diff --git a/.github/ISSUE_TEMPLATE/config.yaml b/.github/ISSUE_TEMPLATE/config.yaml index a413879b3..d82f8a49a 100644 --- a/.github/ISSUE_TEMPLATE/config.yaml +++ b/.github/ISSUE_TEMPLATE/config.yaml @@ -1,5 +1,5 @@ blank_issues_enabled: false contact_links: - name: Usage question - url: https://github.com/NREL/H2Integrate/discussions - about: Have any questions about using H2Integrate? Post in Discussions to engage with the NREL team and H2Integrate community. + url: https://github.com/NatLabRockies/H2Integrate/discussions + about: Have any questions about using H2Integrate? Post in Discussions to engage with the NLR team and H2Integrate community. diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index 38eea35b9..a8e7fdc44 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -78,33 +78,33 @@ IMPORTANT NOTES "A complete thought. [PR XYZ]((https://github.com/NatLabRockies/H2Integrate/pull/XYZ)", where `XYZ` should be replaced with the actual number. -## Section 3: Related Issues +## Section 4: Related Issues -## Section 4: Impacted Areas of the Software +## Section 5: Impacted Areas of the Software -### Section 4.1: New Files +### Section 5.1: New Files - `path/to/file.extension` - `method1`: What and why something was changed in one sentence or less. -### Section 4.2: Modified Files +### Section 5.2: Modified Files - `path/to/file.extension` - `method1`: What and why something was changed in one sentence or less. -## Section 5: Additional Supporting Information +## Section 6: Additional Supporting Information -## Section 6: Test Results, if applicable +## Section 7: Test Results, if applicable -## Section 7 (Optional): New Model Checklist +## Section 8 (Optional): New Model Checklist - [ ] **Model Structure**: - [ ] Follows established naming conventions outlined in `docs/developer_guide/coding_guidelines.md` @@ -128,17 +128,18 @@ failing test cases. - [ ] Model added to the main models list in `docs/user_guide/model_overview.md` - [ ] Model documentation page added to the appropriate `docs/` section - [ ] `.md` is added to the `_toc.yml` + - [ ] Run `generate_class_hierarchy.py` to update the class hierarchy diagram in `docs/developer_guide/class_structure.md` diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index e25ca9bb5..6b362c792 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -1,53 +1,92 @@ name: Testing on: push: - branches: [main, develop] + branches: + - main + - develop pull_request: - branches: [main, develop] + branches: + - main + - develop schedule: - cron: 0 0 * * MON jobs: build: - runs-on: ubuntu-latest strategy: matrix: - python-version: ['3.11', '3.13'] - optional: [false] + os: + - ubuntu-latest + # - windows-latest # Temporarily disabled + python-version: + - '3.11' + - '3.13' + test-type: + - unit + - regression + - integration + optional: + - true include: - python-version: '3.11' - optional: true + optional: false + os: ubuntu-latest + test-type: unit + - python-version: '3.11' + optional: false + os: ubuntu-latest + test-type: regression + - python-version: '3.11' + optional: false + os: ubuntu-latest + test-type: integration + runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@v6 - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v6 + uses: conda-incubator/setup-miniconda@v3 with: + miniconda-version: latest + auto-update-conda: true python-version: ${{ matrix.python-version }} - - name: Install dependencies + auto-activate: true + activate-environment: h2integrate + - name: Install base dependencies + shell: bash -el {0} env: SKLEARN_ALLOW_DEPRECATED_SKLEARN_PACKAGE_INSTALL: true run: | - sudo apt-get update && sudo apt-get install -y libglpk-dev glpk-utils coinor-cbc + conda install -y -c conda-forge glpk coin-or-cbc>=2.10.12 python -m pip install --upgrade pip - if [ "${{ matrix.optional }}" = "true" ]; then - pip install ".[develop]" - else - pip install ".[develop]" - fi + pip install ".[develop]" + - if: ${{ matrix.optional }} + name: Install optional dependencies + shell: bash -el {0} + run: | + conda install -y -c conda-forge wisdem + pip install ".[extras]" - name: Create env file run: | touch .env - - name: Run unit tests - run: | - pytest . -m unit --cov=h2integrate --cov-report=xml:unit-coverage.xml - - name: Run regression tests - run: | - pytest . -m regression --cov=h2integrate --cov-report=xml:regression-coverage.xml - - name: Run integration tests - run: | - pytest . -m integration --cov=h2integrate --cov-report=xml:integration-coverage.xml - - name: Upload unit coverage to - uses: codecov/codecov-action@v5 + - name: Run ${{ matrix.test-type }} tests + shell: bash -el {0} + run: pytest . -m ${{ matrix.test-type }} --cov=h2integrate --cov-report=lcov:./${{ matrix.test-type }}-coverage.lcov + - name: Upload test coverage + uses: coverallsapp/github-action@v2 + if: contains(matrix.os, 'ubuntu') && contains(matrix.python-version, '3.13') + with: + github-token: ${{ secrets.COVERALLS_REPO_TOKEN }} + files: ./${{ matrix.test-type }}-coverage.lcov + flag-name: ${{ matrix.test-type }} + parallel: true + fail-on-error: false + coverage-finish: + needs: build + if: always() + runs-on: ubuntu-latest + steps: + - name: Coveralls finished + uses: coverallsapp/github-action@v2 with: - token: ${{ secrets.CODECOV_TOKEN }} - files: unit-coverage.xml, regression-coverage.xml, integration-coverage.xml - flags: unit,regression,integration + github-token: ${{ secrets.COVERALLS_REPO_TOKEN }} + parallel-finished: true + fail-on-error: false diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 779253020..1e89e3f5a 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -5,7 +5,9 @@ build: tools: python: '3.11' jobs: - pre_build: [jupyter-book config sphinx docs/] + pre_build: + - python docs/generate_class_hierarchy.py + - jupyter-book config sphinx docs/ python: install: - method: pip diff --git a/CHANGELOG.md b/CHANGELOG.md index 7e8c78abd..f1cec9631 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,60 @@ # Changelog +## Unreleased +- Updated README and docs intro page with expanded H2I description, reorganized sections, and streamlined installation instructions [PR 677](https://github.com/NatLabRockies/H2Integrate/pull/677) +- Update energy conversion ratio in H2 SMR model [PR 606](https://github.com/NatLabRockies/H2Integrate/pull/606) +- Update iron models and examples [PR 601](https://github.com/NatLabRockies/H2Integrate/pull/601) + - Remove outdated iron files + - Consolidate iron examples into a single main folder + - Add documentation for Rosner iron DRI and steel EAF models +- Breaks out pyomo controller simulation code from base class to individual controllers. [PR 587](https://github.com/NatLabRockies/H2Integrate/pull/587) +- Add tests for non-one valued charge, discharge, and round-trip efficiencies for the open-loop demand controller [PR 610](https://github.com/NatLabRockies/H2Integrate/pull/610) +- Updated the `StorageAutoSizingModel` and `PassThroughOpenLoopController` so that `commodity_set_point` is used as the storage dispatch command [PR 608](https://github.com/NatLabRockies/H2Integrate/pull/608) +- Updated the `SimpleGenericStorage` and `DemandOpenLoopStorageController` so that `commodity_set_point` is used as the storage dispatch command [PR 612](https://github.com/NatLabRockies/H2Integrate/pull/612) +- Add PySAM marine models [PR 607](https://github.com/NatLabRockies/H2Integrate/pull/607) + - Add tidal resource model + - Add pysam tidal performance model + - Add pysam marine hydrokinetic cost model +- Updated the `StoragePerformanceModel` and `PySAMBatteryPerformanceModel` to be compatible with the open-loop storage control strategies [PR 613](https://github.com/NatLabRockies/H2Integrate/pull/613) + - Removed `SimpleGenericStorage` and replaced usage with `StoragePerformanceModel` + - Renamed `PassThroughOpenLoopController` to `SimpleStorageOpenLoopController` + - Bugfix in pyomo control rules so that units such as `kg/h` can be used + - Bugfix in tests of pyomo control strategies with `StoragePerformanceModel` so that the pathname attribute is correct + - Added `demand_profile` as an input to `StoragePerformanceModel` and `PySAMBatteryPerformanceModel` + - Renamed `xx_charge_fraction` to `xx_soc_fraction` +- Bugfix in `StoragePerformanceModel` and `PySAMBatteryPerformanceModel` for setting control inputs to account for cases with multiple storage technologies with different control strategy types [PR 615](https://github.com/NatLabRockies/H2Integrate/pull/615) +- Bugfix input energy to OAE financial model [PR 617](https://github.com/NatLabRockies/H2Integrate/pull/617) + - Remove `MarineCarbonCapture` base classes +- Added the notion of multivariable commodity streams, which allow users to connect multiple variables between technologies with a single connection specification. [PR 480](https://github.com/NatLabRockies/H2Integrate/pull/480) +- Added base class (`StorageOpenLoopControlBase`) and base configuration class (`StorageOpenLoopControlBaseConfig`) for open-loop storage control strategies and updated the existing open-loop storage control strategies to inherit these [PR 619](https://github.com/NatLabRockies/H2Integrate/pull/619) +- Added a generic cost model for converters [PR 622](https://github.com/NatLabRockies/H2Integrate/pull/622) +- Updated the `StorageAutoSizingModel` model to be compatible with Pyomo control strategies [PR 621](https://github.com/NatLabRockies/H2Integrate/pull/621) +- Removed a few usages of `shape_by_conn` due to issues with OpenMDAO v3.43.0 release on some computers [PR 632](https://github.com/NatLabRockies/H2Integrate/pull/632) +- Made generating an XDSM diagram from connections in a model optional and added documentation on model visualization. [PR 629](https://github.com/NatLabRockies/H2Integrate/pull/629) +- Added a storage performance baseclass model `StoragePerformanceBase` and updated the other storage performance models to inherit it [PR 624](https://github.com/NatLabRockies/H2Integrate/pull/624) +- Added an automated script to crawl through the codebase and generate a visualization of the class hierarchy in H2Integrate. [PR 643](https://github.com/NatLabRockies/H2Integrate/pull/643) +- Modified the calc tilt angle function for pysam solar to support latitudes in the southern hemisphere [PR 646](https://github.com/NatLabRockies/H2Integrate/pull/646) +- Added oxygen production metrics and as outputs to `ECOElectrolyzerPerformanceModel` [PR 642](https://github.com/NatLabRockies/H2Integrate/pull/642) +- Bugfix to allow for one resource to be connected to multiple technologies [PR 655](https://github.com/NatLabRockies/H2Integrate/pull/655) +- Removed the last of the logic that was based on technology names rather than model classes [PR 654](https://github.com/NatLabRockies/H2Integrate/pull/654) +- Add input checking for extraneous or mis-categorized input parameters for technologies that have a defined control strategy or dispatch rule set [PR 647](https://github.com/NatLabRockies/H2Integrate/pull/647) +- Bumps the `coin-or-cbc` dependency to at least 2.10.12 to enable easy Windows compatibility. [PR 590](https://github.com/NatLabRockies/H2Integrate/pull/590) +- Uses the optional installation parameter `extras` to combine all analysis extras, and remove them + from the `develop` options. [PR 590](https://github.com/NatLabRockies/H2Integrate/pull/590) +- Tests reliant on the `gis` optional dependencies are no longer run when the extra dependencies are not installed + similar to the ard tests. [PR 590](https://github.com/NatLabRockies/H2Integrate/pull/590) +- Updates the testing infrastructure to use function-scoped fixtures unless there is a specific need for sharing + data between functions in a module. [PR 590](https://github.com/NatLabRockies/H2Integrate/pull/590) +- Adds `H2IntegrateModel.state` as an `IntEnum` to handle setup and run status checks. + [PR 590](https://github.com/NatLabRockies/H2Integrate/pull/590) +- Added standardized outputs to feedstock model [PR 523](https://github.com/NatLabRockies/H2Integrate/pull/523) +- Reclassified open-loop converter control strategies as demand components and updated output naming convention to align with output naming convention in storage performance models [PR 631](https://github.com/NatLabRockies/H2Integrate/pull/631). + - The `FlexibleDemandOpenLoopConverterController` has been renamed to `FlexibleDemandComponent` + - The `DemandOpenLoopConverterController` has been renamed to `GenericDemandComponent` +- Modified CI setup so Windows is temporarily disabled and also so unit, regression, and integration tests are run in separate jobs to speed up testing and provide more information on test failures. [PR 668](https://github.com/NatLabRockies/H2Integrate/pull/668) +- Added infrastructure for running models with non-hourly time steps via a class attribute `_time_step_bounds` and sets new time step bounds of 5-minutes to 1-hour for the grid components. [PR 653](https://github.com/NatLabRockies/H2Integrate/pull/653) and [PR 671](https://github.com/NatLabRockies/H2Integrate/pull/671) +- Remove demand-related outputs from storage performance models and replace usage with demand components [PR 666](https://github.com/NatLabRockies/H2Integrate/pull/666) + ## 0.7.2 [April 9, 2026] - Bumps the minimum WOMBAT version to v.0.13.3, which fixes Pandas compatibility with the Pandas 2.3.x and 3.x release @@ -31,6 +86,8 @@ - Fixed a bug in the discrete variable instantiation within the iron processing stack that caused a failure with OpenMDAO v3.43 [PR 595](https://github.com/NatLabRockies/H2Integrate/pull/595) - Fixed a bug in model setup where transporters were added to the system at the end of the system instead after their source [PR 591](https://github.com/NatLabRockies/H2Integrate/pull/591) - Fixed a bug in example 1 (steel) where a cable was included between the combiner to steel, but steel uses an internal grid connection [PR 591](https://github.com/NatLabRockies/H2Integrate/pull/591) +- Introduced a keyword arg to `post_process` to allow users to choose if results are printed to the console. [PR 597](https://github.com/NatLabRockies/H2Integrate/pull/597) +- Fixed a bug in charge and discharge efficiency handling in `StoragePerformanceModel` [PR 600](https://github.com/NatLabRockies/H2Integrate/pull/600) ## 0.7 [March 3, 2026] @@ -125,55 +182,55 @@ ### New Features and Technology Models -- Added PySAM Windpower performance model to simulate wind [PR 306](https://github.com/NREL/H2Integrate/pull/306) -- Added `simple_grid_layout.py` for wind plant layout modeling, can model square or rectangular layouts [PR 306](https://github.com/NREL/H2Integrate/pull/306) -- Added ability to visualize the wind plant layout for PySAM Windpower model using `post_process(show_plots=True)` [PR 306](https://github.com/NREL/H2Integrate/pull/306) -- Added Wind Annual Technology Baseline cost model `atb_wind_cost.py` [PR 306](https://github.com/NREL/H2Integrate/pull/306) -- Added resource models to make solar resource API calls to the NREL Developer GOES dataset [PR 279](https://github.com/NREL/H2Integrate/pull/279) -- Added solar resource models for Meteosat Prime Meridian and Himawari datasets available through NSRDB [PR 377](https://github.com/NREL/H2Integrate/pull/377) -- Added wind resource model for API calls to Open-Meteo archive [PR 332](https://github.com/NREL/H2Integrate/pull/332) -- Added PySAM battery model as a storage technology performance model [PR 211](https://github.com/NREL/H2Integrate/pull/211) -- Added framework to run heuristic load following dispatch for storage technologies [PR 211](https://github.com/NREL/H2Integrate/pull/211) -- Added storage auto-sizing performance model based on storage sizing calculations that existed in the coupled hydrogen storage performance and cost model [PR 324](https://github.com/NREL/H2Integrate/pull/324) -- Added grid converter performance and cost model which can be used to buy, sell, or buy and sell electricity to/from the grid [PR 340](https://github.com/NREL/H2Integrate/pull/340) -- Add feature for natural gas plant converter to take electricity demand as an input and added system capacity as an input [PR 334](https://github.com/NREL/H2Integrate/pull/334) -- Added standalone iron mine performance and cost model [PR 364](https://github.com/NREL/H2Integrate/pull/364) -- Add open-loop load demand controllers: `DemandOpenLoopConverterController` and `FlexibleDemandOpenLoopConverterController` [PR 328](https://github.com/NREL/H2Integrate/pull/328) +- Added PySAM Windpower performance model to simulate wind [PR 306](https://github.com/NatLabRockies/H2Integrate/pull/306) +- Added `simple_grid_layout.py` for wind plant layout modeling, can model square or rectangular layouts [PR 306](https://github.com/NatLabRockies/H2Integrate/pull/306) +- Added ability to visualize the wind plant layout for PySAM Windpower model using `post_process(show_plots=True)` [PR 306](https://github.com/NatLabRockies/H2Integrate/pull/306) +- Added Wind Annual Technology Baseline cost model `atb_wind_cost.py` [PR 306](https://github.com/NatLabRockies/H2Integrate/pull/306) +- Added resource models to make solar resource API calls to the NREL Developer GOES dataset [PR 279](https://github.com/NatLabRockies/H2Integrate/pull/279) +- Added solar resource models for Meteosat Prime Meridian and Himawari datasets available through NSRDB [PR 377](https://github.com/NatLabRockies/H2Integrate/pull/377) +- Added wind resource model for API calls to Open-Meteo archive [PR 332](https://github.com/NatLabRockies/H2Integrate/pull/332) +- Added PySAM battery model as a storage technology performance model [PR 211](https://github.com/NatLabRockies/H2Integrate/pull/211) +- Added framework to run heuristic load following dispatch for storage technologies [PR 211](https://github.com/NatLabRockies/H2Integrate/pull/211) +- Added storage auto-sizing performance model based on storage sizing calculations that existed in the coupled hydrogen storage performance and cost model [PR 324](https://github.com/NatLabRockies/H2Integrate/pull/324) +- Added grid converter performance and cost model which can be used to buy, sell, or buy and sell electricity to/from the grid [PR 340](https://github.com/NatLabRockies/H2Integrate/pull/340) +- Add feature for natural gas plant converter to take electricity demand as an input and added system capacity as an input [PR 334](https://github.com/NatLabRockies/H2Integrate/pull/334) +- Added standalone iron mine performance and cost model [PR 364](https://github.com/NatLabRockies/H2Integrate/pull/364) +- Add open-loop load demand controllers: `DemandOpenLoopConverterController` and `FlexibleDemandOpenLoopConverterController` [PR 328](https://github.com/NatLabRockies/H2Integrate/pull/328) ### Improvements and Refactoring -- Updated inputs for the `ATBBatteryCostModel` and `DemandOpenLoopController` so storage capacity and charge rate can be design variables [PR 290](https://github.com/NREL/H2Integrate/pull/290) -- Split out cost models from coupled hydrogen storage performance and cost model [PR 324](https://github.com/NREL/H2Integrate/pull/324) -- Created `ProFastBase`, a base class for the `ProFastLCO` and `ProFastNPV` models [PR 310](https://github.com/NREL/H2Integrate/pull/310) -- Added `ProFastNPV`, a finance model using ProFAST to calculate NPV of the commodity [PR 310](https://github.com/NREL/H2Integrate/pull/310) -- Moved `compute()` from `ProFastBase` to `ProFastLCO` [PR 310](https://github.com/NREL/H2Integrate/pull/310) -- Added `NumpyFinancialNPV`, a finance model that uses NumPy Financial npv to calculate the npv from the cash flows [PR 310](https://github.com/NREL/H2Integrate/pull/310) -- Added capability for user-defined finance models in the H2Integrate framework [PR 247](https://github.com/NREL/H2Integrate/pull/247) -- Enabled dynamic plant component sizing modes through the resizeable model class `ResizeablePerformanceModelBaseClass` [PR 198](https://github.com/NREL/H2Integrate/pull/198) -- Move geologic hydrogen models into specific geoh2 subsurface converters [PR 367](https://github.com/NREL/H2Integrate/pull/367) -- Updated generic combiner to accept any number of inflow streams instead of just 2 [PR 406](https://github.com/NREL/H2Integrate/pull/406) -- Allow multiple instances of the same electricity producing technologies using prefix-based matching [PR 397](https://github.com/NREL/H2Integrate/pull/397) -- Allow multiple instances of custom models in the same hybrid system [PR 397](https://github.com/NREL/H2Integrate/pull/397) -- Removed a large portion of the old GreenHEART code that was no longer being used [PR 384](https://github.com/NREL/H2Integrate/pull/384) -- Moved high-level tests to the appropriate directory and removed defunct tests [PR 412](https://github.com/NREL/H2Integrate/pull/412) +- Updated inputs for the `ATBBatteryCostModel` and `DemandOpenLoopController` so storage capacity and charge rate can be design variables [PR 290](https://github.com/NatLabRockies/H2Integrate/pull/290) +- Split out cost models from coupled hydrogen storage performance and cost model [PR 324](https://github.com/NatLabRockies/H2Integrate/pull/324) +- Created `ProFastBase`, a base class for the `ProFastLCO` and `ProFastNPV` models [PR 310](https://github.com/NatLabRockies/H2Integrate/pull/310) +- Added `ProFastNPV`, a finance model using ProFAST to calculate NPV of the commodity [PR 310](https://github.com/NatLabRockies/H2Integrate/pull/310) +- Moved `compute()` from `ProFastBase` to `ProFastLCO` [PR 310](https://github.com/NatLabRockies/H2Integrate/pull/310) +- Added `NumpyFinancialNPV`, a finance model that uses NumPy Financial npv to calculate the npv from the cash flows [PR 310](https://github.com/NatLabRockies/H2Integrate/pull/310) +- Added capability for user-defined finance models in the H2Integrate framework [PR 247](https://github.com/NatLabRockies/H2Integrate/pull/247) +- Enabled dynamic plant component sizing modes through the resizeable model class `ResizeablePerformanceModelBaseClass` [PR 198](https://github.com/NatLabRockies/H2Integrate/pull/198) +- Move geologic hydrogen models into specific geoh2 subsurface converters [PR 367](https://github.com/NatLabRockies/H2Integrate/pull/367) +- Updated generic combiner to accept any number of inflow streams instead of just 2 [PR 406](https://github.com/NatLabRockies/H2Integrate/pull/406) +- Allow multiple instances of the same electricity producing technologies using prefix-based matching [PR 397](https://github.com/NatLabRockies/H2Integrate/pull/397) +- Allow multiple instances of custom models in the same hybrid system [PR 397](https://github.com/NatLabRockies/H2Integrate/pull/397) +- Removed a large portion of the old GreenHEART code that was no longer being used [PR 384](https://github.com/NatLabRockies/H2Integrate/pull/384) +- Moved high-level tests to the appropriate directory and removed defunct tests [PR 412](https://github.com/NatLabRockies/H2Integrate/pull/412) ### Configuration and Optimization -- Added `tools/run_cases.py` with tools to run different `tech_config` cases from a spreadsheet, with new docs page to describe: docs/user_guide/how_to_run_several_cases_in_sequence.md [PR 242](https://github.com/NREL/H2Integrate/pull/242) -- Updated setting up recorder in `PoseOptimization` [PR 291](https://github.com/NREL/H2Integrate/pull/291) -- Added `create_om_reports` option to driver config to enable/disable OpenMDAO reports (N2 diagrams, etc.) [PR 308](https://github.com/NREL/H2Integrate/pull/308) -- Added design of experiment functionality [PR 314](https://github.com/NREL/H2Integrate/pull/314) -- Added "csvgen" as generator type for design of experiments [PR 314](https://github.com/NREL/H2Integrate/pull/314) -- Added `load_yaml()` function and flexibility to input a config dictionary to H2IntegrateModel rather than a filepath [PR 313](https://github.com/NREL/H2Integrate/pull/313) -- Removed `boundaries` from the necessary keys in `plant_config` validation [PR 361](https://github.com/NREL/H2Integrate/pull/361) -- Added ability for latitude and longitude to be design variables in design sweep [PR 336](https://github.com/NREL/H2Integrate/pull/336) +- Added `tools/run_cases.py` with tools to run different `tech_config` cases from a spreadsheet, with new docs page to describe: docs/user_guide/how_to_run_several_cases_in_sequence.md [PR 242](https://github.com/NatLabRockies/H2Integrate/pull/242) +- Updated setting up recorder in `PoseOptimization` [PR 291](https://github.com/NatLabRockies/H2Integrate/pull/291) +- Added `create_om_reports` option to driver config to enable/disable OpenMDAO reports (N2 diagrams, etc.) [PR 308](https://github.com/NatLabRockies/H2Integrate/pull/308) +- Added design of experiment functionality [PR 314](https://github.com/NatLabRockies/H2Integrate/pull/314) +- Added "csvgen" as generator type for design of experiments [PR 314](https://github.com/NatLabRockies/H2Integrate/pull/314) +- Added `load_yaml()` function and flexibility to input a config dictionary to H2IntegrateModel rather than a filepath [PR 313](https://github.com/NatLabRockies/H2Integrate/pull/313) +- Removed `boundaries` from the necessary keys in `plant_config` validation [PR 361](https://github.com/NatLabRockies/H2Integrate/pull/361) +- Added ability for latitude and longitude to be design variables in design sweep [PR 336](https://github.com/NatLabRockies/H2Integrate/pull/336) ### Documentation, Examples, and Miscellaneous -- Added an optimized offshore methanol production case to examples/03_methanol/co2_hydrogenation_doc [PR 137](https://github.com/NREL/H2Integrate/pull/137) -- Improved the readability of the postprocessing printout [PR 361](https://github.com/NREL/H2Integrate/pull/361) -- Improved readability of the postprocessing printout by simplifying numerical representation, especially for years [PR 378](https://github.com/NREL/H2Integrate/pull/378) -- Fixed stoichiometry mistake in ammonia synloop [PR 363](https://github.com/NREL/H2Integrate/pull/363) +- Added an optimized offshore methanol production case to examples/03_methanol/co2_hydrogenation_doc [PR 137](https://github.com/NatLabRockies/H2Integrate/pull/137) +- Improved the readability of the postprocessing printout [PR 361](https://github.com/NatLabRockies/H2Integrate/pull/361) +- Improved readability of the postprocessing printout by simplifying numerical representation, especially for years [PR 378](https://github.com/NatLabRockies/H2Integrate/pull/378) +- Fixed stoichiometry mistake in ammonia synloop [PR 363](https://github.com/NatLabRockies/H2Integrate/pull/363) ## 0.4.0 [October 1, 2025] @@ -181,70 +238,70 @@ This release introduces significant new technology models and framework capabili ### New Features and Technology Models -- Added capability for user-defined technologies in the H2Integrate framework, allowing for custom models to be integrated into the system [PR 128](https://github.com/NREL/H2Integrate/pull/128). -- Added a check for if a custom model's name clashes with an existing model name in the H2Integrate framework, raising an error if it does [PR 128](https://github.com/NREL/H2Integrate/pull/128). -- Added geologic hydrogen (GeoH2) converter and examples [PR 135](https://github.com/NREL/H2Integrate/pull/135). -- Added methanol production base class [PR 137](https://github.com/NREL/H2Integrate/pull/137). -- Added steam methane reforming methanol production technology [PR 137](https://github.com/NREL/H2Integrate/pull/137). -- Added CO2 hydrogenation methanol production technology [PR 137](https://github.com/NREL/H2Integrate/pull/137). -- Added run of river hydro plant model, an example, and a documentation page [PR 145](https://github.com/NREL/H2Integrate/pull/145). -- Added marine carbon capture base class [PR 165](https://github.com/NREL/H2Integrate/pull/165). -- Added direct ocean capture technology [PR 165](https://github.com/NREL/H2Integrate/pull/165). -- Added ammonia synloop, partially addressing [Issue 169](https://github.com/NREL/H2Integrate/issues/169) [PR 177](https://github.com/NREL/H2Integrate/pull/177). -- Added simple air separation unit (ASU) converter to model nitrogen production [PR 179](https://github.com/NREL/H2Integrate/pull/179). -- Added rule-based storage system control capability (e.g., for battery, H2, CO2) [PR 195](https://github.com/NREL/H2Integrate/pull/195). -- Added ocean alkalinity enhancement technology model [PR 212](https://github.com/NREL/H2Integrate/pull/212). -- Added `natural_gas_performance` and `natural_gas_cost` models, allowing for natural gas power plant modeling [PR 221](https://github.com/NREL/H2Integrate/pull/221). -- Added wind resource model, API baseclasses, updated examples, and documentation [PR 245](https://github.com/NREL/H2Integrate/pull/245). -- Added generic storage model, useful for battery, hydrogen, CO2, or other resource storage [PR 248](https://github.com/NREL/H2Integrate/pull/248). +- Added capability for user-defined technologies in the H2Integrate framework, allowing for custom models to be integrated into the system [PR 128](https://github.com/NatLabRockies/H2Integrate/pull/128). +- Added a check for if a custom model's name clashes with an existing model name in the H2Integrate framework, raising an error if it does [PR 128](https://github.com/NatLabRockies/H2Integrate/pull/128). +- Added geologic hydrogen (GeoH2) converter and examples [PR 135](https://github.com/NatLabRockies/H2Integrate/pull/135). +- Added methanol production base class [PR 137](https://github.com/NatLabRockies/H2Integrate/pull/137). +- Added steam methane reforming methanol production technology [PR 137](https://github.com/NatLabRockies/H2Integrate/pull/137). +- Added CO2 hydrogenation methanol production technology [PR 137](https://github.com/NatLabRockies/H2Integrate/pull/137). +- Added run of river hydro plant model, an example, and a documentation page [PR 145](https://github.com/NatLabRockies/H2Integrate/pull/145). +- Added marine carbon capture base class [PR 165](https://github.com/NatLabRockies/H2Integrate/pull/165). +- Added direct ocean capture technology [PR 165](https://github.com/NatLabRockies/H2Integrate/pull/165). +- Added ammonia synloop, partially addressing [Issue 169](https://github.com/NatLabRockies/H2Integrate/issues/169) [PR 177](https://github.com/NatLabRockies/H2Integrate/pull/177). +- Added simple air separation unit (ASU) converter to model nitrogen production [PR 179](https://github.com/NatLabRockies/H2Integrate/pull/179). +- Added rule-based storage system control capability (e.g., for battery, H2, CO2) [PR 195](https://github.com/NatLabRockies/H2Integrate/pull/195). +- Added ocean alkalinity enhancement technology model [PR 212](https://github.com/NatLabRockies/H2Integrate/pull/212). +- Added `natural_gas_performance` and `natural_gas_cost` models, allowing for natural gas power plant modeling [PR 221](https://github.com/NatLabRockies/H2Integrate/pull/221). +- Added wind resource model, API baseclasses, updated examples, and documentation [PR 245](https://github.com/NatLabRockies/H2Integrate/pull/245). +- Added generic storage model, useful for battery, hydrogen, CO2, or other resource storage [PR 248](https://github.com/NatLabRockies/H2Integrate/pull/248). ### Improvements and Refactoring -- Removed the `to_organize` directory [PR 138](https://github.com/NREL/H2Integrate/pull/138). -- Updated the naming scheme throughout the framework so resources produced always have `_out` and resources consumed always have `_in` in their names [PR 148](https://github.com/NREL/H2Integrate/pull/148). -- Added ability to export ProFAST object to yaml file in `ProFastComp` [PR 207](https://github.com/NREL/H2Integrate/pull/207). -- Refactored `ProFastComp` and put in a new file (`h2integrate/core/profast_financial.py`). Added flexibility to allow users to specify different financial models [PR 218](https://github.com/NREL/H2Integrate/pull/218). -- Revamped the feedstocks technology group to allow for more precise modeling of feedstock supply chains, including capacity constraints and feedstock amount consumed [PR 221](https://github.com/NREL/H2Integrate/pull/221). -- Made `pipe` and `cable` substance-agnostic rather than hard-coded for `hydrogen` and `electricity` [PR 241](https://github.com/NREL/H2Integrate/pull/241). -- Updated option to pass variables in technology interconnections to allow for different variable names from source to destination in the format `[source_tech, dest_tech, (source_tech_variable, dest_tech_variable)]` [PR 236](https://github.com/NREL/H2Integrate/pull/236). -- Split out the electrolyzer cost models `basic` and `singlitico` for clarity [PR 147](https://github.com/NREL/H2Integrate/pull/147). -- Refactored the ammonia production model to use the new H2Integrate framework natively and removed the prior performance and cost functions [PR 163](https://github.com/NREL/H2Integrate/pull/163). -- Added a new ammonia production model which has nitrogen, hydrogen, and electricity inputs and ammonia output, with performance and cost functions [PR 163](https://github.com/NREL/H2Integrate/pull/163). -- Added WOMBAT electrolyzer O&M model [PR 168](https://github.com/NREL/H2Integrate/pull/168). -- Changed electrolyzer capacity to be specified as `n_clusters` rather than `rating` in electrolyzer performance model config [PR 194](https://github.com/NREL/H2Integrate/pull/194). -- Changed electrolyzer capacity to be an input to the electrolyzer cost models rather than pulled from the cost model config [PR 194](https://github.com/NREL/H2Integrate/pull/194). -- Added cost model base class and removed `plant_config['finance_parameters']['discount_years']['tech']`. Cost year is now an optional input (`tech_config[tech]['model_inputs']['cost_parameters']['cost_year']`) and a discrete output [PR 199](https://github.com/NREL/H2Integrate/pull/199). -- Added two ATB-compatible solar-PV cost models [PR 193](https://github.com/NREL/H2Integrate/pull/193). -- Update PySAM solar performance model to allow for more user-configurability [PR 187](https://github.com/NREL/H2Integrate/pull/187). -- Added `"custom_electrolyzer_cost"` model, an electrolyzer cost model that allows for user-defined CapEx and OpEx values [PR 227](https://github.com/NREL/H2Integrate/pull/227). -- Added variable O&M to `CostModelBaseClass` and integrated into finance-related models [PR 235](https://github.com/NREL/H2Integrate/pull/235). -- Improved `h2integrate/transporters/power_combiner.py` and enabled usage of multiple electricity producing technologies [PR 232](https://github.com/NREL/H2Integrate/pull/232). +- Removed the `to_organize` directory [PR 138](https://github.com/NatLabRockies/H2Integrate/pull/138). +- Updated the naming scheme throughout the framework so resources produced always have `_out` and resources consumed always have `_in` in their names [PR 148](https://github.com/NatLabRockies/H2Integrate/pull/148). +- Added ability to export ProFAST object to yaml file in `ProFastComp` [PR 207](https://github.com/NatLabRockies/H2Integrate/pull/207). +- Refactored `ProFastComp` and put in a new file (`h2integrate/core/profast_financial.py`). Added flexibility to allow users to specify different financial models [PR 218](https://github.com/NatLabRockies/H2Integrate/pull/218). +- Revamped the feedstocks technology group to allow for more precise modeling of feedstock supply chains, including capacity constraints and feedstock amount consumed [PR 221](https://github.com/NatLabRockies/H2Integrate/pull/221). +- Made `pipe` and `cable` substance-agnostic rather than hard-coded for `hydrogen` and `electricity` [PR 241](https://github.com/NatLabRockies/H2Integrate/pull/241). +- Updated option to pass variables in technology interconnections to allow for different variable names from source to destination in the format `[source_tech, dest_tech, (source_tech_variable, dest_tech_variable)]` [PR 236](https://github.com/NatLabRockies/H2Integrate/pull/236). +- Split out the electrolyzer cost models `basic` and `singlitico` for clarity [PR 147](https://github.com/NatLabRockies/H2Integrate/pull/147). +- Refactored the ammonia production model to use the new H2Integrate framework natively and removed the prior performance and cost functions [PR 163](https://github.com/NatLabRockies/H2Integrate/pull/163). +- Added a new ammonia production model which has nitrogen, hydrogen, and electricity inputs and ammonia output, with performance and cost functions [PR 163](https://github.com/NatLabRockies/H2Integrate/pull/163). +- Added WOMBAT electrolyzer O&M model [PR 168](https://github.com/NatLabRockies/H2Integrate/pull/168). +- Changed electrolyzer capacity to be specified as `n_clusters` rather than `rating` in electrolyzer performance model config [PR 194](https://github.com/NatLabRockies/H2Integrate/pull/194). +- Changed electrolyzer capacity to be an input to the electrolyzer cost models rather than pulled from the cost model config [PR 194](https://github.com/NatLabRockies/H2Integrate/pull/194). +- Added cost model base class and removed `plant_config['finance_parameters']['discount_years']['tech']`. Cost year is now an optional input (`tech_config[tech]['model_inputs']['cost_parameters']['cost_year']`) and a discrete output [PR 199](https://github.com/NatLabRockies/H2Integrate/pull/199). +- Added two ATB-compatible solar-PV cost models [PR 193](https://github.com/NatLabRockies/H2Integrate/pull/193). +- Update PySAM solar performance model to allow for more user-configurability [PR 187](https://github.com/NatLabRockies/H2Integrate/pull/187). +- Added `"custom_electrolyzer_cost"` model, an electrolyzer cost model that allows for user-defined CapEx and OpEx values [PR 227](https://github.com/NatLabRockies/H2Integrate/pull/227). +- Added variable O&M to `CostModelBaseClass` and integrated into finance-related models [PR 235](https://github.com/NatLabRockies/H2Integrate/pull/235). +- Improved `h2integrate/transporters/power_combiner.py` and enabled usage of multiple electricity producing technologies [PR 232](https://github.com/NatLabRockies/H2Integrate/pull/232). ### Configuration and Optimization -- Updated finance parameter organization naming in `plant_config` [PR 218](https://github.com/NREL/H2Integrate/pull/218). -- Changed finance handling to use `finance_subgroups` and `finance_groups` defined in the `plant_config` rather than previous `financial_groups` in the `tech_config` and `technologies_to_include_in_metrics` in `plant_config` [PR 240](https://github.com/NREL/H2Integrate/pull/240). -- Allow users to specify the technologies to include in the metrics calculations in the plant configuration file [PR 240](https://github.com/NREL/H2Integrate/pull/240). -- Added option for user to provide ProFAST parameters in finance parameters [PR 240](https://github.com/NREL/H2Integrate/pull/240). -- Changed `plant_config` `atb_year` entry to `financial_analysis_start_year` [PR 190](https://github.com/NREL/H2Integrate/pull/190). -- Added `simulation` section under `plant_config['plant']` that has information such as number of timesteps in the simulation, time step interval in seconds, simulation start time, and time zone [PR 219](https://github.com/NREL/H2Integrate/pull/219). -- Moved `overwrite_fin_values` to HOPP [PR 164](https://github.com/NREL/H2Integrate/pull/164). -- Enable optimization with HOPP technology ratings using `recreate_hopp_config_for_optimization` [PR 164](https://github.com/NREL/H2Integrate/pull/164). -- Made caching in the HOPP wrapper optional [PR 164](https://github.com/NREL/H2Integrate/pull/164). -- Added more available constraints from the HOPP wrapper useful for design optimizations [PR 164](https://github.com/NREL/H2Integrate/pull/164). +- Updated finance parameter organization naming in `plant_config` [PR 218](https://github.com/NatLabRockies/H2Integrate/pull/218). +- Changed finance handling to use `finance_subgroups` and `finance_groups` defined in the `plant_config` rather than previous `financial_groups` in the `tech_config` and `technologies_to_include_in_metrics` in `plant_config` [PR 240](https://github.com/NatLabRockies/H2Integrate/pull/240). +- Allow users to specify the technologies to include in the metrics calculations in the plant configuration file [PR 240](https://github.com/NatLabRockies/H2Integrate/pull/240). +- Added option for user to provide ProFAST parameters in finance parameters [PR 240](https://github.com/NatLabRockies/H2Integrate/pull/240). +- Changed `plant_config` `atb_year` entry to `financial_analysis_start_year` [PR 190](https://github.com/NatLabRockies/H2Integrate/pull/190). +- Added `simulation` section under `plant_config['plant']` that has information such as number of timesteps in the simulation, time step interval in seconds, simulation start time, and time zone [PR 219](https://github.com/NatLabRockies/H2Integrate/pull/219). +- Moved `overwrite_fin_values` to HOPP [PR 164](https://github.com/NatLabRockies/H2Integrate/pull/164). +- Enable optimization with HOPP technology ratings using `recreate_hopp_config_for_optimization` [PR 164](https://github.com/NatLabRockies/H2Integrate/pull/164). +- Made caching in the HOPP wrapper optional [PR 164](https://github.com/NatLabRockies/H2Integrate/pull/164). +- Added more available constraints from the HOPP wrapper useful for design optimizations [PR 164](https://github.com/NatLabRockies/H2Integrate/pull/164). ### Documentation, Examples, and Miscellaneous -- Added an example of a user-defined technology in the `examples` directory, demonstrating an extremely simple paper mill model [PR 128](https://github.com/NREL/H2Integrate/pull/128). -- Added example for running with HOPP as the only technology in the H2Integrate system [PR 164](https://github.com/NREL/H2Integrate/pull/164). -- Added an optimization example with a wind plant and electrolyzer to showcase how to define design variables, constraints, and objective functions [PR 126](https://github.com/NREL/H2Integrate/pull/126). -- Expanded docs to include a new section on modifying config dicts after model instantiation [PR 151](https://github.com/NREL/H2Integrate/pull/151). -- Added `*_out/` to `.gitignore` to avoid clutter [PR 191](https://github.com/NREL/H2Integrate/pull/191). -- Bump min Python version and removed unnecessary packages from `pyproject.toml` [PR 150](https://github.com/NREL/H2Integrate/pull/150). -- Bugfix: only run `pyxdsm` when there are connections in the system [PR 201](https://github.com/NREL/H2Integrate/pull/201). +- Added an example of a user-defined technology in the `examples` directory, demonstrating an extremely simple paper mill model [PR 128](https://github.com/NatLabRockies/H2Integrate/pull/128). +- Added example for running with HOPP as the only technology in the H2Integrate system [PR 164](https://github.com/NatLabRockies/H2Integrate/pull/164). +- Added an optimization example with a wind plant and electrolyzer to showcase how to define design variables, constraints, and objective functions [PR 126](https://github.com/NatLabRockies/H2Integrate/pull/126). +- Expanded docs to include a new section on modifying config dicts after model instantiation [PR 151](https://github.com/NatLabRockies/H2Integrate/pull/151). +- Added `*_out/` to `.gitignore` to avoid clutter [PR 191](https://github.com/NatLabRockies/H2Integrate/pull/191). +- Bump min Python version and removed unnecessary packages from `pyproject.toml` [PR 150](https://github.com/NatLabRockies/H2Integrate/pull/150). +- Bugfix: only run `pyxdsm` when there are connections in the system [PR 201](https://github.com/NatLabRockies/H2Integrate/pull/201). ## 0.3.0 [May 2, 2025] @@ -255,7 +312,7 @@ This release introduces significant new technology models and framework capabili ## 0.2.1 Unreleased, TBD -- Fixed iron data save issue [PR 122](https://github.com/NREL/H2Integrate/pull/122) +- Fixed iron data save issue [PR 122](https://github.com/NatLabRockies/H2Integrate/pull/122) - Added optional inputs to electrolyzer model, including curve coefficients and water usage rate. - Bug-fix in electrolyzer outputs (H2_Results) if some stacks are never turned on. @@ -263,7 +320,7 @@ This release introduces significant new technology models and framework capabili - Allow users to save the H2IntegrateOutput class as a yaml file and read that yaml to an instance of the output class - Include new plotting capabilities: (1) hydrogen storage, production, and dispatch; (2) electricity and hydrogen dispatch -- Remove reference_plants from examples. Reference plants can now be found in the [ReferenceHybridSystemDesigns](https://github.com/NREL/ReferenceHybridSystemDesigns) repository. +- Remove reference_plants from examples. Reference plants can now be found in the [ReferenceHybridSystemDesigns](https://github.com/NatLabRockies/ReferenceHybridSystemDesigns) repository. - Use sentence capitalization for plot labels and legends - Use "metric ton" instead of "tonne" or "metric tonne" in all internal naming and plots - Fix bug in hydrogen dispatch plotting by storing time series of hydrogen demand by hour @@ -272,11 +329,11 @@ This release introduces significant new technology models and framework capabili - Removed defunct conda build and upload scripts - Return full solution dictionary from ProFAST, allowing access to CRF and WACC - Renamed code from GreenHEART to H2Integrate -- Added iron processing framework and capabilities [PR 90](https://github.com/NREL/H2Integrate/pull/90) -- Added Martin and Rosner iron ore models, performance and cost for each [PR 90](https://github.com/NREL/H2Integrate/pull/90) -- Added Rosner direct reduction iron (DRI) model, performance and cost [PR 90](https://github.com/NREL/H2Integrate/pull/90) -- Added Martin transport module for performance and cost of iron [PR 90](https://github.com/NREL/H2Integrate/pull/90) -- Added generalized Stinn cost model for electrolysis of arbitrary materials [PR 90](https://github.com/NREL/H2Integrate/pull/90) +- Added iron processing framework and capabilities [PR 90](https://github.com/NatLabRockies/H2Integrate/pull/90) +- Added Martin and Rosner iron ore models, performance and cost for each [PR 90](https://github.com/NatLabRockies/H2Integrate/pull/90) +- Added Rosner direct reduction iron (DRI) model, performance and cost [PR 90](https://github.com/NatLabRockies/H2Integrate/pull/90) +- Added Martin transport module for performance and cost of iron [PR 90](https://github.com/NatLabRockies/H2Integrate/pull/90) +- Added generalized Stinn cost model for electrolysis of arbitrary materials [PR 90](https://github.com/NatLabRockies/H2Integrate/pull/90) ## v0.1.4 [4 February, 2025] diff --git a/README.md b/README.md index 060082d98..33bc4a961 100644 --- a/README.md +++ b/README.md @@ -1,21 +1,58 @@ # H2Integrate: Holistic Hybrids Optimization and Design Tool [![PyPI version](https://badge.fury.io/py/H2Integrate.svg)](https://badge.fury.io/py/H2Integrate) -![CI Tests](https://github.com/NREL/H2Integrate/actions/workflows/ci.yml/badge.svg) +![CI Tests](https://github.com/NatLabRockies/H2Integrate/actions/workflows/ci.yml/badge.svg) [![image](https://img.shields.io/pypi/pyversions/H2Integrate.svg)](https://pypi.python.org/pypi/H2Integrate) [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) -[![DOI:10.5281/zenodo.17903150](https://zenodo.org/badge/DOI/10.5281/zenodo.17903149.svg)](https://zenodo.org/records/17903149) +[![DOI 10.5281/zenodo.17903150](https://zenodo.org/badge/DOI/10.5281/zenodo.17903150.svg)](https://zenodo.org/records/17903150) -[![DOI 10.1088/1742-6596/2767/8/082019](https://img.shields.io/badge/DOI-10.1088%2F1742--6596%2F2767%2F8%2F082019-brightgreen?link=[https://doi.org/10.1088/1742-6596/2767/8/082019](https://doi.org/10.1088/1742-6596/2767/8/082019))](https://iopscience.iop.org/article/10.1088/1742-6596/2767/8/082019/pdf) -[![DOI 10.1088/1742-6596/2767/6/062017](https://img.shields.io/badge/DOI-10.1088%2F1742--6596%2F2767%2F6%2F062017-brightgreen?link=[https://doi.org/10.1088/1742-6596/2767/6/062017](https://doi.org/10.1088/1742-6596/2767/6/062017))](https://iopscience.iop.org/article/10.1088/1742-6596/2767/6/062017/pdf) -[![DOI 10.21203/rs.3.rs-4326648/v1](https://img.shields.io/badge/DOI-10.21203%2Frs.3.rs--4326648%2Fv1-brightgreen?link=[https://doi.org/10.21203/rs.3.rs-4326648/v1](https://doi.org/10.21203/rs.3.rs-4326648/v1))](https://assets-eu.researchsquare.com/files/rs-4326648/v1_covered_338a5071-b74b-4ecd-9d2a-859e8d988b5c.pdf?c=1716199726) +H2Integrate (H2I) is an open-source Python package for hybrid energy systems engineering design and technoeconomic analysis. +It models hybrid systems, especially hybrid energy plants that produce electricity, hydrogen, ammonia, steel, and other products, to perform optimization and scenario analysis. -H2Integrate is an open-source Python package for modeling and designing hybrid energy systems producing electricity, hydrogen, ammonia, steel, and other products. +## Installation -Note: The current version of H2Integrate is under active development and may be missing features that existed previously. H2Integrate v0.2.0 is the last version that uses the prior framework. +The recommended installation method is via pip from PyPI, which will install the latest stable release of H2Integrate and its dependencies: + +```bash +pip install h2integrate +``` + +For installing from source, development setup, and additional installation options, see the [full installation instructions](https://h2integrate.readthedocs.io/en/latest/getting_started/install.html). + +## What H2Integrate Does + +H2Integrate is both a **hybrid systems engineering design tool** and a **technoeconomic analysis (TEA) tool**. It significantly expands beyond generalized tools by offering: + +- **Detailed equipment-level modeling** with a wide range of subsystem variation options +- **High-resolution, location-specific resource data** for site-dependent performance modeling +- **Cost inputs settable by the user** with examples based on the [Annual Technology Baseline (ATB)](https://atb.nlr.gov/) +- **Optimization and scenario analysis** to explore design trade-offs across hybrid plant configurations + +### Available Technologies + +H2I includes models for a broad set of energy generation, conversion, and storage technologies. +This is a non-exhaustive list, and the library of available technologies is actively expanding: + +- **Electricity generation**: solar PV, wind, wave, tidal, natural gas combined cycle (NGCC), natural gas combustion turbines (NGCT), nuclear, grid +- **Hydrogen production**: PEM electrolysis, NG-SMR +- **Energy storage**: Li-ion batteries, long-duration energy storage (LDES), pumped storage hydropower (PSH) +- **Fuel cells**: H2 PEM fuel cells +- **Industrial processes**: ammonia synthesis, iron ore reduction, steel production, and more + +## Getting Started + +See the [Getting Started guide](https://h2integrate.readthedocs.io/en/latest/intro.html) for an introduction to H2Integrate. +The [Examples folder](./examples/) contain Jupyter notebooks, Python scripts, and sample YAML files for common usage scenarios. + +## Publications + +For a full list of publications, see the [Publications section in the documentation](https://h2integrate.readthedocs.io/en/latest/intro.html#publications). +Note: H2Integrate was previously known as GreenHEART, and some publications may refer to it by that name. ## Software Citation +If you use H2I or any of its components in your work, please cite this in your publications using the following BibTeX: + ```bibtex @software{brunik_2025_17903150, author = {Brunik, Kaitlin and @@ -50,204 +87,6 @@ Note: The current version of H2Integrate is under active development and may be } ``` -## Publications where H2Integrate has been used - -For more context about H2Integrate and to see analyses that have been performed using the tool, please see some of these publications. -PDFs are available in the linked titles. - -### Nationwide techno-economic analysis of clean hydrogen production powered by a hybrid renewable energy plant for over 50,000 locations in the United States. - -The levelized cost of hydrogen is calculated for varying technology costs, and tax credits to -explore cost sensitivities independent of plant design, performance, and site selection. Our -findings suggest that strategies for cost reduction include selecting sites with abundant wind -resources, complementary wind and solar resources, and optimizing the sizing of wind and solar -assets to maximize the hybrid plant capacity factor. - -Grant, E., et al. "[Hybrid power plant design for low-carbon hydrogen in the United States.](https://iopscience.iop.org/article/10.1088/1742-6596/2767/8/082019/pdf)" Journal of Physics: Conference Series. Vol. 2767. No. 8. IOP Publishing, 2024. - -### Exploring the role of producing low-carbon hydrogen using water electrolysis powered by offshore wind in facilitating the United States’ transition to a net-zero emissions economy by 2050. - -Conducting a regional techno-economic analysis at four U.S. coastal sites, the study evaluates two -energy transmission configurations and examines associated costs for the years 2025, 2030, and 2035. -The results highlight that locations using fixed-bottom technology may achieve cost-competitive -water electrolysis hydrogen production by 2030 through leveraging geologic hydrogen storage and -federal policy incentives. - -Brunik, K., et al. "[Potential for large-scale deployment of offshore wind-to-hydrogen systems in the United States.](https://iopscience.iop.org/article/10.1088/1742-6596/2767/6/062017/pdf)" Journal of Physics: Conference Series. Vol. 2767. No. 6. IOP Publishing, 2024. - -### Examining how tightly-coupled gigawatt-scale wind- and solar-sourced H2 depends on the ability to store and deliver otherwise-curtailed H2 during times of shortages. - -Modeling results suggest that the levelized cost of storage is highly spatially heterogeneous, with -minor impact on the cost of H2 in the Midwest, and potentially significant impact in areas with -emerging H2 economies such as Central California and the Southeast. While TOL/MCH may be the -cheapest aboveground bulk storage solution evaluated, upfront capital costs, modest energy -efficiency, reliance on critical materials, and greenhouse gas emissions from heating remain -concerns. - -Breunig, Hanna, et al. "[Hydrogen Storage Materials Could Meet Requirements for GW-Scale Seasonal Storage and Green Steel.](https://assets-eu.researchsquare.com/files/rs-4326648/v1_covered_338a5071-b74b-4ecd-9d2a-859e8d988b5c.pdf?c=1716199726)" (2024). - -### DOE Hydrogen Program review presentation of H2Integrate - -King, J. and Hammond, S. "[Integrated Modeling, TEA, and Reference Design for Renewable Hydrogen to Green Steel and Ammonia - GreenHEART](https://www.hydrogen.energy.gov/docs/hydrogenprogramlibraries/pdfs/review24/sdi001_king_2024_o.pdf?sfvrsn=a800ca84_3)" (2024). - -## Software requirements - -- Python version 3.11, 3.12 64-bit -- Other versions may still work, but have not been extensively tested at this time - -## Installing from Package Repositories - -```bash -pip install h2integrate -``` - -> [!NOTE] -> If using the Ard models `h2integrate[ard]`, see the source installation instructions (item 3) for -> creating a conda environment with WISDEM installed through conda, not pip as it can cause issues -> on some machines. - -## Installing from Source - -### Easiest approach (recommended) - -1. Using Git, navigate to a local target directory and clone repository: - - ```bash - git clone https://github.com/NREL/H2Integrate.git - ``` - -2. Navigate to `H2Integrate` - - ```bash - cd H2Integrate - ``` - -3. Create a conda environment and install H2Integrate and all its dependencies. Please read the - following two notes about modified installation steps. - - 1. If on a Unix machine, uncomment line 8 in `environment.yml` to install Cbc. Windows - users will need to manually install from . - 2. If you plan to use Ard, please uncomment line 9 in `environment.yml` to ensure WISDEM (an Ard - dependency) is installed from conda to avoid installation issues with some systems. - - ```bash - conda env create -f environment.yml - ``` - -An additional step can be added if additional dependencies are required, or you plan to use this -environment for development work. - -- Pass `-e` for an editable developer install -- Use one of the extra flags as needed: - - `gis`: adds the iron mapping tools. - - `ard`: adds the Ard-based wind models. - - `develop`: adds developer and documentation tools, plus optional analysis modifiers `gis` and `ard`. - - `examples`: allows you to use the Jupyter Notebooks and all examples (includes `ard` and `gis`). - - `all` simplifies adding all the dependencies. - -This looks like the following for a developer installation: - -```bash -pip install -e ".[all]" -``` - -### Customizable - -1. Using Git, navigate to a local target directory and clone repository: - - ```bash - git clone https://github.com/NREL/H2Integrate.git - ``` - -2. Navigate to `H2Integrate` - - ```bash - cd H2Integrate - ``` - -3. Create a new virtual environment and change to it. Using Conda Python 3.11 (choose your favorite - supported version) and naming it 'h2integrate' (choose your desired name): - - ```bash - conda create --name h2integrate python=3.11 -y - conda activate h2integrate - ``` - -4. Install H2Integrate and its dependencies: - - ```bash - conda install -y -c conda-forge glpk - ``` - - Note: Unix users should install Cbc via: - - ```bash - conda install -y -c conda-forge coin-or-cbc=2.10.8 - ``` - - Windows users will have to manually install Cbc: https://github.com/coin-or/Cbc. - - - If you want to just use H2Integrate: - - ```bash - pip install . - ``` - - - If you want to work with the examples: - - ```bash - pip install ".[examples]" - ``` - - - If you also want development dependencies for running tests and building docs: - - ```bash - pip install -e ".[develop]" - ``` - - Please be sure to also install the pre-commit hooks if contributing code back to the main - repository via the following. This enables a series of automated formatting and code linting - (style and correctness checking) to ensure the code is stylistically consistent. - - ```bash - pre-commit install - ``` - - If a check (or multiple) fails (commit is blocked), and reformatting was done, then restage - (`git add`) your files and commit them again to see if all issues were resolved without user - intervention. If changes are required follow the suggested fix, or resolve the stated - issue(s). Restaging and committing may take multiple attempts steps if errors are unaddressed - or insufficiently addressed. Please see [pre-commit](https://pre-commit.com/), - [ruff](https://docs.astral.sh/ruff/), or [isort](https://pycqa.github.io/isort/) for more - information. - - - In one step, all dependencies can be installed as: - - ```bash - pip install -e ".[all]" - ``` - -5. The functions which download resource data require an NLR API key. Obtain a key from: - - [https://developer.nlr.gov/signup/](https://developer.nlr.gov/signup/) - -6. To set up the `NLR_API_KEY` and `NLR_API_EMAIL` required for resource downloads, follow the steps - outlined in [this doc page](https://h2integrate.readthedocs.io/en/latest/getting_started/environment_variables.html). - -7. Verify setup by running tests: - - ```bash - pytest - ``` - -## Getting Started - -The [Examples](./examples/) contain Jupyter notebooks and sample YAML files for common usage -scenarios in H2Integrate. These are actively maintained and updated to demonstrate H2Integrate's -capabilities. For full details on simulation options and other features, documentation is -forthcoming. - ## Contributing -Interested in improving H2Integrate? Please see the [Contributor's Guide](./docs/CONTRIBUTING.md) -section for more information. +Interested in improving H2Integrate? Please see the [Contributor's Guide](./docs/CONTRIBUTING.md) for more information. diff --git a/docs/CONTRIBUTING.md b/docs/CONTRIBUTING.md index 441544b02..d33ccb64e 100644 --- a/docs/CONTRIBUTING.md +++ b/docs/CONTRIBUTING.md @@ -3,9 +3,9 @@ We welcome contributions in the form of bug reports, bug fixes, improvements to the documentation, ideas for enhancements (or the enhancements themselves!). -You can find a [list of current issues](https://github.com/NREL/H2Integrate/issues) in the project's +You can find a [list of current issues](https://github.com/NatLabRockies/H2Integrate/issues) in the project's GitHub repo. Feel free to tackle any existing bugs or enhancement ideas by submitting a -[pull request](https://github.com/NREL/H2Integrate/pulls). +[pull request](https://github.com/NatLabRockies/H2Integrate/pulls). ## Bug Reports @@ -21,7 +21,7 @@ GitHub repo. Feel free to tackle any existing bugs or enhancement ideas by submi * Docstrings are required and should follow the [Google style](https://www.sphinx-doc.org/en/master/usage/extensions/example_google.html). * When you start working on a pull request, start by creating a new branch pointing at the latest - commit on [main](https://github.com/NREL/H2Integrate). + commit on [main](https://github.com/NatLabRockies/H2Integrate). * Code formatting is enforced using pre-commit hooks and is required for any code pushed up to the repository. The pre-commit package is included in the developer install of the repository. The pre-commit hooks can be installed by running ```bash pre-commit install @@ -31,7 +31,7 @@ in the repository directory. This will automatically run the pre-commit formatti git commit -am "" ``` which will re-add the reformatted files to the commit. -* The H2Integrate copyright policy is detailed in the [`LICENSE`](https://github.com/NREL/H2Integrate/blob/main/LICENSE). +* The H2Integrate copyright policy is detailed in the [`LICENSE`](https://github.com/NatLabRockies/H2Integrate/blob/main/LICENSE). ## Documentation @@ -89,7 +89,7 @@ pytest tests/h2integrate/test_hybrid.py::test_h2integrate_system ``` When you push to your fork, or open a PR, your tests will be run against the -[Continuous Integration (CI)](https://github.com/NREL/HOPP/actions) suite. This will start a build +[Continuous Integration (CI)](https://github.com/NatLabRockies/HOPP/actions) suite. This will start a build that runs all tests on your branch against multiple Python versions, and will also test documentation builds. @@ -142,7 +142,7 @@ met the following steps should be taken to create a new release ``` 6. Check that the - [Test PyPI GitHub Action](https://github.com/NREL/H2Integrate/actions/workflows/publish_to_test_pypi.yml) + [Test PyPI GitHub Action](https://github.com/NatLabRockies/H2Integrate/actions/workflows/publish_to_test_pypi.yml) has run successfully. 1. If the action failed, identify and fix the issue, then 2. delete the local and remote tag using the following (replace "v0.1" in all instances just like @@ -155,7 +155,7 @@ met the following steps should be taken to create a new release 3. Start back at step 1. 7. When the Test PyPI Action has successfully run, - [create a new release](https://github.com/NREL/H2Integrate/releases/new) using the tag created in + [create a new release](https://github.com/NatLabRockies/H2Integrate/releases/new) using the tag created in step 5. ### Patches @@ -167,4 +167,4 @@ below: 1. All CI tests pass and the patch version has been bumped according to the [semantic versioning guidelines](https://semver.org/). 2. Follow steps 4 through 7 above. -3. Merge the NREL main branch back into the develop branch and push the changes. +3. Merge the NLR main branch back into the develop branch and push the changes. diff --git a/docs/_config.yml b/docs/_config.yml index 10effe641..06149877f 100644 --- a/docs/_config.yml +++ b/docs/_config.yml @@ -24,7 +24,7 @@ latex: targetname: book.tex # Information about where the book exists on the web repository: - url: https://github.com/NREL/H2Integrate + url: https://github.com/NatLabRockies/H2Integrate path_to_book: docs branch: main # Add GitHub buttons to your book @@ -39,33 +39,6 @@ sphinx: - sphinx.ext.autosummary - sphinx.ext.napoleon config: - # html_theme: sphinx_book_theme - # html_theme_options: - # use_issues_button: true - # use_repository_button: true - # use_edit_page_button: true - # show_toc_level: 2 - # repository_url: https://github.com/NREL/WAVES - # repository_branch: main - # icon_links: [ - # { - # name: GitHub, - # url: https://github.com/NREL/WAVES, - # icon: fa-brands fa-github, - # }, - # { - # name: PyPI version, - # url: https://pypi.org/project/WAVES/, - # icon: "https://img.shields.io/pypi/v/WAVES?link=https%3A%2F%2Fpypi.org%2Fproject%2FWAVES%2F", - # type: url, - # }, - # { - # name: Binder, - # url: "https://mybinder.org/v2/gh/NREL/WAVES/main?filepath=examples", - # icon: "https://mybinder.org/badge_logo.svg", - # type: url, - # }, - # ] language: python autosummary_generate: true autodoc_default_options: @@ -82,6 +55,8 @@ sphinx: # class-doc-from # no-value autodoc_typehints: description + html_static_path: + - _static napoleon_use_admonition_for_notes: true napoleon_use_rtype: false nb_merge_streams: true diff --git a/docs/_static/class_hierarchy.html b/docs/_static/class_hierarchy.html new file mode 100644 index 000000000..acd5c8d55 --- /dev/null +++ b/docs/_static/class_hierarchy.html @@ -0,0 +1,497 @@ + + + + + + + + + +
+

+
+ + + + + + +
+

+
+ + + + + +
+ + +
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+ + +
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0%
+
+
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+ + + + + +
+ Color = Application Group
+ Chemical
Control
Core / General
Hydrogen
Metal
Other
Other Elec. Generators
Renewables +

+ Shape = Model Category
+ ⬢Control
●Converter
◯Core
★Finance
▲Resource
◆Storage +

+ + Border width = inheritance depth
+ (thicker = higher-level parent)

+ Arrows: parent → child
+ Scroll to zoom | Drag to pan + +
+ + +
+ H2Integrate Class Hierarchy
+ + Interactive visualization — zoom, pan, hover for details + +
+ + + + + + diff --git a/docs/_toc.yml b/docs/_toc.yml index 08d048a91..f5cb561ac 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -12,7 +12,6 @@ parts: chapters: - file: user_guide/model_overview - file: user_guide/how_to_set_up_an_analysis - - file: user_guide/expected_naming_convention - file: user_guide/connecting_technologies - file: user_guide/defining_sites_and_resources - file: user_guide/design_optimization_in_h2i @@ -26,7 +25,9 @@ parts: - caption: Technology Models chapters: - file: technology_models/feedstocks + - file: technology_models/marine_hydrokinetic - file: technology_models/run_of_river + - file: technology_models/tidal - file: technology_models/natural_gas - file: technology_models/nuclear - file: technology_models/wombat_electrolyzer_om @@ -35,17 +36,24 @@ parts: - file: technology_models/atb_costs_pv.md - file: technology_models/atb_costs_wind.md - file: technology_models/co2.md - - file: technology_models/simple_generic_storage.md - file: technology_models/methanol.md - file: technology_models/ammonia.md - - file: technology_models/pysam_battery.md - file: technology_models/wind_plant_ard.md - file: technology_models/geologic_hydrogen.md - file: technology_models/grid - - file: technology_models/hydrogen_storage.md - file: technology_models/h2_fuel_cell.md + - file: technology_models/h2_smr.md - file: technology_models/iron_ewin.md - file: technology_models/iron_mine.md + - file: technology_models/iron_dri.md + - file: technology_models/steel_eaf.md + - caption: Storage Models + chapters: + - file: storage/storage_models_index.md + sections: + - file: storage/simple_generic_storage.md + - file: storage/pysam_battery.md + - file: storage/hydrogen_storage.md - caption: Resource Models chapters: - file: resource/resource_index @@ -58,12 +66,17 @@ parts: - file: resource/himawari_v3_api - file: resource/meteosat_prime_meridian_v4_api - file: resource/openmeteo_solar_archive + - file: resource/tidal_resource - caption: Control chapters: - file: control/control_overview - file: control/open-loop_controllers - file: control/pyomo_controllers - file: control/controller_demonstrations + - caption: Demand + chapters: + - file: demand/demand_components + - file: demand/demand_demo - caption: Finance Models chapters: - file: finance_models/financial_analyses diff --git a/docs/api.md b/docs/api.md index 7c4b9fa4e..6cf44f280 100644 --- a/docs/api.md +++ b/docs/api.md @@ -15,4 +15,5 @@ storage transporters tools + demand ``` diff --git a/docs/build_book.sh b/docs/build_book.sh index 5817e636c..8e9d6f837 100644 --- a/docs/build_book.sh +++ b/docs/build_book.sh @@ -1,2 +1,6 @@ rm -rf _build + +# Generate the interactive class hierarchy diagram +python generate_class_hierarchy.py + jupyter-book build --keep-going . diff --git a/docs/control/control_overview.md b/docs/control/control_overview.md index 7d9da9550..141606339 100644 --- a/docs/control/control_overview.md +++ b/docs/control/control_overview.md @@ -7,10 +7,9 @@ There are two different systematic approaches, or frameworks, in H2Integrate for The first approach, [open-loop control](#open-loop-control), assumes no feedback of any kind to the controller. The open-loop framework does not require a detailed technology performance model and can essentially act as the performance model. The open-loop framework establishes a control component that runs the control and passes out information about `_unmet_demand`, `unused_`, `_out`, and `total__unmet_demand`. Supported controllers: -- [`PassThroughOpenLoopController`](#pass-through-controller) +- [`SimpleStorageOpenLoopController`](#pass-through-controller) - [`DemandOpenLoopStorageController`](#demand-open-loop-storage-controller) -- [`DemandOpenLoopConverterController`](#demand-open-loop-converter-controller) -- [`FlexibleDemandOpenLoopConverterController`](#flexible-demand-open-loop-converter-controller) + (pyomo-control-framework)= @@ -20,4 +19,4 @@ The second systematic control approach, [pyomo control](#pyomo-control), allows In the pyomo control framework in H2Integrate, each technology can have control rules associated with them that are in turn passed to the pyomo control component, which is owned by the storage technology. The pyomo control component combines the technology rules into a single pyomo model, which is then passed to the storage technology performance model inside a callable dispatch function. The dispatch function also accepts a simulation method from the performance model and iterates between the pyomo model for dispatch commands and the performance simulation function to simulated performance with the specified commands. The dispatch function runs in specified time windows for dispatch and performance until the whole simulation time has been run. Supported controllers: -- [`HeuristicLoadFollowingController`](#heuristic-load-following-controller) +- [`HeuristicLoadFollowingStorageController`](#heuristic-load-following-controller) diff --git a/docs/control/controller_demonstrations.md b/docs/control/controller_demonstrations.md index 1bc727bec..82fd0b635 100644 --- a/docs/control/controller_demonstrations.md +++ b/docs/control/controller_demonstrations.md @@ -17,6 +17,8 @@ kernelspec: from pathlib import Path from matplotlib import pyplot as plt from h2integrate.core.h2integrate_model import H2IntegrateModel +from h2integrate import EXAMPLE_DIR + ``` ## Hydrogen Dispatch @@ -24,25 +26,33 @@ from h2integrate.core.h2integrate_model import H2IntegrateModel The following example is an expanded form of `examples/14_wind_hydrogen_dispatch`. Here, we're highlighting the dispatch controller setup from -`examples/14_wind_hydrogen_dispatch/inputs/tech_config.yaml`. Please note some sections are removed -simply to highlight the controller sections +`examples/14_wind_hydrogen_dispatch/inputs/tech_config.yaml`. Please note some sections are removed simply to highlight the controller sections + +```{literalinclude} ../../examples/14_wind_hydrogen_dispatch/inputs/tech_config.yaml +:language: yaml +:lineno-start: 52 +:linenos: true +:lines: 52,53-54,57-58,59-71 +``` + +We also include a demand technology to calculate how much demand is met, how much commodity is unused to meet the demand, and how much demand is remaining: ```{literalinclude} ../../examples/14_wind_hydrogen_dispatch/inputs/tech_config.yaml :language: yaml -:lineno-start: 54 +:lineno-start: 79 :linenos: true -:lines: 54,59-61,67-74 +:lines: 79-86 ``` + Using the primary configuration, we can create, run, and postprocess an H2Integrate model. ```{code-cell} ipython3 # Create an H2Integrate model -model = H2IntegrateModel(Path("../../examples/14_wind_hydrogen_dispatch/inputs/h2i_wind_to_h2_storage.yaml")) +model = H2IntegrateModel(EXAMPLE_DIR/"14_wind_hydrogen_dispatch"/"inputs"/"h2i_wind_to_h2_storage.yaml") # Run the model model.run() -model.post_process() ``` Now, we can visualize the demand profiles over time. @@ -50,20 +60,13 @@ Now, we can visualize the demand profiles over time. ```{code-cell} ipython3 fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(8, 6), layout="tight") -start_hour = 0 -end_hour = 200 -total_time_steps = model.prob.get_val("h2_storage.hydrogen_soc").size -demand_profile = [ - model.technology_config["technologies"]["h2_storage"]["model_inputs"]["control_parameters"][ - "demand_profile" - ] - * 1e-3 -] * total_time_steps +start_hour = 800 +end_hour = 1000 xvals = list(range(start_hour, end_hour)) ax1.plot( xvals, - model.prob.get_val("h2_storage.hydrogen_soc", units="percent")[start_hour:end_hour], + model.prob.get_val("h2_storage.SOC", units="percent")[start_hour:end_hour], label="SOC", ) ax2.plot( @@ -74,25 +77,25 @@ ax2.plot( ) ax2.plot( xvals, - model.prob.get_val("h2_storage.hydrogen_unused_commodity", units="t/h")[start_hour:end_hour], + model.prob.get_val("h2_load_demand.unused_hydrogen_out", units="t/h")[start_hour:end_hour], linestyle=":", label="H$_2$ Unused (kg)", ) ax2.plot( xvals, - model.prob.get_val("h2_storage.hydrogen_unmet_demand", units="t/h")[start_hour:end_hour], + model.prob.get_val("h2_load_demand.unmet_hydrogen_demand_out", units="t/h")[start_hour:end_hour], linestyle=":", label="H$_2$ Unmet Demand (kg)", ) ax2.plot( xvals, - model.prob.get_val("h2_storage.hydrogen_out", units="t/h")[start_hour:end_hour], + model.prob.get_val("h2_load_demand.hydrogen_out", units="t/h")[start_hour:end_hour], linestyle="-", label="H$_2$ Delivered (kg)", ) ax2.plot( xvals, - demand_profile[start_hour:end_hour], + model.prob.get_val("h2_load_demand.hydrogen_demand", units="t/h")[start_hour:end_hour], linestyle="--", label="H$_2$ Demand (kg)", ) @@ -100,15 +103,15 @@ ax2.plot( ax1.set_ylabel("SOC (%)") ax1.grid() ax1.set_axisbelow(True) -ax1.set_xlim(0, 200) -ax1.set_ylim(0, 50) +ax1.set_xlim(start_hour, end_hour) +ax1.set_ylim(60, 100) ax2.set_ylabel("H$_2$ Hourly (t)") ax2.set_xlabel("Timestep (hr)") ax2.grid() ax2.set_axisbelow(True) -ax2.set_ylim(0, 20) -ax2.set_yticks(range(0, 21, 2)) +ax2.set_ylim(0, 18) +ax2.set_yticks(range(0, 19, 2)) plt.legend(ncol=3) fig.show() diff --git a/docs/control/open-loop_controllers.md b/docs/control/open-loop_controllers.md index f8f6743b0..dfb7f241c 100644 --- a/docs/control/open-loop_controllers.md +++ b/docs/control/open-loop_controllers.md @@ -7,10 +7,10 @@ The open-loop storage controllers can be attached as the control strategy in the 2. Demand Open-Loop Storage Controller - uses simple logic to attempt to meet demand using the storage technology. (pass-through-controller)= -### Pass-Through Controller -The `PassThroughOpenLoopController` simply directly passes the input commodity flow to the output without any modifications. It is useful for testing, as a placeholder for more complex controllers, and for maintaining consistency between controlled and uncontrolled frameworks as this 'controller' does not alter the system output in any way. +### Simple Open-Loop Storage Controller +The `SimpleStorageOpenLoopController` passes the input commodity flow to the output, possibly with minor adjustments to meet demand. It is useful for testing and as a placeholder for more complex controllers. -For examples of how to use the `PassThroughOpenLoopController` open-loop control framework, see the following: +For examples of how to use the `SimpleStorageOpenLoopController` open-loop control framework, see the following: - `examples/01_onshore_steel_mn` - `examples/02_texas_ammonia` - `examples/12_ammonia_synloop` @@ -26,93 +26,3 @@ An example of an N2 diagram for a system using the open-loop control framework f For examples of how to use the `DemandOpenLoopStorageController` open-loop control framework, see the following: - `examples/14_wind_hydrogen_dispatch/` - `examples/19_simple_dispatch/` - -## Open-Loop Converter Controllers - -Open-loop converter controllers define rule-based logic for meeting commodity demand profiles without using dynamic system feedback. These controllers operate independently at each timestep. - -This page documents two core controller types: -1. Demand Open-Loop Converter Controller — meets a fixed demand profile. -2. Flexible Demand Open-Loop Converter Controller — adjusts demand up or down within flexible bounds. - -(demand-open-loop-converter-controller)= -### Demand Open-Loop Converter Controller -The `DemandOpenLoopConverterController` allocates commodity input to meet a defined demand profile. It does not contain energy storage logic, only **instantaneous** matching of supply and demand. - -The controller computes each value per timestep: -- Unmet demand (non-zero when supply < demand, otherwise 0.) -- Unused commodity (non-zero when supply > demand, otherwise 0.) -- Delivered output (commodity supplied to demand sink) - -This provides a simple baseline for understanding supply–demand balance before adding complex controls. - -#### Configuration -The controller is defined within the `tech_config` and requires these inputs. - -| Field | Type | Description | -| ----------------- | -------------- | ------------------------------------- | -| `commodity_name` | `str` | Commodity name (e.g., `"hydrogen"`). | -| `commodity_units` | `str` | Units (e.g., `"kg/h"`). | -| `demand_profile` | scalar or list | Timeseries demand or constant demand. | - -```yaml -control_strategy: - model: DemandOpenLoopConverterController -model_inputs: - control_parameters: - commodity_name: hydrogen - commodity_units: kg/h - demand_profile: [10, 10, 12, 15, 14] -``` -For an example of how to use the `DemandOpenLoopConverterController` open-loop control framework, see the following: -- `examples/23_solar_wind_ng_demand` - -(flexible-demand-open-loop-converter-controller)= -### Flexible Demand Open-Loop Converter Controller -The `FlexibleDemandOpenLoopConverterController` extends the fixed-demand controller by allowing the actual demand to flex up or down within defined bounds. This is useful for demand-side management scenarios where: -- Processes can defer demand (e.g., flexible industrial loads) -- The system requires demand elasticity without dynamic optimization - -The controller computes: -- Flexible demand (clamped within allowable ranges) -- Unmet flexible demand -- Unused commodity -- Delivered output - -Everything remains open-loop no storage, no intertemporal coupling. - -For an example of how to use the `FlexibleDemandOpenLoopConverterController` open-loop control framework, see the following: -- `examples/23_solar_wind_ng_demand` - -The flexible demand component takes an input commodity production profile, the maximum demand profile, and various constraints (listed below), and creates a "flexible demand profile" that follows the original input commodity production profile while satisfying varying constraint. -Please see the figure below for an example of how the flexible demand profile can vary from the original demand profile based on the input commodity production profile and the ramp rates. -The axes are unlabeled to allow for generalization to any commodity and unit type. - -| ![Flexible Demand Example](figures/flex_demand_fig.png) | -|-| - - -#### Configuration -The flexible demand controller is defined within the `tech_config` with the following parameters: - -| Field | Type | Description | -| ------------------- | -------------- | -------------------------------------------- | -| `commodity_name` | `str` | Commodity name. | -| `commodity_units` | `str` | Units for all values. | -| `demand_profile` | scalar or list | Default (nominal) demand profile. | -| `turndown_ratio` | float | Minimum fraction of baseline demand allowed. | -| `ramp_down_rate_fraction` | float | Maximum ramp-down rate per timestep expressed as a fraction of baseline demand. | -| `ramp_up_rate_fraction` | float | Maximum ramp-up rate per timestep expressed as a fraction of baseline demand. | -| `min_utilization` | float | Minimum total fraction of baseline demand that must be met over the entire simulation. | - -```yaml -model_inputs: - control_parameters: - commodity_name: hydrogen - commodity_units: kg/h - demand_profile: [10, 12, 10, 8] - turndown_ratio: 0.1 - ramp_down_rate_fraction: 0.5 - ramp_up_rate_fraction: 0.5 - min_utilization: 0 -``` diff --git a/docs/control/pyomo_controllers.md b/docs/control/pyomo_controllers.md index 1f6c517fc..2da23275f 100644 --- a/docs/control/pyomo_controllers.md +++ b/docs/control/pyomo_controllers.md @@ -13,15 +13,15 @@ The Pyomo control framework currently supports both a simple heuristic method an (heuristic-load-following-controller)= ## Heuristic Load Following Controller -The simple heuristic method is specified by setting the storage control to `HeuristicLoadFollowingController`. When using the Pyomo framework, a `dispatch_rule_set` for each technology connected to the storage technology must also be specified. These will typically be `PyomoDispatchGenericConverter` for generating technologies, and `PyomoRuleStorageBaseclass` for storage technologies. More complex rule sets may be developed as needed. +The simple heuristic method is specified by setting the storage control to `HeuristicLoadFollowingStorageController`. When using the Pyomo framework, a `dispatch_rule_set` for each technology connected to the storage technology must also be specified. These will typically be `PyomoDispatchGenericConverter` for generating technologies, and `PyomoRuleStorageBaseclass` for storage technologies. More complex rule sets may be developed as needed. -For an example of how to use the heuristic Pyomo control framework with the `HeuristicLoadFollowingController`, see +For an example of how to use the heuristic Pyomo control framework with the `HeuristicLoadFollowingStorageController`, see - `examples/18_pyomo_heuristic_wind_battery_dispatch` (optimized-load-following-controller)= ## Optimized Load Following Controller -The optimized dispatch method is specified by setting the storage control to `OptimizedDispatchController`. The same `dispatch_rule_set` for each technology connected to the storage technology is followed as in the heuristic case, where each technology must also have a `dispatch_rule_set` defined in the `tech_config`. The optimized dispatch currently uses the same dispatch rules as the heuristic dispatch. This method maximizes the load met while minimizing the cost of the system (operating cost) over each specified time window. +The optimized dispatch method is specified by setting the storage control to `OptimizedDispatchStorageController`. Unlike the heuristic method, the optimized dispatch method does not use `dispatch_rule_set` as an input in the `tech_config`. The `OptimizedDispatchStorageController` method maximizes the load met while minimizing the cost of the system (operating cost) over each specified time window. The optimized dispatch using Pyomo is implemented differently than the heuristic dispatch in order to be able to properly aggregate the individual Pyomo technology models into a cohesive Pyomo plant model for the optimization solver. Practically, this means that the Pyomo elements of the dispatch (including the individual technology models and the plant model) are not exposed to the main H2I code flow, and do not appear in the N2 diagram. The figure below shows a flow diagram of how the dispatch is implemented. The green blocks below represent what is represented in the N2 diagram of the system. The dispatch routine is currently self-contained within the storage technology of the system, though it includes solving an aggregated plant model in the optimization @@ -40,7 +40,7 @@ We have exposed the optimization cost (weighting) values to the user in this imp - The cost values are defined in units of "$/kW". ``` -For an example of how to use the optimized Pyomo control framework with the `OptimizedDispatchController`, see +For an example of how to use the optimized Pyomo control framework with the `OptimizedDispatchStorageController`, see - `examples/27_pyomo_optimized_dispatch` diff --git a/docs/demand/demand_components.md b/docs/demand/demand_components.md new file mode 100644 index 000000000..125973d0c --- /dev/null +++ b/docs/demand/demand_components.md @@ -0,0 +1,109 @@ +# Demand Components + +Demand components define rule-based logic for meeting commodity demand profiles without using dynamic system feedback. These components operate independently at each timestep. + +This page documents two core demand types: +1. [`GenericDemandComponent`](#generic-demand-component) — meets a fixed demand profile. +2. [`FlexibleDemandComponent`](#flexible-demand-component) — adjusts demand up or down within flexible bounds. + + +(demand-component-inputs-and-outputs)= +## Demand component inputs and outputs +The inputs to the demand components are the `{commodity}_demand` profile and the `{commodity}_in` profile. The `{commodity}_in` profile is the initial commodity used to satisfy the demand. Suppose the `{commodity}_in` (pink) and `{commodity}_demand` (green) profiles look like whats shown below: + +![](./figures/demand_inputs.png) + +The demand components then compute the following output profiles: +- `unmet_{commodity}_demand_out`: Unmet demand (non-zero when supply < demand, otherwise 0.) +- `unused_{commodity}_out`: Unused commodity (non-zero when supply > demand, otherwise 0.) + +![](./figures/demand_calcs.png) + +- `commodity_out`: Delivered output (commodity supplied to demand sink) + +![](./figures/demand_commodity_out.png) + +The demand components also compute the following outputs: +- `capacity_factor`: the ratio of the demand that's been met to the full demand +- `rated_commodity_production`: the maximum value of the demand profile + + +(generic-demand-component)= +### Generic Demand Component +The `GenericDemandComponent` allocates commodity input to meet a defined demand profile. It does not contain energy storage logic, only **instantaneous** matching of supply and demand. + +The demand component computes each value per timestep: +- Unmet demand (non-zero when supply < demand, otherwise 0.) +- Unused commodity (non-zero when supply > demand, otherwise 0.) +- Delivered output (commodity supplied to demand sink) + +This provides a simple baseline for understanding supply–demand balance. + +#### Configuration +The demand is defined within the `tech_config` and requires these inputs. + +| Field | Type | Description | +| ----------------- | -------------- | ------------------------------------- | +| `commodity_name` | `str` | Commodity name (e.g., `"hydrogen"`). | +| `commodity_units` | `str` | Units (e.g., `"kg/h"`). | +| `demand_profile` | scalar or list | Timeseries demand or constant demand. | + +```yaml +performance_model: + model: GenericDemandComponent +model_inputs: + performance_parameters: + commodity_name: hydrogen + commodity_units: kg/h + demand_profile: [10, 10, 12, 15, 14] +``` +For an example of how to use the `GenericDemandComponent` framework, see the following: +- `examples/23_solar_wind_ng_demand` + +(flexible-demand-component)= +### Flexible Demand Component +The `FlexibleDemandComponent` extends the generic demand component by allowing the actual demand to flex up or down within defined bounds. This is useful for demand-side management scenarios where: +- Processes can defer demand (e.g., flexible industrial loads) +- The system requires demand elasticity without dynamic optimization + +The component computes: +- Flexible demand (clamped within allowable ranges) +- Unmet flexible demand +- Unused commodity +- Delivered output + +For an example of how to use the `FlexibleDemandComponent` demand component, see the following: +- `examples/23_solar_wind_ng_demand` + +The flexible demand component takes an input commodity production profile, the maximum demand profile, and various constraints (listed below), and creates a "flexible demand profile" that follows the original input commodity production profile while satisfying varying constraint. +Please see the figure below for an example of how the flexible demand profile can vary from the original demand profile based on the input commodity production profile and the ramp rates. +The axes are unlabeled to allow for generalization to any commodity and unit type. + +| ![Flexible Demand Example](figures/flex_demand_fig.png) | +|-| + + +#### Configuration +The flexible demand component is defined within the `tech_config` with the following parameters: + +| Field | Type | Description | +| ------------------- | -------------- | -------------------------------------------- | +| `commodity_name` | `str` | Commodity name. | +| `commodity_units` | `str` | Units for all values. | +| `demand_profile` | scalar or list | Default (nominal) demand profile. | +| `turndown_ratio` | float | Minimum fraction of baseline demand allowed. | +| `ramp_down_rate_fraction` | float | Maximum ramp-down rate per timestep expressed as a fraction of baseline demand. | +| `ramp_up_rate_fraction` | float | Maximum ramp-up rate per timestep expressed as a fraction of baseline demand. | +| `min_utilization` | float | Minimum total fraction of baseline demand that must be met over the entire simulation. | + +```yaml +model_inputs: + performance_parameters: + commodity_name: hydrogen + commodity_units: kg/h + demand_profile: [10, 12, 10, 8] + turndown_ratio: 0.1 + ramp_down_rate_fraction: 0.5 + ramp_up_rate_fraction: 0.5 + min_utilization: 0 +``` diff --git a/docs/demand/demand_demo.md b/docs/demand/demand_demo.md new file mode 100644 index 000000000..fdc076bcc --- /dev/null +++ b/docs/demand/demand_demo.md @@ -0,0 +1,259 @@ +--- +jupytext: + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.18.1 +kernelspec: + display_name: native-ard-h2i + language: python + name: python3 +--- + +# Demand Demonstration +Different usage of demand components are shown in the following examples: +- `13_dispatch_for_electrolyzer`: showcases how to set an overall demand for the system and a separate demand for the battery system. +- `23_solar_wind_ng_demand`: compares usage of the `GenericDemandComponent` and `FlexibleDemandComponent`. +- `24_solar_battery_grid`: highlights how to sell the electricity in excess of the demand to the grid. + +This demonstration will focus on the `13_dispatch_for_electrolyzer` example + +## Electrolyzer load demand + +The following example is an expanded form of `examples/13_dispatch_for_electrolyzer`. + +The technology interconnections: + +```{literalinclude} ../../examples/13_dispatch_for_electrolyzer/plant_config.yaml +:language: yaml +:lineno-start: 16 +:linenos: true +:lines: 16-27 +``` + +Which we can visualize using an XDSM diagram: + +![](./figures/example_13_xdsm.png) + +The electrolyzer system is comprised of 6 stacks, each rated at 10 MW, resulting in a total capacity of 60 MW. The minimum operating point of the electrolyzer (`turndown_ratio`) is 10% of the rated capacity, meaning that the electrolyzer is turned off if there is less than 6 MW of input electricity. + +```{literalinclude} ../../examples/13_dispatch_for_electrolyzer/tech_config.yaml +:language: yaml +:lineno-start: 120 +:linenos: true +:lines: 120-123,125-126,129-130,134 +``` + +We want to dispatch the battery to *keep the electrolyzer on*. We set the demand profile for the battery as the minimum power required to keep the electrolyzer on, 6 MW. + +```{note} +Note: the demand profile for the battery is only used by the battery controller and is not used in the battery performance model for any calculations. +``` + + +```{literalinclude} ../../examples/13_dispatch_for_electrolyzer/tech_config.yaml +:language: yaml +:lineno-start: 79 +:linenos: true +:lines: 79-90, 99 +``` + +We don't want to send more electricity to the electrolyzer than the electrolyzer can use. We use the demand component to saturate the electricity generation to the electrolyzer's rated capacity (equal to 60 MW). + +```{literalinclude} ../../examples/13_dispatch_for_electrolyzer/tech_config.yaml +:language: yaml +:lineno-start: 112 +:linenos: true +:lines: 112-119 +``` + + + +We initialize and setup the H2I model + +```{code-cell} ipython3 +from pathlib import Path +from matplotlib import pyplot as plt +from h2integrate.core.h2integrate_model import H2IntegrateModel +from h2integrate import EXAMPLE_DIR +from h2integrate.core.inputs.validation import load_tech_yaml, load_plant_yaml, load_driver_yaml + +ex_dir = EXAMPLE_DIR / "13_dispatch_for_electrolyzer" +tech_config = load_tech_yaml(ex_dir / "tech_config.yaml") +plant_config = load_plant_yaml(ex_dir / "plant_config.yaml") +driver_config = load_driver_yaml(ex_dir / "driver_config.yaml") + +# modify all the output folders to be full filepaths +driver_config["general"]["folder_output"] = str(Path(ex_dir / "outputs").absolute()) +tech_config["technologies"]["distributed_wind_plant"]["model_inputs"]["performance_parameters"][ + "cache_dir" +] = ex_dir / "cache" + +input_config = { + "plant_config": plant_config, + "technology_config": tech_config, + "driver_config": driver_config, +} + + +# Create an H2Integrate model +h2i = H2IntegrateModel(input_config) + +# Setup the model +h2i.setup() +``` + +If we wanted to change the demand profiles for the battery (`battery`) or the demand component (`elec_load_demand`) to be different than the demand profiles specified in the technology config, we could do that using `set_val`: + +```{code-cell} ipython3 +electrolyzer_capacity_MW = 60 + +## Set the battery demand equal to the minimum electricity needed to keep the electrolyzer on +# h2i.prob.set_val("battery.electricity_demand", 0.1 * electrolyzer_capacity_MW, units="MW") + +## Set the demand of the demand component equal to the rated electrical capacity of the electrolyzer +# h2i.prob.set_val("elec_load_demand.electricity_demand", electrolyzer_capacity_MW, units="MW") +``` + +We then run the model: +```{code-cell} ipython3 +# Run the model +h2i.run() +h2i.prob.get_val("finance_subgroup_hydrogen.LCOH", units="USD/kg")[0] +``` + + +### Plotting outputs + +First, we get the main inputs and outputs of the `GenericDemandComponent` technology: + +```{code-cell} ipython3 +generation_with_battery = h2i.prob.get_val("elec_load_demand.electricity_in", units="MW") +full_demand = h2i.prob.get_val("elec_load_demand.electricity_demand", units="MW") +unmet_demand = h2i.prob.get_val("elec_load_demand.unmet_electricity_demand_out", units="MW") +excess_electricity = h2i.prob.get_val("elec_load_demand.unused_electricity_out", units="MW") +``` + +Plot the inputs to the `GenericDemandComponent` and outputs calculated in the `GenericDemandComponent`: + +```{code-cell} ipython3 +import matplotlib.pyplot as plt +fig, ax = plt.subplots(1, 1, figsize=[6.4, 2.4]) + +start_hour = 800 +end_hour = 1000 + +x = list(range(start_hour, end_hour)) +where_unmet_demand = [True if d>0 else False for d in unmet_demand[start_hour:end_hour]] +where_excess_commodity = [True if d>0 else False for d in excess_electricity[start_hour:end_hour]] + +ax.fill_between(x, full_demand[start_hour:end_hour], generation_with_battery[start_hour:end_hour], where=where_unmet_demand, color="tab:red", alpha=0.5, zorder=0, label="unmet_commodity_demand_out") +ax.fill_between(x, full_demand[start_hour:end_hour], generation_with_battery[start_hour:end_hour], where=where_excess_commodity, color="tab:blue", alpha=0.5, zorder=1, label="unused_commodity_out") +ax.plot(x, full_demand[start_hour:end_hour], color="tab:green", lw=2.0, ls='solid', zorder=4, label="commodity_demand") +ax.plot(x, generation_with_battery[start_hour:end_hour], color="tab:pink", lw=2.0, ls='solid', zorder=3, label="commodity_in") + +ax.set_xlim([start_hour,end_hour]) +ax.spines[['right', 'top']].set_visible(False) +ax.legend(bbox_to_anchor=(0.10, 0.95), loc="lower left", borderaxespad=0.0, framealpha=0.0, ncols=2) +ax.set_ylabel("GenericDemandComponent \nElectricity (MW)") +ax.set_xlabel("Time (hours)") +``` + + +Plot the main inputs and outputs of the `GenericDemandComponent`: + +```{code-cell} ipython3 +fig, ax = plt.subplots(1, 1, figsize=[6.4, 2.4]) +ax.fill_between(x, full_demand[start_hour:end_hour], generation_with_battery[start_hour:end_hour], where=where_excess_commodity, color="tab:grey", alpha=0.25, zorder=1, label="_unused_commodity_out") +ax.plot(x, generation_with_battery[start_hour:end_hour], color="tab:grey", lw=2.0, ls='solid', zorder=2, label="commodity_in", alpha=0.25) + +ax.plot(x, full_demand[start_hour:end_hour], color="tab:green", alpha=0.5, lw=1.5, ls='-.', zorder=3, label="commodity_demand") + +ax.plot(x, h2i.prob.get_val("elec_load_demand.electricity_out", units="MW")[start_hour:end_hour], color="tab:purple", lw=2.0, ls='solid', zorder=4, label="commodity_out") +ax.spines[['right', 'top']].set_visible(False) +ax.set_xlim([start_hour, end_hour]) +ax.set_ylabel("GenericDemandComponent \nElectricity (MW)") +ax.set_xlabel("Time (hours)") +ax.legend(bbox_to_anchor=(0.0, 0.95), loc="lower left", ncols=3, borderaxespad=0.0, framealpha=0.0) +``` + + +Plot the battery performance: + +```{code-cell} ipython3 +fig, ax = plt.subplots(1, 1, figsize=[7.2, 2.4]) + +start_hour = 900 +end_hour = 1000 +x = list(range(start_hour, end_hour)) + +generation = h2i.prob.get_val("battery.electricity_in", units="MW") +battery_demand = h2i.prob.get_val("battery.electricity_demand", units="MW") +battery_charge_discharge = h2i.prob.get_val("battery.electricity_out", units="MW") + +where_charge = [True if d<0 else False for d in battery_charge_discharge[start_hour:end_hour]] +where_discharge = [True if d>0 else False for d in battery_charge_discharge[start_hour:end_hour]] + +ax.plot(x, battery_demand[start_hour:end_hour], color="tab:green", alpha=0.5, lw=1.5, ls='-.', zorder=2, label="battery.electricity_demand") +ax.plot(x, generation[start_hour:end_hour], color="tab:blue", alpha=1.0, lw=1.5, ls='--', zorder=3, label="battery.electricity_in") +ax.plot(x, generation_with_battery[start_hour:end_hour], color="tab:pink", alpha=1.0, lw=1.5, ls='-', zorder=3, label="elec_combiner.electricity_out") +ax.fill_between(x, generation[start_hour:end_hour], generation_with_battery[start_hour:end_hour], where=where_charge, color="tab:cyan", alpha=0.5, zorder=0, label="battery charging") +ax.fill_between(x, generation[start_hour:end_hour], generation_with_battery[start_hour:end_hour], where=where_discharge, color="tab:orange", alpha=0.5, zorder=0, label="battery discharging") + + +ax.spines[['right', 'top']].set_visible(False) +ax.set_xlim([start_hour, end_hour]) +ax.set_ylim([0, 70]) +ax.legend(bbox_to_anchor=(1.0, 0.5), loc="center left", borderaxespad=0, framealpha=0.0) +ax.set_ylabel("Electricity (MW)") +ax.set_xlabel("Time (hours)") +``` + +Plot the battery SOC and charge/discharge profile + +```{code-cell} ipython3 +fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=[7.2, 4.8]) + +battery_SOC = h2i.prob.get_val("battery.SOC", units="percent") + +ax1.plot(x, battery_charge_discharge[start_hour:end_hour], color="tab:blue", alpha=1.0, lw=1.5, ls='-', zorder=3, label="battery.electricity_out") +ax1.spines[['right', 'top']].set_visible(False) +ax1.set_xlim([start_hour, end_hour]) +ax.legend(bbox_to_anchor=(1.0, 0.5), loc="center left", borderaxespad=0, framealpha=0.0) +ax1.set_ylabel("Electricity (MW)") + + +# Plot the SOC +ax2.plot(x, battery_SOC[start_hour:end_hour], color="tab:blue", lw=1.5) + +ax2.set_ylabel("SOC (%)") +ax2.set_ylim([0, 100]) +ax2.spines[['right', 'top']].set_visible(False) +ax2.set_xlabel("Time (hours)") +``` + + +### Changing the battery demand + +Lets see what the LCOH is when the battery is used to keep the electrolyzer on: +```{code-cell} ipython3 +h2i.prob.get_val("finance_subgroup_hydrogen.LCOH", units="USD/kg")[0] +``` + +If we re-run H2I and set the battery demand equal to the electrolyzer capacity instead, we can see that the LCOH increases: + +```{code-cell} ipython3 + +# Set the battery demand equal to the rated electrical capacity of the electrolyzer +h2i.prob.set_val("battery.electricity_demand",electrolyzer_capacity_MW, units="MW") + +# Set the demand of the demand component equal to the rated electrical capacity of the electrolyzer +h2i.prob.set_val("elec_load_demand.electricity_demand", electrolyzer_capacity_MW, units="MW") + +# Re-run H2I +h2i.run() + +# Get the LCOH +h2i.prob.get_val("finance_subgroup_hydrogen.LCOH", units="USD/kg")[0] +``` diff --git a/docs/demand/figures/demand_calcs.png b/docs/demand/figures/demand_calcs.png new file mode 100644 index 000000000..a3e297d93 Binary files /dev/null and b/docs/demand/figures/demand_calcs.png differ diff --git a/docs/demand/figures/demand_commodity_out.png b/docs/demand/figures/demand_commodity_out.png new file mode 100644 index 000000000..ec699a97a Binary files /dev/null and b/docs/demand/figures/demand_commodity_out.png differ diff --git a/docs/demand/figures/demand_inputs.png b/docs/demand/figures/demand_inputs.png new file mode 100644 index 000000000..91e31d8a7 Binary files /dev/null and b/docs/demand/figures/demand_inputs.png differ diff --git a/h2integrate/control/control_strategies/converters/__init__.py b/docs/demand/figures/example_13_xdsm.png similarity index 100% rename from h2integrate/control/control_strategies/converters/__init__.py rename to docs/demand/figures/example_13_xdsm.png diff --git a/docs/control/figures/flex_demand_fig.png b/docs/demand/figures/flex_demand_fig.png similarity index 100% rename from docs/control/figures/flex_demand_fig.png rename to docs/demand/figures/flex_demand_fig.png diff --git a/docs/developer_guide/adding_a_new_technology.md b/docs/developer_guide/adding_a_new_technology.md index 9d30d320b..5c22e6d19 100644 --- a/docs/developer_guide/adding_a_new_technology.md +++ b/docs/developer_guide/adding_a_new_technology.md @@ -258,6 +258,19 @@ In the middle-ground case where the models might use a shared object that is com This would require additional logic to first check if the cached object exists and is valid before attempting to load it, otherwise it would create the object from scratch. There is an example of this in the `hopp_wrapper.py` file. +### Specifying allowable time step for your model +If you want your model to run with time steps other than 1 hour (3600 s), then you must specify the `_time_step_bounds` as a class attribute in each of your model classes. To run a simulation with a given time step, all models in the plant must be compatible with the desired time step. + +```python +class ECOElectrolyzerPerformanceModel(ElectrolyzerPerformanceBaseClass): + """ + An OpenMDAO component that wraps the PEM electrolyzer model. + Takes electricity input and outputs hydrogen and oxygen generation rates. + """ + + # (min, max) time step lengths (in seconds) compatible with this model + _time_step_bounds = (300, 3600) # (5-min, 1-hour) +``` ### Other cases diff --git a/docs/developer_guide/class_structure.md b/docs/developer_guide/class_structure.md index 49631eb14..0b99823ca 100644 --- a/docs/developer_guide/class_structure.md +++ b/docs/developer_guide/class_structure.md @@ -18,6 +18,29 @@ Let us take a PEM electrolyzer model as an example. Each electrolyzer model has shared methods and attributes that would be present in any valid model. These methods are defined at the `ElectrolyzerBaseClass` level, which inherits from `ConverterBaseClass`. Any implemented electrolyzer model should inherit from `ElectrolyzerBaseClass` to make use of its already built out structure and methods. -This is shown below. -![Class structure](fig_of_class_structure.png) +## Interactive class hierarchy + +The diagram below shows **every model class** in H2Integrate and how they inherit from one another. +The visual encoding uses three dimensions: + +- **Color** represents the application group (electricity, chemical, metal, etc.) +- **Shape** represents the model category (converter, storage, transporter, etc.) +- **Border thickness** indicates inheritance depth (thicker borders = higher-level parent classes) + +Arrows point from parent to child. +You can **zoom**, **pan**, **hover** for details, and **drag** nodes to rearrange the layout. + +To regenerate this visualization after code changes, run: + +```bash +python docs/generate_class_hierarchy.py +``` + +```{raw} html +
+ +
+``` diff --git a/docs/developer_guide/why_make_h2integrate.md b/docs/developer_guide/why_make_h2integrate.md index 7d2d48768..851813c36 100644 --- a/docs/developer_guide/why_make_h2integrate.md +++ b/docs/developer_guide/why_make_h2integrate.md @@ -45,7 +45,7 @@ Using OpenMDAO for this gives quite a few benefits: - internal units handling and conversion - built-in nonlinear solvers to resolve model coupling - built-in optimization and parameter sweep drivers -- multiple existing NREL tools use OpenMDAO, including [WISDEM](https://github.com/WISDEM/WISDEM/) and [WEIS](https://github.com/WISDEM/WEIS), so we can draw from institutional knowledge +- multiple existing NLR tools use OpenMDAO, including [WISDEM](https://github.com/NLRWindSystems/WISDEM/) and [WEIS](https://github.com/NLRWindSystems/WEIS), so we can draw from institutional knowledge - set up with gradient-based optimization in mind, which is not currently a focus for H2Integrate but this positions the tool well for potential future additions - parallelization done using MPI, which is also not currently a focus but useful for the future @@ -60,7 +60,7 @@ Additionally, we can code H2Integrate in a way to minimize some of the potential ## Where does HOPP come into play? -[HOPP](https://github.com/NREL/HOPP) is a well-structured and useful tool that analyzes hybrid plants producing electricity. +[HOPP](https://github.com/NatLabRockies/HOPP) is a well-structured and useful tool that analyzes hybrid plants producing electricity. H2Integrate historically calls HOPP to obtain the plant's outputted electricity, which is then used downstream to feed into electrolyzers, steel, and other components. The current setup of H2Integrate largely works in the same way. diff --git a/docs/finance_models/ProFastBase.md b/docs/finance_models/ProFastBase.md index baa212e77..b2ce0798e 100644 --- a/docs/finance_models/ProFastBase.md +++ b/docs/finance_models/ProFastBase.md @@ -1,10 +1,10 @@ (profastmodel:profastmodel)= # ProFAST Financial Model -The Production Financial Analysis Scenario Tool or (ProFAST)[https://www.nrel.gov/hydrogen/profast-access] is a financial modeling tool developed at the NREL based on General Accepted Accounting Principles (GAAP) methodology. The model provides a quick and convenient way to conduct in-depth financial analysis for production system and services. +The Production Financial Analysis Scenario Tool or (ProFAST)[https://www.nlr.gov/hydrogen/profast-access] is a financial modeling tool developed at the NLR based on General Accepted Accounting Principles (GAAP) methodology. The model provides a quick and convenient way to conduct in-depth financial analysis for production system and services. Currently there are two ProFAST models that can be used: -- [``ProFastLCO``](profastcomp:profastcompmodel): A price-taker model that calculates levelized cost of commodity (or breakeven price) using [ProFAST](https://github.com/NREL/ProFAST). -- [``ProFastNPV``](profastnpv:profastnpvmodel): A price-setter model that calculates the net present value of a commodity using [ProFAST](https://github.com/NREL/ProFAST). +- [``ProFastLCO``](profastcomp:profastcompmodel): A price-taker model that calculates levelized cost of commodity (or breakeven price) using [ProFAST](https://github.com/NatLabRockies/ProFAST). +- [``ProFastNPV``](profastnpv:profastnpvmodel): A price-setter model that calculates the net present value of a commodity using [ProFAST](https://github.com/NatLabRockies/ProFAST). (profast:overview)= ## Finance parameters overview diff --git a/docs/finance_models/ProFastComp.md b/docs/finance_models/ProFastComp.md index 6403505d3..e94fe210e 100644 --- a/docs/finance_models/ProFastComp.md +++ b/docs/finance_models/ProFastComp.md @@ -1,7 +1,7 @@ (profastcomp:profastcompmodel)= # ProFastComp -The `ProFastLCO` finance model calculates levelized cost of a commodity using [ProFAST](https://github.com/NREL/ProFAST). +The `ProFastLCO` finance model calculates levelized cost of a commodity using [ProFAST](https://github.com/NatLabRockies/ProFAST). The inputs, outputs, and naming convention for the `ProFastLCO` model are outlined in this doc page. diff --git a/docs/finance_models/ProFastNPV.md b/docs/finance_models/ProFastNPV.md index ffe645f88..70447fc6c 100644 --- a/docs/finance_models/ProFastNPV.md +++ b/docs/finance_models/ProFastNPV.md @@ -1,6 +1,6 @@ (profastnpv:profastnpvmodel)= # ProFastNPV -The `ProFastNPV` finance model calculates the net present value (NPV) of a commodity using [ProFAST](https://github.com/NREL/ProFAST). +The `ProFastNPV` finance model calculates the net present value (NPV) of a commodity using [ProFAST](https://github.com/NatLabRockies/ProFAST). `ProFastNPV` inherits from `ProFastBase` so refer to the [`ProFastBase` documentation](profast:overview) for setting the finance parameters. diff --git a/docs/finance_models/finance_index.md b/docs/finance_models/finance_index.md index ab9f2c018..737ca4332 100644 --- a/docs/finance_models/finance_index.md +++ b/docs/finance_models/finance_index.md @@ -18,15 +18,15 @@ The `commodity_type` and `description` are used in the finance model naming conv (finance:supportedmodels)= ## Currently supported general finance models -- [``ProFastLCO``](profastcomp:profastcompmodel): calculates levelized cost of commodity using [ProFAST](https://github.com/NREL/ProFAST). -- [``ProFastNPV``](profastnpv:profastnpvmodel): calculates the net present value of a commodity using [ProFAST](https://github.com/NREL/ProFAST). +- [``ProFastLCO``](profastcomp:profastcompmodel): calculates levelized cost of commodity using [ProFAST](https://github.com/NatLabRockies/ProFAST). +- [``ProFastNPV``](profastnpv:profastnpvmodel): calculates the net present value of a commodity using [ProFAST](https://github.com/NatLabRockies/ProFAST). - [``NumpyFinancialNPV``](numpyfinancialnpvfinance:numpyfinancialnpvmodel): calculates the net present value of a commodity using the [NumPy Financial npv](https://numpy.org/numpy-financial/latest/npv.html#numpy_financial.npv) method. ## Custom finance models A general finance model can be defined similarly to a custom technology model. A custom finance model should be defined in the plant configuration file within the `finance_groups` section under `finance_parameters`. -Below shows an example, similar to the [Wind Electrolyzer Example](https://github.com/NREL/H2Integrate/tree/develop/examples/08_wind_electrolyzer/) of how to define a custom finance model within the `plant_config.yaml` file: +Below shows an example, similar to the [Wind Electrolyzer Example](https://github.com/NatLabRockies/H2Integrate/tree/develop/examples/08_wind_electrolyzer/) of how to define a custom finance model within the `plant_config.yaml` file: ```yaml finance_parameters: finance_groups: diff --git a/docs/generate_class_hierarchy.py b/docs/generate_class_hierarchy.py new file mode 100644 index 000000000..47918b890 --- /dev/null +++ b/docs/generate_class_hierarchy.py @@ -0,0 +1,673 @@ +""" +Generate an interactive class hierarchy visualization for H2Integrate. + +This script scans the h2integrate package to discover all classes and their +inheritance relationships, then produces an interactive HTML visualization +(zoomable/scrollable) suitable for embedding in the Jupyter Book docs. + +Visual encoding: + - **Shape** → model category (converter, storage, transporter, etc.) + - **Color** → product / application group (electricity, chemical, metal, etc.) + - **Border width** → inheritance depth (thicker = higher-level parent) + +Usage: + python docs/generate_class_hierarchy.py + +Outputs: + docs/_static/class_hierarchy.html — interactive graph +""" + +import os +import re +import ast +import math +from pathlib import Path + +import networkx as nx +from pyvis.network import Network + +from h2integrate import ROOT_DIR + + +# --------------------------------------------------------------------------- +# Configuration +# --------------------------------------------------------------------------- + +REPO_ROOT = ROOT_DIR.parent +OUTPUT_HTML = REPO_ROOT / "docs" / "_static" / "class_hierarchy.html" + +# Directories / path fragments that indicate test code (case-insensitive check) +TEST_INDICATORS = {"test", "tests", "conftest", "test_"} + +# We only show H2I-native classes; external bases (OpenMDAO, attrs, etc.) +# are excluded from the visualization entirely. +EXTERNAL_BASES_TO_KEEP: set[str] = set() + +# --------------------------------------------------------------------------- +# Category detection rules: (directory substring -> (model_category, product)) +# Order matters -- first match wins. +# model_category → controls node SHAPE +# product → controls node COLOR (via color group mapping below) +# --------------------------------------------------------------------------- +CATEGORY_RULES = [ + ("core", ("Core", "General")), + # --- Electricity-producing converters --- + ("converters/wind", ("Converter", "Wind")), + ("converters/solar", ("Converter", "Solar")), + ("converters/nuclear", ("Converter", "Nuclear")), + ("converters/grid", ("Converter", "Grid")), + ("converters/water_power", ("Converter", "Water Power")), + ("converters/natural_gas", ("Converter", "Natural Gas")), + ("converters/hopp", ("Converter", "HOPP")), + # --- Chemical converters --- + ("converters/hydrogen", ("Converter", "Hydrogen")), + ("converters/ammonia", ("Converter", "Ammonia")), + ("converters/methanol", ("Converter", "Methanol")), + ("converters/co2", ("Converter", "CO2")), + ("converters/nitrogen", ("Converter", "Nitrogen")), + ("converters/water", ("Converter", "Water")), + # --- Metal converters --- + ("converters/iron", ("Converter", "Iron")), + ("converters/steel", ("Converter", "Steel")), + # --- Catch-all converter --- + ("converters", ("Converter", "Other")), + # --- Non-converter categories --- + ("storage", ("Storage", "General")), + ("resource", ("Resource", "General")), + ("finances", ("Finance", "General")), + ("transporters", ("Transporter", "General")), + ("control", ("Control", "General")), + ("simulation", ("Simulation", "General")), + ("tools", ("Tools", "General")), + ("postprocess", ("Post-processing", "General")), + ("preprocess", ("Pre-processing", "General")), +] + +# --------------------------------------------------------------------------- +# Shape by model category +# --------------------------------------------------------------------------- +CATEGORY_SHAPES_PYVIS = { + "Core": "ellipse", + "Converter": "dot", + "Storage": "diamond", + "Resource": "triangle", + "Finance": "star", + "Transporter": "square", + "Control": "hexagon", + "Simulation": "triangleDown", + "Tools": "box", + "Post-processing": "box", + "Pre-processing": "box", +} + +# --------------------------------------------------------------------------- +# Color by broad application group +# Products are mapped to a broad group, each group gets one color. +# --------------------------------------------------------------------------- +PRODUCT_TO_GROUP = { + "General": "Core / General", + "Wind": "Renewables", + "Solar": "Renewables", + "Water Power": "Renewables", + "HOPP": "Renewables", + "Nuclear": "Other Elec. Generators", + "Grid": "Other Elec. Generators", + "Natural Gas": "Other Elec. Generators", + "Hydrogen": "Hydrogen", + "Ammonia": "Chemical", + "Methanol": "Chemical", + "CO2": "Chemical", + "Nitrogen": "Chemical", + "Water": "Chemical", + "Iron": "Metal", + "Steel": "Metal", + "Other": "Other", +} + +GROUP_COLORS = { + "Core / General": "#555555", + "Renewables": "#4A90D9", + "Other Elec. Generators": "#1B3A5C", + "Hydrogen": "#2E7D32", + "Chemical": "#66BB6A", + "Metal": "#D84315", + "Control": "#00ACC1", + "Other": "#F5C542", +} + +# Patterns used to detect control-related classes by name. +# If a class name matches any of these, its color group is overridden to "Control". +CONTROL_NAME_PATTERNS = re.compile(r"control|pyomo|openloop|open_loop", re.IGNORECASE) + +# Border width range for inheritance depth +MAX_BORDER_WIDTH = 5 +MIN_BORDER_WIDTH = 1 + + +# --------------------------------------------------------------------------- +# AST helpers +# --------------------------------------------------------------------------- + + +def _is_test_path(filepath: Path) -> bool: + """Return True if the file path looks like it belongs to test code.""" + parts = filepath.parts + for part in parts: + lower = part.lower() + if lower in TEST_INDICATORS or lower.startswith("test_"): + return True + if filepath.stem.lower().startswith("test_") or filepath.stem.lower() == "conftest": + return True + return False + + +def _rel_mod_path(filepath: Path) -> str: + """Return the filepath relative to the repo root using forward slashes.""" + try: + return str(filepath.relative_to(ROOT_DIR)).replace("\\", "/") + except ValueError: + return str(filepath).replace("\\", "/") + + +def _classify(filepath: Path) -> tuple[str, str]: + """Determine the (model_category, product) for a class based on its file path.""" + rel = _rel_mod_path(filepath) + for pattern, cat_tuple in CATEGORY_RULES: + if pattern in rel: + return cat_tuple + return ("Other", "Other") + + +def _resolve_base_name(base_node: ast.expr) -> str | None: + """Extract a human-readable base-class name from an AST node.""" + if isinstance(base_node, ast.Name): + return base_node.id + if isinstance(base_node, ast.Attribute): + # e.g. om.ExplicitComponent -> ExplicitComponent + return base_node.attr + if isinstance(base_node, ast.Subscript): + # e.g. Generic[T] -> Generic + return _resolve_base_name(base_node.value) + return None + + +# --------------------------------------------------------------------------- +# Scanning +# --------------------------------------------------------------------------- + + +def scan_classes(package_root: Path): + """Walk the package tree and return class info. + + Returns + ------- + classes : dict + {class_name: {"bases": [str], "file": Path, "category": str}} + If multiple classes share a name, the module path is prepended to + disambiguate. + """ + raw: list[dict] = [] + + for dirpath, _dirnames, filenames in os.walk(package_root): + dirpath = Path(dirpath) + for fname in filenames: + if not fname.endswith(".py"): + continue + filepath = dirpath / fname + if _is_test_path(filepath): + continue + try: + source = filepath.read_text(encoding="utf-8", errors="replace") + tree = ast.parse(source, filename=str(filepath)) + except SyntaxError: + continue + + for node in ast.walk(tree): + if not isinstance(node, ast.ClassDef): + continue + bases = [] + for b in node.bases: + bname = _resolve_base_name(b) + if bname: + bases.append(bname) + category, subcategory = _classify(filepath) + raw.append( + { + "name": node.name, + "bases": bases, + "file": filepath, + "model_category": category, + "product": subcategory, + } + ) + + # Detect name collisions and disambiguate + from collections import Counter + + name_counts = Counter(r["name"] for r in raw) + classes: dict[str, dict] = {} + for r in raw: + name = r["name"] + if name_counts[name] > 1: + # Prefix with the relative module path to disambiguate + module = _rel_mod_path(r["file"]).replace("/", ".").removesuffix(".py") + key = f"{module}.{name}" + else: + key = name + classes[key] = { + "bases": r["bases"], + "file": r["file"], + "model_category": r["model_category"], + "product": r["product"], + } + + return classes + + +# --------------------------------------------------------------------------- +# Graph construction +# --------------------------------------------------------------------------- + +# Classes to exclude from the visualization entirely. +# We filter out all config/dataclass definitions — only performance, cost, +# and other "model" classes are shown. +CONFIG_PATTERN = re.compile(r"Config$", re.IGNORECASE) +EXCLUDE_CLASSES = { + "BaseConfig", +} + + +def build_graph(classes: dict) -> nx.DiGraph: + """Build a directed graph of class inheritance (edges point from parent to child).""" + G = nx.DiGraph() + + # Build a set of all known H2I class names for quick lookup + known_names = set(classes.keys()) + # Also build short-name -> full-key mapping (for resolving base names) + short_to_full: dict[str, list[str]] = {} + for key in classes: + short = key.rsplit(".", 1)[-1] if "." in key else key + short_to_full.setdefault(short, []).append(key) + + def resolve(base_name: str) -> str | None: + """Resolve a base class name to a node key, or None to skip.""" + if base_name in known_names: + return base_name + candidates = short_to_full.get(base_name, []) + if len(candidates) == 1: + return candidates[0] + if len(candidates) > 1: + # Ambiguous — pick the one from core if available + for c in candidates: + if "core" in c.lower(): + return c + return candidates[0] + # External base — keep only the interesting ones + if base_name in EXTERNAL_BASES_TO_KEEP: + return base_name + return None + + def _is_excluded(name: str) -> bool: + """Return True if the class should be excluded (configs, etc.).""" + if name in EXCLUDE_CLASSES: + return True + if CONFIG_PATTERN.search(name): + return True + return False + + # Add all H2I class nodes (skip configs) + for key, info in classes.items(): + short_name = key.rsplit(".", 1)[-1] if "." in key else key + if _is_excluded(short_name): + continue + model_cat = info.get("model_category", "Other") + product = info.get("product", "General") + G.add_node( + key, + label=short_name, + model_category=model_cat, + product=product, + title=f"{short_name}\n{_rel_mod_path(info['file'])}\n[{model_cat} / {product}]", + ) + + # Add edges (parent -> child) — only between H2I classes already in the graph + for key, info in classes.items(): + short_name = key.rsplit(".", 1)[-1] if "." in key else key + if _is_excluded(short_name): + continue + if key not in G: + continue + for base_name in info["bases"]: + parent = resolve(base_name) + if parent is None: + continue + # Only add edges to parents that are already in the graph (H2I classes). + # Do NOT add external nodes. + if parent not in G: + continue + G.add_edge(parent, key) + + return G + + +# --------------------------------------------------------------------------- +# Inheritance depth computation +# --------------------------------------------------------------------------- + + +def _compute_depths(G: nx.DiGraph) -> dict[str, int]: + """Compute inheritance depth for each node (0 = root, increases for children).""" + depths: dict[str, int] = {} + roots = [n for n in G if G.in_degree(n) == 0] + for root in roots: + queue = [(root, 0)] + visited = {root} + while queue: + node, d = queue.pop(0) + depths[node] = max(depths.get(node, 0), d) + for child in G.successors(node): + if child not in visited: + visited.add(child) + queue.append((child, d + 1)) + for n in G.nodes: + if n not in depths: + depths[n] = 0 + return depths + + +def _border_width(depth: int, max_depth: int) -> float: + """Compute border width: thicker for roots, thinner for deeply inherited.""" + if max_depth == 0: + return MAX_BORDER_WIDTH + return max( + MIN_BORDER_WIDTH, + MAX_BORDER_WIDTH - depth * (MAX_BORDER_WIDTH - MIN_BORDER_WIDTH) / max_depth, + ) + + +# --------------------------------------------------------------------------- +# Visualization — interactive HTML +# --------------------------------------------------------------------------- + + +def build_interactive_html(G: nx.DiGraph, output_path: Path): + """Create a pyvis interactive HTML visualization of the graph.""" + net = Network( + height="900px", + width="100%", + directed=True, + notebook=False, + cdn_resources="in_line", + bgcolor="#FFFFFF", + font_color="#333333", + select_menu=False, + filter_menu=False, + ) + + net.set_options(""" + { + "physics": { + "enabled": true, + "forceAtlas2Based": { + "gravitationalConstant": -80, + "centralGravity": 0.008, + "springLength": 120, + "springConstant": 0.06, + "damping": 0.4, + "avoidOverlap": 0.6 + }, + "solver": "forceAtlas2Based", + "stabilization": { + "enabled": true, + "iterations": 1500, + "updateInterval": 25 + }, + "maxVelocity": 50, + "minVelocity": 0.01 + }, + "layout": { + "improvedLayout": true, + "randomSeed": 42 + }, + "edges": { + "arrows": { "to": { "enabled": true, "scaleFactor": 0.5 } }, + "color": { "color": "#888888", "opacity": 0.5 }, + "smooth": { "type": "continuous" }, + "width": 1.2 + }, + "interaction": { + "hover": true, + "tooltipDelay": 100, + "zoomView": true, + "dragView": true, + "navigationButtons": true, + "keyboard": { "enabled": true } + } + } + """) + + depths = _compute_depths(G) + max_depth = max(depths.values(), default=0) + + # Cluster seeds by model_category + used_categories = sorted({G.nodes[n].get("model_category", "Other") for n in G.nodes}) + cat_to_index = {cat: i for i, cat in enumerate(used_categories)} + n_cats = len(used_categories) + CLUSTER_RADIUS = 600 + INTRA_SCATTER = 180 + _cat_counter: dict[str, int] = {c: 0 for c in used_categories} + + # Determine node sizes based on out-degree + max_degree = max((G.out_degree(n) for n in G.nodes), default=1) or 1 + + for node_id in G.nodes: + data = G.nodes[node_id] + label = data.get("label", node_id) + model_cat = data.get("model_category", "Other") + product = data.get("product", "General") + tooltip = data.get("title", label) + + # Override color group to "Control" for control-related classes + if CONTROL_NAME_PATTERNS.search(label): + color_group = "Control" + else: + color_group = PRODUCT_TO_GROUP.get(product, "Other") + color = GROUP_COLORS.get(color_group, "#BDBDBD") + shape = CATEGORY_SHAPES_PYVIS.get(model_cat, "dot") + bw = _border_width(depths.get(node_id, 0), max_depth) + out_deg = G.out_degree(node_id) + size = 18 + 27 * (out_deg / max_degree) + + # Compute initial position: cluster centre + spiral offset + idx = cat_to_index.get(model_cat, 0) + angle_base = 2 * math.pi * idx / max(n_cats, 1) + cx = CLUSTER_RADIUS * math.cos(angle_base) + cy = CLUSTER_RADIUS * math.sin(angle_base) + seq = _cat_counter[model_cat] + _cat_counter[model_cat] += 1 + spiral_angle = seq * 0.8 + spiral_r = INTRA_SCATTER * (0.3 + 0.7 * (seq / max(1, _cat_counter[model_cat]))) + x = cx + spiral_r * math.cos(spiral_angle) + y = cy + spiral_r * math.sin(spiral_angle) + + net.add_node( + node_id, + label=label, + title=tooltip, + color={ + "background": color, + "border": "#555555", + "highlight": {"background": "#FF6B6B", "border": "#FF0000"}, + "hover": {"background": "#FFD700", "border": "#FF8C00"}, + }, + size=size, + borderWidth=bw, + shape=shape, + font={"size": 14, "face": "Arial"}, + x=x, + y=y, + ) + + for u, v in G.edges: + net.add_edge(u, v) + + # --- Build legend HTML --- + # Color groups: collect the actual group used for each node + used_groups = set() + for n in G.nodes: + label_n = G.nodes[n].get("label", n) + product_n = G.nodes[n].get("product", "General") + if CONTROL_NAME_PATTERNS.search(label_n): + used_groups.add("Control") + else: + used_groups.add(PRODUCT_TO_GROUP.get(product_n, "Other")) + used_groups = sorted(used_groups) + color_items = [] + for group in used_groups: + c = GROUP_COLORS.get(group, "#BDBDBD") + color_items.append( + f'{group}' + ) + color_legend = "
".join(color_items) + + # Model category shapes + shape_labels = { + "ellipse": "◯", # circle-like for Core + "dot": "●", # filled circle for Converter + "diamond": "◆", # diamond for Storage + "triangle": "▲", # triangle for Resource + "star": "★", # star for Finance + "square": "■", # square for Transporter + "hexagon": "⬢", # hexagon for Control + "triangleDown": "▼", # down-triangle for Simulation + "box": "■", # square for Tools + } + used_cats = sorted({G.nodes[n].get("model_category", "Other") for n in G.nodes}) + shape_items = [] + for cat in used_cats: + shape = CATEGORY_SHAPES_PYVIS.get(cat, "dot") + symbol = shape_labels.get(shape, "●") + shape_items.append( + f'' + f"{symbol}{cat}" + ) + shape_legend = "
".join(shape_items) + + # Save and inject legend + net.generate_html() + raw_html = net.html + output_path.write_text(raw_html, encoding="utf-8") + html = output_path.read_text(encoding="utf-8") + + legend_div = f""" +
+ Color = Application Group
+ {color_legend} +

+ Shape = Model Category
+ {shape_legend} +

+ + Border width = inheritance depth
+ (thicker = higher-level parent)

+ Arrows: parent → child
+ Scroll to zoom | Drag to pan + +
+ """ + + title_div = """ +
+ H2Integrate Class Hierarchy
+ + Interactive visualization — zoom, pan, hover for details + +
+ """ + + # Inject a script to disable physics after stabilization so the + # diagram is still unless the user drags a node. + stabilize_script = """ + + """ + + html = html.replace("", f"{legend_div}\n{title_div}\n{stabilize_script}\n") + output_path.write_text(html, encoding="utf-8") + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- + + +def main(): + print(f"Scanning classes in {ROOT_DIR} ...") + classes = scan_classes(ROOT_DIR) + print(f" Found {len(classes)} classes (excluding test files)") + + print("Building inheritance graph ...") + G = build_graph(classes) + print(f" Graph has {G.number_of_nodes()} nodes and {G.number_of_edges()} edges") + + # Remove isolated nodes (no inheritance relationships) to reduce clutter + isolates = list(nx.isolates(G)) + G.remove_nodes_from(isolates) + print( + f" After removing {len(isolates)} isolated classes: " + f"{G.number_of_nodes()} nodes, {G.number_of_edges()} edges" + ) + + print(f"Generating interactive HTML → {OUTPUT_HTML} ...") + OUTPUT_HTML.parent.mkdir(parents=True, exist_ok=True) + build_interactive_html(G, OUTPUT_HTML) + + print("Done!") + + # Print summary by category + cats: dict[str, int] = {} + for n in G.nodes: + cat = G.nodes[n].get("model_category", "Other") + cats[cat] = cats.get(cat, 0) + 1 + print("\nClasses by model category (in graph):") + for cat in sorted(cats, key=cats.get, reverse=True): + print(f" {cat}: {cats[cat]}") + + +if __name__ == "__main__": + main() diff --git a/docs/getting_started/expected_user_knowledge.md b/docs/getting_started/expected_user_knowledge.md index 7c1c6ea96..42b49d554 100644 --- a/docs/getting_started/expected_user_knowledge.md +++ b/docs/getting_started/expected_user_knowledge.md @@ -27,8 +27,8 @@ Users should also be familiar with the [Google style](https://www.sphinx-doc.org H2Integrate itself is a framework that relies on a number of other tools and libraries to function. Users should be familiar with the following tools and libraries to use H2Integrate effectively: -- [HOPP](https://github.com/NREL/HOPP): Used for simulating technologies that produce electricity for other components in H2Integrate -- [PySAM](https://github.com/NREL/pysam): Provides access to the System Advisor Model (SAM) for performance and financial modeling; underlying tool used for modeling certain generation technologies +- [HOPP](https://github.com/NatLabRockies/HOPP): Used for simulating technologies that produce electricity for other components in H2Integrate +- [PySAM](https://github.com/NatLabRockies/pysam): Provides access to the System Advisor Model (SAM) for performance and financial modeling; underlying tool used for modeling certain generation technologies - [OpenMDAO](https://openmdao.org/): A framework for multidisciplinary optimization, analysis, and design; used for data-passing, model organization, and optimization - [Pyomo](http://www.pyomo.org/): A Python-based, open-source optimization modeling language; only useful to understand if you are modifying the dispatch algorithms - [Pandas](https://pandas.pydata.org/): A data manipulation and analysis library used for handling and analyzing data structures; useful for post-processing results diff --git a/docs/getting_started/install.md b/docs/getting_started/install.md index a65b34c3f..4dcba6c06 100644 --- a/docs/getting_started/install.md +++ b/docs/getting_started/install.md @@ -2,7 +2,7 @@ ## Install H2Integrate via PyPI -If you just want to use H2Integrate and aren't developing new models, you can install it from PyPI using pip: +If you just want to use H2Integrate and aren't developing new models, you should install it from PyPI using pip: ```bash pip install h2integrate @@ -12,7 +12,7 @@ pip install h2integrate If you want to develop new models or contribute to H2Integrate, you can install it from source. -### NREL Resource Data +### NLR Resource Data 1. The functions which download resource data require an NLR API key. Obtain a key from: @@ -29,12 +29,12 @@ If you want to develop new models or contribute to H2Integrate, you can install NLR_API_EMAIL=your.name@email.com ``` -### NREL-Provided Conda Environment Specification (recommended) +### NLR-Provided Conda Environment Specification (recommended) 1. Using Git, navigate to a local target directory and clone repository: ```bash - git clone https://github.com/NREL/H2Integrate.git + git clone https://github.com/NatLabRockies/H2Integrate.git ``` 2. Navigate to `H2Integrate` @@ -75,7 +75,8 @@ environment for development work. - Use one of the extra flags as needed: - `gis`: adds the iron mapping tools. - `ard`: adds the Ard-based wind models. - - `develop`: adds developer and documentation tools, plus optional analysis modifiers `gis` and `ard`. + - `extras`: installs all extra analysis tool dependencies, e.g., `ard` or `gis`. + - `develop`: adds developer and documentation tools. - `examples`: allows you to use the Jupyter Notebooks and all examples (includes `ard` and `gis`). - `all` simplifies adding all the dependencies. @@ -90,7 +91,7 @@ pip install -e ".[all]" 1. Using Git, navigate to a local target directory and clone repository: ```bash - git clone https://github.com/NREL/H2Integrate.git + git clone https://github.com/NatLabRockies/H2Integrate.git ``` 2. Navigate to `H2Integrate` @@ -110,19 +111,9 @@ pip install -e ".[all]" 4. Install H2Integrate and its dependencies: ```bash - conda install -y -c conda-forge glpk + conda install -y -c conda-forge glpk coin-or-cbc>=2.10.12 ``` - ````{note} - Unix users should install Cbc via: - - ```bash - conda install -y -c conda-forge coin-or-cbc=2.10.8 - ``` - - Windows users should install Cbc manually according to https://github.com/coin-or/Cbc. - ```` - - If you want to just use H2Integrate: ```bash diff --git a/docs/intro.md b/docs/intro.md index 6a50e4619..356a53451 100644 --- a/docs/intro.md +++ b/docs/intro.md @@ -1,61 +1,23 @@ # H2Integrate - Holistic Hybrids Optimization and Design Tool [![PyPI version](https://badge.fury.io/py/h2integrate.svg)](https://badge.fury.io/py/h2integrate) -![CI Tests](https://github.com/NREL/H2Integrate/actions/workflows/ci.yml/badge.svg) +![CI Tests](https://github.com/NatLabRockies/H2Integrate/actions/workflows/ci.yml/badge.svg) [![image](https://img.shields.io/pypi/pyversions/h2integrate.svg)](https://pypi.python.org/pypi/h2integrate) [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause) -[![DOI:10.5281/zenodo.17903150](https://zenodo.org/badge/DOI/10.5281/zenodo.17903149.svg)](https://zenodo.org/records/17903149) +[![DOI 10.5281/zenodo.17903150](https://zenodo.org/badge/DOI/10.5281/zenodo.17903150.svg)](https://zenodo.org/records/17903150) -H2Integrate is an open-source Python package for modeling and designing hybrid energy systems producing electricity, hydrogen, ammonia, steel, and other products. +H2Integrate (H2I) is an open-source Python package for hybrid systems engineering design and technoeconomic analysis. +It models and optimizes hybrid energy plants that produce electricity, hydrogen, ammonia, steel, and other products, using high-resolution location-specific resource data to perform optimization and scenario analysis. -Browse the example workflows in the GitHub repository: https://github.com/NREL/H2Integrate/tree/main/examples - -```{note} -H2Integrate is under active development and may be missing features that existed in previous versions. H2Integrate v0.2.0 is the last version that uses the prior framework. -``` - -If you use this software in your work, please cite using the following BibTeX: - -```bibtex -@software{brunik_2025_17903150, - author = {Brunik, Kaitlin and - Grant, Elenya and - Thomas, Jared and - Starke, Genevieve M and - Martin, Jonathan and - Ramos, Dakota and - Koleva, Mariya and - Reznicek, Evan and - Hammond, Rob and - Stanislawski, Brooke and - Kiefer, Charlie and - Irmas, Cameron and - Vijayshankar, Sanjana and - Riccobono, Nicholas and - Frontin, Cory and - Clark, Caitlyn and - Barker, Aaron and - Gupta, Abhineet and - Kee, Benjamin (Jamie) and - King, Jennifer and - Jasa, John and - Bay, Christopher}, - title = {H2Integrate: Holistic Hybrids Optimization and Design Tool}, - month = dec, - year = 2025, - publisher = {Zenodo}, - version = {0.4.0}, - doi = {10.5281/zenodo.17903150}, - url = {https://doi.org/10.5281/zenodo.17903150}, -} -``` +Browse the example workflows in the GitHub repository: https://github.com/NatLabRockies/H2Integrate/tree/main/examples ## What is H2Integrate? -H2Integrate is designed to be flexible and extensible, allowing users to create their own components and models for various energy systems. -The tool currently includes renewable energy generation (wind, solar, wave, tidal), battery storage, hydrogen, ammonia, methanol, and steel technologies. -Other elements such as desalination systems, pipelines, compressors, and storage systems can also be included as developed by users. -Some modeling capabilities in H2Integrate are provided by integrating existing tools, such as [HOPP](https://github.com/NREL/HOPP), [PySAM](https://github.com/NREL/pysam), [ORBIT](https://github.com/wisdem/ORBIT), and [ProFAST](https://github.com/NREL/ProFAST). +H2Integrate is designed to be flexible and extensible, allowing users to create their own components and models for various hybrid systems. +The tool currently includes renewable energy generation (land-based wind, offshore wind, solar PV, wave, tidal), conventional generation (natural gas combined cycle, combustion turbines, grid electricity), hydrogen production (PEM electrolysis, NG-SMR), energy storage (Li-ion batteries, long-duration energy storage, pumped storage hydropower), fuel cells, and industrial processes (ammonia synthesis, iron ore reduction, steel production, methanol, and more). +Other elements can also be included as developed by users. +H2Integrate is continually expanding to serve additional hybrid applications -- if you're interested in seeing what's being actively developed, please see the [current pull requests in the GitHub repository](https://github.com/NatLabRockies/H2Integrate/pulls). +Some modeling capabilities in H2Integrate are provided by integrating existing tools, such as [HOPP](https://github.com/NatLabRockies/HOPP), [PySAM](https://github.com/NatLabRockies/pysam), [ORBIT](https://github.com/NLRWindSystems/ORBIT), and [ProFAST](https://github.com/NatLabRockies/ProFAST). The H2Integrate tool is built on top of [NASA's OpenMDAO framework](https://github.com/OpenMDAO/OpenMDAO/), which provides a powerful and flexible environment for modeling and optimization. ```{note} @@ -64,11 +26,17 @@ H2Integrate was previously known as GreenHEART. The name was updated to H2Integr ## How does H2Integrate work? -H2Integrate models energy systems on a yearly basis using hourly timesteps (i.e., 8760 operational data points across a year). +H2Integrate typically models energy systems on a yearly basis using hourly timesteps (i.e., 8760 operational data points across a year). Results from these simulations are then processed across the project's lifecycle to provide insights into the system's performance, costs, and financial viability. Depending on the models used and the size of the system, H2Integrate can simulate systems ranging from the kW to GW scale in seconds on a personal computer. Additionally, H2Integrate tracks the flow of electricity, molecules (e.g., hydrogen, ammonia, methanol), and other products (e.g., steel) between different technologies in the energy system. +```{note} +Some models are now able to operate with non-hourly time steps. +Appropriate time step bounds are included as class attributes when non-hourly time steps are permitted. +Check individual model docs and definitions for time step bounds for individual models. All models in a given simulation must be compatible with the specified time step. +``` + For each technology there are 4 different types of models: control, performance, cost, and finance. These model categories allow for modular pieces to be brought in or re-used throughout H2Integrate, as well as ease of development and organization. Note that the only required models for a technology are performance and cost, while control and finance are optional. The figure below shows these four categories and some of the technologies included in H2Integrate. For a full list of models available, please see [Model Overview](user_guide/model_overview.md). ![A representation of a single technology model in H2Integrate](tech-model.png) @@ -84,18 +52,87 @@ The modular nature of the H2Integrate system makes adding custom models, includi ## How does H2Integrate differ from other tools? -H2Integrate is developed at NREL, which has a long history of developing high-quality tools for renewable energy systems. +H2Integrate is developed at NLR, which has a long history of developing high-quality tools for renewable energy systems. Although there are many tools available for modeling hybrid energy systems, H2Integrate is unique in its focus on component-level modeling and design including nonlinear physics models, as well as its modularity and extensibility. H2Integrate stands out by offering a modular approach that models the entire energy system, from generation to end-use products such as hydrogen, ammonia, methanol, and steel, which is a capability not commonly found in other tools. -[REopt](https://reopt.nrel.gov/tool) is similar to H2Integrate in that it models hybrid energy systems, though it is a higher-level tool that focuses on linear optimization. +[REopt](https://reopt.nlr.gov/tool) is similar to H2Integrate in that it models hybrid energy systems, though it is a higher-level tool that focuses on linear optimization. One significant difference is that REopt can accommodate various external loads such as steel or ammonia, as long as the user provides the load profiles for those end-uses. H2Integrate models the processes themselves and does not require the user to provide a load profile, instead modeling what the load profile would be based on physics-based or analytical models. -[SAM](https://sam.nrel.gov/) is another relevant tool (that H2Integrate partially uses), which gives more detailed performance and financial modeling capabilities than REopt. +[SAM](https://sam.nrl.gov/) is another relevant tool (that H2Integrate partially uses), which gives more detailed performance and financial modeling capabilities than REopt. Like REopt, SAM also does not model loads or end-uses but accepts timeseries data of the loads for design purposes. H2Integrate goes into more component-level details than those tools, especially in terms of nonlinear physics-based modeling and design. +```{note} +H2Integrate was previously known as GreenHEART, and some publications or references may refer to it by that name. +``` + +## Publications + +For more context about H2Integrate and analyses performed using the tool, see the publications below. +PDFs are available in the linked titles. + + +### Techno-economic analysis of low-carbon hydrogen production pathways for decarbonizing steel and ammonia production + +Reznicek, E.P., et al. "[Techno-economic analysis of low-carbon hydrogen production pathways for decarbonizing steel and ammonia production.](https://www.cell.com/cell-reports-sustainability/pdfExtended/S2949-7906(25)00034-5)" Cell Reports Sustainability. Vol. 2. No. 4. Elsevier, 2025. + +### Nationwide techno-economic analysis of clean hydrogen production powered by a hybrid renewable energy plant for over 50,000 locations in the United States + +Grant, E., et al. "[Hybrid power plant design for low-carbon hydrogen in the United States.](https://iopscience.iop.org/article/10.1088/1742-6596/2767/8/082019/pdf)" Journal of Physics: Conference Series. Vol. 2767. No. 8. IOP Publishing, 2024. + +### Exploring the role of producing low-carbon hydrogen using water electrolysis powered by offshore wind in facilitating the United States' transition to a net-zero emissions economy by 2050 + +Brunik, K., et al. "[Potential for large-scale deployment of offshore wind-to-hydrogen systems in the United States.](https://iopscience.iop.org/article/10.1088/1742-6596/2767/6/062017/pdf)" Journal of Physics: Conference Series. Vol. 2767. No. 6. IOP Publishing, 2024. + +### Examining how tightly-coupled gigawatt-scale wind- and solar-sourced H2 depends on the ability to store and deliver otherwise-curtailed H2 during times of shortages + + +Breunig, Hanna, et al. "[Hydrogen Storage Materials Could Meet Requirements for GW-Scale Seasonal Storage and Green Steel.](https://assets-eu.researchsquare.com/files/rs-4326648/v1_covered_338a5071-b74b-4ecd-9d2a-859e8d988b5c.pdf?c=1716199726)" (2024). + +### DOE Hydrogen Program review presentation of H2Integrate + +King, J. and Hammond, S. "[Integrated Modeling, TEA, and Reference Design for Renewable Hydrogen to Green Steel and Ammonia - GreenHEART](https://www.hydrogen.energy.gov/docs/hydrogenprogramlibraries/pdfs/review24/sdi001_king_2024_o.pdf?sfvrsn=a800ca84_3)" (2024). + +## Software Citation + +If you use this software in your work, please cite using the following BibTeX: + +```bibtex +@software{brunik_2025_17903150, + author = {Brunik, Kaitlin and + Grant, Elenya and + Thomas, Jared and + Starke, Genevieve M and + Martin, Jonathan and + Ramos, Dakota and + Koleva, Mariya and + Reznicek, Evan and + Hammond, Rob and + Stanislawski, Brooke and + Kiefer, Charlie and + Irmas, Cameron and + Vijayshankar, Sanjana and + Riccobono, Nicholas and + Frontin, Cory and + Clark, Caitlyn and + Barker, Aaron and + Gupta, Abhineet and + Kee, Benjamin (Jamie) and + King, Jennifer and + Jasa, John and + Bay, Christopher}, + title = {H2Integrate: Holistic Hybrids Optimization and Design Tool}, + month = dec, + year = 2025, + publisher = {Zenodo}, + version = {0.4.0}, + doi = {10.5281/zenodo.17903150}, + url = {https://doi.org/10.5281/zenodo.17903150}, +} +``` + ```{tableofcontents} ``` diff --git a/docs/misc_resources/turbine_models_library_preprocessing.md b/docs/misc_resources/turbine_models_library_preprocessing.md index 10ed2c813..3b67ae2d5 100644 --- a/docs/misc_resources/turbine_models_library_preprocessing.md +++ b/docs/misc_resources/turbine_models_library_preprocessing.md @@ -13,9 +13,9 @@ kernelspec: # Turbine Models Library Pre-Processing Tools -The [turbine-models package](https://github.com/NREL/turbine-models/tree/main) hosts wind turbine data for a variety of wind turbines and has tools that can streamline the process to run new turbines with the [PySAM Windpower model](https://nrel-pysam.readthedocs.io/en/main/modules/Windpower.html) or [FLORIS](https://github.com/NREL/floris/tree/main). +The [turbine-models package](https://github.com/NatLabRockies/turbine-models/tree/main) hosts wind turbine data for a variety of wind turbines and has tools that can streamline the process to run new turbines with the [PySAM Windpower model](https://nrel-pysam.readthedocs.io/en/main/modules/Windpower.html) or [FLORIS](https://github.com/NatLabRockies/floris/tree/main). -The full list of turbine models available in the turbine-models library can be found [here](https://github.com/NREL/turbine-models/blob/main/turbine_models/supported_turbines.py) +The full list of turbine models available in the turbine-models library can be found [here](https://github.com/NatLabRockies/turbine-models/blob/main/turbine_models/supported_turbines.py) H2Integrate has preprocessing tools that leverage the functionality available in the turbine-models library. The function `export_turbine_to_pysam_format()` will save turbine model specifications formatted for the PySAM Windpower model. The PySAM Windpower model is wrapped in H2I and can be utilized with the "pysam_wind_plant_performance" model. Example usage of the `export_turbine_to_pysam_format()` function is demonstrated in the following section using Example 8. diff --git a/docs/resource/resource_index.md b/docs/resource/resource_index.md index 6ea4c7762..32bfc5855 100644 --- a/docs/resource/resource_index.md +++ b/docs/resource/resource_index.md @@ -3,3 +3,4 @@ - [Wind Resource Data](wind_resource:models) - [Solar Resource Data](solar_resource:models) +- [Tidal Resource Data](tidal_resource:models) diff --git a/docs/resource/tidal_resource.md b/docs/resource/tidal_resource.md new file mode 100644 index 000000000..b6b013a41 --- /dev/null +++ b/docs/resource/tidal_resource.md @@ -0,0 +1,19 @@ +(tidal_resource:models)= +# Tidal Resource: Model Overview + +The tidal resource model is essentially a file reader that processes the data for use in the OpenMDAO format of H2I. It expects a CSV file to be input with timeseries tidal data and meta data. + +The tidal resource format is based on the [System Advisor Model (SAM)](https://sam.nlr.gov/). Additional sample tidal resource files can be downloaded via the SAM GUI. + +```{note} +H2I expects the resource date to be in a timeseries format rather than a probability distribution. +``` + +The tidal resource data should be in the format: + - Rows 1 and 2: Header rows with location info. + - Row 3: Column headings for time-series data + - (`Year`, `Month`, `Day`, `Hour`, `Minute`, `Speed`). + - Rows 4+: Data values: + - `Speed` (current speed) in meters/second. + +If the tidal data is greater than hourly the model will interpolate the values in between the timesteps so that the resulting data is in an hourly format. diff --git a/docs/technology_models/hydrogen_storage.md b/docs/storage/hydrogen_storage.md similarity index 99% rename from docs/technology_models/hydrogen_storage.md rename to docs/storage/hydrogen_storage.md index 6cf844182..258e4c00a 100644 --- a/docs/technology_models/hydrogen_storage.md +++ b/docs/storage/hydrogen_storage.md @@ -1,3 +1,4 @@ +(h2-storage-cost)= # Bulk Hydrogen Storage Cost Model ## Storage Types diff --git a/docs/technology_models/pysam_battery.md b/docs/storage/pysam_battery.md similarity index 92% rename from docs/technology_models/pysam_battery.md rename to docs/storage/pysam_battery.md index 0811f8fd9..3f847df02 100644 --- a/docs/technology_models/pysam_battery.md +++ b/docs/storage/pysam_battery.md @@ -1,6 +1,7 @@ +(pysam-battery-performance)= # PySAM Battery Model -The PySAM battery model in H2Integrate is a wrapper that integrates the NREL PySAM `BatteryStateful` model into +The PySAM battery model in H2Integrate is a wrapper that integrates the NLR PySAM `BatteryStateful` model into an OpenMDAO component. For full documentation see the [PySAM battery model documentation](https://nrel-pysam.readthedocs.io/en/main/modules/BatteryStateful.html). The PySAM battery model simulates the response of the battery to control commands. However, the control commands may not be strictly followed. Specifically, the SOC bounds have been seen to be exceeded by nearly 4% SOC for the upper bound and close to 1% SOC on the lower bound. diff --git a/docs/technology_models/simple_generic_storage.md b/docs/storage/simple_generic_storage.md similarity index 50% rename from docs/technology_models/simple_generic_storage.md rename to docs/storage/simple_generic_storage.md index cc31123eb..983787eeb 100644 --- a/docs/technology_models/simple_generic_storage.md +++ b/docs/storage/simple_generic_storage.md @@ -1,21 +1,17 @@ -# Simple Generic Storage Model +(simple-generic-storage-performance)= +# `StoragePerformanceModel` Model -The Simple Generic Storage model provides a flexible framework for modeling various types of energy storage systems in H2Integrate. While particularly useful for battery storage, this model can be used to simulate the storage of different resources including hydrogen, CO2, or any other commodity. +The `StoragePerformanceModel` model provides a flexible framework for modeling various types of energy storage systems in H2Integrate. While particularly useful for battery storage, this model can be used to simulate the storage of different commodities including hydrogen, CO2, or any other commodity. ## Overview -The Simple Generic Storage model consists of two main components: - -1. **SimpleGenericStorage**: A minimal component that defines the input interface for the storage system -2. **DemandOpenLoopStorageController**: The core logic component that handles storage operations, state of charge calculations, and resource management - -This architecture allows the storage system to work with any resource type by simply configuring the resource name and units, making it quite versatile. +The `StoragePerformanceModel` is a component that defines a simple storage performance model for any defined commodity. This model allows the storage system to work with any commodity type by simply configuring the commodity name and units, making it quite versatile. ## Example Applications ### Battery Storage (Example 19) -Example 19 demonstrates a wind-battery dispatch system that showcases the Simple Generic Storage model in action. This example: +Example 19 demonstrates a wind-battery dispatch system that showcases the `StoragePerformanceModel` model in action. This example: - Models a wind farm providing variable electricity generation - Uses battery storage with defined capacity and charge/discharge rates @@ -24,16 +20,17 @@ Example 19 demonstrates a wind-battery dispatch system that showcases the Simple The example produces detailed plots showing: - Battery state of charge over time -- Electricity flows (input, output, curtailed, missed load) +- Electricity flows (input, output) - How the storage system balances variable wind generation with constant demand ### Hydrogen Storage The model can be configured for hydrogen storage systems by setting: ```yaml -commodity_name: "hydrogen" -commodity_units: "kg/h" # commodity_units must by a rate +commodity: "hydrogen" +commodity_rate_units: "kg/h" max_capacity: 1000.0 # kg +commodity_amount_units: "kg" ``` This is useful for modeling hydrogen production from electrolyzers with variable renewable input and steady hydrogen demand for industrial processes. @@ -42,8 +39,9 @@ This is useful for modeling hydrogen production from electrolyzers with variable For carbon capture and utilization systems: ```yaml -commodity_name: "co2" -commodity_units: "kg/h" +commodity: "co2" +commodity_rate_units: "kg/h" +commodity_amount_units: "kg" max_capacity: 50000.0 # kg ``` diff --git a/docs/storage/storage_models_index.md b/docs/storage/storage_models_index.md new file mode 100644 index 000000000..d9a626d42 --- /dev/null +++ b/docs/storage/storage_models_index.md @@ -0,0 +1,63 @@ +# Storage Models + +Storage technologies input and output the 'Storage Commodity' (`commodity`) as a time series. These technologies can be both filled or charged and unfilled or discharged, resulting in a commodity stream that can be positive and negative.These models are usually constrained by two key model parameters: storage capacity and charge/discharge rate. + +## Storage Performance Models + +H2I currently supports the following storage performance models: +- [`StoragePerformanceModel`](#simple-generic-storage-performance) +- [`PySAMBatteryPerformanceModel`](#pysam-battery-performance) +- `StorageAutoSizingModel` + +The following sections detail the inputs and outputs of the storage performance models. + +```{note} +The inputs and outputs of storage performance models are generalized here for any commodity. If input and output names include the word `commodity`, the actual variable name would be the commodity defined for that storage model. For example, the `PySAMBatteryPerformanceModel` can only be used for the commodity `electricity`. Therefore, the `commodity_in` input to the `PySAMBatteryPerformanceModel` is actually named `electricity_in`. +``` + +### Inputs +- `commodity_in`: commodity available to use for charging storage + +If using a **feedback control strategy** (this means that the controller received the actual storage state periodically), the control-related inputs for control to the storage performance include: +- `commodity_demand`: the target demand profile to satisfy with the storage performance model and the input commodity. This is passed to the control strategy through the `pyomo_dispatch_solver` method. +- `pyomo_dispatch_solver`: the control function from the storage controller that outputs dispatch commands to the storage performance model. + +If using an **open-loop control strategy**, the control input to the storage performance model is: +- `commodity_set_point`: the dispatch commands to the storage performance model, negative values indicate charge commands and positive values indicate discharge commands + +Some storage models may also have design inputs of `max_charge_rate`, `storage_capacity`, and `max_discharge_rate`. + +### Calculations and Outputs +The storage performance models output timeseries profiles (length of `n_timesteps`): +- `commodity_out`: storage charge and discharge profile in `commodity_rate_units`, values are negative when charging and positive when discharging. This is equivalent to `storage_commodity_charge + storage_commodity_discharge` +- `storage_commodity_charge`: charge profile the storage, the values are either negative (when charging) or 0 (when doing nothing or discharging). In units of `commodity_rate_units` +- `storage_commodity_discharge`: discharge profile the storage, the values are either positive (when discharging) or 0 (when doing nothing or charging). In units of `commodity_rate_units` +- `SOC`: the storage state-of-charge in units of either `percent` (values between 0 and 100) or `unitless` (values between 0 and 1) + +The aggregated or summarized performance outputs are (single values): +- `storage_duration`: the storage capacity divided by the storage discharge rate. Units are time-based, such as `h` (hours). +- `rated_commodity_production`: the storage discharge rate in units of `commodity_rate_units`. +- `total_commodity_produced`: the summation of `commodity_out` over the simulation in units of `commodity_amount_units`. This value may be negative if the storage charges more than discharges. + +The results that are output per-year of the `plant_life` are: +- `annual_commodity_produced`: each value is the `total_commodity_produced` scaled to a 1-year (8760 hours) simulation. This value may be negative if the storage charges more than discharges. +- `capacity_factor`: the storage capacity factor which is calculated in the same way that capacity factors are calculated in converter technologies, which is a ratio of the sum of *`total_commodity_produced`* to the discharge rate (or `rated_commodity_production`). This value may be negative if the storage charges more than discharges. + $ + CF = \frac{\sum_{t=0}^{n_{\text{timesteps}}}(\text{commodity_out}_t*dt)}{\text{\text{discharge}\_\text{rate}*n_{\text{timesteps}}*dt} + $ +- `standard_capacity_factor`: the storage capacity factor as defined by the [NLR ATB](https://atb.nrel.gov/electricity/2024b/utility-scale_battery_storage). The ratio of the total *commodity discharged* to the discharge rate (or `rated_commodity_production`). This value will always be greater than or equal to zero. + $ + CF_{\text{standard}} = \frac{\sum_{t=0}^{n_{\text{timesteps}}}(\text{storage_commodity_discharge}_t*dt)}{\text{discharge_rate}*n_{\text{timesteps}}*dt} + $ + + +## Storage Cost models: + +The available storage cost models are: +- `ATBBatteryCostModel` +- `GenericStorageCostModel` +- [Hydrogen storage cost models](#h2-storage-cost): + - `LinedRockCavernStorageCostModel` + - `SaltCavernStorageCostModel` + - `PipeStorageCostModel` + - `MCHTOLStorageCostModel` diff --git a/docs/technology_models/atb_costs_pv.md b/docs/technology_models/atb_costs_pv.md index 23415f748..1a5d9d050 100644 --- a/docs/technology_models/atb_costs_pv.md +++ b/docs/technology_models/atb_costs_pv.md @@ -1,12 +1,12 @@ # Solar-PV Cost Models based on ATB-Formatted Cost Data -NREL's [Annual Technology Baseline (ATB)](https://atb.nrel.gov) is commonly referenced for technology costs such as overnight capital cost, fixed operations and maintenance costs, and capital expenditures. Two solar-PV cost models are available in H2I that are intended to be easily used with cost values pulled from [NREL's ATB Excel workbook](https://atb.nrel.gov/electricity/2024/data). +NLR's [Annual Technology Baseline (ATB)](https://atb.nlr.gov) is commonly referenced for technology costs such as overnight capital cost, fixed operations and maintenance costs, and capital expenditures. Two solar-PV cost models are available in H2I that are intended to be easily used with cost values pulled from [NLR's ATB Excel workbook](https://atb.nlr.gov/electricity/2024/data). ```{note} The Annual Technology Baseline (ATB) is updated annually. While we do our best to update our documentation regularly, be sure that you're using the most recent version of the ATB in case our links are pointing to an older version. ``` -As mentioned on the [Utility-Scale PV ATB page](https://atb.nrel.gov/electricity/2024/utility-scale_pv), the costs for utility-scale PV have been published in `$/kW-AC` since 2020. The costs for [Commercial PV](https://atb.nrel.gov/electricity/2024/commercial_pv) and [Residential PV](https://atb.nrel.gov/electricity/2024/residential_pv) are published in `$/kW-DC`. The only difference between the two cost models in H2I are whether costs are input in `$/kW-AC` or `$/kW-DC`. +As mentioned on the [Utility-Scale PV ATB page](https://atb.nlr.gov/electricity/2024/utility-scale_pv), the costs for utility-scale PV have been published in `$/kW-AC` since 2020. The costs for [Commercial PV](https://atb.nlr.gov/electricity/2024/commercial_pv) and [Residential PV](https://atb.nlr.gov/electricity/2024/residential_pv) are published in `$/kW-DC`. The only difference between the two cost models in H2I are whether costs are input in `$/kW-AC` or `$/kW-DC`. - The `"ATBUtilityPVCostModel"` model has costs input in `$/kW-AC` to match the ATB and the outputted capacity in kW-AC from the PV performance model. Example usage of this cost model in the `tech_config.yaml` file is shown [in the first section below](#utility-scale-pv-cost-model). - The `"ATBResComPVCostModel"` model has costs input in `$/kW-DC` and the PV capacity is input in kW-DC from the **shared input parameter** of the PV performance model. Example usage of this cost model in the `tech_config.yaml` file is shown [in the second section below](#commercial-and-residential-pv-cost-model). diff --git a/docs/technology_models/atb_costs_wind.md b/docs/technology_models/atb_costs_wind.md index e3e628b4e..6d2cc58ce 100644 --- a/docs/technology_models/atb_costs_wind.md +++ b/docs/technology_models/atb_costs_wind.md @@ -1,15 +1,15 @@ # Wind Cost Model based on ATB-Formatted Cost Data -NREL's [Annual Technology Baseline (ATB)](https://atb.nrel.gov) is commonly referenced for technology costs such as overnight capital cost, fixed operations and maintenance costs, and capital expenditures. The `ATBWindPlantCostModel` cost model available in H2I that is intended to be easily used with cost values pulled from [NREL's ATB Excel workbook](https://atb.nrel.gov/electricity/2024/data). +NLR's [Annual Technology Baseline (ATB)](https://atb.nlr.gov) is commonly referenced for technology costs such as overnight capital cost, fixed operations and maintenance costs, and capital expenditures. The `ATBWindPlantCostModel` cost model available in H2I that is intended to be easily used with cost values pulled from [NLR's ATB Excel workbook](https://atb.nlr.gov/electricity/2024/data). ```{note} The Annual Technology Baseline (ATB) is updated annually. While we do our best to update our documentation regularly, be sure that you're using the most recent version of the ATB in case our links are pointing to an older version. ``` There are specific costs and performance specifications in the ATB for: -- [Land-Based Wind](https://atb.nrel.gov/electricity/2024/land-based_wind) -- [Offshore Wind](https://atb.nrel.gov/electricity/2024/offshore_wind) -- [Distributed Wind](https://atb.nrel.gov/electricity/2024/distributed_wind) +- [Land-Based Wind](https://atb.nlr.gov/electricity/2024/land-based_wind) +- [Offshore Wind](https://atb.nlr.gov/electricity/2024/offshore_wind) +- [Distributed Wind](https://atb.nlr.gov/electricity/2024/distributed_wind) Example usage of this cost model in the `tech_config.yaml` file is shown [in the first section below](#atb-wind-cost-model). diff --git a/docs/technology_models/co2.md b/docs/technology_models/co2.md index e3869b786..cef6f8f40 100644 --- a/docs/technology_models/co2.md +++ b/docs/technology_models/co2.md @@ -1,14 +1,14 @@ # Marine Carbon Dioxide Capture Models Marine carbon dioxide (CO₂) capture technologies aim to remove CO₂ from the ocean or enhance the ocean’s natural capacity to store atmospheric CO₂. These approaches provide additional pathways for managing carbon in the marine environment and can complement land-based strategies for resource management and ocean health. -This section provides an overview of the marine carbon dioxide capture models integrated into H2Integrate. The models are adapted from and maintained in NREL's [MarineCarbonManagement Repository](https://github.com/Nrel/MarineCarbonManagement) and have been integrated here for ease of scenario analysis and system-level optimization. +This section provides an overview of the marine carbon dioxide capture models integrated into H2Integrate. The models are adapted from and maintained in NLR's [MarineCarbonManagement Repository](https://github.com/NatLabRockies/MarineCarbonManagement) and have been integrated here for ease of scenario analysis and system-level optimization. ## Direct Ocean Capture (DOC) Model Direct Ocean Capture extracts dissolved CO₂ directly from seawater using engineered processes. By reducing the concentration of dissolved inorganic carbon, the ocean naturally reabsorbs an equivalent amount of atmospheric CO₂. The resultant CO₂ can then be used for downstream processes and conversion or storage. -The DOC model is built on electrodialysis-based separation and includes both performance and cost components, allowing users to explore a wide range of system configurations, operational scenarios, and infrastructure options. It is designed for process design, optimization, and cost evaluation of marine carbon capture systems and is integrated from NREL's [MarineCarbonManagement Repository](https://github.com/Nrel/MarineCarbonManagement). Additional information about this specific model can be found in [Niffenegger et al.](https://doi.org/10.3390/cleantechnol7030052) +The DOC models, `DOCPerformanceModel` and `DOCCostModel`, are built on electrodialysis-based separation and includes both performance and cost components, allowing users to explore a wide range of system configurations, operational scenarios, and infrastructure options. It is designed for process design, optimization, and cost evaluation of marine carbon capture systems and is integrated from NLR's [MarineCarbonManagement Repository](https://github.com/NatLabRockies/MarineCarbonManagement). Additional information about this specific model can be found in [Niffenegger et al.](https://doi.org/10.3390/cleantechnol7030052) ### Why Use This Model - Evaluate technology performance — quantify system throughput, process efficiencies, and resource usage under different operating conditions. @@ -55,7 +55,7 @@ The Ocean Alkalinity Enhancement (OAE) Model simulates the process of increasing This model estimates the CO₂ absorption potential of the ocean after the OAE process. CO₂ is not a usable downstream product. ``` -The OAE model is adapted from NREL’s MarineCarbonManagement Repository and includes both performance and cost components, as well as a financial model enabling users to explore a variety of system designs, operational strategies, and infrastructure setups. Additional information about this specific model can be found in [Niffenegger et al.](https://doi.org/10.3390/cleantechnol8010012) +The OAE models, `OAEPerformanceModel` and `OAECostModel`, are adapted from NLR’s MarineCarbonManagement Repository and includes both performance and cost components, as well as a financial model enabling users to explore a variety of system designs, operational strategies, and infrastructure setups. Additional information about this specific model can be found in [Niffenegger et al.](https://doi.org/10.3390/cleantechnol8010012) ### Why Use This Model - Assess system performance — determine processing capacity, flow characteristics, and operational profiles. @@ -95,7 +95,7 @@ Computes capital (CapEx) and operational (OpEx) costs based on plant design and - Annual operating cost (USD/year) #### OAE Cost and Financial Model -Extends the cost model to include financial metrics. Allows calculation of net present value (NPV) and determination of credit values required for financial viability. +The `OAECostAndFinancialModel` extends the cost model to include financial metrics. Allows calculation of net present value (NPV) and determination of credit values required for financial viability. **Additional inputs include:** - Levelized cost of electricity (LCOE) diff --git a/docs/technology_models/feedstocks.md b/docs/technology_models/feedstocks.md index 2d722b351..dc5db0986 100644 --- a/docs/technology_models/feedstocks.md +++ b/docs/technology_models/feedstocks.md @@ -1,6 +1,6 @@ # Feedstock Models -Feedstock models in H2Integrate represent any resource input that is consumed by technologies in your plant, such as natural gas, water, electricity from the grid, or any other material input. +Feedstock models in H2Integrate represent any resource input that is consumed by technologies in your plant that comes from outside your designed system boundary (and not generated internally), such as natural gas, water, electricity from the grid, or any other material input. The feedstock modeling approach provides a flexible way to track resource consumption and calculate associated costs for any type of input material or energy source. Please see the example `16_natural_gas` in the `examples` directory for a complete setup using natural gas as a feedstock. @@ -19,6 +19,7 @@ Each feedstock type requires two model components: - Calculates consumption costs based on actual usage - Takes `{commodity}_consumed` as input - Located after all consuming technologies in the chain + - Calculates the capacity factor of the consumed feedstock ### Technology Interconnections @@ -56,8 +57,8 @@ ng_feedstock: commodity_amount_units: "MMBtu" # optional, if not specified defaults to `commodity_rate_units*h` cost_year: 2023 price: 4.2 # cost in USD/commodity_amount_units - annual_cost: 0. - start_up_cost: 100000. + annual_cost: 0. #cost in USD/year + start_up_cost: 100000. #cost in USD ``` ### Performance Model Parameters @@ -81,3 +82,11 @@ ng_feedstock: ```{tip} The `price` parameter is flexible - you can specify constant pricing with a single value or time-varying pricing with an array of values matching the number of simulation timesteps. ``` + +### Consumed Feedstock Outputs +The feedstock model outputs cost and performance information about the consumed feedstock. The most notable outputs are: +- `VarOpEx`: cost of the feedstock consumed (in `USD/yr`) +- `total_{commodity}_consumed`: total feedstock consumed over simulation (in `commodity_amount_units`) +- `annual_{commodity}_consumed`: annual feedstock consumed (in `commodity_amount_units/yr`) +- `rated_{commodity}_production`: this is equal to the the `rated_capacity` of the feedstock model (in `commodity_rate_units`) +- `capacity_factor`: ratio of the feedstock consumed to the maximum feedstock available diff --git a/docs/technology_models/geologic_hydrogen.md b/docs/technology_models/geologic_hydrogen.md index af74e4cf5..751ca44eb 100644 --- a/docs/technology_models/geologic_hydrogen.md +++ b/docs/technology_models/geologic_hydrogen.md @@ -43,7 +43,7 @@ The modeling approach in this performance model is informed by: The modeling approach in this cost model is based on: - Mathur et al. (Stanford): - NETL Quality Guidelines: -- Drill cost curves are based on an adapted [GETEM model](https://sam.nrel.gov/geothermal.html) +- Drill cost curves are based on an adapted [GETEM model](https://sam.nlr.gov/geothermal.html) ## Hydrogen Surface Processing Models diff --git a/docs/technology_models/grid.md b/docs/technology_models/grid.md index befb8e3f8..ce0ca4902 100644 --- a/docs/technology_models/grid.md +++ b/docs/technology_models/grid.md @@ -67,3 +67,7 @@ The **revenue** of selling electricity to the grid is represented as a variable ```{note} If you're using a price-maker financial model (e.g., calculating the LCOE) and selling all of the electricity to the grid, then the `electricity_sell_price` should most likely be set to 0. since you want to know the breakeven price of selling that electricity. ``` + +```{note} +The grid components are currently compatible with 5-minute (300-second) to 1-hour (3600-second) time steps. +``` diff --git a/docs/technology_models/h2_smr.md b/docs/technology_models/h2_smr.md new file mode 100644 index 000000000..0b7b26685 --- /dev/null +++ b/docs/technology_models/h2_smr.md @@ -0,0 +1,23 @@ +# Hydrogen Steam Methane Reformer (SMR) Model + +This module implements performance and cost models for a steam methane reformer (SMR) hydrogen production plant. The model converts natural gas and electricity inputs into hydrogen output subject to plant capacity constraints and feedstock availability. + +The implementation consists of two components: +1. `SteamMethaneReformerPerformanceModel` – calculates hydrogen production and energy consumption over time. +2. `SteamMethaneReformerCostModel` – computes capital and operating costs based on plant capacity and hydrogen production. + +The model is designed to integrate with the `h2integrate` framework and follows the standard configuration and model structure used for performance and cost models. + +## Model Overview + +Steam methane reforming is a thermochemical process where methane reacts with steam to produce hydrogen and carbon monoxide, followed by water-gas shift reactions to increase hydrogen yield. + +The simplified representation used in this model assumes: +- Hydrogen production is limited by: + - SMR rated capacity + - Natural gas availability + - Electricity availability +- Energy consumption is modeled using constant intensity parameters: + - Natural gas consumption per kg of hydrogen + - Electricity consumption per kg of hydrogen +- Hydrogen demand may be specified and unmet demand is tracked. diff --git a/docs/technology_models/iron_dri.md b/docs/technology_models/iron_dri.md new file mode 100644 index 000000000..a2bbd367e --- /dev/null +++ b/docs/technology_models/iron_dri.md @@ -0,0 +1,27 @@ +# Direct Reduced Iron Models + +H2I contains two direct reduced iron (DRI) models, one is for a facility run using natural gas and another facility run using hydrogen. + +The original models were constructed in Aspen Pro and translated into Python and added to H2I. The models were developed in conjunction with [Lawrence Berkeley National Laboratory (LBNL)](https://www.lbl.gov/). + +The models implemented in H2I are: +- Natural Gas Electric Arc Furnace + - `NaturalGasIronReductionPlantPerformanceComponent` + - `NaturalGasIronReductionPlantCostComponent` +- Hydrogen Electric Arc Furnace + - `HydrogenIronReductionPlantPerformanceComponent` + - `HydrogenIronReductionPlantCostComponent` + +Citation: +```bibtex +@article{rosner2023green, + title={Green steel: design and cost analysis of hydrogen-based direct iron reduction}, + author={Rosner, Fabian and Papadias, Dionissios and Brooks, Kriston and Yoro, Kelvin and Ahluwalia, Rajesh and Autrey, Tom and Breunig, Hanna}, + journal={Energy \& Environmental Science}, + volume={16}, + number={10}, + pages={4121--4134}, + year={2023}, + publisher={Royal Society of Chemistry} +} +``` diff --git a/docs/technology_models/iron_mine.md b/docs/technology_models/iron_mine.md index 25fc92d29..c1fca3d94 100644 --- a/docs/technology_models/iron_mine.md +++ b/docs/technology_models/iron_mine.md @@ -27,7 +27,7 @@ These data were compiled from two sources: - [Minorca Mine](https://minedocs.com/22/Minorca-TR-12312021.pdf) - [Tilden Mine](https://minedocs.com/22/Tilden-TR-12312021.pdf) -To use this model, specify `"iron_mine_performance_martin"` as the performance model and `"iron_mine_cost_martin"` as the cost model. +To use this model, specify `"MartinIronMinePerformanceComponent"` as the performance model and `"MartinIronMineCostComponent"` as the cost model. Currently, no complex calculations occur beyond importing performance and costs. In the performance model, the "wet long tons" (wlt) that ore production is typically reported in are converted to dry metric tons for use in H2I. In the cost model, the total capex costs for a plant are scaled by the amount of are produced annually. diff --git a/docs/technology_models/marine_hydrokinetic.md b/docs/technology_models/marine_hydrokinetic.md new file mode 100644 index 000000000..54089c34e --- /dev/null +++ b/docs/technology_models/marine_hydrokinetic.md @@ -0,0 +1,42 @@ +# Marine Hydrokinetic (MHK) Models + +H2I has several marine hydrokinetic (MHK) models for performance and costs. Marine and Hydrokinetic (MHK) technologies harness renewable energy from waves, tidal currents, ocean currents, and riverine flows to generate electricity. + +Within H2I there are models for: +- [Run-Of-River Hydropower](run_of_river_hydro) +- [Tidal](tidal_performance) +- [MHK Costs](pysam_mhk_costs) + +(pysam_mhk_costs)= +## PySAM MHK Cost Model +The **PySAM Marine Cost Model** estimates capital and operational costs for marine hydrokinetic (MHK) energy systems, including tidal and wave energy technologies. The model wraps the [PySAM MhkCosts module](https://nrel-pysam.readthedocs.io/en/main/modules/MhkCosts.html) implementation of the marine hydrokinetic cost model used in the [System Advisor Model (SAM)](https://sam.nlr.gov/). To use this model, specify `"PySAMMarineCostModel"` as the cost model. + +The cost model leverages technology assumptions and cost structures developed through the U.S. Department of Energy [Reference Model Project](https://energy.sandia.gov/programs/renewable-energy/water-power/projects/reference-model-project-rmp/) for marine energy technologies. These reference models provide standardized system configurations and cost relationships for different MHK device types. + +This component is implemented as an OpenMDAO-compatible cost model and can be integrated into larger hybrid energy system simulations. + +### Model Overview + +The model estimates: +- Capital expenditures (CapEx) for MHK devices and associated infrastructure +- Operational expenditures (OpEx) for maintenance and operations + +Costs are derived using the PySAM MhkCosts module, which calculates costs for: +- Device structural and power take-off systems +- Mooring and foundation systems +- Installation and infrastructure +- Electrical systems +- Project development and financial costs +- Operations and maintenance + +The final outputs are total installed capital cost and annual operating cost. + +The model supports several reference technologies from the DOE Reference Model Project. Each reference model corresponds to cost relationships embedded in the PySAM cost library. + +| Reference Model | Technology | +| --------------- | ------------------------ | +| RM1 | Tidal Current Turbine | +| RM2 | River Current Turbine | +| RM3 | Wave Point Absorber | +| RM5 | Oscillating Surge Flap | +| RM6 | Oscillating Water Column | diff --git a/docs/technology_models/methanol.md b/docs/technology_models/methanol.md index 1e02eea11..86fee1ef8 100644 --- a/docs/technology_models/methanol.md +++ b/docs/technology_models/methanol.md @@ -2,9 +2,9 @@ Methanol is an essential precursor to many chemicals, and is also used as a fuel, especially in marine applications. It can be produced from many substances, which the most conventional route using natural gas as the main feedstock, and other developing technologies that combine hydrogen and carbon dioxide (CO2). A basic framework for modeling methanol production is set up in `h2integrate/converters/methanol/methanol_baseclass.py`, and specific technologies are modeled in inherited classes. Examples of each methanol production method can be found in `examples/03_methanol`. Currently, H2I models two methanol production technologies: -1. Steam Methane Reforming (SMR): This is the most prominent commercial production route for methanol in the United States, and utilizes natural gas as the main feedstock. This model is found at `h2integrate/converters/methanol/smr_methanol_plant.py`. The NREL modeling of this process is based on an [NETL Baseline Analysis of Crude Methanol Production from Coal and Natural Gas](https://doi.org/10.2172/1601964). +1. Steam Methane Reforming (SMR): This is the most prominent commercial production route for methanol in the United States, and utilizes natural gas as the main feedstock. This model is found at `h2integrate/converters/methanol/smr_methanol_plant.py`. The NLR modeling of this process is based on an [NETL Baseline Analysis of Crude Methanol Production from Coal and Natural Gas](https://doi.org/10.2172/1601964). -2. CO2 Hydrogenation: This is a developing methanol production technology that directly combines hydrogen with carbon dioxide to produce methanol. This model is found at `h2integrate/converters/methanol/co2h_methanol_plant.py`. The NREL modeling of this process is based on a combination of three peer-reviewed studies: +2. CO2 Hydrogenation: This is a developing methanol production technology that directly combines hydrogen with carbon dioxide to produce methanol. This model is found at `h2integrate/converters/methanol/co2h_methanol_plant.py`. The NLR modeling of this process is based on a combination of three peer-reviewed studies: - [Perez-Fortes et al.](https://doi.org/10.1016/j.apenergy.2015.07.067) - [Szima et al.](https://doi.org/10.1016/j.jcou.2018.02.007) - [Nyari et al.](https://doi.org/10.1016/j.jcou.2020.101166) diff --git a/docs/technology_models/pvwattsv8_solar_pv.md b/docs/technology_models/pvwattsv8_solar_pv.md index a6e8796f7..dceada8a0 100644 --- a/docs/technology_models/pvwattsv8_solar_pv.md +++ b/docs/technology_models/pvwattsv8_solar_pv.md @@ -54,16 +54,16 @@ $$ - `create_model_from`: this can either be set to `"new"` or `"default"` and defaults to `"new"`. If `create_model_from` is `"new"`, the PV model is initialized using `Pvwattsv8.new()` and *populated* with parameters specified in `pysam_options`. If `create_model_from` is `"default"`, the PV model is initialized using `Pvwattsv8.default(config_name)` (`config_name` is also an input parameter) then *updated* with parameters specified in `pysam_options`. - `config_name`: this is only used if `create_model_from` is `"default"`. The default value for this is `"PVWattsSingleOwner"`. The available options and their default parameters are listed below: - - [PVWattsCommercial](https://github.com/NREL/SAM/blob/develop/api/api_autogen/library/defaults/Pvwattsv8_PVWattsCommercial.json) - - [PVWattsCommunitySolar](https://github.com/NREL/SAM/blob/develop/api/api_autogen/library/defaults/Pvwattsv8_PVWattsCommunitySolar.json) - - [PVWattsHostDeveloper](https://github.com/NREL/SAM/blob/develop/api/api_autogen/library/defaults/Pvwattsv8_PVWattsHostDeveloper.json) - - [PVWattsMerchantPlant](https://github.com/NREL/SAM/blob/develop/api/api_autogen/library/defaults/Pvwattsv8_PVWattsMerchantPlant.json) - - [PVWattsNone](https://github.com/NREL/SAM/blob/develop/api/api_autogen/library/defaults/Pvwattsv8_PVWattsNone.json) - - [PVWattsResidential](https://github.com/NREL/SAM/blob/develop/api/api_autogen/library/defaults/Pvwattsv8_PVWattsResidential.json) - - [PVWattsSaleLeaseback](https://github.com/NREL/SAM/blob/develop/api/api_autogen/library/defaults/Pvwattsv8_PVWattsSaleLeaseback.json) - - [PVWattsSingleOwner](https://github.com/NREL/SAM/blob/develop/api/api_autogen/library/defaults/Pvwattsv8_PVWattsSingleOwner.json) - - [PVWattsThirdParty](https://github.com/NREL/SAM/blob/develop/api/api_autogen/library/defaults/Pvwattsv8_PVWattsThirdParty.json) - - [PVWattsAllEquityPartnershipFlip](https://github.com/NREL/SAM/blob/develop/api/api_autogen/library/defaults/Pvwattsv8_PVWattsAllEquityPartnershipFlip.json) + - [PVWattsCommercial](https://github.com/NatLabRockies/SAM/blob/develop/api/api_autogen/library/defaults/Pvwattsv8_PVWattsCommercial.json) + - [PVWattsCommunitySolar](https://github.com/NatLabRockies/SAM/blob/develop/api/api_autogen/library/defaults/Pvwattsv8_PVWattsCommunitySolar.json) + - [PVWattsHostDeveloper](https://github.com/NatLabRockies/SAM/blob/develop/api/api_autogen/library/defaults/Pvwattsv8_PVWattsHostDeveloper.json) + - [PVWattsMerchantPlant](https://github.com/NatLabRockies/SAM/blob/develop/api/api_autogen/library/defaults/Pvwattsv8_PVWattsMerchantPlant.json) + - [PVWattsNone](https://github.com/NatLabRockies/SAM/blob/develop/api/api_autogen/library/defaults/Pvwattsv8_PVWattsNone.json) + - [PVWattsResidential](https://github.com/NatLabRockies/SAM/blob/develop/api/api_autogen/library/defaults/Pvwattsv8_PVWattsResidential.json) + - [PVWattsSaleLeaseback](https://github.com/NatLabRockies/SAM/blob/develop/api/api_autogen/library/defaults/Pvwattsv8_PVWattsSaleLeaseback.json) + - [PVWattsSingleOwner](https://github.com/NatLabRockies/SAM/blob/develop/api/api_autogen/library/defaults/Pvwattsv8_PVWattsSingleOwner.json) + - [PVWattsThirdParty](https://github.com/NatLabRockies/SAM/blob/develop/api/api_autogen/library/defaults/Pvwattsv8_PVWattsThirdParty.json) + - [PVWattsAllEquityPartnershipFlip](https://github.com/NatLabRockies/SAM/blob/develop/api/api_autogen/library/defaults/Pvwattsv8_PVWattsAllEquityPartnershipFlip.json) - `pysam_options` (dict): The top-level keys correspond to the Groups available in the [Pvwattsv8 module](https://nrel-pysam.readthedocs.io/en/main/modules/Pvwattsv8.html). The next level is the individual attributes a user could set and a full list is available through the PySAM documentation of Pvwattsv8 module. The Groups that users may want to specify specific options for are the: - [SystemDesign](#systemdesign-group) - [SolarResource](#solarresource-group) diff --git a/docs/technology_models/run_of_river.md b/docs/technology_models/run_of_river.md index ee8209fcf..4822d9d87 100644 --- a/docs/technology_models/run_of_river.md +++ b/docs/technology_models/run_of_river.md @@ -1,3 +1,4 @@ +(run_of_river_hydro)= # Run-of-river hydropower model The `RunOfRiverHydro` model simulates the generation of electricity from a river generation source, taking into account the flow rate and the efficiency of the turbine. @@ -74,4 +75,4 @@ Also within the `tech_config` yaml, you can set the performance and cost paramet For additional information regarding hydropower technology and performance see [New Stream-reach Development: A Comprehensive Assessment of Hydropower Energy Potential in the United States](https://info.ornl.gov/sites/publications/Files/Pub46481.pdf) and -for relevant performance and cost assumptions see the [Annual Technology Baseline - Hydropower](https://atb.nrel.gov/electricity/2024/hydropower). +for relevant performance and cost assumptions see the [Annual Technology Baseline - Hydropower](https://atb.nlr.gov/electricity/2024/hydropower). diff --git a/docs/technology_models/steel_eaf.md b/docs/technology_models/steel_eaf.md new file mode 100644 index 000000000..28638556b --- /dev/null +++ b/docs/technology_models/steel_eaf.md @@ -0,0 +1,31 @@ +# Steel Electric Arc Furnace Models + +H2I contains two steel electric arc furnace (EAF) models, one is for a facility that utilizes iron pellets from natural gas (NG) direct iron reduction and another facility that utilizes iron pellets from hydrogen (H2)direct iron reduction. + +The main difference is the required feedstocks for processing the pellets. +- NG-EAF requires: natural gas, water, pig iron and electricity. +- H2-EAF requires: natural gas, water, carbon, lime, pig iron and electricity. + +The original models were constructed in Aspen Pro and translated into Python and added to H2I. The models were developed in conjunction with [Lawrence Berkeley National Laboratory (LBNL)](https://www.lbl.gov/). + +The models implemented in H2I are: +- Natural Gas Electric Arc Furnace + - `NaturalGasEAFPlantPerformanceComponent` + - `NaturalGasEAFPlantCostComponent` +- Hydrogen Electric Arc Furnace + - `HydrogenEAFPlantPerformanceComponent` + - `HydrogenEAFPlantCostComponent` + +Citation: +```bibtex +@article{rosner2023green, + title={Green steel: design and cost analysis of hydrogen-based direct iron reduction}, + author={Rosner, Fabian and Papadias, Dionissios and Brooks, Kriston and Yoro, Kelvin and Ahluwalia, Rajesh and Autrey, Tom and Breunig, Hanna}, + journal={Energy \& Environmental Science}, + volume={16}, + number={10}, + pages={4121--4134}, + year={2023}, + publisher={Royal Society of Chemistry} +} +``` diff --git a/docs/technology_models/tidal.md b/docs/technology_models/tidal.md new file mode 100644 index 000000000..0cae0797d --- /dev/null +++ b/docs/technology_models/tidal.md @@ -0,0 +1,50 @@ +(tidal_performance)= +# Tidal Models + +## PySAM Tidal Performance Model + +The **PySAM Tidal Performance Model** simulates electricity generation from **tidal current energy devices** using a time-series tidal velocity resource. The model wraps the [PySAM MhkTidal module](https://nrel-pysam.readthedocs.io/en/main/modules/MhkTidal.html) implementation of the tidal performance model used in the [System Advisor Model (SAM)](https://sam.nlr.gov/). + +This component integrates the PySAM **MhkTidal** module into an **OpenMDAO-compatible performance model**, allowing it to be used within hybrid energy system simulations. To use this model, specify `"PYSAMTidalPerformanceModel"` as the performance model. + +The model converts a **tidal velocity resource time series** and **device power curve** into: +- time-series electricity generation +- annual energy production +- system capacity factor + +### Model Overview + +The model represents an array of tidal energy devices operating in a tidal current resource. Electricity production is calculated by mapping the instantaneous tidal velocity to the device power curve, scaling by the number of devices, and integrating over time. + +Key capabilities include: +- Time-series tidal resource simulation +- Device-array scaling +- Custom tidal power curves +- Optional automatic power curve scaling to match device rating +- Integration with hybrid energy system simulations + +(pysam-options)= +#### PySAM Options +A user can specify any of the attributes available within the [MhkTidal module](https://nrel-pysam.readthedocs.io/en/main/modules/MhkTidal.html). They can do this using the `pysam_options` dictionary in the when setting up the `PySAMTidalPerformanceModel`. + +The top-level keys of the dictionary correspond to the Groups available in the [MhkTidal module](https://nrel-pysam.readthedocs.io/en/main/modules/MhkTidal.html). The next level is the individual attributes a user could set and a full list is available through the PySAM documentation of MhkTidal module. + +#### Power Curve Scaling + +If `run_recalculate_power_curve=True`, the model rescales the power curve to match the specified device rating. The power curve will be internally scaled based on the new device rating so if the original tidal device rating is updated to a different rating, the model can use the original power curve and scale it if a new power curve is not available. + +Scaling is performed using: + +$$P_{\text{scaled}}(v) = +P_{\text{original}}(v) +\times +\frac{\text{device_rating}}{P_{\text{rated, original}}}$$ + +where: + +- $P_{\text{original}}(v)$ is the original power curve value at velocity \(v\) +- $P_{\text{rated, original}}$ is the maximum value of the original power curve +- `device_rating` is the rated power of the device specified in the configuration + + +This allows a generic power curve to be adapted to different device ratings. diff --git a/docs/technology_models/wind_plant_ard.md b/docs/technology_models/wind_plant_ard.md index 29e83f88b..659f0d33b 100644 --- a/docs/technology_models/wind_plant_ard.md +++ b/docs/technology_models/wind_plant_ard.md @@ -57,18 +57,18 @@ technologies: rated_power: 5000000.0 # W num_blades: 3 rated_thrust_N: 823484.4216152605 # from NREL 5MW definition - gust_velocity_m_per_s: 70.0 # from https://github.com/WISDEM/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml + gust_velocity_m_per_s: 70.0 # from https://github.com/NLRWindSystems/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml blade_surface_area: 69.7974979 tower_mass: 620.4407337521 nacelle_mass: 101.98582836439 hub_mass: 8.38407517646 blade_mass: 14.56341339641 foundation_height: 0.0 - commissioning_cost_kW: 44.0 # from https://github.com/WISDEM/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml - decommissioning_cost_kW: 58.0 # from https://github.com/WISDEM/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml + commissioning_cost_kW: 44.0 # from https://github.com/NLRWindSystems/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml + decommissioning_cost_kW: 58.0 # from https://github.com/NLRWindSystems/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml trench_len_to_substation_km: 50.0 distance_to_interconnect_mi: 4.97096954 - interconnect_voltage_kV: 130.0 # from https://github.com/WISDEM/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml + interconnect_voltage_kV: 130.0 # from https://github.com/NLRWindSystems/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml tcc_per_kW: 1300.00 # (USD/kW) opex_per_kW: 44.00 # (USD/kWh) analysis_options: diff --git a/docs/technology_models/windpower_wind_plant.md b/docs/technology_models/windpower_wind_plant.md index 4f7785b81..36a4a33f4 100644 --- a/docs/technology_models/windpower_wind_plant.md +++ b/docs/technology_models/windpower_wind_plant.md @@ -50,14 +50,14 @@ technologies: - `turbine_rating_kw` (required): rated power of individual wind turbine in kW - `create_model_from`: this can either be set to `"new"` or `"default"` and defaults to `"new"`. If `create_model_from` is `"new"`, the wind model is initialized using `Windpower.new()` and *populated* with parameters specified in `pysam_options`. If `create_model_from` is `"default"`, the wind model is initialized using `Windpower.default(config_name)` (`config_name` is also an input parameter) then *updated* with parameters specified in `pysam_options`. - `config_name`: this is only used if `create_model_from` is `"default"`. The default value for this is `"WindPowerSingleOwner"`. The available options and their default parameters are listed below: - - [WindPowerAllEquityPartnershipFlip](https://github.com/NREL/SAM/blob/develop/api/api_autogen/library/defaults/Windpower_WindPowerAllEquityPartnershipFlip.json) - - [WindPowerCommercial](https://github.com/NREL/SAM/blob/develop/api/api_autogen/library/defaults/Windpower_WindPowerCommercial.json) - - [WindPowerLeveragedPartnershipFlip](https://github.com/NREL/SAM/blob/develop/api/api_autogen/library/defaults/Windpower_WindPowerLeveragedPartnershipFlip.json) - - [WindPowerMerchantPlant](https://github.com/NREL/SAM/blob/develop/api/api_autogen/library/defaults/Windpower_WindPowerMerchantPlant.json) - - [WindPowerNone](https://github.com/NREL/SAM/blob/develop/api/api_autogen/library/defaults/Windpower_WindPowerNone.json) - - [WindPowerResidential](https://github.com/NREL/SAM/blob/develop/api/api_autogen/library/defaults/Windpower_WindPowerResidential.json) - - [WindPowerSaleLeaseback](https://github.com/NREL/SAM/blob/develop/api/api_autogen/library/defaults/Windpower_WindPowerSaleLeaseback.json) - - [WindPowerSingleOwner](https://github.com/NREL/SAM/blob/develop/api/api_autogen/library/defaults/Windpower_WindPowerSingleOwner.json) + - [WindPowerAllEquityPartnershipFlip](https://github.com/NatLabRockies/SAM/blob/develop/api/api_autogen/library/defaults/Windpower_WindPowerAllEquityPartnershipFlip.json) + - [WindPowerCommercial](https://github.com/NatLabRockies/SAM/blob/develop/api/api_autogen/library/defaults/Windpower_WindPowerCommercial.json) + - [WindPowerLeveragedPartnershipFlip](https://github.com/NatLabRockies/SAM/blob/develop/api/api_autogen/library/defaults/Windpower_WindPowerLeveragedPartnershipFlip.json) + - [WindPowerMerchantPlant](https://github.com/NatLabRockies/SAM/blob/develop/api/api_autogen/library/defaults/Windpower_WindPowerMerchantPlant.json) + - [WindPowerNone](https://github.com/NatLabRockies/SAM/blob/develop/api/api_autogen/library/defaults/Windpower_WindPowerNone.json) + - [WindPowerResidential](https://github.com/NatLabRockies/SAM/blob/develop/api/api_autogen/library/defaults/Windpower_WindPowerResidential.json) + - [WindPowerSaleLeaseback](https://github.com/NatLabRockies/SAM/blob/develop/api/api_autogen/library/defaults/Windpower_WindPowerSaleLeaseback.json) + - [WindPowerSingleOwner](https://github.com/NatLabRockies/SAM/blob/develop/api/api_autogen/library/defaults/Windpower_WindPowerSingleOwner.json) - `run_recalculate_power_curve` (optional): Flag whether to recalculate the wind turbine power curve. defaults to True. diff --git a/docs/technology_models/wombat_electrolyzer_om.md b/docs/technology_models/wombat_electrolyzer_om.md index 57f71ebcf..432674770 100644 --- a/docs/technology_models/wombat_electrolyzer_om.md +++ b/docs/technology_models/wombat_electrolyzer_om.md @@ -1,6 +1,6 @@ # WOMBAT Electrolyzer O&M Model -The `WOMBATElectrolyzerModel` provides a detailed, simulation-based approach to estimating the operations and maintenance (O&M) costs and availability of hydrogen electrolyzers. This model leverages the [WOMBAT](https://github.com/WISDEM/WOMBAT) (Windfarm Operations and Maintenance cost-Benefit Analysis Tool) framework, originally developed for wind farm O&M analysis, and adapts it for hydrogen systems. +The `WOMBATElectrolyzerModel` provides a detailed, simulation-based approach to estimating the operations and maintenance (O&M) costs and availability of hydrogen electrolyzers. This model leverages the [WOMBAT](https://github.com/NLRWindSystems/WOMBAT) (Windfarm Operations and Maintenance cost-Benefit Analysis Tool) framework, originally developed for wind farm O&M analysis, and adapts it for hydrogen systems. ## What is WOMBAT? diff --git a/docs/user_guide/connecting_technologies.md b/docs/user_guide/connecting_technologies.md index f85396e7a..e70bbe6b7 100644 --- a/docs/user_guide/connecting_technologies.md +++ b/docs/user_guide/connecting_technologies.md @@ -49,7 +49,7 @@ There are two connection formats: ##### Different shared parameter names ```yaml -["source_tech", "destination_tech", ("source_parameter", "destination_parameter")] +["source_tech", "destination_tech", ["source_parameter", "destination_parameter"]] ``` - **source_tech**: Name of the technology providing the output @@ -57,6 +57,9 @@ There are two connection formats: - **source_parameter**: The name of the parameter within ``"source_tech"`` - **destination_parameter**: The name of the parameter within ``"destination_tech"`` +```{note} +The `source_parameter` and `destination_parameter` should be input into the array as another array. If it's input as a tuple the model will raise an error. +``` ### Internal connection logic @@ -92,6 +95,109 @@ And automatically connects: - `electricity_splitter.electricity_out1` → `electricity_splitter_to_electrolyzer_cable.electricity_in` - `electricity_splitter.electricity_out2` → `electricity_splitter_to_doc_cable.electricity_in` +## Multivariable streams + +Standard connections in H2Integrate transport a single commodity between technologies (e.g., electricity in kW, hydrogen in kg/h). +*Multivariable streams* extend this by bundling several related variables into a single named stream, so that one connection specification in `technology_interconnections` expands into connections for every constituent variable automatically. + +A typical use-case is a gas mixture where you need to transport the mass flow rate, composition fractions, temperature, and pressure together between a producer, a combiner, and a consumer. + +### Defining a multivariable stream + +Multivariable streams are defined in `commodity_stream_definitions.py`. Each stream has a name and a dictionary of constituent variables with their units and descriptions: + +```{literalinclude} ../../h2integrate/core/commodity_stream_definitions.py +:language: python +:lines: 11-35 +:caption: Built-in stream definition from commodity_stream_definitions.py +``` + +To add a new multivariable stream type, add another entry to the `multivariable_streams` dictionary with the stream name as the key and the constituent variables as the value. + +### Variable naming convention + +Multivariable stream variables follow the naming convention `:_in` for inputs and `:_out` for outputs. +The colon separates the stream name from the constituent variable name, making it clear which stream a variable belongs to. + + +### Using multivariable streams in components + +Two helper functions are provided to register all constituent variables of a multivariable stream on an OpenMDAO component: + +```python +from h2integrate.core.commodity_stream_definitions import ( + add_multivariable_output, + add_multivariable_input, +) + +class MyProducer(PerformanceModelBaseClass): + def setup(self): + super().setup() + # Adds all wellhead_gas_mixture variables as outputs + add_multivariable_output(self, "wellhead_gas_mixture", self.n_timesteps) + +class MyConsumer(PerformanceModelBaseClass): + def setup(self): + super().setup() + # Adds all wellhead_gas_mixture variables as inputs + add_multivariable_input(self, "wellhead_gas_mixture", self.n_timesteps) +``` + +These helper functions replace the need for manually iterating over the stream definition dictionary, reducing boilerplate code and ensuring consistency when adding new stream types. + +### Connecting multivariable streams + +Multivariable streams are connected using the same `technology_interconnections` syntax as standard connections. +When H2Integrate encounters a stream name that matches a key in `multivariable_streams`, it automatically expands the connection into individual connections for each constituent variable. + +#### 4-element connections + +```yaml +technology_interconnections: [ + ["gas_producer", "gas_consumer", "wellhead_gas_mixture", "pipe"], +] +``` + +This single line expands into five OpenMDAO connections: +- `gas_producer.wellhead_gas_mixture:mass_flow_out` → `gas_consumer.wellhead_gas_mixture:mass_flow_in` +- `gas_producer.wellhead_gas_mixture:hydrogen_mass_fraction_out` → `gas_consumer.wellhead_gas_mixture:hydrogen_mass_fraction_in` +- `gas_producer.wellhead_gas_mixture:oxygen_mass_fraction_out` → `gas_consumer.wellhead_gas_mixture:oxygen_mass_fraction_in` +- `gas_producer.wellhead_gas_mixture:temperature_out` → `gas_consumer.wellhead_gas_mixture:temperature_in` +- `gas_producer.wellhead_gas_mixture:pressure_out` → `gas_consumer.wellhead_gas_mixture:pressure_in` + + +#### 3-element connections + +Three-element connections also support multivariable streams: + +```yaml +technology_interconnections: [ + ["gas_producer", "gas_consumer", "wellhead_gas_mixture"], +] +``` + +This expands into the same set of individual connections as the 4-element version above. + +#### Combiner and splitter connections + +Multivariable streams work with combiners and splitters using the same naming conventions as standard commodity connections. +The system auto-increments stream indices for combiners and splitters: + +```yaml +technology_interconnections: [ + ["gas_producer_1", "gas_combiner", "wellhead_gas_mixture"], + ["gas_producer_2", "gas_combiner", "wellhead_gas_mixture"], + ["gas_combiner", "gas_consumer", "wellhead_gas_mixture"], +] +``` + +For the combiner inputs, variables are indexed as `wellhead_gas_mixture:_in1`, `wellhead_gas_mixture:_in2`, etc. +For the splitter outputs, variables are indexed as `wellhead_gas_mixture:_out1`, `wellhead_gas_mixture:_out2`, etc. + +### Example + +See [Example 32](https://github.com/NatLabRockies/H2Integrate/tree/main/examples/32_multivariable_streams) for a complete working example that demonstrates two gas producers with different properties feeding into a gas stream combiner, which then feeds a consumer. + ## Generic combiner The generic combiner is a simple but essential component that takes a single commodity from multiple sources and combines the sources into a single output without losses. The following example uses power (kW) as the commodity, but streams of any single commodity can be combined. Any number of sources may be combined, not just two as in the example. diff --git a/docs/user_guide/expected_naming_convention.md b/docs/user_guide/expected_naming_convention.md deleted file mode 100644 index 447e57297..000000000 --- a/docs/user_guide/expected_naming_convention.md +++ /dev/null @@ -1,39 +0,0 @@ -# Expected Technology Naming Convention in H2I - -Some logic within H2I relies on expected naming conventions of technologies in the technology configuration file. The following details the naming convention required for H2I to run properly. - -```yaml -technologies: - tech_name_A: #these are the keys with specific required naming convention - performance_model: - model: - cost_model: - model: - model_inputs: - performance_parameters: -``` - -- if using the `GenericCombinerPerformanceModel` as the technology performance model, the technology name must include the name `combiner`. Some examples of valid technology names are: - + 'combiner' - + 'combiner_1' - + 'electricity_combiner' - + 'hydrogen_combiner_2' -- if using the `GenericSplitterPerformanceModel` as the technology performance model, the technology name must include the name `splitter`. Some examples of valid technology names are: - + 'splitter' - + 'splitter_1' - + 'electricity_splitter' - + 'hydrogen_splitter_2' -- if using the `FeedstockPerformanceModel` as the technology performance model and the `FeedstockCostModel` as the technology cost model, the technology name must include the name `feedstock`. Some examples of valid technology names are: - + 'feedstock' - + 'water_feedstock' - + 'feedstock_iron_ore' - + 'natural_gas_feedstock_tank' -- If a transport component is defined in the technology configuration file (most likely to transport commodities that cannot be transported by 'pipe' or 'cable'), the technology name must include the name `transport`. Some examples of valid technology names are: - + 'transport' - + 'lime_transport' - + 'transport_iron_ore' - + 'reformer_catalyst_transport_tube' - -```{note} -These naming convention limitations are known and you can track them in this issue: https://github.com/NatLabRockies/H2Integrate/issues/374 -``` diff --git a/docs/user_guide/figures/example_08_xdsm.png b/docs/user_guide/figures/example_08_xdsm.png new file mode 100644 index 000000000..a9c6b9301 Binary files /dev/null and b/docs/user_guide/figures/example_08_xdsm.png differ diff --git a/docs/user_guide/how_to_interface_with_user_defined_model.md b/docs/user_guide/how_to_interface_with_user_defined_model.md index 709894cd9..9f13197f5 100644 --- a/docs/user_guide/how_to_interface_with_user_defined_model.md +++ b/docs/user_guide/how_to_interface_with_user_defined_model.md @@ -29,11 +29,11 @@ To demonstrate this capability, we include a minimal example of a custom technol - A `PaperMillCost` model that estimates capital and operational expenditures. - A `PaperMillFinance` technology finance model that computes the levelized cost of paper production (LCOP). -Refer to the [Paper Mill Model Example](https://github.com/NREL/H2Integrate/tree/develop/examples/06_custom_tech/) for a complete walkthrough. +Refer to the [Paper Mill Model Example](https://github.com/NatLabRockies/H2Integrate/tree/develop/examples/06_custom_tech/) for a complete walkthrough. ### Custom General Finance Model Example: simple LCO finance model -To demonstrate this capability, we include an example of a custom general finance model: a **simple LCO (levelized cost of) calculation** found in [Example 08: Wind Electrolyzer Example](https://github.com/NREL/H2Integrate/tree/develop/examples/08_wind_electrolyzer/). This example includes: +To demonstrate this capability, we include an example of a custom general finance model: a **simple LCO (levelized cost of) calculation** found in [Example 08: Wind Electrolyzer Example](https://github.com/NatLabRockies/H2Integrate/tree/develop/examples/08_wind_electrolyzer/). This example includes: - A `SimpleLCOFinanceConfig`: configuration class that is populated with inputs specific to that finance model. - A `SimpleLCOFinance` general finance model that computes the levelized cost of a commodity diff --git a/docs/user_guide/how_to_set_up_an_analysis.md b/docs/user_guide/how_to_set_up_an_analysis.md index 2995ad02d..b64d4c7e3 100644 --- a/docs/user_guide/how_to_set_up_an_analysis.md +++ b/docs/user_guide/how_to_set_up_an_analysis.md @@ -121,6 +121,90 @@ The `resource_to_tech_connections` section defines how resources (like wind or s For more information on how to define and interpret technology interconnections, see the {ref}`connecting_technologies` page. ``` +## Visualizing the model structure +There are two basic methods for visualizing the model structure of your H2Integrate system model. +You can generate a simplified [XDSM diagram](https://openmdao.github.io/PracticalMDO/Notebooks/ModelConstruction/understanding_xdsm_diagrams.html) showing the technologies and connections specified in your config file, or you can generate an interactive [N2 diagram](https://openmdao.org/newdocs/versions/latest/features/model_visualization/n2_details/n2_details.html) of the full OpenMDAO model. +The XDSM diagram is primarily useful for publications and presentations. +The N2 diagram is primarily useful for debugging. Details for generating XDSM and N2 diagrams of your H2Integrate model are given below. + +### XDSM diagram (static and simplified) + +Use the built-in `create_xdsm()` method to generate a static system diagram from the +`technology_interconnections` section of your plant config. + +```python +from h2integrate.core.h2integrate_model import H2IntegrateModel +import os + + +# Change to an example directory +os.chdir("../../examples/08_wind_electrolyzer/") + +# Build the model from the top-level config file +h2i_model = H2IntegrateModel("wind_plant_electrolyzer.yaml") + +# Write XDSM output to connections_xdsm.pdf +h2i_model.create_xdsm(outfile="connections_xdsm") +``` + +This creates a PDF named `connections_xdsm.pdf` in your current working directory. + +```{figure} figures/example_08_xdsm.png +:width: 70% +:align: center +``` +*Figure: XDSM diagram generated from the technology interconnections.* + +### N2 diagram (interactive and complete) + +Use OpenMDAO's `n2` utility to generate an interactive HTML diagram of the full model. + +```{code-cell} ipython3 +from h2integrate.core.h2integrate_model import H2IntegrateModel +import openmdao.api as om +import os + + +# Change to an example directory +os.chdir("../../examples/08_wind_electrolyzer/") + +# Build and set up the model +h2i_model = H2IntegrateModel("wind_plant_electrolyzer.yaml") +h2i_model.setup() + +# Write interactive N2 HTML diagram +om.n2( + h2i_model.prob, + outfile="h2i_n2.html", + display_in_notebook=False, # set to True to display in-line in a notebook + show_browser=False, # set to True to open in a browser at run time +) +``` + +Open `h2i_n2.html` in a browser to explore model groups, components, and variable connections. + +```{code-cell} ipython3 +:tags: [remove-input] +import html +from pathlib import Path +from IPython.display import HTML, display + +n2_html = "h2i_n2.html" +n2_srcdoc = html.escape(Path(n2_html).read_text(encoding="utf-8")) +display( + HTML( + f'
' + f'' + '
' + ) +) +``` +*Figure: Interactive OpenMDAO N2 diagram showing the full model structure and variable connections.* + + + ## Running the analysis Once you have the config files defined, you can run the analysis using a simple Python script that inputs the top-level config yaml. diff --git a/docs/user_guide/model_overview.md b/docs/user_guide/model_overview.md index 5d18c80a4..6523f091d 100644 --- a/docs/user_guide/model_overview.md +++ b/docs/user_guide/model_overview.md @@ -67,6 +67,7 @@ Below summarizes the available performance, cost, and financial models for each - [Storage Models](#storage-models) - [Basic Operations](#basic-operations) - [Control Models](#control-models) + - [DemandModels](#demand-models) (resource-models)= ## Resource models @@ -92,6 +93,9 @@ Below summarizes the available performance, cost, and financial models for each (converter-models)= ## Converter models +- generic models: + - cost models: + + `GenericConverterCostModel` - `wind`: wind turbine - performance models: + `'PYSAMWindPlantPerformanceModel'` @@ -250,8 +254,6 @@ Below summarizes the available performance, cost, and financial models for each (storage-models)= ## Storage Models - `h2_storage`: hydrogen storage - - performance models: - + `'SimpleGenericStorage'` - cost models: + `'LinedRockCavernStorageCostModel'` + `'SaltCavernStorageCostModel'` @@ -259,7 +261,7 @@ Below summarizes the available performance, cost, and financial models for each + `'PipeStorageCostModel'` - `generic_storage`: any resource storage - performance models: - + `'SimpleGenericStorage'` + + `'StoragePerformanceModel'` + `'StorageAutoSizingModel'` - cost models: + `'GenericStorageCostModel'` @@ -279,12 +281,14 @@ Below summarizes the available performance, cost, and financial models for each (control-models)= ## Control Models -- `'PassThroughOpenLoopController'`: open-loop control; directly passes the input resource flow to the output without any modifications - Storage Controllers: + - `'SimpleStorageOpenLoopController'`: open-loop control; manages resource flow based on demand and input commodity - `'DemandOpenLoopStorageController'`: open-loop control; manages resource flow based on demand and storage constraints - - `'HeuristicLoadFollowingController'`: open-loop control that works on a time window basis to set dispatch commands; uses Pyomo -- Converter Controllers: - - `'DemandOpenLoopConverterController'`: open-loop control; manages resource flow based on demand constraints - - `'FlexibleDemandOpenLoopConverterController'`: open-loop control; manages resource flow based on demand and flexibility constraints + - `'HeuristicLoadFollowingStorageController'`: open-loop control that works on a time window basis to set dispatch commands; uses Pyomo - Optimized Dispatch: - - `'OptimizedDispatchController'`: optimization-based dispatch using Pyomo + - `'OptimizedDispatchStorageController'`: optimization-based dispatch using Pyomo + +(demand-models)= +## Demand Models +- `'GenericDemandComponent'`: manages resource flow based on demand constraints +- `'FlexibleDemandComponent'`: manages resource flow based on demand and flexibility constraints diff --git a/docs/user_guide/specifying_finance_parameters.md b/docs/user_guide/specifying_finance_parameters.md index 7ae08217d..3f12b4e4c 100644 --- a/docs/user_guide/specifying_finance_parameters.md +++ b/docs/user_guide/specifying_finance_parameters.md @@ -76,7 +76,7 @@ finance_subgroup_default. ``` Examples: -- [Example 7](https://github.com/NREL/H2Integrate/blob/develop/examples/07_run_of_river_plant/plant_config.yaml) +- [Example 7](https://github.com/NatLabRockies/H2Integrate/blob/develop/examples/07_run_of_river_plant/plant_config.yaml) (finparams:singlemodelsubgroups)= @@ -106,9 +106,9 @@ finance_subgroup_. ``` Examples: -- [Example 02](https://github.com/NREL/H2Integrate/tree/develop/examples/02_texas_ammonia/plant_config.yaml) -- [Example 03 - CO2H](https://github.com/NREL/H2Integrate/tree/develop/examples/03_methanol/co2_hydrogenation/plant_config_co2h.yaml) -- [Example 09 - DOC](https://github.com/NREL/H2Integrate/tree/develop/examples/09_co2/direct_ocean_capture/plant_config.yaml) +- [Example 02](https://github.com/NatLabRockies/H2Integrate/tree/develop/examples/02_texas_ammonia/plant_config.yaml) +- [Example 03 - CO2H](https://github.com/NatLabRockies/H2Integrate/tree/develop/examples/03_methanol/co2_hydrogenation/plant_config_co2h.yaml) +- [Example 09 - DOC](https://github.com/NatLabRockies/H2Integrate/tree/develop/examples/09_co2/direct_ocean_capture/plant_config.yaml) @@ -151,7 +151,7 @@ finance_subgroup_subgroup_b.NPV_hydrogen_delivered_group_b ``` Examples: -- [Example 10](https://github.com/NREL/H2Integrate/blob/develop/examples/10_electrolyzer_om/plant_config.yaml) +- [Example 10](https://github.com/NatLabRockies/H2Integrate/blob/develop/examples/10_electrolyzer_om/plant_config.yaml) #### Key Behaviors - If `finance_parameters` is missing --> no finance model is created. diff --git a/environment.yml b/environment.yml index 3899c89f9..5ca9dbed3 100644 --- a/environment.yml +++ b/environment.yml @@ -3,7 +3,7 @@ channels: [conda-forge, defaults] dependencies: - python=3.11 - glpk - # - coin-or-cbc=2.10.8 # NOTE: uncomment if on Unix + - coin-or-cbc>=2.10.12 # - wisdem # NOTE: uncomment if installing with Ard - pip - pip: [.] diff --git a/examples/01_onshore_steel_mn/plant_config.yaml b/examples/01_onshore_steel_mn/plant_config.yaml index fbd42d963..751cdf96c 100644 --- a/examples/01_onshore_steel_mn/plant_config.yaml +++ b/examples/01_onshore_steel_mn/plant_config.yaml @@ -22,8 +22,15 @@ technology_interconnections: - [wind, combiner, electricity, cable] - [solar, combiner, electricity, cable] - [combiner, battery, electricity, cable] - - [battery, electrolyzer, electricity, cable] + # combine the battery output with the wind and solar generation + - [battery, elec_combiner, electricity, cable] + - [combiner, elec_combiner, electricity, cable] + # connect the electricity supply to the electrolyzer + - [elec_combiner, electrolyzer, electricity, cable] - [electrolyzer, h2_storage, hydrogen, pipe] + # combine the h2 from the electrolyzer and the h2_storage + - [electrolyzer, h2_combiner, hydrogen, pipe] + - [h2_storage, h2_combiner, hydrogen, pipe] - [finance_subgroup_hydrogen, steel, [LCOH_delivered, LCOH]] # etc tech_to_dispatch_connections: @@ -48,7 +55,7 @@ finance_parameters: inflation_rate: 0.0 # 0 for nominal analysis discount_rate: 0.09 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 2.62 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx + property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx total_income_tax_rate: 0.257 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.07375 # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/ diff --git a/examples/01_onshore_steel_mn/tech_config.yaml b/examples/01_onshore_steel_mn/tech_config.yaml index 87a370762..4e3a99fb4 100644 --- a/examples/01_onshore_steel_mn/tech_config.yaml +++ b/examples/01_onshore_steel_mn/tech_config.yaml @@ -60,17 +60,14 @@ technologies: dispatch_rule_set: model: PyomoDispatchGenericConverter model_inputs: - performance_parameters: - commodity: electricity - commodity_rate_units: kW - dispatch_rule_parameters: + shared_parameters: commodity: electricity commodity_rate_units: kW battery: dispatch_rule_set: model: PyomoRuleStorageBaseclass control_strategy: - model: HeuristicLoadFollowingController + model: HeuristicLoadFollowingStorageController performance_model: model: PySAMBatteryPerformanceModel cost_model: @@ -81,19 +78,27 @@ technologies: commodity_rate_units: kW max_charge_rate: 375740.4 # kW max_capacity: 375745.2 # kWh - n_control_window: 24 - init_charge_fraction: 0.9 - max_charge_fraction: 1.0 - min_charge_fraction: 0.2 - system_commodity_interface_limit: 1e12 + init_soc_fraction: 0.9 + max_soc_fraction: 1.0 + min_soc_fraction: 0.2 performance_parameters: - system_model_source: pysam chemistry: LFPGraphite + demand_profile: 720000 # 720 MW cost_parameters: cost_year: 2019 energy_capex: 310 # $/kWh from 2024 ATB year 2025 power_capex: 311 # $/kW from 2024 ATB year 2025 opex_fraction: 0.024999840573439444 + control_parameters: + system_commodity_interface_limit: 1e12 + n_control_window: 24 + elec_combiner: + performance_model: + model: GenericCombinerPerformanceModel + model_inputs: + performance_parameters: + commodity: electricity + commodity_rate_units: kW electrolyzer: performance_model: model: ECOElectrolyzerPerformanceModel @@ -119,18 +124,32 @@ technologies: performance_model: model: StorageAutoSizingModel control_strategy: - model: PassThroughOpenLoopController + model: SimpleStorageOpenLoopController cost_model: model: LinedRockCavernStorageCostModel model_inputs: shared_parameters: commodity: hydrogen commodity_rate_units: kg/h + set_demand_as_avg_commodity_in: true + commodity_amount_units: kg + performance_parameters: + min_soc_fraction: 0.0 + max_soc_fraction: 1.0 + charge_efficiency: 1.0 + discharge_efficiency: 1.0 cost_parameters: # since the storage is being auto-sized by the performance model, # we set the sizing mode to 'auto' rather than defining the capacities # in the input file sizing_mode: auto # set as "auto" so storage capacity doesnt have to be defined + h2_combiner: + performance_model: + model: GenericCombinerPerformanceModel + model_inputs: + performance_parameters: + commodity: hydrogen + commodity_rate_units: kg/h steel: performance_model: model: SteelPerformanceModel diff --git a/examples/02_texas_ammonia/plant_config.yaml b/examples/02_texas_ammonia/plant_config.yaml index 9d42bd2c5..bc4db7f5b 100644 --- a/examples/02_texas_ammonia/plant_config.yaml +++ b/examples/02_texas_ammonia/plant_config.yaml @@ -21,9 +21,17 @@ technology_interconnections: - [wind, combiner, electricity, cable] - [solar, combiner, electricity, cable] - [combiner, battery, electricity, cable] - - [battery, electrolyzer, electricity, cable] + # combine the battery output with the wind and solar generation + - [battery, elec_combiner, electricity, cable] + - [combiner, elec_combiner, electricity, cable] + # connect the electricity supply to the electrolyzer + - [elec_combiner, electrolyzer, electricity, cable] - [electrolyzer, h2_storage, hydrogen, pipe] - - [h2_storage, ammonia, hydrogen, pipe] + # combine the h2 from the electrolyzer and the h2_storage + - [electrolyzer, h2_combiner, hydrogen, pipe] + - [h2_storage, h2_combiner, hydrogen, pipe] + # connect the hydrogen supply to the ammonia model + - [h2_combiner, ammonia, hydrogen, pipe] - [finance_subgroup_hydrogen, ammonia, LCOH] # etc tech_to_dispatch_connections: @@ -48,7 +56,7 @@ finance_parameters: inflation_rate: 0.0 # 0 for real analysis discount_rate: 0.06 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 0.724 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.025 # https://www.house.mn.gov/hrd/issinfo/clsrates.aspx and # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf + property_tax_and_insurance: 0.025 # https://www.house.mn.gov/hrd/issinfo/clsrates.aspx and # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf total_income_tax_rate: 0.2574 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.0 # average combined state and local sales tax https://taxfoundation.org/location/texas/ diff --git a/examples/02_texas_ammonia/tech_config.yaml b/examples/02_texas_ammonia/tech_config.yaml index 032e5d8a6..e1494c63d 100644 --- a/examples/02_texas_ammonia/tech_config.yaml +++ b/examples/02_texas_ammonia/tech_config.yaml @@ -60,17 +60,14 @@ technologies: dispatch_rule_set: model: PyomoDispatchGenericConverter model_inputs: - performance_parameters: - commodity: electricity - commodity_rate_units: kW - dispatch_rule_parameters: + shared_parameters: commodity: electricity commodity_rate_units: kW battery: dispatch_rule_set: model: PyomoRuleStorageBaseclass control_strategy: - model: HeuristicLoadFollowingController + model: HeuristicLoadFollowingStorageController performance_model: model: PySAMBatteryPerformanceModel cost_model: @@ -81,19 +78,27 @@ technologies: commodity_rate_units: kW max_charge_rate: 96.0 # kW max_capacity: 96.0 # kWh - n_control_window: 24 - init_charge_fraction: 0.9 - max_charge_fraction: 1.0 - min_charge_fraction: 0.2 - system_commodity_interface_limit: 1e12 + init_soc_fraction: 0.9 + max_soc_fraction: 1.0 + min_soc_fraction: 0.2 performance_parameters: - system_model_source: pysam chemistry: LFPGraphite + demand_profile: 640000 # 640 MW cost_parameters: cost_year: 2019 energy_capex: 310 # $/kWh from 2024 ATB year 2025 power_capex: 311 # $/kW from 2024 ATB year 2025 opex_fraction: 0.025 + control_parameters: + system_commodity_interface_limit: 1e12 + n_control_window: 24 + elec_combiner: + performance_model: + model: GenericCombinerPerformanceModel + model_inputs: + performance_parameters: + commodity: electricity + commodity_rate_units: kW electrolyzer: performance_model: model: ECOElectrolyzerPerformanceModel @@ -119,18 +124,32 @@ technologies: performance_model: model: StorageAutoSizingModel control_strategy: - model: PassThroughOpenLoopController + model: SimpleStorageOpenLoopController cost_model: model: SaltCavernStorageCostModel model_inputs: shared_parameters: commodity: hydrogen commodity_rate_units: kg/h + commodity_amount_units: kg + set_demand_as_avg_commodity_in: true + performance_parameters: + min_soc_fraction: 0.0 + max_soc_fraction: 1.0 + charge_efficiency: 1.0 + discharge_efficiency: 1.0 cost_parameters: # since the storage is being auto-sized by the performance model, # we set the sizing mode to 'auto' rather than defining the capacities # in the input file sizing_mode: auto # set as "auto" so storage capacity doesnt have to be defined + h2_combiner: + performance_model: + model: GenericCombinerPerformanceModel + model_inputs: + performance_parameters: + commodity: hydrogen + commodity_rate_units: kg/h ammonia: performance_model: model: SimpleAmmoniaPerformanceModel diff --git a/examples/03_methanol/co2_hydrogenation_doc/plant_config_co2h.yaml b/examples/03_methanol/co2_hydrogenation_doc/plant_config_co2h.yaml index c6c0b33df..7a2134f88 100644 --- a/examples/03_methanol/co2_hydrogenation_doc/plant_config_co2h.yaml +++ b/examples/03_methanol/co2_hydrogenation_doc/plant_config_co2h.yaml @@ -15,9 +15,18 @@ technology_interconnections: - [electricity_splitter, doc, electricity, cable] - [electricity_splitter, electrolyzer, electricity, cable] - [electrolyzer, h2_storage, hydrogen, pipe] - - [h2_storage, methanol, hydrogen, pipe] + # combine the h2 from the electrolyzer and the h2_storage + - [electrolyzer, h2_combiner, hydrogen, pipe] + - [h2_storage, h2_combiner, hydrogen, pipe] + # connect the h2 supply to the methanol system + - [h2_combiner, methanol, hydrogen, pipe] + # connect the doc to co2 storage - [doc, co2_storage, co2, pipe] - - [co2_storage, methanol, co2, pipe] + # combine the co2 from the doc and the co2_storage + - [doc, co2_combiner, co2, pipe] + - [co2_storage, co2_combiner, co2, pipe] + # connect the co2 supply to the methanol system + - [co2_combiner, methanol, co2, pipe] resource_to_tech_connections: # connect the wind resource to the wind technology - [site.wind_resource, wind, wind_resource_data] diff --git a/examples/03_methanol/co2_hydrogenation_doc/tech_config_co2h.yaml b/examples/03_methanol/co2_hydrogenation_doc/tech_config_co2h.yaml index 5be75998e..a22248b3b 100644 --- a/examples/03_methanol/co2_hydrogenation_doc/tech_config_co2h.yaml +++ b/examples/03_methanol/co2_hydrogenation_doc/tech_config_co2h.yaml @@ -5,7 +5,7 @@ technologies: performance_model: model: PYSAMWindPlantPerformanceModel cost_model: - model: ATBWindPlantCostModel + model: GenericConverterCostModel model_inputs: performance_parameters: num_turbines: 24 @@ -39,8 +39,12 @@ technologies: row_phase_offset: 0.0 layout_shape: square cost_parameters: - capex_per_kW: 1380.0 - opex_per_kW_per_year: 29.0 + commodity: electricity + commodity_rate_units: kW + commodity_amount_units: kW*h + unit_capex: 1380.0 # USD/kW + unit_opex: 29.0 # USD/kW/year + unit_varopex: 0.0 # USD/kWh cost_year: 2019 electricity_splitter: performance_model: @@ -74,7 +78,7 @@ technologies: replacement_cost_percent: 0.15 # percent of capex - H2A default case h2_storage: performance_model: - model: SimpleGenericStorage + model: StoragePerformanceModel cost_model: model: GenericStorageCostModel control_strategy: @@ -85,10 +89,9 @@ technologies: commodity_rate_units: kg/h max_charge_rate: 50000.0 # kg/time step max_capacity: 103701.401563086 # kg - control_parameters: - max_charge_fraction: 1.0 # fraction (0-1) - min_charge_fraction: 0.1 # fraction (0-1) - init_charge_fraction: 0.25 # fraction (0-1) + max_soc_fraction: 1.0 # fraction (0-1) + min_soc_fraction: 0.1 # fraction (0-1) + init_soc_fraction: 0.25 # fraction (0-1) max_discharge_rate: 5000.0 # kg/time step charge_equals_discharge: false charge_efficiency: 1.0 # fraction (0-1) @@ -99,6 +102,13 @@ technologies: capacity_capex: 383 # $/kg - based on https://www.hydrogen.energy.gov/docs/hydrogenprogramlibraries/pdfs/review22/st235_houchins_2022_p-pdf.pdf?Status=Master charge_capex: .000001 # $/kg/h opex_fraction: .000001 # percent of capex + h2_combiner: + performance_model: + model: GenericCombinerPerformanceModel + model_inputs: + performance_parameters: + commodity: hydrogen + commodity_rate_units: kg/h doc: performance_model: model: DOCPerformanceModel @@ -130,7 +140,7 @@ technologies: infrastructure_type: desal co2_storage: performance_model: - model: SimpleGenericStorage + model: StoragePerformanceModel cost_model: model: GenericStorageCostModel control_strategy: @@ -141,10 +151,9 @@ technologies: commodity_rate_units: kg/h max_charge_rate: 50000.0 # kg/time step max_capacity: 961456.376748904 # kg - control_parameters: - max_charge_fraction: 1.0 # fraction (0-1) - min_charge_fraction: 0.1 # fraction (0-1) - init_charge_fraction: 0.25 # fraction (0-1) + max_soc_fraction: 1.0 # fraction (0-1) + min_soc_fraction: 0.1 # fraction (0-1) + init_soc_fraction: 0.25 # fraction (0-1) max_discharge_rate: 50000.0 # kg/time step charge_efficiency: 1.0 # fraction (0-1) discharge_efficiency: 1.0 # fraction (0-1) @@ -154,6 +163,13 @@ technologies: capacity_capex: 5 # $/kg - based on https://www.maritime.dot.gov/sites/marad.dot.gov/files/2024-11/LCE%20MARAD%20CCS%20TEA%20Final%20Report%20-%2031%20May%202024%20%283%29.pdf charge_capex: .000001 # $/kg/h opex_fraction: .000001 # percent of capex + co2_combiner: + performance_model: + model: GenericCombinerPerformanceModel + model_inputs: + performance_parameters: + commodity: co2 + commodity_rate_units: kg/h methanol: performance_model: model: CO2HMethanolPlantPerformanceModel diff --git a/examples/04_geo_h2/plant_config_natural.yaml b/examples/04_geo_h2/plant_config_natural.yaml index bb828355e..1bf46c69a 100644 --- a/examples/04_geo_h2/plant_config_natural.yaml +++ b/examples/04_geo_h2/plant_config_natural.yaml @@ -19,7 +19,7 @@ finance_parameters: inflation_rate: 0.0 # 0 for nominal analysis discount_rate: 0.09 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 2.62 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx + property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx total_income_tax_rate: 0.257 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.07375 # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/ diff --git a/examples/04_geo_h2/plant_config_stimulated.yaml b/examples/04_geo_h2/plant_config_stimulated.yaml index fa361a863..2fa0f0591 100644 --- a/examples/04_geo_h2/plant_config_stimulated.yaml +++ b/examples/04_geo_h2/plant_config_stimulated.yaml @@ -17,7 +17,7 @@ finance_parameters: inflation_rate: 0.0 # 0 for nominal analysis discount_rate: 0.09 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 2.62 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx + property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx total_income_tax_rate: 0.257 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.07375 # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/ diff --git a/examples/05_wind_h2_opt/plant_config.yaml b/examples/05_wind_h2_opt/plant_config.yaml index a8570032a..027f5f45a 100644 --- a/examples/05_wind_h2_opt/plant_config.yaml +++ b/examples/05_wind_h2_opt/plant_config.yaml @@ -31,7 +31,7 @@ finance_parameters: inflation_rate: 0.0 # 0 for nominal analysis discount_rate: 0.09 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 2.62 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx + property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx total_income_tax_rate: 0.257 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.07375 # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/ diff --git a/examples/06_custom_tech/user_defined_model/paper_mill.py b/examples/06_custom_tech/user_defined_model/paper_mill.py index 34adef7b3..f6ef47c7e 100644 --- a/examples/06_custom_tech/user_defined_model/paper_mill.py +++ b/examples/06_custom_tech/user_defined_model/paper_mill.py @@ -16,6 +16,11 @@ class PaperMillConfig(BaseConfig): class PaperMillPerformance(om.ExplicitComponent): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): self.options.declare("driver_config", types=dict) self.options.declare("plant_config", types=dict) @@ -53,6 +58,11 @@ class PaperMillCostConfig(CostModelBaseConfig): class PaperMillCost(CostModelBaseClass): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = PaperMillCostConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost") diff --git a/examples/07_run_of_river_plant/plant_config.yaml b/examples/07_run_of_river_plant/plant_config.yaml index 215f7a720..0383cf89b 100644 --- a/examples/07_run_of_river_plant/plant_config.yaml +++ b/examples/07_run_of_river_plant/plant_config.yaml @@ -34,7 +34,7 @@ finance_parameters: inflation_rate: 0.0 # 0 for nominal analysis discount_rate: 0.09 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 2.62 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx + property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx total_income_tax_rate: 0.308 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.07375 # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/ diff --git a/examples/09_co2/direct_ocean_capture/plant_config.yaml b/examples/09_co2/direct_ocean_capture/plant_config.yaml index 2bb100ef1..d06252483 100644 --- a/examples/09_co2/direct_ocean_capture/plant_config.yaml +++ b/examples/09_co2/direct_ocean_capture/plant_config.yaml @@ -17,7 +17,11 @@ technology_interconnections: - [hopp, combiner, electricity, cable] - [wind, combiner, electricity, cable] - [combiner, battery, electricity, cable] - - [battery, doc, electricity, cable] + # combine the battery output with the wind and hopp generation + - [battery, elec_combiner, electricity, cable] + - [combiner, elec_combiner, electricity, cable] + # connect the electricity supply to the doc + - [elec_combiner, doc, electricity, cable] # etc tech_to_dispatch_connections: - [combiner, battery] @@ -37,7 +41,7 @@ finance_parameters: inflation_rate: 0.0 # 0 for nominal analysis discount_rate: 0.09 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 2.62 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx + property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx total_income_tax_rate: 0.257 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.07375 # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/ diff --git a/examples/09_co2/direct_ocean_capture/tech_config.yaml b/examples/09_co2/direct_ocean_capture/tech_config.yaml index 4bbf1c526..4a40c0277 100644 --- a/examples/09_co2/direct_ocean_capture/tech_config.yaml +++ b/examples/09_co2/direct_ocean_capture/tech_config.yaml @@ -9,12 +9,11 @@ technologies: dispatch_rule_set: model: PyomoDispatchGenericConverter model_inputs: - shared_parameters: - cost_year: 2022 performance_parameters: cache_dir: cache enable_caching: true hopp_config: !include tech_inputs/hopp_config.yaml + cost_year: 2022 dispatch_rule_parameters: commodity: electricity commodity_rate_units: kW @@ -53,17 +52,14 @@ technologies: dispatch_rule_set: model: PyomoDispatchGenericConverter model_inputs: - performance_parameters: - commodity: electricity - commodity_rate_units: kW - dispatch_rule_parameters: + shared_parameters: commodity: electricity commodity_rate_units: kW battery: dispatch_rule_set: model: PyomoRuleStorageBaseclass control_strategy: - model: HeuristicLoadFollowingController + model: HeuristicLoadFollowingStorageController performance_model: model: PySAMBatteryPerformanceModel cost_model: @@ -74,19 +70,27 @@ technologies: commodity_rate_units: kW max_charge_rate: 50000 # kW max_capacity: 200000 # kWh - n_control_window: 24 - init_charge_fraction: 0.9 - max_charge_fraction: 1.0 - min_charge_fraction: 0.2 - system_commodity_interface_limit: 1e12 + init_soc_fraction: 0.9 + max_soc_fraction: 1.0 + min_soc_fraction: 0.2 performance_parameters: - system_model_source: pysam chemistry: LFPGraphite + demand_profile: 340000 # 340 MW cost_parameters: cost_year: 2022 energy_capex: 246 # $/kWh from 2024 ATB year 2025 power_capex: 317 # $/kW from 2024 ATB year 2025 opex_fraction: 0.02536510376633359 + control_parameters: + n_control_window: 24 + system_commodity_interface_limit: 1e12 + elec_combiner: + performance_model: + model: GenericCombinerPerformanceModel + model_inputs: + performance_parameters: + commodity: electricity + commodity_rate_units: kW doc: performance_model: model: DOCPerformanceModel diff --git a/examples/09_co2/ocean_alkalinity_enhancement/plant_config.yaml b/examples/09_co2/ocean_alkalinity_enhancement/plant_config.yaml index d684aff3e..1f967f058 100644 --- a/examples/09_co2/ocean_alkalinity_enhancement/plant_config.yaml +++ b/examples/09_co2/ocean_alkalinity_enhancement/plant_config.yaml @@ -15,7 +15,11 @@ sites: # this will naturally grow as we mature the interconnected tech technology_interconnections: - [wind, battery, electricity, cable] - - [battery, oae, electricity, cable] + # combine the battery output with the wind generation + - [wind, combiner, electricity, cable] + - [battery, combiner, electricity, cable] + # connect the electricity supply to the oae + - [combiner, oae, electricity, cable] # etc tech_to_dispatch_connections: - [wind, battery] @@ -35,7 +39,7 @@ finance_parameters: inflation_rate: 0.0 # 0 for nominal analysis discount_rate: 0.09 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 2.62 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx + property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx total_income_tax_rate: 0.257 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.07375 # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/ diff --git a/examples/09_co2/ocean_alkalinity_enhancement/tech_config.yaml b/examples/09_co2/ocean_alkalinity_enhancement/tech_config.yaml index 54c242ed8..d35e4bd3d 100644 --- a/examples/09_co2/ocean_alkalinity_enhancement/tech_config.yaml +++ b/examples/09_co2/ocean_alkalinity_enhancement/tech_config.yaml @@ -39,7 +39,7 @@ technologies: dispatch_rule_set: model: PyomoRuleStorageBaseclass control_strategy: - model: HeuristicLoadFollowingController + model: HeuristicLoadFollowingStorageController performance_model: model: PySAMBatteryPerformanceModel cost_model: @@ -50,20 +50,27 @@ technologies: commodity_rate_units: kW max_charge_rate: 50000 # kW max_capacity: 200000 # kWh - n_control_window: 24 - init_charge_fraction: 0.9 - max_charge_fraction: 1.0 - min_charge_fraction: 0.2 - system_commodity_interface_limit: 1e12 + init_soc_fraction: 0.9 + max_soc_fraction: 1.0 + min_soc_fraction: 0.2 performance_parameters: - system_model_source: pysam chemistry: LFPGraphite + demand_profile: 330000 # 330 MW cost_parameters: cost_year: 2022 - commodity_units: kW energy_capex: 246 # $/kWh from 2024 ATB year 2025 power_capex: 317 # $/kW from 2024 ATB year 2025 opex_fraction: 0.02536510376633359 + control_parameters: + n_control_window: 24 + system_commodity_interface_limit: 1e12 + combiner: + performance_model: + model: GenericCombinerPerformanceModel + model_inputs: + performance_parameters: + commodity: electricity + commodity_rate_units: kW oae: performance_model: model: OAEPerformanceModel diff --git a/examples/09_co2/ocean_alkalinity_enhancement_financials/plant_config_financials.yaml b/examples/09_co2/ocean_alkalinity_enhancement_financials/plant_config_financials.yaml index fbb03a22e..bf53e153b 100644 --- a/examples/09_co2/ocean_alkalinity_enhancement_financials/plant_config_financials.yaml +++ b/examples/09_co2/ocean_alkalinity_enhancement_financials/plant_config_financials.yaml @@ -18,6 +18,7 @@ plant: technology_interconnections: - [wind, oae, electricity, cable] - [finance_subgroup_electricity, oae, LCOE] + - [wind, oae, [annual_electricity_produced, annual_input_electricity]] # etc resource_to_tech_connections: # connect the wind resource to the wind technology @@ -32,7 +33,7 @@ finance_parameters: inflation_rate: 0.0 # 0 for nominal analysis discount_rate: 0.09 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 2.62 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx + property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx total_income_tax_rate: 0.257 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.07375 # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/ diff --git a/examples/11_hybrid_energy_plant/plant_config.yaml b/examples/11_hybrid_energy_plant/plant_config.yaml index 3cccee7c8..1b91ae6bc 100644 --- a/examples/11_hybrid_energy_plant/plant_config.yaml +++ b/examples/11_hybrid_energy_plant/plant_config.yaml @@ -17,7 +17,7 @@ finance_parameters: inflation_rate: 0.025 # 0 for nominal analysis discount_rate: 0.089 # nominal return debt_equity_ratio: 2.1746031746031744 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.015 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf + property_tax_and_insurance: 0.015 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf total_income_tax_rate: 0.257 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.045 diff --git a/examples/12_ammonia_synloop/plant_config.yaml b/examples/12_ammonia_synloop/plant_config.yaml index b836b5335..1d2bc2b80 100644 --- a/examples/12_ammonia_synloop/plant_config.yaml +++ b/examples/12_ammonia_synloop/plant_config.yaml @@ -21,9 +21,19 @@ technology_interconnections: - [wind, combiner, electricity, cable] - [solar, combiner, electricity, cable] - [combiner, battery, electricity, cable] - - [battery, electrolyzer, electricity, cable] + # combine the battery output with the wind and solar generation + - [battery, elec_combiner, electricity, cable] + - [combiner, elec_combiner, electricity, cable] + # connect the electricity supply to the electrolyzer + - [elec_combiner, electrolyzer, electricity, cable] - [electrolyzer, h2_storage, hydrogen, pipe] - - [h2_storage, ammonia, hydrogen, pipe] + # combine the h2 from the electrolyzer and the h2_storage + - [electrolyzer, h2_combiner, hydrogen, pipe] + - [h2_storage, h2_combiner, hydrogen, pipe] + # subtract the hydrogen supplied from the hydrogen demand + - [h2_combiner, h2_load_demand, hydrogen, pipe] + # connect the hydrogen supplied as the demand to the ammonia model + - [h2_load_demand, ammonia, hydrogen, pipe] - [n2_feedstock, ammonia, nitrogen, pipe] - [electricity_feedstock, ammonia, electricity, cable] # etc @@ -46,7 +56,7 @@ finance_parameters: inflation_rate: 0.0 # 0 for real analysis discount_rate: 0.06 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 0.724 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.025 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx + property_tax_and_insurance: 0.025 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx total_income_tax_rate: 0.2574 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.0 # average combined state and local sales tax https://taxfoundation.org/location/texas/ @@ -74,5 +84,8 @@ finance_parameters: - battery - electrolyzer - h2_storage - - n2_feedstock - ammonia + n2: + commodity: nitrogen + commodity_stream: n2_feedstock + technologies: [n2_feedstock] diff --git a/examples/12_ammonia_synloop/tech_config.yaml b/examples/12_ammonia_synloop/tech_config.yaml index d35ef157e..ec1b28dd6 100644 --- a/examples/12_ammonia_synloop/tech_config.yaml +++ b/examples/12_ammonia_synloop/tech_config.yaml @@ -60,17 +60,14 @@ technologies: dispatch_rule_set: model: PyomoDispatchGenericConverter model_inputs: - performance_parameters: - commodity: electricity - commodity_rate_units: kW - dispatch_rule_parameters: + shared_parameters: commodity: electricity commodity_rate_units: kW battery: dispatch_rule_set: model: PyomoRuleStorageBaseclass control_strategy: - model: HeuristicLoadFollowingController + model: HeuristicLoadFollowingStorageController performance_model: model: PySAMBatteryPerformanceModel cost_model: @@ -81,19 +78,27 @@ technologies: commodity_rate_units: kW max_charge_rate: 96.0 # kW max_capacity: 96.0 # kWh - n_control_window: 24 - init_charge_fraction: 0.9 - max_charge_fraction: 1.0 - min_charge_fraction: 0.2 - system_commodity_interface_limit: 1e12 + init_soc_fraction: 0.9 + max_soc_fraction: 1.0 + min_soc_fraction: 0.2 performance_parameters: - system_model_source: pysam chemistry: LFPGraphite + demand_profile: 640000 # 640 MW cost_parameters: cost_year: 2019 energy_capex: 310 # $/kWh from 2024 ATB year 2025 power_capex: 311 # $/kW from 2024 ATB year 2025 opex_fraction: 0.025 + control_parameters: + n_control_window: 24 + system_commodity_interface_limit: 1e12 + elec_combiner: + performance_model: + model: GenericCombinerPerformanceModel + model_inputs: + performance_parameters: + commodity: electricity + commodity_rate_units: kW electrolyzer: performance_model: model: ECOElectrolyzerPerformanceModel @@ -119,18 +124,40 @@ technologies: performance_model: model: StorageAutoSizingModel control_strategy: - model: PassThroughOpenLoopController + model: SimpleStorageOpenLoopController cost_model: model: SaltCavernStorageCostModel model_inputs: shared_parameters: commodity: hydrogen commodity_rate_units: kg/h + commodity_amount_units: kg + set_demand_as_avg_commodity_in: true + performance_parameters: + min_soc_fraction: 0.0 + max_soc_fraction: 1.0 + charge_efficiency: 1.0 + discharge_efficiency: 1.0 cost_parameters: # since the storage is being auto-sized by the performance model, # we set the sizing mode to 'auto' rather than defining the capacities # in the input file sizing_mode: auto # set as "auto" so storage capacity doesnt have to be defined + h2_combiner: + performance_model: + model: GenericCombinerPerformanceModel + model_inputs: + performance_parameters: + commodity: hydrogen + commodity_rate_units: kg/h + h2_load_demand: + performance_model: + model: GenericDemandComponent + model_inputs: + performance_parameters: + commodity: hydrogen + commodity_rate_units: kg/h + demand_profile: 9306.754158 # 50 kg/h n2_feedstock: performance_model: model: FeedstockPerformanceModel @@ -144,7 +171,7 @@ technologies: rated_capacity: 50.0 # metric tonnes of N2/hour cost_parameters: cost_year: 2022 - price: 0.0 + price: 5.0 annual_cost: 0. start_up_cost: 0.0 electricity_feedstock: diff --git a/examples/13_dispatch_for_electrolyzer/driver_config.yaml b/examples/13_dispatch_for_electrolyzer/driver_config.yaml new file mode 100644 index 000000000..0f78a81a9 --- /dev/null +++ b/examples/13_dispatch_for_electrolyzer/driver_config.yaml @@ -0,0 +1,15 @@ +name: driver_config +description: driver file +general: + folder_output: outputs + create_om_reports: false +recorder: + # required inputs + flag: true # record outputs + file: cases.sql # this file will be written to the folder `outputs` + + # optional but recommended inputs + overwrite_recorder: true # If True, do not create a unique recorder file for subsequent runs. Defaults to False. + recorder_attachment: model #"driver" or "model", defaults to "model". Use "driver" if running a parallel simulation. + includes: ['*'] #include everything + excludes: ['*resource_data*'] #exclude resource data diff --git a/examples/13_dispatch_for_electrolyzer/plant_config.yaml b/examples/13_dispatch_for_electrolyzer/plant_config.yaml new file mode 100644 index 000000000..6822e682f --- /dev/null +++ b/examples/13_dispatch_for_electrolyzer/plant_config.yaml @@ -0,0 +1,78 @@ +name: plant_config +description: This plant is located in Australia +sites: + site: + latitude: -28.454864 + longitude: 114.551749 + resources: + wind_resource: + resource_model: OpenMeteoHistoricalWindResource + resource_parameters: + resource_year: 2024 + solar_resource: + resource_model: OpenMeteoHistoricalSolarResource + resource_parameters: + resource_year: 2024 +technology_interconnections: + # combine the generation and input it to the battery + - [distributed_wind_plant, gen_combiner, electricity, cable] + - [distributed_pv, gen_combiner, electricity, cable] + - [gen_combiner, battery, electricity, cable] + # combine the battery output with the wind and solar generation + - [battery, elec_combiner, electricity, cable] + - [gen_combiner, elec_combiner, electricity, cable] + # subtract the electricity generated from the electricity demand + - [elec_combiner, elec_load_demand, electricity, cable] + # connect the electricity supplied to the demand to the electrolyzer + - [elec_load_demand, electrolyzer, electricity, cable] +resource_to_tech_connections: + # connect the wind resource to the wind technology + - [site.wind_resource, distributed_wind_plant, wind_resource_data] + - [site.solar_resource, distributed_pv, solar_resource_data] +plant: + plant_life: 30 + simulation: + timezone: 8 +finance_parameters: + finance_groups: + finance_model: ProFastLCO + model_inputs: + params: + analysis_start_year: 2032 + installation_time: 36 # months + inflation_rate: 0.0 # 0 for nominal analysis + discount_rate: 0.09 # 9% discount rate + debt_equity_ratio: 2.62 # + property_tax_and_insurance: 0.03 # 3% + total_income_tax_rate: 0.257 # 25.7% + capital_gains_tax_rate: 0.15 # 15% + sales_tax_rate: 0.07375 # 7.3% + debt_interest_rate: 0.07 # 7% + debt_type: Revolving debt # + loan_period_if_used: 0 # Not used for revolving debt + cash_onhand_months: 1 # months + admin_expense: 0.00 # percent of sales H2FAST default + capital_items: + depr_type: MACRS # can be "MACRS" or "Straight line" + depr_period: 5 # 5 years + refurb: [0.] + finance_subgroups: + generated_electricity: + commodity: electricity + commodity_stream: gen_combiner + technologies: [distributed_wind_plant, distributed_pv] + electrical_system: + commodity: electricity + commodity_stream: elec_combiner + technologies: [distributed_wind_plant, distributed_pv, battery] + electrical_load: + commodity: electricity + commodity_stream: elec_load_demand + technologies: [distributed_wind_plant, distributed_pv, battery] + hydrogen: + commodity: hydrogen + commodity_stream: electrolyzer + technologies: [distributed_wind_plant, distributed_pv, battery, electrolyzer] + cost_adjustment_parameters: + cost_year_adjustment_inflation: 0.025 # used to adjust modeled costs to target_dollar_year + target_dollar_year: 2022 diff --git a/examples/13_dispatch_for_electrolyzer/run_dispatch_for_electrolyzer.py b/examples/13_dispatch_for_electrolyzer/run_dispatch_for_electrolyzer.py new file mode 100644 index 000000000..cf8499968 --- /dev/null +++ b/examples/13_dispatch_for_electrolyzer/run_dispatch_for_electrolyzer.py @@ -0,0 +1,40 @@ +from pathlib import Path + +from h2integrate import EXAMPLE_DIR +from h2integrate.core.h2integrate_model import H2IntegrateModel +from h2integrate.core.inputs.validation import load_tech_yaml, load_plant_yaml, load_driver_yaml + + +this_dir = EXAMPLE_DIR / "13_dispatch_for_electrolyzer" +tech_config = load_tech_yaml(this_dir / "tech_config.yaml") +plant_config = load_plant_yaml(this_dir / "plant_config.yaml") +driver_config = load_driver_yaml(this_dir / "driver_config.yaml") + +# modify all the output folders to be full filepaths +driver_config["general"]["folder_output"] = str(Path(this_dir / "outputs").absolute()) +tech_config["technologies"]["distributed_wind_plant"]["model_inputs"]["performance_parameters"][ + "cache_dir" +] = this_dir / "cache" + +input_config = { + "plant_config": plant_config, + "technology_config": tech_config, + "driver_config": driver_config, +} + +h2i = H2IntegrateModel(input_config) + +h2i.setup() + +electrolyzer_capacity_MW = 60 +h2i.prob.set_val("battery.electricity_demand", 0.1 * electrolyzer_capacity_MW, units="MW") +h2i.prob.set_val("elec_load_demand.electricity_demand", electrolyzer_capacity_MW, units="MW") + +h2i.run() + +h2i.post_process(print_results=False, summarize_sql=True) + +lcoe_gen = h2i.prob.get_val("finance_subgroup_generated_electricity.LCOE", units="USD/(MW*h)")[0] +lcoe_sys = h2i.prob.get_val("finance_subgroup_electrical_system.LCOE", units="USD/(MW*h)")[0] +lcoe_load = h2i.prob.get_val("finance_subgroup_electrical_load.LCOE", units="USD/(MW*h)")[0] +lcoh = h2i.prob.get_val("finance_subgroup_hydrogen.LCOH", units="USD/kg")[0] diff --git a/examples/13_dispatch_for_electrolyzer/tech_config.yaml b/examples/13_dispatch_for_electrolyzer/tech_config.yaml new file mode 100644 index 000000000..fea5f8cec --- /dev/null +++ b/examples/13_dispatch_for_electrolyzer/tech_config.yaml @@ -0,0 +1,143 @@ +name: technology_config +description: This hybrid plant produces hydrogen +technologies: + distributed_wind_plant: + performance_model: + model: FlorisWindPlantPerformanceModel + cost_model: + model: GenericConverterCostModel + model_inputs: + performance_parameters: + num_turbines: 100 # number of turbines in the farm + hub_height: 65.0 # turbine hub-height + operational_losses: 12.83 # percentage of non-wake losses + floris_wake_config: !include "floris_v4_default_template.yaml" #floris wake model file + floris_turbine_config: !include "floris_turbine_Vestas_660kW.yaml" #turbine model file formatted for floris + resource_data_averaging_method: weighted_average #"weighted_average", "average" or "nearest" + operation_model: cosine-loss # turbine operation model + default_turbulence_intensity: 0.06 + enable_caching: true # whether to use cached results + cache_dir: cache # directory to save or load cached data + layout: + layout_mode: basicgrid + layout_options: + row_D_spacing: 5.0 + turbine_D_spacing: 5.0 + rotation_angle_deg: 0.0 + row_phase_offset: 0.0 + layout_shape: square + cost_parameters: + commodity: electricity + commodity_rate_units: kW + commodity_amount_units: kW*h + unit_capex: 2286 # USD/kW distributed wind capex for 2030 + unit_opex: 38 # USD/kW/year + unit_varopex: 0.0 # USD/kW*h/year + cost_year: 2022 + financial_parameters: + capital_items: + depr_period: 7 # 7 year depreciation period + replacement_cost_percent: 0.15 # cost of stack replacement is 15% of CapEx + distributed_pv: + performance_model: + model: PYSAMSolarPlantPerformanceModel + cost_model: + model: ATBResComPVCostModel + model_inputs: + shared_parameters: + pv_capacity_kWdc: 50000 # 50 MWdc + performance_parameters: + dc_ac_ratio: 1.23 # pv inverter capacity is equal to 50 MWdc/1.22 + create_model_from: new + tilt_angle_func: none + tilt: 20.0 + pysam_options: + SystemDesign: + array_type: 0 # fixed open rack + inv_eff: 98.0 # inverter losses + bifaciality: 0.0 + module_type: 0 # standard, 19% efficiency + losses: 10.0 # DC losses + azimuth: 0 # North facing + gcr: 0.2 # groud coverage ratio + SolarResource: + use_wf_albedo: false # don't use albedo from weather file + albedo_default: 0.3 + Lifetime: + dc_degradation: [0] + cost_parameters: + capex_per_kWdc: 1492 #$/kWdc + opex_per_kWdc_per_year: 16.0 # $/kWDC/year + cost_year: 2022 + gen_combiner: # combiner for generation, pv + wind + performance_model: + model: GenericCombinerPerformanceModel + model_inputs: + shared_parameters: + commodity: electricity + commodity_rate_units: kW + battery: + performance_model: + model: StoragePerformanceModel + cost_model: + model: GenericStorageCostModel + control_strategy: + model: DemandOpenLoopStorageController + model_inputs: + shared_parameters: + commodity: electricity + commodity_rate_units: MW + commodity_amount_units: MW*h + max_charge_rate: 12.0 # equal to 2x electrolyzer turndown ratio, 12 MW + max_capacity: 48.0 # 4-hour battery, 48 MW + init_soc_fraction: 0.0 + charge_equals_discharge: true + charge_efficiency: 1.0 + discharge_efficiency: 1.0 + min_soc_fraction: 0.0 + max_soc_fraction: 1.0 + demand_profile: 6.0 # equal to electrolyzer turndown ratio (6 MW) + cost_parameters: + capacity_capex: 173000 # USD/MW*h, 173 USD/kW*h + charge_capex: 750000 # USD/MW (multiplied by charge rate), 750 USD/kW + opex_fraction: 0.025 # 2.5 % of total CapEx + cost_year: 2022 + elec_combiner: # combiner for battery + generation + performance_model: + model: GenericCombinerPerformanceModel + model_inputs: + shared_parameters: + commodity: electricity + commodity_rate_units: kW + elec_load_demand: # load demand component for electrolyzer + performance_model: + model: GenericDemandComponent + model_inputs: + performance_parameters: + commodity: electricity + commodity_rate_units: MW + demand_profile: 60 # Equal to electrolyzer capacity + electrolyzer: + performance_model: + model: ECOElectrolyzerPerformanceModel + cost_model: + model: CustomElectrolyzerCostModel + model_inputs: + performance_parameters: + location: onshore + size_mode: normal + n_clusters: 6 # number of 10 MW clusters + cluster_rating_MW: 10 # 10 MW clusters + eol_eff_percent_loss: 10 # eol defined as x% change in efficiency from bol + uptime_hours_until_eol: 80000. # number of 'on' hours until electrolyzer reaches eol + include_degradation_penalty: true # include degradation + turndown_ratio: 0.1 # turndown_ratio = minimum_cluster_power/cluster_rating_MW + electrolyzer_capex: 1000 # only used for electrolyzer performance + cost_parameters: + capex_USD_per_kW: 1000.0 + fixed_om_USD_per_kW_per_year: 12.8 + cost_year: 2022 + financial_parameters: + capital_items: + depr_period: 7 # based on PEM Electrolysis H2A Production Case Study Documentation estimate of 7 years. also see https://www.irs.gov/publications/p946#en_US_2020_publink1000107507 + replacement_cost_percent: 0.15 # percent of capex - H2A default case diff --git a/examples/14_wind_hydrogen_dispatch/inputs/plant_config.yaml b/examples/14_wind_hydrogen_dispatch/inputs/plant_config.yaml index d0ea32177..60df6a0d6 100644 --- a/examples/14_wind_hydrogen_dispatch/inputs/plant_config.yaml +++ b/examples/14_wind_hydrogen_dispatch/inputs/plant_config.yaml @@ -16,6 +16,11 @@ sites: technology_interconnections: - [wind, electrolyzer, electricity, cable] - [electrolyzer, h2_storage, hydrogen, pipe] + # combine the h2 from the electrolyzer and the h2_storage + - [electrolyzer, h2_combiner, hydrogen, pipe] + - [h2_storage, h2_combiner, hydrogen, pipe] + # subtract hydrogen production from hydrogen demand + - [h2_combiner, h2_load_demand, hydrogen, pipe] resource_to_tech_connections: # connect the wind resource to the wind technology - [site.wind_resource, wind, wind_resource_data] @@ -31,7 +36,7 @@ finance_parameters: inflation_rate: 0.0 # 0 for nominal analysis discount_rate: 0.09 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 2.62 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx + property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx total_income_tax_rate: 0.257 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.07375 # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/ @@ -57,5 +62,9 @@ finance_parameters: technologies: [wind, electrolyzer, h2_storage] dispatched_hydrogen: commodity: hydrogen - commodity_stream: h2_storage # use only dispatched hydrogen from h2_storage controller in finance calc + commodity_stream: h2_load_demand # use only dispatched hydrogen from h2_storage controller in finance calc technologies: [wind, electrolyzer, h2_storage] + oxygen: + commodity: oxygen + commodity_stream: electrolyzer + technologies: [wind, electrolyzer] diff --git a/examples/14_wind_hydrogen_dispatch/inputs/tech_config.yaml b/examples/14_wind_hydrogen_dispatch/inputs/tech_config.yaml index 0771dbac0..3672d7119 100644 --- a/examples/14_wind_hydrogen_dispatch/inputs/tech_config.yaml +++ b/examples/14_wind_hydrogen_dispatch/inputs/tech_config.yaml @@ -51,7 +51,7 @@ technologies: replacement_cost_percent: 0.15 # percent of capex - H2A default case h2_storage: performance_model: - model: SimpleGenericStorage + model: StoragePerformanceModel cost_model: model: LinedRockCavernStorageCostModel control_strategy: @@ -62,11 +62,25 @@ technologies: commodity_rate_units: kg/h max_charge_rate: 12446.00729773 # kg/time step max_capacity: 2987042.0 # kg - control_parameters: - max_charge_fraction: 1.0 # fraction (0-1) - min_charge_fraction: 0.1 # fraction (0-1) - init_charge_fraction: 0.25 # fraction (0-1) + max_soc_fraction: 1.0 # fraction (0-1) + min_soc_fraction: 0.1 # fraction (0-1) + init_soc_fraction: 0.25 # fraction (0-1) max_discharge_rate: 12446.00729773 # kg/time step charge_efficiency: 1.0 # fraction (0-1) discharge_efficiency: 1.0 # fraction (0-1) demand_profile: 5000 # constant demand of 5000 kg per hour (see commodity_rate_units) + h2_combiner: + performance_model: + model: GenericCombinerPerformanceModel + model_inputs: + performance_parameters: + commodity: hydrogen + commodity_rate_units: kg/h + h2_load_demand: + performance_model: + model: GenericDemandComponent + model_inputs: + performance_parameters: + commodity: hydrogen + commodity_rate_units: kg/h + demand_profile: 5000 # 5000 kg/h diff --git a/examples/15_wind_solar_electrolyzer/plant_config.yaml b/examples/15_wind_solar_electrolyzer/plant_config.yaml index 3ed82e0e0..cc45921b4 100644 --- a/examples/15_wind_solar_electrolyzer/plant_config.yaml +++ b/examples/15_wind_solar_electrolyzer/plant_config.yaml @@ -47,7 +47,7 @@ finance_parameters: inflation_rate: 0.0 # 0 for nominal analysis discount_rate: 0.09 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 2.62 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx + property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx total_income_tax_rate: 0.257 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.07375 # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/ diff --git a/examples/16_natural_gas/plant_config.yaml b/examples/16_natural_gas/plant_config.yaml index e01c71feb..aa30629f7 100644 --- a/examples/16_natural_gas/plant_config.yaml +++ b/examples/16_natural_gas/plant_config.yaml @@ -18,13 +18,18 @@ sites: # and not connected to additional technologies. # hence the empty array. technology_interconnections: + # connect solar to battery - [solar, battery, electricity, cable] - # connect solar to battery + # combine the battery output with the solar generation + - [battery, elec_combiner, electricity, cable] + - [solar, elec_combiner, electricity, cable] + # subtract the combined generation from the demand profile + - [elec_combiner, electrical_load_demand, electricity, cable] + # connect the remaining electricity demand to the NG plant + - [electrical_load_demand, natural_gas_plant, [unmet_electricity_demand_out, electricity_demand]] + # connect NG feedstock to NG plant - [ng_feedstock, natural_gas_plant, natural_gas, pipe] - # connect NG feedstock to NG plant - - [battery, natural_gas_plant, [electricity_unmet_demand, electricity_demand]] - # connect electricity demand to NG feedstock - # connect natural gas and battery to combiner + # connect natural gas and solar to finance combiner - [solar, fin_combiner, electricity, cable] - [natural_gas_plant, fin_combiner, electricity, cable] resource_to_tech_connections: diff --git a/examples/16_natural_gas/tech_config.yaml b/examples/16_natural_gas/tech_config.yaml index 3c0b9079a..99361db80 100644 --- a/examples/16_natural_gas/tech_config.yaml +++ b/examples/16_natural_gas/tech_config.yaml @@ -31,7 +31,7 @@ technologies: cost_year: 2024 battery: performance_model: - model: SimpleGenericStorage + model: StoragePerformanceModel cost_model: model: ATBBatteryCostModel control_strategy: @@ -42,10 +42,9 @@ technologies: commodity_rate_units: kW max_charge_rate: 25000.0 # kW/time step, 25 MW max_capacity: 200000.0 # kWh, 200 MWh - control_parameters: - max_charge_fraction: 1.0 # fraction (0-1) - min_charge_fraction: 0.1 # fraction (0-1) - init_charge_fraction: 0.1 # fraction (0-1) + max_soc_fraction: 1.0 # fraction (0-1) + min_soc_fraction: 0.1 # fraction (0-1) + init_soc_fraction: 0.1 # fraction (0-1) max_discharge_rate: 25000.0 # kW/time step charge_efficiency: 1.0 # fraction (0-1) discharge_efficiency: 1.0 # fraction (0-1) @@ -55,6 +54,21 @@ technologies: energy_capex: 300 # $/kWh power_capex: 100 # $/kW opex_fraction: 0.05 # percent of capex + elec_combiner: + performance_model: + model: GenericCombinerPerformanceModel + model_inputs: + performance_parameters: + commodity: electricity + commodity_rate_units: kW + electrical_load_demand: + performance_model: + model: GenericDemandComponent + model_inputs: + performance_parameters: + commodity: electricity + commodity_rate_units: kW + demand_profile: 100000 # kW, 100 MW ng_feedstock: performance_model: model: FeedstockPerformanceModel @@ -68,9 +82,10 @@ technologies: rated_capacity: 750. # MMBtu/h cost_parameters: cost_year: 2023 - price: 4.2 - annual_cost: 0. - start_up_cost: 100000. + commodity_amount_units: MMBtu + price: 4.2 # USD/commodity_amount_units + annual_cost: 0. # USD + start_up_cost: 100000. # USD natural_gas_plant: performance_model: model: NaturalGasPerformanceModel diff --git a/examples/17_splitter_wind_doc_h2/plant_config.yaml b/examples/17_splitter_wind_doc_h2/plant_config.yaml index 9059c57e4..187c6cd12 100644 --- a/examples/17_splitter_wind_doc_h2/plant_config.yaml +++ b/examples/17_splitter_wind_doc_h2/plant_config.yaml @@ -32,7 +32,7 @@ finance_parameters: inflation_rate: 0.0 # 0 for real analysis discount_rate: 0.06 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 0.724 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.025 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx + property_tax_and_insurance: 0.025 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx total_income_tax_rate: 0.2574 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.0 # average combined state and local sales tax https://taxfoundation.org/location/texas/ diff --git a/examples/18_pyomo_heuristic_dispatch/plant_config.yaml b/examples/18_pyomo_heuristic_dispatch/plant_config.yaml index 64e0848c7..36f8084d5 100644 --- a/examples/18_pyomo_heuristic_dispatch/plant_config.yaml +++ b/examples/18_pyomo_heuristic_dispatch/plant_config.yaml @@ -17,6 +17,11 @@ plant: # this will naturally grow as we mature the interconnected tech technology_interconnections: - [wind, battery, electricity, cable] + # combine the electricity from wind and the battery + - [wind, elec_combiner, electricity, cable] + - [battery, elec_combiner, electricity, cable] + # subtract electricity production from electricity demand + - [elec_combiner, electrical_load_demand, electricity, cable] # array of arrays containing left-to-right technology, technology doing the dispatching # in this case, battery is connected to battery because there are controls rules for # the battery and battery is controlling the dispatching @@ -37,7 +42,7 @@ finance_parameters: inflation_rate: 0.0 # 0 for nominal analysis discount_rate: 0.09 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 2.62 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.03 # p-tax https://www.house.mn.gov/hrd/issinfo/clsrates.aspx # insurance percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf + property_tax_and_insurance: 0.03 # p-tax https://www.house.mn.gov/hrd/issinfo/clsrates.aspx # insurance percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf total_income_tax_rate: 0.257 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.07375 # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/ @@ -59,5 +64,5 @@ finance_parameters: technologies: [wind, battery] dispatched_electricity: commodity: electricity - commodity_stream: battery # use only dispatched electricity from battery in finance calc + commodity_stream: electrical_load_demand # use only dispatched electricity from battery in finance calc technologies: [wind, battery] diff --git a/examples/18_pyomo_heuristic_dispatch/tech_config.yaml b/examples/18_pyomo_heuristic_dispatch/tech_config.yaml index 3830c01f7..45b6c1f89 100644 --- a/examples/18_pyomo_heuristic_dispatch/tech_config.yaml +++ b/examples/18_pyomo_heuristic_dispatch/tech_config.yaml @@ -37,7 +37,7 @@ technologies: dispatch_rule_set: model: PyomoRuleStorageBaseclass control_strategy: - model: HeuristicLoadFollowingController + model: HeuristicLoadFollowingStorageController performance_model: model: PySAMBatteryPerformanceModel cost_model: @@ -48,16 +48,32 @@ technologies: commodity_rate_units: kW max_charge_rate: 100000 max_capacity: 500000 - n_control_window: 24 - init_charge_fraction: 0.5 - max_charge_fraction: 0.9 - min_charge_fraction: 0.1 - system_commodity_interface_limit: 1e12 + init_soc_fraction: 0.5 + max_soc_fraction: 0.9 + min_soc_fraction: 0.1 performance_parameters: - system_model_source: pysam chemistry: LFPGraphite + demand_profile: 40000 # 40 MW cost_parameters: cost_year: 2022 energy_capex: 310 # $/kWh from 2024 ATB year 2025 power_capex: 311 # $/kW from 2024 ATB year 2025 opex_fraction: 0.25 # 0.25% of capex per year from 2024 ATB + control_parameters: + n_control_window: 24 + system_commodity_interface_limit: 1e12 + elec_combiner: + performance_model: + model: GenericCombinerPerformanceModel + model_inputs: + performance_parameters: + commodity: electricity + commodity_rate_units: kW + electrical_load_demand: + performance_model: + model: GenericDemandComponent + model_inputs: + performance_parameters: + commodity: electricity + commodity_rate_units: kW + demand_profile: 50000 # 50 MW diff --git a/examples/19_simple_dispatch/plant_config.yaml b/examples/19_simple_dispatch/plant_config.yaml index f43314101..aafa25034 100644 --- a/examples/19_simple_dispatch/plant_config.yaml +++ b/examples/19_simple_dispatch/plant_config.yaml @@ -17,6 +17,11 @@ plant: # this will naturally grow as we mature the interconnected tech technology_interconnections: - [wind, battery, electricity, cable] + # combine the electricity from wind and the battery + - [wind, elec_combiner, electricity, cable] + - [battery, elec_combiner, electricity, cable] + # subtract electricity production from electricity demand + - [elec_combiner, electrical_load_demand, electricity, cable] # etc resource_to_tech_connections: # connect the wind resource to the wind technology @@ -33,7 +38,7 @@ finance_parameters: inflation_rate: 0.0 # 0 for nominal analysis discount_rate: 0.09 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 2.62 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx + property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx total_income_tax_rate: 0.257 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.07375 # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/ @@ -56,7 +61,7 @@ finance_parameters: inflation_rate: 0.0 # 0 for nominal analysis discount_rate: 0.09 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 2.62 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx + property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx total_income_tax_rate: 0.257 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.07375 # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/ @@ -88,7 +93,7 @@ finance_parameters: technologies: [wind] battery: commodity: electricity - commodity_stream: battery # use electricity from battery in finance calc + commodity_stream: electrical_load_demand # use electricity from battery in finance calc commodity_desc: battery_included finance_groups: [profast] technologies: [wind, battery] diff --git a/examples/19_simple_dispatch/run_wind_battery.py b/examples/19_simple_dispatch/run_wind_battery.py index 7fff85678..8e2ecdaf6 100644 --- a/examples/19_simple_dispatch/run_wind_battery.py +++ b/examples/19_simple_dispatch/run_wind_battery.py @@ -42,14 +42,14 @@ ) ax[1].plot( range(start_hour, end_hour), - model.prob.get_val("battery.electricity_unused_commodity", units="MW")[start_hour:end_hour], + model.prob.get_val("battery.unused_electricity_out", units="MW")[start_hour:end_hour], linestyle=":", label="Unused Electricity commodity (MW)", linewidth=2, ) ax[1].plot( range(start_hour, end_hour), - model.prob.get_val("battery.electricity_unmet_demand", units="MW")[start_hour:end_hour], + model.prob.get_val("battery.unmet_electricity_demand_out", units="MW")[start_hour:end_hour], linestyle=":", label="Electricity Unmet Demand (MW)", linewidth=2, diff --git a/examples/19_simple_dispatch/tech_config.yaml b/examples/19_simple_dispatch/tech_config.yaml index b5dae53dd..321e710dc 100644 --- a/examples/19_simple_dispatch/tech_config.yaml +++ b/examples/19_simple_dispatch/tech_config.yaml @@ -30,7 +30,7 @@ technologies: cost_year: 2019 battery: performance_model: - model: SimpleGenericStorage + model: StoragePerformanceModel cost_model: model: ATBBatteryCostModel control_strategy: @@ -41,10 +41,9 @@ technologies: commodity_rate_units: kW max_charge_rate: 5000.0 # kW max_capacity: 30000.0 # kWh - control_parameters: - max_charge_fraction: 1.0 # fraction (0-1) - min_charge_fraction: 0.1 # fraction (0-1) - init_charge_fraction: 0.25 # fraction (0-1) + max_soc_fraction: 1.0 # fraction (0-1) + min_soc_fraction: 0.1 # fraction (0-1) + init_soc_fraction: 0.25 # fraction (0-1) max_discharge_rate: 5000.0 # kW charge_efficiency: 1.0 # fraction (0-1) discharge_efficiency: 1.0 # fraction (0-1) @@ -54,3 +53,18 @@ technologies: energy_capex: 300 # $/kWh power_capex: 100 # $/kW opex_fraction: 0.02 # percent of capex + elec_combiner: + performance_model: + model: GenericCombinerPerformanceModel + model_inputs: + performance_parameters: + commodity: electricity + commodity_rate_units: kW + electrical_load_demand: + performance_model: + model: GenericDemandComponent + model_inputs: + performance_parameters: + commodity: electricity + commodity_rate_units: kW + demand_profile: 5000 # kW diff --git a/examples/21_iron_mn_to_il/driver_config.yaml b/examples/21_iron_examples/iron_dri/driver_config.yaml similarity index 100% rename from examples/21_iron_mn_to_il/driver_config.yaml rename to examples/21_iron_examples/iron_dri/driver_config.yaml diff --git a/examples/21_iron_mn_to_il/plant_config.yaml b/examples/21_iron_examples/iron_dri/plant_config.yaml similarity index 97% rename from examples/21_iron_mn_to_il/plant_config.yaml rename to examples/21_iron_examples/iron_dri/plant_config.yaml index 36e3d356a..83e240172 100644 --- a/examples/21_iron_mn_to_il/plant_config.yaml +++ b/examples/21_iron_examples/iron_dri/plant_config.yaml @@ -40,7 +40,7 @@ finance_parameters: inflation_rate: 0.0 # 0 for nominal analysis discount_rate: 0.09 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 2.62 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx + property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx total_income_tax_rate: 0.257 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.07375 # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/ diff --git a/examples/21_iron_mn_to_il/run_iron.py b/examples/21_iron_examples/iron_dri/run_iron.py similarity index 98% rename from examples/21_iron_mn_to_il/run_iron.py rename to examples/21_iron_examples/iron_dri/run_iron.py index c3388b506..93a73f40b 100644 --- a/examples/21_iron_mn_to_il/run_iron.py +++ b/examples/21_iron_examples/iron_dri/run_iron.py @@ -21,7 +21,7 @@ # Create H2Integrate model -model = H2IntegrateModel("21_iron.yaml") +model = H2IntegrateModel("single_site_iron.yaml") # Load cases case_file = Path("test_inputs.csv") diff --git a/examples/21_iron_mn_to_il/21_iron.yaml b/examples/21_iron_examples/iron_dri/single_site_iron.yaml similarity index 100% rename from examples/21_iron_mn_to_il/21_iron.yaml rename to examples/21_iron_examples/iron_dri/single_site_iron.yaml diff --git a/examples/21_iron_mn_to_il/tech_config.yaml b/examples/21_iron_examples/iron_dri/tech_config.yaml similarity index 100% rename from examples/21_iron_mn_to_il/tech_config.yaml rename to examples/21_iron_examples/iron_dri/tech_config.yaml diff --git a/examples/21_iron_mn_to_il/test_inputs.csv b/examples/21_iron_examples/iron_dri/test_inputs.csv similarity index 100% rename from examples/21_iron_mn_to_il/test_inputs.csv rename to examples/21_iron_examples/iron_dri/test_inputs.csv diff --git a/examples/31_iron_electrowinning/driver_config.yaml b/examples/21_iron_examples/iron_electrowinning/driver_config.yaml similarity index 100% rename from examples/31_iron_electrowinning/driver_config.yaml rename to examples/21_iron_examples/iron_electrowinning/driver_config.yaml diff --git a/examples/31_iron_electrowinning/31_iron_electrowinning.yaml b/examples/21_iron_examples/iron_electrowinning/iron_electrowinning.yaml similarity index 100% rename from examples/31_iron_electrowinning/31_iron_electrowinning.yaml rename to examples/21_iron_examples/iron_electrowinning/iron_electrowinning.yaml diff --git a/examples/31_iron_electrowinning/plant_config.yaml b/examples/21_iron_examples/iron_electrowinning/plant_config.yaml similarity index 96% rename from examples/31_iron_electrowinning/plant_config.yaml rename to examples/21_iron_examples/iron_electrowinning/plant_config.yaml index 274070b36..fe1ed3001 100644 --- a/examples/31_iron_electrowinning/plant_config.yaml +++ b/examples/21_iron_examples/iron_electrowinning/plant_config.yaml @@ -30,7 +30,7 @@ finance_parameters: inflation_rate: 0.0 # 0 for nominal analysis discount_rate: 0.09 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 2.62 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx + property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx total_income_tax_rate: 0.257 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.07375 # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/ diff --git a/examples/31_iron_electrowinning/run_iron.py b/examples/21_iron_examples/iron_electrowinning/run_iron.py similarity index 97% rename from examples/31_iron_electrowinning/run_iron.py rename to examples/21_iron_examples/iron_electrowinning/run_iron.py index 0896d334c..bd4a24c7f 100644 --- a/examples/31_iron_electrowinning/run_iron.py +++ b/examples/21_iron_examples/iron_electrowinning/run_iron.py @@ -16,7 +16,7 @@ # Create H2Integrate model -model = H2IntegrateModel("31_iron_electrowinning.yaml") +model = H2IntegrateModel("iron_electrowinning.yaml") # Define the electrowinning types as a list electrolysis_types = ["ahe", "mse", "moe"] diff --git a/examples/31_iron_electrowinning/tech_config.yaml b/examples/21_iron_examples/iron_electrowinning/tech_config.yaml similarity index 100% rename from examples/31_iron_electrowinning/tech_config.yaml rename to examples/21_iron_examples/iron_electrowinning/tech_config.yaml diff --git a/examples/28_iron_map/driver_config.yaml b/examples/21_iron_examples/iron_mapping/driver_config.yaml similarity index 77% rename from examples/28_iron_map/driver_config.yaml rename to examples/21_iron_examples/iron_mapping/driver_config.yaml index 1cf9ebc1f..f40426217 100644 --- a/examples/28_iron_map/driver_config.yaml +++ b/examples/21_iron_examples/iron_mapping/driver_config.yaml @@ -1,7 +1,7 @@ name: driver_config description: This analysis runs a hybrid plant to match the first example in H2Integrate general: - folder_output: outputs + folder_output: ex_out driver: design_of_experiments: flag: true @@ -21,22 +21,23 @@ design_variables: units: deg lower: -180.0 upper: 180.0 - iron: - LCOE: + dri_grid_feedstock: + price: flag: true units: USD/MW/h lower: 0 upper: 100 - LCOH: + hydrogen_feedstock: + price: flag: true units: USD/kg lower: 0 upper: 10 objective: - name: iron.LCOI + name: finance_subgroup_pig_iron.LCOP recorder: file: cases.sql overwrite_recorder: true flag: true - includes: [site.latitude, site.longitude, iron.LCOI] + includes: [site.latitude, site.longitude, finance_subgroup_pig_iron.LCOP] excludes: ['*'] diff --git a/examples/21_iron_examples/iron_mapping/ex_out/cases.csv b/examples/21_iron_examples/iron_mapping/ex_out/cases.csv new file mode 100644 index 000000000..c2e7a20ae --- /dev/null +++ b/examples/21_iron_examples/iron_mapping/ex_out/cases.csv @@ -0,0 +1,544 @@ +,site.latitude (deg),site.longitude (deg),dri_grid_feedstock.price (USD/MW/h),hydrogen_feedstock.price (USD/kg),finance_subgroup_pig_iron.LCOP (USD/kg) +0,39.179,-94.644,58.24602573,7.819127689,0.7850112572903336 +1,40.639,-94.179,51.41859144,6.486845574,0.7039548783993137 +2,42.135,-74.231,54.87195632,9.087358988,0.8611145933067907 +3,39.836,-86.466,54.08137179,7.389792328,0.7586802397697303 +4,44.836,-72.268,79.67190898,11.19138114,0.9906233427596457 +5,43.297,-83.034,54.46538588,7.030717938,0.7370719861716352 +6,41.522,-97.383,48.48607904,6.892715924,0.7281119355480926 +7,43.745,-75.672,50.22448696,7.602515656,0.7710931829912547 +8,40.915,-93.153,50.25523396,6.52284299,0.7060007884990818 +9,45.619,-97.165,48.01464159,6.479866149,0.7031699430936669 +10,38.376,-93.262,57.32420626,8.084316033,0.8009013974875046 +11,38.785,-86.901,58.74660238,8.268727655,0.8121721113901094 +12,38.792,-95.074,48.66910731,6.576432804,0.709062155450073 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+533,44.348,-74.993,57.4429404,8.078041073,0.800535768626705 +534,44.681,-91.582,55.21964952,7.260808017,0.7510252758352114 +535,40.648,-96.93,46.1520268,6.233359253,0.6881082859225318 +536,43.405,-78.411,52.95839231,7.995269103,0.7950655499818584 +537,44.177,-88.702,54.82723999,6.810351697,0.7238243613769677 +538,44.94,-96.376,53.02000449,7.311072105,0.7538204979035236 +539,38.271,-75.806,56.03213163,9.025428971,0.8575048129981654 +540,42.858,-89.76,53.76259374,6.576633391,0.7096191158290799 +541,43.186,-95.762,49.43314627,6.883232479,0.7276414699804448 +542,44.801,-74.672,61.89315377,8.919900273,0.8517692432827209 diff --git a/examples/28_iron_map/example_ore_prices.csv b/examples/21_iron_examples/iron_mapping/example_ore_prices.csv similarity index 100% rename from examples/28_iron_map/example_ore_prices.csv rename to examples/21_iron_examples/iron_mapping/example_ore_prices.csv diff --git a/examples/28_iron_map/example_shipping_prices.csv b/examples/21_iron_examples/iron_mapping/example_shipping_prices.csv similarity index 100% rename from examples/28_iron_map/example_shipping_prices.csv rename to examples/21_iron_examples/iron_mapping/example_shipping_prices.csv diff --git a/examples/21_iron_examples/iron_mapping/iron_map.yaml b/examples/21_iron_examples/iron_mapping/iron_map.yaml new file mode 100644 index 000000000..f6593fa1d --- /dev/null +++ b/examples/21_iron_examples/iron_mapping/iron_map.yaml @@ -0,0 +1,5 @@ +name: H2Integrate_config +system_summary: Produces iron and at locations across the US. +driver_config: driver_config.yaml +technology_config: tech_config.yaml +plant_config: plant_config.yaml diff --git a/examples/28_iron_map/ned_reduced_sitelist.csv b/examples/21_iron_examples/iron_mapping/ned_reduced_sitelist.csv similarity index 99% rename from examples/28_iron_map/ned_reduced_sitelist.csv rename to examples/21_iron_examples/iron_mapping/ned_reduced_sitelist.csv index ca80a1f57..701620106 100644 --- a/examples/28_iron_map/ned_reduced_sitelist.csv +++ b/examples/21_iron_examples/iron_mapping/ned_reduced_sitelist.csv @@ -1,4 +1,4 @@ -site.latitude,site.longitude,iron.LCOE,iron.LCOH +site.latitude,site.longitude,dri_grid_feedstock.price,hydrogen_feedstock.price 39.179,-94.644,58.24602573,7.819127689 40.639,-94.179,51.41859144,6.486845574 42.135,-74.231,54.87195632,9.087358988 diff --git a/examples/21_iron_examples/iron_mapping/plant_config.yaml b/examples/21_iron_examples/iron_mapping/plant_config.yaml new file mode 100644 index 000000000..e0c8b12f0 --- /dev/null +++ b/examples/21_iron_examples/iron_mapping/plant_config.yaml @@ -0,0 +1,72 @@ +name: plant_config +description: This file contains plant-specific parameters for the iron mapping example. +sites: + site: + latitude: 41.717 + longitude: -88.398 +technology_interconnections: + # Connect feedstocks to iron mine. + - [mine_electricity_feedstock, iron_mine, electricity, cable] + - [mine_crude_ore_feedstock, iron_mine, crude_ore, crude_ore_transport] + # Connect iron_ore price and processed_ore_feedstock to iron_transport + # Connect the LCOI of iron ore to the price of a feedstock component representing the ore to be used in DRI + - [finance_subgroup_iron_ore, processed_ore_feedstock, [price_iron_ore, price]] + # Connect the ore feedstock available to the iron plant + - [processed_ore_feedstock, iron_plant, iron_ore, iron_ore_transport] + - [dri_grid_feedstock, iron_plant, electricity, cable] + - [hydrogen_feedstock, iron_plant, hydrogen, pipe] + - [water_feedstock, iron_plant, water, pipe] + - [natural_gas_feedstock, iron_plant, natural_gas, pipe] + # Use the iron ore consumed by the iron plant to calculate transport costs + - [iron_plant, iron_transport, [iron_ore_consumed, iron_ore_in]] +plant: + plant_life: 30 + simulation: + n_timesteps: 8760 + dt: 3600 + timezone: 0 +finance_parameters: + finance_groups: + finance_model: ProFastLCO + model_inputs: + params: + analysis_start_year: 2032 + installation_time: 36 # months + inflation_rate: 0.0 # 0 for nominal analysis + discount_rate: 0.09 # nominal return based on 2024 ATB baseline workbook for land-based wind + debt_equity_ratio: 2.62 # 2024 ATB uses 72.4% debt for land-based wind + property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx + total_income_tax_rate: 0.257 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) + capital_gains_tax_rate: 0.15 # H2FAST default + sales_tax_rate: 0.07375 # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/ + debt_interest_rate: 0.07 # based on 2024 ATB nominal interest rate for land-based wind + debt_type: Revolving debt # can be "Revolving debt" or "One time loan". Revolving debt is H2FAST default and leads to much lower LCOH + loan_period_if_used: 0 # H2FAST default, not used for revolving debt + cash_onhand_months: 1 # H2FAST default + admin_expense: 0.00 # percent of sales H2FAST default + capital_items: + depr_type: MACRS # can be "MACRS" or "Straight line" + depr_period: 5 # 5 years - for clean energy facilities as specified by the IRS MACRS schedule https://www.irs.gov/publications/p946#en_US_2020_publink1000107507 + refurb: [0.] + cost_adjustment_parameters: + cost_year_adjustment_inflation: 0.025 + target_dollar_year: 2022 + finance_subgroups: + iron_ore: + commodity: iron_ore + commodity_stream: iron_mine + technologies: + - iron_mine + - mine_electricity_feedstock + - mine_crude_ore_feedstock + pig_iron: + commodity: pig_iron + commodity_stream: iron_plant + technologies: + - processed_ore_feedstock + - iron_transport + - iron_plant + - dri_grid_feedstock + - hydrogen_feedstock + - water_feedstock + - natural_gas_feedstock diff --git a/examples/28_iron_map/run_iron.py b/examples/21_iron_examples/iron_mapping/run_iron.py similarity index 91% rename from examples/28_iron_map/run_iron.py rename to examples/21_iron_examples/iron_mapping/run_iron.py index 8537c599e..74f7dc619 100644 --- a/examples/28_iron_map/run_iron.py +++ b/examples/21_iron_examples/iron_mapping/run_iron.py @@ -26,21 +26,23 @@ model = H2IntegrateModel("iron_map.yaml") model.run() + model.post_process(summarize_sql=True) + # Define filepaths -ex_28_dir = EXAMPLE_DIR / "28_iron_map" -ex_28_out_dir = EXAMPLE_DIR / "28_iron_map/ex_28_out" -save_plot_filepath = ex_28_out_dir / "example_28_iron_map.png" +ex_dir = EXAMPLE_DIR / "21_iron_examples/iron_mapping" +ex_out_dir = EXAMPLE_DIR / "21_iron_examples/iron_mapping/ex_out" +save_plot_filepath = ex_out_dir / "example_iron_map.png" save_plot_filepath.unlink(missing_ok=True) -case_results_filepath = ex_28_out_dir / "cases.csv" -ore_prices_filepath = ex_28_dir / "example_ore_prices.csv" +case_results_filepath = ex_out_dir / "cases.csv" +ore_prices_filepath = ex_dir / "example_ore_prices.csv" shipping_coords_filepath = ROOT_DIR / "converters/iron/martin_transport/shipping_coords.csv" -shipping_prices_filepath = ex_28_dir / "example_shipping_prices.csv" +shipping_prices_filepath = ex_dir / "example_shipping_prices.csv" # Plot the LCOI results with geopandas and contextily # NOTE: you can swap './ex_28_out/cases.sql' with './ex_28_out/cases.csv' to read results from csv fig, ax, lcoi_layer_gdf = plot_geospatial_point_heat_map( case_results_fpath=case_results_filepath, - metric_to_plot="iron.LCOI (USD/kg)", + metric_to_plot="finance_subgroup_pig_iron.LCOP (USD/kg)", map_preferences={ "figsize": (10, 8), "colorbar_label": "Levelized Cost of\nIron [$/kg]", diff --git a/examples/21_iron_examples/iron_mapping/tech_config.yaml b/examples/21_iron_examples/iron_mapping/tech_config.yaml new file mode 100644 index 000000000..c9949779a --- /dev/null +++ b/examples/21_iron_examples/iron_mapping/tech_config.yaml @@ -0,0 +1,171 @@ +name: technology_config +description: This hybrid plant produces iron +technologies: + mine_electricity_feedstock: # electricity feedstock for iron ore + performance_model: + model: FeedstockPerformanceModel + cost_model: + model: FeedstockCostModel + model_inputs: + shared_parameters: + commodity: electricity + commodity_rate_units: MW + performance_parameters: + rated_capacity: 30. # MW, need 27.913 MW per timestep for iron ore + cost_parameters: + cost_year: 2022 + price: 0. # Electricity costs are already priced in to current mine model + annual_cost: 0. + start_up_cost: 0. + mine_crude_ore_feedstock: # iron ore feedstock + performance_model: + model: FeedstockPerformanceModel + cost_model: + model: FeedstockCostModel + model_inputs: + shared_parameters: + commodity: crude_ore + commodity_rate_units: t/h + performance_parameters: + rated_capacity: 2000. # need 828.50385048 t/h + cost_parameters: + cost_year: 2022 + price: 0. # USD/t + annual_cost: 0. + start_up_cost: 0. + crude_ore_transport: + performance_model: + model: GenericTransporterPerformanceModel + model_inputs: + performance_parameters: + commodity: crude_ore + commodity_rate_units: t/h + iron_mine: + performance_model: + model: MartinIronMinePerformanceComponent + cost_model: + model: MartinIronMineCostComponent + model_inputs: + shared_parameters: + mine: Northshore + taconite_pellet_type: drg + max_ore_production_rate_tonnes_per_hr: 221.2592636 + iron_ore_transport: + performance_model: + model: GenericTransporterPerformanceModel + model_inputs: + performance_parameters: + commodity: iron_ore + commodity_rate_units: kg/h + processed_ore_feedstock: # iron ore feedstock + performance_model: + model: FeedstockPerformanceModel + cost_model: + model: FeedstockCostModel + model_inputs: + shared_parameters: + commodity: iron_ore + commodity_rate_units: t/h + performance_parameters: + rated_capacity: 250. # need 828.50385048 t/h + cost_parameters: + cost_year: 2022 + price: 0.0 + annual_cost: 0. + start_up_cost: 0. + iron_transport: + performance_model: + model: IronTransportPerformanceComponent + cost_model: + model: IronTransportCostComponent + model_inputs: + performance_parameters: + find_closest_ship_site: false + shipment_site: Chicago + cost_parameters: + transport_year: 2022 + cost_year: 2022 + natural_gas_feedstock: + performance_model: + model: FeedstockPerformanceModel + cost_model: + model: FeedstockCostModel + model_inputs: + shared_parameters: + commodity: natural_gas + commodity_rate_units: MMBtu/h + performance_parameters: + rated_capacity: 1270. # need 1268.934 MMBtu/h + cost_parameters: + cost_year: 2022 + price: 4.0 # USD 4.0/MMBtu + annual_cost: 0. + start_up_cost: 0. + water_feedstock: # for iron reduction + performance_model: + model: FeedstockPerformanceModel + cost_model: + model: FeedstockCostModel + model_inputs: + shared_parameters: + commodity: water + commodity_rate_units: galUS # galUS/h + performance_parameters: + rated_capacity: 40000. # need 38710.49649 galUS/h + cost_parameters: + cost_year: 2022 + price: 0.0016700004398318847 # cost is USD 0.441167535/t, converted to USD/gal + annual_cost: 0. + start_up_cost: 0. + hydrogen_feedstock: # for h2 dri iron reduction + performance_model: + model: FeedstockPerformanceModel + cost_model: + model: FeedstockCostModel + model_inputs: + shared_parameters: + commodity: hydrogen + commodity_rate_units: kg/h + performance_parameters: + rated_capacity: 1000000 # oversized + cost_parameters: + cost_year: 2022 + price: 0. # USD/kg set from the CSV + annual_cost: 0. + start_up_cost: 0. + dri_grid_feedstock: # electricity feedstock for iron dri + performance_model: + model: FeedstockPerformanceModel + cost_model: + model: FeedstockCostModel + model_inputs: + shared_parameters: + commodity: electricity + commodity_rate_units: MW + performance_parameters: + rated_capacity: 270000000. # Oversized to ensure it doesn't constrain the process, need 27.913 MW per timestep for iron ore + cost_parameters: + cost_year: 2022 + price: 0. # USD/MWh set from CSV + annual_cost: 0. + start_up_cost: 0. + iron_plant: + performance_model: + model: HydrogenIronReductionPlantPerformanceComponent + cost_model: + model: HydrogenIronReductionPlantCostComponent + model_inputs: + shared_parameters: + pig_iron_production_rate_tonnes_per_hr: 161.8829908675799 # equivalent to 1418095 t/yr + performance_parameters: + water_density: 1000 # kg/m3 + cost_parameters: + skilled_labor_cost: 40.85 # 2022 USD/hr + unskilled_labor_cost: 30.0 # 2022 USD/hr + pig_iron_transport: + performance_model: + model: GenericTransporterPerformanceModel + model_inputs: + performance_parameters: + commodity: pig_iron + commodity_rate_units: kg/h diff --git a/examples/23_solar_wind_ng_demand/flexible_demand_tech_config.yaml b/examples/23_solar_wind_ng_demand/flexible_demand_tech_config.yaml index 4f10f7ddc..d0c83fab0 100644 --- a/examples/23_solar_wind_ng_demand/flexible_demand_tech_config.yaml +++ b/examples/23_solar_wind_ng_demand/flexible_demand_tech_config.yaml @@ -91,10 +91,10 @@ technologies: variable_opex_per_mwh: 0.0 # $/MWh cost_year: 2023 electrical_load_demand: - control_strategy: - model: FlexibleDemandOpenLoopConverterController + performance_model: + model: FlexibleDemandComponent model_inputs: - control_parameters: + performance_parameters: commodity: electricity commodity_rate_units: kW rated_demand: 100000 diff --git a/examples/23_solar_wind_ng_demand/plant_config.yaml b/examples/23_solar_wind_ng_demand/plant_config.yaml index 555b9b13e..048314999 100644 --- a/examples/23_solar_wind_ng_demand/plant_config.yaml +++ b/examples/23_solar_wind_ng_demand/plant_config.yaml @@ -27,7 +27,7 @@ technology_interconnections: # connect NG feedstock to NG plant - [combiner, electrical_load_demand, [electricity_out, electricity_in]] # subtract wind and solar from demand - - [electrical_load_demand, natural_gas_plant, [electricity_unmet_demand, electricity_demand]] + - [electrical_load_demand, natural_gas_plant, [unmet_electricity_demand_out, electricity_demand]] # give remaining load demand to natural gas plant - [combiner, fin_combiner, electricity, cable] - [natural_gas_plant, fin_combiner, electricity, cable] @@ -51,7 +51,7 @@ finance_parameters: inflation_rate: 0.0 # 0 for nominal analysis discount_rate: 0.09 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 2.62 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx + property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx total_income_tax_rate: 0.257 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.07375 # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/ @@ -74,7 +74,7 @@ finance_parameters: inflation_rate: 0.0 # 0 for nominal analysis discount_rate: 0.09 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 2.62 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx + property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx total_income_tax_rate: 0.257 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.07375 # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/ diff --git a/examples/23_solar_wind_ng_demand/tech_config.yaml b/examples/23_solar_wind_ng_demand/tech_config.yaml index 5fbabe17f..e044c1788 100644 --- a/examples/23_solar_wind_ng_demand/tech_config.yaml +++ b/examples/23_solar_wind_ng_demand/tech_config.yaml @@ -91,10 +91,10 @@ technologies: variable_opex_per_mwh: 0.0 # $/MWh cost_year: 2023 electrical_load_demand: - control_strategy: - model: DemandOpenLoopConverterController + performance_model: + model: GenericDemandComponent model_inputs: - control_parameters: + performance_parameters: commodity: electricity commodity_rate_units: kW demand_profile: 100000 # 100 MW diff --git a/examples/24_solar_battery_grid/README.md b/examples/24_solar_battery_grid/README.md index 2cdf79ef6..cce607d62 100644 --- a/examples/24_solar_battery_grid/README.md +++ b/examples/24_solar_battery_grid/README.md @@ -53,8 +53,8 @@ The grid performance model handles: - `electricity_in`: Power flowing INTO the grid (selling to grid) - limited by interconnection size - `electricity_out`: Power flowing OUT OF the grid (buying from grid) - limited by interconnection size - `electricity_sold`: Actual electricity sold (up to interconnection limit) -- `electricity_unmet_demand`: Demand that couldn't be met due to interconnection limit -- `electricity_excess`: Electricity that couldn't be sold due to interconnection limit +- `unmet_electricity_demand_out`: Demand that couldn't be met due to interconnection limit +- `unused_electricity_out`: Electricity that couldn't be sold due to interconnection limit **Cost Model:** - CapEx: Based on interconnection size ($/kW) plus fixed costs @@ -72,16 +72,16 @@ Solar → Battery → [Grid Buy (purchases) | Grid Sell (sales)] - Solar generates electricity - Battery stores excess and follows 100 MW demand profile -- Grid Buy purchases electricity when battery cannot meet demand (via `electricity_unmet_demand` → `electricity_demand` connection) -- Grid Sell accepts excess electricity when battery has surplus (via `electricity_unused_commodity` → `electricity_in` connection) +- Grid Buy purchases electricity when battery cannot meet demand (via `unmet_electricity_demand_out` → `electricity_demand` connection) +- Grid Sell accepts excess electricity when battery has surplus (via `unused_electricity_out` → `electricity_in` connection) ### Technology Interconnections ```yaml technology_interconnections: [ ["solar", "battery", "electricity", "cable"], - ["battery", "grid_buy", ["electricity_unmet_demand", "electricity_demand"]], - ["battery", "grid_sell", ["electricity_unused_commodity", "electricity_in"]] + ["battery", "grid_buy", ["unmet_electricity_demand_out", "electricity_demand"]], + ["battery", "grid_sell", ["unused_electricity_out", "electricity_in"]] ] ``` diff --git a/examples/24_solar_battery_grid/plant_config.yaml b/examples/24_solar_battery_grid/plant_config.yaml index f9dd733d6..8c9298c03 100644 --- a/examples/24_solar_battery_grid/plant_config.yaml +++ b/examples/24_solar_battery_grid/plant_config.yaml @@ -19,12 +19,18 @@ sites: # - grid_buy: purchases electricity to meet unmet demand (upstream) # - grid_sell: sells excess electricity back to grid (downstream) technology_interconnections: + # connect solar to battery - [solar, battery, electricity, cable] - # connect solar to battery - - [battery, grid_buy, [electricity_unmet_demand, electricity_demand]] - # connect battery demand to grid buying - - [battery, grid_sell, [electricity_unused_commodity, electricity_in]] - # connect battery surplus to grid selling + # combine solar and battery output + - [solar, bat_combiner, electricity, cable] + - [battery, bat_combiner, electricity, cable] + # subtract variable generation from the demand + - [bat_combiner, electrical_load_demand, electricity, cable] + # connect remaining demand to grid buying + - [electrical_load_demand, grid_buy, [unmet_electricity_demand_out, electricity_demand]] + # connect surplus generation to grid selling + - [electrical_load_demand, grid_sell, [unused_electricity_out, electricity_in]] + # combine electricity generated from solar and electricity bought from grid - [solar, combiner, electricity, cable] - [grid_buy, combiner, electricity, cable] resource_to_tech_connections: diff --git a/examples/24_solar_battery_grid/tech_config.yaml b/examples/24_solar_battery_grid/tech_config.yaml index 8229ee64a..969cfbf63 100644 --- a/examples/24_solar_battery_grid/tech_config.yaml +++ b/examples/24_solar_battery_grid/tech_config.yaml @@ -31,7 +31,7 @@ technologies: cost_year: 2024 battery: performance_model: - model: SimpleGenericStorage + model: StoragePerformanceModel cost_model: model: ATBBatteryCostModel control_strategy: @@ -42,10 +42,9 @@ technologies: commodity_rate_units: kW max_charge_rate: 25000.0 # kW/time step, 25 MW max_capacity: 200000.0 # kWh, 200 MWh - control_parameters: - max_charge_fraction: 1.0 # fraction (0-1) - min_charge_fraction: 0.1 # fraction (0-1) - init_charge_fraction: 0.1 # fraction (0-1) + max_soc_fraction: 1.0 # fraction (0-1) + min_soc_fraction: 0.1 # fraction (0-1) + init_soc_fraction: 0.1 # fraction (0-1) max_discharge_rate: 25000.0 # kW/time step charge_efficiency: 1.0 # fraction (0-1) discharge_efficiency: 1.0 # fraction (0-1) @@ -55,6 +54,21 @@ technologies: energy_capex: 300 # $/kWh power_capex: 100 # $/kW opex_fraction: 0.05 # percent of capex + electrical_load_demand: + performance_model: + model: GenericDemandComponent + model_inputs: + performance_parameters: + commodity: electricity + commodity_rate_units: kW + demand_profile: 100000 # kW, 100 MW + bat_combiner: + performance_model: + model: GenericCombinerPerformanceModel + model_inputs: + performance_parameters: + commodity: electricity + commodity_rate_units: kW grid_buy: performance_model: model: GridPerformanceModel diff --git a/examples/25_sizing_modes/plant_config.yaml b/examples/25_sizing_modes/plant_config.yaml index fa237f4af..b5089cf47 100644 --- a/examples/25_sizing_modes/plant_config.yaml +++ b/examples/25_sizing_modes/plant_config.yaml @@ -25,7 +25,7 @@ finance_parameters: inflation_rate: 0.0 # 0 for real analysis discount_rate: 0.06 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 0.724 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.025 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx + property_tax_and_insurance: 0.025 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx total_income_tax_rate: 0.2574 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.0 # average combined state and local sales tax https://taxfoundation.org/location/texas/ diff --git a/examples/25_sizing_modes/tech_inputs/hopp_config_tx.yaml b/examples/25_sizing_modes/tech_inputs/hopp_config_tx.yaml index 71d06343d..6a6b35816 100644 --- a/examples/25_sizing_modes/tech_inputs/hopp_config_tx.yaml +++ b/examples/25_sizing_modes/tech_inputs/hopp_config_tx.yaml @@ -57,7 +57,7 @@ technologies: federal_tax_rate: 21.0 # https://taxsummaries.pwc.com/quick-charts/corporate-income-tax-cit-rates state_tax_rate: 4.74 # 0.75 # https://comptroller.texas.gov/taxes/franchise/ --> The TX franchise tax should be applied to margin but is included in income tax here as an approximation property_tax_rate: 1.47 # average effective real property tax rate in Texas https://taxfoundation.org/data/all/state/property-taxes-by-state-county-2024/ - insurance_rate: 1.0 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf + insurance_rate: 1.0 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf debt_percent: 72.4 # based on 2024 ATB for land-based wind term_int_rate: 4.4 # based on 2024 ATB real interest rate for land-based onshore wind months_working_reserve: 1 @@ -125,7 +125,7 @@ technologies: federal_tax_rate: 21.0 # https://taxsummaries.pwc.com/quick-charts/corporate-income-tax-cit-rates state_tax_rate: 4.74 # 0.75 # https://comptroller.texas.gov/taxes/franchise/ --> The TX franchise tax should be applied to margin but is included in income tax here as an approximation property_tax_rate: 1.47 # average effective real property tax rate in Texas https://taxfoundation.org/data/all/state/property-taxes-by-state-county-2024/ - insurance_rate: 1.0 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf + insurance_rate: 1.0 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf debt_percent: 75.3 # based on 2024 ATB for utility-scale pv solar term_int_rate: 4.4 # based on 2024 ATB real interest rate for utility-scale pv solar months_working_reserve: 1 @@ -171,7 +171,7 @@ technologies: federal_tax_rate: 21.0 # https://taxsummaries.pwc.com/quick-charts/corporate-income-tax-cit-rates state_tax_rate: 4.74 # 0.75 # https://comptroller.texas.gov/taxes/franchise/ --> The TX franchise tax should be applied to margin but is included in income tax here as an approximation property_tax_rate: 1.47 # average effective real property tax rate in Texas https://taxfoundation.org/data/all/state/property-taxes-by-state-county-2024/ - insurance_rate: 1.0 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf + insurance_rate: 1.0 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf debt_percent: 75.4 # based on 2024 ATB for utility-scale pv plus battery term_int_rate: 4.4 # based on 2024 ATB real interest rate for utility-scale battery months_working_reserve: 1 @@ -213,7 +213,7 @@ technologies: federal_tax_rate: 21.0 # https://taxsummaries.pwc.com/quick-charts/corporate-income-tax-cit-rates state_tax_rate: 4.74 # 0.75 # https://comptroller.texas.gov/taxes/franchise/ --> The TX franchise tax should be applied to margin but is included in income tax here as an approximation property_tax_rate: 1.47 # average effective real property tax rate in Texas https://taxfoundation.org/data/all/state/property-taxes-by-state-county-2024/ - insurance_rate: 1.0 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf + insurance_rate: 1.0 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf debt_percent: 72.4 # based on 2024 ATB for land-based wind term_int_rate: 4.4 # based on 2024 ATB real interest rate for land-based onshore wind months_working_reserve: 1 @@ -243,10 +243,10 @@ config: pv_charging_only: false include_lifecycle_count: false cost_info: - wind_installed_cost_mw: 1380000 # (2022USD) 2025 costs in 2022 USD from NREL 2024 ATB for land-based wind. 2025 used as an overnight-capex lock-in date for 2030 operation - solar_installed_cost_mw: 1323000 # (2022USD) 2025 costs in 2022 USD from NREL 2024 ATB for utility-scale pv. 2025 used as an overnight-capex lock-in date for 2030 operation - storage_installed_cost_mwh: 310000 # (2022USD) 2025 battery costs in 2022 USD from NREL 2024 ATB for utility-scale battery storage. 2025 used as an overnight-capex lock-in date for 2030 operation - storage_installed_cost_mw: 311000 # (2022USD) 2025 battery costs in 2022 USD from NREL 2024 ATB for utility-scale battery storage. 2025 used as an overnight-capex lock-in date for 2030 operation + wind_installed_cost_mw: 1380000 # (2022USD) 2025 costs in 2022 USD from NLR 2024 ATB for land-based wind. 2025 used as an overnight-capex lock-in date for 2030 operation + solar_installed_cost_mw: 1323000 # (2022USD) 2025 costs in 2022 USD from NLR 2024 ATB for utility-scale pv. 2025 used as an overnight-capex lock-in date for 2030 operation + storage_installed_cost_mwh: 310000 # (2022USD) 2025 battery costs in 2022 USD from NLR 2024 ATB for utility-scale battery storage. 2025 used as an overnight-capex lock-in date for 2030 operation + storage_installed_cost_mw: 311000 # (2022USD) 2025 battery costs in 2022 USD from NLR 2024 ATB for utility-scale battery storage. 2025 used as an overnight-capex lock-in date for 2030 operation wind_om_per_kw: 29 # (2022USD) 2030 costs in 2022 USD from 2024 ATB for land-based wind - pv_om_per_kw: 18 # (2022USD) 2030 costs in 2022 USD from NREL 2024 ATB for utility-scale PV + pv_om_per_kw: 18 # (2022USD) 2030 costs in 2022 USD from NLR 2024 ATB for utility-scale PV battery_om_per_kw: 15.525 # (2022USD) based on 2.5 percent of battery capex/kW for a 1-hour battery using both power and energy costs above diff --git a/examples/28_iron_map/ex_28_out/cases.csv b/examples/28_iron_map/ex_28_out/cases.csv deleted file mode 100644 index 6834b938c..000000000 --- a/examples/28_iron_map/ex_28_out/cases.csv +++ /dev/null @@ -1,544 +0,0 @@ -,site.latitude (deg),site.longitude (deg),iron.LCOE (USD/MW/h),iron.LCOH (USD/kg),iron.LCOI (USD/kg) -0,39.179,-94.644,58.24602573,7.819127689,0.7397575877065189 -1,40.639,-94.179,51.41859144,6.486845574,0.6645510235049574 -2,42.135,-74.231,54.87195632,9.087358988,0.8103685686602611 -3,39.836,-86.466,54.08137179,7.389792328,0.7153268718671882 -4,44.836,-72.268,79.67190898,11.19138114,0.9305307102908987 -5,43.297,-83.034,54.46538588,7.030717938,0.6952780794865759 -6,41.522,-97.383,48.48607904,6.892715924,0.6869646730441603 -7,43.745,-75.672,50.22448696,7.602515656,0.7268439766785298 -8,40.915,-93.153,50.25523396,6.52284299,0.6664492808830237 -9,45.619,-97.165,48.01464159,6.479866149,0.6638227367462083 -10,38.376,-93.262,57.32420626,8.084316033,0.75450094122482 -11,38.785,-86.901,58.74660238,8.268727655,0.7649582510664614 -12,38.792,-95.074,48.66910731,6.576432804,0.6692897098943401 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-535,40.648,-96.93,46.1520268,6.233359253,0.6498480748809882 -536,43.405,-78.411,52.95839231,7.995269103,0.7490862650931874 -537,44.177,-88.702,54.82723999,6.810351697,0.682986531822166 -538,44.94,-96.376,53.02000449,7.311072105,0.7108178561219106 -539,38.271,-75.806,56.03213163,9.025428971,0.8070193051199421 -540,42.858,-89.76,53.76259374,6.576633391,0.6698064746087723 -541,43.186,-95.762,49.43314627,6.883232479,0.686528160836268 -542,44.801,-74.672,61.89315377,8.919900273,0.8016976697602869 diff --git a/examples/28_iron_map/iron_map.yaml b/examples/28_iron_map/iron_map.yaml deleted file mode 100644 index 1a90e1342..000000000 --- a/examples/28_iron_map/iron_map.yaml +++ /dev/null @@ -1,6 +0,0 @@ -name: H2Integrate_config -system_summary: This reference hybrid plant is located in Minnesota and contains wind, solar, and battery storage technologies. - The system is designed to produce hydrogen using an electrolyzer and also produce steel using a grid-connected plant. -driver_config: driver_config.yaml -technology_config: tech_config.yaml -plant_config: plant_config.yaml diff --git a/examples/28_iron_map/tech_config.yaml b/examples/28_iron_map/tech_config.yaml deleted file mode 100644 index 18f331b4e..000000000 --- a/examples/28_iron_map/tech_config.yaml +++ /dev/null @@ -1,24 +0,0 @@ -name: technology_config -description: This hybrid plant produces iron -technologies: - iron: - performance_model: - model: IronComponent - model_inputs: - cost_parameters: - # h2_kgpy: 68583942.6460997 # 1000000 # 68583942.6460997 - LCOE: 58.02 #$/MWh - LCOH: 7.10 #$/kg - ROM_iron_site_name: Hibbing #"Hibbing", "Northshore", "United", "Minorca" or "Tilden" - iron_ore_product_selection: drg_taconite_pellets #"drg_taconite_pellets" or "std_taconite_pellets" - reduced_iron_product_selection: h2_dri #"h2_dri" or "ng_dri" - structural_iron_product_selection: eaf_steel #"eaf_steel" or "none" - cost_year: 2020 - reduced_iron_site_latitude: 41.717 - reduced_iron_site_longitude: -88.398 - transport_cost_included: true - ng_mod: false - ng_price: 4 - capex_mod: false - capex_mod_pct: 0 -# baseline 843.37604007 diff --git a/examples/29_wind_ard/h2i_inputs/plant_config.yaml b/examples/29_wind_ard/h2i_inputs/plant_config.yaml index b1caecada..be5903a62 100644 --- a/examples/29_wind_ard/h2i_inputs/plant_config.yaml +++ b/examples/29_wind_ard/h2i_inputs/plant_config.yaml @@ -16,9 +16,13 @@ plant: plant_life: 30 technology_interconnections: - [wind, combiner, electricity, cable] - # source_tech, dest_tech, transport_item, transport_type = connection - [solar, combiner, electricity, cable] - [combiner, battery, electricity, cable] + # combine the electricity from wind, solar, and the battery + - [combiner, elec_combiner, electricity, cable] + - [battery, elec_combiner, electricity, cable] + # subtract electricity production from electricity demand + - [elec_combiner, electrical_load_demand, electricity, cable] # etc resource_to_tech_connections: # The wind resource is handled strictly by Ard in this example, @@ -34,7 +38,7 @@ finance_parameters: inflation_rate: 0.0 # 0 for real analysis discount_rate: 0.09 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 2.62 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx + property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx total_income_tax_rate: 0.257 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.07375 # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/ @@ -53,7 +57,7 @@ finance_parameters: finance_subgroups: dispatched_electricity: commodity: electricity - commodity_stream: battery + commodity_stream: electrical_load_demand technologies: [wind, solar, battery] produced_electricity: commodity: electricity diff --git a/examples/29_wind_ard/h2i_inputs/tech_config.yaml b/examples/29_wind_ard/h2i_inputs/tech_config.yaml index 55fff7d6f..dbdc1cacd 100644 --- a/examples/29_wind_ard/h2i_inputs/tech_config.yaml +++ b/examples/29_wind_ard/h2i_inputs/tech_config.yaml @@ -42,18 +42,18 @@ technologies: rated_power: 5000000.0 # W num_blades: 3 rated_thrust_N: 823484.4216152605 # from NREL 5MW definition - gust_velocity_m_per_s: 70.0 # from https://github.com/WISDEM/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml + gust_velocity_m_per_s: 70.0 # from https://github.com/NLRWindSystems/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml blade_surface_area: 69.7974979 tower_mass: 620.4407337521 nacelle_mass: 101.98582836439 hub_mass: 8.38407517646 blade_mass: 14.56341339641 foundation_height: 0.0 - commissioning_cost_kW: 44.0 # from https://github.com/WISDEM/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml - decommissioning_cost_kW: 58.0 # from https://github.com/WISDEM/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml + commissioning_cost_kW: 44.0 # from https://github.com/NLRWindSystems/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml + decommissioning_cost_kW: 58.0 # from https://github.com/NLRWindSystems/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml trench_len_to_substation_km: 50.0 distance_to_interconnect_mi: 4.97096954 - interconnect_voltage_kV: 130.0 # from https://github.com/WISDEM/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml + interconnect_voltage_kV: 130.0 # from https://github.com/NLRWindSystems/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml tcc_per_kW: 1300.00 # (USD/kW) opex_per_kW: 44.00 # (USD/kWh) analysis_options: null @@ -96,7 +96,7 @@ technologies: commodity_rate_units: kW battery: performance_model: - model: SimpleGenericStorage + model: StoragePerformanceModel cost_model: model: ATBBatteryCostModel control_strategy: @@ -107,10 +107,9 @@ technologies: commodity_rate_units: kW max_charge_rate: 95000.0 # kW/time step max_capacity: 190000.0 # kWh - control_parameters: - max_charge_fraction: 1.0 # fraction (0-1) - min_charge_fraction: 0.1 # fraction (0-1) - init_charge_fraction: 0.25 # fraction (0-1) + max_soc_fraction: 1.0 # fraction (0-1) + min_soc_fraction: 0.1 # fraction (0-1) + init_soc_fraction: 0.25 # fraction (0-1) max_discharge_rate: 95000.0 # kW/time step charge_efficiency: 0.985 # fraction (0-1) discharge_efficiency: 0.985 # fraction (0-1) @@ -120,3 +119,18 @@ technologies: energy_capex: 72.54 # $/kWh power_capex: 18.72 # $/kW opex_fraction: 0.02 # percent of capex + elec_combiner: + performance_model: + model: GenericCombinerPerformanceModel + model_inputs: + performance_parameters: + commodity: electricity + commodity_rate_units: kW + electrical_load_demand: + performance_model: + model: GenericDemandComponent + model_inputs: + performance_parameters: + commodity: electricity + commodity_rate_units: kW + demand_profile: 30000 # 30 MW diff --git a/examples/30_pyomo_optimized_dispatch/plant_config.yaml b/examples/30_pyomo_optimized_dispatch/plant_config.yaml index ad929fbfb..ea111f4f8 100644 --- a/examples/30_pyomo_optimized_dispatch/plant_config.yaml +++ b/examples/30_pyomo_optimized_dispatch/plant_config.yaml @@ -17,6 +17,11 @@ plant: # this will naturally grow as we mature the interconnected tech technology_interconnections: - [wind, battery, electricity, cable] + # combine the battery output with the wind generation + - [battery, elec_combiner, electricity, cable] + - [wind, elec_combiner, electricity, cable] + # subtract the generation from the demand + - [elec_combiner, electrical_load_demand, electricity, cable] # array of arrays containing left-to-right technology, technology doing the dispatching # in this case, battery is connected to battery because there are controls rules for # the battery and battery is controlling the dispatching @@ -38,7 +43,7 @@ finance_parameters: inflation_rate: 0.0 # 0 for nominal analysis discount_rate: 0.09 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 2.62 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.03 # p-tax https://www.house.mn.gov/hrd/issinfo/clsrates.aspx # insurance percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf + property_tax_and_insurance: 0.03 # p-tax https://www.house.mn.gov/hrd/issinfo/clsrates.aspx # insurance percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf total_income_tax_rate: 0.257 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.07375 # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/ diff --git a/examples/30_pyomo_optimized_dispatch/tech_config.yaml b/examples/30_pyomo_optimized_dispatch/tech_config.yaml index 947562759..abe1b397a 100644 --- a/examples/30_pyomo_optimized_dispatch/tech_config.yaml +++ b/examples/30_pyomo_optimized_dispatch/tech_config.yaml @@ -6,8 +6,6 @@ technologies: model: PYSAMWindPlantPerformanceModel cost_model: model: ATBWindPlantCostModel - dispatch_rule_set: - model: PyomoDispatchGenericConverter resource: type: pysam_wind wind_speed: 9. @@ -37,39 +35,51 @@ technologies: commodity: electricity commodity_rate_units: kW battery: - dispatch_rule_set: - model: PyomoRuleStorageBaseclass control_strategy: - model: OptimizedDispatchController + model: OptimizedDispatchStorageController performance_model: model: PySAMBatteryPerformanceModel cost_model: model: ATBBatteryCostModel model_inputs: shared_parameters: - commodity: electricity max_charge_rate: 100000 max_capacity: 400000 - init_charge_fraction: 0.5 # Initial SOC for the storage - max_charge_fraction: 0.9 # Maximum SOC allowable for the storage technology - min_charge_fraction: 0.1 # Minimum SOC allowable for the storage technology - system_commodity_interface_limit: 1e12 - charge_efficiency: 0.95 # Charge efficiency of the storage technology - discharge_efficiency: 0.95 # Discharge efficiency of the storage technology + init_soc_fraction: 0.5 # Initial SOC for the storage + max_soc_fraction: 0.9 # Maximum SOC allowable for the storage technology + min_soc_fraction: 0.1 # Minimum SOC allowable for the storage technology commodity_rate_units: kW - # rated_demand: 100000 # in kW performance_parameters: - system_model_source: pysam chemistry: LFPGraphite + demand_profile: 100000 # 100 MW cost_parameters: cost_year: 2022 energy_capex: 310 # $/kWh from 2024 ATB year 2025 power_capex: 311 # $/kW from 2024 ATB year 2025 opex_fraction: 0.25 # 0.25% of capex per year from 2024 ATB control_parameters: + commodity: electricity + charge_efficiency: 0.95 # Charge efficiency of the storage technology + discharge_efficiency: 0.95 # Discharge efficiency of the storage technology cost_per_charge: 0.03 # in $/kW, cost to charge the storage (note that charging is incentivized) cost_per_discharge: 0.05 # in $/kW, cost to discharge the storage commodity_met_value: 0.1 # in $/kW, penalty for not meeting the desired load demand cost_per_production: 0.0 # in $/kW, cost to use the incoming produced commodity (i.e. electricity from wind) time_weighting_factor: 0.995 # This parameter discounts each subsequent time step incrementally in the future in the horizon window by this amount n_control_window: 24 # in timesteps (currently hours), The length of time that the control is applied to in the rolling window optimization + system_commodity_interface_limit: 1e12 + elec_combiner: + performance_model: + model: GenericCombinerPerformanceModel + model_inputs: + performance_parameters: + commodity: electricity + commodity_rate_units: kW + electrical_load_demand: + performance_model: + model: GenericDemandComponent + model_inputs: + performance_parameters: + commodity: electricity + commodity_rate_units: kW + demand_profile: 100000 # 100 MW diff --git a/examples/31_tidal/driver_config.yaml b/examples/31_tidal/driver_config.yaml new file mode 100644 index 000000000..7e4d60c5b --- /dev/null +++ b/examples/31_tidal/driver_config.yaml @@ -0,0 +1,4 @@ +name: driver_config +description: This analysis runs a tidal power plant +general: + folder_output: outputs diff --git a/examples/28_iron_map/plant_config.yaml b/examples/31_tidal/plant_config.yaml similarity index 61% rename from examples/28_iron_map/plant_config.yaml rename to examples/31_tidal/plant_config.yaml index 589985954..e6b501d2a 100644 --- a/examples/28_iron_map/plant_config.yaml +++ b/examples/31_tidal/plant_config.yaml @@ -1,18 +1,32 @@ name: plant_config -description: This plant is located in MN, USA... +description: This plant is located in Washington sites: site: - latitude: 41.717 - longitude: -88.398 + latitude: 44.6899 + longitude: -124.1346 + resources: + tidal_resource: + resource_model: TidalResource + resource_parameters: + resource_dir: resource_files/tidal/ + resource_filename: Tidal_resource_timeseries.csv # array of arrays containing left-to-right technology # interconnections; can support bidirectional connections # with the reverse definition. -# this will naturally grow as we mature the interconnected tech +# in this example the tidal plant is behind-the-meter +# and not connected to additional technologies. +# hence the empty array. technology_interconnections: [] +# resource interconnections +# array of arrays containing left-to-right resource interconnections; +resource_to_tech_connections: + # connect the tidal resource to the tidal technology + - [site.tidal_resource, tidal, tidal_velocity] plant: plant_life: 30 finance_parameters: finance_groups: + commodity: electricity finance_model: ProFastLCO model_inputs: params: @@ -21,8 +35,8 @@ finance_parameters: inflation_rate: 0.0 # 0 for nominal analysis discount_rate: 0.09 # nominal return based on 2024 ATB baseline workbook for land-based wind debt_equity_ratio: 2.62 # 2024 ATB uses 72.4% debt for land-based wind - property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nrel.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx - total_income_tax_rate: 0.257 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) + property_tax_and_insurance: 0.03 # percent of CAPEX estimated based on https://www.nlr.gov/docs/fy25osti/91775.pdf https://www.house.mn.gov/hrd/issinfo/clsrates.aspx + total_income_tax_rate: 0.308 # 0.257 tax rate in 2024 atb baseline workbook, value here is based on federal (21%) and state in MN (9.8) capital_gains_tax_rate: 0.15 # H2FAST default sales_tax_rate: 0.07375 # total state and local sales tax in St. Louis County https://taxmaps.state.mn.us/salestax/ debt_interest_rate: 0.07 # based on 2024 ATB nominal interest rate for land-based wind @@ -31,14 +45,9 @@ finance_parameters: cash_onhand_months: 1 # H2FAST default admin_expense: 0.00 # percent of sales H2FAST default capital_items: - depr_type: MACRS # can be "MACRS" or "Straight line" + depr_type: MACRS # can be "MACRS" or "Straight line" depr_period: 5 # 5 years - for clean energy facilities as specified by the IRS MACRS schedule https://www.irs.gov/publications/p946#en_US_2020_publink1000107507 refurb: [0.] cost_adjustment_parameters: - cost_year_adjustment_inflation: 0.025 + cost_year_adjustment_inflation: 0.025 # used to adjust modeled costs to target_dollar_year target_dollar_year: 2022 - finance_subgroups: - iron: - commodity: iron - commodity_stream: iron - technologies: [iron] diff --git a/examples/31_tidal/run_tidal.py b/examples/31_tidal/run_tidal.py new file mode 100644 index 000000000..516e05ac4 --- /dev/null +++ b/examples/31_tidal/run_tidal.py @@ -0,0 +1,11 @@ +from h2integrate.core.h2integrate_model import H2IntegrateModel + + +# Create an H2I model +h2i = H2IntegrateModel("tidal.yaml") + +# Run the model +h2i.run() + +# Post-process the results +h2i.post_process() diff --git a/examples/31_tidal/tech_config.yaml b/examples/31_tidal/tech_config.yaml new file mode 100644 index 000000000..01f59e20f --- /dev/null +++ b/examples/31_tidal/tech_config.yaml @@ -0,0 +1,63 @@ +name: technology_config +description: This plant produces electricity from tidal +technologies: + tidal: + performance_model: + model: PySAMTidalPerformanceModel + cost_model: + model: PySAMMarineCostModel + model_inputs: + shared_parameters: + device_rating_kw: 1115.0 + num_devices: 20 + performance_parameters: + tidal_power_curve: # [tide_velocity (m/s), power (kW)] + - [0.000000, 0.000000] + - [0.100000, 0.000000] + - [0.200000, 0.000000] + - [0.300000, 0.000000] + - [0.400000, 0.000000] + - [0.500000, 0.000000] + - [0.600000, 10.421100] + - [0.700000, 20.842300] + - [0.800000, 39.968900] + - [0.900000, 59.095600] + - [1.000000, 89.201600] + - [1.100000, 119.308000] + - [1.200000, 160.886000] + - [1.300000, 202.464000] + - [1.400000, 259.292000] + - [1.500000, 316.120000] + - [1.600000, 392.673000] + - [1.700000, 469.226000] + - [1.800000, 570.306000] + - [1.900000, 671.386000] + - [2.000000, 802.908000] + - [2.100000, 934.430000] + - [2.200000, 1024.710000] + - [2.300000, 1115.000000] + - [2.400000, 1115.000000] + - [2.500000, 1115.000000] + - [2.600000, 1115.000000] + - [2.700000, 1115.000000] + - [2.800000, 1115.000000] + - [2.900000, 1115.000000] + - [3.000000, 1115.000000] + - [3.100000, 1115.000000] + - [3.200000, 1085.370000] + - [3.300000, 1055.730000] + pysam_options: + MHKTidal: + loss_array_spacing: 0.0 + loss_resource_overprediction: 0.0 + loss_transmission: 0.0 + loss_downtime: 0.0 + loss_additional: 0.0 + cost_parameters: + reference_model_number: 1 # 1 is for a Tidal Current Turbine + water_depth: 100 + distance_to_shore: 80 + number_rows: 2 + device_spacing: 600 + row_spacing: 600 + cable_system_overbuild: 20 diff --git a/examples/31_tidal/tidal.yaml b/examples/31_tidal/tidal.yaml new file mode 100644 index 000000000..bed612037 --- /dev/null +++ b/examples/31_tidal/tidal.yaml @@ -0,0 +1,5 @@ +name: H2Integrate_config +system_summary: This reference hybrid plant contains a tidal power plant +driver_config: driver_config.yaml +technology_config: tech_config.yaml +plant_config: plant_config.yaml diff --git a/examples/32_multivariable_streams/32_multivariable_streams.yaml b/examples/32_multivariable_streams/32_multivariable_streams.yaml new file mode 100644 index 000000000..6dc90a6ed --- /dev/null +++ b/examples/32_multivariable_streams/32_multivariable_streams.yaml @@ -0,0 +1,11 @@ +name: H2Integrate_config +system_summary: > + This example demonstrates the multivariable streams feature in H2Integrate. + Multivariable streams allow users to connect multiple related variables with + a single connection specification. In this example, we connect a wellhead_gas + stream (containing wellhead_gas_mixture:mass_flow, wellhead_gas_mixture:hydrogen_mass_fraction, + wellhead_gas_mixture:oxygen_mass_fraction, wellhead_gas_mixture:temperature, + and wellhead_gas_mixture:pressure) between a dummy gas producer and consumer. +driver_config: driver_config.yaml +technology_config: tech_config.yaml +plant_config: plant_config.yaml diff --git a/examples/32_multivariable_streams/driver_config.yaml b/examples/32_multivariable_streams/driver_config.yaml new file mode 100644 index 000000000..0991d8889 --- /dev/null +++ b/examples/32_multivariable_streams/driver_config.yaml @@ -0,0 +1,5 @@ +name: driver_config +description: Driver configuration for multivariable streams example +general: + folder_output: outputs + create_om_reports: false diff --git a/examples/32_multivariable_streams/plant_config.yaml b/examples/32_multivariable_streams/plant_config.yaml new file mode 100644 index 000000000..806449d7c --- /dev/null +++ b/examples/32_multivariable_streams/plant_config.yaml @@ -0,0 +1,12 @@ +name: plant_config +description: Demonstrates multivariable streams with a gas combiner +plant: + plant_life: 30 + simulation: + n_timesteps: 8760 + dt: 3600 + timezone: -6 +technology_interconnections: + - [gas_producer_1, gas_combiner, wellhead_gas_mixture] + - [gas_producer_2, gas_combiner, wellhead_gas_mixture] + - [gas_combiner, gas_consumer, wellhead_gas_mixture] diff --git a/examples/32_multivariable_streams/run_multivariable_streams.py b/examples/32_multivariable_streams/run_multivariable_streams.py new file mode 100644 index 000000000..e26ad8585 --- /dev/null +++ b/examples/32_multivariable_streams/run_multivariable_streams.py @@ -0,0 +1,149 @@ +""" +Example 32: Multivariable Streams with Gas Combiner + +This example demonstrates: +1. Multivariable streams - connecting multiple related variables with a single connection +2. Gas stream combiner - combining multiple gas streams with mass-weighted averaging + +Two gas producers with different properties feed into a combiner, which outputs +a single combined stream to a consumer. + +The wellhead_gas_mixture stream includes: +- wellhead_gas_mixture:mass_flow (kg/h): Total mass flow rate +- wellhead_gas_mixture:hydrogen_mass_fraction: Mass fraction of hydrogen +- wellhead_gas_mixture:oxygen_mass_fraction: Mass fraction of oxygen +- wellhead_gas_mixture:temperature (K): Temperature +- wellhead_gas_mixture:pressure (bar): Pressure +""" + +import numpy as np +import matplotlib.pyplot as plt + +from h2integrate.core.h2integrate_model import H2IntegrateModel + + +# Create and setup the H2Integrate model +model = H2IntegrateModel("32_multivariable_streams.yaml") + +model.setup() + +model.run() + + +# Get outputs from gas producers +print("\nGas Producer 1 Outputs:") +flow1 = model.prob.get_val("gas_producer_1.wellhead_gas_mixture:mass_flow_out", units="kg/h") +temp1 = model.prob.get_val("gas_producer_1.wellhead_gas_mixture:temperature_out", units="K") +pres1 = model.prob.get_val("gas_producer_1.wellhead_gas_mixture:pressure_out", units="bar") +print(f" Flow: mean={flow1.mean():.2f} kg/h") +print(f" Temperature: mean={temp1.mean():.1f} K") +print(f" Pressure: mean={pres1.mean():.2f} bar") + +print("\nGas Producer 2 Outputs:") +flow2 = model.prob.get_val("gas_producer_2.wellhead_gas_mixture:mass_flow_out", units="kg/h") +temp2 = model.prob.get_val("gas_producer_2.wellhead_gas_mixture:temperature_out", units="K") +pres2 = model.prob.get_val("gas_producer_2.wellhead_gas_mixture:pressure_out", units="bar") +print(f" Flow: mean={flow2.mean():.2f} kg/h") +print(f" Temperature: mean={temp2.mean():.1f} K") +print(f" Pressure: mean={pres2.mean():.2f} bar") + +# Get outputs from combiner +print("\nGas Combiner Outputs (mass-weighted average):") +flow_out = model.prob.get_val("gas_combiner.wellhead_gas_mixture:mass_flow_out", units="kg/h") +temp_out = model.prob.get_val("gas_combiner.wellhead_gas_mixture:temperature_out", units="K") +pres_out = model.prob.get_val("gas_combiner.wellhead_gas_mixture:pressure_out", units="bar") +h2_out = model.prob.get_val("gas_combiner.wellhead_gas_mixture:hydrogen_mass_fraction_out") +print(f" Total Flow: mean={flow_out.mean():.2f} kg/h (sum of inputs)") +print(f" Temperature: mean={temp_out.mean():.1f} K (weighted avg)") +print(f" Pressure: mean={pres_out.mean():.2f} bar (weighted avg)") +print(f" H2 Fraction: mean={h2_out.mean():.3f} (weighted avg)") + +# Get derived outputs from gas_consumer +print("\nGas Consumer Derived Outputs:") +h2_mass_flow = model.prob.get_val("gas_consumer.hydrogen_out", units="kg/h") +total_consumed = model.prob.get_val("gas_consumer.total_gas_consumed", units="kg") +avg_temp = model.prob.get_val("gas_consumer.avg_temperature", units="K") +avg_pressure = model.prob.get_val("gas_consumer.avg_pressure", units="bar") +print(f" H2 Mass Flow: mean={h2_mass_flow.mean():.2f} kg/h") +print(f" Total Gas Consumed: {total_consumed[0]:,.0f} kg") +print(f" Avg Temperature: {avg_temp[0]:.1f} K") +print(f" Avg Pressure: {avg_pressure[0]:.2f} bar") + +# Time axis in hours +n_timesteps = len(flow1) +time_hours = np.arange(n_timesteps) + +# Create figure with 3 subplots sharing x-axis +fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(12, 10), sharex=True) +fig.suptitle("Gas Stream Time History", fontsize=14, fontweight="bold") + +# Colors for the two streams +color1 = "#1f77b4" # Blue for stream 1 +color2 = "#ff7f0e" # Orange for stream 2 +color_total = "#2ca02c" # Green for total/combined + +# ------------------------------------------------------------ +# Plot 1: Mass Flow Rates (stacked area) +# ------------------------------------------------------------ +# Stack the flows - stream 1 on bottom, stream 2 on top +ax1.fill_between(time_hours, 0, flow1, color=color1, alpha=0.7, label="Stream 1") +ax1.fill_between(time_hours, flow1, flow1 + flow2, color=color2, alpha=0.7, label="Stream 2") +ax1.plot(time_hours, flow_out, color=color_total, linewidth=2, label="Total (Combined)") + +# Add in-area labels at the center of each region +mid_time = n_timesteps // 2 +ax1.text( + mid_time, + flow1.mean() / 2, + "Producer 1", + ha="center", + va="center", + fontsize=10, + fontweight="bold", + color="white", +) +ax1.text( + mid_time, + flow1.mean() + flow2.mean() / 2, + "Producer 2", + ha="center", + va="center", + fontsize=10, + fontweight="bold", + color="white", +) + +ax1.set_ylabel("Mass Flow Rate (kg/h)") +ax1.set_title("Mass Flow Rates") +ax1.legend(loc="upper right") +ax1.grid(True, alpha=0.3) +ax1.set_ylim(bottom=0) + +# ------------------------------------------------------------ +# Plot 2: Pressure +# ------------------------------------------------------------ +ax2.plot(time_hours, pres1, color=color1, linewidth=1.5, label="Stream 1", linestyle="--") +ax2.plot(time_hours, pres2, color=color2, linewidth=1.5, label="Stream 2", linestyle="--") +ax2.plot(time_hours, pres_out, color=color_total, linewidth=2, label="Combined") + +ax2.set_ylabel("Pressure (bar)") +ax2.set_title("Pressure") +ax2.legend(loc="upper right") +ax2.grid(True, alpha=0.3) + +# ------------------------------------------------------------ +# Plot 3: Temperature +# ------------------------------------------------------------ +ax3.plot(time_hours, temp1, color=color1, linewidth=1.5, label="Stream 1", linestyle="--") +ax3.plot(time_hours, temp2, color=color2, linewidth=1.5, label="Stream 2", linestyle="--") +ax3.plot(time_hours, temp_out, color=color_total, linewidth=2, label="Combined") + +ax3.set_xlabel("Time (hours)") +ax3.set_ylabel("Temperature (K)") +ax3.set_title("Temperature") +ax3.legend(loc="upper right") +ax3.grid(True, alpha=0.3) + +# Adjust layout and save +plt.tight_layout() +plt.show() diff --git a/examples/32_multivariable_streams/tech_config.yaml b/examples/32_multivariable_streams/tech_config.yaml new file mode 100644 index 000000000..ce5f5db7c --- /dev/null +++ b/examples/32_multivariable_streams/tech_config.yaml @@ -0,0 +1,60 @@ +name: technology_config +description: > + Technology configuration for multivariable streams example. + Demonstrates two gas producers feeding into a combiner, which then + feeds a consumer. The combiner uses mass-weighted averaging. +technologies: + gas_producer_1: + performance_model: + model: SimpleGasProducerPerformance + cost_model: + model: SimpleGasProducerCost + model_inputs: + performance_parameters: + base_flow_rate: 150.0 # kg/hr + base_temperature: 310.0 # K + base_pressure: 12.0 # bar + flow_variation: 45.0 # ±45 kg/hr variation + temp_variation: 15.0 # ±15 K variation + pressure_variation: 2.0 # ±2 bar variation + random_seed: 42 # for reproducibility + cost_parameters: + capex: 1000000.0 # $1M + opex: 50000.0 # $50k/year + cost_year: 2024 + gas_producer_2: + performance_model: + model: SimpleGasProducerPerformance + cost_model: + model: SimpleGasProducerCost + model_inputs: + performance_parameters: + base_flow_rate: 100.0 # kg/hr - different flow rate + base_temperature: 350.0 # K - hotter stream + base_pressure: 8.0 # bar - lower pressure + flow_variation: 25.0 # ±25 kg/hr variation + temp_variation: 20.0 # ±20 K variation + pressure_variation: 1.5 # ±1.5 bar variation + random_seed: 123 # different seed for different random pattern + cost_parameters: + capex: 800000.0 # $800k + opex: 40000.0 # $40k/year + cost_year: 2024 + gas_combiner: + performance_model: + model: GasStreamCombinerPerformanceModel + model_inputs: + performance_parameters: + commodity: wellhead_gas_mixture + in_streams: 2 + gas_consumer: + performance_model: + model: SimpleGasConsumerPerformance + cost_model: + model: SimpleGasConsumerCost + model_inputs: + performance_parameters: {} + cost_parameters: + capex: 2000000.0 # $2M + opex: 100000.0 # $100k/year + cost_year: 2024 diff --git a/examples/test/conftest.py b/examples/test/conftest.py index 8d49ffb3d..fa8f250cf 100644 --- a/examples/test/conftest.py +++ b/examples/test/conftest.py @@ -1 +1,5 @@ -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) diff --git a/examples/test/test_all_examples.py b/examples/test/test_all_examples.py index efe524ee6..e5a9916e4 100644 --- a/examples/test/test_all_examples.py +++ b/examples/test/test_all_examples.py @@ -1,5 +1,4 @@ import os -import shutil import importlib from pathlib import Path @@ -8,7 +7,7 @@ import pytest import openmdao.api as om -from h2integrate import ROOT_DIR, EXAMPLE_DIR +from h2integrate import ROOT_DIR from h2integrate.core.file_utils import load_yaml from h2integrate.core.h2integrate_model import H2IntegrateModel @@ -16,18 +15,6 @@ ROOT = Path(__file__).parents[1] -@pytest.fixture(scope="function") -def temp_copy_of_example(temp_dir, example_folder, resource_example_folder): - original = EXAMPLE_DIR / example_folder - shutil.copytree(original, temp_dir / example_folder, dirs_exist_ok=True) - if resource_example_folder is not None: - secondary = EXAMPLE_DIR / resource_example_folder - shutil.copytree(secondary, temp_dir / resource_example_folder, dirs_exist_ok=True) - os.chdir(temp_dir / example_folder) - yield temp_dir / example_folder - os.chdir(Path(__file__).parent) - - # docs fencepost start: DO NOT REMOVE @pytest.mark.integration @pytest.mark.parametrize("example_folder,resource_example_folder", [("01_onshore_steel_mn", None)]) @@ -88,7 +75,7 @@ def test_steel_example(subtests, temp_copy_of_example): with subtests.test("Check H2 Storage capacity"): assert ( - pytest.approx(model.prob.get_val("h2_storage.max_capacity", units="kg"), rel=1e-3) + pytest.approx(model.prob.get_val("h2_storage.storage_capacity", units="kg"), rel=1e-3) == 2559669.7759292 ) @@ -388,6 +375,13 @@ def test_ammonia_synloop_example(subtests, temp_copy_of_example): ) == 1.1018637096646757 ) + with subtests.test("Check LCON"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_n2.LCON", units="USD/t")[0], rel=1e-6 + ) + == 5.03140888 + ) @pytest.mark.integration @@ -751,6 +745,106 @@ def test_hybrid_energy_plant_example(subtests, temp_copy_of_example): assert model.prob.get_val("finance_subgroup_default.LCOE", units="USD/(MW*h)")[0] < 83.2123 +@pytest.mark.integration +@pytest.mark.parametrize( + "example_folder,resource_example_folder", [("13_dispatch_for_electrolyzer", None)] +) +def test_electrolyzer_demand(subtests, temp_copy_of_example): + from h2integrate.core.inputs.validation import load_tech_yaml, load_plant_yaml, load_driver_yaml + + example_folder = temp_copy_of_example + + tech_config = load_tech_yaml(example_folder / "tech_config.yaml") + plant_config = load_plant_yaml(example_folder / "plant_config.yaml") + driver_config = load_driver_yaml(example_folder / "driver_config.yaml") + + # modify all the output folders to be full filepaths + driver_config["general"]["folder_output"] = str(Path(example_folder / "outputs").absolute()) + tech_config["technologies"]["distributed_wind_plant"]["model_inputs"]["performance_parameters"][ + "cache_dir" + ] = example_folder / "cache" + + input_config = { + "plant_config": plant_config, + "technology_config": tech_config, + "driver_config": driver_config, + } + + h2i = H2IntegrateModel(input_config) + + h2i.setup() + + electrolyzer_capacity_MW = 60 + + # Set the battery demand as 10% of the electrolyzer capacity + h2i.prob.set_val("battery.electricity_demand", 0.1 * electrolyzer_capacity_MW, units="MW") + h2i.prob.set_val("elec_load_demand.electricity_demand", electrolyzer_capacity_MW, units="MW") + + h2i.run() + + lcoe_gen = h2i.prob.get_val("finance_subgroup_generated_electricity.LCOE", units="USD/(MW*h)")[ + 0 + ] + lcoe_sys = h2i.prob.get_val("finance_subgroup_electrical_system.LCOE", units="USD/(MW*h)")[0] + lcoe_load = h2i.prob.get_val("finance_subgroup_electrical_load.LCOE", units="USD/(MW*h)")[0] + lcoh = h2i.prob.get_val("finance_subgroup_hydrogen.LCOH", units="USD/kg")[0] + + with subtests.test("LCOE of electricity generated"): + assert pytest.approx(217.53810477, rel=1e-6) == lcoe_gen + + with subtests.test("LCOE of electrical load (battery for min power)"): + assert pytest.approx(236.15820250, rel=1e-6) == lcoe_load + + with subtests.test("LCOE of electrical system (battery for min power)"): + assert pytest.approx(235.43108263, rel=1e-6) == lcoe_sys + + with subtests.test("LCOH (battery for min power)"): + assert pytest.approx(16.02862959, rel=1e-3) == lcoh + + with subtests.test("Electrolyzer capacity factor (Year 0) (battery for min power)"): + elec_cf_yr0 = h2i.prob.get_val("electrolyzer.capacity_factor", units="percent")[0] + assert pytest.approx(25.43832863, rel=1e-3) == elec_cf_yr0 + + with subtests.test("Electrical load capacity factor (battery for min power)"): + load_cf = h2i.prob.get_val("elec_load_demand.capacity_factor", units="percent")[0] + assert pytest.approx(24.29709189, rel=1e-6) == load_cf + + with subtests.test("Electricity to electrolyzer (battery for min power)"): + electricity_to_electrolyzer = h2i.prob.get_val("electrolyzer.electricity_in", "MW").sum() + assert pytest.approx(127705.51498100, rel=1e-6) == electricity_to_electrolyzer + # Re-run where we set the battery demand equal to the electrolyzer capacity + + h2i.prob.set_val("battery.electricity_demand", electrolyzer_capacity_MW, units="MW") + h2i.prob.set_val("elec_load_demand.electricity_demand", electrolyzer_capacity_MW, units="MW") + + h2i.run() + + lcoe_sys = h2i.prob.get_val("finance_subgroup_electrical_system.LCOE", units="USD/(MW*h)")[0] + lcoe_load = h2i.prob.get_val("finance_subgroup_electrical_load.LCOE", units="USD/(MW*h)")[0] + lcoh = h2i.prob.get_val("finance_subgroup_hydrogen.LCOH", units="USD/kg")[0] + + with subtests.test("LCOE of electrical load (battery for full power)"): + assert pytest.approx(235.46701455, rel=1e-6) == lcoe_load + + with subtests.test("LCOE of electrical system (battery for full power)"): + assert pytest.approx(235.40978870, rel=1e-6) == lcoe_sys + + with subtests.test("LCOH (battery for full power)"): + assert pytest.approx(17.21768237, rel=1e-6) == lcoh + + with subtests.test("Electrolyzer capacity factor (Year 0) (battery for full power)"): + elec_cf_yr0 = h2i.prob.get_val("electrolyzer.capacity_factor", units="percent")[0] + assert pytest.approx(24.96971302, rel=1e-6) == elec_cf_yr0 + + with subtests.test("Electrical load capacity factor (battery for full power)"): + load_cf = h2i.prob.get_val("elec_load_demand.capacity_factor", units="percent")[0] + assert pytest.approx(24.36841338, rel=1e-6) == load_cf + + with subtests.test("Electricity to electrolyzer (battery for full power)"): + electricity_to_electrolyzer = h2i.prob.get_val("electrolyzer.electricity_in", "MW").sum() + assert pytest.approx(128080.38070512, rel=1e-6) == electricity_to_electrolyzer + + @pytest.mark.integration @pytest.mark.parametrize( "example_folder,resource_example_folder", [("14_wind_hydrogen_dispatch", None)] @@ -827,6 +921,14 @@ def test_hydrogen_dispatch_example(subtests, temp_copy_of_example): ) == 7.564000289456695 ) + with subtests.test("Check LCOO"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_oxygen.LCOO", units="USD/kg")[0], + rel=1e-5, + ) + == 0.666523050 + ) @pytest.mark.integration @@ -850,8 +952,6 @@ def test_wind_wave_oae_example(subtests, temp_copy_of_example): model.post_process() # Subtests for checking specific values - # Note: These are placeholder values. Update with actual values after running the test - # when MCM package is properly installed and configured with subtests.test("Check LCOC"): assert ( pytest.approx( @@ -887,8 +987,6 @@ def test_wind_wave_oae_example_with_finance(subtests, temp_copy_of_example): model.post_process() # Subtests for checking specific values - # Note: These are placeholder values. Update with actual values after running the test - # when MCM package is properly installed and configured with subtests.test("Check LCOE"): assert ( pytest.approx( @@ -901,7 +999,7 @@ def test_wind_wave_oae_example_with_finance(subtests, temp_copy_of_example): with subtests.test("Check Carbon Credit"): assert ( pytest.approx(model.prob.get_val("oae.carbon_credit_value", units="USD/t")[0], rel=1e-3) - == 574.37466 + == 1026.4684117 ) @@ -921,9 +1019,11 @@ def test_natural_gas_example(subtests, temp_copy_of_example): model.post_process() solar_aep = sum(model.prob.get_val("solar.electricity_out", units="kW")) - solar_bat_out_total = sum(model.prob.get_val("battery.electricity_out", units="kW")) + solar_bat_out_total = sum( + model.prob.get_val("electrical_load_demand.electricity_out", units="kW") + ) solar_curtailed_total = sum( - model.prob.get_val("battery.electricity_unused_commodity", units="kW") + model.prob.get_val("electrical_load_demand.unused_electricity_out", units="kW") ) renewable_subgroup_total_electricity = ( @@ -954,7 +1054,9 @@ def test_natural_gas_example(subtests, temp_copy_of_example): # NOTE: battery output power is not included in any of the financials - pre_ng_missed_load = model.prob.get_val("battery.electricity_unmet_demand", units="kW") + pre_ng_missed_load = model.prob.get_val( + "electrical_load_demand.unmet_electricity_demand_out", units="kW" + ) ng_electricity_demand = model.prob.get_val("natural_gas_plant.electricity_demand", units="kW") ng_electricity_production = model.prob.get_val("natural_gas_plant.electricity_out", units="kW") bat_init_charge = 200000.0 * 0.1 # max capacity in kW and initial charge rate percentage @@ -1069,6 +1171,10 @@ def test_natural_gas_example(subtests, temp_copy_of_example): expected_opex = 4.2 * ng_consumed.sum() # price = 4.2 $/MMBtu assert pytest.approx(ng_opex, rel=1e-6) == expected_opex + with subtests.test("Check feedstock capacity factor"): + ng_cf = model.prob.get_val("ng_feedstock.capacity_factor", units="unitless").mean() + assert pytest.approx(ng_cf, rel=1e-6) == 0.5676562763739097 + @pytest.mark.integration @pytest.mark.parametrize( @@ -1231,7 +1337,7 @@ def test_pyomo_heuristic_dispatch_example(subtests, temp_copy_of_example): assert wind_electricity.sum() == pytest.approx(battery_electricity_in.sum(), rel=1e-6) with subtests.test("Check demand satisfaction"): - electricity_out = model.prob.get_val("battery.electricity_out", units="MW") + electricity_out = model.prob.get_val("electrical_load_demand.electricity_out", units="MW") # Battery output should try to meet the 50 MW constant demand # Average output should be close to demand when there's sufficient generation assert electricity_out.mean() >= 45 # MW @@ -1265,14 +1371,14 @@ def test_pyomo_heuristic_dispatch_example(subtests, temp_copy_of_example): # Subtest for electricity unused_commodity with subtests.test("Check electricity unused commodity"): electricity_unused_commodity = np.linalg.norm( - model.prob.get_val("battery.unused_electricity_out", units="MW") + model.prob.get_val("electrical_load_demand.unused_electricity_out", units="MW") ) assert electricity_unused_commodity == pytest.approx(36590.067573337095, rel=1e-6) # Subtest for unmet demand with subtests.test("Check electricity unmet demand"): electricity_unmet_demand = np.linalg.norm( - model.prob.get_val("battery.unmet_electricity_demand_out", units="MW") + model.prob.get_val("electrical_load_demand.unmet_electricity_demand_out", units="MW") ) assert electricity_unmet_demand == pytest.approx(711.1997294551337, rel=1e-6) @@ -1322,7 +1428,7 @@ def test_simple_dispatch_example(subtests, temp_copy_of_example): # Test battery storage functionality with subtests.test("Check battery SOC bounds"): - soc = model.prob.get_val("battery.electricity_soc", units="unitless") + soc = model.prob.get_val("battery.SOC", units="unitless") # SOC should stay within configured bounds (10% to 100%) assert all(soc >= 0.1) assert all(soc <= 1.0) @@ -1336,7 +1442,7 @@ def test_simple_dispatch_example(subtests, temp_copy_of_example): assert pytest.approx(wind_electricity.sum(), rel=1e-6) == battery_electricity_in.sum() with subtests.test("Check demand satisfaction"): - electricity_out = model.prob.get_val("battery.electricity_out", units="MW") + electricity_out = model.prob.get_val("electrical_load_demand.electricity_out", units="MW") # Battery output should try to meet the 5 MW constant demand # Average output should be close to demand when there's sufficient generation assert electricity_out.mean() > 4.20 # MW @@ -1377,14 +1483,14 @@ def test_simple_dispatch_example(subtests, temp_copy_of_example): # Subtest for electricity unused_commodity with subtests.test("Check electricity unused commodity"): electricity_unused_commodity = np.linalg.norm( - model.prob.get_val("battery.electricity_unused_commodity", units="kW") + model.prob.get_val("electrical_load_demand.unused_electricity_out", units="kW") ) assert pytest.approx(electricity_unused_commodity, rel=1e-6) == 412531.73840450746 # Subtest for unmet demand with subtests.test("Check electricity unmet demand"): electricity_unmet_demand = np.linalg.norm( - model.prob.get_val("battery.electricity_unmet_demand", units="kW") + model.prob.get_val("electrical_load_demand.unmet_electricity_demand_out", units="kW") ) assert pytest.approx(electricity_unmet_demand, rel=1e-6) == 165604.70758669 @@ -1417,8 +1523,10 @@ def test_simple_dispatch_example(subtests, temp_copy_of_example): * 8760 ) battery_electricity_performance = ( - model.prob.get_val("battery.rated_electricity_production", units="MW*h/year")[0] - * model.prob.get_val("battery.capacity_factor", units="unitless").mean() + model.prob.get_val( + "electrical_load_demand.rated_electricity_production", units="MW*h/year" + )[0] + * model.prob.get_val("electrical_load_demand.capacity_factor", units="unitless").mean() * 8760 ) assert ( @@ -1482,7 +1590,9 @@ def test_windard_pv_battery_dispatch_example(subtests, temp_copy_of_example): ) with subtests.test("Check demand satisfaction"): - dispatched_electricity = model.prob.get_val("battery.electricity_out", units="MW") + dispatched_electricity = model.prob.get_val( + "electrical_load_demand.electricity_out", units="MW" + ) # Demand should be met for the last part of the year assert np.allclose( dispatched_electricity[8700:], @@ -1520,16 +1630,15 @@ def test_windard_pv_battery_dispatch_example(subtests, temp_copy_of_example): # Subtest for electricity curtailed with subtests.test("Check electricity curtailed"): electricity_curtailed = model.prob.get_val( - "battery.electricity_unused_commodity", units="MW" + "electrical_load_demand.unused_electricity_out", units="MW" ).sum() - # import pdb; pdb.set_trace() assert electricity_curtailed == pytest.approx(20344.97639127703, rel=1e-6) # Subtest for missed load with subtests.test("Check electricity missed load"): electricity_missed_load = np.linalg.norm( - model.prob.get_val("battery.electricity_unmet_demand", units="MW") + model.prob.get_val("electrical_load_demand.unmet_electricity_demand_out", units="MW") ) assert electricity_missed_load == pytest.approx(1403.5372787817894) @@ -1742,6 +1851,69 @@ def test_sweeping_solar_sites_doe(subtests, temp_copy_of_example): assert len(list(set(res_df["LCOE"].to_list()))) == len(res_df) +@pytest.mark.integration +@pytest.mark.parametrize( + "example_folder,resource_example_folder", + [("23_solar_wind_ng_demand", "11_hybrid_energy_plant/")], +) +def test_ng_demand_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + generic_demand_fpath = example_folder / "solar_wind_ng_demand.yaml" + # Create a H2I model + h2i_generic = H2IntegrateModel(generic_demand_fpath) + h2i_generic.run() + + lcoe_renewables_generic = h2i_generic.prob.get_val( + "finance_subgroup_renewables.LCOE_profast_lco", units="USD/(MW*h)" + ) + npv_renewables_generic = h2i_generic.prob.get_val( + "finance_subgroup_renewables.NPV_electricity__profast_npv", units="MUSD" + ) + lcoe_ng_generic = h2i_generic.prob.get_val( + "finance_subgroup_natural_gas.LCOE", units="USD/(MW*h)" + ) + lcoe_electricity_generic = h2i_generic.prob.get_val( + "finance_subgroup_electricity.LCOE", units="USD/(MW*h)" + ) + + with subtests.test("Renewables LCOE with generic demand"): + assert pytest.approx(65.25367747, rel=1e-6) == lcoe_renewables_generic[0] + with subtests.test("Renewables NPV with generic demand"): + assert pytest.approx(-36.0408322, rel=1e-6) == npv_renewables_generic[0] + with subtests.test("Natural gas LCOE with generic demand"): + assert pytest.approx(60.30971126, rel=1e-6) == lcoe_ng_generic[0] + with subtests.test("Electricity LCOE with generic demand"): + assert pytest.approx(62.95948605, rel=1e-6) == lcoe_electricity_generic[0] + + # Run with the flexible load demand + flexible_demand_fpath = example_folder / "solar_wind_ng_flexible_demand.yaml" + h2i_flexible = H2IntegrateModel(flexible_demand_fpath) + h2i_flexible.run() + + lcoe_renewables_flexible = h2i_flexible.prob.get_val( + "finance_subgroup_renewables.LCOE_profast_lco", units="USD/(MW*h)" + ) + npv_renewables_flexible = h2i_flexible.prob.get_val( + "finance_subgroup_renewables.NPV_electricity__profast_npv", units="MUSD" + ) + lcoe_ng_flexible = h2i_flexible.prob.get_val( + "finance_subgroup_natural_gas.LCOE", units="USD/(MW*h)" + ) + lcoe_electricity_flexible = h2i_flexible.prob.get_val( + "finance_subgroup_electricity.LCOE", units="USD/(MW*h)" + ) + + with subtests.test("Renewables LCOE with flexible demand"): + assert pytest.approx(65.25367747, rel=1e-6) == lcoe_renewables_flexible[0] + with subtests.test("Renewables NPV with flexible demand"): + assert pytest.approx(-36.0408322, rel=1e-6) == npv_renewables_flexible[0] + with subtests.test("Natural gas LCOE with flexible demand"): + assert pytest.approx(115.92792486, rel=1e-6) == lcoe_ng_flexible[0] + with subtests.test("Electricity LCOE with flexible demand"): + assert pytest.approx(76.39162926, rel=1e-6) == lcoe_electricity_flexible[0] + + @pytest.mark.integration @pytest.mark.parametrize("example_folder,resource_example_folder", [("26_floris", None)]) def test_floris_example(subtests, temp_copy_of_example): @@ -1912,9 +2084,13 @@ def test_24_solar_battery_grid_example(subtests, temp_copy_of_example): ) electricity_bought = sum(model.prob.get_val("grid_buy.electricity_out", units="kW")) - battery_missed_load = sum(model.prob.get_val("battery.electricity_unmet_demand", units="kW")) + battery_missed_load = sum( + model.prob.get_val("electrical_load_demand.unmet_electricity_demand_out", units="kW") + ) - battery_curtailed = sum(model.prob.get_val("battery.electricity_unused_commodity", units="kW")) + battery_curtailed = sum( + model.prob.get_val("electrical_load_demand.unused_electricity_out", units="kW") + ) electricity_sold = sum(model.prob.get_val("grid_sell.electricity_in", units="kW")) solar_aep = sum(model.prob.get_val("solar.electricity_out", units="kW")) @@ -1936,8 +2112,11 @@ def test_24_solar_battery_grid_example(subtests, temp_copy_of_example): @pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("28_iron_map", None)]) -def test_28_iron_map_example(subtests, temp_copy_of_example): +@pytest.mark.parametrize( + "example_folder,resource_example_folder", [("21_iron_examples/iron_mapping", None)] +) +@pytest.mark.skipif(importlib.util.find_spec("geopandas") is None, reason="`gis` not installed") +def test_iron_mapping_example(subtests, temp_copy_of_example): import geopandas as gpd import matplotlib @@ -1949,19 +2128,19 @@ def test_28_iron_map_example(subtests, temp_copy_of_example): example_folder = temp_copy_of_example # Define filepaths - ex_28_dir = example_folder - ex_28_out_dir = ex_28_dir / "ex_28_out" - ore_prices_filepath = ex_28_dir / "example_ore_prices.csv" + ex_dir = example_folder + ex_out_dir = ex_dir / "ex_out" + ore_prices_filepath = ex_dir / "example_ore_prices.csv" shipping_coords_filepath = ROOT_DIR / "converters/iron/martin_transport/shipping_coords.csv" - shipping_prices_filepath = ex_28_dir / "example_shipping_prices.csv" - cases_csv_fpath = ex_28_out_dir / "cases.csv" - ex_png_fpath = ex_28_out_dir / "example_28_iron_map.png" + shipping_prices_filepath = ex_dir / "example_shipping_prices.csv" + cases_csv_fpath = ex_out_dir / "cases.csv" + ex_png_fpath = ex_out_dir / "example_iron_map.png" ex_png_fpath.unlink(missing_ok=True) # Plot LCOI results from cases.sql file, save sql data to csv fig, ax, lcoi_layer_gdf = plot_geospatial_point_heat_map( case_results_fpath=cases_csv_fpath, - metric_to_plot="iron.LCOI (USD/kg)", + metric_to_plot="finance_subgroup_pig_iron.LCOP (USD/kg)", map_preferences={ "figsize": (10, 8), "colorbar_label": "Levelized Cost of\nIron [$/kg]", @@ -2218,11 +2397,13 @@ def test_stimulated_geoh2(subtests, temp_copy_of_example): @pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("21_iron_mn_to_il", None)]) -def test_21_iron_dri_eaf_example(subtests, temp_copy_of_example): +@pytest.mark.parametrize( + "example_folder,resource_example_folder", [("21_iron_examples/iron_dri", None)] +) +def test_iron_dri_eaf_example(subtests, temp_copy_of_example): example_folder = temp_copy_of_example - h2i = H2IntegrateModel(example_folder / "21_iron.yaml") + h2i = H2IntegrateModel(example_folder / "single_site_iron.yaml") h2i.run() @@ -2241,12 +2422,12 @@ def test_21_iron_dri_eaf_example(subtests, temp_copy_of_example): @pytest.mark.integration @pytest.mark.parametrize( - "example_folder,resource_example_folder", [("31_iron_electrowinning", None)] + "example_folder,resource_example_folder", [("21_iron_examples/iron_electrowinning", None)] ) -def test_31_iron_electrowinning_example(subtests, temp_copy_of_example): +def test_iron_electrowinning_example(subtests, temp_copy_of_example): example_folder = temp_copy_of_example - model = H2IntegrateModel(example_folder / "31_iron_electrowinning.yaml") + model = H2IntegrateModel(example_folder / "iron_electrowinning.yaml") with subtests.test("Value check on AHE"): model.technology_config["technologies"]["iron_plant"]["model_inputs"]["shared_parameters"][ @@ -2415,6 +2596,7 @@ def test_pyomo_optimized_dispatch_example(subtests, temp_copy_of_example): # TODO: Update with demand module once it is developed model.setup() model.prob.set_val("battery.electricity_demand", demand_profile, units="MW") + model.prob.set_val("electrical_load_demand.electricity_demand", demand_profile, units="MW") # Run the model model.run() @@ -2435,11 +2617,15 @@ def test_pyomo_optimized_dispatch_example(subtests, temp_copy_of_example): # Battery checks with subtests.test("Check battery total electricity produced"): - battery_total = model.prob.get_val("battery.total_electricity_produced", units="kW*h")[0] + battery_total = model.prob.get_val( + "electrical_load_demand.total_electricity_produced", units="kW*h" + )[0] assert battery_total == pytest.approx(645_787_407.02, rel=1e-3) with subtests.test("Check battery capacity factor"): - battery_cf = model.prob.get_val("battery.capacity_factor", units="unitless")[0] + battery_cf = model.prob.get_val("electrical_load_demand.capacity_factor", units="unitless")[ + 0 + ] assert battery_cf == pytest.approx(0.7372, rel=1e-3) with subtests.test("Check battery CapEx"): @@ -2486,3 +2672,113 @@ def test_pyomo_optimized_dispatch_example(subtests, temp_copy_of_example): "finance_subgroup_all_electricity.price_electricity", units="USD/(kW*h)" )[0] assert price == pytest.approx(0.134, rel=1e-3) + + +@pytest.mark.integration +@pytest.mark.parametrize("example_folder,resource_example_folder", [("31_tidal", None)]) +def test_tidal_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create the model + model = H2IntegrateModel(example_folder / "tidal.yaml") + + # # Run the model + model.run() + + with subtests.test("AEP"): + tidal_electricity = model.prob.get_val("tidal.electricity_out", units="GW") + assert tidal_electricity.sum() == pytest.approx(60.625515492, rel=1e-4) + + with subtests.test("Capex"): + capex = model.prob.get_val("tidal.CapEx", units="USD") + assert capex == pytest.approx(123902868.63, rel=1e-4) + + with subtests.test("OpEx"): + OpEx = model.prob.get_val("tidal.OpEx", units="USD/yr") + assert OpEx == pytest.approx(4498582.9, rel=1e-4) + + with subtests.test("LCOE"): + lcoe = model.prob.get_val("finance_subgroup_default.LCOE", units="USD/(kW*h)") + assert lcoe == pytest.approx(0.287, rel=1e-4) + + +@pytest.mark.integration +@pytest.mark.parametrize( + "example_folder,resource_example_folder", [("32_multivariable_streams", None)] +) +def test_multivariable_streams_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create the model + model = H2IntegrateModel(example_folder / "32_multivariable_streams.yaml") + + # Run the model + model.run() + + # Gas Producer 1 + with subtests.test("Producer 1 flow"): + flow1 = model.prob.get_val( + "gas_producer_1.wellhead_gas_mixture:mass_flow_out", units="kg/h" + ) + assert flow1.mean() == pytest.approx(149.70, rel=1e-3) + + with subtests.test("Producer 1 temperature"): + temp1 = model.prob.get_val("gas_producer_1.wellhead_gas_mixture:temperature_out", units="K") + assert temp1.mean() == pytest.approx(310.0, rel=1e-3) + + with subtests.test("Producer 1 pressure"): + pres1 = model.prob.get_val("gas_producer_1.wellhead_gas_mixture:pressure_out", units="bar") + assert pres1.mean() == pytest.approx(12.02, rel=1e-3) + + # Gas Producer 2 + with subtests.test("Producer 2 flow"): + flow2 = model.prob.get_val( + "gas_producer_2.wellhead_gas_mixture:mass_flow_out", units="kg/h" + ) + assert flow2.mean() == pytest.approx(99.68, rel=1e-3) + + with subtests.test("Producer 2 temperature"): + temp2 = model.prob.get_val("gas_producer_2.wellhead_gas_mixture:temperature_out", units="K") + assert temp2.mean() == pytest.approx(350.0, rel=1e-3) + + with subtests.test("Producer 2 pressure"): + pres2 = model.prob.get_val("gas_producer_2.wellhead_gas_mixture:pressure_out", units="bar") + assert pres2.mean() == pytest.approx(8.01, rel=1e-3) + + # Gas Combiner + with subtests.test("Combiner total flow"): + flow_out = model.prob.get_val( + "gas_combiner.wellhead_gas_mixture:mass_flow_out", units="kg/h" + ) + assert flow_out.mean() == pytest.approx(249.38, rel=1e-3) + + with subtests.test("Combiner temperature"): + temp_out = model.prob.get_val( + "gas_combiner.wellhead_gas_mixture:temperature_out", units="K" + ) + assert temp_out.mean() == pytest.approx(326.1, rel=1e-3) + + with subtests.test("Combiner pressure"): + pres_out = model.prob.get_val("gas_combiner.wellhead_gas_mixture:pressure_out", units="bar") + assert pres_out.mean() == pytest.approx(10.40, rel=1e-3) + + with subtests.test("Combiner H2 fraction"): + h2_out = model.prob.get_val("gas_combiner.wellhead_gas_mixture:hydrogen_mass_fraction_out") + assert h2_out.mean() == pytest.approx(0.800, rel=1e-3) + + # Gas Consumer + with subtests.test("Consumer H2 mass flow"): + h2_mass_flow = model.prob.get_val("gas_consumer.hydrogen_out", units="kg/h") + assert h2_mass_flow.mean() == pytest.approx(199.55, rel=1e-3) + + with subtests.test("Consumer total gas consumed"): + total_consumed = model.prob.get_val("gas_consumer.total_gas_consumed", units="kg") + assert total_consumed[0] == pytest.approx(2_184_570, rel=1e-3) + + with subtests.test("Consumer avg temperature"): + avg_temp = model.prob.get_val("gas_consumer.avg_temperature", units="K") + assert avg_temp[0] == pytest.approx(326.1, rel=1e-3) + + with subtests.test("Consumer avg pressure"): + avg_pres = model.prob.get_val("gas_consumer.avg_pressure", units="bar") + assert avg_pres[0] == pytest.approx(10.40, rel=1e-3) diff --git a/h2integrate/__init__.py b/h2integrate/__init__.py index 1d00c0667..6f5253a1d 100644 --- a/h2integrate/__init__.py +++ b/h2integrate/__init__.py @@ -1,6 +1,6 @@ from pathlib import Path -__version__ = "0.7.2" +__version__ = "0.8.0" ROOT_DIR = Path(__file__).resolve().parent EXAMPLE_DIR = ROOT_DIR.parent / "examples" diff --git a/h2integrate/control/control_rules/converters/generic_converter.py b/h2integrate/control/control_rules/converters/generic_converter.py index ab069bc4e..fc18bb39f 100644 --- a/h2integrate/control/control_rules/converters/generic_converter.py +++ b/h2integrate/control/control_rules/converters/generic_converter.py @@ -9,6 +9,11 @@ class PyomoDispatchGenericConverter(PyomoRuleBaseClass): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = PyomoRuleBaseConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "dispatch_rule"), @@ -26,6 +31,10 @@ def _create_variables(self, pyomo_model: pyo.ConcreteModel, tech_name: str): variables are created. """ + rate_units_pyo_str = "/".join( + f"pyo.units.{u}" for u in self.config.commodity_rate_units.split("/") + ) + setattr( pyomo_model, f"{tech_name}_{self.config.commodity}", @@ -33,7 +42,7 @@ def _create_variables(self, pyomo_model: pyo.ConcreteModel, tech_name: str): doc=f"{self.config.commodity} generation \ from {tech_name} [{self.config.commodity_rate_units}]", domain=pyo.NonNegativeReals, - units=eval("pyo.units." + self.config.commodity_rate_units), + units=eval(rate_units_pyo_str), initialize=0.0, ), ) diff --git a/h2integrate/control/control_rules/converters/generic_converter_min_operating_cost.py b/h2integrate/control/control_rules/converters/generic_converter_min_operating_cost.py index 1dd66d987..56cd25d47 100644 --- a/h2integrate/control/control_rules/converters/generic_converter_min_operating_cost.py +++ b/h2integrate/control/control_rules/converters/generic_converter_min_operating_cost.py @@ -35,6 +35,14 @@ def __init__( # names and units in the Pyomo model self.commodity_name = commodity_info["commodity_name"] self.commodity_storage_units = commodity_info["commodity_storage_units"] + pyo.units.load_definitions_from_strings(["USD = [currency]"]) + + rate_units_pyo_str = "/".join( + f"pyo.units.{u}" for u in self.commodity_storage_units.split("/") + ) + amount_units_pyo_str = f"({rate_units_pyo_str})*pyo.units.h" + self.rate_units_pyo = eval(rate_units_pyo_str) + self.cost_units_per_amount_pyo = eval(f"pyo.units.USD / ({amount_units_pyo_str})") # The Pyomo model that this class builds off of, where all of the variables, parameters, # constraints, and ports will be added to. @@ -51,14 +59,14 @@ def __init__( # The units of this are in hours, so half an hour would be 0.5, etc. self.time_duration = [time_duration] * len(self.blocks.index_set()) - def initialize_parameters( - self, commodity_in: list, commodity_demand: list, dispatch_inputs: dict - ): + def initialize_parameters(self, inputs: dict, dispatch_inputs: dict): """Initialize parameters for optimization model Args: - commodity_in (list): List of generated commodity in for this time slice. - commodity_demand (list): The demanded commodity for this time slice. + inputs (dict): + Dictionary of numpy arrays (length = self.n_timesteps) containing at least: + f"{commodity}_in" : Available generated commodity profile. + f"{commodity}_demand" : Demanded commodity output profile. dispatch_inputs (dict): Dictionary of the dispatch input parameters from config """ @@ -70,7 +78,7 @@ def dispatch_block_rule_function(self, pyomo_model: pyo.ConcreteModel): This method sets up all model elements (parameters, variables, constraints, and ports) associated with a technology block within the dispatch model. - It is typically called in the setup_pyomo() method of the PyomoControllerBaseClass. + It is typically called in the setup_pyomo() method of the PyomoStorageControllerBaseClass. Args: pyomo_model (pyo.ConcreteModel): The Pyomo model to which the technology @@ -95,12 +103,13 @@ def _create_variables(self, pyomo_model: pyo.ConcreteModel): pyomo_model (pyo.ConcreteModel): pyomo_model the variables should be added to. """ + tech_var = pyo.Var( doc=f"{self.commodity_name} production \ from {self.block_set_name} [{self.commodity_storage_units}]", domain=pyo.NonNegativeReals, bounds=(0, pyomo_model.available_production), - units=eval("pyo.units." + self.commodity_storage_units), + units=self.rate_units_pyo, initialize=0.0, ) @@ -136,6 +145,7 @@ def _create_parameters(self, pyomo_model: pyo.ConcreteModel): ################################## # Parameters # ################################## + pyomo_model.time_duration = pyo.Param( doc=f"{pyomo_model.name} time step [hour]", default=1.0, @@ -148,14 +158,14 @@ def _create_parameters(self, pyomo_model: pyo.ConcreteModel): default=0.0, within=pyo.NonNegativeReals, mutable=True, - units=eval(f"pyo.units.USD / pyo.units.{self.commodity_storage_units}h"), + units=self.cost_units_per_amount_pyo, ) pyomo_model.available_production = pyo.Param( doc=f"Available production for the generator [{self.commodity_storage_units}]", default=0.0, within=pyo.Reals, mutable=True, - units=eval(f"pyo.units.{self.commodity_storage_units}"), + units=self.rate_units_pyo, ) def _create_constraints(self, pyomo_model: pyo.ConcreteModel): @@ -233,7 +243,7 @@ def _create_hybrid_variables(self, hybrid_model: pyo.ConcreteModel, tech_name: s doc=f"{self.commodity_name} production \ from {tech_name} [{self.commodity_storage_units}]", domain=pyo.NonNegativeReals, - units=eval("pyo.units." + self.commodity_storage_units), + units=self.rate_units_pyo, initialize=0.0, ) diff --git a/h2integrate/control/control_rules/plant_dispatch_model.py b/h2integrate/control/control_rules/plant_dispatch_model.py index 298ff6ccd..a28f29007 100644 --- a/h2integrate/control/control_rules/plant_dispatch_model.py +++ b/h2integrate/control/control_rules/plant_dispatch_model.py @@ -98,21 +98,20 @@ def dispatch_block_rule(self, hybrid, t): ################################## self._create_hybrid_constraints(hybrid, t) - def initialize_parameters( - self, commodity_in: list, commodity_demand: list, dispatch_params: dict - ): + def initialize_parameters(self, inputs: dict, dispatch_params: dict): """Initialize parameters for optimization model - Args: - commodity_in (list): List of generated commodity in for this time slice. - commodity_demand (list): The demanded commodity for this time slice. - dispatch_inputs (dict): Dictionary of the dispatch input parameters from config + inputs (dict): + Dictionary of numpy arrays (length = self.n_timesteps) containing at least: + f"{commodity}_in" : Available generated commodity profile. + f"{commodity}_demand" : Demanded commodity output profile. + dispatch_inputs (dict): Dictionary of the dispatch input parameters from config """ self.time_weighting_factor = self.time_weighting_factor_input # Discount factor for tech in self.source_techs: pyomo_block = self.tech_dispatch_models.__getattribute__(f"{tech}_rule") - pyomo_block.initialize_parameters(commodity_in, commodity_demand, dispatch_params) + pyomo_block.initialize_parameters(inputs, dispatch_params) def _create_variables_and_ports(self, hybrid, t): """Connect variables and ports from individual technology model diff --git a/h2integrate/control/control_rules/pyomo_rule_baseclass.py b/h2integrate/control/control_rules/pyomo_rule_baseclass.py index b8f512e57..021596b50 100644 --- a/h2integrate/control/control_rules/pyomo_rule_baseclass.py +++ b/h2integrate/control/control_rules/pyomo_rule_baseclass.py @@ -22,6 +22,11 @@ class PyomoRuleBaseConfig(BaseConfig): class PyomoRuleBaseClass(om.ExplicitComponent): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): self.options.declare("driver_config", types=dict) self.options.declare("plant_config", types=dict) @@ -51,7 +56,7 @@ def dispatch_block_rule_function(self, pyomo_model: pyo.ConcreteModel, tech_name This method sets up all model elements (parameters, variables, constraints, and ports) associated with a technology block within the dispatch model. - It is typically called in the setup_pyomo() method of the PyomoControllerBaseClass. + It is typically called in the setup_pyomo() method of the PyomoStorageControllerBaseClass. Args: pyomo_model (pyo.ConcreteModel): The Pyomo model to which the technology diff --git a/h2integrate/control/control_rules/storage/pyomo_storage_rule_baseclass.py b/h2integrate/control/control_rules/storage/pyomo_storage_rule_baseclass.py index 4bf2bdd3c..e55a9837c 100644 --- a/h2integrate/control/control_rules/storage/pyomo_storage_rule_baseclass.py +++ b/h2integrate/control/control_rules/storage/pyomo_storage_rule_baseclass.py @@ -14,8 +14,8 @@ class PyomoStorageRuleBaseConfig(PyomoRuleBaseConfig): max_capacity: float = field(validator=gt_zero) - min_charge_fraction: float = field(default=0.1, validator=range_val(0, 1)) - max_charge_fraction: float = field(default=0.9, validator=range_val(0, 1)) + min_soc_fraction: float = field(default=0.1, validator=range_val(0, 1)) + max_soc_fraction: float = field(default=0.9, validator=range_val(0, 1)) charge_efficiency: float = field(default=0.938, validator=range_val(0, 1)) discharge_efficiency: float = field(default=0.938, validator=range_val(0, 1)) @@ -24,6 +24,11 @@ class PyomoStorageRuleBaseConfig(PyomoRuleBaseConfig): class PyomoRuleStorageBaseclass(PyomoRuleBaseClass): """Base class defining Pyomo rules for generic commodity storage components.""" + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = PyomoStorageRuleBaseConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "dispatch_rule"), @@ -32,6 +37,10 @@ def setup(self): ) super().setup() + self.rate_units_pyo = eval( + "/".join(f"pyo.units.{u}" for u in self.config.commodity_rate_units.split("/")) + ) + def _create_parameters(self, pyomo_model: pyo.ConcreteModel, t): """Create storage-related parameters in the Pyomo model. @@ -47,6 +56,7 @@ def _create_parameters(self, pyomo_model: pyo.ConcreteModel, t): ################################## # Storage Parameters # ################################## + pyomo_model.time_duration = pyo.Param( doc=pyomo_model.name + " time step [hour]", default=1.0, @@ -62,7 +72,7 @@ def _create_parameters(self, pyomo_model: pyo.ConcreteModel, t): default=0.0, within=pyo.NonNegativeReals, mutable=True, - units=eval("pyo.units." + self.config.commodity_rate_units), + units=self.rate_units_pyo, ) pyomo_model.maximum_storage = pyo.Param( doc=pyomo_model.name @@ -71,18 +81,18 @@ def _create_parameters(self, pyomo_model: pyo.ConcreteModel, t): + "]", within=pyo.NonNegativeReals, mutable=True, - units=eval("pyo.units." + self.config.commodity_rate_units), + units=self.rate_units_pyo, ) pyomo_model.minimum_soc = pyo.Param( doc=pyomo_model.name + " minimum state-of-charge [-]", - default=self.config.min_charge_fraction, + default=self.config.min_soc_fraction, within=pyo.PercentFraction, mutable=True, units=pyo.units.dimensionless, ) pyomo_model.maximum_soc = pyo.Param( doc=pyomo_model.name + " maximum state-of-charge [-]", - default=self.config.max_charge_fraction, + default=self.config.max_soc_fraction, within=pyo.PercentFraction, mutable=True, units=pyo.units.dimensionless, @@ -114,7 +124,7 @@ def _create_parameters(self, pyomo_model: pyo.ConcreteModel, t): default=self.config.max_capacity, within=pyo.NonNegativeReals, mutable=True, - units=eval("pyo.units." + self.config.commodity_rate_units), + units=self.rate_units_pyo, ) def _create_variables(self, pyomo_model: pyo.ConcreteModel, t): @@ -131,6 +141,7 @@ def _create_variables(self, pyomo_model: pyo.ConcreteModel, t): ################################## # Variables # ################################## + pyomo_model.is_charging = pyo.Var( doc="1 if " + pyomo_model.name + " is charging; 0 Otherwise [-]", domain=pyo.Binary, @@ -162,7 +173,7 @@ def _create_variables(self, pyomo_model: pyo.ConcreteModel, t): + self.config.commodity_rate_units + "]", domain=pyo.NonNegativeReals, - units=eval("pyo.units." + self.config.commodity_rate_units), + units=self.rate_units_pyo, ) pyomo_model.discharge_commodity = pyo.Var( doc=self.config.commodity @@ -172,7 +183,7 @@ def _create_variables(self, pyomo_model: pyo.ConcreteModel, t): + self.config.commodity_rate_units + "]", domain=pyo.NonNegativeReals, - units=eval("pyo.units." + self.config.commodity_rate_units), + units=self.rate_units_pyo, ) def _create_constraints(self, pyomo_model: pyo.ConcreteModel, t): diff --git a/h2integrate/control/control_rules/storage/pyomo_storage_rule_min_operating_cost.py b/h2integrate/control/control_rules/storage/pyomo_storage_rule_min_operating_cost.py index 7950984aa..6faf5b513 100644 --- a/h2integrate/control/control_rules/storage/pyomo_storage_rule_min_operating_cost.py +++ b/h2integrate/control/control_rules/storage/pyomo_storage_rule_min_operating_cost.py @@ -38,6 +38,15 @@ def __init__( # This loads the currency unit definition into pyomo pyo.units.load_definitions_from_strings(["USD = [currency]"]) + rate_units_pyo_str = "/".join( + f"pyo.units.{u}" for u in self.commodity_storage_units.split("/") + ) + amount_units_pyo_str = f"({rate_units_pyo_str})*pyo.units.h" + + self.rate_units_pyo = eval(rate_units_pyo_str) + self.amount_units_pyo = eval(amount_units_pyo_str) + self.cost_units_per_amount_pyo = eval(f"pyo.units.USD / ({amount_units_pyo_str})") + # The Pyomo model that this class builds off of, where all of the variables, parameters, # constraints, and ports will be added to. self.model = pyomo_model @@ -53,17 +62,19 @@ def __init__( # The units of this are in hours, so half an hour would be 0.5, etc. self.time_duration = [time_duration] * len(self.blocks.index_set()) - def initialize_parameters( - self, commodity_in: list, commodity_demand: list, dispatch_inputs: dict - ): + def initialize_parameters(self, inputs: dict, dispatch_inputs: dict): """Initialize parameters for optimization model Args: - commodity_in (list): List of generated commodity in for this time slice. - commodity_demand (list): The demanded commodity for this time slice. + inputs (dict): + Dictionary of numpy arrays (length = self.n_timesteps) containing at least: + f"{commodity}_in" : Available generated commodity profile. + f"{commodity}_demand" : Demanded commodity output profile. dispatch_inputs (dict): Dictionary of the dispatch input parameters from config """ + commodity_demand = inputs[f"{self.commodity_name}_demand"] + # Dispatch Parameters self.set_timeseries_parameter("cost_per_charge", dispatch_inputs["cost_per_charge"]) self.set_timeseries_parameter("cost_per_discharge", dispatch_inputs["cost_per_discharge"]) @@ -73,8 +84,8 @@ def initialize_parameters( self.set_timeseries_parameter("minimum_storage", 0.0) self.set_timeseries_parameter("maximum_storage", dispatch_inputs["max_capacity"]) - self.set_timeseries_parameter("minimum_soc", dispatch_inputs["min_charge_fraction"]) - self.set_timeseries_parameter("maximum_soc", dispatch_inputs["max_charge_fraction"]) + self.set_timeseries_parameter("minimum_soc", dispatch_inputs["min_soc_fraction"]) + self.set_timeseries_parameter("maximum_soc", dispatch_inputs["max_soc_fraction"]) self.initial_soc = dispatch_inputs["initial_soc_fraction"] self.charge_efficiency = dispatch_inputs.get("charge_efficiency") @@ -130,11 +141,6 @@ def _create_parameters(self, pyomo_model: pyo.ConcreteModel, t): # Storage Parameters # ################################## - pyo_commodity_storage_unit = eval(f"pyo.units.{self.commodity_storage_units}") - pyo_commodity_storage_unit_hrs = eval(f"pyo.units.{self.commodity_storage_units}h") - pyo_usd_per_commodity_storage_unit_hrs = eval( - f"pyo.units.USD / pyo.units.{self.commodity_storage_units}h" - ) usd_pr_units_str = f"[$/{self.commodity_storage_units}]" pyomo_model.time_duration = pyo.Param( @@ -150,28 +156,28 @@ def _create_parameters(self, pyomo_model: pyo.ConcreteModel, t): default=0.0, within=pyo.NonNegativeReals, mutable=True, - units=pyo_usd_per_commodity_storage_unit_hrs, + units=self.cost_units_per_amount_pyo, ) pyomo_model.cost_per_discharge = pyo.Param( doc=f"Operating cost of {pyomo_model.name} discharging {usd_pr_units_str}", default=0.0, within=pyo.NonNegativeReals, mutable=True, - units=pyo_usd_per_commodity_storage_unit_hrs, + units=self.cost_units_per_amount_pyo, ) pyomo_model.minimum_storage = pyo.Param( doc=f"{pyomo_model.name} minimum storage rating [{self.commodity_storage_units}]", default=0.0, within=pyo.NonNegativeReals, mutable=True, - units=pyo_commodity_storage_unit, + units=self.rate_units_pyo, ) pyomo_model.maximum_storage = pyo.Param( doc=f"{pyomo_model.name} maximum storage rating [{self.commodity_storage_units}]", default=1000.0, within=pyo.NonNegativeReals, mutable=True, - units=pyo_commodity_storage_unit_hrs, + units=self.amount_units_pyo, ) pyomo_model.minimum_soc = pyo.Param( doc=f"{pyomo_model.name} minimum state-of-charge [-]", @@ -212,13 +218,13 @@ def _create_parameters(self, pyomo_model: pyo.ConcreteModel, t): doc=f"{pyomo_model.name} maximum charge [{self.commodity_storage_units}]", within=pyo.NonNegativeReals, mutable=True, - units=pyo_commodity_storage_unit, + units=self.rate_units_pyo, ) pyomo_model.max_discharge = pyo.Param( doc=f"{pyomo_model.name} maximum discharge [{self.commodity_storage_units}]", within=pyo.NonNegativeReals, mutable=True, - units=pyo_commodity_storage_unit, + units=self.rate_units_pyo, ) ################################## # System Parameters # @@ -235,14 +241,14 @@ def _create_parameters(self, pyomo_model: pyo.ConcreteModel, t): default=0.0, within=pyo.Reals, mutable=True, - units=pyo_usd_per_commodity_storage_unit_hrs, + units=self.cost_units_per_amount_pyo, ) pyomo_model.commodity_load_demand = pyo.Param( doc=f"Load demand for the commodity [{self.commodity_storage_units}]", default=1000.0, within=pyo.NonNegativeReals, mutable=True, - units=pyo_commodity_storage_unit, + units=self.rate_units_pyo, ) def _create_variables(self, pyomo_model: pyo.ConcreteModel, t): @@ -260,6 +266,7 @@ def _create_variables(self, pyomo_model: pyo.ConcreteModel, t): ################################## # Variables # ################################## + pyomo_model.is_charging = pyo.Var( doc=f"1 if {pyomo_model.name} is charging; 0 Otherwise [-]", domain=pyo.Binary, @@ -286,12 +293,12 @@ def _create_variables(self, pyomo_model: pyo.ConcreteModel, t): pyomo_model.charge_commodity = pyo.Var( doc=f"{self.commodity_name} into {pyomo_model.name} [{self.commodity_storage_units}]", domain=pyo.NonNegativeReals, - units=eval(f"pyo.units.{self.commodity_storage_units}"), + units=self.rate_units_pyo, ) pyomo_model.discharge_commodity = pyo.Var( doc=f"{self.commodity_name} out of {pyomo_model.name} [{self.commodity_storage_units}]", domain=pyo.NonNegativeReals, - units=eval(f"pyo.units.{self.commodity_storage_units}"), + units=self.rate_units_pyo, ) ################################## # System Variables # @@ -299,18 +306,18 @@ def _create_variables(self, pyomo_model: pyo.ConcreteModel, t): pyomo_model.system_production = pyo.Var( doc=f"System generation [{self.commodity_storage_units}]", domain=pyo.NonNegativeReals, - units=eval(f"pyo.units.{self.commodity_storage_units}"), + units=self.rate_units_pyo, ) pyomo_model.system_load = pyo.Var( doc=f"System load [{self.commodity_storage_units}]", domain=pyo.NonNegativeReals, - units=eval(f"pyo.units.{self.commodity_storage_units}"), + units=self.rate_units_pyo, ) pyomo_model.commodity_out = pyo.Var( doc=f"Commodity out of the system [{self.commodity_storage_units}]", domain=pyo.NonNegativeReals, bounds=(0, pyomo_model.commodity_load_demand), - units=eval(f"pyo.units.{self.commodity_storage_units}"), + units=self.rate_units_pyo, ) pyomo_model.is_generating = pyo.Var( doc="System is producing commodity binary [-]", @@ -537,22 +544,21 @@ def _create_hybrid_variables(self, hybrid_model: pyo.ConcreteModel, tech_name: s ################################## # System Variables # ################################## - pyo_commodity_units = eval("pyo.units." + self.commodity_storage_units) hybrid_model.system_production = pyo.Var( doc=f"System generation [{self.commodity_storage_units}]", domain=pyo.NonNegativeReals, - units=pyo_commodity_units, + units=self.rate_units_pyo, ) hybrid_model.system_load = pyo.Var( doc=f"System load [{self.commodity_storage_units}]", domain=pyo.NonNegativeReals, - units=pyo_commodity_units, + units=self.rate_units_pyo, ) hybrid_model.commodity_out = pyo.Var( doc=f"{self.commodity_name} sold [{self.commodity_storage_units}]", domain=pyo.NonNegativeReals, - units=pyo_commodity_units, + units=self.rate_units_pyo, ) ################################## # Storage Variables # @@ -561,12 +567,12 @@ def _create_hybrid_variables(self, hybrid_model: pyo.ConcreteModel, tech_name: s hybrid_model.charge_commodity = pyo.Var( doc=f"{self.commodity_name} into {tech_name} [{self.commodity_storage_units}]", domain=pyo.NonNegativeReals, - units=pyo_commodity_units, + units=self.rate_units_pyo, ) hybrid_model.discharge_commodity = pyo.Var( doc=f"{self.commodity_name} out of {tech_name} [{self.commodity_storage_units}]", domain=pyo.NonNegativeReals, - units=pyo_commodity_units, + units=self.rate_units_pyo, ) # Returns to power_source_gen_vars and load_vars in hybrid_rule power_source_gen_var = hybrid_model.discharge_commodity diff --git a/h2integrate/control/control_strategies/converters/demand_openloop_controller.py b/h2integrate/control/control_strategies/converters/demand_openloop_controller.py deleted file mode 100644 index bf2aa5c78..000000000 --- a/h2integrate/control/control_strategies/converters/demand_openloop_controller.py +++ /dev/null @@ -1,77 +0,0 @@ -import numpy as np - -from h2integrate.control.control_strategies.demand_openloop_controller import ( - DemandOpenLoopControlBase, - DemandOpenLoopControlBaseConfig, -) - - -class DemandOpenLoopConverterController(DemandOpenLoopControlBase): - """Open-loop controller for converting input supply into met demand. - - This controller computes unmet demand, unused (curtailed) production, and - the resulting commodity output profile based on the incoming supply and an - externally specified demand profile. It uses simple arithmetic rules: - - * If demand exceeds supplied commodity, the difference is unmet demand. - * If supply exceeds demand, the excess is unused (curtailed) commodity. - * Output equals supplied commodity minus curtailed commodity. - - This component relies on configuration provided through the - ``tech_config`` dictionary, which must define the controller's - ``control_parameters``. - """ - - def setup(self): - """Set up the load controller configuration. - - Loads the controller configuration from ``tech_config`` and then calls - the base class ``setup`` to create inputs/outputs. - - Raises: - KeyError: If the expected configuration keys are missing from - ``tech_config``. - """ - self.config = DemandOpenLoopControlBaseConfig.from_dict( - self.options["tech_config"]["model_inputs"]["control_parameters"], - additional_cls_name=self.__class__.__name__, - ) - super().setup() - - def compute(self, inputs, outputs): - """Compute unmet demand, unused commodity, and converter output. - - This method compares the demand profile to the supplied commodity for - each timestep and assigns unmet demand, curtailed production, and - actual delivered output. - - Args: - inputs (dict-like): Mapping of input variable names to their - current values, including: - - * ``{commodity}_demand``: Demand profile. - * ``{commodity}_in``: Supplied commodity. - outputs (dict-like): Mapping of output variable names where results - will be written, including: - - * ``{commodity}_unmet_demand``: Unmet demand. - * ``{commodity}_unused_commodity``: Curtailed production. - * ``{commodity}_out``: Actual output delivered. - - Notes: - All variables operate on a per-timestep basis and typically have - array shape ``(n_timesteps,)``. - """ - commodity = self.config.commodity - remaining_demand = inputs[f"{commodity}_demand"] - inputs[f"{commodity}_in"] - - # Calculate missed load and curtailed production - outputs[f"{commodity}_unmet_demand"] = np.where(remaining_demand > 0, remaining_demand, 0) - outputs[f"{commodity}_unused_commodity"] = np.where( - remaining_demand < 0, -1 * remaining_demand, 0 - ) - - # Calculate actual output based on demand met and curtailment - outputs[f"{commodity}_set_point"] = ( - inputs[f"{commodity}_in"] - outputs[f"{commodity}_unused_commodity"] - ) diff --git a/h2integrate/control/control_strategies/demand_openloop_controller.py b/h2integrate/control/control_strategies/demand_openloop_controller.py deleted file mode 100644 index be0def762..000000000 --- a/h2integrate/control/control_strategies/demand_openloop_controller.py +++ /dev/null @@ -1,121 +0,0 @@ -import openmdao.api as om -from attrs import field, define - -from h2integrate.core.utilities import BaseConfig - - -@define(kw_only=True) -class DemandOpenLoopControlBaseConfig(BaseConfig): - """Configuration for defining an open-loop demand profile. - - This configuration object specifies the commodity being controlled and the - demand profile that should be met by downstream components. - - Attributes: - commodity_rate_units (str): Units of the commodity (e.g., "kg/h"). - commodity (str): Name of the commodity being controlled - (e.g., "hydrogen"). Converted to lowercase and stripped of whitespace. - demand_profile (int | float | list): Demand values for each timestep, in - the same units as `commodity_rate_units`. May be a scalar for constant - demand or a list/array for time-varying demand. - """ - - commodity_rate_units: str = field(converter=str.strip) - commodity: str = field(converter=(str.strip, str.lower)) - demand_profile: int | float | list = field() - - -class DemandOpenLoopControlBase(om.ExplicitComponent): - """Base OpenMDAO component for open-loop demand tracking. - - This component defines the interfaces required for open-loop demand - controllers, including inputs for demand, supplied commodity, and outputs - tracking unmet demand, unused production, and total unmet demand. - Subclasses must implement the :meth:`compute` method to define the - controller behavior. - """ - - def initialize(self): - """Declare component options. - - Options: - driver_config (dict): Driver-level configuration parameters. - plant_config (dict): Plant-level configuration, including number of - simulation timesteps. - tech_config (dict): Technology-specific configuration, including - controller settings. - - """ - self.options.declare("driver_config", types=dict) - self.options.declare("plant_config", types=dict) - self.options.declare("tech_config", types=dict) - - def setup(self): - """Define inputs and outputs for demand control. - - Creates time-series inputs and outputs for commodity demand, supply, - unmet demand, unused commodity, and total unmet demand. Shapes and units - are determined by the plant configuration and controller configuration. - - Raises: - KeyError: If required configuration keys are missing from - ``plant_config`` or ``tech_config``. - """ - n_timesteps = int(self.options["plant_config"]["plant"]["simulation"]["n_timesteps"]) - - commodity = self.config.commodity - - self.add_input( - f"{commodity}_demand", - val=self.config.demand_profile, - shape=(n_timesteps), - units=self.config.commodity_rate_units, # NOTE: hardcoded to align with controllers - desc=f"Demand profile of {commodity}", - ) - - self.add_input( - f"{commodity}_in", - val=0.0, - shape=(n_timesteps), - units=self.config.commodity_rate_units, - desc=f"Amount of {commodity} demand that has already been supplied", - ) - - self.add_output( - f"{commodity}_unmet_demand", - val=self.config.demand_profile, - shape=(n_timesteps), - units=self.config.commodity_rate_units, - desc=f"Remaining demand profile of {commodity}", - ) - - self.add_output( - f"{commodity}_unused_commodity", - val=0.0, - shape=(n_timesteps), - units=self.config.commodity_rate_units, - desc=f"Excess production of {commodity}", - ) - - self.add_output( - f"{commodity}_set_point", - val=0.0, - shape=(n_timesteps), - units=self.config.commodity_rate_units, - desc=f"Production profile of {commodity}", - ) - - self.add_output( - f"total_{commodity}_unmet_demand", - units=self.config.commodity_rate_units, - desc="Total unmet demand", - ) - - def compute(): - """This method must be implemented by subclasses to define the - controller. - - Raises: - NotImplementedError: Always, unless implemented in a subclass. - """ - raise NotImplementedError("This method should be implemented in a subclass.") diff --git a/h2integrate/control/control_strategies/passthrough_openloop_controller.py b/h2integrate/control/control_strategies/passthrough_openloop_controller.py deleted file mode 100644 index 1e3520022..000000000 --- a/h2integrate/control/control_strategies/passthrough_openloop_controller.py +++ /dev/null @@ -1,92 +0,0 @@ -import numpy as np -import openmdao.api as om -from attrs import field, define - -from h2integrate.core.utilities import BaseConfig, merge_shared_inputs - - -@define(kw_only=True) -class PassThroughOpenLoopControllerConfig(BaseConfig): - commodity: str = field() - commodity_rate_units: str = field() - - -class PassThroughOpenLoopController(om.ExplicitComponent): - """ - A simple pass-through controller for open-loop systems. - - This controller directly passes the input commodity flow to the output without any - modifications. It is useful for testing, as a placeholder for more complex controllers, - and for maintaining consistency between controlled and uncontrolled frameworks as this - 'controller' does not alter the system output in any way. - """ - - def initialize(self): - """ - Declare options for the component. See "Attributes" section in class doc strings for - details. - """ - - self.options.declare("driver_config", types=dict) - self.options.declare("plant_config", types=dict) - self.options.declare("tech_config", types=dict) - - def setup(self): - self.config = PassThroughOpenLoopControllerConfig.from_dict( - merge_shared_inputs(self.options["tech_config"]["model_inputs"], "control"), - additional_cls_name=self.__class__.__name__, - ) - - self.add_input( - f"{self.config.commodity}_in", - shape_by_conn=True, - units=self.config.commodity_rate_units, - desc=f"{self.config.commodity} input timeseries from production to storage", - ) - - self.add_output( - f"{self.config.commodity}_set_point", - copy_shape=f"{self.config.commodity}_in", - units=self.config.commodity_rate_units, - desc=f"{self.config.commodity} output timeseries from plant after storage", - ) - - def compute(self, inputs, outputs): - """ - Pass through input to output flows. - - Args: - inputs (dict): Dictionary of input values. - - {commodity}_in: Input commodity flow. - outputs (dict): Dictionary of output values. - - {commodity}_out: Output commodity flow, equal to the input flow. - """ - - # Assign the input to the output - outputs[f"{self.config.commodity}_set_point"] = inputs[f"{self.config.commodity}_in"] - - def setup_partials(self): - """ - Declare partial derivatives as unity throughout the design space. - - This method specifies that the derivative of the output with respect to the input is - always 1.0, consistent with the pass-through behavior. - - Note: - This method is not currently used and isn't strictly needed if you're creating other - controllers; it is included as a nod towards potential future development enabling - more derivative information passing. - """ - - # Get the size of the input/output array - size = self._get_var_meta(f"{self.config.commodity}_in", "size") - - # Declare partials sparsely for all elements as an identity matrix - # (diagonal elements are 1.0, others are 0.0) - self.declare_partials( - of=f"{self.config.commodity}_set_point", - wrt=f"{self.config.commodity}_in", - rows=np.arange(size), - cols=np.arange(size), - val=np.ones(size), # Diagonal elements are 1.0 - ) diff --git a/h2integrate/control/control_strategies/pyomo_controller_baseclass.py b/h2integrate/control/control_strategies/pyomo_storage_controller_baseclass.py similarity index 50% rename from h2integrate/control/control_strategies/pyomo_controller_baseclass.py rename to h2integrate/control/control_strategies/pyomo_storage_controller_baseclass.py index b2693d207..2de73541f 100644 --- a/h2integrate/control/control_strategies/pyomo_controller_baseclass.py +++ b/h2integrate/control/control_strategies/pyomo_storage_controller_baseclass.py @@ -14,7 +14,7 @@ @define(kw_only=True) -class PyomoControllerBaseConfig(BaseConfig): +class PyomoStorageControllerBaseConfig(BaseConfig): """ Configuration data container for Pyomo-based storage / dispatch controllers. @@ -26,13 +26,13 @@ class PyomoControllerBaseConfig(BaseConfig): max_capacity (float): Physical maximum stored commodity capacity (inventory, not a rate). Units correspond to the base commodity units (e.g., kg, MWh). - max_charge_fraction (float): + max_soc_fraction (float): Upper bound on state of charge expressed as a fraction in [0, 1]. 1.0 means the controller may fill to max_capacity. - min_charge_fraction (float): + min_soc_fraction (float): Lower bound on state of charge expressed as a fraction in [0, 1]. 0.0 allows full depletion; >0 reserves minimum inventory. - init_charge_fraction (float): + init_soc_fraction (float): Initial state of charge at simulation start as a fraction in [0, 1]. n_control_window (int): Number of consecutive timesteps processed per control action @@ -55,9 +55,9 @@ class PyomoControllerBaseConfig(BaseConfig): """ max_capacity: float = field() - max_charge_fraction: float = field(validator=range_val(0, 1)) - min_charge_fraction: float = field(validator=range_val(0, 1)) - init_charge_fraction: float = field(validator=range_val(0, 1)) + max_soc_fraction: float = field(validator=range_val(0, 1)) + min_soc_fraction: float = field(validator=range_val(0, 1)) + init_soc_fraction: float = field(validator=range_val(0, 1)) n_control_window: int = field() commodity: str = field() commodity_rate_units: str = field() @@ -74,16 +74,12 @@ def __attrs_post_init__(self): ] * self.n_control_window -def dummy_function(): - """Dummy function used for setting OpenMDAO input/output defaults but otherwise unused. +class PyomoStorageControllerBaseClass(om.ExplicitComponent): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model - Returns: - None: empty output - """ - return None - - -class PyomoControllerBaseClass(om.ExplicitComponent): def initialize(self): """ Declare options for the component. See "Attributes" section in class doc strings for @@ -109,9 +105,7 @@ def dummy_method(self, in1, in2): def setup(self): """Register per-technology dispatch rule inputs and expose the solver callable. - Adds discrete inputs named 'dispatch_block_rule_function' (and variants - suffixed with source tech names for cross-tech connections) plus a - discrete output 'pyomo_dispatch_solver' that will hold the assembled + Adds discrete output 'pyomo_dispatch_solver' that will hold the assembled callable after compute(). """ @@ -123,25 +117,11 @@ def setup(self): # create inputs for all pyomo object creation functions from all connected technologies self.dispatch_connections = self.options["plant_config"]["tech_to_dispatch_connections"] - for connection in self.dispatch_connections: - # get connection definition - source_tech, intended_dispatch_tech = connection - if any(intended_dispatch_tech in name for name in self.tech_group_name): - if source_tech == intended_dispatch_tech: - # When getting rules for the same tech, the tech name is not used in order to - # allow for automatic connections rather than complicating the h2i model set up - self.add_discrete_input("dispatch_block_rule_function", val=self.dummy_method) - else: - self.add_discrete_input( - f"{'dispatch_block_rule_function'}_{source_tech}", val=self.dummy_method - ) - else: - continue # create output for the pyomo control model self.add_discrete_output( "pyomo_dispatch_solver", - val=dummy_function, + val=lambda: None, desc="callable: fully formed pyomo model and execution logic to be run \ by owning technologies performance model", ) @@ -151,43 +131,16 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): discrete_outputs["pyomo_dispatch_solver"] = self.pyomo_setup(discrete_inputs) def pyomo_setup(self, discrete_inputs): - """Create the Pyomo model, attach per-tech Blocks, and return dispatch solver. + """Create the Pyomo model and return dispatch solver. Returns: callable: Function(performance_model, performance_model_kwargs, inputs, commodity) - executing rolling-window heuristic dispatch or optimization and returning: + executing rolling-window dispatch and returning: (total_out, storage_out, unmet_demand, unused_commodity, soc) """ # initialize the pyomo model self.pyomo_model = pyomo.ConcreteModel() - index_set = pyomo.Set(initialize=range(self.config.n_control_window)) - - self.source_techs = [] - self.dispatch_tech = [] - - # run each pyomo rule set up function for each technology - for connection in self.dispatch_connections: - # get connection definition - source_tech, intended_dispatch_tech = connection - # only add connections to intended dispatch tech - if any(intended_dispatch_tech in name for name in self.tech_group_name): - # names are specified differently if connecting within the tech group vs - # connecting from an external tech group. This facilitates OM connections - if source_tech == intended_dispatch_tech: - dispatch_block_rule_function = discrete_inputs["dispatch_block_rule_function"] - self.dispatch_tech.append(source_tech) - else: - dispatch_block_rule_function = discrete_inputs[ - f"{'dispatch_block_rule_function'}_{source_tech}" - ] - # create pyomo block and set attr - blocks = pyomo.Block(index_set, rule=dispatch_block_rule_function) - setattr(self.pyomo_model, source_tech, blocks) - self.source_techs.append(source_tech) - else: - continue - # define dispatch solver def pyomo_dispatch_solver( performance_model: callable, @@ -201,9 +154,9 @@ def pyomo_dispatch_solver( Iterates over the full simulation period in chunks of size `self.config.n_control_window`, (re)configures per-window dispatch - parameters, invokes a heuristic control strategy to set fixed - dispatch decisions, and then calls the provided performance_model - over each window to obtain storage output and SOC trajectories. + parameters, applies the chosen control strategy, and then calls the + provided performance_model over each window to obtain storage output and + SOC trajectories. Args: performance_model (callable): @@ -224,15 +177,9 @@ def pyomo_dispatch_solver( self.config.commodity. Returns: - tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: - total_commodity_out : - Net commodity supplied to demand each timestep (min(demand, storage + gen)). + tuple[np.ndarray, np.ndarray]: storage_commodity_out : Commodity supplied (positive) by the storage asset each timestep. - unmet_demand : - Positive shortfall = demand - total_out (0 if fully met). - unused_commodity : - Surplus generation + storage discharge not used to meet demand. soc : State of charge trajectory (percent of capacity). @@ -245,107 +192,19 @@ def pyomo_dispatch_solver( """ # initialize outputs - unmet_demand = np.zeros(self.n_timesteps) storage_commodity_out = np.zeros(self.n_timesteps) - total_commodity_out = np.zeros(self.n_timesteps) - unused_commodity = np.zeros(self.n_timesteps) soc = np.zeros(self.n_timesteps) - # get the starting index for each control window - window_start_indices = list(range(0, self.n_timesteps, self.config.n_control_window)) - - control_strategy = self.options["tech_config"]["control_strategy"]["model"] - - # TODO: implement optional kwargs for this method: maybe this will remove if statement here - if "Heuristic" in control_strategy: - # Initialize parameters for heuristic dispatch strategy - self.initialize_parameters(inputs) - elif "Optimized" in control_strategy: - # Initialize parameters for optimized dispatch strategy - self.initialize_parameters( - inputs[f"{commodity_name}_in"], inputs[f"{commodity_name}_demand"] - ) - - else: - raise ( - NotImplementedError( - f"Control strategy '{control_strategy}' was given, \ - but has not been implemented yet." - ) - ) - - # loop over all control windows, where t is the starting index of each window - for t in window_start_indices: - # get the inputs over the current control window - commodity_in = inputs[f"{self.config.commodity}_in"][ - t : t + self.config.n_control_window - ] - demand_in = inputs[f"{commodity_name}_demand"][t : t + self.config.n_control_window] - - if "Heuristic" in control_strategy: - # Update time series parameters for the heuristic method - self.update_time_series_parameters() - # determine dispatch commands for the current control window - # using the heuristic method - self.set_fixed_dispatch( - commodity_in, - self.config.system_commodity_interface_limit, - demand_in, - ) - - elif "Optimized" in control_strategy: - # Progress report - if t % (self.n_timesteps // 4) < self.n_control_window: - percentage = round((t / self.n_timesteps) * 100) - print(f"{percentage}% done with optimal dispatch") - # Update time series parameters for the optimization method - self.update_time_series_parameters( - commodity_in=commodity_in, - commodity_demand=demand_in, - updated_initial_soc=self.updated_initial_soc, - ) - # Run dispatch optimization to minimize costs while meeting demand - self.solve_dispatch_model( - start_time=t, - n_days=self.n_timesteps // 24, - ) - - else: - raise ( - NotImplementedError( - f"Control strategy '{control_strategy}' was given, \ - but has not been implemented yet." - ) - ) - - # run the performance/simulation model for the current control window - # using the dispatch commands - storage_commodity_out_control_window, soc_control_window = performance_model( - self.storage_dispatch_commands, - **performance_model_kwargs, - sim_start_index=t, - ) - # update SOC for next time window - self.updated_initial_soc = soc_control_window[-1] / 100 # turn into ratio - - # get a list of all time indices belonging to the current control window - window_indices = list(range(t, t + self.config.n_control_window)) - - # loop over all time steps in the current control window - for j in window_indices: - # save the output for the control window to the output for the full - # simulation - storage_commodity_out[j] = storage_commodity_out_control_window[j - t] - soc[j] = soc_control_window[j - t] - total_commodity_out[j] = np.minimum( - demand_in[j - t], storage_commodity_out[j] + commodity_in[j - t] - ) - unmet_demand[j] = np.maximum(0, demand_in[j - t] - total_commodity_out[j]) - unused_commodity[j] = np.maximum( - 0, storage_commodity_out[j] + commodity_in[j - t] - demand_in[j - t] - ) - - return total_commodity_out, storage_commodity_out, unmet_demand, unused_commodity, soc + # This is where the pyomo controller interface is defined in individual + # pyomo controllers + + # The structure should be as follows: + # 1. initialize_parameters() + # 2. update_time_series_parameters() + # 3. solve dispatch model or set fixed dispatch + # 4. update outputs + + return storage_commodity_out, soc return pyomo_dispatch_solver diff --git a/h2integrate/control/control_strategies/storage/demand_openloop_controller.py b/h2integrate/control/control_strategies/storage/demand_openloop_storage_controller.py similarity index 52% rename from h2integrate/control/control_strategies/storage/demand_openloop_controller.py rename to h2integrate/control/control_strategies/storage/demand_openloop_storage_controller.py index 588b2aa32..e456f8d92 100644 --- a/h2integrate/control/control_strategies/storage/demand_openloop_controller.py +++ b/h2integrate/control/control_strategies/storage/demand_openloop_storage_controller.py @@ -5,27 +5,33 @@ from h2integrate.core.utilities import merge_shared_inputs from h2integrate.core.validators import gte_zero, range_val, range_val_or_none -from h2integrate.control.control_strategies.demand_openloop_controller import ( - DemandOpenLoopControlBase, - DemandOpenLoopControlBaseConfig, +from h2integrate.control.control_strategies.storage.openloop_storage_control_base import ( + StorageOpenLoopControlBase, + StorageOpenLoopControlBaseConfig, ) @define(kw_only=True) -class DemandOpenLoopStorageControllerConfig(DemandOpenLoopControlBaseConfig): +class DemandOpenLoopStorageControllerConfig(StorageOpenLoopControlBaseConfig): """ Configuration class for the DemandOpenLoopStorageController. This class defines the parameters required to configure the `DemandOpenLoopStorageController`. Attributes: + commodity (str): Name of the commodity being controlled + (e.g., "hydrogen"). Stripped of whitespace. + commodity_rate_units (str): Units of the commodity (e.g., "kg/h"). + demand_profile (int | float | list): Demand values for each timestep, in + the same units as `commodity_rate_units`. May be a scalar for constant + demand or a list/array for time-varying demand. max_capacity (float): Maximum storage capacity of the commodity (in non-rate units, e.g., "kg" if `commodity_rate_units` is "kg/h"). - max_charge_fraction (float): Maximum allowable state of charge (SOC) as a fraction + max_soc_fraction (float): Maximum allowable state of charge (SOC) as a fraction of `max_capacity`, between 0 and 1. - min_charge_fraction (float): Minimum allowable SOC as a fraction of `max_capacity`, + min_soc_fraction (float): Minimum allowable SOC as a fraction of `max_capacity`, between 0 and 1. - init_charge_fraction (float): Initial SOC as a fraction of `max_capacity`, + init_soc_fraction (float): Initial SOC as a fraction of `max_capacity`, between 0 and 1. max_charge_rate (float): Maximum rate at which the commodity can be charged (in units per time step, e.g., "kg/time step"). This rate does not include the charge_efficiency. @@ -45,12 +51,14 @@ class DemandOpenLoopStorageControllerConfig(DemandOpenLoopControlBaseConfig): discharging the storage, represented as a decimal between 0 and 1 (e.g., 0.81 for 81% efficiency). Optional if `charge_efficiency` and `discharge_efficiency` are provided. + commodity_amount_units (str | None, optional): Units of the commodity as an amount + (i.e., kW*h or kg). If not provided, defaults to commodity_rate_units*h. """ max_capacity: float = field() - max_charge_fraction: float = field(validator=range_val(0, 1)) - min_charge_fraction: float = field(validator=range_val(0, 1)) - init_charge_fraction: float = field(validator=range_val(0, 1)) + max_soc_fraction: float = field(validator=range_val(0, 1)) + min_soc_fraction: float = field(validator=range_val(0, 1)) + init_soc_fraction: float = field(validator=range_val(0, 1)) max_charge_rate: float = field(validator=gte_zero) charge_equals_discharge: bool = field(default=True) max_discharge_rate: float | None = field(default=None) @@ -67,22 +75,20 @@ def __attrs_post_init__(self): it calculates `charge_efficiency` and `discharge_efficiency` as the square root of `round_trip_efficiency`. """ - if self.round_trip_efficiency is not None: - if self.charge_efficiency is not None or self.discharge_efficiency is not None: - raise ValueError( - "Provide either `round_trip_efficiency` or both `charge_efficiency` " - "and `discharge_efficiency`, but not both." - ) + super().__attrs_post_init__() + + if (self.round_trip_efficiency is not None) and ( + self.charge_efficiency is None and self.discharge_efficiency is None + ): # Calculate charge and discharge efficiencies from round-trip efficiency self.charge_efficiency = np.sqrt(self.round_trip_efficiency) self.discharge_efficiency = np.sqrt(self.round_trip_efficiency) - elif self.charge_efficiency is not None and self.discharge_efficiency is not None: - # Ensure both charge and discharge efficiencies are provided - pass - else: + self.round_trip_efficiency = None + if self.charge_efficiency is None or self.discharge_efficiency is None: raise ValueError( - "You must provide either `round_trip_efficiency` or both " - "`charge_efficiency` and `discharge_efficiency`." + "Exactly one of the following sets of parameters must be set: (a) " + "`round_trip_efficiency`, or (b) both `charge_efficiency` " + "and `discharge_efficiency`." ) if self.charge_equals_discharge: @@ -100,70 +106,21 @@ def __attrs_post_init__(self): self.max_discharge_rate = self.max_charge_rate -class DemandOpenLoopStorageController(DemandOpenLoopControlBase): +class DemandOpenLoopStorageController(StorageOpenLoopControlBase): """ A controller that manages commodity flow based on demand and storage constraints. - The `DemandOpenLoopStorageController` computes the state of charge (SOC), output flow, - curtailment, and missed load for a commodity storage system. It uses a demand profile - and storage parameters to determine how much of the commodity to charge, discharge, - or curtail at each time step. - - Note: the units of the outputs are the same as the commodity units, which is typically a rate - in H2Integrate (e.g. kg/h) - - Attributes: - config (DemandOpenLoopStorageControllerConfig): Configuration object containing parameters - such as commodity name, units, time steps, storage capacity, charge/discharge rates, - efficiencies, and demand profile. - - Inputs: - {commodity}_in (float): Input commodity flow timeseries (e.g., hydrogen production). - - Units: Defined in `commodity_rate_units` (e.g., "kg/h"). - - Outputs: - {commodity}_out (float): Output commodity flow timeseries after storage to meet demand. - - Units: Defined in `commodity_rate_units` (e.g., "kg/h"). - - Note: the may include commodity from commodity_in that was never used to charge the - storage system but was directly dispatched to meet demand. - {commodity}_soc (float): State of charge (SOC) timeseries for the storage system. - - Units: "unitless" (percentage of maximum capacity given as a ratio between 0 and 1). - {commodity}_unused_commodity (float): Curtailment timeseries for unused - input commodity. - - Units: Defined in `commodity_rate_units` (e.g., "kg/h"). - - Note: curtailment in this case does not reduce what the converter produces, but - rather the system just does not use it (throws it away) because this controller is - specific to the storage technology and has no influence on other technologies in - the system. - {commodity}_unmet_demand (float): Unmet demand timeseries when demand exceeds supply. - Same meaning as missed load. - - Units: Defined in `commodity_rate_units` (e.g., "kg/h"). - total_{commodity}_unmet_demand (float): Total unmet demand over the simulation period. - - Units: Defined in `commodity_rate_units` (e.g., "kg"). + The `DemandOpenLoopStorageController` computes the dispatch commands for a commodity storage + system. It uses a demand profile and storage parameters to determine how much of the + commodity to charge, discharge, or curtail at each time step. """ - def setup(self): - """ - Set up inputs, outputs, and configuration for the open-loop storage controller. - - This method initializes the controller configuration from the technology - configuration, establishes the number of simulation time steps, adds inputs - related to storage constraints (e.g., maximum charge rate and storage capacity), - and defines outputs such as the commodity state-of-charge (SOC) timeseries - and the estimated storage duration. + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model - Inputs defined: - * ``max_charge_rate``: Maximum rate at which storage can charge/discharge. - * ``max_capacity``: Maximum total storage capacity. - - Outputs defined: - * ``_soc``: Timeseries of storage state of charge. - * ``storage_duration``: Estimated duration (hours) the storage can - discharge at its maximum rate. - - Returns: - None - """ + def setup(self): self.config = DemandOpenLoopStorageControllerConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "control"), strict=False, @@ -173,8 +130,7 @@ def setup(self): self.n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"] - commodity = self.config.commodity - + # Design constraints of storage system self.add_input( "max_charge_rate", val=self.config.max_charge_rate, @@ -183,24 +139,19 @@ def setup(self): ) self.add_input( - "max_capacity", + "storage_capacity", val=self.config.max_capacity, - units=self.config.commodity_rate_units + "*h", + units=self.config.commodity_amount_units, desc="Maximum storage capacity", ) - self.add_output( - f"{commodity}_soc", - copy_shape=f"{commodity}_in", - units="unitless", - desc=f"{commodity} state of charge timeseries for storage", - ) - - self.add_output( - "storage_duration", - units="h", - desc="Estimated storage duration based on max capacity and discharge rate", - ) + if not self.config.charge_equals_discharge: + self.add_input( + "max_discharge_rate", + val=self.config.max_discharge_rate, + units=self.config.commodity_rate_units, + desc="Storage discharge rate", + ) def compute(self, inputs, outputs): """ @@ -221,12 +172,8 @@ def compute(self, inputs, outputs): * ``max_capacity``: Maximum total storage capacity. Outputs populated: - * ``_soc``: State-of-charge timeseries (unitless). - * ``_out``: Output delivered to meet demand. - * ``_unused_commodity``: Curtailment timeseries. - * ``_unmet_demand``: Missed load timeseries. - * ``total__unmet_demand``: Aggregated unmet demand. - * ``storage_duration``: Estimated discharge duration at maximum rate (hours). + * ``_set_point``: Dispatch command to storage, + negative when charging, positive when discharging. Control logic includes: * Enforcing SOC limits (min, max, and initial conditions). @@ -251,65 +198,63 @@ def compute(self, inputs, outputs): f"{inputs['max_charge_rate']}" ) raise UserWarning(msg) - if inputs["max_capacity"][0] < 0: + if inputs["storage_capacity"][0] < 0: msg = ( - f"max_capacity cannot be less than zero and has value of " - f"{inputs['max_capacity']}" + f"storage_capacity cannot be less than zero and has value of " + f"{inputs['storage_capacity']}" ) raise UserWarning(msg) - max_capacity = inputs["max_capacity"].item() - max_charge_fraction = self.config.max_charge_fraction - min_charge_fraction = self.config.min_charge_fraction - init_charge_fraction = self.config.init_charge_fraction + max_capacity = inputs["storage_capacity"].item() max_charge_rate = inputs["max_charge_rate"].item() + if self.config.charge_equals_discharge: max_discharge_rate = inputs["max_charge_rate"].item() else: - max_discharge_rate = float(self.config.max_discharge_rate) - charge_efficiency = float(self.config.charge_efficiency) - discharge_efficiency = float(self.config.discharge_efficiency) + max_discharge_rate = inputs["max_discharge_rate"].item() + + soc_max = self.config.max_soc_fraction + soc_min = self.config.min_soc_fraction + init_soc_fraction = self.config.init_soc_fraction + + charge_eff = float(self.config.charge_efficiency) + discharge_eff = float(self.config.discharge_efficiency) # Initialize time-step state of charge prior to loop so the loop starts with # the previous time step's value - soc = deepcopy(init_charge_fraction) + soc = deepcopy(init_soc_fraction) demand_profile = inputs[f"{commodity}_demand"] # initialize outputs - soc_array = outputs[f"{commodity}_soc"] - unused_commodity_array = outputs[f"{commodity}_unused_commodity"] - output_array = outputs[f"{commodity}_set_point"] - unmet_demand_array = outputs[f"{commodity}_unmet_demand"] - + soc_array = np.zeros(self.n_timesteps) + set_point_array = np.zeros(self.n_timesteps) + combined_output_array = np.zeros(self.n_timesteps) # Loop through each time step for t, demand_t in enumerate(demand_profile): # Get the input flow at the current time step input_flow = inputs[f"{commodity}_in"][t] # Calculate the available charge/discharge capacity - available_charge = float((max_charge_fraction - soc) * max_capacity) - available_discharge = float((soc - min_charge_fraction) * max_capacity) - - # Initialize persistent variables for curtailment and missed load - unused_input = 0.0 - charge = 0.0 + available_charge = float((soc_max - soc) * max_capacity) + available_discharge = float((soc - soc_min) * max_capacity) # Determine the output flow based on demand_t and SOC if demand_t > input_flow: # Discharge storage to meet demand. # `discharge_needed` is as seen by the storage - discharge_needed = (demand_t - input_flow) / discharge_efficiency + discharge_needed = (demand_t - input_flow) / discharge_eff # `discharge` is as seen by the storage, but `max_discharge_rate` is as observed # outside the storage discharge = min( - discharge_needed, available_discharge, max_discharge_rate / discharge_efficiency + discharge_needed, available_discharge, max_discharge_rate / discharge_eff ) soc -= discharge / max_capacity # soc is a ratio with value between 0 and 1 # output is as observed outside the storage, so we need to adjust `discharge` by # applying `discharge_efficiency`. - output_array[t] = input_flow + discharge * discharge_efficiency + combined_output_array[t] = input_flow + discharge * discharge_eff + set_point_array[t] = discharge * discharge_eff else: # Charge storage with unused input # `unused_input` is as seen outside the storage @@ -319,38 +264,16 @@ def compute(self, inputs, outputs): # seen outside the storage so we need to adjust `available_charge` outside the # storage view and the final result back into the storage view. charge = ( - min(unused_input, available_charge / charge_efficiency, max_charge_rate) - * charge_efficiency + min(unused_input, available_charge / charge_eff, max_charge_rate) * charge_eff ) soc += charge / max_capacity # soc is a ratio with value between 0 and 1 - output_array[t] = demand_t + combined_output_array[t] = demand_t + set_point_array[t] = -1 * charge / charge_eff # Ensure SOC stays within bounds - soc = max(min_charge_fraction, min(max_charge_fraction, soc)) + soc = max(soc_min, min(soc_max, soc)) # Record the SOC for the current time step soc_array[t] = deepcopy(soc) - # Record the curtailment at the current time step. Adjust `charge` from storage view to - # outside view for curtailment - unused_commodity_array[t] = max(0.0, unused_input - charge / charge_efficiency) - - # Record the missed load at the current time step - unmet_demand_array[t] = max(0.0, (demand_t - output_array[t])) - - outputs[f"{commodity}_set_point"] = output_array - - # Return the SOC - outputs[f"{commodity}_soc"] = soc_array - - # Return the unused commodity - outputs[f"{commodity}_unused_commodity"] = unused_commodity_array - - # Return the unmet load demand - outputs[f"{commodity}_unmet_demand"] = unmet_demand_array - - # Calculate and return the total unmet demand over the simulation period - outputs[f"total_{commodity}_unmet_demand"] = np.sum(unmet_demand_array) - - # Output the storage duration in hours - outputs["storage_duration"] = max_capacity / max_discharge_rate + outputs[f"{commodity}_set_point"] = set_point_array diff --git a/h2integrate/control/control_strategies/heuristic_pyomo_controller.py b/h2integrate/control/control_strategies/storage/heuristic_pyomo_controller.py similarity index 58% rename from h2integrate/control/control_strategies/heuristic_pyomo_controller.py rename to h2integrate/control/control_strategies/storage/heuristic_pyomo_controller.py index 85729da98..0c875f06a 100644 --- a/h2integrate/control/control_strategies/heuristic_pyomo_controller.py +++ b/h2integrate/control/control_strategies/storage/heuristic_pyomo_controller.py @@ -1,12 +1,14 @@ from typing import TYPE_CHECKING +import numpy as np +import pyomo.environ as pyomo from attrs import field, define from h2integrate.core.utilities import merge_shared_inputs from h2integrate.core.validators import range_val_or_none -from h2integrate.control.control_strategies.pyomo_controller_baseclass import ( - PyomoControllerBaseClass, - PyomoControllerBaseConfig, +from h2integrate.control.control_strategies.pyomo_storage_controller_baseclass import ( + PyomoStorageControllerBaseClass, + PyomoStorageControllerBaseConfig, ) @@ -15,12 +17,51 @@ @define(kw_only=True) -class HeuristicLoadFollowingControllerConfig(PyomoControllerBaseConfig): +class HeuristicLoadFollowingStorageControllerConfig(PyomoStorageControllerBaseConfig): + """Configuration class for the HeuristicLoadFollowingStorageController. + + Attributes: + charge_efficiency (float | None, optional): Efficiency of charging the storage, represented + as a decimal between 0 and 1 (e.g., 0.9 for 90% efficiency). Must be `None` if + `round_trip_efficiency` is provided. + discharge_efficiency (float | None, optional): Efficiency of discharging the storage, + represented as a decimal between 0 and 1 (e.g., 0.9 for 90% efficiency). Must be `None` + if `round_trip_efficiency` is provided. + round_trip_efficiency (float | None, optional): Combined efficiency of charging and + discharging the storage, represented as a decimal between 0 and 1 (e.g., 0.81 for + 81% efficiency). Must be `None` if `charge_efficiency` or `discharge_efficiency` are + provided. + """ + charge_efficiency: float = field(validator=range_val_or_none(0, 1), default=None) discharge_efficiency: float = field(validator=range_val_or_none(0, 1), default=None) + round_trip_efficiency: float | None = field(default=None, validator=range_val_or_none(0, 1)) + + def __attrs_post_init__(self): + """ + Post-initialization logic to validate and calculate efficiencies. + + Ensures that either `charge_efficiency` and `discharge_efficiency` are provided, + or `round_trip_efficiency` is provided. If `round_trip_efficiency` is provided, + it calculates `charge_efficiency` and `discharge_efficiency` as the square root + of `round_trip_efficiency`. + """ + + super().__attrs_post_init__() + if self.round_trip_efficiency is not None: + if self.charge_efficiency is not None or self.discharge_efficiency is not None: + raise ValueError( + "Exactly one of the following sets of parameters must be set: (a) " + "`round_trip_efficiency`, or (b) both `charge_efficiency` " + "and `discharge_efficiency`." + ) + + # Calculate charge and discharge efficiencies from round-trip efficiency + self.charge_efficiency = np.sqrt(self.round_trip_efficiency) + self.discharge_efficiency = np.sqrt(self.round_trip_efficiency) -class HeuristicLoadFollowingController(PyomoControllerBaseClass): +class HeuristicLoadFollowingStorageController(PyomoStorageControllerBaseClass): """Operates storage based on heuristic rules to meet the demand profile based on available commodity from generation profiles and demand profile. @@ -29,9 +70,14 @@ class HeuristicLoadFollowingController(PyomoControllerBaseClass): """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): """Initialize the heuristic load-following controller.""" - self.config = HeuristicLoadFollowingControllerConfig.from_dict( + self.config = HeuristicLoadFollowingStorageControllerConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "control"), additional_cls_name=self.__class__.__name__, strict=False, @@ -41,6 +87,21 @@ def setup(self): super().setup() + for connection in self.dispatch_connections: + # get connection definition + source_tech, intended_dispatch_tech = connection + if any(intended_dispatch_tech in name for name in self.tech_group_name): + if source_tech == intended_dispatch_tech: + # When getting rules for the same tech, the tech name is not used in order to + # allow for automatic connections rather than complicating the h2i model set up + self.add_discrete_input("dispatch_block_rule_function", val=self.dummy_method) + else: + self.add_discrete_input( + f"{'dispatch_block_rule_function'}_{source_tech}", val=self.dummy_method + ) + else: + continue + self.add_input( "storage_capacity", val=self.config.max_capacity, @@ -48,23 +109,155 @@ def setup(self): desc="Storage capacity", ) - self.max_charge_fraction = [0.0] * self.config.n_control_window + self.max_soc_fraction = [0.0] * self.config.n_control_window self.max_discharge_fraction = [0.0] * self.config.n_control_window self._fixed_dispatch = [0.0] * self.config.n_control_window + def pyomo_setup(self, discrete_inputs): + """Create the Pyomo model, attach per-tech Blocks, and return dispatch solver. + + Returns: + callable: Function(performance_model, performance_model_kwargs, inputs, commodity) + executing rolling-window heuristic dispatch and returning: + (total_out, storage_out, unmet_demand, unused_commodity, soc) + """ + # initialize the pyomo model + self.pyomo_model = pyomo.ConcreteModel() + + index_set = pyomo.Set(initialize=range(self.config.n_control_window)) + + # run each pyomo rule set up function for each technology + for connection in self.dispatch_connections: + # get connection definition + source_tech, intended_dispatch_tech = connection + # only add connections to intended dispatch tech + if any(intended_dispatch_tech in name for name in self.tech_group_name): + # names are specified differently if connecting within the tech group vs + # connecting from an external tech group. This facilitates OM connections + if source_tech == intended_dispatch_tech: + dispatch_block_rule_function = discrete_inputs["dispatch_block_rule_function"] + else: + dispatch_block_rule_function = discrete_inputs[ + f"{'dispatch_block_rule_function'}_{source_tech}" + ] + # create pyomo block and set attr + blocks = pyomo.Block(index_set, rule=dispatch_block_rule_function) + setattr(self.pyomo_model, source_tech, blocks) + else: + continue + + # define dispatch solver + def pyomo_dispatch_solver( + performance_model: callable, + performance_model_kwargs, + inputs, + pyomo_model=self.pyomo_model, + commodity_name: str = self.config.commodity, + ): + """ + Execute rolling-window dispatch for the controlled technology. + + Iterates over the full simulation period in chunks of size + `self.config.n_control_window`, (re)configures per-window dispatch + parameters, invokes a heuristic control strategy to set fixed + dispatch decisions, and then calls the provided performance_model + over each window to obtain storage output and SOC trajectories. + + Args: + performance_model (callable): + Function implementing the technology performance over a control + window. Signature must accept (storage_dispatch_commands, + **performance_model_kwargs, sim_start_index=) + and return (storage_out_window, soc_window) arrays of length + n_control_window. + performance_model_kwargs (dict): + Extra keyword arguments forwarded unchanged to performance_model + at window (e.g., efficiencies, timestep size). + inputs (dict): + Dictionary of numpy arrays (length = self.n_timesteps) containing at least: + f"{commodity}_in" : available generated commodity profile. + f"{commodity}_demand" : demanded commodity output profile. + commodity (str, optional): + Base commodity name (e.g. "electricity", "hydrogen"). Default: + self.config.commodity. + + Returns: + tuple[np.ndarray, np.ndarray]: + storage_commodity_out : + Commodity supplied (positive) by the storage asset each timestep. + soc : + State of charge trajectory (percent of capacity). + + Raises: + NotImplementedError: + If the configured control strategy is not implemented. + + Notes: + 1. Arrays returned have length self.n_timesteps (full simulation period). + """ + + # initialize outputs + storage_commodity_out = np.zeros(self.n_timesteps) + soc = np.zeros(self.n_timesteps) + + # get the starting index for each control window + window_start_indices = list(range(0, self.n_timesteps, self.config.n_control_window)) + + self.initialize_parameters(inputs) + + # loop over all control windows, where t is the starting index of each window + for t in window_start_indices: + # get the inputs over the current control window + commodity_in = inputs[self.config.commodity + "_in"][ + t : t + self.config.n_control_window + ] + demand_in = inputs[f"{commodity_name}_demand"][t : t + self.config.n_control_window] + + # Update time series parameters for the heuristic method + self.update_time_series_parameters() + # determine dispatch commands for the current control window + # using the heuristic method + self.set_fixed_dispatch( + commodity_in, + self.config.system_commodity_interface_limit, + demand_in, + ) + + # run the performance/simulation model for the current control window + # using the dispatch commands + storage_commodity_out_control_window, soc_control_window = performance_model( + self.storage_dispatch_commands, + **performance_model_kwargs, + sim_start_index=t, + ) + + # get a list of all time indices belonging to the current control window + window_indices = list(range(t, t + self.config.n_control_window)) + + # loop over all time steps in the current control window + for j in window_indices: + # save the output for the control window to the output for the full + # simulation + storage_commodity_out[j] = storage_commodity_out_control_window[j - t] + soc[j] = soc_control_window[j - t] + + return storage_commodity_out, soc + + return pyomo_dispatch_solver + + def initialize_parameters(self, inputs): + """Initializes parameters.""" + if self.config.charge_efficiency is not None: self.charge_efficiency = self.config.charge_efficiency if self.config.discharge_efficiency is not None: self.discharge_efficiency = self.config.discharge_efficiency - def initialize_parameters(self, inputs): - """Initializes parameters.""" - self.minimum_storage = 0.0 self.maximum_storage = inputs["storage_capacity"][0] - self.minimum_soc = self.config.min_charge_fraction - self.maximum_soc = self.config.max_charge_fraction - self.initial_soc = self.config.init_charge_fraction + self.minimum_soc = self.config.min_soc_fraction + self.maximum_soc = self.config.max_soc_fraction + self.initial_soc = self.config.init_soc_fraction def update_time_series_parameters(self, start_time: int = 0): """Updates time series parameters. @@ -148,7 +341,7 @@ def _set_commodity_fraction_limits( """ for t in self.blocks.index_set(): - self.max_charge_fraction[t] = self.enforce_power_fraction_simple_bounds( + self.max_soc_fraction[t] = self.enforce_power_fraction_simple_bounds( (commodity_in[t]) / self.maximum_storage, self.minimum_soc, self.maximum_soc ) self.max_discharge_fraction[t] = self.enforce_power_fraction_simple_bounds( @@ -172,8 +365,8 @@ def _heuristic_method(self, commodity_in, commodity_demand): if fd > self.max_discharge_fraction[t]: fd = self.max_discharge_fraction[t] elif fd < 0.0: # Charging - if -fd > self.max_charge_fraction[t]: - fd = -self.max_charge_fraction[t] + if -fd > self.max_soc_fraction[t]: + fd = -self.max_soc_fraction[t] self._fixed_dispatch[t] = fd @staticmethod @@ -240,8 +433,8 @@ def _enforce_power_fraction_limits(self): if fd > self.max_discharge_fraction[t]: fd = self.max_discharge_fraction[t] elif fd < 0.0: # Charging - if -fd > self.max_charge_fraction[t]: - fd = -self.max_charge_fraction[t] + if -fd > self.max_soc_fraction[t]: + fd = -self.max_soc_fraction[t] self._fixed_dispatch[t] = fd def _fix_dispatch_model_variables(self): diff --git a/h2integrate/control/control_strategies/storage/openloop_storage_control_base.py b/h2integrate/control/control_strategies/storage/openloop_storage_control_base.py new file mode 100644 index 000000000..9f013d311 --- /dev/null +++ b/h2integrate/control/control_strategies/storage/openloop_storage_control_base.py @@ -0,0 +1,87 @@ +import openmdao.api as om +from attrs import field, define + +from h2integrate.core.utilities import BaseConfig + + +@define(kw_only=True) +class StorageOpenLoopControlBaseConfig(BaseConfig): + """ + Configuration class for the open-loop storage control models. + + Attributes: + commodity (str): Name of the commodity being stored (e.g., "hydrogen"). + commodity_rate_units (str): Rate units of the commodity (e.g., "kg/h" or "kW"). + demand_profile (int | float | list): Demand values for each timestep, in + the same units as `commodity_rate_units`. May be a scalar for constant + demand or a list/array for time-varying demand. + commodity_amount_units (str | None, optional): Units of the commodity as an amount + (i.e., kW*h or kg). If not provided, defaults to `commodity_rate_units*h`. + + """ + + commodity: str = field() + commodity_rate_units: str = field() + demand_profile: int | float | list = field() + commodity_amount_units: str = field(default=None) + + def __attrs_post_init__(self): + if self.commodity_amount_units is None: + self.commodity_amount_units = f"({self.commodity_rate_units})*h" + + +class StorageOpenLoopControlBase(om.ExplicitComponent): + """Base OpenMDAO component for open-loop demand tracking. + + This component defines the interfaces required for open-loop demand + controllers, including inputs for demand, available commodity, and outputs + dispatch command profile. + """ + + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + + def initialize(self): + self.options.declare("driver_config", types=dict) + self.options.declare("plant_config", types=dict) + self.options.declare("tech_config", types=dict) + + def setup(self): + self.n_timesteps = int(self.options["plant_config"]["plant"]["simulation"]["n_timesteps"]) + + commodity = self.config.commodity + + self.add_input( + f"{commodity}_demand", + val=self.config.demand_profile, + shape=self.n_timesteps, + units=self.config.commodity_rate_units, + desc=f"Demand profile of {commodity}", + ) + + self.add_input( + f"{commodity}_in", + val=0.0, + shape=self.n_timesteps, + units=self.config.commodity_rate_units, + desc=f"Amount of {commodity} demand that has already been supplied", + ) + + self.add_output( + f"{commodity}_set_point", + val=0.0, + shape=self.n_timesteps, + units=self.config.commodity_rate_units, + desc=f"Dispatch commands for {commodity} storage", + ) + + def compute(): + """This method must be implemented by subclasses to define the + controller. + + Raises: + NotImplementedError: Always, unless implemented in a subclass. + """ + raise NotImplementedError("This method should be implemented in a subclass.") diff --git a/h2integrate/control/control_strategies/optimized_pyomo_controller.py b/h2integrate/control/control_strategies/storage/optimized_pyomo_controller.py similarity index 58% rename from h2integrate/control/control_strategies/optimized_pyomo_controller.py rename to h2integrate/control/control_strategies/storage/optimized_pyomo_controller.py index b9f0da125..1b89510c5 100644 --- a/h2integrate/control/control_strategies/optimized_pyomo_controller.py +++ b/h2integrate/control/control_strategies/storage/optimized_pyomo_controller.py @@ -1,5 +1,6 @@ from typing import TYPE_CHECKING +import numpy as np import pyomo.environ as pyomo from attrs import field, define from pyomo.util.check_units import assert_units_consistent @@ -7,12 +8,12 @@ from h2integrate.core.utilities import merge_shared_inputs from h2integrate.core.validators import range_val from h2integrate.control.control_rules.plant_dispatch_model import PyomoDispatchPlantModel -from h2integrate.control.control_strategies.pyomo_controller_baseclass import ( +from h2integrate.control.control_strategies.controller_opt_problem_state import DispatchProblemState +from h2integrate.control.control_strategies.pyomo_storage_controller_baseclass import ( SolverOptions, - PyomoControllerBaseClass, - PyomoControllerBaseConfig, + PyomoStorageControllerBaseClass, + PyomoStorageControllerBaseConfig, ) -from h2integrate.control.control_strategies.controller_opt_problem_state import DispatchProblemState from h2integrate.control.control_rules.storage.pyomo_storage_rule_min_operating_cost import ( PyomoRuleStorageMinOperatingCosts, ) @@ -26,7 +27,7 @@ @define -class OptimizedDispatchControllerConfig(PyomoControllerBaseConfig): +class OptimizedDispatchStorageControllerConfig(PyomoStorageControllerBaseConfig): """ Configuration data container for Pyomo-based optimal dispatch. @@ -79,19 +80,19 @@ def make_dispatch_inputs(self): "cost_per_discharge", "commodity_met_value", "max_capacity", - "max_charge_fraction", - "min_charge_fraction", + "max_soc_fraction", + "min_soc_fraction", "charge_efficiency", "discharge_efficiency", "max_charge_rate", ] dispatch_inputs = {k: self.as_dict()[k] for k in dispatch_keys} - dispatch_inputs.update({"initial_soc_fraction": self.init_charge_fraction}) + dispatch_inputs.update({"initial_soc_fraction": self.init_soc_fraction}) return dispatch_inputs -class OptimizedDispatchController(PyomoControllerBaseClass): +class OptimizedDispatchStorageController(PyomoStorageControllerBaseClass): """Operates storage based on optimization to meet the demand profile based on available commodity from generation profiles and demand profile while minimizing costs. @@ -99,9 +100,14 @@ class OptimizedDispatchController(PyomoControllerBaseClass): """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): """Initialize the optimized dispatch controller.""" - self.config = OptimizedDispatchControllerConfig.from_dict( + self.config = OptimizedDispatchStorageControllerConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "control") ) @@ -124,7 +130,7 @@ def setup(self): super().setup() self.n_control_window = self.config.n_control_window - self.updated_initial_soc = self.config.init_charge_fraction + self.updated_initial_soc = self.config.init_soc_fraction # Is this the best place to put this??? self.commodity_info = { @@ -137,16 +143,158 @@ def setup(self): self.dispatch_inputs = self.config.make_dispatch_inputs() - def initialize_parameters(self, commodity_in, commodity_demand): + def pyomo_setup(self, discrete_inputs): + """Create the Pyomo model, extract dispatch technology names, and return dispatch solver. + + Returns: + callable: Function(performance_model, performance_model_kwargs, inputs, commodity) + executing rolling-window optimization to determine dispatch and returning: + (total_out, storage_out, unmet_demand, unused_commodity, soc) + """ + # initialize the pyomo model + self.pyomo_model = pyomo.ConcreteModel() + + pyomo.Set(initialize=range(self.config.n_control_window)) + + self.source_techs = [] + self.dispatch_tech = [] + + for connection in self.dispatch_connections: + # get connection definition + source_tech, intended_dispatch_tech = connection + # only add connections to intended dispatch tech + if any(intended_dispatch_tech in name for name in self.tech_group_name): + # record source and dispatch techs + if source_tech == intended_dispatch_tech: + self.dispatch_tech.append(source_tech) + self.source_techs.append(source_tech) + else: + continue + + # define dispatch solver + def pyomo_dispatch_solver( + performance_model: callable, + performance_model_kwargs, + inputs, + pyomo_model=self.pyomo_model, + commodity_name: str = self.config.commodity, + ): + """ + Execute rolling-window dispatch for the controlled technology. + + Iterates over the full simulation period in chunks of size + `self.config.n_control_window`, (re)configures per-window dispatch + parameters, solves the Pyomo optimization model to determine + dispatch decisions, and then calls the provided performance_model + over each window to obtain storage output and SOC trajectories. + + Args: + performance_model (callable): + Function implementing the technology performance over a control + window. Signature must accept (storage_dispatch_commands, + **performance_model_kwargs, sim_start_index=) + and return (storage_out_window, soc_window) arrays of length + n_control_window. + performance_model_kwargs (dict): + Extra keyword arguments forwarded unchanged to performance_model + at window (e.g., efficiencies, timestep size). + inputs (dict): + Dictionary of numpy arrays (length = self.n_timesteps) containing at least: + f"{commodity}_in" : available generated commodity profile. + f"{commodity}_demand" : demanded commodity output profile. + commodity (str, optional): + Base commodity name (e.g. "electricity", "hydrogen"). Default: + self.config.commodity. + + Returns: + tuple[np.ndarray, np.ndarray]: + storage_commodity_out : + Commodity supplied (positive) by the storage asset each timestep. + soc : + State of charge trajectory (percent of capacity). + + Raises: + NotImplementedError: + If the configured control strategy is not implemented. + + Notes: + 1. Arrays returned have length self.n_timesteps (full simulation period). + """ + + # initialize outputs + storage_commodity_out = np.zeros(self.n_timesteps) + soc = np.zeros(self.n_timesteps) + + # get the starting index for each control window + window_start_indices = list(range(0, self.n_timesteps, self.config.n_control_window)) + + # Initialize parameters for optimized dispatch strategy + self.initialize_parameters(inputs) + + # loop over all control windows, where t is the starting index of each window + for t in window_start_indices: + # get the inputs over the current control window + commodity_in = inputs[f"{self.config.commodity}_in"][ + t : t + self.config.n_control_window + ] + demand_in = inputs[f"{commodity_name}_demand"][t : t + self.config.n_control_window] + + # Progress report + if t % (self.n_timesteps // 4) < self.n_control_window: + percentage = round((t / self.n_timesteps) * 100) + print(f"{percentage}% done with optimal dispatch") + # Update time series parameters for the optimization method + self.update_time_series_parameters( + commodity_in=commodity_in, + commodity_demand=demand_in, + updated_initial_soc=self.updated_initial_soc, + ) + # Run dispatch optimization to minimize costs while meeting demand + self.solve_dispatch_model( + start_time=t, + n_days=self.n_timesteps // 24, + ) + + # run the performance/simulation model for the current control window + # using the dispatch commands + storage_commodity_out_control_window, soc_control_window = performance_model( + self.storage_dispatch_commands, + **performance_model_kwargs, + sim_start_index=t, + ) + # update SOC for next time window + self.updated_initial_soc = soc_control_window[-1] / 100 # turn into ratio + + # get a list of all time indices belonging to the current control window + window_indices = list(range(t, t + self.config.n_control_window)) + + # loop over all time steps in the current control window + for j in window_indices: + # save the output for the control window to the output for the full + # simulation + storage_commodity_out[j] = storage_commodity_out_control_window[j - t] + soc[j] = soc_control_window[j - t] + + return storage_commodity_out, soc + + return pyomo_dispatch_solver + + def initialize_parameters(self, inputs): """Initialize parameters for optimization model Args: - commodity_in (list): List of generated commodity in for this time slice. - commodity_demand (list): The demanded commodity for this time slice. + inputs (dict): + Dictionary of numpy arrays (length = self.n_timesteps) containing at least: + f"{commodity}_in" : Available generated commodity profile. + f"{commodity}_demand" : Demanded commodity output profile. """ # Where pyomo model communicates with the rest of the controller # self.hybrid_dispatch_model is the pyomo model, this is the thing in hybrid_rule + if "max_charge_rate" in inputs: + self.dispatch_inputs["max_charge_rate"] = inputs["max_charge_rate"][0] + if "storage_capacity" in inputs: + self.dispatch_inputs["max_capacity"] = inputs["storage_capacity"][0] self.hybrid_dispatch_model = self._create_dispatch_optimization_model() self.hybrid_dispatch_rule.create_min_operating_cost_expression() self.hybrid_dispatch_rule.create_arcs() @@ -159,9 +307,7 @@ def initialize_parameters(self, commodity_in, commodity_demand): # hybrid_dispatch_rule is the thing where you can access variables and hybrid_rule \ # functions from - self.hybrid_dispatch_rule.initialize_parameters( - commodity_in, commodity_demand, self.dispatch_inputs - ) + self.hybrid_dispatch_rule.initialize_parameters(inputs, self.dispatch_inputs) def update_time_series_parameters( self, commodity_in=None, commodity_demand=None, updated_initial_soc=None diff --git a/h2integrate/control/control_strategies/storage/simple_openloop_controller.py b/h2integrate/control/control_strategies/storage/simple_openloop_controller.py new file mode 100644 index 000000000..418a8ec05 --- /dev/null +++ b/h2integrate/control/control_strategies/storage/simple_openloop_controller.py @@ -0,0 +1,105 @@ +import numpy as np +from attrs import field, define + +from h2integrate.core.utilities import merge_shared_inputs +from h2integrate.control.control_strategies.storage.openloop_storage_control_base import ( + StorageOpenLoopControlBase, + StorageOpenLoopControlBaseConfig, +) + + +@define(kw_only=True) +class SimpleStorageOpenLoopControllerConfig(StorageOpenLoopControlBaseConfig): + """Configuration class for the SimpleStorageOpenLoopController + + Attributes: + commodity (str): name of commodity + commodity_rate_units (str): Units of the commodity (e.g., kW or kg/h). + set_demand_as_avg_commodity_in (bool): If True, assume the demand is + equal to the mean input commodity. If False, uses the demand input. + demand_profile (int | float | list, optional): Demand values for each timestep, in + the same units as `commodity_rate_units`. May be a scalar for constant + demand or a list/array for time-varying demand. + Only used if `set_demand_as_avg_commodity_in` is False. Defaults to 0. + + """ + + set_demand_as_avg_commodity_in: bool = field() + demand_profile: int | float | list = field(default=0.0) + + def __attrs_post_init__(self): + super().__attrs_post_init__() + + if isinstance(self.demand_profile, list | np.ndarray): + user_input_dmd = True if sum(self.demand_profile) > 0 else False + else: + user_input_dmd = True if self.demand_profile > 0 else False + + if self.set_demand_as_avg_commodity_in and user_input_dmd: + # If using the average commodity in as the demand, + # warn users if they input the demand profile + msg = ( + "A non-zero demand profile was provided but set_demand_as_avg_commodity_in is True." + " The provided demand profile will not be used, the demand profile will be " + f"calculated as the mean of ``{self.commodity}_in``. " + ) + raise ValueError(msg) + + +class SimpleStorageOpenLoopController(StorageOpenLoopControlBase): + """ + A simple open-loop controller for storage systems. + + This controller directly sets a storage control set point as the difference between the + demand and the available input commodity. It is useful for testing, as a placeholder for + more complex storage controllers, and for maintaining consistency between controlled and + uncontrolled frameworks. + """ + + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + + def setup(self): + self.config = SimpleStorageOpenLoopControllerConfig.from_dict( + merge_shared_inputs(self.options["tech_config"]["model_inputs"], "control"), + additional_cls_name=self.__class__.__name__, + strict=False, + ) + + super().setup() + + def compute(self, inputs, outputs): + """ + Simple controller that outputs `commodity_set_point`, + the dispatch set-points for each timestep in `commodity_rate_units`. + Negative values command charging, positive values command discharging. + + """ + + if ( + self.config.set_demand_as_avg_commodity_in + and inputs[f"{self.config.commodity}_demand"].sum() > 0 + ): + msg = ( + "A non-zero demand profile was input but set_demand_as_avg_commodity_in is True." + " The input demand profile will not be used, the demand profile will be " + f"calculated as the mean of ``{self.config.commodity}_in``. " + ) + raise ValueError(msg) + + if self.config.set_demand_as_avg_commodity_in: + # Assume the demand is the average of the input commodity + commodity_demand = np.mean(inputs[f"{self.config.commodity}_in"]) * np.ones( + self.n_timesteps + ) + else: + commodity_demand = inputs[f"{self.config.commodity}_demand"] + + # Assign the set point as the difference between the demand and the input commodity + # when demand > input, the set point is positive to command discharging + # when demand < input, the set point is negative to command charging + outputs[f"{self.config.commodity}_set_point"] = ( + commodity_demand - inputs[f"{self.config.commodity}_in"] + ) diff --git a/h2integrate/control/control_strategies/storage/test/conftest.py b/h2integrate/control/control_strategies/storage/test/conftest.py new file mode 100644 index 000000000..fa8f250cf --- /dev/null +++ b/h2integrate/control/control_strategies/storage/test/conftest.py @@ -0,0 +1,5 @@ +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) diff --git a/h2integrate/control/test/inputs/tech_config.yaml b/h2integrate/control/control_strategies/storage/test/inputs/tech_config.yaml similarity index 93% rename from h2integrate/control/test/inputs/tech_config.yaml rename to h2integrate/control/control_strategies/storage/test/inputs/tech_config.yaml index de88ea0c8..9a1556e0b 100644 --- a/h2integrate/control/test/inputs/tech_config.yaml +++ b/h2integrate/control/control_strategies/storage/test/inputs/tech_config.yaml @@ -3,7 +3,7 @@ description: This hybrid plant produces hydrogen technologies: h2_storage: control_strategy: - model: PassThroughOpenLoopController + model: SimpleStorageOpenLoopController performance_model: model: StorageAutoSizingModel cost_model: diff --git a/h2integrate/control/control_strategies/storage/test/test_heuristic_controllers.py b/h2integrate/control/control_strategies/storage/test/test_heuristic_controllers.py new file mode 100644 index 000000000..bb7114c04 --- /dev/null +++ b/h2integrate/control/control_strategies/storage/test/test_heuristic_controllers.py @@ -0,0 +1,1079 @@ +import numpy as np +import pytest +import openmdao.api as om +from pytest import fixture + +from h2integrate.storage.battery.pysam_battery import PySAMBatteryPerformanceModel +from h2integrate.storage.storage_performance_model import StoragePerformanceModel +from h2integrate.storage.simple_storage_auto_sizing import StorageAutoSizingModel +from h2integrate.control.control_rules.storage.pyomo_storage_rule_baseclass import ( + PyomoRuleStorageBaseclass, +) +from h2integrate.control.control_strategies.storage.heuristic_pyomo_controller import ( + HeuristicLoadFollowingStorageController, +) + + +@fixture +def plant_config_battery(): + plant_config_dict = { + "plant": { + "plant_life": 30, + "simulation": { + "dt": 3600, + "n_timesteps": 8760, + }, + }, + "tech_to_dispatch_connections": [ + ["battery", "battery"], + ], + } + return plant_config_dict + + +@fixture +def tech_config_battery(): + tech_config_dict = { + "name": "technology_config", + "description": "...", + "technologies": { + "battery": { + "dispatch_rule_set": {"model": "PyomoRuleStorageBaseclass"}, + "control_strategy": {"model": "HeuristicLoadFollowingStorageController"}, + "performance_model": {"model": "PySAMBatteryPerformanceModel"}, + "model_inputs": { + "shared_parameters": { + "max_charge_rate": 50000, + "max_capacity": 200000, + "n_control_window": 24, + "n_horizon_window": 48, + "init_soc_fraction": 0.5, + "max_soc_fraction": 0.9, + "min_soc_fraction": 0.1, + }, + "performance_parameters": { + "chemistry": "LFPGraphite", + "control_variable": "input_power", + "demand_profile": 0.0, + }, + "control_parameters": { + "commodity": "electricity", + "commodity_rate_units": "kW", + "tech_name": "battery", + "system_commodity_interface_limit": 1e12, + }, + "dispatch_rule_parameters": { + "commodity": "electricity", + "commodity_rate_units": "kW", + }, + }, + } + }, + } + return tech_config_dict + + +@fixture +def plant_config_h2_storage(): + plant_config = { + "plant": { + "plant_life": 30, + "simulation": { + "dt": 3600, + "n_timesteps": 8760, + }, + }, + "tech_to_dispatch_connections": [ + ["combiner", "h2_storage"], + ["h2_storage", "h2_storage"], + ], + } + return plant_config + + +@fixture +def tech_config_generic(): + tech_config = { + "technologies": { + "h2_storage": { + "dispatch_rule_set": {"model": "PyomoRuleStorageBaseclass"}, + "control_strategy": {"model": "HeuristicLoadFollowingStorageController"}, + "performance_model": {"model": "StoragePerformanceModel"}, + "model_inputs": { + "shared_parameters": { + "max_charge_rate": 10.0, + "max_capacity": 40.0, + "n_control_window": 24, + "init_soc_fraction": 0.1, + "max_soc_fraction": 1.0, + "min_soc_fraction": 0.1, + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + "charge_efficiency": 1.0, + "discharge_efficiency": 1.0, + }, + "performance_parameters": { + "charge_equals_discharge": True, + "commodity_amount_units": "kg", + "demand_profile": 0.0, + }, + "control_parameters": { + "tech_name": "h2_storage", + "system_commodity_interface_limit": 10.0, + }, + }, + } + }, + } + return tech_config + + +@fixture +def tech_config_autosizing(): + tech_config = { + "technologies": { + "h2_storage": { + "dispatch_rule_set": {"model": "PyomoRuleStorageBaseclass"}, + "control_strategy": {"model": "HeuristicLoadFollowingStorageController"}, + "performance_model": {"model": "StorageAutoSizingModel"}, + "model_inputs": { + "shared_parameters": { + "max_soc_fraction": 1.0, + "min_soc_fraction": 0.0, + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + "charge_efficiency": 1.0, + "discharge_efficiency": 1.0, + "max_capacity": 5.0, # shared between control and dispatch rules + }, + "performance_parameters": { + "charge_equals_discharge": True, + "commodity_amount_units": "kg", + "demand_profile": 0.0, + "set_demand_as_avg_commodity_in": False, + }, + "control_parameters": { + "n_control_window": 24, + "tech_name": "h2_storage", + "system_commodity_interface_limit": 10.0, + "init_soc_fraction": 0.1, + }, + }, + } + }, + } + return tech_config + + +def calculate_combined_outputs(storage_charge_discharge, commodity_in, commodity_demand): + combined_commodity_in = commodity_in + storage_charge_discharge + remaining_demand = commodity_demand - combined_commodity_in + unmet_demand = np.where(remaining_demand > 0, remaining_demand, 0) + unused_commodity = np.where(remaining_demand < 0, -1 * remaining_demand, 0) + combined_out_for_demand = combined_commodity_in - unused_commodity + + return unmet_demand, unused_commodity, combined_out_for_demand + + +@pytest.mark.regression +def test_heuristic_load_following_battery_dispatch( + plant_config_battery, tech_config_battery, subtests +): + # Fabricate some oscillating power generation data: 0 kW for the first 12 hours, 10000 kW for + # the second twelve hours, and repeat that daily cycle over a year. + n_look_ahead_half = int(24 / 2) + + electricity_in = np.concatenate( + (np.ones(n_look_ahead_half) * 0, np.ones(n_look_ahead_half) * 10000) + ) + electricity_in = np.tile(electricity_in, 365) + + demand_in = np.ones(8760) * 6000.0 + + # Setup the OpenMDAO problem and add subsystems + prob = om.Problem() + + prob.model.add_subsystem( + "PyomoRuleStorageBaseclass", + PyomoRuleStorageBaseclass( + plant_config=plant_config_battery, + tech_config=tech_config_battery["technologies"]["battery"], + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "battery_heuristic_load_following_controller", + HeuristicLoadFollowingStorageController( + plant_config=plant_config_battery, + tech_config=tech_config_battery["technologies"]["battery"], + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "battery", + PySAMBatteryPerformanceModel( + plant_config=plant_config_battery, + tech_config=tech_config_battery["technologies"]["battery"], + ), + promotes=["*"], + ) + + # Setup the system and required values + prob.setup() + prob.set_val("battery.electricity_in", electricity_in) + prob.set_val("battery.electricity_demand", demand_in) + + # Run the model + prob.run_model() + + # Test the case where the charging/discharging cycle remains within the max and min SOC limits + # Check the expected outputs to actual outputs + expected_electricity_out = [ + 5999.99995059, + 5990.56676743, + 5990.138959, + 5989.64831176, + 5989.08548217, + 5988.44193888, + 5987.70577962, + 5986.86071125, + 5985.88493352, + 5984.7496388, + 5983.41717191, + 5981.839478, + 6000.0, + 6000.0, + 6000.0, + 6000.0, + 6000.0, + 6000.0, + 6000.0, + 6000.0, + 6000.0, + 6000.0, + 6000.0, + 6000.0, + ] + + expected_battery_electricity = [ + 5999.99995059, + 5990.56676743, + 5990.138959, + 5989.64831176, + 5989.08548217, + 5988.44193888, + 5987.70577962, + 5986.86071125, + 5985.88493352, + 5984.7496388, + 5983.41717191, + 5981.839478, + -3988.62235554, + -3989.2357847, + -3989.76832626, + -3990.26170521, + -3990.71676106, + -3991.13573086, + -3991.52143699, + -3991.87684905, + -3992.20485715, + -3992.50815603, + -3992.78920148, + -3993.05020268, + ] + + expected_SOC = [ + 49.39724571, + 46.54631833, + 43.69133882, + 40.83119769, + 37.96394628, + 35.08762294, + 32.20015974, + 29.29919751, + 26.38184809, + 23.44436442, + 20.48162855, + 17.48627159, + 19.47067094, + 21.44466462, + 23.40741401, + 25.36052712, + 27.30530573, + 29.24281439, + 31.17393198, + 33.09939078, + 35.01980641, + 36.93570091, + 38.84752069, + 40.75565055, + ] + + expected_unmet_demand_out = np.array( + [ + 4.93562475e-05, + 9.43323257e00, + 9.86104099e00, + 1.03516883e01, + 1.09145178e01, + 1.15580611e01, + 1.22942204e01, + 1.31392889e01, + 1.41150664e01, + 1.52503612e01, + 1.65828282e01, + 1.81605218e01, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ] + ) + + expected_unused_commodity_out = np.array( + [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 11.37764445, + 10.76421514, + 10.23167373, + 9.73829458, + 9.28323883, + 8.86426912, + 8.47856327, + 8.12315078, + 7.79514283, + 7.49184426, + 7.21079852, + 6.94979705, + ] + ) + + unmet_demand, unused_commodity, combined_out_for_demand = calculate_combined_outputs( + prob.get_val("battery.electricity_out", units="kW"), electricity_in, demand_in + ) + + with subtests.test("Check electricity_out"): + assert pytest.approx(expected_electricity_out) == combined_out_for_demand[0:24] + + with subtests.test("Check battery_electricity"): + assert ( + pytest.approx(expected_battery_electricity) + == prob.get_val("battery.electricity_out", units="kW")[0:24] + ) + + with subtests.test("Check SOC"): + assert pytest.approx(expected_SOC) == prob.get_val("battery.SOC", units="percent")[0:24] + + with subtests.test("Check unmet_demand"): + assert pytest.approx(expected_unmet_demand_out, abs=1e-4) == unmet_demand[0:24] + + with subtests.test("Check unused_electricity_out"): + assert pytest.approx(expected_unused_commodity_out) == unused_commodity[0:24] + + # Test the case where the battery is discharged to its lower SOC limit + electricity_in = np.zeros(8760) + demand_in = np.ones(8760) * 30000 + + # Setup the system and required values + prob.setup() + + prob.set_val("battery.electricity_in", electricity_in) + prob.set_val("battery.electricity_demand", demand_in) + + # Run the model + prob.run_model() + + expected_electricity_out = np.array( + [3.00000000e04, 2.99305601e04, 2.48145097e04, 4.97901621e00, 3.04065390e01] + ) + expected_battery_electricity = expected_electricity_out + expected_SOC = np.array([37.69010284, 22.89921133, 10.00249593, 10.01524461, 10.03556385]) + expected_unmet_demand_out = np.array( + [ + 9.43691703e-09, + 6.94398578e01, + 5.18549025244965e03, + 2.999502098378662e04, + 2.9969593461021406e04, + ] + ) + expected_unused_commodity_out = np.zeros(5) + + unmet_demand, unused_commodity, combined_out_for_demand = calculate_combined_outputs( + prob.get_val("battery.electricity_out", units="kW"), electricity_in, demand_in + ) + + with subtests.test("Check electricity_out for min SOC"): + assert pytest.approx(expected_electricity_out) == combined_out_for_demand[:5] + + with subtests.test("Check battery_electricity for min SOC"): + assert ( + pytest.approx(expected_battery_electricity) + == prob.get_val("battery.electricity_out", units="kW")[:5] + ) + + with subtests.test("Check SOC for min SOC"): + assert pytest.approx(expected_SOC) == prob.get_val("battery.SOC", units="percent")[:5] + + with subtests.test("Check unmet_demand for min SOC"): + assert pytest.approx(expected_unmet_demand_out, abs=1e-6) == unmet_demand[:5] + + with subtests.test("Check unused_commodity_out for min SOC"): + assert pytest.approx(expected_unused_commodity_out) == unused_commodity[:5] + + # Test the case where the battery is charged to its upper SOC limit + electricity_in = np.ones(8760) * 30000.0 + demand_in = np.zeros(8760) + + # Setup the system and required values + prob.setup() + prob.set_val("battery.electricity_in", electricity_in) + prob.set_val("battery.electricity_demand", demand_in) + + # Run the model + prob.run_model() + + expected_electricity_out = [-0.008477085, 0.0, 0.0, 0.0, 0.0] + + # TODO reevaluate the output here + expected_battery_electricity = np.array( + [-30000.00847709, -29973.58679719, -21109.22734423, 0.0, 0.0] + ) + + # expected_SOC = [66.00200558, 79.43840635, 90.0, 90.0, 90.0] + expected_SOC = np.array([66.00200558, 79.43840635, 89.02326413, 89.02326413, 89.02326413]) + expected_unmet_demand_out = np.array([0.00847709, 0.0, 0.0, 0.0, 0.0]) + expected_unused_commodity_out = np.array( + [0.00000000e00, 2.64132028e01, 8.89077266e03, 3.04088135e04, 3.00564087e04] + ) + # I think this is the right expected_electricity_out since the battery won't + # be discharging in this instance + # expected_electricity_out = [0.0, 0.0, 0.0, 0.0, 0.0] + # # expected_electricity_out = [0.0, 0.0, 6150.14483911, 30000.0, 30000.0] + # expected_battery_electricity = [-30000.00847705, -29973.58679681, + # -23310.54620182, 0.0, 0.0] + # expected_SOC = [66.00200558, 79.43840635, 90.0, 90.0, 90.0] + # expected_unmet_demand_out = np.zeros(5) + # expected_unused_commodity_out = [0.0, 0.0, 6150.14483911, 30000.0, 30000.0] + + abs_tol = 1e-6 + rel_tol = 1e-1 + + unmet_demand, unused_commodity, combined_out_for_demand = calculate_combined_outputs( + prob.get_val("battery.electricity_out", units="kW"), electricity_in, demand_in + ) + + with subtests.test("Check electricity_out for max SOC"): + assert ( + pytest.approx(expected_electricity_out, abs=abs_tol, rel=rel_tol) + == combined_out_for_demand[:5] + ) + + with subtests.test("Check battery_electricity for max SOC"): + assert ( + pytest.approx(expected_battery_electricity, abs=abs_tol, rel=rel_tol) + == prob.get_val("battery.electricity_out", units="kW")[:5] + ) + + with subtests.test("Check SOC for max SOC"): + assert ( + pytest.approx(expected_SOC, abs=abs_tol) + == prob.get_val("battery.SOC", units="percent")[:5] + ) + + with subtests.test("Check unmet_demand for max SOC"): + assert pytest.approx(expected_unmet_demand_out, abs=abs_tol) == unmet_demand[:5] + + with subtests.test("Check unused_commodity_out for max SOC"): + assert ( + pytest.approx(expected_unused_commodity_out, abs=abs_tol, rel=rel_tol) + == unused_commodity[:5] + ) + + +@pytest.mark.regression +def test_heuristic_load_following_battery_dispatch_change_capacities( + plant_config_battery, tech_config_battery, subtests +): + # update the battery capacity to be very small in the config + + tech_config_battery["technologies"]["battery"]["model_inputs"]["shared_parameters"].update( + {"max_charge_rate": 1000} + ) + tech_config_battery["technologies"]["battery"]["model_inputs"]["shared_parameters"].update( + {"max_capacity": 1000} + ) + + # Fabricate some oscillating power generation data: 0 kW for the first 12 hours, 10000 kW for + # the second twelve hours, and repeat that daily cycle over a year. + + n_look_ahead_half = int(24 / 2) + + electricity_in = np.concatenate( + (np.ones(n_look_ahead_half) * 0, np.ones(n_look_ahead_half) * 10000) + ) + electricity_in = np.tile(electricity_in, 365) + + demand_in = np.ones(8760) * 6000.0 + + # Setup the OpenMDAO problem and add subsystems + prob = om.Problem() + + prob.model.add_subsystem( + name="IVC1", + subsys=om.IndepVarComp(name="max_charge_rate", val=1000, units="kW"), + promotes=["*"], + ) + + prob.model.add_subsystem( + name="IVC2", + subsys=om.IndepVarComp(name="storage_capacity", val=1000, units="kW*h"), + promotes=["*"], + ) + + prob.model.add_subsystem( + "PyomoRuleStorageBaseclass", + PyomoRuleStorageBaseclass( + plant_config=plant_config_battery, + tech_config=tech_config_battery["technologies"]["battery"], + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "battery_heuristic_load_following_controller", + HeuristicLoadFollowingStorageController( + plant_config=plant_config_battery, + tech_config=tech_config_battery["technologies"]["battery"], + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "battery", + PySAMBatteryPerformanceModel( + plant_config=plant_config_battery, + tech_config=tech_config_battery["technologies"]["battery"], + ), + promotes=["*"], + ) + + # Setup the system and required values + prob.setup() + + prob.set_val("IVC1.max_charge_rate", 50000, units="kW") + prob.set_val("IVC2.storage_capacity", 200000, units="kW*h") + + prob.set_val("battery.electricity_in", electricity_in) + prob.set_val("battery.electricity_demand", demand_in) + + # Run the model + prob.run_model() + + # Test the case where the charging/discharging cycle remains within the max and min SOC limits + # Check the expected outputs to actual outputs + expected_electricity_out = [ + 5999.99995059, + 5990.56676743, + 5990.138959, + 5989.64831176, + 5989.08548217, + 5988.44193888, + 5987.70577962, + 5986.86071125, + 5985.88493352, + 5984.7496388, + 5983.41717191, + 5981.839478, + 6000.0, + 6000.0, + 6000.0, + 6000.0, + 6000.0, + 6000.0, + 6000.0, + 6000.0, + 6000.0, + 6000.0, + 6000.0, + 6000.0, + ] + + expected_battery_electricity = [ + 5999.99995059, + 5990.56676743, + 5990.138959, + 5989.64831176, + 5989.08548217, + 5988.44193888, + 5987.70577962, + 5986.86071125, + 5985.88493352, + 5984.7496388, + 5983.41717191, + 5981.839478, + -3988.62235554, + -3989.2357847, + -3989.76832626, + -3990.26170521, + -3990.71676106, + -3991.13573086, + -3991.52143699, + -3991.87684905, + -3992.20485715, + -3992.50815603, + -3992.78920148, + -3993.05020268, + ] + + expected_SOC = [ + 49.39724571, + 46.54631833, + 43.69133882, + 40.83119769, + 37.96394628, + 35.08762294, + 32.20015974, + 29.29919751, + 26.38184809, + 23.44436442, + 20.48162855, + 17.48627159, + 19.47067094, + 21.44466462, + 23.40741401, + 25.36052712, + 27.30530573, + 29.24281439, + 31.17393198, + 33.09939078, + 35.01980641, + 36.93570091, + 38.84752069, + 40.75565055, + ] + + expected_unmet_demand_out = np.array( + [ + 4.93562475e-05, + 9.43323257e00, + 9.86104099e00, + 1.03516883e01, + 1.09145178e01, + 1.15580611e01, + 1.22942204e01, + 1.31392889e01, + 1.41150664e01, + 1.52503612e01, + 1.65828282e01, + 1.81605218e01, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ] + ) + + expected_unused_commodity_out = np.array( + [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 11.37764445, + 10.76421514, + 10.23167373, + 9.73829458, + 9.28323883, + 8.86426912, + 8.47856327, + 8.12315078, + 7.79514283, + 7.49184426, + 7.21079852, + 6.94979705, + ] + ) + + unmet_demand, unused_commodity, combined_out_for_demand = calculate_combined_outputs( + prob.get_val("battery.electricity_out", units="kW"), electricity_in, demand_in + ) + + with subtests.test("Battery output capacity"): + assert ( + pytest.approx( + prob.get_val("battery.rated_electricity_production", units="MW"), rel=1e-6 + ) + == 50.0 + ) + + with subtests.test("Check electricity_out"): + assert ( + pytest.approx(expected_electricity_out) # TODO: update + == combined_out_for_demand[0:24] + ) + + with subtests.test("Check battery_electricity"): + assert ( + pytest.approx(expected_battery_electricity) + == prob.get_val("battery.electricity_out", units="kW")[0:24] + ) + + with subtests.test("Check SOC"): + assert pytest.approx(expected_SOC) == prob.get_val("battery.SOC", units="percent")[0:24] + + with subtests.test("Check unmet_demand"): + assert pytest.approx(expected_unmet_demand_out, abs=1e-4) == unmet_demand[0:24] + + with subtests.test("Check unused_electricity_out"): + assert pytest.approx(expected_unused_commodity_out) == unused_commodity[0:24] + + +@pytest.mark.regression +def test_heuristic_load_following_dispatch_with_generic_storage( + plant_config_h2_storage, tech_config_generic, subtests +): + commodity_demand = np.full(8760, 5.0) + commodity_in = np.tile(np.concat([np.zeros(3), np.cumsum(np.ones(15)), np.full(6, 4.0)]), 365) + + # Setup the OpenMDAO problem and add subsystems + prob = om.Problem() + + prob.model.add_subsystem( + "PyomoRuleStorageBaseclass", + PyomoRuleStorageBaseclass( + plant_config=plant_config_h2_storage, + tech_config=tech_config_generic["technologies"]["h2_storage"], + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "h2_storage_heuristic_load_following_controller", + HeuristicLoadFollowingStorageController( + plant_config=plant_config_h2_storage, + tech_config=tech_config_generic["technologies"]["h2_storage"], + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "h2_storage", + StoragePerformanceModel( + plant_config=plant_config_h2_storage, + tech_config=tech_config_generic["technologies"]["h2_storage"], + ), + promotes=["*"], + ) + + # Setup the system and required values + prob.setup() + prob.set_val("h2_storage.hydrogen_in", commodity_in) + prob.set_val("h2_storage.hydrogen_demand", commodity_demand) + + # Run the model + prob.run_model() + + charge_rate = prob.get_val("h2_storage.max_charge_rate", units="kg/h")[0] + discharge_rate = prob.get_val("h2_storage.max_charge_rate", units="kg/h")[0] + capacity = prob.get_val("h2_storage.storage_capacity", units="kg")[0] + + # Test that discharge is always positive + with subtests.test("Discharge is always positive"): + assert np.all(prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h") >= 0) + with subtests.test("Charge is always negative"): + assert np.all(prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h") <= 0) + with subtests.test("Charge + Discharge == hydrogen_out"): + charge_plus_discharge = prob.get_val( + "h2_storage.storage_hydrogen_charge", units="kg/h" + ) + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h") + np.testing.assert_allclose( + charge_plus_discharge, prob.get_val("hydrogen_out", units="kg/h"), rtol=1e-6 + ) + with subtests.test("Initial SOC is correct"): + assert ( + pytest.approx(prob.model.get_val("h2_storage.SOC", units="unitless")[0], rel=1e-6) + == 0.1 + ) + + indx_soc_increase = np.argwhere( + np.diff(prob.model.get_val("h2_storage.SOC", units="unitless"), prepend=True) > 0 + ).flatten() + indx_soc_decrease = np.argwhere( + np.diff(prob.model.get_val("h2_storage.SOC", units="unitless"), prepend=False) < 0 + ).flatten() + indx_soc_same = np.argwhere( + np.diff(prob.model.get_val("h2_storage.SOC", units="unitless"), prepend=True) == 0.0 + ).flatten() + + with subtests.test("SOC increases when charging"): + assert np.all( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h")[indx_soc_increase] < 0 + ) + assert np.all( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h")[indx_soc_decrease] == 0 + ) + assert np.all( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h")[indx_soc_same] == 0 + ) + + with subtests.test("SOC decreases when discharging"): + assert np.all( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h")[indx_soc_decrease] + > 0 + ) + assert np.all( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h")[indx_soc_increase] + == 0 + ) + assert np.all( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h")[indx_soc_same] == 0 + ) + + with subtests.test("Max SOC <= Max storage percent"): + assert prob.get_val("h2_storage.SOC", units="unitless").max() <= 1.0 + + with subtests.test("Min SOC >= Min storage percent"): + assert prob.get_val("h2_storage.SOC", units="unitless").min() >= 0.1 + + with subtests.test("Charge never exceeds charge rate"): + assert ( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h").min() + >= -1 * charge_rate + ) + + with subtests.test("Discharge never exceeds discharge rate"): + assert ( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h").max() + <= discharge_rate + ) + + with subtests.test("Discharge never exceeds demand"): + assert np.all( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h").max() + <= commodity_demand + ) + + with subtests.test("Cumulative charge/discharge does not exceed storage capacity"): + assert np.cumsum(prob.get_val("hydrogen_out", units="kg/h")).max() <= capacity + assert np.cumsum(prob.get_val("hydrogen_out", units="kg/h")).min() >= -1 * capacity + + with subtests.test("Expected discharge from hour 10-30"): + expected_discharge = np.concat( + [np.zeros(8), np.ones(6), np.full(3, 5.0), np.array([4, 3, 2])] + ) + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h")[10:30], + expected_discharge, + rtol=1e-6, + ) + + with subtests.test("Expected charge hour 0-20"): + expected_charge = np.concat([np.zeros(8), np.arange(-1, -9, -1), np.zeros(4)]) + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h")[0:20], + expected_charge, + rtol=1e-6, + ) + + +@pytest.mark.regression +def test_heuristic_dispatch_with_autosizing_storage_demand_less_than_avg_in( + plant_config_h2_storage, tech_config_autosizing, subtests +): + commodity_demand = np.full(8760, 5.0) + commodity_in = np.tile(np.concat([np.zeros(3), np.cumsum(np.ones(15)), np.full(6, 4.0)]), 365) + + # Setup the OpenMDAO problem and add subsystems + prob = om.Problem() + + prob.model.add_subsystem( + "PyomoRuleStorageBaseclass", + PyomoRuleStorageBaseclass( + plant_config=plant_config_h2_storage, + tech_config=tech_config_autosizing["technologies"]["h2_storage"], + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "h2_storage_controller", + HeuristicLoadFollowingStorageController( + plant_config=plant_config_h2_storage, + tech_config=tech_config_autosizing["technologies"]["h2_storage"], + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "h2_storage", + StorageAutoSizingModel( + plant_config=plant_config_h2_storage, + tech_config=tech_config_autosizing["technologies"]["h2_storage"], + ), + promotes=["*"], + ) + + # Setup the system and required values + prob.setup() + prob.set_val("h2_storage.hydrogen_in", commodity_in) + prob.set_val("h2_storage.hydrogen_demand", commodity_demand) + + # Run the model + prob.run_model() + + charge_rate = prob.get_val("h2_storage.max_charge_rate", units="kg/h")[0] + discharge_rate = prob.get_val("h2_storage.max_discharge_rate", units="kg/h")[0] + capacity = prob.get_val("h2_storage.storage_capacity", units="kg")[0] + + with subtests.test("Capacity is correct"): + soc_kg = np.cumsum(commodity_demand - commodity_in) + soc_kg_adj = soc_kg + np.abs(np.min(soc_kg)) + expected_usable_capacity = np.max(soc_kg_adj) - np.min(soc_kg_adj) + + assert pytest.approx(capacity, rel=1e-6) == expected_usable_capacity + + with subtests.test("Charge rate is correct"): + assert pytest.approx(charge_rate, rel=1e-6) == max(commodity_in) + with subtests.test("Discharge rate is correct"): + assert pytest.approx(discharge_rate, rel=1e-6) == max(commodity_in) + + with subtests.test("Expected discharge from hour 10-30"): + expected_discharge = np.concat( + [np.zeros(8), np.ones(6), np.full(3, 5.0), np.array([4, 3, 2])] + ) + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h")[10:30], + expected_discharge, + rtol=1e-6, + ) + + with subtests.test("Expected charge hour 0-20"): + expected_charge = np.concat([np.zeros(8), np.arange(-1, -11, -1), np.zeros(2)]) + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h")[0:20], + expected_charge, + rtol=1e-6, + ) + + +@pytest.mark.regression +def test_heuristic_dispatch_with_autosizing_storage_demand_is_avg_in( + plant_config_h2_storage, tech_config_autosizing, subtests +): + commodity_in = np.tile(np.concat([np.zeros(3), np.cumsum(np.ones(15)), np.full(6, 4.0)]), 365) + commodity_demand = np.full(8760, np.mean(commodity_in)) + tech_config_autosizing["technologies"]["h2_storage"]["model_inputs"]["performance_parameters"][ + "set_demand_as_avg_commodity_in" + ] = True + tech_config_autosizing["technologies"]["h2_storage"]["model_inputs"]["performance_parameters"][ + "demand_profile" + ] = 0.0 + + # Setup the OpenMDAO problem and add subsystems + prob = om.Problem() + + prob.model.add_subsystem( + "PyomoRuleStorageBaseclass", + PyomoRuleStorageBaseclass( + plant_config=plant_config_h2_storage, + tech_config=tech_config_autosizing["technologies"]["h2_storage"], + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "h2_storage_controller", + HeuristicLoadFollowingStorageController( + plant_config=plant_config_h2_storage, + tech_config=tech_config_autosizing["technologies"]["h2_storage"], + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "h2_storage", + StorageAutoSizingModel( + plant_config=plant_config_h2_storage, + tech_config=tech_config_autosizing["technologies"]["h2_storage"], + ), + promotes=["*"], + ) + + # Setup the system and required values + prob.setup() + prob.set_val("h2_storage.hydrogen_in", commodity_in) + + # Run the model + prob.run_model() + + charge_rate = prob.get_val("h2_storage.max_charge_rate", units="kg/h")[0] + discharge_rate = prob.get_val("h2_storage.max_discharge_rate", units="kg/h")[0] + capacity = prob.get_val("h2_storage.storage_capacity", units="kg")[0] + + with subtests.test("Capacity is correct"): + soc_kg = np.cumsum(commodity_demand - commodity_in) + soc_kg_adj = soc_kg + np.abs(np.min(soc_kg)) + expected_usable_capacity = np.max(soc_kg_adj) - np.min(soc_kg_adj) + + assert pytest.approx(capacity, rel=1e-6) == expected_usable_capacity + + with subtests.test("Charge rate is correct"): + assert pytest.approx(charge_rate, rel=1e-6) == max(commodity_in) + with subtests.test("Discharge rate is correct"): + assert pytest.approx(discharge_rate, rel=1e-6) == max(commodity_in) + + with subtests.test("Expected discharge from hour 10-30"): + expected_discharge = np.concat( + [np.zeros(8), np.full(6, 2.0), np.full(3, 6.0), np.array([5, 4, 3])] + ) + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h")[10:30], + expected_discharge, + rtol=1e-6, + ) + + with subtests.test("Expected charge hour 0-20"): + expected_charge = np.concat([np.zeros(9), np.arange(-1, -10, -1), np.zeros(2)]) + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h")[0:20], + expected_charge, + rtol=1e-6, + ) diff --git a/h2integrate/control/control_strategies/storage/test/test_multistorage_pyomo_openloop_control.py b/h2integrate/control/control_strategies/storage/test/test_multistorage_pyomo_openloop_control.py new file mode 100644 index 000000000..58c01c3f5 --- /dev/null +++ b/h2integrate/control/control_strategies/storage/test/test_multistorage_pyomo_openloop_control.py @@ -0,0 +1,790 @@ +import numpy as np +import pytest +import openmdao.api as om +from pytest import fixture + +from h2integrate.storage.battery.pysam_battery import PySAMBatteryPerformanceModel +from h2integrate.storage.storage_performance_model import StoragePerformanceModel +from h2integrate.control.control_rules.storage.pyomo_storage_rule_baseclass import ( + PyomoRuleStorageBaseclass, +) +from h2integrate.control.control_strategies.storage.heuristic_pyomo_controller import ( + HeuristicLoadFollowingStorageController, +) +from h2integrate.control.control_strategies.storage.demand_openloop_storage_controller import ( + DemandOpenLoopStorageController, +) + + +@fixture +def plant_config(pyo_controllers): + plant_config_base = { + "plant": { + "plant_life": 30, + "simulation": { + "dt": 3600, + "n_timesteps": 8760, + }, + } + } + + if pyo_controllers == "bat": + tech_to_dispatch = { + "tech_to_dispatch_connections": [ + ["combiner", "battery"], + ["battery", "battery"], + ] + } + plant_config = plant_config_base | tech_to_dispatch + return plant_config + + if pyo_controllers == "h2s": + tech_to_dispatch = { + "tech_to_dispatch_connections": [ + ["combiner", "h2_storage"], + ["h2_storage", "h2_storage"], + ] + } + plant_config = plant_config_base | tech_to_dispatch + return plant_config + + if pyo_controllers == "both": + tech_to_dispatch = { + "tech_to_dispatch_connections": [ + ["elec_combiner", "battery"], + ["battery", "battery"], + ["h2_combiner", "h2_storage"], + ["h2_storage", "h2_storage"], + ] + } + plant_config = plant_config_base | tech_to_dispatch + return plant_config + + if pyo_controllers == "none": + return plant_config_base + + +def make_battery_pyo_group(plant_config_bat): + bat_tech_config = { + "battery": { + "dispatch_rule_set": {"model": "PyomoRuleStorageBaseclass"}, + "control_strategy": {"model": "HeuristicLoadFollowingStorageController"}, + "performance_model": {"model": "PySAMBatteryPerformanceModel"}, + "model_inputs": { + "shared_parameters": { + "max_charge_rate": 50000, + "max_capacity": 200000, + "n_control_window": 24, + "n_horizon_window": 48, + "init_soc_fraction": 0.5, + "max_soc_fraction": 0.9, + "min_soc_fraction": 0.1, + }, + "performance_parameters": { + "chemistry": "LFPGraphite", + "control_variable": "input_power", + "demand_profile": 6000.0, + }, + "control_parameters": { + "commodity": "electricity", + "commodity_rate_units": "kW", + "tech_name": "battery", + "system_commodity_interface_limit": 1e12, + }, + "dispatch_rule_parameters": { + "commodity": "electricity", + "commodity_rate_units": "kW", + }, + }, + } + } + rule_comp = PyomoRuleStorageBaseclass( + plant_config=plant_config_bat, tech_config=bat_tech_config["battery"] + ) + perf_comp = PySAMBatteryPerformanceModel( + plant_config=plant_config_bat, tech_config=bat_tech_config["battery"] + ) + control_comp = HeuristicLoadFollowingStorageController( + plant_config=plant_config_bat, tech_config=bat_tech_config["battery"] + ) + + electricity_in = np.concatenate((np.ones(12) * 0, np.ones(12) * 10000)) + electricity_in = np.tile(electricity_in, 365) + ivc_comp = om.IndepVarComp(name="electricity_in", val=electricity_in, units="kW") + + return rule_comp, perf_comp, control_comp, ivc_comp + + +def make_h2_storage_pyo_group(plant_config_h2s): + h2s_tech_config = { + "h2_storage": { + "dispatch_rule_set": {"model": "PyomoRuleStorageBaseclass"}, + "control_strategy": {"model": "HeuristicLoadFollowingStorageController"}, + "performance_model": {"model": "StoragePerformanceModel"}, + "model_inputs": { + "shared_parameters": { + "max_charge_rate": 10.0, + "max_capacity": 40.0, + "init_soc_fraction": 0.1, + "max_soc_fraction": 1.0, + "min_soc_fraction": 0.1, + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + "charge_efficiency": 1.0, + "discharge_efficiency": 1.0, + }, + "performance_parameters": { + "charge_equals_discharge": True, + "commodity_amount_units": "kg", + "demand_profile": 5.0, + }, + "control_parameters": { + "tech_name": "h2_storage", + "system_commodity_interface_limit": 10.0, + "n_control_window": 24, + }, + }, + } + } + rule_comp = PyomoRuleStorageBaseclass( + plant_config=plant_config_h2s, tech_config=h2s_tech_config["h2_storage"] + ) + perf_comp = StoragePerformanceModel( + plant_config=plant_config_h2s, tech_config=h2s_tech_config["h2_storage"] + ) + control_comp = HeuristicLoadFollowingStorageController( + plant_config=plant_config_h2s, tech_config=h2s_tech_config["h2_storage"] + ) + + hydrogen_in = np.tile(np.concat([np.zeros(3), np.cumsum(np.ones(15)), np.full(6, 4.0)]), 365) + + ivc_comp = om.IndepVarComp(name="hydrogen_in", val=hydrogen_in, units="kg/h") + + return rule_comp, perf_comp, control_comp, ivc_comp + + +def make_h2_storage_openloop_group(plant_config_h2s): + tech_config_h2s = { + "shared_parameters": { + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + "demand_profile": 5.0, + "max_capacity": 40, + "max_charge_rate": 10, + "min_soc_fraction": 0.1, + "max_soc_fraction": 1.0, + "init_soc_fraction": 0.1, + "charge_equals_discharge": True, + "charge_efficiency": 1.0, + "discharge_efficiency": 1.0, + } + } + + h2_in = np.concat([np.zeros(3), np.cumsum(np.ones(15)), np.full(6, 4.0)]) + commodity_in = np.tile(h2_in, 365) + ivc_comp = om.IndepVarComp(name="hydrogen_in", val=commodity_in, units="kg/h") + perf_comp = StoragePerformanceModel( + plant_config=plant_config_h2s, tech_config={"model_inputs": tech_config_h2s} + ) + control_comp = DemandOpenLoopStorageController( + plant_config=plant_config_h2s, tech_config={"model_inputs": tech_config_h2s} + ) + + return ivc_comp, perf_comp, control_comp + + +def make_battery_openloop_group(plant_config_bat): + tech_config_bat = { + "shared_parameters": { + "demand_profile": 5.0, + "max_capacity": 40, + "max_charge_rate": 10, + "min_soc_fraction": 0.1, + "max_soc_fraction": 1.0, + "init_soc_fraction": 1.0, + }, + "performance_parameters": {"chemistry": "LFPGraphite"}, + "control_parameters": { + "commodity": "electricity", + "commodity_rate_units": "kW", + "charge_equals_discharge": True, + "charge_efficiency": 1.0, + "discharge_efficiency": 1.0, + }, + } + + elec_in = np.concat([np.zeros(3), np.cumsum(np.ones(15)), np.full(6, 4.0)]) + commodity_in = np.tile(elec_in, 365) + ivc_comp = om.IndepVarComp(name="electricity_in", val=commodity_in, units="kW") + perf_comp = PySAMBatteryPerformanceModel( + plant_config=plant_config_bat, tech_config={"model_inputs": tech_config_bat} + ) + control_comp = DemandOpenLoopStorageController( + plant_config=plant_config_bat, tech_config={"model_inputs": tech_config_bat} + ) + + return ivc_comp, perf_comp, control_comp + + +@pytest.mark.regression +@pytest.mark.parametrize("pyo_controllers", ["none"]) +def test_h2s_openloop(subtests, plant_config): + # these are the test values for the hydrogen storage that are used in + # test_both_openloop_controllers and test_battery_pyomo_h2s_openloop + h2s_expected_discharge = np.concat([np.zeros(18), np.ones(6)]) + h2s_expected_charge = np.concat([np.zeros(8), np.arange(-1, -9, -1), np.zeros(8)]) + prob = om.Problem() + + # make h2 storage group + h2s_group = prob.model.add_subsystem("h2_storage", om.Group()) + h2s_ivc_comp, h2s_perf_comp, h2s_control_comp = make_h2_storage_openloop_group(plant_config) + h2s_group.add_subsystem("IVC1", h2s_ivc_comp, promotes=["*"]) + h2s_group.add_subsystem("control", h2s_control_comp, promotes=["*"]) + h2s_group.add_subsystem("perf", h2s_perf_comp, promotes=["*"]) + + prob.setup() + prob.run_model() + + with subtests.test("H2 Storage: Expected charge"): + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h")[:24], + h2s_expected_charge, + rtol=1e-6, + ) + with subtests.test("H2 Storage: Expected discharge"): + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h")[:24], + h2s_expected_discharge, + rtol=1e-6, + ) + + +@pytest.mark.regression +@pytest.mark.parametrize("pyo_controllers", ["none"]) +def test_battery_openloop(subtests, plant_config): + # these are the test values for the battery that are used in + # test_both_openloop_controllers and test_h2s_pyomo_batery_openloop + bat_expected_discharge = np.concat( + [ + np.array( + [4.99483673, 4.99483511, 4.99403933, 3.99456736, 2.99545121, 1.99671242, 0.99828405] + ), + np.zeros(11), + np.array([0.99907629, 0.9991349, 0.99911777, 0.99909762, 0.99907648, 0.99905434]), + ] + ) + bat_expected_charge = np.concat( + [ + np.zeros(8), + np.array( + [ + -0.99835716, + -1.9969656, + -2.99592019, + -3.9952197, + -4.99480988, + -5.99461958, + -3.70438813, + ] + ), + np.zeros(9), + ] + ) + + prob = om.Problem() + + # make battery group + bat_group = prob.model.add_subsystem("battery", om.Group()) + bat_ivc_comp, bat_perf_comp, bat_control_comp = make_battery_openloop_group(plant_config) + bat_group.add_subsystem("IVC2", bat_ivc_comp, promotes=["*"]) + bat_group.add_subsystem("control", bat_control_comp, promotes=["*"]) + bat_group.add_subsystem("perf", bat_perf_comp, promotes=["*"]) + + prob.setup() + prob.run_model() + + with subtests.test("Battery: Expected charge"): + np.testing.assert_allclose( + prob.get_val("battery.storage_electricity_charge", units="kW")[:24], + bat_expected_charge, + rtol=1e-6, + ) + with subtests.test("Battery: Expected discharge"): + np.testing.assert_allclose( + prob.get_val("battery.storage_electricity_discharge", units="kW")[:24], + bat_expected_discharge, + rtol=1e-6, + ) + + +@pytest.mark.regression +@pytest.mark.parametrize("pyo_controllers", ["bat"]) +def test_battery_pyo(subtests, plant_config): + # these are the test values for the battery that are used in + # test_both_pyomo_controllers and test_battery_pyomo_h2s_openloop + bat_expected_charge = np.concat( + [ + np.zeros(12), + np.array( + [ + -3988.62235554, + -3989.2357847, + -3989.76832626, + -3990.26170521, + -3990.71676106, + -3991.13573086, + -3991.52143699, + -3991.87684905, + -3992.20485715, + -3992.50815603, + -3992.78920148, + -3993.05020268, + ] + ), + ] + ) + bat_expected_discharge = np.concat( + [ + np.array( + [ + 5999.99995059, + 5990.56676743, + 5990.138959, + 5989.64831176, + 5989.08548217, + 5988.44193888, + 5987.70577962, + 5986.86071125, + 5985.88493352, + 5984.7496388, + 5983.41717191, + 5981.839478, + ] + ), + np.zeros(12), + ] + ) + prob = om.Problem() + + # make battery group + bat_group = prob.model.add_subsystem("battery", om.Group()) + bat_rule_comp, bat_perf_comp, bat_control_comp, electricity_in = make_battery_pyo_group( + plant_config + ) + bat_group.add_subsystem("IVC2", electricity_in, promotes=["*"]) + bat_group.add_subsystem("rule", bat_rule_comp, promotes=["*"]) + bat_group.add_subsystem("control", bat_control_comp, promotes=["*"]) + bat_group.add_subsystem("perf", bat_perf_comp, promotes=["*"]) + + prob.setup() + prob.run_model() + + with subtests.test("Battery: Expected charge"): + np.testing.assert_allclose( + prob.get_val("battery.storage_electricity_charge", units="kW")[:24], + bat_expected_charge, + rtol=1e-6, + ) + with subtests.test("Battery: Expected discharge"): + np.testing.assert_allclose( + prob.get_val("battery.storage_electricity_discharge", units="kW")[:24], + bat_expected_discharge, + rtol=1e-6, + ) + + +@pytest.mark.regression +@pytest.mark.parametrize("pyo_controllers", ["h2s"]) +def test_h2s_pyo(subtests, plant_config): + # these are the test values for the hydrogen storage that are used in + # test_both_pyomo_controllers and test_h2s_pyomo_battery_openloop + + h2s_expected_discharge = np.concat( + [np.zeros(8), np.ones(6), np.full(3, 5.0), np.array([4, 3, 2])] + ) + h2s_expected_charge = np.concat([np.zeros(8), np.arange(-1, -9, -1), np.zeros(8)]) + prob = om.Problem() + + # make hydrogen storage group + h2s_group = prob.model.add_subsystem("h2_storage", om.Group()) + h2s_rule_comp, h2s_perf_comp, h2s_control_comp, hydrogen_in = make_h2_storage_pyo_group( + plant_config + ) + h2s_group.add_subsystem("IVC1", hydrogen_in, promotes=["*"]) + h2s_group.add_subsystem("rule", h2s_rule_comp, promotes=["*"]) + h2s_group.add_subsystem("control", h2s_control_comp, promotes=["*"]) + h2s_group.add_subsystem("perf", h2s_perf_comp, promotes=["*"]) + prob.setup() + prob.run_model() + + with subtests.test("H2 Storage: Expected charge"): + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h")[:24], + h2s_expected_charge, + rtol=1e-6, + ) + with subtests.test("H2 Storage: Expected discharge"): + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h")[10:30], + h2s_expected_discharge, + rtol=1e-6, + ) + + +@pytest.mark.regression +@pytest.mark.parametrize("pyo_controllers", ["both"]) +def test_both_pyomo_controllers(subtests, plant_config): + bat_expected_charge = np.concat( + [ + np.zeros(12), + np.array( + [ + -3988.62235554, + -3989.2357847, + -3989.76832626, + -3990.26170521, + -3990.71676106, + -3991.13573086, + -3991.52143699, + -3991.87684905, + -3992.20485715, + -3992.50815603, + -3992.78920148, + -3993.05020268, + ] + ), + ] + ) + bat_expected_discharge = np.concat( + [ + np.array( + [ + 5999.99995059, + 5990.56676743, + 5990.138959, + 5989.64831176, + 5989.08548217, + 5988.44193888, + 5987.70577962, + 5986.86071125, + 5985.88493352, + 5984.7496388, + 5983.41717191, + 5981.839478, + ] + ), + np.zeros(12), + ] + ) + + h2s_expected_discharge = np.concat( + [np.zeros(8), np.ones(6), np.full(3, 5.0), np.array([4, 3, 2])] + ) + h2s_expected_charge = np.concat([np.zeros(8), np.arange(-1, -9, -1), np.zeros(8)]) + prob = om.Problem() + + # make hydrogen storage group + h2s_group = prob.model.add_subsystem("h2_storage", om.Group()) + h2s_rule_comp, h2s_perf_comp, h2s_control_comp, hydrogen_in = make_h2_storage_pyo_group( + plant_config + ) + h2s_group.add_subsystem("IVC1", hydrogen_in, promotes=["*"]) + h2s_group.add_subsystem("rule", h2s_rule_comp, promotes=["*"]) + h2s_group.add_subsystem("control", h2s_control_comp, promotes=["*"]) + h2s_group.add_subsystem("perf", h2s_perf_comp, promotes=["*"]) + + # make battery group + bat_group = prob.model.add_subsystem("battery", om.Group()) + bat_rule_comp, bat_perf_comp, bat_control_comp, electricity_in = make_battery_pyo_group( + plant_config + ) + bat_group.add_subsystem("IVC2", electricity_in, promotes=["*"]) + bat_group.add_subsystem("rule", bat_rule_comp, promotes=["*"]) + bat_group.add_subsystem("control", bat_control_comp, promotes=["*"]) + bat_group.add_subsystem("perf", bat_perf_comp, promotes=["*"]) + + prob.setup() + prob.run_model() + + with subtests.test("Battery: Expected charge"): + np.testing.assert_allclose( + prob.get_val("battery.storage_electricity_charge", units="kW")[:24], + bat_expected_charge, + rtol=1e-6, + ) + with subtests.test("Battery: Expected discharge"): + np.testing.assert_allclose( + prob.get_val("battery.storage_electricity_discharge", units="kW")[:24], + bat_expected_discharge, + rtol=1e-6, + ) + + with subtests.test("H2 Storage: Expected charge"): + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h")[:24], + h2s_expected_charge, + rtol=1e-6, + ) + with subtests.test("H2 Storage: Expected discharge"): + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h")[10:30], + h2s_expected_discharge, + rtol=1e-6, + ) + + +@pytest.mark.regression +@pytest.mark.parametrize("pyo_controllers", ["none"]) +def test_both_openloop_controllers(subtests, plant_config): + h2s_expected_discharge = np.concat([np.zeros(18), np.ones(6)]) + h2s_expected_charge = np.concat([np.zeros(8), np.arange(-1, -9, -1), np.zeros(8)]) + bat_expected_discharge = np.concat( + [ + np.array( + [4.99483673, 4.99483511, 4.99403933, 3.99456736, 2.99545121, 1.99671242, 0.99828405] + ), + np.zeros(11), + np.array([0.99907629, 0.9991349, 0.99911777, 0.99909762, 0.99907648, 0.99905434]), + ] + ) + bat_expected_charge = np.concat( + [ + np.zeros(8), + np.array( + [ + -0.99835716, + -1.9969656, + -2.99592019, + -3.9952197, + -4.99480988, + -5.99461958, + -3.70438813, + ] + ), + np.zeros(9), + ] + ) + + prob = om.Problem() + + # make h2 storage group + h2s_group = prob.model.add_subsystem("h2_storage", om.Group()) + h2s_ivc_comp, h2s_perf_comp, h2s_control_comp = make_h2_storage_openloop_group(plant_config) + h2s_group.add_subsystem("IVC1", h2s_ivc_comp, promotes=["*"]) + h2s_group.add_subsystem("control", h2s_control_comp, promotes=["*"]) + h2s_group.add_subsystem("perf", h2s_perf_comp, promotes=["*"]) + + # make battery group + bat_group = prob.model.add_subsystem("battery", om.Group()) + bat_ivc_comp, bat_perf_comp, bat_control_comp = make_battery_openloop_group(plant_config) + bat_group.add_subsystem("IVC2", bat_ivc_comp, promotes=["*"]) + bat_group.add_subsystem("control", bat_control_comp, promotes=["*"]) + bat_group.add_subsystem("perf", bat_perf_comp, promotes=["*"]) + + prob.setup() + prob.run_model() + + with subtests.test("Battery: Expected charge"): + np.testing.assert_allclose( + prob.get_val("battery.storage_electricity_charge", units="kW")[:24], + bat_expected_charge, + rtol=1e-6, + ) + with subtests.test("Battery: Expected discharge"): + np.testing.assert_allclose( + prob.get_val("battery.storage_electricity_discharge", units="kW")[:24], + bat_expected_discharge, + rtol=1e-6, + ) + + with subtests.test("H2 Storage: Expected charge"): + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h")[:24], + h2s_expected_charge, + rtol=1e-6, + ) + with subtests.test("H2 Storage: Expected discharge"): + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h")[:24], + h2s_expected_discharge, + rtol=1e-6, + ) + + +@pytest.mark.regression +@pytest.mark.parametrize("pyo_controllers", ["h2s"]) +def test_h2s_pyomo_battery_openloop(subtests, plant_config): + bat_expected_discharge = np.concat( + [ + np.array( + [4.99483673, 4.99483511, 4.99403933, 3.99456736, 2.99545121, 1.99671242, 0.99828405] + ), + np.zeros(11), + np.array([0.99907629, 0.9991349, 0.99911777, 0.99909762, 0.99907648, 0.99905434]), + ] + ) + bat_expected_charge = np.concat( + [ + np.zeros(8), + np.array( + [ + -0.99835716, + -1.9969656, + -2.99592019, + -3.9952197, + -4.99480988, + -5.99461958, + -3.70438813, + ] + ), + np.zeros(9), + ] + ) + h2s_expected_discharge = np.concat( + [np.zeros(8), np.ones(6), np.full(3, 5.0), np.array([4, 3, 2])] + ) + h2s_expected_charge = np.concat([np.zeros(8), np.arange(-1, -9, -1), np.zeros(8)]) + prob = om.Problem() + + # make hydrogen storage group + h2s_group = prob.model.add_subsystem("h2_storage", om.Group()) + h2s_rule_comp, h2s_perf_comp, h2s_control_comp, hydrogen_in = make_h2_storage_pyo_group( + plant_config + ) + h2s_group.add_subsystem("IVC1", hydrogen_in, promotes=["*"]) + h2s_group.add_subsystem("rule", h2s_rule_comp, promotes=["*"]) + h2s_group.add_subsystem("control", h2s_control_comp, promotes=["*"]) + h2s_group.add_subsystem("perf", h2s_perf_comp, promotes=["*"]) + + # make battery group + bat_group = prob.model.add_subsystem("battery", om.Group()) + bat_ivc_comp, bat_perf_comp, bat_control_comp = make_battery_openloop_group(plant_config) + bat_group.add_subsystem("IVC2", bat_ivc_comp, promotes=["*"]) + bat_group.add_subsystem("control", bat_control_comp, promotes=["*"]) + bat_group.add_subsystem("perf", bat_perf_comp, promotes=["*"]) + + prob.setup() + prob.run_model() + + with subtests.test("Battery: Expected charge"): + np.testing.assert_allclose( + prob.get_val("battery.storage_electricity_charge", units="kW")[:24], + bat_expected_charge, + rtol=1e-6, + ) + with subtests.test("Battery: Expected discharge"): + np.testing.assert_allclose( + prob.get_val("battery.storage_electricity_discharge", units="kW")[:24], + bat_expected_discharge, + rtol=1e-6, + ) + + with subtests.test("H2 Storage: Expected charge"): + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h")[:24], + h2s_expected_charge, + rtol=1e-6, + ) + with subtests.test("H2 Storage: Expected discharge"): + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h")[10:30], + h2s_expected_discharge, + rtol=1e-6, + ) + + +@pytest.mark.regression +@pytest.mark.parametrize("pyo_controllers", ["bat"]) +def test_battery_pyomo_h2s_openloop(subtests, plant_config): + h2s_expected_discharge = np.concat([np.zeros(18), np.ones(6)]) + h2s_expected_charge = np.concat([np.zeros(8), np.arange(-1, -9, -1), np.zeros(8)]) + bat_expected_charge = np.concat( + [ + np.zeros(12), + np.array( + [ + -3988.62235554, + -3989.2357847, + -3989.76832626, + -3990.26170521, + -3990.71676106, + -3991.13573086, + -3991.52143699, + -3991.87684905, + -3992.20485715, + -3992.50815603, + -3992.78920148, + -3993.05020268, + ] + ), + ] + ) + bat_expected_discharge = np.concat( + [ + np.array( + [ + 5999.99995059, + 5990.56676743, + 5990.138959, + 5989.64831176, + 5989.08548217, + 5988.44193888, + 5987.70577962, + 5986.86071125, + 5985.88493352, + 5984.7496388, + 5983.41717191, + 5981.839478, + ] + ), + np.zeros(12), + ] + ) + + prob = om.Problem() + + # make h2 storage group + h2s_group = prob.model.add_subsystem("h2_storage", om.Group()) + h2s_ivc_comp, h2s_perf_comp, h2s_control_comp = make_h2_storage_openloop_group(plant_config) + h2s_group.add_subsystem("IVC1", h2s_ivc_comp, promotes=["*"]) + h2s_group.add_subsystem("control", h2s_control_comp, promotes=["*"]) + h2s_group.add_subsystem("perf", h2s_perf_comp, promotes=["*"]) + + # make battery group + bat_rule_comp, bat_perf_comp, bat_control_comp, electricity_in = make_battery_pyo_group( + plant_config + ) + bat_group = prob.model.add_subsystem("battery", om.Group()) + bat_group.add_subsystem("IVC2", electricity_in, promotes=["*"]) + bat_group.add_subsystem("rule", bat_rule_comp, promotes=["*"]) + bat_group.add_subsystem("control", bat_control_comp, promotes=["*"]) + bat_group.add_subsystem("perf", bat_perf_comp, promotes=["*"]) + + prob.setup() + prob.run_model() + + with subtests.test("Battery: Expected charge"): + np.testing.assert_allclose( + prob.get_val("battery.storage_electricity_charge", units="kW")[:24], + bat_expected_charge, + rtol=1e-6, + ) + with subtests.test("Battery: Expected discharge"): + np.testing.assert_allclose( + prob.get_val("battery.storage_electricity_discharge", units="kW")[:24], + bat_expected_discharge, + rtol=1e-6, + ) + + with subtests.test("H2 Storage: Expected charge"): + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h")[:24], + h2s_expected_charge, + rtol=1e-6, + ) + with subtests.test("H2 Storage: Expected discharge"): + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h")[:24], + h2s_expected_discharge, + rtol=1e-6, + ) diff --git a/h2integrate/control/control_strategies/storage/test/test_openloop_controllers.py b/h2integrate/control/control_strategies/storage/test/test_openloop_controllers.py new file mode 100644 index 000000000..e9ded400c --- /dev/null +++ b/h2integrate/control/control_strategies/storage/test/test_openloop_controllers.py @@ -0,0 +1,537 @@ +from copy import deepcopy +from pathlib import Path + +import numpy as np +import pytest +import openmdao.api as om +from pytest import fixture + +from h2integrate.core.file_utils import load_yaml +from h2integrate.storage.storage_performance_model import StoragePerformanceModel +from h2integrate.control.control_strategies.storage.simple_openloop_controller import ( + SimpleStorageOpenLoopController, +) +from h2integrate.control.control_strategies.storage.demand_openloop_storage_controller import ( + DemandOpenLoopStorageController, +) + + +def calculate_combined_outputs(storage_charge_discharge, commodity_in, commodity_demand): + combined_commodity_in = commodity_in + storage_charge_discharge + remaining_demand = commodity_demand - combined_commodity_in + unmet_demand = np.where(remaining_demand > 0, remaining_demand, 0) + unused_commodity = np.where(remaining_demand < 0, -1 * remaining_demand, 0) + combined_out_for_demand = combined_commodity_in - unused_commodity + + return unmet_demand, unused_commodity, combined_out_for_demand + + +@fixture +def variable_h2_production_profile(): + end_use_rated_demand = 10.0 # kg/h + ramp_up_rate_kg = 4.0 + ramp_down_rate_kg = 2.0 + slow_ramp_up = np.arange(0, end_use_rated_demand * 1.1, 0.5) + slow_ramp_down = np.arange(end_use_rated_demand * 1.1, -0.5, -0.5) + fast_ramp_up = np.arange(0, end_use_rated_demand, ramp_up_rate_kg * 1.2) + fast_ramp_down = np.arange(end_use_rated_demand, 0.0, ramp_down_rate_kg * 1.1) + variable_profile = np.concat( + [slow_ramp_up, fast_ramp_down, slow_ramp_up, slow_ramp_down, fast_ramp_up] + ) + variable_h2_profile = np.tile(variable_profile, 2) + return variable_h2_profile + + +@pytest.mark.unit +def test_pass_through_controller(subtests): + # Get the directory of the current script + current_dir = Path(__file__).parent + + # Resolve the paths to the configuration files + tech_config_path = current_dir / "inputs" / "tech_config.yaml" + + # Load the technology configuration + tech_config = load_yaml(tech_config_path) + + tech_config["technologies"]["h2_storage"]["model_inputs"]["shared_parameters"].update( + {"set_demand_as_avg_commodity_in": True} + ) + # Set up the OpenMDAO problem + prob = om.Problem() + + plant_config = {"plant": {"plant_life": 30, "simulation": {"n_timesteps": 10, "dt": 3600}}} + + prob.model.add_subsystem( + name="IVC", + subsys=om.IndepVarComp(name="hydrogen_in", val=np.arange(10), units="kg/h"), + promotes=["*"], + ) + + prob.model.add_subsystem( + "pass_through_controller", + SimpleStorageOpenLoopController( + plant_config=plant_config, tech_config=tech_config["technologies"]["h2_storage"] + ), + promotes=["*"], + ) + + prob.setup() + + prob.run_model() + + # Run the test + with subtests.test("Check output"): + expected_set_point = np.mean(np.arange(10)) - np.arange(10) + assert expected_set_point == ( + pytest.approx(prob.get_val("hydrogen_set_point", units="kg/h"), rel=1e-3) + ) + + +@pytest.mark.regression +def test_storage_demand_controller(subtests): + # Get the directory of the current script + current_dir = Path(__file__).parent + + # Resolve the paths to the configuration files + tech_config_path = current_dir / "inputs" / "tech_config.yaml" + + # Load the technology configuration + tech_config = load_yaml(tech_config_path) + + commodity_in = np.arange(10) + commodity_demand = np.full(10, 1.0) + + tech_config["technologies"]["h2_storage"]["model_inputs"]["shared_parameters"] = { + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + "max_capacity": 10.0, # kg + "max_soc_fraction": 1.0, # fraction (0-1) + "min_soc_fraction": 0.0, # fraction (0-1) + "init_soc_fraction": 1.0, # fraction (0-1) + "max_charge_rate": 1.0, # kg/time step + "max_discharge_rate": 0.5, # kg/time step + "charge_equals_discharge": False, + "charge_efficiency": 1.0, + "discharge_efficiency": 1.0, + "demand_profile": commodity_demand, # Example: 10 time steps with 10 kg/time step demand + } + + plant_config = {"plant": {"plant_life": 30, "simulation": {"n_timesteps": 10, "dt": 3600}}} + + # Set up the OpenMDAO problem + prob = om.Problem() + + prob.model.add_subsystem( + name="IVC", + subsys=om.IndepVarComp(name="hydrogen_in", val=commodity_in, units="kg/h"), + promotes=["*"], + ) + + prob.model.add_subsystem( + "demand_open_loop_storage_controller", + DemandOpenLoopStorageController( + plant_config=plant_config, tech_config=tech_config["technologies"]["h2_storage"] + ), + promotes=["*"], + ) + prob.model.add_subsystem( + "storage", + StoragePerformanceModel( + plant_config=plant_config, tech_config=tech_config["technologies"]["h2_storage"] + ), + promotes=["*"], + ) + + prob.setup() + + prob.run_model() + + unmet_demand, unused_commodity, combined_out_for_demand = calculate_combined_outputs( + prob.get_val("hydrogen_out", units="kg/h"), commodity_in, commodity_demand + ) + # Run the test + with subtests.test("Check output"): + assert combined_out_for_demand == pytest.approx( + [0.5, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + ) + + with subtests.test("Check curtailment"): + assert unused_commodity == pytest.approx([0.0, 0.0, 0.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]) + + with subtests.test("Check soc"): + assert prob.get_val("SOC", units="unitless") == pytest.approx( + [0.95, 0.95, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + ) + + with subtests.test("Check missed load"): + assert unmet_demand == pytest.approx([0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) + + +@pytest.mark.unit +def test_storage_demand_controller_round_trip_efficiency(subtests): + # This tests the behavior of storage efficiencies when the storage is charging and discharging + # Get the directory of the current script + current_dir = Path(__file__).parent + + # Resolve the paths to the configuration files + tech_config_path = current_dir / "inputs" / "tech_config.yaml" + + # Load the technology configuration + tech_config = load_yaml(tech_config_path) + + tech_config["technologies"]["h2_storage"]["model_inputs"]["shared_parameters"] = { + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + "max_capacity": 10.0, # kg + "max_soc_fraction": 1.0, # fraction (0-1) + "min_soc_fraction": 0.0, # fraction (0-1) + "init_soc_fraction": 1.0, # fraction (0-1) + "max_charge_rate": 1.0, # kg/time step + "max_discharge_rate": 0.5, # kg/time step + "charge_equals_discharge": False, + "charge_efficiency": 1.0, + "discharge_efficiency": 1.0, + "demand_profile": [1.0] * 10, # Example: 10 time steps with 10 kg/time step demand + } + + tech_config_rte = deepcopy(tech_config) + tech_config_rte["technologies"]["h2_storage"]["model_inputs"]["shared_parameters"] = { + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + "max_capacity": 10.0, # kg + "max_soc_fraction": 1.0, # fraction (0-1) + "min_soc_fraction": 0.0, # fraction (0-1) + "init_soc_fraction": 1.0, # fraction (0-1) + "max_charge_rate": 1.0, # kg/time step + "max_discharge_rate": 0.5, # kg/time step + "charge_equals_discharge": False, + "round_trip_efficiency": 1.0, + "demand_profile": [1.0] * 10, # Example: 10 time steps with 10 kg/time step demand + } + + plant_config = {"plant": {"plant_life": 30, "simulation": {"n_timesteps": 10, "dt": 3600}}} + + def set_up_and_run_problem(config): + # Set up the OpenMDAO problem + prob = om.Problem() + + prob.model.add_subsystem( + name="IVC", + subsys=om.IndepVarComp( + name="hydrogen_in", + val=np.arange(10), + units="kg/h", + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "demand_openloop_controller", + DemandOpenLoopStorageController( + plant_config=plant_config, tech_config=config["technologies"]["h2_storage"] + ), + promotes=["*"], + ) + prob.model.add_subsystem( + "storage", + StoragePerformanceModel( + plant_config=plant_config, tech_config=tech_config["technologies"]["h2_storage"] + ), + promotes=["*"], + ) + + prob.setup() + + prob.run_model() + + return prob + + prob_ioe = set_up_and_run_problem(tech_config) + prob_rte = set_up_and_run_problem(tech_config_rte) + + # Run the test + unmet_demand_rte, unused_commodity_rte, combined_out_for_demand_rte = ( + calculate_combined_outputs( + prob_rte.get_val("hydrogen_out", units="kg/h"), + prob_rte.get_val("hydrogen_in", units="kg/h"), + prob_rte.get_val("hydrogen_demand", units="kg/h"), + ) + ) + unmet_demand_ioe, unused_commodity_ioe, combined_out_for_demand_ioe = ( + calculate_combined_outputs( + prob_ioe.get_val("hydrogen_out", units="kg/h"), + prob_ioe.get_val("hydrogen_in", units="kg/h"), + prob_ioe.get_val("hydrogen_demand", units="kg/h"), + ) + ) + + with subtests.test("Check output"): + assert combined_out_for_demand_rte == pytest.approx(combined_out_for_demand_ioe) + + with subtests.test("Check curtailment"): + assert unused_commodity_rte == pytest.approx(unused_commodity_ioe) + + with subtests.test("Check soc"): + assert prob_rte.get_val("SOC", units="unitless") == pytest.approx( + prob_ioe.get_val("SOC", units="unitless") + ) + + with subtests.test("Check missed load"): + assert unmet_demand_rte == pytest.approx(unmet_demand_ioe) + + +@pytest.mark.unit +def test_storage_demand_controller_round_trip_with_non_one_efficiencies(subtests): + # Get the directory of the current script + current_dir = Path(__file__).parent + + # Resolve the paths to the configuration files + tech_config_path = current_dir / "inputs" / "tech_config.yaml" + + # Load the technology configuration + tech_config = load_yaml(tech_config_path) + + tech_config["technologies"]["h2_storage"]["control_strategy"]["model"] = ( + "DemandOpenLoopStorageController" + ) + + tech_config["technologies"]["h2_storage"]["performance_model"]["model"] = ( + "StoragePerformanceModel" + ) + + tech_config["technologies"]["h2_storage"]["model_inputs"]["shared_parameters"] = { + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + "max_capacity": 10.0, # kg + "max_soc_fraction": 1.0, # fraction (0-1) + "min_soc_fraction": 0.0, # fraction (0-1) + "init_soc_fraction": 0.75, # fraction (0-1) + "max_charge_rate": 1.0, # kg/time step + "max_discharge_rate": 1.0, # kg/time step + "charge_equals_discharge": False, + "charge_efficiency": 0.5, + "discharge_efficiency": 0.5, + "demand_profile": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 1.0, + 1.0, + 1.0, + 1.0, + 2.0, + ], # Example: 10 time steps + } + + tech_config_rte = deepcopy(tech_config) + tech_config_rte["technologies"]["h2_storage"]["model_inputs"]["shared_parameters"] = { + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + "max_capacity": 10.0, # kg + "max_soc_fraction": 1.0, # fraction (0-1) + "min_soc_fraction": 0.0, # fraction (0-1) + "init_soc_fraction": 0.75, # fraction (0-1) + "max_charge_rate": 1.0, # kg/time step + "max_discharge_rate": 1.0, # kg/time step + "charge_equals_discharge": False, + "round_trip_efficiency": 0.5**2, + "demand_profile": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 1.0, + 1.0, + 1.0, + 1.0, + 2.0, + ], # Example: 10 time steps with 10 kg/time step demand + } + + plant_config = {"plant": {"plant_life": 30, "simulation": {"n_timesteps": 10, "dt": 3600}}} + + def set_up_and_run_problem(config): + # Set up the OpenMDAO problem + prob = om.Problem() + + prob.model.add_subsystem( + name="IVC", + subsys=om.IndepVarComp( + name="hydrogen_in", + val=[2.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], + units="kg/h", + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "demand_openloop_controller", + DemandOpenLoopStorageController( + plant_config=plant_config, tech_config=config["technologies"]["h2_storage"] + ), + promotes=["*"], + ) + prob.model.add_subsystem( + "storage", + StoragePerformanceModel( + plant_config=plant_config, tech_config=tech_config["technologies"]["h2_storage"] + ), + promotes=["*"], + ) + + prob.setup() + + prob.run_model() + + return prob + + prob_ioe = set_up_and_run_problem(tech_config) + prob_rte = set_up_and_run_problem(tech_config_rte) + + # Run the comparison tests between charge/discharge and round trip efficiencies + unmet_demand_rte, unused_commodity_rte, combined_out_for_demand_rte = ( + calculate_combined_outputs( + prob_rte.get_val("hydrogen_out", units="kg/h"), + prob_rte.get_val("hydrogen_in", units="kg/h"), + prob_rte.get_val("hydrogen_demand", units="kg/h"), + ) + ) + unmet_demand_ioe, unused_commodity_ioe, combined_out_for_demand_ioe = ( + calculate_combined_outputs( + prob_ioe.get_val("hydrogen_out", units="kg/h"), + prob_ioe.get_val("hydrogen_in", units="kg/h"), + prob_ioe.get_val("hydrogen_demand", units="kg/h"), + ) + ) + + with subtests.test("Check output match"): + assert combined_out_for_demand_rte == pytest.approx(combined_out_for_demand_ioe) + + with subtests.test("Check curtailment match"): + assert unused_commodity_rte == pytest.approx(unused_commodity_ioe) + + with subtests.test("Check soc match"): + assert prob_rte.get_val("SOC", units="unitless") == pytest.approx( + prob_ioe.get_val("SOC", units="unitless") + ) + + with subtests.test("Check missed load match"): + assert unmet_demand_rte == pytest.approx(unmet_demand_ioe) + + # Run the absolute value tests for charge/discharge and round trip efficiencies + with subtests.test("Check output value"): + assert combined_out_for_demand_rte == pytest.approx( + np.array([0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0]) + ) + + with subtests.test("Check curtailment value"): + assert unused_commodity_rte == pytest.approx( + np.array([1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) + ) + + with subtests.test("Check soc value"): + assert prob_rte.get_val("SOC", units="unitless") == pytest.approx( + np.array([0.8, 0.85, 0.9, 0.95, 1.0, 0.8, 0.6, 0.4, 0.2, 0.0]) + ) + + with subtests.test("Check missed load value"): + assert unmet_demand_rte == pytest.approx( + np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]) + ) + + +@pytest.mark.regression +def test_generic_storage_demand_controller(subtests): + # Test is the same as the demand controller test test_demand_controller for the "h2_storage" + # performance model but with the "StoragePerformanceModel" performance model + + # Get the directory of the current script + current_dir = Path(__file__).parent + + # Resolve the paths to the configuration files + tech_config_path = current_dir / "inputs" / "tech_config.yaml" + + # Load the technology configuration + tech_config = load_yaml(tech_config_path) + + tech_config["technologies"]["h2_storage"] = { + "performance_model": { + "model": "StoragePerformanceModel", + }, + "control_strategy": { + "model": "DemandOpenLoopStorageController", + }, + "model_inputs": { + "shared_parameters": { + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + "max_capacity": 10.0, # kg + "max_charge_rate": 1.0, # fraction (0-1) + "max_soc_fraction": 1.0, # fraction (0-1) + "min_soc_fraction": 0.0, # fraction (0-1) + "init_soc_fraction": 1.0, # fraction (0-1) + "max_discharge_rate": 0.5, # kg/time step + "charge_efficiency": 1.0, + "charge_equals_discharge": False, + "discharge_efficiency": 1.0, + "demand_profile": [1.0] * 10, # Example: 10 time steps with 10 kg/time step demand + }, + }, + } + + plant_config = {"plant": {"plant_life": 30, "simulation": {"n_timesteps": 10, "dt": 3600}}} + + # Set up OpenMDAO problem + prob = om.Problem() + + prob.model.add_subsystem( + name="IVC", + subsys=om.IndepVarComp(name="hydrogen_in", val=np.arange(10), units="kg/h"), + promotes=["*"], + ) + + prob.model.add_subsystem( + "demand_open_loop_storage_controller", + DemandOpenLoopStorageController( + plant_config=plant_config, tech_config=tech_config["technologies"]["h2_storage"] + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "storage", + StoragePerformanceModel( + plant_config=plant_config, tech_config=tech_config["technologies"]["h2_storage"] + ), + promotes=["*"], + ) + + prob.setup() + + prob.run_model() + + unmet_demand, unused_commodity, combined_out_for_demand = calculate_combined_outputs( + prob.get_val("hydrogen_out", units="kg/h"), + prob.get_val("hydrogen_in", units="kg/h"), + prob.get_val("hydrogen_demand", units="kg/h"), + ) + + # # Run the test + with subtests.test("Check output"): + assert combined_out_for_demand == pytest.approx( + [0.5, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + ) + + with subtests.test("Check curtailment"): + assert unused_commodity == pytest.approx([0.0, 0.0, 0.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]) + + with subtests.test("Check soc"): + assert prob.get_val("SOC", units="unitless") == pytest.approx( + [0.95, 0.95, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] + ) + + with subtests.test("Check missed load"): + assert unmet_demand == pytest.approx([0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) diff --git a/h2integrate/control/control_strategies/storage/test/test_openloop_storage_integration.py b/h2integrate/control/control_strategies/storage/test/test_openloop_storage_integration.py new file mode 100644 index 000000000..df1787dfe --- /dev/null +++ b/h2integrate/control/control_strategies/storage/test/test_openloop_storage_integration.py @@ -0,0 +1,575 @@ +import numpy as np +import pytest +import openmdao.api as om +from pytest import fixture + +from h2integrate.storage.battery.pysam_battery import PySAMBatteryPerformanceModel +from h2integrate.storage.storage_performance_model import StoragePerformanceModel +from h2integrate.storage.simple_storage_auto_sizing import StorageAutoSizingModel +from h2integrate.control.control_strategies.storage.simple_openloop_controller import ( + SimpleStorageOpenLoopController, +) +from h2integrate.control.control_strategies.storage.demand_openloop_storage_controller import ( + DemandOpenLoopStorageController, +) + + +@fixture +def plant_config(): + plant = { + "plant": { + "plant_life": 30, + "simulation": { + "dt": 3600, + "n_timesteps": 24, + }, + }, + } + return plant + + +@fixture +def storage_perf_params(storage_model_name): + if storage_model_name == "StoragePerformanceModel": + return { + "max_capacity": 40, + "max_charge_rate": 10, + "min_soc_fraction": 0.1, + "max_soc_fraction": 1.0, + "init_soc_fraction": 0.1, + "charge_equals_discharge": True, + "charge_efficiency": 1.0, + "discharge_efficiency": 1.0, + } + if storage_model_name == "PySAMBatteryPerformanceModel": + return { + "max_capacity": 40, + "max_charge_rate": 10, + "min_soc_fraction": 0.1, + "max_soc_fraction": 1.0, + "init_soc_fraction": 1.0, + "chemistry": "LFPGraphite", + } + if storage_model_name == "StorageAutoSizingModel": + return { + "min_soc_fraction": 0.1, + "max_soc_fraction": 1.0, + "charge_efficiency": 1.0, + "discharge_efficiency": 1.0, + } + + +@pytest.mark.integration +@pytest.mark.parametrize("storage_model_name", ["StoragePerformanceModel"]) +def test_generic_storage_with_demand_openloop(plant_config, storage_perf_params, subtests): + commodity_in = np.concat([np.zeros(3), np.cumsum(np.ones(15)), np.full(6, 4.0)]) + commodity_demand = np.full(24, 5.0) + + shared_params = { + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + "demand_profile": commodity_demand, + } + + model_inputs = {"shared_parameters": storage_perf_params | shared_params} + + prob = om.Problem() + prob.model.add_subsystem( + name="IVC1", + subsys=om.IndepVarComp(name="hydrogen_in", val=commodity_in, units="kg/h"), + promotes=["*"], + ) + + prob.model.add_subsystem( + "h2_storage_control", + DemandOpenLoopStorageController( + plant_config=plant_config, + tech_config={"model_inputs": model_inputs}, + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "h2_storage", + StoragePerformanceModel( + plant_config=plant_config, + tech_config={"model_inputs": model_inputs}, + ), + promotes=["*"], + ) + + prob.setup() + + prob.run_model() + + with subtests.test("Expected discharge"): + expected_discharge = np.concat([np.zeros(18), np.ones(6)]) + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h"), + expected_discharge, + rtol=1e-6, + ) + + with subtests.test("Expected charge"): + expected_charge = np.concat([np.zeros(8), np.arange(-1, -9, -1), np.zeros(8)]) + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h"), + expected_charge, + rtol=1e-6, + ) + + +@pytest.mark.integration +@pytest.mark.parametrize("storage_model_name", ["StoragePerformanceModel"]) +def test_generic_storage_with_simple_openloop(plant_config, storage_perf_params, subtests): + commodity_in = np.concat([np.zeros(3), np.cumsum(np.ones(15)), np.full(6, 4.0)]) + commodity_demand = np.full(24, 5.0) + + shared_params = { + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + "demand_profile": commodity_demand, + } + + model_inputs = { + "shared_parameters": shared_params, + "performance_parameters": storage_perf_params, + "control_parameters": {"set_demand_as_avg_commodity_in": False}, + } + + prob = om.Problem() + prob.model.add_subsystem( + name="IVC1", + subsys=om.IndepVarComp(name="hydrogen_in", val=commodity_in, units="kg/h"), + promotes=["*"], + ) + + prob.model.add_subsystem( + "h2_storage_control", + SimpleStorageOpenLoopController( + plant_config=plant_config, + tech_config={"model_inputs": model_inputs}, + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "h2_storage", + StoragePerformanceModel( + plant_config=plant_config, + tech_config={"model_inputs": model_inputs}, + ), + promotes=["*"], + ) + + prob.setup() + + prob.run_model() + + with subtests.test("Expected discharge"): + expected_discharge = np.concat([np.zeros(18), np.ones(6)]) + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h"), + expected_discharge, + rtol=1e-6, + ) + + with subtests.test("Expected charge"): + expected_charge = np.concat([np.zeros(8), np.arange(-1, -9, -1), np.zeros(8)]) + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h"), + expected_charge, + rtol=1e-6, + ) + + +@pytest.mark.integration +@pytest.mark.parametrize("storage_model_name", ["PySAMBatteryPerformanceModel"]) +def test_pysam_battery_with_demand_openloop(plant_config, storage_perf_params, subtests): + commodity_in = np.concat([np.zeros(3), np.cumsum(np.ones(15)), np.full(6, 4.0)]) + commodity_demand = np.full(24, 5.0) + + shared_params = {"demand_profile": commodity_demand} | { + k: v for k, v in storage_perf_params.items() if k != "chemistry" + } + + model_inputs = { + "shared_parameters": shared_params, + "performance_parameters": {"chemistry": storage_perf_params["chemistry"]}, + "control_parameters": { + "commodity": "electricity", + "commodity_rate_units": "kW", + "charge_equals_discharge": True, + "charge_efficiency": 1.0, + "discharge_efficiency": 1.0, + }, + } + + prob = om.Problem() + prob.model.add_subsystem( + name="IVC1", + subsys=om.IndepVarComp(name="electricity_in", val=commodity_in, units="kW"), + promotes=["*"], + ) + + prob.model.add_subsystem( + "battery_control", + DemandOpenLoopStorageController( + plant_config=plant_config, + tech_config={"model_inputs": model_inputs}, + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "battery", + PySAMBatteryPerformanceModel( + plant_config=plant_config, + tech_config={"model_inputs": model_inputs}, + ), + promotes=["*"], + ) + + prob.setup() + + prob.run_model() + expected_discharge = np.array( + [ + 4.99483673, + 4.99483511, + 4.99403933, + 3.99456736, + 2.99545121, + 1.99671242, + 0.99828405, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.99907629, + 0.9991349, + 0.99911777, + 0.99909762, + 0.99907648, + 0.99905434, + ] + ) + with subtests.test("Expected discharge"): + np.testing.assert_allclose( + prob.get_val("battery.storage_electricity_discharge", units="kW"), + expected_discharge, + rtol=1e-6, + ) + + expected_charge = np.array( + [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + -0.99835716, + -1.9969656, + -2.99592019, + -3.9952197, + -4.99480988, + -5.99461958, + -3.70438813, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + ] + ) + with subtests.test("Expected charge"): + np.testing.assert_allclose( + prob.get_val("battery.storage_electricity_charge", units="kW"), + expected_charge, + rtol=1e-6, + ) + + +@pytest.mark.integration +@pytest.mark.parametrize("storage_model_name", ["PySAMBatteryPerformanceModel"]) +def test_pysam_battery_with_simple_openloop(plant_config, storage_perf_params, subtests): + commodity_in = np.concat([np.zeros(3), np.cumsum(np.ones(15)), np.full(6, 4.0)]) + commodity_demand = np.full(24, 5.0) + + control_params = { + "commodity": "electricity", + "commodity_rate_units": "kW", + "set_demand_as_avg_commodity_in": False, + } + + model_inputs = { + "shared_parameters": {"demand_profile": commodity_demand}, + "performance_parameters": storage_perf_params, + "control_parameters": control_params, + } + + prob = om.Problem() + prob.model.add_subsystem( + name="IVC1", + subsys=om.IndepVarComp(name="electricity_in", val=commodity_in, units="kW"), + promotes=["*"], + ) + + prob.model.add_subsystem( + "battery_control", + SimpleStorageOpenLoopController( + plant_config=plant_config, + tech_config={"model_inputs": model_inputs}, + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "battery", + PySAMBatteryPerformanceModel( + plant_config=plant_config, + tech_config={"model_inputs": model_inputs}, + ), + promotes=["*"], + ) + + prob.setup() + + prob.run_model() + expected_discharge = np.array( + [ + 4.99483673, + 4.99483511, + 4.99403933, + 3.99456736, + 2.99545121, + 1.99671242, + 0.99828405, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.99885513, + 0.99913572, + 0.99912398, + 0.99910425, + 0.99908343, + 0.99906162, + ] + ) + with subtests.test("Expected discharge"): + np.testing.assert_allclose( + prob.get_val("battery.storage_electricity_discharge", units="kW"), + expected_discharge, + rtol=1e-6, + ) + + expected_charge = np.array( + [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + -0.99835716, + -1.9969656, + -2.99592019, + -3.9952197, + -4.99480988, + -5.99461958, + -3.70438813, + -0.31010625, + -0.02143906, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + ] + ) + with subtests.test("Expected charge"): + np.testing.assert_allclose( + prob.get_val("battery.storage_electricity_charge", units="kW"), + expected_charge, + rtol=1e-6, + ) + + +@pytest.mark.integration +@pytest.mark.parametrize("storage_model_name", ["StorageAutoSizingModel"]) +def test_storage_autosizing_with_demand_openloop(plant_config, storage_perf_params, subtests): + commodity_in = np.concat([np.zeros(3), np.cumsum(np.ones(15)), np.full(6, 4.0)]) + commodity_demand = np.full(24, 5.0) + shared_params = { + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + "demand_profile": commodity_demand, + "charge_efficiency": storage_perf_params.pop("charge_efficiency"), + "discharge_efficiency": storage_perf_params.pop("discharge_efficiency"), + "min_soc_fraction": storage_perf_params.pop("min_soc_fraction"), + "max_soc_fraction": storage_perf_params.pop("max_soc_fraction"), + } + + perf_params = storage_perf_params | {"set_demand_as_avg_commodity_in": False} + model_inputs = { + "shared_parameters": shared_params, + "performance_parameters": perf_params, + "control_parameters": { + "max_capacity": 100.0, + "max_charge_rate": 400.0, + "init_soc_fraction": 0.1, + }, + } + + prob = om.Problem() + prob.model.add_subsystem( + name="IVC1", + subsys=om.IndepVarComp(name="hydrogen_in", val=commodity_in, units="kg/h"), + promotes=["*"], + ) + + # NOTE: when these are used together, the controller is not taking + # the charge/discharge rate info output from the performance model + + prob.model.add_subsystem( + "h2_storage_control", + DemandOpenLoopStorageController( + plant_config=plant_config, + tech_config={"model_inputs": model_inputs}, + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "h2_storage", + StorageAutoSizingModel( + plant_config=plant_config, + tech_config={"model_inputs": model_inputs}, + ), + promotes=["*"], + ) + + prob.setup() + + # NOTE: with these combinations, we have to set these values + # or else the 0 value initialized in the storage performance model + # is passed to the demand openloop controller + prob.set_val("h2_storage_control.storage_capacity", 400.0, units="kg") + prob.set_val("h2_storage_control.max_charge_rate", 100.0, units="kg/h") + + prob.run_model() + with subtests.test("Expected discharge"): + expected_discharge = np.concat([np.zeros(18), np.ones(6)]) + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h"), + expected_discharge, + rtol=1e-6, + ) + + with subtests.test("Expected charge"): + expected_charge = np.concat([np.zeros(8), np.arange(-1, -11, -1), np.zeros(6)]) + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h"), + expected_charge, + rtol=1e-6, + ) + + with subtests.test("Expected Capacity"): + assert ( + pytest.approx(prob.get_val("h2_storage.storage_capacity", units="kg"), rel=1e-6) + == 61.11111111111111 + ) + + with subtests.test("Expected Charge Rate"): + assert pytest.approx( + prob.get_val("h2_storage.max_charge_rate", units="kg/h"), rel=1e-6 + ) == np.max(commodity_in) + + +@pytest.mark.integration +@pytest.mark.parametrize("storage_model_name", ["StorageAutoSizingModel"]) +def test_storage_autosizing_with_simple_openloop(plant_config, storage_perf_params, subtests): + commodity_in = np.concat([np.zeros(3), np.cumsum(np.ones(15)), np.full(6, 4.0)]) + commodity_demand = np.full(24, 5.0) + shared_params = { + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + "demand_profile": commodity_demand, + "set_demand_as_avg_commodity_in": False, + } + + model_inputs = { + "shared_parameters": shared_params, + "performance_parameters": storage_perf_params, + } + + prob = om.Problem() + prob.model.add_subsystem( + name="IVC1", + subsys=om.IndepVarComp(name="hydrogen_in", val=commodity_in, units="kg/h"), + promotes=["*"], + ) + + prob.model.add_subsystem( + "h2_storage_control", + SimpleStorageOpenLoopController( + plant_config=plant_config, + tech_config={"model_inputs": model_inputs}, + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "h2_storage", + StorageAutoSizingModel( + plant_config=plant_config, + tech_config={"model_inputs": model_inputs}, + ), + promotes=["*"], + ) + + prob.setup() + + prob.run_model() + + with subtests.test("Expected discharge"): + expected_discharge = np.concat([np.array([5.0, 5.0, 3.88889]), np.zeros(15), np.ones(6)]) + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h"), + expected_discharge, + rtol=1e-6, + ) + + with subtests.test("Expected charge"): + expected_charge = np.concat([np.zeros(8), np.arange(-1, -11, -1), np.zeros(6)]) + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h"), + expected_charge, + rtol=1e-6, + ) diff --git a/h2integrate/control/control_strategies/storage/test/test_optimal_controllers.py b/h2integrate/control/control_strategies/storage/test/test_optimal_controllers.py new file mode 100644 index 000000000..0c0ec2633 --- /dev/null +++ b/h2integrate/control/control_strategies/storage/test/test_optimal_controllers.py @@ -0,0 +1,626 @@ +import numpy as np +import pytest +import openmdao.api as om +from pytest import fixture + +from h2integrate.storage.battery.pysam_battery import PySAMBatteryPerformanceModel +from h2integrate.storage.storage_performance_model import StoragePerformanceModel +from h2integrate.storage.simple_storage_auto_sizing import StorageAutoSizingModel +from h2integrate.control.control_strategies.storage.optimized_pyomo_controller import ( + OptimizedDispatchStorageController, +) + + +@fixture +def plant_config_battery(): + plant_config = { + "plant": { + "plant_life": 1, + "simulation": { + "dt": 3600, + "n_timesteps": 48, + }, + }, + "tech_to_dispatch_connections": [ + ["combiner", "battery"], + ["battery", "battery"], + ], + } + return plant_config + + +@fixture +def tech_config_battery(): + tech_config = { + "technologies": { + "battery": { + "control_strategy": {"model": "OptimizedDispatchStorageController"}, + "performance_model": {"model": "PySAMBatteryPerformanceModel"}, + "model_inputs": { + "shared_parameters": { + "max_charge_rate": 50000, + "max_capacity": 200000, + "init_soc_fraction": 0.5, + "max_soc_fraction": 0.9, + "min_soc_fraction": 0.1, + "commodity": "electricity", + "commodity_rate_units": "kW", + "charge_efficiency": 0.95, + "discharge_efficiency": 0.95, + }, + "performance_parameters": { + "system_model_source": "pysam", + "chemistry": "LFPGraphite", + "control_variable": "input_power", + "demand_profile": 0.0, + }, + "control_parameters": { + "tech_name": "battery", + "system_commodity_interface_limit": 1e12, + "cost_per_charge": 0.004, + "cost_per_discharge": 0.005, + "cost_per_production": 0.0, + "commodity_met_value": 0.1, + "round_digits": 4, + "time_weighting_factor": 0.995, + "n_control_window": 24, + }, + }, + }, + }, + } + return tech_config + + +@fixture +def plant_config_h2_storage(): + plant_config = { + "plant": { + "plant_life": 30, + "simulation": { + "dt": 3600, + "n_timesteps": 48, + }, + }, + "tech_to_dispatch_connections": [ + ["converter", "h2_storage"], + ["h2_storage", "h2_storage"], + ], + } + return plant_config + + +@fixture +def tech_config_generic(): + tech_config = { + "technologies": { + "h2_storage": { + "control_strategy": {"model": "OptimizedDispatchStorageController"}, + "performance_model": {"model": "StoragePerformanceModel"}, + "model_inputs": { + "shared_parameters": { + "max_charge_rate": 10.0, + "max_capacity": 40.0, + "init_soc_fraction": 0.2, + "max_soc_fraction": 1.0, + "min_soc_fraction": 0.1, + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + "charge_efficiency": 1.0, + "discharge_efficiency": 1.0, + }, + "performance_parameters": { + "charge_equals_discharge": True, + "commodity_amount_units": "kg", + "demand_profile": 0.0, + }, + "control_parameters": { + "tech_name": "h2_storage", + "cost_per_charge": 0.03, # USD/kg + "cost_per_discharge": 0.05, # USD/kg + "commodity_met_value": 0.1, # USD/kg + "cost_per_production": 0.0, # USD/kg + "time_weighting_factor": 0.995, + "system_commodity_interface_limit": 10.0, + "n_control_window": 24, + }, + }, + } + }, + } + return tech_config + + +@fixture +def tech_config_autosizing(): + tech_config = { + "technologies": { + "h2_storage": { + "control_strategy": {"model": "OptimizedDispatchStorageController"}, + "performance_model": {"model": "StorageAutoSizingModel"}, + "model_inputs": { + "shared_parameters": { + "max_soc_fraction": 1.0, + "min_soc_fraction": 0.0, + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + "charge_efficiency": 1.0, + "discharge_efficiency": 1.0, + }, + "performance_parameters": { + "charge_equals_discharge": True, + "demand_profile": 0.0, + "commodity_amount_units": "kg", + "set_demand_as_avg_commodity_in": False, + }, + "control_parameters": { + "tech_name": "h2_storage", + "cost_per_charge": 0.03, # USD/kg + "cost_per_discharge": 0.05, # USD/kg + "commodity_met_value": 0.1, # USD/kg + "cost_per_production": 0.0, # USD/kg + "time_weighting_factor": 0.995, + "system_commodity_interface_limit": 10.0, + "n_control_window": 24, + "max_charge_rate": 5.0, + "max_capacity": 5.0, + "init_soc_fraction": 0.1, + }, + }, + } + }, + } + return tech_config + + +@pytest.mark.regression +def test_min_operating_cost_load_following_battery_dispatch( + plant_config_battery, tech_config_battery, subtests +): + # Fabricate some oscillating power generation data: 1000 kW for the first 12 hours, 10000 kW for + # the second twelve hours, and repeat that daily cycle over a year. + n_look_ahead_third = int(24 / 3) + + electricity_in = np.concatenate( + ( + np.ones(n_look_ahead_third) * 6000, + np.ones(n_look_ahead_third) * 1000, + np.ones(n_look_ahead_third) * 10000, + ) + ) + electricity_in = np.tile(electricity_in, 2) + + demand_in = np.ones(48) * 6000.0 + + # Setup the OpenMDAO problem and add subsystems + prob = om.Problem() + + prob.model.add_subsystem( + "battery_optimized_load_following_controller", + OptimizedDispatchStorageController( + plant_config=plant_config_battery, + tech_config=tech_config_battery["technologies"]["battery"], + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "battery", + PySAMBatteryPerformanceModel( + plant_config=plant_config_battery, + tech_config=tech_config_battery["technologies"]["battery"], + ), + promotes=["*"], + ) + + # Setup the system and required values + prob.setup() + prob.set_val("battery.electricity_in", electricity_in) + prob.set_val("battery.electricity_demand", demand_in) + + # Run the model + prob.run_model() + + charge_rate = prob.get_val("battery.max_charge_rate", units="kW")[0] + discharge_rate = prob.get_val("battery.max_charge_rate", units="kW")[0] + capacity = prob.get_val("battery.storage_capacity", units="kW*h")[0] + + # Test that discharge is always positive + with subtests.test("Discharge is always positive"): + assert np.all(prob.get_val("battery.storage_electricity_discharge") >= 0) + with subtests.test("Charge is always negative"): + assert np.all(prob.get_val("battery.storage_electricity_charge") <= 0) + # Set rtol lower b/c the values are in kW + with subtests.test("Charge + Discharge == battery_electricity_out"): + charge_plus_discharge = prob.get_val("battery.storage_electricity_charge") + prob.get_val( + "battery.storage_electricity_discharge" + ) + np.testing.assert_allclose( + charge_plus_discharge, prob.get_val("electricity_out"), rtol=1e-2 + ) + with subtests.test("Initial SOC is correct"): + assert pytest.approx(prob.model.get_val("battery.SOC")[0], rel=1e-2) == 50 + + # Find where the signal increases, decreases, and stays at zero + print("SOC", prob.model.get_val("battery.SOC")) + indx_soc_increase = np.argwhere( + np.diff(prob.model.get_val("battery.SOC", units="unitless"), prepend=True) > 0 + ).flatten() + indx_soc_decrease = np.argwhere( + np.diff(prob.model.get_val("battery.SOC", units="unitless"), prepend=False) < 0 + ).flatten() + indx_soc_same = np.argwhere( + np.diff(prob.model.get_val("battery.SOC", units="unitless"), prepend=True) == 0.0 + ).flatten() + + with subtests.test("SOC increases when charging"): + assert np.all( + prob.get_val("battery.storage_electricity_charge", units="kW")[indx_soc_increase] <= 0 + ) + assert np.all( + prob.get_val("battery.storage_electricity_charge", units="kW")[indx_soc_decrease] == 0 + ) + assert np.all( + prob.get_val("battery.storage_electricity_charge", units="kW")[indx_soc_same] == 0 + ) + + with subtests.test("SOC decreases when discharging"): + assert np.all( + prob.get_val("battery.storage_electricity_discharge", units="kW")[indx_soc_decrease] > 0 + ) + assert np.all( + prob.get_val("battery.storage_electricity_discharge", units="kW")[indx_soc_increase] + == 0 + ) + assert np.all( + prob.get_val("battery.storage_electricity_discharge", units="kW")[indx_soc_same] == 0 + ) + + with subtests.test("Max SOC <= Max storage percent"): + assert prob.get_val("battery.SOC", units="unitless").max() <= 0.9 + + with subtests.test("Min SOC >= Min storage percent"): + assert prob.get_val("battery.SOC", units="unitless").min() >= 0.1 + + with subtests.test("Charge never exceeds charge rate"): + assert ( + prob.get_val("battery.storage_electricity_charge", units="kW").min() >= -1 * charge_rate + ) + + with subtests.test("Discharge never exceeds discharge rate"): + assert ( + prob.get_val("battery.storage_electricity_discharge", units="kW").max() + <= discharge_rate + ) + + with subtests.test("Discharge never exceeds demand"): + assert np.all( + prob.get_val("battery.storage_electricity_discharge", units="kW").max() <= demand_in + ) + + with subtests.test("Sometimes discharges"): + assert any( + k > 1e-3 for k in prob.get_val("battery.storage_electricity_discharge", units="kW") + ) + + with subtests.test("Sometimes charges"): + assert any( + k < -1e-3 for k in prob.get_val("battery.storage_electricity_charge", units="kW") + ) + + with subtests.test("Cumulative charge/discharge does not exceed storage capacity"): + assert np.cumsum(charge_plus_discharge).max() <= capacity + assert np.cumsum(charge_plus_discharge).min() >= -1 * capacity + + with subtests.test("Expected discharge from hour 10-30"): + expected_discharge = np.concat([np.zeros(8), np.ones(8) * 5000, np.zeros(4)]) + np.testing.assert_allclose( + prob.get_val("battery.storage_electricity_discharge", units="kW")[0:20], + expected_discharge, + rtol=1e-2, + ) + + with subtests.test("Expected charge hour 0-24"): + expected_charge = -1 * np.concat([np.zeros(16), np.ones(8) * 4000]) + np.testing.assert_allclose( + prob.get_val("battery.storage_electricity_charge", units="kW")[0:24], + expected_charge, + rtol=1e-2, + ) + + +@pytest.mark.regression +def test_optimal_control_with_generic_storage( + plant_config_h2_storage, tech_config_generic, subtests +): + commodity_demand = np.full(48, 5.0) + commodity_in = np.tile(np.concat([np.zeros(3), np.cumsum(np.ones(15)), np.full(6, 4.0)]), 2) + + # Setup the OpenMDAO problem and add subsystems + prob = om.Problem() + + prob.model.add_subsystem( + "h2_storage_optimized_load_following_controller", + OptimizedDispatchStorageController( + plant_config=plant_config_h2_storage, + tech_config=tech_config_generic["technologies"]["h2_storage"], + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "h2_storage", + StoragePerformanceModel( + plant_config=plant_config_h2_storage, + tech_config=tech_config_generic["technologies"]["h2_storage"], + ), + promotes=["*"], + ) + + # Setup the system and required values + prob.setup() + prob.set_val("h2_storage.hydrogen_in", commodity_in) + prob.set_val("h2_storage.hydrogen_demand", commodity_demand) + + # Run the model + prob.run_model() + + charge_rate = prob.get_val("h2_storage.max_charge_rate", units="kg/h")[0] + discharge_rate = prob.get_val("h2_storage.max_charge_rate", units="kg/h")[0] + capacity = prob.get_val("h2_storage.storage_capacity", units="kg")[0] + + # Test that discharge is always positive + with subtests.test("Discharge is always positive"): + assert np.all(prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h") >= 0) + with subtests.test("Charge is always negative"): + assert np.all(prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h") <= 0) + with subtests.test("Charge + Discharge == storage_hydrogen_out"): + charge_plus_discharge = prob.get_val( + "h2_storage.storage_hydrogen_charge", units="kg/h" + ) + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h") + np.testing.assert_allclose( + charge_plus_discharge, prob.get_val("hydrogen_out", units="kg/h"), rtol=1e-6 + ) + with subtests.test("Initial SOC is correct"): + assert ( + pytest.approx(prob.model.get_val("h2_storage.SOC", units="unitless")[0], rel=1e-6) + == 0.1 + ) + + indx_soc_increase = np.argwhere( + np.diff(prob.model.get_val("h2_storage.SOC", units="unitless"), prepend=True) > 0 + ).flatten() + indx_soc_decrease = np.argwhere( + np.diff(prob.model.get_val("h2_storage.SOC", units="unitless"), prepend=False) < 0 + ).flatten() + indx_soc_same = np.argwhere( + np.diff(prob.model.get_val("h2_storage.SOC", units="unitless"), prepend=True) == 0.0 + ).flatten() + + with subtests.test("SOC increases when charging"): + assert np.all( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h")[indx_soc_increase] < 0 + ) + assert np.all( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h")[indx_soc_decrease] == 0 + ) + assert np.all( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h")[indx_soc_same] == 0 + ) + + with subtests.test("SOC decreases when discharging"): + assert np.all( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h")[indx_soc_decrease] + > 0 + ) + assert np.all( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h")[indx_soc_increase] + == 0 + ) + assert np.all( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h")[indx_soc_same] == 0 + ) + + with subtests.test("Max SOC <= Max storage percent"): + assert prob.get_val("h2_storage.SOC", units="unitless").max() <= 1.0 + + with subtests.test("Min SOC >= Min storage percent"): + assert prob.get_val("h2_storage.SOC", units="unitless").min() >= 0.1 + + with subtests.test("Charge never exceeds charge rate"): + assert ( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h").min() + >= -1 * charge_rate + ) + + with subtests.test("Discharge never exceeds discharge rate"): + assert ( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h").max() + <= discharge_rate + ) + + with subtests.test("Discharge never exceeds demand"): + assert np.all( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h").max() + <= commodity_demand + ) + + with subtests.test("Sometimes discharges"): + assert any( + k > 1e-3 for k in prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h") + ) + + with subtests.test("Sometimes charges"): + assert any( + k < -1e-3 for k in prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h") + ) + + with subtests.test("Cumulative charge/discharge does not exceed storage capacity"): + assert np.cumsum(prob.get_val("hydrogen_out", units="kg/h")).max() <= capacity + assert np.cumsum(prob.get_val("hydrogen_out", units="kg/h")).min() >= -1 * capacity + + with subtests.test("Expected discharge from hour 10-30"): + expected_discharge = np.concat( + [np.zeros(8), np.ones(6), np.full(3, 5.0), np.array([4, 3, 2])] + ) + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h")[10:30], + expected_discharge, + rtol=1e-6, + ) + + with subtests.test("Expected charge hour 0-20"): + expected_charge = np.concat([np.zeros(8), np.arange(-1, -9, -1), np.zeros(4)]) + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h")[0:20], + expected_charge, + rtol=1e-6, + ) + + +@pytest.mark.regression +def test_optimal_dispatch_with_autosizing_storage_demand_less_than_avg_in( + plant_config_h2_storage, tech_config_autosizing, subtests +): + commodity_demand = np.full(48, 5.0) + commodity_in = np.tile(np.concat([np.zeros(3), np.cumsum(np.ones(15)), np.full(6, 4.0)]), 2) + # Setup the OpenMDAO problem and add subsystems + prob = om.Problem() + + prob.model.add_subsystem( + "h2_storage_controller", + OptimizedDispatchStorageController( + plant_config=plant_config_h2_storage, + tech_config=tech_config_autosizing["technologies"]["h2_storage"], + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "h2_storage", + StorageAutoSizingModel( + plant_config=plant_config_h2_storage, + tech_config=tech_config_autosizing["technologies"]["h2_storage"], + ), + promotes=["*"], + ) + + # Setup the system and required values + prob.setup() + prob.set_val("h2_storage.hydrogen_in", commodity_in) + prob.set_val("h2_storage.hydrogen_demand", commodity_demand) + + # Run the model + prob.run_model() + + charge_rate = prob.get_val("h2_storage.max_charge_rate", units="kg/h")[0] + discharge_rate = prob.get_val("h2_storage.max_discharge_rate", units="kg/h")[0] + capacity = prob.get_val("h2_storage.storage_capacity", units="kg")[0] + + with subtests.test("Capacity is correct"): + soc_kg = np.cumsum(commodity_demand - commodity_in) + soc_kg_adj = soc_kg + np.abs(np.min(soc_kg)) + expected_usable_capacity = np.max(soc_kg_adj) - np.min(soc_kg_adj) + + assert pytest.approx(capacity, rel=1e-6) == expected_usable_capacity + + with subtests.test("Charge rate is correct"): + assert pytest.approx(charge_rate, rel=1e-6) == max(commodity_in) + with subtests.test("Discharge rate is correct"): + assert pytest.approx(discharge_rate, rel=1e-6) == max(commodity_in) + + with subtests.test("Expected discharge from hour 10-30"): + expected_discharge = np.concat( + [np.zeros(8), np.ones(6), np.full(3, 5.0), np.array([4, 3, 2])] + ) + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h")[10:30], + expected_discharge, + rtol=1e-6, + ) + + with subtests.test("Expected charge hour 0-20"): + expected_charge = np.concat([np.zeros(8), np.arange(-1, -11, -1), np.zeros(2)]) + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h")[0:20], + expected_charge, + rtol=1e-6, + ) + + +@pytest.mark.regression +def test_optimal_dispatch_with_autosizing_storage_demand_is_avg_in( + plant_config_h2_storage, tech_config_autosizing, subtests +): + commodity_in = np.tile(np.concat([np.zeros(3), np.cumsum(np.ones(15)), np.full(6, 4.0)]), 2) + commodity_demand = np.full(48, np.mean(commodity_in)) + tech_config_autosizing["technologies"]["h2_storage"]["model_inputs"]["performance_parameters"][ + "set_demand_as_avg_commodity_in" + ] = True + tech_config_autosizing["technologies"]["h2_storage"]["model_inputs"]["performance_parameters"][ + "demand_profile" + ] = 0.0 + + # Setup the OpenMDAO problem and add subsystems + prob = om.Problem() + + prob.model.add_subsystem( + "h2_storage_controller", + OptimizedDispatchStorageController( + plant_config=plant_config_h2_storage, + tech_config=tech_config_autosizing["technologies"]["h2_storage"], + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "h2_storage", + StorageAutoSizingModel( + plant_config=plant_config_h2_storage, + tech_config=tech_config_autosizing["technologies"]["h2_storage"], + ), + promotes=["*"], + ) + + # Setup the system and required values + prob.setup() + prob.set_val("h2_storage.hydrogen_in", commodity_in) + + # Run the model + prob.run_model() + + charge_rate = prob.get_val("h2_storage.max_charge_rate", units="kg/h")[0] + discharge_rate = prob.get_val("h2_storage.max_discharge_rate", units="kg/h")[0] + capacity = prob.get_val("h2_storage.storage_capacity", units="kg")[0] + + with subtests.test("Capacity is correct"): + soc_kg = np.cumsum(commodity_demand - commodity_in) + soc_kg_adj = soc_kg + np.abs(np.min(soc_kg)) + expected_usable_capacity = np.max(soc_kg_adj) - np.min(soc_kg_adj) + + assert pytest.approx(capacity, rel=1e-6) == expected_usable_capacity + + with subtests.test("Charge rate is correct"): + assert pytest.approx(charge_rate, rel=1e-6) == max(commodity_in) + with subtests.test("Discharge rate is correct"): + assert pytest.approx(discharge_rate, rel=1e-6) == max(commodity_in) + + with subtests.test("Expected discharge from hour 10-30"): + expected_discharge = np.concat( + [np.zeros(8), np.full(6, 2.0), np.full(3, 6.0), np.array([5, 4, 3])] + ) + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_discharge", units="kg/h")[10:30], + expected_discharge, + rtol=1e-6, + ) + + with subtests.test("Expected charge hour 0-20"): + expected_charge = np.concat( + [np.zeros(9), np.arange(-1, -7, -1), np.array([-1.5]), np.zeros(4)] + ) + np.testing.assert_allclose( + prob.get_val("h2_storage.storage_hydrogen_charge", units="kg/h")[0:20], + expected_charge, + rtol=1e-6, + ) diff --git a/h2integrate/control/test/conftest.py b/h2integrate/control/test/conftest.py deleted file mode 100644 index 8d49ffb3d..000000000 --- a/h2integrate/control/test/conftest.py +++ /dev/null @@ -1 +0,0 @@ -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 diff --git a/h2integrate/control/test/test_heuristic_controllers.py b/h2integrate/control/test/test_heuristic_controllers.py deleted file mode 100644 index 9f95ce87f..000000000 --- a/h2integrate/control/test/test_heuristic_controllers.py +++ /dev/null @@ -1,663 +0,0 @@ -import numpy as np -import pytest -import openmdao.api as om -from pytest import fixture - -from h2integrate.storage.battery.pysam_battery import PySAMBatteryPerformanceModel -from h2integrate.control.control_strategies.heuristic_pyomo_controller import ( - HeuristicLoadFollowingController, -) -from h2integrate.control.control_rules.storage.pyomo_storage_rule_baseclass import ( - PyomoRuleStorageBaseclass, -) - - -@fixture -def plant_config(): - plant_config_dict = { - "plant": { - "plant_life": 30, - "simulation": { - "dt": 3600, - "n_timesteps": 8760, - }, - }, - "tech_to_dispatch_connections": [ - ["battery", "battery"], - ], - } - return plant_config_dict - - -@fixture -def tech_config(): - tech_config_dict = { - "name": "technology_config", - "description": "...", - "technologies": { - "battery": { - "dispatch_rule_set": {"model": "PyomoRuleStorageBaseclass"}, - "control_strategy": {"model": "HeuristicLoadFollowingController"}, - "performance_model": {"model": "PySAMBatteryPerformanceModel"}, - "model_inputs": { - "shared_parameters": { - "max_charge_rate": 50000, - "max_capacity": 200000, - "n_control_window": 24, - "n_horizon_window": 48, - "init_charge_fraction": 0.5, - "max_charge_fraction": 0.9, - "min_charge_fraction": 0.1, - }, - "performance_parameters": { - "chemistry": "LFPGraphite", - "control_variable": "input_power", - }, - "control_parameters": { - "commodity": "electricity", - "commodity_rate_units": "kW", - "tech_name": "battery", - "system_commodity_interface_limit": 1e12, - }, - "dispatch_rule_parameters": { - "commodity": "electricity", - "commodity_rate_units": "kW", - }, - }, - } - }, - } - return tech_config_dict - - -@pytest.mark.regression -def test_heuristic_load_following_battery_dispatch(plant_config, tech_config, subtests): - # Fabricate some oscillating power generation data: 0 kW for the first 12 hours, 10000 kW for - # the second twelve hours, and repeat that daily cycle over a year. - n_look_ahead_half = int(24 / 2) - - electricity_in = np.concatenate( - (np.ones(n_look_ahead_half) * 0, np.ones(n_look_ahead_half) * 10000) - ) - electricity_in = np.tile(electricity_in, 365) - - demand_in = np.ones(8760) * 6000.0 - - # Setup the OpenMDAO problem and add subsystems - prob = om.Problem() - - prob.model.add_subsystem( - "PyomoRuleStorageBaseclass", - PyomoRuleStorageBaseclass( - plant_config=plant_config, tech_config=tech_config["technologies"]["battery"] - ), - promotes=["*"], - ) - - prob.model.add_subsystem( - "battery_heuristic_load_following_controller", - HeuristicLoadFollowingController( - plant_config=plant_config, tech_config=tech_config["technologies"]["battery"] - ), - promotes=["*"], - ) - - prob.model.add_subsystem( - "battery", - PySAMBatteryPerformanceModel( - plant_config=plant_config, tech_config=tech_config["technologies"]["battery"] - ), - promotes=["*"], - ) - - # Setup the system and required values - prob.setup() - prob.set_val("battery.electricity_in", electricity_in) - prob.set_val("battery.electricity_demand", demand_in) - - # Run the model - prob.run_model() - - # Test the case where the charging/discharging cycle remains within the max and min SOC limits - # Check the expected outputs to actual outputs - expected_electricity_out = [ - 5999.99995059, - 5990.56676743, - 5990.138959, - 5989.64831176, - 5989.08548217, - 5988.44193888, - 5987.70577962, - 5986.86071125, - 5985.88493352, - 5984.7496388, - 5983.41717191, - 5981.839478, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - ] - - expected_battery_electricity = [ - 5999.99995059, - 5990.56676743, - 5990.138959, - 5989.64831176, - 5989.08548217, - 5988.44193888, - 5987.70577962, - 5986.86071125, - 5985.88493352, - 5984.7496388, - 5983.41717191, - 5981.839478, - -3988.62235554, - -3989.2357847, - -3989.76832626, - -3990.26170521, - -3990.71676106, - -3991.13573086, - -3991.52143699, - -3991.87684905, - -3992.20485715, - -3992.50815603, - -3992.78920148, - -3993.05020268, - ] - - expected_SOC = [ - 49.39724571, - 46.54631833, - 43.69133882, - 40.83119769, - 37.96394628, - 35.08762294, - 32.20015974, - 29.29919751, - 26.38184809, - 23.44436442, - 20.48162855, - 17.48627159, - 19.47067094, - 21.44466462, - 23.40741401, - 25.36052712, - 27.30530573, - 29.24281439, - 31.17393198, - 33.09939078, - 35.01980641, - 36.93570091, - 38.84752069, - 40.75565055, - ] - - expected_unmet_demand_out = np.array( - [ - 4.93562475e-05, - 9.43323257e00, - 9.86104099e00, - 1.03516883e01, - 1.09145178e01, - 1.15580611e01, - 1.22942204e01, - 1.31392889e01, - 1.41150664e01, - 1.52503612e01, - 1.65828282e01, - 1.81605218e01, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ] - ) - - expected_unused_commodity_out = np.array( - [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 11.37764445, - 10.76421514, - 10.23167373, - 9.73829458, - 9.28323883, - 8.86426912, - 8.47856327, - 8.12315078, - 7.79514283, - 7.49184426, - 7.21079852, - 6.94979705, - ] - ) - - with subtests.test("Check electricity_out"): - assert ( - pytest.approx(expected_electricity_out) - == prob.get_val("battery.electricity_out", units="kW")[0:24] - ) - - with subtests.test("Check battery_electricity"): - assert ( - pytest.approx(expected_battery_electricity) - == prob.get_val("battery.battery_electricity", units="kW")[0:24] - ) - - with subtests.test("Check SOC"): - assert pytest.approx(expected_SOC) == prob.get_val("battery.SOC", units="percent")[0:24] - - with subtests.test("Check unmet_demand"): - assert ( - pytest.approx(expected_unmet_demand_out, abs=1e-4) - == prob.get_val("battery.unmet_electricity_demand_out", units="kW")[0:24] - ) - - with subtests.test("Check unused_electricity_out"): - assert ( - pytest.approx(expected_unused_commodity_out) - == prob.get_val("battery.unused_electricity_out", units="kW")[0:24] - ) - - # Test the case where the battery is discharged to its lower SOC limit - electricity_in = np.zeros(8760) - demand_in = np.ones(8760) * 30000 - - # Setup the system and required values - prob.setup() - - prob.set_val("battery.electricity_in", electricity_in) - prob.set_val("battery.electricity_demand", demand_in) - - # Run the model - prob.run_model() - - expected_electricity_out = np.array( - [3.00000000e04, 2.99305601e04, 2.48145097e04, 4.97901621e00, 3.04065390e01] - ) - expected_battery_electricity = expected_electricity_out - expected_SOC = np.array([37.69010284, 22.89921133, 10.00249593, 10.01524461, 10.03556385]) - expected_unmet_demand_out = np.array( - [ - 9.43691703e-09, - 6.94398578e01, - 5.18549025244965e03, - 2.999502098378662e04, - 2.9969593461021406e04, - ] - ) - expected_unused_commodity_out = np.zeros(5) - - with subtests.test("Check electricity_out for min SOC"): - assert ( - pytest.approx(expected_electricity_out) - == prob.get_val("battery.electricity_out", units="kW")[:5] - ) - - with subtests.test("Check battery_electricity for min SOC"): - assert ( - pytest.approx(expected_battery_electricity) - == prob.get_val("battery.battery_electricity", units="kW")[:5] - ) - - with subtests.test("Check SOC for min SOC"): - assert pytest.approx(expected_SOC) == prob.get_val("battery.SOC", units="percent")[:5] - - with subtests.test("Check unmet_demand for min SOC"): - assert ( - pytest.approx(expected_unmet_demand_out, abs=1e-6) - == prob.get_val("battery.unmet_electricity_demand_out", units="kW")[:5] - ) - - with subtests.test("Check unused_commodity_out for min SOC"): - assert ( - pytest.approx(expected_unused_commodity_out) - == prob.get_val("battery.unused_electricity_out", units="kW")[:5] - ) - - # Test the case where the battery is charged to its upper SOC limit - electricity_in = np.ones(8760) * 30000.0 - demand_in = np.zeros(8760) - - # Setup the system and required values - prob.setup() - prob.set_val("battery.electricity_in", electricity_in) - prob.set_val("battery.electricity_demand", demand_in) - - # Run the model - prob.run_model() - - expected_electricity_out = [-0.008477085, 0.0, 0.0, 0.0, 0.0] - - # TODO reevaluate the output here - expected_battery_electricity = np.array( - [-30000.00847709, -29973.58679719, -21109.22734423, 0.0, 0.0] - ) - - # expected_SOC = [66.00200558, 79.43840635, 90.0, 90.0, 90.0] - expected_SOC = np.array([66.00200558, 79.43840635, 89.02326413, 89.02326413, 89.02326413]) - expected_unmet_demand_out = np.array([0.00847709, 0.0, 0.0, 0.0, 0.0]) - expected_unused_commodity_out = np.array( - [0.00000000e00, 2.64132028e01, 8.89077266e03, 3.04088135e04, 3.00564087e04] - ) - # I think this is the right expected_electricity_out since the battery won't - # be discharging in this instance - # expected_electricity_out = [0.0, 0.0, 0.0, 0.0, 0.0] - # # expected_electricity_out = [0.0, 0.0, 6150.14483911, 30000.0, 30000.0] - # expected_battery_electricity = [-30000.00847705, -29973.58679681, - # -23310.54620182, 0.0, 0.0] - # expected_SOC = [66.00200558, 79.43840635, 90.0, 90.0, 90.0] - # expected_unmet_demand_out = np.zeros(5) - # expected_unused_commodity_out = [0.0, 0.0, 6150.14483911, 30000.0, 30000.0] - - abs_tol = 1e-6 - rel_tol = 1e-1 - with subtests.test("Check electricity_out for max SOC"): - assert ( - pytest.approx(expected_electricity_out, abs=abs_tol, rel=rel_tol) - == prob.get_val("battery.electricity_out", units="kW")[:5] - ) - - with subtests.test("Check battery_electricity for max SOC"): - assert ( - pytest.approx(expected_battery_electricity, abs=abs_tol, rel=rel_tol) - == prob.get_val("battery.battery_electricity", units="kW")[:5] - ) - - with subtests.test("Check SOC for max SOC"): - assert ( - pytest.approx(expected_SOC, abs=abs_tol) - == prob.get_val("battery.SOC", units="percent")[:5] - ) - - with subtests.test("Check unmet_demand for max SOC"): - assert ( - pytest.approx(expected_unmet_demand_out, abs=abs_tol) - == prob.get_val("battery.unmet_electricity_demand_out", units="kW")[:5] - ) - - with subtests.test("Check unused_commodity_out for max SOC"): - assert ( - pytest.approx(expected_unused_commodity_out, abs=abs_tol, rel=rel_tol) - == prob.get_val("battery.unused_electricity_out", units="kW")[:5] - ) - - -@pytest.mark.regression -def test_heuristic_load_following_battery_dispatch_change_capacities( - plant_config, tech_config, subtests -): - # update the battery capacity to be very small in the config - - tech_config["technologies"]["battery"]["model_inputs"]["shared_parameters"].update( - {"max_charge_rate": 1000} - ) - tech_config["technologies"]["battery"]["model_inputs"]["shared_parameters"].update( - {"max_capacity": 1000} - ) - - # Fabricate some oscillating power generation data: 0 kW for the first 12 hours, 10000 kW for - # the second twelve hours, and repeat that daily cycle over a year. - - n_look_ahead_half = int(24 / 2) - - electricity_in = np.concatenate( - (np.ones(n_look_ahead_half) * 0, np.ones(n_look_ahead_half) * 10000) - ) - electricity_in = np.tile(electricity_in, 365) - - demand_in = np.ones(8760) * 6000.0 - - # Setup the OpenMDAO problem and add subsystems - prob = om.Problem() - - prob.model.add_subsystem( - name="IVC1", - subsys=om.IndepVarComp(name="max_charge_rate", val=1000, units="kW"), - promotes=["*"], - ) - - prob.model.add_subsystem( - name="IVC2", - subsys=om.IndepVarComp(name="storage_capacity", val=1000, units="kW*h"), - promotes=["*"], - ) - - prob.model.add_subsystem( - "PyomoRuleStorageBaseclass", - PyomoRuleStorageBaseclass( - plant_config=plant_config, tech_config=tech_config["technologies"]["battery"] - ), - promotes=["*"], - ) - - prob.model.add_subsystem( - "battery_heuristic_load_following_controller", - HeuristicLoadFollowingController( - plant_config=plant_config, tech_config=tech_config["technologies"]["battery"] - ), - promotes=["*"], - ) - - prob.model.add_subsystem( - "battery", - PySAMBatteryPerformanceModel( - plant_config=plant_config, tech_config=tech_config["technologies"]["battery"] - ), - promotes=["*"], - ) - - # Setup the system and required values - prob.setup() - - prob.set_val("IVC1.max_charge_rate", 50000, units="kW") - prob.set_val("IVC2.storage_capacity", 200000, units="kW*h") - - prob.set_val("battery.electricity_in", electricity_in) - prob.set_val("battery.electricity_demand", demand_in) - - # Run the model - prob.run_model() - - # Test the case where the charging/discharging cycle remains within the max and min SOC limits - # Check the expected outputs to actual outputs - expected_electricity_out = [ - 5999.99995059, - 5990.56676743, - 5990.138959, - 5989.64831176, - 5989.08548217, - 5988.44193888, - 5987.70577962, - 5986.86071125, - 5985.88493352, - 5984.7496388, - 5983.41717191, - 5981.839478, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - ] - - expected_battery_electricity = [ - 5999.99995059, - 5990.56676743, - 5990.138959, - 5989.64831176, - 5989.08548217, - 5988.44193888, - 5987.70577962, - 5986.86071125, - 5985.88493352, - 5984.7496388, - 5983.41717191, - 5981.839478, - -3988.62235554, - -3989.2357847, - -3989.76832626, - -3990.26170521, - -3990.71676106, - -3991.13573086, - -3991.52143699, - -3991.87684905, - -3992.20485715, - -3992.50815603, - -3992.78920148, - -3993.05020268, - ] - - expected_SOC = [ - 49.39724571, - 46.54631833, - 43.69133882, - 40.83119769, - 37.96394628, - 35.08762294, - 32.20015974, - 29.29919751, - 26.38184809, - 23.44436442, - 20.48162855, - 17.48627159, - 19.47067094, - 21.44466462, - 23.40741401, - 25.36052712, - 27.30530573, - 29.24281439, - 31.17393198, - 33.09939078, - 35.01980641, - 36.93570091, - 38.84752069, - 40.75565055, - ] - - expected_unmet_demand_out = np.array( - [ - 4.93562475e-05, - 9.43323257e00, - 9.86104099e00, - 1.03516883e01, - 1.09145178e01, - 1.15580611e01, - 1.22942204e01, - 1.31392889e01, - 1.41150664e01, - 1.52503612e01, - 1.65828282e01, - 1.81605218e01, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ] - ) - - expected_unused_commodity_out = np.array( - [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 11.37764445, - 10.76421514, - 10.23167373, - 9.73829458, - 9.28323883, - 8.86426912, - 8.47856327, - 8.12315078, - 7.79514283, - 7.49184426, - 7.21079852, - 6.94979705, - ] - ) - - with subtests.test("Battery output capacity"): - assert ( - pytest.approx( - prob.get_val("battery.rated_electricity_production", units="MW"), rel=1e-6 - ) - == 50.0 - ) - - with subtests.test("Check electricity_out"): - assert ( - pytest.approx(expected_electricity_out) - == prob.get_val("battery.electricity_out", units="kW")[0:24] - ) - - with subtests.test("Check battery_electricity"): - assert ( - pytest.approx(expected_battery_electricity) - == prob.get_val("battery.battery_electricity", units="kW")[0:24] - ) - - with subtests.test("Check SOC"): - assert pytest.approx(expected_SOC) == prob.get_val("battery.SOC", units="percent")[0:24] - - with subtests.test("Check unmet_demand"): - assert ( - pytest.approx(expected_unmet_demand_out, abs=1e-4) - == prob.get_val("battery.unmet_electricity_demand_out", units="kW")[0:24] - ) - - with subtests.test("Check unused_electricity_out"): - assert ( - pytest.approx(expected_unused_commodity_out) - == prob.get_val("battery.unused_electricity_out", units="kW")[0:24] - ) diff --git a/h2integrate/control/test/test_heuristic_with_generic_storage.py b/h2integrate/control/test/test_heuristic_with_generic_storage.py deleted file mode 100644 index 31bb72c65..000000000 --- a/h2integrate/control/test/test_heuristic_with_generic_storage.py +++ /dev/null @@ -1,205 +0,0 @@ -import numpy as np -import pytest -import openmdao.api as om -from pytest import fixture - -from h2integrate.storage.generic_storage_pyo import StoragePerformanceModel -from h2integrate.control.control_strategies.heuristic_pyomo_controller import ( - HeuristicLoadFollowingController, -) -from h2integrate.control.control_rules.storage.pyomo_storage_rule_baseclass import ( - PyomoRuleStorageBaseclass, -) - - -@fixture -def plant_config(): - plant_config = { - "plant": { - "plant_life": 30, - "simulation": { - "dt": 3600, - "n_timesteps": 8760, - }, - }, - "tech_to_dispatch_connections": [ - ["h2_storage", "h2_storage"], - ], - } - return plant_config - - -@fixture -def tech_config_generic(): - tech_config = { - "technologies": { - "h2_storage": { - "dispatch_rule_set": {"model": "PyomoRuleStorageBaseclass"}, - "control_strategy": {"model": "HeuristicLoadFollowingController"}, - "performance_model": {"model": "StoragePerformanceModel"}, - "model_inputs": { - "shared_parameters": { - "max_charge_rate": 10.0, - "max_capacity": 40.0, - "n_control_window": 24, - "init_charge_fraction": 0.1, - "max_charge_fraction": 1.0, - "min_charge_fraction": 0.1, - "commodity": "hydrogen", - "commodity_rate_units": "kg/h", - }, - "performance_parameters": { - "charge_equals_discharge": True, - "commodity_amount_units": "kg", - "charge_efficiency": 1.0, - "discharge_efficiency": 1.0, - }, - "control_parameters": { - "tech_name": "h2_storage", - "system_commodity_interface_limit": 10.0, - }, - }, - } - }, - } - return tech_config - - -@pytest.mark.regression -def test_heuristic_load_following_dispatch_with_generic_storage( - plant_config, tech_config_generic, subtests -): - commodity_demand = np.full(8760, 5.0) - commodity_in = np.tile(np.concat([np.zeros(3), np.cumsum(np.ones(15)), np.full(6, 4.0)]), 365) - - # Setup the OpenMDAO problem and add subsystems - prob = om.Problem() - - prob.model.add_subsystem( - "PyomoRuleStorageBaseclass", - PyomoRuleStorageBaseclass( - plant_config=plant_config, tech_config=tech_config_generic["technologies"]["h2_storage"] - ), - promotes=["*"], - ) - - prob.model.add_subsystem( - "battery_heuristic_load_following_controller", - HeuristicLoadFollowingController( - plant_config=plant_config, tech_config=tech_config_generic["technologies"]["h2_storage"] - ), - promotes=["*"], - ) - - prob.model.add_subsystem( - "storage", - StoragePerformanceModel( - plant_config=plant_config, tech_config=tech_config_generic["technologies"]["h2_storage"] - ), - promotes=["*"], - ) - - # Setup the system and required values - prob.setup() - prob.set_val("storage.hydrogen_in", commodity_in) - prob.set_val("storage.hydrogen_demand", commodity_demand) - - # Run the model - prob.run_model() - - charge_rate = prob.get_val("storage.max_charge_rate", units="kg/h")[0] - discharge_rate = prob.get_val("storage.max_charge_rate", units="kg/h")[0] - capacity = prob.get_val("storage.storage_capacity", units="kg")[0] - - # Test that discharge is always positive - with subtests.test("Discharge is always positive"): - assert np.all(prob.get_val("storage.storage_hydrogen_discharge", units="kg/h") >= 0) - with subtests.test("Charge is always negative"): - assert np.all(prob.get_val("storage.storage_hydrogen_charge", units="kg/h") <= 0) - with subtests.test("Charge + Discharge == storage_hydrogen_out"): - charge_plus_discharge = prob.get_val( - "storage.storage_hydrogen_charge", units="kg/h" - ) + prob.get_val("storage.storage_hydrogen_discharge", units="kg/h") - np.testing.assert_allclose( - charge_plus_discharge, prob.get_val("storage_hydrogen_out", units="kg/h"), rtol=1e-6 - ) - with subtests.test("Initial SOC is correct"): - assert ( - pytest.approx(prob.model.get_val("storage.SOC", units="unitless")[0], rel=1e-6) == 0.1 - ) - - indx_soc_increase = np.argwhere( - np.diff(prob.model.get_val("storage.SOC", units="unitless"), prepend=True) > 0 - ).flatten() - indx_soc_decrease = np.argwhere( - np.diff(prob.model.get_val("storage.SOC", units="unitless"), prepend=False) < 0 - ).flatten() - indx_soc_same = np.argwhere( - np.diff(prob.model.get_val("storage.SOC", units="unitless"), prepend=True) == 0.0 - ).flatten() - - with subtests.test("SOC increases when charging"): - assert np.all( - prob.get_val("storage.storage_hydrogen_charge", units="kg/h")[indx_soc_increase] < 0 - ) - assert np.all( - prob.get_val("storage.storage_hydrogen_charge", units="kg/h")[indx_soc_decrease] == 0 - ) - assert np.all( - prob.get_val("storage.storage_hydrogen_charge", units="kg/h")[indx_soc_same] == 0 - ) - - with subtests.test("SOC decreases when discharging"): - assert np.all( - prob.get_val("storage.storage_hydrogen_discharge", units="kg/h")[indx_soc_decrease] > 0 - ) - assert np.all( - prob.get_val("storage.storage_hydrogen_discharge", units="kg/h")[indx_soc_increase] == 0 - ) - assert np.all( - prob.get_val("storage.storage_hydrogen_discharge", units="kg/h")[indx_soc_same] == 0 - ) - - with subtests.test("Max SOC <= Max storage percent"): - assert prob.get_val("storage.SOC", units="unitless").max() <= 1.0 - - with subtests.test("Min SOC >= Min storage percent"): - assert prob.get_val("storage.SOC", units="unitless").min() >= 0.1 - - with subtests.test("Charge never exceeds charge rate"): - assert ( - prob.get_val("storage.storage_hydrogen_charge", units="kg/h").min() >= -1 * charge_rate - ) - - with subtests.test("Discharge never exceeds discharge rate"): - assert ( - prob.get_val("storage.storage_hydrogen_discharge", units="kg/h").max() <= discharge_rate - ) - - with subtests.test("Discharge never exceeds demand"): - assert np.all( - prob.get_val("storage.storage_hydrogen_discharge", units="kg/h").max() - <= commodity_demand - ) - - with subtests.test("Cumulative charge/discharge does not exceed storage capacity"): - assert np.cumsum(prob.get_val("storage_hydrogen_out", units="kg/h")).max() <= capacity - assert np.cumsum(prob.get_val("storage_hydrogen_out", units="kg/h")).min() >= -1 * capacity - - with subtests.test("Expected discharge from hour 10-30"): - expected_discharge = np.concat( - [np.zeros(8), np.ones(6), np.full(3, 5.0), np.array([4, 3, 2])] - ) - np.testing.assert_allclose( - prob.get_val("storage.storage_hydrogen_discharge", units="kg/h")[10:30], - expected_discharge, - rtol=1e-6, - ) - - with subtests.test("Expected charge hour 0-20"): - expected_charge = np.concat([np.zeros(8), np.arange(-1, -9, -1), np.zeros(4)]) - np.testing.assert_allclose( - prob.get_val("storage.storage_hydrogen_charge", units="kg/h")[0:20], - expected_charge, - rtol=1e-6, - ) diff --git a/h2integrate/control/test/test_openloop_controllers.py b/h2integrate/control/test/test_openloop_controllers.py deleted file mode 100644 index 1a54b7fe6..000000000 --- a/h2integrate/control/test/test_openloop_controllers.py +++ /dev/null @@ -1,613 +0,0 @@ -from copy import deepcopy -from pathlib import Path - -import yaml -import numpy as np -import pytest -import openmdao.api as om -from pytest import fixture -from openmdao.utils.assert_utils import assert_check_totals - -from h2integrate.control.control_strategies.passthrough_openloop_controller import ( - PassThroughOpenLoopController, -) -from h2integrate.control.control_strategies.storage.demand_openloop_controller import ( - DemandOpenLoopStorageController, -) -from h2integrate.control.control_strategies.converters.demand_openloop_controller import ( - DemandOpenLoopConverterController, -) -from h2integrate.control.control_strategies.converters.flexible_demand_openloop_controller import ( - FlexibleDemandOpenLoopConverterController, -) - - -@fixture -def variable_h2_production_profile(): - end_use_rated_demand = 10.0 # kg/h - ramp_up_rate_kg = 4.0 - ramp_down_rate_kg = 2.0 - slow_ramp_up = np.arange(0, end_use_rated_demand * 1.1, 0.5) - slow_ramp_down = np.arange(end_use_rated_demand * 1.1, -0.5, -0.5) - fast_ramp_up = np.arange(0, end_use_rated_demand, ramp_up_rate_kg * 1.2) - fast_ramp_down = np.arange(end_use_rated_demand, 0.0, ramp_down_rate_kg * 1.1) - variable_profile = np.concat( - [slow_ramp_up, fast_ramp_down, slow_ramp_up, slow_ramp_down, fast_ramp_up] - ) - variable_h2_profile = np.tile(variable_profile, 2) - return variable_h2_profile - - -@pytest.mark.unit -def test_pass_through_controller(subtests): - # Get the directory of the current script - current_dir = Path(__file__).parent - - # Resolve the paths to the configuration files - tech_config_path = current_dir / "inputs" / "tech_config.yaml" - - # Load the technology configuration - with tech_config_path.open() as file: - tech_config = yaml.safe_load(file) - - # Set up the OpenMDAO problem - prob = om.Problem() - - prob.model.add_subsystem( - name="IVC", - subsys=om.IndepVarComp(name="hydrogen_in", val=np.arange(10)), - promotes=["*"], - ) - - prob.model.add_subsystem( - "pass_through_controller", - PassThroughOpenLoopController( - plant_config={}, tech_config=tech_config["technologies"]["h2_storage"] - ), - promotes=["*"], - ) - - prob.setup() - - prob.run_model() - - # Run the test - with subtests.test("Check output"): - assert pytest.approx( - prob.get_val("hydrogen_set_point", units="kg/h"), rel=1e-3 - ) == np.arange(10) - - # Run the test - with subtests.test("Check derivatives"): - # check total derivatives using OpenMDAO's check_totals and assert tools - assert_check_totals( - prob.check_totals( - of=[ - "hydrogen_set_point", - ], - wrt=[ - "hydrogen_in", - ], - step=1e-6, - form="central", - show_only_incorrect=False, - out_stream=None, - ) - ) - - -@pytest.mark.regression -def test_storage_demand_controller(subtests): - # Get the directory of the current script - current_dir = Path(__file__).parent - - # Resolve the paths to the configuration files - tech_config_path = current_dir / "inputs" / "tech_config.yaml" - - # Load the technology configuration - with tech_config_path.open() as file: - tech_config = yaml.safe_load(file) - - plant_config = {"plant": {"simulation": {"n_timesteps": 10}}} - - tech_config["technologies"]["h2_storage"]["control_strategy"]["model"] = ( - "DemandOpenLoopStorageController" - ) - - tech_config["technologies"]["h2_storage"]["model_inputs"]["control_parameters"] = { - "max_capacity": 10.0, # kg - "max_charge_fraction": 1.0, # fraction (0-1) - "min_charge_fraction": 0.0, # fraction (0-1) - "init_charge_fraction": 1.0, # fraction (0-1) - "max_charge_rate": 1.0, # kg/time step - "max_discharge_rate": 0.5, # kg/time step - "charge_equals_discharge": False, - "charge_efficiency": 1.0, - "discharge_efficiency": 1.0, - "demand_profile": [1.0] * 10, # Example: 10 time steps with 10 kg/time step demand - } - - plant_config = {"plant": {"plant_life": 30, "simulation": {"n_timesteps": 10}}} - - # Set up the OpenMDAO problem - prob = om.Problem() - - prob.model.add_subsystem( - name="IVC", - subsys=om.IndepVarComp(name="hydrogen_in", val=np.arange(10)), - promotes=["*"], - ) - - prob.model.add_subsystem( - "demand_open_loop_storage_controller", - DemandOpenLoopStorageController( - plant_config=plant_config, tech_config=tech_config["technologies"]["h2_storage"] - ), - promotes=["*"], - ) - - prob.setup() - - prob.run_model() - - # Run the test - with subtests.test("Check output"): - assert pytest.approx([0.5, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]) == prob.get_val( - "hydrogen_set_point", units="kg/h" - ) - - with subtests.test("Check curtailment"): - assert pytest.approx([0.0, 0.0, 0.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]) == prob.get_val( - "hydrogen_unused_commodity", units="kg/h" - ) - - with subtests.test("Check soc"): - assert pytest.approx([0.95, 0.95, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]) == prob.get_val( - "hydrogen_soc", units="unitless" - ) - - with subtests.test("Check missed load"): - assert pytest.approx([0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) == prob.get_val( - "hydrogen_unmet_demand", units="kg/h" - ) - - -@pytest.mark.unit -def test_storage_demand_controller_round_trip_efficiency(subtests): - # Get the directory of the current script - current_dir = Path(__file__).parent - - # Resolve the paths to the configuration files - tech_config_path = current_dir / "inputs" / "tech_config.yaml" - - # Load the technology configuration - with tech_config_path.open() as file: - tech_config = yaml.safe_load(file) - - plant_config = {"plant": {"simulation": {"n_timesteps": 10}}} - - tech_config["technologies"]["h2_storage"]["control_strategy"]["model"] = ( - "DemandOpenLoopStorageController" - ) - tech_config["technologies"]["h2_storage"]["model_inputs"]["control_parameters"] = { - "max_capacity": 10.0, # kg - "max_charge_fraction": 1.0, # fraction (0-1) - "min_charge_fraction": 0.0, # fraction (0-1) - "init_charge_fraction": 1.0, # fraction (0-1) - "max_charge_rate": 1.0, # kg/time step - "max_discharge_rate": 0.5, # kg/time step - "charge_equals_discharge": False, - "charge_efficiency": 1.0, - "discharge_efficiency": 1.0, - "demand_profile": [1.0] * 10, # Example: 10 time steps with 10 kg/time step demand - } - - tech_config_rte = deepcopy(tech_config) - tech_config_rte["technologies"]["h2_storage"]["model_inputs"]["control_parameters"] = { - "max_capacity": 10.0, # kg - "max_charge_fraction": 1.0, # fraction (0-1) - "min_charge_fraction": 0.0, # fraction (0-1) - "init_charge_fraction": 1.0, # fraction (0-1) - "max_charge_rate": 1.0, # kg/time step - "max_discharge_rate": 0.5, # kg/time step - "charge_equals_discharge": False, - "round_trip_efficiency": 1.0, - "demand_profile": [1.0] * 10, # Example: 10 time steps with 10 kg/time step demand - } - - plant_config = {"plant": {"plant_life": 30, "simulation": {"n_timesteps": 10}}} - - def set_up_and_run_problem(config): - # Set up the OpenMDAO problem - prob = om.Problem() - - prob.model.add_subsystem( - name="IVC", - subsys=om.IndepVarComp(name="hydrogen_in", val=np.arange(10)), - promotes=["*"], - ) - - prob.model.add_subsystem( - "demand_openloop_controller", - DemandOpenLoopStorageController( - plant_config=plant_config, tech_config=config["technologies"]["h2_storage"] - ), - promotes=["*"], - ) - - prob.setup() - - prob.run_model() - - return prob - - prob_ioe = set_up_and_run_problem(tech_config) - prob_rte = set_up_and_run_problem(tech_config_rte) - - # Run the test - with subtests.test("Check output"): - assert pytest.approx( - prob_ioe.get_val("hydrogen_set_point", units="kg/h") - ) == prob_rte.get_val("hydrogen_set_point", units="kg/h") - - with subtests.test("Check curtailment"): - assert pytest.approx( - prob_ioe.get_val("hydrogen_unused_commodity", units="kg/h") - ) == prob_rte.get_val("hydrogen_unused_commodity", units="kg/h") - - with subtests.test("Check soc"): - assert pytest.approx( - prob_ioe.get_val("hydrogen_soc", units="unitless") - ) == prob_rte.get_val("hydrogen_soc", units="unitless") - - with subtests.test("Check missed load"): - assert pytest.approx( - prob_ioe.get_val("hydrogen_unmet_demand", units="kg/h") - ) == prob_rte.get_val("hydrogen_unmet_demand", units="kg/h") - - -@pytest.mark.regression -def test_generic_storage_demand_controller(subtests): - # Test is the same as the demand controller test test_demand_controller for the "h2_storage" - # performance model but with the "SimpleGenericStorage" performance model - - # Get the directory of the current script - current_dir = Path(__file__).parent - - # Resolve the paths to the configuration files - tech_config_path = current_dir / "inputs" / "tech_config.yaml" - - # Load the technology configuration - with tech_config_path.open() as file: - tech_config = yaml.safe_load(file) - - tech_config["technologies"]["h2_storage"] = { - "performance_model": { - "model": "SimpleGenericStorage", - }, - "control_strategy": { - "model": "DemandOpenLoopStorageController", - }, - "model_inputs": { - "shared_parameters": { - "commodity": "hydrogen", - "commodity_rate_units": "kg", - "max_capacity": 10.0, # kg - "max_charge_rate": 1.0, # fraction (0-1) - }, - "control_parameters": { - "max_charge_fraction": 1.0, # fraction (0-1) - "min_charge_fraction": 0.0, # fraction (0-1) - "init_charge_fraction": 1.0, # fraction (0-1) - "max_discharge_rate": 0.5, # kg/time step - "charge_efficiency": 1.0, - "charge_equals_discharge": False, - "discharge_efficiency": 1.0, - "demand_profile": [1.0] * 10, # Example: 10 time steps with 10 kg/time step demand - }, - }, - } - - plant_config = {"plant": {"plant_life": 30, "simulation": {"n_timesteps": 10}}} - - # Set up OpenMDAO problem - prob = om.Problem() - - prob.model.add_subsystem( - name="IVC", - subsys=om.IndepVarComp(name="hydrogen_in", val=np.arange(10)), - promotes=["*"], - ) - - prob.model.add_subsystem( - "demand_open_loop_storage_controller", - DemandOpenLoopStorageController( - plant_config=plant_config, tech_config=tech_config["technologies"]["h2_storage"] - ), - promotes=["*"], - ) - - prob.setup() - - prob.run_model() - - # # Run the test - with subtests.test("Check output"): - assert pytest.approx([0.5, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]) == prob.get_val( - "hydrogen_set_point" - ) - - with subtests.test("Check curtailment"): - assert pytest.approx([0.0, 0.0, 0.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]) == prob.get_val( - "hydrogen_unused_commodity" - ) - - with subtests.test("Check soc"): - assert pytest.approx([0.95, 0.95, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]) == prob.get_val( - "hydrogen_soc" - ) - - with subtests.test("Check missed load"): - assert pytest.approx([0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) == prob.get_val( - "hydrogen_unmet_demand" - ) - - -@pytest.mark.regression -def test_demand_converter_controller(subtests): - # Test is the same as the demand controller test test_demand_controller for the "h2_storage" - # performance model but with the "SimpleGenericStorage" performance model - - # Get the directory of the current script - current_dir = Path(__file__).parent - - # Resolve the paths to the configuration files - tech_config_path = current_dir / "inputs" / "tech_config.yaml" - - # Load the technology configuration - with tech_config_path.open() as file: - tech_config = yaml.safe_load(file) - - tech_config["technologies"]["load"] = { - "control_strategy": { - "model": "DemandOpenLoopConverterController", - }, - "model_inputs": { - "control_parameters": { - "commodity": "hydrogen", - "commodity_rate_units": "kg", - "demand_profile": [5.0] * 10, # Example: 10 time steps with 5 kg/time step demand - }, - }, - } - - plant_config = {"plant": {"plant_life": 30, "simulation": {"n_timesteps": 10}}} - - # Set up OpenMDAO problem - prob = om.Problem() - - prob.model.add_subsystem( - name="IVC", - subsys=om.IndepVarComp(name="hydrogen_in", val=np.arange(10)), - promotes=["*"], - ) - - prob.model.add_subsystem( - "demand_open_loop_storage_controller", - DemandOpenLoopConverterController( - plant_config=plant_config, tech_config=tech_config["technologies"]["load"] - ), - promotes=["*"], - ) - - prob.setup() - - prob.run_model() - - # # Run the test - with subtests.test("Check output"): - assert pytest.approx([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0]) == prob.get_val( - "hydrogen_set_point" - ) - - with subtests.test("Check curtailment"): - assert pytest.approx([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0]) == prob.get_val( - "hydrogen_unused_commodity" - ) - - with subtests.test("Check missed load"): - assert pytest.approx([5.0, 4.0, 3.0, 2.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0]) == prob.get_val( - "hydrogen_unmet_demand" - ) - - -### Add test for flexible load demand controller here - - -@pytest.mark.unit -def test_flexible_demand_converter_controller(subtests, variable_h2_production_profile): - # Get the directory of the current script - current_dir = Path(__file__).parent - - # Resolve the paths to the configuration files - tech_config_path = current_dir / "inputs" / "tech_config.yaml" - - # Load the technology configuration - with tech_config_path.open() as file: - tech_config = yaml.safe_load(file) - - end_use_rated_demand = 10.0 # kg/h - ramp_up_rate_kg = 4.0 - ramp_down_rate_kg = 2.0 - min_demand_kg = 2.5 - tech_config["technologies"]["load"] = { - "control_strategy": { - "model": "FlexibleDemandOpenLoopConverterController", - }, - "model_inputs": { - "control_parameters": { - "commodity": "hydrogen", - "commodity_rate_units": "kg", - "rated_demand": end_use_rated_demand, - "demand_profile": end_use_rated_demand, # flat demand profile - "turndown_ratio": min_demand_kg / end_use_rated_demand, - "ramp_down_rate_fraction": ramp_down_rate_kg / end_use_rated_demand, - "ramp_up_rate_fraction": ramp_up_rate_kg / end_use_rated_demand, - "min_utilization": 0.1, - }, - }, - } - - plant_config = { - "plant": { - "plant_life": 30, - "simulation": {"n_timesteps": len(variable_h2_production_profile)}, - } - } - - # Set up OpenMDAO problem - prob = om.Problem() - - prob.model.add_subsystem( - name="IVC", - subsys=om.IndepVarComp(name="hydrogen_in", val=variable_h2_production_profile), - promotes=["*"], - ) - - prob.model.add_subsystem( - "flexible_demand_open_loop_converter_controller", - FlexibleDemandOpenLoopConverterController( - plant_config=plant_config, tech_config=tech_config["technologies"]["load"] - ), - promotes=["*"], - ) - - prob.setup() - - prob.run_model() - - flexible_total_demand = prob.get_val("hydrogen_flexible_demand_profile", units="kg") - - rated_production = end_use_rated_demand * len(variable_h2_production_profile) - - with subtests.test("Check that total demand profile is less than rated"): - assert np.all(flexible_total_demand <= end_use_rated_demand) - - with subtests.test("Check curtailment"): # failed - assert ( - pytest.approx(np.sum(prob.get_val("hydrogen_unused_commodity", units="kg")), rel=1e-3) - == 6.6 - ) - - # check ramping constraints and turndown constraints are met - with subtests.test("Check turndown ratio constraint"): - assert np.all(flexible_total_demand >= min_demand_kg) - - ramping_down = np.where( - np.diff(flexible_total_demand) < 0, -1 * np.diff(flexible_total_demand), 0 - ) - ramping_up = np.where(np.diff(flexible_total_demand) > 0, np.diff(flexible_total_demand), 0) - - with subtests.test("Check ramping down constraint"): - assert pytest.approx(np.max(ramping_down), rel=1e-6) == ramp_down_rate_kg - - with subtests.test("Check ramping up constraint"): # failed - assert pytest.approx(np.max(ramping_up), rel=1e-6) == ramp_up_rate_kg - - with subtests.test("Check min utilization constraint"): - assert np.sum(flexible_total_demand) / rated_production >= 0.1 - - with subtests.test("Check min utilization value"): - flexible_demand_utilization = np.sum(flexible_total_demand) / rated_production - assert pytest.approx(flexible_demand_utilization, rel=1e-6) == 0.5822142857142857 - - # flexible_demand_profile[i] >= commodity_in[i] (as long as you are not curtailing - # any commodity in) - with subtests.test("Check that flexible demand is greater than hydrogen_in"): - hydrogen_available = variable_h2_production_profile - prob.get_val( - "hydrogen_unused_commodity", units="kg" - ) - assert np.all(flexible_total_demand >= hydrogen_available) - - with subtests.test("Check that remaining demand was calculated properly"): - unmet_demand = flexible_total_demand - hydrogen_available - assert np.all(unmet_demand == prob.get_val("hydrogen_unmet_demand", units="kg")) - - -@pytest.mark.regression -def test_flexible_demand_converter_controller_min_utilization( - subtests, variable_h2_production_profile -): - # give it a min utilization larger than utilization resulting from above test - - # Get the directory of the current script - current_dir = Path(__file__).parent - - # Resolve the paths to the configuration files - tech_config_path = current_dir / "inputs" / "tech_config.yaml" - - # Load the technology configuration - with tech_config_path.open() as file: - tech_config = yaml.safe_load(file) - - end_use_rated_demand = 10.0 # kg/h - ramp_up_rate_kg = 4.0 - ramp_down_rate_kg = 2.0 - min_demand_kg = 2.5 - tech_config["technologies"]["load"] = { - "control_strategy": { - "model": "FlexibleDemandOpenLoopConverterController", - }, - "model_inputs": { - "control_parameters": { - "commodity": "hydrogen", - "commodity_rate_units": "kg", - "rated_demand": end_use_rated_demand, - "demand_profile": end_use_rated_demand, # flat demand profile - "turndown_ratio": min_demand_kg / end_use_rated_demand, - "ramp_down_rate_fraction": ramp_down_rate_kg / end_use_rated_demand, - "ramp_up_rate_fraction": ramp_up_rate_kg / end_use_rated_demand, - "min_utilization": 0.8, - }, - }, - } - - plant_config = { - "plant": { - "plant_life": 30, - "simulation": {"n_timesteps": len(variable_h2_production_profile)}, - } - } - - # Set up OpenMDAO problem - prob = om.Problem() - - prob.model.add_subsystem( - name="IVC", - subsys=om.IndepVarComp(name="hydrogen_in", val=variable_h2_production_profile), - promotes=["*"], - ) - - prob.model.add_subsystem( - "DemandOpenLoopStorageController", - FlexibleDemandOpenLoopConverterController( - plant_config=plant_config, tech_config=tech_config["technologies"]["load"] - ), - promotes=["*"], - ) - - prob.setup() - - prob.run_model() - - flexible_total_demand = prob.get_val("hydrogen_flexible_demand_profile", units="kg") - - rated_production = end_use_rated_demand * len(variable_h2_production_profile) - - flexible_demand_utilization = np.sum(flexible_total_demand) / rated_production - - with subtests.test("Check min utilization constraint"): - assert flexible_demand_utilization >= 0.8 - - with subtests.test("Check min utilization value"): - assert pytest.approx(flexible_demand_utilization, rel=1e-6) == 0.8010612244 diff --git a/h2integrate/control/test/test_optimal_controller_with_generic_storage.py b/h2integrate/control/test/test_optimal_controller_with_generic_storage.py deleted file mode 100644 index d7867f089..000000000 --- a/h2integrate/control/test/test_optimal_controller_with_generic_storage.py +++ /dev/null @@ -1,208 +0,0 @@ -import numpy as np -import pytest -import openmdao.api as om -from pytest import fixture - -from h2integrate.storage.generic_storage_pyo import StoragePerformanceModel -from h2integrate.control.control_strategies.optimized_pyomo_controller import ( - OptimizedDispatchController, -) -from h2integrate.control.control_rules.storage.pyomo_storage_rule_baseclass import ( - PyomoRuleStorageBaseclass, -) - - -@fixture -def plant_config(): - plant_config = { - "plant": { - "plant_life": 30, - "simulation": { - "dt": 3600, - "n_timesteps": 8760, - }, - }, - "tech_to_dispatch_connections": [ - ["h2_storage", "h2_storage"], - ], - } - return plant_config - - -@fixture -def tech_config_generic(): - tech_config = { - "technologies": { - "h2_storage": { - "dispatch_rule_set": {"model": "PyomoRuleStorageBaseclass"}, - "control_strategy": {"model": "OptimizedDispatchController"}, - "performance_model": {"model": "StoragePerformanceModel"}, - "model_inputs": { - "shared_parameters": { - "max_charge_rate": 10.0, - "max_capacity": 40.0, - "n_control_window": 24, - "init_charge_fraction": 0.1, - "max_charge_fraction": 1.0, - "min_charge_fraction": 0.1, - "commodity": "hydrogen", - "commodity_rate_units": "kg/h", - "charge_efficiency": 1.0, - "discharge_efficiency": 1.0, - }, - "performance_parameters": { - "charge_equals_discharge": True, - "commodity_amount_units": "kg", - }, - "control_parameters": { - "tech_name": "h2_storage", - "cost_per_charge": 0.03, # USD/kg - "cost_per_discharge": 0.05, # USD/kg - "commodity_met_value": 0.1, # USD/kg - "cost_per_production": 0.0, # USD/kg - "time_weighting_factor": 0.995, - "system_commodity_interface_limit": 10.0, - }, - }, - } - }, - } - return tech_config - - -@pytest.mark.regression -def test_optimal_control_with_generic_storage(plant_config, tech_config_generic, subtests): - commodity_demand = np.full(8760, 5.0) - commodity_in = np.tile(np.concat([np.zeros(3), np.cumsum(np.ones(15)), np.full(6, 4.0)]), 365) - - # Setup the OpenMDAO problem and add subsystems - prob = om.Problem() - - prob.model.add_subsystem( - "PyomoRuleStorageBaseclass", - PyomoRuleStorageBaseclass( - plant_config=plant_config, tech_config=tech_config_generic["technologies"]["h2_storage"] - ), - promotes=["*"], - ) - - prob.model.add_subsystem( - "battery_optimized_load_following_controller", - OptimizedDispatchController( - plant_config=plant_config, tech_config=tech_config_generic["technologies"]["h2_storage"] - ), - promotes=["*"], - ) - - prob.model.add_subsystem( - "storage", - StoragePerformanceModel( - plant_config=plant_config, tech_config=tech_config_generic["technologies"]["h2_storage"] - ), - promotes=["*"], - ) - - # Setup the system and required values - prob.setup() - prob.set_val("storage.hydrogen_in", commodity_in) - prob.set_val("storage.hydrogen_demand", commodity_demand) - - # Run the model - prob.run_model() - - charge_rate = prob.get_val("storage.max_charge_rate", units="kg/h")[0] - discharge_rate = prob.get_val("storage.max_charge_rate", units="kg/h")[0] - capacity = prob.get_val("storage.storage_capacity", units="kg")[0] - - # Test that discharge is always positive - with subtests.test("Discharge is always positive"): - assert np.all(prob.get_val("storage.storage_hydrogen_discharge", units="kg/h") >= 0) - with subtests.test("Charge is always negative"): - assert np.all(prob.get_val("storage.storage_hydrogen_charge", units="kg/h") <= 0) - with subtests.test("Charge + Discharge == storage_hydrogen_out"): - charge_plus_discharge = prob.get_val( - "storage.storage_hydrogen_charge", units="kg/h" - ) + prob.get_val("storage.storage_hydrogen_discharge", units="kg/h") - np.testing.assert_allclose( - charge_plus_discharge, prob.get_val("storage_hydrogen_out", units="kg/h"), rtol=1e-6 - ) - with subtests.test("Initial SOC is correct"): - assert ( - pytest.approx(prob.model.get_val("storage.SOC", units="unitless")[0], rel=1e-6) == 0.1 - ) - - indx_soc_increase = np.argwhere( - np.diff(prob.model.get_val("storage.SOC", units="unitless"), prepend=True) > 0 - ).flatten() - indx_soc_decrease = np.argwhere( - np.diff(prob.model.get_val("storage.SOC", units="unitless"), prepend=False) < 0 - ).flatten() - indx_soc_same = np.argwhere( - np.diff(prob.model.get_val("storage.SOC", units="unitless"), prepend=True) == 0.0 - ).flatten() - - with subtests.test("SOC increases when charging"): - assert np.all( - prob.get_val("storage.storage_hydrogen_charge", units="kg/h")[indx_soc_increase] < 0 - ) - assert np.all( - prob.get_val("storage.storage_hydrogen_charge", units="kg/h")[indx_soc_decrease] == 0 - ) - assert np.all( - prob.get_val("storage.storage_hydrogen_charge", units="kg/h")[indx_soc_same] == 0 - ) - - with subtests.test("SOC decreases when discharging"): - assert np.all( - prob.get_val("storage.storage_hydrogen_discharge", units="kg/h")[indx_soc_decrease] > 0 - ) - assert np.all( - prob.get_val("storage.storage_hydrogen_discharge", units="kg/h")[indx_soc_increase] == 0 - ) - assert np.all( - prob.get_val("storage.storage_hydrogen_discharge", units="kg/h")[indx_soc_same] == 0 - ) - - with subtests.test("Max SOC <= Max storage percent"): - assert prob.get_val("storage.SOC", units="unitless").max() <= 1.0 - - with subtests.test("Min SOC >= Min storage percent"): - assert prob.get_val("storage.SOC", units="unitless").min() >= 0.1 - - with subtests.test("Charge never exceeds charge rate"): - assert ( - prob.get_val("storage.storage_hydrogen_charge", units="kg/h").min() >= -1 * charge_rate - ) - - with subtests.test("Discharge never exceeds discharge rate"): - assert ( - prob.get_val("storage.storage_hydrogen_discharge", units="kg/h").max() <= discharge_rate - ) - - with subtests.test("Discharge never exceeds demand"): - assert np.all( - prob.get_val("storage.storage_hydrogen_discharge", units="kg/h").max() - <= commodity_demand - ) - - with subtests.test("Cumulative charge/discharge does not exceed storage capacity"): - assert np.cumsum(prob.get_val("storage_hydrogen_out", units="kg/h")).max() <= capacity - assert np.cumsum(prob.get_val("storage_hydrogen_out", units="kg/h")).min() >= -1 * capacity - - with subtests.test("Expected discharge from hour 10-30"): - expected_discharge = np.concat( - [np.zeros(8), np.ones(6), np.full(3, 5.0), np.array([4, 3, 2])] - ) - np.testing.assert_allclose( - prob.get_val("storage.storage_hydrogen_discharge", units="kg/h")[10:30], - expected_discharge, - rtol=1e-6, - ) - - with subtests.test("Expected charge hour 0-20"): - expected_charge = np.concat([np.zeros(8), np.arange(-1, -9, -1), np.zeros(4)]) - np.testing.assert_allclose( - prob.get_val("storage.storage_hydrogen_charge", units="kg/h")[0:20], - expected_charge, - rtol=1e-6, - ) diff --git a/h2integrate/control/test/test_optimal_controllers.py b/h2integrate/control/test/test_optimal_controllers.py deleted file mode 100644 index 3dd9b1018..000000000 --- a/h2integrate/control/test/test_optimal_controllers.py +++ /dev/null @@ -1,351 +0,0 @@ -import numpy as np -import pytest - -from h2integrate.core.h2integrate_model import H2IntegrateModel - - -plant_config = { - "name": "plant_config", - "description": "...", - "sites": { - "site": {"latitude": 35.2018863, "longitude": -101.945027}, - }, - "plant": { - "plant_life": 1, - "grid_connection": False, - "ppa_price": 0.025, - "hybrid_electricity_estimated_cf": 0.492, - "simulation": { - "dt": 3600, - "n_timesteps": 8760, - }, - }, - "technology_interconnections": [["combiner", "battery", "electricity", "cable"]], - "tech_to_dispatch_connections": [ - ["combiner", "battery"], - ["battery", "battery"], - ], -} - -driver_config = { - "name": "driver_config", - "description": "Pyomo optimal min operating cost test", - "general": {}, -} - -tech_config = { - "name": "technology_config", - "description": "...", - "technologies": { - "battery": { - "dispatch_rule_set": {"model": "PyomoRuleStorageBaseclass"}, - "control_strategy": {"model": "OptimizedDispatchController"}, - "performance_model": {"model": "PySAMBatteryPerformanceModel"}, - "model_inputs": { - "shared_parameters": { - "max_charge_rate": 50000, - "max_capacity": 200000, - "n_control_window": 24, - "init_charge_fraction": 0.5, - "max_charge_fraction": 0.9, - "min_charge_fraction": 0.1, - "commodity": "electricity", - "commodity_rate_units": "kW", - "time_weighting_factor": 0.995, - "charge_efficiency": 0.95, - "discharge_efficiency": 0.95, - "cost_per_charge": 0.004, - "cost_per_discharge": 0.005, - "cost_per_production": 0.0, - "commodity_met_value": 0.1, - "round_digits": 4, - }, - "performance_parameters": { - "system_model_source": "pysam", - "chemistry": "LFPGraphite", - "control_variable": "input_power", - }, - "control_parameters": { - "tech_name": "battery", - "system_commodity_interface_limit": 1e12, - }, - }, - }, - "combiner": { - "performance_model": {"model": "GenericCombinerPerformanceModel"}, - "dispatch_rule_set": {"model": "PyomoDispatchGenericConverter"}, - "model_inputs": { - "performance_parameters": { - "commodity": "electricity", - "commodity_rate_units": "kW", - "in_streams": 1, - }, - "dispatch_rule_parameters": { - "commodity": "electricity", - "commodity_rate_units": "kW", - }, - }, - }, - }, -} - - -@pytest.mark.integration -def test_min_operating_cost_load_following_battery_dispatch(subtests): - # Fabricate some oscillating power generation data: 1000 kW for the first 12 hours, 10000 kW for - # the second twelve hours, and repeat that daily cycle over a year. - n_look_ahead_half = int(24 / 2) - - electricity_in = np.concatenate( - (np.ones(n_look_ahead_half) * 1000, np.ones(n_look_ahead_half) * 10000) - ) - electricity_in = np.tile(electricity_in, 365) - - demand_in = np.ones(8760) * 6000.0 - - # Create an H2Integrate model - model = H2IntegrateModel( - { - "driver_config": driver_config, - "technology_config": tech_config, - "plant_config": plant_config, - } - ) - - # Setup the system and required values - model.setup() - model.prob.set_val("combiner.electricity_in1", electricity_in) - model.prob.set_val("battery.electricity_demand", demand_in) - - # Run the model - model.prob.run_model() - - # Test the case where the charging/discharging cycle remains within the max and min SOC limits - # Check the expected outputs to actual outputs - expected_electricity_out = [ - 5999.99997732, - 5992.25494845, - 5991.96052468, - 5991.63342842, - 5991.26824325, - 5990.86174194, - 5990.40961477, - 5989.90607785, - 5989.34362595, - 5988.71271658, - 5988.00134229, - 5987.19448473, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - 6000.0, - ] - - expected_battery_electricity = [ - 4999.99997732, - 4992.25494845, - 4991.96052468, - 4991.63342842, - 4991.26824325, - 4990.86174194, - 4990.40961477, - 4989.90607785, - 4989.34362595, - 4988.71271658, - 4988.00134229, - 4987.19448473, - -3990.28117686, - -3990.74350731, - -3991.15657455, - -3991.53821244, - -3991.89075932, - -3992.21669822, - -3992.51846124, - -3992.79833559, - -3993.05841677, - -3993.30060036, - -3993.52658465, - -3993.73788536, - ] - - expected_battery_soc = [ - 49.87479765, - 47.50390223, - 45.12932011, - 42.75108798, - 40.3682277, - 37.9797332, - 35.58459541, - 33.18174418, - 30.76997461, - 28.34786178, - 25.91365422, - 23.4651282, - 25.4168656, - 27.36180226, - 29.29938411, - 31.23051435, - 33.15594392, - 35.07630244, - 36.99212221, - 38.90385706, - 40.81189705, - 42.71658006, - 44.61820098, - 46.517019, - 44.14026872, - 41.75708657, - 39.3692475, - 36.97562686, - 34.57512794, - 32.16658699, - 29.7486839, - 27.31984419, - 24.87811568, - 22.42099596, - 19.94517118, - 17.44609219, - ] - - expected_unmet_demand = np.array( - [ - 2.26821512e-05, - 7.74505155, - 8.03947532, - 8.36657158, - 8.73175675, - 9.13825806, - 9.59038523, - 1.00939222e01, - 1.06563740e01, - 1.12872834e01, - 1.19986577e01, - 1.28055153e01, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ] - ) - - expected_unused_electricity = np.array( - [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 9.71882314, - 9.25649269, - 8.84342545, - 8.46178756, - 8.10924068, - 7.78330178, - 7.48153876, - 7.20166441, - 6.94158323, - 6.69939964, - 6.47341535, - 6.26211464, - ] - ) - - with subtests.test("Check battery.electricity_out"): - assert ( - pytest.approx(expected_electricity_out) - == model.prob.get_val("battery.electricity_out", units="kW")[0:24] - ) - - with subtests.test("Check battery_electricity"): - assert ( - pytest.approx(expected_battery_electricity) - == model.prob.get_val("battery.battery_electricity", units="kW")[0:24] - ) - - # Check a longer portion of SOC to make sure SOC is getting linked between optimization periods - with subtests.test("Check SOC"): - assert ( - pytest.approx(expected_battery_soc) - == model.prob.get_val("battery.SOC", units="percent")[0:36] - ) - - with subtests.test("Check unmet_demand"): - assert ( - pytest.approx(expected_unmet_demand, abs=1e-4) - == model.prob.get_val("battery.unmet_electricity_demand_out", units="kW")[0:24] - ) - - with subtests.test("Check unused_electricity_out"): - assert ( - pytest.approx(expected_unused_electricity) - == model.prob.get_val("battery.unused_electricity_out", units="kW")[0:24] - ) - - # Test the case where the battery efficiency is lower - tech_config["technologies"]["battery"]["model_inputs"]["shared_parameters"][ - "charge_efficiency" - ] = 0.632 - tech_config["technologies"]["battery"]["model_inputs"]["shared_parameters"][ - "discharge_efficiency" - ] = 0.632 - - model = H2IntegrateModel( - { - "driver_config": driver_config, - "technology_config": tech_config, - "plant_config": plant_config, - } - ) - - # Setup the system and required values - model.setup() - model.prob.set_val("combiner.electricity_in1", electricity_in) - model.prob.set_val("battery.electricity_demand", demand_in) - - # Run the model - model.prob.run_model() - - expected_electricity_out = [ - 5999.99997732, - 5992.25494845, - 5991.96052468, - 5991.63342842, - 5991.26824325, - 5990.86174194, - 5990.40961477, - 5989.90607785, - 5989.34362595, - 5988.71271658, - 1558.72773849, - 1000.0, - ] - - # Make sure output changes if efficiency is changed - with subtests.test("Check electricity_out for different efficiency"): - assert ( - pytest.approx(expected_electricity_out) - == model.prob.get_val("battery.electricity_out", units="kW")[:12] - ) diff --git a/h2integrate/converters/ammonia/ammonia_synloop.py b/h2integrate/converters/ammonia/ammonia_synloop.py index 3e360478d..ac1308c29 100644 --- a/h2integrate/converters/ammonia/ammonia_synloop.py +++ b/h2integrate/converters/ammonia/ammonia_synloop.py @@ -139,6 +139,11 @@ class AmmoniaSynLoopPerformanceModel(ResizeablePerformanceModelBaseClass): conversion efficiency up to the limiting reagent or energy input. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() self.commodity = "ammonia" @@ -423,6 +428,11 @@ class AmmoniaSynLoopCostModel(CostModelBaseClass): Annual maintenance cost """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): target_cost_year = self.options["plant_config"]["finance_parameters"][ "cost_adjustment_parameters" diff --git a/h2integrate/converters/ammonia/simple_ammonia_model.py b/h2integrate/converters/ammonia/simple_ammonia_model.py index 9f609d232..527e2333b 100644 --- a/h2integrate/converters/ammonia/simple_ammonia_model.py +++ b/h2integrate/converters/ammonia/simple_ammonia_model.py @@ -30,6 +30,11 @@ class SimpleAmmoniaPerformanceModel(PerformanceModelBaseClass): Computes annual ammonia production based on plant capacity and capacity factor. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() self.commodity = "ammonia" @@ -108,6 +113,11 @@ class SimpleAmmoniaCostModel(CostModelBaseClass): Includes CapEx, OpEx, and byproduct credits, and exposes all detailed cost outputs. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = AmmoniaCostModelConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"), diff --git a/h2integrate/converters/ammonia/test/conftest.py b/h2integrate/converters/ammonia/test/conftest.py index 8d49ffb3d..fa8f250cf 100644 --- a/h2integrate/converters/ammonia/test/conftest.py +++ b/h2integrate/converters/ammonia/test/conftest.py @@ -1 +1,5 @@ -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) diff --git a/h2integrate/converters/co2/marine/direct_ocean_capture.py b/h2integrate/converters/co2/marine/direct_ocean_capture.py index 19c0d5862..0702aab32 100644 --- a/h2integrate/converters/co2/marine/direct_ocean_capture.py +++ b/h2integrate/converters/co2/marine/direct_ocean_capture.py @@ -1,13 +1,9 @@ from attrs import field, define from mcm.capture import echem_mcc -from h2integrate.core.utilities import merge_shared_inputs -from h2integrate.core.validators import must_equal -from h2integrate.converters.co2.marine.marine_carbon_capture_baseclass import ( - MarineCarbonCaptureCostBaseClass, - MarineCarbonCapturePerformanceConfig, - MarineCarbonCapturePerformanceBaseClass, -) +from h2integrate.core.utilities import BaseConfig, merge_shared_inputs +from h2integrate.core.validators import gt_zero, contains, gte_zero, range_val, must_equal +from h2integrate.core.model_baseclasses import CostModelBaseClass, PerformanceModelBaseClass def setup_electrodialysis_inputs(config): @@ -29,10 +25,14 @@ def setup_electrodialysis_inputs(config): @define(kw_only=True) -class DOCPerformanceConfig(MarineCarbonCapturePerformanceConfig): +class DOCPerformanceConfig(BaseConfig): """Extended configuration for Direct Ocean Capture (DOC) performance model. Attributes: + number_ed_min (int): Minimum number of ED units to operate. + number_ed_max (int): Maximum number of ED units available. + use_storage_tanks (bool): Flag indicating whether to use storage tanks. + store_hours (float): Number of hours of CO₂ storage capacity (hours). power_single_ed_w (float): Power requirement of a single electrodialysis (ED) unit (watts). flow_rate_single_ed_m3s (float): Flow rate of a single ED unit (cubic meters per second). E_HCl (float): Energy required per mole of HCl produced (kWh/mol). @@ -50,39 +50,42 @@ class DOCPerformanceConfig(MarineCarbonCapturePerformanceConfig): save_plots (bool, optional): If true, save plots of results. Defaults to False. """ - power_single_ed_w: float = field() - flow_rate_single_ed_m3s: float = field() - E_HCl: float = field() - E_NaOH: float = field() - y_ext: float = field() - y_pur: float = field() - y_vac: float = field() - frac_ed_flow: float = field() - temp_C: float = field() - sal: float = field() - dic_i: float = field() - pH_i: float = field() - initial_tank_volume_m3: float = field() + number_ed_min: int = field(validator=gt_zero) + number_ed_max: int = field(validator=gt_zero) + use_storage_tanks: bool = field() + store_hours: float = field(validator=gte_zero) + power_single_ed_w: float = field(validator=gte_zero) + flow_rate_single_ed_m3s: float = field(validator=gt_zero) + E_HCl: float = field(validator=gte_zero) + E_NaOH: float = field(validator=gte_zero) + y_ext: float = field(validator=range_val(0, 1)) + y_pur: float = field(validator=range_val(0, 1)) + y_vac: float = field(validator=range_val(0, 1)) + frac_ed_flow: float = field(validator=range_val(0, 1)) + temp_C: float = field(validator=gte_zero) + sal: float = field(validator=gte_zero) + dic_i: float = field(validator=gte_zero) + pH_i: float = field(validator=gte_zero) + initial_tank_volume_m3: float = field(validator=gte_zero) save_outputs: bool = field(default=False) save_plots: bool = field(default=False) -class DOCPerformanceModel(MarineCarbonCapturePerformanceBaseClass): +class DOCPerformanceModel(PerformanceModelBaseClass): """ An OpenMDAO component for modeling the performance of a Direct Ocean Capture (DOC) plant. - - Extends: - MarineCarbonCapturePerformanceBaseClass - - Computes: - - co2_out: Hourly CO2 capture rate (kg/h) - - co2_capture_mtpy: Annual CO2 capture (t/year) - - total_tank_volume_m3: Total tank volume (m^3) - - plant_mCC_capacity_mtph: Plant carbon capture capacity (t/h) """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() + self.commodity = "co2" + self.commodity_rate_units = "kg/h" + self.commodity_amount_units = "kg" def setup(self): self.config = DOCPerformanceConfig.from_dict( @@ -90,14 +93,17 @@ def setup(self): additional_cls_name=self.__class__.__name__, ) super().setup() - self.add_output( - "plant_mCC_capacity_mtph", + + self.add_input( + "electricity_in", val=0.0, - units="t/h", - desc="Theoretical maximum CO₂ capture (t/h)", + shape=self.n_timesteps, + units="W", + desc="Hourly input electricity (W)", ) + self.add_output( - "total_tank_volume_m3", + "total_tank_volume", val=0.0, units="m**3", ) @@ -123,9 +129,7 @@ def compute(self, inputs, outputs): ) outputs["co2_out"] = ed_outputs.ED_outputs["mCC"] * 1000 # kg/h - outputs["co2_capture_mtpy"] = max(ed_outputs.mCC_yr, 1e-6) # Must be >0 #TODO: remove - outputs["total_tank_volume_m3"] = range_outputs.V_aT_max + range_outputs.V_bT_max - outputs["plant_mCC_capacity_mtph"] = max(range_outputs.S1["mCC"]) # TODO: remove + outputs["total_tank_volume"] = range_outputs.V_aT_max + range_outputs.V_bT_max outputs["rated_co2_production"] = (ed_outputs.mCC_yr_MaxPwr / 8760) * 1e3 outputs["total_co2_produced"] = outputs["co2_out"].sum() @@ -141,23 +145,24 @@ class DOCCostModelConfig(DOCPerformanceConfig): """Configuration for the DOC cost model. Attributes: - infrastructure_type (str): Type of infrastructure (e.g., "desal", "swCool", "new""). + infrastructure_type (str): Type of infrastructure (e.g., "desal", "swCool", "new"). cost_year (int): dollar year corresponding to cost values """ - infrastructure_type: str = field() + infrastructure_type: str = field(validator=contains(["desal", "swCool", "new"])) cost_year: int = field(default=2023, converter=int, validator=must_equal(2023)) -class DOCCostModel(MarineCarbonCaptureCostBaseClass): +class DOCCostModel(CostModelBaseClass): """OpenMDAO component for computing capital (CapEx) and operational (OpEx) costs of a - direct ocean capture (DOC) system. - - Computes: - - CapEx (USD) - - OpEx (USD/year) + direct ocean capture (DOC) system. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() @@ -168,18 +173,27 @@ def setup(self): ) super().setup() + plant_life = int(self.options["plant_config"]["plant"]["plant_life"]) self.add_input( - "total_tank_volume_m3", + "total_tank_volume", val=0.0, units="m**3", ) self.add_input( - "plant_mCC_capacity_mtph", # TODO: replace with rated_co2_production + "annual_co2_produced", + val=0.0, + shape=plant_life, + units="t/year", + desc="Annual co2 captured", + ) + + self.add_input( + "rated_co2_production", val=0.0, units="t/h", - desc="Theoretical plant maximum CO₂ capture (t/h)", + desc="Theoretical plant maximum CO₂ capture", ) def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): @@ -189,15 +203,13 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): res = echem_mcc.electrodialysis_cost_model( echem_mcc.ElectrodialysisCostInputs( electrodialysis_inputs=ED_inputs, - mCC_yr=inputs["co2_capture_mtpy"], # TODO: replace with annual_co2_produced - total_tank_volume=inputs["total_tank_volume_m3"], + mCC_yr=inputs["annual_co2_produced"], + total_tank_volume=inputs["total_tank_volume"], infrastructure_type=self.config.infrastructure_type, - max_theoretical_mCC=inputs[ - "plant_mCC_capacity_mtph" - ], # TODO: replaced with rated_co2_production + max_theoretical_mCC=inputs["rated_co2_production"], ) ) # Calculate CapEx outputs["CapEx"] = res.initial_capital_cost - outputs["OpEx"] = res.yearly_operational_cost + outputs["OpEx"] = res.yearly_operational_cost[0] diff --git a/h2integrate/converters/co2/marine/marine_carbon_capture_baseclass.py b/h2integrate/converters/co2/marine/marine_carbon_capture_baseclass.py deleted file mode 100644 index 805ced48f..000000000 --- a/h2integrate/converters/co2/marine/marine_carbon_capture_baseclass.py +++ /dev/null @@ -1,78 +0,0 @@ -from attrs import field, define - -from h2integrate.core.utilities import BaseConfig -from h2integrate.core.model_baseclasses import CostModelBaseClass, PerformanceModelBaseClass - - -@define(kw_only=True) -class MarineCarbonCapturePerformanceConfig(BaseConfig): - """Configuration options for marine carbon capture performance modeling. - - Attributes: - number_ed_min (int): Minimum number of ED units to operate. - number_ed_max (int): Maximum number of ED units available. - use_storage_tanks (bool): Flag indicating whether to use storage tanks. - store_hours (float): Number of hours of CO₂ storage capacity (hours). - """ - - number_ed_min: int = field() - number_ed_max: int = field() - use_storage_tanks: bool = field() - store_hours: float = field() - - -class MarineCarbonCapturePerformanceBaseClass(PerformanceModelBaseClass): - """Base OpenMDAO component for modeling marine carbon capture performance. - - This class provides the basic input/output setup and requires subclassing to - implement actual CO₂ capture calculations. - - Attributes: - plant_config (dict): Configuration dictionary for plant-level parameters. - tech_config (dict): Configuration dictionary for technology-specific parameters. - """ - - def initialize(self): - super().initialize() - self.commodity = "co2" - self.commodity_rate_units = "kg/h" - self.commodity_amount_units = "kg" - - def setup(self): - super().setup() - - self.add_input( - "electricity_in", - val=0.0, - shape=self.n_timesteps, - units="W", - desc="Hourly input electricity (W)", - ) - - # TODO: remove this output once finance models are updated - self.add_output("co2_capture_mtpy", units="t/year", desc="Annual CO₂ captured (t/year)") - - -class MarineCarbonCaptureCostBaseClass(CostModelBaseClass): - """Base OpenMDAO component for modeling marine carbon capture costs. - - This class defines the input/output structure for cost evaluation and should - be subclassed for implementation. - - Attributes: - plant_config (dict): Configuration dictionary for plant-level parameters. - tech_config (dict): Configuration dictionary for technology-specific parameters. - """ - - def setup(self): - super().setup() - self.add_input( - "electricity_in", val=0.0, shape=8760, units="W", desc="Hourly input electricity (W)" - ) - # TODO: replaced with annual_co2_produced - self.add_input( - "co2_capture_mtpy", - val=0.0, - units="t/year", - desc="Annual CO₂ captured (t/year)", - ) diff --git a/h2integrate/converters/co2/marine/ocean_alkalinity_enhancement.py b/h2integrate/converters/co2/marine/ocean_alkalinity_enhancement.py index cd6cd9ca1..96995c8f5 100644 --- a/h2integrate/converters/co2/marine/ocean_alkalinity_enhancement.py +++ b/h2integrate/converters/co2/marine/ocean_alkalinity_enhancement.py @@ -2,11 +2,11 @@ from mcm.capture import echem_oae from h2integrate.core.utilities import BaseConfig, merge_shared_inputs -from h2integrate.core.validators import must_equal -from h2integrate.converters.co2.marine.marine_carbon_capture_baseclass import ( - MarineCarbonCaptureCostBaseClass, - MarineCarbonCapturePerformanceConfig, - MarineCarbonCapturePerformanceBaseClass, +from h2integrate.core.validators import gt_zero, contains, gte_zero, range_val, must_equal +from h2integrate.core.model_baseclasses import ( + CostModelBaseClass, + CostModelBaseConfig, + PerformanceModelBaseClass, ) @@ -25,10 +25,14 @@ def setup_ocean_alkalinity_enhancement_inputs(config): @define(kw_only=True) -class OAEPerformanceConfig(MarineCarbonCapturePerformanceConfig): +class OAEPerformanceConfig(BaseConfig): """Extended configuration for Ocean Alkalinity Enhancement (OAE) performance model. Attributes: + number_ed_min (int): Minimum number of ED units to operate. + number_ed_max (int): Maximum number of ED units available. + use_storage_tanks (bool): Flag indicating whether to use storage tanks. + store_hours (float): Number of hours of CO₂ storage capacity (hours). assumed_CDR_rate (float): Mole of CO2 per mole of NaOH (unitless). frac_base_flow (float): Fraction of base flow in the system (unitless). max_ed_system_flow_rate_m3s (float): Maximum flow rate through the ED system (m³/s). @@ -37,40 +41,44 @@ class OAEPerformanceConfig(MarineCarbonCapturePerformanceConfig): initial_dic_mol_per_L (float): Initial dissolved inorganic carbon (mol/L). initial_pH (float): Initial pH of seawater. initial_tank_volume_m3 (float): Initial volume of the tank (m³). - acid_disposal_method (str): Method for acid disposal. + acid_disposal_method (str): Method for acid disposal. Options are + "sell acid", "sell rca", "acid disposal". save_outputs (bool, optional): If true, save results to .csv files. Defaults to False. save_plots (bool, optional): If true, save plots of results. Defaults to False. """ - assumed_CDR_rate: float = field() - frac_base_flow: float = field() - max_ed_system_flow_rate_m3s: float = field() - initial_temp_C: float = field() - initial_salinity_ppt: float = field() - initial_dic_mol_per_L: float = field() - initial_pH: float = field() - initial_tank_volume_m3: float = field() - acid_disposal_method: str = field() + number_ed_min: int = field(validator=gt_zero) + number_ed_max: int = field(validator=gt_zero) + use_storage_tanks: bool = field() + store_hours: float = field(validator=gte_zero) + assumed_CDR_rate: float = field(validator=range_val(0, 1)) + frac_base_flow: float = field(validator=range_val(0, 1)) + max_ed_system_flow_rate_m3s: float = field(validator=gt_zero) + initial_temp_C: float = field(validator=gte_zero) + initial_salinity_ppt: float = field(validator=gte_zero) + initial_dic_mol_per_L: float = field(validator=gte_zero) + initial_pH: float = field(validator=gte_zero) + initial_tank_volume_m3: float = field(validator=gte_zero) + acid_disposal_method: str = field( + validator=contains(["sell acid", "sell rca", "acid disposal"]) + ) save_outputs: bool = field(default=False) save_plots: bool = field(default=False) -class OAEPerformanceModel(MarineCarbonCapturePerformanceBaseClass): - """OpenMDAO component for modeling Ocean Alkalinity Enhancement (OAE) performance. - - Extends: - MarineCarbonCapturePerformanceBaseClass +class OAEPerformanceModel(PerformanceModelBaseClass): + """OpenMDAO component for modeling Ocean Alkalinity Enhancement (OAE) performance.""" - Computes: - - co2_out: Hourly CO₂ capture rate (kg/h). - - co2_capture_mtpy: Annual CO₂ captured in metric tons per year. - - Notes: - This component requires the mcm.capture.echem_oae module for calculations. - """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model def initialize(self): super().initialize() + self.commodity = "co2" + self.commodity_rate_units = "kg/h" + self.commodity_amount_units = "kg" def setup(self): self.config = OAEPerformanceConfig.from_dict( @@ -78,105 +86,105 @@ def setup(self): additional_cls_name=self.__class__.__name__, ) super().setup() - n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"] - self.add_output( - "plant_mCC_capacity_mtph", + self.add_input( + "electricity_in", val=0.0, - units="t/h", - desc="Theoretical maximum CO₂ capture (t/h)", + shape=self.n_timesteps, + units="W", + desc="Hourly input electricity (W)", ) self.add_output( "alkaline_seawater_flow_rate", - shape=n_timesteps, + shape=self.n_timesteps, val=0.0, units="m**3/s", - desc="Alkaline seawater flow rate (m³/s)", + desc="Alkaline seawater flow rate", ) self.add_output( "alkaline_seawater_pH", val=0.0, - shape=n_timesteps, + shape=self.n_timesteps, units="unitless", desc="pH of the alkaline seawater", ) self.add_output( "alkaline_seawater_dic", val=0.0, - shape=n_timesteps, + shape=self.n_timesteps, units="mol/L", desc="Dissolved inorganic carbon concentration in the alkaline seawater", ) self.add_output( "alkaline_seawater_ta", val=0.0, - shape=n_timesteps, + shape=self.n_timesteps, units="mol/L", desc="Total alkalinity of the alkaline seawater", ) self.add_output( "alkaline_seawater_salinity", val=0.0, - shape=n_timesteps, + shape=self.n_timesteps, units="ppt", desc="Salinity of the alkaline seawater", ) self.add_output( "alkaline_seawater_temp", val=0.0, - shape=n_timesteps, - units="C", - desc="Temperature of the alkaline seawater (°C)", + shape=self.n_timesteps, + units="degC", + desc="Temperature of the alkaline seawater", ) self.add_output( "excess_acid", val=0.0, - shape=n_timesteps, + shape=self.n_timesteps, units="m**3", - desc="Excess acid produced (m³)", + desc="Excess acid produced", ) self.add_output( "mass_sellable_product", val=0.0, units="t/year", - desc="Mass of sellable product (acid or RCA) produced per year (tonnes)", + desc="Mass of sellable product (acid or RCA) produced per year", ) self.add_output( "value_products", val=0.0, units="USD/year", - desc="Value of products (acid or RCA) (USD/year)", + desc="Value of products (acid or RCA)", ) self.add_output( "mass_acid_disposed", val=0.0, units="t/year", - desc="Mass of acid disposed per year (tonnes)", + desc="Mass of acid disposed per year", ) self.add_output( "cost_acid_disposal", val=0.0, units="USD/year", - desc="Cost of acid disposal (USD/year)", + desc="Cost of acid disposal", ) self.add_output( "based_added_seawater_max_power", val=0.0, units="mol/year", - desc="Maximum power for base added seawater per year (mol/year)", + desc="Maximum power for base added seawater per year", ) self.add_output( "mass_rca", val=0.0, units="g", - desc="Mass of RCA tumbler slurry produced (grams)", + desc="Mass of RCA tumbler slurry produced", ) self.add_output( "unused_energy", val=0.0, - shape=n_timesteps, + shape=self.n_timesteps, units="W", - desc="Unused energy unused by OAE system (W)", + desc="Unused energy unused by OAE system", ) def compute(self, inputs, outputs): @@ -220,11 +228,6 @@ def compute(self, inputs, outputs): oae_outputs.M_co2est * 1e3 ) # convert from metric tons/year to kg/year outputs["capacity_factor"] = oae_outputs.oae_capacity_factor - - outputs["co2_capture_mtpy"] = oae_outputs.M_co2est # TODO: remove - outputs["plant_mCC_capacity_mtph"] = max( - range_outputs.S1["mass_CO2_absorbed"] / 1000 - ) # TODO: remove outputs["alkaline_seawater_flow_rate"] = oae_outputs.OAE_outputs["Qout"] outputs["alkaline_seawater_pH"] = oae_outputs.OAE_outputs["pH_f"] outputs["alkaline_seawater_dic"] = oae_outputs.OAE_outputs["dic_f"] @@ -242,7 +245,7 @@ def compute(self, inputs, outputs): @define(kw_only=True) -class OAECostModelConfig(BaseConfig): +class OAECostModelConfig(CostModelBaseConfig): """Configuration for the OAE cost model. Attributes: @@ -252,15 +255,16 @@ class OAECostModelConfig(BaseConfig): cost_year: int = field(default=2024, converter=int, validator=must_equal(2024)) -class OAECostModel(MarineCarbonCaptureCostBaseClass): +class OAECostModel(CostModelBaseClass): """OpenMDAO component for computing capital (CapEx) and operational (OpEx) costs of a - ocean alkalinity enhancement (OAE) system. - - Computes: - - CapEx (USD) - - OpEx (USD/year) + ocean alkalinity enhancement (OAE) system. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() @@ -276,41 +280,49 @@ def setup(self): additional_cls_name=self.__class__.__name__, ) super().setup() + plant_life = int(self.options["plant_config"]["plant"]["plant_life"]) + + self.add_input( + "annual_co2_produced", + val=0.0, + shape=plant_life, + units="t/year", + ) self.add_input( "mass_sellable_product", val=0.0, units="t/year", - desc="Mass of sellable product (acid or RCA) produced per year (tonnes)", + desc="Mass of sellable product (acid or RCA) produced per year", ) self.add_input( "value_products", val=0.0, units="USD/year", - desc="Value of products (acid or RCA) (USD/year)", + desc="Value of products (acid or RCA)", ) self.add_input( "mass_acid_disposed", val=0.0, units="t/year", - desc="Mass of acid disposed per year (tonnes)", + desc="Mass of acid disposed per year", ) self.add_input( "cost_acid_disposal", val=0.0, units="USD/year", - desc="Cost of acid disposal (USD/year)", + desc="Cost of acid disposal", ) self.add_input( "based_added_seawater_max_power", val=0.0, units="mol/year", - desc="Maximum power for base added seawater per year (mol/year)", + desc="Maximum power for base added seawater per year", ) self.add_input( "mass_rca", val=0.0, units="g", - desc="Mass of RCA tumbler slurry produced (grams)", + desc="Mass of RCA tumbler slurry produced", ) def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): @@ -319,7 +331,7 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): value_product=inputs["value_products"][0], waste_mass=inputs["mass_acid_disposed"][0], waste_disposal_cost=inputs["cost_acid_disposal"][0], - estimated_cdr=inputs["co2_capture_mtpy"][0], # TODO: replace with annual_co2_produced + estimated_cdr=inputs["annual_co2_produced"][0], base_added_seawater_max_power=inputs["based_added_seawater_max_power"][0], mass_rca=inputs["mass_rca"][0], annual_energy_cost=0, # Energy costs are calculated within H2I and added to LCOC calc @@ -332,19 +344,24 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): outputs["OpEx"] = results["Annual Operating Cost ($/yr)"] -class OAECostAndFinancialModel(MarineCarbonCaptureCostBaseClass): +class OAECostAndFinancialModel(CostModelBaseClass): """OpenMDAO component for calculating costs and financial metrics of an Ocean Alkalinity Enhancement (OAE) system. The financial model calculates the carbon credit value that would be required to achieve a net present value (NPV) of zero for the overall system costs. Computes: - - CapEx (USD) - - OpEx (USD/year) - - NPV ($) - - Carbon Credit Value (USD/tCO2) + - CapEx + - OpEx + - NPV + - Carbon Credit Value """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() @@ -361,85 +378,103 @@ def setup(self): ) super().setup() n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"] + plant_life = int(self.options["plant_config"]["plant"]["plant_life"]) + + self.add_input( + "annual_co2_produced", + val=0.0, + shape=plant_life, + units="t/year", + desc="Annual co2 captured", + ) self.add_input( "LCOE", val=0.0, units="USD/(kW*h)", - desc="Levelized cost of electricity (USD/kWh)", + desc="Levelized cost of electricity", ) self.add_input( - "annual_energy", + "annual_input_electricity", val=0.0, + shape=plant_life, units="kW*h/year", - desc="Annual energy production in kWac", + desc="Annual energy input to the OAE", ) self.add_input( "unused_energy", val=0.0, shape=n_timesteps, - units="W", - desc="Unused energy unused by OAE system (W)", + units="kW", + desc="Unused energy unused by OAE system", ) self.add_input( "mass_sellable_product", val=0.0, units="t/year", - desc="Mass of sellable product (acid or RCA) produced per year (tonnes)", + desc="Mass of sellable product (acid or RCA) produced per year", ) self.add_input( "value_products", val=0.0, units="USD/year", - desc="Value of products (acid or RCA) (USD/year)", + desc="Value of products (acid or RCA)", ) self.add_input( "mass_acid_disposed", val=0.0, units="t/year", - desc="Mass of acid disposed per year (tonnes)", + desc="Mass of acid disposed per year", ) self.add_input( "cost_acid_disposal", val=0.0, units="USD/year", - desc="Cost of acid disposal (USD/year)", + desc="Cost of acid disposal", ) self.add_input( "based_added_seawater_max_power", val=0.0, units="mol/year", - desc="Maximum power for base added seawater per year (mol/year)", + desc="Maximum power for base added seawater per year", ) self.add_input( "mass_rca", val=0.0, units="g", - desc="Mass of RCA tumbler slurry produced (grams)", + desc="Mass of RCA tumbler slurry produced", ) self.add_output( "NPV", val=0.0, units="USD", - desc="Net Present Value of the OAE system (USD)", + desc="Net Present Value of the OAE system", ) self.add_output( "carbon_credit_value", val=0.0, units="USD/t", - desc="Carbon credit value required to achieve NPV of zero (USD/tCO2)", + desc="Carbon credit value required to achieve NPV of zero", ) def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): + """Model assume that you only pay for the energy you use for OAE.""" + if not inputs["annual_input_electricity"][0]: + msg = ( + "the annual_input_electricity needs to be connected to " + "an annual electricity stream in the technology_interconnections " + "in the plant_config." + ) + raise AttributeError(msg) annual_energy_cost_usd_yr = inputs["LCOE"] * ( - inputs["annual_energy"] - (sum(inputs["unused_energy"]) / 1000) # Convert W to kW + inputs["annual_input_electricity"][0] - (sum(inputs["unused_energy"])) ) # remove unused power from the annual energy cost only used power considered costs = echem_oae.OAECosts( mass_product=inputs["mass_sellable_product"][0], value_product=inputs["value_products"][0], waste_mass=inputs["mass_acid_disposed"][0], waste_disposal_cost=inputs["cost_acid_disposal"][0], - estimated_cdr=inputs["co2_capture_mtpy"][0], # TODO: replace with annual_co2_produced + estimated_cdr=inputs["annual_co2_produced"][0], base_added_seawater_max_power=inputs["based_added_seawater_max_power"][0], mass_rca=inputs["mass_rca"][0], annual_energy_cost=annual_energy_cost_usd_yr[0], diff --git a/h2integrate/converters/co2/marine/test/conftest.py b/h2integrate/converters/co2/marine/test/conftest.py index 7dc54f763..4a4191a52 100644 --- a/h2integrate/converters/co2/marine/test/conftest.py +++ b/h2integrate/converters/co2/marine/test/conftest.py @@ -2,7 +2,11 @@ import pytest -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) has_mcm = importlib.util.find_spec("mcm") is not None @@ -23,7 +27,7 @@ def plant_config(): # docs fencepost start: DO NOT REMOVE -@pytest.fixture(scope="module") +@pytest.fixture(scope="function") def driver_config(temp_dir): # noqa: F811 driver_config = { "general": { diff --git a/h2integrate/converters/co2/marine/test/test_doc.py b/h2integrate/converters/co2/marine/test/test_doc.py index de49b79e3..6c03bec40 100644 --- a/h2integrate/converters/co2/marine/test/test_doc.py +++ b/h2integrate/converters/co2/marine/test/test_doc.py @@ -147,8 +147,8 @@ def test_doc_standard_outputs(driver_config, plant_config, tech_config, subtests prob.model.add_subsystem("comp", doc_model, promotes=["*"]) prob.setup() rng = np.random.default_rng(seed=42) - base_power = np.linspace(3.0e8, 2.0e8, 8760) # 5 MW to 10 MW over 8760 hours - noise = rng.normal(loc=0, scale=0.5e8, size=8760) # ±0.5 MW noise + base_power = np.linspace(3.0e8, 2.0e8, 8760) # 300 MW to 200 MW over 8760 hours + noise = rng.normal(loc=0, scale=0.5e8, size=8760) # ±50 MW noise power_profile = base_power + noise prob.set_val("comp.electricity_in", power_profile, units="W") @@ -158,12 +158,6 @@ def test_doc_standard_outputs(driver_config, plant_config, tech_config, subtests int(plant_config["plant"]["plant_life"]) int(plant_config["plant"]["simulation"]["n_timesteps"]) - with subtests.test("co2 captured mtpy == annual co2 produced"): - assert ( - pytest.approx(prob.get_val("comp.co2_capture_mtpy", units="t/yr")[0], rel=1e-6) - == prob.get_val("comp.annual_co2_produced", units="t/yr")[0] - ) - annual_co2_from_cf_calc = ( prob.get_val("comp.capacity_factor", units="unitless") * prob.get_val("comp.rated_co2_production", units="t/h") @@ -173,7 +167,7 @@ def test_doc_standard_outputs(driver_config, plant_config, tech_config, subtests with subtests.test("CF calculated properly"): assert ( pytest.approx(annual_co2_from_cf_calc[0], rel=1e-6) - == prob.get_val("comp.co2_capture_mtpy", units="t/yr")[0] + == prob.get_val("comp.annual_co2_produced", units="t/yr")[0] ) @@ -192,8 +186,8 @@ def test_performance_model(tech_config, plant_config, driver_config): # Set inputs rng = np.random.default_rng(seed=42) - base_power = np.linspace(3.0e8, 2.0e8, 8760) # 5 MW to 10 MW over 8760 hours - noise = rng.normal(loc=0, scale=0.5e8, size=8760) # ±0.5 MW noise + base_power = np.linspace(3.0e8, 2.0e8, 8760) # 300 MW to 200 MW over 8760 hours + noise = rng.normal(loc=0, scale=0.5e8, size=8760) # ±50 MW noise power_profile = base_power + noise prob.set_val("DOC.electricity_in", power_profile, units="W") @@ -201,10 +195,10 @@ def test_performance_model(tech_config, plant_config, driver_config): prob.run_model() # Additional asserts for output values - co2_out = prob.get_val("co2_out") - co2_capture_mtpy = prob.get_val("co2_capture_mtpy") - plant_mCC_capacity_mtph = prob.get_val("plant_mCC_capacity_mtph") - total_tank_volume_m3 = prob.get_val("total_tank_volume_m3") + co2_out = prob.get_val("co2_out", units="kg/h") + co2_capture_mtpy = prob.get_val("annual_co2_produced", units="t/year")[0] + plant_mCC_capacity_mtph = prob.get_val("rated_co2_production", units="t/h") + total_tank_volume_m3 = prob.get_val("total_tank_volume") # Assert values (allowing for small numerical tolerance) assert_near_equal(np.linalg.norm(co2_out), 11394970.06218, tolerance=1e-1) diff --git a/h2integrate/converters/co2/marine/test/test_oae.py b/h2integrate/converters/co2/marine/test/test_oae.py index f4d992353..9f0cf5279 100644 --- a/h2integrate/converters/co2/marine/test/test_oae.py +++ b/h2integrate/converters/co2/marine/test/test_oae.py @@ -30,12 +30,12 @@ def tech_config(): @pytest.mark.unit -def test_doc_outputs(driver_config, plant_config, tech_config, subtests): - doc_model = OAEPerformanceModel( +def test_oae_outputs(driver_config, plant_config, tech_config, subtests): + oae_model = OAEPerformanceModel( driver_config=driver_config, plant_config=plant_config, tech_config=tech_config ) prob = om.Problem(model=om.Group()) - prob.model.add_subsystem("comp", doc_model, promotes=["*"]) + prob.model.add_subsystem("comp", oae_model, promotes=["*"]) prob.setup() rng = np.random.default_rng(seed=42) @@ -128,11 +128,11 @@ def test_doc_outputs(driver_config, plant_config, tech_config, subtests): @pytest.mark.regression def test_oae_standard_outputs(driver_config, plant_config, tech_config, subtests): - doc_model = OAEPerformanceModel( + oae_model = OAEPerformanceModel( driver_config=driver_config, plant_config=plant_config, tech_config=tech_config ) prob = om.Problem(model=om.Group()) - prob.model.add_subsystem("comp", doc_model, promotes=["*"]) + prob.model.add_subsystem("comp", oae_model, promotes=["*"]) prob.setup() rng = np.random.default_rng(seed=42) @@ -144,12 +144,6 @@ def test_oae_standard_outputs(driver_config, plant_config, tech_config, subtests # Run the model prob.run_model() - with subtests.test("co2 captured mtpy == annual co2 produced"): - assert ( - pytest.approx(prob.get_val("comp.co2_capture_mtpy", units="t/yr")[0], rel=1e-6) - == prob.get_val("comp.annual_co2_produced", units="t/yr")[0] - ) - annual_co2_from_cf_calc = ( prob.get_val("comp.capacity_factor", units="unitless") * prob.get_val("comp.rated_co2_production", units="t/h") @@ -159,7 +153,7 @@ def test_oae_standard_outputs(driver_config, plant_config, tech_config, subtests with subtests.test("CF calculated properly"): assert ( pytest.approx(annual_co2_from_cf_calc[0], rel=1e-6) - == prob.get_val("comp.co2_capture_mtpy", units="t/yr")[0] + == prob.get_val("comp.annual_co2_produced", units="t/yr")[0] ) @@ -184,15 +178,15 @@ def test_performance_model(tech_config, plant_config, driver_config): # Get output values to determine expected values co2_out = prob.get_val("co2_out", units="kg/h") - co2_capture_mtpy = prob.get_val("co2_capture_mtpy", units="t/year") - plant_mCC_capacity_mtph = prob.get_val("plant_mCC_capacity_mtph", units="t/h") + co2_capture_mtpy = prob.get_val("annual_co2_produced", units="t/year") + plant_mCC_capacity_mtph = prob.get_val("rated_co2_production", units="t/h") alkaline_seawater_flow_rate = prob.get_val("alkaline_seawater_flow_rate", units="m**3/s") alkaline_seawater_pH = prob.get_val("alkaline_seawater_pH", units="unitless") excess_acid = prob.get_val("excess_acid", units="m**3") # Assert values (allowing for small numerical tolerance) assert_near_equal(np.mean(co2_out), 1108.394704250361, tolerance=1e-3) - assert_near_equal(co2_capture_mtpy, [9709.53760923], tolerance=1e-6) + assert_near_equal(co2_capture_mtpy[0], [9709.53760923], tolerance=1e-6) assert_near_equal(plant_mCC_capacity_mtph, [1.10854656], tolerance=1e-6) assert_near_equal(np.mean(alkaline_seawater_flow_rate), 3.2395561643835618, tolerance=1e-6) assert_near_equal(np.mean(alkaline_seawater_pH), 9.145157555568293, tolerance=1e-6) diff --git a/h2integrate/converters/generic_converter_cost.py b/h2integrate/converters/generic_converter_cost.py new file mode 100644 index 000000000..ed2a03e9e --- /dev/null +++ b/h2integrate/converters/generic_converter_cost.py @@ -0,0 +1,124 @@ +from attrs import field, define + +from h2integrate.core.utilities import merge_shared_inputs +from h2integrate.core.validators import gte_zero, range_val_or_none +from h2integrate.core.model_baseclasses import CostModelBaseClass, CostModelBaseConfig + + +@define(kw_only=True) +class GenericConverterCostConfig(CostModelBaseConfig): + """Configuration class for the GenericConverterCostModel with costs based on rated capacity. + The cost units must compatible with the units of the commodity produced by the converter. + + Attributes: + commodity (str): name of commodity + commodity_rate_units (str): Units of the commodity (e.g., "kg/h" or "kW"). + unit_capex (float | int): capital cost in units of `USD/commodity_rate_units`. + Must be greater than or equal to zero. + unit_varopex (float | int): variable O&M cost in units of `USD/commodity_amount_units` + unit_opex (float | int | None): fixed O&M cost in units of `USD/commodity_rate_units/year`. + Only required if `opex_fraction` is None. Defaults to None. + opex_fraction (float | int | None): the fixed O&M cost as a ratio of the CapEx. + Must be between 0 or 1. Only required if `unit_opex` is None. Defaults to None. + cost_year (int): dollar year of input costs + commodity_amount_units (str | None, optional): Units of the commodity as an amount + (i.e., "kW*h" or "kg"). If not provided, defaults to `commodity_rate_units*h`. + """ + + commodity: str = field(converter=str.strip) + commodity_rate_units: str = field(converter=str.strip) + unit_capex: float | int = field(validator=gte_zero) + unit_varopex: float = field() + + unit_opex: float | int | None = field(default=None) + opex_fraction: float | None = field(default=None, validator=range_val_or_none(0, 1)) + commodity_amount_units: str = field(default=None) + + def __attrs_post_init__(self): + # If both or neither OpEx value was input, raise an error + if (self.unit_opex is None and self.opex_fraction is None) or ( + self.unit_opex is not None and self.opex_fraction is not None + ): + msg = ( + "Please provide either a value for `unit_opex` or a value for " + + "`opex_fraction` in the generic converter cost config, but not both." + ) + raise KeyError(msg) + + if self.commodity_amount_units is None: + self.commodity_amount_units = f"({self.commodity_rate_units})*h" + + +class GenericConverterCostModel(CostModelBaseClass): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + + def setup(self): + self.config = GenericConverterCostConfig.from_dict( + merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"), + strict=True, + additional_cls_name=self.__class__.__name__, + ) + plant_life = int(self.options["plant_config"]["plant"]["plant_life"]) + + super().setup() + + # Inputs that are outputs of the performance model + self.add_input( + f"rated_{self.config.commodity}_production", + val=0.0, + units=self.config.commodity_rate_units, + ) + self.add_input( + f"annual_{self.config.commodity}_produced", + val=0.0, + shape=plant_life, + units=f"({self.config.commodity_amount_units})/year", + ) + + # Cost parameter inputs + self.add_input( + "unit_capex", + val=self.config.unit_capex, + units=f"USD/({self.config.commodity_rate_units})", + desc="Unit CapEx", + ) + + self.add_input( + "unit_varopex", + val=self.config.unit_varopex, + units=f"USD/({self.config.commodity_amount_units})", + desc="Unit Variable O&M", + ) + + if self.config.opex_fraction is not None: + # opex is expressed as a fraction of CapEx + self.add_input( + "fixed_opex_ratio", + val=self.config.opex_fraction, + units="unitless", + desc="Fixed OpEx as a fraction of the total CapEx", + ) + else: + # opex is expressed as a multiplier of rated capacity + self.add_input( + "unit_opex", + val=self.config.unit_opex, + units=f"USD/({self.config.commodity_rate_units})/year", + desc="Unit Fixed OpEx", + ) + + def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): + tot_capex = inputs[f"rated_{self.config.commodity}_production"] * inputs["unit_capex"] + outputs["CapEx"] = tot_capex + if "unit_opex" in inputs: + outputs["OpEx"] = ( + inputs[f"rated_{self.config.commodity}_production"] * inputs["unit_opex"] + ) + else: + outputs["OpEx"] = tot_capex * inputs["fixed_opex_ratio"] + outputs["VarOpEx"] = ( + inputs[f"annual_{self.config.commodity}_produced"] * inputs["unit_varopex"] + ) diff --git a/h2integrate/converters/grid/grid.py b/h2integrate/converters/grid/grid.py index 0765ac426..dfde27149 100644 --- a/h2integrate/converters/grid/grid.py +++ b/h2integrate/converters/grid/grid.py @@ -36,6 +36,8 @@ class GridPerformanceModel(PerformanceModelBaseClass): different grid connection points (for example, one for buying upstream and another for selling downstream). + This model is compatible with time steps ranging from 5-minutes to 1-hour. + Inputs interconnection_size (float): Maximum power capacity for grid connection (kW). electricity_in (array): Power flowing into the grid (selling) (kW). @@ -45,6 +47,11 @@ class GridPerformanceModel(PerformanceModelBaseClass): electricity_out (array): Power flowing out of the grid (buying) (kW). """ + _time_step_bounds = ( + 300, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() self.commodity = "electricity" @@ -174,10 +181,15 @@ class GridCostModel(CostModelBaseClass): - Revenue from electricity sales (sell mode) - Support for time-varying electricity prices - Note: Although the electricity units are in kW and the prices are in USD/kWh, - this model assumes that each timestep represents 1 hour. + This model is compatible with time steps ranging from 5-minutes to 1-hour. + """ + _time_step_bounds = ( + 300, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = GridCostModelConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"), @@ -277,13 +289,13 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): electricity_out = inputs["electricity_out"] buy_price = inputs["electricity_buy_price"] # Buying costs money (positive VarOpEx) - varopex += np.sum(electricity_out * buy_price) + varopex += np.sum((self.dt / 3600) * electricity_out * buy_price) # Add selling revenue if sell price is configured # electricity_sold represents power flowing INTO grid (selling) if self.config.electricity_sell_price is not None: sell_price = inputs["electricity_sell_price"] # Selling generates revenue (negative VarOpEx) - varopex -= np.sum(inputs["electricity_sold"] * sell_price) + varopex -= np.sum((self.dt / 3600) * inputs["electricity_sold"] * sell_price) outputs["VarOpEx"] = varopex diff --git a/h2integrate/converters/grid/test/conftest.py b/h2integrate/converters/grid/test/conftest.py index 8d49ffb3d..fa8f250cf 100644 --- a/h2integrate/converters/grid/test/conftest.py +++ b/h2integrate/converters/grid/test/conftest.py @@ -1 +1,5 @@ -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) diff --git a/h2integrate/converters/grid/test/test_grid.py b/h2integrate/converters/grid/test/test_grid.py index 5af52832c..ee6f54121 100644 --- a/h2integrate/converters/grid/test/test_grid.py +++ b/h2integrate/converters/grid/test/test_grid.py @@ -4,6 +4,7 @@ from pytest import fixture from h2integrate.converters.grid.grid import GridCostModel, GridPerformanceModel +from h2integrate.core.h2integrate_model import H2IntegrateModel @fixture @@ -281,6 +282,138 @@ def test_varying_demand_profile(plant_config, n_timesteps): np.testing.assert_array_almost_equal(electricity_out, expected) +@pytest.mark.unit +@pytest.mark.parametrize("n_timesteps", [10]) +def test_non_hourly_dt_demand_profile(subtests, plant_config, n_timesteps): + """Test with time-varying demand profile.""" + plant_config["plant"]["simulation"]["dt"] = 300 + + prob = om.Problem() + commodity = "electricity" + + tech_config = {"model_inputs": {"shared_parameters": {"interconnection_size": 100000.0}}} + + prob.model.add_subsystem( + "grid", + GridPerformanceModel(driver_config={}, plant_config=plant_config, tech_config=tech_config), + ) + + prob.setup() + + # Create varying demand profile + demand = np.array([10000, 20000, 30000, 50000, 70000, 90000, 110000, 80000, 60000, 40000]) + prob.set_val("grid.electricity_demand", demand, units="kW") + + prob.run_model() + + interconnection_size = tech_config["model_inputs"]["shared_parameters"]["interconnection_size"] + dt_seconds = plant_config["plant"]["simulation"]["dt"] + expected = np.clip(demand, 0, interconnection_size) + expected_total = expected.sum() * dt_seconds / 3600.0 + fraction_of_year = n_timesteps * dt_seconds / 31536000.0 + expected_annual_value = expected_total / fraction_of_year + + with subtests.test(f"annual_{commodity}_produced length"): + electricity_out = prob.get_val("grid.electricity_out", units="kW") + np.testing.assert_array_almost_equal(electricity_out, expected) + + with subtests.test("cf"): + cf = prob.get_val("grid.capacity_factor", units="unitless") + expected_capacity_factor = expected.mean() / interconnection_size + assert cf == pytest.approx(expected_capacity_factor) + + with subtests.test("total production"): + total_energy = prob.get_val(f"grid.total_{commodity}_produced", units="kW*h") + np.testing.assert_allclose(np.atleast_1d(total_energy), [expected_total]) + + with subtests.test("annual production"): + annual_energy = prob.get_val(f"grid.annual_{commodity}_produced", units="kW*h/year") + np.testing.assert_allclose( + np.atleast_1d(annual_energy), + np.full(np.atleast_1d(annual_energy).shape, expected_annual_value), + ) + + +@pytest.mark.integration +def test_grid_integration_dt_1800(subtests, tmp_path): + """Integration test: run an H2IntegrateModel with only grid technology at dt=1800 s.""" + n_timesteps = 8760 * 2 + dt_seconds = 1800 + interconnection_size = 100000.0 + demand_kw = 40000.0 + + driver_config = { + "name": "driver_config", + "description": "Integration test driver config", + "general": { + "folder_output": str(tmp_path / "output"), + "create_om_reports": False, + }, + } + + tech_config = { + "name": "technology_config", + "description": "Grid-only integration test", + "technologies": { + "grid": { + "performance_model": {"model": "GridPerformanceModel"}, + "model_inputs": { + "shared_parameters": { + "interconnection_size": interconnection_size, + } + }, + } + }, + } + + plant_config = { + "name": "plant_config", + "description": "Grid-only integration test plant", + "plant": { + "plant_life": 30, + "simulation": { + "n_timesteps": n_timesteps, + "dt": dt_seconds, + }, + }, + } + + h2i = H2IntegrateModel( + { + "name": "h2i_grid_integration_test", + "system_summary": "Grid-only integration model", + "driver_config": driver_config, + "technology_config": tech_config, + "plant_config": plant_config, + } + ) + h2i.setup() + + demand = np.full(n_timesteps, demand_kw) + h2i.prob.set_val("grid.electricity_demand", demand, units="kW") + h2i.prob.run_model() + + expected_out = np.full(n_timesteps, demand_kw) + expected_total = expected_out.sum() * (dt_seconds / 3600) + expected_annual = expected_total * (365 * 24 * 3600) / (n_timesteps * dt_seconds) + + with subtests.test("electricity_out equals demand when below interconnection limit"): + electricity_out = h2i.prob.get_val("grid.electricity_out", units="kW") + np.testing.assert_array_almost_equal(electricity_out, expected_out) + + with subtests.test("capacity factor reflects 40 percent loading"): + capacity_factor = h2i.prob.get_val("grid.capacity_factor", units="unitless") + np.testing.assert_array_almost_equal(capacity_factor, np.full_like(capacity_factor, 0.4)) + + with subtests.test("total electricity produced scales with 1800 second timestep"): + total_energy = h2i.prob.get_val("grid.total_electricity_produced", units="kW*h") + assert total_energy == pytest.approx(expected_total) + + with subtests.test("annual electricity produced scales from simulated fraction of year"): + annual_energy = h2i.prob.get_val("grid.annual_electricity_produced", units="kW*h/year") + assert annual_energy == pytest.approx(expected_annual) + + @pytest.mark.unit @pytest.mark.parametrize("n_timesteps", [24]) def test_buy_only_mode(plant_config, n_timesteps): diff --git a/h2integrate/converters/hopp/hopp_wrapper.py b/h2integrate/converters/hopp/hopp_wrapper.py index 2ac08b08f..06579c601 100644 --- a/h2integrate/converters/hopp/hopp_wrapper.py +++ b/h2integrate/converters/hopp/hopp_wrapper.py @@ -28,6 +28,11 @@ class HOPPComponent(PerformanceModelBaseClass, CacheBaseClass): computed results when the same configuration is encountered. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() self.commodity = "electricity" @@ -37,7 +42,7 @@ def initialize(self): def setup(self): self.config = HOPPComponentModelConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance"), - strict=False, + strict=True, additional_cls_name=self.__class__.__name__, ) diff --git a/h2integrate/converters/hopp/test/conftest.py b/h2integrate/converters/hopp/test/conftest.py index 8d49ffb3d..fa8f250cf 100644 --- a/h2integrate/converters/hopp/test/conftest.py +++ b/h2integrate/converters/hopp/test/conftest.py @@ -1 +1,5 @@ -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) diff --git a/h2integrate/converters/hydrogen/basic_cost_model.py b/h2integrate/converters/hydrogen/basic_cost_model.py index 574993abb..9f6cbdb50 100644 --- a/h2integrate/converters/hydrogen/basic_cost_model.py +++ b/h2integrate/converters/hydrogen/basic_cost_model.py @@ -35,6 +35,11 @@ class BasicElectrolyzerCostModel(ElectrolyzerCostBaseClass): An OpenMDAO component that computes the cost of a PEM electrolyzer. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = BasicElectrolyzerCostModelConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"), diff --git a/h2integrate/converters/hydrogen/custom_electrolyzer_cost_model.py b/h2integrate/converters/hydrogen/custom_electrolyzer_cost_model.py index 851b2538c..89085c6ff 100644 --- a/h2integrate/converters/hydrogen/custom_electrolyzer_cost_model.py +++ b/h2integrate/converters/hydrogen/custom_electrolyzer_cost_model.py @@ -27,6 +27,11 @@ class CustomElectrolyzerCostModel(ElectrolyzerCostBaseClass): An OpenMDAO component that computes the cost of a PEM electrolyzer. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = CustomElectrolyzerCostModelConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"), diff --git a/h2integrate/converters/hydrogen/electrolyzer_baseclass.py b/h2integrate/converters/hydrogen/electrolyzer_baseclass.py index 39112d648..c0934a013 100644 --- a/h2integrate/converters/hydrogen/electrolyzer_baseclass.py +++ b/h2integrate/converters/hydrogen/electrolyzer_baseclass.py @@ -5,6 +5,11 @@ class ElectrolyzerPerformanceBaseClass(ResizeablePerformanceModelBaseClass): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() self.commodity = "hydrogen" @@ -28,6 +33,11 @@ def compute(self, inputs, outputs): class ElectrolyzerCostBaseClass(CostModelBaseClass): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"] super().setup() diff --git a/h2integrate/converters/hydrogen/geologic/aspen_surface_processing.py b/h2integrate/converters/hydrogen/geologic/aspen_surface_processing.py index e929950ed..98a041968 100644 --- a/h2integrate/converters/hydrogen/geologic/aspen_surface_processing.py +++ b/h2integrate/converters/hydrogen/geologic/aspen_surface_processing.py @@ -76,6 +76,11 @@ class AspenGeoH2SurfacePerformanceModel(GeoH2SurfacePerformanceBaseClass): Hourly steam production profile (8760 hours), in kW thermal. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = AspenGeoH2SurfacePerformanceConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance"), @@ -244,6 +249,11 @@ class AspenGeoH2SurfaceCostModel(GeoH2SurfaceCostBaseClass): All inherited from GeoH2SurfaceCostBaseClass """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = AspenGeoH2SurfaceCostConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"), diff --git a/h2integrate/converters/hydrogen/geologic/h2_well_subsurface_baseclass.py b/h2integrate/converters/hydrogen/geologic/h2_well_subsurface_baseclass.py index f22dd3010..fc3c24fea 100644 --- a/h2integrate/converters/hydrogen/geologic/h2_well_subsurface_baseclass.py +++ b/h2integrate/converters/hydrogen/geologic/h2_well_subsurface_baseclass.py @@ -86,6 +86,11 @@ class GeoH2SubsurfacePerformanceBaseClass(PerformanceModelBaseClass): The total hydrogen produced over the plant lifetime, in kilograms per year. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() self.commodity = "hydrogen" @@ -169,6 +174,11 @@ class GeoH2SubsurfaceCostBaseClass(CostModelBaseClass): Variable OPEX per kilogram of wellhead gas produced, in USD/kg. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): super().setup() n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"] diff --git a/h2integrate/converters/hydrogen/geologic/h2_well_surface_baseclass.py b/h2integrate/converters/hydrogen/geologic/h2_well_surface_baseclass.py index 3fca88df0..e9107249c 100644 --- a/h2integrate/converters/hydrogen/geologic/h2_well_surface_baseclass.py +++ b/h2integrate/converters/hydrogen/geologic/h2_well_surface_baseclass.py @@ -66,6 +66,11 @@ class GeoH2SurfacePerformanceBaseClass(PerformanceModelBaseClass): The wellhead gas flow in kg/hour used for sizing the system - passed to the cost model. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() self.commodity = "hydrogen" @@ -147,6 +152,11 @@ class GeoH2SurfaceCostBaseClass(CostModelBaseClass): Variable OPEX per kilogram of hydrogen produced, in USD/year. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): super().setup() n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"] diff --git a/h2integrate/converters/hydrogen/geologic/simple_natural_geoh2.py b/h2integrate/converters/hydrogen/geologic/simple_natural_geoh2.py index 8f26ddacd..94079bc00 100644 --- a/h2integrate/converters/hydrogen/geologic/simple_natural_geoh2.py +++ b/h2integrate/converters/hydrogen/geologic/simple_natural_geoh2.py @@ -141,6 +141,11 @@ class NaturalGeoH2PerformanceModel(GeoH2SubsurfacePerformanceBaseClass): of annual hydrogen production to the maximum hydrogen production of the well. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = NaturalGeoH2PerformanceConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance"), diff --git a/h2integrate/converters/hydrogen/geologic/templeton_serpentinization.py b/h2integrate/converters/hydrogen/geologic/templeton_serpentinization.py index bc99d3c3d..88390de19 100644 --- a/h2integrate/converters/hydrogen/geologic/templeton_serpentinization.py +++ b/h2integrate/converters/hydrogen/geologic/templeton_serpentinization.py @@ -73,6 +73,11 @@ class StimulatedGeoH2PerformanceModel(GeoH2SubsurfacePerformanceBaseClass): over one year (8760 hours) [kg/h]. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"] self.config = StimulatedGeoH2PerformanceConfig.from_dict( diff --git a/h2integrate/converters/hydrogen/geologic/test/conftest.py b/h2integrate/converters/hydrogen/geologic/test/conftest.py index 8d49ffb3d..fa8f250cf 100644 --- a/h2integrate/converters/hydrogen/geologic/test/conftest.py +++ b/h2integrate/converters/hydrogen/geologic/test/conftest.py @@ -1 +1,5 @@ -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) diff --git a/h2integrate/converters/hydrogen/h2_fuel_cell.py b/h2integrate/converters/hydrogen/h2_fuel_cell.py index e7851a94b..e4675768a 100644 --- a/h2integrate/converters/hydrogen/h2_fuel_cell.py +++ b/h2integrate/converters/hydrogen/h2_fuel_cell.py @@ -38,6 +38,11 @@ class LinearH2FuelCellPerformanceModel(PerformanceModelBaseClass): - HHV_hydrogen is the higher heating value of hydrogen (approximately 142 MJ/kg) """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() self.commodity = "electricity" @@ -144,6 +149,11 @@ class H2FuelCellCostModel(CostModelBaseClass): specified cost parameters. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = H2FuelCellCostConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"), diff --git a/h2integrate/converters/hydrogen/pem_electrolyzer.py b/h2integrate/converters/hydrogen/pem_electrolyzer.py index 0baa49eb2..41a86b21e 100644 --- a/h2integrate/converters/hydrogen/pem_electrolyzer.py +++ b/h2integrate/converters/hydrogen/pem_electrolyzer.py @@ -59,6 +59,11 @@ class ECOElectrolyzerPerformanceModel(ElectrolyzerPerformanceBaseClass): Takes electricity input and outputs hydrogen and oxygen generation rates. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = ECOElectrolyzerPerformanceModelConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance"), @@ -90,6 +95,11 @@ def setup(self): units="MW", desc="Size of the electrolyzer in MW", ) + + self.add_output("oxygen_out", val=0, shape=self.n_timesteps, units="kg/h") + self.add_output("rated_oxygen_production", val=0, shape=1, units="kg/h") + self.add_output("annual_oxygen_produced", val=0, shape=self.plant_life, units="kg/yr") + self.add_input("cluster_size", val=-1.0, units="MW") self.add_input("max_hydrogen_capacity", val=1000.0, units="kg/h") # TODO: add feedstock inputs and consumption outputs @@ -188,8 +198,13 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): "Capacity Factor [-]" ].values outputs["annual_hydrogen_produced"] = H2_Results["Life: Annual H2 production [kg/year]"] - # TODO: replace above line w below # outputs["annual_hydrogen_produced"] = H2_Results["Performance Schedules"][ # "Annual H2 Production [kg/year]" # ].values + + outputs["oxygen_out"] = H2_Results["Oxygen Hourly Production [kg/hr]"] + outputs["rated_oxygen_production"] = H2_Results["Rated BOL: O2 Production [kg/hr]"] + outputs["annual_oxygen_produced"] = H2_Results["Performance Schedules"][ + "Annual O2 Production [kg/year]" + ] diff --git a/h2integrate/converters/hydrogen/pem_model/PEM_H2_LT_electrolyzer_Clusters.py b/h2integrate/converters/hydrogen/pem_model/PEM_H2_LT_electrolyzer_Clusters.py index baaf946fd..63dd2f271 100644 --- a/h2integrate/converters/hydrogen/pem_model/PEM_H2_LT_electrolyzer_Clusters.py +++ b/h2integrate/converters/hydrogen/pem_model/PEM_H2_LT_electrolyzer_Clusters.py @@ -29,6 +29,8 @@ import rainflow from scipy import interpolate +from h2integrate.tools.constants import O2_MW + np.set_printoptions(threshold=sys.maxsize) @@ -174,7 +176,7 @@ def run(self, input_external_power_kw): cluster_cycling = np.array(cluster_cycling) # how much to reduce h2 by based on cycling status - h2_multiplier = np.where(cluster_cycling > 0, startup_ratio, 1) + production_multiplier = np.where(cluster_cycling > 0, startup_ratio, 1) # number of "stacks" on at a single time self.n_stacks_op = self.max_stacks * self.cluster_status # n_stacks_op is now either number of pem per cluster or 0 if cluster is off! @@ -218,10 +220,15 @@ def run(self, input_external_power_kw): # h20_gal_used_system=self.water_supply(h2_kg_hr_system_init) p_consumed_max, rated_h2_hr = self.rated_h2_prod() # scales h2 production to account for start-up time if going from off->on - h2_kg_hr_system = h2_kg_hr_system_init * h2_multiplier + h2_kg_hr_system = h2_kg_hr_system_init * production_multiplier h20_gal_used_system = self.water_supply(h2_kg_hr_system) + # Get oxygen production and rated used + rated_o2_hr = self.rated_o2_prod() + o2_kg_hr_system_init = self.o2_production_rate(stack_current, self.n_stacks_op) + o2_kg_hr_system = o2_kg_hr_system_init * production_multiplier + pem_cf = np.sum(h2_kg_hr_system) / (rated_h2_hr * len(input_power_kw) * self.max_stacks) efficiency = self.system_efficiency(input_power_kw, stack_current) # Efficiency as %-HHV @@ -231,6 +238,7 @@ def run(self, input_external_power_kw): h2_results["hydrogen production no start-up time"] = h2_kg_hr_system_init h2_results["hydrogen_hourly_production"] = h2_kg_hr_system h2_results["water_hourly_usage_kg"] = h20_gal_used_system * 3.79 + h2_results["oxygen_hourly_production"] = o2_kg_hr_system h2_results["electrolyzer_total_efficiency_perc"] = efficiency h2_results["kwh_per_kgH2"] = input_power_kw / h2_kg_hr_system h2_results["Power Consumed [kWh]"] = system_power_consumed @@ -246,6 +254,7 @@ def run(self, input_external_power_kw): p_consumed_max * self.max_stacks ) h2_results_aggregates["Cluster Rated H2 Production [kg/hr]"] = rated_h2_hr * self.max_stacks + h2_results_aggregates["Cluster Rated O2 Production [kg/hr]"] = rated_o2_hr * self.max_stacks h2_results_aggregates["gal H20 per kg H2"] = np.sum(h20_gal_used_system) / np.sum( h2_kg_hr_system ) @@ -253,6 +262,9 @@ def run(self, input_external_power_kw): h2_results_aggregates["Cluster Rated H2 Production [kg/yr]"] = ( rated_h2_hr * len(input_power_kw) * self.max_stacks ) + h2_results_aggregates["Cluster Rated O2 Production [kg/yr]"] = ( + rated_o2_hr * len(input_power_kw) * self.max_stacks + ) h2_results_aggregates["Operational Time / Simulation Time (ratio)"] = ( self.percent_of_sim_operating ) # added @@ -367,13 +379,17 @@ def make_yearly_performance_dict(self, power_in_kW, V_deg, V_cell, I_op, grid_co cluster_cycling = [0, *list(np.diff(self.cluster_status))] # no delay at beginning of sim cluster_cycling = np.array(cluster_cycling) startup_ratio = 1 - (600 / 3600) # TODO: don't have this hard-coded - h2_multiplier = np.where(cluster_cycling > 0, startup_ratio, 1) + production_multiplier = np.where(cluster_cycling > 0, startup_ratio, 1) _, rated_h2_pr_stack_BOL = self.rated_h2_prod() + rated_o2_pr_stack_BOL = self.rated_o2_prod() rated_h2_pr_sim = rated_h2_pr_stack_BOL * self.max_stacks * sim_length + rated_o2_pr_sim = rated_o2_pr_stack_BOL * self.max_stacks * sim_length kg_h2_pr_sim = np.zeros(int(self.plant_life_years)) + kg_o2_pr_sim = np.zeros(int(self.plant_life_years)) capfac_per_sim = np.zeros(int(self.plant_life_years)) + o2_capfac_per_sim = np.zeros(int(self.plant_life_years)) d_sim = np.zeros(int(self.plant_life_years)) power_pr_yr_kWh = np.zeros(int(self.plant_life_years)) Vdeg0 = 0 @@ -396,20 +412,28 @@ def make_yearly_performance_dict(self, power_in_kW, V_deg, V_cell, I_op, grid_co power_in_kW, V_cell, V_deg_pr_sim ) h2_kg_hr_system_init = self.h2_production_rate(stack_current, self.n_stacks_op) + o2_kg_hr_system_init = self.o2_production_rate(stack_current, self.n_stacks_op) # total_sim_input_power = self.max_stacks*np.sum(power_in_kW) power_pr_yr_kWh[i] = self.max_stacks * np.sum(power_in_kW) else: h2_kg_hr_system_init = self.h2_production_rate(I_op, self.n_stacks_op) h2_kg_hr_system_init = h2_kg_hr_system_init * np.ones(len(power_in_kW)) + + o2_kg_hr_system_init = self.o2_production_rate(I_op, self.n_stacks_op) + o2_kg_hr_system_init = o2_kg_hr_system_init * np.ones(len(power_in_kW)) + annual_power_consumed_kWh = ( self.max_stacks * I_op * (V_cell + V_deg_pr_sim) * self.N_cells / 1000 ) # total_sim_input_power = np.sum(annual_power_consumed_kWh) power_pr_yr_kWh[i] = np.sum(annual_power_consumed_kWh) - h2_kg_hr_system = h2_kg_hr_system_init * h2_multiplier + h2_kg_hr_system = h2_kg_hr_system_init * production_multiplier + o2_kg_hr_system = o2_kg_hr_system_init * production_multiplier + kg_o2_pr_sim[i] = np.sum(o2_kg_hr_system) kg_h2_pr_sim[i] = np.sum(h2_kg_hr_system) capfac_per_sim[i] = np.sum(h2_kg_hr_system) / rated_h2_pr_sim + o2_capfac_per_sim[i] = np.sum(o2_kg_hr_system) / rated_o2_pr_sim d_sim[i] = V_deg_pr_sim[sim_length - 1] Vdeg0 = V_deg_pr_sim[sim_length - 1] performance_by_year = {} @@ -427,7 +451,8 @@ def make_yearly_performance_dict(self, power_in_kW, V_deg, V_cell, I_op, grid_co zip(year, self.eta_h2_hhv / (power_pr_yr_kWh / kg_h2_pr_sim)) ) performance_by_year["Annual Energy Used [kWh/year]"] = dict(zip(year, power_pr_yr_kWh)) - + performance_by_year["Annual O2 Production [kg/year]"] = dict(zip(year, kg_o2_pr_sim)) + performance_by_year["O2 Capacity Factor [-]"] = dict(zip(year, o2_capfac_per_sim)) return performance_by_year def reset_uptime_degradation_rate(self, uptime_hours_until_eol): @@ -606,6 +631,22 @@ def rated_h2_prod(self): max_h2_stack_kg = self.h2_production_rate(I_max, 1) return P_consumed_stack_kw, max_h2_stack_kg + def rated_o2_prod(self): + """Calculates the oxygen production per stack at the rated current in kg + + Returns: + float: rated oxygen production in kg/dt + """ + i = self.output_dict["BOL Efficiency Curve Info"].index[ + self.output_dict["BOL Efficiency Curve Info"]["Power Sent [kWh]"] + == self.stack_rating_kW + ] + I_max = self.output_dict["BOL Efficiency Curve Info"]["Current"].iloc[i].values[0] + # I_max = calc_current((self.stack_rating_kW,self.T_C),*self.curve_coeff) + + max_o2_stack_kg = self.o2_production_rate(I_max, 1) + return max_o2_stack_kg + def external_power_supply(self, input_external_power_kw): """ External power source (grid or REG) which will need to be stepped @@ -892,6 +933,33 @@ def h2_production_rate(self, stack_current, n_stacks_op): return h2_produced_kg_hr_system + def o2_production_rate(self, stack_current, n_stacks_op): + """Calculate the oxygen production rate of the cluster without warm-up losses. + These equations are based on Faraday's law: + + O2 [mol/sec] = eta_F*(n_cells*I)/(4F) + + where eta_F is the faradaic efficiency. This can be found in Equation 12 of + `Simple PEM water electrolyser model and experimental validation `_. + + Args: + stack_current (float | np.array): current of the stack in A + n_stacks_op (int | np.array[int]): number of stacks that are operating in the stack. + + Returns: + float | np.array: oxygen production profile of the cluster in kg/dt + """ + # Calculate the faradaic efficiency + n_Tot = self.faradaic_efficiency(stack_current) + # Faraday's law + o2_production_rate = n_Tot * ((self.N_cells * stack_current) / (4 * self.F)) # mol/s + # O2_MW is in g/mol + # mol/s * g/mol = g/s + o2_production_rate_g_s = o2_production_rate * O2_MW + o2_produced_kg_dt = o2_production_rate_g_s * self.dt / 1000 + o2_produced_kg_dt_system = o2_produced_kg_dt * n_stacks_op + return o2_produced_kg_dt_system + def water_supply(self, h2_kg_hr): """ Calculate water supply rate based system efficiency and H2 production @@ -920,7 +988,7 @@ def run_grid_connected_workaround(self, power_input_signal, current_signal): cluster_cycling = np.array(cluster_cycling) power_per_stack = np.where(self.n_stacks_op > 0, power_input_signal / self.n_stacks_op, 0) - h2_multiplier = np.where(cluster_cycling > 0, startup_ratio, 1) + production_multiplier = np.where(cluster_cycling > 0, startup_ratio, 1) self.n_stacks_op = self.max_stacks * self.cluster_status V_init = self.cell_design(self.T_C, current_signal) @@ -936,7 +1004,9 @@ def run_grid_connected_workaround(self, power_input_signal, current_signal): h2_kg_hr_system_init = self.h2_production_rate(current_signal, self.n_stacks_op) p_consumed_max, rated_h2_hr = self.rated_h2_prod() - h2_kg_hr_system = h2_kg_hr_system_init * h2_multiplier # scales h2 production to account + h2_kg_hr_system = ( + h2_kg_hr_system_init * production_multiplier + ) # scales h2 production to account # for start-up time if going from off->on h20_gal_used_system = self.water_supply(h2_kg_hr_system) diff --git a/h2integrate/converters/hydrogen/pem_model/run_h2_PEM.py b/h2integrate/converters/hydrogen/pem_model/run_h2_PEM.py index e44ef7ff3..c62c5f200 100644 --- a/h2integrate/converters/hydrogen/pem_model/run_h2_PEM.py +++ b/h2integrate/converters/hydrogen/pem_model/run_h2_PEM.py @@ -13,6 +13,8 @@ def clean_up_final_outputs(h2_tot, h2_ts): "Cluster Rated H2 Production [kg/yr]", "Stack Rated H2 Production [kg/hr]", "Stack Rated Power Consumed [kWh]", + "Cluster Rated O2 Production [kg/hr]", + "Cluster Rated O2 Production [kg/yr]", ] ) h2_ts.sum(axis=1) @@ -21,6 +23,7 @@ def clean_up_final_outputs(h2_tot, h2_ts): "Power Consumed [kWh]", "hydrogen production no start-up time", "hydrogen_hourly_production", + "oxygen_hourly_production", "water_hourly_usage_kg", ] @@ -89,6 +92,7 @@ def run_h2_PEM( # time-series info (unchanged) energy_input_to_electrolyzer = h2_ts.loc["Input Power [kWh]"].sum() hydrogen_hourly_production = h2_ts.loc["hydrogen_hourly_production"].sum() + oxygen_hourly_production = h2_ts.loc["oxygen_hourly_production"].sum() hourly_system_electrical_usage = h2_ts.loc["Power Consumed [kWh]"].sum() water_hourly_usage = h2_ts.loc["water_hourly_usage_kg"].sum() avg_eff_perc = eta_h2_hhv * hydrogen_hourly_production / hourly_system_electrical_usage @@ -102,6 +106,7 @@ def run_h2_PEM( # Beginning of Life (BOL) Rated Specs (attributes/system design) max_h2_pr_hr = h2_tot.loc["Cluster Rated H2 Production [kg/hr]"].sum() + max_o2_pr_hr = h2_tot.loc["Cluster Rated O2 Production [kg/hr]"].sum() max_pwr_pr_hr = h2_tot.loc["Cluster Rated Power Consumed [kWh]"].sum() rated_kWh_pr_kg = h2_tot.loc["Stack Rated Efficiency [kWh/kg]"].mean() elec_rated_h2_capacity_kgpy = h2_tot.loc["Cluster Rated H2 Production [kg/yr]"].sum() @@ -110,6 +115,7 @@ def run_h2_PEM( atrribute_desc = [ "Efficiency [kWh/kg]", "H2 Production [kg/hr]", + "O2 Production [kg/hr]", "Power Consumed [kWh]", "Annual H2 Production [kg/year]", "Gal H2O per kg-H2", @@ -118,6 +124,7 @@ def run_h2_PEM( attributes = [ rated_kWh_pr_kg, max_h2_pr_hr, + max_o2_pr_hr, max_pwr_pr_hr, elec_rated_h2_capacity_kgpy, gal_h20_pr_kg_h2, @@ -188,6 +195,7 @@ def run_h2_PEM( H2_Results.update(dict(zip(life_desc, life_vals))) H2_Results.update({"Performance Schedules": pd.DataFrame(annual_avg_performance)}) H2_Results.update({"Hydrogen Hourly Production [kg/hr]": hydrogen_hourly_production}) + H2_Results.update({"Oxygen Hourly Production [kg/hr]": oxygen_hourly_production}) H2_Results.update({"Water Hourly Consumption [kg/hr]": water_hourly_usage}) if not debug_mode: diff --git a/h2integrate/converters/hydrogen/singlitico_cost_model.py b/h2integrate/converters/hydrogen/singlitico_cost_model.py index 8c36a7d9d..19b5642e7 100644 --- a/h2integrate/converters/hydrogen/singlitico_cost_model.py +++ b/h2integrate/converters/hydrogen/singlitico_cost_model.py @@ -29,6 +29,11 @@ class SingliticoCostModel(ElectrolyzerCostBaseClass): An OpenMDAO component that computes the cost of a PEM electrolyzer. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = SingliticoCostModelConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"), diff --git a/h2integrate/converters/hydrogen/steam_methane_reformer.py b/h2integrate/converters/hydrogen/steam_methane_reformer.py index 18b8fa287..104478dc8 100644 --- a/h2integrate/converters/hydrogen/steam_methane_reformer.py +++ b/h2integrate/converters/hydrogen/steam_methane_reformer.py @@ -1,9 +1,9 @@ import numpy as np from attrs import field, define +from openmdao.utils import units from h2integrate.core.utilities import BaseConfig, merge_shared_inputs from h2integrate.core.validators import gt_zero, gte_zero -from h2integrate.tools.constants import HHV_H2_MJ_PER_KG from h2integrate.core.model_baseclasses import ( CostModelBaseClass, CostModelBaseConfig, @@ -44,6 +44,11 @@ class SteamMethaneReformerPerformanceModel(PerformanceModelBaseClass): unmet_hydrogen_demand (array): Unmet hydrogen demand in kg/h for each timestep """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() self.commodity = "hydrogen" @@ -144,6 +149,14 @@ def setup(self): desc="Unmet hydrogen demand for SMR plant", ) + self.add_output( + "total_energy_conversion_ratio", + val=0.0, + shape=1, + units="kW*h/kg", + desc="Net energy conversion ratio", + ) + def compute(self, inputs, outputs): """ Compute hydrogen output from natural gas input. @@ -163,7 +176,7 @@ def compute(self, inputs, outputs): # calculate max input and output system_capacity_kg_per_hour = inputs["system_capacity"] * ( 1000 / 24 - ) # plant capacity in kg/h + ) # plant capacity in kg/h from tonnes per day natural_gas_usage_mmbtu_per_kg = inputs["natural_gas_usage_rate"] max_natural_gas_consumption = system_capacity_kg_per_hour * natural_gas_usage_mmbtu_per_kg electricity_usage_kWh_per_kg = inputs["electricity_usage_rate"] @@ -202,16 +215,22 @@ def compute(self, inputs, outputs): hydrogen_out = np.minimum.reduce([hydrogen_out_ng, hydrogen_out_elec]) outputs["hydrogen_out"] = hydrogen_out - outputs["natural_gas_consumed"] = natural_gas_consumed - outputs["electricity_consumed"] = electricity_consumed + outputs["natural_gas_consumed"] = hydrogen_out * natural_gas_usage_mmbtu_per_kg + outputs["electricity_consumed"] = hydrogen_out * electricity_usage_kWh_per_kg outputs["rated_hydrogen_production"] = system_capacity_kg_per_hour # kg/h - # Convert HHV of H2 from MJ/kg to kW*h/kg - hhv_h2_kWh_per_kg = HHV_H2_MJ_PER_KG / (3600.0 * 0.001) + # Convert natural gas usage from MMBtu/kg to kW*h/kg + energy_conversion_ratio_ng = units.convert_units( + inputs["natural_gas_usage_rate"], "MMBtu/kg", "kW*h/kg" + ) + total_energy_conversion_ratio = ( + energy_conversion_ratio_ng + inputs["electricity_usage_rate"] + ) + outputs["electrical_rated_hydrogen_production"] = ( - system_capacity_kg_per_hour * hhv_h2_kWh_per_kg - ) / 1000 # convert kg/h to MW using HHV of hydrogen + system_capacity_kg_per_hour * total_energy_conversion_ratio + ) / 1000 # convert kg/h to MW using energy conversion ratio max_production = system_capacity_kg_per_hour * len(hydrogen_out) * (self.dt / 3600) @@ -223,6 +242,7 @@ def compute(self, inputs, outputs): 1 / self.fraction_of_year_simulated ) outputs["unmet_hydrogen_demand"] = inputs["hydrogen_demand"] - hydrogen_out + outputs["total_energy_conversion_ratio"] = total_energy_conversion_ratio @define(kw_only=True) @@ -256,6 +276,11 @@ class SteamMethaneReformerCostModel(CostModelBaseClass): cost_year (int): Dollar year for the costs """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = SteamMethaneReformerCostModelConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"), @@ -266,7 +291,6 @@ def setup(self): super().setup() - # Add inputs specific to the cost model with config values as defaults self.add_input( "annual_hydrogen_produced", val=0.0, @@ -274,12 +298,20 @@ def setup(self): units="kg/year", desc="Annual hydrogen output from performance model", ) + self.add_input( + "total_energy_conversion_ratio", + val=0.0, + units="(kW*h)/kg", + desc="Plant electricity usage rate in kWh/kg", + ) self.add_input( "electrical_rated_hydrogen_production", val=0.0, units="kW", desc="Electrical equivalent rated hydrogen production from performance model", ) + + # Add inputs specific to the cost model with config values as defaults self.add_input( "unit_capex", val=self.config.capex_per_kw, @@ -319,12 +351,9 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): hydrogen_out = inputs["annual_hydrogen_produced"] # kg/year annual profile - # Convert HHV of H2 from MJ/kg to kW*h/kg - hhv_h2_kWh_per_kg = HHV_H2_MJ_PER_KG / (3600.0 * 0.001) - - # Convert the variable O&M from USD/kWh to USD/kg using the HHV of H2 + # Convert the variable O&M from USD/kWh to USD/kg using the energy conversion ratio # USD/kW*h * kW*h/kg = USD/kg - variable_opex_per_kg = variable_opex_per_kwh * hhv_h2_kWh_per_kg + variable_opex_per_kg = variable_opex_per_kwh * inputs["total_energy_conversion_ratio"][0] # Calculate variable operating expenses over project life variable_om = variable_opex_per_kg * hydrogen_out diff --git a/h2integrate/converters/hydrogen/test/conftest.py b/h2integrate/converters/hydrogen/test/conftest.py index 8d49ffb3d..fa8f250cf 100644 --- a/h2integrate/converters/hydrogen/test/conftest.py +++ b/h2integrate/converters/hydrogen/test/conftest.py @@ -1 +1,5 @@ -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) diff --git a/h2integrate/converters/hydrogen/test/test_basic_cost_model.py b/h2integrate/converters/hydrogen/test/test_basic_cost_model.py index c11cdb69b..bce2205ea 100644 --- a/h2integrate/converters/hydrogen/test/test_basic_cost_model.py +++ b/h2integrate/converters/hydrogen/test/test_basic_cost_model.py @@ -34,6 +34,7 @@ def _create_problem(self, location, electrolyzer_size_mw, electrical_generation_ "plant_life": self.useful_life, "simulation": { "n_timesteps": self.n_timesteps, + "dt": 3600, }, }, }, diff --git a/h2integrate/converters/hydrogen/test/test_custom_electrolyzer_cost_model.py b/h2integrate/converters/hydrogen/test/test_custom_electrolyzer_cost_model.py index 3a4771fd8..e4aa77f05 100644 --- a/h2integrate/converters/hydrogen/test/test_custom_electrolyzer_cost_model.py +++ b/h2integrate/converters/hydrogen/test/test_custom_electrolyzer_cost_model.py @@ -26,6 +26,7 @@ def test_custom_electrolyzer_cost_model(subtests): "plant_life": 30, "simulation": { "n_timesteps": 8760, # Default number of timesteps for the simulation + "dt": 3600, # Default time step length in seconds (1 hour) }, }, } diff --git a/h2integrate/converters/hydrogen/test/test_pem_electrolyzer_performance.py b/h2integrate/converters/hydrogen/test/test_pem_electrolyzer_performance.py index 081d39ae4..88a0cfdc0 100644 --- a/h2integrate/converters/hydrogen/test/test_pem_electrolyzer_performance.py +++ b/h2integrate/converters/hydrogen/test/test_pem_electrolyzer_performance.py @@ -129,3 +129,62 @@ def test_electrolyzer_outputs(tech_config, plant_config, subtests): assert prob.get_val("comp.operational_life", units="yr") == plant_life with subtests.test("replacement_schedule value"): assert np.any(prob.get_val("comp.replacement_schedule", units="unitless") == 0) + + +@pytest.mark.regression +def test_electrolyzer_results(tech_config, plant_config, subtests): + prob = om.Problem() + comp = ECOElectrolyzerPerformanceModel( + plant_config=plant_config, tech_config=tech_config, driver_config={} + ) + prob.model.add_subsystem("comp", comp, promotes=["*"]) + prob.setup() + power_profile = np.full(8760, 32.0) + prob.set_val("comp.electricity_in", power_profile, units="MW") + + prob.run_model() + + with subtests.test("Total hydrogen produced"): + assert ( + pytest.approx(5297814.89908964, rel=1e-6) + == prob.get_val("comp.hydrogen_out", units="kg/h").sum() + ) + + with subtests.test("Total oxygen produced"): + assert ( + pytest.approx(42045916.90741967, rel=1e-6) + == prob.get_val("comp.oxygen_out", units="kg/h").sum() + ) + + with subtests.test("Year 0 capacity factor"): + assert ( + pytest.approx(77.10460139, rel=1e-6) + == prob.get_val("comp.capacity_factor", units="percent")[0] + ) + + with subtests.test("Rated H2 production"): + assert pytest.approx(784.3544735823235, rel=1e-6) == prob.get_val( + "comp.rated_hydrogen_production", units="kg/h" + ) + + with subtests.test("Rated O2 production"): + assert pytest.approx(6225.00099576, rel=1e-6) == prob.get_val( + "comp.rated_oxygen_production", units="kg/h" + ) + + with subtests.test("H2: CF*Rated = Annual"): + np.testing.assert_allclose( + prob.get_val("comp.rated_hydrogen_production", units="kg/h") + * prob.get_val("comp.capacity_factor", units="unitless") + * 8760, + prob.get_val("comp.annual_hydrogen_produced", units="kg/yr"), + rtol=1e-6, + ) + with subtests.test("O2: CF*Rated = Annual"): + np.testing.assert_allclose( + prob.get_val("comp.rated_oxygen_production", units="kg/h") + * prob.get_val("comp.capacity_factor", units="unitless") + * 8760, + prob.get_val("comp.annual_oxygen_produced", units="kg/yr"), + rtol=1e-6, + ) diff --git a/h2integrate/converters/hydrogen/test/test_singlitico_cost_model.py b/h2integrate/converters/hydrogen/test/test_singlitico_cost_model.py index 5fa41ab82..76b448726 100644 --- a/h2integrate/converters/hydrogen/test/test_singlitico_cost_model.py +++ b/h2integrate/converters/hydrogen/test/test_singlitico_cost_model.py @@ -38,6 +38,7 @@ def _create_problem(self, location): "plant_life": 30, "simulation": { "n_timesteps": 8760, + "dt": 3600, }, }, }, diff --git a/h2integrate/converters/hydrogen/test/test_steam_methane_reformer.py b/h2integrate/converters/hydrogen/test/test_steam_methane_reformer.py index a236f3218..3cdd6812e 100644 --- a/h2integrate/converters/hydrogen/test/test_steam_methane_reformer.py +++ b/h2integrate/converters/hydrogen/test/test_steam_methane_reformer.py @@ -178,7 +178,7 @@ def test_h2_smr_performance_outputs(plant_config, smr_performance_params, subtes pytest.approx( prob.get_val("comp.electrical_rated_hydrogen_production", units="MW"), rel=1e-6 ) - == 394.05555 + == 571.49645 ) with subtests.test("total_hydrogen_produced value"): assert ( @@ -231,15 +231,15 @@ def test_h2_smr_cost(smr_performance_params, smr_cost_params, plant_config, subt with subtests.test("capex value"): assert pytest.approx(prob.get_val("cost_comp.CapEx", units="USD"), rel=1e-6) == ( - 394.05555 * 1000.0 * 500.0 + 571.49645 * 1000.0 * 500.0 ) with subtests.test("opex value"): assert pytest.approx(prob.get_val("cost_comp.OpEx", units="USD/year"), rel=1e-6) == ( - 394.05555 * 1000.0 * 20.0 # fixed opex + 571.49645 * 1000.0 * 20.0 # fixed opex ) with subtests.test("var opex value"): assert pytest.approx(prob.get_val("cost_comp.VarOpEx", units="USD/year"), rel=1e-6) == ( - 3451926666.666666 * 0.05 # variable opex + 250315447.17 # variable opex ) diff --git a/h2integrate/converters/hydrogen/wombat_model.py b/h2integrate/converters/hydrogen/wombat_model.py index 84fdc04be..58edd06e8 100644 --- a/h2integrate/converters/hydrogen/wombat_model.py +++ b/h2integrate/converters/hydrogen/wombat_model.py @@ -35,6 +35,11 @@ class WOMBATElectrolyzerModel(ECOElectrolyzerPerformanceModel): lost due to operations and maintenance (O&M), and electrolyzer availability. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): super().setup() self.config = WOMBATModelConfig.from_dict( diff --git a/h2integrate/converters/iron/humbert_ewin_perf.py b/h2integrate/converters/iron/humbert_ewin_perf.py index 7a8888933..1fc65f46d 100644 --- a/h2integrate/converters/iron/humbert_ewin_perf.py +++ b/h2integrate/converters/iron/humbert_ewin_perf.py @@ -75,6 +75,11 @@ class HumbertEwinPerformanceComponent(PerformanceModelBaseClass): """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): self.commodity = "sponge_iron" self.commodity_rate_units = "kg/h" diff --git a/h2integrate/converters/iron/humbert_stinn_ewin_cost.py b/h2integrate/converters/iron/humbert_stinn_ewin_cost.py index 374ee1732..6eabd529b 100644 --- a/h2integrate/converters/iron/humbert_stinn_ewin_cost.py +++ b/h2integrate/converters/iron/humbert_stinn_ewin_cost.py @@ -117,6 +117,11 @@ class HumbertStinnEwinCostComponent(CostModelBaseClass): """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): self.options.declare("driver_config", types=dict) self.options.declare("plant_config", types=dict) diff --git a/h2integrate/converters/iron/iron.py b/h2integrate/converters/iron/iron.py deleted file mode 100644 index 3f3ced9ec..000000000 --- a/h2integrate/converters/iron/iron.py +++ /dev/null @@ -1,366 +0,0 @@ -import copy -import importlib -from pathlib import Path -from dataclasses import field - -import pandas as pd -from attrs import define -from pandas import DataFrame - -from h2integrate.core.file_utils import load_yaml - - -# Get model locations loaded up to refer to -CD = Path(__file__).parent -model_locs_fp = CD / "model_locations.yaml" -model_locs = load_yaml(model_locs_fp) - - -@define(kw_only=True) -class IronPerformanceModelConfig: - """ - Configuration inputs for the iron performance model. - - Attributes: - product_selection (str): The particular iron product selected. - site (dict): Contains information on the site where iron is being reduced. - model (dict): Contains name of performance model and, if necessary, filepaths to - secure location passed from input if not part of public H2Integrate. - Also contains 'refit_coeffs' boolean to re-do - model coefficient curve fitting. - params (dict): The rest of the parameters for the performance model. - """ - - product_selection: str = "" - site: dict = field(default_factory=dict) - model: dict = field(default_factory=dict) - params: dict = field(default_factory=dict) - - def __attrs_post_init__(self): - if self.product_selection == "": - raise ValueError("Iron performance product_selection must be set.") - if self.site == {}: - raise ValueError("Iron performance site must be set.") - if self.model == {}: - raise ValueError("Iron performance model must be set.") - for fieldname in ["model_fp", "inputs_fp", "coeffs_fp", "refit_coeffs"]: - if fieldname not in self.model.keys(): - if fieldname != "refit_coeffs": - self.model[fieldname] = "" - else: - self.model[fieldname] = False - - -@define(kw_only=True) -class IronPerformanceModelOutputs: - """ - Outputs from the iron performance model. - - Attributes: - performances_df (DataFrame): Contains locations and modeled iron plant performance outputs. - """ - - performances_df: DataFrame = pd.DataFrame() - - def __attrs_post_init__(self): - if len(self.performances_df) == 0: - raise ValueError("No iron performance data has been calculated.") - - -def run_size_iron_plant_performance( - config: IronPerformanceModelConfig, -) -> IronPerformanceModelOutputs: - """ - Calculates either the annual iron production in metric tons based on plant capacity and - available hydrogen or the amount of required hydrogen based on a desired iron production. - - Args: - config (IronPerformanceModelConfig): - Configuration object containing all necessary parameters for the capacity sizing, - including capacity factor estimate and feedstock costs. - - Returns: - IronPerformanceModelOutputs: An object containing iron plant capacity in metric tons - per year and amount of hydrogen required in kilograms per year. - - """ - - perf_model = config.model["name"] - if config.model["model_fp"] == "": - config.model["model_fp"] = model_locs["performance"][perf_model]["model"] - if config.model["inputs_fp"] == "": - config.model["inputs_fp"] = model_locs["performance"][perf_model]["inputs"] - if config.model["coeffs_fp"] == "": - config.model["coeffs_fp"] = model_locs["performance"][perf_model]["coeffs"] - model = importlib.import_module(config.model["model_fp"]) - model_outputs = model.main(config) - performances_df = model_outputs - - return IronPerformanceModelOutputs(performances_df=performances_df) - - -@define(kw_only=True) -class IronCostModelConfig: - """ - Configuration inputs for the iron cost model. - - Attributes: - performance(IronPerformanceModelOutputs): Outputs from the performance model. - product_selection (str): The particular iron product selected. - site (dict): Contains information on the site where iron is being reduced. - model (dict): Contains name of cost model and, if necessary, filepaths to - secure location passed from input if not part of public H2Integrate. - Also contains 'refit_coeffs' boolean to re-do - model coefficient curve fitting. - params (dict): The rest of the parameters for the cost model. - """ - - performance: IronPerformanceModelOutputs - product_selection: str = "" - site: dict = field(default_factory=dict) - model: dict = field(default_factory=dict) - params: dict = field(default_factory=dict) - - def __attrs_post_init__(self): - if self.product_selection == "": - raise ValueError("Iron cost product_selection must be set.") - if self.site == {}: - raise ValueError("Iron cost site must be set.") - if self.model == {}: - raise ValueError("Iron cost model must be set.") - for fieldname in ["model_fp", "inputs_fp", "coeffs_fp", "refit_coeffs"]: - if fieldname not in self.model.keys(): - if fieldname != "refit_coeffs": - self.model[fieldname] = "" - else: - self.model[fieldname] = False - - -@define(kw_only=True) -class IronCostModelOutputs: - """ - Outputs from the iron cost model. - - Attributes: - costs_df (DataFrame): Contains locations and modeled iron plant cost outputs. - """ - - costs_df: DataFrame = pd.DataFrame() - - def __attrs_post_init__(self): - if len(self.costs_df) == 0: - raise ValueError("No iron performance data has been calculated.") - - -def run_iron_cost_model(config: IronCostModelConfig) -> IronCostModelOutputs: - """ - Calculates the capital expenditure (CapEx) and operating expenditure (OpEx) for - a iron manufacturing plant based on the provided configuration. - - Args: - config (IronCostModelConfig): - Configuration object containing all necessary parameters for the cost - model, including plant capacity, feedstock costs, and integration options - for oxygen and heat. - - Returns: - IronCostModelOutputs: An object containing detailed breakdowns of capital and - operating costs, as well as total plant cost and other financial metrics. - - Note: - The calculation includes various cost components such as electric arc furnace - (EAF) casting, shaft furnace, oxygen supply, hydrogen preheating, cooling tower, - and more, adjusted based on the Chemical Engineering Plant Cost Index (CEPCI). - """ - # If cost model name is "placeholder", use the code that was copied over from Green Steel - cost_model = config.model["name"] - if config.model["model_fp"] == "": - config.model["model_fp"] = model_locs["cost"][cost_model]["model"] - if config.model["inputs_fp"] == "": - config.model["inputs_fp"] = model_locs["cost"][cost_model]["inputs"] - if config.model["coeffs_fp"] == "": - config.model["coeffs_fp"] = model_locs["cost"][cost_model]["coeffs"] - model = importlib.import_module(config.model["model_fp"]) - model_outputs = model.main(config) - - return IronCostModelOutputs(costs_df=model_outputs) - - -@define(kw_only=True) -class IronFinanceModelConfig(IronCostModelConfig): - """ - Configuration inputs for the iron finance model. - - Attributes: - cost(IronCostModelOutputs): Outputs from the cost model. - performance(IronPerformanceModelOutputs): Outputs from the performance model. - product_selection (str): The particular iron product selected. - site (dict): Contains information on the site where iron is being reduced. - model (dict): Contains name of finance model and, if necessary, filepaths to - secure location passed from input if not part of public H2Integrate. - Also contains 'refit_coeffs' boolean to re-do - model coefficient curve fitting. - params (dict): The rest of the parameters for the finance model. - pf (dict): Optional dictionary of the ProFAST object. - """ - - cost: IronCostModelOutputs - performance: IronPerformanceModelOutputs - product_selection: str = "" - site: dict = field(default_factory=dict) - model: dict = field(default_factory=dict) - params: dict = field(default_factory=dict) - pf: dict | None = field(init=False) - - def __attrs_post_init__(self): - if self.product_selection == "": - raise ValueError("Iron finance product_selection must be set.") - if self.site == {}: - raise ValueError("Iron finance site must be set.") - if self.model == {}: - raise ValueError("Iron finance model must be set.") - for fieldname in ["model_fp", "inputs_fp", "coeffs_fp", "refit_coeffs"]: - if fieldname not in self.model.keys(): - if fieldname != "refit_coeffs": - self.model[fieldname] = "" - else: - self.model[fieldname] = False - - -@define(kw_only=True) -class IronFinanceModelOutputs: - """ - Represents the outputs of the iron finance model, encapsulating the - results of financial analysis for iron production. - - Attributes: - sol (dict): - A dictionary containing the solution to the financial model, including key - financial indicators such as NPV (Net Present Value), IRR (Internal Rate of - Return), and breakeven price. - summary (dict): - A summary of key results from the financial analysis, providing a - high-level overview of financial metrics and performance indicators. - price_breakdown (pd.DataFrame): - A Pandas DataFrame detailing the cost breakdown for producing iron, - including both capital and operating expenses, as well as the impact of - various cost factors on the overall price of iron. - pf (object): - ProFAST object. - """ - - sol: dict - summary: dict - price_breakdown: pd.DataFrame - pf: object - - -def run_iron_finance_model( - config: IronFinanceModelConfig, -) -> IronFinanceModelOutputs: - """ - Executes the financial model for iron production, calculating the breakeven price - of iron and other financial metrics based on the provided configuration and cost - models. - - This function integrates various cost components, including capital expenditures - (CapEx), operating expenses (OpEx), and owner's costs. It leverages the ProFAST - financial analysis software framework. - - Args: - config (IronFinanceModelConfig): - Configuration object containing all necessary parameters and assumptions - for the financial model, including plant characteristics, cost inputs, - financial assumptions, and grid prices. - - Returns: - IronFinanceModelOutputs: - Object containing detailed financial analysis results, including solution - metrics, summary values, price breakdown, iron price breakdown per - tonne, and the ProFAST object. This output is instrumental in assessing the - financial performance and breakeven price for the iron production facility. - """ - - finance_model = config.model["name"] - if config.model["model_fp"] == "": - config.model["model_fp"] = model_locs["finance"][finance_model]["model"] - model = importlib.import_module(config.model["model_fp"]) - model_outputs = model.main(config) - - sol, summary, price_breakdown, pf = model_outputs - return IronFinanceModelOutputs(sol=sol, summary=summary, price_breakdown=price_breakdown, pf=pf) - - -def run_iron_full_model( - h2integrate_config: dict, -) -> tuple[IronPerformanceModelOutputs, IronCostModelOutputs, IronFinanceModelOutputs]: - """ - Runs the full iron model, including capacity (performance), cost, and finance models. - - Args: - h2integrate_config (dict): The configuration for the h2integrate model. - - Returns: - Tuple[IronPerformanceModelOutputs, IronCostModelOutputs, IronFinanceModelOutputs]: - A tuple containing the outputs of the iron capacity, cost, and finance models. - """ - # this is likely to change as we refactor to use config dataclasses, but for now - # we'll just copy the config and modify it as needed - iron_config = copy.deepcopy(h2integrate_config["iron"]) - - if iron_config["costs"]["lcoh"] != iron_config["finances"]["lcoh"]: - raise ( - ValueError( - "iron cost LCOH and iron finance LCOH are not equal. \ - You must specify both values or neither. \ - If neither is specified, LCOH will be calculated." - ) - ) - iron_product_selection = iron_config["product_selection"] - iron_site = iron_config["site"] - - iron_performance_inputs = iron_config["performance"] - performance_model = iron_config["performance_model"] - - iron_cost_inputs = iron_performance_inputs | iron_config["costs"] - cost_model = iron_config["cost_model"] - - iron_finance_inputs = iron_cost_inputs | iron_config["finances"] - finance_model = iron_config["finance_model"] - - iron_finance_inputs["operational_year"] = iron_cost_inputs["operational_year"] - iron_finance_inputs["installation_years"] = iron_cost_inputs["installation_years"] - iron_finance_inputs["plant_life"] = iron_cost_inputs["plant_life"] - iron_finance_inputs["cost_year"] = h2integrate_config["project_parameters"]["cost_year"] - - # run iron performance model to get iron plant size - performance_config = IronPerformanceModelConfig( - product_selection=iron_product_selection, - site=iron_site, - model=performance_model, - params=iron_performance_inputs, - ) - iron_performance = run_size_iron_plant_performance(performance_config) - - # run iron cost model to get iron plant costs - cost_config = IronCostModelConfig( - product_selection=iron_product_selection, - site=iron_site, - model=cost_model, - params=iron_cost_inputs, - performance=iron_performance, - ) - iron_cost = run_iron_cost_model(cost_config) - - # run iron finance model to get iron plant finances - finance_config = IronFinanceModelConfig( - product_selection=iron_product_selection, - site=iron_site, - model=finance_model, - params=iron_finance_inputs, - performance=iron_performance, - cost=iron_cost, - ) - iron_finance = run_iron_finance_model(finance_config) - - return (iron_performance, iron_cost, iron_finance) diff --git a/h2integrate/converters/iron/iron_dri_base.py b/h2integrate/converters/iron/iron_dri_base.py index bc7d9f26d..a24d9636d 100644 --- a/h2integrate/converters/iron/iron_dri_base.py +++ b/h2integrate/converters/iron/iron_dri_base.py @@ -30,6 +30,11 @@ class IronReductionPerformanceBaseConfig(BaseConfig): class IronReductionPlantBasePerformanceComponent(PerformanceModelBaseClass): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() self.commodity = "pig_iron" @@ -295,6 +300,11 @@ class IronReductionPlantBaseCostComponent(CostModelBaseClass): steel_to_iron_ratio (float): steel/pig iron ratio """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"] @@ -327,7 +337,7 @@ def setup(self): self.config = IronReductionCostBaseConfig.from_dict( config_dict, - strict=False, + strict=True, additional_cls_name=self.__class__.__name__, ) diff --git a/h2integrate/converters/iron/iron_dri_plant.py b/h2integrate/converters/iron/iron_dri_plant.py index 25a631ff9..a1d5e551d 100644 --- a/h2integrate/converters/iron/iron_dri_plant.py +++ b/h2integrate/converters/iron/iron_dri_plant.py @@ -15,6 +15,11 @@ class HydrogenIronReductionPlantCostComponent(IronReductionPlantBaseCostComponen steel_to_iron_ratio (float): steel/pig iron ratio """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.product = "h2_dri" super().setup() @@ -31,6 +36,11 @@ class NaturalGasIronReductionPlantCostComponent(IronReductionPlantBaseCostCompon steel_to_iron_ratio (float): steel/pig iron ratio """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.product = "ng_dri" super().setup() @@ -47,6 +57,11 @@ class HydrogenIronReductionPlantPerformanceComponent(IronReductionPlantBasePerfo steel_to_iron_ratio (float): steel/pig iron ratio """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.product = "h2_dri" self.feedstocks_to_units = { @@ -70,6 +85,11 @@ class NaturalGasIronReductionPlantPerformanceComponent(IronReductionPlantBasePer steel_to_iron_ratio (float): steel/pig iron ratio """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.feedstocks_to_units = { "natural_gas": "MMBtu/h", diff --git a/h2integrate/converters/iron/iron_mine.py b/h2integrate/converters/iron/iron_mine.py deleted file mode 100644 index 336527290..000000000 --- a/h2integrate/converters/iron/iron_mine.py +++ /dev/null @@ -1,238 +0,0 @@ -import numpy as np -import pandas as pd -import openmdao.api as om -from attrs import field, define - -from h2integrate.core.utilities import BaseConfig, merge_shared_inputs -from h2integrate.core.validators import contains, range_val -from h2integrate.converters.iron.iron import ( - IronCostModelConfig, - IronPerformanceModelConfig, - IronPerformanceModelOutputs, - run_iron_cost_model, - run_size_iron_plant_performance, -) -from h2integrate.core.model_baseclasses import CostModelBaseClass -from h2integrate.tools.inflation.inflate import inflate_cpi -from h2integrate.converters.iron.martin_ore.variable_om_cost import martin_ore_variable_om_cost -from h2integrate.converters.iron.rosner_ore.variable_om_cost import rosner_ore_variable_om_cost - - -@define(kw_only=True) -class IronMineBaseConfig(BaseConfig): - mine: str = field(validator=contains(["Hibbing", "Northshore", "United", "Minorca", "Tilden"])) - - # product_selection - taconite_pellet_type: str = field( - converter=(str.lower, str.strip), validator=contains(["std", "drg"]) - ) - - model_name: str = field(default="martine_ore") # only option at the moment - model_fp: str = field(default="") - inputs_fp: str = field(default="") - coeffs_fp: str = field(default="") - refit_coeffs: bool = field(default=False) - - def make_model_dict(self): - keys = ["model_fp", "inputs_fp", "coeffs_fp", "refit_coeffs"] - d = self.as_dict() - model_dict = {k: v for k, v in d.items() if k in keys} - model_dict.update({"name": self.model_name}) - return model_dict - - def make_site_dict(self): - return {"name": self.mine} - - -@define(kw_only=True) -class IronMinePerformanceConfig(IronMineBaseConfig): - ore_cf_estimate: float = field(default=0.9, validator=range_val(0, 1)) # ore - - def make_model_dict(self): - keys = ["model_fp", "inputs_fp", "coeffs_fp", "refit_coeffs"] - d = self.as_dict() - model_dict = {k: v for k, v in d.items() if k in keys} - model_dict.update({"name": self.model_name}) - return model_dict - - -class IronMinePerformanceComponent(om.ExplicitComponent): - def initialize(self): - self.options.declare("driver_config", types=dict) - self.options.declare("plant_config", types=dict) - self.options.declare("tech_config", types=dict) - - def setup(self): - n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"] - self.config = IronMinePerformanceConfig.from_dict( - merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance"), - strict=False, - additional_cls_name=self.__class__.__name__, - ) - self.add_discrete_output( - "iron_mine_performance", val=pd.DataFrame(), desc="iron mine performance results" - ) - self.add_output("iron_ore_out", val=0.0, shape=n_timesteps, units="kg/h") - self.add_output("total_iron_ore_produced", val=0.0, units="t/year") - - def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): - n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"] - ore_performance_inputs = {"input_capacity_factor_estimate": self.config.ore_cf_estimate} - ore_model_inputs = self.config.make_model_dict() - iron_mine_site = self.config.make_site_dict() - performance_config = IronPerformanceModelConfig( - product_selection=f"{self.config.taconite_pellet_type}_taconite_pellets", - site=iron_mine_site, - model=ore_model_inputs, - params=ore_performance_inputs, - ) - iron_mine_performance = run_size_iron_plant_performance(performance_config) - # wltpy = wet long tons per year - ore_produced_wltpy = iron_mine_performance.performances_df.set_index("Name").loc[ - "Ore pellets produced" - ][self.config.mine] - ore_produced_wmtpy = ore_produced_wltpy * 1.016047 # wmtpy = wet metric tonnes per year - ore_produced_mtpy = ore_produced_wmtpy * 0.98 # mtpy = dry metric tonnes per year - discrete_outputs["iron_mine_performance"] = iron_mine_performance.performances_df - outputs["iron_ore_out"] = ore_produced_mtpy * 1000 / n_timesteps - outputs["total_iron_ore_produced"] = ore_produced_mtpy - - -@define(kw_only=True) -class IronMineCostConfig(IronMineBaseConfig): - LCOE: float = field(kw_only=True) # $/MWh - LCOH: float = field(kw_only=True) # $/kg - varom_model_name: str = field( - default="martin_ore", validator=contains(["martin_ore", "rosner_ore"]) - ) - operational_year: int = field(converter=int, kw_only=True) - installation_years: int | float = field(kw_only=True) - plant_life: int = field(converter=int, kw_only=True) - cost_year: int = field(converter=int, kw_only=True) - - def make_model_dict(self): - keys = ["model_fp", "inputs_fp", "coeffs_fp", "refit_coeffs"] - d = self.as_dict() - model_dict = {k: v for k, v in d.items() if k in keys} - model_dict.update({"name": self.model_name}) - return model_dict - - def make_cost_dict(self): - keys = ["operational_year", "installation_years", "plant_life"] - d = self.as_dict() - cost_dict = {k: v for k, v in d.items() if k in keys} - return cost_dict - - -class IronMineCostComponent(CostModelBaseClass): - def setup(self): - self.target_dollar_year = self.options["plant_config"]["finance_parameters"][ - "cost_adjustment_parameters" - ]["target_dollar_year"] - self.plant_life = self.options["plant_config"]["plant"]["plant_life"] - - config_dict = merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost") - config_dict.update({"cost_year": self.target_dollar_year}) - config_dict.update({"plant_life": self.plant_life}) - - self.config = IronMineCostConfig.from_dict( - config_dict, - strict=False, - additional_cls_name=self.__class__.__name__, - ) - - super().setup() - self.add_input("LCOE", val=self.config.LCOE, units="USD/MW/h") - self.add_input("LCOH", val=self.config.LCOH, units="USD/kg") - self.add_input("total_iron_ore_produced", val=1.0, units="t/year") - self.add_discrete_input( - "iron_mine_performance", val=pd.DataFrame(), desc="iron mine performance results" - ) - self.add_discrete_output( - "iron_mine_cost", val=pd.DataFrame(), desc="iron mine cost results" - ) - - def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): - ore_performance = IronPerformanceModelOutputs( - performances_df=discrete_inputs["iron_mine_performance"] - ) - - ore_cost_inputs = { - "lcoe": inputs["LCOE"][0] / 1e3, - "lcoh": inputs["LCOH"][0], - } - cost_dict = self.config.make_cost_dict() - ore_cost_inputs.update(cost_dict) - - ore_model_inputs = self.config.make_model_dict() - iron_mine_site = self.config.make_site_dict() - cost_config = IronCostModelConfig( - product_selection=f"{self.config.taconite_pellet_type}_taconite_pellets", - site=iron_mine_site, - model=ore_model_inputs, - params=ore_cost_inputs, - performance=ore_performance, - ) - iron_ore_cost = run_iron_cost_model(cost_config) - - discrete_outputs["iron_mine_cost"] = iron_ore_cost.costs_df - - # Now taking some stuff from finance - cost_df = iron_ore_cost.costs_df.set_index("Name") - cost_ds = cost_df.loc[:, self.config.mine] - cost_names = cost_df.index.values - cost_types = cost_df.loc[:, "Type"].values - cost_units = cost_df.loc[:, "Unit"].values - - capex = 0 - fixed_om = 0 - variable_om = 0 - capital_idxs = np.where(cost_types == "capital")[0] - for idx in capital_idxs: - cost_names[idx] - unit = cost_units[idx] # Units for capital costs should be " $"" - source_year = int(unit[:4]) - source_year_cost = cost_ds.iloc[idx] - cost = inflate_cpi(source_year_cost, source_year, self.config.cost_year) - capex += cost - - # Add fixed opex costs - fixed_idxs = np.where(cost_types == "fixed opex")[0] - for idx in fixed_idxs: - cost_names[idx] - unit = cost_units[idx] # Units for fixed opex costs should be " $ per year" - source_year = int(unit[:4]) - source_year_cost = cost_ds.iloc[idx] - fixed_cost = inflate_cpi(source_year_cost, source_year, self.config.cost_year) - fixed_om += fixed_cost - # NOTE: why are we double counting this cost? - fixed_om * 6 / 12 # TODO: output installation cost? - - var_idxs = np.where(cost_types == "variable opex")[0] - for idx in var_idxs: - unit = cost_units[idx] # Should be " $ per " - source_year = int(unit[:4]) - source_year_cost = cost_ds.iloc[idx] - cost = inflate_cpi(source_year_cost, source_year, self.config.cost_year) - variable_om += cost - - analysis_start = self.config.operational_year - self.config.installation_years - var_om_td = 0 - # below is for rosner finance model - if "rosner" in self.config.varom_model_name: - var_om_td = rosner_ore_variable_om_cost( - self.config.mine, cost_df, analysis_start, self.config.cost_year, self.plant_life - ) - else: - var_om_td = martin_ore_variable_om_cost( - self.config.mine, cost_df, analysis_start, self.config.cost_year, self.plant_life - ) - - variable_om += var_om_td - - total_production = inputs["total_iron_ore_produced"] - variable_om = np.multiply(total_production, variable_om) - - outputs["CapEx"] = capex - outputs["OpEx"] = fixed_om - outputs["VarOpEx"] = variable_om diff --git a/h2integrate/converters/iron/iron_plant.py b/h2integrate/converters/iron/iron_plant.py deleted file mode 100644 index 79339af4b..000000000 --- a/h2integrate/converters/iron/iron_plant.py +++ /dev/null @@ -1,288 +0,0 @@ -import numpy as np -import pandas as pd -import openmdao.api as om -from attrs import field, define - -from h2integrate.core.utilities import BaseConfig, merge_shared_inputs -from h2integrate.core.validators import contains -from h2integrate.converters.iron.iron import ( - IronCostModelConfig, - IronPerformanceModelConfig, - IronPerformanceModelOutputs, - run_iron_cost_model, - run_size_iron_plant_performance, -) -from h2integrate.core.model_baseclasses import CostModelBaseClass -from h2integrate.tools.inflation.inflate import inflate_cpi, inflate_cepci -from h2integrate.converters.iron.load_top_down_coeffs import load_top_down_coeffs - - -@define(kw_only=True) -class IronPlantBaseConfig(BaseConfig): - winning_type: str = field( - kw_only=True, converter=(str.lower, str.strip), validator=contains(["h2", "ng"]) - ) # product selection - iron_win_capacity: float | int = field(default=1418095) # plant_capacity_mtpy - win_capacity_demon: str = field( - default="iron", converter=(str.lower, str.strip), validator=contains(["iron", "steel"]) - ) # capacity_denominator - - model_name: str = field(default="rosner") # only option at the moment - model_fp: str = field(default="") - inputs_fp: str = field(default="") - coeffs_fp: str = field(default="") - refit_coeffs: bool = field(default=False) - site_name: str = field(default="winning_site") - - def make_model_dict(self): - keys = ["model_fp", "inputs_fp", "coeffs_fp", "refit_coeffs"] - d = self.as_dict() - model_dict = {k: v for k, v in d.items() if k in keys} - model_dict.update({"name": self.model_name}) - return model_dict - - def make_site_dict(self): - return {"name": self.site_name} - - -@define(kw_only=True) -class IronPlantPerformanceConfig(IronPlantBaseConfig): - def make_model_dict(self): - keys = ["model_fp", "inputs_fp", "coeffs_fp", "refit_coeffs"] - d = self.as_dict() - model_dict = {k: v for k, v in d.items() if k in keys} - model_dict.update({"name": self.model_name}) - return model_dict - - -class IronPlantPerformanceComponent(om.ExplicitComponent): - def initialize(self): - self.options.declare("driver_config", types=dict) - self.options.declare("plant_config", types=dict) - self.options.declare("tech_config", types=dict) - - def setup(self): - n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"] - self.config = IronPlantPerformanceConfig.from_dict( - merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance"), - strict=False, - additional_cls_name=self.__class__.__name__, - ) - self.add_input("iron_ore_in", val=0.0, shape=n_timesteps, units="t/h") - self.add_discrete_output( - "iron_plant_performance", val=pd.DataFrame(), desc="iron plant performance results" - ) - self.add_output("pig_iron_out", val=0.0, shape=n_timesteps, units="kg/h") - self.add_output("total_pig_iron_produced", val=0.0, units="t/year") - # self.add_output("total_steel_produced", val=0.0, units="t/year") - - def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): - n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"] - iron_plant_performance_inputs = { - "plant_capacity_mtpy": self.config.iron_win_capacity, - "capacity_denominator": self.config.win_capacity_demon, - } - iron_plant_model_inputs = self.config.make_model_dict() - iron_plant_site = self.config.make_site_dict() - performance_config = IronPerformanceModelConfig( - product_selection=f"{self.config.winning_type}_dri", - site=iron_plant_site, - model=iron_plant_model_inputs, - params=iron_plant_performance_inputs, - ) - iron_plant_performance = run_size_iron_plant_performance(performance_config) - # wltpy = wet long tons per year - pig_iron_produced_mtpy = iron_plant_performance.performances_df.set_index("Name").loc[ - "Pig Iron Production" - ]["Model"] - outputs["pig_iron_out"] = pig_iron_produced_mtpy * 1000 / n_timesteps - outputs["total_pig_iron_produced"] = pig_iron_produced_mtpy - discrete_outputs["iron_plant_performance"] = iron_plant_performance.performances_df - - -@define(kw_only=True) -class IronPlantCostConfig(IronPlantBaseConfig): - LCOE: float = field(kw_only=True) # $/MWh - LCOH: float = field(kw_only=True) # $/kg - LCOI_ore: float = field(kw_only=True) - iron_transport_cost: float = field(kw_only=True) - ore_profit_pct: float = field(kw_only=True) - - # varom_model_name is unused at the moment - varom_model_name: str = field( - default="rosner", validator=contains(["rosner", "rosner_override"]) - ) - operational_year: int = field(converter=int, kw_only=True) - installation_years: int | float = field(kw_only=True) - plant_life: int = field(converter=int, kw_only=True) - cost_year: int = field(converter=int, kw_only=True) - - def make_model_dict(self): - keys = ["model_fp", "inputs_fp", "coeffs_fp", "refit_coeffs"] - d = self.as_dict() - model_dict = {k: v for k, v in d.items() if k in keys} - model_dict.update({"name": self.model_name}) - return model_dict - - def make_cost_dict(self): - keys = ["operational_year", "installation_years", "plant_life"] - d = self.as_dict() - cost_dict = {k: v for k, v in d.items() if k in keys} - return cost_dict - - -class IronPlantCostComponent(CostModelBaseClass): - def setup(self): - self.target_dollar_year = self.options["plant_config"]["finance_parameters"][ - "cost_adjustment_parameters" - ]["target_dollar_year"] - self.plant_life = self.options["plant_config"]["plant"]["plant_life"] - - config_dict = merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost") - config_dict.update({"cost_year": self.target_dollar_year}) - config_dict.update({"plant_life": self.plant_life}) - - self.config = IronPlantCostConfig.from_dict( - config_dict, - additional_cls_name=self.__class__.__name__, - ) - - super().setup() - self.add_input("LCOE", val=self.config.LCOE, units="USD/MW/h") - self.add_input("LCOH", val=self.config.LCOH, units="USD/kg") - self.add_input("price_iron_ore", val=self.config.LCOI_ore, units="USD/t") - self.add_input("iron_transport_cost", val=self.config.iron_transport_cost, units="USD/t") - self.add_input("ore_profit_pct", val=self.config.ore_profit_pct, units="USD/t") - self.add_input("total_pig_iron_produced", val=1.0, units="t/year") - self.add_discrete_input( - "iron_plant_performance", val=pd.DataFrame(), desc="iron plant performance results" - ) - self.add_discrete_output( - "iron_plant_cost", val=pd.DataFrame(), desc="iron plant cost results" - ) - - def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): - iron_plant_performance = IronPerformanceModelOutputs( - performances_df=discrete_inputs["iron_plant_performance"] - ) - - iron_plant_cost_inputs = { - "lcoe": inputs["LCOE"][0] / 1e3, - "lcoh": inputs["LCOH"][0], - "lco_iron_ore_tonne": inputs["price_iron_ore"], - "iron_transport_tonne": inputs["iron_transport_cost"][0], - "plant_capacity_mtpy": self.config.iron_win_capacity, - "capacity_denominator": self.config.win_capacity_demon, - } - cost_dict = self.config.make_cost_dict() - iron_plant_cost_inputs.update(cost_dict) - - iron_plant_model_inputs = self.config.make_model_dict() - iron_ore_site = self.config.make_site_dict() - cost_config = IronCostModelConfig( - product_selection=f"{self.config.winning_type}_dri", - site=iron_ore_site, - model=iron_plant_model_inputs, - params=iron_plant_cost_inputs, - performance=iron_plant_performance, - ) - iron_plant_cost = run_iron_cost_model(cost_config) - - discrete_outputs["iron_plant_cost"] = iron_plant_cost.costs_df - - # Now taking some stuff from finance - cost_df = iron_plant_cost.costs_df.set_index("Name") - cost_ds = cost_df.loc[:, self.config.site_name] - - cost_names = cost_df.index.values - cost_types = cost_df.loc[:, "Type"].values - cost_units = cost_df.loc[:, "Unit"].values - - # add capital items - capex = 0 - fixed_om = 0 - variable_om = 0 - capital_idxs = np.where(cost_types == "capital")[0] - for idx in capital_idxs: - cost_names[idx] - unit = cost_units[idx] # Units for capital costs should be " $"" - source_year = int(unit[:4]) - source_year_cost = cost_ds.iloc[idx] - capex += inflate_cepci(source_year_cost, source_year, self.config.cost_year) - - # add fixed costs - fixed_idxs = np.where(cost_types == "fixed opex")[0] - for idx in fixed_idxs: - cost_names[idx] - unit = cost_units[idx] # Units for fixed opex costs should be " $ per year" - source_year = int(unit[:4]) - source_year_cost = cost_ds.iloc[idx] - fixed_om += inflate_cpi(source_year_cost, source_year, self.config.cost_year) - - # add feedstock costs - perf_df = iron_plant_performance.performances_df.set_index("Name") - perf_ds = perf_df.loc[:, "Model"] - - coeff_dict = load_top_down_coeffs( - [ - "Raw Water", - "Lime", - "Carbon", - "Slag Disposal", - "Hydrogen", - "Natural Gas", - "Electricity", - "Inflation Rate", - ] - ) - - years = list(coeff_dict["years"]) - analysis_start = self.config.operational_year - self.config.installation_years - start_idx = years.index(analysis_start) - if len(years) > (start_idx + self.config.plant_life + self.config.installation_years + 1): - end_idx = years.index( - analysis_start + self.config.plant_life + self.config.installation_years + 1 - ) - indices = list(np.arange(start_idx, end_idx)) - else: - end_idx = len(years) - 1 - indices = list(np.arange(start_idx, end_idx)) - repeats = ( - start_idx + self.config.plant_life + self.config.installation_years + 2 - len(years) - ) - for _i in range(repeats): - indices.append(end_idx) - - raw_water_unitcost_tonne = coeff_dict["Raw Water"]["values"][indices].astype(float) - lime_unitcost_tonne = coeff_dict["Lime"]["values"][indices].astype(float) - carbon_unitcost_tonne = coeff_dict["Carbon"]["values"][indices].astype(float) - slag_disposal_unitcost_tonne = coeff_dict["Slag Disposal"]["values"][indices].astype(float) - - # TODO: make natural gas costs an input - natural_gas_prices_MMBTU = coeff_dict["Natural Gas"]["values"][indices].astype(float) - natural_gas_prices_GJ = natural_gas_prices_MMBTU / 1.05506 # Convert to GJ - - iron_ore_pellet_unitcost_tonne = inputs["price_iron_ore"] - if inputs["iron_transport_cost"] > 0: - iron_transport_cost_tonne = inputs["iron_transport_cost"][0] - ore_profit_pct = inputs["ore_profit_pct"][0] - iron_ore_pellet_unitcost_tonne = ( - iron_ore_pellet_unitcost_tonne + iron_transport_cost_tonne - ) * (1 + ore_profit_pct / 100) - - v_start = years.index(self.config.operational_year) - years.index(analysis_start) + 1 - variable_om += perf_ds["Raw Water Withdrawal"] * raw_water_unitcost_tonne - variable_om += perf_ds["Lime"] * lime_unitcost_tonne - variable_om += perf_ds["Carbon (Coke)"] * carbon_unitcost_tonne - variable_om += perf_ds["Iron Ore"] * iron_ore_pellet_unitcost_tonne - variable_om += perf_ds["Hydrogen"] * inputs["LCOH"][0] * 1000 - variable_om += perf_ds["Natural Gas"] * natural_gas_prices_GJ - variable_om += perf_ds["Electricity"] * inputs["LCOE"][0] - variable_om += perf_ds["Slag"] * slag_disposal_unitcost_tonne - - total_production = inputs["total_pig_iron_produced"] - variable_om = np.multiply(total_production, variable_om) - - outputs["CapEx"] = capex - outputs["OpEx"] = fixed_om - outputs["VarOpEx"] = variable_om[v_start:] diff --git a/h2integrate/converters/iron/iron_transport.py b/h2integrate/converters/iron/iron_transport.py index 38b43699e..93223b453 100644 --- a/h2integrate/converters/iron/iron_transport.py +++ b/h2integrate/converters/iron/iron_transport.py @@ -29,6 +29,11 @@ def __attrs_post_init__(self): class IronTransportPerformanceComponent(om.ExplicitComponent): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): self.options.declare("driver_config", types=dict) self.options.declare("plant_config", types=dict) @@ -37,7 +42,7 @@ def initialize(self): def setup(self): self.config = IronTransportPerformanceConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance"), - strict=False, + strict=True, additional_cls_name=self.__class__.__name__, ) self.add_output("land_transport_distance", val=0.0, units="km") @@ -163,6 +168,11 @@ class IronTransportCostConfig(BaseConfig): class IronTransportCostComponent(CostModelBaseClass): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): self.options.declare("driver_config", types=dict) self.options.declare("plant_config", types=dict) diff --git a/h2integrate/converters/iron/iron_wrapper.py b/h2integrate/converters/iron/iron_wrapper.py deleted file mode 100644 index 59d87cf3f..000000000 --- a/h2integrate/converters/iron/iron_wrapper.py +++ /dev/null @@ -1,278 +0,0 @@ -import copy -from pathlib import Path - -from attrs import field, define -from hopp.utilities import load_yaml - -import h2integrate.tools.profast_tools as pf_tools -from h2integrate.core.utilities import merge_shared_inputs -from h2integrate.core.validators import contains, range_val -from h2integrate.converters.iron.iron import run_iron_full_model -from h2integrate.core.model_baseclasses import CostModelBaseClass, CostModelBaseConfig -from h2integrate.converters.iron.martin_transport.iron_transport import calc_iron_ship_cost - - -@define(kw_only=True) -class IronConfig(CostModelBaseConfig): - """Configuration class for IronComponent. - - Attributes: - LCOE (float): cost of electricity in USD/MW/h - LCOH (float): cost of hydrogen in USD/kg - ROM_iron_site_name (str): mine for Iron Ore. - Options are "Hibbing", "Northshore", "United", "Minorca" or "Tilden". - iron_ore_product_selection (str): iron ore pellet type. - Options are "drg_taconite_pellets" or "std_taconite_pellets". - reduced_iron_product_selection (str): material for iron electrowinning process. - Options are "h2_dri" or "ng_dri" - structural_iron_product_selection (str): iron processing method. - Options are "eaf_steel" or "none". - iron_capacity_denom (str): Capacity denominator to use in iron modeling. - Options are "iron" or "steel". - eaf_capacity (float, optional): Capacity of electric arc furnace in - metric tonnes of iron per year. Defaults to 1000000. - dri_capacity (float, optional): Capacity of direct reduced iron plant in - metric tonnes of iron per year. Defaults to 1418095. - iron_ore_cf_estimate (float, optional): Estimated capacity factor of iron ore mine. - Defaults to 0.9. Must be between 0 and 1. - - """ - - # Comments below denote which step each config variable applies to in run_iron_full_model - - LCOE: float = field() # $/MWh - LCOH: float = field() # $/kg - ROM_iron_site_name: str = field( - validator=contains(["Hibbing", "Northshore", "United", "Minorca", "Tilden"]) - ) # ore - iron_ore_product_selection: str = field( - converter=(str.lower, str.strip), - validator=contains(["drg_taconite_pellets", "std_taconite_pellets"]), - ) # ore - reduced_iron_site_latitude: float = field() - reduced_iron_site_longitude: float = field() - reduced_iron_product_selection: str = field( - converter=(str.lower, str.strip), validator=contains(["h2_dri", "ng_dri"]) - ) # win - structural_iron_product_selection: str = field( - converter=(str.lower, str.strip), validator=contains(["eaf_steel", "none"]) - ) # post - iron_capacity_denom: str = field( - default="iron", converter=(str.lower, str.strip), validator=contains(["iron", "steel"]) - ) # win - eaf_capacity: float | int = field(default=1000000) # post - dri_capacity: float | int = field(default=1418095) # win - iron_ore_cf_estimate: float = field(default=0.9, validator=range_val(0, 1)) # ore - transport_cost_included: bool = field(default=True) - ng_mod: bool = field(default=False) - ng_price: float = field(default=4.00) # $/MMBTU - capex_mod: bool = field(default=False) - capex_mod_pct: float = field(default=0.0) # Fraction of orignal capex value to modify by - - -class IronComponent(CostModelBaseClass): - """ - A simple OpenMDAO component that represents an Iron model from old GreenHEART code. - - This component uses caching to store and retrieve results of the iron model - based on the configuration. - """ - - def setup(self): - plant_life = self.options["plant_config"]["plant"]["plant_life"] - - n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"] - self.config = IronConfig.from_dict( - merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"), - strict=False, - additional_cls_name=self.__class__.__name__, - ) - super().setup() - - CD = Path(__file__).parent - old_input_path = CD / "old_input" - h2i_config_old_fn = "h2integrate_config_modular.yaml" - self.h2i_config_old = load_yaml(old_input_path / h2i_config_old_fn) - - self.add_output("iron_out", val=0.0, shape=n_timesteps, units="kg/h") - - self.add_input("LCOE", val=self.config.LCOE, units="USD/MW/h") - self.add_input("LCOH", val=self.config.LCOH, units="USD/kg") - - self.add_output("annual_iron_produced", val=0.0, shape=plant_life, units="kg/year") - self.add_output("LCOI", val=0.0, units="USD/kg") - - def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): - # Parse in values from config - mine_site = self.config.ROM_iron_site_name - ore_type = self.config.iron_ore_product_selection - red_site_lat = self.config.reduced_iron_site_latitude - red_site_lon = self.config.reduced_iron_site_longitude - red_iron_type = self.config.reduced_iron_product_selection - struct_iron_type = self.config.structural_iron_product_selection - denom = self.config.iron_capacity_denom - eaf_cap = self.config.eaf_capacity - dri_cap = self.config.dri_capacity - ore_cf = self.config.iron_ore_cf_estimate - trans_incl = self.config.transport_cost_included - ng_mod = self.config.ng_mod - ng_price = self.config.ng_price - capex_mod = self.config.capex_mod - capex_pct = self.config.capex_mod_pct - - # BELOW: Copy-pasted from ye olde h2integrate_simulation.py (the 1000+ line monster) - - iron_config = copy.deepcopy(self.h2i_config_old) - - # This is not the most graceful way to do this... but it avoids copied imports - # and copying iron.py - iron_ore_config = copy.deepcopy(iron_config) - iron_win_config = copy.deepcopy(iron_config) - iron_post_config = copy.deepcopy(iron_config) - - iron_ore_config["iron"] = iron_config["iron_ore"] - iron_win_config["iron"] = iron_config["iron_win"] - iron_post_config["iron"] = iron_config["iron_post"] - for sub_iron_config in [ - iron_ore_config, - iron_win_config, - iron_post_config, - ]: # ,iron_post_config]: # iron_pre_config, iron_post_config - sub_iron_config["iron"]["costs"]["lcoe"] = inputs["LCOE"][0] / 1e3 - sub_iron_config["iron"]["finances"]["lcoe"] = inputs["LCOE"][0] / 1e3 - sub_iron_config["iron"]["costs"]["lcoh"] = inputs["LCOH"][0] - sub_iron_config["iron"]["finances"]["lcoh"] = inputs["LCOH"][0] - - # Update ore config - iron_ore_config["iron"]["site"]["name"] = mine_site - iron_ore_config["iron"]["performance"]["input_capacity_factor_estimate"] = ore_cf - iron_ore_config["iron"]["product_selection"] = ore_type - - # Update win config - iron_win_config["iron"]["product_selection"] = red_iron_type - iron_win_config["iron"]["performance"]["plant_capacity_mtpy"] = dri_cap - iron_win_config["iron"]["site"]["lat"] = red_site_lat - iron_win_config["iron"]["site"]["lon"] = red_site_lon - - # Update post config - if struct_iron_type == "none": - iron_post_config["iron"]["product_selection"] = "none" - elif struct_iron_type == "eaf_steel": - if red_iron_type == "ng_dri": - iron_post_config["iron"]["product_selection"] = "ng_eaf" - elif red_iron_type == "h2_dri": - iron_post_config["iron"]["product_selection"] = "h2_eaf" - else: - msg = f"The EAF steel model cannot (yet) use {red_iron_type} as input" - raise NotImplementedError(msg) - iron_post_config["iron"]["performance"]["capacity_denominator"] = denom - iron_post_config["iron"]["performance"]["plant_capacity_mtpy"] = eaf_cap - - # Run iron model for iron ore - iron_ore_config["iron"]["finances"]["ng_mod"] = ng_mod - iron_ore_config["iron"]["finances"]["ng_price"] = ng_price - iron_ore_config["iron"]["costs"]["capex_mod"] = capex_mod - iron_ore_config["iron"]["costs"]["capex_pct"] = capex_pct - iron_ore_performance, iron_ore_costs, iron_ore_finance = run_iron_full_model( - iron_ore_config - ) - - # Run iron transport model - # Determine whether to ship from "Duluth", "Chicago", "Cleveland" or "Buffalo" - # To electrowinning site - if trans_incl: - iron_transport_cost_tonne, ore_profit_pct = calc_iron_ship_cost(iron_win_config) - else: - iron_transport_cost_tonne = 0 - ore_profit_pct = 6 - - ### DRI ---------------------------------------------------------------------------- - ### Electrowinning - - iron_win_config["iron"]["finances"]["ng_mod"] = ng_mod - iron_win_config["iron"]["finances"]["ng_price"] = ng_price - iron_win_config["iron"]["costs"]["capex_mod"] = capex_mod - iron_win_config["iron"]["costs"]["capex_pct"] = capex_pct - iron_win_config["iron"]["finances"]["ore_profit_pct"] = ore_profit_pct - iron_win_config["iron"]["costs"]["iron_transport_tonne"] = iron_transport_cost_tonne - iron_win_config["iron"]["costs"]["lco_iron_ore_tonne"] = iron_ore_finance.sol["lco"] - iron_win_performance, iron_win_costs, iron_win_finance = run_iron_full_model( - iron_win_config - ) - - ### EAF ---------------------------------------------------------------------------- - if iron_post_config["iron"]["product_selection"] == "none": - iron_performance = iron_win_performance - iron_costs = iron_win_costs - iron_finance = iron_win_finance - - else: - if iron_post_config["iron"]["product_selection"] not in ["ng_eaf", "h2_eaf"]: - raise ValueError( - "The product selection for the iron post module must be either \ - 'ng_eaf' or 'h2_eaf'" - ) - pf_config = pf_tools.make_pf_config_from_profast( - iron_win_finance.pf - ) # dictionary of profast objects - pf_dict = pf_tools.convert_pf_res_to_pf_config( - copy.deepcopy(pf_config) - ) # profast dictionary of values - iron_post_config["iron"]["finances"]["pf"] = pf_dict - iron_post_config["iron"]["costs"]["lco_iron_ore_tonne"] = iron_ore_finance.sol["lco"] - iron_post_config["iron"]["finances"]["ng_mod"] = ng_mod - iron_post_config["iron"]["finances"]["ng_price"] = ng_price - iron_post_config["iron"]["costs"]["capex_mod"] = capex_mod - iron_post_config["iron"]["costs"]["capex_pct"] = capex_pct - - iron_post_performance, iron_post_costs, iron_post_finance = run_iron_full_model( - iron_post_config - ) - - iron_performance = iron_post_performance - iron_costs = iron_post_costs - iron_finance = iron_post_finance - - perf_df = iron_performance.performances_df - iron_mtpy = perf_df.loc[perf_df["Name"] == "Pig Iron Production", "Model"].values[0] - - # ABOVE: Copy-pasted from ye olde h2integrate_simulation.py (the 1000+ line monster) - - outputs["iron_out"] = iron_mtpy * 1000 / 8760 - outputs["annual_iron_produced"] = iron_mtpy * 1000 - - cost_df = iron_costs.costs_df - capex = 0 - opex = 0 - capex_list = [ - "EAF & Casting", - "Shaft Furnace", - "Reformer", - "Recycle Compressor", - "Oxygen Supply", - "H2 Pre-heating", - "Cooling Tower", - "Piping", - "Electrical & Instrumentation", - "Buildings, Storage, Water Service", - "Other Miscellaneous Cost", - ] - opex_list = [ - "labor_cost_annual_operation", - "labor_cost_maintenance", - "labor_cost_admin_support", - "property_tax_insurance", - "maintenance_materials", - ] - - location = cost_df.columns.values[-1] - for capex_item in capex_list: - capex += cost_df.loc[cost_df["Name"] == capex_item, location].values[0] - for opex_item in opex_list: - opex += cost_df.loc[cost_df["Name"] == opex_item, location].values[0] - - outputs["CapEx"] = capex - outputs["OpEx"] = opex - - lcoi = iron_finance.sol["lco"] - outputs["LCOI"] = lcoi / 1000 diff --git a/h2integrate/converters/iron/martin_mine_cost_model.py b/h2integrate/converters/iron/martin_mine_cost_model.py index 2f5554b57..912b46933 100644 --- a/h2integrate/converters/iron/martin_mine_cost_model.py +++ b/h2integrate/converters/iron/martin_mine_cost_model.py @@ -39,6 +39,11 @@ class MartinIronMineCostConfig(BaseConfig): class MartinIronMineCostComponent(CostModelBaseClass): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): # merge inputs from performance parameters and cost parameters config_dict = merge_shared_inputs( diff --git a/h2integrate/converters/iron/martin_mine_perf_model.py b/h2integrate/converters/iron/martin_mine_perf_model.py index b886ca7cf..6a3d5ab1f 100644 --- a/h2integrate/converters/iron/martin_mine_perf_model.py +++ b/h2integrate/converters/iron/martin_mine_perf_model.py @@ -31,6 +31,11 @@ class MartinIronMinePerformanceConfig(BaseConfig): class MartinIronMinePerformanceComponent(PerformanceModelBaseClass): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() self.commodity = "iron_ore" diff --git a/h2integrate/converters/iron/martin_ore/cost_inputs.csv b/h2integrate/converters/iron/martin_ore/cost_inputs.csv deleted file mode 100644 index baddd2ad2..000000000 --- a/h2integrate/converters/iron/martin_ore/cost_inputs.csv +++ /dev/null @@ -1,27 +0,0 @@ -Product,Name,Type,Unit,Northshore,United,Hibbing,Minorca,Tilden -std_taconite_pellets,Latitude,location,deg N,47.29415278,47.34858889,47.53,47.55833333,46.48333333 -std_taconite_pellets,Longitude,location,deg E,-91.25649444,-92.58361944,-92.91,-92.525,-87.66666667 -std_taconite_pellets,Crude ore processed,capacity,wltpy,17000000,15000000,24000000,8600000,20500000 -std_taconite_pellets,Ore pellets produced,capacity,wltpy,4540000,4600000,6300000,2790000,7700000 -std_taconite_pellets,Estimated Reserves,capacity,mlt,1158,774.6,109,109.7,520 -std_taconite_pellets,Buildings and other structures,capital,2021 $,802586723.3,782080397.9,1078561311,552238204.9,1340439971 -std_taconite_pellets,Mineral exploration and development,capital,2021 $,92838867.94,90466807.72,124762107.6,63879912.64,155054807 -std_taconite_pellets,Mineral land and rights,capital,2021 $,1041461.66,1014852.01,1399574.925,716601.5841,1739396.873 -std_taconite_pellets,Mining,variable opex,2021 $ per wlt pellet,16.875,15.49,19.87,16.84,15.3 -std_taconite_pellets,Processing,variable opex,2021 $ per wlt pellet,40.135,37.62,34.57,45.56,42.79 -std_taconite_pellets,Site administration,variable opex,2021 $ per wlt pellet,2.37,2.14,2.3,2.2,2.84 -std_taconite_pellets,New equipment purchases,variable opex,2021 $ per wlt pellet,4.2,4.46,4.54,5.63,4.65 -std_taconite_pellets,General/other,variable opex,2021 $ per wlt pellet,8.165,9.29,8.2,10.1,5.07 -drg_taconite_pellets,Latitude,location,deg N,47.29415278,47.34858889,47.53,47.55833333,46.48333333 -drg_taconite_pellets,Longitude,location,deg E,-91.25649444,-92.58361944,-92.91,-92.525,-87.66666667 -drg_taconite_pellets,Crude ore processed,capacity,wltpy,17000000,15000000,24000000,0,0 -drg_taconite_pellets,Ore pellets produced,capacity,wltpy,4540000,4600000,6300000,0,0 -drg_taconite_pellets,Estimated Reserves,capacity,mlt,1158,774.6,109,0,0 -drg_taconite_pellets,Buildings and other structures,capital,2021 $,1012588246,1118066189,949301480.9,0,0 -drg_taconite_pellets,Mineral exploration and development,capital,2021 $,117130702.2,129331817,109810033.3,0,0 -drg_taconite_pellets,Mineral land and rights,capital,2021 $,1313966.21,1450837.691,1231843.322,0,0 -drg_taconite_pellets,Mining,variable opex,2021 $ per wlt pellet,22.1175,26.60498583,22.58913891,0,0 -drg_taconite_pellets,Processing,variable opex,2021 $ per wlt pellet,43.8175,51.61628024,43.8251436,0,0 -drg_taconite_pellets,Site administration,variable opex,2021 $ per wlt pellet,4.515,5.521117114,4.687740946,0,0 -drg_taconite_pellets,New equipment purchases,variable opex,2021 $ per wlt pellet,4.2,4.46,4.54,0,0 -drg_taconite_pellets,General/other,variable opex,2021 $ per wlt pellet,15.8675,15.39868427,13.07435456,0,0 diff --git a/h2integrate/converters/iron/martin_ore/cost_model.py b/h2integrate/converters/iron/martin_ore/cost_model.py deleted file mode 100644 index d34c354dc..000000000 --- a/h2integrate/converters/iron/martin_ore/cost_model.py +++ /dev/null @@ -1,127 +0,0 @@ -""" -Direct Reduced Grade (DR-Grade) iron ore model developed by Jonathan Martin at NREL -in conjunction with UMN-Duluth's NRRI (Brett Spigarelli, Rod Johnson, Matt Aro) -""" - -from pathlib import Path - -import numpy as np -import pandas as pd - -from h2integrate.core.file_utils import load_yaml - - -CD = Path(__file__).parent - -# Get model locations loaded up to refer to -model_locs_fp = CD / "../model_locations.yaml" -model_locs = load_yaml(model_locs_fp) - - -def main(config): - """Loads and processes Direct Reduced Grade (DR-Grade) iron ore cost coefficients. - - This function imports "top-down" costs, loads coefficients for a specified - iron ore product and site, and converts units from wet long tons to dry - metric tonnes. If refitting the model, it loads an input dataframe and saves - the updated coefficients. Otherwise, it loads pre-existing coefficients. - - Args: - config (object): Configuration object containing: - - model (dict): Includes: - - ``refit_coeffs`` (bool): Whether to refit model coefficients. - - ``inputs_fp`` (str): File path for input coefficients (if refitting). - - ``coeffs_fp`` (str): File path for stored coefficients. - - product_selection (str): Selected iron ore product. - - site (dict): Contains ``name`` (str), the site name. - - Returns: - pd.DataFrame: DataFrame containing processed cost coefficients for the - selected product and site. - - Raises: - ValueError: If the selected product or site is not found in the coefficients data. - - Notes: - Direct Reduced Grade (DR-Grade) iron ore model developed by Jonathan Martin at NREL - in conjunction with UMN-Duluth's NRRI (Brett Spigarelli, Rod Johnson, Matt Aro) - """ - - # --------------- capital items ---------------- - # If re-fitting the model, load an inputs dataframe, otherwise, load up the coeffs - if config.model["refit_coeffs"]: - input_df = pd.read_csv(CD / config.model["inputs_fp"]) - - # Right now all the performance modeling is constant - input_df.insert(3, "Coeff", np.full((len(input_df),), "constant")) - - coeff_df = input_df - coeff_df.to_csv(CD / config.model["coeffs_fp"]) - - else: - coeff_df = pd.read_csv(CD / config.model["coeffs_fp"], index_col=0) - - prod = config.product_selection - site = config.site["name"] - - rows = np.where(coeff_df.loc[:, "Product"] == prod)[0] - col = np.where(coeff_df.columns == site)[0] - cols = [0, 1, 2, 3, 4] - cols.extend(list(col)) - - if len(rows) == 0: - raise ValueError(f'Product "{prod}" not found in coeffs data!') - if len(cols) == 0: - raise ValueError(f'Site "{site}" not found in coeffs data!') - - prod_df = coeff_df.iloc[rows, cols] - - # Convert per unit wet long ton to per unit dry metric tonne - prod_df = prod_df.set_index("Name") - for name, item in prod_df.iterrows(): - unit = item["Unit"] - if "per wlt" in unit: - LT_idx = unit.index("per wlt") - if len(unit) == LT_idx + 7: - new_unit = unit[:LT_idx] + "per mt" - else: - new_unit = unit[:LT_idx] + "per mt" + unit[(LT_idx + 7) :] - LT_value = item[site] - mt_value = ( - LT_value - / 1.016047 # Long tons to metric tons - / 0.98 - ) # Wet tons to dry tons (2% moisture) - prod_df.loc[name, site] = mt_value - prod_df.loc[name, "Unit"] = new_unit - if "wltpy" in unit: - LT_idx = unit.index("wltpy") - if len(unit) == LT_idx + 5: - new_unit = unit[:LT_idx] + "mtpy" - else: - new_unit = unit[:LT_idx] + "mtpy" + unit[(LT_idx + 5) :] - LT_value = item[site] - mt_value = ( - LT_value - * 1.016047 # Long tons to metric tons - * 0.98 - ) # Wet tons to dry tons (2% moisture) - prod_df.loc[name, site] = mt_value - prod_df.loc[name, "Unit"] = new_unit - prod_df = prod_df.reset_index().set_index("Product").reset_index() - - # Right now, the there is no need to scale the coefficients. - # cost_df will contain the same values as coeff_df - # This will change when scaling/extrapolating mining operations - prod_df = coeff_df.iloc[rows, cols] - length, width = prod_df.shape - cost_cols = list(prod_df.columns.values) - cost_cols.remove("Coeff") - col_idxs = list(range(width)) - col_idxs.remove(3) - cost_df = pd.DataFrame([], columns=cost_cols) - for row in range(length): - if prod_df.iloc[row, 3] == "constant": - cost_df.loc[len(cost_df)] = prod_df.iloc[row, col_idxs] - - return cost_df diff --git a/h2integrate/converters/iron/martin_ore/finance_model.py b/h2integrate/converters/iron/martin_ore/finance_model.py deleted file mode 100644 index 3bf6b1aee..000000000 --- a/h2integrate/converters/iron/martin_ore/finance_model.py +++ /dev/null @@ -1,212 +0,0 @@ -import numpy as np -import ProFAST - -from h2integrate.tools.inflation.inflate import inflate_cpi -from h2integrate.converters.iron.load_top_down_coeffs import load_top_down_coeffs - - -def main(config): - """Sets up a ProFAST financial model for an iron reduction plant, including costs, - production capacity, and economic assumptions. - - This function initializes and configures a ProFAST financial model using - cost data, site-specific parameters, and inflation adjustments. It categorizes - costs into capital, fixed operational expenses, and variable operational expenses, - including both model-defined and top-down costs. - - Args: - config (object): Configuration object containing: - - cost (object): Includes ``costs_df`` (DataFrame) with cost names, types, - units, and site-specific costs. - - site (dict): Contains ``name`` (str), the site name. - - params (dict): Contains various financial and operational parameters, including: - - ``operational_year`` (int): Year the plant starts operation. - - ``installation_years`` (int): Number of years for installation. - - ``plant_life`` (int): Expected plant lifetime in years. - - ``gen_inflation`` (float): General inflation rate. - - ``cost_year`` (int): Reference year for cost inflation adjustments. - - ``financial_assumptions`` (dict): Key-value pairs of financial settings. - - Returns: - tuple: A tuple containing: - - sol (float): Solved price required for economic viability. - - summary (dict): Summary of key financial outputs from ProFAST. - - price_breakdown (dict): Detailed breakdown of cost components. - - pf (ProFAST.ProFAST): The ProFAST model instance used for the calculations. - """ - # Get reduction plant costs into data frame/series with cost names as index - costs = config.cost - cost_df = costs.costs_df.set_index("Name") - cost_ds = cost_df.loc[:, config.site["name"]] - cost_names = cost_df.index.values - cost_types = cost_df.loc[:, "Type"].values - cost_units = cost_df.loc[:, "Unit"].values - - operational_year = config.params["operational_year"] - install_years = config.params["installation_years"] - plant_life = config.params["plant_life"] - gen_inflation = config.params["gen_inflation"] - cost_year = config.params["cost_year"] - - analysis_start = operational_year - install_years - - # Set up ProFAST - pf = ProFAST.ProFAST("blank") - - # Apply all params passed through from config - for param, val in config.params["financial_assumptions"].items(): - pf.set_params(param, val) - pf.set_params("analysis start year", analysis_start) - pf.set_params("operating life", plant_life) - pf.set_params("installation months", 12 * install_years) - pf.set_params("general inflation rate", gen_inflation) - - # Set the commodity produced as processed iron ore - pf.set_params( - "commodity", - { - "name": "processed iron ore", - "unit": "wet metric tonnes", - "initial price": 80, - "escalation": gen_inflation, - }, - ) - - # Set plant production capacity - ore_produced_wltpy = cost_ds.loc["Ore pellets produced"] # wltpy = wet long tons per year - ore_produced_wmtpy = ore_produced_wltpy * 1.016047 # wmtpy = wet metric tonnes per year - ore_produced_mtpy = ore_produced_wmtpy * 0.98 # mtpy = dry metric tonnes per year - pf.set_params("capacity", ore_produced_mtpy / 365) # units/day - - # Set default parameters - pf.set_params("sell undepreciated cap", True) - pf.set_params("tax losses monetized", True) - pf.set_params("debt type", "Revolving debt") - - # Set unused parameters to zeros and ones - pf.set_params("maintenance", {"value": 0, "escalation": gen_inflation}) - pf.set_params("non depr assets", 0) - pf.set_params("end of proj sale non depr assets", 0) - pf.set_params("demand rampup", 0) - pf.set_params("long term utilization", 1) - pf.set_params("credit card fees", 0) - pf.set_params("sales tax", 0) - pf.set_params("license and permit", {"value": 00, "escalation": gen_inflation}) - pf.set_params("rent", {"value": 0, "escalation": gen_inflation}) - pf.set_params("property tax and insurance", 0) - pf.set_params("admin expense", 0) - pf.set_params("cash onhand", 1) - - """ - Add the costs - find the indices ("idxs") of the costs in the cost dataframe - and loop through each index ("idx") to add the name, unit, and value to ProFAST - """ - - # Add capital items - capital_idxs = np.where(cost_types == "capital")[0] - for idx in capital_idxs: - name = cost_names[idx] - unit = cost_units[idx] # Units for capital costs should be " $"" - source_year = int(unit[:4]) - source_year_cost = cost_ds.iloc[idx] - cost = inflate_cpi(source_year_cost, source_year, cost_year) - - pf.add_capital_item( - name=name, - cost=cost, - depr_type="MACRS", - depr_period=7, - refurb=[0], - ) - - # Add fixed opex costs - fixed_idxs = np.where(cost_types == "fixed opex")[0] - installation_cost = 0 - for idx in fixed_idxs: - name = cost_names[idx] - unit = cost_units[idx] # Units for fixed opex costs should be " $ per year" - source_year = int(unit[:4]) - source_year_cost = cost_ds.iloc[idx] - cost = inflate_cpi(source_year_cost, source_year, cost_year) - pf.add_fixed_cost( - name=name, - usage=1, - unit="$/year", - cost=cost, - escalation=gen_inflation, - ) - installation_cost += cost - - # Installation costs = 6 months fixed opex cost - installation_cost = installation_cost * 6 / 12 - pf.set_params( - "installation cost", - { - "value": installation_cost, - "depr type": "Straight line", - "depr period": 4, - "depreciable": False, - }, - ) - - """ - In the Martin model, the ProFAST 'feedstocks' cost class covers both the Martin model's - 'variable opex' costs, which are defined solely by the model for the specific technology, - and the Martin model's 'variable opex td' costs, which are UNIVERSAL, 'top-down' costs where - the price of the feedstock (e.g. natural gas) changes in tandem across multiple modules. - """ - - # Add variable opex costs (defined in model) - var_idxs = np.where(cost_types == "variable opex")[0] - installation_cost = 0 - for idx in var_idxs: - name = cost_names[idx] - unit = cost_units[idx] # Should be " $ per " - source_year = int(unit[:4]) - source_year_cost = cost_ds.iloc[idx] - cost = inflate_cpi(source_year_cost, source_year, cost_year) - pf.add_feedstock( - name=name, - usage=1.0, - unit=unit, - cost=cost, - escalation=gen_inflation, - ) - - # Add variable opex costs (look up price from 'top-down' inputs) - var_td_idxs = np.where(cost_types == "variable opex td")[0] - var_td_names = cost_names[var_td_idxs] - var_td_input_costs = load_top_down_coeffs(var_td_names, cost_year) - var_td_years = var_td_input_costs["years"] - year_start_idx = np.where(var_td_years == analysis_start)[0][0] - analysis_end = min(max(var_td_years), analysis_start + plant_life) - year_end_idx = np.where(var_td_years == analysis_end)[0][0] - year_idxs = range(year_start_idx, year_end_idx) - installation_cost = 0 - for idx in var_td_idxs: - name = cost_names[idx] - unit1 = cost_units[idx] # Should be " per " - var_td_usage = cost_ds.iloc[idx] - - var_td_dict = var_td_input_costs[name] - unit2 = var_td_dict["unit"] # Should be " $ per per " - var_td_usage = cost_ds.iloc[idx] - - var_td_dict = var_td_input_costs[name] - var_td_dict["unit"] # Should be " $ per see files/task1/regulation and policy revue/ page 4 of 13 middle - read this - # and look at assumptions - electricity_itc: 0 - electricity_ptc: 0 - h2_ptc: 0 - h2_storage_itc: 0 - option2: # base credit levels with H2 - electricity_itc: 0 - electricity_ptc: 0.003 # $0.003/kW (this is base, see inflation adjustment in option 3) - h2_ptc: 0.6 # $0.60/kg h2 produced - assumes net zero but not meeting prevailing wage requirements - does this need to be - # adjusted for inflation from 2022 dollars to claim date, probably constant after claim date? - h2_storage_itc: 0.06 - option3: # same as option 5, but assuming prevailing wages are met --> 5x multiplier on both PTCs - electricity_itc: 0 - electricity_ptc: 0.015 # $/kWh 1992 dollars - h2_ptc: 3.00 # $/kg 2022 dollars - do not adjust for inflation - h2_storage_itc: 0.3 - # bonus options, option 5 and 6 but ITC equivalents - option4: # prevailing wages not met - electricity_itc: 0.06 # %/100 capex - electricity_ptc: 0.00 # $/kW 1992 dollars - h2_ptc: 0.6 # $0.60/kg produced 2022 dollars - assumes net zero but not meeting prevailing wage requirements - does this need to be - # do not adjust for inflation, probably constant after claim date? - h2_storage_itc: 0.06 - option5: # prevailing wages met - electricity_itc: 0.30 # %/100 capex - electricity_ptc: 0.0 # $/kWh 1992 dollars - h2_ptc: 3.00 # $/kg of h2 produced 2022 dollars - do adjust for inflation every year applied and until application year - h2_storage_itc: 0.3 - option6: # assumes prevailing wages are met, and includes 10% bonus credit of domestic content (100% of steel and iron - # and mfg. components from the US) - electricity_itc: 0.40 # %/100 capex - electricity_ptc: 0.0 # $/kWh 1992 dollars - h2_ptc: 3.00 # $/kg of h2 produced 2022 dollars - do adjust for inflation every year applied and until application year - h2_storage_itc: 0.4 - option7: # assumes prevailing wages are met, and includes 10% bonus credit of domestic content (100% of steel and iron - # and mfg. components from the US) - electricity_itc: 0.0 # %/100 capex - electricity_ptc: 0.0165 # $/kWh 1992 dollars (0.015*1.1) - h2_ptc: 3.00 # $/kg of h2 produced 2022 dollars - do adjust for inflation every year applied and until application year - h2_storage_itc: 0 - # you can elect itc_for_h2 in leu of the h2_ptc - this choice is independent of the other tech credit selections - # 6% or %50 for itc_for_h2 -plant_design: - scenario0: - electrolyzer_location: platform # can be one of ["onshore", "turbine", "platform"] - transportation: pipeline # can be one of ["hvdc", "pipeline", "none", hvdc+pipeline, "colocated"] - h2_storage_location: platform # can be one of ["onshore", "turbine", "platform"] - wind_location: offshore # can be one of ["onshore", "offshore"] - pv_location: none # can be one of ["none", "onshore", "platform"] - battery_location: none # can be one of ["none", "onshore", "platform"] - scenario1: - electrolyzer_location: onshore # can be one of ["onshore", "turbine", "platform"] - transportation: hvdc # can be one of ["hvdc", "pipeline", "none", hvdc+pipeline, "colocated"] - h2_storage_location: onshore # can be one of ["onshore", "turbine", "platform"] - wind_location: offshore # can be one of ["onshore", "offshore"] - pv_location: none # can be one of ["none", "onshore", "platform"] - battery_location: none # can be one of ["none", "onshore", "platform"] - scenario2: - electrolyzer_location: onshore # can be one of ["onshore", "turbine", "platform"] - transportation: hvdc # can be one of ["hvdc", "pipeline", "none", hvdc+pipeline, "colocated"] - h2_storage_location: platform # can be one of ["onshore", "turbine", "platform"] - wind_location: offshore # can be one of ["onshore", "offshore"] - pv_location: none # can be one of ["none", "onshore", "platform"] - battery_location: none # can be one of ["none", "onshore", "platform"] - scenario3: - electrolyzer_location: turbine # can be one of ["onshore", "turbine", "platform"] - transportation: none # can be one of ["hvdc", "pipeline", "none", hvdc+pipeline, "colocated"] - h2_storage_location: turbine # can be one of ["onshore", "turbine", "platform"] - wind_location: offshore # can be one of ["onshore", "offshore"] - pv_location: none # can be one of ["none", "onshore", "platform"] - battery_location: none # can be one of ["none", "onshore", "platform"] - scenario4: - electrolyzer_location: turbine # can be one of ["onshore", "turbine", "platform"] - transportation: none # can be one of ["hvdc", "pipeline", "none", hvdc+pipeline, "colocated"] - h2_storage_location: platform # can be one of ["onshore", "turbine", "platform"] - wind_location: offshore # can be one of ["onshore", "offshore"] - pv_location: none # can be one of ["none", "onshore", "platform"] - battery_location: none # can be one of ["none", "onshore", "platform"] - scenario5: - electrolyzer_location: turbine # can be one of ["onshore", "turbine", "platform"] - transportation: pipeline # can be one of ["hvdc", "pipeline", "none", hvdc+pipeline, "colocated"] - h2_storage_location: onshore # can be one of ["onshore", "turbine", "platform"] - wind_location: offshore # can be one of ["onshore", "offshore"] - pv_location: none # can be one of ["none", "onshore", "platform"] - battery_location: none # can be one of ["none", "onshore", "platform"] - scenario6: - electrolyzer_location: platform # can be one of ["onshore", "turbine", "platform"] - transportation: none # can be one of ["hvdc", "pipeline", "none", hvdc+pipeline, "colocated"] - h2_storage_location: platform # can be one of ["onshore", "turbine", "platform"] - wind_location: offshore # can be one of ["onshore", "offshore"] - pv_location: none # can be one of ["none", "onshore", "platform"] - battery_location: none # can be one of ["none", "onshore", "platform"] - scenario7: - electrolyzer_location: platform # can be one of ["onshore", "turbine", "platform"] - transportation: pipeline # can be one of ["hvdc", "pipeline", "none", hvdc+pipeline, "colocated"] - h2_storage_location: onshore # can be one of ["onshore", "turbine", "platform"] - wind_location: offshore # can be one of ["onshore", "offshore"] - pv_location: none # can be one of ["none", "onshore", "platform"] - battery_location: none # can be one of ["none", "onshore", "platform"] - scenario8: - electrolyzer_location: platform # can be one of ["onshore", "turbine", "platform"] - transportation: hvdc+pipeline # can be one of ["hvdc", "pipeline", "none", hvdc+pipeline, "colocated"] - h2_storage_location: onshore # can be one of ["onshore", "turbine", "platform"] - wind_location: offshore # can be one of ["onshore", "offshore"] - scenario9: - electrolyzer_location: onshore # can be one of ["onshore", "turbine", "platform"] - transportation: colocated # can be one of ["hvdc", "pipeline", "none", hvdc+pipeline, "colocated"] - h2_storage_location: onshore # can be one of ["onshore", "turbine", "platform"] - wind_location: onshore # can be one of ["onshore", "offshore"] - pv_location: onshore # can be one of ["none", "onshore", "platform"] - battery_location: onshore # can be one of ["none", "onshore", "platform"] - scenario10: - electrolyzer_location: platform # can be one of ["onshore", "turbine", "platform"] - transportation: pipeline # can be one of ["hvdc", "pipeline", "none", hvdc+pipeline, "colocated"] - h2_storage_location: onshore # can be one of ["onshore", "turbine", "platform"] - pv_location: platform # can be one of ["none", "onshore", "platform"] - battery_location: platform # can be one of ["none", "onshore", "platform"] -iron_ore: - site: - lat: 47.29415278 - lon: -91.25649444 - resource_dir: /../data_library/weather/ - name: Northshore - product_selection: drg_taconite_pellets #'drg_taconite_pellets' # 'std_taconite_pellets' # - performance_model: - name: martin_ore # 'martin_ore' - refit_coeffs: false - cost_model: - name: martin_ore # 'martin_ore' - refit_coeffs: false - finance_model: - name: martin_ore # 'martin_ore', 'rosner_ore' - performance: - input_capacity_factor_estimate: 0.9 - costs: - operational_year: 2035 - installation_years: 3 - plant_life: 30 - o2_heat_integration: false - finances: - gen_inflation: 0.025 - # Additional parameters passed to ProFAST - financial_assumptions: - total income tax rate: 0.2574 - capital gains tax rate: 0.15 - leverage after tax nominal discount rate: 0.10893 - debt equity ratio of initial financing: 0.624788 - debt interest rate: 0.050049 -iron_win: - site: - lat: 41.717 - lon: -88.398 - resource_dir: /../data_library/weather/ - name: IL - product_selection: ng_dri # 'h2_dri' # 'ng_dri' - performance_model: - name: rosner # 'rosner' - refit_coeffs: false - cost_model: - name: rosner # 'rosner' - refit_coeffs: false - finance_model: - name: rosner # 'rosner', 'rosner_override' - performance: - plant_capacity_mtpy: 1418095 - capacity_denominator: iron # steel - costs: - operational_year: 2035 - installation_years: 3 - plant_life: 30 - o2_heat_integration: false - finances: - gen_inflation: 0.025 - # Additional parameters passed to ProFAST - financial_assumptions: - total income tax rate: 0.2574 - capital gains tax rate: 0.15 - leverage after tax nominal discount rate: 0.10893 - debt equity ratio of initial financing: 0.624788 - debt interest rate: 0.050049 -iron_post: - site: - lat: 41.717 - lon: -88.398 - resource_dir: /../data_library/weather/ - name: IL - product_selection: ng_eaf #''h2_eaf' # 'ng_eaf' - performance_model: - name: rosner # 'rosner' - refit_coeffs: false - cost_model: - name: rosner # 'rosner' - refit_coeffs: false - finance_model: - name: rosner # 'rosner', 'rosner_override' - performance: - plant_capacity_mtpy: 1000000 - costs: - operational_year: 2035 - installation_years: 3 - plant_life: 30 - o2_heat_integration: false - finances: - gen_inflation: 0.025 - # Additional parameters passed to ProFAST - financial_assumptions: - total income tax rate: 0.2574 - capital gains tax rate: 0.15 - leverage after tax nominal discount rate: 0.10893 - debt equity ratio of initial financing: 0.624788 - debt interest rate: 0.050049 -lca_config: - run_lca: true # True - electrolyzer_type: pem # alkaline, soec - feedstock_water_type: ground # desal, surface - cambium: # cambium API argument, see cambium_data.py for additional argument options - project_uuid: 0f92fe57-3365-428a-8fe8-0afc326b3b43 - scenario: Mid-case with 100% decarbonization by 2035 - location_type: GEA Regions 2023 - time_type: hourly -opt_options: - opt_flag: true - general: - folder_output: output - fname_output: optimization_log - design_variables: - electrolyzer_rating_kw: - flag: true - lower: 1000.0 - upper: 2000000.0 - units: kW - pv_capacity_kw: - flag: false - lower: 1000.0 - upper: 2000000.0 - units: kW - wave_capacity_kw: - flag: false - lower: 1000.0 - upper: 2000000.0 - units: kW*h - battery_capacity_kw: - flag: false - lower: 1000.0 - upper: 2000000.0 - units: kW - battery_capacity_kwh: - flag: false - lower: 1000.0 - upper: 2000000.0 - units: kW*h - turbine_x: - flag: false - lower: 0.0 - upper: 20000.0 - units: m - turbine_y: - flag: false - lower: 0.0 - upper: 20000.0 - units: m - constraints: - turbine_spacing: - flag: false - lower: 0.0 - boundary_distance: - flag: false - lower: 0.0 - pv_to_platform_area_ratio: - flag: false - upper: 1.0 # relative size of solar pv area to platform area - user: {} - merit_figure: lcoi - merit_figure_user: - name: lcoi - max_flag: false - ref: 1.0 # value of objective that scales to 1.0 - driver: - optimization: - flag: true - solver: SNOPT - tol: 1E-6 - gradient_method: snopt_fd # can be one of ['pyopt_fd', 'openmdao', 'snopt_fd'] - max_major_iter: 10 - max_minor_iter: 30 - # time_limit: 10 # (sec) optional - # "hist_file_name: "snopt_history.txt", # optional - verify_level: 0 # optional - step_calc: None - form: forward # type of finite differences to use, can be one of ["forward", "backward", "central"] - debug_print: false - design_of_experiments: - flag: false - run_parallel: false - generator: FullFact # [Uniform, FullFact, PlackettBurman, BoxBehnken, LatinHypercube] - num_samples: 5 # Number of samples to evaluate model at (Uniform and LatinHypercube only) - seed: 2 - levels: 5 # Number of evenly spaced levels between each design variable lower and upper bound (FullFactorial only) - criterion: None # [None, center, c, maximin, m, centermaximin, cm, correelation, corr] - iterations: 1 - debug_print: false - step_size_study: - flag: false - recorder: - flag: true - file_name: record.sql - includes: false diff --git a/h2integrate/converters/iron/old_input/hopp_config.yaml b/h2integrate/converters/iron/old_input/hopp_config.yaml deleted file mode 100644 index 3e9621a21..000000000 --- a/h2integrate/converters/iron/old_input/hopp_config.yaml +++ /dev/null @@ -1,95 +0,0 @@ -site: #!include flatirons_site.yaml - data: - lat: 47.5233 - lon: -92.5366 - elev: 1099 - year: 2013 - tz: -6 - site_boundaries: - verts: - - [0.0, 0.0] - - [0.0, 20000.0] - - [20000.0, 20000.0] - - [20000.0, 0.0] - verts_simple: - - [0.0, 0.0] - - [0.0, 20000.0] - - [20000.0, 20000.0] - - [20000.0, 0.0] - solar_resource_file: ../../../../resource_files/solar/47.5233_-92.5366_psmv3_60_2013.csv - wind_resource_file: ../../../../resource_files/wind/47.5233_-92.5366_windtoolkit_2013_60min_100m_120m.srw - wave_resource_file: '' - grid_resource_file: '' - hub_height: 115.0 - capacity_hours: [] - solar: true - wind: true - wave: false - wind_resource_origin: WTK -technologies: - wind: - num_turbines: 216 - turbine_rating_kw: 6000.0 - model_name: floris - timestep: [0, 8760] - floris_config: input_files/floris/floris_input_lbw_6MW.yaml - fin_model: !include default_fin_config.yaml - hub_height: 115.0 - pv: - system_capacity_kw: 100000 - dc_degradation: - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - fin_model: !include default_fin_config.yaml - battery: - system_capacity_kwh: 100000 - system_capacity_kw: 100000 - minimum_SOC: 20.0 - maximum_SOC: 100.0 - initial_SOC: 90.0 - fin_model: !include default_fin_config.yaml - grid: - interconnect_kw: 2000000 # Set higher than rated generation capacity - fin_model: !include default_fin_config.yaml -config: - simulation_options: - wind: - skip_financial: false - dispatch_options: - battery_dispatch: load_following_heuristic # load_following_heuristic # heuristic - solver: cbc - n_look_ahead_periods: 48 - grid_charging: false - pv_charging_only: false - include_lifecycle_count: false - cost_info: # Costs from GS Manuscript Supplemental Table S11 - wind_installed_cost_mw: 1098000 # (2020USD) - solar_installed_cost_mw: 991000 # (2020USD) - storage_installed_cost_mwh: 158000 # (2020USD) - storage_installed_cost_mw: 212000 # (2020USD) - wind_om_per_kw: 25.8 # (2020USD) - pv_om_per_kw: 17.2 # (2020USD) - battery_om_per_kw: 9.25 # (2020USD) based on 0.025 percent of battery capex with a 100MW 1hr battery diff --git a/h2integrate/converters/iron/peters/cost_coeffs.csv b/h2integrate/converters/iron/peters/cost_coeffs.csv deleted file mode 100644 index 2836b54da..000000000 --- a/h2integrate/converters/iron/peters/cost_coeffs.csv +++ /dev/null @@ -1,3 +0,0 @@ -Name,Type,Coeff,Unit,A -Annual Operating Labor Cost,fixed opex,lin,hr/day/step/(kg/day),4.42732 -Annual Operating Labor Cost,fixed opex,exp,-,0.25242 diff --git a/h2integrate/converters/iron/peters/cost_inputs.csv b/h2integrate/converters/iron/peters/cost_inputs.csv deleted file mode 100644 index 8cdb6d2c1..000000000 --- a/h2integrate/converters/iron/peters/cost_inputs.csv +++ /dev/null @@ -1,3 +0,0 @@ -Name,Type,Unit,,,,,,,,,,,,, -Plant Size,capacity,kg/day,2732.869921,4379.474018,7403.041639,11296.91978,17315.77767,30062.34547,44074.13326,69075.8528,114196.8106,193037.7348,309346.4996,491342.1164,783889.5626 -Operating Labor,fixed opex,hr/day/process step,32.55405119,36.73889167,41.86477288,46.79161847,52.29827868,59.88401692,65.96715145,73.37467971,83.61205692,95.27777281,108.0471822,120.7626885,136.2867959 diff --git a/h2integrate/converters/iron/peters/cost_model.py b/h2integrate/converters/iron/peters/cost_model.py deleted file mode 100644 index 847caf288..000000000 --- a/h2integrate/converters/iron/peters/cost_model.py +++ /dev/null @@ -1,8 +0,0 @@ -""" -Opex model developed by Peters et al. -Plant Design and Economics for Chemical Engineers, 5th ed. -""" - - -def main(): - raise NotImplementedError("Peters et al. OpEx model not yet implemented.") diff --git a/h2integrate/converters/iron/rosner/__init__.py b/h2integrate/converters/iron/rosner/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/h2integrate/converters/iron/rosner/cost_inputs.csv b/h2integrate/converters/iron/rosner/cost_inputs.csv deleted file mode 100644 index 80b2b82c5..000000000 --- a/h2integrate/converters/iron/rosner/cost_inputs.csv +++ /dev/null @@ -1,72 +0,0 @@ -Product,Name,Type,Unit,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,,,,,,,,,,,,,,,,,,,,, -h2_dri_eaf,Capacity Factor,capacity,%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,,,,,,,,,,,,,,,,,,,,, -h2_dri_eaf,Steel Slab,capacity,mtpy,1953600,1855920,1758240,1660560,1562880,1465200,1367520,1269840,1162077.47,976800,879120,781440,683760,586080,488400,,,,,,,,,,,,,,,,,,,,, -h2_dri_eaf,EAF & Casting,capital,2022 $,260255522.8,254238851.7,248047319.7,241665683.2,235076376.8,228258989.6,221189578.3,213839753.4,205364842.6,189727496.6,180827659.3,171371781.1,161248239.8,150302860.7,138312234.7,,,,,,,,,,,,,,,,,,,,, -h2_dri_eaf,Shaft Furnace,capital,2022 $,189167703.1,180245439.2,171339493.9,162448492.9,153570754.7,144704212.1,135846309.2,126993862.1,117229159.8,100423410.7,91539783.55,82625838.87,73665745.24,64636998.8,55506165.19,,,,,,,,,,,,,,,,,,,,, -h2_dri_eaf,Oxygen Supply,capital,2022 $,19803012.38,19156134.82,18497276.17,17825524.6,17139840.36,16439028.46,15721703.48,14986243.13,14151511.46,12649792.98,11818078.61,10953276.74,10049616.88,9099314.638,8091391.698,,,,,,,,,,,,,,,,,,,,, -h2_dri_eaf,H2 Pre-heating,capital,2022 $,12748756.83,12194648.33,11636658.34,11074540.3,10508016.47,9936771.729,9360445.679,8778622.438,8129781.283,6994858.784,6385188.139,5766411.648,5137214.284,4495870.843,3840025.155,,,,,,,,,,,,,,,,,,,,, -h2_dri_eaf,Cooling Tower,capital,2022 $,24211545.45,23436000.52,22645521.65,21838964.04,21015021.6,20172192.55,19308734.81,18422607.29,17415796.02,15601435.51,14594752.86,13546545,12449519.48,11293828.55,10065567.56,,,,,,,,,,,,,,,,,,,,, -h2_dri_eaf,Piping,capital,2022 $,70654088.85,68365967.71,66048982.85,63700332.79,61316781.93,58894562.17,56429244.11,53915565.49,51078684.26,46013679.72,43226867.91,40340339.97,37333250.44,34176804.85,30829299.67,,,,,,,,,,,,,,,,,,,,, -h2_dri_eaf,Electrical & Instrumentation,capital,2022 $,47102725.9,45577311.81,44032655.23,42466888.53,40877854.62,39263041.45,37619496.07,35943710.32,34052456.17,30675786.48,28817911.94,26893559.98,24888833.62,22784536.57,20552866.45,,,,,,,,,,,,,,,,,,,,, -h2_dri_eaf,"Buildings, Storage, Water Service",capital,2022 $,118358421.5,113599926,108791050.9,103928415.2,99008197.5,94026046.36,88976966.75,83855171.16,78112171.69,67979062.02,62484050.6,56865276.8,51103847.67,45174792.69,39043718.36,,,,,,,,,,,,,,,,,,,,, -h2_dri_eaf,Other Miscellaneous Cost,capital,2022 $,47102725.9,45577311.81,44032655.23,42466888.53,40877854.62,39263041.45,37619496.07,35943710.32,34052456.17,30675786.48,28817911.94,26893559.98,24888833.62,22784536.57,20552866.45,,,,,,,,,,,,,,,,,,,,, -h2_dri,Capacity Factor,capacity,%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,,,,,,,,,,,,,,,,,,,,, -h2_dri,Steel Slab,capacity,mtpy,1953600,1855920,1758240,1660560,1562880,1465200,1367520,1269840,1162077.47,976800,879120,781440,683760,586080,488400,,,,,,,,,,,,,,,,,,,,, -h2_dri,Steel Liquid,capacity,mtpy,2000000,1900000,1800000,1700000,1600000,1500000,1400000,1300000,1189678,1000000,900000,800000,700000,600000,500000,,,,,,,,,,,,,,,,,,,,, -h2_dri,Pig Iron,capacity,mtpy,2328689.051,2212254.598,2095820.146,1979385.693,1862951.241,1746516.788,1630082.336,1513647.883,1385195.066,1164344.526,1047910.073,931475.6204,815041.1679,698606.7153,582172.2628,,,,,,,,,,,,,,,,,,,,, -h2_dri,EAF & Casting,capital,2022 $,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,,,,,,,,,,,,,,,, -h2_dri,Shaft Furnace,capital,2022 $,189390550.8,180465334.6,171556319.8,162662120.4,153781041.3,144910999.5,136049420.3,127193097.1,117423853.5,100609508.1,91720852.25,82801446.65,73835360.85,64799950.79,55661572.89,,,,,,,,,,,,,,,,,,,,, -h2_dri,Oxygen Supply,capital,2022 $,13343949.9,12915276.82,12478447.69,12032836.22,11577727.75,11112300.32,10635600.23,10146509.77,9590962.663,8590229.013,8035204.66,7457451.766,6852966.973,6216340.633,5539928.599,,,,,,,,,,,,,,,,,,,,, -h2_dri,H2 Pre-heating,capital,2022 $,12748756.83,12194648.33,11636658.34,11074540.3,10508016.47,9936771.729,9360445.679,8778622.438,8129781.283,6994858.784,6385188.139,5766411.648,5137214.284,4495870.843,3840025.155,,,,,,,,,,,,,,,,,,,,, -h2_dri,Cooling Tower,capital,2022 $,21190954.21,20515950.93,19827895.04,19125783.63,18408472.21,17674644.5,16922773.21,16151068.41,15274139.21,13693464.2,12816205.63,11902557.23,10946107.5,9938192.428,8866568.634,,,,,,,,,,,,,,,,,,,,, -h2_dri,Piping,capital,2022 $,30392341.75,29048457.94,27702678.21,26354542.59,25003512.82,23648954.17,22290111.66,20926078.37,19413897.55,16790455.92,15391628.85,13978482.46,12547251.51,11092719.52,9607326.862,,,,,,,,,,,,,,,,,,,,, -h2_dri,Electrical & Instrumentation,capital,2022 $,20261561.16,19365638.63,18468452.14,17569695.06,16669008.54,15765969.44,14860074.44,13950718.91,12942598.37,11193637.28,10261085.9,9318988.307,8364834.337,7395146.345,6404884.575,,,,,,,,,,,,,,,,,,,,, -h2_dri,"Buildings, Storage, Water Service",capital,2022 $,67979062.02,65246024.09,62484050.6,59691199.81,56865276.8,54003782.39,51103847.67,48162148.58,44863661.56,39043718.36,35887663.07,32660524.96,29351452.87,25946105.02,22424727.53,,,,,,,,,,,,,,,,,,,,, -h2_dri,Other Miscellaneous Cost,capital,2022 $,20261561.16,19365638.63,18468452.14,17569695.06,16669008.54,15765969.44,14860074.44,13950718.91,12942598.37,11193637.28,10261085.9,9318988.307,8364834.337,7395146.345,6404884.575,,,,,,,,,,,,,,,,,,,,, -h2_eaf,Capacity Factor,capacity,%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,,,,,,,,,,,,,,,,,,,,, -h2_eaf,Steel Slab,capacity,mtpy,1953600,1855920,1758240,1660560,1562880,1465200,1367520,1269840,1162077.47,976800,879120,781440,683760,586080,488400,,,,,,,,,,,,,,,,,,,,, -h2_eaf,EAF & Casting,capital,2022 $,260255522.8,254238851.7,248047319.7,241665683.2,235076376.8,228258989.6,221189578.3,213839753.4,205364842.6,189727496.6,180827659.3,171371781.1,161248239.8,150302860.7,138312234.7,,,,,,,,,,,,,,,,,,,,, -h2_eaf,Shaft Furnace,capital,2022 $,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,,,,,,,,,,,,,,,, -h2_eaf,Oxygen Supply,capital,2022 $,6459062.48,6240858,6018828.48,5792688.38,5562112.61,5326728.14,5086103.25,4839733.36,4560548.797,4059563.967,3782873.95,3495824.974,3196649.907,2882974.005,2551463.099,,,,,,,,,,,,,,,,,,,,, -h2_eaf,H2 Pre-heating,capital,2022 $,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,,,,,,,,,,,,,,,, -h2_eaf,Cooling Tower,capital,2022 $,3020591.24,2920049.59,2817626.61,2713180.41,2606549.39,2497548.05,2385961.6,2271538.88,2141656.81,1907971.31,1778547.23,1643987.77,1503411.98,1355636.122,1198998.926,,,,,,,,,,,,,,,,,,,,, -h2_eaf,Piping,capital,2022 $,40261747.1,39317509.77,38346304.64,37345790.2,36313269.11,35245608,34139132.45,32989487.12,31664786.71,29223223.8,27835239.06,26361857.51,24785998.93,23084085.33,21221972.81,,,,,,,,,,,,,,,,,,,,, -h2_eaf,Electrical & Instrumentation,capital,2022 $,26841164.74,26211673.18,25564203.09,24897193.47,24208846.08,23497072.01,22759421.63,21992991.41,21109857.8,19482149.2,18556826.04,17574571.67,16523999.28,15389390.23,14147981.88,,,,,,,,,,,,,,,,,,,,, -h2_eaf,"Buildings, Storage, Water Service",capital,2022 $,50379359.48,48353901.91,46307000.3,44237215.39,42142920.7,40022263.97,37873119.08,35693022.58,33248510.13,28935343.66,26596387.53,24204751.84,21752394.8,19228687.67,16618990.83,,,,,,,,,,,,,,,,,,,,, -h2_eaf,Other Miscellaneous Cost,capital,2022 $,26618317.04,25991777.78,25347377.19,24683565.97,23998559.48,23290284.61,22556310.53,21793756.41,20915164.1,19296051.8,18375757.34,17398963.89,16354383.67,15226438.24,13992574.18,,,,,,,,,,,,,,,,,,,,, -ng_dri_eaf,Capacity Factor,capacity,%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,,,,,,,,,,,,,,,,,,,,, -ng_dri_eaf,Steel Slab,capacity,mtpy,1953600,1855920,1758240,1660560,1562880,1465200,1367520,1269840,1189722,976800,879120,781440,683760,586080,488400,,,,,,,,,,,,,,,,,,,,, -ng_dri_eaf,EAF & Casting,capital,2022 $,257484642.6,251532029.6,245406417.4,239092724.7,232573573.1,225828769,218834624.1,211563051.2,205368306,187707511.9,178902429.1,169547225.5,159531467.2,148702621,136839656.4,,,,,,,,,,,,,,,,,,,,, -ng_dri_eaf,Shaft Furnace,capital,2022 $,165527681,160054337.4,154481860.4,148802740.6,143008418.3,137089060.4,131033271.8,124827716,119615081.2,105137857.9,98138556.42,90866839.64,83275476.47,75300822.77,66853240.14,,,,,,,,,,,,,,,,,,,,, -ng_dri_eaf,Oxygen Supply,capital,2022 $,13734411.21,13286338.24,12829949.23,12364610.84,11889600.84,11404089.11,10907113.21,10397546.05,9969266.546,8778527.165,8202117.982,7602726.696,6976342.098,6317552.322,5618723.867,,,,,,,,,,,,,,,,,,,,, -ng_dri_eaf,Reformer,capital,2022 $,155515479.7,150412197,145214142.4,139914133.2,134503982.3,128974285,123314143,117510801.7,112633449.3,99074627.27,92512331.92,85689569.9,78561196.09,71066388.76,63119505.7,,,,,,,,,,,,,,,,,,,,, -ng_dri_eaf,Recycle Compressor,capital,2022 $,28009339.48,27007639.45,25990524.82,24956880.8,23905440.91,22834755.43,21743150.7,20628675.8,19695901.24,17122673.3,15888531.24,14613877.46,13292025.9,11914037.38,10467435.02,,,,,,,,,,,,,,,,,,,,, -ng_dri_eaf,Cooling Tower,capital,2022 $,16524489.01,15997098.2,15459524.15,14910985.05,14350588.81,13777309.51,13189956.87,12587136.09,12080000.14,10667564.33,9982444.377,9268960.317,8522120.373,7735180.647,6898613.857,,,,,,,,,,,,,,,,,,,,, -ng_dri_eaf,Piping,capital,2022 $,90856917.47,88235300.53,85556351.08,82815434.77,80007241.31,77125636.47,74163469.32,71112316.96,68536326.07,61313421.04,57781339.33,54080687.4,50180016.81,46036400.54,41588462.91,,,,,,,,,,,,,,,,,,,,, -ng_dri_eaf,Electrical & Instrumentation,capital,2022 $,60571278.32,58823533.68,57037567.39,55210289.85,53338160.87,51417090.98,49442312.88,47408211.31,45690884.05,40875614.03,38520892.89,36053791.6,33453344.54,30690933.7,27725641.94,,,,,,,,,,,,,,,,,,,,, -ng_dri_eaf,"Buildings, Storage, Water Service",capital,2022 $,116156544.8,111486573.9,106767160.5,101994986.7,97166302.06,92276836.19,87321686.95,82295174.5,78114482.85,66714415.99,61321630.82,55807385.67,50153139.08,44334385.06,38317369.95,,,,,,,,,,,,,,,,,,,,, -ng_dri_eaf,Other Miscellaneous Cost,capital,2022 $,60571278.32,58823533.68,57037567.39,55210289.85,53338160.87,51417090.98,49442312.88,47408211.31,45690884.05,40875614.03,38520892.89,36053791.6,33453344.54,30690933.7,27725641.94,,,,,,,,,,,,,,,,,,,,, -ng_dri,Capacity Factor,capacity,%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,,,,,,,,,,,,,,,,,,,,, -ng_dri,Steel Slab,capacity,mtpy,1953600,1855920,1758240,1660560,1562880,1465200,1367520,1269840,1189722,976800,879120,781440,683760,586080,488400,,,,,,,,,,,,,,,,,,,,, -ng_dri,EAF & Casting,capital,2022 $,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,,,,,,,,,,,,,,,, -ng_dri,Shaft Furnace,capital,2022 $,165527681,160054337.4,154481860.4,148802740.6,143008418.3,137089060.4,131033271.8,124827716,119615081.2,105137857.9,98138556.42,90866839.64,83275476.47,75300822.77,66853240.14,,,,,,,,,,,,,,,,,,,,, -ng_dri,Oxygen Supply,capital,2022 $,10432717.02,10096184.15,9753290.492,9403548.672,9046402.626,8681212.934,8307237.826,7923608.146,7601028.016,6703390.286,6268417.391,5815757.607,5342303.03,4843855.705,4314486.461,,,,,,,,,,,,,,,,,,,,, -ng_dri,Reformer,capital,2022 $,155515479.7,150412197,145214142.4,139914133.2,134503982.3,128974285,123314143,117510801.7,112633449.3,99074627.27,92512331.92,85689569.9,78561196.09,71066388.76,63119505.7,,,,,,,,,,,,,,,,,,,,, -ng_dri,Recycle Compressor,capital,2022 $,28009339.48,27007639.45,25990524.82,24956880.8,23905440.91,22834755.43,21743150.7,20628675.8,19695901.24,17122673.3,15888531.24,14613877.46,13292025.9,11914037.38,10467435.02,,,,,,,,,,,,,,,,,,,,, -ng_dri,Cooling Tower,capital,2022 $,16505954.56,15978809.29,15441490.53,14893217.45,14333099.07,13760110.8,13173063.93,12570565.52,12063707.99,10652086.43,9967384.718,9254354.849,8508013.279,7721627.773,6885688.449,,,,,,,,,,,,,,,,,,,,, -ng_dri,Piping,capital,2022 $,51795405.61,50081454.67,48336380.82,46557831.31,44743124.71,42889181.22,40992432.52,39048703.14,37415930.5,32880989.75,30688418.56,28410499.86,26032470.76,23534446.21,20888432.63,,,,,,,,,,,,,,,,,,,,, -ng_dri,Electrical & Instrumentation,capital,2022 $,34530270.4,33387636.45,32224253.88,31038554.21,29828749.8,28592787.48,27328288.35,26032468.76,24943953.66,21920659.84,20458945.71,18940333.24,17354980.51,15689630.81,13925621.75,,,,,,,,,,,,,,,,,,,,, -ng_dri,"Buildings, Storage, Water Service",capital,2022 $,66714415.99,64032222.03,61321630.82,58580736.73,55807385.67,52999124.97,50153139.08,47266165.79,44864988.97,38317369.95,35220028.22,32052926.06,28805414.18,25463417.59,22007549.92,,,,,,,,,,,,,,,,,,,,, -ng_dri,Other Miscellaneous Cost,capital,2022 $,34530270.4,33387636.45,32224253.88,31038554.21,29828749.8,28592787.48,27328288.35,26032468.76,24943953.66,21920659.84,20458945.71,18940333.24,17354980.51,15689630.81,13925621.75,,,,,,,,,,,,,,,,,,,,, -ng_eaf,Capacity Factor,capacity,%,100,100,100,100,100,100,100,100,100,100,100,100,100,100,100,,,,,,,,,,,,,,,,,,,,, -ng_eaf,Steel Slab,capacity,mtpy,1953600,1855920,1758240,1660560,1562880,1465200,1367520,1269840,1189722,976800,879120,781440,683760,586080,488400,,,,,,,,,,,,,,,,,,,,, -ng_eaf,EAF & Casting,capital,2022 $,257484642.6,251532029.6,245406417.4,239092724.7,232573573.1,225828769,218834624.1,211563051.2,205368306,187707511.9,178902429.1,169547225.5,159531467.2,148702621,136839656.4,,,,,,,,,,,,,,,,,,,,, -ng_eaf,Shaft Furnace,capital,2022 $,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,,,,,,,,,,,,,,,, -ng_eaf,Oxygen Supply,capital,2022 $,3301694.19,3190154.09,3076658.738,2961062.168,2843198.214,2722876.176,2599875.384,2473937.904,2368238.53,2075136.879,1933700.591,1786969.089,1634039.068,1473696.617,1304237.406,,,,,,,,,,,,,,,,,,,,, -ng_eaf,Reformer,capital,2022 $,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,,,,,,,,,,,,,,,, -ng_eaf,Recycle Compressor,capital,2022 $,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,,,,,,,,,,,,,,,,, -ng_eaf,Cooling Tower,capital,2022 $,18534.45,18288.91,18033.62,17767.6,17489.74,17198.71,16892.94,16570.57,16292.15,15477.9,15059.659,14605.468,14107.094,13552.874,12925.408,,,,,,,,,,,,,,,,,,,,, -ng_eaf,Piping,capital,2022 $,39061511.86,38153845.86,37219970.26,36257603.46,35264116.6,34236455.25,33171036.8,32063613.82,31120395.57,28432431.29,27092920.77,25670187.54,24147546.05,22501954.33,20700030.28,,,,,,,,,,,,,,,,,,,,, -ng_eaf,Electrical & Instrumentation,capital,2022 $,26041007.92,25435897.23,24813313.51,24171735.64,23509411.07,22824303.5,22114024.53,21375742.55,20746930.39,18954954.19,18061947.18,17113458.36,16098364.03,15001302.89,13800020.19,,,,,,,,,,,,,,,,,,,,, -ng_eaf,"Buildings, Storage, Water Service",capital,2022 $,49442128.81,47454351.87,45445529.68,43414249.97,41358916.39,39277711.22,37168547.87,35029008.71,33249493.88,28397046.04,26101602.6,23754459.61,21347724.9,18870967.47,16309820.03,,,,,,,,,,,,,,,,,,,,, -ng_eaf,Other Miscellaneous Cost,capital,2022 $,26041007.92,25435897.23,24813313.51,24171735.64,23509411.07,22824303.5,22114024.53,21375742.55,20746930.39,18954954.19,18061947.18,17113458.36,16098364.03,15001302.89,13800020.19,,,,,,,,,,,,,,,,,,,,, diff --git a/h2integrate/converters/iron/rosner/cost_model.py b/h2integrate/converters/iron/rosner/cost_model.py deleted file mode 100644 index db293686e..000000000 --- a/h2integrate/converters/iron/rosner/cost_model.py +++ /dev/null @@ -1,342 +0,0 @@ -""" -Direct Reduced Iron (DRI) model developed by Rosner et al. -Energy Environ. Sci., 2023, 16, 4121 -doi.org/10.1039/d3ee01077e -""" - -from pathlib import Path - -import numpy as np -import pandas as pd - -from h2integrate.core.file_utils import load_yaml -from h2integrate.converters.iron.load_top_down_coeffs import load_top_down_coeffs - - -CD = Path(__file__).parent - -# Get model locations loaded up to refer to -model_locs_fp = CD / "../model_locations.yaml" -model_locs = load_yaml(model_locs_fp) - -capital_costs = {} - - -def main(config): - """Calculates the capital, fixed O&M, and owner's costs for a Direct Reduced Iron (DRI) plant or - a Electric Arc Furnace (EAF) plant. - - This function processes economic and performance data to estimate the total capital cost, - fixed operational expenses, and other costs associated with a DRI and/or EAF plant based on the - methodology presented by Rosner et al. - (Energy Environ. Sci., 2023, 16, 4121, doi:10.1039/d3ee01077e). - - The function uses predefined coefficients and refits cost models if needed. - It also considers various cost components, including labor, maintenance, - property tax, installation, and consumables. - - Args: - config (Config): A configuration object containing parameters such as plant capacity, - operational year, plant life, product selection, site information, and performance data. - - Returns: - pandas.DataFrame: A DataFrame containing cost estimates with columns: - - "Product": The product type (e.g., 'ng_dri', 'h2_dri', 'ng_eaf', 'h2_eaf'). - - "Name": Cost component name. - - "Type": Cost category (e.g., 'capital', 'fixed opex', 'other'). - - "Unit": Currency unit and reference year. - - "Value": Estimated cost for the given category. - """ - # Import 'top-down' costs - top_down_coeffs = load_top_down_coeffs() - - model_year = 2022 # Rosner paper: All economic data in 2022 $ - - start_year = config.params["operational_year"] - end_year = start_year + config.params["plant_life"] - end_year = min(end_year, top_down_coeffs["years"][-1]) - td_start_idx = np.where(top_down_coeffs["years"] == start_year)[0][0] - td_end_idx = np.where(top_down_coeffs["years"] == end_year)[0][0] - - # Get plant performances into data frame/series with performance names as index - perf_df = config.performance.performances_df.set_index("Name") - perf_ds = perf_df.loc[:, "Model"] - - plant_capacity_mtpy = config.params["plant_capacity_mtpy"] # In metric tonnes per year - - # Convert iron to steel if needed - if config.params["capacity_denominator"] == "iron": - steel_cap = perf_ds["Steel Production"] - iron_cap = perf_ds["Pig Iron Production"] - plant_capacity_mtpy *= steel_cap / iron_cap - - lcoh = config.params["lcoh"] - - # Set up dataframe to store costs - cols = ["Product", "Name", "Type", "Unit", config.site["name"]] - costs_df = pd.DataFrame([], columns=cols) - - # If re-fitting the model, load an inputs dataframe, otherwise, load up the coeffs - if config.model["refit_coeffs"]: - input_df = pd.read_csv(CD / config.model["inputs_fp"]) - products = np.unique(input_df.loc[:, "Product"].values) - coeff_df = pd.read_csv(CD / config.model["coeffs_fp"], index_col=0) - - for product in products: - prod_rows = np.where(input_df.loc[:, "Product"].values == product)[0] - prod_df = input_df.iloc[prod_rows, :] - product_col = np.where(coeff_df.columns.values == product)[0] - - remove_rows = ["Capacity Factor", "Pig Iron", "Steel Liquid"] - keep_rows = np.where( - np.logical_not([i in remove_rows for i in prod_df.loc[:, "Name"].values]) - ) - prod_df = prod_df.iloc[keep_rows] - - keys = prod_df.iloc[:, 1] # Extract name - values = prod_df.iloc[:, 4:19] # Extract values for cost re-fitting - - # Create dictionary with keys for name and arrays of values - array_dict = { - key: np.array(row) - for key, row in zip(keys, values.itertuples(index=False, name=None)) - } - - # This determines that costs are a function of steel slab output - x = np.log(array_dict["Steel Slab"]) - - del array_dict["Steel Slab"] - - for key in array_dict: - y = np.log(array_dict[key] + 1e-10) # Add a small value to avoid log(0) - # Fit the curve - coeffs = np.polyfit(x, y, 1) - - # Extract coefficients - a = np.exp(coeffs[1]) - b = coeffs[0] - - # Ensure all values are real - a = 0 if np.isnan(a) else a - b = 0 if np.isnan(b) else b - - # Store the parameters in the coeff dataframe - lin_row = np.where( - np.logical_and(coeff_df.index.values == key, coeff_df.loc[:, "Coeff"] == "lin") - )[0][0] - exp_row = np.where( - np.logical_and(coeff_df.index.values == key, coeff_df.loc[:, "Coeff"] == "exp") - )[0][0] - coeff_df.iloc[lin_row, product_col] = a - coeff_df.iloc[exp_row, product_col] = b - - coeff_df.to_csv(CD / config.model["coeffs_fp"]) - coeff_df = pd.read_csv(CD / config.model["coeffs_fp"], index_col=[0, 1, 2, 3]) - product = config.product_selection - - prod_coeffs = coeff_df[[product]].reset_index() - - # Workaround for changing capex - if "capex_mod" in list(config.params.keys()): - if config.params["capex_mod"]: - capex_lin = prod_coeffs.iloc[ - np.where((prod_coeffs["Type"] == "capital") & (prod_coeffs["Coeff"] == "lin"))[0], - -1, - ].values - capex_lin = [i * (1 + config.params["capex_pct"]) for i in capex_lin] - prod_coeffs.iloc[ - np.where((prod_coeffs["Type"] == "capital") & (prod_coeffs["Coeff"] == "lin"))[0], - -1, - ] = capex_lin - - # Add unique capital items based on the "Name" column for product - for item_name in prod_coeffs[prod_coeffs["Type"] == "capital"]["Name"].unique(): - # Filter for this item and get the lin and exp coefficients - item_data = prod_coeffs[ - (prod_coeffs["Name"] == item_name) & (prod_coeffs["Type"] == "capital") - ] - lin_coeff = item_data[item_data["Coeff"] == "lin"][product].values[0] - exp_coeff = item_data[item_data["Coeff"] == "exp"][product].values[0] - - # Calculate the capital cost for the item - capital_costs[item_name] = lin_coeff * plant_capacity_mtpy**exp_coeff - - total_plant_cost = sum(capital_costs.values()) - - for cost_name, cost in capital_costs.items(): - new_cost = [product, cost_name, "capital", str(model_year) + " $", cost] - costs_df.loc[len(costs_df)] = new_cost - - # -------------------------------Fixed O&M Costs------------------------------ - - # Import Peters opex model - if config.model["refit_coeffs"]: - input_df = pd.read_csv(CD / "../peters" / model_locs["cost"]["peters"]["inputs"]) - keys = input_df.iloc[:, 0] # Extract name - values = input_df.iloc[:, 3:16] # Extract values for cost re-fitting - - # Create dictionary with keys for name and arrays of values - array_dict = { - key: np.array(row) for key, row in zip(keys, values.itertuples(index=False, name=None)) - } - - x = np.log(array_dict["Plant Size"]) - y = np.log(array_dict["Operating Labor"]) - - # Fit the curve - coeffs = np.polyfit(x, y, 1) - - # Extract coefficients - Peters_coeffs_lin = np.exp(coeffs[1]) - Peters_coeffs_exp = coeffs[0] - - else: - coeff_df = pd.read_csv( - CD / "../peters" / model_locs["cost"]["peters"]["coeffs"], index_col=[0, 1, 2, 3] - ) - Peters_coeffs = coeff_df["A"] - Peters_coeffs_lin = Peters_coeffs.loc["Annual Operating Labor Cost", :, "lin"].values[0] - Peters_coeffs_exp = Peters_coeffs.loc["Annual Operating Labor Cost", :, "exp"].values[0] - - # Peters model - employee-hours/day/process step * # of process steps - fixed_costs = {} - - cost = ( - 365 - * ( - prod_coeffs.loc[prod_coeffs["Name"] == "% Skilled Labor", product].values[0] - / 100 - * np.mean(top_down_coeffs["Skilled Labor Cost"]["values"][td_start_idx:td_end_idx]) - + prod_coeffs.loc[prod_coeffs["Name"] == "% Unskilled Labor", product].values[0] - / 100 - * np.mean(top_down_coeffs["Unskilled Labor Cost"]["values"][td_start_idx:td_end_idx]) - ) - * prod_coeffs.loc[prod_coeffs["Name"] == "Processing Steps", product].values[0] - * Peters_coeffs_lin - * (plant_capacity_mtpy / 365 * 1000) ** Peters_coeffs_exp - ) - labor_cost_annual_operation = cost - fixed_costs["labor_cost_annual_operation"] = cost - - cost = ( - prod_coeffs.loc[prod_coeffs["Name"] == "Maintenance Labor Cost", product].values[0] - * total_plant_cost - ) - labor_cost_maintenance = cost - fixed_costs["labor_cost_maintenance"] = cost - - cost = prod_coeffs.loc[ - prod_coeffs["Name"] == "Administrative & Support Labor Cost", product - ].values[0] * (labor_cost_annual_operation + labor_cost_maintenance) - labor_cost_admin_support = cost - fixed_costs["labor_cost_admin_support"] = cost - - cost = ( - prod_coeffs.loc[prod_coeffs["Name"] == "Property Tax & Insurance", product].values[0] - * total_plant_cost - ) - property_tax_insurance = cost - fixed_costs["property_tax_insurance"] = cost - - cost = ( - prod_coeffs.loc[prod_coeffs["Name"] == "Maintenance Materials", product].values[0] - * plant_capacity_mtpy - ) - maintenance_materials = cost - fixed_costs["maintenance_materials"] = cost - - ( - labor_cost_annual_operation - + labor_cost_maintenance - + labor_cost_admin_support - + property_tax_insurance - + maintenance_materials - ) - - for cost_name, cost in fixed_costs.items(): - new_cost = [product, cost_name, "fixed opex", str(model_year) + " $ per year", cost] - costs_df.loc[len(costs_df)] = new_cost - - # ---------------------- Owner's (Installation) Costs -------------------------- - if config.product_selection == "ng_dri" or "h2_dri" or "ng_dri_eaf" or "h2_dri_eaf": - labor_cost_fivemonth = ( - 5 - / 12 - * (labor_cost_annual_operation + labor_cost_maintenance + labor_cost_admin_support) - ) - else: - labor_cost_fivemonth = 0 - - ( - prod_coeffs.loc[prod_coeffs["Name"] == "Maintenance Materials", product].values[0] - * plant_capacity_mtpy - / 12 - ) - non_fuel_consumables_onemonth = ( - plant_capacity_mtpy - * ( - perf_ds["Raw Water Withdrawal"] * top_down_coeffs["Raw Water"]["values"][td_start_idx] - + perf_ds["Lime"] * top_down_coeffs["Lime"]["values"][td_start_idx] - + perf_ds["Carbon (Coke)"] * top_down_coeffs["Carbon"]["values"][td_start_idx] - + perf_ds["Iron Ore"] * top_down_coeffs["Iron Ore Pellets"]["values"][td_start_idx] - + perf_ds["Reformer Catalyst"] - * top_down_coeffs["Reformer Catalyst"]["values"][td_start_idx] - ) - / 12 - ) - - ( - plant_capacity_mtpy - * perf_ds["Slag"] - * top_down_coeffs["Slag Disposal"]["values"][td_start_idx] - / 12 - ) - - ( - plant_capacity_mtpy - * ( - perf_ds["Hydrogen"] * lcoh * 1000 - + perf_ds["Natural Gas"] * top_down_coeffs["Natural Gas"]["values"][td_start_idx] - + perf_ds["Electricity"] * top_down_coeffs["Electricity"]["values"][td_start_idx] - ) - / 12 - ) - preproduction_cost = ( - prod_coeffs.loc[prod_coeffs["Name"] == "Preproduction", product].values[0] - * total_plant_cost - ) - - fuel_consumables_60day_supply_cost = non_fuel_consumables_onemonth * 12 / 365 * 60 - - spare_parts_cost = ( - prod_coeffs.loc[prod_coeffs["Name"] == "Spare Parts", product].values[0] * total_plant_cost - ) - land_cost = ( - prod_coeffs.loc[prod_coeffs["Name"] == "Land", product].values[0] * plant_capacity_mtpy - ) - misc_owners_costs = ( - prod_coeffs.loc[prod_coeffs["Name"] == "Other Owners's Costs", product].values[0] - * total_plant_cost - ) - - installation_cost = ( - labor_cost_fivemonth - + preproduction_cost - + fuel_consumables_60day_supply_cost - + spare_parts_cost - + misc_owners_costs - ) - - new_cost = [product, "Land cost", "other", str(model_year) + " $ per year", land_cost] - costs_df.loc[len(costs_df)] = new_cost - - new_cost = [ - product, - "Installation cost", - "other", - str(model_year) + " $ per year", - installation_cost, - ] - costs_df.loc[len(costs_df)] = new_cost - - return costs_df diff --git a/h2integrate/converters/iron/rosner/finance_model.py b/h2integrate/converters/iron/rosner/finance_model.py deleted file mode 100644 index 46032d3bd..000000000 --- a/h2integrate/converters/iron/rosner/finance_model.py +++ /dev/null @@ -1,269 +0,0 @@ -import numpy as np -import ProFAST - -import h2integrate.tools.profast_tools as pf_tools -from h2integrate.tools.inflation.inflate import inflate_cpi, inflate_cepci -from h2integrate.converters.iron.load_top_down_coeffs import load_top_down_coeffs - - -def main(config): - """Performs financial analysis for a Direct Reduced Iron (DRI) plant or - a Electric Arc Furnace (EAF) plant using ProFAST. - - This function models the finances of a DRI or EAF - plant based on input parameters, cost data, and plant performance metrics. It - uses ProFAST for financial modeling, including capital costs, operational expenses, - feedstock costs, and coproduct revenues. - - Args: - config (Config): Configuration object containing plant parameters, - cost data, performance metrics, and financial assumptions. - - Returns: - tuple: A tuple containing the following elements: - - sol (float): Solved price required to meet financial constraints. - - summary (dict): Summary of key financial metrics. - - price_breakdown (dict): Detailed breakdown of cost contributions. - - pf (ProFAST.ProFAST): Configured ProFAST object for further analysis. - """ - # First thing - get iron pellet cost from previous modules - iron_ore_pellet_unitcost_tonne = config.params["lco_iron_ore_tonne"] - if "iron_transport_tonne" in config.params.keys(): - iron_transport_cost_tonne = config.params["iron_transport_tonne"] - ore_profit_pct = config.params["ore_profit_pct"] - iron_ore_pellet_unitcost_tonne = ( - iron_ore_pellet_unitcost_tonne + iron_transport_cost_tonne - ) * (1 + ore_profit_pct / 100) - - # Determine years for analyzing cost - operational_year = config.params["operational_year"] - install_years = config.params["installation_years"] - plant_life = config.params["plant_life"] - cost_year = config.params["cost_year"] - analysis_start = operational_year - install_years - - # Get feedstock costs from input sheets - coeff_dict = load_top_down_coeffs( - [ - "Raw Water", - "Lime", - "Carbon", - "Slag Disposal", - "Hydrogen", - "Natural Gas", - "Electricity", - "Inflation Rate", - ] - ) - - # Workaround for changing ng_price - if "ng_mod" in list(config.params.keys()): - if config.params["ng_mod"]: - ng_prices = coeff_dict["Natural Gas"]["values"] - ng_prices = [config.params["ng_price"] for i in ng_prices] - coeff_dict["Natural Gas"]["values"] = np.array(ng_prices) - - years = list(coeff_dict["years"]) - start_idx = years.index(analysis_start) - if len(years) > (start_idx + plant_life + install_years + 1): - end_idx = years.index(analysis_start + plant_life + install_years + 1) - indices = list(np.arange(start_idx, end_idx)) - else: - end_idx = len(years) - 1 - indices = list(np.arange(start_idx, end_idx)) - repeats = start_idx + plant_life + install_years + 2 - len(years) - for _i in range(repeats): - indices.append(end_idx) - - raw_water_unitcost_tonne = coeff_dict["Raw Water"]["values"][indices].astype(float) - lime_unitcost_tonne = coeff_dict["Lime"]["values"][indices].astype(float) - carbon_unitcost_tonne = coeff_dict["Carbon"]["values"][indices].astype(float) - slag_disposal_unitcost_tonne = coeff_dict["Slag Disposal"]["values"][indices].astype(float) - hydrogen_cost_kg = coeff_dict["Hydrogen"]["values"][indices].astype(float) - natural_gas_prices_MMBTU = coeff_dict["Natural Gas"]["values"][indices].astype(float) - electricity_cost_kwh = coeff_dict["Electricity"]["values"][indices].astype(float) - gen_inflation_pct = coeff_dict["Inflation Rate"]["values"][indices].astype(float) - gen_inflation = np.mean(gen_inflation_pct) / 100 - - # Choose whether to override top-down electricity, hydrogen, and TODO: natural gas - electricity_cost_kwh = config.params["lcoe"] # Originally in $/kWh - lcoe_dollar_MWH = electricity_cost_kwh * 1000 - hydrogen_cost_kg = config.params["lcoh"] # Originally in $/kg - lcoh_dollar_metric_tonne = hydrogen_cost_kg * 1000 - # TODO: Natural Gas - natural_gas_prices_GJ = natural_gas_prices_MMBTU * 1.05506 # Convert to GJ - - # Get plant performances into data frame/series with performance names as index - performance = config.performance - perf_df = performance.performances_df.set_index("Name") - perf_ds = perf_df.loc[:, "Model"] - - plant_capacity_mtpy = config.params["plant_capacity_mtpy"] # In metric tonnes per year - plant_capacity_factor = perf_ds["Capacity Factor"] / 100 # Fractional - - # Get reduction plant costs into data frame/series with cost names as index - costs = config.cost - cost_df = costs.costs_df.set_index("Name") - cost_ds = cost_df.loc[:, config.site["name"]] - cost_names = cost_df.index.values - cost_types = cost_df.loc[:, "Type"].values - cost_units = cost_df.loc[:, "Unit"].values - - installation_cost = cost_ds["Installation cost"] - land_cost = cost_ds["Land cost"] - - if "pf" in config.params: - pf = pf_tools.create_and_populate_profast(config.params["pf"]) - else: - # Set up ProFAST - pf = ProFAST.ProFAST("blank") - - product_name = config.params["capacity_denominator"] - module_label = config.product_selection - - # apply all params passed through from config - for param, val in config.params["financial_assumptions"].items(): - pf.set_params(param, val) - - pf.set_params( - "commodity", - { - "name": f"{product_name}", - "unit": "metric tonnes", - "initial price": 1000, - "escalation": gen_inflation, - }, - ) - pf.set_params("capacity", plant_capacity_mtpy / 365) # units/day - pf.set_params("maintenance", {"value": 0, "escalation": gen_inflation}) - pf.set_params("analysis start year", analysis_start) - pf.set_params("operating life", plant_life) - pf.set_params("installation months", 12 * install_years) - pf.set_params( - "installation cost", - { - "value": installation_cost, - "depr type": "Straight line", - "depr period": 4, - "depreciable": False, - }, - ) - pf.set_params("non depr assets", land_cost) - pf.set_params( - "end of proj sale non depr assets", - land_cost * (1 + gen_inflation) ** plant_life, - ) - pf.set_params("demand rampup", 5.3) - pf.set_params("long term utilization", plant_capacity_factor) - pf.set_params("credit card fees", 0) - pf.set_params("sales tax", 0) - pf.set_params("license and permit", {"value": 00, "escalation": gen_inflation}) - pf.set_params("rent", {"value": 0, "escalation": gen_inflation}) - pf.set_params("property tax and insurance", 0) - pf.set_params("admin expense", 0) - pf.set_params("sell undepreciated cap", True) - pf.set_params("tax losses monetized", True) - pf.set_params("general inflation rate", gen_inflation) - pf.set_params("debt type", "Revolving debt") - pf.set_params("cash onhand", 1) - - # ----------------------------------- Add capital items to ProFAST ---------------- - capital_idxs = np.where(cost_types == "capital")[0] - for idx in capital_idxs: - name = cost_names[idx] - unit = cost_units[idx] # Units for capital costs should be " $"" - source_year = int(unit[:4]) - source_year_cost = cost_ds.iloc[idx] - cost = inflate_cepci(source_year_cost, source_year, cost_year) - - pf.add_capital_item( - name=f"{module_label}: {name}", - cost=cost, - depr_type="MACRS", - depr_period=7, - refurb=[0], - ) - - # -------------------------------------- Add fixed costs-------------------------------- - fixed_idxs = np.where(cost_types == "fixed opex")[0] - for idx in fixed_idxs: - name = cost_names[idx] - unit = cost_units[idx] # Units for fixed opex costs should be " $ per year" - source_year = int(unit[:4]) - source_year_cost = cost_ds.iloc[idx] - cost = inflate_cpi(source_year_cost, source_year, cost_year) - pf.add_fixed_cost( - name=f"{module_label}: {name}", - usage=1, - unit="$/year", - cost=cost, - escalation=gen_inflation, - ) - # Putting property tax and insurance here to zero out depreciation/escalation. - # Could instead put it in set_params if we think that is more accurate - - # ---------------------- Add feedstocks, note the various cost options------------------- - pf.add_feedstock( - name=f"{module_label}: Raw Water Withdrawal", - usage=perf_ds["Raw Water Withdrawal"], - unit="metric tonnes of water per metric tonne of iron", - cost=raw_water_unitcost_tonne, - escalation=gen_inflation, - ) - pf.add_feedstock( - name=f"{module_label}: Lime", - usage=perf_ds["Lime"], - unit="metric tonnes of lime per metric tonne of iron", - cost=lime_unitcost_tonne, - escalation=gen_inflation, - ) - pf.add_feedstock( - name=f"{module_label}: Carbon", - usage=perf_ds["Carbon (Coke)"], - unit="metric tonnes of carbon per metric tonne of iron", - cost=carbon_unitcost_tonne, - escalation=gen_inflation, - ) - pf.add_feedstock( - name=f"{module_label}: Iron Ore", - usage=perf_ds["Iron Ore"], - unit="metric tonnes of iron ore per metric tonne of iron", - cost=iron_ore_pellet_unitcost_tonne, - escalation=gen_inflation, - ) - pf.add_feedstock( - name=f"{module_label}: Hydrogen", - usage=perf_ds["Hydrogen"], - unit="metric tonnes of hydrogen per metric tonne of iron", - cost=lcoh_dollar_metric_tonne, - escalation=gen_inflation, - ) - pf.add_feedstock( - name=f"{module_label}: Natural Gas", - usage=perf_ds["Natural Gas"], - unit="GJ-LHV per metric tonne of iron", - cost=natural_gas_prices_GJ, - escalation=gen_inflation, - ) - pf.add_feedstock( - name=f"{module_label}: Electricity", - usage=perf_ds["Electricity"], - unit="MWh per metric tonne of iron", - cost=lcoe_dollar_MWH, - escalation=gen_inflation, - ) - pf.add_feedstock( - name=f"{module_label}: Slag Disposal", - usage=perf_ds["Slag"], - unit="metric tonnes of slag per metric tonne of iron", - cost=slag_disposal_unitcost_tonne, - escalation=gen_inflation, - ) - - # ------------------------------ Set up outputs --------------------------- - - sol = pf.solve_price() - summary = pf.get_summary_vals() - price_breakdown = pf.get_cost_breakdown() - - return sol, summary, price_breakdown, pf diff --git a/h2integrate/converters/iron/rosner/perf_inputs.csv b/h2integrate/converters/iron/rosner/perf_inputs.csv deleted file mode 100644 index 3b70c37ed..000000000 --- a/h2integrate/converters/iron/rosner/perf_inputs.csv +++ /dev/null @@ -1,145 +0,0 @@ -Product,Name,Type,Unit,Model -h2_dri_eaf,Steel Production,capacity,mtpy,1189678 -h2_dri_eaf,Pig Iron Production,capacity,mtpy,1418094.867 -h2_dri_eaf,Capacity Factor,capacity,%,100 -h2_dri_eaf,Iron Ore,feed,mt ore / mt steel,1.62927 -h2_dri_eaf,Reformer Catalyst,feed,m3 catalyst/mt steel,0 -h2_dri_eaf,Hydrogen,feed,mt H2 / mt steel,0.06596 -h2_dri_eaf,Natural Gas,feed,GJ-LHV NG / mt steel,0.71657 -h2_dri_eaf,Electricity,feed,MWh / mt steel,0.5502 -h2_dri_eaf,Carbon (Coke),feed,mt C / mt steel,0.0538 -h2_dri_eaf,Lime,feed,mt lime / mt steel,0.01812 -h2_dri_eaf,Raw Water Withdrawal,feed,mt H2O / mt steel,0.80367 -h2_dri_eaf,Carbon Dioxide (Methane as Fuel),emission,mt CO2 / mt steel,0.03929 -h2_dri_eaf,Carbon Dioxide (Carbon),emission,mt CO2 / mt steel,0.17466 -h2_dri_eaf,Slag,emission,mt slag / mt steel,0.17433 -h2_dri_eaf,Surface Water Discharge,emission,mt H2O discharged/mt H2O withdrawn,0.42113 -h2_dri_eaf,Steelmaking efficiency,efficiency,%,60.07 -h2_dri_eaf,Reformer Furnace & Stack Air Blower,feed,kW / mtpy steel,0 -h2_dri_eaf,Process Gas recycle,feed,kW / mtpy steel,0 -h2_dri_eaf,DRI Scrubber Electricity,feed,kW / mtpy steel,0.000122575 -h2_dri_eaf,Cooling Tower & Water Circulation Electricity,feed,kW / mtpy steel,0.000760857 -h2_dri_eaf,Oxygen Supply Electricity,feed,kW / mtpy steel,0.010389687 -h2_dri_eaf,Electric Arc Furnace Electricity,feed,kW / mtpy steel,0.041202278 -h2_dri_eaf,Ladle Refining Electricity,feed,kW / mtpy steel,0.003218638 -h2_dri_eaf,Miscellaneous Balance of Plant Electricity,feed,kW / mtpy steel,0.007113805 -h2_dri,Steel Production,capacity,mtpy,1189678 -h2_dri,Pig Iron Production,capacity,mtpy,1418094.867 -h2_dri,Capacity Factor,capacity,%,100 -h2_dri,Iron Ore,feed,mt ore / mt steel,1.62927 -h2_dri,Reformer Catalyst,feed,m3 catalyst/mt steel,0 -h2_dri,Hydrogen,feed,mt H2 / mt steel,0.06596 -h2_dri,Natural Gas,feed,GJ-LHV NG / mt steel,0.622287755 -h2_dri,Electricity,feed,MWh / mt steel,0.117029561 -h2_dri,Carbon (Coke),feed,mt C / mt steel,0 -h2_dri,Lime,feed,mt lime / mt steel,0 -h2_dri,Raw Water Withdrawal,feed,mt H2O / mt steel,0.642938077 -h2_dri,Carbon Dioxide (Methane as Fuel),emission,mt CO2 / mt steel,0.029631801 -h2_dri,Carbon Dioxide (Carbon),emission,mt CO2 / mt steel,0.377646164 -h2_dri,Slag,emission,mt slag / mt steel,0 -h2_dri,Surface Water Discharge,emission,mt H2O discharged/mt H2O withdrawn,0.270760315 -h2_dri,Steelmaking efficiency,efficiency,%,87.988 -h2_dri,Reformer Furnace & Stack Air Blower,feed,kW / mtpy steel,0 -h2_dri,Process Gas recycle,feed,kW / mtpy steel,0 -h2_dri,DRI Scrubber Electricity,feed,kW / mtpy steel,0.000122575 -h2_dri,Cooling Tower & Water Circulation Electricity,feed,kW / mtpy steel,0.000608686 -h2_dri,Oxygen Supply Electricity,feed,kW / mtpy steel,0.005507939 -h2_dri,Electric Arc Furnace Electricity,feed,kW / mtpy steel,0.000006534 -h2_dri,Ladle Refining Electricity,feed,kW / mtpy steel,0 -h2_dri,Miscellaneous Balance of Plant Electricity,feed,kW / mtpy steel,0.007113805 -h2_eaf,Steel Production,capacity,mtpy,1189678 -h2_eaf,Pig Iron Production,capacity,mtpy,1418094.867 -h2_eaf,Capacity Factor,capacity,%,100 -h2_eaf,Iron Ore,feed,mt ore / mt steel,0 -h2_eaf,Reformer Catalyst,feed,m3 catalyst/mt steel,0 -h2_eaf,Hydrogen,feed,mt H2 / mt steel,0 -h2_eaf,Natural Gas,feed,GJ-LHV NG / mt steel,0.094282245 -h2_eaf,Electricity,feed,MWh / mt steel,0.433170439 -h2_eaf,Carbon (Coke),feed,mt C / mt steel,0.0538 -h2_eaf,Lime,feed,mt lime / mt steel,0.01812 -h2_eaf,Raw Water Withdrawal,feed,mt H2O / mt steel,0.160731923 -h2_eaf,Carbon Dioxide (Methane as Fuel),emission,mt CO2 / mt steel,0.009658199 -h2_eaf,Carbon Dioxide (Carbon),emission,mt CO2 / mt steel,0 -h2_eaf,Slag,emission,mt slag / mt steel,0.17433 -h2_eaf,Surface Water Discharge,emission,mt H2O discharged/mt H2O withdrawn,0.150369685 -h2_eaf,Steelmaking efficiency,efficiency,%,60.07 -h2_eaf,Reformer Furnace & Stack Air Blower,feed,kW / mtpy steel,0 -h2_eaf,Process Gas recycle,feed,kW / mtpy steel,0 -h2_eaf,DRI Scrubber Electricity,feed,kW / mtpy steel,0 -h2_eaf,Cooling Tower & Water Circulation Electricity,feed,kW / mtpy steel,0.000152171 -h2_eaf,Oxygen Supply Electricity,feed,kW / mtpy steel,0.004881748 -h2_eaf,Electric Arc Furnace Electricity,feed,kW / mtpy steel,0.041195744 -h2_eaf,Ladle Refining Electricity,feed,kW / mtpy steel,0.003218638 -h2_eaf,Miscellaneous Balance of Plant Electricity,feed,kW / mtpy steel,0 -ng_dri_eaf,Steel Production,capacity,mtpy,1189772 -ng_dri_eaf,Pig Iron Production,capacity,mtpy,1418206.915 -ng_dri_eaf,Capacity Factor,capacity,%,100 -ng_dri_eaf,Iron Ore,feed,mt ore / mt steel,1.629206363 -ng_dri_eaf,Reformer Catalyst,feed,m3 catalyst/mt steel,0.000003493 -ng_dri_eaf,Hydrogen,feed,mt H2 / mt steel,0 -ng_dri_eaf,Natural Gas,feed,GJ-LHV NG / mt steel,10.41822203 -ng_dri_eaf,Electricity,feed,MWh / mt steel,0.583453494 -ng_dri_eaf,Carbon (Coke),feed,mt C / mt steel,0.047169223 -ng_dri_eaf,Lime,feed,mt lime / mt steel,0.018120393 -ng_dri_eaf,Raw Water Withdrawal,feed,mt H2O / mt steel,1.158363659 -ng_dri_eaf,Carbon Dioxide (Methane as Fuel),emission,mt CO2 / mt steel,0 -ng_dri_eaf,Carbon Dioxide (Carbon),emission,mt CO2 / mt steel,0.721051998 -ng_dri_eaf,Slag,emission,mt slag / mt steel,0.174325548 -ng_dri_eaf,Surface Water Discharge,emission,mt H2O discharged/mt H2O withdrawn,0.326557377 -ng_dri_eaf,Steelmaking efficiency,efficiency,%,52.854 -ng_dri_eaf,Reformer Furnace & Stack Air Blower,feed,kW / mtpy steel,0.004095664 -ng_dri_eaf,Process Gas recycle,feed,kW / mtpy steel,0.00451324 -ng_dri_eaf,DRI Scrubber Electricity,feed,kW / mtpy steel,0.00011255 -ng_dri_eaf,Cooling Tower & Water Circulation Electricity,feed,kW / mtpy steel,0.000717118 -ng_dri_eaf,Oxygen Supply Electricity,feed,kW / mtpy steel,0.004981703 -ng_dri_eaf,Electric Arc Furnace Electricity,feed,kW / mtpy steel,0.040593161 -ng_dri_eaf,Ladle Refining Electricity,feed,kW / mtpy steel,0.003143966 -ng_dri_eaf,Miscellaneous Balance of Plant Electricity,feed,kW / mtpy steel,0.006901666 -ng_dri,Steel Production,capacity,mtpy,1189772 -ng_dri,Pig Iron Production,capacity,mtpy,1418206.915 -ng_dri,Capacity Factor,capacity,%,100 -ng_dri,Iron Ore,feed,mt ore / mt steel,1.629206363 -ng_dri,Reformer Catalyst,feed,m3 catalyst/mt steel,0.000003493 -ng_dri,Hydrogen,feed,mt H2 / mt steel,0 -ng_dri,Natural Gas,feed,GJ-LHV NG / mt steel,8.270148697 -ng_dri,Electricity,feed,MWh / mt steel,0.16645596 -ng_dri,Carbon (Coke),feed,mt C / mt steel,0 -ng_dri,Lime,feed,mt lime / mt steel,0 -ng_dri,Raw Water Withdrawal,feed,mt H2O / mt steel,0.905191697 -ng_dri,Carbon Dioxide (Methane as Fuel),emission,mt CO2 / mt steel,0 -ng_dri,Carbon Dioxide (Carbon),emission,mt CO2 / mt steel,0.721051998 -ng_dri,Slag,emission,mt slag / mt steel,0 -ng_dri,Surface Water Discharge,emission,mt H2O discharged/mt H2O withdrawn,0.281912658 -ng_dri,Steelmaking efficiency,efficiency,%,52.853 -ng_dri,Reformer Furnace & Stack Air Blower,feed,kW / mtpy steel,0.004095663 -ng_dri,Process Gas recycle,feed,kW / mtpy steel,0.00451324 -ng_dri,DRI Scrubber Electricity,feed,kW / mtpy steel,0.00011255 -ng_dri,Cooling Tower & Water Circulation Electricity,feed,kW / mtpy steel,0.000573694 -ng_dri,Oxygen Supply Electricity,feed,kW / mtpy steel,0.002805009 -ng_dri,Electric Arc Furnace Electricity,feed,kW / mtpy steel,0 -ng_dri,Ladle Refining Electricity,feed,kW / mtpy steel,0 -ng_dri,Miscellaneous Balance of Plant Electricity,feed,kW / mtpy steel,0.006901666 -ng_eaf,Steel Production,capacity,mtpy,1189772 -ng_eaf,Pig Iron Production,capacity,mtpy,1418206.915 -ng_eaf,Capacity Factor,capacity,%,100 -ng_eaf,Iron Ore,feed,mt ore / mt steel,0 -ng_eaf,Reformer Catalyst,feed,m3 catalyst/mt steel,0 -ng_eaf,Hydrogen,feed,mt H2 / mt steel,0 -ng_eaf,Natural Gas,feed,GJ-LHV NG / mt steel,2.148073333 -ng_eaf,Electricity,feed,MWh / mt steel,0.416997534 -ng_eaf,Carbon (Coke),feed,mt C / mt steel,0 -ng_eaf,Lime,feed,mt lime / mt steel,0 -ng_eaf,Raw Water Withdrawal,feed,mt H2O / mt steel,0.253171962 -ng_eaf,Carbon Dioxide (Methane as Fuel),emission,mt CO2 / mt steel,0 -ng_eaf,Carbon Dioxide (Carbon),emission,mt CO2 / mt steel,0 -ng_eaf,Slag,emission,mt slag / mt steel,0.174325548 -ng_eaf,Surface Water Discharge,emission,mt H2O discharged/mt H2O withdrawn,0.044644719 -ng_eaf,Steelmaking efficiency,efficiency,%,52.854 -ng_eaf,Reformer Furnace & Stack Air Blower,feed,kW / mtpy steel,0.000000001 -ng_eaf,Process Gas recycle,feed,kW / mtpy steel,0 -ng_eaf,DRI Scrubber Electricity,feed,kW / mtpy steel,0 -ng_eaf,Cooling Tower & Water Circulation Electricity,feed,kW / mtpy steel,0.000143424 -ng_eaf,Oxygen Supply Electricity,feed,kW / mtpy steel,0.002176694 -ng_eaf,Electric Arc Furnace Electricity,feed,kW / mtpy steel,0.040593161 -ng_eaf,Ladle Refining Electricity,feed,kW / mtpy steel,0.003143966 -ng_eaf,Miscellaneous Balance of Plant Electricity,feed,kW / mtpy steel,0 diff --git a/h2integrate/converters/iron/rosner/perf_model.py b/h2integrate/converters/iron/rosner/perf_model.py deleted file mode 100644 index 6afe032e3..000000000 --- a/h2integrate/converters/iron/rosner/perf_model.py +++ /dev/null @@ -1,106 +0,0 @@ -""" -Direct Reduced Iron (DRI) model developed by Rosner et al. -Energy Environ. Sci., 2023, 16, 4121 -doi.org/10.1039/d3ee01077e -""" - -import copy -from pathlib import Path - -import numpy as np -import pandas as pd - - -CD = Path(__file__).parent - - -def main(config): - """Processes and retrieves performance data for Direct Reduced Iron (DRI) modeling - or Electric Arc Furnace (EAF) modeling. - - This function either fits a new model by processing input data or loads precomputed - coefficients from a file. It then extracts performance data for the selected product - and site, and converts units from per-unit steel to per-unit iron if necessary. - - Args: - config (object): Configuration object containing: - - model (dict): Model-related settings including: - - refit_coeffs (bool): Whether to refit coefficients from input data. - - inputs_fp (str): File path to input data (if refitting). - - coeffs_fp (str): File path to coefficient data. - - product_selection (str): Selected product for analysis. - - params (dict): Additional parameters, including: - - capacity_denominator (str): Determines unit conversion (e.g., "iron"). - - Returns: - pd.DataFrame: Processed performance data with relevant coefficients. - - Raises: - ValueError: If the selected product or site is not found in the coefficient data. - - References: - Rosner et al., "Direct Reduced Iron (DRI) Model," Energy Environ. Sci., 2023, 16, 4121. - DOI: https://doi.org/10.1039/d3ee01077e - """ - # If re-fitting the model, load an inputs dataframe, otherwise, load up the coeffs - if config.model["refit_coeffs"]: - input_df = pd.read_csv(CD / config.model["inputs_fp"]) - - # Right now all the performance modeling is linear - input_df.insert(3, "Coeff", np.full((len(input_df),), "lin")) - - # Right now, no curve fitting - just copy input DataFrame to make coeff DataFrame - coeff_df = copy.deepcopy(input_df) - - coeff_df.to_csv(CD / config.model["coeffs_fp"]) - else: - coeff_df = pd.read_csv(CD / config.model["coeffs_fp"], index_col=0) - - prod = config.product_selection - site = "Model" - - rows = np.where(coeff_df.loc[:, "Product"] == prod)[0] - col = np.where(coeff_df.columns == site)[0] - cols = [0, 1, 2, 3, 4] - cols.extend(list(col)) - - if len(rows) == 0: - raise ValueError(f'Product "{prod}" not found in coeffs data!') - if len(cols) == 0: - raise ValueError(f'Site "{site}" not found in coeffs data!') - - prod_df = coeff_df.iloc[rows, cols] - - # Convert per unit steel to per unit iron - if config.params["capacity_denominator"] == "iron": - prod_df = prod_df.set_index("Name") - steel_cap = prod_df.loc["Steel Production", "Model"] - iron_cap = prod_df.loc["Pig Iron Production", "Model"] - for name, item in prod_df.iterrows(): - unit = item["Unit"] - if "steel" in unit: - steel_idx = unit.index("steel") - if len(unit) == steel_idx + 5: - new_unit = unit[:steel_idx] + "iron" - else: - new_unit = unit[:steel_idx] + "iron" + unit[steel_idx + 5 :] - steel_value = item["Model"] - iron_value = steel_value * steel_cap / iron_cap - prod_df.loc[name, "Model"] = iron_value - prod_df.loc[name, "Unit"] = new_unit - prod_df = prod_df.reset_index().set_index("Product").reset_index() - - # Right now, the there is no need to scale the coefficients. - # perf_df will contain the same values as coeff_df - # This will change when scaling/extrapolating mining operations - length, width = prod_df.shape - perf_cols = list(prod_df.columns.values) - perf_cols.remove("Coeff") - col_idxs = list(range(width)) - col_idxs.remove(3) - perf_df = pd.DataFrame([], columns=perf_cols) - for row in range(length): - if prod_df.iloc[row, 3] == "lin": - perf_df.loc[len(perf_df)] = prod_df.iloc[row, col_idxs] - - return perf_df diff --git a/h2integrate/converters/iron/rosner_ore/__init__.py b/h2integrate/converters/iron/rosner_ore/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/h2integrate/converters/iron/rosner_ore/finance_model.py b/h2integrate/converters/iron/rosner_ore/finance_model.py deleted file mode 100644 index 6ba726de6..000000000 --- a/h2integrate/converters/iron/rosner_ore/finance_model.py +++ /dev/null @@ -1,211 +0,0 @@ -import numpy as np -import ProFAST - -from h2integrate.tools.inflation.inflate import inflate_cpi -from h2integrate.converters.iron.load_top_down_coeffs import load_top_down_coeffs - - -def main(config): - """ - Runs a techno-economic analysis of a reduction plant using ProFAST. - - This function retrieves cost data, configures ProFAST with financial assumptions, - and calculates the levelized cost of processed iron ore. It processes capital, - fixed, and variable operational costs while adjusting for inflation. - - Args: - config (object): Configuration object containing input parameters, including: - cost (object): Contains cost data in a DataFrame. - site (dict): Contains site-specific parameters, including the site name. - params (dict): Contains financial and operational parameters such as: - - - operational_year (int): The first year of plant operation. - - installation_years (int): Duration of installation in years. - - plant_life (int): Expected operational life of the plant in years. - - gen_inflation (float): General inflation rate. - - cost_year (int): The reference year for cost adjustments. - - financial_assumptions (dict): Key-value pairs of financial assumptions. - - Returns: - tuple: A tuple containing: - - sol (dict): Solution output from ProFAST, including the levelized cost. - - summary (dict): Summary values from the ProFAST model. - - price_breakdown (dict): Cost breakdown from ProFAST. - - pf (ProFAST.ProFAST): The ProFAST model instance with applied parameters. - """ - # Get reduction plant costs into data frame/series with cost names as index - costs = config.cost - cost_df = costs.costs_df.set_index("Name") - cost_ds = cost_df.loc[:, config.site["name"]] - cost_names = cost_df.index.values - cost_types = cost_df.loc[:, "Type"].values - cost_units = cost_df.loc[:, "Unit"].values - - operational_year = config.params["operational_year"] - install_years = config.params["installation_years"] - plant_life = config.params["plant_life"] - gen_inflation = config.params["gen_inflation"] - cost_year = config.params["cost_year"] - - analysis_start = operational_year - install_years - - # Set up ProFAST - pf = ProFAST.ProFAST("blank") - - # Apply all params passed through from config - for param, val in config.params["financial_assumptions"].items(): - pf.set_params(param, val) - pf.set_params("analysis start year", analysis_start) - pf.set_params("operating life", plant_life) - pf.set_params("installation months", 12 * install_years) - pf.set_params("general inflation rate", gen_inflation) - - # Set the commodity produced as processed iron ore - pf.set_params( - "commodity", - { - "name": "processed iron ore", - "unit": "wet metric tonnes", - "initial price": 80, - "escalation": gen_inflation, - }, - ) - - # Set plant production capacity - ore_produced_wltpy = cost_ds.loc["Ore pellets produced"] # wltpy = wet long tons per year - ore_produced_wmtpy = ore_produced_wltpy * 1.016047 # wmtpy = wet metric tones per year - pf.set_params("capacity", ore_produced_wmtpy / 365) # units/day - - # Set default parameters - pf.set_params("sell undepreciated cap", True) - pf.set_params("tax losses monetized", True) - pf.set_params("debt type", "Revolving debt") - - # Set unused parameters to zeros and ones - pf.set_params("maintenance", {"value": 0, "escalation": gen_inflation}) - pf.set_params("non depr assets", 0) - pf.set_params("end of proj sale non depr assets", 0) - pf.set_params("demand rampup", 0) - pf.set_params("long term utilization", 1) - pf.set_params("credit card fees", 0) - pf.set_params("sales tax", 0) - pf.set_params("license and permit", {"value": 00, "escalation": gen_inflation}) - pf.set_params("rent", {"value": 0, "escalation": gen_inflation}) - pf.set_params("property tax and insurance", 0) - pf.set_params("admin expense", 0) - pf.set_params("cash onhand", 1) - - """ - Add the costs - find the indices ("idxs") of the costs in the cost dataframe - and loop through each index ("idx") to add the name, unit, and value to ProFAST - """ - - # Add capital items - capital_idxs = np.where(cost_types == "capital")[0] - for idx in capital_idxs: - name = cost_names[idx] - unit = cost_units[idx] # Units for capital costs should be " $"" - source_year = int(unit[:4]) - source_year_cost = cost_ds.iloc[idx] - cost = inflate_cpi(source_year_cost, source_year, cost_year) - - pf.add_capital_item( - name=name, - cost=cost, - depr_type="MACRS", - depr_period=7, - refurb=[0], - ) - - # Add fixed opex costs - fixed_idxs = np.where(cost_types == "fixed opex")[0] - installation_cost = 0 - for idx in fixed_idxs: - name = cost_names[idx] - unit = cost_units[idx] # Units for fixed opex costs should be " $ per year" - source_year = int(unit[:4]) - source_year_cost = cost_ds.iloc[idx] - cost = inflate_cpi(source_year_cost, source_year, cost_year) - pf.add_fixed_cost( - name=name, - usage=1, - unit="$/year", - cost=cost, - escalation=gen_inflation, - ) - installation_cost += cost - - # Installation costs = 6 months fixed opex cost - installation_cost = installation_cost * 6 / 12 - pf.set_params( - "installation cost", - { - "value": installation_cost, - "depr type": "Straight line", - "depr period": 4, - "depreciable": False, - }, - ) - - """ - In the Martin model, the ProFAST 'feedstocks' cost class covers both the Martin model's - 'variable opex' costs, which are defined solely by the model for the specific technology, - and the Martin model's 'variable opex td' costs, which are UNIVERSAL, 'top-down' costs where - the price of the feedstock (e.g. natural gas) changes in tandem across multiple modules. - """ - - # Add variable opex costs (defined in model) - var_idxs = np.where(cost_types == "variable opex")[0] - installation_cost = 0 - for idx in var_idxs: - name = cost_names[idx] - unit = cost_units[idx] # Should be " $ per " - source_year = int(unit[:4]) - source_year_cost = cost_ds.iloc[idx] - cost = inflate_cpi(source_year_cost, source_year, cost_year) - pf.add_feedstock( - name=name, - usage=1.0, - unit=unit, - cost=cost, - escalation=gen_inflation, - ) - - # Add variable opex costs (look up price from 'top-down' inputs) - var_td_idxs = np.where(cost_types == "variable opex td")[0] - var_td_names = cost_names[var_td_idxs] - var_td_input_costs = load_top_down_coeffs(var_td_names, cost_year) - var_td_years = var_td_input_costs["years"] - year_start_idx = np.where(var_td_years == analysis_start)[0][0] - analysis_end = min(max(var_td_years), analysis_start + plant_life) - year_end_idx = np.where(var_td_years == analysis_end)[0][0] - year_idxs = range(year_start_idx, year_end_idx) - installation_cost = 0 - for idx in var_td_idxs: - name = cost_names[idx] - unit1 = cost_units[idx] # Should be " per " - var_td_usage = cost_ds.iloc[idx] - - var_td_dict = var_td_input_costs[name] - unit2 = var_td_dict["unit"] # Should be " $ per per " - var_td_usage = cost_ds.iloc[idx] - - var_td_dict = var_td_input_costs[name] - var_td_dict["unit"] # Should be " $ per $"" - source_year = int(unit[:4]) - source_year_cost = cost_ds.iloc[idx] - cost = inflate_cepci(source_year_cost, source_year, cost_year) - - pf.add_capital_item( - name=f"{module_label}: {name}", - cost=cost, - depr_type="MACRS", - depr_period=7, - refurb=[0], - ) - - # -------------------------------------- Add fixed costs-------------------------------- - fixed_idxs = np.where(cost_types == "fixed opex")[0] - for idx in fixed_idxs: - name = cost_names[idx] - unit = cost_units[idx] # Units for fixed opex costs should be " $ per year" - source_year = int(unit[:4]) - source_year_cost = cost_ds.iloc[idx] - cost = inflate_cpi(source_year_cost, source_year, cost_year) - pf.add_fixed_cost( - name=f"{module_label}: {name}", - usage=1, - unit="$/year", - cost=cost, - escalation=gen_inflation, - ) - # Putting property tax and insurance here to zero out depcreciation/escalation. - # Could instead put it in set_params if we think that is more accurate - - # ---------------------- Add feedstocks, note the various cost options------------------- - pf.add_feedstock( - name=f"{module_label}: Raw Water Withdrawal", - usage=perf_ds["Raw Water Withdrawal"], - unit="metric tonnes of water per metric tonne of iron", - cost=raw_water_unitcost, - escalation=gen_inflation, - ) - pf.add_feedstock( - name=f"{module_label}: Lime", - usage=perf_ds["Lime"], - unit="metric tonnes of lime per metric tonne of iron", - cost=lime_unitcost, - escalation=gen_inflation, - ) - pf.add_feedstock( - name=f"{module_label}: Carbon", - usage=perf_ds["Carbon (Coke)"], - unit="metric tonnes of carbon per metric tonne of iron", - cost=carbon_unitcost, - escalation=gen_inflation, - ) - pf.add_feedstock( - name=f"{module_label}: Iron Ore", - usage=perf_ds["Iron Ore"], - unit="metric tonnes of iron ore per metric tonne of iron", - cost=iron_ore_pellet_unitcost, - escalation=gen_inflation, - ) - pf.add_feedstock( - name=f"{module_label}: Hydrogen", - usage=perf_ds["Hydrogen"], - unit="metric tonnes of hydrogen per metric tonne of iron", - cost=lcoh_dollar_metric_tonne, - escalation=gen_inflation, - ) - pf.add_feedstock( - name=f"{module_label}: Natural Gas", - usage=perf_ds["Natural Gas"], - unit="GJ-LHV per metric tonne of iron", - cost=natural_gas_prices, - escalation=gen_inflation, - ) - pf.add_feedstock( - name=f"{module_label}: Electricity", - usage=perf_ds["Electricity"], - unit="MWh per metric tonne of iron", - cost=lcoe_dollar_MWH, - escalation=gen_inflation, - ) - pf.add_feedstock( - name=f"{module_label}: Slag Disposal", - usage=perf_ds["Slag"], - unit="metric tonnes of slag per metric tonne of iron", - cost=slag_disposal_unitcost, - escalation=gen_inflation, - ) - - pf.add_coproduct( - name=f"{module_label}: Oxygen sales", - usage=unused_oxygen, - unit="kg O2 per metric tonne of iron", - cost=oxygen_market_price, - escalation=gen_inflation, - ) - - # ------------------------------ Set up outputs --------------------------- - - sol = pf.solve_price() - summary = pf.get_summary_vals() - price_breakdown = pf.get_cost_breakdown() - - return sol, summary, price_breakdown, pf diff --git a/h2integrate/converters/iron/test/conftest.py b/h2integrate/converters/iron/test/conftest.py index 107ff3d86..3fa460d19 100644 --- a/h2integrate/converters/iron/test/conftest.py +++ b/h2integrate/converters/iron/test/conftest.py @@ -3,12 +3,18 @@ from h2integrate import EXAMPLE_DIR from h2integrate.core.inputs.validation import load_driver_yaml -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) @fixture def driver_config(): - driver_config = load_driver_yaml(EXAMPLE_DIR / "21_iron_mn_to_il" / "driver_config.yaml") + driver_config = load_driver_yaml( + EXAMPLE_DIR / "21_iron_examples" / "iron_dri" / "driver_config.yaml" + ) return driver_config diff --git a/h2integrate/converters/iron/test/input/default_fin_config.yaml b/h2integrate/converters/iron/test/input/default_fin_config.yaml deleted file mode 100644 index 6a87adbad..000000000 --- a/h2integrate/converters/iron/test/input/default_fin_config.yaml +++ /dev/null @@ -1,39 +0,0 @@ -battery_system: - batt_replacement_schedule_percent: [0] - batt_bank_replacement: [0] - batt_replacement_option: 0 - batt_computed_bank_capacity: 0 - batt_meter_position: 0 -system_costs: - om_fixed: [1] - om_production: [2] - om_capacity: [0] - om_batt_fixed_cost: 42.888 - om_batt_variable_cost: [0] - om_batt_capacity_cost: 0 - om_batt_replacement_cost: 0 - om_replacement_cost_escal: 0 -revenue: - ppa_price_input: [0.01] # cents/kWh - ppa_escalation: 1 # % -system_use_lifetime_output: 0 -financial_parameters: - inflation_rate: 2.5 - real_discount_rate: 6.98 - federal_tax_rate: 21.0 - state_tax_rate: 4.74 - property_tax_rate: 1.0 - insurance_rate: 0.5 - debt_percent: 68.5 - term_int_rate: 4.6 - months_working_reserve: 1 - analysis_start_year: 2025 - installation_months: 36 - sales_tax_rate_state: 0.0 - admin_expense_percent_of_sales: 1.0 - capital_gains_tax_rate: 15.0 - debt_type: Revolving debt - depreciation_method: MACRS - depreciation_period: 5 -cp_capacity_credit_percent: [0] -degradation: [0] diff --git a/h2integrate/converters/iron/test/input/h2integrate_config_modular.yaml b/h2integrate/converters/iron/test/input/h2integrate_config_modular.yaml deleted file mode 100644 index d0f6ef653..000000000 --- a/h2integrate/converters/iron/test/input/h2integrate_config_modular.yaml +++ /dev/null @@ -1,393 +0,0 @@ -site: - mean_windspeed: false - depth: 0 # m - wind_layout: - row_spacing: 5 # Also defined in ORBIT config for offshore layout. H2Integrate config values override the values in ORBIT. - turbine_spacing: 5 # Also defined in ORBIT config for offshore layout. H2Integrate config values override the values in ORBIT. - grid_angle: 0 # wind layout grid angle in degrees where 0 is north, increasing clockwise - row_phase_offset: 0 # wind layout offset of turbines along row from one row to the next -project_parameters: - project_lifetime: 30 - grid_connection: false # option, can be turned on or off - ppa_price: 0.025 # $/kWh based on 2022 land based wind market report (ERCOT area ppa prices) https://www.energy.gov/sites/default/files/2022-08/land_based_wind_market_report_2202.pdf - hybrid_electricity_estimated_cf: 0.492 # should equal 1 if grid_connection = True - financial_analysis_start_year: 2032 - cost_year: 2022 # to match ATB - installation_time: 36 # months -finance_parameters: - costing_general_inflation: 0.025 # used to adjust modeled costs to cost_year - inflation_rate: 0 # based on 2022 ATB - discount_rate: 0.0948 # nominal return based on 2022 ATB basline workbook - debt_equity_ratio: 1.72 - property_tax: 0.01 # percent of CAPEX # combined with property insurance then between H2A and H2FAST defaults - property_insurance: 0.005 # percent of CAPEX # combined with property tax then between H2A and H2FAST defaults - total_income_tax_rate: 0.2574 # 0.257 tax rate in 2022 atb baseline workbook # current federal income tax rate, but proposed 2023 rate is 0.28. No state income tax in Texas - capital_gains_tax_rate: 0.15 # H2FAST default - sales_tax_rate: 0.0 # Verify that a different rate shouldn't be used # minimum total sales tax rate in Corpus Christi https://www.cctexas.com/detail/corpus-christi-type-fund-purpose - does this apply to H2? - debt_interest_rate: 0.046 - debt_type: Revolving debt # can be "Revolving debt" or "One time loan". Revolving debt is H2FAST default and leads to much lower LCOH - loan_period: 0 # H2FAST default, not used for revolving debt - cash_onhand_months: 1 # H2FAST default - administrative_expense_percent_of_sales: 0.00 # Check this # percent of sales H2FAST default - depreciation_method: MACRS # can be "MACRS" or "Straight line" - MACRS may be better and can reduce LCOH by more than $1/kg and is spec'd in the IRS MACRS schedule https://www.irs.gov/publications/p946#en_US_2020_publink1000107507 - depreciation_period: 7 # years - as specified by the IRS MACRS schedule https://www.irs.gov/publications/p946#en_US_2020_publink1000107507 - depreciation_period_electrolyzer: 7 # based on PEM Electrolysis H2A Production Case Study Documentation estimate of 7 years. also see https://www.irs.gov/publications/p946#en_US_2020_publink1000107507 - discount_years: - wind: 2020 # Updated for GS - wind_and_electrical: 2020 # Updated for GS - wave: 2020 # confirmed by Kaitlin Brunik 20240103 - solar: 2020 # Updated for GS - battery: 2020 # Updated for GS - platform: 2022 # TODO ask Nick and Charlie - electrical_export_system: 2022 # also from ORBIT, so match wind assumptions. TODO ask Sophie Bradenkamp - desal: 2013 # from code citation: https://www.nrel.gov/docs/fy16osti/66073.pdf - electrolyzer: 2020 # 2020 for singlitico2021, 2016 # for simple h2 cost model in hopp (see https://www.hydrogen.energy.gov/pdfs/19009_h2_production_cost_pem_electrolysis_2019.pdf) ## 2020 # based on IRENA report https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2020/Dec/IRENA_Green_hydrogen_cost_2020.pdf - h2_transport_compressor: 2016 # listed in code header - h2_storage: - pressure_vessel: 2022 # based on readme for Compressed_gas_function - pipe: 2019 # Papadias 2021 - salt_cavern: 2019 # Papadias 2021 - turbine: 2003 # assumed based on Kottenstette 2004 - lined_rock_cavern: 2018 # based on Papadias 2021 and HD SAM - none: 2022 # arbitrary - h2_pipe_array: 2018 # ANL costs - h2_transport_pipeline: 2018 # same model for costs as the h2_pipe_array - wind: - expected_plant_cost: none -electrolyzer: - size_mode: normal - rating: 1160 # MW - cluster_rating_MW: 40 - eol_eff_percent_loss: 13 # eol defined as x% change in efficiency from bol - uptime_hours_until_eol: 77600 # number of 'on' hours until electrolyzer reaches eol - include_degradation_penalty: true # include degradation - turndown_ratio: 0.1 # turndown_ratio = minimum_cluster_power/cluster_rating_MW - electrolyzer_capex: 1600 # $/kW - time_between_replacement: 62320 # 62320 based on PEM Electrolysis H2A Production Case Study Documentation estimate of 7 years, 40000 based on current est (see unpublished report), 80000 hours based on OSW atb_year = 2025 - replacement_cost_percent: 0.15 # percent of capex - H2A default case - cost_model: singlitico2021 # "basic" is a basic cost model based on H2a and HFTO program record for PEM electrolysis. "singlitico2021" uses cost estimates from that paper -h2_transport_compressor: - outlet_pressure: 68 # bar based on HDSAM -h2_storage_compressor: - output_pressure: 100 # bar (1 bar = 100 kPa) - flow_rate: 89 # kg/hr - energy_rating: 802 # kWe (aka 1 kWh) - mean_days_between_failures: 200 # days -h2_transport_pipe: - outlet_pressure: 10 # bar - from example in code from Jamie # TODO check this value -h2_storage: - size_capacity_from_demand: - flag: true # If True, then storage is sized to provide steady-state storage - capacity_from_max_on_turbine_storage: false # if True, then days of storage is ignored and storage capacity is based on how much h2 storage fits on the turbines in the plant using Kottenstete 2003. - type: lined_rock_cavern # can be one of ["none", "pipe", "turbine", "pressure_vessel", "salt_cavern", "lined_rock_cavern"] - days: 0 # 8.57267 # from `hydrogen_storage_duration_hr` = 205.74419987482239 [days] how many days worth of production we should be able to store (this is ignored if `capacity_from_max_on_turbine_storage` is set to True) -policy_parameters: # these should be adjusted for inflation prior to application - order of operations: rate in 1992 $, -# then prevailing wage multiplier if applicable, then inflation - option1: # base # no policy included ---> see files/task1/regulation and policy revue/ page 4 of 13 middle - read this - # and look at assumptions - electricity_itc: 0 - electricity_ptc: 0 - h2_ptc: 0 - h2_storage_itc: 0 - option2: # base credit levels with H2 - electricity_itc: 0 - electricity_ptc: 0.003 # $0.003/kW (this is base, see inflation adjustment in option 3) - h2_ptc: 0.6 # $0.60/kg h2 produced - assumes net zero but not meeting prevailing wage requirements - does this need to be - # adjusted for inflation from 2022 dollars to claim date, probably constant after claim date? - h2_storage_itc: 0.06 - option3: # same as option 5, but assuming prevailing wages are met --> 5x multiplier on both PTCs - electricity_itc: 0 - electricity_ptc: 0.015 # $/kWh 1992 dollars - h2_ptc: 3.00 # $/kg 2022 dollars - do not adjust for inflation - h2_storage_itc: 0.3 - # bonus options, option 5 and 6 but ITC equivalents - option4: # prevailing wages not met - electricity_itc: 0.06 # %/100 capex - electricity_ptc: 0.00 # $/kW 1992 dollars - h2_ptc: 0.6 # $0.60/kg produced 2022 dollars - assumes net zero but not meeting prevailing wage requirements - does this need to be - # do not adjust for inflation, probably constant after claim date? - h2_storage_itc: 0.06 - option5: # prevailing wages met - electricity_itc: 0.30 # %/100 capex - electricity_ptc: 0.0 # $/kWh 1992 dollars - h2_ptc: 3.00 # $/kg of h2 produced 2022 dollars - do adjust for inflation every year applied and until application year - h2_storage_itc: 0.3 - option6: # assumes prevailing wages are met, and includes 10% bonus credit of domestic content (100% of steel and iron - # and mfg. components from the US) - electricity_itc: 0.40 # %/100 capex - electricity_ptc: 0.0 # $/kWh 1992 dollars - h2_ptc: 3.00 # $/kg of h2 produced 2022 dollars - do adjust for inflation every year applied and until application year - h2_storage_itc: 0.4 - option7: # assumes prevailing wages are met, and includes 10% bonus credit of domestic content (100% of steel and iron - # and mfg. components from the US) - electricity_itc: 0.0 # %/100 capex - electricity_ptc: 0.0165 # $/kWh 1992 dollars (0.015*1.1) - h2_ptc: 3.00 # $/kg of h2 produced 2022 dollars - do adjust for inflation every year applied and until application year - h2_storage_itc: 0 - # you can elect itc_for_h2 in leu of the h2_ptc - this choice is independent of the other tech credit selections - # 6% or %50 for itc_for_h2 -plant_design: - scenario0: - electrolyzer_location: platform # can be one of ["onshore", "turbine", "platform"] - transportation: pipeline # can be one of ["hvdc", "pipeline", "none", hvdc+pipeline, "colocated"] - h2_storage_location: platform # can be one of ["onshore", "turbine", "platform"] - wind_location: offshore # can be one of ["onshore", "offshore"] - pv_location: none # can be one of ["none", "onshore", "platform"] - battery_location: none # can be one of ["none", "onshore", "platform"] - scenario1: - electrolyzer_location: onshore # can be one of ["onshore", "turbine", "platform"] - transportation: hvdc # can be one of ["hvdc", "pipeline", "none", hvdc+pipeline, "colocated"] - h2_storage_location: onshore # can be one of ["onshore", "turbine", "platform"] - wind_location: offshore # can be one of ["onshore", "offshore"] - pv_location: none # can be one of ["none", "onshore", "platform"] - battery_location: none # can be one of ["none", "onshore", "platform"] - scenario2: - electrolyzer_location: onshore # can be one of ["onshore", "turbine", "platform"] - transportation: hvdc # can be one of ["hvdc", "pipeline", "none", hvdc+pipeline, "colocated"] - h2_storage_location: platform # can be one of ["onshore", "turbine", "platform"] - wind_location: offshore # can be one of ["onshore", "offshore"] - pv_location: none # can be one of ["none", "onshore", "platform"] - battery_location: none # can be one of ["none", "onshore", "platform"] - scenario3: - electrolyzer_location: turbine # can be one of ["onshore", "turbine", "platform"] - transportation: none # can be one of ["hvdc", "pipeline", "none", hvdc+pipeline, "colocated"] - h2_storage_location: turbine # can be one of ["onshore", "turbine", "platform"] - wind_location: offshore # can be one of ["onshore", "offshore"] - pv_location: none # can be one of ["none", "onshore", "platform"] - battery_location: none # can be one of ["none", "onshore", "platform"] - scenario4: - electrolyzer_location: turbine # can be one of ["onshore", "turbine", "platform"] - transportation: none # can be one of ["hvdc", "pipeline", "none", hvdc+pipeline, "colocated"] - h2_storage_location: platform # can be one of ["onshore", "turbine", "platform"] - wind_location: offshore # can be one of ["onshore", "offshore"] - pv_location: none # can be one of ["none", "onshore", "platform"] - battery_location: none # can be one of ["none", "onshore", "platform"] - scenario5: - electrolyzer_location: turbine # can be one of ["onshore", "turbine", "platform"] - transportation: pipeline # can be one of ["hvdc", "pipeline", "none", hvdc+pipeline, "colocated"] - h2_storage_location: onshore # can be one of ["onshore", "turbine", "platform"] - wind_location: offshore # can be one of ["onshore", "offshore"] - pv_location: none # can be one of ["none", "onshore", "platform"] - battery_location: none # can be one of ["none", "onshore", "platform"] - scenario6: - electrolyzer_location: platform # can be one of ["onshore", "turbine", "platform"] - transportation: none # can be one of ["hvdc", "pipeline", "none", hvdc+pipeline, "colocated"] - h2_storage_location: platform # can be one of ["onshore", "turbine", "platform"] - wind_location: offshore # can be one of ["onshore", "offshore"] - pv_location: none # can be one of ["none", "onshore", "platform"] - battery_location: none # can be one of ["none", "onshore", "platform"] - scenario7: - electrolyzer_location: platform # can be one of ["onshore", "turbine", "platform"] - transportation: pipeline # can be one of ["hvdc", "pipeline", "none", hvdc+pipeline, "colocated"] - h2_storage_location: onshore # can be one of ["onshore", "turbine", "platform"] - wind_location: offshore # can be one of ["onshore", "offshore"] - pv_location: none # can be one of ["none", "onshore", "platform"] - battery_location: none # can be one of ["none", "onshore", "platform"] - scenario8: - electrolyzer_location: platform # can be one of ["onshore", "turbine", "platform"] - transportation: hvdc+pipeline # can be one of ["hvdc", "pipeline", "none", hvdc+pipeline, "colocated"] - h2_storage_location: onshore # can be one of ["onshore", "turbine", "platform"] - wind_location: offshore # can be one of ["onshore", "offshore"] - scenario9: - electrolyzer_location: onshore # can be one of ["onshore", "turbine", "platform"] - transportation: colocated # can be one of ["hvdc", "pipeline", "none", hvdc+pipeline, "colocated"] - h2_storage_location: onshore # can be one of ["onshore", "turbine", "platform"] - wind_location: onshore # can be one of ["onshore", "offshore"] - pv_location: onshore # can be one of ["none", "onshore", "platform"] - battery_location: onshore # can be one of ["none", "onshore", "platform"] - scenario10: - electrolyzer_location: platform # can be one of ["onshore", "turbine", "platform"] - transportation: pipeline # can be one of ["hvdc", "pipeline", "none", hvdc+pipeline, "colocated"] - h2_storage_location: onshore # can be one of ["onshore", "turbine", "platform"] - pv_location: platform # can be one of ["none", "onshore", "platform"] - battery_location: platform # can be one of ["none", "onshore", "platform"] -iron_ore: - site: - lat: 47.29415278 - lon: -91.25649444 - resource_dir: /../data_library/weather/ - name: Northshore - product_selection: drg_taconite_pellets #'drg_taconite_pellets' # 'std_taconite_pellets' # - performance_model: - name: martin_ore # 'martin_ore' - refit_coeffs: false - cost_model: - name: martin_ore # 'martin_ore' - refit_coeffs: false - finance_model: - name: martin_ore # 'martin_ore', 'rosner_ore' - performance: - input_capacity_factor_estimate: 0.9 - costs: - operational_year: 2035 - installation_years: 3 - plant_life: 30 - o2_heat_integration: false - finances: - gen_inflation: 0.025 - # Additional parameters passed to ProFAST - financial_assumptions: - total income tax rate: 0.2574 - capital gains tax rate: 0.15 - leverage after tax nominal discount rate: 0.10893 - debt equity ratio of initial financing: 0.624788 - debt interest rate: 0.050049 -iron_win: - site: - lat: 41 - lon: -78 - resource_dir: /../data_library/weather/ - name: Mid WI - product_selection: ng_dri # 'h2_dri' # 'ng_dri' - performance_model: - name: rosner # 'rosner' - refit_coeffs: false - cost_model: - name: rosner # 'rosner' - refit_coeffs: false - finance_model: - name: rosner # 'rosner', 'rosner_override' - performance: - plant_capacity_mtpy: 1418095 - capacity_denominator: iron # steel - costs: - operational_year: 2035 - installation_years: 3 - plant_life: 30 - o2_heat_integration: false - finances: - gen_inflation: 0.025 - # Additional parameters passed to ProFAST - financial_assumptions: - total income tax rate: 0.2574 - capital gains tax rate: 0.15 - leverage after tax nominal discount rate: 0.10893 - debt equity ratio of initial financing: 0.624788 - debt interest rate: 0.050049 -iron_post: - site: - lat: 41 - lon: -84 - resource_dir: /../data_library/weather/ - name: Mid WI - product_selection: ng_eaf #''h2_eaf' # 'ng_eaf' - performance_model: - name: rosner # 'rosner' - refit_coeffs: false - cost_model: - name: rosner # 'rosner' - refit_coeffs: false - finance_model: - name: rosner # 'rosner', 'rosner_override' - performance: - plant_capacity_mtpy: 1000000 - costs: - operational_year: 2035 - installation_years: 3 - plant_life: 30 - o2_heat_integration: false - finances: - gen_inflation: 0.025 - # Additional parameters passed to ProFAST - financial_assumptions: - total income tax rate: 0.2574 - capital gains tax rate: 0.15 - leverage after tax nominal discount rate: 0.10893 - debt equity ratio of initial financing: 0.624788 - debt interest rate: 0.050049 -lca_config: - run_lca: true # True - electrolyzer_type: pem # alkaline, soec - feedstock_water_type: ground # desal, surface - cambium: # cambium API argument, see cambium_data.py for additional argument options - project_uuid: 0f92fe57-3365-428a-8fe8-0afc326b3b43 - scenario: Mid-case with 100% decarbonization by 2035 - location_type: GEA Regions 2023 - time_type: hourly -opt_options: - opt_flag: true - general: - folder_output: output - fname_output: optimization_log - design_variables: - electrolyzer_rating_kw: - flag: true - lower: 1000.0 - upper: 2000000.0 - units: kW - pv_capacity_kw: - flag: false - lower: 1000.0 - upper: 2000000.0 - units: kW - wave_capacity_kw: - flag: false - lower: 1000.0 - upper: 2000000.0 - units: kW*h - battery_capacity_kw: - flag: false - lower: 1000.0 - upper: 2000000.0 - units: kW - battery_capacity_kwh: - flag: false - lower: 1000.0 - upper: 2000000.0 - units: kW*h - turbine_x: - flag: false - lower: 0.0 - upper: 20000.0 - units: m - turbine_y: - flag: false - lower: 0.0 - upper: 20000.0 - units: m - constraints: - turbine_spacing: - flag: false - lower: 0.0 - boundary_distance: - flag: false - lower: 0.0 - pv_to_platform_area_ratio: - flag: false - upper: 1.0 # relative size of solar pv area to platform area - user: {} - merit_figure: lcoi - merit_figure_user: - name: lcoi - max_flag: false - ref: 1.0 # value of objective that scales to 1.0 - driver: - optimization: - flag: true - solver: SNOPT - tol: 1E-6 - gradient_method: snopt_fd # can be one of ['pyopt_fd', 'openmdao', 'snopt_fd'] - max_major_iter: 10 - max_minor_iter: 30 - # time_limit: 10 # (sec) optional - # "hist_file_name: "snopt_history.txt", # optional - verify_level: 0 # optional - step_calc: None - form: forward # type of finite differences to use, can be one of ["forward", "backward", "central"] - debug_print: false - design_of_experiments: - flag: false - run_parallel: false - generator: FullFact # [Uniform, FullFact, PlackettBurman, BoxBehnken, LatinHypercube] - num_samples: 5 # Number of samples to evaluate model at (Uniform and LatinHypercube only) - seed: 2 - levels: 5 # Number of evenly spaced levels between each design variable lower and upper bound (FullFactorial only) - criterion: None # [None, center, c, maximin, m, centermaximin, cm, correelation, corr] - iterations: 1 - debug_print: false - step_size_study: - flag: false - recorder: - flag: true - file_name: record.sql - includes: false diff --git a/h2integrate/converters/iron/test/input/hopp_config.yaml b/h2integrate/converters/iron/test/input/hopp_config.yaml deleted file mode 100644 index 3e9621a21..000000000 --- a/h2integrate/converters/iron/test/input/hopp_config.yaml +++ /dev/null @@ -1,95 +0,0 @@ -site: #!include flatirons_site.yaml - data: - lat: 47.5233 - lon: -92.5366 - elev: 1099 - year: 2013 - tz: -6 - site_boundaries: - verts: - - [0.0, 0.0] - - [0.0, 20000.0] - - [20000.0, 20000.0] - - [20000.0, 0.0] - verts_simple: - - [0.0, 0.0] - - [0.0, 20000.0] - - [20000.0, 20000.0] - - [20000.0, 0.0] - solar_resource_file: ../../../../resource_files/solar/47.5233_-92.5366_psmv3_60_2013.csv - wind_resource_file: ../../../../resource_files/wind/47.5233_-92.5366_windtoolkit_2013_60min_100m_120m.srw - wave_resource_file: '' - grid_resource_file: '' - hub_height: 115.0 - capacity_hours: [] - solar: true - wind: true - wave: false - wind_resource_origin: WTK -technologies: - wind: - num_turbines: 216 - turbine_rating_kw: 6000.0 - model_name: floris - timestep: [0, 8760] - floris_config: input_files/floris/floris_input_lbw_6MW.yaml - fin_model: !include default_fin_config.yaml - hub_height: 115.0 - pv: - system_capacity_kw: 100000 - dc_degradation: - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - - 0 - fin_model: !include default_fin_config.yaml - battery: - system_capacity_kwh: 100000 - system_capacity_kw: 100000 - minimum_SOC: 20.0 - maximum_SOC: 100.0 - initial_SOC: 90.0 - fin_model: !include default_fin_config.yaml - grid: - interconnect_kw: 2000000 # Set higher than rated generation capacity - fin_model: !include default_fin_config.yaml -config: - simulation_options: - wind: - skip_financial: false - dispatch_options: - battery_dispatch: load_following_heuristic # load_following_heuristic # heuristic - solver: cbc - n_look_ahead_periods: 48 - grid_charging: false - pv_charging_only: false - include_lifecycle_count: false - cost_info: # Costs from GS Manuscript Supplemental Table S11 - wind_installed_cost_mw: 1098000 # (2020USD) - solar_installed_cost_mw: 991000 # (2020USD) - storage_installed_cost_mwh: 158000 # (2020USD) - storage_installed_cost_mw: 212000 # (2020USD) - wind_om_per_kw: 25.8 # (2020USD) - pv_om_per_kw: 17.2 # (2020USD) - battery_om_per_kw: 9.25 # (2020USD) based on 0.025 percent of battery capex with a 100MW 1hr battery diff --git a/h2integrate/converters/iron/test/test_iron_mine.py b/h2integrate/converters/iron/test/test_iron_mine.py deleted file mode 100644 index 953bde012..000000000 --- a/h2integrate/converters/iron/test/test_iron_mine.py +++ /dev/null @@ -1,153 +0,0 @@ -import pytest -import openmdao.api as om -from pytest import fixture - -from h2integrate.converters.iron.iron_mine import ( - IronMineCostComponent, - IronMinePerformanceComponent, -) - - -@fixture -def iron_ore_config_martin_om(): - shared_params = { - "mine": "Northshore", - "taconite_pellet_type": "drg", - } - performance_params = {"ore_cf_estimate": 0.9, "model_name": "martin_ore"} - cost_params = { - "LCOE": 58.02, - "LCOH": 7.10, - "model_name": "martin_ore", - "varom_model_name": "martin_ore", - "installation_years": 3, - "operational_year": 2035, - # 'plant_life': 30, - } - - tech_config = { - "model_inputs": { - "cost_parameters": cost_params, - "performance_parameters": performance_params, - "shared_parameters": shared_params, - } - } - return tech_config - - -@fixture -def iron_ore_config_rosner_om(): - shared_params = { - "mine": "Northshore", - "taconite_pellet_type": "drg", - } - performance_params = {"ore_cf_estimate": 0.9, "model_name": "martin_ore"} - cost_params = { - "LCOE": 58.02, - "LCOH": 7.10, - "model_name": "martin_ore", - "varom_model_name": "rosner_ore", - "installation_years": 3, - "operational_year": 2035, - # 'plant_life': 30, - } - - tech_config = { - "model_inputs": { - "cost_parameters": cost_params, - "performance_parameters": performance_params, - "shared_parameters": shared_params, - } - } - return tech_config - - -# baseline case -@pytest.mark.regression -def test_baseline_iron_ore_costs_martin( - plant_config, driver_config, iron_ore_config_martin_om, subtests -): - martin_ore_capex = 1221599018.626594 - martin_ore_var_om = 441958721.59532887 - martin_ore_fixed_om = 0.0 - - prob = om.Problem() - iron_ore_perf = IronMinePerformanceComponent( - plant_config=plant_config, - tech_config=iron_ore_config_martin_om, - driver_config=driver_config, - ) - - iron_ore_cost = IronMineCostComponent( - plant_config=plant_config, - tech_config=iron_ore_config_martin_om, - driver_config=driver_config, - ) - - prob.model.add_subsystem("ore_perf", iron_ore_perf, promotes=["*"]) - prob.model.add_subsystem("ore_cost", iron_ore_cost, promotes=["*"]) - prob.setup() - prob.run_model() - - annual_ore = prob.get_val("ore_perf.total_iron_ore_produced", units="t/year") - with subtests.test("Annual Ore"): - assert pytest.approx(annual_ore[0] / 365, rel=1e-6) == 12385.195376438356 - with subtests.test("CapEx"): - assert ( - pytest.approx(prob.get_val("ore_cost.CapEx", units="USD")[0], rel=1e-6) - == martin_ore_capex - ) - with subtests.test("OpEx"): - assert ( - pytest.approx(prob.get_val("ore_cost.OpEx", units="USD/year")[0], rel=1e-6) - == martin_ore_fixed_om - ) - with subtests.test("VarOpEx"): - assert ( - pytest.approx(prob.get_val("ore_cost.VarOpEx", units="USD/year")[0], rel=1e-6) - == martin_ore_var_om - ) - - -@pytest.mark.regression -def test_baseline_iron_ore_costs_rosner( - plant_config, driver_config, iron_ore_config_rosner_om, subtests -): - rosner_ore_capex = 1221599018.626594 - rosner_ore_var_om = 441958721.59532887 - rosner_ore_fixed_om = 0.0 - prob = om.Problem() - iron_ore_perf = IronMinePerformanceComponent( - plant_config=plant_config, - tech_config=iron_ore_config_rosner_om, - driver_config=driver_config, - ) - - iron_ore_cost = IronMineCostComponent( - plant_config=plant_config, - tech_config=iron_ore_config_rosner_om, - driver_config=driver_config, - ) - - prob.model.add_subsystem("ore_perf", iron_ore_perf, promotes=["*"]) - prob.model.add_subsystem("ore_cost", iron_ore_cost, promotes=["*"]) - prob.setup() - prob.run_model() - annual_ore = prob.get_val("ore_perf.total_iron_ore_produced", units="t/year") - with subtests.test("Annual Ore"): - assert pytest.approx(annual_ore[0] / 365, rel=1e-6) == 12385.195376438356 - with subtests.test("CapEx"): - assert ( - pytest.approx(prob.get_val("ore_cost.CapEx", units="USD")[0], rel=1e-6) - == rosner_ore_capex - ) - with subtests.test("OpEx"): - assert ( - pytest.approx(prob.get_val("ore_cost.OpEx", units="USD/year")[0], rel=1e-6) - == rosner_ore_fixed_om - ) - with subtests.test("VarOpEx"): - assert ( - pytest.approx(prob.get_val("ore_cost.VarOpEx", units="USD/year")[0], rel=1e-6) - == rosner_ore_var_om - ) diff --git a/h2integrate/converters/iron/test/test_iron_ore.py b/h2integrate/converters/iron/test/test_iron_ore.py deleted file mode 100644 index 727fadcfb..000000000 --- a/h2integrate/converters/iron/test/test_iron_ore.py +++ /dev/null @@ -1,219 +0,0 @@ -import copy -import shutil -from pathlib import Path - -import pytest -from pytest import approx, fixture - -from h2integrate.converters.iron import iron - - -@fixture -def iron_ore(): - iron_ore = { - "project_parameters": { - "cost_year": 2022, - "project_lifetime": 30, - "grid_connection": False, - "ppa_price": 0.025, - "hybrid_electricity_estimated_cf": 0.492, - "financial_analysis_start_year": 2032, - "installation_time": 36, - }, - "iron": { - "site": { - "lat": 47.29415278, - "lon": -91.25649444, - "resource_dir": "/../data_library/weather/", - "name": "Northshore", - }, - "product_selection": "drg_taconite_pellets", # 'std_taconite_pellets' - "performance_model": { - "name": "martin_ore", - "refit_coeffs": False, - }, - "cost_model": { - "name": "martin_ore", - "refit_coeffs": False, - }, - "finance_model": { - "name": "martin_ore", # 'rosner_ore' also an option - }, - "performance": { - "input_capacity_factor_estimate": 0.9, - }, - "costs": { - "operational_year": 2035, - "installation_years": 3, - "plant_life": 30, - "o2_heat_integration": False, - "lcoh": 2.80, - }, - "finances": { - "lcoh": 2.80, - "gen_inflation": 0.025, - "financial_assumptions": { - "total income tax rate": 0.2574, - "capital gains tax rate": 0.15, - "leverage after tax nominal discount rate": 0.10893, - "debt equity ratio of initial financing": 0.624788, - "debt interest rate": 0.050049, - }, - }, - }, - } - return iron_ore - - -@pytest.mark.regression -def test_run_martin_iron_ore(iron_ore, subtests): - performance, cost, finance = iron.run_iron_full_model(iron_ore) - perf_df = performance.performances_df - cost_df = cost.costs_df - - with subtests.test("performance model: drg_taconite_pellets"): - assert perf_df.loc[perf_df["Name"] == "Pellet Fe Content", "Northshore"].values[ - 0 - ] == approx(67.35, 1e-3) - with subtests.test("cost model: drg_taconite_pellets"): - assert cost_df.loc[ - cost_df["Name"] == "Buildings and other structures", "Northshore" - ].values[0] == approx(1012588246, 1e-3) - with subtests.test("finance model: drg_taconite_pellets"): - # TODO: verify value - just copied result - assert finance.sol["lco"] == approx(125.25, 1e-3) - - # Test a different ore type - iron_ore_copy = copy.deepcopy(iron_ore) # Create an isolated copy - iron_ore_copy["iron"]["product_selection"] = "std_taconite_pellets" - performance, cost, finance = iron.run_iron_full_model(iron_ore_copy) - perf_df = performance.performances_df - cost_df = cost.costs_df - with subtests.test("performance model: std_taconite_pellets"): - assert perf_df.loc[perf_df["Name"] == "Pellet Fe Content", "Northshore"].values[ - 0 - ] == approx(65.0, 1e-3) - with subtests.test("cost model: std_taconite_pellets"): - assert cost_df.loc[ - cost_df["Name"] == "Buildings and other structures", "Northshore" - ].values[0] == approx(802586723.3, 1e-3) - with subtests.test("finance model: std_taconite_pellets"): - # TODO: verify value - just copied result - assert finance.sol["lco"] == approx(99.28, 1e-3) - - # Test a different mine location - # TODO: check that site lat/lon get updated if you're changing site name - iron_ore_copy = copy.deepcopy(iron_ore) # Create an isolated copy - iron_ore_copy["iron"]["site"]["name"] = "United" - performance, cost, finance = iron.run_iron_full_model(iron_ore_copy) - perf_df = performance.performances_df - cost_df = cost.costs_df - with subtests.test("performance model: different location"): - assert perf_df.loc[perf_df["Name"] == "Pellet Fe Content", "United"].values[0] == approx( - 67.63, 1e-3 - ) - with subtests.test("cost model: different location"): - assert cost_df.loc[cost_df["Name"] == "Buildings and other structures", "United"].values[ - 0 - ] == approx(1118066189.0, 1e-3) - with subtests.test("finance model: different location"): - # TODO: verify value - just copied result - assert finance.sol["lco"] == approx(141.91, 1e-3) - - -@pytest.mark.regression -def test_refit_coefficients(iron_ore, subtests): - # Determine the model directory based on the model name - iron_tech_dir = Path(__file__).parents[3] / "h2integrate" / "converters" / "iron" - model_name = iron_ore["iron"]["cost_model"]["name"] - model_dir = iron_tech_dir / model_name - - # Backup both performance and cost coefficient files - cost_coeffs_file = model_dir / "cost_coeffs.csv" - cost_backup_file = model_dir / "cost_coeffs_backup.csv" - perf_coeffs_file = model_dir / "perf_coeffs.csv" - perf_backup_file = model_dir / "perf_coeffs_backup.csv" - - if cost_coeffs_file.exists(): - shutil.copy2(cost_coeffs_file, cost_backup_file) - if perf_coeffs_file.exists(): - shutil.copy2(perf_coeffs_file, perf_backup_file) - - # Non-refit coefficients - performance, cost, finance = iron.run_iron_full_model(iron_ore) - perf_df = performance.performances_df - cost_df = cost.costs_df - - # Refit coefficients performance model - iron_ore_copy = copy.deepcopy(iron_ore) - iron_ore_copy["iron"]["performance_model"]["refit_coeffs"] = True - performance2, cost2, finance2 = iron.run_iron_full_model(iron_ore_copy) - perf_df2 = performance2.performances_df - cost_df2 = cost2.costs_df - with subtests.test("performance model: refit coefficients"): - assert perf_df2.loc[perf_df2["Name"] == "Pellet Fe Content", "Northshore"].values[ - 0 - ] == approx( - perf_df.loc[perf_df["Name"] == "Pellet Fe Content", "Northshore"].values[0], 1e-3 - ) - with subtests.test("cost model: refit coefficients"): - assert cost_df2.loc[ - cost_df2["Name"] == "Buildings and other structures", "Northshore" - ].values[0] == approx( - cost_df.loc[cost_df["Name"] == "Buildings and other structures", "Northshore"].values[ - 0 - ], - 1e-3, - ) - with subtests.test("finance model: refit coefficients"): - assert finance2.sol["lco"] == approx(finance.sol["lco"], 1e-3) - - # Refit coefficients cost model - iron_ore_copy = copy.deepcopy(iron_ore) - iron_ore_copy["iron"]["cost_model"]["refit_coeffs"] = True - performance2, cost2, finance2 = iron.run_iron_full_model(iron_ore_copy) - perf_df2 = performance2.performances_df - cost_df2 = cost2.costs_df - with subtests.test("performance model: refit coefficients"): - assert perf_df2.loc[perf_df2["Name"] == "Pellet Fe Content", "Northshore"].values[ - 0 - ] == approx( - perf_df.loc[perf_df["Name"] == "Pellet Fe Content", "Northshore"].values[0], 1e-3 - ) - with subtests.test("cost model: refit coefficients"): - assert cost_df2.loc[ - cost_df2["Name"] == "Buildings and other structures", "Northshore" - ].values[0] == approx( - cost_df.loc[cost_df["Name"] == "Buildings and other structures", "Northshore"].values[ - 0 - ], - 1e-3, - ) - with subtests.test("finance model: refit coefficients"): - assert finance2.sol["lco"] == approx(finance.sol["lco"], 1e-3) - - # Restore the original coefficient files - if cost_backup_file.exists(): - shutil.move(cost_backup_file, cost_coeffs_file) - if perf_backup_file.exists(): - shutil.move(perf_backup_file, perf_coeffs_file) - - -@pytest.mark.regression -def test_run_rosner_iron_ore(iron_ore, subtests): - iron_ore_copy = copy.deepcopy(iron_ore) - iron_ore_copy["iron"]["finance_model"]["name"] = "rosner_ore" - performance, cost, finance = iron.run_iron_full_model(iron_ore_copy) - perf_df = performance.performances_df - cost_df = cost.costs_df - with subtests.test("performance model: drg_taconite_pellets"): - assert perf_df.loc[perf_df["Name"] == "Pellet Fe Content", "Northshore"].values[ - 0 - ] == approx(67.35, 1e-3) - with subtests.test("cost model: drg_taconite_pellets"): - assert cost_df.loc[ - cost_df["Name"] == "Buildings and other structures", "Northshore" - ].values[0] == approx(1012588246, 1e-3) - with subtests.test("finance model: drg_taconite_pellets"): - # TODO: verify value - just copied result - assert finance.sol["lco"] == approx(207.66, 1e-3) diff --git a/h2integrate/converters/iron/test/test_iron_plant.py b/h2integrate/converters/iron/test/test_iron_plant.py deleted file mode 100644 index 865936330..000000000 --- a/h2integrate/converters/iron/test/test_iron_plant.py +++ /dev/null @@ -1,133 +0,0 @@ -import pytest -import openmdao.api as om -from pytest import fixture - -from h2integrate.converters.iron.iron_plant import ( - IronPlantCostComponent, - IronPlantPerformanceComponent, -) - - -@fixture -def iron_dri_config_rosner_ng(): - shared_params = { - "winning_type": "ng", - "iron_win_capacity": 1418095, - "site_name": "IL", - } - performance_params = {"win_capacity_demon": "iron", "model_name": "rosner"} - cost_params = { - "LCOE": 58.02, - "LCOH": 7.10, - "LCOI_ore": 125.25996463784443, - "iron_transport_cost": 30.566808424134745, - "ore_profit_pct": 6.0, - "varom_model_name": "rosner", - "installation_years": 3, - "operational_year": 2035, - # 'plant_life': 30, - } - - tech_config = { - "model_inputs": { - "cost_parameters": cost_params, - "performance_parameters": performance_params, - "shared_parameters": shared_params, - } - } - return tech_config - - -@pytest.mark.regression -def test_baseline_iron_dri_costs_rosner_ng( - plant_config, driver_config, iron_dri_config_rosner_ng, subtests -): - expected_capex = 403808062.6981323 - expected_var_om = 369445330.68402404 - expected_fixed_om = 60103761.59958463 - capacity = 3885.1917808219177 - - prob = om.Problem() - iron_dri_perf = IronPlantPerformanceComponent( - plant_config=plant_config, - tech_config=iron_dri_config_rosner_ng, - driver_config=driver_config, - ) - - iron_dri_cost = IronPlantCostComponent( - plant_config=plant_config, - tech_config=iron_dri_config_rosner_ng, - driver_config=driver_config, - ) - - prob.model.add_subsystem("dri_perf", iron_dri_perf, promotes=["*"]) - prob.model.add_subsystem("dri_cost", iron_dri_cost, promotes=["*"]) - prob.setup() - prob.run_model() - - annual_pig_iron = prob.get_val("dri_perf.total_pig_iron_produced", units="t/year") - with subtests.test("Annual Ore"): - assert pytest.approx(annual_pig_iron[0] / 365, rel=1e-3) == capacity - with subtests.test("CapEx"): - assert ( - pytest.approx(prob.get_val("dri_cost.CapEx", units="USD")[0], rel=1e-6) - == expected_capex - ) - with subtests.test("OpEx"): - assert ( - pytest.approx(prob.get_val("dri_cost.OpEx", units="USD/year")[0], rel=1e-6) - == expected_fixed_om - ) - with subtests.test("VarOpEx"): - assert ( - pytest.approx(prob.get_val("dri_cost.VarOpEx", units="USD/year")[0], rel=1e-6) - == expected_var_om - ) - - -@pytest.mark.regression -def test_baseline_iron_dri_costs_rosner_h2( - plant_config, driver_config, iron_dri_config_rosner_ng, subtests -): - expected_capex = 246546589.2914324 - expected_var_om = 888529893.2442514 - expected_fixed_om = 53360873.348792635 - capacity = 3885.1917808219177 - - iron_dri_config_rosner_ng["model_inputs"]["shared_parameters"].update({"winning_type": "h2"}) - prob = om.Problem() - iron_dri_perf = IronPlantPerformanceComponent( - plant_config=plant_config, - tech_config=iron_dri_config_rosner_ng, - driver_config=driver_config, - ) - - iron_dri_cost = IronPlantCostComponent( - plant_config=plant_config, - tech_config=iron_dri_config_rosner_ng, - driver_config=driver_config, - ) - - prob.model.add_subsystem("dri_perf", iron_dri_perf, promotes=["*"]) - prob.model.add_subsystem("dri_cost", iron_dri_cost, promotes=["*"]) - prob.setup() - prob.run_model() - - annual_pig_iron = prob.get_val("dri_perf.total_pig_iron_produced", units="t/year") - with subtests.test("Annual Ore"): - assert pytest.approx(annual_pig_iron[0] / 365, rel=1e-3) == capacity - with subtests.test("CapEx"): - assert ( - pytest.approx(prob.get_val("dri_cost.CapEx", units="USD")[0], rel=1e-6) - == expected_capex - ) - with subtests.test("OpEx"): - assert ( - pytest.approx(prob.get_val("dri_cost.OpEx", units="USD/year")[0], rel=1e-6) - == expected_fixed_om - ) - with subtests.test("VarOpEx"): - assert ( - pytest.approx(prob.get_val("dri_cost.VarOpEx", units="USD/year")[0], rel=1e-6) - == expected_var_om - ) diff --git a/h2integrate/converters/iron/test/test_iron_post.py b/h2integrate/converters/iron/test/test_iron_post.py deleted file mode 100644 index 44aa0f4c1..000000000 --- a/h2integrate/converters/iron/test/test_iron_post.py +++ /dev/null @@ -1,239 +0,0 @@ -import copy -import shutil -from pathlib import Path - -import pytest -from pytest import approx, fixture - -from h2integrate.converters.iron import iron - - -@fixture -def iron_post(): - iron_post = { - "project_parameters": { - "cost_year": 2022, - "project_lifetime": 30, - "grid_connection": False, - "ppa_price": 0.025, - "hybrid_electricity_estimated_cf": 0.492, - "financial_analysis_start_year": 2032, - "installation_time": 36, - }, - "iron": { - "site": { - "lat": 41, - "lon": -84, - "resource_dir": "/../data_library/weather/", - "name": "Mid WI", - }, - "product_selection": "ng_eaf", # Alternative: 'h2_eaf' - "performance_model": { - "name": "rosner", - "refit_coeffs": False, - }, - "cost_model": { - "name": "rosner", - "refit_coeffs": False, - }, - "finance_model": { - "name": "rosner", # Alternative: 'rosner_override' - }, - "performance": { - "plant_capacity_mtpy": 1000000, - "capacity_denominator": "iron", # Alternative: 'steel' - }, - "costs": { - "lcoh": 2.80, - "lcoe": 0.050, - "lco_iron_ore_tonne": 313, - "operational_year": 2035, - "installation_years": 3, - "plant_life": 30, - "o2_heat_integration": False, - }, - "finances": { - "lcoh": 2.80, - "gen_inflation": 0.025, - "financial_assumptions": { - "total income tax rate": 0.2574, - "capital gains tax rate": 0.15, - "leverage after tax nominal discount rate": 0.10893, - "debt equity ratio of initial financing": 0.624788, - "debt interest rate": 0.050049, - }, - }, - }, - } - return iron_post - - -@pytest.mark.regression -def test_ng_eaf(iron_post, subtests): - performance, cost, finance = iron.run_iron_full_model(iron_post) - perf_df = performance.performances_df - cost_df = cost.costs_df - - with subtests.test("performance model: NG"): - # TODO: verify conversion is correct - assert perf_df.loc[perf_df["Name"] == "Natural Gas", "Model"].values[0] == approx( - 1.80, 1e-2 - ) - with subtests.test("performance model: H2"): - # TODO: verify conversion is correct - assert perf_df.loc[perf_df["Name"] == "Hydrogen", "Model"].values[0] == approx(0, 1e-3) - with subtests.test("cost model"): - # TODO: verify value - just copied result - assert cost_df.loc[cost_df["Name"] == "EAF & Casting", "Mid WI"].values[0] == approx( - 175125136, 1e-3 - ) - with subtests.test("finance model"): - # TODO: verify value - just copied result - assert finance.sol["lco"] == approx(119.38, 1e-3) - - -@pytest.mark.regression -def test_h2_eaf(iron_post, subtests): - iron_post_copy = copy.deepcopy(iron_post) - iron_post_copy["iron"]["product_selection"] = "h2_eaf" - performance, cost, finance = iron.run_iron_full_model(iron_post_copy) - perf_df = performance.performances_df - cost_df = cost.costs_df - - with subtests.test("performance model: NG"): - # TODO: verify conversion is correct - assert perf_df.loc[perf_df["Name"] == "Natural Gas", "Model"].values[0] == approx( - 0.079, 1e-2 - ) - with subtests.test("performance model: H2"): - # TODO: verify conversion is correct - assert perf_df.loc[perf_df["Name"] == "Hydrogen", "Model"].values[0] == approx(0, 1e-2) - with subtests.test("cost model"): - # TODO: verify value - just copied result - assert cost_df.loc[cost_df["Name"] == "EAF & Casting", "Mid WI"].values[0] == approx( - 177009717, 1e-3 - ) - with subtests.test("finance model"): - # TODO: verify value - just copied result - assert finance.sol["lco"] == approx(124.68, 1e-3) - - -@pytest.mark.regression -def test_steel_capacity_denominator(iron_post, subtests): - iron_post_copy = copy.deepcopy(iron_post) - iron_post_copy["iron"]["performance"]["capacity_denominator"] = "steel" - iron_post_copy["iron"]["product_selection"] = "h2_eaf" - performance, cost, finance = iron.run_iron_full_model(iron_post_copy) - perf_df = performance.performances_df - cost_df = cost.costs_df - - with subtests.test("performance model: NG"): - assert perf_df.loc[perf_df["Name"] == "Natural Gas", "Model"].values[0] == approx( - 0.0942, 1e-2 - ) - with subtests.test("performance model: H2"): - assert perf_df.loc[perf_df["Name"] == "Hydrogen", "Model"].values[0] == approx(0, 1e-2) - with subtests.test("cost model"): - # TODO: verify value - just copied result - assert cost_df.loc[cost_df["Name"] == "EAF & Casting", "Mid WI"].values[0] == approx( - 191769218, 1e-3 - ) - with subtests.test("finance model"): - # TODO: verify value - just copied result - assert finance.sol["lco"] == approx(138.31, 1e-3) - - -@pytest.mark.regression -def test_rosner_override(iron_post, subtests): - iron_post_copy = copy.deepcopy(iron_post) - iron_post_copy["iron"]["finance_model"]["name"] = "rosner_override" - performance, cost, finance = iron.run_iron_full_model(iron_post_copy) - perf_df = performance.performances_df - cost_df = cost.costs_df - - with subtests.test("performance model: NG"): - assert perf_df.loc[perf_df["Name"] == "Natural Gas", "Model"].values[0] == approx( - 1.80, 1e-2 - ) - with subtests.test("performance model: H2"): - assert perf_df.loc[perf_df["Name"] == "Hydrogen", "Model"].values[0] == approx(0, 1e-2) - with subtests.test("cost model"): - # TODO: verify value - just copied result - assert cost_df.loc[cost_df["Name"] == "EAF & Casting", "Mid WI"].values[0] == approx( - 175125136, 1e-3 - ) - with subtests.test("finance model"): - # TODO: verify value - just copied result - assert finance.sol["lco"] == approx(119.0, 1e-3) - - -@pytest.mark.regression -def test_refit_coefficients(iron_post, subtests): - # Determine the model directory based on the model name - iron_tech_dir = Path(__file__).parents[3] / "h2integrate" / "converters" / "iron" - model_name = iron_post["iron"]["cost_model"]["name"] - model_dir = iron_tech_dir / model_name - - # Backup both performance and cost coefficient files - cost_coeffs_file = model_dir / "cost_coeffs.csv" - cost_backup_file = model_dir / "cost_coeffs_backup.csv" - perf_coeffs_file = model_dir / "perf_coeffs.csv" - perf_backup_file = model_dir / "perf_coeffs_backup.csv" - - if cost_coeffs_file.exists(): - shutil.copy2(cost_coeffs_file, cost_backup_file) - if perf_coeffs_file.exists(): - shutil.copy2(perf_coeffs_file, perf_backup_file) - - # Non-refit coefficients - performance, cost, finance = iron.run_iron_full_model(iron_post) - perf_df = performance.performances_df - cost_df = cost.costs_df - - # Refit coefficients performance model - iron_post_copy = copy.deepcopy(iron_post) - iron_post_copy["iron"]["performance_model"]["refit_coeffs"] = True - performance2, cost2, finance2 = iron.run_iron_full_model(iron_post_copy) - perf_df2 = performance2.performances_df - cost_df2 = cost2.costs_df - with subtests.test("performance model: NG"): - assert perf_df2.loc[perf_df2["Name"] == "Natural Gas", "Model"].values[0] == approx( - perf_df.loc[perf_df["Name"] == "Natural Gas", "Model"].values[0], 1e-3 - ) - with subtests.test("performance model: H2"): - assert perf_df2.loc[perf_df2["Name"] == "Hydrogen", "Model"].values[0] == approx( - perf_df.loc[perf_df["Name"] == "Hydrogen", "Model"].values[0], 1e-3 - ) - with subtests.test("cost model"): - assert cost_df2.loc[cost_df2["Name"] == "EAF & Casting", "Mid WI"].values[0] == approx( - cost_df.loc[cost_df["Name"] == "EAF & Casting", "Mid WI"].values[0], 1e-3 - ) - with subtests.test("finance model"): - assert finance.sol["lco"] == approx(finance.sol["lco"], 1e-3) - - # Refit coefficients cost model - iron_post_copy = copy.deepcopy(iron_post) - iron_post_copy["iron"]["cost_model"]["refit_coeffs"] = True - performance2, cost2, finance2 = iron.run_iron_full_model(iron_post_copy) - perf_df2 = performance2.performances_df - cost_df2 = cost2.costs_df - with subtests.test("performance model: NG"): - assert perf_df2.loc[perf_df2["Name"] == "Natural Gas", "Model"].values[0] == approx( - perf_df.loc[perf_df["Name"] == "Natural Gas", "Model"].values[0], 1e-3 - ) - with subtests.test("performance model: H2"): - assert perf_df2.loc[perf_df2["Name"] == "Hydrogen", "Model"].values[0] == approx( - perf_df.loc[perf_df["Name"] == "Hydrogen", "Model"].values[0], 1e-3 - ) - with subtests.test("cost model"): - assert cost_df2.loc[cost_df2["Name"] == "EAF & Casting", "Mid WI"].values[0] == approx( - cost_df.loc[cost_df["Name"] == "EAF & Casting", "Mid WI"].values[0], 1e-3 - ) - with subtests.test("finance model"): - assert finance.sol["lco"] == approx(finance.sol["lco"], 1e-3) - - # Restore the original coefficient files - if cost_backup_file.exists(): - shutil.move(cost_backup_file, cost_coeffs_file) - if perf_backup_file.exists(): - shutil.move(perf_backup_file, perf_coeffs_file) diff --git a/h2integrate/converters/iron/test/test_iron_win.py b/h2integrate/converters/iron/test/test_iron_win.py deleted file mode 100644 index 08303334f..000000000 --- a/h2integrate/converters/iron/test/test_iron_win.py +++ /dev/null @@ -1,241 +0,0 @@ -import copy -import shutil -from pathlib import Path - -import pytest -from pytest import approx, fixture - -from h2integrate.converters.iron import iron - - -@fixture -def iron_win(): - iron_win = { - "project_parameters": { - "cost_year": 2022, - "project_lifetime": 30, - "grid_connection": False, - "ppa_price": 0.025, - "hybrid_electricity_estimated_cf": 0.492, - "financial_analysis_start_year": 2032, - "installation_time": 36, - }, - "iron": { - "site": { - "lat": 41, - "lon": -78, - "resource_dir": "/../data_library/weather/", - "name": "Mid WI", - }, - "product_selection": "ng_dri", # Alternative: 'h2_dri' - "performance_model": { - "name": "rosner", - "refit_coeffs": False, - }, - "cost_model": { - "name": "rosner", - "refit_coeffs": False, - }, - "finance_model": { - "name": "rosner", # Alternative: 'rosner_override' - }, - "performance": { - "plant_capacity_mtpy": 1418095, - "capacity_denominator": "iron", # Alternative: 'steel' - }, - "costs": { - "lcoh": 2.80, - "lcoe": 0.050, - "lco_iron_ore_tonne": 125, - "operational_year": 2035, - "installation_years": 3, - "plant_life": 30, - "o2_heat_integration": False, - }, - "finances": { - "lcoh": 2.80, - "gen_inflation": 0.025, - "financial_assumptions": { - "total income tax rate": 0.2574, - "capital gains tax rate": 0.15, - "leverage after tax nominal discount rate": 0.10893, - "debt equity ratio of initial financing": 0.624788, - "debt interest rate": 0.050049, - }, - }, - }, - } - return iron_win - - -@pytest.mark.regression -def test_ng_dri(iron_win, subtests): - performance, cost, finance = iron.run_iron_full_model(iron_win) - perf_df = performance.performances_df - cost_df = cost.costs_df - - with subtests.test("performance model: NG"): - # TODO: verify conversion is correct - assert perf_df.loc[perf_df["Name"] == "Natural Gas", "Model"].values[0] == approx( - 6.938, 1e-3 - ) - with subtests.test("performance model: H2"): - # TODO: verify conversion is correct - assert perf_df.loc[perf_df["Name"] == "Hydrogen", "Model"].values[0] == approx(0, 1e-3) - with subtests.test("cost model"): - # TODO: verify value - just copied result - assert cost_df.loc[cost_df["Name"] == "Shaft Furnace", "Mid WI"].values[0] == approx( - 119635267, 1e-3 - ) - with subtests.test("finance model"): - # TODO: verify value - just copied result - assert finance.sol["lco"] == approx(313.56, 1e-3) - - -@pytest.mark.regression -def test_h2_dri(iron_win, subtests): - iron_win_copy = copy.deepcopy(iron_win) - iron_win_copy["iron"]["product_selection"] = "h2_dri" - performance, cost, finance = iron.run_iron_full_model(iron_win_copy) - perf_df = performance.performances_df - cost_df = cost.costs_df - - with subtests.test("performance model: NG"): - # TODO: verify conversion is correct - assert perf_df.loc[perf_df["Name"] == "Natural Gas", "Model"].values[0] == approx( - 0.52, 1e-2 - ) - with subtests.test("performance model: H2"): - # TODO: verify conversion is correct - assert perf_df.loc[perf_df["Name"] == "Hydrogen", "Model"].values[0] == approx(0.055, 1e-2) - with subtests.test("cost model"): - # TODO: verify value - just copied result - assert cost_df.loc[cost_df["Name"] == "Shaft Furnace", "Mid WI"].values[0] == approx( - 120794215.5, 1e-3 - ) - with subtests.test("finance model"): - # TODO: verify value - just copied result - assert finance.sol["lco"] == approx(418.08, 1e-3) - - -@pytest.mark.regression -def test_steel_capacity_denominator(iron_win, subtests): - iron_win_copy = copy.deepcopy(iron_win) - iron_win_copy["iron"]["performance"]["capacity_denominator"] = "steel" - iron_win_copy["iron"]["product_selection"] = "h2_dri" - performance, cost, finance = iron.run_iron_full_model(iron_win_copy) - perf_df = performance.performances_df - cost_df = cost.costs_df - - with subtests.test("performance model: NG"): - assert perf_df.loc[perf_df["Name"] == "Natural Gas", "Model"].values[0] == approx( - 0.62, 1e-2 - ) - with subtests.test("performance model: H2"): - assert perf_df.loc[perf_df["Name"] == "Hydrogen", "Model"].values[0] == approx( - 0.06596, 1e-2 - ) - with subtests.test("cost model"): - # TODO: verify value - just copied result - assert cost_df.loc[cost_df["Name"] == "Shaft Furnace", "Mid WI"].values[0] == approx( - 141139018.10, 1e-3 - ) - with subtests.test("finance model"): - # TODO: verify value - just copied result - assert finance.sol["lco"] == approx(492.62, 1e-3) - - -@pytest.mark.regression -def test_rosner_override(iron_win, subtests): - iron_win_copy = copy.deepcopy(iron_win) - iron_win_copy["iron"]["finance_model"]["name"] = "rosner_override" - performance, cost, finance = iron.run_iron_full_model(iron_win_copy) - perf_df = performance.performances_df - cost_df = cost.costs_df - - with subtests.test("performance model: NG"): - assert perf_df.loc[perf_df["Name"] == "Natural Gas", "Model"].values[0] == approx( - 6.938, 1e-2 - ) - with subtests.test("performance model: H2"): - assert perf_df.loc[perf_df["Name"] == "Hydrogen", "Model"].values[0] == approx(0, 1e-2) - with subtests.test("cost model"): - # TODO: verify value - just copied result - assert cost_df.loc[cost_df["Name"] == "Shaft Furnace", "Mid WI"].values[0] == approx( - 119635267, 1e-3 - ) - with subtests.test("finance model"): - # TODO: verify value - just copied result - assert finance.sol["lco"] == approx(427.2, 1e-3) - - -@pytest.mark.regression -def test_refit_coefficients(iron_win, subtests): - # Determine the model directory based on the model name - iron_tech_dir = Path(__file__).parents[3] / "h2integrate" / "converters" / "iron" - model_name = iron_win["iron"]["cost_model"]["name"] - model_dir = iron_tech_dir / model_name - - # Backup both performance and cost coefficient files - cost_coeffs_file = model_dir / "cost_coeffs.csv" - cost_backup_file = model_dir / "cost_coeffs_backup.csv" - perf_coeffs_file = model_dir / "perf_coeffs.csv" - perf_backup_file = model_dir / "perf_coeffs_backup.csv" - - if cost_coeffs_file.exists(): - shutil.copy2(cost_coeffs_file, cost_backup_file) - if perf_coeffs_file.exists(): - shutil.copy2(perf_coeffs_file, perf_backup_file) - - # Non-refit coefficients - performance, cost, finance = iron.run_iron_full_model(iron_win) - perf_df = performance.performances_df - cost_df = cost.costs_df - - # Refit coefficients performance model - iron_win_copy = copy.deepcopy(iron_win) - iron_win_copy["iron"]["performance_model"]["refit_coeffs"] = True - performance2, cost2, finance2 = iron.run_iron_full_model(iron_win_copy) - perf_df2 = performance2.performances_df - cost_df2 = cost2.costs_df - with subtests.test("performance model: NG"): - assert perf_df2.loc[perf_df2["Name"] == "Natural Gas", "Model"].values[0] == approx( - perf_df.loc[perf_df["Name"] == "Natural Gas", "Model"].values[0], 1e-3 - ) - with subtests.test("performance model: H2"): - assert perf_df2.loc[perf_df2["Name"] == "Hydrogen", "Model"].values[0] == approx( - perf_df.loc[perf_df["Name"] == "Hydrogen", "Model"].values[0], 1e-3 - ) - with subtests.test("cost model"): - assert cost_df2.loc[cost_df2["Name"] == "Shaft Furnace", "Mid WI"].values[0] == approx( - cost_df.loc[cost_df["Name"] == "Shaft Furnace", "Mid WI"].values[0], 1e-3 - ) - with subtests.test("finance model"): - assert finance.sol["lco"] == approx(finance.sol["lco"], 1e-3) - - # Refit coefficients cost model - iron_win_copy = copy.deepcopy(iron_win) - iron_win_copy["iron"]["cost_model"]["refit_coeffs"] = True - performance2, cost2, finance2 = iron.run_iron_full_model(iron_win_copy) - perf_df2 = performance2.performances_df - cost_df2 = cost2.costs_df - with subtests.test("performance model: NG"): - assert perf_df2.loc[perf_df2["Name"] == "Natural Gas", "Model"].values[0] == approx( - perf_df.loc[perf_df["Name"] == "Natural Gas", "Model"].values[0], 1e-3 - ) - with subtests.test("performance model: H2"): - assert perf_df2.loc[perf_df2["Name"] == "Hydrogen", "Model"].values[0] == approx( - perf_df.loc[perf_df["Name"] == "Hydrogen", "Model"].values[0], 1e-3 - ) - with subtests.test("cost model"): - assert cost_df2.loc[cost_df2["Name"] == "Shaft Furnace", "Mid WI"].values[0] == approx( - cost_df.loc[cost_df["Name"] == "Shaft Furnace", "Mid WI"].values[0], 1e-3 - ) - with subtests.test("finance model"): - assert finance.sol["lco"] == approx(finance.sol["lco"], 1e-3) - - # Restore the original coefficient files - if cost_backup_file.exists(): - shutil.move(cost_backup_file, cost_coeffs_file) - if perf_backup_file.exists(): - shutil.move(perf_backup_file, perf_coeffs_file) diff --git a/h2integrate/converters/iron/test/test_iron_wrapper_baseline.py b/h2integrate/converters/iron/test/test_iron_wrapper_baseline.py deleted file mode 100644 index a5de0895b..000000000 --- a/h2integrate/converters/iron/test/test_iron_wrapper_baseline.py +++ /dev/null @@ -1,277 +0,0 @@ -import pytest -import openmdao.api as om -from pytest import fixture - -from h2integrate import EXAMPLE_DIR -from h2integrate.core.inputs.validation import load_plant_yaml -from h2integrate.converters.iron.iron_wrapper import IronComponent - - -@fixture -def iron_config(lcoe, lcoh, site): - iron_config = { - "LCOE": lcoe, - "LCOH": lcoh, - "ROM_iron_site_name": site, - "iron_ore_product_selection": "drg_taconite_pellets", - "reduced_iron_site_latitude": 41.717, - "reduced_iron_site_longitude": -88.398, - "reduced_iron_product_selection": "ng_dri", - "structural_iron_product_selection": "none", - "win_capacity_denom": "iron", - "iron_post_capacity": 1000000, - "iron_win_capacity": 1418095, - "ore_cf_estimate": 0.9, - "cost_year": 2020, - } - return iron_config - - -@fixture -def location_iron_tech(): - iron_config = { - "LCOE": 60.48, - "LCOH": 8.86, - "ROM_iron_site_name": "Northshore", - "iron_ore_product_selection": "drg_taconite_pellets", - "reduced_iron_site_latitude": 41.2, - "reduced_iron_site_longitude": -81.7, - "reduced_iron_product_selection": "ng_dri", - "structural_iron_product_selection": "none", - "win_capacity_denom": "iron", - "iron_post_capacity": 1000000, - "iron_win_capacity": 1418095, - "ore_cf_estimate": 0.9, - "cost_year": 2020, - } - return iron_config - - -@fixture -def plant_config(): - plant_config = load_plant_yaml(EXAMPLE_DIR / "21_iron_mn_to_il" / "plant_config.yaml") - return plant_config - - -@pytest.mark.regression -@pytest.mark.parametrize("lcoe,lcoh,site", [(58.02, 7.1, "Northshore")]) -def test_baseline_iron(plant_config, driver_config, iron_config, subtests): - test_cases = { - "ng/none": { - "reduced_iron_product_selection": "ng_dri", - "structural_iron_product_selection": "none", - }, - "ng/eaf": { - "reduced_iron_product_selection": "ng_dri", - "structural_iron_product_selection": "eaf_steel", - }, - "h2/none": { - "reduced_iron_product_selection": "h2_dri", - "structural_iron_product_selection": "none", - }, - "h2/eaf": { - "reduced_iron_product_selection": "h2_dri", - "structural_iron_product_selection": "eaf_steel", - }, - } - expected_lcoi = { - "ng/none": 370.212189551055, # USD/t - "ng/eaf": 513.8574754088993, - "h2/none": 715.1015416039348, - "h2/eaf": 858.9727023405763, - } - - for test_name, test_inputs in test_cases.items(): - iron_config.update(test_inputs) - prob = om.Problem() - comp = IronComponent( - plant_config=plant_config, - tech_config={"model_inputs": {"cost_parameters": iron_config}}, - driver_config=driver_config, - ) - prob.model.add_subsystem("iron", comp) - prob.setup() - prob.run_model() - - with subtests.test(f"baseline LCOI for {test_name}"): - lcoi = prob.get_val("iron.LCOI", units="USD/t") - assert pytest.approx(lcoi, abs=0.3) == expected_lcoi[test_name] - - -@pytest.mark.regression -@pytest.mark.parametrize("lcoe,lcoh,site", [(58.02, 7.1, "Hibbing")]) -def test_changing_mine_iron(plant_config, driver_config, iron_config, subtests): - test_cases = { - "ng/none": { - "reduced_iron_product_selection": "ng_dri", - "structural_iron_product_selection": "none", - }, - "ng/eaf": { - "reduced_iron_product_selection": "ng_dri", - "structural_iron_product_selection": "eaf_steel", - }, - "h2/none": { - "reduced_iron_product_selection": "h2_dri", - "structural_iron_product_selection": "none", - }, - "h2/eaf": { - "reduced_iron_product_selection": "h2_dri", - "structural_iron_product_selection": "eaf_steel", - }, - } - expected_lcoi = { - "ng/none": 354.77730320952014, - "ng/eaf": 498.42258906736447, - "h2/none": 699.666052372724, - "h2/eaf": 843.5372131093657, - } - for test_name, test_inputs in test_cases.items(): - iron_config.update(test_inputs) - prob = om.Problem() - comp = IronComponent( - plant_config=plant_config, - tech_config={"model_inputs": {"cost_parameters": iron_config}}, - driver_config=driver_config, - ) - prob.model.add_subsystem("iron", comp) - prob.setup() - prob.run_model() - - with subtests.test(f"Hibbing mine LCOI for {test_name}"): - lcoi = prob.get_val("iron.LCOI", units="USD/t") - assert pytest.approx(lcoi, abs=0.3) == expected_lcoi[test_name] - - -@pytest.mark.regression -@pytest.mark.parametrize("lcoe,lcoh,site", [(50.0, 7.1, "Northshore")]) -def test_lcoe50_iron(plant_config, driver_config, iron_config, subtests): - test_cases = { - "ng/none": { - "reduced_iron_product_selection": "ng_dri", - "structural_iron_product_selection": "none", - }, - "ng/eaf": { - "reduced_iron_product_selection": "ng_dri", - "structural_iron_product_selection": "eaf_steel", - }, - "h2/none": { - "reduced_iron_product_selection": "h2_dri", - "structural_iron_product_selection": "none", - }, - "h2/eaf": { - "reduced_iron_product_selection": "h2_dri", - "structural_iron_product_selection": "eaf_steel", - }, - } - - expected_lcoi = { - "ng/none": 369.07934010565174, - "ng/eaf": 509.8866655496638, - "h2/none": 714.305073437314, - "h2/eaf": 855.2282058088431, - } - for test_name, test_inputs in test_cases.items(): - iron_config.update(test_inputs) - prob = om.Problem() - comp = IronComponent( - plant_config=plant_config, - tech_config={"model_inputs": {"cost_parameters": iron_config}}, - driver_config=driver_config, - ) - prob.model.add_subsystem("iron", comp) - prob.setup() - prob.run_model() - - with subtests.test(f"Hibbing mine LCOI for {test_name}"): - lcoi = prob.get_val("iron.LCOI", units="USD/t") - assert pytest.approx(lcoi, abs=0.3) == expected_lcoi[test_name] - - -@pytest.mark.regression -@pytest.mark.parametrize("lcoe,lcoh,site", [(58.02, 6.0, "Northshore")]) -def test_lcoh6_iron(plant_config, driver_config, iron_config, subtests): - test_cases = { - "ng/none": { - "reduced_iron_product_selection": "ng_dri", - "structural_iron_product_selection": "none", - }, - "ng/eaf": { - "reduced_iron_product_selection": "ng_dri", - "structural_iron_product_selection": "eaf_steel", - }, - "h2/none": { - "reduced_iron_product_selection": "h2_dri", - "structural_iron_product_selection": "none", - }, - "h2/eaf": { - "reduced_iron_product_selection": "h2_dri", - "structural_iron_product_selection": "eaf_steel", - }, - } - - expected_lcoi = { - "ng/none": 370.212189551055, - "ng/eaf": 513.8574754088993, - "h2/none": 653.4076529067631, - "h2/eaf": 797.2788136434048, - } - for test_name, test_inputs in test_cases.items(): - iron_config.update(test_inputs) - prob = om.Problem() - comp = IronComponent( - plant_config=plant_config, - tech_config={"model_inputs": {"cost_parameters": iron_config}}, - driver_config=driver_config, - ) - prob.model.add_subsystem("iron", comp) - prob.setup() - prob.run_model() - - with subtests.test(f"Hibbing mine LCOI for {test_name}"): - lcoi = prob.get_val("iron.LCOI", units="USD/t") - assert pytest.approx(lcoi, abs=0.3) == expected_lcoi[test_name] - - -@pytest.mark.regression -def test_location_iron(plant_config, driver_config, location_iron_tech, subtests): - test_cases = { - "ng/none": { - "reduced_iron_product_selection": "ng_dri", - "structural_iron_product_selection": "none", - }, - "ng/eaf": { - "reduced_iron_product_selection": "ng_dri", - "structural_iron_product_selection": "eaf_steel", - }, - "h2/none": { - "reduced_iron_product_selection": "h2_dri", - "structural_iron_product_selection": "none", - }, - "h2/eaf": { - "reduced_iron_product_selection": "h2_dri", - "structural_iron_product_selection": "eaf_steel", - }, - } - - expected_lcoi = { - "ng/none": 365.2571816, - "ng/eaf": 509.7708257, - "h2/none": 808.3401864, - "h2/eaf": 953.1133839, - } - - for test_name, test_inputs in test_cases.items(): - location_iron_tech.update(test_inputs) - prob = om.Problem() - comp = IronComponent( - plant_config=plant_config, - tech_config={"model_inputs": {"cost_parameters": location_iron_tech}}, - driver_config=driver_config, - ) - prob.model.add_subsystem("iron", comp) - prob.setup() - prob.run_model() - - with subtests.test(f"Hibbing mine LCOI for {test_name}"): - lcoi = prob.get_val("iron.LCOI", units="USD/t") - assert pytest.approx(lcoi, abs=0.3) == expected_lcoi[test_name] diff --git a/h2integrate/converters/methanol/co2h_methanol_plant.py b/h2integrate/converters/methanol/co2h_methanol_plant.py index abf2cbbce..d9c561a52 100644 --- a/h2integrate/converters/methanol/co2h_methanol_plant.py +++ b/h2integrate/converters/methanol/co2h_methanol_plant.py @@ -49,6 +49,11 @@ class CO2HMethanolPlantPerformanceModel(MethanolPerformanceBaseClass): - methanol_out: methanol produced in kg/h """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"] self.config = CO2HPerformanceConfig.from_dict( @@ -161,6 +166,11 @@ class CO2HMethanolPlantCostModel(MethanolCostBaseClass): co2_cost: annual cost of CO2 (USD/year) """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"] self.config = CO2HCostConfig.from_dict( diff --git a/h2integrate/converters/methanol/methanol_baseclass.py b/h2integrate/converters/methanol/methanol_baseclass.py index ad0babc2a..1d918303d 100644 --- a/h2integrate/converters/methanol/methanol_baseclass.py +++ b/h2integrate/converters/methanol/methanol_baseclass.py @@ -34,6 +34,11 @@ class MethanolPerformanceBaseClass(PerformanceModelBaseClass): - h2o_consumption: h2o consumption in kg/h """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() self.commodity = "methanol" @@ -87,6 +92,11 @@ class MethanolCostBaseClass(CostModelBaseClass): - Variable_OpEx: all methanol plant variable operating expenses (vary with production rate) """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"] super().setup() diff --git a/h2integrate/converters/methanol/smr_methanol_plant.py b/h2integrate/converters/methanol/smr_methanol_plant.py index 9ab9fe600..46f48e0bd 100644 --- a/h2integrate/converters/methanol/smr_methanol_plant.py +++ b/h2integrate/converters/methanol/smr_methanol_plant.py @@ -42,6 +42,11 @@ class SMRMethanolPlantPerformanceModel(MethanolPerformanceBaseClass): - electricity_out: hourly electricity production (kW*h/h) """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"] self.config = SMRPerformanceConfig.from_dict( @@ -145,6 +150,11 @@ class SMRMethanolPlantCostModel(MethanolCostBaseClass): cost_year: dollar year for output costs """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = SMRCostConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"), diff --git a/h2integrate/converters/methanol/test/conftest.py b/h2integrate/converters/methanol/test/conftest.py index 8d49ffb3d..fa8f250cf 100644 --- a/h2integrate/converters/methanol/test/conftest.py +++ b/h2integrate/converters/methanol/test/conftest.py @@ -1 +1,5 @@ -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) diff --git a/h2integrate/converters/natural_gas/dummy_gas_components.py b/h2integrate/converters/natural_gas/dummy_gas_components.py new file mode 100644 index 000000000..96525ff5f --- /dev/null +++ b/h2integrate/converters/natural_gas/dummy_gas_components.py @@ -0,0 +1,270 @@ +""" +Dummy components for demonstrating multivariable streams, nominally based +on wellhead gas mixtures. These components are not meant to represent any real +physical processes or technologies, but simply to provide a realistic example +of producing and consuming a multivariable stream with multiple constituent +variables. + +These components are used in example 32 to showcase the multivariable stream +connection feature. They produce and consume wellhead_gas_mixture streams with +5 constituent variables. +""" + +import numpy as np +from attrs import field, define + +from h2integrate.core.utilities import BaseConfig, merge_shared_inputs +from h2integrate.core.validators import gt_zero, gte_zero +from h2integrate.core.model_baseclasses import ( + CostModelBaseClass, + CostModelBaseConfig, + PerformanceModelBaseClass, +) +from h2integrate.core.commodity_stream_definitions import ( + add_multivariable_input, + add_multivariable_output, +) + + +@define(kw_only=True) +class SimpleGasProducerPerformanceConfig(BaseConfig): + """ + Configuration class for dummy gas producer performance model. + + Attributes: + base_flow_rate: Base gas flow rate in kg/h + base_temperature: Base gas temperature in K + base_pressure: Base gas pressure in bar + flow_variation: Absolute variation in flow rate in kg/h + temp_variation: Variation in temperature in K + pressure_variation: Variation in pressure in bar + random_seed: Seed for random number generator (for reproducibility) + """ + + base_flow_rate: float = field(default=100.0, validator=gt_zero) + base_temperature: float = field(default=300.0, validator=gt_zero) + base_pressure: float = field(default=10.0, validator=gt_zero) + flow_variation: float = field(default=20.0, validator=gte_zero) + temp_variation: float = field(default=10.0, validator=gte_zero) + pressure_variation: float = field(default=1.0, validator=gte_zero) + random_seed: int | None = field(default=None) + + +class SimpleGasProducerPerformance(PerformanceModelBaseClass): + """ + A dummy gas producer component that outputs a 'wellhead_gas_mixture' multivariable stream. + + This component produces time-varying outputs for each constituent variable + of the wellhead_gas_mixture stream (mass_flow, hydrogen_mass_fraction, oxygen_mass_fraction, + temperature, pressure). + + The outputs use random variations around base values. + """ + + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + + def initialize(self): + super().initialize() + self.commodity = "gas" + self.commodity_rate_units = "kg/h" + self.commodity_amount_units = "kg" + + def setup(self): + self.config = SimpleGasProducerPerformanceConfig.from_dict( + merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance") + ) + super().setup() + + # Add all wellhead_gas_mixture stream outputs + add_multivariable_output(self, "wellhead_gas_mixture", self.n_timesteps) + + def compute(self, inputs, outputs): + # Set random seed for reproducibility if specified + rng = np.random.default_rng(self.config.random_seed) + + base_flow = self.config.base_flow_rate + base_temp = self.config.base_temperature + base_pressure = self.config.base_pressure + + # Gas flow varies randomly within ±flow_variation (absolute, kg/h) + flow_noise = rng.uniform( + -self.config.flow_variation, self.config.flow_variation, self.n_timesteps + ) + outputs["wellhead_gas_mixture:mass_flow_out"] = base_flow + flow_noise + + # Hydrogen fraction: 0.7 to 0.9 (random) + outputs["wellhead_gas_mixture:hydrogen_mass_fraction_out"] = rng.uniform( + 0.7, 0.9, self.n_timesteps + ) + + # Oxygen mass fraction: 0.0 to 0.05 (random) + outputs["wellhead_gas_mixture:oxygen_mass_fraction_out"] = rng.uniform( + 0.0, 0.05, self.n_timesteps + ) + + # Temperature varies randomly within ±temp_variation K + temp_noise = rng.uniform( + -self.config.temp_variation, self.config.temp_variation, self.n_timesteps + ) + outputs["wellhead_gas_mixture:temperature_out"] = base_temp + temp_noise + + # Pressure varies randomly within ±pressure_variation bar + pres_noise = rng.uniform( + -self.config.pressure_variation, self.config.pressure_variation, self.n_timesteps + ) + outputs["wellhead_gas_mixture:pressure_out"] = base_pressure + pres_noise + + # Standardized outputs from PerformanceModelBaseClass + rated_production = base_flow + self.config.flow_variation + outputs["gas_out"] = outputs["wellhead_gas_mixture:mass_flow_out"] + outputs["total_gas_produced"] = np.sum(outputs["wellhead_gas_mixture:mass_flow_out"]) * ( + self.dt / 3600 + ) + outputs["rated_gas_production"] = rated_production + outputs["annual_gas_produced"] = ( + outputs["total_gas_produced"] / self.fraction_of_year_simulated + ) + outputs["capacity_factor"] = outputs["total_gas_produced"] / ( + rated_production * self.n_timesteps * (self.dt / 3600) + ) + + +class SimpleGasConsumerPerformance(PerformanceModelBaseClass): + """ + A dummy gas consumer component that takes in a 'wellhead_gas_mixture' multivariable stream. + + This component demonstrates receiving all constituent variables of a + wellhead_gas_mixture stream (mass_flow, hydrogen_mass_fraction, oxygen_mass_fraction, + temperature, pressure) and performing simple calculations. + + The component calculates some derived quantities from the input stream. + The primary commodity output is hydrogen (extracted from the gas stream). + """ + + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + + def initialize(self): + super().initialize() + self.commodity = "hydrogen" + self.commodity_rate_units = "kg/h" + self.commodity_amount_units = "kg" + + def setup(self): + super().setup() + + # Add all wellhead_gas_mixture stream inputs + add_multivariable_input(self, "wellhead_gas_mixture", self.n_timesteps) + + # Add some derived outputs + self.add_output( + "total_gas_consumed", val=0.0, units="kg", desc="Total gas consumed over the simulation" + ) + self.add_output("avg_temperature", val=0.0, units="K", desc="Average gas temperature") + self.add_output("avg_pressure", val=0.0, units="bar", desc="Average gas pressure") + + def compute(self, inputs, outputs): + # Calculate derived quantities from the stream inputs + gas_flow = inputs["wellhead_gas_mixture:mass_flow_in"] + h2_fraction = inputs["wellhead_gas_mixture:hydrogen_mass_fraction_in"] + temperature = inputs["wellhead_gas_mixture:temperature_in"] + pressure = inputs["wellhead_gas_mixture:pressure_in"] + + # Hydrogen mass flow is total flow times hydrogen fraction + hydrogen_mass_flow = gas_flow * h2_fraction + + # Total gas consumed (assuming hourly data, sum all flow rates) + outputs["total_gas_consumed"] = np.sum(gas_flow) * (self.dt / 3600) + + # Average temperature and pressure + outputs["avg_temperature"] = np.mean(temperature) + outputs["avg_pressure"] = np.mean(pressure) + + # Standardized outputs from PerformanceModelBaseClass + outputs["hydrogen_out"] = hydrogen_mass_flow + outputs["total_hydrogen_produced"] = np.sum(hydrogen_mass_flow) * (self.dt / 3600) + outputs["rated_hydrogen_production"] = np.max(hydrogen_mass_flow) + outputs["annual_hydrogen_produced"] = ( + outputs["total_hydrogen_produced"] / self.fraction_of_year_simulated + ) + max_possible = np.max(hydrogen_mass_flow) * self.n_timesteps * (self.dt / 3600) + outputs["capacity_factor"] = ( + outputs["total_hydrogen_produced"] / max_possible if max_possible > 0 else 0.0 + ) + + +@define(kw_only=True) +class SimpleGasProducerCostConfig(CostModelBaseConfig): + """ + Configuration class for dummy gas producer cost model. + + Attributes: + capex: Capital expenditure in USD + opex: Fixed operational expenditure in USD/year + """ + + capex: float = field(default=1_000_000.0, validator=gte_zero) + opex: float = field(default=50_000.0, validator=gte_zero) + + +class SimpleGasProducerCost(CostModelBaseClass): + """ + Simple cost model for the dummy gas producer. + """ + + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + + def setup(self): + self.config = SimpleGasProducerCostConfig.from_dict( + merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost") + ) + + super().setup() + + def compute(self, inputs, outputs, discrete_inputs=None, discrete_outputs=None): + outputs["CapEx"] = self.config.capex + outputs["OpEx"] = self.config.opex + + +@define(kw_only=True) +class SimpleGasConsumerCostConfig(CostModelBaseConfig): + """ + Configuration class for dummy gas consumer cost model. + + Attributes: + capex: Capital expenditure in USD + opex: Fixed operational expenditure in USD/year + """ + + capex: float = field(default=2_000_000.0, validator=gte_zero) + opex: float = field(default=100_000.0, validator=gte_zero) + + +class SimpleGasConsumerCost(CostModelBaseClass): + """ + Simple cost model for the dummy gas consumer. + """ + + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + + def setup(self): + self.config = SimpleGasConsumerCostConfig.from_dict( + merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost") + ) + + super().setup() + + def compute(self, inputs, outputs, discrete_inputs=None, discrete_outputs=None): + outputs["CapEx"] = self.config.capex + outputs["OpEx"] = self.config.opex diff --git a/h2integrate/converters/natural_gas/natural_gas_cc_ct.py b/h2integrate/converters/natural_gas/natural_gas_cc_ct.py index 36f1b0010..173e2163a 100644 --- a/h2integrate/converters/natural_gas/natural_gas_cc_ct.py +++ b/h2integrate/converters/natural_gas/natural_gas_cc_ct.py @@ -56,6 +56,11 @@ class NaturalGasPerformanceModel(PerformanceModelBaseClass): """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() self.commodity = "electricity" @@ -84,7 +89,7 @@ def setup(self): self.add_input( "heat_rate_mmbtu_per_mwh", val=self.config.heat_rate_mmbtu_per_mwh, - units="MMBtu/MW/h", + units="MMBtu/(MW*h)", desc="Plant heat rate in MMBtu/MWh", ) @@ -246,6 +251,11 @@ class NaturalGasCostModel(CostModelBaseClass): cost_year (int): Dollar year for the costs """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = NaturalGasCostModelConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"), diff --git a/h2integrate/converters/natural_gas/test/conftest.py b/h2integrate/converters/natural_gas/test/conftest.py index 8d49ffb3d..fa8f250cf 100644 --- a/h2integrate/converters/natural_gas/test/conftest.py +++ b/h2integrate/converters/natural_gas/test/conftest.py @@ -1 +1,5 @@ -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) diff --git a/h2integrate/converters/nitrogen/simple_ASU.py b/h2integrate/converters/nitrogen/simple_ASU.py index d32399a98..12ef88b48 100644 --- a/h2integrate/converters/nitrogen/simple_ASU.py +++ b/h2integrate/converters/nitrogen/simple_ASU.py @@ -65,6 +65,11 @@ class SimpleASUPerformanceModel(PerformanceModelBaseClass): Air Separation Unit. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() self.commodity = "nitrogen" @@ -278,6 +283,11 @@ def __attrs_post_init__(self): class SimpleASUCostModel(CostModelBaseClass): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): self.options.declare("plant_config", types=dict) self.options.declare("tech_config", types=dict) diff --git a/h2integrate/converters/nitrogen/test/conftest.py b/h2integrate/converters/nitrogen/test/conftest.py index 8d49ffb3d..fa8f250cf 100644 --- a/h2integrate/converters/nitrogen/test/conftest.py +++ b/h2integrate/converters/nitrogen/test/conftest.py @@ -1 +1,5 @@ -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) diff --git a/h2integrate/converters/nuclear/nuclear_plant.py b/h2integrate/converters/nuclear/nuclear_plant.py index 212c3f44f..832dbb4b3 100644 --- a/h2integrate/converters/nuclear/nuclear_plant.py +++ b/h2integrate/converters/nuclear/nuclear_plant.py @@ -2,7 +2,7 @@ from attrs import field, define from h2integrate.core.utilities import BaseConfig, merge_shared_inputs -from h2integrate.core.validators import gt_zero +from h2integrate.core.validators import gt_zero, gte_zero from h2integrate.core.model_baseclasses import ( CostModelBaseClass, CostModelBaseConfig, @@ -33,6 +33,11 @@ class QuinnNuclearPerformanceModel(PerformanceModelBaseClass): https://doi.org/10.1016/j.apenergy.2023.120669 """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() self.commodity = "electricity" @@ -99,9 +104,9 @@ class QuinnNuclearCostModelConfig(CostModelBaseConfig): """ system_capacity_kw: float = field(validator=gt_zero) - capex_per_kw: float = field(validator=gt_zero) - fixed_opex_per_kw_year: float = field(validator=gt_zero) - variable_opex_per_mwh: float = field(validator=gt_zero) + capex_per_kw: float = field(validator=gte_zero) + fixed_opex_per_kw_year: float = field(validator=gte_zero) + variable_opex_per_mwh: float = field(validator=gte_zero) reference_capacity_kw: float | None = field(default=None) capex_scaling_exponent: float = field(default=1.0, validator=gt_zero) @@ -124,6 +129,11 @@ class QuinnNuclearCostModel(CostModelBaseClass): https://doi.org/10.1016/j.apenergy.2023.120669 """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = QuinnNuclearCostModelConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"), diff --git a/h2integrate/converters/solar/atb_res_com_pv_cost.py b/h2integrate/converters/solar/atb_res_com_pv_cost.py index b5e8540b7..43baef180 100644 --- a/h2integrate/converters/solar/atb_res_com_pv_cost.py +++ b/h2integrate/converters/solar/atb_res_com_pv_cost.py @@ -10,12 +10,11 @@ class ATBResComPVCostModelConfig(CostModelBaseConfig): """Configuration class for the ATBResComPVCostModel with costs based on DC capacity. Recommended to use with commercial or residential PV models. More information on ATB methodology and representative PV technologies can be found - `here for commercial PV `_ and - `here for residential PV `_. + `here for commercial PV `_ and + `here for residential PV `_. Reference cost values can be found on the `Solar - PV Dist. Comm` or `Solar - PV Dist. Res` sheets of the - `NREL ATB workbook `_. - + `NLR ATB workbook `_. Attributes: capex_per_kWdc (float|int): capital cost of solar-PV system in $/kW-DC opex_per_kWdc_per_year (float|int): annual operating cost of solar-PV @@ -30,6 +29,11 @@ class ATBResComPVCostModelConfig(CostModelBaseConfig): class ATBResComPVCostModel(CostModelBaseClass): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = ATBResComPVCostModelConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"), diff --git a/h2integrate/converters/solar/atb_utility_pv_cost.py b/h2integrate/converters/solar/atb_utility_pv_cost.py index 25a223dd1..2fca31e22 100644 --- a/h2integrate/converters/solar/atb_utility_pv_cost.py +++ b/h2integrate/converters/solar/atb_utility_pv_cost.py @@ -10,9 +10,9 @@ class ATBUtilityPVCostModelConfig(CostModelBaseConfig): """Configuration class for the ATBUtilityPVCostModel with costs based on AC capacity. Recommended to use with utility-scale PV models. More information on ATB methodology and representative utility-scale PV technologies can be found - `here `_ + `here `_ Reference cost values can be found on the `Solar - Utility PV` sheet of the - `NREL ATB workbook `_. + `NLR ATB workbook `_. Attributes: capex_per_kWac (float|int): capital cost of solar-PV system in $/kW-AC @@ -26,6 +26,11 @@ class ATBUtilityPVCostModelConfig(CostModelBaseConfig): class ATBUtilityPVCostModel(CostModelBaseClass): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = ATBUtilityPVCostModelConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"), diff --git a/h2integrate/converters/solar/solar_baseclass.py b/h2integrate/converters/solar/solar_baseclass.py index c27ebb322..0bfa4019c 100644 --- a/h2integrate/converters/solar/solar_baseclass.py +++ b/h2integrate/converters/solar/solar_baseclass.py @@ -2,6 +2,11 @@ class SolarPerformanceBaseClass(PerformanceModelBaseClass): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() self.commodity = "electricity" diff --git a/h2integrate/converters/solar/solar_pysam.py b/h2integrate/converters/solar/solar_pysam.py index ac6b3535b..2fefd5986 100644 --- a/h2integrate/converters/solar/solar_pysam.py +++ b/h2integrate/converters/solar/solar_pysam.py @@ -142,12 +142,17 @@ class PYSAMSolarPlantPerformanceModel(SolarPerformanceBaseClass): It takes solar parameters as input and outputs power generation data. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): super().setup() self.design_config = PYSAMSolarPlantPerformanceModelDesignConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance"), - strict=False, + strict=True, additional_cls_name=self.__class__.__name__, ) self.add_input( @@ -206,20 +211,24 @@ def calc_tilt_angle(self, latitude): # Return user-specified tilt return self.design_config.tilt + # Use absolute value of latitude for tilt calculations + # to support southern hemisphere (negative) latitudes + abs_latitude = abs(latitude) + # If tilt angle function is 'lat', use the latitude as the tilt if self.design_config.tilt_angle_func == "lat": - return latitude + return abs_latitude # If tilt angle function is 'lat-func', use empirical formulas based on latitude if self.design_config.tilt_angle_func == "lat-func": - if latitude <= 25: + if abs_latitude <= 25: # For latitudes <= 25, use 0.87 * latitude - return latitude * 0.87 - if 25 < latitude <= 50: + return abs_latitude * 0.87 + if 25 < abs_latitude <= 50: # For latitudes between 25 and 50, use 0.76 * latitude + 3.1 - return (latitude * 0.76) + 3.1 + return (abs_latitude * 0.76) + 3.1 # For latitudes > 50, use latitude directly - return latitude + return abs_latitude def format_resource_data(self, solar_resource_data): """Format solar resource data into the format required for the diff --git a/h2integrate/converters/solar/test/conftest.py b/h2integrate/converters/solar/test/conftest.py index fe767140b..2a0ca26e7 100644 --- a/h2integrate/converters/solar/test/conftest.py +++ b/h2integrate/converters/solar/test/conftest.py @@ -2,7 +2,11 @@ from h2integrate import EXAMPLE_DIR -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) @pytest.fixture diff --git a/h2integrate/converters/solar/test/test_pysam_solar.py b/h2integrate/converters/solar/test/test_pysam_solar.py index 931455a41..6df3286a5 100644 --- a/h2integrate/converters/solar/test/test_pysam_solar.py +++ b/h2integrate/converters/solar/test/test_pysam_solar.py @@ -1,3 +1,5 @@ +from unittest.mock import MagicMock + import numpy as np import pytest import openmdao.api as om @@ -7,6 +9,106 @@ from h2integrate.resource.solar.nlr_developer_goes_api_models import GOESAggregatedSolarAPI +@pytest.mark.unit +class TestCalcTiltAngle: + """Unit tests for PYSAMSolarPlantPerformanceModel.calc_tilt_angle + with various latitudes including southern hemisphere (negative) values. + """ + + def _make_model(self, tilt_angle_func, tilt=None, create_model_from="default"): + """Create a lightweight mock of PYSAMSolarPlantPerformanceModel + with the minimum attributes needed by calc_tilt_angle.""" + model = MagicMock(spec=PYSAMSolarPlantPerformanceModel) + model.design_config = MagicMock() + model.design_config.tilt_angle_func = tilt_angle_func + model.design_config.tilt = tilt + model.design_config.create_model_from = create_model_from + model.design_config.pysam_options = {} + model.system_model = MagicMock() + model.system_model.value.return_value = 20.0 # default tilt from PySAM model + return model + + # --- tilt_angle_func = "lat" --- + @pytest.mark.parametrize( + "latitude, expected_tilt", + [ + (30.0, 30.0), + (-30.0, 30.0), + (0.0, 0.0), + (45.0, 45.0), + (-45.0, 45.0), + (90.0, 90.0), + (-90.0, 90.0), + ], + ) + def test_lat_mode(self, latitude, expected_tilt): + model = self._make_model(tilt_angle_func="lat") + result = PYSAMSolarPlantPerformanceModel.calc_tilt_angle(model, latitude) + assert result == pytest.approx(expected_tilt) + + # --- tilt_angle_func = "lat-func" --- + @pytest.mark.parametrize( + "latitude, expected_tilt", + [ + # |lat| <= 25: tilt = 0.87 * |lat| + (10.0, 10.0 * 0.87), + (-10.0, 10.0 * 0.87), + (25.0, 25.0 * 0.87), + (-25.0, 25.0 * 0.87), + (0.0, 0.0), + # 25 < |lat| <= 50: tilt = 0.76 * |lat| + 3.1 + (30.0, 30.0 * 0.76 + 3.1), + (-30.0, 30.0 * 0.76 + 3.1), + (50.0, 50.0 * 0.76 + 3.1), + (-50.0, 50.0 * 0.76 + 3.1), + # |lat| > 50: tilt = |lat| + (60.0, 60.0), + (-60.0, 60.0), + (80.0, 80.0), + (-80.0, 80.0), + ], + ) + def test_lat_func_mode(self, latitude, expected_tilt): + model = self._make_model(tilt_angle_func="lat-func") + result = PYSAMSolarPlantPerformanceModel.calc_tilt_angle(model, latitude) + assert result == pytest.approx(expected_tilt) + + def test_lat_func_symmetric(self): + """Verify that positive and negative latitudes produce identical tilt angles.""" + model = self._make_model(tilt_angle_func="lat-func") + for lat in [5, 15, 25, 30, 40, 50, 55, 70, 85]: + pos = PYSAMSolarPlantPerformanceModel.calc_tilt_angle(model, lat) + neg = PYSAMSolarPlantPerformanceModel.calc_tilt_angle(model, -lat) + assert pos == pytest.approx(neg), f"Mismatch at latitude {lat}: {pos} != {neg}" + + # --- tilt_angle_func = "none" --- + def test_none_mode_default_with_user_tilt(self): + model = self._make_model(tilt_angle_func="none", tilt=15.0, create_model_from="default") + result = PYSAMSolarPlantPerformanceModel.calc_tilt_angle(model, -33.0) + assert result == pytest.approx(15.0) + + def test_none_mode_default_without_user_tilt(self): + model = self._make_model(tilt_angle_func="none", tilt=None, create_model_from="default") + result = PYSAMSolarPlantPerformanceModel.calc_tilt_angle(model, -33.0) + assert result == pytest.approx(20.0) # from system_model.value("tilt") + + def test_none_mode_new_with_user_tilt(self): + model = self._make_model(tilt_angle_func="none", tilt=10.0, create_model_from="new") + result = PYSAMSolarPlantPerformanceModel.calc_tilt_angle(model, -33.0) + assert result == pytest.approx(10.0) + + def test_none_mode_new_without_user_tilt(self): + model = self._make_model(tilt_angle_func="none", tilt=None, create_model_from="new") + model.design_config.pysam_options = {"SystemDesign": {"tilt": 22.0}} + result = PYSAMSolarPlantPerformanceModel.calc_tilt_angle(model, -33.0) + assert result == pytest.approx(22.0) + + def test_none_mode_new_no_tilt_anywhere(self): + model = self._make_model(tilt_angle_func="none", tilt=None, create_model_from="new") + result = PYSAMSolarPlantPerformanceModel.calc_tilt_angle(model, -33.0) + assert result == pytest.approx(0) # default fallback + + @fixture def basic_pysam_options(): pysam_options = { diff --git a/h2integrate/converters/steel/electric_arc_furnance.py b/h2integrate/converters/steel/electric_arc_furnance.py deleted file mode 100644 index cb2ab9bce..000000000 --- a/h2integrate/converters/steel/electric_arc_furnance.py +++ /dev/null @@ -1,273 +0,0 @@ -import numpy as np -import pandas as pd -import openmdao.api as om -from attrs import field, define - -from h2integrate.core.utilities import BaseConfig, merge_shared_inputs -from h2integrate.core.validators import contains -from h2integrate.converters.iron.iron import ( - IronCostModelConfig, - IronPerformanceModelConfig, - IronPerformanceModelOutputs, - run_iron_cost_model, - run_size_iron_plant_performance, -) -from h2integrate.core.model_baseclasses import CostModelBaseClass -from h2integrate.tools.inflation.inflate import inflate_cpi, inflate_cepci -from h2integrate.converters.iron.load_top_down_coeffs import load_top_down_coeffs - - -@define(kw_only=True) -class EAFPlantBaseConfig(BaseConfig): - eaf_type: str = field( - kw_only=True, converter=(str.lower, str.strip), validator=contains(["h2", "ng"]) - ) # product selection - eaf_capacity: float | int = field(default=1418095) # plant_capacity_mtpy - eaf_capacity_denominator: str = field( - default="iron", converter=(str.lower, str.strip), validator=contains(["iron", "steel"]) - ) # how is this being used for steel? - - model_name: str = field(default="rosner") # only option at the moment - model_fp: str = field(default="") - inputs_fp: str = field(default="") - coeffs_fp: str = field(default="") - refit_coeffs: bool = field(default=False) - site_name: str = field(default="winning_site") - - def make_site_dict(self): - return {"name": self.site_name} - - -@define(kw_only=True) -class EAFPlantPerformanceConfig(EAFPlantBaseConfig): - def make_model_dict(self): - keys = ["model_fp", "inputs_fp", "coeffs_fp", "refit_coeffs"] - d = self.as_dict() - model_dict = {k: v for k, v in d.items() if k in keys} - model_dict.update({"name": self.model_name}) - return model_dict - - -class EAFPlantPerformanceComponent(om.ExplicitComponent): - def initialize(self): - self.options.declare("driver_config", types=dict) - self.options.declare("plant_config", types=dict) - self.options.declare("tech_config", types=dict) - - def setup(self): - self.config = EAFPlantPerformanceConfig.from_dict( - merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance"), - strict=False, - additional_cls_name=self.__class__.__name__, - ) - - self.add_discrete_output( - "steel_plant_performance", val=pd.DataFrame(), desc="steel plant performance results" - ) - - self.add_output("total_steel_produced", val=0.0, units="t/year") - - def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): - steel_plant_performance_inputs = { - "plant_capacity_mtpy": self.config.eaf_capacity, - "capacity_denominator": self.config.eaf_capacity_denominator, - } - steel_plant_model_inputs = self.config.make_model_dict() - steel_plant_site = self.config.make_site_dict() - performance_config = IronPerformanceModelConfig( - product_selection=f"{self.config.eaf_type}_eaf", - site=steel_plant_site, - model=steel_plant_model_inputs, - params=steel_plant_performance_inputs, - ) - iron_post_plant_performance = run_size_iron_plant_performance(performance_config) - # wltpy = wet long tons per year - steel_produced_mtpy = iron_post_plant_performance.performances_df.set_index("Name").loc[ - "Steel Production" - ]["Model"] - outputs["total_steel_produced"] = steel_produced_mtpy - discrete_outputs["steel_plant_performance"] = iron_post_plant_performance.performances_df - - -@define(kw_only=True) -class EAFPlantCostConfig(EAFPlantBaseConfig): - LCOE: float = field(kw_only=True) # $/MWh - LCOH: float = field(kw_only=True) # $/kg - LCOI_ore: float = field(kw_only=True) - iron_transport_cost: float = field(kw_only=True) - ore_profit_pct: float = field(kw_only=True) - - # varom_model_name is unused at the moment - varom_model_name: str = field( - default="rosner", validator=contains(["rosner", "rosner_override"]) - ) - operational_year: int = field(converter=int, kw_only=True) - installation_years: int | float = field(kw_only=True) - plant_life: int = field(converter=int, kw_only=True) - cost_year: int = field(converter=int, kw_only=True) - - def make_model_dict(self): - keys = ["model_fp", "inputs_fp", "coeffs_fp", "refit_coeffs"] - d = self.as_dict() - model_dict = {k: v for k, v in d.items() if k in keys} - model_dict.update({"name": self.model_name}) - return model_dict - - def make_cost_dict(self): - keys = ["operational_year", "installation_years", "plant_life"] - d = self.as_dict() - cost_dict = {k: v for k, v in d.items() if k in keys} - return cost_dict - - -class EAFPlantCostComponent(CostModelBaseClass): - def setup(self): - self.target_dollar_year = self.options["plant_config"]["finance_parameters"][ - "cost_adjustment_parameters" - ]["target_dollar_year"] - self.plant_life = self.options["plant_config"]["plant"]["plant_life"] - - config_dict = merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost") - config_dict.update({"cost_year": self.target_dollar_year}) - config_dict.update({"plant_life": self.plant_life}) - - self.config = EAFPlantCostConfig.from_dict( - config_dict, - additional_cls_name=self.__class__.__name__, - ) - - super().setup() - self.add_input("LCOE", val=self.config.LCOE, units="USD/MW/h") - self.add_input("LCOH", val=self.config.LCOH, units="USD/kg") - self.add_input("LCOI_ore", val=self.config.LCOI_ore, units="USD/t") - self.add_input("iron_transport_cost", val=self.config.iron_transport_cost, units="USD/t") - self.add_input("ore_profit_pct", val=self.config.ore_profit_pct, units="USD/t") - self.add_discrete_input( - "steel_plant_performance", val=pd.DataFrame(), desc="steel plant performance results" - ) - self.add_discrete_output( - "steel_plant_cost", val=pd.DataFrame(), desc="steel plant cost results" - ) - - def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): - steel_plant_performance = IronPerformanceModelOutputs( - performances_df=discrete_inputs["steel_plant_performance"] - ) - - steel_plant_cost_inputs = { - "lcoe": inputs["LCOE"][0] / 1e3, - "lcoh": inputs["LCOH"][0], - "lco_iron_ore_tonne": inputs["LCOI_ore"][0], - "iron_transport_tonne": inputs["iron_transport_cost"][0], - "plant_capacity_mtpy": self.config.eaf_capacity, - "capacity_denominator": self.config.eaf_capacity_denominator, - } - cost_dict = self.config.make_cost_dict() - steel_plant_cost_inputs.update(cost_dict) - - steel_plant_model_inputs = self.config.make_model_dict() - iron_ore_site = self.config.make_site_dict() - cost_config = IronCostModelConfig( - product_selection=f"{self.config.eaf_type}_eaf", - site=iron_ore_site, - model=steel_plant_model_inputs, - params=steel_plant_cost_inputs, - performance=steel_plant_performance, - ) - steel_plant_cost = run_iron_cost_model(cost_config) - - discrete_outputs["steel_plant_cost"] = steel_plant_cost.costs_df - - # Now taking some stuff from finance - cost_df = steel_plant_cost.costs_df.set_index("Name") - cost_ds = cost_df.loc[:, self.config.site_name] - - cost_names = cost_df.index.values - cost_types = cost_df.loc[:, "Type"].values - cost_units = cost_df.loc[:, "Unit"].values - - # add capital items - capex = 0 - fixed_om = 0 - variable_om = 0 - capital_idxs = np.where(cost_types == "capital")[0] - for idx in capital_idxs: - cost_names[idx] - unit = cost_units[idx] # Units for capital costs should be " $"" - source_year = int(unit[:4]) - source_year_cost = cost_ds.iloc[idx] - capex += inflate_cepci(source_year_cost, source_year, self.config.cost_year) - - # add fixed costs - fixed_idxs = np.where(cost_types == "fixed opex")[0] - for idx in fixed_idxs: - cost_names[idx] - unit = cost_units[idx] # Units for fixed opex costs should be " $ per year" - source_year = int(unit[:4]) - source_year_cost = cost_ds.iloc[idx] - fixed_om += inflate_cpi(source_year_cost, source_year, self.config.cost_year) - - # add feedstock costs - perf_df = steel_plant_performance.performances_df.set_index("Name") - perf_ds = perf_df.loc[:, "Model"] - - coeff_dict = load_top_down_coeffs( - [ - "Raw Water", - "Lime", - "Carbon", - "Slag Disposal", - "Hydrogen", - "Natural Gas", - "Electricity", - "Inflation Rate", - ] - ) - - years = list(coeff_dict["years"]) - analysis_start = self.config.operational_year - self.config.installation_years - start_idx = years.index(analysis_start) - if len(years) > (start_idx + self.config.plant_life + self.config.installation_years + 1): - end_idx = years.index( - analysis_start + self.config.plant_life + self.config.installation_years + 1 - ) - indices = list(np.arange(start_idx, end_idx)) - else: - end_idx = len(years) - 1 - indices = list(np.arange(start_idx, end_idx)) - repeats = ( - start_idx + self.config.plant_life + self.config.installation_years + 2 - len(years) - ) - for _i in range(repeats): - indices.append(end_idx) - - raw_water_unitcost_tonne = coeff_dict["Raw Water"]["values"][indices].astype(float) - lime_unitcost_tonne = coeff_dict["Lime"]["values"][indices].astype(float) - carbon_unitcost_tonne = coeff_dict["Carbon"]["values"][indices].astype(float) - slag_disposal_unitcost_tonne = coeff_dict["Slag Disposal"]["values"][indices].astype(float) - - # TODO: make natural gas costs an input - natural_gas_prices_MMBTU = coeff_dict["Natural Gas"]["values"][indices].astype(float) - natural_gas_prices_GJ = natural_gas_prices_MMBTU / 1.05506 # Convert to GJ - - iron_ore_pellet_unitcost_tonne = inputs["LCOI_ore"][0] - if inputs["iron_transport_cost"] > 0: - iron_transport_cost_tonne = inputs["iron_transport_cost"][0] - ore_profit_pct = inputs["ore_profit_pct"][0] - iron_ore_pellet_unitcost_tonne = ( - iron_ore_pellet_unitcost_tonne + iron_transport_cost_tonne - ) * (1 + ore_profit_pct / 100) - - v_start = years.index(self.config.operational_year) - years.index(analysis_start) + 1 - variable_om += perf_ds["Raw Water Withdrawal"] * raw_water_unitcost_tonne - variable_om += perf_ds["Lime"] * lime_unitcost_tonne - variable_om += perf_ds["Carbon (Coke)"] * carbon_unitcost_tonne - variable_om += perf_ds["Iron Ore"] * iron_ore_pellet_unitcost_tonne - variable_om += perf_ds["Hydrogen"] * inputs["LCOH"][0] * 1000 - variable_om += perf_ds["Natural Gas"] * natural_gas_prices_GJ - variable_om += perf_ds["Electricity"] * inputs["LCOE"][0] - variable_om += perf_ds["Slag"] * slag_disposal_unitcost_tonne - - outputs["CapEx"] = capex - outputs["OpEx"] = fixed_om - outputs["VarOpEx"] = variable_om[v_start:] diff --git a/h2integrate/converters/steel/steel.py b/h2integrate/converters/steel/steel.py index 753c26fd1..599d65729 100644 --- a/h2integrate/converters/steel/steel.py +++ b/h2integrate/converters/steel/steel.py @@ -21,6 +21,11 @@ class SteelPerformanceModel(SteelPerformanceBaseClass): Computes annual steel production based on plant capacity and capacity factor. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): super().setup() self.config = SteelPerformanceModelConfig.from_dict( @@ -84,6 +89,11 @@ class SteelCostAndFinancialModel(SteelCostBaseClass): Includes CapEx, OpEx, and byproduct credits. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = SteelCostAndFinancialModelConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"), diff --git a/h2integrate/converters/steel/steel_baseclass.py b/h2integrate/converters/steel/steel_baseclass.py index 16c11c032..24eb57f5c 100644 --- a/h2integrate/converters/steel/steel_baseclass.py +++ b/h2integrate/converters/steel/steel_baseclass.py @@ -2,6 +2,11 @@ class SteelPerformanceBaseClass(PerformanceModelBaseClass): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() self.commodity = "steel" @@ -26,6 +31,11 @@ def compute(self, inputs, outputs): class SteelCostBaseClass(CostModelBaseClass): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): # Inputs for cost model configuration super().setup() diff --git a/h2integrate/converters/steel/steel_eaf_base.py b/h2integrate/converters/steel/steel_eaf_base.py index 9f83ca8f7..bdd8b606f 100644 --- a/h2integrate/converters/steel/steel_eaf_base.py +++ b/h2integrate/converters/steel/steel_eaf_base.py @@ -30,6 +30,11 @@ class ElectricArcFurnacePerformanceBaseConfig(BaseConfig): class ElectricArcFurnacePlantBasePerformanceComponent(PerformanceModelBaseClass): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() self.commodity = "steel" @@ -300,6 +305,11 @@ class ElectricArcFurnacePlantBaseCostComponent(CostModelBaseClass): coeff_df (pd.DataFrame): cost coefficient dataframe """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"] @@ -332,7 +342,7 @@ def setup(self): self.config = ElectricArcFurnaceCostBaseConfig.from_dict( config_dict, - strict=False, + strict=True, additional_cls_name=self.__class__.__name__, ) diff --git a/h2integrate/converters/steel/steel_eaf_plant.py b/h2integrate/converters/steel/steel_eaf_plant.py index 6aa5b5e93..50e35f366 100644 --- a/h2integrate/converters/steel/steel_eaf_plant.py +++ b/h2integrate/converters/steel/steel_eaf_plant.py @@ -14,6 +14,11 @@ class HydrogenEAFPlantCostComponent(ElectricArcFurnacePlantBaseCostComponent): coeff_df (pd.DataFrame): cost coefficient dataframe """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.product = "h2_eaf" super().setup() @@ -29,6 +34,11 @@ class NaturalGasEAFPlantCostComponent(ElectricArcFurnacePlantBaseCostComponent): coeff_df (pd.DataFrame): cost coefficient dataframe """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.product = "ng_eaf" super().setup() @@ -44,6 +54,11 @@ class HydrogenEAFPlantPerformanceComponent(ElectricArcFurnacePlantBasePerformanc coeff_df (pd.DataFrame): performance coefficient dataframe """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.product = "h2_eaf" self.feedstocks_to_units = { @@ -67,6 +82,11 @@ class NaturalGasEAFPlantPerformanceComponent(ElectricArcFurnacePlantBasePerforma coeff_df (pd.DataFrame): performance coefficient dataframe """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.feedstocks_to_units = { "natural_gas": "MMBtu/h", diff --git a/h2integrate/converters/steel/test/conftest.py b/h2integrate/converters/steel/test/conftest.py index 934d70cd2..6029ca9d3 100644 --- a/h2integrate/converters/steel/test/conftest.py +++ b/h2integrate/converters/steel/test/conftest.py @@ -3,11 +3,17 @@ from h2integrate import EXAMPLE_DIR from h2integrate.core.inputs.validation import load_driver_yaml -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) @pytest.fixture def driver_config(temp_dir): # noqa: F811 # NOTE: no idea why this error is raised - driver_config = load_driver_yaml(EXAMPLE_DIR / "21_iron_mn_to_il" / "driver_config.yaml") + driver_config = load_driver_yaml( + EXAMPLE_DIR / "21_iron_examples" / "iron_dri" / "driver_config.yaml" + ) driver_config["general"]["folder_output"] = temp_dir return driver_config diff --git a/h2integrate/converters/steel/test/test_electric_arc_furnance.py b/h2integrate/converters/steel/test/test_electric_arc_furnance.py deleted file mode 100644 index 1041c6985..000000000 --- a/h2integrate/converters/steel/test/test_electric_arc_furnance.py +++ /dev/null @@ -1,101 +0,0 @@ -import pytest -import openmdao.api as om -from pytest import fixture - -from h2integrate.converters.steel.electric_arc_furnance import ( - EAFPlantCostComponent, - EAFPlantPerformanceComponent, -) - - -@fixture -def steel_eaf_config_rosner_ng(): - shared_params = { - "eaf_type": "ng", - "eaf_capacity": 1000000, - "site_name": "IL", - } - performance_params = {"model_name": "rosner"} - cost_params = { - "LCOE": 58.02, - "LCOH": 7.10, - "LCOI_ore": 125.25996463784443, - "iron_transport_cost": 30.566808424134745, - "ore_profit_pct": 6.0, - "varom_model_name": "rosner", - "installation_years": 3, - "operational_year": 2035, - # 'plant_life': 30, - } - - tech_config = { - "model_inputs": { - "cost_parameters": cost_params, - "performance_parameters": performance_params, - "shared_parameters": shared_params, - } - } - return tech_config - - -@fixture -def plant_config(): - plant_config = { - "plant": { - "plant_life": 30, - }, - "finance_parameters": { - "cost_adjustment_parameters": { - "cost_year_adjustment_inflation": 0.025, - "target_dollar_year": 2022, - } - }, - } - return plant_config - - -@pytest.mark.regression -def test_baseline_steel_eaf_costs_rosner_ng( - plant_config, driver_config, steel_eaf_config_rosner_ng, subtests -): - expected_capex = 264034898.3329662 - expected_var_om = 31.31789319 - expected_fixed_om = 38298777.651658 - capacity = 1189772 / 365 # t/day - - prob = om.Problem() - steel_eaf_perf = EAFPlantPerformanceComponent( - plant_config=plant_config, - tech_config=steel_eaf_config_rosner_ng, - driver_config=driver_config, - ) - - steel_eaf_cost = EAFPlantCostComponent( - plant_config=plant_config, - tech_config=steel_eaf_config_rosner_ng, - driver_config=driver_config, - ) - - prob.model.add_subsystem("eaf_perf", steel_eaf_perf, promotes=["*"]) - prob.model.add_subsystem("eaf_cost", steel_eaf_cost, promotes=["*"]) - prob.setup() - prob.run_model() - - annual_steel = prob.get_val("eaf_perf.total_steel_produced", units="t/year") - with subtests.test("Annual Steel"): - assert pytest.approx(annual_steel[0] / 365, rel=1e-3) == capacity - with subtests.test("CapEx"): - assert ( - pytest.approx(prob.get_val("eaf_cost.CapEx", units="USD")[0], rel=1e-6) - == expected_capex - ) - with subtests.test("OpEx"): - assert ( - pytest.approx(prob.get_val("eaf_cost.OpEx", units="USD/year")[0], rel=1e-6) - == expected_fixed_om - ) - with subtests.test("VarOpEx"): - assert ( - pytest.approx(prob.get_val("eaf_cost.VarOpEx", units="USD/year")[0], rel=1e-6) - == expected_var_om - ) diff --git a/h2integrate/converters/test/conftest.py b/h2integrate/converters/test/conftest.py index aec692e38..a4763a760 100644 --- a/h2integrate/converters/test/conftest.py +++ b/h2integrate/converters/test/conftest.py @@ -4,7 +4,11 @@ import os -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) def pytest_sessionstart(session): diff --git a/h2integrate/converters/test/test_generic_converter_cost.py b/h2integrate/converters/test/test_generic_converter_cost.py new file mode 100644 index 000000000..b4089f505 --- /dev/null +++ b/h2integrate/converters/test/test_generic_converter_cost.py @@ -0,0 +1,184 @@ +import numpy as np +import pytest +import openmdao.api as om +from pytest import fixture + +from h2integrate.core.utilities import merge_shared_inputs +from h2integrate.converters.generic_converter_cost import ( + GenericConverterCostModel, + GenericConverterCostConfig, +) + + +@fixture +def plant_config(): + plant_config = { + "plant": { + "plant_life": 30, + "simulation": { + "dt": 3600, + "n_timesteps": 8760, + }, + }, + } + return plant_config + + +@fixture +def model_inputs(): + tech_config = { + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + "commodity_amount_units": "kg", + "unit_capex": 10.0, + "unit_opex": None, + "opex_fraction": 0.1, + "unit_varopex": 0.0, + "cost_year": 2022, + } + return {"cost_parameters": tech_config} + + +@pytest.mark.unit +def test_generic_converter_cost_config(model_inputs, subtests): + expected_msg = "Please provide either a value for unit_opex or a value for opex_fraction" + + model_inputs["cost_parameters"]["unit_opex"] = 10.0 + model_inputs["cost_parameters"]["opex_fraction"] = 0.1 + with subtests.test("Config error: two opex inputs provided"): + with pytest.raises(KeyError) as excinfo: + config = GenericConverterCostConfig.from_dict( + merge_shared_inputs(model_inputs, "cost"), + strict=True, + additional_cls_name="test", + ) + assert str(excinfo.value) == expected_msg + + model_inputs["cost_parameters"]["unit_opex"] = None + model_inputs["cost_parameters"]["opex_fraction"] = None + with subtests.test("Config error: No opex input provided"): + with pytest.raises(KeyError) as excinfo: + GenericConverterCostConfig.from_dict( + merge_shared_inputs(model_inputs, "cost"), + strict=True, + additional_cls_name="test", + ) + assert str(excinfo.value) == expected_msg + + model_inputs["cost_parameters"]["unit_opex"] = None + model_inputs["cost_parameters"]["opex_fraction"] = 0.1 + with subtests.test("Config instantiation: opex fraction provided"): + config = GenericConverterCostConfig.from_dict( + merge_shared_inputs(model_inputs, "cost"), + strict=True, + additional_cls_name="test", + ) + assert config.opex_fraction == 0.1 + + model_inputs["cost_parameters"]["unit_opex"] = 10.0 + model_inputs["cost_parameters"]["opex_fraction"] = None + with subtests.test("Config instantiation: unit opex provided"): + config = GenericConverterCostConfig.from_dict( + merge_shared_inputs(model_inputs, "cost"), + strict=True, + additional_cls_name="test", + ) + assert config.unit_opex == 10.0 + + +@pytest.mark.unit +def test_generic_converter_cost_opex_fraction(plant_config, model_inputs, subtests): + model_inputs["cost_parameters"]["unit_opex"] = None + model_inputs["cost_parameters"]["opex_fraction"] = 0.1 + + prob = om.Problem() + + comp = GenericConverterCostModel( + plant_config=plant_config, + tech_config={"model_inputs": model_inputs}, + ) + + opex_fraction = 0.1 + unit_capex = 10.0 + unit_varom = 5.0 + hourly_rated_production = 1.0 + annual_production = 8760 * hourly_rated_production + rated_prod_comp = om.IndepVarComp( + name="rated_hydrogen_production", val=hourly_rated_production, units="kg/h" + ) + annual_prod_comp = om.IndepVarComp( + name="annual_hydrogen_produced", val=annual_production, shape=30, units="kg/yr" + ) + prob.model.add_subsystem("cost", comp, promotes=["*"]) + prob.model.add_subsystem("IVC1", rated_prod_comp, promotes=["*"]) + prob.model.add_subsystem("IVC2", annual_prod_comp, promotes=["*"]) + + prob.setup() + + prob.set_val("cost.fixed_opex_ratio", val=opex_fraction, units="unitless") + prob.set_val("cost.unit_capex", val=unit_capex, units="USD/(kg/h)") + prob.set_val("cost.unit_varopex", val=unit_varom, units="USD/kg") + prob.run_model() + + with subtests.test("CapEx"): + expected_capex = unit_capex * hourly_rated_production + assert pytest.approx(expected_capex, rel=1e-6) == prob.get_val("CapEx", units="USD") + + with subtests.test("Fixed OpEx"): + expected_opex = expected_capex * opex_fraction + assert pytest.approx(expected_opex, rel=1e-6) == prob.get_val("OpEx", units="USD/year") + + with subtests.test("Variable OpEx"): + expected_varopex = np.full(30, unit_varom * annual_production) + assert pytest.approx(expected_varopex, rel=1e-6) == prob.get_val( + "VarOpEx", units="USD/year" + ) + + +@pytest.mark.unit +def test_generic_converter_cost_opex_value(plant_config, model_inputs, subtests): + model_inputs["cost_parameters"]["unit_opex"] = 1.0 + model_inputs["cost_parameters"]["opex_fraction"] = None + + prob = om.Problem() + + comp = GenericConverterCostModel( + plant_config=plant_config, + tech_config={"model_inputs": model_inputs}, + ) + + unit_opex = 1.0 + unit_capex = 10.0 + unit_varom = 5.0 + hourly_rated_production = 1.0 + annual_production = 8760 * hourly_rated_production + rated_prod_comp = om.IndepVarComp( + name="rated_hydrogen_production", val=hourly_rated_production, units="kg/h" + ) + annual_prod_comp = om.IndepVarComp( + name="annual_hydrogen_produced", val=annual_production, shape=30, units="kg/yr" + ) + prob.model.add_subsystem("cost", comp, promotes=["*"]) + prob.model.add_subsystem("IVC1", rated_prod_comp, promotes=["*"]) + prob.model.add_subsystem("IVC2", annual_prod_comp, promotes=["*"]) + + prob.setup() + + prob.set_val("cost.unit_opex", val=unit_opex, units="USD/(kg/h)/year") + prob.set_val("cost.unit_capex", val=unit_capex, units="USD/(kg/h)") + prob.set_val("cost.unit_varopex", val=unit_varom, units="USD/kg") + prob.run_model() + + with subtests.test("CapEx"): + expected_capex = unit_capex * hourly_rated_production + assert pytest.approx(expected_capex, rel=1e-6) == prob.get_val("CapEx", units="USD") + + with subtests.test("Fixed OpEx"): + expected_opex = hourly_rated_production * unit_opex + assert pytest.approx(expected_opex, rel=1e-6) == prob.get_val("OpEx", units="USD/year") + + with subtests.test("Variable OpEx"): + expected_varopex = np.full(30, unit_varom * annual_production) + assert pytest.approx(expected_varopex, rel=1e-6) == prob.get_val( + "VarOpEx", units="USD/year" + ) diff --git a/h2integrate/converters/water/desal/desalination.py b/h2integrate/converters/water/desal/desalination.py index 7f2bff123..1444b59c3 100644 --- a/h2integrate/converters/water/desal/desalination.py +++ b/h2integrate/converters/water/desal/desalination.py @@ -32,6 +32,11 @@ class ReverseOsmosisPerformanceModel(DesalinationPerformanceBaseClass): Takes plantcapacitykgph input and outputs fresh water and electricity required. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): super().setup() self.config = ReverseOsmosisPerformanceModelConfig.from_dict( @@ -132,6 +137,11 @@ class ReverseOsmosisCostModel(DesalinationCostBaseClass): An OpenMDAO component that computes the cost of a reverse osmosis desalination system. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = ReverseOsmosisCostModelConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"), @@ -140,7 +150,7 @@ def setup(self): super().setup() def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): - """Cost reference: Table 3 of https://www.nrel.gov/docs/fy16osti/66073.pdf. + """Cost reference: Table 3 of https://www.nlr.gov/docs/fy16osti/66073.pdf. CapEx includes 2.55% financing factor, numbers based on INL report https://doi.org/10.2172/1236837 (in Table 10) which came from this report https://www-pub.iaea.org/MTCD/Publications/PDF/te_1561_web.pdf diff --git a/h2integrate/converters/water/desal/desalination_baseclass.py b/h2integrate/converters/water/desal/desalination_baseclass.py index 55fd58c4c..fcabf728e 100644 --- a/h2integrate/converters/water/desal/desalination_baseclass.py +++ b/h2integrate/converters/water/desal/desalination_baseclass.py @@ -2,6 +2,11 @@ class DesalinationPerformanceBaseClass(PerformanceModelBaseClass): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() self.commodity = "water" @@ -25,6 +30,11 @@ def compute(self, inputs, outputs): class DesalinationCostBaseClass(CostModelBaseClass): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): super().setup() # Inputs for cost model configuration diff --git a/h2integrate/converters/water/desal/test/conftest.py b/h2integrate/converters/water/desal/test/conftest.py index aec692e38..a4763a760 100644 --- a/h2integrate/converters/water/desal/test/conftest.py +++ b/h2integrate/converters/water/desal/test/conftest.py @@ -4,7 +4,11 @@ import os -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) def pytest_sessionstart(session): diff --git a/h2integrate/converters/water_power/hydro_plant_run_of_river.py b/h2integrate/converters/water_power/hydro_plant_run_of_river.py index 780b5b950..c00575901 100644 --- a/h2integrate/converters/water_power/hydro_plant_run_of_river.py +++ b/h2integrate/converters/water_power/hydro_plant_run_of_river.py @@ -36,6 +36,11 @@ class RunOfRiverHydroPerformanceModel(PerformanceModelBaseClass): Computes annual electricity production based on water flow rate and turbine efficiency. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() self.commodity = "electricity" @@ -107,6 +112,11 @@ class RunOfRiverHydroCostModel(CostModelBaseClass): Just a placeholder for now, but can be extended with more detailed cost models. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = RunOfRiverHydroCostConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"), diff --git a/h2integrate/converters/water_power/pysam_marine_cost.py b/h2integrate/converters/water_power/pysam_marine_cost.py new file mode 100644 index 000000000..32791347b --- /dev/null +++ b/h2integrate/converters/water_power/pysam_marine_cost.py @@ -0,0 +1,291 @@ +import PySAM.MhkCosts as MhkCost +from attrs import field, define + +from h2integrate.core.utilities import merge_shared_inputs +from h2integrate.core.validators import gt_zero, contains, range_val, must_equal +from h2integrate.core.model_baseclasses import CostModelBaseClass, CostModelBaseConfig + + +@define(kw_only=True) +class PySAMMarineCostConfig(CostModelBaseConfig): + """Configuration class for the PySAMMarineCostModel. + + Args: + device_rating_kw (float): Rated power of the MHK device [kW] + num_devices (int): Number of MHK tidal devices in the system + reference_model_number (int): Reference model number from the + Department of Energy Reference Model Project + (1, 2, 3, 5 or 6). + water_depth (float): Water depth in meters. + distance_to_shore (float): Distance to shore in meters. + number_rows (int): Number of rows in the device layout. + device_spacing (float): Spacing between devices in a row in meters. + row_spacing (float): Spacing between rows in meters. + cable_system_overbuild: Cable system overbuild percentage. + pysam_cost_options (dict, optional): dictionary of MhkCosts input parameters with + top-level keys corresponding to the different MhkCosts variable groups. + (please refer to MhkCosts documentation + `here `__ + ) + + Note: + More information about the reference models and their + associated costs can be found in the + [Reference Model Project](https://energy.sandia.gov/programs/renewable-energy/water-power/projects/reference-model-project-rmp/) + + The supported reference models in this cost model are: + - Reference Model 1: Tidal Current Turbine + - Reference Model 2: River Current Turbine + - Reference Model 3: Wave Point Absorber + - Reference Model 5: Oscillating Surge Flap + - Reference Model 6: Oscillating Water Column + + Additional MHK cost model information can be found + through the [System Advisor Model](https://sam.nlr.gov/) + """ + + device_rating_kw: float = field(validator=gt_zero) + num_devices: int = field(validator=gt_zero) + reference_model_number: int = field(validator=contains([1, 2, 3, 5, 6])) + water_depth: float = field(validator=gt_zero) + distance_to_shore: float = field(validator=gt_zero) + number_rows: int = field(validator=gt_zero) + device_spacing: float = field(validator=gt_zero) + row_spacing: float = field(validator=gt_zero) + cable_system_overbuild: float = field(validator=range_val(0, 100)) + pysam_cost_options: dict = field(default={}) + cost_year: int = field( + default=2022, converter=int, validator=must_equal(2022) + ) # TODO update based on feedback from SAM team + + def __attrs_post_init__(self): + # if pysam_cost_options is not an empty dictionary + if not self.pysam_cost_options: + self.check_pysam_cost_options() + + def check_pysam_cost_options(self): + """Checks that top-level keys of pysam_cost_options dictionary are valid and that + system capacity is not given in pysam_cost_options. + + Raises: + ValueError: if top-level keys of pysam_cost_options are not valid. + ValueError: if device_rated_power is provide in pysam_cost_options["MHKCosts"] + """ + valid_groups = [ + "MHKCosts", + ] + if bool(self.pysam_cost_options): + invalid_groups = [k for k in self.pysam_cost_options if k not in valid_groups] + if len(invalid_groups) > 0: + msg = ( + f"Invalid group(s) found in pysam_cost_options: {invalid_groups}. " + f"Valid groups are: {valid_groups}." + ) + raise ValueError(msg) + + if ( + self.pysam_cost_options.get("MHKCosts", {}).get("device_rated_power", None) + is not None + ): + msg = ( + "Please do not specify device_rated_power in the pysam_cost_options " + "dictionary. The device rated power should be set with the " + "'device_rating' in the cost parameter." + ) + raise ValueError(msg) + return + + def create_input_dict(self): + """Create dictionary of inputs to over-write the default values + associated with the specified MHKCosts configuration. + + Returns: + dict: dictionary of MHKCosts group parameters from user-input. + """ + design_dict = { + "MHKCosts": { + "device_rated_power": self.device_rating_kw, + }, + } + + # check if custom cost values are input + cost_keys_map = [ + "structural_assembly_cost", + "power_takeoff_system_cost", + "mooring_found_substruc_cost", + "development_cost", + "eng_and_mgmt_cost", + "assembly_and_install_cost", + "other_infrastructure_cost", + "array_cable_system_cost", + "export_cable_system_cost", + "onshore_substation_cost", + "offshore_substation_cost", + "other_elec_infra_cost", + ] + + for key in cost_keys_map: + design_dict["MHKCosts"][f"{key}_method"] = 2 # used modeled values + design_dict["MHKCosts"][f"{key}_input"] = 0 + if self.pysam_cost_options.get("MHKCosts", {}).get(f"{key}_input", None) is not None: + design_dict["MHKCosts"][f"{key}_method"] = self.pysam_cost_options.get( + "MHKCosts" + ).get(f"{key}_input") + design_dict["MHKCosts"][f"{key}_input"] = self.pysam_cost_options.get( + "MHKCosts" + ).get(f"{key}_method") + + return design_dict + + +class PySAMMarineCostModel(CostModelBaseClass): + """An OpenMDAO component for calculating the costs associated + with Marine Hydrokinetic (MHK) energy systems. + + The class initializes and configures cost calculations for MHK systems. + It uses the PySAM library for cost modeling which is based on industry + input as well as the Department of Energy + [Reference Model Project](https://energy.sandia.gov/programs/renewable-energy/water-power/projects/reference-model-project-rmp/). + Additional MHK cost model information can be found + through the [System Advisor Model](https://sam.nlr.gov/). + + Note: + The supported reference models in this cost model are: + - Reference Model 1: Tidal Current Turbine + - Reference Model 2: River Current Turbine + - Reference Model 3: Wave Point Absorber + - Reference Model 5: Oscillating Surge Flap + - Reference Model 6: Oscillating Water Column + + """ + + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + + def setup(self): + self.config = PySAMMarineCostConfig.from_dict( + merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"), + additional_cls_name=self.__class__.__name__, + ) + super().setup() + + self.add_input( + "device_rating", + val=self.config.device_rating_kw, + units="kW", + desc="Rated power of the MHK device", + ) + + self.add_input( + "num_devices", + val=self.config.num_devices, + units="unitless", + desc="Number of MHK devices in the system", + ) + + self.cost_model = MhkCost.new() + + design_dict = self.config.create_input_dict() + if bool(self.config.pysam_cost_options): + for group, group_parameters in self.config.pysam_cost_options.items(): + if group in design_dict: + design_dict[group].update(group_parameters) + else: + design_dict.update({group: group_parameters}) + self.cost_model.assign(design_dict) + + def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): + # Assign + number_devices = inputs["num_devices"][0] + device_rating = inputs["device_rating"][0] + self.cost_model.value("device_rated_power", device_rating) + self.cost_model.value("system_capacity", device_rating * number_devices) + + if number_devices < self.config.number_rows: + raise Exception("number_of_rows exceeds num_devices") + else: + if (number_devices / self.config.number_rows).is_integer(): + self.cost_model.value("devices_per_row", number_devices / self.config.number_rows) + else: + raise Exception( + "Layout must be square or rectangular. Modify 'number_rows' or 'num_devices'." + ) + + # Assign type of MHK device + ref_model_num = f"RM{self.config.reference_model_number}" + self.cost_model.value("lib_wave_device", ref_model_num) + if ref_model_num == "RM3" or ref_model_num == "RM5" or ref_model_num == "RM6": + self.cost_model.value("marine_energy_tech", 0) # Wave + self.cost_model.value("lib_wave_device", ref_model_num) + elif ref_model_num == "RM1" or ref_model_num == "RM2": + self.cost_model.value("marine_energy_tech", 1) # Tidal + self.cost_model.value("lib_tidal_device", ref_model_num) + else: + self.cost_model.value("marine_energy_tech", 0) # Generic + self.cost_model.value("library_or_input_wec", 0) + + # Inter-array cable length, m + # The total length of cable used within the array of devices + array_cable_length = (self.cost_model.value("devices_per_row") - 1) * ( + self.config.device_spacing * self.config.number_rows + ) + self.config.row_spacing * (self.config.number_rows - 1) + self.cost_model.value("inter_array_cable_length", array_cable_length) + + # Export cable length, m + # The length of cable between the array and onshore grid connection point + export_cable_length = (self.config.water_depth + self.config.distance_to_shore) * ( + 1 + self.config.cable_system_overbuild / 100 + ) + self.cost_model.value("export_cable_length", export_cable_length) + + # Riser cable length, m + # The length of cable from the seabed to the water surface that + # connects the floating device to the seabed cabling. + # Applies only to floating array + riser_cable_length = ( + 1.5 + * self.config.water_depth + * number_devices + * (1 + self.config.cable_system_overbuild / 100) + ) + self.cost_model.value("riser_cable_length", riser_cable_length) + + self.cost_model.execute(1) + + cost_dict = self.cost_model.Outputs.export() + + capex = ( + cost_dict["structural_assembly_cost_modeled"] + + cost_dict["power_takeoff_system_cost_modeled"] + + cost_dict["mooring_found_substruc_cost_modeled"] + ) + bos = ( + cost_dict["development_cost_modeled"] + + cost_dict["eng_and_mgmt_cost_modeled"] + + cost_dict["plant_commissioning_cost_modeled"] + + cost_dict["site_access_port_staging_cost_modeled"] + + cost_dict["assembly_and_install_cost_modeled"] + + cost_dict["other_infrastructure_cost_modeled"] + ) + elec_infrastruc_costs = ( + cost_dict["array_cable_system_cost_modeled"] + + cost_dict["export_cable_system_cost_modeled"] + + cost_dict["onshore_substation_cost_modeled"] + + cost_dict["offshore_substation_cost_modeled"] + + cost_dict["other_elec_infra_cost_modeled"] + ) + financial = ( + cost_dict["project_contingency"] + + cost_dict["insurance_during_construction"] + + cost_dict["reserve_accounts"] + ) + + total_installed_cost = capex + bos + elec_infrastruc_costs + financial + + outputs["CapEx"] = total_installed_cost + + opex = cost_dict["maintenance_cost"] + cost_dict["operations_cost"] + + outputs["OpEx"] = opex diff --git a/h2integrate/converters/water_power/test/conftest.py b/h2integrate/converters/water_power/test/conftest.py index aec692e38..a4763a760 100644 --- a/h2integrate/converters/water_power/test/conftest.py +++ b/h2integrate/converters/water_power/test/conftest.py @@ -4,7 +4,11 @@ import os -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) def pytest_sessionstart(session): diff --git a/h2integrate/converters/water_power/test/test_marine_pysam_cost.py b/h2integrate/converters/water_power/test/test_marine_pysam_cost.py new file mode 100644 index 000000000..0578527e5 --- /dev/null +++ b/h2integrate/converters/water_power/test/test_marine_pysam_cost.py @@ -0,0 +1,310 @@ +import pytest +import openmdao.api as om + +from h2integrate.resource.tidal import TidalResource +from h2integrate.converters.water_power.tidal_pysam import PySAMTidalPerformanceModel +from h2integrate.converters.water_power.pysam_marine_cost import PySAMMarineCostModel + + +@pytest.fixture +def plant_config(): + plant_config = { + "plant": { + "plant_life": 30, + "simulation": { + "n_timesteps": 8760, + "dt": 3600, + }, + }, + "site": { + "latitude": 47.5233, + "longitude": -92.5366, + "resources": { + "tidal_resource": { + "resource_parameters": { + "resource_dir": "resource_files/tidal/", + "resource_filename": "Tidal_resource_timeseries.csv", + } + } + }, + }, + } + return plant_config + + +@pytest.fixture +def cost_config(): + cost_config = { + "model_inputs": { + "cost_parameters": { + "device_rating_kw": 1115, + "num_devices": 20, + "reference_model_number": 1, + "water_depth": 100, + "distance_to_shore": 80, + "number_rows": 2, + "device_spacing": 600, + "row_spacing": 600, + "cable_system_overbuild": 20, + } + } + } + return cost_config + + +# Loop through reference model number and test capex and opex values +@pytest.mark.unit +def test_ref_model_number1(cost_config, plant_config, subtests): + prob = om.Problem() + + cost = PySAMMarineCostModel( + plant_config=plant_config, + tech_config=cost_config, + driver_config={}, + ) + + prob.model.add_subsystem("cost", cost, promotes=["*"]) + prob.setup() + + prob.run_model() + + with subtests.test("RM1 Capex"): + assert ( + pytest.approx(prob.get_val("cost.CapEx", units="USD"), rel=1e-6) == 123902868.62743238 + ) + + with subtests.test("RM1 Opex"): + assert pytest.approx(prob.get_val("cost.OpEx", units="USD/year"), rel=1e-6) == 4498582.9 + + +@pytest.mark.unit +def test_ref_model_number2(cost_config, plant_config, subtests): + prob = om.Problem() + + cost_config["model_inputs"]["cost_parameters"]["reference_model_number"] = 2 + cost = PySAMMarineCostModel( + plant_config=plant_config, + tech_config=cost_config, + driver_config={}, + ) + + prob.model.add_subsystem("cost", cost, promotes=["*"]) + prob.setup() + + prob.run_model() + + with subtests.test("RM2 Capex"): + assert ( + pytest.approx(prob.get_val("cost.CapEx", units="USD"), rel=1e-6) == 194855694.06743234 + ) + + with subtests.test("RM2 Opex"): + assert pytest.approx(prob.get_val("cost.OpEx", units="USD/year"), rel=1e-6) == 4498582.9 + + +@pytest.mark.unit +def test_ref_model_number3(cost_config, plant_config, subtests): + prob = om.Problem() + + cost_config["model_inputs"]["cost_parameters"]["reference_model_number"] = 3 + cost = PySAMMarineCostModel( + plant_config=plant_config, + tech_config=cost_config, + driver_config={}, + ) + + prob.model.add_subsystem("cost", cost, promotes=["*"]) + prob.setup() + + prob.run_model() + + with subtests.test("RM3 Capex"): + assert ( + pytest.approx(prob.get_val("cost.CapEx", units="USD"), rel=1e-6) == 366034529.39826673 + ) + + with subtests.test("RM3 Opex"): + assert pytest.approx(prob.get_val("cost.OpEx", units="USD/year"), rel=1e-6) == 4498582.9 + + +@pytest.mark.unit +def test_ref_model_number5(cost_config, plant_config, subtests): + prob = om.Problem() + + cost_config["model_inputs"]["cost_parameters"]["reference_model_number"] = 5 + cost = PySAMMarineCostModel( + plant_config=plant_config, + tech_config=cost_config, + driver_config={}, + ) + + prob.model.add_subsystem("cost", cost, promotes=["*"]) + prob.setup() + + prob.run_model() + + with subtests.test("RM5 Capex"): + assert ( + pytest.approx(prob.get_val("cost.CapEx", units="USD"), rel=1e-6) == 366895942.95049995 + ) + + with subtests.test("RM5 Opex"): + assert pytest.approx(prob.get_val("cost.OpEx", units="USD/year"), rel=1e-6) == 4498582.9 + + +@pytest.mark.unit +def test_ref_model_number6(cost_config, plant_config, subtests): + prob = om.Problem() + + cost_config["model_inputs"]["cost_parameters"]["reference_model_number"] = 6 + cost = PySAMMarineCostModel( + plant_config=plant_config, + tech_config=cost_config, + driver_config={}, + ) + + prob.model.add_subsystem("cost", cost, promotes=["*"]) + prob.setup() + + prob.run_model() + + with subtests.test("RM6 Capex"): + assert pytest.approx(prob.get_val("cost.CapEx", units="USD"), rel=1e-6) == 580646799.6967537 + + with subtests.test("RM6 Opex"): + assert pytest.approx(prob.get_val("cost.OpEx", units="USD/year"), rel=1e-6) == 4498582.9 + + +@pytest.mark.unit +def test_custom_cost(cost_config, plant_config, subtests): + prob = om.Problem() + + cost_config["model_inputs"]["cost_parameters"]["pysam_cost_options"] = { + "MHKCosts": { + "structural_assembly_cost_input": 20, # $/kw + "structural_assembly_cost_method": 0, # Enter in $/kw + } + } + cost = PySAMMarineCostModel( + plant_config=plant_config, + tech_config=cost_config, + driver_config={}, + ) + + prob.model.add_subsystem("cost", cost, promotes=["*"]) + prob.setup() + + prob.run_model() + + with subtests.test("capex is not equal to RM1 capex"): + assert ( + pytest.approx(prob.get_val("cost.CapEx", units="USD"), rel=1e-6) + != 123902868.62743238 # Value from test_ref_model_number1 subtest [RM1 Capex] + ) + with subtests.test("Adjusted RM1 Capex"): + assert ( + pytest.approx(prob.get_val("cost.CapEx", units="USD"), rel=1e-6) == 122936916.35143237 + ) + + +@pytest.mark.integration +def test_performance_cost_with_pysam_options(plant_config, cost_config, subtests): + prob = om.Problem() + + tidal_resource = TidalResource( + plant_config=plant_config, + resource_config=plant_config["site"]["resources"]["tidal_resource"]["resource_parameters"], + driver_config={}, + ) + + prob.model.add_subsystem("tidal_resource", tidal_resource, promotes=["*"]) + + tidal_config = { + "model_inputs": { + "performance_parameters": { + "create_model_from": "default", + "num_devices": 20, + "device_rating_kw": 1115, + "pysam_options": { + "MHKTidal": { + "loss_downtime": 10.0, + } + }, + } + } + } + comp = PySAMTidalPerformanceModel( + plant_config=plant_config, + tech_config=tidal_config, + driver_config={}, + ) + prob.model.add_subsystem("comp", comp, promotes=["*"]) + + cost_config["model_inputs"]["cost_parameters"]["pysam_cost_options"] = { + "MHKCosts": { + "structural_assembly_cost_input": 20, # $/kw + "structural_assembly_cost_method": 0, # Enter in $/kw + } + } + cost = PySAMMarineCostModel( + plant_config=plant_config, + tech_config=cost_config, + driver_config={}, + ) + + prob.model.add_subsystem("cost", cost, promotes=["*"]) + prob.setup() + + prob.run_model() + with subtests.test("total_electricity_produced value"): + assert ( + pytest.approx(prob.get_val("comp.total_electricity_produced", units="kW*h"), rel=1e-6) + == 51531688.16819879 + ) + + with subtests.test("capex is not equal to RM1 capex"): + assert ( + pytest.approx(prob.get_val("cost.CapEx", units="USD"), rel=1e-6) + != 123902868.62743238 # Value from test_ref_model_number1 subtest [RM1 Capex] + ) + with subtests.test("Adjusted RM1 Capex"): + assert ( + pytest.approx(prob.get_val("cost.CapEx", units="USD"), rel=1e-6) == 122936916.35143237 + ) + + +@pytest.mark.unit +def test_rows_and_device_error(cost_config, plant_config): + prob = om.Problem() + + msg = "number_of_rows exceeds num_devices" + with pytest.raises(Exception, match=msg): + cost_config["model_inputs"]["cost_parameters"]["number_rows"] = 100 + cost = PySAMMarineCostModel( + plant_config=plant_config, + tech_config=cost_config, + driver_config={}, + ) + + prob.model.add_subsystem("cost", cost, promotes=["*"]) + prob.setup() + + prob.run_model() + + +@pytest.mark.unit +def test_layout_error(cost_config, plant_config): + prob = om.Problem() + msg = "Layout must be square or rectangular. Modify 'number_rows' or 'num_devices'." + with pytest.raises(Exception, match=msg): + cost_config["model_inputs"]["cost_parameters"]["num_devices"] = 25 + cost = PySAMMarineCostModel( + plant_config=plant_config, + tech_config=cost_config, + driver_config={}, + ) + + prob.model.add_subsystem("cost", cost, promotes=["*"]) + prob.setup() + + prob.run_model() diff --git a/h2integrate/converters/water_power/test/test_tidal_pysam.py b/h2integrate/converters/water_power/test/test_tidal_pysam.py new file mode 100644 index 000000000..58d2be0af --- /dev/null +++ b/h2integrate/converters/water_power/test/test_tidal_pysam.py @@ -0,0 +1,360 @@ +import numpy as np +import pytest +import openmdao.api as om + +from h2integrate.resource.tidal import TidalResource +from h2integrate.converters.water_power.tidal_pysam import PySAMTidalPerformanceModel + + +@pytest.fixture +def tidal_config(): + config = { + "model_inputs": { + "performance_parameters": { + "device_rating_kw": 1115, # [kW] + "num_devices": 20, + "tidal_power_curve": [ + (0.0, 0.0), + (0.1, 0.0), + (0.2, 0.0), + (0.3, 0.0), + (0.4, 0.0), + (0.5, 0.0), + (0.6, 10.4211), + (0.7, 20.8423), + (0.8, 39.9689), + (0.9, 59.0956), + (1.0, 89.2016), + (1.1, 119.3080), + (1.2, 160.8860), + (1.3, 202.4640), + (1.4, 259.2920), + (1.5, 316.1200), + (1.6, 392.6730), + (1.7, 469.2260), + (1.8, 570.3060), + (1.9, 671.3860), + (2.0, 802.9080), + (2.1, 934.4300), + (2.2, 1024.7100), + (2.3, 1115.0000), + (2.4, 1115.0000), + (2.5, 1115.0000), + (2.6, 1115.0000), + (2.7, 1115.0000), + (2.8, 1115.0000), + (2.9, 1115.0000), + (3.0, 1115.0000), + (3.1, 1115.0000), + (3.2, 1085.3700), + (3.3, 1055.7300), + ], + } + } + } + return config + + +@pytest.fixture +def pysam_options(): + pysam_options = { + "MHKTidal": { + "loss_array_spacing": 0.0, + "loss_resource_overprediction": 0.0, + "loss_transmission": 0.0, + "loss_downtime": 0.0, + "loss_additional": 0.0, + } + } + return pysam_options + + +@pytest.fixture +def plant_config(): + plant_config = { + "plant": { + "plant_life": 30, + "simulation": { + "n_timesteps": 8760, + "dt": 3600, + }, + }, + "site": { + "latitude": 47.5233, + "longitude": -92.5366, + "resources": { + "tidal_resource": { + "resource_parameters": { + "resource_dir": "resource_files/tidal/", + "resource_filename": "Tidal_resource_timeseries.csv", + } + } + }, + }, + } + return plant_config + + +@pytest.mark.unit +def test_tidal_pysam_outputs(plant_config, tidal_config, pysam_options, subtests): + prob = om.Problem() + + tidal_resource = TidalResource( + plant_config=plant_config, + resource_config=plant_config["site"]["resources"]["tidal_resource"]["resource_parameters"], + driver_config={}, + ) + + prob.model.add_subsystem("tidal_resource", tidal_resource, promotes=["*"]) + + tidal_config["model_inputs"]["performance_parameters"]["pysam_options"] = pysam_options + comp = PySAMTidalPerformanceModel( + plant_config=plant_config, + tech_config=tidal_config, + driver_config={}, + ) + prob.model.add_subsystem("comp", comp, promotes=["*"]) + prob.setup() + prob.run_model() + + commodity = "electricity" + commodity_amount_units = "kW*h" + commodity_rate_units = "kW" + plant_life = int(plant_config["plant"]["plant_life"]) + n_timesteps = int(plant_config["plant"]["simulation"]["n_timesteps"]) + + # Check that replacement schedule is between 0 and 1 + with subtests.test("0 <= replacement_schedule <=1"): + assert np.all(prob.get_val("comp.replacement_schedule", units="unitless") >= 0) + assert np.all(prob.get_val("comp.replacement_schedule", units="unitless") <= 1) + + with subtests.test("replacement_schedule length"): + assert len(prob.get_val("comp.replacement_schedule", units="unitless")) == plant_life + + # Check that capacity factor is between 0 and 1 with units of "unitless" + with subtests.test("0 <= capacity_factor (unitless) <=1"): + assert np.all(prob.get_val("comp.capacity_factor", units="unitless") >= 0) + assert np.all(prob.get_val("comp.capacity_factor", units="unitless") <= 1) + + # Check that capacity factor is between 1 and 100 with units of "percent" + with subtests.test("1 <= capacity_factor (percent) <=1"): + assert np.all(prob.get_val("comp.capacity_factor", units="percent") >= 1) + assert np.all(prob.get_val("comp.capacity_factor", units="percent") <= 100) + + with subtests.test("capacity_factor length"): + assert len(prob.get_val("comp.capacity_factor", units="unitless")) == plant_life + + # Test that rated commodity production is greater than zero + with subtests.test(f"rated_{commodity}_production > 0"): + assert np.all( + prob.get_val(f"comp.rated_{commodity}_production", units=commodity_rate_units) > 0 + ) + + with subtests.test(f"rated_{commodity}_production length"): + assert ( + len(prob.get_val(f"comp.rated_{commodity}_production", units=commodity_rate_units)) == 1 + ) + + # Test that total commodity production is greater than zero + with subtests.test(f"total_{commodity}_produced > 0"): + assert np.all( + prob.get_val(f"comp.total_{commodity}_produced", units=commodity_amount_units) > 0 + ) + with subtests.test(f"total_{commodity}_produced length"): + assert ( + len(prob.get_val(f"comp.total_{commodity}_produced", units=commodity_amount_units)) == 1 + ) + + # Test that annual commodity production is greater than zero + with subtests.test(f"annual_{commodity}_produced > 0"): + assert np.all( + prob.get_val(f"comp.annual_{commodity}_produced", units=f"{commodity_amount_units}/yr") + > 0 + ) + + with subtests.test(f"annual_{commodity}_produced[1:] == annual_{commodity}_produced[0]"): + annual_production = prob.get_val( + f"comp.annual_{commodity}_produced", units=f"{commodity_amount_units}/yr" + ) + assert np.all(annual_production[1:] == annual_production[0]) + + with subtests.test(f"annual_{commodity}_produced length"): + assert len(annual_production) == plant_life + + # Test that commodity output has some values greater than zero + with subtests.test(f"Some of {commodity}_out > 0"): + assert np.any(prob.get_val(f"comp.{commodity}_out", units=commodity_rate_units) > 0) + + with subtests.test(f"{commodity}_out length"): + assert len(prob.get_val(f"comp.{commodity}_out", units=commodity_rate_units)) == n_timesteps + + # Test default values + with subtests.test("operational_life default value"): + assert prob.get_val("comp.operational_life", units="yr") == plant_life + with subtests.test("replacement_schedule value"): + assert np.all(prob.get_val("comp.replacement_schedule", units="unitless") == 0) + + +@pytest.mark.unit +def test_tidal_performance_values(plant_config, tidal_config, pysam_options, subtests): + """Add tests for values from performance model.""" + prob = om.Problem() + + tidal_resource = TidalResource( + plant_config=plant_config, + resource_config=plant_config["site"]["resources"]["tidal_resource"]["resource_parameters"], + driver_config={}, + ) + + prob.model.add_subsystem("tidal_resource", tidal_resource, promotes=["*"]) + + tidal_config["model_inputs"]["performance_parameters"]["pysam_options"] = pysam_options + comp = PySAMTidalPerformanceModel( + plant_config=plant_config, + tech_config=tidal_config, + driver_config={}, + ) + prob.model.add_subsystem("comp", comp, promotes=["*"]) + prob.setup() + prob.run_model() + + # Test output values + with subtests.test("electricity_out value"): + assert ( + pytest.approx(np.sum(prob.get_val("comp.electricity_out", units="kW")), rel=1e-6) + == 60625515.492 + ) + + with subtests.test("rated_electricity_production value"): + assert ( + pytest.approx(prob.get_val("comp.rated_electricity_production", units="kW"), rel=1e-6) + == 1115 * 20 + ) + with subtests.test("total_electricity_produced value"): + assert ( + pytest.approx(prob.get_val("comp.total_electricity_produced", units="kW*h"), rel=1e-6) + == 60625515.492 + ) + with subtests.test("capacity_factor value"): + assert ( + pytest.approx(prob.get_val("comp.capacity_factor", units="unitless"), rel=1e-6) + == 0.310346 + ) + + with subtests.test("annual_electricity_produced value"): + assert ( + pytest.approx( + prob.get_val("comp.annual_electricity_produced", units="kW*h/yr"), rel=1e-6 + ) + == 60625515.492 + ) + + +@pytest.mark.unit +### Test run_recalculate_power_curve method +def test_recalculate_power_curve(plant_config, tidal_config, pysam_options, subtests): + prob = om.Problem() + + tidal_resource = TidalResource( + plant_config=plant_config, + resource_config=plant_config["site"]["resources"]["tidal_resource"]["resource_parameters"], + driver_config={}, + ) + + prob.model.add_subsystem("tidal_resource", tidal_resource, promotes=["*"]) + + tidal_config["model_inputs"]["performance_parameters"]["pysam_options"] = pysam_options + tidal_config["model_inputs"]["performance_parameters"]["run_recalculate_power_curve"] = True + tidal_config["model_inputs"]["performance_parameters"]["device_rating_kw"] = ( + 2230 # 2x the original rating + ) + + comp = PySAMTidalPerformanceModel( + plant_config=plant_config, + tech_config=tidal_config, + driver_config={}, + ) + prob.model.add_subsystem("comp", comp, promotes=["*"]) + prob.setup() + prob.run_model() + + device_rating_kw = 2230 # 2x the original rating + + with subtests.test("correctly runs in model"): + # Check that the model runs with the recalculated power curve and produces expected outputs + assert ( + pytest.approx(prob.get_val("comp.rated_electricity_production", units="kW"), rel=1e-6) + == device_rating_kw * 20 + ) + + with subtests.test("annual_electricity_produced value recalculated power curve"): + assert ( + pytest.approx( + prob.get_val("comp.annual_electricity_produced", units="kW*h/yr"), rel=1e-6 + ) + == 60625515.492 * 2 + ) + + +@pytest.mark.unit +def test_tidal_default_model(plant_config, subtests): + """Add tests for values from performance model.""" + prob = om.Problem() + + tidal_resource = TidalResource( + plant_config=plant_config, + resource_config=plant_config["site"]["resources"]["tidal_resource"]["resource_parameters"], + driver_config={}, + ) + + prob.model.add_subsystem("tidal_resource", tidal_resource, promotes=["*"]) + + tidal_config = { + "model_inputs": { + "performance_parameters": { + "create_model_from": "default", + "num_devices": 20, + "device_rating_kw": 1115, + } + } + } + comp = PySAMTidalPerformanceModel( + plant_config=plant_config, + tech_config=tidal_config, + driver_config={}, + ) + prob.model.add_subsystem("comp", comp, promotes=["*"]) + prob.setup() + prob.run_model() + + # Test output values + with subtests.test("electricity_out value"): + assert ( + pytest.approx(np.sum(prob.get_val("comp.electricity_out", units="kW")), rel=1e-6) + == 56381729.40755999 + ) + + # test that it correctly adds the number of devices (overwriting PySAM json defaults) + with subtests.test("rated_electricity_production value"): + assert ( + pytest.approx(prob.get_val("comp.rated_electricity_production", units="kW"), rel=1e-6) + == 1115 * 20 + ) + with subtests.test("total_electricity_produced value"): + assert ( + pytest.approx(prob.get_val("comp.total_electricity_produced", units="kW*h"), rel=1e-6) + == 56381729.40755999 + ) + with subtests.test("capacity_factor value"): + assert ( + pytest.approx(prob.get_val("comp.capacity_factor", units="unitless"), rel=1e-6) + == 0.28862199 + ) + + with subtests.test("annual_electricity_produced value"): + assert ( + pytest.approx( + prob.get_val("comp.annual_electricity_produced", units="kW*h/yr"), rel=1e-6 + ) + == 56381729.40755999 + ) diff --git a/h2integrate/converters/water_power/tidal_pysam.py b/h2integrate/converters/water_power/tidal_pysam.py new file mode 100644 index 000000000..f0e961175 --- /dev/null +++ b/h2integrate/converters/water_power/tidal_pysam.py @@ -0,0 +1,230 @@ +import PySAM.MhkTidal as MhkTidal +from attrs import field, define + +from h2integrate.core.utilities import BaseConfig, merge_shared_inputs +from h2integrate.core.validators import gt_zero, contains +from h2integrate.core.model_baseclasses import PerformanceModelBaseClass + + +@define(kw_only=True) +class PySAMTidalPerformanceConfig(BaseConfig): + """ + Configuration class for MHKTidalPlant. + + Args: + device_rating_kw (float): Rated power of the MHK device [kW] + num_devices (int): Number of MHK tidal devices in the system + tidal_power_curve (List[List[float]]): Power curve of tidal energy device as + function of stream speeds [kW]. Required if create_model_from == 'new'. + create_model_from (str): + - 'default': instantiate MhkTidal model from the default config 'config_name' + - 'new': instantiate new MhkTidal model (default). Requires pysam_options. + config_name (str,optional): PySAM.MhkTidal configuration name for non-hybrid wind systems. + Defaults to 'MEtidalNone'. Only used if create_model_from='default'. + pysam_options (dict, optional): dictionary of MhkTidal input parameters with + top-level keys corresponding to the different MhkTidal variable groups. + (please refer to MhkTidal documentation + `here `__ + ) + run_recalculate_power_curve (bool, optional): If True, the tidal device power curve will be + recalculated based on the device_rating_kw and original power_curve. Defaults to False. + + """ + + device_rating_kw: float = field(validator=gt_zero) + num_devices: int = field(validator=gt_zero) + tidal_power_curve: list[list[float]] | None = field(default=None) + + create_model_from: str = field( + default="new", validator=contains(["default", "new"]), converter=(str.strip, str.lower) + ) + + config_name: str = field( + default="MEtidalNone", + validator=contains( + [ + "MEtidalLCOECalculator", + "MEtidalNone", + ] + ), + ) + pysam_options: dict = field(default={}) + run_recalculate_power_curve: bool = field(default=False) + + def __attrs_post_init__(self): + if self.create_model_from == "new" and not bool(self.pysam_options): + msg = ( + "To create a new MhkTidal object, please provide a dictionary " + "of MhkTidal design variables for the 'pysam_options' key." + ) + raise ValueError(msg) + + if self.create_model_from == "new" and self.tidal_power_curve is None: + msg = ( + "To create a new MhkTidal object, please provide a " + "tidal_power_curve in the config." + ) + raise ValueError(msg) + + self.check_pysam_options() + + def check_pysam_options(self): + """Checks that top-level keys of pysam_options dictionary are valid and that + system capacity is not given in pysam_options. + + Raises: + ValueError: if top-level keys of pysam_options are not valid. + ValueError: if number_devices is provided in pysam_options["MHKTidal"] + """ + valid_groups = [ + "MHKTidal", + "AdjustmentFactors", + ] + if bool(self.pysam_options): + invalid_groups = [k for k in self.pysam_options if k not in valid_groups] + if len(invalid_groups) > 0: + msg = ( + f"Invalid group(s) found in pysam_options: {invalid_groups}. " + f"Valid groups are: {valid_groups}." + ) + raise ValueError(msg) + + if self.pysam_options.get("MHKTidal", {}).get("number_devices", None) is not None: + msg = ( + "Please do not specify number_devices in the pysam_options dictionary. " + "The number of tidal devices should be set with the 'num_devices' " + "performance parameter." + ) + raise ValueError(msg) + + return + + def create_input_dict(self): + """Create dictionary of inputs to over-write the default values + associated with the specified MhkTidal configuration. + + Returns: + dict: dictionary of MHKTidal group parameters from user-input. + """ + design_dict = { + "MHKTidal": { + "number_devices": self.num_devices, + }, + } + + return design_dict + + +class PySAMTidalPerformanceModel(PerformanceModelBaseClass): + """An OpenMDAO component that wraps the PySAM MhkTidal model. + It takes tidal parameters as input and outputs power generation data. + """ + + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + + def initialize(self): + super().initialize() + self.commodity = "electricity" + self.commodity_rate_units = "kW" + self.commodity_amount_units = "kW*h" + + def setup(self): + super().setup() + self.config = PySAMTidalPerformanceConfig.from_dict( + merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance"), + additional_cls_name=self.__class__.__name__, + ) + + #### Tidal Resource #### + self.add_input( + "tidal_velocity", + val=0.0, + shape=self.n_timesteps, + units="m/s", + ) + + #### Tidal Device Parameters #### + self.add_input( + "num_devices", + val=self.config.num_devices, + units="unitless", + desc="Number of tidal devices in the system", + ) + + self.add_input( + "device_rating", + val=self.config.device_rating_kw, + units="kW", + desc="Rated power of the tidal energy device", + ) + + if self.config.create_model_from == "default": + self.system_model = MhkTidal.default(self.config.config_name) + elif self.config.create_model_from == "new": + self.system_model = MhkTidal.new() + self.system_model.value("tidal_power_curve", self.config.tidal_power_curve) + + design_dict = self.config.create_input_dict() + if bool(self.config.pysam_options): + for group, group_parameters in self.config.pysam_options.items(): + if group in design_dict: + design_dict[group].update(group_parameters) + else: + design_dict.update({group: group_parameters}) + self.system_model.assign(design_dict) + + def recalculate_power_curve(self, device_rating_kw, power_curve): + """Recalculate tidal device power curve based on the device rating. + + Args: + device_rating_kw (float): Rated power of the tidal energy device in kW. + power_curve (List[List[float]]): Original power curve of tidal energy + device as function of stream speeds [kW]. + + Returns: + List[List[float]]: Recalculated power curve based on the device rating. + """ + original_rated_power = max([point[1] for point in power_curve]) + scaling_factor = device_rating_kw[0] / original_rated_power + recalculated_power_curve = [(point[0], point[1] * scaling_factor) for point in power_curve] + + return recalculated_power_curve + + def compute(self, inputs, outputs): + # set tidal resource model choice + self.system_model.MHKTidal.tidal_resource_model_choice = ( + 1 # Time-series data=1 JPD=0 (Joint-probability distribution) + ) + + # assign resource to tidal model + tidal_velocity = inputs["tidal_velocity"] + self.system_model.value("tidal_velocity", tidal_velocity) + + # recalculate power curve if specified in config + if self.config.run_recalculate_power_curve: + recalculated_power_curve = self.recalculate_power_curve( + device_rating_kw=inputs["device_rating"], + power_curve=self.config.tidal_power_curve, + ) + self.system_model.value("tidal_power_curve", recalculated_power_curve) + + # calculate system capacity + system_capacity_kw = inputs["num_devices"][0] * inputs["device_rating"][0] + self.system_model.value("system_capacity", system_capacity_kw) + self.system_model.value("number_devices", inputs["num_devices"][0]) + + # run the model + self.system_model.execute(0) + + outputs["electricity_out"] = self.system_model.Outputs.gen + outputs["rated_electricity_production"] = system_capacity_kw + + outputs["total_electricity_produced"] = outputs["electricity_out"].sum() * (self.dt / 3600) + outputs["annual_electricity_produced"] = self.system_model.Outputs.annual_energy + + outputs["capacity_factor"] = ( + self.system_model.Outputs.capacity_factor / 100 + ) # divide by 100 to make it unitless diff --git a/h2integrate/converters/wind/atb_wind_cost.py b/h2integrate/converters/wind/atb_wind_cost.py index 7aaf35edf..1f9a75809 100644 --- a/h2integrate/converters/wind/atb_wind_cost.py +++ b/h2integrate/converters/wind/atb_wind_cost.py @@ -10,10 +10,10 @@ class ATBWindPlantCostModelConfig(CostModelBaseConfig): """Configuration class for the ATBWindCostModel. Recommended to use with wind models (Land-Based, Offshore and Distributed More information on ATB methodology and representative wind technologies can - be found `here `_ + be found `here `_ Reference cost values can be found on the `Land-Based Wind`, `Fixed-Bottom Offshore Wind`, `Floating Offshore Wind` or `Distributed Wind` - sheet of the `NREL ATB workbook `_. + sheet of the `NLR ATB workbook `_. Attributes: capex_per_kW (float|int): capital cost of wind system in $/kW @@ -48,6 +48,11 @@ class ATBWindPlantCostModel(CostModelBaseClass): Annual operating expenditure of the wind plant. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = ATBWindPlantCostModelConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"), diff --git a/h2integrate/converters/wind/floris.py b/h2integrate/converters/wind/floris.py index 773e73a2a..02c26ef1e 100644 --- a/h2integrate/converters/wind/floris.py +++ b/h2integrate/converters/wind/floris.py @@ -104,6 +104,11 @@ class FlorisWindPlantPerformanceModel(WindPerformanceBaseClass, CacheBaseClass): outputs power generation data. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.n_timesteps = int(self.options["plant_config"]["plant"]["simulation"]["n_timesteps"]) diff --git a/h2integrate/converters/wind/layout/test/conftest.py b/h2integrate/converters/wind/layout/test/conftest.py index 8d49ffb3d..fa8f250cf 100644 --- a/h2integrate/converters/wind/layout/test/conftest.py +++ b/h2integrate/converters/wind/layout/test/conftest.py @@ -1 +1,5 @@ -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) diff --git a/h2integrate/converters/wind/test/conftest.py b/h2integrate/converters/wind/test/conftest.py index 3c9a44d85..81f4ba75b 100644 --- a/h2integrate/converters/wind/test/conftest.py +++ b/h2integrate/converters/wind/test/conftest.py @@ -3,7 +3,11 @@ import pytest from hopp import TEST_ENV_VAR -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) def pytest_sessionstart(session): diff --git a/h2integrate/converters/wind/test/test_ard_wind.py b/h2integrate/converters/wind/test/test_ard_wind.py index 0fbf47dd6..6afcbf8be 100644 --- a/h2integrate/converters/wind/test/test_ard_wind.py +++ b/h2integrate/converters/wind/test/test_ard_wind.py @@ -55,18 +55,18 @@ def ard_config(): "rated_power": 5000000.0, # W "num_blades": 3, "rated_thrust_N": 823484.4216152605, # from NREL 5MW definition - "gust_velocity_m_per_s": 70.0, # from https://github.com/WISDEM/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml + "gust_velocity_m_per_s": 70.0, # from https://github.com/NLRWindSystems/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml "blade_surface_area": 69.7974979, "tower_mass": 620.4407337521, "nacelle_mass": 101.98582836439, "hub_mass": 8.38407517646, "blade_mass": 14.56341339641, "foundation_height": 0.0, - "commissioning_cost_kW": 44.0, # from https://github.com/WISDEM/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml - "decommissioning_cost_kW": 58.0, # from https://github.com/WISDEM/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml + "commissioning_cost_kW": 44.0, # from https://github.com/NLRWindSystems/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml + "decommissioning_cost_kW": 58.0, # from https://github.com/NLRWindSystems/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml "trench_len_to_substation_km": 50.0, "distance_to_interconnect_mi": 4.97096954, - "interconnect_voltage_kV": 130.0, # from https://github.com/WISDEM/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml + "interconnect_voltage_kV": 130.0, # from https://github.com/NLRWindSystems/WISDEM/blob/master/examples/02_reference_turbines/nrel5mw.yaml "tcc_per_kW": 1300.00, # (USD/kW) "opex_per_kW": 44.00, # (USD/kWh) }, diff --git a/h2integrate/converters/wind/tools/test/conftest.py b/h2integrate/converters/wind/tools/test/conftest.py index 8d49ffb3d..fa8f250cf 100644 --- a/h2integrate/converters/wind/tools/test/conftest.py +++ b/h2integrate/converters/wind/tools/test/conftest.py @@ -1 +1,5 @@ -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) diff --git a/h2integrate/converters/wind/wind_plant_ard.py b/h2integrate/converters/wind/wind_plant_ard.py index 43204e623..fedb9ab33 100644 --- a/h2integrate/converters/wind/wind_plant_ard.py +++ b/h2integrate/converters/wind/wind_plant_ard.py @@ -37,19 +37,13 @@ class WindArdPerformanceCompatibilityComponent(PerformanceModelBaseClass): H2Integrate. """ + _time_step_bounds = (3600, 3600) # (min, max) time step lengths compatible with this model + def initialize(self): super().initialize() self.commodity = "electricity" self.commodity_rate_units = "kW" self.commodity_amount_units = "kW*h" - if set_up_ard_model is None: - msg = ( - "Please install `ard-nrel` or `h2integrate[ard]` to use the" - " `WindArdPerformanceCompatibilityComponent` Ard-based model." - " It is highly recommended to run `conda install wisdem` first. See H2I's" - "installation instructions for further details." - ) - raise ModuleNotFoundError(msg) def setup(self): self.config = WindPlantArdModelConfig.from_dict( @@ -99,6 +93,11 @@ class WindArdCostCompatibilityComponent(CostModelBaseClass): requires a self.config attribute to be defined, so we create this minimal subclass. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = CostModelBaseConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost") @@ -145,11 +144,24 @@ class ArdWindPlantModel(om.Group): VarOpEx: Variable operating expenditure (currently placeholder). """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): self.options.declare("driver_config", types=dict) self.options.declare("plant_config", types=dict) self.options.declare("tech_config", types=dict) + if set_up_ard_model is None: + msg = ( + "Please install `ard-nrel` or `h2integrate[ard]` to use the" + " `ArdWindPlantModel`. See H2I's installation instructions " + "for further details." + ) + raise ModuleNotFoundError(msg) + def setup(self): self.config = WindPlantArdModelConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance") diff --git a/h2integrate/converters/wind/wind_plant_baseclass.py b/h2integrate/converters/wind/wind_plant_baseclass.py index 0355996fb..6e4de0bd2 100644 --- a/h2integrate/converters/wind/wind_plant_baseclass.py +++ b/h2integrate/converters/wind/wind_plant_baseclass.py @@ -2,6 +2,11 @@ class WindPerformanceBaseClass(PerformanceModelBaseClass): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() self.commodity = "electricity" diff --git a/h2integrate/converters/wind/wind_pysam.py b/h2integrate/converters/wind/wind_pysam.py index 074ea9eb6..46dd84a2e 100644 --- a/h2integrate/converters/wind/wind_pysam.py +++ b/h2integrate/converters/wind/wind_pysam.py @@ -182,6 +182,11 @@ class PYSAMWindPlantPerformanceModel(WindPerformanceBaseClass): It takes wind parameters as input and outputs power generation data. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): super().setup() @@ -247,7 +252,7 @@ def setup(self): if self.config.create_model_from == "default": self.system_model = Windpower.default(self.config.config_name) elif self.config.create_model_from == "new": - self.system_model = Windpower.new(self.config.config_name) + self.system_model = Windpower.new() design_dict = self.config.create_input_dict() if bool(self.config.pysam_options): diff --git a/h2integrate/core/commodity_stream_definitions.py b/h2integrate/core/commodity_stream_definitions.py new file mode 100644 index 000000000..22e20bddb --- /dev/null +++ b/h2integrate/core/commodity_stream_definitions.py @@ -0,0 +1,114 @@ +""" +Commodity stream definitions for H2Integrate. + +This module contains: +1. multivariable_streams: Definitions for streams that bundle multiple related variables +2. add_multivariable_output / add_multivariable_input: Helpers to register all + constituent variables of a multivariable stream on an OpenMDAO component +3. is_electricity_producer: Helper function to identify electricity-producing technologies +""" + +multivariable_streams = { + "wellhead_gas_mixture": { + "mass_flow": { + "units": "kg/h", + "desc": "Total mass flow rate of gas in the stream", + }, + "hydrogen_mass_fraction": { + "units": "unitless", + "desc": "Mass fraction of hydrogen in the gas stream", + }, + "oxygen_mass_fraction": { + "units": "unitless", + "desc": "Mass fraction of oxygen in the gas stream", + }, + "temperature": { + "units": "K", + "desc": "Temperature of the gas stream", + }, + "pressure": { + "units": "bar", + "desc": "Pressure of the gas stream", + }, + }, + # Future multivariable stream definitions can be added here +} + + +def add_multivariable_output(component, stream_name: str, n_timesteps: int) -> None: + """Add all constituent variables of a multivariable stream as outputs. + + For each variable defined in ``multivariable_streams[stream_name]``, an + output named ``:_out`` is added to *component*. + + Args: + component: An OpenMDAO component instance (must have ``add_output``). + stream_name: Key into :data:`multivariable_streams`. + n_timesteps: Length of the time-series dimension. + + Raises: + KeyError: If *stream_name* is not in :data:`multivariable_streams`. + """ + for var_name, var_props in multivariable_streams[stream_name].items(): + component.add_output( + f"{stream_name}:{var_name}_out", + val=0.0, + shape=n_timesteps, + units=var_props.get("units"), + desc=var_props.get("desc", ""), + ) + + +def add_multivariable_input(component, stream_name: str, n_timesteps: int) -> None: + """Add all constituent variables of a multivariable stream as inputs. + + For each variable defined in ``multivariable_streams[stream_name]``, an + input named ``:_in`` is added to *component*. + + Args: + component: An OpenMDAO component instance (must have ``add_input``). + stream_name: Key into :data:`multivariable_streams`. + n_timesteps: Length of the time-series dimension. + + Raises: + KeyError: If *stream_name* is not in :data:`multivariable_streams`. + """ + for var_name, var_props in multivariable_streams[stream_name].items(): + component.add_input( + f"{stream_name}:{var_name}_in", + val=0.0, + shape=n_timesteps, + units=var_props.get("units"), + desc=var_props.get("desc", ""), + ) + + +def is_electricity_producer(tech_name: str) -> bool: + """Check if a technology is an electricity producer. + + Args: + tech_name: The name of the technology to check. + Returns: + True if tech_name starts with any of the known electricity producing + tech prefixes (e.g., 'wind', 'solar', 'pv', 'grid_buy', etc.). + Note: + This uses prefix matching, so 'grid_buy_1' and 'grid_buy_2' would both + be considered electricity producers. Be careful when naming technologies + to avoid unintended matches (e.g., 'pv_battery' would be incorrectly + identified as an electricity producer). + """ + + # add any new electricity producing technologies to this list + electricity_producing_techs = [ + "wind", + "solar", + "pv", + "tidal", + "river", + "hopp", + "natural_gas_plant", + "grid_buy", + "h2_fuel_cell", + ] + + return any(tech_name.startswith(elem) for elem in electricity_producing_techs) diff --git a/h2integrate/core/dict_utils.py b/h2integrate/core/dict_utils.py index 1404024ac..34b5c3483 100644 --- a/h2integrate/core/dict_utils.py +++ b/h2integrate/core/dict_utils.py @@ -1,6 +1,7 @@ import copy import operator from functools import reduce +from collections import Counter import numpy as np @@ -217,3 +218,139 @@ def rename_dict_keys(input_dict, init_keyname, new_keyname): input_dict = update_keyname(input_dict, init_keyname, new_keyname) input_dict = remove_keynames(input_dict, init_keyname) return input_dict + + +def check_inputs(prob, tech: str, tech_info: dict, tech_config_path: str): + """Check the user-input technology configuration inputs against the + instantiated technology configuration classes to ensure that: + + 1. All user-input parameters are used in at least 1 configuration class + 2. User-input `shared_parameters` are shared across at least 2 configuration classes + 3. User-input parameters that are not shared are only used in 1 configuration class + + Args: + prob (om.Problem): OpenMDAO problem defined in H2IntegrateModel + tech (str): name of technology that the tech_info is for. + tech_info (dict): technology input dictionary, including the + technology model names and `model_inputs`. + tech_config_path (str or Path, optional): path to the technology + configuration file. Used in error messages to help the user + locate the problematic section. + + Raises: + AttributeError: Raised if any of the 3 conditions are not met. + """ + # Only check models that have a control strategy or dispatch rule set + if not {"control_strategy", "dispatch_rule_set"}.intersection(tech_info): + return + + # Only check for shared inputs when the system contains at least one technology + # in addition to a performance and control model + check_keys = ("control_strategy", "dispatch_rule_set", "cost_model", "performance_model") + minimal_keys = {"control_strategy", "performance_model"} + overlap = set(tech_info).intersection(check_keys) + if not overlap.difference(minimal_keys): + return + + msg = None + control_sys = None + dispatch_sys = None + cost_sys = None + perf_sys = None + group = getattr(prob.model.plant, tech) + + # Rebuild the model inputs dictionary from the initialized technology parameters + control_params = {} + dispatch_params = {} + cost_params = {} + performance_params = {} + if "control_strategy" in tech_info: + if (control_sys := getattr(group, tech_info["control_strategy"]["model"])) is not None: + control_params = control_sys.config.as_dict() + if "dispatch_rule_set" in tech_info: + if (dispatch_sys := getattr(group, tech_info["dispatch_rule_set"]["model"])) is not None: + dispatch_params = dispatch_sys.config.as_dict() + if "cost_model" in tech_info: + if (cost_sys := getattr(group, tech_info["cost_model"]["model"])) is not None: + cost_params = cost_sys.config.as_dict() + if "performance_model" in tech_info: + if (perf_sys := getattr(group, tech_info["performance_model"]["model"])) is not None: + performance_params = perf_sys.config.as_dict() + if "cost_model" in tech_info and "performance_model" in tech_info: + # Handle case with combined cost and performance model + if tech_info["cost_model"]["model"] == tech_info["performance_model"]["model"]: + cost_sys = None + cost_params = {} + + # Check for overlapping keys between any two sets of configurations to reconstruct + # the shared parameters, and create a restructured configuration + all_parameters = (control_params, dispatch_params, cost_params, performance_params) + _share_check = Counter([x for el in all_parameters for x in set(el)]) + shared = {k for k, v in _share_check.items() if v > 1} + shared_params = {k: control_params.pop(k) for k in shared.intersection(control_params)} + shared_params |= {k: dispatch_params.pop(k) for k in shared.intersection(dispatch_params)} + shared_params |= {k: cost_params.pop(k) for k in shared.intersection(cost_params)} + shared_params |= {k: performance_params.pop(k) for k in shared.intersection(performance_params)} + restructured_params = { + "control_parameters": control_params, + "dispatch_parameters": dispatch_params, + "cost_parameters": cost_params, + "performance_parameters": performance_params, + "shared_parameters": shared_params, + } + + tech_location = f"the '{tech}' section of {tech_config_path}" + + # Flag any extra parameterizations provided by the user that should have either been + # shared but were not or were inappropriately shared + for param_key, vals in restructured_params.items(): + # check that the parameter key exists in both the user-provided model_inputs and + # the restructured parameters + if (user_params := tech_info["model_inputs"].get(param_key)) is None: + continue + + # Only throw errors when the user provided extraneous parameterizations + user_extras = set(user_params).difference(vals) + if not user_extras: + continue + + if param_key == "shared_parameters": + unnecessary_shared = [ + (user_extras.intersection(other_params), other_key) + for other_key, other_params in restructured_params.items() + ] + unnecessary_shared = [el for el in unnecessary_shared if el[0]] # remove the empty sets + if unnecessary_shared: + if len(unnecessary_shared) == 1: + unshared_params, other_key = unnecessary_shared[0] + msg = ( + f"The parameter(s): {unnecessary_shared} found in shared_parameters" + f" but should be in {other_key} for {tech_location}" + ) + else: + mapping = "\n\t".join( + f"{level} should contain: {keys}" for keys, level in unnecessary_shared + ) + msg = ( + f"The following parameter sets were found in shared_parameters but should" + f" be in the following sections for {tech_location}:" + f"\n\t{mapping}" + ) + else: + msg = ( + f"The parameter(s): {user_extras} found in shared_parameters" + f" are not used by any of the models for {tech_location}" + ) + raise AttributeError(msg) + + shared_overlap = user_extras.intersection(restructured_params.get("shared_parameters", {})) + if shared_overlap: + msg = ( + f"The parameter(s) {shared_overlap} found in {param_key}" + f" should be under shared_parameters for {tech_location}" + ) + msg = ( + f"The parameter(s) {user_extras} found in {param_key} are not used for " + f"{tech_location}" + ) + raise AttributeError(msg) diff --git a/h2integrate/core/feedstocks.py b/h2integrate/core/feedstocks.py index 3a4fc5fd0..a0f457c3c 100644 --- a/h2integrate/core/feedstocks.py +++ b/h2integrate/core/feedstocks.py @@ -23,6 +23,11 @@ class FeedstockPerformanceConfig(BaseConfig): class FeedstockPerformanceModel(om.ExplicitComponent): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): self.options.declare("driver_config", types=dict) self.options.declare("plant_config", types=dict) @@ -75,7 +80,6 @@ class FeedstockCostConfig(CostModelBaseConfig): price: int | float | list = field() annual_cost: float = field(default=0.0) start_up_cost: float = field(default=0.0) - commodity_amount_units: str | None = field(default=None) def __attrs_post_init__(self): @@ -84,34 +88,107 @@ def __attrs_post_init__(self): class FeedstockCostModel(CostModelBaseClass): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = FeedstockCostConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"), additional_cls_name=self.__class__.__name__, ) - n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"] + self.n_timesteps = int(self.options["plant_config"]["plant"]["simulation"]["n_timesteps"]) plant_life = int(self.options["plant_config"]["plant"]["plant_life"]) + # Set cost outputs super().setup() self.add_input( f"{self.config.commodity}_consumed", val=0.0, - shape=int(n_timesteps), + shape=self.n_timesteps, units=self.config.commodity_rate_units, desc=f"Consumption profile of {self.config.commodity}", ) + self.add_input( + f"{self.config.commodity}_out", + val=0, + shape=self.n_timesteps, + units=self.config.commodity_rate_units, + ) + self.add_input( "price", val=self.config.price, units=f"USD/({self.config.commodity_amount_units})", - desc=f"Consumption profile of {self.config.commodity}", + desc=f"Price profile of {self.config.commodity}", + ) + + self.dt = self.options["plant_config"]["plant"]["simulation"]["dt"] + self.plant_life = int(self.options["plant_config"]["plant"]["plant_life"]) + hours_per_year = 8760 + hours_simulated = (self.dt / 3600) * self.n_timesteps + self.fraction_of_year_simulated = hours_simulated / hours_per_year + # since feedstocks are consumed, some outputs are appended + # with 'consumed' rather than 'produced' + + self.add_output( + f"total_{self.config.commodity}_consumed", + val=0.0, + units=self.config.commodity_amount_units, + ) + + self.add_output( + f"annual_{self.config.commodity}_consumed", + val=0.0, + shape=self.plant_life, + units=f"({self.config.commodity_amount_units})/year", + ) + + # Capacity factor is feedstock_consumed/max_feedstock_available + self.add_output( + "capacity_factor", + val=0.0, + shape=self.plant_life, + units="unitless", + desc="Capacity factor", + ) + + # The should be equal to the commodity_capacity input of the FeedstockPerformanceModel + self.add_output( + f"rated_{self.config.commodity}_production", + val=0, + units=self.config.commodity_rate_units, ) # lifetime estimate of item replacements, represented as a fraction of the capacity. self.add_output("replacement_schedule", val=0.0, shape=plant_life, units="unitless") def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): + # Capacity factor is the total amount consumed / the total amount available + outputs["capacity_factor"] = ( + inputs[f"{self.config.commodity}_consumed"].sum() + / inputs[f"{self.config.commodity}_out"].sum() + ) + + # Sum the amount consumed + outputs[f"total_{self.config.commodity}_consumed"] = inputs[ + f"{self.config.commodity}_consumed" + ].sum() * (self.dt / 3600) + + # Estimate annual consumption based on consumption over the simulation + # NOTE: once we standardize feedstock consumption outputs in models, this should + # be updated to handle consumption that varies over years of operation + outputs[f"annual_{self.config.commodity}_consumed"] = outputs[ + f"total_{self.config.commodity}_consumed" + ] * (1 / self.fraction_of_year_simulated) + + outputs[f"rated_{self.config.commodity}_production"] = inputs[ + f"{self.config.commodity}_out" + ].max() + + # Calculate costs price = inputs["price"] hourly_consumption = inputs[f"{self.config.commodity}_consumed"] cost_per_year = sum(price * hourly_consumption) diff --git a/h2integrate/core/file_utils.py b/h2integrate/core/file_utils.py index 73266e416..9f19d411e 100644 --- a/h2integrate/core/file_utils.py +++ b/h2integrate/core/file_utils.py @@ -143,7 +143,7 @@ def find_file(filename: str | Path, root_folder: str | Path | None = None): f"the root directory {root_folder}." ) raise ValueError( - f"Cannot find unique file: found {len(files_cwd)} files relative to cwd, " + f"Cannot find unique file for {filename}: found {len(files_cwd)} files relative to cwd, " f"{len(files_h2i)} files relative to H2Integrate root directory, " f"{len(files)} files relative to the root folder." ) diff --git a/h2integrate/core/h2integrate_model.py b/h2integrate/core/h2integrate_model.py index b401f39c2..e7c3e80c0 100644 --- a/h2integrate/core/h2integrate_model.py +++ b/h2integrate/core/h2integrate_model.py @@ -1,4 +1,5 @@ import importlib.util +from enum import IntEnum import numpy as np import networkx as nx @@ -7,14 +8,23 @@ from h2integrate.core.sites import SiteLocationComponent from h2integrate.core.utilities import create_xdsm_from_config +from h2integrate.core.dict_utils import check_inputs from h2integrate.core.file_utils import get_path, find_file, load_yaml from h2integrate.finances.finances import AdjustedCapexOpexComp -from h2integrate.core.supported_models import supported_models, is_electricity_producer +from h2integrate.core.supported_models import ( + no_cost_models, + supported_models, + no_replacement_schedule_models, +) from h2integrate.core.inputs.validation import load_tech_yaml, load_plant_yaml, load_driver_yaml from h2integrate.core.pose_optimization import PoseOptimization from h2integrate.postprocess.sql_to_csv import convert_sql_to_csv_summary -from h2integrate.control.control_strategies.pyomo_controller_baseclass import ( - PyomoControllerBaseClass, +from h2integrate.core.commodity_stream_definitions import ( + multivariable_streams, + is_electricity_producer, +) +from h2integrate.control.control_strategies.pyomo_storage_controller_baseclass import ( + PyomoStorageControllerBaseClass, ) @@ -24,6 +34,13 @@ pyxdsm = None +class State(IntEnum): + INITIALIZED = 0 + SETUP = 1 + RUN = 2 + POST_PROCESS = 3 + + class H2IntegrateModel: def __init__(self, config_input): # read in config file; it's a yaml dict that looks like this: @@ -40,9 +57,6 @@ def __init__(self, config_input): self.prob = om.Problem(reports=create_om_reports) self.model = self.prob.model - # track if setup has been called via boolean - self.setup_has_been_called = False - # initialize recorder_path attribute self.recorder_path = None @@ -71,6 +85,8 @@ def __init__(self, config_input): # might be an analysis or optimization self.create_driver_model() + self.state = State.INITIALIZED + def _load_component_config(self, config_key, config_value, config_path, validator_func): """Helper method to load and validate a component configuration. @@ -192,7 +208,7 @@ def load_config(self, config_input): controller_model_name = vals["control_strategy"]["model"] controller_cls = supported_models.get(controller_model_name) if controller_cls is not None and issubclass( - controller_cls, PyomoControllerBaseClass + controller_cls, PyomoStorageControllerBaseClass ): model_inputs = self.technology_config["technologies"][name]["model_inputs"] if ( @@ -346,6 +362,9 @@ def create_site_model(self): and resources models (if provided in the configuration) for that site. """ # Loop through each site defined in the plant config + # If no sites defined in plant_config, nothing to do + if "sites" not in self.plant_config or not self.plant_config["sites"]: + return for site_name, site_info in self.plant_config["sites"].items(): # Reorganize the plant config to be formatted as expected by the # resource models @@ -480,6 +499,7 @@ def create_technology_models(self): plant_config=self.plant_config, tech_config=individual_tech_config, ) + self._check_time_step(perf_model, comp) self.plant.add_subsystem(f"{tech_name}_source", comp) else: tech_group = self.plant.add_subsystem(tech_name, om.Group()) @@ -489,6 +509,7 @@ def create_technology_models(self): # and in combined_performance_and_cost_models perf_model = individual_tech_config.get("performance_model", {}).get("model") cost_model = individual_tech_config.get("cost_model", {}).get("model") + individual_tech_config.get("finance_model", {}).get("model") if ( perf_model @@ -514,7 +535,8 @@ def create_technology_models(self): plant_config=self.plant_config, tech_config=individual_tech_config, ) - om_model_object = tech_group.add_subsystem(tech_name, comp, promotes=["*"]) + self._check_time_step(perf_model, comp) + om_model_object = tech_group.add_subsystem(perf_model, comp, promotes=["*"]) self.performance_models.append(om_model_object) self.cost_models.append(om_model_object) self.finance_models.append(om_model_object) @@ -563,18 +585,23 @@ def create_technology_models(self): for tech_name, individual_tech_config in self.technology_config["technologies"].items(): cost_model = individual_tech_config.get("cost_model", {}).get("model") + if cost_model == "FeedstockCostModel": comp = self.supported_models[cost_model]( driver_config=self.driver_config, plant_config=self.plant_config, tech_config=individual_tech_config, ) + self._check_time_step(tech_name, comp) self.plant.add_subsystem(tech_name, comp) def _process_model(self, model_type, individual_tech_config, tech_group): # Generalized function to process model definitions model_name = individual_tech_config[model_type]["model"] model_object = self.supported_models[model_name] + + self._check_time_step(model_name, model_object) + om_model_object = tech_group.add_subsystem( model_name, model_object( @@ -584,8 +611,23 @@ def _process_model(self, model_type, individual_tech_config, tech_group): ), promotes=["*"], ) + return om_model_object + def _check_time_step(self, model_name, model_object): + dt = int(self.plant_config["plant"]["simulation"]["dt"]) + + min_ts = model_object._time_step_bounds[0] + max_ts = model_object._time_step_bounds[1] + if dt < min_ts or dt > max_ts: + msg = ( + f"Model {model_name} is compatible with time steps " + f"between {min_ts} (s) and {max_ts} (s), but a time step of {dt} (s) " + "was specified. Please set plant_config['plant']['simulation']['dt'] to a" + f" value within the range [{min_ts}, {max_ts}]." + ) + raise ValueError(msg) + def create_finance_model(self): """ Create and configure the finance model(s) for the plant. @@ -753,7 +795,12 @@ def create_finance_model(self): f"technologies: {list(self.technology_config['technologies'].keys())}" ) if commodity_stream is not None: - if "combiner" not in commodity_stream and commodity_stream not in tech_names: + commodity_stream_has_cost = ( + self.technology_config["technologies"] + .get(commodity_stream, {}) + .get("cost_model", False) + ) + if commodity_stream_has_cost and commodity_stream not in tech_names: raise UserWarning( f"The technology specific for the commodity_stream '{commodity_stream}' " f"is not included in subgroup '{subgroup_name}' technologies list." @@ -947,6 +994,50 @@ def create_finance_model(self): self.finance_subgroups = finance_subgroups + def _connect_multivariable_stream( + self, source_tech, dest_tech, stream_name, combiner_counts, splitter_counts + ): + """Connect a multivariable stream between source and destination technologies. + + Handles combiner indexing (numbered inputs), splitter indexing (numbered outputs), + and direct connections. Updates combiner_counts/splitter_counts dicts in-place. + + Args: + source_tech (str): Name of the source technology. + dest_tech (str): Name of the destination technology. + stream_name (str): Name of the multivariable stream (key in multivariable_streams). + combiner_counts (dict): Tracks the next input index per combiner technology. + splitter_counts (dict): Tracks the next output index per splitter technology. + """ + if "combiner" in dest_tech: + if dest_tech not in combiner_counts: + combiner_counts[dest_tech] = 1 + else: + combiner_counts[dest_tech] += 1 + stream_index = combiner_counts[dest_tech] + for var_name in multivariable_streams[stream_name]: + self.plant.connect( + f"{source_tech}.{stream_name}:{var_name}_out", + f"{dest_tech}.{stream_name}:{var_name}_in{stream_index}", + ) + elif "splitter" in source_tech: + if source_tech not in splitter_counts: + splitter_counts[source_tech] = 1 + else: + splitter_counts[source_tech] += 1 + stream_index = splitter_counts[source_tech] + for var_name in multivariable_streams[stream_name]: + self.plant.connect( + f"{source_tech}.{stream_name}:{var_name}_out{stream_index}", + f"{dest_tech}.{stream_name}:{var_name}_in", + ) + else: + for var_name in multivariable_streams[stream_name]: + self.plant.connect( + f"{source_tech}.{stream_name}:{var_name}_out", + f"{dest_tech}.{stream_name}:{var_name}_in", + ) + def connect_technologies(self): technology_interconnections = self.plant_config.get("technology_interconnections", []) @@ -959,6 +1050,18 @@ def connect_technologies(self): if len(connection) == 4: source_tech, dest_tech, transport_item, transport_type = connection + # Check if this is a multivariable stream connection + # Format: [source, dest, stream_name, transport_type] + if transport_item in multivariable_streams: + self._connect_multivariable_stream( + source_tech, + dest_tech, + transport_item, + combiner_counts, + splitter_counts, + ) + continue # Skip the rest of the 4-element handling + if transport_type in self.tech_names: # if the transport type is already a technology, skip creating a new component connection_name = f"{transport_type}" @@ -978,6 +1081,11 @@ def connect_technologies(self): f"{dest_tech}.{transport_item}_consumed", f"{source_tech}.{transport_item}_consumed", ) + # Connect the feedstock performance model output to the cost model input + self.plant.connect( + f"{source_tech}_source.{transport_item}_out", + f"{source_tech}.{transport_item}_out", + ) if perf_model_name == "FeedstockPerformanceModel": source_tech = f"{source_tech}_source" @@ -989,10 +1097,11 @@ def connect_technologies(self): pass else: connection_component = self.supported_models[transport_type]( - transport_item=transport_item + transport_item=transport_item, plant_config=self.plant_config ) # Add the connection component to the model + self._check_time_step(transport_type, connection_component) self.plant.add_subsystem(connection_name, connection_component) # Reorder the subsystems so transporters comes after their source technology @@ -1061,13 +1170,34 @@ def connect_technologies(self): source_tech, dest_tech, connected_parameter = connection if isinstance(connected_parameter, tuple | list): source_parameter, dest_parameter = connected_parameter - self.plant.connect( - f"{source_tech}.{source_parameter}", f"{dest_tech}.{dest_parameter}" - ) + # Check if this is a multivariable stream connection + if source_parameter in multivariable_streams: + self._connect_multivariable_stream( + source_tech, + dest_tech, + source_parameter, + combiner_counts, + splitter_counts, + ) + else: + self.plant.connect( + f"{source_tech}.{source_parameter}", f"{dest_tech}.{dest_parameter}" + ) else: - self.plant.connect( - f"{source_tech}.{connected_parameter}", f"{dest_tech}.{connected_parameter}" - ) + # Check if the connected_parameter is a multivariable stream + if connected_parameter in multivariable_streams: + self._connect_multivariable_stream( + source_tech, + dest_tech, + connected_parameter, + combiner_counts, + splitter_counts, + ) + else: + self.plant.connect( + f"{source_tech}.{connected_parameter}", + f"{dest_tech}.{connected_parameter}", + ) else: err_msg = f"Invalid connection: {connection}" @@ -1079,51 +1209,51 @@ def connect_technologies(self): resource_models = {} for site_grp, site_grp_inputs in self.plant_config["sites"].items(): for resource_key, resource_params in site_grp_inputs.get("resources", {}).items(): - resource_models[f"{site_grp}-{resource_key}"] = resource_params - - resource_source_connections = [c[0] for c in resource_to_tech_connections] - # Check if there is a missing resource to tech connection or missing resource model - if len(resource_models) != len(resource_source_connections): - if len(resource_models) > len(resource_source_connections): - # more resource models than resources connected to technologies - non_connected_resource = [ - k for k in resource_models if k not in resource_source_connections - ] - # check if theres a resource model that isn't connected to a technology - if len(non_connected_resource) > 0: - msg = ( - "Some resources are not connected to a technology. Resource models " - f"{non_connected_resource} are not included in " - "`resource_to_tech_connections`. Please connect these resources " - "to their technologies under `resource_to_tech_connections` in " - "the plant config file." - ) - raise ValueError(msg) - if len(resource_source_connections) > len(resource_models): - # more resources connected than resource models - missing_resource = [ - k for k in resource_source_connections if k not in resource_models - ] - # check if theres a resource model that isn't connected to a technology - if len(missing_resource) > 0: - msg = ( - "Missing resource(s) are not defined but are connected to a technology. " - f"Missing resource(s) are {missing_resource}. " - "Please check ``resource_to_tech_connections`` in the plant config file " - "or add the missing resources" - " to plant_config['site']['resources']." - ) - raise ValueError(msg) + resource_models[f"{site_grp}.{resource_key}"] = resource_params + + resource_source_connections = [c[0] for c in resource_to_tech_connections] + # Check if there is a missing resource to tech connection or missing resource model + if len(resource_models) != len(resource_source_connections): + if len(resource_models) > len(resource_source_connections): + # more resource models than resources connected to technologies + non_connected_resource = [ + k for k in resource_models if k not in resource_source_connections + ] + # check if theres a resource model that isn't connected to a technology + if len(non_connected_resource) > 0: + msg = ( + "Some resources are not connected to a technology. Resource models " + f"{non_connected_resource} are not included in " + "`resource_to_tech_connections`. Please connect these resources " + "to their technologies under `resource_to_tech_connections` in " + "the plant config file." + ) + raise ValueError(msg) + if len(resource_source_connections) > len(resource_models): + # more resources connected than resource models + missing_resource = [ + k for k in resource_source_connections if k not in resource_models + ] + # check if theres a resource model that isn't connected to a technology + if len(missing_resource) > 0: + msg = ( + "Missing resource(s) are not defined but are connected to a" + f" technology. Missing resource(s) are {missing_resource}. " + "Please check ``resource_to_tech_connections`` in the plant" + " config file or add the missing resources" + " to plant_config['site']['resources']." + ) + raise ValueError(msg) - for connection in resource_to_tech_connections: - if len(connection) != 3: - err_msg = f"Invalid resource to tech connection: {connection}" - raise ValueError(err_msg) + for connection in resource_to_tech_connections: + if len(connection) != 3: + err_msg = f"Invalid resource to tech connection: {connection}" + raise ValueError(err_msg) - resource_name, tech_name, variable = connection + resource_name, tech_name, variable = connection - # Connect the resource output to the technology input - self.model.connect(f"{resource_name}.{variable}", f"{tech_name}.{variable}") + # Connect the resource output to the technology input + self.model.connect(f"{resource_name}.{variable}", f"{tech_name}.{variable}") # connect outputs of the technology models to the cost and finance models of the # same name if the cost and finance models are not None @@ -1150,10 +1280,9 @@ def connect_technologies(self): # Only connect technologies that are included in the finance stackup for tech_name in tech_configs.keys(): - # For now, assume splitters and combiners do not add any costs - if "splitter" in tech_name or "combiner" in tech_name: - continue - if tech_name == "cable" or tech_name == "pipe": + # Skip technologies whose models doesn't add costs + perf_model = tech_configs[tech_name].get("performance_model").get("model") + if perf_model in no_cost_models: continue self.plant.connect( @@ -1171,7 +1300,7 @@ def connect_technologies(self): f"finance_subgroup_{group_id}.cost_year_{tech_name}", ) - if is_system_finance_model and "transport" not in tech_name: + if is_system_finance_model and perf_model not in no_replacement_schedule_models: # connect replacement schedule to system-level finance models self.plant.connect( f"{tech_name}.replacement_schedule", @@ -1208,17 +1337,16 @@ def connect_technologies(self): if tech_name == dispatching_tech_name: continue else: - # Connect the dispatch rules output to the dispatching_tech_name input - self.model.connect( - f"{tech_name}.dispatch_block_rule_function", - f"{dispatching_tech_name}.dispatch_block_rule_function_{tech_name}", + # Only connect dispatch rules if they are defined in the tech_config + tech_dispatch_rule = self.technology_config.get(tech_name, {}).get( + "dispatch_rule_set", False ) - - if (pyxdsm is not None) and (len(technology_interconnections) > 0): - try: - create_xdsm_from_config(self.plant_config) - except FileNotFoundError as e: - print(f"Unable to create system XDSM diagram. Error: {e}") + if tech_dispatch_rule: + # Connect the dispatch rules output to the dispatching_tech_name input + self.model.connect( + f"{tech_name}.dispatch_block_rule_function", + f"{dispatching_tech_name}.dispatch_block_rule_function_{tech_name}", + ) def create_driver_model(self): """ @@ -1239,17 +1367,26 @@ def setup(self): """ Extremely light wrapper to setup the OpenMDAO problem and track setup status. """ - self.setup_has_been_called = True self.prob.setup() + self.state = State.SETUP + + for tech, tech_info in self.technology_config["technologies"].items(): + check_inputs(self.prob, tech, tech_info, self.tech_config_path) def run(self): # do model setup based on the driver config # might add a recorder, driver, set solver tolerances, etc - if not self.setup_has_been_called: + if self.state < State.SETUP: self.prob.setup() - self.setup_has_been_called = True + + if self.state < State.RUN: + # OpenMDAO will skip this step if it encounters an issue leading to silent failures + # TODO: remove this step when OpenMDAO implements cursor closure + if self.recorder_path is not None: + self.recorder_path.unlink(missing_ok=True) self.prob.run_driver() + self.state = State.RUN def post_process(self, print_results=True, summarize_sql=False, show_plots=False): """Post-process the results of the OpenMDAO model. @@ -1266,6 +1403,8 @@ def post_process(self, print_results=True, summarize_sql=False, show_plots=False show_plots (bool): If True, run post-processing plots for any performance models that support them. Defaults to False. """ + if self.state < State.RUN: + raise RuntimeError("`run` not called, so `post_process` cannot be called.") if print_results: # Use custom summary printer instead of OpenMDAO's built-in printing so we can # suppress internal value printing and display only mean values. @@ -1279,6 +1418,7 @@ def post_process(self, print_results=True, summarize_sql=False, show_plots=False model.post_process(show_plots=show_plots) if show_plots: plt.show() + self.state = State.POST_PROCESS @staticmethod def print_results(model, includes=None, excludes=None, show_units=True): @@ -1444,3 +1584,29 @@ def _structured(meta_list): "explicit_outputs": _structured(explicit_meta), "implicit_outputs": _structured(implicit_meta), } + + def create_xdsm(self, outfile="connections_xdsm"): + """Create an XDSM diagram from the plant technology interconnections. + + This method reads ``technology_interconnections`` from ``self.plant_config`` + and delegates diagram generation to + :func:`h2integrate.core.utilities.create_xdsm_from_config`. + + Args: + outfile (str, optional): Base filename for the generated XDSM output. + The default is ``"connections_xdsm"``. + + Raises: + ValueError: If ``technology_interconnections`` is empty or missing from + the plant configuration. + """ + + technology_interconnections = self.plant_config.get("technology_interconnections", []) + + if len(technology_interconnections) > 0: + create_xdsm_from_config(self.plant_config, output_file=outfile) + else: + raise ValueError( + "Generating an XDSM diagram requires technology interconnections, " + "but none were found." + ) diff --git a/h2integrate/core/inputs/plant_schema.yaml b/h2integrate/core/inputs/plant_schema.yaml index 603f94e09..eabcf4280 100644 --- a/h2integrate/core/inputs/plant_schema.yaml +++ b/h2integrate/core/inputs/plant_schema.yaml @@ -72,4 +72,4 @@ properties: items: type: [string, array] description: Technology names and connection types -required: [name, description, sites] +required: [name, description] diff --git a/h2integrate/core/inputs/tech_schema.yaml b/h2integrate/core/inputs/tech_schema.yaml index b85ac3294..a25530379 100644 --- a/h2integrate/core/inputs/tech_schema.yaml +++ b/h2integrate/core/inputs/tech_schema.yaml @@ -52,5 +52,5 @@ properties: type: object description: Financial model details (optional) additionalProperties: true - required: [] + required: [performance_model] required: [name, description, technologies] diff --git a/h2integrate/core/inputs/validation.py b/h2integrate/core/inputs/validation.py index d9cc6f90d..927691cc7 100644 --- a/h2integrate/core/inputs/validation.py +++ b/h2integrate/core/inputs/validation.py @@ -1,5 +1,5 @@ """ -Code adapted from NREL's WISDEM tool. +Code adapted from NLR's WISDEM tool. """ from pathlib import Path @@ -140,20 +140,18 @@ def load_tech_yaml(finput): def load_plant_yaml(finput): plant_config = _validate(finput, fschema_plant) - if int(plant_config["plant"]["simulation"]["n_timesteps"]) != 8760: + n_timesteps = plant_config["plant"]["simulation"]["n_timesteps"] + dt = plant_config["plant"]["simulation"]["dt"] + + if int(n_timesteps) * int(dt) != 31536000: # seconds in simulation must be seconds/year msg = ( "H2Integrate does not currently support simulations that are less than or " "greater than 1-year. Please ensure that " - "plant_config['plant']['simulation']['n_timesteps'] is set to 8760." - ) - raise ValueError(msg) - if int(plant_config["plant"]["simulation"]["dt"]) != 3600: - msg = ( - "H2Integrate does not currently support simulations with a time step that is " - "less than or greater than 1-hour. Please ensure that " - "plant_config['plant']['simulation']['dt'] is set to 3600." + "plant_config['plant']['simulation']['n_timesteps'] times " + "plant_config['plant']['simulation']['dt'] equals 31536000 (s)." ) raise ValueError(msg) + return plant_config diff --git a/h2integrate/core/model_baseclasses.py b/h2integrate/core/model_baseclasses.py index 60226ce08..c6adf5cd6 100644 --- a/h2integrate/core/model_baseclasses.py +++ b/h2integrate/core/model_baseclasses.py @@ -32,7 +32,7 @@ def setup(self): self.n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"] # dt is seconds per timestep - self.dt = self.options["plant_config"]["plant"]["simulation"]["dt"] + self.dt = int(self.options["plant_config"]["plant"]["simulation"]["dt"]) # plant_life is number of years the plant is expected to operate for self.plant_life = int(self.options["plant_config"]["plant"]["plant_life"]) @@ -151,6 +151,9 @@ def setup(self): "cost_year", val=self.config.cost_year, desc="Dollar year for costs" ) + # dt is seconds per timestep + self.dt = int(self.options["plant_config"]["plant"]["simulation"]["dt"]) + def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): """ Computation for the OM component. diff --git a/h2integrate/core/pose_optimization.py b/h2integrate/core/pose_optimization.py index 323d06842..b139f5d5b 100644 --- a/h2integrate/core/pose_optimization.py +++ b/h2integrate/core/pose_optimization.py @@ -1,5 +1,5 @@ """ -This file is based on the WISDEM file of the same name: https://github.com/WISDEM/WISDEM +This file is based on the WISDEM file of the same name: https://github.com/NLRWindSystems/WISDEM and also based off of the H2Integrate file of the same name originally adapted by Jared Thomas. """ diff --git a/h2integrate/core/supported_models.py b/h2integrate/core/supported_models.py index a96c9f95a..f57d46a1f 100644 --- a/h2integrate/core/supported_models.py +++ b/h2integrate/core/supported_models.py @@ -1,25 +1,18 @@ from h2integrate.resource.river import RiverResource +from h2integrate.resource.tidal import TidalResource from h2integrate.core.feedstocks import FeedstockCostModel, FeedstockPerformanceModel from h2integrate.transporters.pipe import PipePerformanceModel from h2integrate.transporters.cable import CablePerformanceModel from h2integrate.converters.grid.grid import GridCostModel, GridPerformanceModel from h2integrate.finances.profast_lco import ProFastLCO from h2integrate.finances.profast_npv import ProFastNPV +from h2integrate.demand.generic_demand import GenericDemandComponent from h2integrate.converters.steel.steel import SteelPerformanceModel, SteelCostAndFinancialModel from h2integrate.converters.wind.floris import FlorisWindPlantPerformanceModel -from h2integrate.converters.iron.iron_mine import ( - IronMineCostComponent, - IronMinePerformanceComponent, -) -from h2integrate.converters.iron.iron_plant import ( - IronPlantCostComponent, - IronPlantPerformanceComponent, -) +from h2integrate.demand.flexible_demand import FlexibleDemandComponent from h2integrate.converters.wind.wind_pysam import PYSAMWindPlantPerformanceModel -from h2integrate.storage.generic_storage_pyo import StoragePerformanceModel from h2integrate.transporters.generic_summer import GenericSummerPerformanceModel from h2integrate.converters.hopp.hopp_wrapper import HOPPComponent -from h2integrate.converters.iron.iron_wrapper import IronComponent from h2integrate.converters.solar.solar_pysam import PYSAMSolarPlantPerformanceModel from h2integrate.finances.numpy_financial_npv import NumpyFinancialNPV from h2integrate.resource.wind.openmeteo_wind import OpenMeteoHistoricalWindResource @@ -42,7 +35,6 @@ from h2integrate.converters.nitrogen.simple_ASU import SimpleASUCostModel, SimpleASUPerformanceModel from h2integrate.converters.wind.wind_plant_ard import ArdWindPlantModel from h2integrate.resource.solar.openmeteo_solar import OpenMeteoHistoricalSolarResource -from h2integrate.storage.simple_generic_storage import SimpleGenericStorage from h2integrate.converters.hydrogen.h2_fuel_cell import ( H2FuelCellCostModel, LinearH2FuelCellPerformanceModel, @@ -64,12 +56,16 @@ SaltCavernStorageCostModel, LinedRockCavernStorageCostModel, ) +from h2integrate.transporters.gas_stream_combiner import GasStreamCombinerPerformanceModel from h2integrate.transporters.generic_transporter import GenericTransporterPerformanceModel +from h2integrate.converters.generic_converter_cost import GenericConverterCostModel from h2integrate.converters.iron.humbert_ewin_perf import HumbertEwinPerformanceComponent +from h2integrate.storage.storage_performance_model import StoragePerformanceModel from h2integrate.converters.ammonia.ammonia_synloop import ( AmmoniaSynLoopCostModel, AmmoniaSynLoopPerformanceModel, ) +from h2integrate.converters.water_power.tidal_pysam import PySAMTidalPerformanceModel from h2integrate.storage.simple_storage_auto_sizing import StorageAutoSizingModel from h2integrate.converters.water.desal.desalination import ( ReverseOsmosisCostModel, @@ -101,12 +97,19 @@ NaturalGasCostModel, NaturalGasPerformanceModel, ) +from h2integrate.converters.water_power.pysam_marine_cost import PySAMMarineCostModel from h2integrate.converters.hydrogen.singlitico_cost_model import SingliticoCostModel from h2integrate.converters.co2.marine.direct_ocean_capture import DOCCostModel, DOCPerformanceModel from h2integrate.converters.hydrogen.steam_methane_reformer import ( SteamMethaneReformerCostModel, SteamMethaneReformerPerformanceModel, ) +from h2integrate.converters.natural_gas.dummy_gas_components import ( + SimpleGasConsumerCost, + SimpleGasProducerCost, + SimpleGasConsumerPerformance, + SimpleGasProducerPerformance, +) from h2integrate.converters.hydrogen.geologic.mathur_modified import GeoH2SubsurfaceCostModel from h2integrate.resource.solar.nlr_developer_goes_api_models import ( GOESTMYSolarAPI, @@ -137,12 +140,6 @@ from h2integrate.converters.hydrogen.custom_electrolyzer_cost_model import ( CustomElectrolyzerCostModel, ) -from h2integrate.control.control_strategies.heuristic_pyomo_controller import ( - HeuristicLoadFollowingController, -) -from h2integrate.control.control_strategies.optimized_pyomo_controller import ( - OptimizedDispatchController, -) from h2integrate.converters.hydrogen.geologic.aspen_surface_processing import ( AspenGeoH2SurfaceCostModel, AspenGeoH2SurfacePerformanceModel, @@ -153,18 +150,18 @@ from h2integrate.control.control_rules.storage.pyomo_storage_rule_baseclass import ( PyomoRuleStorageBaseclass, ) -from h2integrate.control.control_strategies.passthrough_openloop_controller import ( - PassThroughOpenLoopController, -) from h2integrate.resource.solar.nlr_developer_meteosat_prime_meridian_models import ( MeteosatPrimeMeridianSolarAPI, MeteosatPrimeMeridianTMYSolarAPI, ) -from h2integrate.control.control_strategies.storage.demand_openloop_controller import ( - DemandOpenLoopStorageController, +from h2integrate.control.control_strategies.storage.heuristic_pyomo_controller import ( + HeuristicLoadFollowingStorageController, +) +from h2integrate.control.control_strategies.storage.optimized_pyomo_controller import ( + OptimizedDispatchStorageController, ) -from h2integrate.control.control_strategies.converters.demand_openloop_controller import ( - DemandOpenLoopConverterController, +from h2integrate.control.control_strategies.storage.simple_openloop_controller import ( + SimpleStorageOpenLoopController, ) from h2integrate.control.control_rules.storage.pyomo_storage_rule_min_operating_cost import ( PyomoRuleStorageMinOperatingCosts, @@ -172,13 +169,14 @@ from h2integrate.control.control_rules.converters.generic_converter_min_operating_cost import ( PyomoDispatchGenericConverterMinOperatingCosts, ) -from h2integrate.control.control_strategies.converters.flexible_demand_openloop_controller import ( - FlexibleDemandOpenLoopConverterController, +from h2integrate.control.control_strategies.storage.demand_openloop_storage_controller import ( + DemandOpenLoopStorageController, ) supported_models = { # Resources + "TidalResource": TidalResource, "RiverResource": RiverResource, "WTKNLRDeveloperAPIWindResource": WTKNLRDeveloperAPIWindResource, "OpenMeteoHistoricalWindResource": OpenMeteoHistoricalWindResource, @@ -193,6 +191,7 @@ "Himawari8SolarAPI": Himawari8SolarAPI, "HimawariTMYSolarAPI": HimawariTMYSolarAPI, # Converters + "GenericConverterCostModel": GenericConverterCostModel, "ATBWindPlantCostModel": ATBWindPlantCostModel, "PYSAMWindPlantPerformanceModel": PYSAMWindPlantPerformanceModel, "FlorisWindPlantPerformanceModel": FlorisWindPlantPerformanceModel, @@ -200,6 +199,8 @@ "PYSAMSolarPlantPerformanceModel": PYSAMSolarPlantPerformanceModel, "ATBUtilityPVCostModel": ATBUtilityPVCostModel, "ATBResComPVCostModel": ATBResComPVCostModel, + "PySAMTidalPerformanceModel": PySAMTidalPerformanceModel, + "PySAMMarineCostModel": PySAMMarineCostModel, "RunOfRiverHydroPerformanceModel": RunOfRiverHydroPerformanceModel, "RunOfRiverHydroCostModel": RunOfRiverHydroCostModel, "ECOElectrolyzerPerformanceModel": ECOElectrolyzerPerformanceModel, @@ -214,11 +215,6 @@ "SimpleASUCostModel": SimpleASUCostModel, "SimpleASUPerformanceModel": SimpleASUPerformanceModel, "HOPPComponent": HOPPComponent, - "IronComponent": IronComponent, - "IronMinePerformanceComponent": IronMinePerformanceComponent, - "IronMineCostComponent": IronMineCostComponent, - "IronPlantPerformanceComponent": IronPlantPerformanceComponent, - "IronPlantCostComponent": IronPlantCostComponent, "MartinIronMinePerformanceComponent": MartinIronMinePerformanceComponent, # standalone model "MartinIronMineCostComponent": MartinIronMineCostComponent, # standalone model "NaturalGasIronReductionPlantPerformanceComponent": NaturalGasIronReductionPlantPerformanceComponent, # noqa: E501 @@ -279,14 +275,13 @@ "PipeStorageCostModel": PipeStorageCostModel, "ATBBatteryCostModel": ATBBatteryCostModel, "GenericStorageCostModel": GenericStorageCostModel, - "SimpleGenericStorage": SimpleGenericStorage, # Control - "PassThroughOpenLoopController": PassThroughOpenLoopController, + "SimpleStorageOpenLoopController": SimpleStorageOpenLoopController, "DemandOpenLoopStorageController": DemandOpenLoopStorageController, - "HeuristicLoadFollowingController": HeuristicLoadFollowingController, - "OptimizedDispatchController": OptimizedDispatchController, - "DemandOpenLoopConverterController": DemandOpenLoopConverterController, - "FlexibleDemandOpenLoopConverterController": FlexibleDemandOpenLoopConverterController, + "HeuristicLoadFollowingStorageController": HeuristicLoadFollowingStorageController, + "OptimizedDispatchStorageController": OptimizedDispatchStorageController, + "GenericDemandComponent": GenericDemandComponent, + "FlexibleDemandComponent": FlexibleDemandComponent, # Dispatch "PyomoDispatchGenericConverter": PyomoDispatchGenericConverter, "PyomoRuleStorageBaseclass": PyomoRuleStorageBaseclass, @@ -302,34 +297,32 @@ "ProFastLCO": ProFastLCO, "ProFastNPV": ProFastNPV, "NumpyFinancialNPV": NumpyFinancialNPV, + # Dummy components for multivariable stream demonstrations + "SimpleGasProducerPerformance": SimpleGasProducerPerformance, + "SimpleGasProducerCost": SimpleGasProducerCost, + "SimpleGasConsumerPerformance": SimpleGasConsumerPerformance, + "SimpleGasConsumerCost": SimpleGasConsumerCost, + "GasStreamCombinerPerformanceModel": GasStreamCombinerPerformanceModel, } -def is_electricity_producer(tech_name: str) -> bool: - """Check if a technology is an electricity producer. +# This next section is to demarcate specific models that belong to certain categories that are +# relevant for processing in the model stackup. Right now, these designations are +# used in `h2integrate_model.py`. - Args: - tech_name: The name of the technology to check. - Returns: - True if tech_name starts with any of the known electricity producing - tech prefixes (e.g., 'wind', 'solar', 'pv', 'grid_buy', etc.). - Note: - This uses prefix matching, so 'grid_buy_1' and 'grid_buy_2' would both - be considered electricity producers. Be careful when naming technologies - to avoid unintended matches (e.g., 'pv_battery' would be incorrectly - identified as an electricity producer). - """ - # add any new electricity producing technologies to this list - electricity_producing_techs = [ - "wind", - "solar", - "pv", - "river", - "hopp", - "natural_gas_plant", - "grid_buy", - "h2_fuel_cell", - ] +# Model classes that do not contribute costs to the finance stackup because they are essentially +# internal-only models that aren't categorized as a specific technology (e.g. a generic combiner +# or splitter, or a model that is only used for performance modeling within another model and +# doesn't have an independent cost model). +no_cost_models = { + "GenericSplitterPerformanceModel", + "GenericCombinerPerformanceModel", + "GasStreamCombinerPerformanceModel", + "CablePerformanceModel", + "PipePerformanceModel", +} - return any(tech_name.startswith(elem) for elem in electricity_producing_techs) +no_replacement_schedule_models = { + "IronTransportPerformanceComponent", +} diff --git a/h2integrate/core/test/conftest.py b/h2integrate/core/test/conftest.py index 144c38689..378efd555 100644 --- a/h2integrate/core/test/conftest.py +++ b/h2integrate/core/test/conftest.py @@ -6,7 +6,13 @@ from h2integrate import EXAMPLE_DIR -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_dir_module, + temp_copy_of_example, + pytest_collection_modifyitems, + temp_copy_of_example_module_scope, +) def pytest_sessionstart(session): diff --git a/h2integrate/core/test/inputs/duplicate_keys.yaml b/h2integrate/core/test/inputs/duplicate_keys.yaml index da5f98faf..b06b7b9ba 100644 --- a/h2integrate/core/test/inputs/duplicate_keys.yaml +++ b/h2integrate/core/test/inputs/duplicate_keys.yaml @@ -43,7 +43,7 @@ technologies: dispatch_rule_set: model: "PyomoRuleStorageBaseclass" control_strategy: - model: "HeuristicLoadFollowingController" + model: "HeuristicLoadFollowingStorageController" performance_model: model: "PySAMBatteryPerformanceModel" cost_model: @@ -55,9 +55,9 @@ technologies: max_charge_rate: 50000 #kW max_capacity: 200000 #kWh n_control_window: 24 - init_charge_fraction: 0.9 - max_charge_fraction: 1.0 - min_charge_fraction: 0.2 + init_soc_fraction: 0.9 + max_soc_fraction: 1.0 + min_soc_fraction: 0.2 system_commodity_interface_limit: 1e12 performance_parameters: system_model_source: "pysam" diff --git a/h2integrate/core/test/inputs/no_duplicates.yaml b/h2integrate/core/test/inputs/no_duplicates.yaml index d1d272d4a..7be0b79bb 100644 --- a/h2integrate/core/test/inputs/no_duplicates.yaml +++ b/h2integrate/core/test/inputs/no_duplicates.yaml @@ -42,7 +42,7 @@ technologies: dispatch_rule_set: model: "PyomoRuleStorageBaseclass" control_strategy: - model: "HeuristicLoadFollowingController" + model: "HeuristicLoadFollowingStorageController" performance_model: model: "PySAMBatteryPerformanceModel" cost_model: @@ -54,16 +54,16 @@ technologies: max_charge_rate: 50000 #kW max_capacity: 200000 #kWh n_control_window: 24 - init_charge_fraction: 0.9 - max_charge_fraction: 1.0 - min_charge_fraction: 0.2 + init_soc_fraction: 0.9 + max_soc_fraction: 1.0 + min_soc_fraction: 0.2 system_commodity_interface_limit: 1e12 performance_parameters: system_model_source: "pysam" chemistry: "LFPGraphite" + demand_profile: 50.0 cost_parameters: cost_year: 2022 - commodity_units: "kW" energy_capex: 246 # $/kWh from 2024 ATB year 2025 power_capex: 317 # $/kW from 2024 ATB year 2025 opex_fraction: 0.02536510376633359 diff --git a/h2integrate/core/test/inputs/no_duplicates_use_include.yaml b/h2integrate/core/test/inputs/no_duplicates_use_include.yaml index 255959518..69b568024 100644 --- a/h2integrate/core/test/inputs/no_duplicates_use_include.yaml +++ b/h2integrate/core/test/inputs/no_duplicates_use_include.yaml @@ -75,7 +75,7 @@ technologies: dispatch_rule_set: model: "PyomoRuleStorageBaseclass" control_strategy: - model: "HeuristicLoadFollowingController" + model: "HeuristicLoadFollowingStorageController" performance_model: model: "PySAMBatteryPerformanceModel" cost_model: @@ -87,9 +87,9 @@ technologies: max_charge_rate: 375740.4 #kW max_capacity: 375745.2 #kWh n_control_window: 24 - init_charge_fraction: 0.9 - max_charge_fraction: 1.0 - min_charge_fraction: 0.2 + init_soc_fraction: 0.9 + max_soc_fraction: 1.0 + min_soc_fraction: 0.2 system_commodity_interface_limit: 1e12 performance_parameters: system_model_source: "pysam" @@ -126,7 +126,7 @@ technologies: performance_model: model: "StorageAutoSizingModel" control_strategy: - model: "PassThroughOpenLoopController" + model: "SimpleStorageOpenLoopController" cost_model: model: "LinedRockCavernStorageCostModel" model_inputs: diff --git a/h2integrate/core/test/test_commodity_stream_definitions.py b/h2integrate/core/test/test_commodity_stream_definitions.py new file mode 100644 index 000000000..7eb29c5b5 --- /dev/null +++ b/h2integrate/core/test/test_commodity_stream_definitions.py @@ -0,0 +1,111 @@ +from unittest.mock import MagicMock, call + +import pytest + +from h2integrate.core.commodity_stream_definitions import ( + multivariable_streams, + add_multivariable_input, + is_electricity_producer, + add_multivariable_output, +) + + +@pytest.mark.unit +def test_is_electricity_producer(subtests): + with subtests.test("exact match"): + assert is_electricity_producer("grid_buy") + + with subtests.test("partial starts-with match"): + assert is_electricity_producer("grid_buy_1") + + with subtests.test("partial ends-with match fails"): + assert not is_electricity_producer("wrong_grid_buy") + + with subtests.test("empty string fails"): + assert not is_electricity_producer("") + + with subtests.test("non-electricity producing tech fails"): + assert not is_electricity_producer("battery") + + +@pytest.mark.unit +def test_add_multivariable_output(subtests): + stream_name = "wellhead_gas_mixture" + n_timesteps = 8760 + component = MagicMock() + + add_multivariable_output(component, stream_name, n_timesteps) + + stream_def = multivariable_streams[stream_name] + + with subtests.test("called once per variable"): + assert component.add_output.call_count == len(stream_def) + + with subtests.test("correct variable names and kwargs"): + expected_calls = [ + call( + f"{stream_name}:{var_name}_out", + val=0.0, + shape=n_timesteps, + units=var_props.get("units"), + desc=var_props.get("desc", ""), + ) + for var_name, var_props in stream_def.items() + ] + component.add_output.assert_has_calls(expected_calls, any_order=False) + + with subtests.test("uses stream_name:var_name prefix"): + called_names = [c.args[0] for c in component.add_output.call_args_list] + for name in called_names: + assert name.startswith(f"{stream_name}:") + assert name.endswith("_out") + + with subtests.test("all variables are unique"): + called_names = [c.args[0] for c in component.add_output.call_args_list] + assert len(called_names) == len(set(called_names)) + + +@pytest.mark.unit +def test_add_multivariable_input(subtests): + stream_name = "wellhead_gas_mixture" + n_timesteps = 8760 + component = MagicMock() + + add_multivariable_input(component, stream_name, n_timesteps) + + stream_def = multivariable_streams[stream_name] + + with subtests.test("called once per variable"): + assert component.add_input.call_count == len(stream_def) + + with subtests.test("correct variable names and kwargs"): + expected_calls = [ + call( + f"{stream_name}:{var_name}_in", + val=0.0, + shape=n_timesteps, + units=var_props.get("units"), + desc=var_props.get("desc", ""), + ) + for var_name, var_props in stream_def.items() + ] + component.add_input.assert_has_calls(expected_calls, any_order=False) + + with subtests.test("uses stream_name:var_name prefix"): + called_names = [c.args[0] for c in component.add_input.call_args_list] + for name in called_names: + assert name.startswith(f"{stream_name}:") + assert name.endswith("_in") + + with subtests.test("all variables are unique"): + called_names = [c.args[0] for c in component.add_input.call_args_list] + assert len(called_names) == len(set(called_names)) + + +@pytest.mark.unit +def test_add_multivariable_invalid_stream(): + component = MagicMock() + with pytest.raises(KeyError): + add_multivariable_output(component, "nonexistent_stream", 10) + with pytest.raises(KeyError): + add_multivariable_input(component, "nonexistent_stream", 10) diff --git a/h2integrate/core/test/test_feedstocks.py b/h2integrate/core/test/test_feedstocks.py index ef9ca7acb..886658c6f 100644 --- a/h2integrate/core/test/test_feedstocks.py +++ b/h2integrate/core/test/test_feedstocks.py @@ -8,10 +8,90 @@ import numpy as np import pytest import openmdao.api as om +from pytest import fixture from h2integrate.core.feedstocks import FeedstockCostModel, FeedstockPerformanceModel +@fixture +def plant_config(): + return { + "plant": { + "plant_life": 30, + "simulation": { + "n_timesteps": 8760, + "dt": 3600, + }, + }, + } + + +@fixture +def ng_feedstock_input_config(): + tech_config = { + "model_inputs": { + "shared_parameters": { + "commodity": "natural_gas", + "commodity_rate_units": "MMBtu/h", + }, + "performance_parameters": { + "rated_capacity": 100.0, + }, + "cost_parameters": { + "price": 4.2, # USD/MMBtu + "annual_cost": 0, + "start_up_cost": 0, + "cost_year": 2023, + "commodity_amount_units": "MMBtu", # optional + }, + } + } + return tech_config + + +@pytest.mark.unit +def test_feedstock_standard_outputs(plant_config, ng_feedstock_input_config, subtests): + perf_model = FeedstockPerformanceModel( + plant_config=plant_config, tech_config=ng_feedstock_input_config, driver_config={} + ) + cost_model = FeedstockCostModel( + plant_config=plant_config, tech_config=ng_feedstock_input_config, driver_config={} + ) + prob = om.Problem() + prob.model.add_subsystem("ng_feedstock_source", perf_model) + prob.model.add_subsystem("ng_feedstock", cost_model) + # Connect the feedstock performance model output to the cost model input + prob.model.connect( + "ng_feedstock_source.natural_gas_out", + "ng_feedstock.natural_gas_out", + ) + + prob.setup() + # Set some consumption values + consumption = np.full(8760, 50.0) # 50 MMBtu/hour + prob.set_val("ng_feedstock.natural_gas_consumed", consumption) + prob.run_model() + with subtests.test("Check feedstock capacity factor"): + ng_cf = prob.get_val("ng_feedstock.capacity_factor", units="unitless").mean() + assert pytest.approx(ng_cf, rel=1e-6) == 0.5 + with subtests.test("Check feedstock rated production"): + rated_production_source = prob.get_val( + "ng_feedstock_source.natural_gas_capacity", units="MMBtu/h" + ) + rated_production = prob.get_val( + "ng_feedstock.rated_natural_gas_production", units="MMBtu/h" + ) + assert pytest.approx(rated_production, rel=1e-6) == rated_production_source + with subtests.test("Check feedstock total consumption"): + total_consumption = prob.get_val("ng_feedstock.total_natural_gas_consumed", units="MMBtu") + assert pytest.approx(total_consumption, rel=1e-6) == consumption.sum() + with subtests.test("Check feedstock annual consumption"): + annual_consumption = prob.get_val( + "ng_feedstock.annual_natural_gas_consumed", units="MMBtu/yr" + ) + assert pytest.approx(annual_consumption, rel=1e-6) == consumption.sum() + + def create_basic_feedstock_config( feedstock_type="natural_gas", units="MMBtu/h", @@ -38,7 +118,7 @@ def create_basic_feedstock_config( }, } } - plant_config = {"plant": {"plant_life": 30, "simulation": {"n_timesteps": 8760}}} + plant_config = {"plant": {"plant_life": 30, "simulation": {"n_timesteps": 8760, "dt": 3600}}} driver_config = {} return tech_config, plant_config, driver_config diff --git a/h2integrate/core/test/test_framework.py b/h2integrate/core/test/test_framework.py index 524968a64..b932c51c1 100644 --- a/h2integrate/core/test/test_framework.py +++ b/h2integrate/core/test/test_framework.py @@ -1,11 +1,14 @@ +import os import shutil from copy import deepcopy from pathlib import Path +from unittest.mock import patch import yaml import numpy as np import pytest +import h2integrate.core.h2integrate_model as h2i_model_module from h2integrate import EXAMPLE_DIR from h2integrate.core.h2integrate_model import H2IntegrateModel from h2integrate.core.inputs.validation import load_tech_yaml, load_plant_yaml, load_driver_yaml @@ -179,9 +182,37 @@ def test_unsupported_simulation_parameters(temp_dir): with pytest.raises(ValueError, match="greater than 1-year"): load_plant_yaml(plant_config_data_ntimesteps) - # check that error is thrown when loading config with invalid time interval - with pytest.raises(ValueError, match="with a time step that"): - load_plant_yaml(plant_config_data_dt) + +@pytest.mark.unit +def test_check_time_step_with_model_bounds_allows_supported_dt(): + class DummyModel: + _time_step_bounds = (900, 3600) + + model = object.__new__(H2IntegrateModel) + model.plant_config = {"plant": {"simulation": {"dt": 1800}}} + + model._check_time_step("DummyModel", DummyModel) + + +@pytest.mark.unit +def test_check_time_step_with_model_bounds_raises_for_unsupported_dt(): + class DummyModel: + _time_step_bounds = ( + 900, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + + model = object.__new__(H2IntegrateModel) + model.plant_config = {"plant": {"simulation": {"dt": 7200}}} + + with pytest.raises( + ValueError, + match=( + r"Model DummyModel is compatible with time steps between " + r"900 \(s\) and 3600 \(s\), but a time step of 7200 \(s\) was specified" + ), + ): + model._check_time_step("DummyModel", DummyModel) @pytest.mark.unit @@ -203,9 +234,9 @@ def test_technology_connections(temp_dir): new_connection = (["finance_subgroup_electricity", "steel", ("LCOE", "electricity_cost")],) new_tech_interconnections = ( - plant_config_data["technology_interconnections"][0:4] + plant_config_data["technology_interconnections"][0:9] + list(new_connection) - + [plant_config_data["technology_interconnections"][4]] + + [plant_config_data["technology_interconnections"][9]] ) plant_config_data["technology_interconnections"] = new_tech_interconnections @@ -321,6 +352,33 @@ def test_resource_connection_error_missing_resource(temp_dir): temp_highlevel_yaml.unlink(missing_ok=True) +@pytest.mark.unit +def test_no_resource_connection_error_resource_to_multiple_techs(temp_dir): + # Path to the original plant_config.yaml and high-level yaml in the example directory + + driver_config = load_driver_yaml(EXAMPLE_DIR / "08_wind_electrolyzer" / "driver_config.yaml") + tech_config = load_tech_yaml(EXAMPLE_DIR / "08_wind_electrolyzer" / "tech_config.yaml") + plant_config = load_plant_yaml(EXAMPLE_DIR / "08_wind_electrolyzer" / "plant_config.yaml") + # Add a second wind technology + wind_tech = tech_config["technologies"]["wind"] + tech_config["technologies"].update({"wind_plant2": wind_tech}) + resource_to_tech_connections = [ + ["site.wind_resource", "wind", "wind_resource_data"], + ["site.wind_resource", "wind_plant2", "wind_resource_data"], + ] + plant_config["resource_to_tech_connections"] = resource_to_tech_connections + input_config = { + "plant_config": plant_config, + "technology_config": tech_config, + "driver_config": driver_config, + } + h2i_model = H2IntegrateModel(input_config) + h2i_model.setup() + # Need to call final_setup to trigger the potential error related to the resource connections + h2i_model.prob.final_setup() + assert True + + @pytest.mark.unit def test_reports_turned_off(temp_dir): # Path to the original config files in the example directory @@ -457,12 +515,18 @@ def test_system_order(subtests): "solar", "solar_to_combiner_cable", "combiner", + "combiner_to_elec_combiner_cable", "combiner_to_battery_cable", "battery", - "battery_to_electrolyzer_cable", + "battery_to_elec_combiner_cable", + "elec_combiner", + "elec_combiner_to_electrolyzer_cable", "electrolyzer", + "electrolyzer_to_h2_combiner_pipe", "electrolyzer_to_h2_storage_pipe", "h2_storage", + "h2_storage_to_h2_combiner_pipe", + "h2_combiner", "steel", "finance_subgroup_electricity", "finance_subgroup_hydrogen", @@ -470,9 +534,98 @@ def test_system_order(subtests): ] names = [sys.name for sys in h2i.model.plant.system_iter(include_self=False, recurse=False)] - with subtests.test("Test expected names are all present"): assert sorted(names) == sorted(expected_names) with subtests.test("Test expected names are in the correct order"): assert names == expected_names + + +@pytest.mark.unit +def test_no_sites_entry(temp_dir): + """Verify that a model can set up and run without a ``sites`` entry in the plant config. + + Uses Example 32 (multivariable streams), whose plant_config intentionally + omits the ``sites`` key. + """ + example_folder = EXAMPLE_DIR / "32_multivariable_streams" + shutil.copytree(example_folder, temp_dir / "32_multivariable_streams", dirs_exist_ok=True) + + os.chdir(temp_dir / "32_multivariable_streams") + + model = H2IntegrateModel( + temp_dir / "32_multivariable_streams" / "32_multivariable_streams.yaml" + ) + model.run() + + # Smoke-check: combiner output flow should be the sum of the two producers + flow_out = model.prob.get_val("gas_combiner.wellhead_gas_mixture:mass_flow_out", units="kg/h") + assert flow_out.mean() > 0.0 + + os.chdir(Path(__file__).parent) + + +@pytest.mark.unit +def test_create_xdsm_calls_create_xdsm_from_config_default_outfile(): + plant_config = {"technology_interconnections": [("wind", "electrolyzer", "electricity")]} + model = object.__new__(H2IntegrateModel) + model.plant_config = plant_config + + with patch.object(h2i_model_module, "create_xdsm_from_config") as mock_fn: + model.create_xdsm() + + mock_fn.assert_called_once_with(plant_config, output_file="connections_xdsm") + + +@pytest.mark.unit +def test_create_xdsm_calls_create_xdsm_from_config_custom_outfile(): + plant_config = {"technology_interconnections": [("wind", "electrolyzer", "electricity")]} + model = object.__new__(H2IntegrateModel) + model.plant_config = plant_config + outfile = "my_custom_xdsm" + + with patch.object(h2i_model_module, "create_xdsm_from_config") as mock_fn: + model.create_xdsm(outfile=outfile) + + mock_fn.assert_called_once_with(plant_config, output_file=outfile) + + +@pytest.mark.unit +def test_create_xdsm_raises_when_no_interconnections(): + plant_config = {"technology_interconnections": []} + model = object.__new__(H2IntegrateModel) + model.plant_config = plant_config + + with patch.object(h2i_model_module, "create_xdsm_from_config") as mock_fn: + with pytest.raises(ValueError, match="requires technology interconnections"): + model.create_xdsm() + + mock_fn.assert_not_called() + + +@pytest.mark.unit +def test_create_xdsm_raises_when_interconnections_key_missing(): + plant_config = {} + model = object.__new__(H2IntegrateModel) + model.plant_config = plant_config + + with patch.object(h2i_model_module, "create_xdsm_from_config") as mock_fn: + with pytest.raises(ValueError, match="requires technology interconnections"): + model.create_xdsm() + + mock_fn.assert_not_called() + + +@pytest.mark.unit +def test_create_xdsm_propagates_file_not_found_error(): + plant_config = {"technology_interconnections": [("wind", "electrolyzer", "electricity")]} + model = object.__new__(H2IntegrateModel) + model.plant_config = plant_config + + with patch.object( + h2i_model_module, + "create_xdsm_from_config", + side_effect=FileNotFoundError("latex not found"), + ): + with pytest.raises(FileNotFoundError, match="latex not found"): + model.create_xdsm() diff --git a/h2integrate/core/test/test_recorder.py b/h2integrate/core/test/test_recorder.py index 316a25f80..804f54a87 100644 --- a/h2integrate/core/test/test_recorder.py +++ b/h2integrate/core/test/test_recorder.py @@ -1,8 +1,7 @@ -import os +import sys import pytest -from h2integrate import EXAMPLE_DIR from h2integrate.core.h2integrate_model import H2IntegrateModel from h2integrate.core.inputs.validation import load_driver_yaml @@ -13,23 +12,21 @@ @pytest.mark.unit -def test_output_folder_creation_first_run(temp_dir, subtests): - # Test that the sql file is written to the output folder - # with the specified name - - # change to example dir - os.chdir(EXAMPLE_DIR / "05_wind_h2_opt") +@pytest.mark.parametrize("example_folder,resource_example_folder", [("05_wind_h2_opt", None)]) +def test_output_folder_creation_first_run(temp_copy_of_example_module_scope, subtests): + """Test that the sql file is written to the output folder with the specified name.""" # initialize H2I using non-optimization config - input_file = EXAMPLE_DIR / "05_wind_h2_opt" / "wind_plant_electrolyzer0.yaml" + example_folder = temp_copy_of_example_module_scope + input_file = example_folder / "wind_plant_electrolyzer0.yaml" h2i = H2IntegrateModel(input_file) # load driver config for optimization run - driver_config = load_driver_yaml(EXAMPLE_DIR / "05_wind_h2_opt" / "driver_config.yaml") + driver_config = load_driver_yaml(example_folder / "driver_config.yaml") # update driver config params with test variables filename_initial = TEST_RECORDER_OUTPUT_FILE0 - driver_config["general"]["folder_output"] = temp_dir + output_folder = example_folder / driver_config["general"]["folder_output"] driver_config["recorder"]["file"] = filename_initial driver_config["driver"]["optimization"]["max_iter"] = 5 # to prevent tests taking too long @@ -40,10 +37,8 @@ def test_output_folder_creation_first_run(temp_dir, subtests): h2i.create_driver_model() # check if output folder and output files exist - output_folder_exists = (EXAMPLE_DIR / "05_wind_h2_opt" / temp_dir).exists() - output_file_exists_prerun = ( - EXAMPLE_DIR / "05_wind_h2_opt" / temp_dir / filename_initial - ).exists() + output_folder_exists = output_folder.exists() + output_file_exists_prerun = (output_folder / filename_initial).exists() with subtests.test("Run 0: output folder exists"): assert output_folder_exists is True @@ -54,32 +49,31 @@ def test_output_folder_creation_first_run(temp_dir, subtests): h2i.run() # check that recorder file was created - output_file_exists_postrun = ( - EXAMPLE_DIR / "05_wind_h2_opt" / temp_dir / filename_initial - ).exists() + output_file_exists_postrun = (output_folder / filename_initial).exists() with subtests.test("Run 0: recorder output file exists after run"): assert output_file_exists_postrun is True @pytest.mark.unit -def test_output_new_recorder_filename_second_run(temp_dir, subtests): - # Test that the sql file is written to the output folder - # with the specified base name and an appended 0 - # change to example dir - os.chdir(EXAMPLE_DIR / "05_wind_h2_opt") +@pytest.mark.parametrize("example_folder,resource_example_folder", [("05_wind_h2_opt", None)]) +def test_output_new_recorder_filename_second_run(temp_copy_of_example_module_scope, subtests): + """Test that the sql file is written to the output folder with the specified base name and + an appended 0. + """ # initialize H2I using non-optimization config - input_file = EXAMPLE_DIR / "05_wind_h2_opt" / "wind_plant_electrolyzer0.yaml" + example_folder = temp_copy_of_example_module_scope + input_file = example_folder / "wind_plant_electrolyzer0.yaml" h2i = H2IntegrateModel(input_file) # load driver config for optimization run - driver_config = load_driver_yaml(EXAMPLE_DIR / "05_wind_h2_opt" / "driver_config.yaml") + driver_config = load_driver_yaml(example_folder / "driver_config.yaml") # update driver config params with test variables filename_initial = TEST_RECORDER_OUTPUT_FILE0 filename_expected = TEST_RECORDER_OUTPUT_FILE1 - driver_config["general"]["folder_output"] = temp_dir + output_folder = example_folder / driver_config["general"]["folder_output"] driver_config["recorder"]["file"] = filename_initial driver_config["driver"]["optimization"]["max_iter"] = 5 # to prevent tests taking too long @@ -90,43 +84,35 @@ def test_output_new_recorder_filename_second_run(temp_dir, subtests): h2i.create_driver_model() # check if output folder and output files exist - output_folder_exists = (EXAMPLE_DIR / "05_wind_h2_opt" / temp_dir).exists() - output_file_exists_prerun = ( - EXAMPLE_DIR / "05_wind_h2_opt" / temp_dir / filename_initial - ).exists() - with subtests.test("Run 1: output folder exists"): - assert output_folder_exists is True + assert output_folder.exists() with subtests.test("Run 1: initial recorder output file exists"): - assert output_file_exists_prerun is True + assert (output_folder / filename_initial).exists() # run the model h2i.run() # check that the new recorder file was created - new_output_file_exists = ( - EXAMPLE_DIR / "05_wind_h2_opt" / temp_dir / filename_expected - ).exists() with subtests.test("Run 1: new recorder output file was made"): - assert new_output_file_exists is True + assert (output_folder / filename_expected).exists() @pytest.mark.unit -def test_output_new_recorder_overwrite_first_run(temp_dir, subtests): - # change to example dir - os.chdir(EXAMPLE_DIR / "05_wind_h2_opt") - +@pytest.mark.parametrize("example_folder,resource_example_folder", [("05_wind_h2_opt", None)]) +@pytest.mark.xfail(sys.platform == "win32", reason="OpenMDAO incorrectly ends SQL processes") +def test_output_new_recorder_overwrite_first_run(temp_copy_of_example_module_scope, subtests): # initialize H2I using non-optimization config - input_file = EXAMPLE_DIR / "05_wind_h2_opt" / "wind_plant_electrolyzer0.yaml" + example_folder = temp_copy_of_example_module_scope + input_file = example_folder / "wind_plant_electrolyzer0.yaml" h2i = H2IntegrateModel(input_file) # load driver config for optimization run - driver_config = load_driver_yaml(EXAMPLE_DIR / "05_wind_h2_opt" / "driver_config.yaml") + driver_config = load_driver_yaml(example_folder / "driver_config.yaml") # update driver config params with test variables filename_initial = TEST_RECORDER_OUTPUT_FILE0 filename_exists_if_failed = TEST_RECORDER_OUTPUT_FILE2 - driver_config["general"]["folder_output"] = temp_dir + output_folder = example_folder / driver_config["general"]["folder_output"] driver_config["recorder"]["file"] = filename_initial # specify that we want the previous file overwritten rather @@ -141,44 +127,35 @@ def test_output_new_recorder_overwrite_first_run(temp_dir, subtests): h2i.create_driver_model() # check if output folder and output files exist - output_folder_exists = (EXAMPLE_DIR / "05_wind_h2_opt" / temp_dir).exists() - output_file_exists_prerun = ( - EXAMPLE_DIR / "05_wind_h2_opt" / temp_dir / filename_initial - ).exists() - with subtests.test("Run 2: output folder exists"): - assert output_folder_exists is True + assert output_folder.exists() with subtests.test("Run 2: initial recorder output file exists"): - assert output_file_exists_prerun is True + assert (output_folder / filename_initial).exists() # run the model h2i.run() # check that recorder file was overwritten - new_output_file_exists = ( - EXAMPLE_DIR / "05_wind_h2_opt" / temp_dir / filename_exists_if_failed - ).exists() with subtests.test("Run 2: initial output file was overwritten"): - assert new_output_file_exists is False + assert not (output_folder / filename_exists_if_failed).exists() @pytest.mark.unit -def test_output_new_recorder_filename_third_run(temp_dir, subtests): - # change to example dir - os.chdir(EXAMPLE_DIR / "05_wind_h2_opt") - +@pytest.mark.parametrize("example_folder,resource_example_folder", [("05_wind_h2_opt", None)]) +def test_output_new_recorder_filename_third_run(temp_copy_of_example_module_scope, subtests): # initialize H2I using non-optimization config - input_file = EXAMPLE_DIR / "05_wind_h2_opt" / "wind_plant_electrolyzer0.yaml" + example_folder = temp_copy_of_example_module_scope + input_file = example_folder / "wind_plant_electrolyzer0.yaml" h2i = H2IntegrateModel(input_file) # load driver config for optimization run - driver_config = load_driver_yaml(EXAMPLE_DIR / "05_wind_h2_opt" / "driver_config.yaml") + driver_config = load_driver_yaml(example_folder / "driver_config.yaml") # update driver config params with test variables filename_initial = TEST_RECORDER_OUTPUT_FILE0 filename_second = TEST_RECORDER_OUTPUT_FILE1 filename_expected = TEST_RECORDER_OUTPUT_FILE2 - driver_config["general"]["folder_output"] = temp_dir + output_folder = example_folder / driver_config["general"]["folder_output"] driver_config["recorder"]["file"] = filename_initial driver_config["driver"]["optimization"]["max_iter"] = 5 # to prevent tests taking too long @@ -189,26 +166,16 @@ def test_output_new_recorder_filename_third_run(temp_dir, subtests): h2i.create_driver_model() # check if output folder and output files exist - output_folder_exists = (EXAMPLE_DIR / "05_wind_h2_opt" / temp_dir).exists() - output_file_exists_prerun = ( - EXAMPLE_DIR / "05_wind_h2_opt" / temp_dir / filename_initial - ).exists() - run1_output_file_exists_prerun = ( - EXAMPLE_DIR / "05_wind_h2_opt" / temp_dir / filename_second - ).exists() with subtests.test("Run 3: output folder exists"): - assert output_folder_exists is True + assert output_folder.exists() with subtests.test("Run 3: initial recorder output file exists"): - assert output_file_exists_prerun is True + assert (output_folder / filename_initial).exists() with subtests.test("Run 3: second recorder output file exists"): - assert run1_output_file_exists_prerun is True + assert (output_folder / filename_second).exists() # run the model h2i.run() # check that the new recorder file was created - new_output_file_exists = ( - EXAMPLE_DIR / "05_wind_h2_opt" / temp_dir / filename_expected - ).exists() with subtests.test("Run 3: new recorder output file was made"): - assert new_output_file_exists is True + assert (output_folder / filename_expected).exists() diff --git a/h2integrate/core/test/test_supported_models.py b/h2integrate/core/test/test_supported_models.py index 271817695..da94d49dd 100644 --- a/h2integrate/core/test/test_supported_models.py +++ b/h2integrate/core/test/test_supported_models.py @@ -1,24 +1,6 @@ import pytest -from h2integrate.core.supported_models import supported_models, is_electricity_producer - - -@pytest.mark.unit -def test_is_electricity_producer(subtests): - with subtests.test("exact match"): - assert is_electricity_producer("grid_buy") - - with subtests.test("partial starts-with match"): - assert is_electricity_producer("grid_buy_1") - - with subtests.test("partial ends-with match fails"): - assert not is_electricity_producer("wrong_grid_buy") - - with subtests.test("empty string fails"): - assert not is_electricity_producer("") - - with subtests.test("non-electricity producing tech fails"): - assert not is_electricity_producer("battery") +from h2integrate.core.supported_models import supported_models @pytest.mark.unit diff --git a/h2integrate/core/test/test_utilities.py b/h2integrate/core/test/test_utilities.py index e7556382d..62f1ed5ef 100644 --- a/h2integrate/core/test/test_utilities.py +++ b/h2integrate/core/test/test_utilities.py @@ -5,12 +5,14 @@ import yaml import numpy as np import pytest +import openmdao.api as om from attrs import field, define from h2integrate import ROOT_DIR, EXAMPLE_DIR, RESOURCE_DEFAULT_DIR from h2integrate.core.utilities import BaseConfig -from h2integrate.core.dict_utils import dict_to_yaml_formatting +from h2integrate.core.dict_utils import check_inputs, dict_to_yaml_formatting from h2integrate.core.file_utils import get_path, find_file, load_yaml, make_unique_case_name +from h2integrate.core.supported_models import supported_models from h2integrate.core.inputs.validation import load_tech_yaml @@ -547,3 +549,193 @@ def traverse_dict(sample_dict): sample = load_yaml(inputs / fn) traverse_dict(sample) load_tech_yaml(inputs / fn) + + +def create_om_problem(tech_config): + plant_config_base = { + "plant": { + "plant_life": 30, + "simulation": { + "dt": 3600, + "n_timesteps": 8760, + }, + }, + "tech_to_dispatch_connections": [ + ["wind", "battery"], + ["battery", "battery"], + ], + } + + prob = om.Problem(reports=False) + model = prob.model + plant_group = om.Group() + plant = model.add_subsystem("plant", plant_group, promotes=["*"]) + + model_types = [ + "dispatch_rule_set", + "control_strategy", + "performance_model", + "cost_model", + ] + for tech_name, individual_tech_config in tech_config["technologies"].items(): + tech_group = plant.add_subsystem(tech_name, om.Group()) + + for model_type in model_types: + if model_type in individual_tech_config: + if model_type == "control_strategy": + control_params = individual_tech_config["model_inputs"].get( + "control_parameters", {} + ) + control_params["tech_name"] = tech_name + individual_tech_config["model_inputs"].update( + {"control_parameters": control_params} + ) + model_name = individual_tech_config[model_type]["model"] + model_object = supported_models[model_name] + tech_group.add_subsystem( + model_name, + model_object( + driver_config={}, + plant_config=plant_config_base, + tech_config=individual_tech_config, + ), + promotes=["*"], + ) + + prob.setup() + return prob + + +@pytest.mark.unit +def test_check_inputs(subtests): + tech_config_fpath = Path(__file__).parent / "inputs" / "no_duplicates.yaml" + + # 1: check for an unused parameter under performance_parameters + tech_config = load_tech_yaml(tech_config_fpath) + prob = create_om_problem(tech_config) + + for tech, tech_info in tech_config["technologies"].items(): + if tech == "battery": + with pytest.raises(AttributeError) as excinfo: + check_inputs(prob, tech, tech_info, tech_config_fpath) + expected_error = ( + "The parameter(s) ['system_model_source'] found in performance_parameters " + f"are not used for the 'battery' section of {tech_config_fpath}" + ) + assert expected_error == str(excinfo.value) + else: + check_inputs(prob, tech, tech_info, tech_config_fpath) + + # 2: check when not-shared parameters are under shared_parameters + tech_config = load_tech_yaml(tech_config_fpath) + tech_config["technologies"]["battery"]["model_inputs"]["performance_parameters"].pop( + "system_model_source" + ) + prob = create_om_problem(tech_config) + + for tech, tech_info in tech_config["technologies"].items(): + if tech == "battery": + with pytest.raises(AttributeError) as excinfo: + check_inputs(prob, tech, tech_info, tech_config_fpath) + expected_error = ( + "The parameter(s) ['n_control_window', 'system_commodity_interface_limit'] " + "found in shared_parameters but should be in control_parameters for " + f"the 'battery' section of {tech_config_fpath}" + ) + assert expected_error == str(excinfo.value) + else: + check_inputs(prob, tech, tech_info, tech_config_fpath) + + # 3: check when multiple unshared parameters from different categories are under shared\ + key = "opex_fraction" + val = tech_config["technologies"]["battery"]["model_inputs"]["cost_parameters"].pop(key) + tech_config["technologies"]["battery"]["model_inputs"]["shared_parameters"][key] = val + for tech, tech_info in tech_config["technologies"].items(): + if tech == "battery": + with pytest.raises(AttributeError) as excinfo: + check_inputs(prob, tech, tech_info, tech_config_fpath) + expected_error = ( + "The following parameter sets were found in shared_parameters but should be" + " contained in the following sections for the 'battery' section of " + f"{tech_config_fpath}:" + "\n\tcontrol_parameters should contain" + " ['n_control_window', 'system_commodity_interface_limit']" + "\n\tcost_parameters should contain ['opex_fraction]" + ) + assert expected_error == str(excinfo.value) + else: + check_inputs(prob, tech, tech_info, tech_config_fpath) + + # 4: check when an unused parameter is under shared_parameters + tech_config = load_tech_yaml(tech_config_fpath) + control_parameters = {} + tech_config["technologies"]["battery"]["model_inputs"]["performance_parameters"].pop( + "system_model_source" + ) + control_parameters["n_control_window"] = tech_config["technologies"]["battery"]["model_inputs"][ + "shared_parameters" + ].pop("n_control_window") + control_parameters["system_commodity_interface_limit"] = tech_config["technologies"]["battery"][ + "model_inputs" + ]["shared_parameters"].pop("system_commodity_interface_limit") + tech_config["technologies"]["battery"]["model_inputs"].update( + {"control_parameters": control_parameters} + ) + # add unused parameter to shared + tech_config["technologies"]["battery"]["model_inputs"]["shared_parameters"].update( + {"test_unused_input": "fake"} + ) + prob = create_om_problem(tech_config) + + for tech, tech_info in tech_config["technologies"].items(): + if tech == "battery": + with pytest.raises(AttributeError) as excinfo: + check_inputs(prob, tech, tech_info, tech_config_fpath) + expected_error = ( + "The parameter(s) ['test_unused_input'] found in " + f"shared_parameters are not used by any of the models for the " + f"'battery' section of {tech_config_fpath}" + ) + assert expected_error == str(excinfo.value) + else: + check_inputs(prob, tech, tech_info, tech_config_fpath) + + # 5: check when parameters are shared but specified individually + combiner_tech = { + "performance_model": {"model": "GenericCombinerPerformanceModel"}, + "dispatch_rule_set": {"model": "PyomoDispatchGenericConverter"}, + "model_inputs": { + "performance_parameters": {"commodity": "electricity", "commodity_rate_units": "kW"}, + "dispatch_rule_parameters": {"commodity": "electricity", "commodity_rate_units": "kW"}, + }, + } + + tech_config = load_tech_yaml(tech_config_fpath) + control_parameters = {} + tech_config["technologies"]["battery"]["model_inputs"]["performance_parameters"].pop( + "system_model_source" + ) + control_parameters["n_control_window"] = tech_config["technologies"]["battery"]["model_inputs"][ + "shared_parameters" + ].pop("n_control_window") + control_parameters["system_commodity_interface_limit"] = tech_config["technologies"]["battery"][ + "model_inputs" + ]["shared_parameters"].pop("system_commodity_interface_limit") + tech_config["technologies"]["battery"]["model_inputs"].update( + {"control_parameters": control_parameters} + ) + tech_config["technologies"].update({"combiner": combiner_tech}) + prob = create_om_problem(tech_config) + + for tech, tech_info in tech_config["technologies"].items(): + if tech == "combiner": + with pytest.raises(AttributeError) as excinfo: + check_inputs(prob, tech, tech_info, tech_config_fpath) + expected_error = ( + "The parameter(s) ['commodity', 'commodity_rate_units] found in " + "performance_parameters should be under shared_parameter(s) for " + f"the 'combiner' section of {tech_config_fpath}" + ) + assert expected_error == str(excinfo.value) + else: + check_inputs(prob, tech, tech_info, tech_config_fpath) diff --git a/h2integrate/converters/iron/martin_ore/__init__.py b/h2integrate/demand/__init__.py similarity index 100% rename from h2integrate/converters/iron/martin_ore/__init__.py rename to h2integrate/demand/__init__.py diff --git a/h2integrate/demand/demand_base.py b/h2integrate/demand/demand_base.py new file mode 100644 index 000000000..09072594f --- /dev/null +++ b/h2integrate/demand/demand_base.py @@ -0,0 +1,153 @@ +import numpy as np +from attrs import field, define + +from h2integrate.core.utilities import BaseConfig +from h2integrate.core.model_baseclasses import PerformanceModelBaseClass + + +@define(kw_only=True) +class DemandComponentBaseConfig(BaseConfig): + """Configuration for defining a demand profile. + + This configuration object specifies the commodity being demanded and the + demand profile that should be met by downstream components. + + Attributes: + commodity (str): Name of the commodity being demanded + (e.g., "hydrogen"). Converted to lowercase and stripped of whitespace. + commodity_rate_units (str): Units of the commodity (e.g., "kg/h"). + demand_profile (int | float | list): Demand values for each timestep, in + the same units as `commodity_rate_units`. May be a scalar for constant + demand or a list/array for time-varying demand. + commodity_amount_units (str | None, optional): Units of the commodity as an amount + (i.e., kW*h or kg). If not provided, defaults to commodity_rate_units*h. + """ + + commodity: str = field(converter=str.strip) + commodity_rate_units: str = field(converter=str.strip) + demand_profile: int | float | list = field() + commodity_amount_units: str = field(default=None) + + def __attrs_post_init__(self): + if self.commodity_amount_units is None: + self.commodity_amount_units = f"({self.commodity_rate_units})*h" + + +class DemandComponentBase(PerformanceModelBaseClass): + """Base OpenMDAO component for open-loop demand tracking. + + This component defines the interfaces required for demand + components, including inputs for demand, supplied commodity, and outputs + tracking unmet demand, unused production, and total unmet demand. + Subclasses must implement the :meth:`compute` method to define the + demand component behavior. + """ + + _time_step_bounds = (3600, 3600) # (min, max) time step lengths compatible with this model + + def setup(self): + """Define inputs and outputs for demand component. + + Creates time-series inputs and outputs for commodity demand, supply, + unmet demand, unused commodity, and total unmet demand. Shapes and units + are determined by the plant configuration and load component configuration. + + Raises: + KeyError: If required configuration keys are missing from + ``plant_config`` or ``tech_config``. + """ + self.commodity = self.config.commodity + self.commodity_rate_units = self.config.commodity_rate_units + self.commodity_amount_units = self.config.commodity_amount_units + + super().setup() + + self.add_input( + f"{self.commodity}_demand", + val=self.config.demand_profile, + shape=self.n_timesteps, + units=self.commodity_rate_units, + desc=f"Demand profile of {self.commodity}", + ) + + self.add_input( + f"{self.commodity}_in", + val=0.0, + shape=self.n_timesteps, + units=self.commodity_rate_units, + desc=f"Amount of {self.commodity} demand that has already been supplied", + ) + + self.add_output( + f"unmet_{self.commodity}_demand_out", + val=self.config.demand_profile, + shape=self.n_timesteps, + units=self.commodity_rate_units, + desc=f"Remaining demand profile of {self.commodity}", + ) + + self.add_output( + f"unused_{self.commodity}_out", + val=0.0, + shape=self.n_timesteps, + units=self.commodity_rate_units, + desc=f"Excess production of {self.commodity}", + ) + + def compute(): + """This method must be implemented by subclasses to define the + demand component. + + Raises: + NotImplementedError: Always, unless implemented in a subclass. + """ + raise NotImplementedError("This method should be implemented in a subclass.") + + def calculate_outputs(self, commodity_in, commodity_demand, outputs): + """Compute unmet demand, unused commodity, and converter output. + + This method compares the demand profile to the supplied commodity for + each timestep and assigns unmet demand, curtailed production, and + actual delivered output. + + Args: + commodity_in (np.array): supplied commodity profile + commodity_demand (np.array): entire commodity demand profile + outputs (dict-like): Mapping of output variable names where results + will be written, including: + + * ``unmet_{commodity}_demand_out``: Unmet demand. + * ``unused_{commodity}_out``: Curtailed production. + * ``{commodity}_out``: Actual output delivered. + + Notes: + All variables operate on a per-timestep basis and typically have + array shape ``(n_timesteps,)``. + """ + + remaining_demand = commodity_demand - commodity_in + + # Calculate missed load and curtailed production + outputs[f"unmet_{self.commodity}_demand_out"] = np.where( + remaining_demand > 0, remaining_demand, 0 + ) + outputs[f"unused_{self.commodity}_out"] = np.where( + remaining_demand < 0, -1 * remaining_demand, 0 + ) + + # Calculate actual output based on demand met and curtailment + outputs[f"{self.commodity}_out"] = commodity_in - outputs[f"unused_{self.commodity}_out"] + + outputs[f"rated_{self.commodity}_production"] = commodity_demand.mean() + + outputs[f"total_{self.commodity}_produced"] = np.sum(outputs[f"{self.commodity}_out"]) * ( + self.dt / 3600 + ) + + outputs[f"annual_{self.commodity}_produced"] = ( + outputs[f"total_{self.commodity}_produced"] / self.fraction_of_year_simulated + ) + + outputs["capacity_factor"] = outputs[f"{self.commodity}_out"].sum() / commodity_demand.sum() + + return outputs diff --git a/h2integrate/control/control_strategies/converters/flexible_demand_openloop_controller.py b/h2integrate/demand/flexible_demand.py similarity index 81% rename from h2integrate/control/control_strategies/converters/flexible_demand_openloop_controller.py rename to h2integrate/demand/flexible_demand.py index c3dc37fe0..f4bcf23cc 100644 --- a/h2integrate/control/control_strategies/converters/flexible_demand_openloop_controller.py +++ b/h2integrate/demand/flexible_demand.py @@ -1,18 +1,16 @@ import numpy as np from attrs import field, define +from h2integrate.core.utilities import merge_shared_inputs from h2integrate.core.validators import gte_zero, range_val -from h2integrate.control.control_strategies.demand_openloop_controller import ( - DemandOpenLoopControlBase, - DemandOpenLoopControlBaseConfig, -) +from h2integrate.demand.demand_base import DemandComponentBase, DemandComponentBaseConfig @define(kw_only=True) -class FlexibleDemandOpenLoopConverterControllerConfig(DemandOpenLoopControlBaseConfig): - """Configuration for defining a flexible demand open-loop controller. +class FlexibleDemandComponentConfig(DemandComponentBaseConfig): + """Configuration for defining a flexible demand component. - Extends :class:`DemandOpenLoopControlBaseConfig` with additional parameters + Extends :class:`DemandComponentBaseConfig` with additional parameters required for dynamically adjusting demand based on turndown, ramping, and minimum utilization constraints. These parameters are expressed as fractions of ``maximum_demand`` and must lie within ``(0, 1)``. @@ -42,37 +40,37 @@ class FlexibleDemandOpenLoopConverterControllerConfig(DemandOpenLoopControlBaseC min_utilization: float = field(validator=range_val(0, 1.0)) -class FlexibleDemandOpenLoopConverterController(DemandOpenLoopControlBase): - """Open-loop controller for flexible demand with ramping and utilization constraints. +class FlexibleDemandComponent(DemandComponentBase): + """Demand component for flexible demand with ramping and utilization constraints. - This controller extends the base demand controller by allowing the effective + This component extends the base demand component by allowing the effective demand to vary dynamically based on turndown constraints, ramp-rate limits, and minimum-utilization requirements. A flexible demand profile is generated and used to compute unmet demand, unused commodity, and delivered output. """ + _time_step_bounds = (3600, 3600) # (min, max) time step lengths compatible with this model + def setup(self): - """Set up component inputs and outputs for flexible demand control. + """Set up component inputs and outputs for flexible demand. Adds inputs for turndown ratio, ramp up/down rates, and minimum utilization, all expressed as fractions of maximum demand. Adds the flexible demand output profile, which will be populated in ``compute``. """ - self.config = FlexibleDemandOpenLoopConverterControllerConfig.from_dict( - self.options["tech_config"]["model_inputs"]["control_parameters"], + self.config = FlexibleDemandComponentConfig.from_dict( + merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance"), + strict=True, additional_cls_name=self.__class__.__name__, ) super().setup() - n_timesteps = int(self.options["plant_config"]["plant"]["simulation"]["n_timesteps"]) - commodity = self.config.commodity - self.add_input( - f"rated_{commodity}_demand", + f"rated_{self.commodity}_demand", val=self.config.demand_profile, - shape=(n_timesteps), - units=self.config.commodity_rate_units, - desc=f"Rated demand of {commodity}", + shape=self.n_timesteps, + units=self.commodity_rate_units, + desc=f"Rated demand of {self.commodity}", ) self.add_input( @@ -104,11 +102,11 @@ def setup(self): ) self.add_output( - f"{commodity}_flexible_demand_profile", + f"{self.commodity}_flexible_demand_profile", val=0.0, - shape=(n_timesteps), + shape=self.n_timesteps, units=self.config.commodity_rate_units, - desc=f"Flexible demand profile of {commodity}", + desc=f"Flexible demand profile of {self.commodity}", ) def adjust_demand_for_ramping(self, pre_demand_met_clipped, demand_bounds, ramp_rate_bounds): @@ -250,7 +248,7 @@ def compute(self, inputs, outputs): """Compute unmet demand, unused commodity, and output under flexible demand. If ``min_utilization == 1.0``, the behavior matches the regular open-loop - controller with no flexible demand adjustments. + component with no flexible demand adjustments. Otherwise: * Construct a flexible demand profile. @@ -263,40 +261,33 @@ def compute(self, inputs, outputs): outputs (dict-like): Mapping where computed outputs are written. """ - commodity = self.config.commodity - remaining_demand = inputs[f"{commodity}_demand"] - inputs[f"{commodity}_in"] if self.config.min_utilization == 1.0: + outputs[f"{self.commodity}_flexible_demand_profile"] = inputs[ + f"{self.commodity}_demand" + ] + # Calculate missed load and curtailed production - outputs[f"{commodity}_unmet_demand"] = np.where( - remaining_demand > 0, remaining_demand, 0 - ) - outputs[f"{commodity}_unused_commodity"] = np.where( - remaining_demand < 0, -1 * remaining_demand, 0 + outputs = self.calculate_outputs( + inputs[f"{self.commodity}_in"], inputs[f"{self.commodity}_demand"], outputs ) + else: + remaining_demand = inputs[f"{self.commodity}_demand"] - inputs[f"{self.commodity}_in"] + # when remaining demand is less than 0, that means input exceeds demand # multiply by -1 to make it positive curtailed = np.where(remaining_demand < 0, -1 * remaining_demand, 0) # subtract out the excess input commodity - inflexible_out = inputs[f"{commodity}_in"] - curtailed + inflexible_out = inputs[f"{self.commodity}_in"] - curtailed flexible_demand_profile = self.make_flexible_demand( - inputs[f"{commodity}_demand"], inflexible_out, inputs + inputs[f"{self.commodity}_demand"], inflexible_out, inputs ) - outputs[f"{commodity}_flexible_demand_profile"] = flexible_demand_profile - flexible_remaining_demand = flexible_demand_profile - inputs[f"{commodity}_in"] + outputs[f"{self.commodity}_flexible_demand_profile"] = flexible_demand_profile - outputs[f"{commodity}_unmet_demand"] = np.where( - flexible_remaining_demand > 0, flexible_remaining_demand, 0 - ) - outputs[f"{commodity}_unused_commodity"] = np.where( - flexible_remaining_demand < 0, -1 * flexible_remaining_demand, 0 + outputs = self.calculate_outputs( + inputs[f"{self.commodity}_in"], flexible_demand_profile, outputs ) - - # Calculate actual output based on demand met and curtailment - outputs[f"{commodity}_set_point"] = ( - inputs[f"{commodity}_in"] - outputs[f"{commodity}_unused_commodity"] - ) diff --git a/h2integrate/demand/generic_demand.py b/h2integrate/demand/generic_demand.py new file mode 100644 index 000000000..b65e713f1 --- /dev/null +++ b/h2integrate/demand/generic_demand.py @@ -0,0 +1,56 @@ +from h2integrate.core.utilities import merge_shared_inputs +from h2integrate.demand.demand_base import DemandComponentBase, DemandComponentBaseConfig + + +class GenericDemandComponent(DemandComponentBase): + """Component for for converting input supply into met demand. + + This component computes unmet demand, unused (curtailed) production, and + the resulting commodity output profile based on the incoming supply and an + externally specified demand profile. It uses simple arithmetic rules: + + * If demand exceeds supplied commodity, the difference is unmet demand. + * If supply exceeds demand, the excess is unused (curtailed) commodity. + * Output equals supplied commodity minus curtailed commodity. + + This component relies on configuration provided through the + ``tech_config`` dictionary, which must define the demand's + ``performance_parameters``. + """ + + def setup(self): + self.config = DemandComponentBaseConfig.from_dict( + merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance"), + strict=True, + additional_cls_name=self.__class__.__name__, + ) + super().setup() + + def compute(self, inputs, outputs): + """Compute unmet demand, unused commodity, and converter output. + + This method compares the demand profile to the supplied commodity for + each timestep and assigns unmet demand, curtailed production, and + actual delivered output. + + Args: + inputs (dict-like): Mapping of input variable names to their + current values, including: + + * ``{commodity}_demand``: Demand profile. + * ``{commodity}_in``: Supplied commodity. + outputs (dict-like): Mapping of output variable names where results + will be written, including: + + * ``unmet_{commodity}_demand_out``: Unmet demand. + * ``unused_{commodity}_out``: Curtailed production. + * ``{commodity}_out``: Actual output delivered. + + Notes: + All variables operate on a per-timestep basis and typically have + array shape ``(n_timesteps,)``. + """ + + outputs = self.calculate_outputs( + inputs[f"{self.commodity}_in"], inputs[f"{self.commodity}_demand"], outputs + ) diff --git a/h2integrate/demand/test/test_demand_components.py b/h2integrate/demand/test/test_demand_components.py new file mode 100644 index 000000000..45cbca3b9 --- /dev/null +++ b/h2integrate/demand/test/test_demand_components.py @@ -0,0 +1,256 @@ +import numpy as np +import pytest +import openmdao.api as om +from pytest import fixture + +from h2integrate.demand.generic_demand import GenericDemandComponent +from h2integrate.demand.flexible_demand import FlexibleDemandComponent + + +@fixture +def variable_h2_production_profile(): + end_use_rated_demand = 10.0 # kg/h + ramp_up_rate_kg = 4.0 + ramp_down_rate_kg = 2.0 + slow_ramp_up = np.arange(0, end_use_rated_demand * 1.1, 0.5) + slow_ramp_down = np.arange(end_use_rated_demand * 1.1, -0.5, -0.5) + fast_ramp_up = np.arange(0, end_use_rated_demand, ramp_up_rate_kg * 1.2) + fast_ramp_down = np.arange(end_use_rated_demand, 0.0, ramp_down_rate_kg * 1.1) + variable_profile = np.concat( + [slow_ramp_up, fast_ramp_down, slow_ramp_up, slow_ramp_down, fast_ramp_up] + ) + variable_h2_profile = np.tile(variable_profile, 2) + return variable_h2_profile + + +@pytest.mark.regression +def test_demand_converter_controller(subtests): + # Test is the same as the demand controller test test_demand_controller for the "h2_storage" + # performance model but with the "StoragePerformanceModel" performance model + + # Define the technology configuration + tech_config = {"technologies": {}} + + tech_config["technologies"]["load"] = { + "performance_model": { + "model": "GenericDemandComponent", + }, + "model_inputs": { + "performance_parameters": { + "commodity": "hydrogen", + "commodity_rate_units": "kg", + "demand_profile": [5.0] * 10, # Example: 10 time steps with 5 kg/time step demand + }, + }, + } + + plant_config = {"plant": {"plant_life": 30, "simulation": {"n_timesteps": 10, "dt": 3600}}} + + # Set up OpenMDAO problem + prob = om.Problem() + + prob.model.add_subsystem( + name="IVC", + subsys=om.IndepVarComp(name="hydrogen_in", val=np.arange(10)), + promotes=["*"], + ) + + prob.model.add_subsystem( + "demand_open_loop_storage_controller", + GenericDemandComponent( + plant_config=plant_config, tech_config=tech_config["technologies"]["load"] + ), + promotes=["*"], + ) + + prob.setup() + + prob.run_model() + + # # Run the test + with subtests.test("Check output"): + assert prob.get_val("hydrogen_out") == pytest.approx( + [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0] + ) + + with subtests.test("Check curtailment"): + assert prob.get_val("unused_hydrogen_out") == pytest.approx( + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0] + ) + + with subtests.test("Check missed load"): + assert prob.get_val("unmet_hydrogen_demand_out") == pytest.approx( + [5.0, 4.0, 3.0, 2.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0] + ) + + +@pytest.mark.unit +def test_flexible_demand_converter_controller(subtests, variable_h2_production_profile): + # Define the technology configuration + tech_config = {"technologies": {}} + + end_use_rated_demand = 10.0 # kg/h + ramp_up_rate_kg = 4.0 + ramp_down_rate_kg = 2.0 + min_demand_kg = 2.5 + tech_config["technologies"]["load"] = { + "performance_model": { + "model": "FlexibleDemandComponent", + }, + "model_inputs": { + "performance_parameters": { + "commodity": "hydrogen", + "commodity_rate_units": "kg", + "rated_demand": end_use_rated_demand, + "demand_profile": end_use_rated_demand, # flat demand profile + "turndown_ratio": min_demand_kg / end_use_rated_demand, + "ramp_down_rate_fraction": ramp_down_rate_kg / end_use_rated_demand, + "ramp_up_rate_fraction": ramp_up_rate_kg / end_use_rated_demand, + "min_utilization": 0.1, + }, + }, + } + + plant_config = { + "plant": { + "plant_life": 30, + "simulation": {"n_timesteps": len(variable_h2_production_profile), "dt": 3600}, + } + } + + # Set up OpenMDAO problem + prob = om.Problem() + + prob.model.add_subsystem( + name="IVC", + subsys=om.IndepVarComp(name="hydrogen_in", val=variable_h2_production_profile), + promotes=["*"], + ) + + prob.model.add_subsystem( + "flexible_demand_open_loop_converter_controller", + FlexibleDemandComponent( + plant_config=plant_config, tech_config=tech_config["technologies"]["load"] + ), + promotes=["*"], + ) + + prob.setup() + + prob.run_model() + + flexible_total_demand = prob.get_val("hydrogen_flexible_demand_profile", units="kg") + + rated_production = end_use_rated_demand * len(variable_h2_production_profile) + + with subtests.test("Check that total demand profile is less than rated"): + assert np.all(flexible_total_demand <= end_use_rated_demand) + + with subtests.test("Check curtailment"): # failed + assert np.sum(prob.get_val("unused_hydrogen_out", units="kg")) == pytest.approx(6.6) + + # check ramping constraints and turndown constraints are met + with subtests.test("Check turndown ratio constraint"): + assert np.all(flexible_total_demand >= min_demand_kg) + + ramping_down = np.where( + np.diff(flexible_total_demand) < 0, -1 * np.diff(flexible_total_demand), 0 + ) + ramping_up = np.where(np.diff(flexible_total_demand) > 0, np.diff(flexible_total_demand), 0) + + with subtests.test("Check ramping down constraint"): + assert np.max(ramping_down) == pytest.approx(ramp_down_rate_kg, rel=1e-6) + + with subtests.test("Check ramping up constraint"): # failed + assert np.max(ramping_up) == pytest.approx(ramp_up_rate_kg, rel=1e-6) + + with subtests.test("Check min utilization constraint"): + assert np.sum(flexible_total_demand) / rated_production >= 0.1 + + with subtests.test("Check min utilization value"): + flexible_demand_utilization = np.sum(flexible_total_demand) / rated_production + assert flexible_demand_utilization == pytest.approx(0.5822142857142857, rel=1e-6) + + # flexible_demand_profile[i] >= commodity_in[i] (as long as you are not curtailing + # any commodity in) + with subtests.test("Check that flexible demand is greater than hydrogen_in"): + hydrogen_available = variable_h2_production_profile - prob.get_val( + "unused_hydrogen_out", units="kg" + ) + assert np.all(flexible_total_demand >= hydrogen_available) + + with subtests.test("Check that remaining demand was calculated properly"): + unmet_demand = flexible_total_demand - hydrogen_available + assert np.all(unmet_demand == prob.get_val("unmet_hydrogen_demand_out", units="kg")) + + +@pytest.mark.regression +def test_flexible_demand_converter_controller_min_utilization( + subtests, variable_h2_production_profile +): + # give it a min utilization larger than utilization resulting from above test + + # Define the technology configuration + tech_config = {"technologies": {}} + + end_use_rated_demand = 10.0 # kg/h + ramp_up_rate_kg = 4.0 + ramp_down_rate_kg = 2.0 + min_demand_kg = 2.5 + tech_config["technologies"]["load"] = { + "performance_model": { + "model": "FlexibleDemandComponent", + }, + "model_inputs": { + "performance_parameters": { + "commodity": "hydrogen", + "commodity_rate_units": "kg", + "rated_demand": end_use_rated_demand, + "demand_profile": end_use_rated_demand, # flat demand profile + "turndown_ratio": min_demand_kg / end_use_rated_demand, + "ramp_down_rate_fraction": ramp_down_rate_kg / end_use_rated_demand, + "ramp_up_rate_fraction": ramp_up_rate_kg / end_use_rated_demand, + "min_utilization": 0.8, + }, + }, + } + + plant_config = { + "plant": { + "plant_life": 30, + "simulation": {"n_timesteps": len(variable_h2_production_profile), "dt": 3600}, + } + } + + # Set up OpenMDAO problem + prob = om.Problem() + + prob.model.add_subsystem( + name="IVC", + subsys=om.IndepVarComp(name="hydrogen_in", val=variable_h2_production_profile), + promotes=["*"], + ) + + prob.model.add_subsystem( + "DemandOpenLoopStorageController", + FlexibleDemandComponent( + plant_config=plant_config, tech_config=tech_config["technologies"]["load"] + ), + promotes=["*"], + ) + + prob.setup() + + prob.run_model() + + flexible_total_demand = prob.get_val("hydrogen_flexible_demand_profile", units="kg") + + rated_production = end_use_rated_demand * len(variable_h2_production_profile) + + flexible_demand_utilization = np.sum(flexible_total_demand) / rated_production + + with subtests.test("Check min utilization constraint"): + assert flexible_demand_utilization >= 0.8 + + with subtests.test("Check min utilization value"): + assert flexible_demand_utilization == pytest.approx(0.8010612244, rel=1e-6) diff --git a/h2integrate/finances/profast_base.py b/h2integrate/finances/profast_base.py index fe0e0ea4b..3bccca45d 100644 --- a/h2integrate/finances/profast_base.py +++ b/h2integrate/finances/profast_base.py @@ -447,7 +447,7 @@ class ProFastBase(om.ExplicitComponent): Reference: ProFAST Documentation: - https://www.nrel.gov/hydrogen/profast-access + https://www.nlr.gov/hydrogen/profast-access Attributes: diff --git a/h2integrate/finances/test/conftest.py b/h2integrate/finances/test/conftest.py index aec692e38..a4763a760 100644 --- a/h2integrate/finances/test/conftest.py +++ b/h2integrate/finances/test/conftest.py @@ -4,7 +4,11 @@ import os -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) def pytest_sessionstart(session): diff --git a/h2integrate/postprocess/test/conftest.py b/h2integrate/postprocess/test/conftest.py index 8d49ffb3d..fa8f250cf 100644 --- a/h2integrate/postprocess/test/conftest.py +++ b/h2integrate/postprocess/test/conftest.py @@ -1 +1,5 @@ -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) diff --git a/h2integrate/postprocess/test/test_mapping_tools.py b/h2integrate/postprocess/test/test_mapping_tools.py index 285e411b2..8da878ac7 100644 --- a/h2integrate/postprocess/test/test_mapping_tools.py +++ b/h2integrate/postprocess/test/test_mapping_tools.py @@ -1,15 +1,21 @@ import numpy as np import pandas as pd import pytest -import geopandas as gpd from shapely.geometry import Polygon -from h2integrate.postprocess.mapping import ( - auto_colorbar_limits, - validate_gdfs_are_same_crs, - auto_detect_lat_long_columns, - calculate_geodataframe_total_bounds, -) + +gis_extras = True +try: + import geopandas as gpd + + from h2integrate.postprocess.mapping import ( + auto_colorbar_limits, + validate_gdfs_are_same_crs, + auto_detect_lat_long_columns, + calculate_geodataframe_total_bounds, + ) +except ModuleNotFoundError: + gis_extras = False # Define geometries to be used in testing of GeoDataFrames @@ -32,6 +38,7 @@ @pytest.mark.unit +@pytest.mark.skipif(not gis_extras, reason="`gis` dependencies not installed") def test_calculate_geodataframe_total_bounds(subtests): with subtests.test("Check invalid argument type"): expected_msg = "Must provide at least one GeoDataFrame." @@ -98,6 +105,7 @@ def test_calculate_geodataframe_total_bounds(subtests): @pytest.mark.unit +@pytest.mark.skipif(not gis_extras, reason="`gis` dependencies not installed") def test_auto_detect_lat_long_columns(subtests): test_good_results_df1 = pd.DataFrame(columns=["index", "latitude", "longitude"]) test_good_results_df2 = pd.DataFrame(columns=["index", "lat", "long"]) @@ -163,6 +171,7 @@ def test_auto_detect_lat_long_columns(subtests): @pytest.mark.unit +@pytest.mark.skipif(not gis_extras, reason="`gis` dependencies not installed") def test_validate_gdfs_are_same_crs(subtests): gdf_1 = gpd.GeoDataFrame( data={"index": [0], "test1": ["larger_square"], "test2": [larger_square_coords]}, @@ -211,6 +220,7 @@ def test_validate_gdfs_are_same_crs(subtests): @pytest.mark.unit +@pytest.mark.skipif(not gis_extras, reason="`gis` dependencies not installed") def test_auto_colorbar_limits(subtests): with subtests.test("Test good value input types"): vmin, vmax = auto_colorbar_limits(values=pd.Series([0.62, 0.75, 0.93])) diff --git a/h2integrate/postprocess/test/test_sql_timeseries_to_csv.py b/h2integrate/postprocess/test/test_sql_timeseries_to_csv.py index dae722dc5..b23867f08 100644 --- a/h2integrate/postprocess/test/test_sql_timeseries_to_csv.py +++ b/h2integrate/postprocess/test/test_sql_timeseries_to_csv.py @@ -1,27 +1,27 @@ -import os from pathlib import Path import numpy as np import pytest from pytest import fixture -from h2integrate import EXAMPLE_DIR from h2integrate.core.h2integrate_model import H2IntegrateModel from h2integrate.core.inputs.validation import load_yaml, load_tech_yaml, load_driver_yaml from h2integrate.postprocess.sql_timeseries_to_csv import save_case_timeseries_as_csv @fixture -def configuration(temp_dir): - config = load_yaml(EXAMPLE_DIR / "02_texas_ammonia" / "02_texas_ammonia.yaml") +def configuration(temp_copy_of_example): + example_folder = temp_copy_of_example + config = load_yaml(example_folder / "02_texas_ammonia.yaml") - driver_config = load_driver_yaml(EXAMPLE_DIR / "02_texas_ammonia" / "driver_config.yaml") - driver_config["general"]["folder_output"] = str(temp_dir) + driver_config = load_driver_yaml(example_folder / "driver_config.yaml") + output_folder = example_folder / driver_config["general"]["folder_output"] + driver_config["general"]["folder_output"] = str(output_folder) config["driver_config"] = driver_config - tech_config = load_tech_yaml(EXAMPLE_DIR / "02_texas_ammonia" / "tech_config.yaml") + tech_config = load_tech_yaml(example_folder / "tech_config.yaml") tech_config["technologies"]["wind"]["model_inputs"]["performance_parameters"]["cache_dir"] = ( - str(temp_dir) + str(output_folder) ) config["technology_config"] = tech_config return config @@ -30,11 +30,13 @@ def configuration(temp_dir): @fixture def run_example_02_sql_fpath(configuration): # check if case file exists, if so, return the filepath - sql_fpath = EXAMPLE_DIR / "02_texas_ammonia" / "outputs" / "cases.sql" + output_folder = ( + Path(configuration["driver_config"]["general"]["folder_output"]).resolve().parent + ) + sql_fpath = output_folder / "cases.sql" if sql_fpath.exists(): return sql_fpath else: - os.chdir(EXAMPLE_DIR / "02_texas_ammonia") # Create a H2Integrate model h2i = H2IntegrateModel(configuration) @@ -50,6 +52,7 @@ def run_example_02_sql_fpath(configuration): @pytest.mark.unit +@pytest.mark.parametrize("example_folder,resource_example_folder", [("02_texas_ammonia", None)]) def test_save_csv_all_results(subtests, configuration, run_example_02_sql_fpath): expected_csv_fpath = ( Path(configuration["driver_config"]["general"]["folder_output"]) / "cases_Case-1.csv" @@ -57,7 +60,7 @@ def test_save_csv_all_results(subtests, configuration, run_example_02_sql_fpath) res = save_case_timeseries_as_csv(run_example_02_sql_fpath, save_to_file=True) with subtests.test("Check number of columns"): - assert len(res.columns.to_list()) == 34 + assert len(res.columns.to_list()) == 49 with subtests.test("Check number of rows"): assert len(res) == 8760 @@ -67,6 +70,7 @@ def test_save_csv_all_results(subtests, configuration, run_example_02_sql_fpath) @pytest.mark.unit +@pytest.mark.parametrize("example_folder,resource_example_folder", [("02_texas_ammonia", None)]) def test_make_df_from_varname_list(subtests, run_example_02_sql_fpath): vars_to_save = [ "electrolyzer.hydrogen_out", @@ -91,6 +95,7 @@ def test_make_df_from_varname_list(subtests, run_example_02_sql_fpath): @pytest.mark.unit +@pytest.mark.parametrize("example_folder,resource_example_folder", [("02_texas_ammonia", None)]) def test_make_df_from_varname_unit_dict(subtests, run_example_02_sql_fpath): vars_units_to_save = { "ammonia.hydrogen_in": "kg/h", @@ -116,6 +121,7 @@ def test_make_df_from_varname_unit_dict(subtests, run_example_02_sql_fpath): @pytest.mark.unit +@pytest.mark.parametrize("example_folder,resource_example_folder", [("02_texas_ammonia", None)]) def test_alternative_column_names(subtests, run_example_02_sql_fpath): vars_to_save = { "electrolyzer.hydrogen_out": {"alternative_name": "Electrolyzer Hydrogen Output"}, diff --git a/h2integrate/postprocess/test/test_sql_to_csv.py b/h2integrate/postprocess/test/test_sql_to_csv.py index 3fde7ae2e..19fb3a773 100644 --- a/h2integrate/postprocess/test/test_sql_to_csv.py +++ b/h2integrate/postprocess/test/test_sql_to_csv.py @@ -27,7 +27,7 @@ EXAMPLE_19_DIR = EXAMPLE_DIR / "19_simple_dispatch" -@fixture +@fixture(scope="function") def configuration(temp_dir): """Load and patch the example-19 configuration so outputs go to a temp dir.""" config = load_yaml(EXAMPLE_19_DIR / "wind_battery_dispatch.yaml") diff --git a/h2integrate/preprocess/test/conftest.py b/h2integrate/preprocess/test/conftest.py index aec692e38..a4763a760 100644 --- a/h2integrate/preprocess/test/conftest.py +++ b/h2integrate/preprocess/test/conftest.py @@ -4,7 +4,11 @@ import os -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) def pytest_sessionstart(session): diff --git a/h2integrate/resource/resource_base.py b/h2integrate/resource/resource_base.py index d0bbef18a..35fb46636 100644 --- a/h2integrate/resource/resource_base.py +++ b/h2integrate/resource/resource_base.py @@ -90,6 +90,7 @@ def setup(self): self.resource_data = None self.resource_site = [self.config.latitude, self.config.longitude] self.dt = self.options["plant_config"]["plant"]["simulation"]["dt"] + self.n_timesteps = self.options["plant_config"]["plant"]["simulation"]["n_timesteps"] self.add_input("latitude", self.config.latitude, units="deg") self.add_input("longitude", self.config.longitude, units="deg") @@ -178,6 +179,60 @@ def add_resource_start_end_times(self, data: dict): return data + def process_leap_day(self, data: dict): + """Process leap day data by optionally removing it and validating data length. + + Checks whether the provided resource data contains a leap day (February 29th). + If ``include_leap_day`` is set to False in the config and the data contains a + leap day, the leap day entries are removed. After processing, validates that + the length of the data matches the expected number of timesteps. + + Args: + data (dict): DataFrame-like dictionary of resource data containing + "Month" and "Day" columns. + Returns: + dict: Processed resource data with leap day handled according to configuration. + + Raises: + ValueError: If the length of the data does not match ``self.n_timesteps`` + after leap day processing. + """ + + # Check if data includes leap day + data_has_leap_day = int(data[data["Month"] == 2]["Day"].max()) == 29 + + # Remove leap day if needed + if not self.config.include_leap_day and data_has_leap_day: + # Get index of dataframe that includes leap day + leap_day_index = ( + data.reset_index(drop=False) + .set_index(keys=["Month", "Day"], drop=True) + .loc[(2, 29)]["index"] + .to_list() + ) + # Drop the leap day data from the dataframe + data = data.drop(index=leap_day_index) + + # Check if data is the same length as the number of timesteps + if len(data) != self.n_timesteps: + leap_day_msg = "" + if data_has_leap_day and len(data) > self.n_timesteps: + # Add extra detail to error message if error may be due to leap day + leap_day_msg = ( + "This may be because the resource data includes a leap day. ", + "To remove data from a leap day from resource data, please set " + "`include_leap_day` to False.", + ) + + msg = ( + f"{self.__class__.__name__}: Resource data is not the same length as n_timesteps. " + f"Resource data has length {len(data)}, n_timesteps is {self.n_timesteps}. " + f"{leap_day_msg}" + ) + raise ValueError(msg) + + return data + def create_filename(self, latitude, longitude): """Create default filename to save downloaded data to. Suggested filename formatting is: diff --git a/h2integrate/resource/solar/openmeteo_solar.py b/h2integrate/resource/solar/openmeteo_solar.py index 19adb7a40..fff03d7c9 100644 --- a/h2integrate/resource/solar/openmeteo_solar.py +++ b/h2integrate/resource/solar/openmeteo_solar.py @@ -21,6 +21,8 @@ class OpenMeteoHistoricalSolarAPIConfig(ResourceBaseAPIConfig): Args: resource_year (int): Year to use for resource data. Must been between 1940 the year before the current calendar year. (inclusive). + include_leap_day (bool, optional): If False, remove data from leap day if the + resource_year is a leap year. Otherwise, leave leap day data in. Defaults to False. resource_data (dict | object, optional): Dictionary of user-input resource data. Defaults to an empty dictionary. resource_dir (str | Path, optional): Folder to save resource files to or @@ -42,6 +44,7 @@ class OpenMeteoHistoricalSolarAPIConfig(ResourceBaseAPIConfig): """ resource_year: int = field(converter=int, validator=range_val(1940, datetime.now().year - 1)) + include_leap_day: bool = field(default=False) dataset_desc: str = "openmeteo_archive_solar" resource_type: str = "solar" valid_intervals: list[int] = field(factory=lambda: [60]) @@ -286,7 +289,10 @@ def load_data(self, fpath): data["Minute"] = time.minute data = data[data["Year"] == self.config.resource_year] - # TODO: throw error if data isn't proper length + + data = data.reset_index(drop=True) + + data = self.process_leap_day(data) data, data_units = self.format_timeseries_data(data) # make units for data in openmdao-compatible units diff --git a/h2integrate/resource/solar/test/conftest.py b/h2integrate/resource/solar/test/conftest.py index fb3a637e2..b8bb99aee 100644 --- a/h2integrate/resource/solar/test/conftest.py +++ b/h2integrate/resource/solar/test/conftest.py @@ -5,4 +5,8 @@ pytest_sessionfinish, ) -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) diff --git a/h2integrate/resource/solar/test/test_pvwatts_integration.py b/h2integrate/resource/solar/test/test_pvwatts_integration.py index 63b2e7433..7bd871ac6 100644 --- a/h2integrate/resource/solar/test/test_pvwatts_integration.py +++ b/h2integrate/resource/solar/test/test_pvwatts_integration.py @@ -72,6 +72,7 @@ def pysam_performance_model(): ("MeteosatPrimeMeridianSolarAPI", "solar", 41.9077, 12.4368, 2008, "nsrdb_msg_v4", 0, 2e-2, 410211.9419), # noqa: E501 pytest.param("MeteosatPrimeMeridianTMYSolarAPI", "solar", -27.3649, 152.67935, "tmy-2022", "himawari_tmy_v3", 0, 1e-3, 510709.633402, marks=pytest.mark.xfail(reason="Longitude mismatch")), # noqa: E501 ("OpenMeteoHistoricalSolarResource", "solar", 44.04218, -95.19757, 2023, "openmeteo_archive_solar", 0, 1e-3, 443558.17053592583), # noqa: E501 + ("OpenMeteoHistoricalSolarResource", "solar", -28.454864, 114.551749, 2024, "openmeteo_archive_solar", 8, 2e-2, 192656.49240723), # noqa: E501 ], ids=[ "Himawari7SolarAPI", @@ -80,6 +81,7 @@ def pysam_performance_model(): "MeteosatPrimeMeridianSolarAPI", "MeteosatPrimeMeridianTMYSolarAPI", "OpenMeteoHistoricalSolarResource", + "OpenMeteoHistoricalSolarResource-LeapYear", ] ) # fmt: on diff --git a/h2integrate/resource/solar/test/test_resource_models.py b/h2integrate/resource/solar/test/test_resource_models.py index aa997f26a..b064593fa 100644 --- a/h2integrate/resource/solar/test/test_resource_models.py +++ b/h2integrate/resource/solar/test/test_resource_models.py @@ -30,7 +30,7 @@ ] ) # fmt: on -def test_nrel_solar_resource_file_downloads( +def test_nlr_solar_resource_file_downloads( subtests, plant_simulation, site_config, @@ -197,3 +197,61 @@ def test_solar_resource_h2i_download( assert solar_data["start_time"] == f"{resource_year}/01/01 00:00:00 (+0000)" with subtests.test("Time step"): assert solar_data["dt"] == plant_simulation["simulation"]["dt"] + + + +# fmt: off +@pytest.mark.unit +@pytest.mark.parametrize( + "model,which,lat,lon,resource_year,model_name,timezone", + [("OpenMeteoHistoricalSolarResource", "solar", -28.454864, 114.551749, 2024, "openmeteo_archive_solar", 8)], # noqa: E501 + ids=["OpenMeteoHistoricalSolarResource-LeapYear"] +) +# fmt: on +def test_solar_resource_h2i_download_leap_year( + plant_simulation, + site_config, + subtests, + model, + which, + lat, + lon, + resource_year, + model_name, +): + plant_config = { + "site": site_config, + "plant": plant_simulation, + } + + prob = om.Problem() + comp = supported_models[model]( + plant_config=plant_config, + resource_config=plant_config["site"]["resources"]["solar_resource"]["resource_parameters"], + driver_config={}, + ) + prob.model.add_subsystem("resource", comp) + prob.setup() + prob.run_model() + solar_data = prob.get_val("resource.solar_resource_data") + name_expected = f"{lat}_{lon}_{resource_year}_{model_name}_60min_local_tz.csv" + with subtests.test("filepath for data was found where expected"): + assert Path(solar_data["filepath"]).exists() + assert Path(solar_data["filepath"]).name == name_expected + + with subtests.test("Data timezone"): + assert pytest.approx(solar_data["data_tz"], rel=1e-6) == 8 + with subtests.test("Site Elevation"): + assert pytest.approx(solar_data["elevation"], rel=1e-6) == 71 + + data_keys = [k for k, v in solar_data.items() if not isinstance(v, float | int | str)] + for k in data_keys: + with subtests.test(f"{k} resource data is 8760"): + assert len(solar_data[k]) == 8760 + + with subtests.test("There are 16 timeseries data keys"): + assert len(data_keys) == 16 + with subtests.test("Start time"): + assert solar_data["start_time"] == f"{resource_year}/01/01 00:00:00 (+0800)" + with subtests.test("Time step"): + assert solar_data["dt"] == plant_simulation["simulation"]["dt"] diff --git a/h2integrate/resource/test/conftest.py b/h2integrate/resource/test/conftest.py index 204de72db..5cd0ab0ea 100644 --- a/h2integrate/resource/test/conftest.py +++ b/h2integrate/resource/test/conftest.py @@ -4,7 +4,11 @@ from h2integrate.resource.utilities.nlr_developer_api_keys import set_nlr_key_dot_env -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) # docs fencepost start: DO NOT REMOVE diff --git a/h2integrate/resource/tidal.py b/h2integrate/resource/tidal.py new file mode 100644 index 000000000..723560724 --- /dev/null +++ b/h2integrate/resource/tidal.py @@ -0,0 +1,140 @@ +from pathlib import Path + +import pandas as pd +import openmdao.api as om +import PySAM.TidalFileReader as tidalfile +from attrs import field, define + +from h2integrate.core.utilities import BaseConfig +from h2integrate.core.file_utils import get_path, find_file + + +@define(kw_only=True) +class TidalResourceConfig(BaseConfig): + """ + Args: + resource_dir (str | Path, optional): Folder to save resource files to or + load resource files from. Defaults to "". + resource_filename (str, optional): Filename to save resource data to or load + resource data from. Defaults to None. + """ + + resource_dir: Path | str | None = field(default=None) + resource_filename: Path | str = field(default="") + + +class TidalResource(om.ExplicitComponent): + """ + A resource component for processing tidal data from a CSV file. + + This component reads a CSV file containing tidal data, processes it, + and outputs hourly tidal velocity values for a full year (8760 hours). The input + file is expected to have specific formatting, including metadata and day and time, + and speed data columns with some error handling for missing or malformed data. + + Notes: + The tidal resource data should be in the format: + - Rows 1 and 2: Header rows with location info. + - Row 3: Column headings for time-series data + - (`Year`, `Month`, `Day`, `Hour`, `Minute`, `Speed`). + - Rows 4+: Data values: + - `Speed` (current speed) in meters/second. + + + Methods: + initialize(): + Declares the options for the component, including the required "filename" option. + setup(): + Defines the outputs for the component, in this case just the "tidal_velocity" array. + compute(inputs, outputs): + Reads, processes, and resamples the data from the input file. + Outputs the hourly tidal velocity values. + + Raises: + FileNotFoundError: If the specified file does not exist. + ValueError: If the file does not contain sufficient data or the required + speed column is not found. + """ + + def initialize(self): + self.options.declare("plant_config", types=dict) + self.options.declare("resource_config", types=dict) + self.options.declare("driver_config", types=dict) + + def setup(self): + # Define inputs and outputs + self.config = TidalResourceConfig.from_dict( + self.options["resource_config"], + additional_cls_name=self.__class__.__name__, + ) + site_config = self.options["plant_config"]["site"] + + self.add_input("latitude", site_config.get("longitude"), units="deg") + self.add_input("longitude", site_config.get("longitude"), units="deg") + self.add_output("tidal_velocity", shape=8760, val=0.0, units="m/s") + + def compute(self, inputs, outputs): + # Read the CSV file + # Check if the file exists + + resource_dir = get_path(self.config.resource_dir) + filename = find_file(self.config.resource_filename, resource_dir) + + # filename = resource_dir / self.config.resource_filename + + tidalfile_model = tidalfile.new() + # Load resource file + tidalfile_model.WeatherReader.tidal_resource_filename = str(filename) + tidalfile_model.WeatherReader.tidal_resource_model_choice = 1 # Time-series=1 JPD=0 + + # Read in resource file, output time series arrays to pass to wave performance module + tidalfile_model.execute() + hours = tidalfile_model.Outputs.hour + + if len(hours) < 8760: + # code that makes/modifies data_df + # Set up dataframe for data manipulation + df = pd.DataFrame() + df["year"] = tidalfile_model.Outputs.year + df["month"] = tidalfile_model.Outputs.month + df["day"] = tidalfile_model.Outputs.day + df["hour"] = tidalfile_model.Outputs.hour + df["minute"] = tidalfile_model.Outputs.minute + df["date_time"] = pd.to_datetime( + { + "year": df.year, + "month": df.month, + "day": df.day, + "hour": df.hour, + "minute": df.minute, + } + ) + df = df.drop(["year", "month", "day", "hour", "minute"], axis=1) + df = df.set_index(["date_time"]) + df["tidal_velocity"] = tidalfile_model.Outputs.tidal_velocity + + # Resample data and linearly interpolate to hourly data + data_df = df.resample("h").mean() + data_df = data_df.interpolate(method="linear") + + # If data cannot interpolate last hours + if len(data_df["tidal_velocity"]) < 8760: + last_hour = data_df.index.max() + missing_hours = 8760 - len(data_df["tidal_velocity"]) + + missing_time = pd.date_range( + last_hour + pd.Timedelta(hours=1), periods=missing_hours, freq="h" + ) + missing_rows = pd.DataFrame(index=missing_time, columns=df.columns) + data_df = pd.concat([data_df, missing_rows]).sort_index() + data_df = data_df.ffill() # forward fill + + data_df = data_df.reset_index() + outputs["tidal_velocity"] = data_df["tidal_velocity"] + return + + if len(hours) == 8760: + outputs["tidal_velocity"] = tidalfile_model.Outputs.tidal_velocity + return + + raise ValueError("Resource time-series cannot be subhourly.") diff --git a/h2integrate/resource/utilities/test/conftest.py b/h2integrate/resource/utilities/test/conftest.py index 6a746b5c5..a7b6c2f5e 100644 --- a/h2integrate/resource/utilities/test/conftest.py +++ b/h2integrate/resource/utilities/test/conftest.py @@ -2,7 +2,11 @@ from hopp import TEST_ENV_VAR -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) def pytest_sessionstart(session): diff --git a/h2integrate/resource/wind/openmeteo_wind.py b/h2integrate/resource/wind/openmeteo_wind.py index 18cee50fa..a3b684528 100644 --- a/h2integrate/resource/wind/openmeteo_wind.py +++ b/h2integrate/resource/wind/openmeteo_wind.py @@ -22,6 +22,8 @@ class OpenMeteoHistoricalWindAPIConfig(ResourceBaseAPIConfig): Args: resource_year (int): Year to use for resource data. Must been between 1940 the year before the current calendar year. (inclusive). + include_leap_day (bool, optional): If False, remove data from leap day if the + resource_year is a leap year. Otherwise, leave leap day data in. Defaults to False. verify_download (bool, optional): Whether to verify the API download from the url. If an `openmeteo_requests.Client.OpenMeteoRequestsError` error is thrown, try setting to True. Defaults to False. @@ -37,6 +39,7 @@ class OpenMeteoHistoricalWindAPIConfig(ResourceBaseAPIConfig): """ resource_year: int = field(converter=int, validator=range_val(1940, datetime.now().year - 1)) + include_leap_day: bool = field(default=False) dataset_desc: str = "openmeteo_archive" resource_type: str = "wind" valid_intervals: list[int] = field(factory=lambda: [60]) @@ -279,7 +282,10 @@ def load_data(self, fpath): data["Minute"] = time.minute data = data[data["Year"] == self.config.resource_year] - # TODO: throw error if data isn't proper length + + data = data.reset_index(drop=True) + + data = self.process_leap_day(data) data, data_units = self.format_timeseries_data(data) # make units for data in openmdao-compatible units @@ -325,7 +331,7 @@ def format_timeseries_data(self, data): if "is_day" in c: data_rename_mapper.update({c: "is_day"}) - data_units.update({"is_day": "percent"}) + data_units.update({"is_day": "unitless"}) if "surface" in c: new_c += "_0m" diff --git a/h2integrate/resource/wind/test/conftest.py b/h2integrate/resource/wind/test/conftest.py index fb3a637e2..b8bb99aee 100644 --- a/h2integrate/resource/wind/test/conftest.py +++ b/h2integrate/resource/wind/test/conftest.py @@ -5,4 +5,8 @@ pytest_sessionfinish, ) -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) diff --git a/h2integrate/resource/wind/test/test_openmeteo_wind_api.py b/h2integrate/resource/wind/test/test_openmeteo_wind_api.py index 63995bd8f..b0d289998 100644 --- a/h2integrate/resource/wind/test/test_openmeteo_wind_api.py +++ b/h2integrate/resource/wind/test/test_openmeteo_wind_api.py @@ -127,3 +127,63 @@ def test_wind_resource_h2i_download( assert wind_data["start_time"] == f"{resource_year}/01/01 00:00:00 (-0600)" with subtests.test("Time step"): assert wind_data["dt"] == plant_simulation["simulation"]["dt"] + + + +# fmt: off +@pytest.mark.unit +@pytest.mark.parametrize( + "model,which,lat,lon,resource_year,model_name,timezone,elevation", + [("OpenMeteoHistoricalWindResource", "wind", -28.454864, 114.551749, 2024, "openmeteo_archive", 8, 71.0)], # noqa: E501 + ids=["Non-UTC Leap Year"], +) +# fmt: on +def test_wind_resource_h2i_download_leap_year( + plant_simulation, + site_config, + subtests, + model, + lat, + lon, + resource_year, + model_name, + timezone, + elevation, +): + plant_config = { + "site": site_config, + "plant": plant_simulation, + } + + prob = om.Problem() + comp = supported_models[model]( + plant_config=plant_config, + resource_config=plant_config["site"]["resources"]["wind_resource"]["resource_parameters"], + driver_config={}, + ) + prob.model.add_subsystem("resource", comp) + prob.setup() + prob.run_model() + wind_data = prob.get_val("resource.wind_resource_data") + + expected_fn = f"{lat}_{lon}_{resource_year}_{model_name}_60min_local_tz.csv" + with subtests.test("filepath for data was found where expected"): + assert Path(wind_data["filepath"]).exists() + assert Path(wind_data["filepath"]).name == expected_fn + + with subtests.test("Data timezone"): + assert pytest.approx(wind_data["data_tz"], rel=1e-6) == timezone + with subtests.test("Site Elevation"): + assert pytest.approx(wind_data["elevation"], rel=1e-6) == elevation + + data_keys = [k for k, v in wind_data.items() if not isinstance(v, float | int | str)] + for k in data_keys: + with subtests.test(f"{k} resource data is 8760"): + assert len(wind_data[k]) == 8760 + + with subtests.test("theres 14 timeseries data keys"): + assert len(data_keys) == 14 + with subtests.test("Start time"): + assert wind_data["start_time"] == f"{resource_year}/01/01 00:00:00 (+0800)" + with subtests.test("Time step"): + assert wind_data["dt"] == plant_simulation["simulation"]["dt"] diff --git a/h2integrate/storage/battery/atb_battery_cost.py b/h2integrate/storage/battery/atb_battery_cost.py index 1e5f77e49..f63e943fe 100644 --- a/h2integrate/storage/battery/atb_battery_cost.py +++ b/h2integrate/storage/battery/atb_battery_cost.py @@ -11,10 +11,10 @@ class ATBBatteryCostConfig(CostModelBaseConfig): """Configuration class for the ATBBatteryCostModel with costs based on storage capacity and charge rate. More information on ATB methodology and representative battery technologies can be found - `here `_ + `here `_ Reference cost values can be found on the `Utility-Scale Battery Storage`, `Commercial Battery Storage`, and `Residential Battery Storage` sheets of the - `NREL ATB workbook `_. + `NLR ATB workbook `_. Attributes: energy_capex (float|int): battery energy capital cost in $/kWh @@ -27,6 +27,8 @@ class ATBBatteryCostConfig(CostModelBaseConfig): per time step, e.g., "kW/time step"). commodity_rate_units (str): Units of the electricity resource used to define the max_capacity and max_charge_rate. Must have a base of Watts ('W'). + commodity_amount_units (str | None, optional): Units of the commodity as an amount + (i.e., kW*h or kg). If not provided, defaults to commodity_rate_units*h. """ energy_capex: float | int = field(validator=gte_zero) @@ -35,6 +37,11 @@ class ATBBatteryCostConfig(CostModelBaseConfig): max_capacity: float = field() max_charge_rate: float = field() commodity_rate_units: str = field(validator=contains(["W", "kW", "MW", "GW", "TW"])) + commodity_amount_units: str = field(default=None) + + def __attrs_post_init__(self): + if self.commodity_amount_units is None: + self.commodity_amount_units = f"({self.commodity_rate_units})*h" class ATBBatteryCostModel(CostModelBaseClass): @@ -54,6 +61,11 @@ class ATBBatteryCostModel(CostModelBaseClass): """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = ATBBatteryCostConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"), @@ -66,13 +78,13 @@ def setup(self): self.add_input( "max_charge_rate", val=self.config.max_charge_rate, - units=f"{self.config.commodity_rate_units}", + units=self.config.commodity_rate_units, desc="Battery charge/discharge rate", ) self.add_input( - "max_capacity", + "storage_capacity", val=self.config.max_capacity, - units=f"{self.config.commodity_rate_units}*h", + units=self.config.commodity_amount_units, desc="Battery storage capacity", ) @@ -81,7 +93,7 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): # convert the input capacity to units of kW*h max_capacity_kWh = units.convert_units( - inputs["max_capacity"], f"{self.config.commodity_rate_units}*h", "kW*h" + inputs["storage_capacity"], self.config.commodity_amount_units, "kW*h" ) # convert the input charge rate to units of kW diff --git a/h2integrate/storage/battery/battery_baseclass.py b/h2integrate/storage/battery/battery_baseclass.py deleted file mode 100644 index 29ad18e6e..000000000 --- a/h2integrate/storage/battery/battery_baseclass.py +++ /dev/null @@ -1,45 +0,0 @@ -from h2integrate.core.model_baseclasses import PerformanceModelBaseClass - - -class BatteryPerformanceBaseClass(PerformanceModelBaseClass): - def initialize(self): - super().initialize() - self.commodity = "electricity" - self.commodity_rate_units = "kW" - self.commodity_amount_units = "kW*h" - - def setup(self): - super().setup() - - self.add_input( - "electricity_in", - val=0.0, - shape=self.n_timesteps, - units="kW", - desc="Power input to Battery", - ) - - self.add_output( - "SOC", - val=0.0, - shape=self.n_timesteps, - units="percent", - desc="State of charge of Battery", - ) - - self.add_output( - "battery_electricity", - val=0.0, - shape=self.n_timesteps, - units="kW", - desc="Electricity output from Battery only", - ) - - def compute(self, inputs, outputs): - """ - Computation for the OM component. - - For a template class this is not implemented and raises an error. - """ - - raise NotImplementedError("This method should be implemented in a subclass.") diff --git a/h2integrate/storage/battery/pysam_battery.py b/h2integrate/storage/battery/pysam_battery.py index 310d1df89..8e64b4b2d 100644 --- a/h2integrate/storage/battery/pysam_battery.py +++ b/h2integrate/storage/battery/pysam_battery.py @@ -3,13 +3,16 @@ import PySAM.BatteryStateful as BatteryStateful from attrs import field, define -from h2integrate.core.utilities import BaseConfig, merge_shared_inputs +from h2integrate.core.utilities import merge_shared_inputs from h2integrate.core.validators import gt_zero, contains, range_val -from h2integrate.storage.battery.battery_baseclass import BatteryPerformanceBaseClass +from h2integrate.storage.storage_baseclass import ( + StoragePerformanceBase, + StoragePerformanceBaseConfig, +) @define(kw_only=True) -class PySAMBatteryPerformanceModelConfig(BaseConfig): +class PySAMBatteryPerformanceModelConfig(StoragePerformanceBaseConfig): """Configuration class for battery performance models. This class defines configuration parameters for simulating battery @@ -18,35 +21,27 @@ class PySAMBatteryPerformanceModelConfig(BaseConfig): and reference module characteristics. Attributes: - max_capacity (float): - Maximum battery energy capacity in kilowatt-hours (kWh). + max_capacity (float): Maximum battery energy capacity in kilowatt-hours (kWh). Must be greater than zero. - max_charge_rate (float): - Rated power capacity of the battery in kilowatts (kW). + max_charge_rate (float): Rated power capacity of the battery in kilowatts (kW). Must be greater than zero. - + demand_profile (int | float | list): Demand values for each timestep, in + the same units as `commodity_rate_units`. May be a scalar for constant + demand or a list/array for time-varying demand. chemistry (str): Battery chemistry option. "LDES" has not been brought over from HOPP yet. Supported values include: - PySAM: ``"LFPGraphite"``, ``"LMOLTO"``, ``"LeadAcid"``, ``"NMCGraphite"`` - min_charge_fraction (float): - Minimum allowable state of charge as a fraction (0 to 1). - max_charge_fraction (float): - Maximum allowable state of charge as a fraction (0 to 1). - init_charge_fraction (float): - Initial state of charge as a fraction (0 to 1). - n_control_window (int, optional): - Number of timesteps in the control window. Defaults to 24. - control_variable (str): - Control mode for the PySAM battery, either ``"input_power"`` + min_soc_fraction (float): Minimum allowable state of charge as a fraction (0 to 1). + max_soc_fraction (float): Maximum allowable state of charge as a fraction (0 to 1). + init_soc_fraction (float): Initial state of charge as a fraction (0 to 1). + control_variable (str): Control mode for the PySAM battery, either ``"input_power"`` or ``"input_current"``. - ref_module_capacity (int | float, optional): - Reference module capacity in kilowatt-hours (kWh). + ref_module_capacity (int | float, optional): Reference module capacity in kWh. Defaults to 400. - ref_module_surface_area (int | float, optional): - Reference module surface area in square meters (m²). + ref_module_surface_area (int | float, optional): Reference module surface area in m². Defaults to 30. Cp (int | float, optional): Battery specific heat capacity [J/kg*K]. Defaults to 900. @@ -62,10 +57,8 @@ class PySAMBatteryPerformanceModelConfig(BaseConfig): chemistry: str = field( validator=contains(["LFPGraphite", "LMOLTO", "LeadAcid", "NMCGraphite"]), ) - min_charge_fraction: float = field(validator=range_val(0, 1)) - max_charge_fraction: float = field(validator=range_val(0, 1)) - init_charge_fraction: float = field(validator=range_val(0, 1)) - n_control_window: int = field(validator=gt_zero, default=24) + + init_soc_fraction: float = field(validator=range_val(0, 1)) control_variable: str = field( default="input_power", validator=contains(["input_power", "input_current"]) ) @@ -76,10 +69,10 @@ class PySAMBatteryPerformanceModelConfig(BaseConfig): resistance: int | float = field(default=0.001) -class PySAMBatteryPerformanceModel(BatteryPerformanceBaseClass): +class PySAMBatteryPerformanceModel(StoragePerformanceBase): """OpenMDAO component wrapping the PySAM Battery Performance model. - This class integrates the NREL PySAM ``BatteryStateful`` model into + This class integrates the NLR PySAM ``BatteryStateful`` model into an OpenMDAO component. It provides inputs and outputs for battery capacity, charge/discharge power, state of charge, and unmet or unused demand. @@ -88,70 +81,31 @@ class PySAMBatteryPerformanceModel(BatteryPerformanceBaseClass): bounds set by the user. It may exceed the bounds by up to 5% SOC. Attributes: - config (PySAMBatteryPerformanceModelConfig): - Configuration parameters for the battery performance model. - system_model (``BatteryStateful``): - Instance of the PySAM BatteryStateful model, initialized with - the selected chemistry and configuration parameters. - outputs (BatteryOutputs): - Container for simulation outputs such as SOC, chargeable/dischargeable - power, unmet demand, and unused commodities. - unmet_demand (float): - Tracks unmet demand during simulation (kW). - unused_commodity (float): - Tracks unused commodity during simulation (kW). - - Inputs: - max_charge_rate (float): - Battery charge rate in kilowatts per hour (kW). - storage_capacity (float): - Total energy storage capacity in kilowatt-hours (kWh). - electricity_demand (ndarray): - Power demand time series (kW). - electricity_in (ndarray): - Commanded input electricity (kW), typically from dispatch. - - Outputs: - unmet_demand_out (ndarray): - Remaining unmet demand after discharge (kW). - unused_commodity_out (ndarray): - Unused energy not absorbed by the battery (kW). - electricity_out (ndarray): - Dispatched electricity to meet demand (kW), including electricity from - electricity_in that was never used to charge the battery and - battery_electricity. - SOC (ndarray): - Battery state of charge (%). - battery_electricity (ndarray): - Electricity output from the battery model (kW). + system_model (``BatteryStateful``): Instance of the PySAM BatteryStateful model, initialized + with the selected chemistry and configuration parameters. + Methods: - setup(): - Defines model inputs, outputs, configuration, and connections - to plant-level dispatch (if applicable). compute(inputs, outputs, discrete_inputs, discrete_outputs): Runs the PySAM BatteryStateful model for a simulation timestep, updating outputs such as SOC, charge/discharge limits, unmet demand, and unused commodities. - simulate(electricity_in, electricity_demand, time_step_duration, control_variable, - sim_start_index=0): - Simulates the battery behavior across timesteps using either - input power or input current as control. This method is similar to what is - provided in typical compute methods in H2Integrate for running models, but - needs to be a separate method here to allow the dispatch function to call - and manage the performance model. _set_control_mode(control_mode=1.0, input_power=0.0, input_current=0.0, control_variable="input_power"): Sets the battery control mode (power or current). - - Notes: - - Default timestep is 1 hour (``dt=1.0``). - - State of charge (SOC) bounds are set using the configuration's - ``min_charge_fraction`` and ``max_charge_fraction``. - - If a Pyomo dispatch solver is provided, the battery will simulate - dispatch decisions using solver inputs. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + + def initialize(self): + super().initialize() + self.commodity = "electricity" + self.commodity_rate_units = "kW" + self.commodity_amount_units = "kW*h" + def setup(self): """Set up the PySAM Battery Performance model in OpenMDAO. @@ -166,99 +120,17 @@ def setup(self): additional_cls_name=self.__class__.__name__, ) - self.add_input( - "max_charge_rate", - val=self.config.max_charge_rate, - units="kW", - desc="Battery charge rate", - ) - - self.add_input( - "storage_capacity", - val=self.config.max_capacity, - units="kW*h", - desc="Battery storage capacity", - ) - super().setup() - self.add_input( - "electricity_demand", - val=0.0, - shape=self.n_timesteps, - units="kW", - desc="Power demand", - ) - - self.add_output( - "unmet_electricity_demand_out", - val=0.0, - shape=self.n_timesteps, - units="kW", - desc="Unmet power demand", - ) - - self.add_output( - "unused_electricity_out", - val=0.0, - shape=self.n_timesteps, - units="kW", - desc="Unused generated commodity", - ) - - self.add_output( - "battery_electricity_discharge", - val=0.0, - shape=self.n_timesteps, - units="kW", - desc="Electricity discharged from battery", - ) - - self.add_output( - "battery_electricity_charge", - val=0.0, - shape=self.n_timesteps, - units="kW", - desc="Electricity to charge battery", - ) - # Initialize the PySAM BatteryStateful model with defaults self.system_model = BatteryStateful.default(self.config.chemistry) - self.dt_hr = int(self.options["plant_config"]["plant"]["simulation"]["dt"]) / ( - 60**2 - ) # convert from seconds to hours - - # create inputs for pyomo control model - if "tech_to_dispatch_connections" in self.options["plant_config"]: - # get technology group name - # TODO: The split below seems brittle - self.tech_group_name = self.pathname.split(".") - for _source_tech, intended_dispatch_tech in self.options["plant_config"][ - "tech_to_dispatch_connections" - ]: - if any(intended_dispatch_tech in name for name in self.tech_group_name): - self.add_discrete_input("pyomo_dispatch_solver", val=dummy_function) - break - def compute(self, inputs, outputs, discrete_inputs=[], discrete_outputs=[]): """Run the PySAM Battery model for one simulation step. Configures the battery stateful model parameters (SOC limits, timestep, thermal properties, etc.), executes the simulation, and stores the results in OpenMDAO outputs. - - Args: - inputs (dict): - Continuous input values (e.g., electricity_in, electricity_demand) or - battery design parameters. - outputs (dict): - Dictionary where model outputs (SOC, battery_discharge, unmet demand, etc.) - are written. - discrete_inputs (dict): - Discrete inputs such as control mode or Pyomo solver. - discrete_outputs (dict): - Discrete outputs (unused in this component). """ # Size the battery based on inputs -> method brought from HOPP @@ -286,9 +158,9 @@ def compute(self, inputs, outputs, discrete_inputs=[], discrete_outputs=[]): self.system_model.value("input_current", 0.0) self.system_model.value("dt_hr", self.dt_hr) - self.system_model.value("minimum_SOC", self.config.min_charge_fraction * 100) - self.system_model.value("maximum_SOC", self.config.max_charge_fraction * 100) - self.system_model.value("initial_SOC", self.config.init_charge_fraction * 100) + self.system_model.value("minimum_SOC", self.config.min_soc_fraction * 100) + self.system_model.value("maximum_SOC", self.config.max_soc_fraction * 100) + self.system_model.value("initial_SOC", self.config.init_soc_fraction * 100) # Setup PySAM battery model using PySAM method self.system_model.setup() @@ -298,81 +170,13 @@ def compute(self, inputs, outputs, discrete_inputs=[], discrete_outputs=[]): self.system_model.value("input_power", 0.0) self.system_model.execute(0) - if "pyomo_dispatch_solver" in discrete_inputs: - # Simulate the battery with provided dispatch inputs - dispatch = discrete_inputs["pyomo_dispatch_solver"] - kwargs = { - "charge_rate": inputs["max_charge_rate"][0], - "discharge_rate": inputs["max_charge_rate"][0], - "storage_capacity": inputs["storage_capacity"][0], - "control_variable": self.config.control_variable, - } - ( - total_power_out, - battery_power, - unmet_demand, - unused_commodity, - soc, - ) = dispatch(self.simulate, kwargs, inputs) - - battery_power = np.array(battery_power) - - else: - # Simulate the battery with provided inputs and no controller. - # This essentially asks for discharge when demand exceeds input - # and requests charge when input exceeds demand - - # update the control window to be the number of timesteps in the simulation - self.config.n_control_window = self.n_timesteps - - # estimate required dispatch commands - pseudo_commands = inputs["electricity_demand"] - inputs["electricity_in"] - - battery_power, soc = self.simulate( - storage_dispatch_commands=pseudo_commands, - charge_rate=inputs["max_charge_rate"][0], - discharge_rate=inputs["max_charge_rate"][0], - storage_capacity=inputs["storage_capacity"][0], - control_variable=self.config.control_variable, - ) - - # battery_power is positive when the battery is discharged - # and negative when the battery is charged - battery_power = np.array(battery_power) - - # calculate combined power out from inflow source and battery (note: battery_power - # is negative when charging) - combined_power_out = inputs["electricity_in"] + np.array(battery_power) - - # find the total power out to meet demand - total_power_out = np.minimum(inputs["electricity_demand"], combined_power_out) - - # determine how much of the inflow electricity was unused - unused_commodity = np.maximum(0, combined_power_out - inputs["electricity_demand"]) - - # determine how much demand was not met - unmet_demand = np.maximum(0, inputs["electricity_demand"] - combined_power_out) - - outputs["unmet_electricity_demand_out"] = unmet_demand - outputs["unused_electricity_out"] = unused_commodity - outputs["battery_electricity"] = battery_power - - # separate out the charge and discharge profiles from battery_power - # battery_electricity_charge is always <= zero, battery_electricity_discharge is always >=0 - outputs["battery_electricity_charge"] = np.where(battery_power < 0, battery_power, 0) - outputs["battery_electricity_discharge"] = np.where(battery_power > 0, battery_power, 0) - - outputs["electricity_out"] = total_power_out - outputs["SOC"] = soc - - outputs["rated_electricity_production"] = inputs["max_charge_rate"] - - outputs["total_electricity_produced"] = np.sum(total_power_out) - outputs["annual_electricity_produced"] = outputs["total_electricity_produced"] * ( - 1 / self.fraction_of_year_simulated - ) - outputs["capacity_factor"] = outputs["total_electricity_produced"] / ( - outputs["rated_electricity_production"] * self.n_timesteps + outputs = self.run_storage( + inputs["max_charge_rate"][0], + inputs["max_charge_rate"][0], + inputs["storage_capacity"][0], + inputs, + outputs, + discrete_inputs, ) def simulate( @@ -381,7 +185,6 @@ def simulate( charge_rate: float, discharge_rate: float, storage_capacity: float, - control_variable: str, sim_start_index: int = 0, ): """Run the PySAM BatteryStateful model over a control window. @@ -418,8 +221,9 @@ def simulate( self.system_model.value("dt_hr", self.dt_hr) # initialize outputs - storage_power_out_timesteps = np.zeros(self.config.n_control_window) - soc_timesteps = np.zeros(self.config.n_control_window) + n = len(storage_dispatch_commands) + storage_power_out_timesteps = np.zeros(n) + soc_timesteps = np.zeros(n) # get constant battery parameters needed during all time steps soc_max = self.system_model.value("maximum_SOC") / 100.0 @@ -466,7 +270,7 @@ def simulate( cmd = actual_discharge # Set the input variable to the desired value - self.system_model.value(control_variable, cmd) + self.system_model.value(self.config.control_variable, cmd) # Simulate the PySAM BatteryStateful model self.system_model.execute(0) @@ -509,8 +313,3 @@ def _set_control_mode( self.system_model.value("input_current", input_current) # Either `input_power` or `input_current`; need to adjust `control_mode` above self.control_variable = control_variable - - -def dummy_function(): - # this function is required for initializing the pyomo control input and nothing else - pass diff --git a/h2integrate/storage/battery/test/conftest.py b/h2integrate/storage/battery/test/conftest.py index 1003a1f0a..349aa222a 100644 --- a/h2integrate/storage/battery/test/conftest.py +++ b/h2integrate/storage/battery/test/conftest.py @@ -1,3 +1,7 @@ from h2integrate.storage.test.conftest import plant_config # noqa: F401 -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) diff --git a/h2integrate/storage/battery/test/inputs/tech_config.yaml b/h2integrate/storage/battery/test/inputs/tech_config.yaml index 8d27fa7b2..a80912b29 100644 --- a/h2integrate/storage/battery/test/inputs/tech_config.yaml +++ b/h2integrate/storage/battery/test/inputs/tech_config.yaml @@ -12,14 +12,16 @@ technologies: shared_parameters: max_charge_rate: 50000 max_capacity: 200000 - n_control_window: 24 dt: 1.0 n_timesteps: 24 - init_charge_fraction: 0.5 - max_charge_fraction: 0.9 - min_charge_fraction: 0.1 + init_soc_fraction: 0.5 + max_soc_fraction: 0.9 + min_soc_fraction: 0.1 performance_parameters: chemistry: LFPGraphite + demand_profile: 0.0 dispatch_rule_parameters: commodity_name: electricity commodity_storage_units: kW + control_parameters: + n_control_window: 24 diff --git a/h2integrate/storage/battery/test/test_battery_cost.py b/h2integrate/storage/battery/test/test_battery_cost.py index 9e7520b82..0ffa3c0a2 100644 --- a/h2integrate/storage/battery/test/test_battery_cost.py +++ b/h2integrate/storage/battery/test/test_battery_cost.py @@ -4,7 +4,7 @@ from pytest import fixture from h2integrate.storage.battery.atb_battery_cost import ATBBatteryCostModel -from h2integrate.control.control_strategies.storage.demand_openloop_controller import ( +from h2integrate.control.control_strategies.storage.demand_openloop_storage_controller import ( DemandOpenLoopStorageController, ) @@ -17,7 +17,7 @@ def electricity_profile_kW(): @fixture def battery_tech_config_kW(): battery_inputs = { - "performance_model": {"model": "SimpleGenericStorage"}, + "performance_model": {"model": "StoragePerformanceModel"}, "cost_model": {"model": "ATBBatteryCostModel"}, "control_strategy": {"model": "DemandOpenLoopStorageController"}, "model_inputs": { @@ -28,9 +28,9 @@ def battery_tech_config_kW(): "max_capacity": 30000.0, }, "control_parameters": { - "max_charge_fraction": 1.0, - "min_charge_fraction": 0.1, - "init_charge_fraction": 0.25, + "max_soc_fraction": 1.0, + "min_soc_fraction": 0.1, + "init_soc_fraction": 0.25, "max_discharge_rate": 5000.0, "charge_efficiency": 1.0, "discharge_efficiency": 1.0, @@ -50,7 +50,7 @@ def battery_tech_config_kW(): @fixture def battery_tech_config_MW(): battery_inputs = { - "performance_model": {"model": "SimpleGenericStorage"}, + "performance_model": {"model": "StoragePerformanceModel"}, "cost_model": {"model": "ATBBatteryCostModel"}, "control_strategy": {"model": "DemandOpenLoopStorageController"}, "model_inputs": { @@ -61,9 +61,9 @@ def battery_tech_config_MW(): "max_capacity": 30.0, }, "control_parameters": { - "max_charge_fraction": 1.0, - "min_charge_fraction": 0.1, - "init_charge_fraction": 0.25, + "max_soc_fraction": 1.0, + "min_soc_fraction": 0.1, + "init_soc_fraction": 0.25, "max_discharge_rate": 5.0, "charge_efficiency": 1.0, "discharge_efficiency": 1.0, diff --git a/h2integrate/storage/battery/test/test_pysam_battery.py b/h2integrate/storage/battery/test/test_pysam_battery.py index 96f4b6369..815fcd260 100644 --- a/h2integrate/storage/battery/test/test_pysam_battery.py +++ b/h2integrate/storage/battery/test/test_pysam_battery.py @@ -28,7 +28,7 @@ def test_pysam_battery_performance_model_without_controller(plant_config, subtes # Set up the OpenMDAO problem prob = om.Problem() - n_control_window = tech_config["technologies"]["battery"]["model_inputs"]["shared_parameters"][ + n_control_window = tech_config["technologies"]["battery"]["model_inputs"]["control_parameters"][ "n_control_window" ] @@ -56,6 +56,14 @@ def test_pysam_battery_performance_model_without_controller(plant_config, subtes promotes=["*"], ) + prob.model.add_subsystem( + name="IVC4", + subsys=om.IndepVarComp( + name="electricity_set_point", val=electricity_demand - electricity_in, units="kW" + ), + promotes=["*"], + ) + prob.model.add_subsystem( "pysam_battery", PySAMBatteryPerformanceModel( @@ -159,7 +167,7 @@ def test_pysam_battery_performance_model_without_controller(plant_config, subtes with subtests.test("expected_battery_power"): np.testing.assert_allclose( - prob.get_val("battery_electricity", units="kW"), + prob.get_val("electricity_out", units="kW"), expected_battery_power, rtol=1e-2, ) @@ -170,15 +178,21 @@ def test_pysam_battery_performance_model_without_controller(plant_config, subtes ) with subtests.test("expected_battery_unmet_demand"): + combined_out = electricity_in + prob.get_val("electricity_out", units="kW") + combined_commodity_to_demand = np.clip(combined_out, a_min=0, a_max=electricity_demand) + unmet_demand = electricity_demand - combined_commodity_to_demand np.testing.assert_allclose( - prob.get_val("unmet_electricity_demand_out", units="kW"), + unmet_demand, expected_unment_demand, rtol=1e-2, ) with subtests.test("expected_battery_unused_commodity"): + unused_electricity = np.clip( + electricity_in - combined_commodity_to_demand, a_min=0, a_max=None + ) np.testing.assert_allclose( - prob.get_val("unused_electricity_out", units="kW"), + unused_electricity, expected_unused_electricity, rtol=1e-2, ) @@ -191,9 +205,10 @@ def test_battery_config(subtests): "max_capacity": batt_kw * 4, "max_charge_rate": batt_kw, "chemistry": "LFPGraphite", - "init_charge_fraction": 0.1, - "max_charge_fraction": 0.9, - "min_charge_fraction": 0.1, + "init_soc_fraction": 0.1, + "max_soc_fraction": 0.9, + "min_soc_fraction": 0.1, + "demand_profile": 0.0, } config = PySAMBatteryPerformanceModelConfig.from_dict(config_data) @@ -204,20 +219,17 @@ def test_battery_config(subtests): assert config.max_capacity == batt_kw * 4 with subtests.test("with minimal params minimum_SOC"): assert ( - config.min_charge_fraction == 0.1 + config.min_soc_fraction == 0.1 ) # Decimal percent as compared to test_battery.py in HOPP 10% with subtests.test("with minimal params maximum_SOC"): assert ( - config.max_charge_fraction == 0.9 + config.max_soc_fraction == 0.9 ) # Decimal percent as compared to test_battery.py in HOPP 90% with subtests.test("with minimal params initial_SOC"): assert ( - config.init_charge_fraction == 0.1 + config.init_soc_fraction == 0.1 ) # Decimal percent as compared to test_battery.py in HOPP 10% - with subtests.test("with minimal params n_control_window"): - assert config.n_control_window == 24 - with subtests.test("with invalid capacity"): with pytest.raises(ValueError): data = deepcopy(config_data) @@ -233,17 +245,17 @@ def test_battery_config(subtests): # SOC values must be between 0-100 with pytest.raises(ValueError): data = deepcopy(config_data) - data["min_charge_fraction"] = -1.0 + data["min_soc_fraction"] = -1.0 PySAMBatteryPerformanceModelConfig.from_dict(data) with pytest.raises(ValueError): data = deepcopy(config_data) - data["max_charge_fraction"] = 120.0 + data["max_soc_fraction"] = 120.0 PySAMBatteryPerformanceModelConfig.from_dict(data) with pytest.raises(ValueError): data = deepcopy(config_data) - data["init_charge_fraction"] = 120.0 + data["init_soc_fraction"] = 120.0 PySAMBatteryPerformanceModelConfig.from_dict(data) @@ -306,11 +318,11 @@ def test_pysam_battery_no_controller_change_capacity(plant_config, subtests): "max_charge_rate": init_charge_rate, "max_capacity": init_capacity, "n_control_window": 48, - "init_charge_fraction": 0.1, - "max_charge_fraction": 1.0, - "min_charge_fraction": 0.1, + "init_soc_fraction": 0.1, + "max_soc_fraction": 1.0, + "min_soc_fraction": 0.1, }, - "performance_parameters": {"chemistry": "LFPGraphite"}, + "performance_parameters": {"chemistry": "LFPGraphite", "demand_profile": 0.0}, } } # Set up the OpenMDAO problem @@ -327,6 +339,14 @@ def test_pysam_battery_no_controller_change_capacity(plant_config, subtests): promotes=["*"], ) + prob_init.model.add_subsystem( + name="IVC3", + subsys=om.IndepVarComp( + name="electricity_set_point", val=electricity_demand - electricity_in, units="kW" + ), + promotes=["*"], + ) + prob_init.model.add_subsystem( "pysam_battery", PySAMBatteryPerformanceModel( @@ -342,21 +362,21 @@ def test_pysam_battery_no_controller_change_capacity(plant_config, subtests): with subtests.test("5 MW battery discharge profile within charge rate bounds"): assert ( - prob_init.get_val("pysam_battery.battery_electricity_discharge", units="kW").max() + prob_init.get_val("pysam_battery.storage_electricity_discharge", units="kW").max() < init_charge_rate ) assert ( - prob_init.get_val("pysam_battery.battery_electricity_discharge", units="kW").min() + prob_init.get_val("pysam_battery.storage_electricity_discharge", units="kW").min() >= 0.0 ) with subtests.test("5 MW battery charge profile within charge rate bounds"): assert ( - prob_init.get_val("pysam_battery.battery_electricity_charge", units="kW").min() + prob_init.get_val("pysam_battery.storage_electricity_charge", units="kW").min() > -1 * init_charge_rate ) assert ( - prob_init.get_val("pysam_battery.battery_electricity_charge", units="kW").max() <= 0.0 + prob_init.get_val("pysam_battery.storage_electricity_charge", units="kW").max() <= 0.0 ) with subtests.test("5 MW battery rated production == charge rate"): @@ -382,6 +402,14 @@ def test_pysam_battery_no_controller_change_capacity(plant_config, subtests): promotes=["*"], ) + prob.model.add_subsystem( + name="IVC3", + subsys=om.IndepVarComp( + name="electricity_set_point", val=electricity_demand - electricity_in, units="kW" + ), + promotes=["*"], + ) + prob.model.add_subsystem( "pysam_battery", PySAMBatteryPerformanceModel( @@ -399,33 +427,33 @@ def test_pysam_battery_no_controller_change_capacity(plant_config, subtests): with subtests.test("2.5 MW battery discharge profile within charge rate bounds"): assert ( - prob.get_val("pysam_battery.battery_electricity_discharge", units="kW").max() + prob.get_val("pysam_battery.storage_electricity_discharge", units="kW").max() < init_charge_rate / 2 ) - assert prob.get_val("pysam_battery.battery_electricity_discharge", units="kW").min() >= 0.0 + assert prob.get_val("pysam_battery.storage_electricity_discharge", units="kW").min() >= 0.0 with subtests.test("2.5 MW battery charge profile within charge rate bounds"): assert ( - prob.get_val("pysam_battery.battery_electricity_charge", units="kW").min() + prob.get_val("pysam_battery.storage_electricity_charge", units="kW").min() > -1 * init_charge_rate / 2 ) - assert prob.get_val("pysam_battery.battery_electricity_charge", units="kW").max() <= 0.0 + assert prob.get_val("pysam_battery.storage_electricity_charge", units="kW").max() <= 0.0 with subtests.test("2.5 MW battery discharge < charge rate"): assert prob.get_val( - "pysam_battery.battery_electricity_discharge", units="MW" + "pysam_battery.storage_electricity_discharge", units="MW" ).max() < init_charge_rate / (2 * 1e3) with subtests.test("2.5 MW battery discharge <= 5 MW battery discharge"): assert ( - prob.get_val("pysam_battery.battery_electricity_discharge", units="MW").max() - < prob_init.get_val("pysam_battery.battery_electricity_discharge", units="MW").max() + prob.get_val("pysam_battery.storage_electricity_discharge", units="MW").max() + < prob_init.get_val("pysam_battery.storage_electricity_discharge", units="MW").max() ) with subtests.test("5 MW battery charge <= 2.5 MW battery charge"): assert ( - prob.get_val("pysam_battery.battery_electricity_discharge", units="MW").min() - <= prob_init.get_val("pysam_battery.battery_electricity_discharge", units="MW").min() + prob.get_val("pysam_battery.storage_electricity_discharge", units="MW").min() + <= prob_init.get_val("pysam_battery.storage_electricity_discharge", units="MW").min() ) with subtests.test("2.5 MW battery rated production == charge rate"): diff --git a/h2integrate/storage/generic_storage_cost.py b/h2integrate/storage/generic_storage_cost.py index 8138ddf2e..4e4cd7bbe 100644 --- a/h2integrate/storage/generic_storage_cost.py +++ b/h2integrate/storage/generic_storage_cost.py @@ -1,4 +1,5 @@ from attrs import field, define +from openmdao.utils import units from h2integrate.core.utilities import merge_shared_inputs from h2integrate.core.validators import contains, gte_zero, range_val @@ -26,7 +27,13 @@ class GenericStorageCostConfig(CostModelBaseConfig): max_charge_rate: float = field() commodity_rate_units: str = field( validator=contains(["W", "kW", "MW", "GW", "TW", "g/h", "kg/h", "t/h", "MMBtu/h"]) - ) # TODO: udpate to commodity_rate_units + ) + + commodity_amount_units: str = field(default=None) + + def __attrs_post_init__(self): + if self.commodity_amount_units is None: + self.commodity_amount_units = f"({self.commodity_rate_units})*h" class GenericStorageCostModel(CostModelBaseClass): @@ -43,6 +50,11 @@ class GenericStorageCostModel(CostModelBaseClass): """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = GenericStorageCostConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "cost"), @@ -54,7 +66,7 @@ def setup(self): charge_units = self.config.commodity_rate_units - capacity_units = f"({self.config.commodity_rate_units})*h" + capacity_units = self.config.commodity_amount_units self.add_input( "max_charge_rate", @@ -63,7 +75,7 @@ def setup(self): desc="Storage charge/discharge rate", ) self.add_input( - "max_capacity", + "storage_capacity", val=self.config.max_capacity, units=capacity_units, desc="Storage storage capacity", @@ -91,7 +103,11 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): storage_duration_hrs = 0.0 if inputs["max_charge_rate"] > 0: - storage_duration_hrs = inputs["max_capacity"] / inputs["max_charge_rate"] + storage_duration_hrs = units.convert_units( + inputs["storage_capacity"] / inputs["max_charge_rate"], + f"({self.config.commodity_amount_units})/({self.config.commodity_rate_units})", + "h", + ) if inputs["max_charge_rate"] < 0: msg = ( f"max_charge_rate cannot be less than zero and has value of " diff --git a/h2integrate/storage/generic_storage_pyo.py b/h2integrate/storage/generic_storage_pyo.py deleted file mode 100644 index da922d8d7..000000000 --- a/h2integrate/storage/generic_storage_pyo.py +++ /dev/null @@ -1,545 +0,0 @@ -import numpy as np -from attrs import field, define - -from h2integrate.core.utilities import BaseConfig, merge_shared_inputs -from h2integrate.core.validators import gt_zero, range_val, range_val_or_none -from h2integrate.core.model_baseclasses import PerformanceModelBaseClass - - -@define(kw_only=True) -class StoragePerformanceModelConfig(BaseConfig): - """Configuration class for storage performance models. - - This class defines configuration parameters for simulating storage - performance with the Pyomo controllers. It includes - specifications such as capacity, charge rate, state-of-charge limits, - and charge/discharge efficiencies. - - Attributes: - commodity (str): name of commodity - commodity_rate_units (str): Units of the commodity (e.g., "kg/h"). - max_capacity (float): - Maximum storage energy capacity in commodity_amount_units. - Must be greater than zero. - max_charge_rate (float): - Rated commodity capacity of the storage in commodity_rate_units. - Must be greater than zero. - min_charge_fraction (float): - Minimum allowable state of charge as a fraction (0 to 1). - max_charge_fraction (float): - Maximum allowable state of charge as a fraction (0 to 1). - init_charge_fraction (float): - Initial state of charge as a fraction (0 to 1). - n_control_window (int, optional): - Number of timesteps in the control window. Defaults to 24. - commodity_amount_units (str | None, optional): Units of the commodity as an amount - (i.e., kW*h or kg). If not provided, defaults to commodity_rate_units*h. - max_discharge_rate (float | None, optional): Maximum rate at which the commodity can be - discharged (in units per time step, e.g., "kg/time step"). This rate does not include - the discharge_efficiency. Only required if `charge_equals_discharge` is False. - charge_equals_discharge (bool, optional): If True, set the max_discharge_rate equal to the - max_charge_rate. If False, specify the max_discharge_rate as a value different than - the max_charge_rate. Defaults to True. - charge_efficiency (float | None, optional): Efficiency of charging the storage, represented - as a decimal between 0 and 1 (e.g., 0.9 for 90% efficiency). Optional if - `round_trip_efficiency` is provided. - discharge_efficiency (float | None, optional): Efficiency of discharging the storage, - represented as a decimal between 0 and 1 (e.g., 0.9 for 90% efficiency). Optional if - `round_trip_efficiency` is provided. - round_trip_efficiency (float | None, optional): Combined efficiency of charging and - discharging the storage, represented as a decimal between 0 and 1 (e.g., 0.81 for - 81% efficiency). Optional if `charge_efficiency` and `discharge_efficiency` are - provided. - - """ - - commodity: str = field() - commodity_rate_units: str = field() - - max_capacity: float = field(validator=gt_zero) - max_charge_rate: float = field(validator=gt_zero) - - min_charge_fraction: float = field(validator=range_val(0, 1)) - max_charge_fraction: float = field(validator=range_val(0, 1)) - init_charge_fraction: float = field(validator=range_val(0, 1)) - n_control_window: int = field(validator=gt_zero, default=24) - - commodity_amount_units: str = field(default=None) - max_discharge_rate: float | None = field(default=None) - charge_equals_discharge: bool = field(default=True) - - charge_efficiency: float | None = field(default=None, validator=range_val_or_none(0, 1)) - discharge_efficiency: float | None = field(default=None, validator=range_val_or_none(0, 1)) - round_trip_efficiency: float | None = field(default=None, validator=range_val_or_none(0, 1)) - - def __attrs_post_init__(self): - """ - Post-initialization logic to validate and calculate efficiencies. - - Ensures that either `charge_efficiency` and `discharge_efficiency` are provided, - or `round_trip_efficiency` is provided. If `round_trip_efficiency` is provided, - it calculates `charge_efficiency` and `discharge_efficiency` as the square root - of `round_trip_efficiency`. - """ - if self.round_trip_efficiency is not None: - if self.charge_efficiency is not None or self.discharge_efficiency is not None: - raise ValueError( - "Exactly one of the following sets of parameters must be set: (a) " - "`round_trip_efficiency`, or (b) both `charge_efficiency` " - "and `discharge_efficiency`." - ) - - # Calculate charge and discharge efficiencies from round-trip efficiency - self.charge_efficiency = np.sqrt(self.round_trip_efficiency) - self.discharge_efficiency = np.sqrt(self.round_trip_efficiency) - elif self.charge_efficiency is not None and self.discharge_efficiency is not None: - # Ensure both charge and discharge efficiencies are provided - pass - else: - raise ValueError( - "Exactly one of the following sets of parameters must be set: (a) " - "`round_trip_efficiency`, or (b) both `charge_efficiency` " - "and `discharge_efficiency`." - ) - - if self.charge_equals_discharge: - if ( - self.max_discharge_rate is not None - and self.max_discharge_rate != self.max_charge_rate - ): - msg = ( - "Max discharge rate does not equal max charge rate but charge_equals_discharge " - f"is True. Discharge rate is {self.max_discharge_rate} and charge rate " - f"is {self.max_charge_rate}." - ) - raise ValueError(msg) - - self.max_discharge_rate = self.max_charge_rate - - if not self.charge_equals_discharge and self.max_discharge_rate is None: - msg = ( - "max_discharge_rate is required when charge_equals_discharge is False. " - "Please input the discharge rate using the key `max_discharge_rate`." - ) - raise ValueError(msg) - - if self.commodity_amount_units is None: - self.commodity_amount_units = f"({self.commodity_rate_units})*h" - - -class StoragePerformanceModel(PerformanceModelBaseClass): - """OpenMDAO component for a storage component. - - Attributes: - config (StoragePerformanceModelConfig): - Configuration parameters for the storage performance model. - current_soc (float): soc at the start of each interval that the simulate() - method is called - dt_hr (float): timestep in hours. - - Inputs: - max_charge_rate (float): - storage charge rate in commodity_rate_units - storage_capacity (float): - Total energy storage capacity in commodity_amount_units - commodity_demand (ndarray): - Commodity demand time series (commodity_rate_units). - commodity_in (ndarray): - Commanded input commodity (commodity_rate_units), typically from dispatch. - - Outputs: - unmet_demand_out (ndarray): - Remaining unmet demand after discharge in commodity_rate_units. - unused_commodity_out (ndarray): - Unused energy not absorbed by the storage in commodity_rate_units. - commodity_out (ndarray): - Dispatched commodity to meet demand in commodity_rate_units, including commodity from - commodity_in that was never used to charge the storage and - storage_commodity_discharge. - SOC (ndarray): - storage state of charge (%). - storage_commodity_discharge (ndarray): - commodity output from the storage model in commodity_rate_units. - - Methods: - setup(): - Defines model inputs, outputs, configuration, and connections - to plant-level dispatch (if applicable). - compute(inputs, outputs, discrete_inputs, discrete_outputs): - Runs the storage model for a simulation timestep, - updating outputs such as SOC, charge/discharge limits, unmet - demand, and unused commodities. - simulate(commodity_in, commodity_demand, time_step_duration, control_variable, - sim_start_index=0): - Simulates the storage behavior across timesteps using input commodity as control. - This method is similar to what is - provided in typical compute methods in H2Integrate for running models, but - needs to be a separate method here to allow the dispatch function to call - and manage the performance model. - - - Notes: - - Default timestep is 1 hour (``dt=1.0``). - - State of charge (SOC) bounds are set using the configuration's - ``min_charge_fraction`` and ``max_charge_fraction``. - - If a Pyomo dispatch solver is provided, the storage will simulate - dispatch decisions using solver inputs. - """ - - def setup(self): - """Set up the storage performance model in OpenMDAO. - - Initializes the configuration and defines inputs/outputs for OpenMDAO. - If dispatch connections are specified, it also sets up a discrete - input for Pyomo solver integration. - """ - self.config = StoragePerformanceModelConfig.from_dict( - merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance"), - strict=False, - additional_cls_name=self.__class__.__name__, - ) - - self.commodity = self.config.commodity - self.commodity_rate_units = self.config.commodity_rate_units - self.commodity_amount_units = self.config.commodity_amount_units - super().setup() - - self.add_input( - f"{self.commodity}_in", - val=0.0, - shape=self.n_timesteps, - units=self.commodity_rate_units, - desc=f"{self.commodity} input to storage", - ) - - self.add_output( - "SOC", - val=0.0, - shape=self.n_timesteps, - units="percent", - desc="State of charge of storage", - ) - - self.add_output( - f"storage_{self.commodity}_discharge", - val=0.0, - shape=self.n_timesteps, - units=self.commodity_rate_units, - desc=f"{self.commodity} output from storage only", - ) - - self.add_output( - f"storage_{self.commodity}_charge", - val=0.0, - shape=self.n_timesteps, - units=self.commodity_rate_units, - desc=f"{self.commodity} input to storage only", - ) - - self.add_output( - f"storage_{self.commodity}_out", - val=0.0, - shape=self.n_timesteps, - units=self.commodity_rate_units, - desc=f"{self.commodity} input and output from storage", - ) - - self.add_input( - "max_charge_rate", - val=self.config.max_charge_rate, - units=self.config.commodity_rate_units, - desc="Storage charge rate", - ) - - if not self.config.charge_equals_discharge: - self.add_input( - "max_discharge_rate", - val=self.config.max_discharge_rate, - units=self.config.commodity_rate_units, - desc="Storage discharge rate", - ) - - self.add_input( - "storage_capacity", - val=self.config.max_capacity, - units=self.commodity_amount_units, - desc="Storage capacity", - ) - - self.add_input( - f"{self.commodity}_demand", - val=0.0, - shape=self.n_timesteps, - units=self.commodity_rate_units, - desc=f"{self.commodity} demand", - ) - - self.add_output( - f"unmet_{self.commodity}_demand_out", - val=0.0, - shape=self.n_timesteps, - units=self.commodity_rate_units, - desc=f"Unmet {self.commodity} demand", - ) - - self.add_output( - f"unused_{self.commodity}_out", - val=0.0, - shape=self.n_timesteps, - units=self.commodity_rate_units, - desc="Unused generated commodity", - ) - - self.dt_hr = int(self.options["plant_config"]["plant"]["simulation"]["dt"]) / ( - 60**2 - ) # convert from seconds to hours - - # create inputs for pyomo control model - if "tech_to_dispatch_connections" in self.options["plant_config"]: - # get technology group name - # TODO: The split below seems brittle - self.tech_group_name = self.pathname.split(".") - for _source_tech, intended_dispatch_tech in self.options["plant_config"][ - "tech_to_dispatch_connections" - ]: - if any(intended_dispatch_tech in name for name in self.tech_group_name): - self.add_discrete_input("pyomo_dispatch_solver", val=dummy_function) - break - - def compute(self, inputs, outputs, discrete_inputs=[], discrete_outputs=[]): - """Run the storage model. - - Configures the storage stateful model parameters (SOC limits, timestep, - thermal properties, etc.), executes the simulation, and stores the - results in OpenMDAO outputs. - - Args: - inputs (dict): - Continuous input values (e.g., commodity_in, commodity_demand). - outputs (dict): - Dictionary where model outputs (SOC, unmet demand, etc.) - are written. - discrete_inputs (dict): - Discrete inputs such as control mode or Pyomo solver. - discrete_outputs (dict): - Discrete outputs (unused in this component). - """ - # Size the storage based on inputs -> method brought from HOPP - if self.config.charge_equals_discharge: - max_discharge_rate = inputs["max_charge_rate"][0] - else: - max_discharge_rate = inputs["max_discharge_rate"][0] - - self.current_soc = self.config.init_charge_fraction - - if "pyomo_dispatch_solver" in discrete_inputs: - # Simulate the storage with provided dispatch inputs - dispatch = discrete_inputs["pyomo_dispatch_solver"] - # kwargs are tech-specific inputs to the simulate() method - kwargs = { - "charge_rate": inputs["max_charge_rate"][0], - "discharge_rate": max_discharge_rate, - "storage_capacity": inputs["storage_capacity"][0], - } - ( - total_commodity_out, - storage_commodity_out, - unmet_demand, - unused_commodity, - soc, - ) = dispatch(self.simulate, kwargs, inputs) - - storage_commodity_out = np.array(storage_commodity_out) - - else: - # Simulate the storage with provided inputs and no controller. - # This essentially asks for discharge when demand exceeds input - # and requests charge when input exceeds demand - - # update the control window to be the number of timesteps in the simulation - self.config.n_control_window = self.n_timesteps - - # estimate required dispatch commands - pseudo_commands = inputs[f"{self.commodity}_demand"] - inputs[f"{self.commodity}_in"] - - storage_commodity_out, soc = self.simulate( - storage_dispatch_commands=pseudo_commands, - charge_rate=inputs["max_charge_rate"][0], - discharge_rate=max_discharge_rate, - storage_capacity=inputs["storage_capacity"][0], - ) - - # determine storage charge and discharge - # storage_commodity_out is positive when the storage is discharged - # and negative when the storage is charged - storage_commodity_out = np.array(storage_commodity_out) - - # calculate combined commodity out from inflow source and storage - # (note: storage_commodity_out is negative when charging) - combined_commodity_out = inputs[f"{self.commodity}_in"] + storage_commodity_out - - # find the total commodity out to meet demand - total_commodity_out = np.minimum( - inputs[f"{self.commodity}_demand"], combined_commodity_out - ) - - # determine how much of the inflow commodity was unused - unused_commodity = np.maximum( - 0, combined_commodity_out - inputs[f"{self.commodity}_demand"] - ) - - # determine how much demand was not met - unmet_demand = np.maximum( - 0, inputs[f"{self.commodity}_demand"] - combined_commodity_out - ) - - outputs[f"storage_{self.commodity}_charge"] = np.where( - storage_commodity_out < 0, storage_commodity_out, 0 - ) - outputs[f"storage_{self.commodity}_discharge"] = np.where( - storage_commodity_out > 0, storage_commodity_out, 0 - ) - - outputs[f"unmet_{self.commodity}_demand_out"] = unmet_demand - outputs[f"unused_{self.commodity}_out"] = unused_commodity - outputs[f"storage_{self.commodity}_out"] = storage_commodity_out - outputs[f"{self.commodity}_out"] = total_commodity_out - - outputs["SOC"] = soc - outputs[f"rated_{self.commodity}_production"] = max_discharge_rate - outputs[f"total_{self.commodity}_produced"] = np.sum(total_commodity_out) - outputs[f"annual_{self.commodity}_produced"] = outputs[ - f"total_{self.commodity}_produced" - ] * (1 / self.fraction_of_year_simulated) - - if outputs[f"rated_{self.commodity}_production"] <= 0: - outputs["capacity_factor"] = 0.0 - else: - outputs["capacity_factor"] = outputs[f"total_{self.commodity}_produced"] / ( - outputs[f"rated_{self.commodity}_production"] * self.n_timesteps - ) - - def simulate( - self, - storage_dispatch_commands: list, - charge_rate: float, - discharge_rate: float, - storage_capacity: float, - sim_start_index: int = 0, - ): - """Run the storage model over a control window of ``n_control_window`` timesteps. - - Iterates through ``storage_dispatch_commands`` one timestep at a time. - A negative command requests charging; a positive command requests - discharging. Each command is clipped to the most restrictive of three - limits before it is applied: - - 1. **SOC headroom** - the remaining capacity (charge) or remaining - stored commodity (discharge), converted to a rate via - ``storage_capacity / dt_hr``. - 2. **Hardware rate limit** - ``charge_rate`` or ``discharge_rate``, - divided by the corresponding efficiency so the limit is expressed - in pre-efficiency rate units. - 3. **Commanded magnitude** - the absolute value of the dispatch command - itself (we never exceed what was asked for). - - After clipping, the result is scaled by the charge or discharge - efficiency to obtain the actual commodity flow into or out of the - storage, and the SOC is updated accordingly. - - This method is separated from ``compute()`` so the Pyomo dispatch - controller can call it directly to evaluate candidate schedules. - - Args: - storage_dispatch_commands (array_like[float]): - Dispatch set-points for each timestep in ``commodity_rate_units``. - Negative values command charging; positive values command - discharging. Length must equal ``config.n_control_window``. - charge_rate (float): - Maximum commodity input rate to storage in - ``commodity_rate_units`` (before charge efficiency is applied). - discharge_rate (float): - Maximum commodity output rate from storage in - ``commodity_rate_units`` (before discharge efficiency is applied). - storage_capacity (float): - Rated storage capacity in ``commodity_amount_units``. - sim_start_index (int, optional): - Starting index for writing into persistent output arrays. - Defaults to 0. - - Returns: - tuple[np.ndarray, np.ndarray] - storage_commodity_out_timesteps : - Commodity flow per timestep in ``commodity_rate_units``. - Positive = discharge (commodity leaving storage), - negative = charge (commodity entering storage). - soc_timesteps : - State of charge at the end of each timestep, in percent - (0-100). - """ - - n = self.config.n_control_window - storage_commodity_out_timesteps = np.zeros(n) - soc_timesteps = np.zeros(n) - - # Early return when storage cannot operate: zero capacity or both - # charge and discharge rates are zero. - if storage_capacity <= 0 or (charge_rate <= 0 and discharge_rate <= 0): - soc_timesteps[:] = self.current_soc * 100.0 - return storage_commodity_out_timesteps, soc_timesteps - - # Pre-compute scalar constants to avoid repeated attribute lookups - # and redundant divisions inside the per-timestep loop. - charge_eff = self.config.charge_efficiency - discharge_eff = self.config.discharge_efficiency - soc_max = self.config.max_charge_fraction - soc_min = self.config.min_charge_fraction - - # max_charge_input / max_discharge_input are the hardware rate limits - # expressed in *pre-efficiency* rate units so they can be compared - # directly against the SOC headroom and the raw command magnitude. - max_charge_input = charge_rate / charge_eff - max_discharge_input = discharge_rate / discharge_eff - - commands = np.asarray(storage_dispatch_commands, dtype=float) - soc = float(self.current_soc) - - for t, cmd in enumerate(commands): - if cmd < 0.0: - # --- Charging --- - # headroom: how much more commodity the storage can accept, - # expressed as a rate (commodity_rate_units). - headroom = (soc_max - soc) * storage_capacity / self.dt_hr - - # Clip to the most restrictive limit, then apply efficiency. - # max(0, ...) guards against negative headroom when SOC - # slightly exceeds soc_max. - actual_charge = max(0.0, min(headroom, max_charge_input, -cmd)) * charge_eff - - # Update SOC (actual_charge is in post-efficiency units) - soc += actual_charge / storage_capacity - storage_commodity_out_timesteps[t] = -actual_charge - else: - # --- Discharging --- - # headroom: how much commodity can still be drawn before - # hitting the minimum SOC, expressed as a rate. - headroom = (soc - soc_min) * storage_capacity / self.dt_hr - - # Clip and apply discharge efficiency. - actual_discharge = max(0.0, min(headroom, max_discharge_input, cmd)) * discharge_eff - - # Update SOC (actual_discharge is in post-efficiency units) - soc -= actual_discharge / storage_capacity - storage_commodity_out_timesteps[t] = actual_discharge - - soc_timesteps[t] = soc * 100.0 - - # Persist the final SOC so subsequent simulate() calls (e.g. from the - # Pyomo controller across rolling windows) start where we left off. - self.current_soc = soc - return storage_commodity_out_timesteps, soc_timesteps - - -def dummy_function(): - # this function is required for initializing the pyomo control input and nothing else - pass diff --git a/h2integrate/storage/hydrogen/h2_storage_cost.py b/h2integrate/storage/hydrogen/h2_storage_cost.py index 970889605..ab392c5b2 100644 --- a/h2integrate/storage/hydrogen/h2_storage_cost.py +++ b/h2integrate/storage/hydrogen/h2_storage_cost.py @@ -74,6 +74,11 @@ def make_model_dict(self): class HydrogenStorageBaseCostModel(CostModelBaseClass): + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() @@ -96,7 +101,7 @@ def setup(self): ) self.add_input( - "max_capacity", + "storage_capacity", val=self.config.max_capacity, units=f"{self.config.commodity_units}*h", desc="Hydrogen storage capacity", @@ -117,7 +122,7 @@ def make_storage_input_dict(self, inputs): # convert capacity to kg max_capacity_kg = units.convert_units( - inputs["max_capacity"], f"({self.config.commodity_units})*h", "kg" + inputs["storage_capacity"], f"({self.config.commodity_units})*h", "kg" ) storage_input["h2_storage_kg"] = max_capacity_kg[0] @@ -151,6 +156,11 @@ class LinedRockCavernStorageCostModel(HydrogenStorageBaseCostModel): [3] HDSAM V4.0 Gaseous H2 Geologic Storage sheet """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): """Calculate installed capital and O&M costs for lined rock cavern hydrogen storage. @@ -276,6 +286,11 @@ class SaltCavernStorageCostModel(HydrogenStorageBaseCostModel): [3] HDSAM V4.0 Gaseous H2 Geologic Storage sheet """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): """Calculate installed capital and O&M costs for salt cavern hydrogen storage. @@ -406,6 +421,11 @@ class PipeStorageCostModel(HydrogenStorageBaseCostModel): [3] HDSAM V4.0 Gaseous H2 Geologic Storage sheet """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): """Calculate installed capital and O&M costs for underground pipe hydrogen storage. diff --git a/h2integrate/storage/hydrogen/mch_storage.py b/h2integrate/storage/hydrogen/mch_storage.py index f6a53b663..4888519c4 100644 --- a/h2integrate/storage/hydrogen/mch_storage.py +++ b/h2integrate/storage/hydrogen/mch_storage.py @@ -54,6 +54,11 @@ class MCHTOLStorageCostModel(CostModelBaseClass): """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): super().initialize() diff --git a/h2integrate/storage/hydrogen/test/conftest.py b/h2integrate/storage/hydrogen/test/conftest.py index abdb88bca..1600cde57 100644 --- a/h2integrate/storage/hydrogen/test/conftest.py +++ b/h2integrate/storage/hydrogen/test/conftest.py @@ -6,7 +6,11 @@ from h2integrate.storage.test.conftest import plant_config # noqa: F401 -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) def pytest_sessionstart(session): diff --git a/h2integrate/storage/simple_generic_storage.py b/h2integrate/storage/simple_generic_storage.py deleted file mode 100644 index 528d474a2..000000000 --- a/h2integrate/storage/simple_generic_storage.py +++ /dev/null @@ -1,66 +0,0 @@ -from attrs import field, define - -from h2integrate.core.utilities import BaseConfig, merge_shared_inputs -from h2integrate.core.validators import gte_zero -from h2integrate.core.model_baseclasses import PerformanceModelBaseClass - - -@define(kw_only=True) -class SimpleGenericStorageConfig(BaseConfig): - commodity: str = field() - commodity_rate_units: str = field() # TODO: update to commodity_rate_units - max_charge_rate: float = field(validator=gte_zero) - - -class SimpleGenericStorage(PerformanceModelBaseClass): - """ - Simple generic storage model that acts as a pass-through component. - - Note: this storage performance model is intended to be used with the - `DemandOpenLoopStorageController` controller and has not been tested - with other controllers. - - """ - - def setup(self): - self.config = SimpleGenericStorageConfig.from_dict( - merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance"), - strict=False, - additional_cls_name=self.__class__.__name__, - ) - self.commodity = self.config.commodity - self.commodity_rate_units = self.config.commodity_rate_units - self.commodity_amount_units = f"({self.commodity_rate_units})*h" - super().setup() - self.add_input( - f"{self.commodity}_set_point", - val=0.0, - shape=self.n_timesteps, - units=self.commodity_rate_units, - ) - self.add_input( - "max_charge_rate", - val=self.config.max_charge_rate, - units=self.config.commodity_rate_units, - desc="Storage charge/discharge rate", - ) - - def compute(self, inputs, outputs): - # Pass the commodity_out as the commodity_set_point - outputs[f"{self.commodity}_out"] = inputs[f"{self.commodity}_set_point"] - - # Set the rated commodity production from the max_charge_rate input - outputs[f"rated_{self.commodity}_production"] = inputs["max_charge_rate"] - - # Calculate the total and annual commodity produced - outputs[f"total_{self.commodity}_produced"] = outputs[f"{self.commodity}_out"].sum() - outputs[f"annual_{self.commodity}_produced"] = outputs[ - f"total_{self.commodity}_produced" - ] * (1 / self.fraction_of_year_simulated) - - # Calculate the maximum theoretical commodity production over the simulation - rated_production = ( - outputs[f"rated_{self.commodity}_production"] * self.n_timesteps * (self.dt / 3600) - ) - - outputs["capacity_factor"] = outputs[f"total_{self.commodity}_produced"] / rated_production diff --git a/h2integrate/storage/simple_storage_auto_sizing.py b/h2integrate/storage/simple_storage_auto_sizing.py index 8d914df2f..c84dad87d 100644 --- a/h2integrate/storage/simple_storage_auto_sizing.py +++ b/h2integrate/storage/simple_storage_auto_sizing.py @@ -1,25 +1,100 @@ -from copy import deepcopy - import numpy as np from attrs import field, define -from h2integrate.core.utilities import BaseConfig, merge_shared_inputs -from h2integrate.core.model_baseclasses import PerformanceModelBaseClass +from h2integrate.core.utilities import merge_shared_inputs +from h2integrate.core.validators import range_val_or_none +from h2integrate.storage.storage_baseclass import ( + StoragePerformanceBase, + StoragePerformanceBaseConfig, +) @define(kw_only=True) -class StorageSizingModelConfig(BaseConfig): +class StorageSizingModelConfig(StoragePerformanceBaseConfig): """Configuration class for the StorageAutoSizingModel. - Fields include `commodity`, `commodity_rate_units`, and `demand_profile`. + Attributes: + commodity (str): name of commodity + commodity_rate_units (str): Units of the commodity (e.g., kW or kg/h). + min_soc_fraction (float): Minimum allowable state of charge as a fraction (0 to 1). + max_soc_fraction (float): Maximum allowable state of charge as a fraction (0 to 1). + set_demand_as_avg_commodity_in (bool): If True, assume the demand is + equal to the mean input commodity. If False, uses the demand input. + demand_profile (int | float | list, optional): Demand values for each timestep, in + the same units as `commodity_rate_units`. May be a scalar for constant + demand or a list/array for time-varying demand. + Only used if `set_demand_as_avg_commodity_in` is False. Defaults to 0. + charge_efficiency (float | None, optional): Efficiency of charging the storage, represented + as a decimal between 0 and 1 (e.g., 0.9 for 90% efficiency). Optional if + `round_trip_efficiency` is provided. + discharge_efficiency (float | None, optional): Efficiency of discharging the storage, + represented as a decimal between 0 and 1 (e.g., 0.9 for 90% efficiency). Optional if + `round_trip_efficiency` is provided. + round_trip_efficiency (float | None, optional): Combined efficiency of charging and + discharging the storage, represented as a decimal between 0 and 1 (e.g., 0.81 for + 81% efficiency). Optional if `charge_efficiency` and `discharge_efficiency` are + provided. + commodity_amount_units (str | None, optional): Units of the commodity as an amount + (i.e., kW*h or kg). If not provided, defaults to commodity_rate_units*h. """ - commodity: str = field(default="hydrogen") - commodity_rate_units: str = field(default="kg/h") # TODO: update to commodity_rate_units - demand_profile: int | float | list = field(default=0.0) + commodity: str = field(converter=(str.strip, str.lower)) + commodity_rate_units: str = field(converter=str.strip) + # TODO: add in logic for having different discharge rate + # charge_equals_discharge: bool = field(default=True) + set_demand_as_avg_commodity_in: bool = field() + demand_profile: int | float | list = field(default=0.0) -class StorageAutoSizingModel(PerformanceModelBaseClass): + charge_efficiency: float | None = field(default=None, validator=range_val_or_none(0, 1)) + discharge_efficiency: float | None = field(default=None, validator=range_val_or_none(0, 1)) + round_trip_efficiency: float | None = field(default=None, validator=range_val_or_none(0, 1)) + + commodity_amount_units: str = field(default=None) + + def __attrs_post_init__(self): + """ + Post-initialization logic to validate and calculate efficiencies. + + Ensures that either `charge_efficiency` and `discharge_efficiency` are provided, + or `round_trip_efficiency` is provided. If `round_trip_efficiency` is provided, + it calculates `charge_efficiency` and `discharge_efficiency` as the square root + of `round_trip_efficiency`. + """ + if (self.round_trip_efficiency is not None) and ( + self.charge_efficiency is None and self.discharge_efficiency is None + ): + # Calculate charge and discharge efficiencies from round-trip efficiency + self.charge_efficiency = np.sqrt(self.round_trip_efficiency) + self.discharge_efficiency = np.sqrt(self.round_trip_efficiency) + self.round_trip_efficiency = None + if self.charge_efficiency is None or self.discharge_efficiency is None: + raise ValueError( + "Exactly one of the following sets of parameters must be set: (a) " + "`round_trip_efficiency`, or (b) both `charge_efficiency` " + "and `discharge_efficiency`." + ) + + # Set the default commodity_amount_units as the commodity_rate_units*h + if self.commodity_amount_units is None: + self.commodity_amount_units = f"({self.commodity_rate_units})*h" + + # Check if the user provided a non-zero demand profile + user_input_dmd = True if np.sum(self.demand_profile) > 0 else False + + # Check that the demand profile is zero if set_demand_as_avg_commodity_in is True + if self.set_demand_as_avg_commodity_in and user_input_dmd: + # If using the average commodity in as the demand, + # warn users if they input the demand profile + msg = ( + "A non-zero demand profile was provided but set_demand_as_avg_commodity_in is True." + " The provided demand profile will not be used, the demand profile will be " + f"calculated as the mean of ``{self.commodity}_in``. " + ) + raise ValueError(msg) + + +class StorageAutoSizingModel(StoragePerformanceBase): """Performance model that calculates the storage charge rate and capacity needed to either: @@ -28,40 +103,13 @@ class StorageAutoSizingModel(PerformanceModelBaseClass): Then simulates performance of a basic storage component using the charge rate and capacity calculated. - - Note: this storage performance model is intended to be used with the - `PassThroughOpenLoopController` controller and is not compatible with the - `DemandOpenLoopStorageController` controller. - - Inputs: - {commodity}_in (float): Input commodity flow timeseries (e.g., hydrogen production) - used to estimate the demand if `commodity_demand_profile` is zero. - - Units: Defined in `commodity_rate_units` (e.g., "kg/h"). - {commodity}_set_point (float): Input commodity flow timeseries (e.g., hydrogen production) - used as the available input commodity to meet the demand. - {commodity}_demand_profile (float): Demand profile of commodity. - - Units: Defined in `commodity_rate_units` (e.g., "kg/h"). - - Outputs: - max_capacity (float): Maximum storage capacity of the commodity. - - Units: in non-rate units, e.g., "kg" if `commodity_rate_units` is "kg/h" - max_charge_rate (float): Maximum rate at which the commodity can be charged - - Units: Defined in `commodity_rate_units` (e.g., "kg/h"). - Assumed to also be the discharge rate. - {commodity}_out (np.ndarray): the commodity used to meet demand from the available - input commodity and storage component. Defined in `commodity_rate_units`. - total_{commodity}_produced (float): sum of commodity discharged from storage over - the simulation. Defined in `commodity_rate_units*h` - rated_{commodity}_production (float): maximum commodity that could be discharged - in a timestep. Defined in `commodity_rate_units` - annual_{commodity}_produced (np.ndarray): total commodity discharged per year. - Defined in `commodity_rate_units*h/year` - capacity_factor (np.ndarray): ratio of commodity discharged to the maximum - commodity that could be discharged over the simulation. - Defined as a ratio (units of `unitless`) - """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def setup(self): self.config = StorageSizingModelConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance"), @@ -71,147 +119,162 @@ def setup(self): self.commodity = self.config.commodity self.commodity_rate_units = self.config.commodity_rate_units - self.commodity_amount_units = f"({self.commodity_rate_units})*h" + self.commodity_amount_units = self.config.commodity_amount_units super().setup() - self.add_input( - f"{self.commodity}_demand_profile", - units=f"{self.config.commodity_rate_units}", - val=self.config.demand_profile, - shape=self.n_timesteps, - desc=f"{self.commodity} demand profile timeseries", - ) - - self.add_input( - f"{self.commodity}_in", - shape_by_conn=True, - units=f"{self.config.commodity_rate_units}", - desc=f"{self.commodity} input timeseries from production to storage", - ) - - self.add_input( - f"{self.commodity}_set_point", - shape_by_conn=True, - units=f"{self.config.commodity_rate_units}", - desc=f"{self.commodity} input set point from controller", + # Capacity outputs + self.add_output( + "storage_capacity", + val=0.0, + shape=1, + units=self.commodity_amount_units, ) self.add_output( - "max_capacity", + "max_charge_rate", val=0.0, shape=1, - units=f"({self.config.commodity_rate_units})*h", + units=self.commodity_rate_units, ) self.add_output( - "max_charge_rate", + "max_discharge_rate", val=0.0, shape=1, - units=f"{self.config.commodity_rate_units}", + units=self.commodity_rate_units, ) - def compute(self, inputs, outputs): - # Step 1: Auto-size the storage to meet the demand + # Check if we need to have an input for demand + # If using the actual demand profile and using + # open-loop control, add demand as an input + if not self.config.set_demand_as_avg_commodity_in and not self.using_feedback_control: + self.add_input( + f"{self.commodity}_demand", + val=self.config.demand_profile, + shape=self.n_timesteps, + units=self.commodity_rate_units, + desc=f"{self.commodity} demand profile", + ) + + def compute(self, inputs, outputs, discrete_inputs=[], discrete_outputs=[]): + """ + Part 0: get demand profile based on user input parameters: + + 1) Estimate the demand profile from either the input `commodity_demand` or assume + the demand is the average of the `commodity_in` profile + + Part 1: calculate the storage sizes (charge rate, discharge rate, and capacity) + needed to meet the demand. The steps to do this are: + + 1) Calculate the max charge and discharge rate as the maximum of the `commodity_in` + profile and oversize to account for charge/discharge efficiencies. + 2) Estimate the storage SOC (in `commodity_amount_units`). The SOC increases when + charging and decreases when discharging. If `commodity_set_point` is input, + calculate the storage SOC as the cumulative summation of the negative of + `commodity_set_point` input (`commodity_set_point` input is + negative when charging and positive when discharging). + Otherwise, calculate the storage SOC as the cumulative summation of + `commodity_in - demand`. + 3) If needed, adjust the SOC profile from Step 2 so that the minimum SOC is positive + 4) Calculate the usable storage capacity as the difference between the + maximum SOC and minimum SOC from Steps 2 and 3. + 5) Calculate the rated storage capacity as the usable storage capacity + (calculated in Step 4) divided by + `config.max_soc_fraction - config.min_soc_fraction` + + Part 2: Simulate the performance of that storage model. The steps of this are: + + 1) Estimate the starting SOC (as a fraction) at the start of the simulation. + Take the first value in the SOC profile (in `commodity_amount_units`) + and divide by the storage capacity + 2) Make an input dictionary containing the calculated demand profile, + storage capacity, and storage fill rate, and run the storage performance. + 3) Calculate the outputs + """ + + # Part 0: get demand profile based on user input parameters + # 1. Calculate the demand profile + if self.config.set_demand_as_avg_commodity_in: + if dict(inputs.items()).get(f"{self.commodity}_demand", np.array([0])).sum() > 0: + msg = ( + "A non-zero demand profile was input when set_demand_as_avg_commodity_in is " + "True. When set_demand_as_avg_commodity_in is True, the input demand profile " + f"cannot be used. Please ensure that ``{self.config.commodity}_in`` is zero or " + "set set_demand_as_avg_commodity_in as False." + ) + raise ValueError(msg) - # Auto-size the fill rate as the max of the input commodity - storage_max_fill_rate = np.max(inputs[f"{self.commodity}_in"]) + commodity_demand = np.mean(inputs[f"{self.commodity}_in"]) * np.ones(self.n_timesteps) - # Set the demand profile - if np.sum(inputs[f"{self.commodity}_demand_profile"]) > 0: - commodity_demand = inputs[f"{self.commodity}_demand_profile"] else: - # If the commodity_demand_profile is zero, use the average - # commodity_in as the demand - commodity_demand = np.mean(inputs[f"{self.commodity}_in"]) * np.ones( - self.n_timesteps - ) # TODO: update demand based on end-use needs - - # The commodity_set_point is the production set by the controller - desired_commodity_production = inputs[f"{self.commodity}_set_point"] - - # TODO: SOC is just an absolute value and is not a percentage. Ideally would calculate as shortfall in future. - # Size the storage capacity to meet the demand as much as possible - commodity_storage_soc = [] - for j in range(len(desired_commodity_production)): - if j == 0: - commodity_storage_soc.append(desired_commodity_production[j] - commodity_demand[j]) - else: - commodity_storage_soc.append( - commodity_storage_soc[j - 1] - + desired_commodity_production[j] - - commodity_demand[j] - ) + commodity_demand = inputs[f"{self.commodity}_demand"] + # Part 1: Auto-size the storage to meet the demand + # 1. Auto-size the fill rate as the max of the input commodity + storage_max_fill_rate = np.max(inputs[f"{self.commodity}_in"]) + # Auto-size the empty rate as the max of the input commodity + storage_max_empty_rate = np.max(inputs[f"{self.commodity}_in"]) + + # Auto-size the storage capacity to meet the demand as much as possible + # 2. Estimate the storage SOC in `commodity_amount_units` + # NOTE: commodity_storage_soc is just an absolute value and is not a percentage. + if f"{self.commodity}_set_point" in inputs: + # `{self.commodity}_set_point` is negative when charging and positive when + # discharging, the negative of `{self.commodity}_set_point` can be used to + # estimate the SOC (which increases when charging and decreases when discharging) + commodity_storage_soc = np.cumsum(-1 * inputs[f"{self.commodity}_set_point"]) + else: + # estimate the SOC (which increases when charging and decreases when discharging) + # based on the demand profile and the input commodity + commodity_storage_soc = np.cumsum( + inputs[f"{self.config.commodity}_in"] - commodity_demand + ) + + # 3. If needed, adjust the SOC profile from Step 2 so that the minimum SOC is positive minimum_soc = np.min(commodity_storage_soc) # Adjust soc so it's not negative. if minimum_soc < 0: - commodity_storage_soc = [x + np.abs(minimum_soc) for x in commodity_storage_soc] - - # Calculate the maximum hydrogen storage capacity needed to meet the demand - commodity_storage_capacity_kg = np.max(commodity_storage_soc) - np.min( - commodity_storage_soc - ) - - # Step 2: Simulate the storage performance based on the sizes calculated - - # Initialize output arrays of charge and discharge - discharge_storage = np.zeros(self.n_timesteps) - charge_storage = np.zeros(self.n_timesteps) - output_array = np.zeros(self.n_timesteps) - - # Initialize state-of-charge value as the soc at t=0 - soc = deepcopy(commodity_storage_soc[0]) - - # Simulate a basic storage component - for t, demand_t in enumerate(commodity_demand): - input_flow = desired_commodity_production[t] - available_charge = float(commodity_storage_capacity_kg - soc) - available_discharge = float(soc) - - # If demand is greater than the input, discharge storage - if demand_t > input_flow: - # Discharge storage to meet demand. - discharge_needed = demand_t - input_flow - discharge = min(discharge_needed, available_discharge, storage_max_fill_rate) - # Update SOC - soc -= discharge - - discharge_storage[t] = discharge - output_array[t] = input_flow + discharge - - # If input is greater than the demand, charge storage - else: - # Charge storage with unused input - unused_input = input_flow - demand_t - charge = min(unused_input, available_charge, storage_max_fill_rate) - # Update SOC - soc += charge - - charge_storage[t] = charge - output_array[t] = demand_t - - # Output the storage sizes (charge rate and capacity) - outputs["max_charge_rate"] = storage_max_fill_rate - outputs["max_capacity"] = commodity_storage_capacity_kg + commodity_storage_soc = commodity_storage_soc + np.abs(minimum_soc) - # commodity_out is the commodity_set_point - charge_storage + discharge_storage - outputs[f"{self.commodity}_out"] = output_array + # 4. Calculate the maximum usable storage capacity needed to meet the demand + max_usable_storage_capacity = np.max(commodity_storage_soc) - np.min(commodity_storage_soc) - # The rated_commodity_production is based on the discharge rate - # (which is assumed equal to the charge rate) - outputs[f"rated_{self.commodity}_production"] = storage_max_fill_rate + # 5. Calculate the storage capacity to account for SOC limits + rated_storage_capacity = max_usable_storage_capacity / ( + self.config.max_soc_fraction - self.config.min_soc_fraction + ) - # The total_commodity_produced is the sum of the commodity discharged from storage - outputs[f"total_{self.commodity}_produced"] = discharge_storage.sum() - # Adjust the total_commodity_produced to a year-long simulation - outputs[f"annual_{self.commodity}_produced"] = outputs[ - f"total_{self.commodity}_produced" - ] * (1 / self.fraction_of_year_simulated) + # Part 2: Simulate the storage performance based on the sizes calculated + # Estimate the initial SOC - # The maximum production is based on the charge/discharge rate - max_production = storage_max_fill_rate * self.n_timesteps * (self.dt / 3600) + # 1. Set the starting SOC (as a fraction) at the start of the simulation. + self.current_soc = np.max( + [self.config.min_soc_fraction, commodity_storage_soc[0] / rated_storage_capacity] + ) - # Capacity factor is total discharged commodity / maximum discharged commodity possible - outputs["capacity_factor"] = outputs[f"total_{self.commodity}_produced"] / max_production + # Output the calculated storage sizes (charge rate and capacity) + outputs["max_charge_rate"] = storage_max_fill_rate + outputs["max_discharge_rate"] = storage_max_empty_rate + outputs["storage_capacity"] = rated_storage_capacity + + # 2. Make dictionary of inputs containing information to pass to the controller + # (such as demand profile, charge rate, and storage capacity) + inputs_adjusted = dict(inputs.items()) + if self.config.set_demand_as_avg_commodity_in: + inputs_adjusted[f"{self.commodity}_demand"] = commodity_demand + + if "pyomo_dispatch_solver" in discrete_inputs: + inputs_adjusted["storage_capacity"] = np.array([rated_storage_capacity]) + inputs_adjusted["max_charge_rate"] = np.array([storage_max_fill_rate]) + + # 3. Simulate the storage performance and calculate outputs + outputs = self.run_storage( + storage_max_fill_rate, + storage_max_empty_rate, + rated_storage_capacity, + inputs_adjusted, + outputs, + discrete_inputs, + ) diff --git a/h2integrate/storage/storage_baseclass.py b/h2integrate/storage/storage_baseclass.py new file mode 100644 index 000000000..05a614da6 --- /dev/null +++ b/h2integrate/storage/storage_baseclass.py @@ -0,0 +1,439 @@ +import numpy as np +from attrs import field, define + +from h2integrate.core.utilities import BaseConfig +from h2integrate.core.validators import range_val +from h2integrate.core.model_baseclasses import PerformanceModelBaseClass + + +@define(kw_only=True) +class StoragePerformanceBaseConfig(BaseConfig): + """ + Configuration class for the StoragePerformanceBase model. + + Attributes: + min_soc_fraction (float): Minimum allowable state of charge as a fraction (0 to 1). + max_soc_fraction (float): Maximum allowable state of charge as a fraction (0 to 1). + demand_profile (int | float | list): Demand values for each timestep, in + the same units as `commodity_rate_units`. May be a scalar for constant + demand or a list/array for time-varying demand. + """ + + # Below are used in all storage models + min_soc_fraction: float = field(validator=range_val(0, 1)) + max_soc_fraction: float = field(validator=range_val(0, 1)) + demand_profile: int | float | list = field() + + +class StoragePerformanceBase(PerformanceModelBaseClass): + """ + Baseclass for storage performance models + + Attributes: + config (StoragePerformanceModelConfig): + Configuration parameters for the storage performance model. + current_soc (float): soc at the start of each interval that the simulate() + method is called + dt_hr (float): timestep in hours. + + + Notes: + - Default timestep is 1 hour (``dt=3600.0``). + - State of charge (SOC) bounds are set using the configuration's + ``min_soc_fraction`` and ``max_soc_fraction``. + - If a Pyomo dispatch solver is provided, the storage will simulate + dispatch decisions using solver inputs. + """ + + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + + def setup(self): + """Set up the storage performance model in OpenMDAO. + + Initializes the configuration and defines inputs/outputs for OpenMDAO. + If dispatch connections are specified, it also sets up a discrete + input for Pyomo solver integration. + """ + + # Below should be done in models that inherit it + # self.commodity = self.config.commodity + # self.commodity_rate_units = self.config.commodity_rate_units + # self.commodity_amount_units = self.config.commodity_amount_units + + # Initialize standard performance model outputs + super().setup() + + commodity = self.commodity + commodity_rate_units = self.commodity_rate_units + commodity_amount_units = self.commodity_amount_units + n_timesteps = self.n_timesteps + + # Input timeseries + self.add_input( + f"{commodity}_in", + val=0, + shape=n_timesteps, + units=commodity_rate_units, + desc=f"{commodity} input", + ) + + # Input storage design parameters + if hasattr(self.config, "max_charge_rate"): + self.add_input( + "max_charge_rate", + val=self.config.max_charge_rate, + units=commodity_rate_units, + desc="Storage charge rate", + ) + + if hasattr(self.config, "max_capacity"): + self.add_input( + "storage_capacity", + val=self.config.max_capacity, + units=commodity_amount_units, + desc="Storage capacity", + ) + + if not getattr(self.config, "charge_equals_discharge", True): + # add max_discharge_rate if discharge rate != charge rate + self.add_input( + "max_discharge_rate", + val=self.config.max_discharge_rate, + units=commodity_rate_units, + desc="Storage discharge rate", + ) + + # Storage design outputs: + default_storage_duration = 0.0 + if hasattr(self.config, "max_charge_rate") and hasattr(self.config, "max_capacity"): + default_storage_duration = self.config.max_capacity / self.config.max_charge_rate + + self.add_output( + "storage_duration", + val=default_storage_duration, + units=f"({commodity_amount_units})/({commodity_rate_units})", + desc="Storage duration capacity", + ) + + # Storage performance outputs + self.add_output( + "SOC", + val=0.0, + shape=n_timesteps, + units="percent", + desc="State of charge of storage", + ) + + self.add_output( + f"storage_{commodity}_discharge", + val=0.0, + shape=n_timesteps, + units=commodity_rate_units, + desc=f"{commodity} output from storage only", + ) + + self.add_output( + f"storage_{commodity}_charge", + val=0.0, + shape=n_timesteps, + units=commodity_rate_units, + desc=f"{commodity} input to storage only", + ) + + self.add_output( + "standard_capacity_factor", + val=0.0, + shape=self.plant_life, + units="unitless", + desc=f"Capacity factor of {commodity} discharged from storage", + ) + + # create a variable to determine whether we are using feedback control + # for this technology + using_feedback_control = False + # create inputs for pyomo control model + if "tech_to_dispatch_connections" in self.options["plant_config"]: + # get technology group name + # TODO: The split below seems brittle + self.tech_group_name = self.pathname.split(".") + for _source_tech, intended_dispatch_tech in self.options["plant_config"][ + "tech_to_dispatch_connections" + ]: + if any(intended_dispatch_tech in name for name in self.tech_group_name): + self.add_input( + f"{commodity}_demand", + val=self.config.demand_profile, + shape=n_timesteps, + units=commodity_rate_units, + desc=f"{commodity} demand profile", + ) + self.add_discrete_input("pyomo_dispatch_solver", val=lambda: None) + # the controller gets demand from the storage model + # set the using feedback control variable to True + using_feedback_control = True + break + if not using_feedback_control: + # using an open-loop storage controller + self.add_input( + f"{commodity}_set_point", + val=0.0, + shape=n_timesteps, + units=commodity_rate_units, + ) + + self.using_feedback_control = using_feedback_control + # convert from seconds to hours + self.dt_hr = int(self.options["plant_config"]["plant"]["simulation"]["dt"]) / ( + 3600 + ) # convert from seconds to hours + + def compute(self, inputs, outputs, discrete_inputs=[], discrete_outputs=[]): + """Run the storage model. + + Configures the storage stateful model parameters (SOC limits, timestep, + thermal properties, etc.), executes the simulation, and stores the + results in OpenMDAO outputs. + + Args: + inputs (dict): Continuous input values (e.g., commodity_in, commodity_demand). + outputs (dict): Dictionary where model outputs (SOC, unmet demand, etc.) + are written. + discrete_inputs (dict): Discrete inputs such as control mode or Pyomo solver. + discrete_outputs (dict): Discrete outputs (unused in this component). + """ + # Below is an example of what the compute method would look like in the + # StoragePerformanceModel + # Do whatever pre-calculations are necessary, then run storage + # self.current_soc = self.config.init_soc_fraction + + # charge_rate = inputs["max_charge_rate"][0] + # if "max_discharge_rate" in inputs: + # discharge_rate = inputs["max_discharge_rate"][0] + # else: + # discharge_rate = inputs["max_charge_rate"][0] + # storage_capacity = inputs["storage_capacity"][0] + # outputs = self.run_storage( + # charge_rate, discharge_rate, storage_capacity, inputs, outputs, discrete_inputs + # ) + raise NotImplementedError("This method should be implemented in a subclass") + + def run_storage( + self, charge_rate, discharge_rate, storage_capacity, inputs, outputs, discrete_inputs + ): + """Run the storage performance model and calculate the outputs. This method should + be called in the `compute()` method of a subclass. + + Example: + >>> # In the `compute()` method: + >>> self.current_soc = self.config.init_soc_fraction + >>> charge_rate = inputs["max_charge_rate"][0] + >>> discharge_rate = inputs["max_discharge_rate"][0] + >>> storage_capacity = inputs["storage_capacity"] + >>> outputs = self.run_storage( + ... charge_rate, discharge_rate, storage_capacity, inputs, outputs, discrete_inputs + >>> ) + + Args: + charge_rate (float): storage charge rate in commodity_rate_units + discharge_rate (float): storage discharge rate in commodity_rate_units + storage_capacity (float): storage capacity in commodity_amount_units + inputs (om.vectors.default_vector.DefaultVector | dict): OpenMDAO inputs + to the `compute()` method. This should at least include the commodity + demand profile and input commodity profile. + outputs (om.vectors.default_vector.DefaultVector): OpenMDAO outputs + from the `compute()` method + discrete_inputs (om.core.component._DictValues, optional): OpenMDAO discrete + inputs to the `compute()` method. This is only required if using a + feedback control strategy and should contain the discrete input + 'pyomo_dispatch_solver'. + + Returns: + om.vectors.default_vector.DefaultVector: calculated OpenMDAO outputs. + """ + if "pyomo_dispatch_solver" in discrete_inputs: + dispatch = discrete_inputs["pyomo_dispatch_solver"] + # kwargs are tech-specific inputs to the simulate() method + kwargs = { + "charge_rate": charge_rate, + "discharge_rate": discharge_rate, + "storage_capacity": storage_capacity, + } + storage_commodity_out, soc = dispatch(self.simulate, kwargs, inputs) + + else: + storage_commodity_out, soc = self.simulate( + storage_dispatch_commands=inputs[f"{self.commodity}_set_point"], + charge_rate=charge_rate, + discharge_rate=discharge_rate, + storage_capacity=storage_capacity, + ) + + # determine storage charge and discharge + # storage_commodity_out is positive when the storage is discharged + # and negative when the storage is charged + storage_commodity_out = np.array(storage_commodity_out) + + # Storage design outputs + if discharge_rate > 0: + outputs["storage_duration"] = storage_capacity / discharge_rate + else: + outputs["storage_duration"] = 0 + + # Storage specific timeseries outputs + outputs[f"storage_{self.commodity}_charge"] = np.where( + storage_commodity_out < 0, storage_commodity_out, 0 + ) + outputs[f"storage_{self.commodity}_discharge"] = np.where( + storage_commodity_out > 0, storage_commodity_out, 0 + ) + outputs["SOC"] = soc + outputs[f"{self.commodity}_out"] = storage_commodity_out + + # Performance model outputs + outputs[f"rated_{self.commodity}_production"] = discharge_rate + outputs[f"total_{self.commodity}_produced"] = np.sum(storage_commodity_out) + outputs[f"annual_{self.commodity}_produced"] = outputs[ + f"total_{self.commodity}_produced" + ] * (1 / self.fraction_of_year_simulated) + + if outputs[f"rated_{self.commodity}_production"] <= 0: + outputs["capacity_factor"] = 0.0 + outputs["standard_capacity_factor"] = 0.0 + else: + outputs["capacity_factor"] = outputs[f"total_{self.commodity}_produced"] / ( + outputs[f"rated_{self.commodity}_production"] * self.n_timesteps + ) + # standard_capacity_factor is the ratio of commodity discharged to the discharge rate + total_commodity_discharged = outputs[f"storage_{self.commodity}_discharge"].sum() + outputs["standard_capacity_factor"] = total_commodity_discharged / ( + outputs[f"rated_{self.commodity}_production"] * self.n_timesteps + ) + return outputs + + def simulate( + self, + storage_dispatch_commands: list, + charge_rate: float, + discharge_rate: float, + storage_capacity: float, + sim_start_index: int = 0, + ): + """Run the storage model over a control window of ``n_control_window`` timesteps. + + Iterates through ``storage_dispatch_commands`` one timestep at a time. + A negative command requests charging; a positive command requests + discharging. Each command is clipped to the most restrictive of three + limits before it is applied: + + 1. **SOC headroom** - the remaining capacity (charge) or remaining + stored commodity (discharge), converted to a rate via + ``storage_capacity / dt_hr``. + 2. **Hardware rate limit** - ``charge_rate`` or ``discharge_rate``, + divided by the corresponding efficiency so the limit is expressed + in pre-efficiency rate units. + 3. **Commanded magnitude** - the absolute value of the dispatch command + itself (we never exceed what was asked for). + + After clipping, the result is scaled by the charge or discharge + efficiency to obtain the actual commodity flow into or out of the + storage, and the SOC is updated accordingly. + + This method is separated from ``compute()`` so the Pyomo dispatch + controller can call it directly to evaluate candidate schedules. + + Args: + storage_dispatch_commands (array_like[float]): + Dispatch set-points for each timestep in ``commodity_rate_units``. + Negative values command charging; positive values command + discharging. Length must equal ``config.n_control_window``. + charge_rate (float): + Maximum commodity input rate to storage in + ``commodity_rate_units`` (before charge efficiency is applied). + discharge_rate (float): + Maximum commodity output rate from storage in + ``commodity_rate_units`` (before discharge efficiency is applied). + storage_capacity (float): + Rated storage capacity in ``commodity_amount_units``. + sim_start_index (int, optional): + Starting index for writing into persistent output arrays. + Defaults to 0. + + Returns: + tuple[np.ndarray, np.ndarray] + storage_commodity_out_timesteps : + Commodity flow per timestep in ``commodity_rate_units``. + Positive = discharge (commodity leaving storage), + negative = charge (commodity entering storage). + soc_timesteps : + State of charge at the end of each timestep, in percent + (0-100). + """ + + n = len(storage_dispatch_commands) + storage_commodity_out_timesteps = np.zeros(n) + soc_timesteps = np.zeros(n) + + # Early return when storage cannot operate: zero capacity or both + # charge and discharge rates are zero. + if storage_capacity <= 0 or (charge_rate <= 0 and discharge_rate <= 0): + soc_timesteps[:] = self.current_soc * 100.0 + return storage_commodity_out_timesteps, soc_timesteps + + # Pre-compute scalar constants to avoid repeated attribute lookups + # and redundant divisions inside the per-timestep loop. + charge_eff = self.config.charge_efficiency + discharge_eff = self.config.discharge_efficiency + soc_max = self.config.max_soc_fraction + soc_min = self.config.min_soc_fraction + + commands = np.asarray(storage_dispatch_commands, dtype=float) + soc = float(self.current_soc) + + for t, cmd in enumerate(commands): + if cmd < 0.0: + # --- Charging --- + # headroom: how much more commodity the storage can accept, + # expressed as a rate (commodity_rate_units). + headroom = (soc_max - soc) * storage_capacity / self.dt_hr + + # Clip to the most restrictive limit, then apply efficiency. + # max(0, ...) guards against negative headroom when SOC + # slightly exceeds soc_max. + # correct headroom to not include charge_eff. + actual_charge = max(0.0, min(headroom / charge_eff, charge_rate, -cmd)) * charge_eff + + # Update SOC (actual_charge is in post-efficiency units) + soc += actual_charge / storage_capacity + + # Update the amount of commodity used to charge from the input stream + # If charge_eff<1, more commodity is pulled from the input stream than + # the commodity that goes into the storage. + storage_commodity_out_timesteps[t] = -actual_charge / charge_eff + else: + # --- Discharging --- + # headroom: how much commodity can still be drawn before + # hitting the minimum SOC, expressed as a rate. + headroom = (soc - soc_min) * storage_capacity / self.dt_hr + + # Clip to the most restrictive limit without applied efficiency. + # Efficiency losses occur as energy leaves storage. + actual_discharge = max( + 0.0, min(headroom, discharge_rate / discharge_eff, cmd / discharge_eff) + ) + + # Update SOC (actual_discharge is before efficiency losses are applied.) + soc -= actual_discharge / storage_capacity + + # If discharge_eff<1, then less commodity is output from the storage + # than the commodity discharged from storage + storage_commodity_out_timesteps[t] = actual_discharge * discharge_eff + + soc_timesteps[t] = soc * 100.0 + + # Persist the final SOC so subsequent simulate() calls (e.g. from the + # Pyomo controller across rolling windows) start where we left off. + self.current_soc = soc + return storage_commodity_out_timesteps, soc_timesteps diff --git a/h2integrate/storage/storage_performance_model.py b/h2integrate/storage/storage_performance_model.py new file mode 100644 index 000000000..f4eed89c7 --- /dev/null +++ b/h2integrate/storage/storage_performance_model.py @@ -0,0 +1,152 @@ +import numpy as np +from attrs import field, define + +from h2integrate.core.utilities import merge_shared_inputs +from h2integrate.core.validators import gt_zero, range_val, range_val_or_none +from h2integrate.storage.storage_baseclass import ( + StoragePerformanceBase, + StoragePerformanceBaseConfig, +) + + +@define(kw_only=True) +class StoragePerformanceModelConfig(StoragePerformanceBaseConfig): + """Configuration class for storage performance models. + + This class defines configuration parameters for simulating storage + performance with the Pyomo controllers. It includes + specifications such as capacity, charge rate, state-of-charge limits, + and charge/discharge efficiencies. + + Attributes: + commodity (str): name of commodity + commodity_rate_units (str): Units of the commodity (e.g., "kg/h"). + demand_profile (int | float | list): Demand values for each timestep, in + the same units as `commodity_rate_units`. May be a scalar for constant + demand or a list/array for time-varying demand. + max_capacity (float): Maximum storage energy capacity in commodity_amount_units. + Must be greater than zero. + max_charge_rate (float): Rated commodity capacity of the storage in commodity_rate_units. + Must be greater than zero. + min_soc_fraction (float): Minimum allowable state of charge as a fraction (0 to 1). + max_soc_fraction (float): Maximum allowable state of charge as a fraction (0 to 1). + init_soc_fraction (float): Initial state of charge as a fraction (0 to 1). + commodity_amount_units (str | None, optional): Units of the commodity as an amount + (i.e., kW*h or kg). If not provided, defaults to commodity_rate_units*h. + max_discharge_rate (float | None, optional): Maximum rate at which the commodity can be + discharged (in units per time step, e.g., "kg/time step"). This rate does not include + the discharge_efficiency. Only required if `charge_equals_discharge` is False. + charge_equals_discharge (bool, optional): If True, set the max_discharge_rate equal to the + max_charge_rate. If False, specify the max_discharge_rate as a value different than + the max_charge_rate. Defaults to True. + charge_efficiency (float | None, optional): Efficiency of charging the storage, represented + as a decimal between 0 and 1 (e.g., 0.9 for 90% efficiency). Optional if + `round_trip_efficiency` is provided. + discharge_efficiency (float | None, optional): Efficiency of discharging the storage, + represented as a decimal between 0 and 1 (e.g., 0.9 for 90% efficiency). Optional if + `round_trip_efficiency` is provided. + round_trip_efficiency (float | None, optional): Combined efficiency of charging and + discharging the storage, represented as a decimal between 0 and 1 (e.g., 0.81 for + 81% efficiency). Optional if `charge_efficiency` and `discharge_efficiency` are + provided. + + """ + + commodity: str = field() + commodity_rate_units: str = field() + + max_capacity: float = field(validator=gt_zero) + max_charge_rate: float = field(validator=gt_zero) + + init_soc_fraction: float = field(validator=range_val(0, 1)) + + commodity_amount_units: str = field(default=None) + max_discharge_rate: float | None = field(default=None) + charge_equals_discharge: bool = field(default=True) + + charge_efficiency: float | None = field(default=None, validator=range_val_or_none(0, 1)) + discharge_efficiency: float | None = field(default=None, validator=range_val_or_none(0, 1)) + round_trip_efficiency: float | None = field(default=None, validator=range_val_or_none(0, 1)) + + def __attrs_post_init__(self): + """ + Post-initialization logic to validate and calculate efficiencies. + + Ensures that either `charge_efficiency` and `discharge_efficiency` are provided, + or `round_trip_efficiency` is provided. If `round_trip_efficiency` is provided, + it calculates `charge_efficiency` and `discharge_efficiency` as the square root + of `round_trip_efficiency`. + """ + if (self.round_trip_efficiency is not None) and ( + self.charge_efficiency is None and self.discharge_efficiency is None + ): + # Calculate charge and discharge efficiencies from round-trip efficiency + self.charge_efficiency = np.sqrt(self.round_trip_efficiency) + self.discharge_efficiency = np.sqrt(self.round_trip_efficiency) + self.round_trip_efficiency = None + if self.charge_efficiency is None or self.discharge_efficiency is None: + raise ValueError( + "Exactly one of the following sets of parameters must be set: (a) " + "`round_trip_efficiency`, or (b) both `charge_efficiency` " + "and `discharge_efficiency`." + ) + + if self.charge_equals_discharge: + if ( + self.max_discharge_rate is not None + and self.max_discharge_rate != self.max_charge_rate + ): + msg = ( + "Max discharge rate does not equal max charge rate but charge_equals_discharge " + f"is True. Discharge rate is {self.max_discharge_rate} and charge rate " + f"is {self.max_charge_rate}." + ) + raise ValueError(msg) + + self.max_discharge_rate = self.max_charge_rate + + if not self.charge_equals_discharge and self.max_discharge_rate is None: + msg = ( + "max_discharge_rate is required when charge_equals_discharge is False. " + "Please input the discharge rate using the key `max_discharge_rate`." + ) + raise ValueError(msg) + + if self.commodity_amount_units is None: + self.commodity_amount_units = f"({self.commodity_rate_units})*h" + + +class StoragePerformanceModel(StoragePerformanceBase): + """OpenMDAO component for a storage component.""" + + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + + def setup(self): + self.config = StoragePerformanceModelConfig.from_dict( + merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance"), + strict=False, + additional_cls_name=self.__class__.__name__, + ) + + self.commodity = self.config.commodity + self.commodity_rate_units = self.config.commodity_rate_units + self.commodity_amount_units = self.config.commodity_amount_units + + super().setup() + + def compute(self, inputs, outputs, discrete_inputs=[], discrete_outputs=[]): + """Run the storage performance model.""" + self.current_soc = self.config.init_soc_fraction + + charge_rate = inputs["max_charge_rate"][0] + if "max_discharge_rate" in inputs: + discharge_rate = inputs["max_discharge_rate"][0] + else: + discharge_rate = inputs["max_charge_rate"][0] + storage_capacity = inputs["storage_capacity"][0] + outputs = self.run_storage( + charge_rate, discharge_rate, storage_capacity, inputs, outputs, discrete_inputs + ) diff --git a/h2integrate/storage/test/conftest.py b/h2integrate/storage/test/conftest.py index 3ae271aaf..be1f5dacd 100644 --- a/h2integrate/storage/test/conftest.py +++ b/h2integrate/storage/test/conftest.py @@ -6,7 +6,11 @@ import pytest -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) def pytest_sessionstart(session): diff --git a/h2integrate/storage/test/test_storage_auto_sizing.py b/h2integrate/storage/test/test_storage_auto_sizing.py new file mode 100644 index 000000000..dffd1951f --- /dev/null +++ b/h2integrate/storage/test/test_storage_auto_sizing.py @@ -0,0 +1,516 @@ +import numpy as np +import pytest +import openmdao.api as om + +from h2integrate.storage.simple_storage_auto_sizing import StorageAutoSizingModel +from h2integrate.control.control_strategies.storage.simple_openloop_controller import ( + SimpleStorageOpenLoopController, +) + + +@pytest.mark.regression +@pytest.mark.parametrize("n_timesteps", [24]) +def test_storage_autosizing_basic_performance_no_losses(plant_config, subtests): + # Basic test to ensure that storage outputs (charge profile, discharge profile, SOC) + # don't violate any performance constraints and that the calculated storage sizes + # are as expected + performance_model_config = { + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + "set_demand_as_avg_commodity_in": True, + "min_soc_fraction": 0.0, + "max_soc_fraction": 1.0, + "commodity_amount_units": "kg", + "charge_efficiency": 1.0, + "discharge_efficiency": 1.0, + } + + commodity_in = np.concat( + [np.full(3, 12.0), np.cumsum(np.ones(15)), np.full(3, 4.0), np.zeros(3)] + ) + commodity_demand = np.full(24, np.mean(commodity_in)) + + prob = om.Problem() + + prob.model.add_subsystem( + name="IVC1", + subsys=om.IndepVarComp(name="hydrogen_in", val=commodity_in, units="kg/h"), + promotes=["*"], + ) + + prob.model.add_subsystem( + name="IVC2", + subsys=om.IndepVarComp( + name="hydrogen_set_point", val=commodity_demand - commodity_in, units="kg/h" + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "storage", + StorageAutoSizingModel( + plant_config=plant_config, + tech_config={"model_inputs": {"performance_parameters": performance_model_config}}, + ), + promotes=["*"], + ) + + prob.setup() + + prob.run_model() + + charge_rate = prob.get_val("storage.max_charge_rate", units="kg/h")[0] + capacity = prob.get_val("storage.storage_capacity", units="kg")[0] + + with subtests.test("Charge rate value"): + assert pytest.approx(charge_rate, rel=1e-6) == np.max(commodity_in) + + with subtests.test("Storage capacity value"): + soc_kg = np.cumsum(commodity_demand - commodity_in) + soc_kg_adj = soc_kg + np.abs(np.min(soc_kg)) + expected_capacity = np.max(soc_kg_adj) - np.min(soc_kg_adj) + assert pytest.approx(capacity, rel=1e-6) == expected_capacity + + with subtests.test("Storage duration"): + expected_storage_duration = expected_capacity / np.max(commodity_in) + assert ( + pytest.approx(prob.get_val("storage_duration", units="h"), rel=1e-6) + == expected_storage_duration + ) + + # Basic sanity-check unit tests on storage performance + with subtests.test("Discharge is always positive"): + assert np.all(prob.get_val("storage.storage_hydrogen_discharge", units="kg/h") >= 0) + + with subtests.test("Charge is always negative"): + assert np.all(prob.get_val("storage.storage_hydrogen_charge", units="kg/h") <= 0) + + with subtests.test("Charge + Discharge == hydrogen_out"): + charge_plus_discharge = prob.get_val( + "storage.storage_hydrogen_charge", units="kg/h" + ) + prob.get_val("storage.storage_hydrogen_discharge", units="kg/h") + np.testing.assert_allclose( + charge_plus_discharge, prob.get_val("hydrogen_out", units="kg/h"), rtol=1e-6 + ) + + # Check that never charging and discharging at the same time + indx_soc_increase = np.argwhere( + np.diff(prob.model.get_val("storage.SOC", units="unitless"), prepend=True) > 0 + ).flatten() + indx_soc_decrease = np.argwhere( + np.diff(prob.model.get_val("storage.SOC", units="unitless"), prepend=False) < 0 + ).flatten() + indx_soc_same = np.argwhere( + np.diff(prob.model.get_val("storage.SOC", units="unitless"), prepend=True) == 0.0 + ).flatten() + + with subtests.test("SOC increases when charging"): + assert np.all( + prob.get_val("storage.storage_hydrogen_charge", units="kg/h")[indx_soc_increase] < 0 + ) + assert np.all( + prob.get_val("storage.storage_hydrogen_charge", units="kg/h")[indx_soc_decrease] == 0 + ) + assert np.all( + prob.get_val("storage.storage_hydrogen_charge", units="kg/h")[indx_soc_same] == 0 + ) + + with subtests.test("SOC decreases when discharging"): + assert np.all( + prob.get_val("storage.storage_hydrogen_discharge", units="kg/h")[indx_soc_decrease] > 0 + ) + assert np.all( + prob.get_val("storage.storage_hydrogen_discharge", units="kg/h")[indx_soc_increase] == 0 + ) + assert np.all( + prob.get_val("storage.storage_hydrogen_discharge", units="kg/h")[indx_soc_same] == 0 + ) + + # Check that charge rate limits are respected + with subtests.test("Charge never exceeds charge rate"): + assert np.all( + prob.get_val("storage.storage_hydrogen_charge", units="kg/h") >= -1 * charge_rate + ) + + with subtests.test("Discharge never exceeds discharge rate"): + assert np.all( + prob.get_val("storage.storage_hydrogen_discharge", units="kg/h") <= charge_rate + ) + + with subtests.test("Discharge never exceeds demand"): + assert np.all( + prob.get_val("storage.storage_hydrogen_discharge", units="kg/h") <= commodity_demand + ) + + with subtests.test("Cumulative charge/discharge does not exceed storage capacity"): + assert np.cumsum(prob.get_val("hydrogen_out", units="kg/h")).max() <= capacity + assert np.cumsum(prob.get_val("hydrogen_out", units="kg/h")).min() >= -1 * capacity + + # Check that demand is fully met, this is because this test starts off with charging the storage + # enough. In cases where the storage is not charged enough at the start, the demand may not + # fully be met + with subtests.test("Demand is fully met"): + combined_out = commodity_in + prob.get_val("hydrogen_out", units="kg/h") + combined_commodity_to_demand = np.clip(combined_out, a_min=0, a_max=commodity_demand) + np.testing.assert_allclose( + combined_commodity_to_demand, commodity_demand, rtol=1e-6, atol=1e-10 + ) + + with subtests.test("Unmet demand"): + unmet_demand = commodity_demand - combined_commodity_to_demand + np.testing.assert_allclose( + unmet_demand, + np.zeros(len(commodity_demand)), + rtol=1e-6, + atol=1e-10, + ) + + with subtests.test("Discharge Profile"): + expected_discharge = np.concat( + [np.zeros(3), np.arange(6, 0, -1), np.zeros(9), np.full(3, 3.0), np.full(3, 7.0)] + ) + np.testing.assert_allclose( + prob.get_val("storage_hydrogen_discharge", units="kg/h"), + expected_discharge, + rtol=1e-6, + atol=1e-10, + ) + + with subtests.test("Charge Profile"): + expected_charge = np.concat( + [np.full(3, -5), np.zeros(7), np.arange(-1, -8, -1), np.array([-3]), np.zeros(6)] + ) + np.testing.assert_allclose( + prob.get_val("storage_hydrogen_charge", units="kg/h"), + expected_charge, + rtol=1e-6, + atol=1e-10, + ) + + with subtests.test("Total unused commodity"): + combined_out = prob.get_val("hydrogen_out", units="kg/h") + commodity_in + unused_commodity_out = combined_out - commodity_demand + assert pytest.approx(unused_commodity_out.sum(), rel=1e-6) == 5.0 + + +@pytest.mark.regression +@pytest.mark.parametrize("n_timesteps", [24]) +def test_storage_autosizing_soc_bounds(plant_config, subtests): + performance_model_config = { + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + "set_demand_as_avg_commodity_in": True, + "min_soc_fraction": 0.1, + "max_soc_fraction": 0.9, + "commodity_amount_units": "kg", + "charge_efficiency": 1.0, + "discharge_efficiency": 1.0, + } + + commodity_in = np.concat( + [np.full(3, 12.0), np.cumsum(np.ones(15)), np.full(3, 4.0), np.zeros(3)] + ) + commodity_demand = np.full(24, np.mean(commodity_in)) + + prob = om.Problem() + + prob.model.add_subsystem( + name="IVC1", + subsys=om.IndepVarComp(name="hydrogen_in", val=commodity_in, units="kg/h"), + promotes=["*"], + ) + + prob.model.add_subsystem( + name="IVC2", + subsys=om.IndepVarComp( + name="hydrogen_set_point", val=commodity_demand - commodity_in, units="kg/h" + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "storage", + StorageAutoSizingModel( + plant_config=plant_config, + tech_config={"model_inputs": {"performance_parameters": performance_model_config}}, + ), + promotes=["*"], + ) + + prob.setup() + + prob.run_model() + + charge_rate = prob.get_val("storage.max_charge_rate", units="kg/h")[0] + capacity = prob.get_val("storage.storage_capacity", units="kg")[0] + + with subtests.test("Charge rate value"): + assert pytest.approx(charge_rate, rel=1e-6) == np.max(commodity_in) + + with subtests.test("Storage capacity value"): + soc_kg = np.cumsum(commodity_demand - commodity_in) + soc_kg_adj = soc_kg + np.abs(np.min(soc_kg)) + expected_usable_capacity = np.max(soc_kg_adj) - np.min(soc_kg_adj) + + expected_capacity = expected_usable_capacity / ( + performance_model_config["max_soc_fraction"] + - performance_model_config["min_soc_fraction"] + ) + assert pytest.approx(capacity, rel=1e-6) == expected_capacity + + with subtests.test("Storage duration"): + expected_storage_duration = expected_capacity / np.max(commodity_in) + assert ( + pytest.approx(prob.get_val("storage_duration", units="h"), rel=1e-6) + == expected_storage_duration + ) + + with subtests.test("SOC >= min SOC fraction"): + assert np.all( + prob.get_val("storage.SOC", units="unitless") + >= performance_model_config["min_soc_fraction"] + ) + + with subtests.test("SOC <= max SOC fraction"): + assert np.all( + prob.get_val("storage.SOC", units="unitless") + <= performance_model_config["max_soc_fraction"] + ) + + with subtests.test("Cumulative charge/discharge does not exceed storage capacity"): + assert np.cumsum(prob.get_val("hydrogen_out", units="kg/h")).max() <= capacity + assert np.cumsum(prob.get_val("hydrogen_out", units="kg/h")).min() >= -1 * capacity + + # Check that demand is fully met, this is because this test starts off with charging the storage + # enough. In cases where the storage is not charged enough at the start, the demand may not + # fully be met + with subtests.test("Demand is fully met"): + combined_out = commodity_in + prob.get_val("hydrogen_out", units="kg/h") + np.testing.assert_allclose( + np.clip(combined_out, a_min=0, a_max=commodity_demand), + commodity_demand, + rtol=1e-6, + atol=1e-10, + ) + + +@pytest.mark.regression +@pytest.mark.parametrize("n_timesteps", [24]) +def test_storage_autosizing_losses(plant_config, subtests): + charge_eff = 0.80 + discharge_eff = 0.75 + performance_model_config = { + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + "set_demand_as_avg_commodity_in": True, + "min_soc_fraction": 0.0, + "max_soc_fraction": 1.0, + "commodity_amount_units": "kg", + "charge_efficiency": charge_eff, + "discharge_efficiency": discharge_eff, + } + + commodity_in = np.concat( + [np.full(3, 12.0), np.cumsum(np.ones(15)), np.full(3, 4.0), np.zeros(3)] + ) + commodity_demand = np.full(24, np.mean(commodity_in)) + + prob = om.Problem() + + prob.model.add_subsystem( + name="IVC1", + subsys=om.IndepVarComp(name="hydrogen_in", val=commodity_in, units="kg/h"), + promotes=["*"], + ) + + prob.model.add_subsystem( + name="IVC2", + subsys=om.IndepVarComp( + name="hydrogen_set_point", val=commodity_demand - commodity_in, units="kg/h" + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "storage", + StorageAutoSizingModel( + plant_config=plant_config, + tech_config={"model_inputs": {"performance_parameters": performance_model_config}}, + ), + promotes=["*"], + ) + + prob.setup() + + prob.run_model() + + charge_rate = prob.get_val("storage.max_charge_rate", units="kg/h")[0] + capacity = prob.get_val("storage.storage_capacity", units="kg")[0] + + with subtests.test("Discharge never exceeds discharge rate"): + assert np.all( + prob.get_val("storage.storage_hydrogen_discharge", units="kg/h") + <= charge_rate * discharge_eff + ) + + with subtests.test("Charge never exceeds charge rate"): + assert np.all( + prob.get_val("storage.storage_hydrogen_charge", units="kg/h") + >= -1 * charge_rate / charge_eff + ) + + # When charging, the increase in the SOC (in kg) should be less than the commodity taken + # from the available commodity to charge + indx_soc_increase = np.argwhere( + np.diff(prob.model.get_val("storage.SOC", units="unitless"), prepend=True) > 0 + ).flatten() + soc_kg = prob.model.get_val("storage.SOC", units="unitless") * capacity + commodity_to_soc_when_charging = np.diff(soc_kg, prepend=True)[indx_soc_increase] + commodity_from_instream_when_charging = ( + -1 * prob.get_val("storage.storage_hydrogen_charge", units="kg/h")[indx_soc_increase] + ) + charge_losses = 1 - (commodity_to_soc_when_charging / commodity_from_instream_when_charging) + + with subtests.test( + "Charge efficiency: commodity from available > commodity to storage when charging" + ): + assert np.allclose(charge_losses, 1 - charge_eff, rtol=1e-6, atol=1e-10) + + # When discharging, the decrease in the SOC (in kg) should be more than the commodity added + # to the available commodity from discharging + indx_soc_decrease = np.argwhere( + np.diff(prob.model.get_val("storage.SOC", units="unitless"), prepend=False) < 0 + ).flatten() + commodity_from_soc_when_discharging = -1 * np.diff(soc_kg, prepend=False)[indx_soc_decrease] + commodity_to_outstream_when_discharging = prob.get_val( + "storage.storage_hydrogen_discharge", units="kg/h" + )[indx_soc_decrease] + discharge_losses = 1 - ( + commodity_to_outstream_when_discharging / commodity_from_soc_when_discharging + ) + + with subtests.test( + "Discharge efficiency: commodity to available < commodity from storage when discharging" + ): + assert np.allclose(discharge_losses, 1 - discharge_eff, rtol=1e-6, atol=1e-10) + + with subtests.test("Charge Profile"): + expected_charge = np.concat( + [np.full(3, -5), np.zeros(7), np.arange(-1, -9, -1), np.zeros(6)] + ) + np.testing.assert_allclose( + prob.get_val("storage_hydrogen_charge", units="kg/h"), + expected_charge, + rtol=1e-6, + atol=1e-10, + ) + + with subtests.test("Discharge Profile"): + expected_discharge = np.concat( + [ + np.zeros(3), + np.arange(6, 3, -1), + np.array([2.25]), + np.zeros(11), + np.full(3, 3.0), + np.array([7, 5.6, 0.0]), + ] + ) + np.testing.assert_allclose( + prob.get_val("storage_hydrogen_discharge", units="kg/h"), + expected_discharge, + rtol=1e-6, + atol=1e-10, + ) + + +@pytest.mark.regression +@pytest.mark.parametrize("n_timesteps", [24]) +def test_storage_autosizing_with_passthrough_controller(plant_config, subtests): + # Basic test to ensure that storage performance model + # works as-expected with the SimpleStorageOpenLoopController. + # This test should have the same results as test_storage_autosizing_basic_performance_no_losses + + tech_config = { + "shared_parameters": { + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + "set_demand_as_avg_commodity_in": True, + }, + "performance_parameters": { + "min_soc_fraction": 0.0, + "max_soc_fraction": 1.0, + "commodity_amount_units": "kg", + "charge_efficiency": 1.0, + "discharge_efficiency": 1.0, + }, + } + + commodity_in = np.concat( + [np.full(3, 12.0), np.cumsum(np.ones(15)), np.full(3, 4.0), np.zeros(3)] + ) + commodity_demand = np.full(24, np.mean(commodity_in)) + + prob = om.Problem() + + prob.model.add_subsystem( + name="IVC1", + subsys=om.IndepVarComp(name="hydrogen_in", val=commodity_in, units="kg/h"), + promotes=["*"], + ) + + prob.model.add_subsystem( + "controller", + SimpleStorageOpenLoopController( + plant_config=plant_config, + tech_config={"model_inputs": tech_config}, + ), + promotes=["*"], + ) + + prob.model.add_subsystem( + "storage", + StorageAutoSizingModel( + plant_config=plant_config, + tech_config={"model_inputs": tech_config}, + ), + promotes=["*"], + ) + + prob.setup() + + prob.run_model() + + charge_rate = prob.get_val("storage.max_charge_rate", units="kg/h")[0] + capacity = prob.get_val("storage.storage_capacity", units="kg")[0] + + with subtests.test("Charge rate value"): + assert pytest.approx(charge_rate, rel=1e-6) == np.max(commodity_in) + + with subtests.test("Storage capacity value"): + soc_kg = np.cumsum(commodity_demand - commodity_in) + soc_kg_adj = soc_kg + np.abs(np.min(soc_kg)) + expected_capacity = np.max(soc_kg_adj) - np.min(soc_kg_adj) + assert pytest.approx(capacity, rel=1e-6) == expected_capacity + + with subtests.test("Discharge Profile"): + expected_discharge = np.concat( + [np.zeros(3), np.arange(6, 0, -1), np.zeros(9), np.full(3, 3.0), np.full(3, 7.0)] + ) + np.testing.assert_allclose( + prob.get_val("storage_hydrogen_discharge", units="kg/h"), + expected_discharge, + rtol=1e-6, + atol=1e-10, + ) + + with subtests.test("Charge Profile"): + expected_charge = np.concat( + [np.full(3, -5), np.zeros(7), np.arange(-1, -8, -1), np.array([-3]), np.zeros(6)] + ) + np.testing.assert_allclose( + prob.get_val("storage_hydrogen_charge", units="kg/h"), + expected_charge, + rtol=1e-6, + atol=1e-10, + ) diff --git a/h2integrate/storage/test/test_generic_storage_pyo.py b/h2integrate/storage/test/test_storage_performance_model.py similarity index 66% rename from h2integrate/storage/test/test_generic_storage_pyo.py rename to h2integrate/storage/test/test_storage_performance_model.py index af26e05da..aa2996939 100644 --- a/h2integrate/storage/test/test_generic_storage_pyo.py +++ b/h2integrate/storage/test/test_storage_performance_model.py @@ -2,26 +2,34 @@ import pytest import openmdao.api as om -from h2integrate.storage.generic_storage_pyo import StoragePerformanceModel +from h2integrate.storage.storage_performance_model import StoragePerformanceModel +from h2integrate.control.control_strategies.storage.simple_openloop_controller import ( + SimpleStorageOpenLoopController, +) @pytest.mark.regression @pytest.mark.parametrize("n_timesteps", [24]) -def test_generic_storage_without_controller_dmd_lessthan_charge_rate(plant_config, subtests): +def test_generic_storage_with_simple_control_dmd_lessthan_charge_rate(plant_config, subtests): # this tests a case where the demand < charge rate and charge_rate=discharge_rate - performance_model_config = { - "commodity": "hydrogen", - "commodity_rate_units": "kg/h", - "max_capacity": 40, - "max_charge_rate": 10, - "min_charge_fraction": 0.1, - "max_charge_fraction": 1.0, - "init_charge_fraction": 0.1, - "n_control_window": 24, - "commodity_amount_units": "kg", - "charge_equals_discharge": True, - "charge_efficiency": 1.0, - "discharge_efficiency": 1.0, + model_inputs = { + "shared_parameters": { + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + }, + "performance_parameters": { + "max_capacity": 40, + "max_charge_rate": 10, + "min_soc_fraction": 0.1, + "max_soc_fraction": 1.0, + "init_soc_fraction": 0.1, + "commodity_amount_units": "kg", + "charge_equals_discharge": True, + "charge_efficiency": 1.0, + "discharge_efficiency": 1.0, + "demand_profile": 0.0, + }, + "control_parameters": {"set_demand_as_avg_commodity_in": False}, } prob = om.Problem() @@ -41,11 +49,20 @@ def test_generic_storage_without_controller_dmd_lessthan_charge_rate(plant_confi promotes=["*"], ) + prob.model.add_subsystem( + "control", + SimpleStorageOpenLoopController( + plant_config=plant_config, + tech_config={"model_inputs": model_inputs}, + ), + promotes=["*"], + ) + prob.model.add_subsystem( "storage", StoragePerformanceModel( plant_config=plant_config, - tech_config={"model_inputs": {"performance_parameters": performance_model_config}}, + tech_config={"model_inputs": model_inputs}, ), promotes=["*"], ) @@ -54,6 +71,8 @@ def test_generic_storage_without_controller_dmd_lessthan_charge_rate(plant_confi prob.run_model() + performance_model_config = model_inputs["performance_parameters"] + charge_rate = prob.get_val("storage.max_charge_rate", units="kg/h")[0] discharge_rate = prob.get_val("storage.max_charge_rate", units="kg/h")[0] capacity = prob.get_val("storage.storage_capacity", units="kg")[0] @@ -72,17 +91,17 @@ def test_generic_storage_without_controller_dmd_lessthan_charge_rate(plant_confi with subtests.test("Charge is always negative"): assert np.all(prob.get_val("storage.storage_hydrogen_charge", units="kg/h") <= 0) - with subtests.test("Charge + Discharge == storage_hydrogen_out"): + with subtests.test("Charge + Discharge == hydrogen_out"): charge_plus_discharge = prob.get_val( "storage.storage_hydrogen_charge", units="kg/h" ) + prob.get_val("storage.storage_hydrogen_discharge", units="kg/h") np.testing.assert_allclose( - charge_plus_discharge, prob.get_val("storage_hydrogen_out", units="kg/h"), rtol=1e-6 + charge_plus_discharge, prob.get_val("hydrogen_out", units="kg/h"), rtol=1e-6 ) with subtests.test("Initial SOC is correct"): assert ( pytest.approx(prob.model.get_val("storage.SOC", units="unitless")[0], rel=1e-6) - == performance_model_config["init_charge_fraction"] + == performance_model_config["init_soc_fraction"] ) indx_soc_increase = np.argwhere( @@ -120,13 +139,13 @@ def test_generic_storage_without_controller_dmd_lessthan_charge_rate(plant_confi with subtests.test("Max SOC <= Max storage percent"): assert ( prob.get_val("storage.SOC", units="unitless").max() - <= performance_model_config["max_charge_fraction"] + <= performance_model_config["max_soc_fraction"] ) with subtests.test("Min SOC >= Min storage percent"): assert ( prob.get_val("storage.SOC", units="unitless").min() - >= performance_model_config["min_charge_fraction"] + >= performance_model_config["min_soc_fraction"] ) with subtests.test("Charge never exceeds charge rate"): @@ -146,8 +165,8 @@ def test_generic_storage_without_controller_dmd_lessthan_charge_rate(plant_confi ) with subtests.test("Cumulative charge/discharge does not exceed storage capacity"): - assert np.cumsum(prob.get_val("storage_hydrogen_out", units="kg/h")).max() <= capacity - assert np.cumsum(prob.get_val("storage_hydrogen_out", units="kg/h")).min() >= -1 * capacity + assert np.cumsum(prob.get_val("hydrogen_out", units="kg/h")).max() <= capacity + assert np.cumsum(prob.get_val("hydrogen_out", units="kg/h")).min() >= -1 * capacity with subtests.test("Expected discharge"): expected_discharge = np.concat([np.zeros(18), np.ones(6)]) @@ -165,24 +184,42 @@ def test_generic_storage_without_controller_dmd_lessthan_charge_rate(plant_confi rtol=1e-6, ) + with subtests.test("Expected capacity factor"): + assert ( + pytest.approx(-12.5, rel=1e-6) + == prob.get_val("storage.capacity_factor", units="percent")[0] + ) + + with subtests.test("Expected standard capacity factor"): + assert ( + pytest.approx(2.5, rel=1e-6) + == prob.get_val("storage.standard_capacity_factor", units="percent")[0] + ) + @pytest.mark.regression @pytest.mark.parametrize("n_timesteps", [24]) -def test_generic_storage_without_controller_charge_rate_lessthan_demand(plant_config, subtests): +def test_generic_storage_with_simple_control_charge_rate_lessthan_demand(plant_config, subtests): # this tests a case where the charge_rate < demand and charge_rate=discharge_rate - performance_model_config = { - "commodity": "hydrogen", - "commodity_rate_units": "kg/h", - "max_capacity": 400, - "max_charge_rate": 100, - "min_charge_fraction": 0.1, - "max_charge_fraction": 1.0, - "init_charge_fraction": 0.1, - "n_control_window": 24, - "commodity_amount_units": "kg", - "charge_equals_discharge": True, - "charge_efficiency": 1.0, - "discharge_efficiency": 1.0, + model_inputs = { + "shared_parameters": { + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + }, + "performance_parameters": { + "max_capacity": 400, + "max_charge_rate": 100, + "min_soc_fraction": 0.1, + "max_soc_fraction": 1.0, + "init_soc_fraction": 0.1, + "n_control_window": 24, + "commodity_amount_units": "kg", + "charge_equals_discharge": True, + "charge_efficiency": 1.0, + "discharge_efficiency": 1.0, + "demand_profile": 0.0, + }, + "control_parameters": {"set_demand_as_avg_commodity_in": False}, } prob = om.Problem() @@ -202,11 +239,20 @@ def test_generic_storage_without_controller_charge_rate_lessthan_demand(plant_co promotes=["*"], ) + prob.model.add_subsystem( + "control", + SimpleStorageOpenLoopController( + plant_config=plant_config, + tech_config={"model_inputs": model_inputs}, + ), + promotes=["*"], + ) + prob.model.add_subsystem( "storage", StoragePerformanceModel( plant_config=plant_config, - tech_config={"model_inputs": {"performance_parameters": performance_model_config}}, + tech_config={"model_inputs": model_inputs}, ), promotes=["*"], ) @@ -220,6 +266,8 @@ def test_generic_storage_without_controller_charge_rate_lessthan_demand(plant_co prob.run_model() + performance_model_config = model_inputs["performance_parameters"] + charge_rate = prob.get_val("storage.max_charge_rate", units="kg/h")[0] discharge_rate = prob.get_val("storage.max_charge_rate", units="kg/h")[0] capacity = prob.get_val("storage.storage_capacity", units="kg")[0] @@ -238,17 +286,17 @@ def test_generic_storage_without_controller_charge_rate_lessthan_demand(plant_co with subtests.test("Charge is always negative"): assert np.all(prob.get_val("storage.storage_hydrogen_charge", units="kg/h") <= 0) - with subtests.test("Charge + Discharge == storage_hydrogen_out"): + with subtests.test("Charge + Discharge == hydrogen_out"): charge_plus_discharge = prob.get_val( "storage.storage_hydrogen_charge", units="kg/h" ) + prob.get_val("storage.storage_hydrogen_discharge", units="kg/h") np.testing.assert_allclose( - charge_plus_discharge, prob.get_val("storage_hydrogen_out", units="kg/h"), rtol=1e-6 + charge_plus_discharge, prob.get_val("hydrogen_out", units="kg/h"), rtol=1e-6 ) with subtests.test("Initial SOC is correct"): init_soc_expected = ( - performance_model_config["init_charge_fraction"] - - prob.get_val("storage_hydrogen_out", units="kg/h")[0] / capacity + performance_model_config["init_soc_fraction"] + - prob.get_val("hydrogen_out", units="kg/h")[0] / capacity ) assert ( pytest.approx(prob.model.get_val("storage.SOC", units="unitless")[0], rel=1e-6) @@ -290,13 +338,13 @@ def test_generic_storage_without_controller_charge_rate_lessthan_demand(plant_co with subtests.test("Max SOC <= Max storage percent"): assert ( prob.get_val("storage.SOC", units="unitless").max() - <= performance_model_config["max_charge_fraction"] + <= performance_model_config["max_soc_fraction"] ) with subtests.test("Min SOC >= Min storage percent"): assert ( prob.get_val("storage.SOC", units="unitless").min() - >= performance_model_config["min_charge_fraction"] + >= performance_model_config["min_soc_fraction"] ) with subtests.test("Charge never exceeds charge rate"): @@ -316,8 +364,8 @@ def test_generic_storage_without_controller_charge_rate_lessthan_demand(plant_co ) with subtests.test("Cumulative charge/discharge does not exceed storage capacity"): - assert np.cumsum(prob.get_val("storage_hydrogen_out", units="kg/h")).max() <= capacity - assert np.cumsum(prob.get_val("storage_hydrogen_out", units="kg/h")).min() >= -1 * capacity + assert np.cumsum(prob.get_val("hydrogen_out", units="kg/h")).max() <= capacity + assert np.cumsum(prob.get_val("hydrogen_out", units="kg/h")).min() >= -1 * capacity with subtests.test("Expected discharge"): expected_discharge = np.concat( @@ -340,25 +388,51 @@ def test_generic_storage_without_controller_charge_rate_lessthan_demand(plant_co rtol=1e-6, ) + with subtests.test("Total charge = total discharge"): + assert ( + pytest.approx( + -1 * prob.get_val("storage.storage_hydrogen_charge", units="kg/h").sum(), rel=1e-6 + ) + == prob.get_val("storage.storage_hydrogen_discharge", units="kg/h").sum() + ) + + with subtests.test("Expected capacity factor"): + assert ( + pytest.approx(0.0, rel=1e-6) + == prob.get_val("storage.capacity_factor", units="percent")[0] + ) + + with subtests.test("Expected standard capacity factor"): + assert ( + pytest.approx(15.416666, rel=1e-6) + == prob.get_val("storage.standard_capacity_factor", units="percent")[0] + ) + @pytest.mark.regression @pytest.mark.parametrize("n_timesteps", [24]) -def test_generic_storage_without_controller_zero_size(plant_config, subtests): +def test_generic_storage_with_simple_control_zero_size(plant_config, subtests): # this tests a case where the charge_rate < demand and charge_rate=discharge_rate - performance_model_config = { - "commodity": "hydrogen", - "commodity_rate_units": "kg/h", - "max_capacity": 40, - "max_charge_rate": 10, - "max_discharge_rate": 10, - "min_charge_fraction": 0.1, - "max_charge_fraction": 1.0, - "init_charge_fraction": 0.1, - "n_control_window": 24, - "commodity_amount_units": "kg", - "charge_equals_discharge": False, - "charge_efficiency": 1.0, - "discharge_efficiency": 1.0, + model_inputs = { + "shared_parameters": { + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + }, + "performance_parameters": { + "max_capacity": 40, + "max_charge_rate": 10, + "max_discharge_rate": 10, + "min_soc_fraction": 0.1, + "max_soc_fraction": 1.0, + "init_soc_fraction": 0.1, + "n_control_window": 24, + "commodity_amount_units": "kg", + "charge_equals_discharge": False, + "charge_efficiency": 1.0, + "discharge_efficiency": 1.0, + "demand_profile": 0.0, + }, + "control_parameters": {"set_demand_as_avg_commodity_in": False}, } prob = om.Problem() @@ -378,11 +452,20 @@ def test_generic_storage_without_controller_zero_size(plant_config, subtests): promotes=["*"], ) + prob.model.add_subsystem( + "control", + SimpleStorageOpenLoopController( + plant_config=plant_config, + tech_config={"model_inputs": model_inputs}, + ), + promotes=["*"], + ) + prob.model.add_subsystem( "storage", StoragePerformanceModel( plant_config=plant_config, - tech_config={"model_inputs": {"performance_parameters": performance_model_config}}, + tech_config={"model_inputs": model_inputs}, ), promotes=["*"], ) @@ -398,6 +481,8 @@ def test_generic_storage_without_controller_zero_size(plant_config, subtests): prob.run_model() + performance_model_config = model_inputs["performance_parameters"] + charge_rate = prob.get_val("storage.max_charge_rate", units="kg/h")[0] discharge_rate = prob.get_val("storage.max_discharge_rate", units="kg/h")[0] capacity = prob.get_val("storage.storage_capacity", units="kg")[0] @@ -447,7 +532,7 @@ def test_generic_storage_without_controller_zero_size(plant_config, subtests): with subtests.test("SOC never changes"): assert np.all( prob.get_val("storage.SOC", units="unitless") - == performance_model_config["init_charge_fraction"] + == performance_model_config["init_soc_fraction"] ) # Test when capacity is zero @@ -481,33 +566,56 @@ def test_generic_storage_without_controller_zero_size(plant_config, subtests): with subtests.test("SOC never changes"): assert np.all( prob.get_val("storage.SOC", units="unitless") - == performance_model_config["init_charge_fraction"] + == performance_model_config["init_soc_fraction"] + ) + + with subtests.test("Expected capacity factor"): + assert ( + pytest.approx(0.0, rel=1e-6) + == prob.get_val("storage.capacity_factor", units="percent")[0] + ) + + with subtests.test("Expected standard capacity factor"): + assert ( + pytest.approx(0.0, rel=1e-6) + == prob.get_val("storage.standard_capacity_factor", units="percent")[0] ) @pytest.mark.regression @pytest.mark.parametrize("n_timesteps", [24]) -def test_generic_storage_without_controller_with_losses(plant_config, subtests): +def test_generic_storage_with_simple_control_with_losses(plant_config, subtests): # this tests a case where the demand < charge rate and charge_rate=discharge_rate - performance_model_config = { - "commodity": "hydrogen", - "commodity_rate_units": "kg/h", - "max_capacity": 40, - "max_charge_rate": 10, - "min_charge_fraction": 0.1, - "max_charge_fraction": 1.0, - "init_charge_fraction": 0.1, - "n_control_window": 24, - "commodity_amount_units": "kg", - "charge_equals_discharge": True, - "charge_efficiency": 0.80, - "discharge_efficiency": 0.75, + charge_eff = 0.80 + discharge_eff = 0.75 + model_inputs = { + "shared_parameters": { + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + }, + "performance_parameters": { + "max_capacity": 40, + "max_charge_rate": 10, + "min_soc_fraction": 0.1, + "max_soc_fraction": 1.0, + "init_soc_fraction": 0.1, + "n_control_window": 24, + "commodity_amount_units": "kg", + "charge_equals_discharge": True, + "charge_efficiency": charge_eff, + "discharge_efficiency": discharge_eff, + "demand_profile": 0.0, + }, + "control_parameters": {"set_demand_as_avg_commodity_in": False}, } prob = om.Problem() - commodity_demand = np.full(24, 5.0) - commodity_in = np.concat([np.zeros(3), np.cumsum(np.ones(15)), np.full(6, 4.0)]) + # demand is below then above the charge rate + commodity_demand = np.concat([np.full(12, 5.0), np.full(12, 20.0)]) + # start with charging for first 3 hours (in>demand), + # then discharging at last 6 hours (in= Min storage percent"): assert ( prob.get_val("storage.SOC", units="unitless").min() - >= performance_model_config["min_charge_fraction"] + >= performance_model_config["min_soc_fraction"] ) with subtests.test("Charge never exceeds charge rate"): assert ( prob.get_val("storage.storage_hydrogen_charge", units="kg/h").min() - >= -1 * charge_rate * performance_model_config["charge_efficiency"] + >= -1 * charge_rate / charge_eff ) with subtests.test("Discharge never exceeds discharge rate"): assert ( - prob.get_val("storage.storage_hydrogen_discharge", units="kg/h").max() - <= discharge_rate * performance_model_config["discharge_efficiency"] + prob.get_val("storage.storage_hydrogen_discharge", units="kg/h").max() <= discharge_rate ) with subtests.test("Discharge never exceeds demand"): assert np.all( - prob.get_val("storage.storage_hydrogen_discharge", units="kg/h").max() - <= commodity_demand + prob.get_val("storage.storage_hydrogen_discharge", units="kg/h") <= commodity_demand ) with subtests.test("Cumulative charge/discharge does not exceed storage capacity"): - assert np.cumsum(prob.get_val("storage_hydrogen_out", units="kg/h")).max() <= capacity - assert np.cumsum(prob.get_val("storage_hydrogen_out", units="kg/h")).min() >= -1 * capacity + assert ( + np.cumsum(prob.get_val("hydrogen_out", units="kg/h")).max() <= capacity * discharge_eff + ) + assert ( + np.cumsum(prob.get_val("hydrogen_out", units="kg/h")).min() + >= -1 * capacity / charge_eff + ) + + # When charging, the increase in the SOC (in kg) should be less than the commodity taken + # from the available commodity to charge + soc_kg = prob.model.get_val("storage.SOC", units="unitless") * capacity + commodity_to_soc_when_charging = np.diff(soc_kg, prepend=True)[indx_soc_increase] + commodity_from_instream_when_charging = ( + -1 * prob.get_val("storage.storage_hydrogen_charge", units="kg/h")[indx_soc_increase] + ) + charge_losses = 1 - (commodity_to_soc_when_charging / commodity_from_instream_when_charging) + + with subtests.test( + "Charge efficiency: commodity from available > commodity to storage when charging" + ): + assert np.allclose(charge_losses, 1 - charge_eff, rtol=1e-6, atol=1e-10) + + # When discharging, the decrease in the SOC (in kg) should be more than the commodity added + # to the available commodity from discharging + commodity_from_soc_when_discharging = -1 * np.diff(soc_kg, prepend=False)[indx_soc_decrease] + commodity_to_outstream_when_discharging = prob.get_val( + "storage.storage_hydrogen_discharge", units="kg/h" + )[indx_soc_decrease] + discharge_losses = 1 - ( + commodity_to_outstream_when_discharging / commodity_from_soc_when_discharging + ) + + with subtests.test( + "Discharge efficiency: commodity to available < commodity from storage when discharging" + ): + assert np.allclose(discharge_losses, 1 - discharge_eff, rtol=1e-6, atol=1e-10) with subtests.test("Expected discharge"): expected_discharge = np.concat( - [np.zeros(18), performance_model_config["discharge_efficiency"] * np.ones(6)] + [np.zeros(3), np.array([4, 1.4]), np.zeros(7), np.array([6]), np.zeros(11)] ) np.testing.assert_allclose( prob.get_val("storage.storage_hydrogen_discharge", units="kg/h"), @@ -643,7 +799,7 @@ def test_generic_storage_without_controller_with_losses(plant_config, subtests): with subtests.test("Expected charge"): expected_charge = np.concat( - [np.zeros(8), np.arange(-0.8, -7.2, -0.8), np.array([-5.76, -1.152]), np.zeros(6)] + [np.full(3, -3.0), np.zeros(5), np.arange(-1, -5, -1), np.zeros(12)] ) np.testing.assert_allclose( prob.get_val("storage.storage_hydrogen_charge", units="kg/h"), @@ -651,23 +807,44 @@ def test_generic_storage_without_controller_with_losses(plant_config, subtests): rtol=1e-6, ) + with subtests.test("Expected capacity factor"): + assert ( + pytest.approx(-3.16666666, rel=1e-6) + == prob.get_val("storage.capacity_factor", units="percent")[0] + ) + + with subtests.test("Expected standard capacity factor"): + assert ( + pytest.approx(4.750, rel=1e-6) + == prob.get_val("storage.standard_capacity_factor", units="percent")[0] + ) + @pytest.mark.regression @pytest.mark.parametrize("n_timesteps", [24]) -def test_generic_storage_without_controller_with_losses_round_trip(plant_config, subtests): +def test_generic_storage_with_simple_control_with_losses_round_trip(plant_config, subtests): # this tests a case where the demand < charge rate and charge_rate=discharge_rate - performance_model_config = { - "commodity": "hydrogen", - "commodity_rate_units": "kg/h", - "max_capacity": 40, - "max_charge_rate": 10, - "min_charge_fraction": 0.1, - "max_charge_fraction": 1.0, - "init_charge_fraction": 0.1, - "n_control_window": 24, - "commodity_amount_units": "kg", - "charge_equals_discharge": True, - "round_trip_efficiency": 0.6, + charge_eff = 0.75 + discharge_eff = 0.75 + round_trip_eff = charge_eff * discharge_eff + model_inputs = { + "shared_parameters": { + "commodity": "hydrogen", + "commodity_rate_units": "kg/h", + }, + "performance_parameters": { + "max_capacity": 40, + "max_charge_rate": 10, + "min_soc_fraction": 0.1, + "max_soc_fraction": 1.0, + "init_soc_fraction": 0.1, + "n_control_window": 24, + "commodity_amount_units": "kg", + "charge_equals_discharge": True, + "round_trip_efficiency": round_trip_eff, + "demand_profile": 0.0, + }, + "control_parameters": {"set_demand_as_avg_commodity_in": False}, } prob = om.Problem() @@ -687,11 +864,20 @@ def test_generic_storage_without_controller_with_losses_round_trip(plant_config, promotes=["*"], ) + prob.model.add_subsystem( + "control", + SimpleStorageOpenLoopController( + plant_config=plant_config, + tech_config={"model_inputs": model_inputs}, + ), + promotes=["*"], + ) + prob.model.add_subsystem( "storage", StoragePerformanceModel( plant_config=plant_config, - tech_config={"model_inputs": {"performance_parameters": performance_model_config}}, + tech_config={"model_inputs": model_inputs}, ), promotes=["*"], ) @@ -700,6 +886,8 @@ def test_generic_storage_without_controller_with_losses_round_trip(plant_config, prob.run_model() + performance_model_config = model_inputs["performance_parameters"] + charge_rate = prob.get_val("storage.max_charge_rate", units="kg/h")[0] discharge_rate = prob.get_val("storage.max_charge_rate", units="kg/h")[0] capacity = prob.get_val("storage.storage_capacity", units="kg")[0] @@ -718,17 +906,17 @@ def test_generic_storage_without_controller_with_losses_round_trip(plant_config, with subtests.test("Charge is always negative"): assert np.all(prob.get_val("storage.storage_hydrogen_charge", units="kg/h") <= 0) - with subtests.test("Charge + Discharge == storage_hydrogen_out"): + with subtests.test("Charge + Discharge == hydrogen_out"): charge_plus_discharge = prob.get_val( "storage.storage_hydrogen_charge", units="kg/h" ) + prob.get_val("storage.storage_hydrogen_discharge", units="kg/h") np.testing.assert_allclose( - charge_plus_discharge, prob.get_val("storage_hydrogen_out", units="kg/h"), rtol=1e-6 + charge_plus_discharge, prob.get_val("hydrogen_out", units="kg/h"), rtol=1e-6 ) with subtests.test("Initial SOC is correct"): assert ( pytest.approx(prob.model.get_val("storage.SOC", units="unitless")[0], rel=1e-6) - == performance_model_config["init_charge_fraction"] + == performance_model_config["init_soc_fraction"] ) indx_soc_increase = np.argwhere( @@ -766,19 +954,20 @@ def test_generic_storage_without_controller_with_losses_round_trip(plant_config, with subtests.test("Max SOC <= Max storage percent"): assert ( prob.get_val("storage.SOC", units="unitless").max() - <= performance_model_config["max_charge_fraction"] + <= performance_model_config["max_soc_fraction"] ) with subtests.test("Min SOC >= Min storage percent"): assert ( prob.get_val("storage.SOC", units="unitless").min() - >= performance_model_config["min_charge_fraction"] + >= performance_model_config["min_soc_fraction"] ) with subtests.test("Charge never exceeds charge rate"): - assert prob.get_val( - "storage.storage_hydrogen_charge", units="kg/h" - ).min() >= -1 * charge_rate * np.sqrt(performance_model_config["round_trip_efficiency"]) + assert ( + prob.get_val("storage.storage_hydrogen_charge", units="kg/h").min() + >= -1 * charge_rate / charge_eff + ) with subtests.test("Discharge never exceeds discharge rate"): assert prob.get_val( @@ -792,13 +981,16 @@ def test_generic_storage_without_controller_with_losses_round_trip(plant_config, ) with subtests.test("Cumulative charge/discharge does not exceed storage capacity"): - assert np.cumsum(prob.get_val("storage_hydrogen_out", units="kg/h")).max() <= capacity - assert np.cumsum(prob.get_val("storage_hydrogen_out", units="kg/h")).min() >= -1 * capacity + assert ( + np.cumsum(prob.get_val("hydrogen_out", units="kg/h")).max() <= capacity * discharge_eff + ) + assert ( + np.cumsum(prob.get_val("hydrogen_out", units="kg/h")).min() + >= -1 * capacity / charge_eff + ) with subtests.test("Expected discharge"): - expected_discharge = np.concat( - [np.zeros(18), np.sqrt(performance_model_config["round_trip_efficiency"]) * np.ones(6)] - ) + expected_discharge = np.concat([np.zeros(18), np.ones(6)]) np.testing.assert_allclose( prob.get_val("storage.storage_hydrogen_discharge", units="kg/h"), expected_discharge, @@ -809,12 +1001,8 @@ def test_generic_storage_without_controller_with_losses_round_trip(plant_config, expected_charge = np.concat( [ np.zeros(8), - np.arange( - -1 * np.sqrt(performance_model_config["round_trip_efficiency"]), - -6.5, - -np.sqrt(performance_model_config["round_trip_efficiency"]), - ), - np.array([-6.28548009, -1.41676815]), + np.arange(-1, -10, -1), + np.array([-3]), np.zeros(6), ] ) @@ -823,3 +1011,15 @@ def test_generic_storage_without_controller_with_losses_round_trip(plant_config, expected_charge, rtol=1e-6, ) + + with subtests.test("Expected capacity factor"): + assert ( + pytest.approx(-17.5, rel=1e-6) + == prob.get_val("storage.capacity_factor", units="percent")[0] + ) + + with subtests.test("Expected standard capacity factor"): + assert ( + pytest.approx(2.5, rel=1e-6) + == prob.get_val("storage.standard_capacity_factor", units="percent")[0] + ) diff --git a/h2integrate/test/conftest.py b/h2integrate/test/conftest.py index aec692e38..a4763a760 100644 --- a/h2integrate/test/conftest.py +++ b/h2integrate/test/conftest.py @@ -4,7 +4,11 @@ import os -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) def pytest_sessionstart(session): diff --git a/h2integrate/tools/profast_tools.py b/h2integrate/tools/profast_tools.py index 8aed16123..33d0c810b 100644 --- a/h2integrate/tools/profast_tools.py +++ b/h2integrate/tools/profast_tools.py @@ -134,11 +134,16 @@ def make_price_breakdown(price_breakdown, pf_config): total_price_capex += cash_outflow_prices.loc[ cash_outflow_prices["Name"] == item, "NPV" ].tolist()[0] - for item in capital_items: - capex_fraction[item] = ( - cash_outflow_prices.loc[cash_outflow_prices["Name"] == item, "NPV"].tolist()[0] - / total_price_capex - ) + if total_price_capex != 0: + for item in capital_items: + capex_fraction[item] = ( + cash_outflow_prices.loc[cash_outflow_prices["Name"] == item, "NPV"].tolist()[0] + / total_price_capex + ) + else: + for item in capital_items: + capex_fraction[item] = 0 + cap_expense = ( price_breakdown.loc[price_breakdown["Name"] == "Repayment of debt", "NPV"].tolist()[0] + price_breakdown.loc[price_breakdown["Name"] == "Interest expense", "NPV"].tolist()[0] diff --git a/h2integrate/tools/test/conftest.py b/h2integrate/tools/test/conftest.py index aec692e38..a4763a760 100644 --- a/h2integrate/tools/test/conftest.py +++ b/h2integrate/tools/test/conftest.py @@ -4,7 +4,11 @@ import os -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) def pytest_sessionstart(session): diff --git a/h2integrate/transporters/cable.py b/h2integrate/transporters/cable.py index 5b369367b..95730cad5 100644 --- a/h2integrate/transporters/cable.py +++ b/h2integrate/transporters/cable.py @@ -6,24 +6,29 @@ class CablePerformanceModel(om.ExplicitComponent): Pass-through cable with no losses. """ + _time_step_bounds = ( + 1, + 1e9, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): self.options.declare("transport_item", values=["electricity"]) + self.options.declare("plant_config", types=dict) def setup(self): + n_timesteps = int(self.options["plant_config"]["plant"]["simulation"]["n_timesteps"]) self.input_name = self.options["transport_item"] + "_in" self.output_name = self.options["transport_item"] + "_out" self.add_input( self.input_name, val=-1.0, - shape_by_conn=True, - copy_shape=self.output_name, + shape=n_timesteps, units="kW", ) self.add_output( self.output_name, val=-1.0, - shape_by_conn=True, - copy_shape=self.input_name, + shape=n_timesteps, units="kW", ) diff --git a/h2integrate/transporters/gas_stream_combiner.py b/h2integrate/transporters/gas_stream_combiner.py new file mode 100644 index 000000000..9a53bb936 --- /dev/null +++ b/h2integrate/transporters/gas_stream_combiner.py @@ -0,0 +1,113 @@ +""" +Gas stream combiner for multivariable streams. + +Combines multiple gas streams using mass-weighted averaging for intensive properties +(temperature, pressure, composition) while summing extensive properties (mass flow). +""" + +import numpy as np +import openmdao.api as om +from attrs import field, define + +from h2integrate.core.utilities import BaseConfig, merge_shared_inputs +from h2integrate.core.commodity_stream_definitions import multivariable_streams + + +@define(kw_only=True) +class GasStreamCombinerConfig(BaseConfig): + """Configuration for the gas stream combiner. + + Attributes: + commodity: Type of multivariable stream (e.g., 'wellhead_gas_mixture') + in_streams: Number of inflow streams to combine + """ + + commodity: str = field(default="wellhead_gas_mixture") + in_streams: int = field(default=2) + + def __attrs_post_init__(self): + if self.commodity not in multivariable_streams: + raise ValueError( + f"Unknown commodity '{self.commodity}'. " + f"Available: {list(multivariable_streams.keys())}" + ) + + +class GasStreamCombinerPerformanceModel(om.ExplicitComponent): + """ + Combine multiple gas streams into one using mass-weighted averaging. + + Total mass flow is summed. Temperature, pressure, and compositions are + mass-weighted averages of the input streams. + """ + + _time_step_bounds = ( + 1, + 1e9, + ) # (min, max) time step lengths (in seconds) compatible with this model + + def initialize(self): + self.options.declare("driver_config", types=dict) + self.options.declare("plant_config", types=dict) + self.options.declare("tech_config", types=dict) + + def setup(self): + self.config = GasStreamCombinerConfig.from_dict( + merge_shared_inputs(self.options["tech_config"]["model_inputs"], "performance") + ) + + n_timesteps = int(self.options["plant_config"]["plant"]["simulation"]["n_timesteps"]) + stream_def = multivariable_streams[self.config.commodity] + stream_name = self.config.commodity + + # Add inputs for each stream + for i in range(1, self.config.in_streams + 1): + for var_name, var_props in stream_def.items(): + self.add_input( + f"{stream_name}:{var_name}_in{i}", + val=0.0, + shape=n_timesteps, + units=var_props.get("units"), + desc=f"Stream {i}: {var_props.get('desc', '')}", + ) + + # Add outputs + for var_name, var_props in stream_def.items(): + self.add_output( + f"{stream_name}:{var_name}_out", + val=0.0, + shape=n_timesteps, + units=var_props.get("units"), + desc=f"Combined: {var_props.get('desc', '')}", + ) + + # Identify the flow variable for weighting + self._flow_var = next((v for v in stream_def.keys() if "flow" in v.lower()), None) + if self._flow_var is None: + raise ValueError(f"No flow variable found in '{self.config.commodity}'") + + def compute(self, inputs, outputs): + n_streams = self.config.in_streams + stream_def = multivariable_streams[self.config.commodity] + stream_name = self.config.commodity + flow_var = self._flow_var + + # Collect mass flows + mass_flows = [inputs[f"{stream_name}:{flow_var}_in{i}"] for i in range(1, n_streams + 1)] + total_mass_flow = sum(mass_flows) + outputs[f"{stream_name}:{flow_var}_out"] = total_mass_flow + + # Mass-weighted average for other variables + for var_name in stream_def.keys(): + if var_name == flow_var: + continue + + weighted_sum = sum( + inputs[f"{stream_name}:{var_name}_in{i}"] * mass_flows[i - 1] + for i in range(1, n_streams + 1) + ) + + with np.errstate(divide="ignore", invalid="ignore"): + outputs[f"{stream_name}:{var_name}_out"] = np.where( + total_mass_flow > 0, weighted_sum / total_mass_flow, 0.0 + ) diff --git a/h2integrate/transporters/generic_combiner.py b/h2integrate/transporters/generic_combiner.py index d6aea2e4b..d9d11a2ef 100644 --- a/h2integrate/transporters/generic_combiner.py +++ b/h2integrate/transporters/generic_combiner.py @@ -38,6 +38,11 @@ class GenericCombinerPerformanceModel(om.ExplicitComponent): the output commodity profile is the element-wise sum of all input profiles. """ + _time_step_bounds = ( + 1, + 1e9, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): self.options.declare("driver_config", types=dict) self.options.declare("plant_config", types=dict) diff --git a/h2integrate/transporters/generic_splitter.py b/h2integrate/transporters/generic_splitter.py index ddfe4df6d..3f07a3df8 100644 --- a/h2integrate/transporters/generic_splitter.py +++ b/h2integrate/transporters/generic_splitter.py @@ -63,6 +63,11 @@ class GenericSplitterPerformanceModel(om.ExplicitComponent): losses or other considerations from system components. """ + _time_step_bounds = ( + 1, + 1e9, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): self.options.declare("driver_config", types=dict, default={}) self.options.declare("plant_config", types=dict, default={}) @@ -75,10 +80,12 @@ def setup(self): additional_cls_name=self.__class__.__name__, ) + n_timesteps = int(self.options["plant_config"]["plant"]["simulation"]["n_timesteps"]) + self.add_input( f"{self.config.commodity}_in", val=0.0, - shape_by_conn=True, + shape=n_timesteps, units=self.config.commodity_rate_units, ) @@ -94,7 +101,7 @@ def setup(self): self.add_input( "prescribed_commodity_to_priority_tech", val=self.config.prescribed_commodity_to_priority_tech, - copy_shape=f"{self.config.commodity}_in", + shape=n_timesteps, units=self.config.commodity_rate_units, desc="Prescribed amount of commodity to send to the priority technology", ) @@ -102,14 +109,14 @@ def setup(self): self.add_output( f"{self.config.commodity}_out1", val=0.0, - copy_shape=f"{self.config.commodity}_in", + shape=n_timesteps, units=self.config.commodity_rate_units, desc=f"{self.config.commodity} output to the first technology", ) self.add_output( f"{self.config.commodity}_out2", val=0.0, - copy_shape=f"{self.config.commodity}_in", + shape=n_timesteps, units=self.config.commodity_rate_units, desc=f"{self.config.commodity} output to the second technology", ) diff --git a/h2integrate/transporters/generic_summer.py b/h2integrate/transporters/generic_summer.py index 32e27c338..c0ef79a15 100644 --- a/h2integrate/transporters/generic_summer.py +++ b/h2integrate/transporters/generic_summer.py @@ -26,6 +26,11 @@ class GenericSummerPerformanceModel(om.ExplicitComponent): Sum the production or consumption profile of some commodity from a single source. """ + _time_step_bounds = ( + 3600, + 3600, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): self.options.declare("driver_config", types=dict) self.options.declare("plant_config", types=dict) diff --git a/h2integrate/transporters/generic_transporter.py b/h2integrate/transporters/generic_transporter.py index c94606ac7..642b98777 100644 --- a/h2integrate/transporters/generic_transporter.py +++ b/h2integrate/transporters/generic_transporter.py @@ -23,6 +23,11 @@ class GenericTransporterPerformanceModel(om.ExplicitComponent): losses or other considerations from system components. """ + _time_step_bounds = ( + 1, + 1e9, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): self.options.declare("driver_config", types=dict) self.options.declare("plant_config", types=dict) diff --git a/h2integrate/transporters/pipe.py b/h2integrate/transporters/pipe.py index f0edbf669..45c6cda3e 100644 --- a/h2integrate/transporters/pipe.py +++ b/h2integrate/transporters/pipe.py @@ -6,6 +6,11 @@ class PipePerformanceModel(om.ExplicitComponent): Pass-through pipe with no losses. """ + _time_step_bounds = ( + 1, + 1e9, + ) # (min, max) time step lengths (in seconds) compatible with this model + def initialize(self): self.options.declare( "transport_item", @@ -20,12 +25,15 @@ def initialize(self): "water", ], ) + self.options.declare("plant_config", types=dict) def setup(self): transport_item = self.options["transport_item"] self.input_name = transport_item + "_in" self.output_name = transport_item + "_out" + n_timesteps = int(self.options["plant_config"]["plant"]["simulation"]["n_timesteps"]) + if transport_item == "natural_gas": units = "MMBtu/h" elif transport_item == "water": @@ -38,15 +46,13 @@ def setup(self): self.add_input( self.input_name, val=-1.0, - shape_by_conn=True, - copy_shape=self.output_name, + shape=n_timesteps, units=units, ) self.add_output( self.output_name, val=-1.0, - shape_by_conn=True, - copy_shape=self.input_name, + shape=n_timesteps, units=units, ) diff --git a/h2integrate/transporters/test/conftest.py b/h2integrate/transporters/test/conftest.py index aec692e38..a4763a760 100644 --- a/h2integrate/transporters/test/conftest.py +++ b/h2integrate/transporters/test/conftest.py @@ -4,7 +4,11 @@ import os -from test.conftest import temp_dir, pytest_collection_modifyitems # noqa: F401 +from test.conftest import ( # noqa: F401 + temp_dir, + temp_copy_of_example, + pytest_collection_modifyitems, +) def pytest_sessionstart(session): diff --git a/h2integrate/transporters/test/test_generic_splitter.py b/h2integrate/transporters/test/test_generic_splitter.py index ee0c7411f..7f9f2cfb3 100644 --- a/h2integrate/transporters/test/test_generic_splitter.py +++ b/h2integrate/transporters/test/test_generic_splitter.py @@ -25,10 +25,16 @@ def splitter_tech_config_hydrogen(): rng = np.random.default_rng(seed=0) +N_TIMESTEPS = 10 + + +@fixture +def plant_config(): + return {"plant": {"simulation": {"n_timesteps": N_TIMESTEPS}}} @pytest.mark.regression -def test_splitter_ratio_mode_edge_cases_electricity(splitter_tech_config_electricity): +def test_splitter_ratio_mode_edge_cases_electricity(splitter_tech_config_electricity, plant_config): """Test the splitter in fraction mode with edge case fractions.""" performance_config = { "split_mode": "fraction", @@ -40,45 +46,45 @@ def test_splitter_ratio_mode_edge_cases_electricity(splitter_tech_config_electri tech_config = {"model_inputs": {"performance_parameters": performance_config}} prob = om.Problem() - comp = GenericSplitterPerformanceModel(tech_config=tech_config) + comp = GenericSplitterPerformanceModel(tech_config=tech_config, plant_config=plant_config) prob.model.add_subsystem("comp", comp, promotes=["*"]) ivc = om.IndepVarComp() - ivc.add_output("electricity_in", val=100.0, units="kW") + ivc.add_output("electricity_in", val=np.full(N_TIMESTEPS, 100.0), units="kW") ivc.add_output("fraction_to_priority_tech", val=0.0) prob.model.add_subsystem("ivc", ivc, promotes=["*"]) prob.setup() - electricity_input = 100.0 + electricity_input = np.full(N_TIMESTEPS, 100.0) prob.set_val("electricity_in", electricity_input, units="kW") prob.set_val("fraction_to_priority_tech", 0.0) prob.run_model() - assert prob.get_val("electricity_out1", units="kW") == approx(0.0, abs=1e-10) + assert prob.get_val("electricity_out1", units="kW") == approx(np.zeros(N_TIMESTEPS), abs=1e-10) assert prob.get_val("electricity_out2", units="kW") == approx(electricity_input, rel=1e-5) prob.set_val("fraction_to_priority_tech", 1.0) prob.run_model() assert prob.get_val("electricity_out1", units="kW") == approx(electricity_input, rel=1e-5) - assert prob.get_val("electricity_out2", units="kW") == approx(0.0, abs=1e-10) + assert prob.get_val("electricity_out2", units="kW") == approx(np.zeros(N_TIMESTEPS), abs=1e-10) prob.set_val("fraction_to_priority_tech", 1.5) prob.run_model() assert prob.get_val("electricity_out1", units="kW") == approx(electricity_input, rel=1e-5) - assert prob.get_val("electricity_out2", units="kW") == approx(0.0, abs=1e-10) + assert prob.get_val("electricity_out2", units="kW") == approx(np.zeros(N_TIMESTEPS), abs=1e-10) prob.set_val("fraction_to_priority_tech", -0.5) prob.run_model() - assert prob.get_val("electricity_out1", units="kW") == approx(0.0, abs=1e-10) + assert prob.get_val("electricity_out1", units="kW") == approx(np.zeros(N_TIMESTEPS), abs=1e-10) assert prob.get_val("electricity_out2", units="kW") == approx(electricity_input, rel=1e-5) @pytest.mark.regression -def test_splitter_prescribed_electricity_mode(splitter_tech_config_electricity): +def test_splitter_prescribed_electricity_mode(splitter_tech_config_electricity, plant_config): """Test the splitter in prescribed_electricity mode.""" performance_config = { "split_mode": "prescribed_commodity", @@ -90,17 +96,17 @@ def test_splitter_prescribed_electricity_mode(splitter_tech_config_electricity): tech_config = {"model_inputs": {"performance_parameters": performance_config}} prob = om.Problem() - comp = GenericSplitterPerformanceModel(tech_config=tech_config) + comp = GenericSplitterPerformanceModel(tech_config=tech_config, plant_config=plant_config) prob.model.add_subsystem("comp", comp, promotes=["*"]) ivc = om.IndepVarComp() - ivc.add_output("electricity_in", val=np.zeros(8760), units="kW") - ivc.add_output("prescribed_commodity_to_priority_tech", val=np.zeros(8760), units="kW") + ivc.add_output("electricity_in", val=np.zeros(N_TIMESTEPS), units="kW") + ivc.add_output("prescribed_commodity_to_priority_tech", val=np.zeros(N_TIMESTEPS), units="kW") prob.model.add_subsystem("ivc", ivc, promotes=["*"]) prob.setup() - electricity_input = rng.random(8760) * 500 + 300 - prescribed_electricity = np.full(8760, 200.0) + electricity_input = rng.random(N_TIMESTEPS) * 500 + 300 + prescribed_electricity = np.full(N_TIMESTEPS, 200.0) prob.set_val("electricity_in", electricity_input, units="kW") prob.set_val("prescribed_commodity_to_priority_tech", prescribed_electricity, units="kW") @@ -119,7 +125,9 @@ def test_splitter_prescribed_electricity_mode(splitter_tech_config_electricity): @pytest.mark.regression -def test_splitter_prescribed_electricity_mode_limited_input(splitter_tech_config_electricity): +def test_splitter_prescribed_electricity_mode_limited_input( + splitter_tech_config_electricity, plant_config +): """ Test the splitter in prescribed_electricity mode when input is less than prescribed electricity. @@ -135,31 +143,31 @@ def test_splitter_prescribed_electricity_mode_limited_input(splitter_tech_config tech_config = {"model_inputs": {"performance_parameters": performance_config}} prob = om.Problem() - comp = GenericSplitterPerformanceModel(tech_config=tech_config) + comp = GenericSplitterPerformanceModel(tech_config=tech_config, plant_config=plant_config) prob.model.add_subsystem("comp", comp, promotes=["*"]) ivc = om.IndepVarComp() - ivc.add_output("electricity_in", val=np.zeros(8760), units="kW") - ivc.add_output("prescribed_commodity_to_priority_tech", val=np.zeros(8760), units="kW") + ivc.add_output("electricity_in", val=np.zeros(N_TIMESTEPS), units="kW") + ivc.add_output("prescribed_commodity_to_priority_tech", val=np.zeros(N_TIMESTEPS), units="kW") prob.model.add_subsystem("ivc", ivc, promotes=["*"]) prob.setup() - electricity_input = np.full(8760, 100.0) - prescribed_electricity = np.full(8760, 150.0) + electricity_input = np.full(N_TIMESTEPS, 100.0) + prescribed_electricity = np.full(N_TIMESTEPS, 150.0) prob.set_val("electricity_in", electricity_input, units="kW") prob.set_val("prescribed_commodity_to_priority_tech", prescribed_electricity, units="kW") prob.run_model() expected_output1 = electricity_input - expected_output2 = np.zeros(8760) + expected_output2 = np.zeros(N_TIMESTEPS) assert prob.get_val("electricity_out1", units="kW") == approx(expected_output1, rel=1e-5) assert prob.get_val("electricity_out2", units="kW") == approx(expected_output2, abs=1e-10) @pytest.mark.unit -def test_splitter_invalid_mode(splitter_tech_config_electricity): +def test_splitter_invalid_mode(splitter_tech_config_electricity, plant_config): """Test that an invalid split mode raises an error.""" performance_config = { "split_mode": "invalid_mode", @@ -174,13 +182,13 @@ def test_splitter_invalid_mode(splitter_tech_config_electricity): match="Item invalid_mode not found in list", ): prob = om.Problem() - comp = GenericSplitterPerformanceModel(tech_config=tech_config) + comp = GenericSplitterPerformanceModel(tech_config=tech_config, plant_config=plant_config) prob.model.add_subsystem("comp", comp, promotes=["*"]) prob.setup() @pytest.mark.regression -def test_splitter_scalar_inputs(splitter_tech_config_electricity): +def test_splitter_scalar_inputs(splitter_tech_config_electricity, plant_config): """Test the splitter with scalar inputs instead of arrays.""" performance_config_ratio = { "split_mode": "fraction", @@ -191,18 +199,22 @@ def test_splitter_scalar_inputs(splitter_tech_config_electricity): tech_config_ratio = {"model_inputs": {"performance_parameters": performance_config_ratio}} prob = om.Problem() - comp = GenericSplitterPerformanceModel(tech_config=tech_config_ratio) + comp = GenericSplitterPerformanceModel(tech_config=tech_config_ratio, plant_config=plant_config) prob.model.add_subsystem("comp", comp, promotes=["*"]) ivc = om.IndepVarComp() - ivc.add_output("electricity_in", val=100.0, units="kW") + ivc.add_output("electricity_in", val=np.full(N_TIMESTEPS, 100.0), units="kW") ivc.add_output("fraction_to_priority_tech", val=0.4) prob.model.add_subsystem("ivc", ivc, promotes=["*"]) prob.setup() prob.run_model() - assert prob.get_val("electricity_out1", units="kW") == approx(40.0, rel=1e-5) - assert prob.get_val("electricity_out2", units="kW") == approx(60.0, rel=1e-5) + assert prob.get_val("electricity_out1", units="kW") == approx( + np.full(N_TIMESTEPS, 40.0), rel=1e-5 + ) + assert prob.get_val("electricity_out2", units="kW") == approx( + np.full(N_TIMESTEPS, 60.0), rel=1e-5 + ) performance_config_prescribed = { "split_mode": "prescribed_commodity", @@ -216,22 +228,32 @@ def test_splitter_scalar_inputs(splitter_tech_config_electricity): } prob2 = om.Problem() - comp2 = GenericSplitterPerformanceModel(tech_config=tech_config_prescribed) + comp2 = GenericSplitterPerformanceModel( + tech_config=tech_config_prescribed, plant_config=plant_config + ) prob2.model.add_subsystem("comp", comp2, promotes=["*"]) ivc2 = om.IndepVarComp() - ivc2.add_output("electricity_in", val=100.0, units="kW") - ivc2.add_output("prescribed_commodity_to_priority_tech", val=30.0, units="kW") + ivc2.add_output("electricity_in", val=np.full(N_TIMESTEPS, 100.0), units="kW") + ivc2.add_output( + "prescribed_commodity_to_priority_tech", val=np.full(N_TIMESTEPS, 30.0), units="kW" + ) prob2.model.add_subsystem("ivc", ivc2, promotes=["*"]) prob2.setup() prob2.run_model() - assert prob2.get_val("electricity_out1", units="kW") == approx(30.0, rel=1e-5) - assert prob2.get_val("electricity_out2", units="kW") == approx(70.0, rel=1e-5) + assert prob2.get_val("electricity_out1", units="kW") == approx( + np.full(N_TIMESTEPS, 30.0), rel=1e-5 + ) + assert prob2.get_val("electricity_out2", units="kW") == approx( + np.full(N_TIMESTEPS, 70.0), rel=1e-5 + ) @pytest.mark.regression -def test_splitter_prescribed_electricity_varied_array(splitter_tech_config_electricity): +def test_splitter_prescribed_electricity_varied_array( + splitter_tech_config_electricity, plant_config +): """Test the splitter in prescribed_electricity mode with a varied array (50-100 MW).""" performance_config = { "split_mode": "prescribed_commodity", @@ -245,20 +267,20 @@ def test_splitter_prescribed_electricity_varied_array(splitter_tech_config_elect tech_config.update(splitter_tech_config_electricity) prob = om.Problem() - comp = GenericSplitterPerformanceModel(tech_config=tech_config) + comp = GenericSplitterPerformanceModel(tech_config=tech_config, plant_config=plant_config) prob.model.add_subsystem("comp", comp, promotes=["*"]) ivc = om.IndepVarComp() - ivc.add_output("electricity_in", val=np.zeros(8760), units="kW") - ivc.add_output("prescribed_commodity_to_priority_tech", val=np.zeros(8760), units="kW") + ivc.add_output("electricity_in", val=np.zeros(N_TIMESTEPS), units="kW") + ivc.add_output("prescribed_commodity_to_priority_tech", val=np.zeros(N_TIMESTEPS), units="kW") prob.model.add_subsystem("ivc", ivc, promotes=["*"]) prob.setup() # Generate varied prescribed electricity array between 50-100 MW (50,000-100,000 kW) - prescribed_electricity = rng.random(8760) * 50000 + 50000 # 50-100 MW range + prescribed_electricity = rng.random(N_TIMESTEPS) * 50000 + 50000 # 50-100 MW range # Input electricity should be higher than prescribed to test both scenarios - electricity_input = rng.random(8760) * 30000 + 120000 # 120-150 MW range + electricity_input = rng.random(N_TIMESTEPS) * 30000 + 120000 # 120-150 MW range prob.set_val("electricity_in", electricity_input, units="kW") prob.set_val("prescribed_commodity_to_priority_tech", prescribed_electricity, units="kW") @@ -278,7 +300,7 @@ def test_splitter_prescribed_electricity_varied_array(splitter_tech_config_elect assert total_output == approx(electricity_input, rel=1e-5) # Test with some time steps where prescribed > available - electricity_input_limited = rng.random(8760) * 30000 + 20000 # 20-50 MW range + electricity_input_limited = rng.random(N_TIMESTEPS) * 30000 + 20000 # 20-50 MW range prob.set_val("electricity_in", electricity_input_limited, units="kW") prob.run_model() diff --git a/h2integrate/transporters/test/test_pipe.py b/h2integrate/transporters/test/test_pipe.py index 638f216c4..ab139ddbb 100644 --- a/h2integrate/transporters/test/test_pipe.py +++ b/h2integrate/transporters/test/test_pipe.py @@ -1,16 +1,28 @@ +import numpy as np import pytest import openmdao.api as om -from pytest import approx +from pytest import approx, fixture from h2integrate.transporters.pipe import PipePerformanceModel +@fixture +def plant_config(): + plant_dict = { + "plant": { + "plant_life": 30, + "simulation": {"n_timesteps": 8760, "dt": 3600}, + } + } + return plant_dict + + @pytest.mark.unit -def test_pipe_with_hydrogen(): +def test_pipe_with_hydrogen(plant_config): """Test the pipe transport with hydrogen as transport_item.""" # Create the pipe component with hydrogen as transport item - pipe = PipePerformanceModel(transport_item="hydrogen") + pipe = PipePerformanceModel(plant_config=plant_config, transport_item="hydrogen") # Create OpenMDAO problem and add the component prob = om.Problem() @@ -18,12 +30,13 @@ def test_pipe_with_hydrogen(): # Add independent variable component for input ivc = om.IndepVarComp() - ivc.add_output("hydrogen_in", val=10.0, units="kg/s") + hydrogen_profile = np.full(8760, 10.0) + ivc.add_output("hydrogen_in", val=hydrogen_profile, units="kg/s") prob.model.add_subsystem("ivc", ivc, promotes=["*"]) # Setup and run the model prob.setup() - prob.set_val("hydrogen_in", 10.0, units="kg/s") + prob.set_val("hydrogen_in", val=hydrogen_profile, units="kg/s") prob.run_model() # Check that output equals input (pass-through pipe with no losses) diff --git a/pyproject.toml b/pyproject.toml index c6780fa8c..0a67eb712 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,8 +28,8 @@ authors = [ {name = "Jennifer King"}, ] maintainers = [ - {name = "John Jasa", email = "john.jasa@nrel.gov"}, - {name = "Christopher Bay", email = "christopher.bay@nrel.gov"}, + {name = "John Jasa", email = "john.jasa@nlr.gov"}, + {name = "Christopher Bay", email = "christopher.bay@nlr.gov"}, ] readme = {file = "README.md", content-type = "text/markdown"} description = "Holistic Hybrids Optimization and Design Tool" @@ -86,21 +86,22 @@ classifiers = [ # https://pypi.org/classifiers/ ] [project.urls] -source = "https://github.com/NREL/H2Integrate" +source = "https://github.com/NatLabRockies/H2Integrate" documentation = "https://h2integrate.readthedocs.io/" -issues = "https://github.com/NREL/H2Integrate/issues" -changelog = "https://github.com/NREL/H2Integrate/blob/main/CHANGELOG.md" +issues = "https://github.com/NatLabRockies/H2Integrate/issues" +changelog = "https://github.com/NatLabRockies/H2Integrate/blob/main/CHANGELOG.md" [project.optional-dependencies] develop = [ "isort", "jupyter-book<2", + "networkx", "pre-commit", + "pyvis", "pytest>=9", "pytest-cov", "ruff", "sphinxcontrib-napoleon", - "h2integrate[gis,ard]", "yamlfix" ] examples = [ @@ -112,7 +113,8 @@ gis = [ "contextily" ] ard = ["ard-nrel"] -all = ["h2integrate[develop,examples]"] +extras = ["h2integrate[gis,ard]"] +all = ["h2integrate[develop,extras,examples]"] [tool.setuptools] include-package-data = true diff --git a/resource_files/solar/-28.454864_114.551749_2024_openmeteo_archive_solar_60min_local_tz.csv b/resource_files/solar/-28.454864_114.551749_2024_openmeteo_archive_solar_60min_local_tz.csv new file mode 100644 index 000000000..27bb80f08 --- /dev/null +++ b/resource_files/solar/-28.454864_114.551749_2024_openmeteo_archive_solar_60min_local_tz.csv @@ -0,0 +1,8836 @@ +latitude,longitude,elevation,utc_offset_seconds,timezone,timezone_abbreviation +-28.43585205078125,114.54545593261719,71.0,28800,Australia/Perth,GMT+8 + +time,wind_speed_10m (m/s),wind_direction_10m (deg),temperature_2m (C),surface_pressure (hPa),relative_humidity_2m (percent),shortwave_radiation (W/m**2),direct_normal_irradiance (W/m**2),diffuse_radiation (W/m**2),dew_point_2m (C),snow_depth (m),rain (mm),albedo (percent) +2023-12-30 16:00:00,2.7802877,232.3057,20.8,1005.47546,90.28737,0.0,0.0,0.0,19.15,0.0,0.0, +2023-12-30 17:00:00,2.86007,233.53067,20.85,1004.9811,90.01005,0.0,0.0,0.0,19.15,0.0,0.0, +2023-12-30 18:00:00,3.4409301,234.46223,20.75,1004.8791,90.002846,0.0,0.0,0.0,19.05,0.0,0.0, +2023-12-30 19:00:00,4.0360875,228.01286,20.55,1004.7743,90.26981,0.0,0.0,0.0,18.9,0.0,0.0, +2023-12-30 20:00:00,4.4598207,222.27362,20.75,1004.8791,88.60926,0.0,0.0,0.0,18.8,0.0,0.0, +2023-12-30 21:00:00,4.327817,220.31403,20.6,1005.07306,88.87449,0.0,0.0,0.0,18.7,0.0,0.0, +2023-12-30 22:00:00,4.4687805,220.46214,20.85,1005.77454,86.971245,12.0,42.034687,9.0,18.6,0.0,0.0, +2023-12-30 23:00:00,4.9819674,218.48022,22.2,1006.2092,78.337654,141.0,357.5579,67.0,18.25,0.0,0.0, +2023-12-31 00:00:00,4.816638,221.63345,23.45,1006.73987,70.15413,364.0,621.9316,106.0,17.7,0.0,0.0, +2023-12-31 01:00:00,5.0990195,221.82008,24.35,1007.1613,66.8793,491.0,477.5966,201.0,17.8,0.0,0.0, +2023-12-31 02:00:00,5.0931325,223.4089,25.15,1007.18335,62.170498,686.0,586.27783,234.0,17.4,0.0,0.0, +2023-12-31 03:00:00,5.4451814,224.25603,25.65,1007.1969,60.923904,844.0,649.19244,263.0,17.55,0.0,0.0, +2023-12-31 04:00:00,5.5865912,225.72514,26.0,1007.0084,60.240345,902.0,607.7862,312.0,17.7,0.0,0.0, +2023-12-31 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+2025-01-01 13:00:00,4.0718546,155.32315,27.4,1001.888,55.29976,0.0,0.0,0.0,17.65,0.0,0.0, +2025-01-01 14:00:00,4.0131035,149.28117,27.75,1001.99664,50.85719,0.0,0.0,0.0,16.65,0.0,0.0, +2025-01-01 15:00:00,2.6800187,165.96373,27.2,1002.18024,51.857697,0.0,0.0,0.0,16.45,0.0,0.0, diff --git a/resource_files/tidal/Tidal_resource_timeseries.csv b/resource_files/tidal/Tidal_resource_timeseries.csv new file mode 100644 index 000000000..144b27fd2 --- /dev/null +++ b/resource_files/tidal/Tidal_resource_timeseries.csv @@ -0,0 +1,8763 @@ +Source,Location ID,Jurisdiction,Latitude,Longitude,Time Zone,Local Time Zone,Distance to Shore,Directionality Coefficient,Energy Period,Maximum Energy Direction,Mean Absolute Period,Mean Wave Direction,Mean Zero-Crossing Period,Omni-Directional Wave Power,Peak Period,Significant Wave Height,Spectral Width,Water Depth,Version +CMIST,PUG1527,The Narrows 0.3 miles North of Bridge,47.27432,-122.54532,0,-10,0,-,s,deg,s,deg,s,W/m,s,m,-,42.9,- 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b/resource_files/wind/-28.454864_114.551749_2024_openmeteo_archive_60min_local_tz.csv @@ -0,0 +1,8836 @@ +latitude,longitude,elevation,utc_offset_seconds,timezone,timezone_abbreviation +-28.43585205078125,114.54545593261719,71.0,28800,Australia/Perth,GMT+8 + +time,wind_speed_10m (m/s),wind_speed_100m (m/s),wind_direction_10m (deg),wind_direction_100m (deg),temperature_2m (degC),surface_pressure (hPa),precipitation (mm/h),relative_humidity_2m (unitless),is_day (percent) +2023-12-30 16:00:00,2.7802877,4.763402,232.3057,230.9645,20.8,1005.47546,0.0,90.28737,0.0 +2023-12-30 17:00:00,2.86007,4.7801676,233.53067,232.65057,20.85,1004.9811,0.0,90.01005,0.0 +2023-12-30 18:00:00,3.4409301,5.7801385,234.46223,232.73352,20.75,1004.8791,0.0,90.002846,0.0 +2023-12-30 19:00:00,4.0360875,6.652819,228.01286,227.43669,20.55,1004.7743,0.0,90.26981,0.0 +2023-12-30 20:00:00,4.4598207,7.1505246,222.27362,222.16582,20.75,1004.8791,0.0,88.60926,0.0 +2023-12-30 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18:00:00,2.147091,4.7405167,117.75845,142.28395,13.55,1008.9349,0.0,83.45532,0.0 +2024-10-17 19:00:00,2.2005682,4.295637,91.301926,129.80553,12.9,1008.6181,0.0,84.781334,0.0 +2024-10-17 20:00:00,2.1965883,3.758989,78.178535,118.61037,12.45,1008.5053,0.0,86.44813,0.0 +2024-10-17 21:00:00,2.5773048,3.981206,71.916466,101.59215,12.05,1008.69183,0.0,88.16021,0.0 +2024-10-17 22:00:00,3.0842342,4.956309,73.033775,92.89121,12.0,1009.2853,0.0,87.56827,1.0 +2024-10-17 23:00:00,3.7980258,5.8806887,69.17903,84.14409,14.3,1009.8494,0.0,77.92054,1.0 +2024-10-18 00:00:00,3.2984846,4.5304523,75.96373,79.18617,17.2,1010.3313,0.0,66.03374,1.0 +2024-10-18 01:00:00,3.1575305,3.9714608,100.95402,100.88548,19.05,1010.1862,0.0,59.18245,1.0 +2024-10-18 02:00:00,2.074247,2.6476402,105.376305,100.88548,20.95,1009.84375,0.0,52.26612,1.0 +2024-10-18 03:00:00,0.99624294,1.2020816,162.47433,135.0001,22.55,1009.3929,0.0,47.084576,1.0 +2024-10-18 04:00:00,3.1100645,3.4132097,216.50153,211.82741,23.15,1008.9134,0.0,48.674816,1.0 +2024-10-18 05:00:00,5.561025,6.7055945,213.26144,210.96368,21.85,1008.778,0.0,59.075863,1.0 +2024-10-18 06:00:00,5.500909,6.8660393,207.03078,205.44536,21.45,1008.37,0.0,59.173096,1.0 +2024-10-18 07:00:00,5.1429563,6.397656,206.56499,204.96275,21.25,1008.16595,0.0,59.31888,1.0 +2024-10-18 08:00:00,4.8787804,6.0730963,204.20097,202.76505,20.6,1008.1476,0.0,61.93926,1.0 +2024-10-18 09:00:00,4.526036,5.776028,196.03241,196.08173,19.65,1008.2199,0.0,65.26001,1.0 +2024-10-18 10:00:00,4.2005954,5.71358,188.90164,188.55548,18.75,1008.4918,0.0,71.31161,1.0 +2024-10-18 11:00:00,3.463019,6.0667124,175.03035,175.74625,17.45,1008.8508,0.0,78.6479,0.0 +2024-10-18 12:00:00,2.7060118,5.408558,163.90912,166.0922,16.8,1009.1296,0.0,83.29161,0.0 +2024-10-18 13:00:00,2.8622541,5.7293105,152.98741,159.03421,16.35,1009.21576,0.0,87.67328,0.0 +2024-10-18 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00:00:00,2.2896507,3.8849065,121.60746,121.849014,18.0,1007.37933,0.0,67.7502,1.0 +2024-10-19 01:00:00,2.687936,3.451449,133.49263,128.53021,21.0,1007.4647,0.0,57.535885,1.0 +2024-10-19 02:00:00,2.0670028,2.5811818,147.84773,135.78474,23.6,1007.0415,0.0,50.094124,1.0 +2024-10-19 03:00:00,2.3286262,2.4505103,194.93147,178.83089,25.05,1006.5853,0.0,48.864574,1.0 +2024-10-19 04:00:00,5.1198144,5.948319,211.82741,205.91882,24.2,1006.1652,0.0,59.06239,1.0 +2024-10-19 05:00:00,6.44748,8.217208,203.78204,201.41304,22.9,1005.73254,0.0,62.660507,1.0 +2024-10-19 06:00:00,5.8122716,7.3859663,201.70995,199.78436,22.7,1005.42957,0.0,59.6624,1.0 +2024-10-19 07:00:00,5.6135993,7.1221485,197.62811,195.89021,22.55,1005.128,0.0,60.013824,1.0 +2024-10-19 08:00:00,5.2540464,6.6940274,196.02036,193.82867,21.75,1004.9072,0.0,61.999687,1.0 +2024-10-19 09:00:00,5.0224495,6.6680207,189.74358,188.19078,20.5,1005.0702,0.0,71.17592,1.0 +2024-10-19 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23:00:00,3.8832977,7.648529,145.49142,131.82008,16.05,1010.89276,0.0,54.968765,1.0 +2024-10-27 00:00:00,3.8967936,5.249762,146.51381,139.63554,19.55,1010.79565,0.0,42.462944,1.0 +2024-10-27 01:00:00,4.2573466,5.448853,139.76372,132.76881,23.1,1010.499,0.0,34.159393,1.0 +2024-10-27 02:00:00,4.103657,5.287958,136.97493,130.01361,26.4,1010.0941,0.0,27.190992,1.0 +2024-10-27 03:00:00,3.77955,4.89183,152.41806,139.14471,28.15,1009.44727,0.0,24.53976,1.0 +2024-10-27 04:00:00,4.8438106,6.203225,187.7118,178.15244,28.45,1008.7608,0.0,28.41787,1.0 +2024-10-27 05:00:00,6.5134478,8.950558,192.86275,186.09268,27.25,1008.13336,0.0,40.72277,1.0 +2024-10-27 06:00:00,8.029477,11.154484,195.1613,189.54669,26.35,1007.5138,0.0,45.97952,1.0 +2024-10-27 07:00:00,9.447884,13.633048,193.15372,188.01114,25.0,1006.7825,0.0,45.46191,1.0 +2024-10-27 08:00:00,9.056766,13.5000925,182.2147,179.7878,23.95,1006.85284,0.0,47.778896,1.0 +2024-10-27 09:00:00,8.438008,11.802224,174.55975,172.45357,23.8,1007.0468,0.0,49.332596,1.0 +2024-10-27 10:00:00,7.488825,12.992786,171.16667,168.90631,21.7,1007.5835,0.0,57.330975,1.0 +2024-10-27 11:00:00,8.285228,13.64597,161.34627,160.96745,20.35,1008.33905,0.0,60.08314,0.0 +2024-10-27 12:00:00,8.022,12.93619,163.71115,162.68552,19.95,1008.72424,0.0,64.05729,0.0 +2024-10-27 13:00:00,6.150203,10.331021,152.39342,152.93898,18.95,1009.29083,0.0,75.63251,0.0 +2024-10-27 14:00:00,4.9648767,9.22456,145.66972,144.93158,18.7,1008.986,0.0,73.897415,0.0 +2024-10-27 15:00:00,3.9604292,8.24151,133.97707,133.27922,18.45,1008.6816,0.0,65.64708,0.0 +2024-10-27 16:00:00,3.6173193,8.371529,130.5153,127.96508,18.55,1008.18854,0.0,50.764156,0.0 +2024-10-27 17:00:00,3.7259226,8.491908,130.10085,125.65542,18.8,1007.89795,0.0,34.117317,0.0 +2024-10-27 18:00:00,3.8425252,8.833177,122.24244,117.29019,19.0,1007.6062,0.0,26.671545,0.0 +2024-10-27 19:00:00,3.782195,8.681157,114.19321,109.16555,18.9,1007.50415,0.0,26.161634,0.0 +2024-10-27 20:00:00,4.040421,9.051243,102.144264,98.57764,18.65,1007.5964,0.0,25.996521,0.0 +2024-10-27 21:00:00,4.90408,10.304368,92.33726,91.668304,18.7,1007.8951,0.0,25.25827,0.0 +2024-10-27 22:00:00,5.867282,11.221853,85.60138,86.423744,19.05,1008.5003,0.0,24.531239,1.0 +2024-10-27 23:00:00,7.008031,12.21935,82.62156,83.89292,21.35,1009.1606,0.0,22.400852,1.0 +2024-10-28 00:00:00,6.930548,10.607191,84.618546,84.04764,24.5,1009.34784,0.0,19.62184,1.0 +2024-10-28 01:00:00,6.8311787,9.141116,81.15827,79.919464,27.5,1009.0327,0.0,21.184072,1.0 +2024-10-28 02:00:00,5.947479,7.735955,82.75559,81.45101,30.25,1008.80884,0.0,19.243965,1.0 +2024-10-28 03:00:00,5.100245,6.5017304,89.43831,88.678055,31.85,1008.2557,0.0,15.692143,1.0 +2024-10-28 04:00:00,3.8327537,4.973178,105.12406,99.841064,33.0,1007.19476,0.0,14.344523,1.0 +2024-10-28 05:00:00,3.3455193,4.1785164,170.53775,158.96242,32.2,1006.4795,0.0,15.995173,1.0 +2024-10-28 06:00:00,6.4903774,8.855789,201.22772,197.412,30.05,1006.02576,0.0,31.567715,1.0 +2024-10-28 07:00:00,7.3691926,10.454664,199.41856,194.96677,29.2,1005.4082,0.0,33.593643,1.0 +2024-10-28 08:00:00,8.414571,12.46846,196.9276,192.9774,27.9,1005.4723,0.0,36.108498,1.0 +2024-10-28 09:00:00,8.414867,12.750882,183.40637,180.67403,25.85,1006.0123,0.0,39.933094,1.0 +2024-10-28 10:00:00,6.7175145,9.982485,171.86998,169.0278,24.75,1006.5771,0.0,44.067104,1.0 +2024-10-28 11:00:00,8.000625,12.681088,161.79143,161.13632,23.0,1007.42145,0.0,42.143814,0.0 +2024-10-28 12:00:00,8.048758,12.997308,160.77705,161.14674,22.3,1007.69946,0.0,45.77715,0.0 +2024-10-28 13:00:00,7.1317954,11.985408,162.45418,162.01848,21.25,1008.5627,0.0,50.46568,0.0 +2024-10-28 14:00:00,6.9420824,11.748723,160.65144,160.87097,20.85,1008.25397,0.0,54.003403,0.0 +2024-10-28 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01:00:00,2.5675864,2.6655207,186.70975,156.80139,27.5,1007.3464,0.0,36.840485,1.0 +2024-10-29 02:00:00,3.8078866,4.020572,203.19861,190.75092,29.15,1007.0931,0.0,39.937664,1.0 +2024-10-29 03:00:00,5.415025,6.527251,208.69481,202.04596,28.9,1006.888,0.0,43.498173,1.0 +2024-10-29 04:00:00,6.4189177,8.586763,205.36736,199.3846,28.05,1006.5677,0.0,42.986485,1.0 +2024-10-29 05:00:00,6.4724417,8.942176,200.81514,195.90146,28.1,1006.4697,0.0,40.69331,1.0 +2024-10-29 06:00:00,6.642665,9.286146,197.0711,191.49141,28.1,1006.27136,0.0,43.279045,1.0 +2024-10-29 07:00:00,7.131795,10.225947,192.96451,189.00168,27.15,1006.0474,0.0,47.402523,1.0 +2024-10-29 08:00:00,7.021574,9.560596,184.4925,182.69775,26.0,1006.314,0.0,50.72619,1.0 +2024-10-29 09:00:00,6.609274,9.376167,176.9645,175.71855,24.9,1006.6805,0.0,54.677467,1.0 +2024-10-29 10:00:00,6.4001956,9.658286,180.4476,177.62645,23.1,1007.3251,0.0,60.725544,1.0 +2024-10-29 11:00:00,6.0133185,9.712491,176.186,173.49704,21.75,1008.2794,0.0,66.985695,0.0 +2024-10-29 12:00:00,6.4878736,10.564208,166.17792,164.91132,21.1,1008.9553,0.0,68.59593,0.0 +2024-10-29 13:00:00,5.632051,9.543715,173.8846,170.04425,19.9,1009.814,0.0,75.543846,0.0 +2024-10-29 14:00:00,5.873883,9.702706,168.21184,166.89473,19.35,1009.9965,0.0,81.22615,0.0 +2024-10-29 15:00:00,5.5915112,9.192388,167.08218,165.5102,18.95,1010.0842,0.0,85.97073,0.0 +2024-10-29 16:00:00,5.4692326,9.024965,165.70964,164.57777,18.6,1009.3798,0.0,90.70397,0.0 +2024-10-29 17:00:00,5.3284616,8.839259,161.39494,160.84752,18.3,1008.97473,0.0,91.84119,0.0 +2024-10-29 18:00:00,4.9340653,8.334416,160.46326,160.3692,18.15,1008.5736,0.0,91.83216,0.0 +2024-10-29 19:00:00,4.7594643,8.190543,160.99393,161.12253,17.85,1008.36664,0.0,92.693565,0.0 +2024-10-29 20:00:00,4.9206195,8.458132,154.73694,155.55606,17.7,1008.36224,0.0,93.27651,0.0 +2024-10-29 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19:00:00,3.8849065,5.899576,211.84901,211.13025,17.15,1007.4539,0.0,72.19073,0.0 +2024-11-04 20:00:00,2.8900692,5.2478566,210.11365,210.96368,16.7,1007.34174,0.0,70.92655,0.0 +2024-11-04 21:00:00,3.9003205,6.300793,216.13547,215.9606,17.2,1007.7529,0.0,63.445644,0.0 +2024-11-04 22:00:00,4.4654226,6.6567636,214.04597,214.28693,17.7,1008.36224,0.0,59.053707,1.0 +2024-11-04 23:00:00,4.9872336,6.574192,205.53738,206.17523,18.3,1009.07385,0.0,57.829056,1.0 +2024-11-05 00:00:00,5.079862,6.4445715,208.8354,208.75227,19.5,1009.4058,0.0,53.475487,1.0 +2024-11-05 01:00:00,5.1224995,6.4031243,218.65984,218.65984,21.2,1010.2474,0.0,45.014374,1.0 +2024-11-05 02:00:00,5.6731825,7.045566,214.95073,214.59235,21.35,1010.2515,0.0,47.228622,1.0 +2024-11-05 03:00:00,5.423329,6.6567636,214.86227,214.28693,21.5,1010.1568,0.0,48.55176,1.0 +2024-11-05 04:00:00,5.3333855,6.591092,218.91148,217.91316,21.6,1010.0602,0.0,48.741215,1.0 +2024-11-05 05:00:00,5.129571,6.2659793,223.02507,222.08908,22.3,1009.6831,0.0,46.548138,1.0 +2024-11-05 06:00:00,5.287958,6.504806,220.01361,219.0725,22.35,1009.0896,0.0,45.485737,1.0 +2024-11-05 07:00:00,5.6579585,7.0032134,213.83054,213.34972,22.0,1009.07983,0.0,47.72386,1.0 +2024-11-05 08:00:00,5.8619537,7.3824115,208.53235,208.30066,21.05,1008.9536,0.0,51.601646,1.0 +2024-11-05 09:00:00,5.727128,7.3824115,204.77512,204.82932,20.4,1008.93524,0.0,54.608593,1.0 +2024-11-05 10:00:00,5.60803,7.400169,201.99118,202.23279,19.65,1009.1125,0.0,58.160072,1.0 +2024-11-05 11:00:00,4.918587,6.8007355,197.14563,197.10281,19.05,1009.3928,0.0,61.37981,0.0 +2024-11-05 12:00:00,4.4322677,6.677762,191.05637,191.22575,18.6,1010.1732,0.0,63.969646,0.0 +2024-11-05 13:00:00,4.2005954,6.6830006,188.90164,189.0394,18.35,1010.5626,0.0,63.282654,0.0 +2024-11-05 14:00:00,4.2074337,6.8222065,183.40637,184.62407,18.0,1010.652,0.0,64.47456,0.0 +2024-11-05 15:00:00,3.6585517,6.2289643,183.91817,185.52745,17.7,1010.34564,0.0,65.70344,0.0 +2024-11-05 16:00:00,3.4058774,5.9801755,183.3664,185.75816,17.55,1010.1431,0.0,65.88946,0.0 +2024-11-05 17:00:00,3.0149627,5.7212324,185.7105,189.05064,17.05,1009.6327,0.0,68.91219,0.0 +2024-11-05 18:00:00,2.6504717,5.427937,181.0809,185.81554,16.9,1009.13245,0.0,69.1109,0.0 +2024-11-05 19:00:00,4.2107005,7.0087447,175.91446,177.13765,16.75,1009.22723,0.0,78.289024,0.0 +2024-11-05 20:00:00,4.1303754,6.6816916,173.04713,174.41748,16.9,1009.3308,0.0,80.38019,0.0 +2024-11-05 21:00:00,4.7183156,7.6447697,170.2383,170.96901,16.55,1009.71735,0.0,81.91902,0.0 +2024-11-05 22:00:00,4.9175706,7.4,161.01228,161.07526,16.5,1010.6083,0.0,83.52721,1.0 +2024-11-05 23:00:00,5.3935146,7.110731,169.31514,169.8754,17.5,1011.52997,0.0,76.385284,1.0 +2024-11-06 00:00:00,6.278734,8.194205,174.5165,174.04599,18.35,1011.95105,0.1,68.26855,1.0 +2024-11-06 01:00:00,5.5145264,6.856566,184.15956,182.50764,20.35,1012.4053,0.0,51.244812,1.0 +2024-11-06 02:00:00,5.5301447,6.7574034,194.6645,192.39084,21.25,1012.7282,0.0,47.04298,1.0 +2024-11-06 03:00:00,5.4134097,6.609463,201.11357,198.9833,21.65,1012.5414,0.0,41.05926,1.0 +2024-11-06 04:00:00,5.897669,7.18401,198.28143,196.16457,22.3,1011.8652,0.0,41.94671,1.0 +2024-11-06 05:00:00,7.040064,8.731695,196.50444,194.5933,22.0,1011.6585,0.0,45.5413,1.0 +2024-11-06 06:00:00,7.28869,9.133044,194.7036,192.97112,21.65,1011.3512,0.0,47.46938,1.0 +2024-11-06 07:00:00,7.6095333,9.65984,191.7529,190.43742,21.25,1010.94293,0.0,49.13675,1.0 +2024-11-06 08:00:00,7.622664,9.823568,187.91742,187.0164,20.55,1010.8242,0.0,53.03724,1.0 +2024-11-06 09:00:00,7.3744493,9.668634,184.66678,183.55779,19.95,1010.807,0.0,55.04053,1.0 +2024-11-06 10:00:00,7.0007143,9.5,180.81844,180.0,19.05,1011.0788,0.0,57.820507,1.0 +2024-11-06 11:00:00,6.129437,9.199184,174.3825,174.07259,18.45,1011.45844,0.0,60.632477,0.0 +2024-11-06 12:00:00,5.396758,8.477323,166.60748,166.70142,18.0,1011.84204,0.0,63.203915,0.0 +2024-11-06 13:00:00,5.440588,8.640023,162.89719,163.87234,17.15,1012.70984,0.0,67.128876,0.0 +2024-11-06 14:00:00,5.110039,8.345208,163.51547,164.71472,16.85,1012.80035,0.0,69.10095,0.0 +2024-11-06 15:00:00,5.015227,8.301355,159.57759,161.0193,16.45,1012.39197,0.0,70.87911,0.0 +2024-11-06 16:00:00,4.7853947,8.1024685,153.97047,155.96571,16.0,1011.982,0.0,74.404625,0.0 +2024-11-06 17:00:00,4.6219583,7.8971515,148.7174,151.6502,15.45,1011.5691,0.0,77.06917,0.0 +2024-11-06 18:00:00,4.6400433,8.043164,142.88306,146.40869,14.85,1011.15497,0.0,75.20287,0.0 +2024-11-06 19:00:00,4.609772,8.150153,139.39879,143.48152,14.25,1011.0381,0.0,70.93368,0.0 +2024-11-06 20:00:00,4.5254836,8.194663,135.0001,139.70213,13.65,1011.1195,0.0,72.02667,0.0 +2024-11-06 21:00:00,4.313931,7.9682183,134.0609,138.30684,13.3,1011.20825,0.0,72.69642,0.0 +2024-11-06 22:00:00,4.457017,7.9644523,133.18172,137.79897,13.45,1011.51013,0.0,71.0205,1.0 +2024-11-06 23:00:00,4.526036,7.079901,135.89508,137.86247,15.6,1011.87115,0.0,61.396706,1.0 +2024-11-07 00:00:00,5.4801917,7.19809,148.05006,147.19304,18.3,1012.049,0.0,48.4986,1.0 +2024-11-07 01:00:00,5.553602,7.038466,159.44388,157.44272,20.45,1012.11066,0.0,43.750614,1.0 +2024-11-07 02:00:00,5.515886,6.8170743,171.13545,167.28886,21.9,1011.3581,0.0,41.551056,1.0 +2024-11-07 03:00:00,5.7054796,6.952877,182.5113,178.35168,22.8,1010.4908,0.0,42.08757,1.0 +2024-11-07 04:00:00,7.0092797,8.649278,194.03627,190.6603,22.5,1010.08563,0.0,47.85991,1.0 +2024-11-07 05:00:00,8.065358,10.254024,197.31174,196.13734,21.15,1010.0475,0.0,53.19304,1.0 +2024-11-07 06:00:00,7.5960517,9.897601,196.04907,195.22978,19.75,1009.8095,0.0,57.60904,1.0 +2024-11-07 07:00:00,6.851642,8.901263,193.93486,193.64597,19.95,1009.5177,0.0,54.857628,1.0 +2024-11-07 08:00:00,6.858936,8.803551,182.92491,181.62724,20.35,1008.63654,0.0,54.05419,1.0 +2024-11-07 09:00:00,6.88077,9.011381,174.5795,173.30884,19.85,1007.82886,0.0,55.382607,1.0 +2024-11-07 10:00:00,6.896557,9.298521,173.33879,171.65213,19.0,1007.6062,0.0,60.56081,1.0 +2024-11-07 11:00:00,6.0876927,8.889601,165.73546,166.6672,18.05,1007.9757,0.0,66.65289,0.0 +2024-11-07 12:00:00,6.140033,9.099726,173.45381,174.00757,17.4,1007.95703,1.8,74.156075,0.0 +2024-11-07 13:00:00,5.8200088,8.42882,171.10422,172.15839,17.8,1007.6709,0.0,69.746254,0.0 +2024-11-07 14:00:00,5.0707493,7.664366,167.4712,166.41702,17.7,1007.27136,0.0,72.05442,0.0 +2024-11-07 15:00:00,5.3183646,8.254847,189.19658,186.60837,16.9,1007.2483,0.0,78.311325,0.0 +2024-11-07 16:00:00,5.8045244,8.798011,197.03024,196.1691,16.75,1006.9464,0.0,78.289024,0.0 +2024-11-07 17:00:00,6.2211337,9.229437,201.20288,201.28271,16.65,1006.84436,0.0,76.49994,0.0 +2024-11-07 18:00:00,5.3521023,8.30542,181.60597,182.06999,16.35,1006.43915,1.3,85.430534,0.0 +2024-11-07 19:00:00,4.7523675,7.35017,181.8087,180.38976,16.3,1006.3384,0.0,87.10315,0.0 +2024-11-07 20:00:00,4.4553337,7.107215,189.0394,187.27492,16.1,1006.53076,0.0,87.08449,0.0 +2024-11-07 21:00:00,4.516913,7.316078,206.28134,204.63838,16.15,1006.82983,0.0,83.214745,0.0 +2024-11-07 22:00:00,4.405111,6.797794,206.27415,206.18805,16.7,1007.34174,0.0,78.02601,1.0 +2024-11-07 23:00:00,6.6912255,9.021363,204.26678,203.86577,17.05,1008.1452,0.1,77.317726,1.0 +2024-11-08 00:00:00,5.3202443,6.8709536,201.5015,201.33693,18.3,1008.37964,0.0,68.03465,1.0 +2024-11-08 01:00:00,6.2433968,7.9147015,211.90807,210.77747,20.65,1007.2565,0.0,57.26107,1.0 +2024-11-08 02:00:00,6.3780875,7.9832635,221.18582,220.17308,20.9,1007.2635,0.0,56.386745,1.0 +2024-11-08 03:00:00,7.184184,9.021225,227.53859,227.02136,21.2,1007.7679,0.0,55.906933,1.0 +2024-11-08 04:00:00,7.9556584,10.07931,224.23619,223.59308,21.05,1007.6643,0.1,56.238644,1.0 +2024-11-08 05:00:00,7.8946185,10.073356,218.82983,218.34894,21.0,1007.3656,0.1,55.85681,1.0 +2024-11-08 06:00:00,7.9600563,10.137184,213.98956,213.53333,21.2,1006.8753,0.0,54.451763,1.0 +2024-11-08 07:00:00,8.276473,10.647065,208.11311,208.00908,20.85,1006.6672,0.0,55.451775,1.0 +2024-11-08 08:00:00,8.6339445,11.32166,199.27448,199.0752,20.6,1006.2632,0.0,56.68501,1.0 +2024-11-08 09:00:00,8.619309,11.521827,191.37508,191.26114,19.65,1006.13727,0.0,63.99124,1.0 +2024-11-08 10:00:00,7.67219,10.493807,187.86598,187.66672,19.4,1006.4275,0.0,63.517532,1.0 +2024-11-08 11:00:00,6.4381676,9.044888,186.24182,185.7105,18.9,1007.2066,0.0,67.269806,0.0 +2024-11-08 12:00:00,6.00354,8.564608,187.65762,187.04195,18.5,1007.69116,0.0,71.7313,0.0 +2024-11-08 13:00:00,4.851031,7.2515516,181.18117,181.18524,17.6,1008.45856,0.0,74.1904,0.0 +2024-11-08 14:00:00,4.8002605,7.5001664,180.5968,180.38196,17.4,1008.4529,0.0,75.3786,0.0 +2024-11-08 15:00:00,4.7265205,7.2339478,173.92764,174.44713,17.45,1008.2558,0.0,75.141,0.0 +2024-11-08 16:00:00,4.2005954,6.527059,171.09836,171.18718,17.45,1007.75995,0.0,75.141,0.0 +2024-11-08 17:00:00,3.7716045,6.404881,165.41104,167.37418,16.9,1007.44666,0.0,77.54767,0.0 +2024-11-08 18:00:00,3.5584404,6.230971,160.29193,162.72816,16.75,1007.04553,0.0,77.77874,0.0 +2024-11-08 19:00:00,3.3837848,6.036969,145.84026,148.54799,16.25,1007.1301,0.0,78.987785,0.0 +2024-11-08 20:00:00,3.0955615,5.7812195,121.122406,130.43997,15.45,1007.40424,0.0,82.04787,0.0 +2024-11-08 21:00:00,3.5850384,6.652819,129.90784,137.43669,15.45,1007.9001,0.0,80.1816,0.0 +2024-11-08 22:00:00,4.210997,7.1431785,132.594,136.13434,15.85,1008.40765,0.0,77.641685,1.0 +2024-11-08 23:00:00,3.740655,5.4426556,127.94221,128.65984,17.25,1009.04346,0.0,71.971924,1.0 +2024-11-09 00:00:00,3.9322386,5.1470866,131.39143,129.87679,19.8,1009.4144,0.0,60.15106,1.0 +2024-11-09 01:00:00,3.3286633,4.1309805,147.26479,141.88168,22.05,1009.37866,0.0,49.680897,1.0 +2024-11-09 02:00:00,3.473111,4.050309,186.61336,179.2927,23.65,1008.729,0.0,42.32625,1.0 +2024-11-09 03:00:00,6.148374,7.653757,211.36325,208.90909,22.0,1008.3855,0.0,60.27445,1.0 +2024-11-09 04:00:00,6.983552,8.879471,209.1342,207.86363,21.5,1008.1732,0.0,62.14409,1.0 +2024-11-09 05:00:00,7.3370633,9.270383,204.99327,203.52322,22.3,1007.7986,0.0,57.10468,1.0 +2024-11-09 06:00:00,7.2235723,9.158739,201.94876,200.1168,22.7,1007.61163,0.0,55.37175,1.0 +2024-11-09 07:00:00,7.8781343,10.077947,196.59483,194.6568,22.2,1007.3994,0.0,58.967525,1.0 +2024-11-09 08:00:00,8.487785,11.186263,185.40823,184.61469,21.25,1007.27325,0.0,63.719852,1.0 +2024-11-09 09:00:00,8.923704,11.912703,184.17703,182.64618,20.75,1007.1602,0.0,66.99578,1.0 +2024-11-09 10:00:00,8.958934,12.417025,177.44106,176.9994,19.4,1007.6176,0.0,72.59743,1.0 +2024-11-09 11:00:00,8.180006,12.010099,175.09102,174.26576,18.65,1007.8939,0.0,76.81513,0.0 +2024-11-09 12:00:00,7.8447433,11.923925,168.9766,168.87859,18.25,1008.08057,0.0,77.00152,0.0 +2024-11-09 13:00:00,6.5736213,10.165136,165.01239,164.59651,17.95,1009.2621,0.0,77.707375,0.0 +2024-11-09 14:00:00,6.594885,10.326302,162.80138,162.53004,17.65,1009.1543,0.0,80.74255,0.0 +2024-11-09 15:00:00,6.1856694,9.7965555,159.66069,159.06738,17.35,1008.94714,0.0,79.92099,0.0 +2024-11-09 16:00:00,5.8587537,9.405583,158.47093,158.48132,16.9,1008.5375,0.0,77.80148,0.0 +2024-11-09 17:00:00,5.5542774,9.073313,156.66594,156.96722,16.4,1008.2254,0.0,77.7256,0.0 +2024-11-09 18:00:00,4.9648767,8.385255,152.4028,153.43501,15.9,1008.0126,0.0,77.13947,0.0 +2024-11-09 19:00:00,4.4102154,7.780906,147.0306,147.33115,15.3,1008.1931,0.0,76.03125,0.0 +2024-11-09 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23:00:00,5.104165,7.1417785,147.39967,146.42116,16.55,1009.9157,0.0,74.74431,1.0 +2024-11-12 00:00:00,5.1441717,6.686179,154.68036,152.86017,18.9,1010.08264,0.0,61.344986,1.0 +2024-11-12 01:00:00,5.0289164,6.3612103,162.64589,159.28575,21.35,1009.85486,0.0,53.59801,1.0 +2024-11-12 02:00:00,5.5226803,6.855837,174.80566,169.91946,23.5,1009.51825,0.0,46.567562,1.0 +2024-11-12 03:00:00,6.412488,7.900158,183.57626,179.63737,24.7,1008.85724,0.0,45.230553,1.0 +2024-11-12 04:00:00,7.686514,9.691877,188.6044,185.32814,24.75,1008.2633,0.0,50.398167,1.0 +2024-11-12 05:00:00,8.541955,10.955363,188.4146,185.76254,24.6,1007.7633,0.0,52.3578,1.0 +2024-11-12 06:00:00,8.812775,11.439514,186.84268,184.76355,24.3,1007.25916,0.0,52.62044,1.0 +2024-11-12 07:00:00,8.646965,11.321661,185.97423,183.5447,23.95,1006.9521,0.0,56.044445,1.0 +2024-11-12 08:00:00,8.46788,11.307077,183.72398,182.02728,22.85,1006.9215,0.0,60.863342,1.0 +2024-11-12 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19:00:00,3.7259226,7.074249,139.89915,136.71832,16.6,1005.5539,0.0,74.507164,0.0 +2024-11-12 20:00:00,4.0672474,7.8873315,136.49428,129.3405,16.8,1005.5596,0.0,71.18038,0.0 +2024-11-12 21:00:00,3.9698865,8.18917,130.91429,121.26367,17.9,1005.59143,0.0,62.55412,0.0 +2024-11-12 22:00:00,3.46013,7.4781346,127.36675,113.65133,19.1,1006.2206,0.0,53.551044,1.0 +2024-11-12 23:00:00,3.7165172,8.041144,146.5237,111.1399,21.5,1006.5863,0.0,44.49377,1.0 +2024-11-13 00:00:00,3.952847,5.911218,145.30478,125.70678,24.75,1006.27954,0.0,34.39376,1.0 +2024-11-13 01:00:00,3.240756,4.3304157,141.89238,126.0761,28.1,1005.57697,0.0,27.655502,1.0 +2024-11-13 02:00:00,3.662308,4.365776,175.3014,159.90468,30.45,1005.0445,0.0,24.89532,1.0 +2024-11-13 03:00:00,5.082568,6.278734,195.40349,185.4835,30.0,1004.5365,0.0,35.984917,1.0 +2024-11-13 04:00:00,5.7293105,7.4394217,200.96579,188.89247,30.15,1003.8461,0.0,41.791256,1.0 +2024-11-13 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+@pytest.fixture(scope="function") def temp_dir(tmp_path_factory): """Temp directory for YAML outputs.""" temp_dir = tmp_path_factory.mktemp("temp_dir") yield temp_dir - shutil.rmtree(str(temp_dir)) + + +@pytest.fixture(scope="module") +def temp_dir_module(tmp_path_factory): + """Temp directory for YAML outputs.""" + temp_dir = tmp_path_factory.mktemp("temp_dir") + yield temp_dir + + +@pytest.fixture(scope="function") +def temp_copy_of_example(temp_dir, example_folder, resource_example_folder): + original = EXAMPLE_DIR / example_folder + shutil.copytree(original, temp_dir / example_folder, dirs_exist_ok=True) + if resource_example_folder is not None: + secondary = EXAMPLE_DIR / resource_example_folder + shutil.copytree(secondary, temp_dir / resource_example_folder, dirs_exist_ok=True) + os.chdir(temp_dir / example_folder) + yield temp_dir / example_folder + os.chdir(Path(__file__).parent) + + +@pytest.fixture(scope="function") +def temp_copy_of_example_module_scope(temp_dir_module, example_folder, resource_example_folder): + original = EXAMPLE_DIR / example_folder + shutil.copytree(original, temp_dir_module / example_folder, dirs_exist_ok=True) + if resource_example_folder is not None: + secondary = EXAMPLE_DIR / resource_example_folder + shutil.copytree(secondary, temp_dir_module / resource_example_folder, dirs_exist_ok=True) + os.chdir(temp_dir_module / example_folder) + yield temp_dir_module / example_folder + os.chdir(Path(__file__).parent)