ReEDS India

Welcome to the Regional Energy Deployment System (ReEDS) Model!

This GitHub repository contains the source code for NLR's ReEDS India model. This version was adapted from the ReEDS model developed for North America. Table 2 summarizes differences in model design and capabilities between the two versions.

Users of this source code agree to the ReEDS licensing agreement https://nlr.gov/analysis/reeds/request-access.html. The ReEDS India Version 2019 source code is available at no cost from the National Laboratory of the Rockies. The ReEDS India model can be downloaded or cloned from https://github.com/NLR/ReEDS_OpenAccess (Branch: India). New users must request access to the ReEDS repository through https://nlr.gov/analysis/reeds/request-access.html.

Contents

• Introduction
• Required Software
• Setting up your computer to run ReEDS for the first time (for Microsoft Windows 10)
  • ReEDS India Repository Configuration
  • Python Configuration
  • GAMS Configuration
  • R Configuration
• Guide to the ReEDS India Respository
• Executing the Model
• Debugging Tips
• Documentation
• Frequently Asked Questions
  • How much are the GAMS licensing fees?
  • Is there a trial version of the GAMS license so I can test ReEDS?
  • What computer hardware is necessary to run ReEDS India?
  • Can I adjust the model input parameters or constraints?
• Contact Us
• Appendix
  • ReEDS India Model Switches

Introduction (https://www.nlr.gov/analysis/reeds/)

The Regional Energy Deployment System (ReEDS) India model is a capacity planning and dispatch model for the Indian electricity system.

As NLR's flagship long-term power sector model, ReEDS has served as the primary analytic tool for many studies (https://www.nlr.gov/analysis/reeds/publications.html) of important energy sector research questions, including clean energy policy, renewable grid integration, technology innovation, and forward-looking issues of the generation and transmission infrastructure. The ReEDS India model extends this capability to investigate critical power sector question for India.

ReEDS uses high spatial resolution and high-fidelity modeling. Though it covers a broad geographic and technological scope, ReEDS is designed to reflect the regional attributes of energy production and consumption. Unique among long-term capacity expansion models, ReEDS possesses advanced algorithms and data to represent the cost and value of variable renewable energy; the full suite of other major generation technologies, including fossil and nuclear; and transmission and storage expansion options. Used in combination with other NLR tools, data, and expertise, ReEDS can provide objective and comprehensive electricity system futures.

Required Software

The ReEDS model is written primarily in GAMS with auxiliary modules written in Python and R. At present, NLR uses the following software versions: GAMS 24.7.4; Python 3.6.5; R 3.4.4;. Other versions of these software may be compatible with ReEDS, but NLR has not tested other versions at this time.

GAMS is a mathematical programming software from the GAMS Development Corporation. "The use of GAMS beyond the limits of the free demo system requires the presence of a valid GAMS license file." [1] The ReEDS model requires the GAMS Base Module and a linear programming (LP) solver (e.g., CPLEX). The LP solver should be connected to GAMS with either a GAMS/Solver license or a GAMS/Solver-Link license. "A GAMS/Solver connects the GAMS Base module to a particular solver and includes a license for this solver to be used through GAMS. It is not necessary to install additional software. A GAMS/Solver-Link connects the GAMS Base Module to a particular solver, but does not include a license for the solver. It may be necessary to install additional software before the solver can be used." [2]

NLR subscribes to the GAMS/CPLEX license for the LP solver, but open-source and free, internet-based services are also available. The COIN-OR Optimization Suite includes open-source solvers that can be linked with GAMS through the GAMS Base Module. NLR has not tested the performance of these open-source solvers for ReEDS. The NEOS Server is a free, internet-based service for solving numerical optimization problems. Links with NEOS can be made through KESTREL which is included in GAMS Base Module. In its current form, ReEDS cannot be solved using NEOS due to the 16 MB limit on submissions to the server. However, modifications could be made to ReEDS to potentially reduce the data below to the required submission size.

Python is "an object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java." [3] " Python is developed under an OSI-approved open source license, making it freely usable and distributable, even for commercial use. Python's license is administered by the Python Software Foundation." [4]. NLR uses Conda to build the python environment necessary for ReEDS. Conda is a "package, dependency and environment management for any language." [5]

"R is a language and environment for statistical computing and graphics…R is available as Free Software under the terms of the Free Software Foundation's GNU General Public License in source code form." [6]

Setting up your computer to run ReEDS for the first time (for Microsoft Windows 10)

The setup and execution of the ReEDS model can be accomplished using a command-line interpreter application and launching a command line interface (referred to as a "terminal window" in this document). For example, initiating the Windows Command Prompt application, i.e., cmd.exe, will launch a terminal window (Figure 1).

Figure 1. Screenshot of a Windows Command Prompt terminal window.

While these installation instructions are specific to Windows 10, this version of ReEDS India has been tested to be compatible with MacOS High Sierra 10.12 as well as Linux CentOX 7.4 operating systems.

SUGGESTON: use a command line emulator such as ConEmu (https://conemu.github.io/) for a more user-friendly terminal. The screenshots of terminal windows shown in this document are taken using ConEmu.

IMPORTANT: Users should exercise Administrative Privileges when installing software. For example, right click on the installer executable for one of the required software (e.g., Anaconda3-2019.07-Windows-x86_64.exe) and click on "Run as administrator" (Figure 2). Alternatively, right click on the executable for the command line interface (e.g., Command Prompt) and click on "Run as administrator" (Figure 3). Then run the required software installer executables from the command line.

Figure 2. Screenshot of running an installer executable using "Run as administrator".

Figure 3. Screenshot of running "Command Prompt" with "Run as administrator".

ReEDS India Repository Configuration

The ReEDS India source code is hosted on GitHub: https://github.com/NLR/ReEDS\_OpenAccess

1. Request access to the ReEDS GitHub repository at https://nlr.gov/analysis/reeds/request-access.html.
2. Clone the ReEDS-2.0 repository on your desktop and use the repository with GitHub Desktop. Alternatively, download a ZIP from GitHub (Figure 4).

Figure 4. Screenshot of GitHub links to clone the ReEDS repository or download ZIP of the ReEDS files.

Python Configuration

Install Anaconda: https://www.anaconda.com/distribution/#download-section. NLR recommends Python 3.7, but has also used Python 3.6.5 and 3.7.1 successfully.

IMPORTANT : Be sure to download the correct version of the installer for your operating system.

Add Python to the "path" environment variable

1. In the Windows start menu, search for "environment variables' and click "Edit the system environment variables" (Figure 5). This will open the "System Properties" window (Figure 6).
2. Click the "Environment Variables" button on the bottom right of the window (Figure 6). This will open the "Environment Variables" window (Figure 7).
3. Highlight the Path variable and click "Edit" (Figure 7). This will open the "Edit environment variable" window (Figure 8).
4. Click "New" (Figure 8) and add the directory locations for \Anaconda\ and \Anaconda\Scripts to the environment path.

IMPORTANT : Test the Python installation from the command line by typing "python" (no quotes) in the terminal window. The Python program should initiate (Figure 9).

Install the gdxpds package from the command line by typing "pip install gdxpds" (no quotes) in the terminal window (Figure 10).The gdxpds package is required for reading GAMS Data Exchange files (.gdx) into Python.

Figure 5. Screenshot of a search for "environment variables" in the Windows start menu.

Figure 6. Screenshot of the "System Properties" window.

Figure 7. Edit the Path environment variable.

Figure 8. Append the Path environment.

Figure 9. Screenshot of a test of Python in the terminal window.

GAMS Configuration

Install GAMS: https://www.gams.com/download-old/. NLR uses GAMS version 24.7 but has not tested the compatibility of ReEDS with newer versions of GAMS. A valid GAMS license must be installed. Please refer to the Required Software section above for more information.

Add GAMS to the "path" environment variable. Follow the same instructions as for adding Python to the path in the Python Configuration section above. Append the environment path with the directory location for the gams.exe application (e.g., C:\GAMS\win64\24.7).

IMPORTANT : Test the GAMS installation from the command line by typing "gams" (no quotes) in the terminal window. The GAMS program should initiate (Figure 10).

Figure 10. Screenshot of a test of GAMS from the terminal window.

R Configuration

Install R 3.4.4: https://cran.r-project.org/bin/windows/base/old/3.4.4/. NLR has observed compatibility issues with other versions of R. NLR has not tested R versions more recent than 3.4.4. Optionally, install RStudio: https://www.rstudio.com/products/rstudio/download/#download.

Add R to the "path" environment variable. Follow the same instructions as for adding Python to the path in the Python Configuration section above. Append the environment path with the directory location for the R.exe and Rscript.exe applications (e.g., C:\Program Files\R\R-3.4.4\bin).

IMPORTANT : Test the R installation from the command line by typing "r" (no quotes) in the terminal window. The R program should initiate (Figure 11).

Figure 11. Screenshot of a test of R from the terminal window.

Guide to the ReEDS India Respository

The ReEDS India repository is organized into six (6) folders. In the main folder there are two files. “runmodel.py” is a python file with a command line interface to manage all model runs. “cplex.op4” defines the options for the cplex solver.

Table 1 summarizes the model files in the ReEDS India repository.

Table 1. Organization of model files in ReEDS India repository.

A_Inputs	Contains all model inputs and GAMS code to read in inputs and format them for model execution
a_inputs.gms	read in and format all model inputs
cases.csv	define all model scenarios
inputs folder	series of csv files organized in subfolders containing all model inputs
B_Equations	Declares model variables, constraints, and objective function
b1_model_constraints.gms	define all variables and constraints
b2_objective_function.gms	define the objective function, broken into two parts (investment costs and operating costs)
C_Solve	Initiates all solve parameters and solve the model
c1_Solveprep.gms	initiate the LP solve and compute parameters based on switch values and configurations defined in “cases.csv”
c2_Solve_Int.gms	execute the model using the intertemporal solve option
c3_augur_data_dump.gms	create a gdx file with all of the data necessary for the Augur module to solve
D_Augur	Computes capacity credit and curtailment using hourly load and generation data
d00_pickle_prep.py	create pickle files for hourly load and resource data
d0_ReEDS_augur.py	call the below scripts in order and write results to a gdx file that is read back into ReEDS
d1_prep_data.py	prepare data for Osprey and package it into a single gdx file
d2_osprey.gms	optimize generation, transmission, and storage with hourly time resolution
d3_gdx_dump.gms	output raw data from Osprey as .csv files
d4_process_osprey_results.py	standardize generator results and aggregate transmission flows to a regional level
d5_existing_curtailment.gms	compute the curtailment of existing resources based on results from Osprey
d6_marginal_curtailment.gms	compute the curtailment of marginal variable generation based on results from Osprey
d7_condor.R	calculate the energy arbitrage revenue for marginal storage additions
d8_capacity_credit.R	calculate capacity credit of existing and marginal variable generation
E_Outputs	Generates gdx files with model results
e1_create_report.gms	format results and write them to a gdx file
e2_process_outputs.py	process ReEDS model results from gams and save key results in Excel format
runs folder	solution folders for each model scenario saved with the name “{runname}_{scenarioname}”
F_Analysis	Create html and figures of model outputs
ReEDS-India-markdown.Rmd	generate html of model results for all user-defined scenarios
data folder	external data used for generating plots and tables (i.e., geographic coordinates for mapping)
scripts folder	R scripts used to query gdx solution file and generate plots and tables
valuestreams folder	calculate and store shadow price of each model constraint to identify sources of value for each investment decision

Executing the Model

The execution of the ReEDS India model can be accomplished by using a command-line interpreter application and launching a command line interface.

A ReEDS India scenario (also referred to as a "run", "case" or "instance") is executed through a python-based case batching program called "runmodel.py". The user can execute a single case or a batch of cases using this program.

Step 1 : Specify the ReEDS scenario name(s) and configuration(s) in the case configuration file.(Figure 12).

The scenario configuration file name is called "cases.csv" in the "A_Inputs" folder. Within "cases.csv", the data in Column A are the model "switches" (also referred to as "options"). Column B provides brief descriptions of the switches. The data in Column C are the default values of the switches. Every subsequent column represents an individual model scenario, beginning with Column D. The scenario name is specified in Row 1. The value for each switch is specified beginning in Row 2. If a switch value is left blank, the default value from Column C is used. Users can tailor the default scenarios in two ways: 1.) adjust the default switch values for an existing scenario or 2.) create a new scenario by adding a new column with a scenario name in Row 1 and desired switch values beginning in Row 2.

A complete list of switches is provided in the Appendix of this document.

Figure 12. Screenshot of cases.csv.

Step 2 : Compile and run the model from the command line

1. Initiate a command-line interpreter application and launch a command line interface.
2. Navigate to the ReEDS India model directory in the command line.
3. Enter

   python runmodel.py

in the command line and press "Enter/Return" to initiate the ReEDS India model

4. You will then be asked to name the model run. Enter a name using valid characters and press return:

   Specify the name of the run(s)

   Run Name (default is a timestamp): testrun

5. Next, you will be asked which scenario you would like to run. This list is generated from the column headers in "cases.csv". The following options are shown as examples. Please select which scenarios you would like to run Enter each selected number separated by a comma (i.e. 1, 3)

---

0 -- Base

1 -- CoalFlex

2 -- FlatLoad

3 -- LowLoad

4 -- LowSolarCost

5 -- NewGas

6 –- LowBatCost

7 -- toy

8 -- RUN ALL

Selected scenarios (default 8): 0,4,6

6. Next, you will be asked if you want to use capacity credit and curtailment capabilities in ReEDS. These modules use the investment and generation outputs from ReEDS and calculate capacity credit and curtailment for variable generation and storage technologies. The results are fed back into the ReEDS model to adjust the variable decisions. The number of iterations between the ReEDS optimization problem and capacity credit and curtailment calculations is defined in "cases.csv".

Do you want to iteratively calculate capacity value and curtailment? (0=no / 1=yes, default 1): 0

7. Select whether you wish to compile a model, which will execute scripts contained in the 'A_Inputs' and 'B_Equations' modules of ReEDS. This option should almost always be selected, unless you are attempting to run a previously compiled model.

Compile inputs and model equations? (0=no / 1=yes, default 1): 1

8. Select whether you want to run the model, which will execute scripts contained in the 'C_Solve', 'D_8760' and 'E_Outputs' modules of ReEDS. Only select this option if you are on the computer which you wish to run ReEDS on. By default, this will run the selected scenarios in cases.csv sequentially. If you wish to model scenarios in parallel and have available computational resources (such as a High Performance Computing system), you might be better off selecting '0' at this point, as you should then be asked if you want to create batch files capable of simultaneously running multiple cases.

Run model? (0=no / 1=yes, default 1): 1

9. Once all responses are received, the batching program will execute the scenario(s) specified. Each scenario selected to run now has a folder in &quotE_Outputs/runs/" containing two .bat files:
   • "compile_{runname}_{scenarioname}.bat"
   • "run_{runname}_{scenarioname}.bat"

Either of these files can be manually executed in order to compile or run specific modules of ReEDS from outside the command line interface prompt. Either step can be executed by double clicking on the appropriate .bat file in "E_Outputs/runs/".

E_Outputs/runs/testrun_Base/compile_testrun_Base.bat

compile the model (evaluate A_Inputs and B_Equations)

E_Outputs/runs/testrun_Base/run_testrun_Base.bat

solve the model, requires that a model has already been compiled

Step 3 : Wait for each case to finish, check for successful completion, and view outputs. Following a successful run, a .gdx file with all model results will be stored in "E_Outputs/gdxfiles/output_{runname}_{scenarioname}.gdx". If R is correctly installed, a standard .html visualization report will automatically open and is also stored in "F_Analysis"

Debugging Tips

GAMS will produced files unique to each case that can help the user with error debugging:

1. GAMS Log File

• Path: "E_Outputs\runs{runname}_{scenarioname}\gamslog.txt"
• Purpose: contains the log outputs for all execution statements from the case batch file
• SUGGESTION : This is a good place to check in which execution step errors may have occurred.

2. GAMS listing files (*.lst)

• Path: "E_Outputs\runs{runname}_{scenarioname}\lstfiles\ "
• Purpose: contains the listing files for GAMS executions
• SUGGESTION : This is a good place to check in which line of the source code errors may have occurred

3. GAMS workfile (*.g00)

• Path: "E_Outputs\runs{runname}_{scenarioname}\g00files\ "
• Purpose: stores a snapshot all the model information available to GAMS at that point in the case execution.
• For more information about GAMS work files: https://www.gams.com/latest/docs/UG_SaveRestart.html
• SUGGESTION : A failed case can be restarted from this snapshot point. The user can rerun the batch file for the case (E_Outputs\runs{runname}{scenarioname}\run{runname}_{scenarioname}.bat) after commenting out a execution statements that completed successfully.

Documentation

This model is based on the ReEDS Version 2018 created for the United States. All input parameters have been updated to reflect the India context including electricity demand, technology cost and performance, data on existing and planned infrastructure, resource data, and operating and policy constraints. The ReEDS Version 2018 Documentation on which the ReEDS India model is based is available at no cost from the National Renewable Energy Laboratory: https://www.nlr.gov/docs/fy19osti/72023.pdf

Table 2 summarizes difference between model design and capabilities in US ReEDS Version 2019 and ReEDS India.

Table 2. Differences between US ReEDS Version 2019 and ReEDS India.

Model Features	US Version 2019	ReEDS India
Geographic resolution and hierarchy	3 interconnections, 18 RTOs, 19 NERC subregions, 9 census divisions, 134 Balancing Areas, 356 resource regions	5 interconnected regions, 34 Balancing Areas, 146 resource regions
Time slices	17 time slices: 4 seasons each with 4 representative times per day (overnight, morning, afternoon, evening) and 1 summer "superpeak"	35 time slices: 5 seasons each with 7 representative times per day (night, sunrise, morning, afternoon, sunset, evening, peak)
Gas fuel costs and constraints	Gas supply curve	Gas fuel price is fixed; Annual limits on total available gas fuel for electricity generation
Geothermal and CSP	Included	Not included
Cooling technologies	Included	Not included
Water use accounting	Included	Not included
Load from EVs	Included	Not included
Clean energy policies	Policies for generation, capacity, emissions, and tax incentives	National and state capacity targets for wind and solar
Operating reserves	Spinning, Regulation, and Flexibility reserve requirements defined for each BA	Spinning reserve requirement for entire country
Planning reserve margin	Planning reserve margin set at NERC reference levels for all years, except ERCOT in 2018 and 2019 is set to actual values because the actuals were lower than the NERC reference levels	Seasonal planning reserve margin requirement of 7% peak demand defined for each interconnected balancing area (i.e., states and union territories)
Technology performance bins	Units binned according to costs and heat rates	Units binned according to reported variable costs which internalize variations in fuel cost and heat rates
Absolute growth constraint	Not included	Absolute growth constraint applied for select technologies, which can be specified in "growth_limit_absolute.csv"
Technology Phase Out	Not included	Forced phase out of select technologies, which can be specified in "tech_phase_out_schedule.csv"
RE diversity constraint	Not included	Mandate that no more than 15% of new wind and solar additions can be located in any one resource region
Coal cost adder	Not included	State wise variable cost adder for coal based on rail transport charges

Frequently Asked Questions

How much are the GAMS licensing fees?

The GAMS licensing price list is available on the GAMS website (https://www.gams.com/products/buy-gams/). Prices listed are "for an unrestricted, perpetual, single named user on a specific platform." The prices for additional users are a function of the single user price. Table 3 and Table 4 below are examples of the pricing for a multi-user licenses for the Base Module and GAMS\CPLEX . The prices listed in the table are not offical quotes. Please contact GAMS for an offical quote.

Table 3. Example of initial GAMS licensing fees (USD) based on the number of users for a Standard License on a single platform (e.g., Windows).

Number of Users	GAMS Base Module	GAMS\CPLEX	Total
1	3,200	9,600	12,800
5	6,400	19,200	25,600
10	9,600	28,800	38,400
20	12,800	38,400	51,200
30	16,000	48,000	64,000

Table 4. Example of initial GAMS licensing fee example (USD) based on the number of users for an Academic License on a single platform (e.g., Windows).

Number of Users	GAMS Base Module	GAMS\CPLEX	Total
1	640	1,280	1,920
5	1,280	2,560	3,840
10	1,920	3,840	5,760
20	2,560	5,120	7,680
30	3,200	6,400	9,600

Is there a trial version of the GAMS license so that I can test ReEDS?

You may be able to request a temporary evaluation licenses of the GAMS base module and a GAMS\Solver license. Please contact GAMS for more information.

What computer hardware is necessary to run ReEDS?

NLR uses Windows servers to execute the ReEDS model. These servers have Intel(R) Xeon(R) CPUs at 2-2.4GHz and 10-14 cores with 160-320GB of RAM. These servers can execute multiple ReEDS cases in parallel.

Table 5 summarizes RAM usage and total clock time necessary to execute the default ReEDS configuration for the three different solve structures (sequential, window, and intertemporal) using one of these servers.

Table 5. Ballpark RAM usage and clock time for select model configurations executed on NLR servers.

Solve Structure	Solve Steps	Approximate LP size per instance (after presolve)	Threads	RAM(GB per instance)	Total Clock Time (minutes)
Intertemporal	10 sample years 2017-2047	Rows:1,832k Columns: 837k Non-Zeros: 7,637k	8	5GB+	40-60

Can I adjust the model input parameters or constraints?

You may update some of the model inputs or constraints to reflect your own data or research questions. Some updates may be simple (e.g., change the values in one input csv to test different technology costs) while others may require advanced knowledge of GAMS (e.g., adding a new constraint).

Contact Us:

If you have comments and/or questions, please contacts the ReEDS team:

ReEDS.Inquiries@nlr.gov

Appendix

ReEDS India Model Switches

Switch	Input Type	Description	Defaut Value
Model Settings: General
timetype	string	Define how the model portrays foresight. This version only includes the intertemporal setting (int) that assumes full foresight of all modeled years	int
solver	string	Solver for GAMS to use	CPLEX
augur_workers	integer	Number of windows for augur module runs	36
yearset	string	File pointer to specify the years to model	yearset_2070.csv
endyear	integer	Last year to be modeled	2070
WeatherYear	integer	Weather year being used	2014
Scenario Inputs: Demand
HourlyLoadFile	string	Load scenario name for capacity value and curtailment load duration curve data	NLR_India_Baseline_8760
Load_file	string	File name of the load in each time block to be used in the scenario run	load_NLR_India_Baseline.csv
PeakDemRegion_file	string	File name of the peak demand by region to be used in the scenario run	peakdem_region_NLR_India_Baseline.csv
PeakDemState_file	string	File name of the peak demand by state to be used in the scenario run	peakdem_state_NLR_India_Baseline.csv
Hours_file	string	File name of the hours in each time block to be used in the scenario run	hours_NLR_India_Baseline.csv
Scenario Inputs: Generation
Binnedcap_file	string	File name of binned non-RE capacity by BA, year, and VO&M bin	binned_capacity.csv
Maxage_file	string	File name of maximum generator age file	maxage.csv
PRMregion_file	string	File name of the PRM (planning reserve margin) by region	prm_region.csv
PRMstate_file	string	File name of PRM (planning reserve margin) by state	prm_state.csv
FuelLimit_file	string	File name of the fuel limit file to be used in scenario run	fuel_limit.csv
FuelPrice_file	string	File name of the fuel prices to be used in scenario run	fprice.csv
TechCost_file	string	File name for the technology cost (units: INR/MW) and heat rate (units: kBtu/kWh) inputs	tech_costs_itc22.csv
MinLoad_file	string	File name of the minimum loading limits to be used in the scenario run	minloadfrac0.csv
IVT_file	string	File name of the technology, vintage and year combinations file used in the scenario run	ivt.csv
RECapMandate_file	string	File name of the RE capacity mandate to be used in the scenario run	recap_mandate.csv
RECapFracMandate_file	string	File name of the non-fossil fractional capacity mandate to be used in the scenario run	recapfrac_mandate.csv
RECapManTech_file	string	File name of set of technologies that qualify for RE capacity mandate	capmandate_tech_set.csv
REGenMandate_file	string	File name of the RE generation mandate to be used in the scenario run	regen_mandate.csv
REGenManTech_file	string	File name of set of technologies that qualify for RE generation mandate	genmandate_tech_set.csv
AbsGrowLim_file	string	File name of the absolute growth limits for different technologies and states	growth_limit_absolute.csv
GrowthLimTech_file	string	File name of the technology growth limits for different technologies	growth_limit_by_tech.csv
CapNonRsc_file	string	File name of the capacity of non-renewable resources in each state	capnonrsc.csv
CapRsc_file	string	File name of the capacity of hydropower in each state	caprsc.csv
PrescribedNonRsc_file	string	File name of the prescribed additions of nonrenewable capacity in each state	prescribednonrsc.csv
PrescribedRsc_file	string	File name of the prescribed additions of renewable capacity in each state	prescribedrsc.csv
CfOut_file	string	File name of the capacity factors of solar and wind in each resource region and in each season	cfout_2014.csv
RscDat_file	string	File name of the technical potential capacity of renewable resources (wind and solar) in each state	rsc_dat_2014.csv
Scenario Inputs: Transmission
Trancap_file	string	File name of the exogenous transmission capacity	trancap.csv
Trancost_file	string	File name of the maximum transmission capacities and costs for each state	trancost.csv
InterTrancost_file	string	File name of the transmission cost between states	inter_transcost.csv
Model Settings: Generation
numclass	integer	Maximum number of technology classes	4
numhintage	integer	Number of technology bins to create when grouping units with similar performance characteristics	7
retireyear	integer	First year when economic capacity retirements are allowed	2026
REdiversity	scalar	Maximum fraction of new VRE investments that can go in any one	0.15
GSw_GrowthRel	binary	Turn relative growth constraint on [1] or off [0]	1
GSw_GrowthAbs	binary	Turn absoluate growth constraint on [1] or off [0]	1
GSw_GrowthLimTech	binary	Turn annual growth limit for techs on [1] or off [0]	1
GSw_Refurb	binary	Turn refurbishments on [1] or off [0]	1
GSw_Storage	binary	Turn all storage on [1] or off [0]	1
GSw_CurtStorage	scalar	Fraction of storage that counts toward reducing curtailment	1
GSw_StorCC	scalar	Turn storage contribution to reserve margin on [1] or off [0]	1
GSw_StorOpres	scalar	Turn storage contribution to operating reserve on [1] or off [0]	1
GSw_Prescribed	binary	Turn prescribed capacity additions on [1] or off [0]	1
GSw_ForcePrescription	binary	Turn forced prescribed capacity additions on [1] or off [0]	0
GSw_Retire	binary	Turn endogenous retirements on [1] or off [0]	1
GSw_REdiveresity	binary	Turn geographic diversity constraint for RE additions on [1] or off [0]	1
GSw_SolarPlusStorage	binary	Mandate UPV (utility-scale solar PV) investment to include storage: yes [1] or no [0]	0
GSw_H2	binary	Turn H2-CT (hydrogen combustion turbine) and H2-CC (hydrogen combined cycle) technologies on [1] or off [0]	1
GSw_OfsWind	binary	Turn offshore wind technology on [1] or off [0]	1
GSw_BanGas	binary	Ban new gas investments in India: on [1] or off [0]	0
Model Settings: Capacity Credit and Curtailment
CC/Curtailment Iterations	integer	Number of iterations between ReEDS LP and CC/Curtailment modules	4
GSw_CCcurtAvg	integer	Select method for average CC/curt calculations	2
GSw_CurtFlow	binary	Turn curtailment trading on [1] or off [0]	0
GSw_Int_CC	integer	Select intertemporal capacity credit method (0 = average undifferentiated, 1 = average differentiated, 2 = marginal differentiated)	0
GSw_Int_Curt	integer	Select intertemporal curtailment method (0=average undifferentiated, 1=average differentiated , 2=marginal undifferentiated, 3=marginal differentiated)	0
GSw_CurtLim	binary	Turn curtailment limits on [1] or off [0]	0
Model Settings: Reserves
GSw_MinCF	binary	Turn minimum CF (capacity factors) for new capacity on [1] or off [0]	1
GSw_OpRes	binary	Turn operating reserve constraints on [1] or off [0]	1
GSw_OpResTrade	binary	Tun operating reserve trading between regions on [1] or off [0]	0
GSw_PRMregion	binary	Turn regional planning reserve margin on [1] or off [0]	0
GSw_PRMstate	binary	Turn state planning reserve margin on [1] or off [0]	1
GSw_PRMTrade	binary	Turn planning reserve margin trading between regions on [1] or off [0]	1
Model Settings: System Operations
GSw_FuelSupply	binary	Turn fuel supply constraints on [1] or off [0]	1
GSw_MinGen	binary	Turn min loading constraint on [1] or off [0]	1
GSw_SAsia_Trade	binary	Turn South Asia trade on [1] or off [0]	1
GSw_SAsia_PRM	binary	Importing countries able to contribute to India's PRM: yes [1] or no [0]	0
GSw_StorHAV	binary	Turn HAV (Hourly Arbitrage Value) for storage on [1] or off [0]	1
GSw_FocusRegionZeroTXCost	binary	Zero transmission capital cost between focus regions: yes [1] or no [0]	0
GSw_TxLimit	binary	Turn transmission flow limits on [1] or off [0]	1
Model Settings: Policy
GSw_RECapMandate	binary	Turn RE capacity targets on [1] or off [0]	1
GSw_RECapFracMandate	binary	Turn fractional non-fossil capacity target on [1] or off [0]	0
GSw_REGenMandate	binary	Turn RE generation target on [1] or off [0]	0
GSw_TechPhaseOut	binary	Turn forced phase out of select technologies on [1] or off [0]	1
GSw_CarbonTax	binary	Turn CO2 tax on [1] or off [0]	0
GSw_CO2Limit	binary	Turn national CO2 emissions limit on [1] or off [0]	0
carbonpolicystartyear	integer	First year with carbon policy	2030
Model Settings: Linear Program
GSw_ValStr	binary	Turn value streams on [1] or off [0]	0
GSw_Loadpoint	binary	Use [1] or disable [0] a loadpoint for the intertemporal solve after the first iteration	1
GSw_gopt	integer	Select solver option file to be used	4