From 410e48890381ecc34b9bf7d19f687b1606ba31ac Mon Sep 17 00:00:00 2001 From: svijaysh Date: Wed, 22 Apr 2026 15:40:12 -0600 Subject: [PATCH 01/55] Add PLM optimized dispatch controller, example 34, and tests --- .../34_plm_optimized_dispatch.yaml | 5 + .../demand_profiles/demand_profile.yaml | 3361 +++++++ .../demand_profiles/supervisory_signal.yaml | 8760 +++++++++++++++++ .../driver_config.yaml | 5 + .../plant_config.yaml | 12 + .../run_plm_optimized_dispatch.py | 94 + .../tech_config.yaml | 54 + .../plm_optimized_storage_controller.py | 536 + .../test_plm_optimized_storage_controller.py | 270 + h2integrate/core/supported_models.py | 59 +- 10 files changed, 13144 insertions(+), 12 deletions(-) create mode 100644 examples/34_plm_optimized_dispatch/34_plm_optimized_dispatch.yaml create mode 100644 examples/34_plm_optimized_dispatch/demand_profiles/demand_profile.yaml create mode 100644 examples/34_plm_optimized_dispatch/demand_profiles/supervisory_signal.yaml create mode 100644 examples/34_plm_optimized_dispatch/driver_config.yaml create mode 100644 examples/34_plm_optimized_dispatch/plant_config.yaml create mode 100644 examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py create mode 100644 examples/34_plm_optimized_dispatch/tech_config.yaml create mode 100644 h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py create mode 100644 h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py diff --git a/examples/34_plm_optimized_dispatch/34_plm_optimized_dispatch.yaml b/examples/34_plm_optimized_dispatch/34_plm_optimized_dispatch.yaml new file mode 100644 index 000000000..6f4c89466 --- /dev/null +++ b/examples/34_plm_optimized_dispatch/34_plm_optimized_dispatch.yaml @@ -0,0 +1,5 @@ +name: H2Integrate_config +system_summary: PLM MILP-optimized battery dispatch +driver_config: driver_config.yaml +technology_config: tech_config.yaml +plant_config: plant_config.yaml diff --git a/examples/34_plm_optimized_dispatch/demand_profiles/demand_profile.yaml b/examples/34_plm_optimized_dispatch/demand_profiles/demand_profile.yaml new file mode 100644 index 000000000..7f14e933f --- /dev/null +++ b/examples/34_plm_optimized_dispatch/demand_profiles/demand_profile.yaml @@ -0,0 +1,3361 @@ +- 212.4377 +- 201.6801 +- 216.0036 +- 217.5245 +- 194.3917 +- 199.5826 +- 251.0227 +- 307.4701 +- 309.8656 +- 303.1756 +- 257.0352 +- 256.2223 +- 250.5282 +- 259.0179 +- 433.7401 +- 423.1257 +- 432.9500 +- 422.3289 +- 437.0276 +- 429.6006 +- 248.5211 +- 244.5526 +- 219.7803 +- 208.7638 +- 206.5734 +- 207.1829 +- 214.2585 +- 212.9236 +- 213.3019 +- 213.4466 +- 267.1332 +- 306.7487 +- 305.9021 +- 303.4898 +- 254.9278 +- 259.0318 +- 249.0884 +- 243.2787 +- 423.4042 +- 435.2047 +- 435.9460 +- 434.3452 +- 424.6759 +- 431.8573 +- 250.9335 +- 251.7495 +- 216.9714 +- 211.7888 +- 215.4313 +- 210.5406 +- 212.3130 +- 215.0503 +- 198.3428 +- 207.4426 +- 246.2370 +- 304.8890 +- 307.7989 +- 321.9595 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8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 28.0000 +- 28.0000 +- 28.0000 +- 28.0000 +- 81.5338 +- 71.4758 +- 74.2712 +- 74.3409 +- 69.6864 +- 23.0000 +- 23.0000 +- 23.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 28.0000 +- 28.0000 +- 28.0000 +- 28.0000 +- 71.5507 +- 114.5877 +- 68.5757 +- 84.7005 +- 69.4464 +- 23.0000 +- 23.0000 +- 23.0000 +- 8.0000 +- 8.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 38.0000 +- 38.0000 +- 38.0000 +- 38.0000 +- 79.3612 +- 99.0560 +- 90.6929 +- 114.6070 +- 93.6671 +- 33.0000 +- 33.0000 +- 33.0000 +- 18.0000 +- 18.0000 diff --git a/examples/34_plm_optimized_dispatch/driver_config.yaml b/examples/34_plm_optimized_dispatch/driver_config.yaml new file mode 100644 index 000000000..0b40f01a0 --- /dev/null +++ b/examples/34_plm_optimized_dispatch/driver_config.yaml @@ -0,0 +1,5 @@ +name: driver_config +description: Driver configuration for PLM MILP-optimized battery dispatch example +general: + folder_output: outputs + create_om_reports: false diff --git a/examples/34_plm_optimized_dispatch/plant_config.yaml b/examples/34_plm_optimized_dispatch/plant_config.yaml new file mode 100644 index 000000000..69e0909fc --- /dev/null +++ b/examples/34_plm_optimized_dispatch/plant_config.yaml @@ -0,0 +1,12 @@ +name: plant_config +description: Simulation for PLM MILP-optimized battery dispatch +plant: + plant_life: 1 + simulation: + n_timesteps: 8760 # full year (hourly) + dt: 3600 # 1-hour timesteps + timezone: -6 # MDT (UTC-6) + start_time: "2025/01/01 00:00:00" +tech_to_dispatch_connections: + - [grid_buy, battery] + - [battery, battery] diff --git a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py new file mode 100644 index 000000000..a833d2cc9 --- /dev/null +++ b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py @@ -0,0 +1,94 @@ +""" + +This example demonstrates demand-response storage dispatch using a rolling-horizon +MILP controller. The battery is scheduled to discharge during high-LMP peak hours +to reduce facility demand charges and earn performance incentives. +""" + +from pathlib import Path + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt + +from h2integrate.core.h2integrate_model import H2IntegrateModel +from h2integrate.core.utilities import build_time_series_from_plant_config + +EXAMPLE_DIR = Path(__file__).parent + + +model = H2IntegrateModel(EXAMPLE_DIR / "34_plm_optimized_dispatch.yaml") +model.setup() + +N = model.plant_config["plant"]["simulation"]["n_timesteps"] +percentile = model.technology_config["technologies"]["battery"]["model_inputs"]["control_parameters"]["signal_threshold_percentile"] + +model.run() + +lmp = np.array( + model.technology_config["technologies"]["battery"]["model_inputs"] + ["control_parameters"]["supervisory_signal"] +)[:N] + +time_index = pd.DatetimeIndex(build_time_series_from_plant_config(model.plant_config)) +battery_power = model.prob.get_val("battery.storage_electricity_discharge", units="kW") +soc_pct = model.prob.get_val("battery.SOC", units="percent") + +peak_mask = (time_index.hour >= 14) & (time_index.hour <= 18) +threshold_pct = np.percentile(lmp, percentile) +discharge_mask = battery_power > 1.0 + + +plt.rcParams.update({"axes.spines.top": False, "axes.spines.right": False}) +fig, axes = plt.subplots(3, 1, sharex=True, figsize=(11, 7)) +days = pd.date_range(time_index[0].normalize(), periods=14, freq="D", tz=time_index.tz) +time_window = 14 * 24 + + +def shade_peaks(ax): + for day in days: + ax.axvspan( + day + pd.Timedelta(hours=14), + day + pd.Timedelta(hours=18), + color="orange", alpha=0.10, linewidth=0, zorder=0, + ) + + +w_discharge = discharge_mask[:time_window] + +ax = axes[0] +shade_peaks(ax) +ax.plot(time_index[:time_window], lmp[:time_window], color="steelblue", linewidth=1.0) +ax.axhline(threshold_pct, color="crimson", linestyle="--", linewidth=0.8) +ax.plot(time_index[:time_window][w_discharge], lmp[:time_window][w_discharge], + "r*", markersize=8, zorder=5) +ax.set_ylabel("LMP ($/MWh)", fontsize=8) +ax.set_ylim(bottom=0) + +# Panel 2: SOC +ax = axes[1] +ax.plot(time_index[:time_window], soc_pct[:time_window], color="tab:green", linewidth=1.0) +ax.axhline(90, color="gray", linestyle=":", linewidth=0.7) +ax.axhline(10, color="gray", linestyle=":", linewidth=0.7) +ax.set_ylabel("SOC (%)", fontsize=8) +ax.set_ylim([0, 105]) + +ax = axes[2] +shade_peaks(ax) +ax.axhline(0, color="black", linewidth=0.4) +width = pd.Timedelta(hours=0.8) +pos_mask = (battery_power > 1)[:time_window] +neg_mask = (battery_power < -1)[:time_window] +ax.bar(time_index[:time_window][pos_mask], battery_power[:time_window][pos_mask] * 1e-3, + width=width, color="tab:red", alpha=0.8) +ax.bar(time_index[:time_window][neg_mask], battery_power[:time_window][neg_mask] * 1e-3, + width=width, color="tab:blue", alpha=0.8) +ax.set_ylabel("Power (MW)", fontsize=8) +ax.tick_params(axis="x", labelrotation=30, labelsize=7) + +for ax in axes: + ax.tick_params(labelsize=7) + ax.grid(True, which="major", alpha=0.3, linewidth=0.5) + +plt.tight_layout() +plt.savefig("plm_optimized_dispatch.png", dpi=150, bbox_inches="tight") diff --git a/examples/34_plm_optimized_dispatch/tech_config.yaml b/examples/34_plm_optimized_dispatch/tech_config.yaml new file mode 100644 index 000000000..6bd644775 --- /dev/null +++ b/examples/34_plm_optimized_dispatch/tech_config.yaml @@ -0,0 +1,54 @@ +name: technology_config +description: 300 kW / 1.2 MWh battery with PLM MILP-optimized dispatch controller +technologies: + battery: + performance_model: + model: StoragePerformanceModel + cost_model: + model: ATBBatteryCostModel + control_strategy: + model: PLMOptimizedStorageController + model_inputs: + shared_parameters: + commodity: electricity + commodity_rate_units: kW + max_charge_rate: 300.0 # kW rated power + max_capacity: 1200.0 # kWh usable capacity (4-hour battery) + max_soc_fraction: 0.90 + min_soc_fraction: 0.10 + init_soc_fraction: 0.90 + charge_efficiency: 0.95 + discharge_efficiency: 0.95 + + performance_parameters: + demand_profile: 0.0 + control_parameters: + system_commodity_interface_limit: 1.0e9 + supervisory_signal: !include demand_profiles/supervisory_signal.yaml + peak_window: + start: "14:00:00" # 2 pm MDT + end: "18:00:00" # 6 pm MDT + performance_incentive: 14.0 # $/kW per dispatch hour + n_max_events: 10 # maximum discharge events per month + signal_threshold_percentile: 95.0 # only dispatch when LMP >= this percentile + n_control_window: 10 # number of time steps in the control window (e.g., 10 hours) + round_digits: 4 + cost_parameters: + cost_year: 2024 + energy_capex: 408 # $/kWh (2024 ATB) + power_capex: 379 # $/kW + opex_fraction: 0.025 + grid_buy: + performance_model: + model: GridPerformanceModel + cost_model: + model: GridCostModel + model_inputs: + shared_parameters: + interconnection_size: 100000 # kW + cost_parameters: + cost_year: 2024 + fixed_interconnection_cost: 0.0 + interconnection_capex_per_kw: 0.0 + interconnection_opex_per_kw: 0.0 + electricity_buy_price: 0.09 # $/kWh diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py new file mode 100644 index 000000000..a4721825f --- /dev/null +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -0,0 +1,536 @@ +from datetime import datetime, timedelta + +import numpy as np +import pandas as pd +import pyomo.environ as pyomo +from attrs import field, define + +from h2integrate.core.utilities import merge_shared_inputs +from h2integrate.core.validators import range_val +from h2integrate.control.control_strategies.pyomo_storage_controller_baseclass import ( + SolverOptions, + PyomoStorageControllerBaseClass, + PyomoStorageControllerBaseConfig, +) +from h2integrate.control.control_strategies.controller_opt_problem_state import ( + DispatchProblemState, +) +from matplotlib import pyplot as plt + + +@define +class PLMOptimizedControllerConfig(PyomoStorageControllerBaseConfig): + """Configuration for the PLM DR optimized storage controller. + + Inherits base fields from ``PyomoControllerBaseConfig``: + ``max_capacity``, ``max_soc_fraction``, ``min_soc_fraction``, + ``init_soc_fraction``, ``n_control_window``, ``commodity``, + ``commodity_rate_units``, ``tech_name``, + ``system_commodity_interface_limit``, ``round_digits``. + + Attributes: + max_charge_rate (float): Maximum charge and discharge rate (kW). + supervisory_signal (list[float]): Price, demand, or price*demand + forecast time series. The rolling solver uses one window of + length ``n_control_window`` per solve. + peak_window (dict): Hours eligible for dispatch. Keys ``'start'`` + and ``'end'`` must be strings in ``HH:MM:SS`` format. + performance_incentive (float): Incentive revenue ($/kW per + dispatch hour). + charge_efficiency (float): Charge efficiency in [0, 1]. + Defaults to 1.0. + discharge_efficiency (float): Discharge efficiency in [0, 1]. + Defaults to 1.0. + n_max_events (int): Maximum discharge events per calendar month. + Defaults to 10. + n_control_window (int): Number of timesteps per rolling solve + window. Defaults to ``24 * 30`` (one month of hourly data). + signal_threshold_percentile (float): Percentile (0–100) used to + compute the signal threshold for each rolling window. Only + hours at or above this percentile of the window signal are + eligible for dispatch. Defaults to 0.0 (all hours eligible). + """ + + max_charge_rate: float = field() + supervisory_signal: list = field() + peak_window: dict = field() + performance_incentive: float = field() + charge_efficiency: float = field(validator=range_val(0, 1), default=1.0) + discharge_efficiency: float = field(validator=range_val(0, 1), default=1.0) + n_max_events: int = field(default=10) + n_control_window: int = field(default=24 * 30) # one month of hourly data + signal_threshold_percentile: float = field(default=0.0, validator=range_val(0,100)) + + +class PLMOptimizedStorageController(PyomoStorageControllerBaseClass): + """Demand-response storage controller using a rolling-horizon MILP. + + Each call to the dispatch solver iterates over the full simulation in + windows of length ``n_control_window``. For each window it receives + the monthly LMP forecast, solves the MILP to maximize incentive + revenue, then passes the resulting dispatch commands to the + performance model. The terminal SOC of each window is carried forward + as the initial SOC of the next window. + """ + + def setup(self): + """Initialize config, register OpenMDAO inputs, and pre-compute static masks. + + Raises: + ValueError: If the length of the time series built from + ``plant_config`` does not match ``n_timesteps``. + """ + self.config = PLMOptimizedControllerConfig.from_dict( + merge_shared_inputs(self.options["tech_config"]["model_inputs"], "control") + ) + + self.add_input( + "max_charge_rate", + val=self.config.max_charge_rate, + units=self.config.commodity_rate_units, + desc="Maximum charge/discharge rate P_max", + ) + self.add_input( + "storage_capacity", + val=self.config.max_capacity, + units=f"{self.config.commodity_rate_units}*h", + desc="Total storage capacity", + ) + + self.n_timesteps = self.options["plant_config"]["plant"]["simulation"][ + "n_timesteps" + ] + super().setup() + + self.updated_initial_soc = self.config.init_soc_fraction + + self.commodity_info = { + "commodity_name": self.config.commodity, + "commodity_storage_units": self.config.commodity_rate_units, + } + + self.time_index = self._build_time_index(self.options["plant_config"]) + + if len(self.time_index) != self.n_timesteps: + raise ValueError( + f"Time series length {len(self.time_index)} != n_timesteps {self.n_timesteps}" + ) + + self.in_peak_window = self._compute_peak_window_mask() # bool array, shape (T,) + self.month_ids = self._compute_month_ids() # int array, shape (T,) + + @staticmethod + def _build_time_index(plant_config: dict) -> pd.DatetimeIndex: + """Build a timezone-aware DatetimeIndex from simulation settings in plant_config. + + Args: + plant_config (dict): Plant configuration dict. Must contain + ``plant.simulation`` with keys ``n_timesteps`` (int), + ``dt`` (int, seconds), ``timezone`` (int, UTC offset), + and ``start_time`` (str). + + Returns: + pd.DatetimeIndex: DatetimeIndex of length ``n_timesteps`` spaced + ``dt`` seconds apart, starting at ``start_time`` in the given timezone. + """ + sim = plant_config["plant"]["simulation"] + n_timesteps = int(sim["n_timesteps"]) + dt_seconds = int(sim["dt"]) + tz = int(sim["timezone"]) + start = pd.Timestamp(sim["start_time"], tz=tz) + freq = pd.to_timedelta(dt_seconds, unit="s") + return pd.date_range(start=start, periods=n_timesteps, freq=freq) + + def _parse_peak_window(self) -> tuple: + """Parse the ``peak_window`` config entry into ``datetime.time`` objects. + + Returns: + tuple[datetime.time, datetime.time]: ``(start, end)`` times. + + Raises: + ValueError: If ``'start'`` or ``'end'`` keys are missing, or + if either value is not a string in ``HH:MM:SS`` format. + """ + pw = dict(self.config.peak_window) + if "start" not in pw or "end" not in pw: + raise ValueError("peak_window must contain 'start' and 'end' keys") + for key in ("start", "end"): + val = pw[key] + if not isinstance(val, str) or len(val.split(":")) != 3: + raise ValueError( + f"peak_window {key} value must be a string in HH:MM:SS format, got {val}." + ) + pw[key] = datetime.strptime(val, "%H:%M:%S").time() + return pw["start"], pw["end"] + + def _compute_peak_window_mask(self) -> np.ndarray: + """Build a boolean mask that is ``True`` for timesteps inside the peak window. + + Returns: + np.ndarray: Boolean array of shape ``(n_timesteps,)``. + + Raises: + ValueError: If ``peak_window`` end time is before start time. + """ + start, end = self._parse_peak_window() + times = pd.DatetimeIndex(self.time_index).time + if end < start: + raise ValueError("peak_window end time must be after start time.") + return np.array([start <= t <= end for t in times]) + + def _compute_month_ids(self) -> np.ndarray: + """Return the calendar month index (1–12) for each timestep. + + Returns: + np.ndarray: Integer array of shape ``(n_timesteps,)``. + """ + return pd.DatetimeIndex(self.time_index).month.to_numpy() + + def _compute_eligible_mask(self, signal_window: np.ndarray) -> np.ndarray: + """Build a boolean mask for timesteps whose signal meets the dispatch threshold. + + The threshold is derived only from ``signal_window`` — it does not + assume the full simulation signal is known in advance. When + ``signal_threshold_percentile`` is 0.0 all hours are eligible. + + Args: + signal_window (np.ndarray): Signal values for the current + rolling window. + + Returns: + np.ndarray: Boolean array of shape ``(len(signal_window),)``. + ``True`` where ``signal_t >= threshold``. + """ + eligible = np.ones(len(signal_window), dtype=bool) + + if self.config.signal_threshold_percentile > 0.0: + threshold = np.percentile( + signal_window, self.config.signal_threshold_percentile + ) + eligible = signal_window >= threshold + + return eligible + + def pyomo_setup(self, discrete_inputs): + """Return the rolling-horizon dispatch solver callable. + + Args: + discrete_inputs (dict): OpenMDAO discrete inputs (unused + directly here; passed for API compatibility). + + Returns: + callable: ``pyomo_dispatch_solver(performance_model, + performance_model_kwargs, inputs)`` that iterates over the + simulation in windows of ``n_control_window`` timesteps. + For each window it: + + 1. Builds a fresh MILP from the window's signal slice. + 2. Solves the MILP with GLPK. + 3. Calls ``performance_model`` with the resulting dispatch + commands. + 4. Carries the terminal SOC into the next window. + + Returns ``(storage_out, soc_out)`` — two ``np.ndarray`` of + length ``n_timesteps``. + """ + + def pyomo_dispatch_solver( + performance_model, + performance_model_kwargs, + inputs, + commodity_name=self.config.commodity, + ): + storage_out = np.zeros(self.n_timesteps) + soc_out = np.zeros(self.n_timesteps) + + # Track events used per calendar month so the monthly cap is + # respected across window boundaries. + events_used_per_month = {} + + n_w = self.config.n_control_window + window_start_indices = list(range(0, self.n_timesteps, n_w)) + + for window_start in window_start_indices: + window_len = min(n_w, self.n_timesteps - window_start) + + n_windows = len(window_start_indices) + report_every = max(1, n_windows // 4) + window_idx = window_start // n_w + if window_idx % report_every == 0: + pct = round(window_start / self.n_timesteps * 100) + print(f"{pct}% done with PLM rolling dispatch") + + month_ids_w = self.month_ids[window_start : window_start + window_len] + remaining_budget = { + int(m): max( + 0, + self.config.n_max_events - events_used_per_month.get(int(m), 0), + ) + for m in np.unique(month_ids_w) + } + + self.dr_model = self._build_dr_model( + window_start=window_start, + window_len=window_len, + init_soc=self.updated_initial_soc, + remaining_budget=remaining_budget, + ) + self.problem_state = DispatchProblemState() + + self.solve_dispatch_model( + start_time=window_start, + n_days=self.n_timesteps // 24, + ) + + for t in range(window_len): + if pyomo.value(self.dr_model.discharge[t]) > 0.5: + m = int(month_ids_w[t]) + events_used_per_month[m] = events_used_per_month.get(m, 0) + 1 + + storage_out_window, soc_window = performance_model( + self.storage_dispatch_commands, + **performance_model_kwargs, + sim_start_index=window_start, + ) + + # Performance model returns SOC in percent. + self.updated_initial_soc = soc_window[-1] / 100.0 + + for j in range(window_len): + storage_out[window_start + j] = storage_out_window[j] + soc_out[window_start + j] = soc_window[j] + + print( + f" Events per month: {dict(events_used_per_month)} " + f"(limit: {self.config.n_max_events}/month)" + ) + return storage_out, soc_out + + return pyomo_dispatch_solver + + def initialize_parameters(self, inputs): + """Sync OpenMDAO inputs into the config. + + Args: + inputs (dict): OpenMDAO inputs dict. Recognised keys are + ``'max_charge_rate'`` and ``'storage_capacity'``. + """ + if "max_charge_rate" in inputs: + object.__setattr__( + self.config, "max_charge_rate", float(inputs["max_charge_rate"][0]) + ) + if "storage_capacity" in inputs: + object.__setattr__( + self.config, "max_capacity", float(inputs["storage_capacity"][0]) + ) + + def _build_dr_model( + self, + window_start: int, + window_len: int, + init_soc: float, + remaining_budget: dict, + ) -> pyomo.ConcreteModel: + """Build the DR MILP for a single rolling window. + + Args: + window_start (int): Global timestep index of the first hour + in this window. + window_len (int): Number of timesteps in this window + (``n_control_window`` except possibly the last window). + init_soc (float): State-of-charge fraction at the start of + this window. + remaining_budget (dict): Mapping of ``month_id (int)`` to + remaining event slots for that month. Computed by + subtracting events already dispatched in earlier windows + from ``n_max_events``, so the monthly cap is respected + across windows. + + Returns: + pyomo.ConcreteModel: Fully formed MILP ready to solve. + """ + m = pyomo.ConcreteModel(name="plm_dr") + + P_max = self.config.max_charge_rate + E_max = self.config.max_capacity * ( + self.config.max_soc_fraction - self.config.min_soc_fraction + ) + eta_c = self.config.charge_efficiency + eta_d = self.config.discharge_efficiency + soc_max = self.config.max_soc_fraction + soc_min = self.config.min_soc_fraction + incentive = self.config.performance_incentive + N_max = self.config.n_max_events + + w = slice(window_start, window_start + window_len) + in_peak_window_w = self.in_peak_window[w] + month_ids_w = self.month_ids[w] + signal_w = np.asarray(self.config.supervisory_signal, dtype=float)[w] + eligible_t_w = self._compute_eligible_mask(signal_w) + + months_in_window = np.unique(month_ids_w).tolist() + + m.T = pyomo.Set(initialize=range(window_len), doc="Timesteps in window") + m.M = pyomo.Set(initialize=months_in_window, doc="Months in window") + + m.discharge = pyomo.Var(m.T, domain=pyomo.Binary, doc="Discharge binary: 1 = discharging at timestep t") + m.charge = pyomo.Var(m.T, domain=pyomo.Binary, doc="Charge binary: 1 = charging at timestep t") + m.soc = pyomo.Var( + m.T, + domain=pyomo.NonNegativeReals, + bounds=(soc_min, soc_max), + doc="State of charge SoC_t", + ) + + m.objective = pyomo.Objective( + expr=-incentive * P_max * sum(m.discharge[t] for t in m.T), + sense=pyomo.minimize, + ) + + m.peak_window_only = pyomo.Constraint( + m.T, + rule=lambda mdl, t: ( + mdl.discharge[t] == 0 if not in_peak_window_w[t] else pyomo.Constraint.Skip + ), + ) + + m.high_signal_only = pyomo.Constraint( + m.T, + rule=lambda mdl, t: mdl.discharge[t] <= int(eligible_t_w[t]), + ) + + def max_events_rule(mdl, month): + ts_in_month = [t for t in mdl.T if month_ids_w[t] == month] + if not ts_in_month: + return pyomo.Constraint.Skip + budget = remaining_budget.get(month, N_max) + return sum(mdl.discharge[t] for t in ts_in_month) <= budget + + m.max_events = pyomo.Constraint(m.M, rule=max_events_rule) + + m.soc_init = pyomo.Constraint(expr=m.soc[0] == init_soc) + + def soc_evolution_rule(mdl, t): + if t == 0: + return pyomo.Constraint.Skip + return mdl.soc[t] == ( + mdl.soc[t - 1] + + eta_c * mdl.charge[t] * P_max / E_max + - mdl.discharge[t] * P_max / (eta_d * E_max) + ) + + m.soc_evolution = pyomo.Constraint(m.T, rule=soc_evolution_rule) + + m.no_simultaneous = pyomo.Constraint( + m.T, + rule=lambda mdl, t: mdl.discharge[t] + mdl.charge[t] <= 1, + ) + + m.no_charge_in_window = pyomo.Constraint( + m.T, + rule=lambda mdl, t: ( + mdl.charge[t] == 0 if in_peak_window_w[t] else pyomo.Constraint.Skip + ), + ) + + return m + + def solve_dispatch_model(self, start_time: int = 0, n_days: int = 0): + """Solve the DR MILP for the current window and record solver metrics. + + Args: + start_time (int): Global timestep index of the window start. + Used only for error messages and metrics. Defaults to 0. + n_days (int): Total simulation days. Passed to + ``DispatchProblemState.store_problem_metrics``. + Defaults to 0. + + Raises: + RuntimeError: If GLPK returns a non-OK status or an + unacceptable termination condition. + """ + from pyomo.opt import SolverStatus, TerminationCondition + + solver_results = self.glpk_solve_call(self.dr_model) + + status = solver_results.solver.status + tc = solver_results.solver.termination_condition + acceptable = ( + TerminationCondition.optimal, + TerminationCondition.feasible, + TerminationCondition.maxTimeLimit, + ) + if status != SolverStatus.ok or tc not in acceptable: + raise RuntimeError( + f"PLM MILP solver failed at window start={start_time}: " + f"status={status}, termination={tc}. " + f"init_soc={self.updated_initial_soc:.4f}, " + f"window_len={len(list(self.dr_model.T))}" + ) + if tc == TerminationCondition.maxTimeLimit: + print( + f" WARNING: solver hit time limit at window start={start_time} " + f"— using best solution found so far" + ) + + self.problem_state.store_problem_metrics( + solver_results, + start_time, + n_days, + pyomo.value(self.dr_model.objective), + ) + + def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): + """Build the DR dispatch solver and write it to discrete outputs. + + Args: + inputs (dict): OpenMDAO continuous inputs. + outputs (dict): OpenMDAO continuous outputs. + discrete_inputs (dict): OpenMDAO discrete inputs. + discrete_outputs (dict): OpenMDAO discrete outputs. The key + ``'pyomo_dispatch_solver'`` is set to the callable + returned by :meth:`pyomo_setup`. + """ + discrete_outputs["pyomo_dispatch_solver"] = self.pyomo_setup(discrete_inputs) + + @staticmethod + def glpk_solve_call( + pyomo_model: pyomo.ConcreteModel, + log_name: str = "", + user_solver_options: dict | None = None, + ): + """Solve a Pyomo MILP with GLPK. + + Args: + pyomo_model (pyomo.ConcreteModel): The model to solve. + log_name (str): Optional log file name passed to + ``SolverOptions``. Defaults to ``''``. + user_solver_options (dict | None): Optional overrides for + GLPK solver options. Defaults to ``None``. + + Returns: + pyomo.opt.SolverResults: Raw results object from GLPK. + """ + glpk_solver_options = {"cuts": None, "presol": None, "tmlim": 300} + solver_options = SolverOptions( + glpk_solver_options, log_name, user_solver_options, "log" + ) + with pyomo.SolverFactory("glpk") as solver: + results = solver.solve( + pyomo_model, options=solver_options.constructed, tee=False + ) + return results + + @property + def storage_dispatch_commands(self) -> list: + """Net dispatch commands for the solved window. + + Returns: + list[float]: ``(u_t - v_t) * P_max`` for each timestep in + the solved window. Positive = discharge, negative = charge. + """ + P_max = self.config.max_charge_rate + return [ + (pyomo.value(self.dr_model.discharge[t]) - pyomo.value(self.dr_model.charge[t])) * P_max + for t in self.dr_model.T + ] diff --git a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py new file mode 100644 index 000000000..40b37863e --- /dev/null +++ b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py @@ -0,0 +1,270 @@ +from types import SimpleNamespace + +from attrs import evolve +import numpy as np +import pandas as pd +import pyomo.environ as pyomo +import pytest + +from h2integrate.control.control_strategies.storage.plm_optimized_storage_controller import ( + PLMOptimizedControllerConfig, + PLMOptimizedStorageController, +) +from h2integrate.storage.storage_baseclass import StoragePerformanceBase + + +def _make_controller(): + return object.__new__(PLMOptimizedStorageController) + + +def _make_controller_with_config(config, n_timesteps=24): + """Build a controller with pre-computed masks, bypassing OpenMDAO setup.""" + controller = _make_controller() + controller.config = config + controller.updated_initial_soc = config.init_soc_fraction + controller.time_index = pd.date_range("2024-01-01", periods=n_timesteps, freq="h") + controller.in_peak_window = controller._compute_peak_window_mask() + controller.month_ids = controller._compute_month_ids() + return controller + + +@pytest.fixture +def base_config(): + """24-hour PLM config shared by optimizer regression tests. + + Storage: 10 kWh capacity, 1 kW max rate, starts fully charged. + Peak window: 08:00-18:00 + Signal: monotonically increasing. + """ + n = 24 + return PLMOptimizedControllerConfig( + max_capacity=10.0, + max_soc_fraction=1.0, + min_soc_fraction=0.0, + init_soc_fraction=1.0, + n_control_window=n, + commodity="electricity", + commodity_rate_units="kW", + tech_name="battery", + system_commodity_interface_limit=100.0, + max_charge_rate=1.0, + supervisory_signal=list(range(n)), + peak_window={"start": "08:00:00", "end": "18:00:00"}, + performance_incentive=10.0, + n_max_events=24, + signal_threshold_percentile=0.0, + ) + + +@pytest.mark.unit +def test_parse_peak_window(): + controller = _make_controller() + controller.config = SimpleNamespace( + peak_window={"start": "08:00:00", "end": "18:40:20"} + ) + start, end = controller._parse_peak_window() + assert start.hour == 8 + assert start.minute == 0 + assert start.second == 0 + assert end.hour == 18 + assert end.minute == 40 + assert end.second == 20 + + +@pytest.mark.unit +def test_parse_peak_window_invalid_format(): + controller = _make_controller() + controller.config = SimpleNamespace(peak_window={"start": "08", "end": "18:40:20"}) + with pytest.raises( + ValueError, match="peak_window start value must be a string in HH:MM:SS format" + ): + controller._parse_peak_window() + + +@pytest.mark.unit +def test_parse_peak_window_int_raises(): + controller = _make_controller() + controller.config = SimpleNamespace(peak_window={"start": 8, "end": 9}) + with pytest.raises(ValueError): + controller._parse_peak_window() + + +@pytest.mark.unit +def test_parse_peak_window_missing_key_raises(): + controller = _make_controller() + controller.config = SimpleNamespace(peak_window={"start": "08:00:00"}) + with pytest.raises( + ValueError, match="peak_window must contain 'start' and 'end' keys" + ): + controller._parse_peak_window() + + +@pytest.mark.unit +def test_compute_peak_window_mask(): + controller = _make_controller() + controller.config = SimpleNamespace( + peak_window={"start": "00:00:00", "end": "02:00:00"} + ) + controller.time_index = pd.date_range("2024-01-01", periods=24, freq="h") + mask = controller._compute_peak_window_mask() + expected = np.array([i <= 2 for i in range(24)]) + assert isinstance(mask, np.ndarray) + assert np.array_equal(mask, expected) + + +@pytest.mark.unit +def test_compute_month_ids(): + # Jan 2024: 744h, Feb 2024 (leap year): 696h, Mar 2024: 744h + controller = _make_controller() + controller.time_index = pd.date_range( + "2024-01-01", periods=744 + 696 + 744, freq="h" + ) + month_ids = controller._compute_month_ids() + expected = np.array([1] * 744 + [2] * 696 + [3] * 744) + assert np.array_equal(month_ids, expected) + + +@pytest.mark.unit +def test_compute_eligible_mask_zero_percentile_all_eligible(): + """All timesteps are eligible when signal_threshold_percentile=0.""" + controller = _make_controller() + controller.config = SimpleNamespace(signal_threshold_percentile=0.0) + signal = np.array([1.0, 5.0, 3.0, 2.0, 8.0]) + mask = controller._compute_eligible_mask(signal) + assert isinstance(mask, np.ndarray) + assert mask.dtype == bool + assert len(mask) == len(signal) + assert mask.all() + + +@pytest.mark.unit +def test_compute_eligible_mask_50th_percentile(): + """Only values at or above the 50th percentile are eligible.""" + controller = _make_controller() + controller.config = SimpleNamespace(signal_threshold_percentile=50.0) + signal = np.array([1.0, 2.0, 3.0, 4.0, 5.0]) + mask = controller._compute_eligible_mask(signal) + expected = signal >= np.percentile(signal, 50.0) + assert np.array_equal(mask, expected) + + +@pytest.mark.unit +def test_compute_eligible_mask_100th_percentile_only_max_eligible(): + """Only the maximum value(s) are eligible at percentile=100.""" + controller = _make_controller() + controller.config = SimpleNamespace(signal_threshold_percentile=100.0) + signal = np.array([1.0, 2.0, 10.0, 3.0, 10.0]) + mask = controller._compute_eligible_mask(signal) + expected = np.array([False, False, True, False, True]) + assert np.array_equal(mask, expected) + + +@pytest.mark.unit +def test_compute_eligible_mask_uniform_signal_all_eligible(): + """All timesteps are eligible when the signal is uniform, regardless of percentile.""" + controller = _make_controller() + controller.config = SimpleNamespace(signal_threshold_percentile=75.0) + signal = np.full(10, 5.0) + mask = controller._compute_eligible_mask(signal) + assert mask.all() + + +@pytest.mark.regression +def test_optimizer_dispatch_only_in_peak_window(base_config): + """Solver must never set 1 outside the peak window.""" + # + controller = _make_controller_with_config(base_config) + model = controller._build_dr_model( + window_start=0, + window_len=24, + init_soc=base_config.init_soc_fraction, + remaining_budget={1: base_config.n_max_events}, + ) + + PLMOptimizedStorageController.glpk_solve_call(model) + + peak_start, peak_end = controller._parse_peak_window() + print("Peak window:", peak_start, "-", peak_end) + for t in range(24): + hour = pd.Timestamp("2024-01-01") + pd.Timedelta(hours=t) + in_window = peak_start <= hour.time() <= peak_end + if not in_window: + assert pyomo.value(model.discharge[t]) < 1e-3 + + +@pytest.mark.regression +def test_optimizer_dispatch_only_on_eligible_timesteps(base_config): + """Solver must never set 1 on ineligible timesteps.""" + controller = _make_controller_with_config(base_config) + model = controller._build_dr_model( + window_start=0, + window_len=24, + init_soc=base_config.init_soc_fraction, + remaining_budget={1: base_config.n_max_events}, + ) + + PLMOptimizedStorageController.glpk_solve_call(model) + + signal = np.array(controller.config.supervisory_signal) + eligible_mask = controller._compute_eligible_mask(signal) + for t in range(24): + if not eligible_mask[t]: + assert pyomo.value(model.discharge[t]) < 1e-3 + + +@pytest.mark.regression +def test_optimizer_dispatch_respects_event_budget(base_config): + """Solver must never set more than n_max_events timesteps to 1.""" + controller = _make_controller_with_config(base_config) + model = controller._build_dr_model( + window_start=0, + window_len=24, + init_soc=base_config.init_soc_fraction, + remaining_budget={1: base_config.n_max_events}, + ) + + PLMOptimizedStorageController.glpk_solve_call(model) + + total_events = sum(pyomo.value(model.discharge[t]) for t in range(24)) + assert total_events <= base_config.n_max_events + 1e-3 + + +@pytest.mark.regression +def test_optimizer_dispatch_respects_soc_constraints(base_config): + """Solver must never violate SOC constraints.""" + controller = _make_controller_with_config(base_config) + model = controller._build_dr_model( + window_start=0, + window_len=24, + init_soc=base_config.init_soc_fraction, + remaining_budget={2: base_config.n_max_events}, + ) + + PLMOptimizedStorageController.glpk_solve_call(model) + + soc = base_config.init_soc_fraction * base_config.max_capacity + for t in range(24): + charge = pyomo.value(model.charge[t]) + discharge = pyomo.value(model.discharge[t]) + soc += charge - discharge + assert soc >= base_config.min_soc_fraction * base_config.max_capacity + assert soc <= base_config.max_soc_fraction * base_config.max_capacity + + +@pytest.mark.regression +def test_optimizer_dispatch_respects_charge_discharge_exclusivity(base_config): + """Solver must never set charge and discharge to 1 in the same timestep.""" + controller = _make_controller_with_config(base_config) + model = controller._build_dr_model( + window_start=0, + window_len=24, + init_soc=base_config.init_soc_fraction, + remaining_budget={2: base_config.n_max_events}, + ) + + PLMOptimizedStorageController.glpk_solve_call(model) + + for t in range(24): + charge = pyomo.value(model.charge[t]) + discharge = pyomo.value(model.discharge[t]) + assert not (charge > 0.5 and discharge > 0.5) diff --git a/h2integrate/core/supported_models.py b/h2integrate/core/supported_models.py index f57d46a1f..cd8285931 100644 --- a/h2integrate/core/supported_models.py +++ b/h2integrate/core/supported_models.py @@ -7,7 +7,10 @@ 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.steel.steel import ( + SteelPerformanceModel, + SteelCostAndFinancialModel, +) from h2integrate.converters.wind.floris import FlorisWindPlantPerformanceModel from h2integrate.demand.flexible_demand import FlexibleDemandComponent from h2integrate.converters.wind.wind_pysam import PYSAMWindPlantPerformanceModel @@ -32,7 +35,10 @@ IronTransportCostComponent, IronTransportPerformanceComponent, ) -from h2integrate.converters.nitrogen.simple_ASU import SimpleASUCostModel, SimpleASUPerformanceModel +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.converters.hydrogen.h2_fuel_cell import ( @@ -56,10 +62,16 @@ SaltCavernStorageCostModel, LinedRockCavernStorageCostModel, ) -from h2integrate.transporters.gas_stream_combiner import GasStreamCombinerPerformanceModel -from h2integrate.transporters.generic_transporter import GenericTransporterPerformanceModel +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.converters.iron.humbert_ewin_perf import ( + HumbertEwinPerformanceComponent, +) from h2integrate.storage.storage_performance_model import StoragePerformanceModel from h2integrate.converters.ammonia.ammonia_synloop import ( AmmoniaSynLoopCostModel, @@ -71,13 +83,21 @@ ReverseOsmosisCostModel, ReverseOsmosisPerformanceModel, ) -from h2integrate.resource.wind.nlr_developer_wtk_api import WTKNLRDeveloperAPIWindResource +from h2integrate.resource.wind.nlr_developer_wtk_api import ( + WTKNLRDeveloperAPIWindResource, +) from h2integrate.converters.hydrogen.basic_cost_model import BasicElectrolyzerCostModel -from h2integrate.converters.hydrogen.pem_electrolyzer import ECOElectrolyzerPerformanceModel +from h2integrate.converters.hydrogen.pem_electrolyzer import ( + ECOElectrolyzerPerformanceModel, +) from h2integrate.converters.solar.atb_res_com_pv_cost import ATBResComPVCostModel from h2integrate.converters.solar.atb_utility_pv_cost import ATBUtilityPVCostModel -from h2integrate.converters.iron.martin_mine_cost_model import MartinIronMineCostComponent -from h2integrate.converters.iron.martin_mine_perf_model import MartinIronMinePerformanceComponent +from h2integrate.converters.iron.martin_mine_cost_model import ( + MartinIronMineCostComponent, +) +from h2integrate.converters.iron.martin_mine_perf_model import ( + MartinIronMinePerformanceComponent, +) from h2integrate.converters.methanol.smr_methanol_plant import ( SMRMethanolPlantCostModel, SMRMethanolPlantFinanceModel, @@ -87,7 +107,9 @@ SimpleAmmoniaCostModel, SimpleAmmoniaPerformanceModel, ) -from h2integrate.converters.iron.humbert_stinn_ewin_cost import HumbertStinnEwinCostComponent +from h2integrate.converters.iron.humbert_stinn_ewin_cost import ( + HumbertStinnEwinCostComponent, +) from h2integrate.converters.methanol.co2h_methanol_plant import ( CO2HMethanolPlantCostModel, CO2HMethanolPlantFinanceModel, @@ -99,7 +121,10 @@ ) 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.co2.marine.direct_ocean_capture import ( + DOCCostModel, + DOCPerformanceModel, +) from h2integrate.converters.hydrogen.steam_methane_reformer import ( SteamMethaneReformerCostModel, SteamMethaneReformerPerformanceModel, @@ -110,7 +135,9 @@ SimpleGasConsumerPerformance, SimpleGasProducerPerformance, ) -from h2integrate.converters.hydrogen.geologic.mathur_modified import GeoH2SubsurfaceCostModel +from h2integrate.converters.hydrogen.geologic.mathur_modified import ( + GeoH2SubsurfaceCostModel, +) from h2integrate.resource.solar.nlr_developer_goes_api_models import ( GOESTMYSolarAPI, GOESConusSolarAPI, @@ -163,6 +190,12 @@ from h2integrate.control.control_strategies.storage.simple_openloop_controller import ( SimpleStorageOpenLoopController, ) +from h2integrate.control.control_strategies.storage.plm_openloop_storage_controller import ( + PeakLoadManagementOpenLoopStorageController, +) +from h2integrate.control.control_strategies.storage.plm_optimized_storage_controller import ( + PLMOptimizedStorageController, +) from h2integrate.control.control_rules.storage.pyomo_storage_rule_min_operating_cost import ( PyomoRuleStorageMinOperatingCosts, ) @@ -278,6 +311,8 @@ # Control "SimpleStorageOpenLoopController": SimpleStorageOpenLoopController, "DemandOpenLoopStorageController": DemandOpenLoopStorageController, + "PeakLoadManagementOpenLoopStorageController": PeakLoadManagementOpenLoopStorageController, + "PLMOptimizedStorageController": PLMOptimizedStorageController, "HeuristicLoadFollowingStorageController": HeuristicLoadFollowingStorageController, "OptimizedDispatchStorageController": OptimizedDispatchStorageController, "GenericDemandComponent": GenericDemandComponent, From cf46abef91cca147c5910c042971e519e12955dc Mon Sep 17 00:00:00 2001 From: svijaysh Date: Wed, 22 Apr 2026 16:05:42 -0600 Subject: [PATCH 02/55] Make sure the example runs --- .../run_plm_optimized_dispatch.py | 55 ++++++++++++++----- .../plm_optimized_storage_controller.py | 2 +- .../test_plm_optimized_storage_controller.py | 6 -- h2integrate/core/supported_models.py | 4 -- 4 files changed, 43 insertions(+), 24 deletions(-) diff --git a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py index a833d2cc9..3dff042d2 100644 --- a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py +++ b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py @@ -12,7 +12,6 @@ import matplotlib.pyplot as plt from h2integrate.core.h2integrate_model import H2IntegrateModel -from h2integrate.core.utilities import build_time_series_from_plant_config EXAMPLE_DIR = Path(__file__).parent @@ -21,16 +20,26 @@ model.setup() N = model.plant_config["plant"]["simulation"]["n_timesteps"] -percentile = model.technology_config["technologies"]["battery"]["model_inputs"]["control_parameters"]["signal_threshold_percentile"] +percentile = model.technology_config["technologies"]["battery"]["model_inputs"][ + "control_parameters" +]["signal_threshold_percentile"] model.run() lmp = np.array( - model.technology_config["technologies"]["battery"]["model_inputs"] - ["control_parameters"]["supervisory_signal"] + model.technology_config["technologies"]["battery"]["model_inputs"][ + "control_parameters" + ]["supervisory_signal"] )[:N] -time_index = pd.DatetimeIndex(build_time_series_from_plant_config(model.plant_config)) +sim = model.plant_config["plant"]["simulation"] +n_timesteps = int(sim["n_timesteps"]) +dt_seconds = int(sim["dt"]) +tz = int(sim["timezone"]) +start = pd.Timestamp(sim["start_time"], tz=tz) +freq = pd.to_timedelta(dt_seconds, unit="s") +time_index = pd.date_range(start=start, periods=n_timesteps, freq=freq) + battery_power = model.prob.get_val("battery.storage_electricity_discharge", units="kW") soc_pct = model.prob.get_val("battery.SOC", units="percent") @@ -50,7 +59,10 @@ def shade_peaks(ax): ax.axvspan( day + pd.Timedelta(hours=14), day + pd.Timedelta(hours=18), - color="orange", alpha=0.10, linewidth=0, zorder=0, + color="orange", + alpha=0.10, + linewidth=0, + zorder=0, ) @@ -60,14 +72,21 @@ def shade_peaks(ax): shade_peaks(ax) ax.plot(time_index[:time_window], lmp[:time_window], color="steelblue", linewidth=1.0) ax.axhline(threshold_pct, color="crimson", linestyle="--", linewidth=0.8) -ax.plot(time_index[:time_window][w_discharge], lmp[:time_window][w_discharge], - "r*", markersize=8, zorder=5) +ax.plot( + time_index[:time_window][w_discharge], + lmp[:time_window][w_discharge], + "r*", + markersize=8, + zorder=5, +) ax.set_ylabel("LMP ($/MWh)", fontsize=8) ax.set_ylim(bottom=0) # Panel 2: SOC ax = axes[1] -ax.plot(time_index[:time_window], soc_pct[:time_window], color="tab:green", linewidth=1.0) +ax.plot( + time_index[:time_window], soc_pct[:time_window], color="tab:green", linewidth=1.0 +) ax.axhline(90, color="gray", linestyle=":", linewidth=0.7) ax.axhline(10, color="gray", linestyle=":", linewidth=0.7) ax.set_ylabel("SOC (%)", fontsize=8) @@ -79,10 +98,20 @@ def shade_peaks(ax): width = pd.Timedelta(hours=0.8) pos_mask = (battery_power > 1)[:time_window] neg_mask = (battery_power < -1)[:time_window] -ax.bar(time_index[:time_window][pos_mask], battery_power[:time_window][pos_mask] * 1e-3, - width=width, color="tab:red", alpha=0.8) -ax.bar(time_index[:time_window][neg_mask], battery_power[:time_window][neg_mask] * 1e-3, - width=width, color="tab:blue", alpha=0.8) +ax.bar( + time_index[:time_window][pos_mask], + battery_power[:time_window][pos_mask] * 1e-3, + width=width, + color="tab:red", + alpha=0.8, +) +ax.bar( + time_index[:time_window][neg_mask], + battery_power[:time_window][neg_mask] * 1e-3, + width=width, + color="tab:blue", + alpha=0.8, +) ax.set_ylabel("Power (MW)", fontsize=8) ax.tick_params(axis="x", labelrotation=30, labelsize=7) diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index a4721825f..3e0524801 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -59,7 +59,7 @@ class PLMOptimizedControllerConfig(PyomoStorageControllerBaseConfig): discharge_efficiency: float = field(validator=range_val(0, 1), default=1.0) n_max_events: int = field(default=10) n_control_window: int = field(default=24 * 30) # one month of hourly data - signal_threshold_percentile: float = field(default=0.0, validator=range_val(0,100)) + signal_threshold_percentile: float = field(default=0.0, validator=range_val(0,100)) # make sure this is valid class PLMOptimizedStorageController(PyomoStorageControllerBaseClass): diff --git a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py index 40b37863e..f3aaa1ae3 100644 --- a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py @@ -30,12 +30,6 @@ def _make_controller_with_config(config, n_timesteps=24): @pytest.fixture def base_config(): - """24-hour PLM config shared by optimizer regression tests. - - Storage: 10 kWh capacity, 1 kW max rate, starts fully charged. - Peak window: 08:00-18:00 - Signal: monotonically increasing. - """ n = 24 return PLMOptimizedControllerConfig( max_capacity=10.0, diff --git a/h2integrate/core/supported_models.py b/h2integrate/core/supported_models.py index cd8285931..8e5dad1b3 100644 --- a/h2integrate/core/supported_models.py +++ b/h2integrate/core/supported_models.py @@ -190,9 +190,6 @@ from h2integrate.control.control_strategies.storage.simple_openloop_controller import ( SimpleStorageOpenLoopController, ) -from h2integrate.control.control_strategies.storage.plm_openloop_storage_controller import ( - PeakLoadManagementOpenLoopStorageController, -) from h2integrate.control.control_strategies.storage.plm_optimized_storage_controller import ( PLMOptimizedStorageController, ) @@ -311,7 +308,6 @@ # Control "SimpleStorageOpenLoopController": SimpleStorageOpenLoopController, "DemandOpenLoopStorageController": DemandOpenLoopStorageController, - "PeakLoadManagementOpenLoopStorageController": PeakLoadManagementOpenLoopStorageController, "PLMOptimizedStorageController": PLMOptimizedStorageController, "HeuristicLoadFollowingStorageController": HeuristicLoadFollowingStorageController, "OptimizedDispatchStorageController": OptimizedDispatchStorageController, From e32b73003087d69566d4c5abae975144eae44f2f Mon Sep 17 00:00:00 2001 From: svijaysh Date: Thu, 23 Apr 2026 10:08:39 -0600 Subject: [PATCH 03/55] Add docs --- docs/control/pyomo_controllers.md | 87 +++++++++++++++++++++++++++++++ docs/user_guide/model_overview.md | 1 + 2 files changed, 88 insertions(+) diff --git a/docs/control/pyomo_controllers.md b/docs/control/pyomo_controllers.md index 2da23275f..ac6a1ddc0 100644 --- a/docs/control/pyomo_controllers.md +++ b/docs/control/pyomo_controllers.md @@ -58,3 +58,90 @@ tech_to_dispatch_connections: [ ["battery", "battery"], ] ``` + +# Optimized Demand Response Controller + +This controller optimizes the dispatch of a Battery Energy Storage System (BESS) based on a pre-defined supervisory signal. This pre-defined signal could be the Locational Marginal Price (LMP) or a demand profile. It could also be a $LMP\times demand$ depending on the application. + +## Definitions + +**Given:** +- $\lambda_t$ := `supervisory_signal`: price, demand, or price $\times$ demand time series at time $t$ +- $\mathcal{W}$ := `peak_window`: set of hours eligible for dispatch (e.g., 12:00--19:00) +- $\gamma$ := performance incentive (\$/kW per dispatch hour) +- $\bar{P}$ := `max_charge_rate` (kW): maximum charge and discharge rate, used as deemed capacity since the battery is assumed to always dispatch at full rated power +- $E_{\max} :=$ `max_capacity` $\times$ (`max_soc_fraction` $-$ `min_soc_fraction`): usable energy capacity (kWh) +- $\eta_c$ := `charge_efficiency`, $\quad \eta_d$ := `discharge_efficiency` +- $\overline{\text{SoC}}$ := `max_soc_fraction`, $\quad \underline{\text{SoC}}$ := `min_soc_fraction` +- `n_control_window` := Horizon length for optimization +- $\mathcal{T} := \{0, 1, \ldots, T\}$: hourly time steps over `n_control_window` +- $\mathcal{M}_m$ := set of hours in month $m$, for $m = 1, \ldots, 12$ + +## Decision Variables + +- $u_t \in \{0, 1\}$ := discharge binary: 1 if battery dispatches at hour $t$, 0 otherwise +- $v_t \in \{0, 1\}$ := charge binary: 1 if battery charges at hour $t$, 0 otherwise + +## Optimization Problem + +This optimization is executed for each window, during which the performance model is invoked and the initial conditions are set. + +### Objective + +Maximize total annual incentive revenue: + +$$ +\max_{u_t,\, v_t} \quad \gamma \cdot \bar{P} \sum_{t \in \mathcal{T}} u_t +$$ + +### Constraints + +- Dispatch only within peak window: + +$$ +u_t = 0 \qquad \forall\, t \notin \mathcal{W} +$$ + +- Dispatch only on high supervisory signal: + +$$ +u_t = 1 \implies \lambda_t \geq \lambda^*_m \qquad \forall\, t \in \mathcal{M}_m +$$ + +where $\lambda^*_m$ is the threshold selecting the high LMP/peak load hours within month $m$. + +- Maximum 10 events per month: + +$$ +\sum_{t \in \mathcal{M}_m} u_t \leq N_{\max} \qquad \forall\, m, \quad N_{\max} = 10 +$$ + +- SoC evolution with charge and discharge: + +$$ +\text{SoC}_{t+1} = \text{SoC}_t + \frac{\eta_c \cdot v_t \cdot \bar{P}}{E_{\max}} - \frac{u_t \cdot \bar{P}}{\eta_d \cdot E_{\max}} \qquad \forall\, t \in \mathcal{T} +$$ + +- SoC bounds: + +$$ +\underline{\text{SoC}} \leq \text{SoC}_t \leq \overline{\text{SoC}} \qquad \forall\, t \in \mathcal{T} +$$ + +- No simultaneous charge and discharge: + +$$ +u_t + v_t \leq 1 \qquad \forall\, t \in \mathcal{T} +$$ + +- No charging during dispatch window (battery reserved for discharge): + +$$ +v_t = 0 \qquad \forall\, t \in \mathcal{W} +$$ + +- Binary variables: + +$$ +u_t \in \{0, 1\}, \quad v_t \in \{0, 1\}, \quad \text{SoC}_t \in [0, 1] \qquad \forall\, t, m +$$ diff --git a/docs/user_guide/model_overview.md b/docs/user_guide/model_overview.md index 6523f091d..3471df07b 100644 --- a/docs/user_guide/model_overview.md +++ b/docs/user_guide/model_overview.md @@ -285,6 +285,7 @@ Below summarizes the available performance, cost, and financial models for each - `'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 - `'HeuristicLoadFollowingStorageController'`: open-loop control that works on a time window basis to set dispatch commands; uses Pyomo + - `'PLMOptimizedStorageController'`: optimized controller for demand response that works on a time window basis. - Optimized Dispatch: - `'OptimizedDispatchStorageController'`: optimization-based dispatch using Pyomo From 561a6da314852d60fce44ba29a0c3b0c3897a158 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Thu, 23 Apr 2026 13:21:14 -0600 Subject: [PATCH 04/55] Update docs and add figure --- docs/control/pyomo_controllers.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/control/pyomo_controllers.md b/docs/control/pyomo_controllers.md index ac6a1ddc0..cece55d33 100644 --- a/docs/control/pyomo_controllers.md +++ b/docs/control/pyomo_controllers.md @@ -61,13 +61,13 @@ tech_to_dispatch_connections: [ # Optimized Demand Response Controller -This controller optimizes the dispatch of a Battery Energy Storage System (BESS) based on a pre-defined supervisory signal. This pre-defined signal could be the Locational Marginal Price (LMP) or a demand profile. It could also be a $LMP\times demand$ depending on the application. +This controller optimizes the dispatch of a Battery Energy Storage System (BESS) based on a pre-defined supervisory signal. This signal could be the Locational Marginal Price (LMP), a demand profile, or a $LMP\times demand$ product depending on the application. The objective is to maximize incentive payments to the battery, subject to constraints on the maximum number of dispatch events per month and on the battery state of charge. ## Definitions **Given:** - $\lambda_t$ := `supervisory_signal`: price, demand, or price $\times$ demand time series at time $t$ -- $\mathcal{W}$ := `peak_window`: set of hours eligible for dispatch (e.g., 12:00--19:00) +- $\mathcal{W}$ := `peak_window`: set of hours eligible for d`ispatch (e.g., 12:00--19:00) - $\gamma$ := performance incentive (\$/kW per dispatch hour) - $\bar{P}$ := `max_charge_rate` (kW): maximum charge and discharge rate, used as deemed capacity since the battery is assumed to always dispatch at full rated power - $E_{\max} :=$ `max_capacity` $\times$ (`max_soc_fraction` $-$ `min_soc_fraction`): usable energy capacity (kWh) @@ -145,3 +145,4 @@ $$ $$ u_t \in \{0, 1\}, \quad v_t \in \{0, 1\}, \quad \text{SoC}_t \in [0, 1] \qquad \forall\, t, m $$ +![](./figures/plm_optimized_dispatch.png) From 7afb43ac94c2a1cf7620ee40a2ab8aed2e84e371 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Thu, 23 Apr 2026 16:12:20 -0600 Subject: [PATCH 05/55] Edit docs page --- docs/control/pyomo_controllers.md | 8 ++-- .../run_plm_optimized_dispatch.py | 38 +++---------------- .../plm_optimized_storage_controller.py | 11 +++--- 3 files changed, 15 insertions(+), 42 deletions(-) diff --git a/docs/control/pyomo_controllers.md b/docs/control/pyomo_controllers.md index cece55d33..4fdcdf233 100644 --- a/docs/control/pyomo_controllers.md +++ b/docs/control/pyomo_controllers.md @@ -67,7 +67,7 @@ This controller optimizes the dispatch of a Battery Energy Storage System (BESS) **Given:** - $\lambda_t$ := `supervisory_signal`: price, demand, or price $\times$ demand time series at time $t$ -- $\mathcal{W}$ := `peak_window`: set of hours eligible for d`ispatch (e.g., 12:00--19:00) +- $\mathcal{W}$ := `peak_window`: set of hours eligible for dispatch (e.g., 12:00--19:00) - $\gamma$ := performance incentive (\$/kW per dispatch hour) - $\bar{P}$ := `max_charge_rate` (kW): maximum charge and discharge rate, used as deemed capacity since the battery is assumed to always dispatch at full rated power - $E_{\max} :=$ `max_capacity` $\times$ (`max_soc_fraction` $-$ `min_soc_fraction`): usable energy capacity (kWh) @@ -110,10 +110,10 @@ $$ where $\lambda^*_m$ is the threshold selecting the high LMP/peak load hours within month $m$. -- Maximum 10 events per month: +- Maximum $N_{max}$ events per month: $$ -\sum_{t \in \mathcal{M}_m} u_t \leq N_{\max} \qquad \forall\, m, \quad N_{\max} = 10 +\sum_{t \in \mathcal{M}_m} u_t \leq N_{\max} \qquad \forall\, m, \quad N_{\max}. $$ - SoC evolution with charge and discharge: @@ -145,4 +145,6 @@ $$ $$ u_t \in \{0, 1\}, \quad v_t \in \{0, 1\}, \quad \text{SoC}_t \in [0, 1] \qquad \forall\, t, m $$ +Example 34 performs the optimization with a synthetic LMP signal. The look-ahead horizon is set to 10 hours. As this value increases, the computational complexity grows and the solver may take significantly longer to run or fail to converge. Care should be taken when choosing this parameter: a short horizon limits visibility, making it difficult for the optimizer to identify the best dispatch opportunities across the full month. See figure below for the results. + ![](./figures/plm_optimized_dispatch.png) diff --git a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py index 3dff042d2..547d030bc 100644 --- a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py +++ b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py @@ -45,11 +45,11 @@ peak_mask = (time_index.hour >= 14) & (time_index.hour <= 18) threshold_pct = np.percentile(lmp, percentile) -discharge_mask = battery_power > 1.0 +discharge_mask = battery_power > 0 plt.rcParams.update({"axes.spines.top": False, "axes.spines.right": False}) -fig, axes = plt.subplots(3, 1, sharex=True, figsize=(11, 7)) +fig, axes = plt.subplots(2, 1, sharex=True, figsize=(11, 7)) days = pd.date_range(time_index[0].normalize(), periods=14, freq="D", tz=time_index.tz) time_window = 14 * 24 @@ -71,7 +71,7 @@ def shade_peaks(ax): ax = axes[0] shade_peaks(ax) ax.plot(time_index[:time_window], lmp[:time_window], color="steelblue", linewidth=1.0) -ax.axhline(threshold_pct, color="crimson", linestyle="--", linewidth=0.8) +ax.axhline(threshold_pct, color="k", linestyle="--", linewidth=0.8) ax.plot( time_index[:time_window][w_discharge], lmp[:time_window][w_discharge], @@ -82,42 +82,14 @@ def shade_peaks(ax): ax.set_ylabel("LMP ($/MWh)", fontsize=8) ax.set_ylim(bottom=0) -# Panel 2: SOC ax = axes[1] -ax.plot( - time_index[:time_window], soc_pct[:time_window], color="tab:green", linewidth=1.0 -) +shade_peaks(ax) +ax.plot(time_index[:time_window], soc_pct[:time_window], color="g", linewidth=1.0) ax.axhline(90, color="gray", linestyle=":", linewidth=0.7) ax.axhline(10, color="gray", linestyle=":", linewidth=0.7) ax.set_ylabel("SOC (%)", fontsize=8) ax.set_ylim([0, 105]) -ax = axes[2] -shade_peaks(ax) -ax.axhline(0, color="black", linewidth=0.4) -width = pd.Timedelta(hours=0.8) -pos_mask = (battery_power > 1)[:time_window] -neg_mask = (battery_power < -1)[:time_window] -ax.bar( - time_index[:time_window][pos_mask], - battery_power[:time_window][pos_mask] * 1e-3, - width=width, - color="tab:red", - alpha=0.8, -) -ax.bar( - time_index[:time_window][neg_mask], - battery_power[:time_window][neg_mask] * 1e-3, - width=width, - color="tab:blue", - alpha=0.8, -) -ax.set_ylabel("Power (MW)", fontsize=8) -ax.tick_params(axis="x", labelrotation=30, labelsize=7) - -for ax in axes: - ax.tick_params(labelsize=7) - ax.grid(True, which="major", alpha=0.3, linewidth=0.5) plt.tight_layout() plt.savefig("plm_optimized_dispatch.png", dpi=150, bbox_inches="tight") diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index 3e0524801..80dc9f1e6 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -45,7 +45,7 @@ class PLMOptimizedControllerConfig(PyomoStorageControllerBaseConfig): Defaults to 10. n_control_window (int): Number of timesteps per rolling solve window. Defaults to ``24 * 30`` (one month of hourly data). - signal_threshold_percentile (float): Percentile (0–100) used to + signal_threshold_percentile (float): Percentile (0-100) used to compute the signal threshold for each rolling window. Only hours at or above this percentile of the window signal are eligible for dispatch. Defaults to 0.0 (all hours eligible). @@ -179,7 +179,7 @@ def _compute_peak_window_mask(self) -> np.ndarray: return np.array([start <= t <= end for t in times]) def _compute_month_ids(self) -> np.ndarray: - """Return the calendar month index (1–12) for each timestep. + """Return the calendar month index (1-12) for each timestep. Returns: np.ndarray: Integer array of shape ``(n_timesteps,)``. @@ -215,8 +215,7 @@ def pyomo_setup(self, discrete_inputs): """Return the rolling-horizon dispatch solver callable. Args: - discrete_inputs (dict): OpenMDAO discrete inputs (unused - directly here; passed for API compatibility). + discrete_inputs (dict): OpenMDAO discrete inputs. Returns: callable: ``pyomo_dispatch_solver(performance_model, @@ -230,7 +229,7 @@ def pyomo_setup(self, discrete_inputs): commands. 4. Carries the terminal SOC into the next window. - Returns ``(storage_out, soc_out)`` — two ``np.ndarray`` of + Returns ``(storage_out, soc_out)`` - two ``np.ndarray`` of length ``n_timesteps``. """ @@ -439,7 +438,7 @@ def solve_dispatch_model(self, start_time: int = 0, n_days: int = 0): """Solve the DR MILP for the current window and record solver metrics. Args: - start_time (int): Global timestep index of the window start. + start_time (int): Timestep index of the window start. Used only for error messages and metrics. Defaults to 0. n_days (int): Total simulation days. Passed to ``DispatchProblemState.store_problem_metrics``. From 9b9ac9b47d52fa800215d3ec9bc20900a516a738 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Thu, 23 Apr 2026 16:40:42 -0600 Subject: [PATCH 06/55] Edit docstrings to include raises --- .../storage/plm_optimized_storage_controller.py | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index 80dc9f1e6..3e9bf6959 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -44,7 +44,7 @@ class PLMOptimizedControllerConfig(PyomoStorageControllerBaseConfig): n_max_events (int): Maximum discharge events per calendar month. Defaults to 10. n_control_window (int): Number of timesteps per rolling solve - window. Defaults to ``24 * 30`` (one month of hourly data). + window. Defaults to ``24`` (one day). signal_threshold_percentile (float): Percentile (0-100) used to compute the signal threshold for each rolling window. Only hours at or above this percentile of the window signal are @@ -58,7 +58,7 @@ class PLMOptimizedControllerConfig(PyomoStorageControllerBaseConfig): charge_efficiency: float = field(validator=range_val(0, 1), default=1.0) discharge_efficiency: float = field(validator=range_val(0, 1), default=1.0) n_max_events: int = field(default=10) - n_control_window: int = field(default=24 * 30) # one month of hourly data + n_control_window: int = field(default=24) # one month of hourly data signal_threshold_percentile: float = field(default=0.0, validator=range_val(0,100)) # make sure this is valid @@ -121,12 +121,12 @@ def setup(self): @staticmethod def _build_time_index(plant_config: dict) -> pd.DatetimeIndex: - """Build a timezone-aware DatetimeIndex from simulation settings in plant_config. + """Build a DatetimeIndex from simulation settings in plant_config. Args: plant_config (dict): Plant configuration dict. Must contain ``plant.simulation`` with keys ``n_timesteps`` (int), - ``dt`` (int, seconds), ``timezone`` (int, UTC offset), + ``dt`` (int, seconds), ``timezone`` (int), and ``start_time`` (str). Returns: @@ -189,7 +189,7 @@ def _compute_month_ids(self) -> np.ndarray: def _compute_eligible_mask(self, signal_window: np.ndarray) -> np.ndarray: """Build a boolean mask for timesteps whose signal meets the dispatch threshold. - The threshold is derived only from ``signal_window`` — it does not + The threshold is derived only from ``signal_window``. It does not assume the full simulation signal is known in advance. When ``signal_threshold_percentile`` is 0.0 all hours are eligible. @@ -333,10 +333,9 @@ def _build_dr_model( """Build the DR MILP for a single rolling window. Args: - window_start (int): Global timestep index of the first hour + window_start (int): Timestep index of the first hour in this window. window_len (int): Number of timesteps in this window - (``n_control_window`` except possibly the last window). init_soc (float): State-of-charge fraction at the start of this window. remaining_budget (dict): Mapping of ``month_id (int)`` to @@ -445,7 +444,7 @@ def solve_dispatch_model(self, start_time: int = 0, n_days: int = 0): Defaults to 0. Raises: - RuntimeError: If GLPK returns a non-OK status or an + RuntimeError: If GLPK returns a not OK status or an unacceptable termination condition. """ from pyomo.opt import SolverStatus, TerminationCondition From 3d4735f9a919d239dde8fcd6f03fa62bd5441903 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Sat, 25 Apr 2026 13:29:12 -0600 Subject: [PATCH 07/55] Add integartion tests --- .../run_plm_optimized_dispatch.py | 88 +++++++++---------- examples/test/test_all_examples.py | 52 +++++++++++ .../plm_optimized_storage_controller.py | 40 ++++++--- 3 files changed, 121 insertions(+), 59 deletions(-) diff --git a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py index 547d030bc..a9294e484 100644 --- a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py +++ b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py @@ -1,8 +1,6 @@ """ - This example demonstrates demand-response storage dispatch using a rolling-horizon MILP controller. The battery is scheduled to discharge during high-LMP peak hours -to reduce facility demand charges and earn performance incentives. """ from pathlib import Path @@ -15,43 +13,52 @@ EXAMPLE_DIR = Path(__file__).parent - model = H2IntegrateModel(EXAMPLE_DIR / "34_plm_optimized_dispatch.yaml") model.setup() -N = model.plant_config["plant"]["simulation"]["n_timesteps"] -percentile = model.technology_config["technologies"]["battery"]["model_inputs"][ - "control_parameters" -]["signal_threshold_percentile"] - -model.run() +battery_config = model.technology_config["technologies"]["battery"]["model_inputs"] +sim = model.plant_config["plant"]["simulation"] -lmp = np.array( - model.technology_config["technologies"]["battery"]["model_inputs"][ - "control_parameters" - ]["supervisory_signal"] -)[:N] +N = int(sim["n_timesteps"]) +percentile = battery_config["control_parameters"]["signal_threshold_percentile"] -sim = model.plant_config["plant"]["simulation"] -n_timesteps = int(sim["n_timesteps"]) -dt_seconds = int(sim["dt"]) -tz = int(sim["timezone"]) -start = pd.Timestamp(sim["start_time"], tz=tz) -freq = pd.to_timedelta(dt_seconds, unit="s") -time_index = pd.date_range(start=start, periods=n_timesteps, freq=freq) +model.run() +lmp = np.array(battery_config["control_parameters"]["supervisory_signal"])[:N] battery_power = model.prob.get_val("battery.storage_electricity_discharge", units="kW") +battery_charge = model.prob.get_val("battery.storage_electricity_charge", units="kW") soc_pct = model.prob.get_val("battery.SOC", units="percent") +unmet_demand = model.prob.get_val("grid_buy.electricity_unmet_demand", units="kW") + +capex = model.prob.get_val("battery.CapEx", units="USD")[0] +opex = model.prob.get_val("battery.OpEx", units="USD/year")[0] +n_discharge_events = int((battery_power > 0).sum()) +total_energy_discharged_kwh = battery_power.sum() # kW * 1 hr per timestep +total_energy_charged_kwh = abs(battery_charge.sum()) +incentive_revenue = ( + n_discharge_events + * battery_config["shared_parameters"]["max_charge_rate"] + * battery_config["control_parameters"]["performance_incentive"] +) -peak_mask = (time_index.hour >= 14) & (time_index.hour <= 18) -threshold_pct = np.percentile(lmp, percentile) -discharge_mask = battery_power > 0 - +print(f"\n--- Results Summary ---") +print(f"Battery CapEx: ${capex:>12,.0f}") +print(f"Battery OpEx: ${opex:>12,.0f} /year") +print(f"Discharge events (annual): {n_discharge_events:>12}") +print(f"Total energy discharged: {total_energy_discharged_kwh:>12,.1f} kWh") +print(f"Total energy charged: {total_energy_charged_kwh:>12,.1f} kWh") +print(f"Estimated incentive rev: ${incentive_revenue:>12,.0f} /year") +print(f"Total unmet demand: {unmet_demand.sum():>12,.1f} kWh") + +time_index = pd.date_range( + start=pd.Timestamp(sim["start_time"], tz=int(sim["timezone"])), + periods=N, + freq=pd.to_timedelta(int(sim["dt"]), unit="s"), +) -plt.rcParams.update({"axes.spines.top": False, "axes.spines.right": False}) -fig, axes = plt.subplots(2, 1, sharex=True, figsize=(11, 7)) -days = pd.date_range(time_index[0].normalize(), periods=14, freq="D", tz=time_index.tz) -time_window = 14 * 24 +W = 14 * 24 # plot first 14 days +ti = time_index[:W] +days = pd.date_range(ti[0].normalize(), periods=14, freq="D", tz=ti.tz) def shade_peaks(ax): @@ -59,37 +66,28 @@ def shade_peaks(ax): ax.axvspan( day + pd.Timedelta(hours=14), day + pd.Timedelta(hours=18), - color="orange", - alpha=0.10, - linewidth=0, - zorder=0, + color="orange", alpha=0.10, linewidth=0, zorder=0, ) -w_discharge = discharge_mask[:time_window] +plt.rcParams.update({"axes.spines.top": False, "axes.spines.right": False}) +fig, axes = plt.subplots(2, 1, sharex=True, figsize=(11, 7)) ax = axes[0] shade_peaks(ax) -ax.plot(time_index[:time_window], lmp[:time_window], color="steelblue", linewidth=1.0) -ax.axhline(threshold_pct, color="k", linestyle="--", linewidth=0.8) -ax.plot( - time_index[:time_window][w_discharge], - lmp[:time_window][w_discharge], - "r*", - markersize=8, - zorder=5, -) +ax.plot(ti, lmp[:W], color="steelblue", linewidth=1.0) +ax.axhline(np.percentile(lmp, percentile), color="k", linestyle="--", linewidth=0.8) +ax.plot(ti[battery_power[:W] > 0], lmp[:W][battery_power[:W] > 0], "r*", markersize=8, zorder=5) ax.set_ylabel("LMP ($/MWh)", fontsize=8) ax.set_ylim(bottom=0) ax = axes[1] shade_peaks(ax) -ax.plot(time_index[:time_window], soc_pct[:time_window], color="g", linewidth=1.0) +ax.plot(ti, soc_pct[:W], color="g", linewidth=1.0) ax.axhline(90, color="gray", linestyle=":", linewidth=0.7) ax.axhline(10, color="gray", linestyle=":", linewidth=0.7) ax.set_ylabel("SOC (%)", fontsize=8) ax.set_ylim([0, 105]) - plt.tight_layout() plt.savefig("plm_optimized_dispatch.png", dpi=150, bbox_inches="tight") diff --git a/examples/test/test_all_examples.py b/examples/test/test_all_examples.py index 4bf2a1229..911b77300 100644 --- a/examples/test/test_all_examples.py +++ b/examples/test/test_all_examples.py @@ -2782,3 +2782,55 @@ def test_multivariable_streams_example(subtests, temp_copy_of_example): 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) + +@pytest.mark.integration +@pytest.mark.parametrize( + "example_folder,resource_example_folder", [("34_plm_optimized_dispatch", None)] +) +def test_plm_optimized_dispatch_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create a H2Integrate model + model = H2IntegrateModel(example_folder / "34_plm_optimized_dispatch.yaml") + model.setup() + + # Run the model + model.run() + + battery_power = model.prob.get_val( + "battery.storage_electricity_discharge", units="kW" + ) + soc_pct = model.prob.get_val("battery.SOC", units="percent") + + with subtests.test("Check battery power is discharging at some point"): + assert (battery_power >= 0).all() + + with subtests.test("Check SOC is between 10 and 90%"): + assert (soc_pct >= 10 - 1e-2).all() + assert (soc_pct <= 90 + 1e-2).all() + + with subtests.test("Check battery CAPEX"): + battery_capex = model.prob.get_val("battery.CapEx", units="USD")[0] + assert pytest.approx(battery_capex, rel=1e-6) == 603300.0 + + with subtests.test("Check battery OPEX"): + battery_opex = model.prob.get_val("battery.OpEx", units="USD/year")[0] + assert pytest.approx(battery_opex, rel=1e-1) == 15082.0 + + with subtests.test("Check number of discharge events"): + # With the given demand profile and battery size, there should be 2 discharge events + num_discharge_events = np.sum( + battery_power > 1e-3 + ) # Count timesteps with discharge + assert num_discharge_events == 115 + + with subtests.test("Check total energy discharged"): + total_energy_discharged = battery_power.sum() * (1 / 60) # kWh, 1 min timestep + assert pytest.approx(total_energy_discharged, rel=1e-2) == 522.55 + + with subtests.test("Check total energy charged"): + battery_charge = model.prob.get_val( + "battery.storage_electricity_charge", units="kW" + ) + total_energy_charged = battery_charge.sum() * (1 / 60) # kWh, 1 min timestep + assert pytest.approx(total_energy_charged, rel=1e-3) == -565.55 \ No newline at end of file diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index 3e9bf6959..f0849d893 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -59,7 +59,9 @@ class PLMOptimizedControllerConfig(PyomoStorageControllerBaseConfig): discharge_efficiency: float = field(validator=range_val(0, 1), default=1.0) n_max_events: int = field(default=10) n_control_window: int = field(default=24) # one month of hourly data - signal_threshold_percentile: float = field(default=0.0, validator=range_val(0,100)) # make sure this is valid + signal_threshold_percentile: float = field( + default=0.0, validator=range_val(0, 100) + ) # make sure this is valid class PLMOptimizedStorageController(PyomoStorageControllerBaseClass): @@ -117,7 +119,7 @@ def setup(self): ) self.in_peak_window = self._compute_peak_window_mask() # bool array, shape (T,) - self.month_ids = self._compute_month_ids() # int array, shape (T,) + self.month_ids = self._compute_month_ids() # int array, shape (T,) @staticmethod def _build_time_index(plant_config: dict) -> pd.DatetimeIndex: @@ -215,7 +217,7 @@ def pyomo_setup(self, discrete_inputs): """Return the rolling-horizon dispatch solver callable. Args: - discrete_inputs (dict): OpenMDAO discrete inputs. + discrete_inputs (dict): OpenMDAO discrete inputs. Returns: callable: ``pyomo_dispatch_solver(performance_model, @@ -225,7 +227,7 @@ def pyomo_setup(self, discrete_inputs): 1. Builds a fresh MILP from the window's signal slice. 2. Solves the MILP with GLPK. - 3. Calls ``performance_model`` with the resulting dispatch + 3. Calls ``performance_model`` with the optimized dispatch commands. 4. Carries the terminal SOC into the next window. @@ -371,8 +373,14 @@ def _build_dr_model( m.T = pyomo.Set(initialize=range(window_len), doc="Timesteps in window") m.M = pyomo.Set(initialize=months_in_window, doc="Months in window") - m.discharge = pyomo.Var(m.T, domain=pyomo.Binary, doc="Discharge binary: 1 = discharging at timestep t") - m.charge = pyomo.Var(m.T, domain=pyomo.Binary, doc="Charge binary: 1 = charging at timestep t") + m.discharge = pyomo.Var( + m.T, + domain=pyomo.Binary, + doc="Discharge binary: 1 = discharging at timestep t", + ) + m.charge = pyomo.Var( + m.T, domain=pyomo.Binary, doc="Charge binary: 1 = charging at timestep t" + ) m.soc = pyomo.Var( m.T, domain=pyomo.NonNegativeReals, @@ -388,7 +396,9 @@ def _build_dr_model( m.peak_window_only = pyomo.Constraint( m.T, rule=lambda mdl, t: ( - mdl.discharge[t] == 0 if not in_peak_window_w[t] else pyomo.Constraint.Skip + mdl.discharge[t] == 0 + if not in_peak_window_w[t] + else pyomo.Constraint.Skip ), ) @@ -485,9 +495,7 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): inputs (dict): OpenMDAO continuous inputs. outputs (dict): OpenMDAO continuous outputs. discrete_inputs (dict): OpenMDAO discrete inputs. - discrete_outputs (dict): OpenMDAO discrete outputs. The key - ``'pyomo_dispatch_solver'`` is set to the callable - returned by :meth:`pyomo_setup`. + discrete_outputs (dict): OpenMDAO discrete outputs. """ discrete_outputs["pyomo_dispatch_solver"] = self.pyomo_setup(discrete_inputs) @@ -502,9 +510,9 @@ def glpk_solve_call( Args: pyomo_model (pyomo.ConcreteModel): The model to solve. log_name (str): Optional log file name passed to - ``SolverOptions``. Defaults to ``''``. - user_solver_options (dict | None): Optional overrides for - GLPK solver options. Defaults to ``None``. + ``SolverOptions``. + user_solver_options (dict): Optional overrides for + GLPK solver options.. Returns: pyomo.opt.SolverResults: Raw results object from GLPK. @@ -529,6 +537,10 @@ def storage_dispatch_commands(self) -> list: """ P_max = self.config.max_charge_rate return [ - (pyomo.value(self.dr_model.discharge[t]) - pyomo.value(self.dr_model.charge[t])) * P_max + ( + pyomo.value(self.dr_model.discharge[t]) + - pyomo.value(self.dr_model.charge[t]) + ) + * P_max for t in self.dr_model.T ] From 42b63c3bd176fe4e4c7544ac226ae1ce890e517f Mon Sep 17 00:00:00 2001 From: svijaysh Date: Sat, 25 Apr 2026 20:22:34 -0600 Subject: [PATCH 08/55] Add PLM optimized dispatch controller, example 34, and tests --- .../34_plm_optimized_dispatch.yaml | 5 + .../demand_profiles/demand_profile.yaml | 3361 +++++++ .../demand_profiles/supervisory_signal.yaml | 8760 +++++++++++++++++ .../driver_config.yaml | 5 + .../plant_config.yaml | 12 + .../run_plm_optimized_dispatch.py | 94 + .../tech_config.yaml | 54 + .../plm_optimized_storage_controller.py | 536 + .../test_plm_optimized_storage_controller.py | 270 + h2integrate/core/supported_models.py | 59 +- 10 files changed, 13144 insertions(+), 12 deletions(-) create mode 100644 examples/34_plm_optimized_dispatch/34_plm_optimized_dispatch.yaml create mode 100644 examples/34_plm_optimized_dispatch/demand_profiles/demand_profile.yaml create mode 100644 examples/34_plm_optimized_dispatch/demand_profiles/supervisory_signal.yaml create mode 100644 examples/34_plm_optimized_dispatch/driver_config.yaml create mode 100644 examples/34_plm_optimized_dispatch/plant_config.yaml create mode 100644 examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py create mode 100644 examples/34_plm_optimized_dispatch/tech_config.yaml create mode 100644 h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py create mode 100644 h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py diff --git a/examples/34_plm_optimized_dispatch/34_plm_optimized_dispatch.yaml b/examples/34_plm_optimized_dispatch/34_plm_optimized_dispatch.yaml new file mode 100644 index 000000000..6f4c89466 --- /dev/null +++ b/examples/34_plm_optimized_dispatch/34_plm_optimized_dispatch.yaml @@ -0,0 +1,5 @@ +name: H2Integrate_config +system_summary: PLM MILP-optimized battery dispatch +driver_config: driver_config.yaml +technology_config: tech_config.yaml +plant_config: plant_config.yaml diff --git a/examples/34_plm_optimized_dispatch/demand_profiles/demand_profile.yaml b/examples/34_plm_optimized_dispatch/demand_profiles/demand_profile.yaml new file mode 100644 index 000000000..7f14e933f --- /dev/null +++ b/examples/34_plm_optimized_dispatch/demand_profiles/demand_profile.yaml @@ -0,0 +1,3361 @@ +- 212.4377 +- 201.6801 +- 216.0036 +- 217.5245 +- 194.3917 +- 199.5826 +- 251.0227 +- 307.4701 +- 309.8656 +- 303.1756 +- 257.0352 +- 256.2223 +- 250.5282 +- 259.0179 +- 433.7401 +- 423.1257 +- 432.9500 +- 422.3289 +- 437.0276 +- 429.6006 +- 248.5211 +- 244.5526 +- 219.7803 +- 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79.0599 +- 85.0666 +- 85.3182 +- 79.7008 +- 33.0000 +- 33.0000 +- 33.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 38.0000 +- 38.0000 +- 38.0000 +- 38.0000 +- 82.8949 +- 89.1500 +- 78.6225 +- 83.3834 +- 101.5483 +- 33.0000 +- 33.0000 +- 33.0000 +- 18.0000 +- 18.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 28.0000 +- 28.0000 +- 28.0000 +- 28.0000 +- 81.5338 +- 71.4758 +- 74.2712 +- 74.3409 +- 69.6864 +- 23.0000 +- 23.0000 +- 23.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 8.0000 +- 28.0000 +- 28.0000 +- 28.0000 +- 28.0000 +- 71.5507 +- 114.5877 +- 68.5757 +- 84.7005 +- 69.4464 +- 23.0000 +- 23.0000 +- 23.0000 +- 8.0000 +- 8.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 18.0000 +- 38.0000 +- 38.0000 +- 38.0000 +- 38.0000 +- 79.3612 +- 99.0560 +- 90.6929 +- 114.6070 +- 93.6671 +- 33.0000 +- 33.0000 +- 33.0000 +- 18.0000 +- 18.0000 diff --git a/examples/34_plm_optimized_dispatch/driver_config.yaml b/examples/34_plm_optimized_dispatch/driver_config.yaml new file mode 100644 index 000000000..0b40f01a0 --- /dev/null +++ b/examples/34_plm_optimized_dispatch/driver_config.yaml @@ -0,0 +1,5 @@ +name: driver_config +description: Driver configuration for PLM MILP-optimized battery dispatch example +general: + folder_output: outputs + create_om_reports: false diff --git a/examples/34_plm_optimized_dispatch/plant_config.yaml b/examples/34_plm_optimized_dispatch/plant_config.yaml new file mode 100644 index 000000000..69e0909fc --- /dev/null +++ b/examples/34_plm_optimized_dispatch/plant_config.yaml @@ -0,0 +1,12 @@ +name: plant_config +description: Simulation for PLM MILP-optimized battery dispatch +plant: + plant_life: 1 + simulation: + n_timesteps: 8760 # full year (hourly) + dt: 3600 # 1-hour timesteps + timezone: -6 # MDT (UTC-6) + start_time: "2025/01/01 00:00:00" +tech_to_dispatch_connections: + - [grid_buy, battery] + - [battery, battery] diff --git a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py new file mode 100644 index 000000000..a833d2cc9 --- /dev/null +++ b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py @@ -0,0 +1,94 @@ +""" + +This example demonstrates demand-response storage dispatch using a rolling-horizon +MILP controller. The battery is scheduled to discharge during high-LMP peak hours +to reduce facility demand charges and earn performance incentives. +""" + +from pathlib import Path + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt + +from h2integrate.core.h2integrate_model import H2IntegrateModel +from h2integrate.core.utilities import build_time_series_from_plant_config + +EXAMPLE_DIR = Path(__file__).parent + + +model = H2IntegrateModel(EXAMPLE_DIR / "34_plm_optimized_dispatch.yaml") +model.setup() + +N = model.plant_config["plant"]["simulation"]["n_timesteps"] +percentile = model.technology_config["technologies"]["battery"]["model_inputs"]["control_parameters"]["signal_threshold_percentile"] + +model.run() + +lmp = np.array( + model.technology_config["technologies"]["battery"]["model_inputs"] + ["control_parameters"]["supervisory_signal"] +)[:N] + +time_index = pd.DatetimeIndex(build_time_series_from_plant_config(model.plant_config)) +battery_power = model.prob.get_val("battery.storage_electricity_discharge", units="kW") +soc_pct = model.prob.get_val("battery.SOC", units="percent") + +peak_mask = (time_index.hour >= 14) & (time_index.hour <= 18) +threshold_pct = np.percentile(lmp, percentile) +discharge_mask = battery_power > 1.0 + + +plt.rcParams.update({"axes.spines.top": False, "axes.spines.right": False}) +fig, axes = plt.subplots(3, 1, sharex=True, figsize=(11, 7)) +days = pd.date_range(time_index[0].normalize(), periods=14, freq="D", tz=time_index.tz) +time_window = 14 * 24 + + +def shade_peaks(ax): + for day in days: + ax.axvspan( + day + pd.Timedelta(hours=14), + day + pd.Timedelta(hours=18), + color="orange", alpha=0.10, linewidth=0, zorder=0, + ) + + +w_discharge = discharge_mask[:time_window] + +ax = axes[0] +shade_peaks(ax) +ax.plot(time_index[:time_window], lmp[:time_window], color="steelblue", linewidth=1.0) +ax.axhline(threshold_pct, color="crimson", linestyle="--", linewidth=0.8) +ax.plot(time_index[:time_window][w_discharge], lmp[:time_window][w_discharge], + "r*", markersize=8, zorder=5) +ax.set_ylabel("LMP ($/MWh)", fontsize=8) +ax.set_ylim(bottom=0) + +# Panel 2: SOC +ax = axes[1] +ax.plot(time_index[:time_window], soc_pct[:time_window], color="tab:green", linewidth=1.0) +ax.axhline(90, color="gray", linestyle=":", linewidth=0.7) +ax.axhline(10, color="gray", linestyle=":", linewidth=0.7) +ax.set_ylabel("SOC (%)", fontsize=8) +ax.set_ylim([0, 105]) + +ax = axes[2] +shade_peaks(ax) +ax.axhline(0, color="black", linewidth=0.4) +width = pd.Timedelta(hours=0.8) +pos_mask = (battery_power > 1)[:time_window] +neg_mask = (battery_power < -1)[:time_window] +ax.bar(time_index[:time_window][pos_mask], battery_power[:time_window][pos_mask] * 1e-3, + width=width, color="tab:red", alpha=0.8) +ax.bar(time_index[:time_window][neg_mask], battery_power[:time_window][neg_mask] * 1e-3, + width=width, color="tab:blue", alpha=0.8) +ax.set_ylabel("Power (MW)", fontsize=8) +ax.tick_params(axis="x", labelrotation=30, labelsize=7) + +for ax in axes: + ax.tick_params(labelsize=7) + ax.grid(True, which="major", alpha=0.3, linewidth=0.5) + +plt.tight_layout() +plt.savefig("plm_optimized_dispatch.png", dpi=150, bbox_inches="tight") diff --git a/examples/34_plm_optimized_dispatch/tech_config.yaml b/examples/34_plm_optimized_dispatch/tech_config.yaml new file mode 100644 index 000000000..6bd644775 --- /dev/null +++ b/examples/34_plm_optimized_dispatch/tech_config.yaml @@ -0,0 +1,54 @@ +name: technology_config +description: 300 kW / 1.2 MWh battery with PLM MILP-optimized dispatch controller +technologies: + battery: + performance_model: + model: StoragePerformanceModel + cost_model: + model: ATBBatteryCostModel + control_strategy: + model: PLMOptimizedStorageController + model_inputs: + shared_parameters: + commodity: electricity + commodity_rate_units: kW + max_charge_rate: 300.0 # kW rated power + max_capacity: 1200.0 # kWh usable capacity (4-hour battery) + max_soc_fraction: 0.90 + min_soc_fraction: 0.10 + init_soc_fraction: 0.90 + charge_efficiency: 0.95 + discharge_efficiency: 0.95 + + performance_parameters: + demand_profile: 0.0 + control_parameters: + system_commodity_interface_limit: 1.0e9 + supervisory_signal: !include demand_profiles/supervisory_signal.yaml + peak_window: + start: "14:00:00" # 2 pm MDT + end: "18:00:00" # 6 pm MDT + performance_incentive: 14.0 # $/kW per dispatch hour + n_max_events: 10 # maximum discharge events per month + signal_threshold_percentile: 95.0 # only dispatch when LMP >= this percentile + n_control_window: 10 # number of time steps in the control window (e.g., 10 hours) + round_digits: 4 + cost_parameters: + cost_year: 2024 + energy_capex: 408 # $/kWh (2024 ATB) + power_capex: 379 # $/kW + opex_fraction: 0.025 + grid_buy: + performance_model: + model: GridPerformanceModel + cost_model: + model: GridCostModel + model_inputs: + shared_parameters: + interconnection_size: 100000 # kW + cost_parameters: + cost_year: 2024 + fixed_interconnection_cost: 0.0 + interconnection_capex_per_kw: 0.0 + interconnection_opex_per_kw: 0.0 + electricity_buy_price: 0.09 # $/kWh diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py new file mode 100644 index 000000000..a4721825f --- /dev/null +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -0,0 +1,536 @@ +from datetime import datetime, timedelta + +import numpy as np +import pandas as pd +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 +from h2integrate.control.control_strategies.pyomo_storage_controller_baseclass import ( + SolverOptions, + PyomoStorageControllerBaseClass, + PyomoStorageControllerBaseConfig, +) +from h2integrate.control.control_strategies.controller_opt_problem_state import ( + DispatchProblemState, +) +from matplotlib import pyplot as plt + + +@define +class PLMOptimizedControllerConfig(PyomoStorageControllerBaseConfig): + """Configuration for the PLM DR optimized storage controller. + + Inherits base fields from ``PyomoControllerBaseConfig``: + ``max_capacity``, ``max_soc_fraction``, ``min_soc_fraction``, + ``init_soc_fraction``, ``n_control_window``, ``commodity``, + ``commodity_rate_units``, ``tech_name``, + ``system_commodity_interface_limit``, ``round_digits``. + + Attributes: + max_charge_rate (float): Maximum charge and discharge rate (kW). + supervisory_signal (list[float]): Price, demand, or price*demand + forecast time series. The rolling solver uses one window of + length ``n_control_window`` per solve. + peak_window (dict): Hours eligible for dispatch. Keys ``'start'`` + and ``'end'`` must be strings in ``HH:MM:SS`` format. + performance_incentive (float): Incentive revenue ($/kW per + dispatch hour). + charge_efficiency (float): Charge efficiency in [0, 1]. + Defaults to 1.0. + discharge_efficiency (float): Discharge efficiency in [0, 1]. + Defaults to 1.0. + n_max_events (int): Maximum discharge events per calendar month. + Defaults to 10. + n_control_window (int): Number of timesteps per rolling solve + window. Defaults to ``24 * 30`` (one month of hourly data). + signal_threshold_percentile (float): Percentile (0–100) used to + compute the signal threshold for each rolling window. Only + hours at or above this percentile of the window signal are + eligible for dispatch. Defaults to 0.0 (all hours eligible). + """ + + max_charge_rate: float = field() + supervisory_signal: list = field() + peak_window: dict = field() + performance_incentive: float = field() + charge_efficiency: float = field(validator=range_val(0, 1), default=1.0) + discharge_efficiency: float = field(validator=range_val(0, 1), default=1.0) + n_max_events: int = field(default=10) + n_control_window: int = field(default=24 * 30) # one month of hourly data + signal_threshold_percentile: float = field(default=0.0, validator=range_val(0,100)) + + +class PLMOptimizedStorageController(PyomoStorageControllerBaseClass): + """Demand-response storage controller using a rolling-horizon MILP. + + Each call to the dispatch solver iterates over the full simulation in + windows of length ``n_control_window``. For each window it receives + the monthly LMP forecast, solves the MILP to maximize incentive + revenue, then passes the resulting dispatch commands to the + performance model. The terminal SOC of each window is carried forward + as the initial SOC of the next window. + """ + + def setup(self): + """Initialize config, register OpenMDAO inputs, and pre-compute static masks. + + Raises: + ValueError: If the length of the time series built from + ``plant_config`` does not match ``n_timesteps``. + """ + self.config = PLMOptimizedControllerConfig.from_dict( + merge_shared_inputs(self.options["tech_config"]["model_inputs"], "control") + ) + + self.add_input( + "max_charge_rate", + val=self.config.max_charge_rate, + units=self.config.commodity_rate_units, + desc="Maximum charge/discharge rate P_max", + ) + self.add_input( + "storage_capacity", + val=self.config.max_capacity, + units=f"{self.config.commodity_rate_units}*h", + desc="Total storage capacity", + ) + + self.n_timesteps = self.options["plant_config"]["plant"]["simulation"][ + "n_timesteps" + ] + super().setup() + + self.updated_initial_soc = self.config.init_soc_fraction + + self.commodity_info = { + "commodity_name": self.config.commodity, + "commodity_storage_units": self.config.commodity_rate_units, + } + + self.time_index = self._build_time_index(self.options["plant_config"]) + + if len(self.time_index) != self.n_timesteps: + raise ValueError( + f"Time series length {len(self.time_index)} != n_timesteps {self.n_timesteps}" + ) + + self.in_peak_window = self._compute_peak_window_mask() # bool array, shape (T,) + self.month_ids = self._compute_month_ids() # int array, shape (T,) + + @staticmethod + def _build_time_index(plant_config: dict) -> pd.DatetimeIndex: + """Build a timezone-aware DatetimeIndex from simulation settings in plant_config. + + Args: + plant_config (dict): Plant configuration dict. Must contain + ``plant.simulation`` with keys ``n_timesteps`` (int), + ``dt`` (int, seconds), ``timezone`` (int, UTC offset), + and ``start_time`` (str). + + Returns: + pd.DatetimeIndex: DatetimeIndex of length ``n_timesteps`` spaced + ``dt`` seconds apart, starting at ``start_time`` in the given timezone. + """ + sim = plant_config["plant"]["simulation"] + n_timesteps = int(sim["n_timesteps"]) + dt_seconds = int(sim["dt"]) + tz = int(sim["timezone"]) + start = pd.Timestamp(sim["start_time"], tz=tz) + freq = pd.to_timedelta(dt_seconds, unit="s") + return pd.date_range(start=start, periods=n_timesteps, freq=freq) + + def _parse_peak_window(self) -> tuple: + """Parse the ``peak_window`` config entry into ``datetime.time`` objects. + + Returns: + tuple[datetime.time, datetime.time]: ``(start, end)`` times. + + Raises: + ValueError: If ``'start'`` or ``'end'`` keys are missing, or + if either value is not a string in ``HH:MM:SS`` format. + """ + pw = dict(self.config.peak_window) + if "start" not in pw or "end" not in pw: + raise ValueError("peak_window must contain 'start' and 'end' keys") + for key in ("start", "end"): + val = pw[key] + if not isinstance(val, str) or len(val.split(":")) != 3: + raise ValueError( + f"peak_window {key} value must be a string in HH:MM:SS format, got {val}." + ) + pw[key] = datetime.strptime(val, "%H:%M:%S").time() + return pw["start"], pw["end"] + + def _compute_peak_window_mask(self) -> np.ndarray: + """Build a boolean mask that is ``True`` for timesteps inside the peak window. + + Returns: + np.ndarray: Boolean array of shape ``(n_timesteps,)``. + + Raises: + ValueError: If ``peak_window`` end time is before start time. + """ + start, end = self._parse_peak_window() + times = pd.DatetimeIndex(self.time_index).time + if end < start: + raise ValueError("peak_window end time must be after start time.") + return np.array([start <= t <= end for t in times]) + + def _compute_month_ids(self) -> np.ndarray: + """Return the calendar month index (1–12) for each timestep. + + Returns: + np.ndarray: Integer array of shape ``(n_timesteps,)``. + """ + return pd.DatetimeIndex(self.time_index).month.to_numpy() + + def _compute_eligible_mask(self, signal_window: np.ndarray) -> np.ndarray: + """Build a boolean mask for timesteps whose signal meets the dispatch threshold. + + The threshold is derived only from ``signal_window`` — it does not + assume the full simulation signal is known in advance. When + ``signal_threshold_percentile`` is 0.0 all hours are eligible. + + Args: + signal_window (np.ndarray): Signal values for the current + rolling window. + + Returns: + np.ndarray: Boolean array of shape ``(len(signal_window),)``. + ``True`` where ``signal_t >= threshold``. + """ + eligible = np.ones(len(signal_window), dtype=bool) + + if self.config.signal_threshold_percentile > 0.0: + threshold = np.percentile( + signal_window, self.config.signal_threshold_percentile + ) + eligible = signal_window >= threshold + + return eligible + + def pyomo_setup(self, discrete_inputs): + """Return the rolling-horizon dispatch solver callable. + + Args: + discrete_inputs (dict): OpenMDAO discrete inputs (unused + directly here; passed for API compatibility). + + Returns: + callable: ``pyomo_dispatch_solver(performance_model, + performance_model_kwargs, inputs)`` that iterates over the + simulation in windows of ``n_control_window`` timesteps. + For each window it: + + 1. Builds a fresh MILP from the window's signal slice. + 2. Solves the MILP with GLPK. + 3. Calls ``performance_model`` with the resulting dispatch + commands. + 4. Carries the terminal SOC into the next window. + + Returns ``(storage_out, soc_out)`` — two ``np.ndarray`` of + length ``n_timesteps``. + """ + + def pyomo_dispatch_solver( + performance_model, + performance_model_kwargs, + inputs, + commodity_name=self.config.commodity, + ): + storage_out = np.zeros(self.n_timesteps) + soc_out = np.zeros(self.n_timesteps) + + # Track events used per calendar month so the monthly cap is + # respected across window boundaries. + events_used_per_month = {} + + n_w = self.config.n_control_window + window_start_indices = list(range(0, self.n_timesteps, n_w)) + + for window_start in window_start_indices: + window_len = min(n_w, self.n_timesteps - window_start) + + n_windows = len(window_start_indices) + report_every = max(1, n_windows // 4) + window_idx = window_start // n_w + if window_idx % report_every == 0: + pct = round(window_start / self.n_timesteps * 100) + print(f"{pct}% done with PLM rolling dispatch") + + month_ids_w = self.month_ids[window_start : window_start + window_len] + remaining_budget = { + int(m): max( + 0, + self.config.n_max_events - events_used_per_month.get(int(m), 0), + ) + for m in np.unique(month_ids_w) + } + + self.dr_model = self._build_dr_model( + window_start=window_start, + window_len=window_len, + init_soc=self.updated_initial_soc, + remaining_budget=remaining_budget, + ) + self.problem_state = DispatchProblemState() + + self.solve_dispatch_model( + start_time=window_start, + n_days=self.n_timesteps // 24, + ) + + for t in range(window_len): + if pyomo.value(self.dr_model.discharge[t]) > 0.5: + m = int(month_ids_w[t]) + events_used_per_month[m] = events_used_per_month.get(m, 0) + 1 + + storage_out_window, soc_window = performance_model( + self.storage_dispatch_commands, + **performance_model_kwargs, + sim_start_index=window_start, + ) + + # Performance model returns SOC in percent. + self.updated_initial_soc = soc_window[-1] / 100.0 + + for j in range(window_len): + storage_out[window_start + j] = storage_out_window[j] + soc_out[window_start + j] = soc_window[j] + + print( + f" Events per month: {dict(events_used_per_month)} " + f"(limit: {self.config.n_max_events}/month)" + ) + return storage_out, soc_out + + return pyomo_dispatch_solver + + def initialize_parameters(self, inputs): + """Sync OpenMDAO inputs into the config. + + Args: + inputs (dict): OpenMDAO inputs dict. Recognised keys are + ``'max_charge_rate'`` and ``'storage_capacity'``. + """ + if "max_charge_rate" in inputs: + object.__setattr__( + self.config, "max_charge_rate", float(inputs["max_charge_rate"][0]) + ) + if "storage_capacity" in inputs: + object.__setattr__( + self.config, "max_capacity", float(inputs["storage_capacity"][0]) + ) + + def _build_dr_model( + self, + window_start: int, + window_len: int, + init_soc: float, + remaining_budget: dict, + ) -> pyomo.ConcreteModel: + """Build the DR MILP for a single rolling window. + + Args: + window_start (int): Global timestep index of the first hour + in this window. + window_len (int): Number of timesteps in this window + (``n_control_window`` except possibly the last window). + init_soc (float): State-of-charge fraction at the start of + this window. + remaining_budget (dict): Mapping of ``month_id (int)`` to + remaining event slots for that month. Computed by + subtracting events already dispatched in earlier windows + from ``n_max_events``, so the monthly cap is respected + across windows. + + Returns: + pyomo.ConcreteModel: Fully formed MILP ready to solve. + """ + m = pyomo.ConcreteModel(name="plm_dr") + + P_max = self.config.max_charge_rate + E_max = self.config.max_capacity * ( + self.config.max_soc_fraction - self.config.min_soc_fraction + ) + eta_c = self.config.charge_efficiency + eta_d = self.config.discharge_efficiency + soc_max = self.config.max_soc_fraction + soc_min = self.config.min_soc_fraction + incentive = self.config.performance_incentive + N_max = self.config.n_max_events + + w = slice(window_start, window_start + window_len) + in_peak_window_w = self.in_peak_window[w] + month_ids_w = self.month_ids[w] + signal_w = np.asarray(self.config.supervisory_signal, dtype=float)[w] + eligible_t_w = self._compute_eligible_mask(signal_w) + + months_in_window = np.unique(month_ids_w).tolist() + + m.T = pyomo.Set(initialize=range(window_len), doc="Timesteps in window") + m.M = pyomo.Set(initialize=months_in_window, doc="Months in window") + + m.discharge = pyomo.Var(m.T, domain=pyomo.Binary, doc="Discharge binary: 1 = discharging at timestep t") + m.charge = pyomo.Var(m.T, domain=pyomo.Binary, doc="Charge binary: 1 = charging at timestep t") + m.soc = pyomo.Var( + m.T, + domain=pyomo.NonNegativeReals, + bounds=(soc_min, soc_max), + doc="State of charge SoC_t", + ) + + m.objective = pyomo.Objective( + expr=-incentive * P_max * sum(m.discharge[t] for t in m.T), + sense=pyomo.minimize, + ) + + m.peak_window_only = pyomo.Constraint( + m.T, + rule=lambda mdl, t: ( + mdl.discharge[t] == 0 if not in_peak_window_w[t] else pyomo.Constraint.Skip + ), + ) + + m.high_signal_only = pyomo.Constraint( + m.T, + rule=lambda mdl, t: mdl.discharge[t] <= int(eligible_t_w[t]), + ) + + def max_events_rule(mdl, month): + ts_in_month = [t for t in mdl.T if month_ids_w[t] == month] + if not ts_in_month: + return pyomo.Constraint.Skip + budget = remaining_budget.get(month, N_max) + return sum(mdl.discharge[t] for t in ts_in_month) <= budget + + m.max_events = pyomo.Constraint(m.M, rule=max_events_rule) + + m.soc_init = pyomo.Constraint(expr=m.soc[0] == init_soc) + + def soc_evolution_rule(mdl, t): + if t == 0: + return pyomo.Constraint.Skip + return mdl.soc[t] == ( + mdl.soc[t - 1] + + eta_c * mdl.charge[t] * P_max / E_max + - mdl.discharge[t] * P_max / (eta_d * E_max) + ) + + m.soc_evolution = pyomo.Constraint(m.T, rule=soc_evolution_rule) + + m.no_simultaneous = pyomo.Constraint( + m.T, + rule=lambda mdl, t: mdl.discharge[t] + mdl.charge[t] <= 1, + ) + + m.no_charge_in_window = pyomo.Constraint( + m.T, + rule=lambda mdl, t: ( + mdl.charge[t] == 0 if in_peak_window_w[t] else pyomo.Constraint.Skip + ), + ) + + return m + + def solve_dispatch_model(self, start_time: int = 0, n_days: int = 0): + """Solve the DR MILP for the current window and record solver metrics. + + Args: + start_time (int): Global timestep index of the window start. + Used only for error messages and metrics. Defaults to 0. + n_days (int): Total simulation days. Passed to + ``DispatchProblemState.store_problem_metrics``. + Defaults to 0. + + Raises: + RuntimeError: If GLPK returns a non-OK status or an + unacceptable termination condition. + """ + from pyomo.opt import SolverStatus, TerminationCondition + + solver_results = self.glpk_solve_call(self.dr_model) + + status = solver_results.solver.status + tc = solver_results.solver.termination_condition + acceptable = ( + TerminationCondition.optimal, + TerminationCondition.feasible, + TerminationCondition.maxTimeLimit, + ) + if status != SolverStatus.ok or tc not in acceptable: + raise RuntimeError( + f"PLM MILP solver failed at window start={start_time}: " + f"status={status}, termination={tc}. " + f"init_soc={self.updated_initial_soc:.4f}, " + f"window_len={len(list(self.dr_model.T))}" + ) + if tc == TerminationCondition.maxTimeLimit: + print( + f" WARNING: solver hit time limit at window start={start_time} " + f"— using best solution found so far" + ) + + self.problem_state.store_problem_metrics( + solver_results, + start_time, + n_days, + pyomo.value(self.dr_model.objective), + ) + + def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): + """Build the DR dispatch solver and write it to discrete outputs. + + Args: + inputs (dict): OpenMDAO continuous inputs. + outputs (dict): OpenMDAO continuous outputs. + discrete_inputs (dict): OpenMDAO discrete inputs. + discrete_outputs (dict): OpenMDAO discrete outputs. The key + ``'pyomo_dispatch_solver'`` is set to the callable + returned by :meth:`pyomo_setup`. + """ + discrete_outputs["pyomo_dispatch_solver"] = self.pyomo_setup(discrete_inputs) + + @staticmethod + def glpk_solve_call( + pyomo_model: pyomo.ConcreteModel, + log_name: str = "", + user_solver_options: dict | None = None, + ): + """Solve a Pyomo MILP with GLPK. + + Args: + pyomo_model (pyomo.ConcreteModel): The model to solve. + log_name (str): Optional log file name passed to + ``SolverOptions``. Defaults to ``''``. + user_solver_options (dict | None): Optional overrides for + GLPK solver options. Defaults to ``None``. + + Returns: + pyomo.opt.SolverResults: Raw results object from GLPK. + """ + glpk_solver_options = {"cuts": None, "presol": None, "tmlim": 300} + solver_options = SolverOptions( + glpk_solver_options, log_name, user_solver_options, "log" + ) + with pyomo.SolverFactory("glpk") as solver: + results = solver.solve( + pyomo_model, options=solver_options.constructed, tee=False + ) + return results + + @property + def storage_dispatch_commands(self) -> list: + """Net dispatch commands for the solved window. + + Returns: + list[float]: ``(u_t - v_t) * P_max`` for each timestep in + the solved window. Positive = discharge, negative = charge. + """ + P_max = self.config.max_charge_rate + return [ + (pyomo.value(self.dr_model.discharge[t]) - pyomo.value(self.dr_model.charge[t])) * P_max + for t in self.dr_model.T + ] diff --git a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py new file mode 100644 index 000000000..40b37863e --- /dev/null +++ b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py @@ -0,0 +1,270 @@ +from types import SimpleNamespace + +from attrs import evolve +import numpy as np +import pandas as pd +import pyomo.environ as pyomo +import pytest + +from h2integrate.control.control_strategies.storage.plm_optimized_storage_controller import ( + PLMOptimizedControllerConfig, + PLMOptimizedStorageController, +) +from h2integrate.storage.storage_baseclass import StoragePerformanceBase + + +def _make_controller(): + return object.__new__(PLMOptimizedStorageController) + + +def _make_controller_with_config(config, n_timesteps=24): + """Build a controller with pre-computed masks, bypassing OpenMDAO setup.""" + controller = _make_controller() + controller.config = config + controller.updated_initial_soc = config.init_soc_fraction + controller.time_index = pd.date_range("2024-01-01", periods=n_timesteps, freq="h") + controller.in_peak_window = controller._compute_peak_window_mask() + controller.month_ids = controller._compute_month_ids() + return controller + + +@pytest.fixture +def base_config(): + """24-hour PLM config shared by optimizer regression tests. + + Storage: 10 kWh capacity, 1 kW max rate, starts fully charged. + Peak window: 08:00-18:00 + Signal: monotonically increasing. + """ + n = 24 + return PLMOptimizedControllerConfig( + max_capacity=10.0, + max_soc_fraction=1.0, + min_soc_fraction=0.0, + init_soc_fraction=1.0, + n_control_window=n, + commodity="electricity", + commodity_rate_units="kW", + tech_name="battery", + system_commodity_interface_limit=100.0, + max_charge_rate=1.0, + supervisory_signal=list(range(n)), + peak_window={"start": "08:00:00", "end": "18:00:00"}, + performance_incentive=10.0, + n_max_events=24, + signal_threshold_percentile=0.0, + ) + + +@pytest.mark.unit +def test_parse_peak_window(): + controller = _make_controller() + controller.config = SimpleNamespace( + peak_window={"start": "08:00:00", "end": "18:40:20"} + ) + start, end = controller._parse_peak_window() + assert start.hour == 8 + assert start.minute == 0 + assert start.second == 0 + assert end.hour == 18 + assert end.minute == 40 + assert end.second == 20 + + +@pytest.mark.unit +def test_parse_peak_window_invalid_format(): + controller = _make_controller() + controller.config = SimpleNamespace(peak_window={"start": "08", "end": "18:40:20"}) + with pytest.raises( + ValueError, match="peak_window start value must be a string in HH:MM:SS format" + ): + controller._parse_peak_window() + + +@pytest.mark.unit +def test_parse_peak_window_int_raises(): + controller = _make_controller() + controller.config = SimpleNamespace(peak_window={"start": 8, "end": 9}) + with pytest.raises(ValueError): + controller._parse_peak_window() + + +@pytest.mark.unit +def test_parse_peak_window_missing_key_raises(): + controller = _make_controller() + controller.config = SimpleNamespace(peak_window={"start": "08:00:00"}) + with pytest.raises( + ValueError, match="peak_window must contain 'start' and 'end' keys" + ): + controller._parse_peak_window() + + +@pytest.mark.unit +def test_compute_peak_window_mask(): + controller = _make_controller() + controller.config = SimpleNamespace( + peak_window={"start": "00:00:00", "end": "02:00:00"} + ) + controller.time_index = pd.date_range("2024-01-01", periods=24, freq="h") + mask = controller._compute_peak_window_mask() + expected = np.array([i <= 2 for i in range(24)]) + assert isinstance(mask, np.ndarray) + assert np.array_equal(mask, expected) + + +@pytest.mark.unit +def test_compute_month_ids(): + # Jan 2024: 744h, Feb 2024 (leap year): 696h, Mar 2024: 744h + controller = _make_controller() + controller.time_index = pd.date_range( + "2024-01-01", periods=744 + 696 + 744, freq="h" + ) + month_ids = controller._compute_month_ids() + expected = np.array([1] * 744 + [2] * 696 + [3] * 744) + assert np.array_equal(month_ids, expected) + + +@pytest.mark.unit +def test_compute_eligible_mask_zero_percentile_all_eligible(): + """All timesteps are eligible when signal_threshold_percentile=0.""" + controller = _make_controller() + controller.config = SimpleNamespace(signal_threshold_percentile=0.0) + signal = np.array([1.0, 5.0, 3.0, 2.0, 8.0]) + mask = controller._compute_eligible_mask(signal) + assert isinstance(mask, np.ndarray) + assert mask.dtype == bool + assert len(mask) == len(signal) + assert mask.all() + + +@pytest.mark.unit +def test_compute_eligible_mask_50th_percentile(): + """Only values at or above the 50th percentile are eligible.""" + controller = _make_controller() + controller.config = SimpleNamespace(signal_threshold_percentile=50.0) + signal = np.array([1.0, 2.0, 3.0, 4.0, 5.0]) + mask = controller._compute_eligible_mask(signal) + expected = signal >= np.percentile(signal, 50.0) + assert np.array_equal(mask, expected) + + +@pytest.mark.unit +def test_compute_eligible_mask_100th_percentile_only_max_eligible(): + """Only the maximum value(s) are eligible at percentile=100.""" + controller = _make_controller() + controller.config = SimpleNamespace(signal_threshold_percentile=100.0) + signal = np.array([1.0, 2.0, 10.0, 3.0, 10.0]) + mask = controller._compute_eligible_mask(signal) + expected = np.array([False, False, True, False, True]) + assert np.array_equal(mask, expected) + + +@pytest.mark.unit +def test_compute_eligible_mask_uniform_signal_all_eligible(): + """All timesteps are eligible when the signal is uniform, regardless of percentile.""" + controller = _make_controller() + controller.config = SimpleNamespace(signal_threshold_percentile=75.0) + signal = np.full(10, 5.0) + mask = controller._compute_eligible_mask(signal) + assert mask.all() + + +@pytest.mark.regression +def test_optimizer_dispatch_only_in_peak_window(base_config): + """Solver must never set 1 outside the peak window.""" + # + controller = _make_controller_with_config(base_config) + model = controller._build_dr_model( + window_start=0, + window_len=24, + init_soc=base_config.init_soc_fraction, + remaining_budget={1: base_config.n_max_events}, + ) + + PLMOptimizedStorageController.glpk_solve_call(model) + + peak_start, peak_end = controller._parse_peak_window() + print("Peak window:", peak_start, "-", peak_end) + for t in range(24): + hour = pd.Timestamp("2024-01-01") + pd.Timedelta(hours=t) + in_window = peak_start <= hour.time() <= peak_end + if not in_window: + assert pyomo.value(model.discharge[t]) < 1e-3 + + +@pytest.mark.regression +def test_optimizer_dispatch_only_on_eligible_timesteps(base_config): + """Solver must never set 1 on ineligible timesteps.""" + controller = _make_controller_with_config(base_config) + model = controller._build_dr_model( + window_start=0, + window_len=24, + init_soc=base_config.init_soc_fraction, + remaining_budget={1: base_config.n_max_events}, + ) + + PLMOptimizedStorageController.glpk_solve_call(model) + + signal = np.array(controller.config.supervisory_signal) + eligible_mask = controller._compute_eligible_mask(signal) + for t in range(24): + if not eligible_mask[t]: + assert pyomo.value(model.discharge[t]) < 1e-3 + + +@pytest.mark.regression +def test_optimizer_dispatch_respects_event_budget(base_config): + """Solver must never set more than n_max_events timesteps to 1.""" + controller = _make_controller_with_config(base_config) + model = controller._build_dr_model( + window_start=0, + window_len=24, + init_soc=base_config.init_soc_fraction, + remaining_budget={1: base_config.n_max_events}, + ) + + PLMOptimizedStorageController.glpk_solve_call(model) + + total_events = sum(pyomo.value(model.discharge[t]) for t in range(24)) + assert total_events <= base_config.n_max_events + 1e-3 + + +@pytest.mark.regression +def test_optimizer_dispatch_respects_soc_constraints(base_config): + """Solver must never violate SOC constraints.""" + controller = _make_controller_with_config(base_config) + model = controller._build_dr_model( + window_start=0, + window_len=24, + init_soc=base_config.init_soc_fraction, + remaining_budget={2: base_config.n_max_events}, + ) + + PLMOptimizedStorageController.glpk_solve_call(model) + + soc = base_config.init_soc_fraction * base_config.max_capacity + for t in range(24): + charge = pyomo.value(model.charge[t]) + discharge = pyomo.value(model.discharge[t]) + soc += charge - discharge + assert soc >= base_config.min_soc_fraction * base_config.max_capacity + assert soc <= base_config.max_soc_fraction * base_config.max_capacity + + +@pytest.mark.regression +def test_optimizer_dispatch_respects_charge_discharge_exclusivity(base_config): + """Solver must never set charge and discharge to 1 in the same timestep.""" + controller = _make_controller_with_config(base_config) + model = controller._build_dr_model( + window_start=0, + window_len=24, + init_soc=base_config.init_soc_fraction, + remaining_budget={2: base_config.n_max_events}, + ) + + PLMOptimizedStorageController.glpk_solve_call(model) + + for t in range(24): + charge = pyomo.value(model.charge[t]) + discharge = pyomo.value(model.discharge[t]) + assert not (charge > 0.5 and discharge > 0.5) diff --git a/h2integrate/core/supported_models.py b/h2integrate/core/supported_models.py index 62e197de9..7120d47a1 100644 --- a/h2integrate/core/supported_models.py +++ b/h2integrate/core/supported_models.py @@ -7,7 +7,10 @@ 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.steel.steel import ( + SteelPerformanceModel, + SteelCostAndFinancialModel, +) from h2integrate.converters.wind.floris import FlorisWindPlantPerformanceModel from h2integrate.demand.flexible_demand import FlexibleDemandComponent from h2integrate.converters.wind.wind_pysam import PYSAMWindPlantPerformanceModel @@ -33,7 +36,10 @@ IronTransportCostComponent, IronTransportPerformanceComponent, ) -from h2integrate.converters.nitrogen.simple_ASU import SimpleASUCostModel, SimpleASUPerformanceModel +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.converters.hydrogen.h2_fuel_cell import ( @@ -58,10 +64,16 @@ CompressedGasStorageCostModel, LinedRockCavernStorageCostModel, ) -from h2integrate.transporters.gas_stream_combiner import GasStreamCombinerPerformanceModel -from h2integrate.transporters.generic_transporter import GenericTransporterPerformanceModel +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.converters.iron.humbert_ewin_perf import ( + HumbertEwinPerformanceComponent, +) from h2integrate.storage.storage_performance_model import StoragePerformanceModel from h2integrate.converters.ammonia.ammonia_synloop import ( AmmoniaSynLoopCostModel, @@ -73,13 +85,21 @@ ReverseOsmosisCostModel, ReverseOsmosisPerformanceModel, ) -from h2integrate.resource.wind.nlr_developer_wtk_api import WTKNLRDeveloperAPIWindResource +from h2integrate.resource.wind.nlr_developer_wtk_api import ( + WTKNLRDeveloperAPIWindResource, +) from h2integrate.converters.hydrogen.basic_cost_model import BasicElectrolyzerCostModel -from h2integrate.converters.hydrogen.pem_electrolyzer import ECOElectrolyzerPerformanceModel +from h2integrate.converters.hydrogen.pem_electrolyzer import ( + ECOElectrolyzerPerformanceModel, +) from h2integrate.converters.solar.atb_res_com_pv_cost import ATBResComPVCostModel from h2integrate.converters.solar.atb_utility_pv_cost import ATBUtilityPVCostModel -from h2integrate.converters.iron.martin_mine_cost_model import MartinIronMineCostComponent -from h2integrate.converters.iron.martin_mine_perf_model import MartinIronMinePerformanceComponent +from h2integrate.converters.iron.martin_mine_cost_model import ( + MartinIronMineCostComponent, +) +from h2integrate.converters.iron.martin_mine_perf_model import ( + MartinIronMinePerformanceComponent, +) from h2integrate.converters.methanol.smr_methanol_plant import ( SMRMethanolPlantCostModel, SMRMethanolPlantFinanceModel, @@ -89,7 +109,9 @@ SimpleAmmoniaCostModel, SimpleAmmoniaPerformanceModel, ) -from h2integrate.converters.iron.humbert_stinn_ewin_cost import HumbertStinnEwinCostComponent +from h2integrate.converters.iron.humbert_stinn_ewin_cost import ( + HumbertStinnEwinCostComponent, +) from h2integrate.converters.methanol.co2h_methanol_plant import ( CO2HMethanolPlantCostModel, CO2HMethanolPlantFinanceModel, @@ -101,7 +123,10 @@ ) 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.co2.marine.direct_ocean_capture import ( + DOCCostModel, + DOCPerformanceModel, +) from h2integrate.converters.hydrogen.steam_methane_reformer import ( SteamMethaneReformerCostModel, SteamMethaneReformerPerformanceModel, @@ -112,7 +137,9 @@ SimpleGasConsumerPerformance, SimpleGasProducerPerformance, ) -from h2integrate.converters.hydrogen.geologic.mathur_modified import GeoH2SubsurfaceCostModel +from h2integrate.converters.hydrogen.geologic.mathur_modified import ( + GeoH2SubsurfaceCostModel, +) from h2integrate.resource.solar.nlr_developer_goes_api_models import ( GOESTMYSolarAPI, GOESConusSolarAPI, @@ -171,6 +198,12 @@ from h2integrate.control.control_strategies.storage.simple_openloop_controller import ( SimpleStorageOpenLoopController, ) +from h2integrate.control.control_strategies.storage.plm_openloop_storage_controller import ( + PeakLoadManagementOpenLoopStorageController, +) +from h2integrate.control.control_strategies.storage.plm_optimized_storage_controller import ( + PLMOptimizedStorageController, +) from h2integrate.control.control_strategies.storage.plm_openloop_storage_controller import ( PeakLoadManagementHeuristicOpenLoopStorageController, ) @@ -295,6 +328,8 @@ # Control "SimpleStorageOpenLoopController": SimpleStorageOpenLoopController, "DemandOpenLoopStorageController": DemandOpenLoopStorageController, + "PeakLoadManagementOpenLoopStorageController": PeakLoadManagementOpenLoopStorageController, + "PLMOptimizedStorageController": PLMOptimizedStorageController, "PeakLoadManagementHeuristicOpenLoopStorageController": ( PeakLoadManagementHeuristicOpenLoopStorageController ), From b0a030cf183cb484223abbd7c8bf11a9ae70baac Mon Sep 17 00:00:00 2001 From: svijaysh Date: Wed, 22 Apr 2026 16:05:42 -0600 Subject: [PATCH 09/55] Make sure the example runs --- .../run_plm_optimized_dispatch.py | 55 ++++++++++++++----- .../plm_optimized_storage_controller.py | 2 +- .../test_plm_optimized_storage_controller.py | 6 -- h2integrate/core/supported_models.py | 4 -- 4 files changed, 43 insertions(+), 24 deletions(-) diff --git a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py index a833d2cc9..3dff042d2 100644 --- a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py +++ b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py @@ -12,7 +12,6 @@ import matplotlib.pyplot as plt from h2integrate.core.h2integrate_model import H2IntegrateModel -from h2integrate.core.utilities import build_time_series_from_plant_config EXAMPLE_DIR = Path(__file__).parent @@ -21,16 +20,26 @@ model.setup() N = model.plant_config["plant"]["simulation"]["n_timesteps"] -percentile = model.technology_config["technologies"]["battery"]["model_inputs"]["control_parameters"]["signal_threshold_percentile"] +percentile = model.technology_config["technologies"]["battery"]["model_inputs"][ + "control_parameters" +]["signal_threshold_percentile"] model.run() lmp = np.array( - model.technology_config["technologies"]["battery"]["model_inputs"] - ["control_parameters"]["supervisory_signal"] + model.technology_config["technologies"]["battery"]["model_inputs"][ + "control_parameters" + ]["supervisory_signal"] )[:N] -time_index = pd.DatetimeIndex(build_time_series_from_plant_config(model.plant_config)) +sim = model.plant_config["plant"]["simulation"] +n_timesteps = int(sim["n_timesteps"]) +dt_seconds = int(sim["dt"]) +tz = int(sim["timezone"]) +start = pd.Timestamp(sim["start_time"], tz=tz) +freq = pd.to_timedelta(dt_seconds, unit="s") +time_index = pd.date_range(start=start, periods=n_timesteps, freq=freq) + battery_power = model.prob.get_val("battery.storage_electricity_discharge", units="kW") soc_pct = model.prob.get_val("battery.SOC", units="percent") @@ -50,7 +59,10 @@ def shade_peaks(ax): ax.axvspan( day + pd.Timedelta(hours=14), day + pd.Timedelta(hours=18), - color="orange", alpha=0.10, linewidth=0, zorder=0, + color="orange", + alpha=0.10, + linewidth=0, + zorder=0, ) @@ -60,14 +72,21 @@ def shade_peaks(ax): shade_peaks(ax) ax.plot(time_index[:time_window], lmp[:time_window], color="steelblue", linewidth=1.0) ax.axhline(threshold_pct, color="crimson", linestyle="--", linewidth=0.8) -ax.plot(time_index[:time_window][w_discharge], lmp[:time_window][w_discharge], - "r*", markersize=8, zorder=5) +ax.plot( + time_index[:time_window][w_discharge], + lmp[:time_window][w_discharge], + "r*", + markersize=8, + zorder=5, +) ax.set_ylabel("LMP ($/MWh)", fontsize=8) ax.set_ylim(bottom=0) # Panel 2: SOC ax = axes[1] -ax.plot(time_index[:time_window], soc_pct[:time_window], color="tab:green", linewidth=1.0) +ax.plot( + time_index[:time_window], soc_pct[:time_window], color="tab:green", linewidth=1.0 +) ax.axhline(90, color="gray", linestyle=":", linewidth=0.7) ax.axhline(10, color="gray", linestyle=":", linewidth=0.7) ax.set_ylabel("SOC (%)", fontsize=8) @@ -79,10 +98,20 @@ def shade_peaks(ax): width = pd.Timedelta(hours=0.8) pos_mask = (battery_power > 1)[:time_window] neg_mask = (battery_power < -1)[:time_window] -ax.bar(time_index[:time_window][pos_mask], battery_power[:time_window][pos_mask] * 1e-3, - width=width, color="tab:red", alpha=0.8) -ax.bar(time_index[:time_window][neg_mask], battery_power[:time_window][neg_mask] * 1e-3, - width=width, color="tab:blue", alpha=0.8) +ax.bar( + time_index[:time_window][pos_mask], + battery_power[:time_window][pos_mask] * 1e-3, + width=width, + color="tab:red", + alpha=0.8, +) +ax.bar( + time_index[:time_window][neg_mask], + battery_power[:time_window][neg_mask] * 1e-3, + width=width, + color="tab:blue", + alpha=0.8, +) ax.set_ylabel("Power (MW)", fontsize=8) ax.tick_params(axis="x", labelrotation=30, labelsize=7) diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index a4721825f..3e0524801 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -59,7 +59,7 @@ class PLMOptimizedControllerConfig(PyomoStorageControllerBaseConfig): discharge_efficiency: float = field(validator=range_val(0, 1), default=1.0) n_max_events: int = field(default=10) n_control_window: int = field(default=24 * 30) # one month of hourly data - signal_threshold_percentile: float = field(default=0.0, validator=range_val(0,100)) + signal_threshold_percentile: float = field(default=0.0, validator=range_val(0,100)) # make sure this is valid class PLMOptimizedStorageController(PyomoStorageControllerBaseClass): diff --git a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py index 40b37863e..f3aaa1ae3 100644 --- a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py @@ -30,12 +30,6 @@ def _make_controller_with_config(config, n_timesteps=24): @pytest.fixture def base_config(): - """24-hour PLM config shared by optimizer regression tests. - - Storage: 10 kWh capacity, 1 kW max rate, starts fully charged. - Peak window: 08:00-18:00 - Signal: monotonically increasing. - """ n = 24 return PLMOptimizedControllerConfig( max_capacity=10.0, diff --git a/h2integrate/core/supported_models.py b/h2integrate/core/supported_models.py index 7120d47a1..4b39a7df5 100644 --- a/h2integrate/core/supported_models.py +++ b/h2integrate/core/supported_models.py @@ -198,9 +198,6 @@ from h2integrate.control.control_strategies.storage.simple_openloop_controller import ( SimpleStorageOpenLoopController, ) -from h2integrate.control.control_strategies.storage.plm_openloop_storage_controller import ( - PeakLoadManagementOpenLoopStorageController, -) from h2integrate.control.control_strategies.storage.plm_optimized_storage_controller import ( PLMOptimizedStorageController, ) @@ -328,7 +325,6 @@ # Control "SimpleStorageOpenLoopController": SimpleStorageOpenLoopController, "DemandOpenLoopStorageController": DemandOpenLoopStorageController, - "PeakLoadManagementOpenLoopStorageController": PeakLoadManagementOpenLoopStorageController, "PLMOptimizedStorageController": PLMOptimizedStorageController, "PeakLoadManagementHeuristicOpenLoopStorageController": ( PeakLoadManagementHeuristicOpenLoopStorageController From 125b48f4106b976cbca067cb2f6f0fb20aa66491 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Sat, 25 Apr 2026 20:26:05 -0600 Subject: [PATCH 10/55] Add docs --- docs/control/pyomo_controllers.md | 87 +++++++++++++++++++++++++++++++ docs/user_guide/model_overview.md | 1 + 2 files changed, 88 insertions(+) diff --git a/docs/control/pyomo_controllers.md b/docs/control/pyomo_controllers.md index 2da23275f..ac6a1ddc0 100644 --- a/docs/control/pyomo_controllers.md +++ b/docs/control/pyomo_controllers.md @@ -58,3 +58,90 @@ tech_to_dispatch_connections: [ ["battery", "battery"], ] ``` + +# Optimized Demand Response Controller + +This controller optimizes the dispatch of a Battery Energy Storage System (BESS) based on a pre-defined supervisory signal. This pre-defined signal could be the Locational Marginal Price (LMP) or a demand profile. It could also be a $LMP\times demand$ depending on the application. + +## Definitions + +**Given:** +- $\lambda_t$ := `supervisory_signal`: price, demand, or price $\times$ demand time series at time $t$ +- $\mathcal{W}$ := `peak_window`: set of hours eligible for dispatch (e.g., 12:00--19:00) +- $\gamma$ := performance incentive (\$/kW per dispatch hour) +- $\bar{P}$ := `max_charge_rate` (kW): maximum charge and discharge rate, used as deemed capacity since the battery is assumed to always dispatch at full rated power +- $E_{\max} :=$ `max_capacity` $\times$ (`max_soc_fraction` $-$ `min_soc_fraction`): usable energy capacity (kWh) +- $\eta_c$ := `charge_efficiency`, $\quad \eta_d$ := `discharge_efficiency` +- $\overline{\text{SoC}}$ := `max_soc_fraction`, $\quad \underline{\text{SoC}}$ := `min_soc_fraction` +- `n_control_window` := Horizon length for optimization +- $\mathcal{T} := \{0, 1, \ldots, T\}$: hourly time steps over `n_control_window` +- $\mathcal{M}_m$ := set of hours in month $m$, for $m = 1, \ldots, 12$ + +## Decision Variables + +- $u_t \in \{0, 1\}$ := discharge binary: 1 if battery dispatches at hour $t$, 0 otherwise +- $v_t \in \{0, 1\}$ := charge binary: 1 if battery charges at hour $t$, 0 otherwise + +## Optimization Problem + +This optimization is executed for each window, during which the performance model is invoked and the initial conditions are set. + +### Objective + +Maximize total annual incentive revenue: + +$$ +\max_{u_t,\, v_t} \quad \gamma \cdot \bar{P} \sum_{t \in \mathcal{T}} u_t +$$ + +### Constraints + +- Dispatch only within peak window: + +$$ +u_t = 0 \qquad \forall\, t \notin \mathcal{W} +$$ + +- Dispatch only on high supervisory signal: + +$$ +u_t = 1 \implies \lambda_t \geq \lambda^*_m \qquad \forall\, t \in \mathcal{M}_m +$$ + +where $\lambda^*_m$ is the threshold selecting the high LMP/peak load hours within month $m$. + +- Maximum 10 events per month: + +$$ +\sum_{t \in \mathcal{M}_m} u_t \leq N_{\max} \qquad \forall\, m, \quad N_{\max} = 10 +$$ + +- SoC evolution with charge and discharge: + +$$ +\text{SoC}_{t+1} = \text{SoC}_t + \frac{\eta_c \cdot v_t \cdot \bar{P}}{E_{\max}} - \frac{u_t \cdot \bar{P}}{\eta_d \cdot E_{\max}} \qquad \forall\, t \in \mathcal{T} +$$ + +- SoC bounds: + +$$ +\underline{\text{SoC}} \leq \text{SoC}_t \leq \overline{\text{SoC}} \qquad \forall\, t \in \mathcal{T} +$$ + +- No simultaneous charge and discharge: + +$$ +u_t + v_t \leq 1 \qquad \forall\, t \in \mathcal{T} +$$ + +- No charging during dispatch window (battery reserved for discharge): + +$$ +v_t = 0 \qquad \forall\, t \in \mathcal{W} +$$ + +- Binary variables: + +$$ +u_t \in \{0, 1\}, \quad v_t \in \{0, 1\}, \quad \text{SoC}_t \in [0, 1] \qquad \forall\, t, m +$$ diff --git a/docs/user_guide/model_overview.md b/docs/user_guide/model_overview.md index 6b8d6344f..2756730c2 100644 --- a/docs/user_guide/model_overview.md +++ b/docs/user_guide/model_overview.md @@ -288,6 +288,7 @@ Below summarizes the available performance, cost, and financial models for each - `'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 - `'HeuristicLoadFollowingStorageController'`: open-loop control that works on a time window basis to set dispatch commands; uses Pyomo + - `'PLMOptimizedStorageController'`: optimized controller for demand response that works on a time window basis. - `'PeakLoadManagementHeuristicOpenLoopStorageController'`: open-loop control that reduces peaks rather than trying to meet a load - Optimized Dispatch: - `'OptimizedDispatchStorageController'`: optimization-based dispatch using Pyomo From 7903e39b496e5bd7e7ee80506dc2267e1ccd0e80 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Thu, 23 Apr 2026 10:08:39 -0600 Subject: [PATCH 11/55] Add docs --- examples/test/test_all_examples.py | 2785 ++++++++++++++++++++++++++++ 1 file changed, 2785 insertions(+) diff --git a/examples/test/test_all_examples.py b/examples/test/test_all_examples.py index 5d9c3cad4..11e75ee03 100644 --- a/examples/test/test_all_examples.py +++ b/examples/test/test_all_examples.py @@ -2882,3 +2882,2788 @@ def test_peak_load_management_example(subtests, temp_copy_of_example): ) grid_purchase = model.prob.get_val("grid_buy.electricity_out", units="kW") assert battery_unmet_demand.sum() == pytest.approx(grid_purchase.sum(), rel=1e-3) + +import os +import importlib +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest +import openmdao.api as om + +from h2integrate import ROOT_DIR +from h2integrate.core.file_utils import load_yaml +from h2integrate.core.h2integrate_model import H2IntegrateModel + + +ROOT = Path(__file__).parents[1] + + +# docs fencepost start: DO NOT REMOVE +@pytest.mark.integration +@pytest.mark.parametrize("example_folder,resource_example_folder", [("01_onshore_steel_mn", None)]) +def test_steel_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create a H2Integrate model + model = H2IntegrateModel(example_folder / "01_onshore_steel_mn.yaml") + # docs fencepost end: DO NOT REMOVE + # Set battery demand profile to electrolyzer capacity + demand_profile = np.ones(8760) * 720.0 + model.setup() + model.prob.set_val("battery.electricity_demand", demand_profile, units="MW") + + # Run the model + model.run() + + model.post_process() + # Subtests for checking specific values + with subtests.test("Check total electricity produced"): + assert ( + pytest.approx( + model.prob.get_val("combiner.electricity_out", units="MW").sum(), + rel=1e-3, + ) + == 5901098.278035271 + ) + + with subtests.test("Check total adjusted CapEx (electricity)"): + assert ( + pytest.approx( + model.prob.get_val( + "finance_subgroup_electricity.total_capex_adjusted", units="USD" + )[0], + rel=1e-3, + ) + == 4314364438.840067 + ) + with subtests.test("Check total adjusted OpEx (electricity)"): + assert ( + pytest.approx( + model.prob.get_val( + "finance_subgroup_electricity.total_opex_adjusted", units="USD/year" + )[0], + rel=1e-3, + ) + == 75831805.27785796 + ) + + with subtests.test("Check LCOE"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0], + rel=1e-3, + ) + == 90.8231905486079 + ) + + with subtests.test("Check H2 Storage capacity"): + assert ( + pytest.approx(model.prob.get_val("h2_storage.storage_capacity", units="kg"), rel=1e-3) + == 2559669.7759292 + ) + + with subtests.test("Check LCOH"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_hydrogen.LCOH_delivered", units="USD/kg")[0], + rel=1e-3, + ) + == 8.235313509720276 + ) + + with subtests.test("Check LCOS"): + assert ( + pytest.approx(model.prob.get_val("steel.LCOS", units="USD/t")[0], rel=1e-3) + == 1264.2821232584045 + ) + + with subtests.test("Check total adjusted CapEx"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_hydrogen.total_capex_adjusted", units="USD")[ + 0 + ], + rel=1e-3, + ) + == 5129491338.670795 + ) + + with subtests.test("Check total adjusted OpEx"): + assert ( + pytest.approx( + model.prob.get_val( + "finance_subgroup_hydrogen.total_opex_adjusted", units="USD/year" + )[0], + rel=1e-3, + ) + == 97887982.86294547 + ) + + with subtests.test("Check steel CapEx"): + assert ( + pytest.approx(model.prob.get_val("steel.CapEx", units="USD"), rel=1e-3) == 5.78060014e08 + ) + + with subtests.test("Check steel OpEx"): + assert ( + pytest.approx(model.prob.get_val("steel.OpEx", units="USD/year"), rel=1e-3) + == 1.0129052e08 + ) + + +@pytest.mark.integration +@pytest.mark.parametrize("example_folder,resource_example_folder", [("02_texas_ammonia", None)]) +def test_simple_ammonia_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create a H2Integrate model + model = H2IntegrateModel(example_folder / "02_texas_ammonia.yaml") + + # Set battery demand profile to electrolyzer capacity + demand_profile = np.ones(8760) * 640.0 + model.setup() + model.prob.set_val("battery.electricity_demand", demand_profile, units="MW") + + # Run the model + model.run() + + model.post_process() + + # Subtests for checking specific values + with subtests.test("Check Wind+PV CapEx"): + wind_pv_capex = ( + model.prob.get_val("wind.CapEx", units="USD")[0] + + model.prob.get_val("solar.CapEx", units="USD")[0] + ) + assert pytest.approx(wind_pv_capex, rel=1e-3) == 1.75469962e09 + + with subtests.test("Check Wind+PV OpEx"): + wind_pv_opex = ( + model.prob.get_val("wind.OpEx", units="USD/yr")[0] + + model.prob.get_val("solar.OpEx", units="USD/yr")[0] + ) + assert pytest.approx(wind_pv_opex, rel=1e-3) == 32953490.4 + + with subtests.test("Check electrolyzer CapEx"): + assert ( + pytest.approx(model.prob.get_val("electrolyzer.CapEx", units="USD"), rel=1e-3) + == 6.00412524e08 + ) + + with subtests.test("Check electrolyzer OpEx"): + assert ( + pytest.approx(model.prob.get_val("electrolyzer.OpEx", units="USD/year"), rel=1e-3) + == 14703155.39207595 + ) + + with subtests.test("Check H2 storage CapEx"): + assert ( + pytest.approx(model.prob.get_val("h2_storage.CapEx", units="USD")[0], rel=1e-3) + == 64599012.73829915 + ) + + with subtests.test("Check H2 storage OpEx"): + assert ( + pytest.approx(model.prob.get_val("h2_storage.OpEx", units="USD/year")[0], rel=1e-3) + == 2975616.8932987223 + ) + + with subtests.test("Check ammonia CapEx"): + assert ( + pytest.approx(model.prob.get_val("ammonia.CapEx", units="USD"), rel=1e-3) + == 1.0124126e08 + ) + + with subtests.test("Check ammonia OpEx"): + assert ( + pytest.approx(model.prob.get_val("ammonia.OpEx", units="USD/year"), rel=1e-3) + == 11178036.31197754 + ) + + with subtests.test("Check total adjusted CapEx"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_hydrogen.total_capex_adjusted", units="USD")[ + 0 + ], + rel=1e-3, + ) + == 2577162708.3 + ) + + with subtests.test("Check total adjusted OpEx"): + assert ( + pytest.approx( + model.prob.get_val( + "finance_subgroup_hydrogen.total_opex_adjusted", units="USD/year" + )[0], + rel=1e-3, + ) + == 53842563.43404999 + ) + + # Currently underestimated compared to the Reference Design Doc + with subtests.test("Check LCOH"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_hydrogen.LCOH", units="USD/kg")[0], + rel=1e-3, + ) + == 4.0155433 + ) + + with subtests.test("Check price of hydrogen"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_hydrogen.price_hydrogen", units="USD/kg")[0], + rel=1e-3, + ) + == 4.0155433 + ) + + # Currently underestimated compared to the Reference Design Doc + with subtests.test("Check LCOA"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_ammonia.LCOA", units="USD/kg")[0], + rel=1e-3, + ) + == 1.027395 + ) + + # Check that the expected output files exist + outputs_dir = example_folder / "outputs" + assert ( + outputs_dir / "profast_output_ammonia_config.yaml" + ).is_file(), "profast_output_ammonia.yaml not found" + assert ( + outputs_dir / "profast_output_electricity_config.yaml" + ).is_file(), "profast_output_electricity.yaml not found" + assert ( + outputs_dir / "profast_output_hydrogen_config.yaml" + ).is_file(), "profast_output_hydrogen.yaml not found" + + +@pytest.mark.integration +@pytest.mark.parametrize("example_folder,resource_example_folder", [("12_ammonia_synloop", None)]) +def test_ammonia_synloop_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create a H2Integrate model + model = H2IntegrateModel(example_folder / "12_ammonia_synloop.yaml") + + # Set battery demand profile to electrolyzer capacity + demand_profile = np.ones(8760) * 640.0 + model.setup() + model.prob.set_val("battery.electricity_demand", demand_profile, units="MW") + + # Run the model + model.run() + + model.post_process() + + # Subtests for checking specific values + with subtests.test("Check HOPP CapEx"): + wind_pv_capex = ( + model.prob.get_val("wind.CapEx", units="USD")[0] + + model.prob.get_val("solar.CapEx", units="USD")[0] + ) + battery_capex = model.prob.get_val("battery.CapEx", units="USD") + re_capex = wind_pv_capex + battery_capex + assert pytest.approx(re_capex, rel=1e-6) == 1.75469962e09 + + with subtests.test("Check HOPP OpEx"): + wind_pv_opex = ( + model.prob.get_val("wind.OpEx", units="USD/yr")[0] + + model.prob.get_val("solar.OpEx", units="USD/yr")[0] + ) + battery_opex = model.prob.get_val("battery.OpEx", units="USD/year") + re_opex = wind_pv_opex + battery_opex + assert pytest.approx(re_opex, rel=1e-6) == 32953490.4 + + with subtests.test("Check electrolyzer CapEx"): + assert ( + pytest.approx(model.prob.get_val("electrolyzer.CapEx", units="USD"), rel=1e-6) + == 6.00412524e08 + ) + + with subtests.test("Check electrolyzer OpEx"): + assert ( + pytest.approx(model.prob.get_val("electrolyzer.OpEx", units="USD/year"), rel=1e-6) + == 14703155.39207595 + ) + + with subtests.test("Check H2 storage CapEx"): + assert ( + pytest.approx(model.prob.get_val("h2_storage.CapEx", units="USD"), rel=1e-6) + == 64553014.22218219 + ) + + with subtests.test("Check H2 storage OpEx"): + assert ( + pytest.approx(model.prob.get_val("h2_storage.OpEx", units="USD/year"), rel=1e-6) + == 2975616.89 + ) + + with subtests.test("Check ammonia CapEx"): + assert ( + pytest.approx(model.prob.get_val("ammonia.CapEx", units="USD"), rel=1e-6) + == 1.15173753e09 + ) + + with subtests.test("Check ammonia OpEx"): + assert ( + pytest.approx(model.prob.get_val("ammonia.OpEx", units="USD/year")[0], rel=1e-4) + == 25414748.989416014 + ) + + with subtests.test("Check ammonia production"): + assert ( + pytest.approx( + model.prob.get_val("ammonia.annual_ammonia_produced", units="t/yr").mean(), rel=1e-4 + ) + == 406333.161 + ) + + with subtests.test("Check total adjusted CapEx"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_nh3.total_capex_adjusted", units="USD")[0], + rel=1e-6, + ) + == 3728034379.0699997 + ) + + with subtests.test("Check total adjusted OpEx"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_nh3.total_opex_adjusted", units="USD/year")[0], + rel=1e-6, + ) + == 79257312.42365658 + ) + + with subtests.test("Check LCOH"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_h2.LCOH", units="USD/kg")[0], rel=1e-6 + ) + == 4.013427289493614 + ) + + with subtests.test("Check LCOA"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_nh3.LCOA", units="USD/kg")[0], rel=1e-6 + ) + == 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 +@pytest.mark.parametrize("example_folder,resource_example_folder", [("03_methanol/smr", None)]) +def test_smr_methanol_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create a H2Integrate model + model = H2IntegrateModel(example_folder / "03_smr_methanol.yaml") + + # Run the model + model.run() + + model.post_process() + + # Check levelized cost of methanol (LCOM) + with subtests.test("Check SMR LCOM"): + assert ( + pytest.approx(model.prob.get_val("methanol.LCOM", units="USD/kg"), rel=1e-6) + == 0.22116813 + ) + + +@pytest.mark.integration +@pytest.mark.parametrize( + "example_folder,resource_example_folder", [("03_methanol/co2_hydrogenation", None)] +) +def test_co2h_methanol_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create a H2Integrate model + model = H2IntegrateModel(example_folder / "03_co2h_methanol.yaml") + + # Run the model + model.run() + + model.post_process() + + # Below is used as an integration test for the combiner + with subtests.test("combiner rated production"): + combined_rated_input = model.prob.get_val( + "wind.rated_electricity_production", units="MW" + ) + model.prob.get_val("solar.rated_electricity_production", units="MW") + assert ( + pytest.approx( + model.prob.get_val("combiner.rated_electricity_production", units="MW"), rel=1e-6 + ) + == combined_rated_input + ) + with subtests.test("combiner weighted CF"): + wind_weighted_cf = model.prob.get_val( + "wind.rated_electricity_production", units="MW" + ) * model.prob.get_val("wind.capacity_factor", units="unitless") + solar_weighted_cf = model.prob.get_val( + "solar.rated_electricity_production", units="MW" + ) * model.prob.get_val("solar.capacity_factor", units="unitless") + combined_cf = (wind_weighted_cf + solar_weighted_cf) / combined_rated_input + assert ( + pytest.approx( + model.prob.get_val("combiner.capacity_factor", units="unitless"), + rel=1e-6, + ) + == combined_cf + ) + + # Check levelized cost of methanol (LCOM) + with subtests.test("Check CO2 Hydrogenation LCOM"): + assert ( + pytest.approx(model.prob.get_val("methanol.LCOM", units="USD/kg")[0], rel=1e-6) + == 1.7516172 + ) + + +@pytest.mark.integration +@pytest.mark.parametrize( + "example_folder,resource_example_folder", [("03_methanol/co2_hydrogenation_doc", None)] +) +def test_doc_methanol_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create a H2Integrate model + model = H2IntegrateModel(example_folder / "03_co2h_methanol.yaml") + + # Run the model + model.run() + + model.post_process() + + # Check levelized cost of methanol (LCOM) + with subtests.test("Check CO2 Hydrogenation LCOM"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_default.LCOM", units="USD/kg")[0], + rel=1e-4, + ) + == 2.5252588 + ) + + +@pytest.mark.integration +@pytest.mark.parametrize("example_folder,resource_example_folder", [("05_wind_h2_opt", None)]) +def test_wind_h2_opt_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Run without optimization + model_init = H2IntegrateModel(example_folder / "wind_plant_electrolyzer0.yaml") + + # Run the model + model_init.run() + + model_init.post_process() + + annual_h20 = model_init.prob.get_val("electrolyzer.annual_hydrogen_produced", units="kg/year")[ + 0 + ] + + # Create a H2Integrate model + model = H2IntegrateModel(example_folder / "wind_plant_electrolyzer.yaml") + + # Run the model + model.run() + + with subtests.test("Check initial H2 production"): + assert annual_h20 < (60500000 - 10000) + + with subtests.test("Check LCOE"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(kW*h)")[0], + rel=1e-3, + ) + == 0.059096 + ) + + with subtests.test("Check electrolyzer size"): + assert ( + pytest.approx( + model.prob.get_val("electrolyzer.electrolyzer_size_mw", units="MW")[0], + rel=1e-3, + ) + == 320.0 + ) + # Read the resulting SQL file and compare initial and final LCOH values + + sql_path = None + for root, _dirs, files in os.walk(example_folder): + for file in files: + if file == "wind_h2_opt.sql": + sql_path = Path(root) / file + break + if sql_path: + break + assert ( + sql_path is not None + ), "wind_h2_opt.sql file not found in current working directory or subdirectories." + + cr = om.CaseReader(str(sql_path)) + cases = list(cr.get_cases()) + assert len(cases) > 1, "Not enough cases recorded in SQL file." + + # Get initial and final LCOH values + + initial_lcoh = cases[0].outputs["finance_subgroup_hydrogen.LCOH"][0] + final_lcoh = cases[-1].outputs["finance_subgroup_hydrogen.LCOH"][0] + + with subtests.test("Check LCOH changed"): + assert final_lcoh != initial_lcoh + + with subtests.test("Check total adjusted CapEx"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_hydrogen.total_capex_adjusted", units="USD")[ + 0 + ], + rel=1e-3, + ) + == 978075832.46 + ) + with subtests.test("Check total adjusted OpEx"): + assert ( + pytest.approx( + model.prob.get_val( + "finance_subgroup_hydrogen.total_opex_adjusted", units="USD/year" + )[0], + rel=1e-3, + ) + == 27646299.56 + ) + + with subtests.test("Check minimum total hydrogen produced"): + assert ( + pytest.approx( + model.prob.get_val("electrolyzer.annual_hydrogen_produced", units="kg/year")[0], + abs=15000, + ) + == 29028700 + ) + + +@pytest.mark.integration +@pytest.mark.parametrize("example_folder,resource_example_folder", [("06_custom_tech", None)]) +def test_paper_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create a H2Integrate model + model = H2IntegrateModel(example_folder / "wind_plant_paper.yaml") + + # Run the model + model.run() + + model.post_process() + + # Subtests for checking specific values + with subtests.test("Check LCOP"): + assert ( + pytest.approx(model.prob.get_val("paper_mill.LCOP", units="USD/t"), rel=1e-3) + == 51.733275 + ) + + +@pytest.mark.integration +@pytest.mark.parametrize( + "example_folder,resource_example_folder", [("09_co2/direct_ocean_capture", None)] +) +def test_wind_wave_doc_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create a H2Integrate model + model = H2IntegrateModel(example_folder / "offshore_plant_doc.yaml") + # Set battery demand profile + demand_profile = np.ones(8760) * 340.0 + model.setup() + model.prob.set_val("battery.electricity_demand", demand_profile, units="MW") + # Run the model + model.run() + + model.post_process() + + # Subtests for checking specific values + with subtests.test("Check LCOC"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_co2.LCOC", units="USD/kg")[0], rel=1e-3 + ) + == 1.803343170781246 + ) + + with subtests.test("Check LCOE"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0], + rel=1e-3, + ) + == 243.723825 + ) + + +@pytest.mark.integration +@pytest.mark.parametrize( + "example_folder,resource_example_folder", [("17_splitter_wind_doc_h2", None)] +) +def test_splitter_wind_doc_h2_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create a H2Integrate model + model = H2IntegrateModel(example_folder / "offshore_plant_splitter_doc_h2.yaml") + + # Run the model + model.run() + + model.post_process() + + # Subtests for checking specific values + with subtests.test("Check Electrical AEP"): + electrical_aep = ( + model.prob.get_val( + "finance_subgroup_electricity.rated_electricity_production", + units="MW", + ) + * model.prob.get_val( + "finance_subgroup_electricity.capacity_factor", + units="unitless", + ).mean() + * 8760 + ) + + assert pytest.approx(electrical_aep[0], rel=1e-3) == 511267.03627 + + with subtests.test("Check LCOH"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_hydrogen.LCOH", units="USD/kg")[0], + rel=1e-3, + ) + == 9.8059083 + ) + + with subtests.test("Check LCOC"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_co2.LCOC", units="USD/kg")[0], rel=1e-3 + ) + == 13.655268 + ) + + with subtests.test("Check LCOE"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0], + rel=1e-3, + ) + == 132.395036462 + ) + + +@pytest.mark.integration +@pytest.mark.parametrize( + "example_folder,resource_example_folder", [("07_run_of_river_plant", None)] +) +def test_hydro_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create a H2Integrate model + model = H2IntegrateModel(example_folder / "07_run_of_river.yaml") + + # Run the model + model.run() + + model.post_process() + + print(model.prob.get_val("finance_subgroup_default.LCOE", units="USD/(kW*h)")) + + # Subtests for checking specific values + with subtests.test("Check LCOE"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_default.LCOE", units="USD/(kW*h)")[0], + rel=1e-3, + ) + == 0.17653979 + ) + + +@pytest.mark.integration +@pytest.mark.parametrize( + "example_folder,resource_example_folder", [("11_hybrid_energy_plant", None)] +) +def test_hybrid_energy_plant_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create a H2Integrate model + model = H2IntegrateModel(example_folder / "wind_pv_battery.yaml") + + # Run the model + model.run() + + model.post_process() + + # Subtests for checking specific values + with subtests.test("Check LCOE"): + 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)] +) +def test_hydrogen_dispatch_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create a H2Integrate model + model = H2IntegrateModel(example_folder / "inputs" / "h2i_wind_to_h2_storage.yaml") + + model.run() + + model.post_process() + + with subtests.test("Check LCOE"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0], + rel=1e-5, + ) + == 59.0962072084844 + ) + + with subtests.test("Check all h2 total_hydrogen_produced"): + all_h2_annual_prod = ( + model.prob.get_val( + "finance_subgroup_all_hydrogen.rated_hydrogen_production", units="kg/h" + )[0] + * model.prob.get_val( + "finance_subgroup_all_hydrogen.capacity_factor", units="unitless" + ).mean() + * 8760 + ) + assert ( + pytest.approx( + all_h2_annual_prod, + rel=1e-5, + ) + == model.prob.get_val("electrolyzer.annual_hydrogen_produced", units="kg/year")[0] + ) + + with subtests.test("Check total_hydrogen_produced"): + assert ( + pytest.approx( + model.prob.get_val("electrolyzer.total_hydrogen_produced", units="kg")[0], + rel=1e-5, + ) + == 61656526.36295184 + ) + + with subtests.test("Check annual hydrogen production"): + assert ( + pytest.approx( + model.prob.get_val("electrolyzer.annual_hydrogen_produced", units="kg/year")[0], + rel=1e-5, + ) + == 58458965.601815335 + ) + + with subtests.test("Check all h2 LCOH"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_all_hydrogen.LCOH", units="USD/kg")[0], + rel=1e-5, + ) + == 5.647544985152393 + ) + + with subtests.test("Check dispatched h2 LCOH"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_dispatched_hydrogen.LCOH", units="USD/kg")[0], + rel=1e-5, + ) + == 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 +@pytest.mark.parametrize( + "example_folder,resource_example_folder", [("09_co2/ocean_alkalinity_enhancement", None)] +) +def test_wind_wave_oae_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create a H2Integrate model + model = H2IntegrateModel(example_folder / "offshore_plant_oae.yaml") + + # Set battery demand profile + demand_profile = np.ones(8760) * 330.0 + model.setup() + model.prob.set_val("battery.electricity_demand", demand_profile, units="MW") + + # Run the model + model.run() + + model.post_process() + + # Subtests for checking specific values + with subtests.test("Check LCOC"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_co2.LCOC", units="USD/kg")[0], rel=1e-3 + ) + == 41.156 + ) + + with subtests.test("Check LCOE"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0], + rel=1e-3, + ) + == 263.130 + ) + + +@pytest.mark.integration +@pytest.mark.parametrize( + "example_folder,resource_example_folder", + [("09_co2/ocean_alkalinity_enhancement_financials", None)], +) +def test_wind_wave_oae_example_with_finance(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create a H2Integrate model + model = H2IntegrateModel(example_folder / "offshore_plant_oae.yaml") + + # Run the model + model.run() + + model.post_process() + + # Subtests for checking specific values + with subtests.test("Check LCOE"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0], + rel=1e-3, + ) + == 92.269663 + ) + + with subtests.test("Check Carbon Credit"): + assert ( + pytest.approx(model.prob.get_val("oae.carbon_credit_value", units="USD/t")[0], rel=1e-3) + == 1026.4684117 + ) + + +@pytest.mark.integration +@pytest.mark.parametrize( + "example_folder,resource_example_folder", [("16_natural_gas", "11_hybrid_energy_plant")] +) +def test_natural_gas_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create a H2Integrate model + model = H2IntegrateModel(example_folder / "natgas.yaml") + + # Run the model + + model.run() + + model.post_process() + solar_aep = sum(model.prob.get_val("solar.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("electrical_load_demand.unused_electricity_out", units="kW") + ) + + renewable_subgroup_total_electricity = ( + model.prob.get_val("finance_subgroup_renewables.rated_electricity_production", units="kW")[ + 0 + ] + * model.prob.get_val("finance_subgroup_renewables.capacity_factor", units="unitless").mean() + * 8760 + ) + electricity_subgroup_total_electricity = ( + model.prob.get_val("finance_subgroup_electricity.rated_electricity_production", units="kW")[ + 0 + ] + * model.prob.get_val( + "finance_subgroup_electricity.capacity_factor", units="unitless" + ).mean() + * 8760 + ) + natural_gas_subgroup_total_electricity = ( + model.prob.get_val("finance_subgroup_natural_gas.rated_electricity_production", units="kW")[ + 0 + ] + * model.prob.get_val( + "finance_subgroup_natural_gas.capacity_factor", units="unitless" + ).mean() + * 8760 + ) + + # NOTE: battery output power is not included in any of the financials + + 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 + + with subtests.test( + "Check solar AEP is greater than battery output (solar oversized relative to demand" + ): + assert solar_aep > solar_bat_out_total + + with subtests.test( + "Check battery outputs against battery inputs (solar oversized relative to demand" + ): + assert ( + pytest.approx(solar_bat_out_total + solar_curtailed_total, abs=bat_init_charge) + == solar_aep + ) + + with subtests.test("Check solar AEP equals total electricity for renewables subgroup"): + assert pytest.approx(solar_aep, rel=1e-6) == renewable_subgroup_total_electricity + + with subtests.test("Check natural gas AEP equals total electricity for natural_gas subgroup"): + assert ( + pytest.approx(sum(ng_electricity_production), rel=1e-6) + == natural_gas_subgroup_total_electricity + ) + + with subtests.test( + "Check natural gas + solar AEP equals total electricity for electricity subgroup" + ): + assert ( + pytest.approx(electricity_subgroup_total_electricity, rel=1e-6) + == sum(ng_electricity_production) + solar_aep + ) + + with subtests.test("Check missed load is natural gas plant electricity demand"): + assert pytest.approx(ng_electricity_demand, rel=1e-6) == pre_ng_missed_load + + with subtests.test("Check natural_gas_plant electricity out equals demand"): + assert pytest.approx(ng_electricity_demand, rel=1e-6) == ng_electricity_production + + # Subtests for checking specific values + with subtests.test("Check Natural Gas CapEx"): + capex = model.prob.get_val("natural_gas_plant.CapEx", units="USD")[0] + assert pytest.approx(capex, rel=1e-6) == 1e8 + + with subtests.test("Check Natural Gas OpEx"): + opex = model.prob.get_val("natural_gas_plant.OpEx", units="USD/year")[0] + assert pytest.approx(opex, rel=1e-6) == 2243167.24525 + + with subtests.test("Check total electricity produced"): + assert pytest.approx(natural_gas_subgroup_total_electricity, rel=1e-6) == 497266898.10354495 + + with subtests.test("Check opex adjusted ng_feedstock"): + opex_ng_feedstock = model.prob.get_val( + "finance_subgroup_natural_gas.varopex_adjusted_ng_feedstock", + units="USD/year", + )[0] + assert pytest.approx(opex_ng_feedstock, rel=1e-6) == 15281860.770986987 + + with subtests.test("Check capex adjusted natural_gas_plant"): + capex_ng_plant = model.prob.get_val( + "finance_subgroup_natural_gas.capex_adjusted_natural_gas_plant", units="USD" + )[0] + assert pytest.approx(capex_ng_plant, rel=1e-6) == 97560975.60975611 + + with subtests.test("Check opex adjusted natural_gas_plant"): + opex_ng_plant = model.prob.get_val( + "finance_subgroup_natural_gas.opex_adjusted_natural_gas_plant", units="USD/year" + )[0] + assert pytest.approx(opex_ng_plant, rel=1e-6) == 2188455.8490330363 + + with subtests.test("Check total adjusted CapEx for natural gas subgroup"): + total_capex = model.prob.get_val( + "finance_subgroup_natural_gas.total_capex_adjusted", units="USD" + )[0] + assert pytest.approx(total_capex, rel=1e-6) == 97658536.58536586 + + with subtests.test("Check LCOE (natural gas plant)"): + lcoe_ng = model.prob.get_val("finance_subgroup_natural_gas.LCOE", units="USD/(kW*h)")[0] + assert pytest.approx(lcoe_ng, rel=1e-6) == 0.05811033466 + + with subtests.test("Check LCOE (renewables plant)"): + lcoe_re = model.prob.get_val("finance_subgroup_renewables.LCOE", units="USD/(kW*h)")[0] + assert pytest.approx(lcoe_re, rel=1e-6) == 0.07102560120 + + with subtests.test("Check LCOE (renewables and natural gas plant)"): + lcoe_tot = model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(kW*h)")[0] + assert pytest.approx(lcoe_tot, rel=1e-6) == 0.063997927290 + + # Test feedstock-specific values + with subtests.test("Check feedstock output"): + ng_output = model.prob.get_val("ng_feedstock_source.natural_gas_out", units="MMBtu/h") + # Should be rated capacity (750 MMBtu/h) for all timesteps + assert all(ng_output == 750.0) + + with subtests.test("Check feedstock consumption"): + ng_consumed = model.prob.get_val("ng_feedstock.natural_gas_consumed", units="MMBtu/h") + # Total consumption should match what the natural gas plant uses + expected_consumption = ( + model.prob.get_val("natural_gas_plant.electricity_out", units="MW") * 7.5 + ) # Convert MWh to MMBtu using heat rate + assert pytest.approx(ng_consumed.sum(), rel=1e-3) == expected_consumption.sum() + + with subtests.test("Check feedstock CapEx"): + ng_capex = model.prob.get_val("ng_feedstock.CapEx", units="USD")[0] + assert pytest.approx(ng_capex, rel=1e-6) == 100000.0 # start_up_cost + + with subtests.test("Check feedstock OpEx"): + ng_opex = model.prob.get_val("ng_feedstock.VarOpEx", units="USD/year")[0] + # OpEx should be annual_cost (0) + price * consumption + ng_consumed = model.prob.get_val("ng_feedstock.natural_gas_consumed", units="MMBtu/h") + 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( + "example_folder,resource_example_folder", + [("15_wind_solar_electrolyzer", "11_hybrid_energy_plant/")], +) +def test_wind_solar_electrolyzer_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create a H2Integrate model + model = H2IntegrateModel(example_folder / "15_wind_solar_electrolyzer.yaml") + model.run() + + solar_fpath = model.model.get_val("solar_site.solar_resource.solar_resource_data")["filepath"] + wind_fpath = model.model.get_val("wind_site.wind_resource.wind_resource_data")["filepath"] + + with subtests.test("Wind resource file"): + assert Path(wind_fpath).name == "35.2018863_-101.945027_2012_wtk_v2_60min_utc_tz.csv" + + with subtests.test("Solar resource file"): + assert Path(solar_fpath).name == "30.6617_-101.7096_psmv3_60_2013.csv" + model.post_process() + + wind_aep = sum(model.prob.get_val("wind.electricity_out", units="kW")) + solar_aep = sum(model.prob.get_val("solar.electricity_out", units="kW")) + total_aep = model.prob.get_val("combiner.electricity_out", units="kW").sum() + + with subtests.test("Check total energy production"): + assert pytest.approx(wind_aep + solar_aep, rel=1e-6) == total_aep + + with subtests.test("Check LCOE"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0], + rel=1e-5, + ) + == 53.9306558 + ) + + with subtests.test("Check LCOH"): + assert ( + pytest.approx( + model.prob.get_val("finance_subgroup_hydrogen.LCOH", units="USD/kg")[0], + rel=1e-5, + ) + == 5.3063358423 + ) + + wind_generation = model.prob.get_val("wind.electricity_out", units="kW") + solar_generation = model.prob.get_val("solar.electricity_out", units="kW") + total_generation = model.prob.get_val("combiner.electricity_out", units="kW") + total_energy_to_electrolyzer = model.prob.get_val("electrolyzer.electricity_in", units="kW") + with subtests.test("Check combiner output"): + assert ( + pytest.approx(wind_generation.sum() + solar_generation.sum(), rel=1e-5) + == total_generation.sum() + ) + with subtests.test("Check electrolyzer input power"): + assert pytest.approx(total_generation.sum(), rel=1e-5) == total_energy_to_electrolyzer.sum() + + +@pytest.mark.integration +@pytest.mark.parametrize("example_folder,resource_example_folder", [("10_electrolyzer_om", None)]) +def test_electrolyzer_om_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create a H2Integrate model + model = H2IntegrateModel(example_folder / "electrolyzer_om.yaml") + + model.run() + + lcoe = model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0] + lcoh_with_lcoh_finance = model.prob.get_val( + "finance_subgroup_hydrogen.LCOH_lcoh_financials", units="USD/kg" + )[0] + lcoh_with_lcoe_finance = model.prob.get_val( + "finance_subgroup_hydrogen.LCOH_lcoe_financials", units="USD/kg" + )[0] + with subtests.test("Check LCOE"): + assert pytest.approx(lcoe, rel=1e-4) == 39.98869 + with subtests.test("Check LCOH with lcoh_financials"): + assert pytest.approx(lcoh_with_lcoh_finance, rel=1e-4) == 16.9204156301 + with subtests.test("Check LCOH with lcoe_financials"): + assert pytest.approx(lcoh_with_lcoe_finance, rel=1e-4) == 10.3360027653 + + +@pytest.mark.integration +@pytest.mark.parametrize("example_folder,resource_example_folder", [("08_wind_electrolyzer", None)]) +def test_wombat_electrolyzer_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create a H2Integrate model + model = H2IntegrateModel(example_folder / "wind_plant_electrolyzer.yaml") + + model.run() + + lcoe_with_profast_model = model.prob.get_val( + "finance_subgroup_electricity_profast.LCOE", units="USD/(MW*h)" + )[0] + lcoe_with_custom_model = model.prob.get_val( + "finance_subgroup_electricity_custom.LCOE", units="USD/(MW*h)" + )[0] + + lcoh_with_custom_model = model.prob.get_val( + "finance_subgroup_hydrogen.LCOH_produced_custom_model", units="USD/kg" + )[0] + lcoh_with_profast_model = model.prob.get_val( + "finance_subgroup_hydrogen.LCOH_produced_profast_model", units="USD/kg" + )[0] + + with subtests.test("Check LCOH from custom model"): + assert pytest.approx(lcoh_with_custom_model, rel=1e-5) == 4.1783979573 + with subtests.test("Check LCOH from ProFAST model"): + assert pytest.approx(lcoh_with_profast_model, rel=1e-5) == 5.3086307305 + with subtests.test("Check LCOE from custom model"): + assert pytest.approx(lcoe_with_custom_model, rel=1e-5) == 51.17615298 + with subtests.test("Check LCOE from ProFAST model"): + assert pytest.approx(lcoe_with_profast_model, rel=1e-5) == 59.0962084 + + +@pytest.mark.integration +@pytest.mark.parametrize( + "example_folder,resource_example_folder", [("18_pyomo_heuristic_dispatch", None)] +) +def test_pyomo_heuristic_dispatch_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create a H2Integrate model + model = H2IntegrateModel(example_folder / "pyomo_heuristic_dispatch.yaml") + + demand_profile = np.ones(8760) * 50.0 + + # TODO: Update with demand module once it is developed + model.setup() + model.prob.set_val("battery.electricity_demand", demand_profile, units="MW") + + # Run the model + model.run() + + model.post_process() + + # Test battery storage functionality + # SOC should stay within configured bounds (10% to 90%) + # Due to pysam simulation, bounds may not be fully respected, + # but should not exceed the upper bound more than 4% SOC + # and the lower bound more than 1% SOC + soc = model.prob.get_val("battery.SOC", units="percent") + with subtests.test("Check battery SOC lower bound"): + assert all(soc >= 9.0) + with subtests.test("Check battery SOC upper bound"): + assert all(soc <= 94.0) + + with subtests.test("Check wind generation out of the wind plant"): + # Wind should generate some electricity + wind_electricity = model.prob.get_val("wind.electricity_out", units="MW") + assert wind_electricity.sum() > 0 + # Wind electricity should match battery input (direct connection) + with subtests.test("Check wind generation in to battery"): + battery_electricity_in = model.prob.get_val("battery.electricity_in", units="MW") + 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("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 + + # Subtest for LCOE + with subtests.test("Check all LCOE value"): + lcoe = model.prob.get_val("finance_subgroup_all_electricity.LCOE", units="USD/(kW*h)")[0] + assert lcoe == pytest.approx(0.08157197567200995, rel=1e-6) + + with subtests.test("Check dispatched LCOE value"): + lcoe = model.prob.get_val( + "finance_subgroup_dispatched_electricity.LCOE", units="USD/(kW*h)" + )[0] + assert lcoe == pytest.approx(0.5975902853904799, rel=1e-6) + + # Subtest for total electricity produced + with subtests.test("Check total electricity produced"): + total_electricity = ( + model.prob.get_val( + name="finance_subgroup_all_electricity.rated_electricity_production", + units="MW", + )[0] + * model.prob.get_val( + name="finance_subgroup_all_electricity.capacity_factor", + units="unitless", + ).mean() + * 8760 + ) + assert total_electricity == pytest.approx(3125443.1089529935, rel=1e-6) + + # Subtest for electricity unused_commodity + with subtests.test("Check electricity unused commodity"): + electricity_unused_commodity = np.linalg.norm( + 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("electrical_load_demand.unmet_electricity_demand_out", units="MW") + ) + assert electricity_unmet_demand == pytest.approx(711.1997294551337, rel=1e-6) + + # Check that incorrect and no tech name provided will be replaced and validate + model_config = load_yaml(example_folder / "pyomo_heuristic_dispatch.yaml") + tech = load_yaml(example_folder / "tech_config.yaml") + with subtests.test("Ensure no-tech name validates"): + tech["technologies"]["battery"]["model_inputs"]["control_parameters"] = None + model_config["technology_config"] = tech + model = H2IntegrateModel(model_config) + + with subtests.test("Ensure incorrect name is corrected"): + tech["technologies"]["battery"]["model_inputs"]["control_parameters"] = { + "tech_name": "goose" + } + model_config["technology_config"] = tech + model = H2IntegrateModel(model_config) + + +@pytest.mark.integration +@pytest.mark.parametrize("example_folder,resource_example_folder", [("19_simple_dispatch", None)]) +def test_simple_dispatch_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create a H2Integrate model + model = H2IntegrateModel(example_folder / "wind_battery_dispatch.yaml") + + # Run the model + model.run() + + model.post_process() + + wind_aep = sum(model.prob.get_val("wind.electricity_out", units="kW")) + aep_for_finance = ( + model.prob.get_val("finance_subgroup_electricity.rated_electricity_production", units="kW")[ + 0 + ] + * model.prob.get_val( + "finance_subgroup_electricity.capacity_factor", units="unitless" + ).mean() + * 8760 + ) + battery_init_energy = 30000.0 * 0.25 # max capacity in kW and initial charge rate percentage + + with subtests.test("Check electricity is not double counted"): + assert aep_for_finance <= wind_aep + battery_init_energy + + # Test battery storage functionality + with subtests.test("Check battery SOC bounds"): + 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) + + with subtests.test("Check wind generation"): + # Wind should generate some electricity + wind_electricity = model.prob.get_val("wind.electricity_out", units="kW") + assert wind_electricity.sum() > 0 + # Wind electricity should match battery input (direct connection) + battery_electricity_in = model.prob.get_val("battery.electricity_in", units="kW") + 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("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 + + # Subtest for LCOE + with subtests.test("Check LCOE value"): + lcoe = model.prob.get_val( + "finance_subgroup_electricity.LCOE_all_electricity_profast", units="USD/(kW*h)" + )[0] + assert pytest.approx(lcoe, rel=1e-6) == 0.07801723344476236 + + # Subtest for NPV + with subtests.test("Check NPV value (numpy financial)"): + npv = model.prob.get_val( + "finance_subgroup_electricity.NPV_electricity_all_electricity_npv", units="USD" + )[0] + assert pytest.approx(npv, rel=1e-6) == 3791194.71 + + # Subtest for ProFAST NPV + with subtests.test("Check NPV value (profast)"): + npv = model.prob.get_val( + "finance_subgroup_electricity.NPV_electricity_all_electricity_profast_npv", + units="USD", + )[0] + assert pytest.approx(npv, rel=1e-6) == 7518969.18 + + # Subtest for total electricity produced + with subtests.test("Check total electricity produced"): + total_electricity = ( + model.prob.get_val( + "finance_subgroup_electricity.rated_electricity_production", units="kW" + )[0] + * model.prob.get_val("finance_subgroup_electricity.capacity_factor").mean() + * 8760 + ) + assert pytest.approx(total_electricity, rel=1e-6) == 62797265.9296355 + + # Subtest for electricity unused_commodity + with subtests.test("Check electricity unused commodity"): + electricity_unused_commodity = np.linalg.norm( + 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("electrical_load_demand.unmet_electricity_demand_out", units="kW") + ) + assert pytest.approx(electricity_unmet_demand, rel=1e-6) == 165604.70758669 + + # Subtest for total electricity produced from wind, should be equal to total + # electricity produced from finance_subgroup_electricity + with subtests.test("Check total electricity produced from wind"): + wind_electricity_finance = ( + model.prob.get_val("finance_subgroup_wind.rated_electricity_production", units="kW")[0] + * model.prob.get_val("finance_subgroup_wind.capacity_factor", units="unitless").mean() + * 8760 + ) + assert pytest.approx(wind_electricity_finance, rel=1e-6) == total_electricity + + with subtests.test("Check total electricity produced from wind compared to wind aep"): + wind_electricity_performance = np.sum( + model.prob.get_val("wind.electricity_out", units="kW") + ) + assert pytest.approx(wind_electricity_performance, rel=1e-6) == wind_electricity_finance + + # Subtest for total electricity produced from battery, should be equal + # to sum of "battery.electricity_out" + with subtests.test("Check total electricity produced from battery"): + battery_electricity_finance = ( + model.prob.get_val( + "finance_subgroup_battery.rated_electricity_production", units="MW*h/year" + )[0] + * model.prob.get_val( + "finance_subgroup_battery.capacity_factor", units="unitless" + ).mean() + * 8760 + ) + battery_electricity_performance = ( + 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 ( + pytest.approx(battery_electricity_finance, rel=1e-6) == battery_electricity_performance + ) + + wind_lcoe = model.prob.get_val("finance_subgroup_wind.LCOE_wind_only", units="USD/(MW*h)")[0] + battery_lcoe = model.prob.get_val( + "finance_subgroup_battery.LCOE_battery_included", units="USD/(MW*h)" + )[0] + electricity_lcoe = model.prob.get_val( + "finance_subgroup_electricity.LCOE_all_electricity_profast", units="USD/(MW*h)" + )[0] + + with subtests.test("Check electricity LCOE is greater than wind LCOE"): + assert electricity_lcoe > wind_lcoe + + with subtests.test("Check battery LCOE is greater than electricity LCOE"): + assert battery_lcoe > electricity_lcoe + + with subtests.test("Check battery LCOE"): + assert pytest.approx(battery_lcoe, rel=1e-6) == 131.781997 + + with subtests.test("Check wind LCOE"): + assert pytest.approx(wind_lcoe, rel=1e-6) == 58.8248 + + with subtests.test("Check electricity LCOE"): + assert pytest.approx(electricity_lcoe, rel=1e-6) == 78.01723 + + +@pytest.mark.integration +@pytest.mark.skipif(importlib.util.find_spec("ard") is None, reason="ard is not installed") +@pytest.mark.parametrize("example_folder,resource_example_folder", [("29_wind_ard", None)]) +def test_windard_pv_battery_dispatch_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create the model + model = H2IntegrateModel(example_folder / "h2i_inputs/wind_pv_battery.yaml") + + # Run the model + model.run() + + # Post-process the results + model.post_process() + + with subtests.test("Check wind generation"): + # Wind should generate some electricity + wind_electricity = model.prob.get_val("wind.electricity_out", units="GW") + assert wind_electricity.sum() == pytest.approx(150.88490967164714, rel=1e-4) + + with subtests.test("Check solar generation"): + # Solar should generate some electricity + solar_electricity = model.prob.get_val("solar.electricity_out", units="GW") + assert solar_electricity.sum() == pytest.approx(44.22139046811775, rel=1e-4) + + with subtests.test("Check battery gets wind and solar output"): + # Wind plus solar electricity should match battery input (direct connection) + battery_electricity_in = model.prob.get_val("battery.electricity_in", units="GW") + assert wind_electricity.sum() + solar_electricity.sum() == pytest.approx( + battery_electricity_in.sum(), rel=1e-6 + ) + + with subtests.test("Check demand satisfaction"): + 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:], + model.prob.get_val("battery.electricity_demand", units="MW")[8700:], + ) + + # Subtest for LCOE + with subtests.test("Check dispatched LCOE value"): + lcoe = model.prob.get_val( + "finance_subgroup_dispatched_electricity.LCOE", units="USD/(kW*h)" + )[0] + assert pytest.approx(lcoe, rel=1e-6) == 0.09289430342906849 + + with subtests.test("Check generation LCOE value (excludes battery)"): + lcoe = model.prob.get_val("finance_subgroup_produced_electricity.LCOE", units="USD/(kW*h)")[ + 0 + ] + assert pytest.approx(lcoe, rel=1e-6) == 0.07204429286793802 + + # Subtest for total electricity produced + with subtests.test("Check total electricity dispatched"): + total_electricity_year_one = ( + model.prob.get_val( + "finance_subgroup_dispatched_electricity.rated_electricity_production", + units="MW", + )[0] + * model.prob.get_val( + "finance_subgroup_dispatched_electricity.capacity_factor", + units="unitless", + )[0] + * 8760 + ) + assert total_electricity_year_one == pytest.approx(dispatched_electricity.sum()) + + # Subtest for electricity curtailed + with subtests.test("Check electricity curtailed"): + electricity_curtailed = model.prob.get_val( + "electrical_load_demand.unused_electricity_out", units="MW" + ).sum() + + 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("electrical_load_demand.unmet_electricity_demand_out", units="MW") + ) + assert electricity_missed_load == pytest.approx(1403.5372787817894) + + +@pytest.mark.integration +@pytest.mark.parametrize( + "example_folder,resource_example_folder", [("20_solar_electrolyzer_doe", None)] +) +def test_csvgen_design_of_experiments(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + with pytest.raises(UserWarning) as excinfo: + model = H2IntegrateModel(example_folder / "20_solar_electrolyzer_doe.yaml") + assert "There may be issues with the csv file csv_doe_cases.csv" in str(excinfo.value) + + from h2integrate.core.dict_utils import update_defaults + from h2integrate.core.file_utils import check_file_format_for_csv_generator + from h2integrate.core.inputs.validation import write_yaml, load_driver_yaml + + # load the driver config file + driver_config = load_driver_yaml("driver_config.yaml") + # specify the filepath to the csv file + csv_fpath = Path(driver_config["driver"]["design_of_experiments"]["filename"]).absolute() + # run the csv checker method, we want it to write the csv file to a new filepath so + # set overwrite_file=False + new_csv_filename = check_file_format_for_csv_generator( + csv_fpath, driver_config, check_only=False, overwrite_file=False + ) + + # update the csv filename in the driver config dictionary + updated_driver = update_defaults(driver_config["driver"], "filename", new_csv_filename.name) + driver_config["driver"].update(updated_driver) + + # save the updated driver to a new file + new_driver_fpath = example_folder / "driver_config_test.yaml" + new_toplevel_fpath = example_folder / "20_solar_electrolyzer_doe_test.yaml" + write_yaml(driver_config, new_driver_fpath) + + # update the driver config filename in the top-level config + main_config = load_yaml("20_solar_electrolyzer_doe.yaml") + main_config["driver_config"] = new_driver_fpath.name + + # save the updated top-level config file to a new file + write_yaml(main_config, new_toplevel_fpath) + + # Run the model + model = H2IntegrateModel(new_toplevel_fpath) + model.run() + + # summarize sql file + model.post_process(summarize_sql=True) + + with subtests.test("Check that sql file was summarized"): + assert model.recorder_path is not None + summarized_filepath = model.recorder_path.parent / f"{model.recorder_path.stem}.csv" + assert summarized_filepath.is_file() + with subtests.test("Check that sql summary file was written as expected"): + summary = pd.read_csv(summarized_filepath, index_col="Unnamed: 0") + assert len(summary) == 10 + d_var_cols = ["solar.system_capacity_DC (kW)", "electrolyzer.n_clusters (unitless)"] + assert summary.columns.to_list()[0] in d_var_cols + assert summary.columns.to_list()[1] in d_var_cols + assert "finance_subgroup_hydrogen.LCOH_optimistic (USD/kg)" in summary.columns.to_list() + # delete summary file + summarized_filepath.unlink() + + sql_fpath = example_folder / "ex_20_out" / "cases.sql" + cr = om.CaseReader(str(sql_fpath)) + cases = list(cr.get_cases()) + + with subtests.test("Check solar capacity in case 0"): + assert ( + pytest.approx(cases[0].get_val("solar.system_capacity_DC", units="MW"), rel=1e-6) + == 25.0 + ) + with subtests.test("Check solar capacity in case 9"): + assert ( + pytest.approx(cases[-1].get_val("solar.system_capacity_DC", units="MW"), rel=1e-6) + == 500.0 + ) + + with subtests.test("Check electrolyzer capacity in case 0"): + assert ( + pytest.approx( + cases[0].get_val("electrolyzer.electrolyzer_size_mw", units="MW"), rel=1e-6 + ) + == 10.0 * 5 + ) + + with subtests.test("Check electrolyzer capacity in case 9"): + assert ( + pytest.approx( + cases[-1].get_val("electrolyzer.electrolyzer_size_mw", units="MW"), rel=1e-6 + ) + == 10.0 * 10 + ) + + min_lcoh_val = 100000.0 + min_lcoh_case_num = 0 + for i, case in enumerate(cases): + lcoh = case.get_val("finance_subgroup_hydrogen.LCOH_optimistic", units="USD/kg")[0] + if lcoh < min_lcoh_val: + min_lcoh_val = np.min([lcoh, min_lcoh_val]) + min_lcoh_case_num = i + + with subtests.test("Min LCOH value"): + assert pytest.approx(min_lcoh_val, rel=1e-6) == 4.663014422338 + + with subtests.test("Min LCOH case number"): + assert min_lcoh_case_num == 6 + + with subtests.test("Min LCOH case LCOH value"): + assert ( + pytest.approx( + cases[min_lcoh_case_num].get_val( + "finance_subgroup_hydrogen.LCOH_optimistic", units="USD/kg" + ), + rel=1e-6, + ) + == min_lcoh_val + ) + + with subtests.test("Min LCOH case has lower LCOH than other cases"): + for i, case in enumerate(cases): + lcoh_case = case.get_val("finance_subgroup_hydrogen.LCOH_optimistic", units="USD/kg") + if i != min_lcoh_case_num: + assert lcoh_case > min_lcoh_val + + with subtests.test("Min LCOH solar capacity"): + assert ( + pytest.approx( + cases[min_lcoh_case_num].get_val("solar.system_capacity_DC", units="MW"), rel=1e-6 + ) + == 200.0 + ) + + with subtests.test("Min LCOH electrolyzer capacity"): + assert ( + pytest.approx( + cases[min_lcoh_case_num].get_val("electrolyzer.electrolyzer_size_mw", units="MW"), + rel=1e-6, + ) + == 100.0 + ) + + # remove files created + new_driver_fpath.unlink() + new_toplevel_fpath.unlink() + new_csv_filename.unlink() + + +@pytest.mark.integration +@pytest.mark.parametrize("example_folder,resource_example_folder", [("22_site_doe", None)]) +def test_sweeping_solar_sites_doe(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create the model + model = H2IntegrateModel(example_folder / "22_solar_site_doe.yaml") + + # Run the model + model.run() + + # Specify the filepath to the sql file, the folder and filename are in the driver_config + sql_fpath = example_folder / "ex_22_out" / "cases.sql" + + # load the cases + cr = om.CaseReader(sql_fpath) + + cases = list(cr.get_cases()) + + res_df = pd.DataFrame() + for ci, case in enumerate(cases): + solar_resource_data = case.get_val("site.solar_resource.solar_resource_data") + lat_lon = ( + f"{case.get_val('site.latitude', units='deg')[0]} " + f"{case.get_val('site.longitude', units='deg')[0]}" + ) + solar_capacity = case.get_design_vars()["solar.system_capacity_DC"][0] + aep = case.get_val("solar.annual_electricity_produced", units="MW*h/yr")[0] + lcoe = case.get_val("finance_subgroup_electricity.LCOE_optimistic", units="USD/(MW*h)")[0] + + site_res = pd.DataFrame( + [aep, lcoe, solar_capacity], index=["AEP", "LCOE", "solar_capacity"], columns=[lat_lon] + ).T + res_df = pd.concat([site_res, res_df], axis=0) + + with subtests.test(f"Case {ci}: Solar resource latitude matches site latitude"): + assert ( + pytest.approx(case.get_val("site.latitude", units="deg"), abs=0.1) + == solar_resource_data["site_lat"] + ) + with subtests.test(f"Case {ci}: Solar resource longitude matches site longitude"): + assert ( + pytest.approx(case.get_val("site.longitude", units="deg"), abs=0.1) + == solar_resource_data["site_lon"] + ) + + locations = list(set(res_df.index.to_list())) + solar_sizes = list(set(res_df["solar_capacity"].to_list())) + + with subtests.test("Two solar sizes per site"): + assert len(solar_sizes) == 2 + with subtests.test("Two unique sites"): + assert len(locations) == 2 + + with subtests.test("Unique AEPs per case"): + assert len(list(set(res_df["AEP"].to_list()))) == len(res_df) + + with subtests.test("Unique LCOEs per case"): + 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): + example_folder = temp_copy_of_example + + driver_config = load_yaml(example_folder / "driver_config.yaml") + tech_config = load_yaml(example_folder / "tech_config.yaml") + plant_config = load_yaml(example_folder / "plant_config.yaml") + + h2i_config = { + "name": "H2Integrate_config", + "system_summary": "", + "driver_config": driver_config, + "technology_config": tech_config, + "plant_config": plant_config, + } + + # Create a H2I model + h2i = H2IntegrateModel(h2i_config) + + # Run the model + h2i.run() + + with subtests.test("Distributed LCOE"): + assert ( + pytest.approx( + h2i.prob.get_val("finance_subgroup_distributed.LCOE", units="USD/MW/h")[0], rel=1e-6 + ) + == 99.872209 + ) + with subtests.test("Utility LCOE"): + assert ( + pytest.approx( + h2i.prob.get_val("finance_subgroup_utility.LCOE", units="USD/MW/h")[0], rel=1e-6 + ) + == 54.2709437311 + ) + + with subtests.test("Total LCOE"): + assert ( + pytest.approx( + h2i.prob.get_val("finance_subgroup_total_electricity.LCOE", units="USD/MW/h")[0], + rel=1e-6, + ) + == 65.2444127137 + ) + + with subtests.test("Distributed wind plant capacity"): + assert ( + pytest.approx( + h2i.prob.get_val("distributed_wind_plant.rated_electricity_production", units="MW"), + rel=1e-6, + ) + == 66.0 + ) + + with subtests.test("Total distributed electricity production"): + assert ( + pytest.approx( + np.sum( + h2i.prob.get_val( + "distributed_wind_plant.total_electricity_produced", units="MW*h" + ) + ), + rel=1e-6, + ) + == 128948.21977 + ) + + with subtests.test("Total utility electricity production"): + assert ( + pytest.approx( + h2i.prob.get_val("utility_wind_plant.electricity_out", units="MW").sum(), rel=1e-6 + ) + == 406908.03381618496 + ) + + with subtests.test("Distributed wind capacity factor"): + assert ( + pytest.approx( + h2i.prob.get_val("distributed_wind_plant.capacity_factor", units="percent")[0], + rel=1e-6, + ) + == 22.30320668 + ) + + with subtests.test("Utility wind plant capacity"): + assert ( + pytest.approx( + h2i.prob.get_val("utility_wind_plant.rated_electricity_production", units="MW"), + rel=1e-6, + ) + == 120.0 + ) + + with subtests.test("Distributed wind site location"): + assert ( + pytest.approx(h2i.prob.get_val("distributed_wind_site.latitude", units="deg"), rel=1e-6) + == 44.04218 + ) + assert ( + pytest.approx( + h2i.prob.get_val("distributed_wind_site.longitude", units="deg"), rel=1e-6 + ) + == -95.19757 + ) + + with subtests.test("Distributed wind plant resource location"): + assert ( + pytest.approx( + h2i.prob.get_val("distributed_wind_plant.wind_resource_data")["site_lat"], abs=1e-2 + ) + == 44.04218 + ) + assert ( + pytest.approx( + h2i.prob.get_val("distributed_wind_plant.wind_resource_data")["site_lon"], abs=1e-2 + ) + == -95.19757 + ) + + with subtests.test("Utility wind site location"): + assert ( + pytest.approx(h2i.prob.get_val("utility_wind_site.latitude", units="deg"), rel=1e-6) + == 35.2018863 + ) + assert ( + pytest.approx(h2i.prob.get_val("utility_wind_site.longitude", units="deg"), rel=1e-6) + == -101.945027 + ) + + with subtests.test("Utility wind plant resource location"): + assert ( + pytest.approx( + h2i.prob.get_val("utility_wind_plant.wind_resource_data")["site_lat"], abs=1e-2 + ) + == 35.2018863 + ) + assert ( + pytest.approx( + h2i.prob.get_val("utility_wind_plant.wind_resource_data")["site_lon"], abs=1e-2 + ) + == -101.945027 + ) + + +@pytest.mark.integration +@pytest.mark.parametrize( + "example_folder,resource_example_folder", [("24_solar_battery_grid", "11_hybrid_energy_plant/")] +) +def test_24_solar_battery_grid_example(subtests, temp_copy_of_example): + # NOTE: would be good to compare LCOE against the same example without grid selling + # and see that LCOE reduces with grid selling + example_folder = temp_copy_of_example + + model = H2IntegrateModel(example_folder / "solar_battery_grid.yaml") + + model.run() + + model.post_process() + + energy_for_financials = ( + model.prob.get_val("finance_subgroup_renewables.rated_electricity_production", units="kW")[ + 0 + ] + * model.prob.get_val("finance_subgroup_renewables.capacity_factor", units="unitless").mean() + * 8760 + ) + + electricity_bought = sum(model.prob.get_val("grid_buy.electricity_out", 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("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")) + + with subtests.test("Behavior check battery missed load is electricity bought"): + assert pytest.approx(battery_missed_load, rel=1e-6) == electricity_bought + + with subtests.test("Behavior check battery curtailed energy is electricity sold"): + assert pytest.approx(battery_curtailed, rel=1e-6) == electricity_sold + + with subtests.test( + "Behavior check energy for financials; include solar aep and electricity bought" + ): + assert pytest.approx(energy_for_financials, rel=1e-6) == (solar_aep + electricity_bought) + + with subtests.test("Value check on LCOE"): + lcoe = model.prob.get_val("finance_subgroup_renewables.LCOE", units="USD/(MW*h)")[0] + assert pytest.approx(lcoe, rel=1e-4) == 91.7057887 + + +@pytest.mark.integration +@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 + + from h2integrate.postprocess.mapping import ( + plot_geospatial_point_heat_map, + plot_straight_line_shipping_routes, + ) + + example_folder = temp_copy_of_example + + # Define filepaths + 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_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="finance_subgroup_sponge_iron.LCOS (USD/kg)", + map_preferences={ + "figsize": (10, 8), + "colorbar_label": "Levelized Cost of\nIron [$/kg]", + "colorbar_limits": (0.6, 1.0), + }, + ) + # Add a layer for example ore cost prices from select mines + fig, ax, ore_cost_layer_gdf = plot_geospatial_point_heat_map( + case_results_fpath=ore_prices_filepath, + metric_to_plot="ore_cost_per_kg", + map_preferences={ + "colormap": "Greens", + "marker": "o", + "colorbar_bbox_to_anchor": (0.025, 0.97, 1, 1), + "colorbar_label": "Levelized Cost of\nIron Ore Pellets\n[$/kg ore]", + "colorbar_limits": (0.11, 0.14), + }, + fig=fig, + ax=ax, + base_layer_gdf=lcoi_layer_gdf, + ) + # Add a layer for example waterway shipping cost from select mines to select ports + fig, ax, shipping_cost_layer_gdf = plot_geospatial_point_heat_map( + case_results_fpath=shipping_prices_filepath, + metric_to_plot="shipping_cost_per_kg", + map_preferences={ + "colormap": "Greys", + "marker": "d", + "markersize": 80, + "colorbar_bbox_to_anchor": (0.4, 0.97, 1, 1), + "colorbar_label": "Waterway Shipping Cost\n[$/kg ore]", + "colorbar_limits": (0.11, 0.14), + }, + fig=fig, + ax=ax, + base_layer_gdf=[lcoi_layer_gdf, ore_cost_layer_gdf], + ) + + # Define example water way shipping routes for plotting straight line transport + cleveland_route = [ + "Duluth", + "Keweenaw", + "Sault St Marie", + "De Tour", + "Lake Huron", + "Port Huron", + "Erie", + "Cleveland", + ] + buffalo_route = [ + "Duluth", + "Keweenaw", + "Sault St Marie", + "De Tour", + "Lake Huron", + "Port Huron", + "Erie", + "Cleveland", + "Buffalo", + ] + chicago_route = [ + "Duluth", + "Keweenaw", + "Sault St Marie", + "De Tour", + "Mackinaw", + "Manistique", + "Chicago", + ] + + # Add cleveland route as layer + fig, ax, transport_layer1_gdf = plot_straight_line_shipping_routes( + shipping_coords_fpath=shipping_coords_filepath, + shipping_route=cleveland_route, + map_preferences={}, + fig=fig, + ax=ax, + base_layer_gdf=[lcoi_layer_gdf, ore_cost_layer_gdf, shipping_cost_layer_gdf], + ) + # Add buffalo route as layer + fig, ax, transport_layer2_gdf = plot_straight_line_shipping_routes( + shipping_coords_fpath=shipping_coords_filepath, + shipping_route=buffalo_route, + map_preferences={}, + fig=fig, + ax=ax, + base_layer_gdf=[ + lcoi_layer_gdf, + ore_cost_layer_gdf, + shipping_cost_layer_gdf, + transport_layer1_gdf, + ], + ) + # Add chicago route as layer + fig, ax, transport_layer3_gdf = plot_straight_line_shipping_routes( + shipping_coords_fpath=shipping_coords_filepath, + shipping_route=chicago_route, + map_preferences={"figure_title": "Example H2 DRI Iron Costs"}, + fig=fig, + ax=ax, + base_layer_gdf=[ + lcoi_layer_gdf, + ore_cost_layer_gdf, + shipping_cost_layer_gdf, + transport_layer1_gdf, + transport_layer2_gdf, + ], + save_plot_fpath=ex_png_fpath, + ) + + with subtests.test("Type check on fig, ax, and lcoi_layer_gdf"): + assert isinstance( + fig, matplotlib.figure.Figure + ), f"Expected matplotlib.figure.Figure but got{type(fig)}" + assert isinstance( + ax, matplotlib.axes._axes.Axes + ), f"Expected matplotlib.axes._axes.Axes but got{type(ax)}" + assert isinstance( + lcoi_layer_gdf, gpd.geodataframe.GeoDataFrame + ), f"Expected gpd.geodataframe.GeoDataFrame but got{type(lcoi_layer_gdf)}" + assert isinstance( + lcoi_layer_gdf, gpd.geodataframe.GeoDataFrame + ), f"Expected gpd.geodataframe.GeoDataFrame but got{type(transport_layer1_gdf)}" + + with subtests.test("Check example_28_iron_map.png was saved"): + assert (ex_png_fpath).is_file(), "example_28_iron_map.png file not found" + + # Clean up any output files/dirs created + ex_png_fpath.unlink(missing_ok=True) + + +@pytest.mark.integration +@pytest.mark.parametrize("example_folder,resource_example_folder", [("04_geo_h2", None)]) +def test_natural_geoh2(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + h2i_nat = H2IntegrateModel(example_folder / "04_geo_h2_natural.yaml") + h2i_nat.run() + + with subtests.test("H2 Production"): + assert ( + pytest.approx( + np.mean(h2i_nat.model.get_val("geoh2_well_subsurface.hydrogen_out", units="kg/h")), + rel=1e-6, + ) + == 606.1508855232839 + ) + + with subtests.test("integrated LCOH"): + assert ( + pytest.approx( + h2i_nat.prob.get_val("finance_subgroup_h2.LCOH", units="USD/kg"), rel=1e-6 + ) + == 1.3089029 + ) + with subtests.test("subsurface Capex"): + assert ( + pytest.approx( + h2i_nat.model.get_val("geoh2_well_subsurface.CapEx", units="USD"), rel=1e-6 + ) + == 7667341.11417252 + ) + with subtests.test("subsurface fixed Opex"): + assert ( + pytest.approx( + h2i_nat.model.get_val("geoh2_well_subsurface.OpEx", units="USD/year"), rel=1e-6 + ) + == 215100.7857875 + ) + with subtests.test("subsurface variable Opex"): + assert ( + pytest.approx( + h2i_nat.model.get_val("geoh2_well_subsurface.VarOpEx", units="USD/year"), + rel=1e-6, + ) + == 0.0 + ) + with subtests.test("subsurface adjusted opex"): + adjusted_opex = h2i_nat.prob.get_val( + "finance_subgroup_h2.opex_adjusted_geoh2_well_subsurface", units="USD/year" + ) + assert pytest.approx(adjusted_opex, rel=1e-6) == 215100.7857875 + + with subtests.test("surface Capex"): + assert ( + pytest.approx(h2i_nat.model.get_val("geoh2_well_surface.CapEx", units="USD"), rel=1e-6) + == 1800711.83796 + ) + with subtests.test("surface fixed Opex"): + assert ( + pytest.approx( + h2i_nat.model.get_val("geoh2_well_surface.OpEx", units="USD/year"), rel=1e-6 + ) + == 4567464 + ) + with subtests.test("surface variable Opex"): + assert ( + pytest.approx( + h2i_nat.model.get_val("geoh2_well_surface.VarOpEx", units="USD/year"), rel=1e-6 + ) + == 989213.8787 + ) + with subtests.test("surface adjusted opex"): + surface_adjusted_opex = h2i_nat.prob.get_val( + "finance_subgroup_h2.opex_adjusted_geoh2_well_surface", units="USD/year" + ) + assert pytest.approx(surface_adjusted_opex, rel=1e-6) == 4798691.865 + + +@pytest.mark.integration +@pytest.mark.parametrize("example_folder,resource_example_folder", [("04_geo_h2", None)]) +def test_stimulated_geoh2(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + h2i_stim = H2IntegrateModel(example_folder / "04_geo_h2_stimulated.yaml") + h2i_stim.run() + + h2_prod = h2i_stim.model.get_val("geoh2_well_subsurface.hydrogen_out", units="kg/h") + + with subtests.test("H2 Production"): + assert pytest.approx(np.mean(h2_prod), rel=1e-6) == 155.03934945719536 + + with subtests.test("integrate LCOH"): + lcoh = h2i_stim.prob.get_val("finance_subgroup_default.LCOH", units="USD/kg") + assert lcoh == pytest.approx( + 2.29337734, 1e-6 + ) # previous val from custom finance model was 1.74903827 + + # failure is expected because we are inflating using general inflation rather than CPI and CEPCI + with subtests.test("Capex"): + assert ( + pytest.approx( + h2i_stim.model.get_val("geoh2_well_subsurface.CapEx", units="USD"), rel=1e-6 + ) + == 19520122.88478073 + ) + with subtests.test("fixed Opex"): + assert ( + pytest.approx( + h2i_stim.model.get_val("geoh2_well_subsurface.OpEx", units="USD/year"), rel=1e-6 + ) + == 215100.7857875 + ) + with subtests.test("variable Opex"): + var_om_pr_h2 = h2i_stim.model.get_val( + "geoh2_well_subsurface.VarOpEx", units="USD/year" + ) / np.sum(h2_prod) + assert pytest.approx(var_om_pr_h2, rel=1e-6) == 0.32105362 + with subtests.test("adjusted Opex"): + adjusted_opex = h2i_stim.prob.get_val( + "finance_subgroup_default.opex_adjusted_geoh2_well_subsurface", units="USD/year" + ) + assert pytest.approx(adjusted_opex, rel=1e-6) == 215100.7857875 + + +@pytest.mark.integration +@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 / "single_site_iron.yaml") + + h2i.run() + + with subtests.test("Value check on LCOI"): + lcoi = h2i.model.get_val("finance_subgroup_iron_ore.LCOI", units="USD/t")[0] + assert pytest.approx(lcoi, rel=1e-4) == 135.3741358811098 + + with subtests.test("Value check on LCOS"): + lcos = h2i.model.get_val("finance_subgroup_sponge_iron.LCOS", units="USD/t")[0] + assert pytest.approx(lcos, rel=1e-4) == 359.670379351 + + with subtests.test("Value check on LCOS"): + lcos = h2i.model.get_val("finance_subgroup_steel.LCOS", units="USD/t")[0] + assert pytest.approx(lcos, rel=1e-4) == 531.5842266865 + + +@pytest.mark.integration +@pytest.mark.parametrize( + "example_folder,resource_example_folder", [("21_iron_examples/iron_electrowinning", None)] +) +def test_iron_electrowinning_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + model = H2IntegrateModel(example_folder / "iron_electrowinning.yaml") + + with subtests.test("Value check on AHE"): + model.technology_config["technologies"]["iron_plant"]["model_inputs"]["shared_parameters"][ + "electrolysis_type" + ] = "ahe" + model.setup() + model.run() + lcoi = model.model.get_val("finance_subgroup_sponge_iron.LCOS", units="USD/kg")[0] + assert pytest.approx(lcoi, rel=1e-4) == 2.187185703820872 + + with subtests.test("Value check on MSE"): + model.technology_config["technologies"]["iron_plant"]["model_inputs"]["shared_parameters"][ + "electrolysis_type" + ] = "mse" + model.technology_config["technologies"]["ewin_NaOH_feedstock"]["model_inputs"][ + "performance_parameters" + ]["rated_capacity"] = 0 + model.technology_config["technologies"]["ewin_CaCl2_feedstock"]["model_inputs"][ + "performance_parameters" + ]["rated_capacity"] = 179.0 + + model.setup() + model.run() + lcoi = model.model.get_val("finance_subgroup_sponge_iron.LCOS", units="USD/kg")[0] + assert pytest.approx(lcoi, rel=1e-4) == 3.3399342887615115 + + with subtests.test("Value check on MOE"): + model.technology_config["technologies"]["iron_plant"]["model_inputs"]["shared_parameters"][ + "electrolysis_type" + ] = "moe" + model.technology_config["technologies"]["ewin_NaOH_feedstock"]["model_inputs"][ + "performance_parameters" + ]["rated_capacity"] = 0 + model.setup() + model.run() + lcoi = model.model.get_val("finance_subgroup_sponge_iron.LCOS", units="USD/kg")[0] + assert pytest.approx(lcoi, rel=1e-4) == 2.2802793527655987 + + +@pytest.mark.integration +@pytest.mark.parametrize("example_folder,resource_example_folder", [("27_site_doe_diff", None)]) +def test_sweeping_different_resource_sites_doe(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create the model + model = H2IntegrateModel(example_folder / "27_wind_solar_site_doe.yaml") + + # # Run the model + model.run() + + # Specify the filepath to the sql file, the folder and filename are in the driver_config + sql_fpath = example_folder / "ex_27_out" / "cases.sql" + + # load the cases + cr = om.CaseReader(sql_fpath) + + cases = list(cr.get_cases()) + + res_df = pd.DataFrame() + for ci, case in enumerate(cases): + solar_resource_data = case.get_val("solar_site.solar_resource.solar_resource_data") + wind_resource_data = case.get_val("wind_site.wind_resource.wind_resource_data") + with subtests.test(f"Case {ci}: Solar resource latitude matches site latitude"): + assert ( + pytest.approx( + case.get_val("solar_site.solar_resource.latitude", units="deg"), abs=0.1 + ) + == solar_resource_data["site_lat"] + ) + with subtests.test(f"Case {ci}: Wind resource latitude matches site latitude"): + assert ( + pytest.approx( + case.get_val("wind_site.wind_resource.latitude", units="deg"), abs=0.1 + ) + == wind_resource_data["site_lat"] + ) + + s_lat = case.get_val("solar_site.solar_resource.latitude", units="deg")[0] + s_lon = case.get_val("solar_site.solar_resource.longitude", units="deg")[0] + solar_lat_lon = f"{s_lat} {s_lon}" + w_lat = case.get_val("wind_site.wind_resource.latitude", units="deg")[0] + w_lon = case.get_val("wind_site.wind_resource.longitude", units="deg")[0] + wind_lat_lon = f"{w_lat} {w_lon}" + + solar_capacity = case.get_design_vars()["solar.system_capacity_DC"][0] + + solar_aep = np.sum(case.get_val("solar.electricity_out", units="MW")) + solar_lcoe = case.get_val("finance_subgroup_solar.LCOE", units="USD/(MW*h)")[0] + + wind_aep = np.sum(case.get_val("wind.electricity_out", units="MW")) + wind_lcoe = case.get_val("finance_subgroup_wind.LCOE", units="USD/(MW*h)")[0] + + combiner_aep = np.sum(case.get_val("combiner.electricity_out", units="MW")) + combiner_lcoe = case.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0] + + index_cols = [ + "solar site", + "wind site", + "solar AEP", + "solar LCOE", + "solar size", + "wind AEP", + "wind LCOE", + "combiner AEP", + "combiner LCOE", + ] + vals = [ + solar_lat_lon, + wind_lat_lon, + solar_aep, + solar_lcoe, + solar_capacity, + wind_aep, + wind_lcoe, + combiner_aep, + combiner_lcoe, + ] + + site_res = pd.DataFrame(vals, index=index_cols, columns=[ci]).T + + res_df = pd.concat([site_res, res_df], axis=0) + + with subtests.test("Two unique solar capacities"): + solar_sizes = list(set(res_df["solar site"].to_list())) + assert len(solar_sizes) == 2 + + with subtests.test("Two unique solar sites"): + solar_locations = list(set(res_df["solar site"])) + assert len(solar_locations) == 2 + + with subtests.test("Two unique wind sites"): + wind_locations = list(set(res_df["wind site"])) + assert len(wind_locations) == 2 + + with subtests.test("Unique solar AEPS"): + assert len(list(set(res_df["solar AEP"].to_list()))) == 4 + + with subtests.test("Unique solar LCOEs"): + assert len(list(set(res_df["solar LCOE"].to_list()))) == 4 + + with subtests.test("Unique wind AEPS"): + assert len(list(set(res_df["wind AEP"].to_list()))) == 2 + + with subtests.test("Unique wind LCOEs"): + assert len(list(set(res_df["wind LCOE"].to_list()))) == 2 + + with subtests.test("Unique combiner AEPS"): + assert len(list(set(res_df["combiner AEP"].to_list()))) == len(res_df) + + with subtests.test("Unique LCOEs per case"): + assert len(list(set(res_df["combiner LCOE"].to_list()))) == len(res_df) + + +@pytest.mark.integration +@pytest.mark.parametrize( + "example_folder,resource_example_folder", [("30_pyomo_optimized_dispatch", None)] +) +def test_pyomo_optimized_dispatch_example(subtests, temp_copy_of_example): + example_folder = temp_copy_of_example + + # Create a H2Integrate model + model = H2IntegrateModel(example_folder / "pyomo_optimized_dispatch.yaml") + + demand_profile = np.ones(8760) * 100.0 + + # 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() + + model.post_process() + + with subtests.test("Check wind total electricity produced"): + wind_total = model.prob.get_val("wind.total_electricity_produced", units="kW*h")[0] + assert wind_total == pytest.approx(781_472_811.8, rel=1e-3) + + with subtests.test("Check wind capacity factor"): + wind_cf = model.prob.get_val("wind.capacity_factor", units="unitless")[0] + assert wind_cf == pytest.approx(0.4299, rel=1e-3) + + with subtests.test("Check wind CapEx"): + wind_capex = model.prob.get_val("wind.CapEx", units="USD")[0] + assert wind_capex == pytest.approx(311_250_000.0, rel=1e-3) + + # Battery checks + with subtests.test("Check battery total electricity produced"): + 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("electrical_load_demand.capacity_factor", units="unitless")[ + 0 + ] + assert battery_cf == pytest.approx(0.7372, rel=1e-3) + + with subtests.test("Check battery CapEx"): + battery_capex = model.prob.get_val("battery.CapEx", units="USD")[0] + assert battery_capex == pytest.approx(155_100_000.0, rel=1e-3) + + with subtests.test("Check battery OpEx"): + battery_opex = model.prob.get_val("battery.OpEx", units="USD/year")[0] + assert battery_opex == pytest.approx(38_775_000.0, rel=1e-3) + + # Finance checks + with subtests.test("Check LCOE"): + lcoe = model.prob.get_val("finance_subgroup_all_electricity.LCOE", units="USD/(kW*h)")[0] + assert lcoe == pytest.approx(0.134, rel=1e-3) + + with subtests.test("Check total adjusted CapEx"): + total_capex = model.prob.get_val( + "finance_subgroup_all_electricity.total_capex_adjusted", units="USD" + )[0] + assert total_capex == pytest.approx(490_282_207.03, rel=1e-3) + + with subtests.test("Check total adjusted OpEx"): + total_opex = model.prob.get_val( + "finance_subgroup_all_electricity.total_opex_adjusted", units="USD/year" + )[0] + assert total_opex == pytest.approx(48_830_466.21, rel=1e-3) + + with subtests.test("Check total electricity produced"): + total_electricity = ( + model.prob.get_val( + "finance_subgroup_all_electricity.rated_electricity_production", + units="kW", + )[0] + * model.prob.get_val( + "finance_subgroup_all_electricity.capacity_factor", + units="unitless", + ).mean() + * 8760 + ) + assert total_electricity == pytest.approx(781_472_811.8, rel=1e-3) + + with subtests.test("Check electricity price"): + price = model.prob.get_val( + "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) From eb9b449490b30416ea325355ec74d64fef6445c7 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Thu, 23 Apr 2026 13:21:14 -0600 Subject: [PATCH 12/55] Update docs and add figure --- docs/control/pyomo_controllers.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/control/pyomo_controllers.md b/docs/control/pyomo_controllers.md index ac6a1ddc0..cece55d33 100644 --- a/docs/control/pyomo_controllers.md +++ b/docs/control/pyomo_controllers.md @@ -61,13 +61,13 @@ tech_to_dispatch_connections: [ # Optimized Demand Response Controller -This controller optimizes the dispatch of a Battery Energy Storage System (BESS) based on a pre-defined supervisory signal. This pre-defined signal could be the Locational Marginal Price (LMP) or a demand profile. It could also be a $LMP\times demand$ depending on the application. +This controller optimizes the dispatch of a Battery Energy Storage System (BESS) based on a pre-defined supervisory signal. This signal could be the Locational Marginal Price (LMP), a demand profile, or a $LMP\times demand$ product depending on the application. The objective is to maximize incentive payments to the battery, subject to constraints on the maximum number of dispatch events per month and on the battery state of charge. ## Definitions **Given:** - $\lambda_t$ := `supervisory_signal`: price, demand, or price $\times$ demand time series at time $t$ -- $\mathcal{W}$ := `peak_window`: set of hours eligible for dispatch (e.g., 12:00--19:00) +- $\mathcal{W}$ := `peak_window`: set of hours eligible for d`ispatch (e.g., 12:00--19:00) - $\gamma$ := performance incentive (\$/kW per dispatch hour) - $\bar{P}$ := `max_charge_rate` (kW): maximum charge and discharge rate, used as deemed capacity since the battery is assumed to always dispatch at full rated power - $E_{\max} :=$ `max_capacity` $\times$ (`max_soc_fraction` $-$ `min_soc_fraction`): usable energy capacity (kWh) @@ -145,3 +145,4 @@ $$ $$ u_t \in \{0, 1\}, \quad v_t \in \{0, 1\}, \quad \text{SoC}_t \in [0, 1] \qquad \forall\, t, m $$ +![](./figures/plm_optimized_dispatch.png) From 053e5868719aaff3ec55f430e1f5b0ab5ec21ac1 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Thu, 23 Apr 2026 16:12:20 -0600 Subject: [PATCH 13/55] Edit docs page --- docs/control/pyomo_controllers.md | 8 ++-- .../run_plm_optimized_dispatch.py | 38 +++---------------- .../plm_optimized_storage_controller.py | 11 +++--- 3 files changed, 15 insertions(+), 42 deletions(-) diff --git a/docs/control/pyomo_controllers.md b/docs/control/pyomo_controllers.md index cece55d33..4fdcdf233 100644 --- a/docs/control/pyomo_controllers.md +++ b/docs/control/pyomo_controllers.md @@ -67,7 +67,7 @@ This controller optimizes the dispatch of a Battery Energy Storage System (BESS) **Given:** - $\lambda_t$ := `supervisory_signal`: price, demand, or price $\times$ demand time series at time $t$ -- $\mathcal{W}$ := `peak_window`: set of hours eligible for d`ispatch (e.g., 12:00--19:00) +- $\mathcal{W}$ := `peak_window`: set of hours eligible for dispatch (e.g., 12:00--19:00) - $\gamma$ := performance incentive (\$/kW per dispatch hour) - $\bar{P}$ := `max_charge_rate` (kW): maximum charge and discharge rate, used as deemed capacity since the battery is assumed to always dispatch at full rated power - $E_{\max} :=$ `max_capacity` $\times$ (`max_soc_fraction` $-$ `min_soc_fraction`): usable energy capacity (kWh) @@ -110,10 +110,10 @@ $$ where $\lambda^*_m$ is the threshold selecting the high LMP/peak load hours within month $m$. -- Maximum 10 events per month: +- Maximum $N_{max}$ events per month: $$ -\sum_{t \in \mathcal{M}_m} u_t \leq N_{\max} \qquad \forall\, m, \quad N_{\max} = 10 +\sum_{t \in \mathcal{M}_m} u_t \leq N_{\max} \qquad \forall\, m, \quad N_{\max}. $$ - SoC evolution with charge and discharge: @@ -145,4 +145,6 @@ $$ $$ u_t \in \{0, 1\}, \quad v_t \in \{0, 1\}, \quad \text{SoC}_t \in [0, 1] \qquad \forall\, t, m $$ +Example 34 performs the optimization with a synthetic LMP signal. The look-ahead horizon is set to 10 hours. As this value increases, the computational complexity grows and the solver may take significantly longer to run or fail to converge. Care should be taken when choosing this parameter: a short horizon limits visibility, making it difficult for the optimizer to identify the best dispatch opportunities across the full month. See figure below for the results. + ![](./figures/plm_optimized_dispatch.png) diff --git a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py index 3dff042d2..547d030bc 100644 --- a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py +++ b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py @@ -45,11 +45,11 @@ peak_mask = (time_index.hour >= 14) & (time_index.hour <= 18) threshold_pct = np.percentile(lmp, percentile) -discharge_mask = battery_power > 1.0 +discharge_mask = battery_power > 0 plt.rcParams.update({"axes.spines.top": False, "axes.spines.right": False}) -fig, axes = plt.subplots(3, 1, sharex=True, figsize=(11, 7)) +fig, axes = plt.subplots(2, 1, sharex=True, figsize=(11, 7)) days = pd.date_range(time_index[0].normalize(), periods=14, freq="D", tz=time_index.tz) time_window = 14 * 24 @@ -71,7 +71,7 @@ def shade_peaks(ax): ax = axes[0] shade_peaks(ax) ax.plot(time_index[:time_window], lmp[:time_window], color="steelblue", linewidth=1.0) -ax.axhline(threshold_pct, color="crimson", linestyle="--", linewidth=0.8) +ax.axhline(threshold_pct, color="k", linestyle="--", linewidth=0.8) ax.plot( time_index[:time_window][w_discharge], lmp[:time_window][w_discharge], @@ -82,42 +82,14 @@ def shade_peaks(ax): ax.set_ylabel("LMP ($/MWh)", fontsize=8) ax.set_ylim(bottom=0) -# Panel 2: SOC ax = axes[1] -ax.plot( - time_index[:time_window], soc_pct[:time_window], color="tab:green", linewidth=1.0 -) +shade_peaks(ax) +ax.plot(time_index[:time_window], soc_pct[:time_window], color="g", linewidth=1.0) ax.axhline(90, color="gray", linestyle=":", linewidth=0.7) ax.axhline(10, color="gray", linestyle=":", linewidth=0.7) ax.set_ylabel("SOC (%)", fontsize=8) ax.set_ylim([0, 105]) -ax = axes[2] -shade_peaks(ax) -ax.axhline(0, color="black", linewidth=0.4) -width = pd.Timedelta(hours=0.8) -pos_mask = (battery_power > 1)[:time_window] -neg_mask = (battery_power < -1)[:time_window] -ax.bar( - time_index[:time_window][pos_mask], - battery_power[:time_window][pos_mask] * 1e-3, - width=width, - color="tab:red", - alpha=0.8, -) -ax.bar( - time_index[:time_window][neg_mask], - battery_power[:time_window][neg_mask] * 1e-3, - width=width, - color="tab:blue", - alpha=0.8, -) -ax.set_ylabel("Power (MW)", fontsize=8) -ax.tick_params(axis="x", labelrotation=30, labelsize=7) - -for ax in axes: - ax.tick_params(labelsize=7) - ax.grid(True, which="major", alpha=0.3, linewidth=0.5) plt.tight_layout() plt.savefig("plm_optimized_dispatch.png", dpi=150, bbox_inches="tight") diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index 3e0524801..80dc9f1e6 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -45,7 +45,7 @@ class PLMOptimizedControllerConfig(PyomoStorageControllerBaseConfig): Defaults to 10. n_control_window (int): Number of timesteps per rolling solve window. Defaults to ``24 * 30`` (one month of hourly data). - signal_threshold_percentile (float): Percentile (0–100) used to + signal_threshold_percentile (float): Percentile (0-100) used to compute the signal threshold for each rolling window. Only hours at or above this percentile of the window signal are eligible for dispatch. Defaults to 0.0 (all hours eligible). @@ -179,7 +179,7 @@ def _compute_peak_window_mask(self) -> np.ndarray: return np.array([start <= t <= end for t in times]) def _compute_month_ids(self) -> np.ndarray: - """Return the calendar month index (1–12) for each timestep. + """Return the calendar month index (1-12) for each timestep. Returns: np.ndarray: Integer array of shape ``(n_timesteps,)``. @@ -215,8 +215,7 @@ def pyomo_setup(self, discrete_inputs): """Return the rolling-horizon dispatch solver callable. Args: - discrete_inputs (dict): OpenMDAO discrete inputs (unused - directly here; passed for API compatibility). + discrete_inputs (dict): OpenMDAO discrete inputs. Returns: callable: ``pyomo_dispatch_solver(performance_model, @@ -230,7 +229,7 @@ def pyomo_setup(self, discrete_inputs): commands. 4. Carries the terminal SOC into the next window. - Returns ``(storage_out, soc_out)`` — two ``np.ndarray`` of + Returns ``(storage_out, soc_out)`` - two ``np.ndarray`` of length ``n_timesteps``. """ @@ -439,7 +438,7 @@ def solve_dispatch_model(self, start_time: int = 0, n_days: int = 0): """Solve the DR MILP for the current window and record solver metrics. Args: - start_time (int): Global timestep index of the window start. + start_time (int): Timestep index of the window start. Used only for error messages and metrics. Defaults to 0. n_days (int): Total simulation days. Passed to ``DispatchProblemState.store_problem_metrics``. From 2f0a325ac2f1fea7dc26da80505b875af3562660 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Thu, 23 Apr 2026 16:40:42 -0600 Subject: [PATCH 14/55] Edit docstrings to include raises --- .../storage/plm_optimized_storage_controller.py | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index 80dc9f1e6..3e9bf6959 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -44,7 +44,7 @@ class PLMOptimizedControllerConfig(PyomoStorageControllerBaseConfig): n_max_events (int): Maximum discharge events per calendar month. Defaults to 10. n_control_window (int): Number of timesteps per rolling solve - window. Defaults to ``24 * 30`` (one month of hourly data). + window. Defaults to ``24`` (one day). signal_threshold_percentile (float): Percentile (0-100) used to compute the signal threshold for each rolling window. Only hours at or above this percentile of the window signal are @@ -58,7 +58,7 @@ class PLMOptimizedControllerConfig(PyomoStorageControllerBaseConfig): charge_efficiency: float = field(validator=range_val(0, 1), default=1.0) discharge_efficiency: float = field(validator=range_val(0, 1), default=1.0) n_max_events: int = field(default=10) - n_control_window: int = field(default=24 * 30) # one month of hourly data + n_control_window: int = field(default=24) # one month of hourly data signal_threshold_percentile: float = field(default=0.0, validator=range_val(0,100)) # make sure this is valid @@ -121,12 +121,12 @@ def setup(self): @staticmethod def _build_time_index(plant_config: dict) -> pd.DatetimeIndex: - """Build a timezone-aware DatetimeIndex from simulation settings in plant_config. + """Build a DatetimeIndex from simulation settings in plant_config. Args: plant_config (dict): Plant configuration dict. Must contain ``plant.simulation`` with keys ``n_timesteps`` (int), - ``dt`` (int, seconds), ``timezone`` (int, UTC offset), + ``dt`` (int, seconds), ``timezone`` (int), and ``start_time`` (str). Returns: @@ -189,7 +189,7 @@ def _compute_month_ids(self) -> np.ndarray: def _compute_eligible_mask(self, signal_window: np.ndarray) -> np.ndarray: """Build a boolean mask for timesteps whose signal meets the dispatch threshold. - The threshold is derived only from ``signal_window`` — it does not + The threshold is derived only from ``signal_window``. It does not assume the full simulation signal is known in advance. When ``signal_threshold_percentile`` is 0.0 all hours are eligible. @@ -333,10 +333,9 @@ def _build_dr_model( """Build the DR MILP for a single rolling window. Args: - window_start (int): Global timestep index of the first hour + window_start (int): Timestep index of the first hour in this window. window_len (int): Number of timesteps in this window - (``n_control_window`` except possibly the last window). init_soc (float): State-of-charge fraction at the start of this window. remaining_budget (dict): Mapping of ``month_id (int)`` to @@ -445,7 +444,7 @@ def solve_dispatch_model(self, start_time: int = 0, n_days: int = 0): Defaults to 0. Raises: - RuntimeError: If GLPK returns a non-OK status or an + RuntimeError: If GLPK returns a not OK status or an unacceptable termination condition. """ from pyomo.opt import SolverStatus, TerminationCondition From a7fc28cc55397b01f474b285e1e36c39174876d5 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Mon, 27 Apr 2026 12:17:34 -0600 Subject: [PATCH 15/55] FIX formatting in supported modesl --- h2integrate/core/supported_models.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/h2integrate/core/supported_models.py b/h2integrate/core/supported_models.py index a4ddf6550..e6705467e 100644 --- a/h2integrate/core/supported_models.py +++ b/h2integrate/core/supported_models.py @@ -198,6 +198,12 @@ from h2integrate.control.control_strategies.storage.simple_openloop_controller import ( SimpleStorageOpenLoopController, ) +from h2integrate.control.control_strategies.storage.plm_optimized_storage_controller import ( + PLMOptimizedStorageController, +) +from h2integrate.control.control_strategies.storage.plm_openloop_storage_controller import ( + PeakLoadManagementHeuristicOpenLoopStorageController, +) from h2integrate.control.control_rules.storage.pyomo_storage_rule_min_operating_cost import ( PyomoRuleStorageMinOperatingCosts, ) @@ -319,6 +325,10 @@ # Control "SimpleStorageOpenLoopController": SimpleStorageOpenLoopController, "DemandOpenLoopStorageController": DemandOpenLoopStorageController, + "PeakLoadManagementHeuristicOpenLoopStorageController": ( + PeakLoadManagementHeuristicOpenLoopStorageController + ), + "PLMOptimizedStorageController": PLMOptimizedStorageController, "HeuristicLoadFollowingStorageController": HeuristicLoadFollowingStorageController, "OptimizedDispatchStorageController": OptimizedDispatchStorageController, "GenericDemandComponent": GenericDemandComponent, From 057d8faa3a8d03ffd99605195fadc1c4edc846b6 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Mon, 27 Apr 2026 12:24:51 -0600 Subject: [PATCH 16/55] Fix formatting in test_all_ex --- examples/test/test_all_examples.py | 5670 ---------------------------- 1 file changed, 5670 deletions(-) diff --git a/examples/test/test_all_examples.py b/examples/test/test_all_examples.py index c815436b3..b9d86ea5c 100644 --- a/examples/test/test_all_examples.py +++ b/examples/test/test_all_examples.py @@ -2883,5676 +2883,6 @@ def test_peak_load_management_example(subtests, temp_copy_of_example): grid_purchase = model.prob.get_val("grid_buy.electricity_out", units="kW") assert battery_unmet_demand.sum() == pytest.approx(grid_purchase.sum(), rel=1e-3) -import os -import importlib -from pathlib import Path - -import numpy as np -import pandas as pd -import pytest -import openmdao.api as om - -from h2integrate import ROOT_DIR -from h2integrate.core.file_utils import load_yaml -from h2integrate.core.h2integrate_model import H2IntegrateModel - - -ROOT = Path(__file__).parents[1] - - -# docs fencepost start: DO NOT REMOVE -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("01_onshore_steel_mn", None)]) -def test_steel_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "01_onshore_steel_mn.yaml") - # docs fencepost end: DO NOT REMOVE - # Set battery demand profile to electrolyzer capacity - demand_profile = np.ones(8760) * 720.0 - model.setup() - model.prob.set_val("battery.electricity_demand", demand_profile, units="MW") - - # Run the model - model.run() - - model.post_process() - # Subtests for checking specific values - with subtests.test("Check total electricity produced"): - assert ( - pytest.approx( - model.prob.get_val("combiner.electricity_out", units="MW").sum(), - rel=1e-3, - ) - == 5901098.278035271 - ) - - with subtests.test("Check total adjusted CapEx (electricity)"): - assert ( - pytest.approx( - model.prob.get_val( - "finance_subgroup_electricity.total_capex_adjusted", units="USD" - )[0], - rel=1e-3, - ) - == 4314364438.840067 - ) - with subtests.test("Check total adjusted OpEx (electricity)"): - assert ( - pytest.approx( - model.prob.get_val( - "finance_subgroup_electricity.total_opex_adjusted", units="USD/year" - )[0], - rel=1e-3, - ) - == 75831805.27785796 - ) - - with subtests.test("Check LCOE"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0], - rel=1e-3, - ) - == 90.8231905486079 - ) - - with subtests.test("Check H2 Storage capacity"): - assert ( - pytest.approx(model.prob.get_val("h2_storage.storage_capacity", units="kg"), rel=1e-3) - == 2559669.7759292 - ) - - with subtests.test("Check LCOH"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_hydrogen.LCOH_delivered", units="USD/kg")[0], - rel=1e-3, - ) - == 8.235313509720276 - ) - - with subtests.test("Check LCOS"): - assert ( - pytest.approx(model.prob.get_val("steel.LCOS", units="USD/t")[0], rel=1e-3) - == 1264.2821232584045 - ) - - with subtests.test("Check total adjusted CapEx"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_hydrogen.total_capex_adjusted", units="USD")[ - 0 - ], - rel=1e-3, - ) - == 5129491338.670795 - ) - - with subtests.test("Check total adjusted OpEx"): - assert ( - pytest.approx( - model.prob.get_val( - "finance_subgroup_hydrogen.total_opex_adjusted", units="USD/year" - )[0], - rel=1e-3, - ) - == 97887982.86294547 - ) - - with subtests.test("Check steel CapEx"): - assert ( - pytest.approx(model.prob.get_val("steel.CapEx", units="USD"), rel=1e-3) == 5.78060014e08 - ) - - with subtests.test("Check steel OpEx"): - assert ( - pytest.approx(model.prob.get_val("steel.OpEx", units="USD/year"), rel=1e-3) - == 1.0129052e08 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("02_texas_ammonia", None)]) -def test_simple_ammonia_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "02_texas_ammonia.yaml") - - # Set battery demand profile to electrolyzer capacity - demand_profile = np.ones(8760) * 640.0 - model.setup() - model.prob.set_val("battery.electricity_demand", demand_profile, units="MW") - - # Run the model - model.run() - - model.post_process() - - # Subtests for checking specific values - with subtests.test("Check Wind+PV CapEx"): - wind_pv_capex = ( - model.prob.get_val("wind.CapEx", units="USD")[0] - + model.prob.get_val("solar.CapEx", units="USD")[0] - ) - assert pytest.approx(wind_pv_capex, rel=1e-3) == 1.75469962e09 - - with subtests.test("Check Wind+PV OpEx"): - wind_pv_opex = ( - model.prob.get_val("wind.OpEx", units="USD/yr")[0] - + model.prob.get_val("solar.OpEx", units="USD/yr")[0] - ) - assert pytest.approx(wind_pv_opex, rel=1e-3) == 32953490.4 - - with subtests.test("Check electrolyzer CapEx"): - assert ( - pytest.approx(model.prob.get_val("electrolyzer.CapEx", units="USD"), rel=1e-3) - == 6.00412524e08 - ) - - with subtests.test("Check electrolyzer OpEx"): - assert ( - pytest.approx(model.prob.get_val("electrolyzer.OpEx", units="USD/year"), rel=1e-3) - == 14703155.39207595 - ) - - with subtests.test("Check H2 storage CapEx"): - assert ( - pytest.approx(model.prob.get_val("h2_storage.CapEx", units="USD")[0], rel=1e-3) - == 64599012.73829915 - ) - - with subtests.test("Check H2 storage OpEx"): - assert ( - pytest.approx(model.prob.get_val("h2_storage.OpEx", units="USD/year")[0], rel=1e-3) - == 2975616.8932987223 - ) - - with subtests.test("Check ammonia CapEx"): - assert ( - pytest.approx(model.prob.get_val("ammonia.CapEx", units="USD"), rel=1e-3) - == 1.0124126e08 - ) - - with subtests.test("Check ammonia OpEx"): - assert ( - pytest.approx(model.prob.get_val("ammonia.OpEx", units="USD/year"), rel=1e-3) - == 11178036.31197754 - ) - - with subtests.test("Check total adjusted CapEx"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_hydrogen.total_capex_adjusted", units="USD")[ - 0 - ], - rel=1e-3, - ) - == 2577162708.3 - ) - - with subtests.test("Check total adjusted OpEx"): - assert ( - pytest.approx( - model.prob.get_val( - "finance_subgroup_hydrogen.total_opex_adjusted", units="USD/year" - )[0], - rel=1e-3, - ) - == 53842563.43404999 - ) - - # Currently underestimated compared to the Reference Design Doc - with subtests.test("Check LCOH"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_hydrogen.LCOH", units="USD/kg")[0], - rel=1e-3, - ) - == 4.0155433 - ) - - with subtests.test("Check price of hydrogen"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_hydrogen.price_hydrogen", units="USD/kg")[0], - rel=1e-3, - ) - == 4.0155433 - ) - - # Currently underestimated compared to the Reference Design Doc - with subtests.test("Check LCOA"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_ammonia.LCOA", units="USD/kg")[0], - rel=1e-3, - ) - == 1.027395 - ) - - # Check that the expected output files exist - outputs_dir = example_folder / "outputs" - assert ( - outputs_dir / "profast_output_ammonia_config.yaml" - ).is_file(), "profast_output_ammonia.yaml not found" - assert ( - outputs_dir / "profast_output_electricity_config.yaml" - ).is_file(), "profast_output_electricity.yaml not found" - assert ( - outputs_dir / "profast_output_hydrogen_config.yaml" - ).is_file(), "profast_output_hydrogen.yaml not found" - - -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("12_ammonia_synloop", None)]) -def test_ammonia_synloop_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "12_ammonia_synloop.yaml") - - # Set battery demand profile to electrolyzer capacity - demand_profile = np.ones(8760) * 640.0 - model.setup() - model.prob.set_val("battery.electricity_demand", demand_profile, units="MW") - - # Run the model - model.run() - - model.post_process() - - # Subtests for checking specific values - with subtests.test("Check HOPP CapEx"): - wind_pv_capex = ( - model.prob.get_val("wind.CapEx", units="USD")[0] - + model.prob.get_val("solar.CapEx", units="USD")[0] - ) - battery_capex = model.prob.get_val("battery.CapEx", units="USD") - re_capex = wind_pv_capex + battery_capex - assert pytest.approx(re_capex, rel=1e-6) == 1.75469962e09 - - with subtests.test("Check HOPP OpEx"): - wind_pv_opex = ( - model.prob.get_val("wind.OpEx", units="USD/yr")[0] - + model.prob.get_val("solar.OpEx", units="USD/yr")[0] - ) - battery_opex = model.prob.get_val("battery.OpEx", units="USD/year") - re_opex = wind_pv_opex + battery_opex - assert pytest.approx(re_opex, rel=1e-6) == 32953490.4 - - with subtests.test("Check electrolyzer CapEx"): - assert ( - pytest.approx(model.prob.get_val("electrolyzer.CapEx", units="USD"), rel=1e-6) - == 6.00412524e08 - ) - - with subtests.test("Check electrolyzer OpEx"): - assert ( - pytest.approx(model.prob.get_val("electrolyzer.OpEx", units="USD/year"), rel=1e-6) - == 14703155.39207595 - ) - - with subtests.test("Check H2 storage CapEx"): - assert ( - pytest.approx(model.prob.get_val("h2_storage.CapEx", units="USD"), rel=1e-6) - == 64553014.22218219 - ) - - with subtests.test("Check H2 storage OpEx"): - assert ( - pytest.approx(model.prob.get_val("h2_storage.OpEx", units="USD/year"), rel=1e-6) - == 2975616.89 - ) - - with subtests.test("Check ammonia CapEx"): - assert ( - pytest.approx(model.prob.get_val("ammonia.CapEx", units="USD"), rel=1e-6) - == 1.15173753e09 - ) - - with subtests.test("Check ammonia OpEx"): - assert ( - pytest.approx(model.prob.get_val("ammonia.OpEx", units="USD/year")[0], rel=1e-4) - == 25414748.989416014 - ) - - with subtests.test("Check ammonia production"): - assert ( - pytest.approx( - model.prob.get_val("ammonia.annual_ammonia_produced", units="t/yr").mean(), rel=1e-4 - ) - == 406333.161 - ) - - with subtests.test("Check total adjusted CapEx"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_nh3.total_capex_adjusted", units="USD")[0], - rel=1e-6, - ) - == 3728034379.0699997 - ) - - with subtests.test("Check total adjusted OpEx"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_nh3.total_opex_adjusted", units="USD/year")[0], - rel=1e-6, - ) - == 79257312.42365658 - ) - - with subtests.test("Check LCOH"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_h2.LCOH", units="USD/kg")[0], rel=1e-6 - ) - == 4.013427289493614 - ) - - with subtests.test("Check LCOA"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_nh3.LCOA", units="USD/kg")[0], rel=1e-6 - ) - == 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 -@pytest.mark.parametrize("example_folder,resource_example_folder", [("03_methanol/smr", None)]) -def test_smr_methanol_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "03_smr_methanol.yaml") - - # Run the model - model.run() - - model.post_process() - - # Check levelized cost of methanol (LCOM) - with subtests.test("Check SMR LCOM"): - assert ( - pytest.approx(model.prob.get_val("methanol.LCOM", units="USD/kg"), rel=1e-6) - == 0.22116813 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("03_methanol/co2_hydrogenation", None)] -) -def test_co2h_methanol_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "03_co2h_methanol.yaml") - - # Run the model - model.run() - - model.post_process() - - # Below is used as an integration test for the combiner - with subtests.test("combiner rated production"): - combined_rated_input = model.prob.get_val( - "wind.rated_electricity_production", units="MW" - ) + model.prob.get_val("solar.rated_electricity_production", units="MW") - assert ( - pytest.approx( - model.prob.get_val("combiner.rated_electricity_production", units="MW"), rel=1e-6 - ) - == combined_rated_input - ) - with subtests.test("combiner weighted CF"): - wind_weighted_cf = model.prob.get_val( - "wind.rated_electricity_production", units="MW" - ) * model.prob.get_val("wind.capacity_factor", units="unitless") - solar_weighted_cf = model.prob.get_val( - "solar.rated_electricity_production", units="MW" - ) * model.prob.get_val("solar.capacity_factor", units="unitless") - combined_cf = (wind_weighted_cf + solar_weighted_cf) / combined_rated_input - assert ( - pytest.approx( - model.prob.get_val("combiner.capacity_factor", units="unitless"), - rel=1e-6, - ) - == combined_cf - ) - - # Check levelized cost of methanol (LCOM) - with subtests.test("Check CO2 Hydrogenation LCOM"): - assert ( - pytest.approx(model.prob.get_val("methanol.LCOM", units="USD/kg")[0], rel=1e-6) - == 1.7516172 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("03_methanol/co2_hydrogenation_doc", None)] -) -def test_doc_methanol_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "03_co2h_methanol.yaml") - - # Run the model - model.run() - - model.post_process() - - # Check levelized cost of methanol (LCOM) - with subtests.test("Check CO2 Hydrogenation LCOM"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_default.LCOM", units="USD/kg")[0], - rel=1e-4, - ) - == 2.5252588 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("05_wind_h2_opt", None)]) -def test_wind_h2_opt_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Run without optimization - model_init = H2IntegrateModel(example_folder / "wind_plant_electrolyzer0.yaml") - - # Run the model - model_init.run() - - model_init.post_process() - - annual_h20 = model_init.prob.get_val("electrolyzer.annual_hydrogen_produced", units="kg/year")[ - 0 - ] - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "wind_plant_electrolyzer.yaml") - - # Run the model - model.run() - - with subtests.test("Check initial H2 production"): - assert annual_h20 < (60500000 - 10000) - - with subtests.test("Check LCOE"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(kW*h)")[0], - rel=1e-3, - ) - == 0.059096 - ) - - with subtests.test("Check electrolyzer size"): - assert ( - pytest.approx( - model.prob.get_val("electrolyzer.electrolyzer_size_mw", units="MW")[0], - rel=1e-3, - ) - == 320.0 - ) - # Read the resulting SQL file and compare initial and final LCOH values - - sql_path = None - for root, _dirs, files in os.walk(example_folder): - for file in files: - if file == "wind_h2_opt.sql": - sql_path = Path(root) / file - break - if sql_path: - break - assert ( - sql_path is not None - ), "wind_h2_opt.sql file not found in current working directory or subdirectories." - - cr = om.CaseReader(str(sql_path)) - cases = list(cr.get_cases()) - assert len(cases) > 1, "Not enough cases recorded in SQL file." - - # Get initial and final LCOH values - - initial_lcoh = cases[0].outputs["finance_subgroup_hydrogen.LCOH"][0] - final_lcoh = cases[-1].outputs["finance_subgroup_hydrogen.LCOH"][0] - - with subtests.test("Check LCOH changed"): - assert final_lcoh != initial_lcoh - - with subtests.test("Check total adjusted CapEx"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_hydrogen.total_capex_adjusted", units="USD")[ - 0 - ], - rel=1e-3, - ) - == 978075832.46 - ) - with subtests.test("Check total adjusted OpEx"): - assert ( - pytest.approx( - model.prob.get_val( - "finance_subgroup_hydrogen.total_opex_adjusted", units="USD/year" - )[0], - rel=1e-3, - ) - == 27646299.56 - ) - - with subtests.test("Check minimum total hydrogen produced"): - assert ( - pytest.approx( - model.prob.get_val("electrolyzer.annual_hydrogen_produced", units="kg/year")[0], - abs=15000, - ) - == 29028700 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("06_custom_tech", None)]) -def test_paper_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "wind_plant_paper.yaml") - - # Run the model - model.run() - - model.post_process() - - # Subtests for checking specific values - with subtests.test("Check LCOP"): - assert ( - pytest.approx(model.prob.get_val("paper_mill.LCOP", units="USD/t"), rel=1e-3) - == 51.733275 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("09_co2/direct_ocean_capture", None)] -) -def test_wind_wave_doc_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "offshore_plant_doc.yaml") - # Set battery demand profile - demand_profile = np.ones(8760) * 340.0 - model.setup() - model.prob.set_val("battery.electricity_demand", demand_profile, units="MW") - # Run the model - model.run() - - model.post_process() - - # Subtests for checking specific values - with subtests.test("Check LCOC"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_co2.LCOC", units="USD/kg")[0], rel=1e-3 - ) - == 1.803343170781246 - ) - - with subtests.test("Check LCOE"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0], - rel=1e-3, - ) - == 243.723825 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("17_splitter_wind_doc_h2", None)] -) -def test_splitter_wind_doc_h2_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "offshore_plant_splitter_doc_h2.yaml") - - # Run the model - model.run() - - model.post_process() - - # Subtests for checking specific values - with subtests.test("Check Electrical AEP"): - electrical_aep = ( - model.prob.get_val( - "finance_subgroup_electricity.rated_electricity_production", - units="MW", - ) - * model.prob.get_val( - "finance_subgroup_electricity.capacity_factor", - units="unitless", - ).mean() - * 8760 - ) - - assert pytest.approx(electrical_aep[0], rel=1e-3) == 511267.03627 - - with subtests.test("Check LCOH"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_hydrogen.LCOH", units="USD/kg")[0], - rel=1e-3, - ) - == 9.8059083 - ) - - with subtests.test("Check LCOC"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_co2.LCOC", units="USD/kg")[0], rel=1e-3 - ) - == 13.655268 - ) - - with subtests.test("Check LCOE"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0], - rel=1e-3, - ) - == 132.395036462 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("07_run_of_river_plant", None)] -) -def test_hydro_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "07_run_of_river.yaml") - - # Run the model - model.run() - - model.post_process() - - print(model.prob.get_val("finance_subgroup_default.LCOE", units="USD/(kW*h)")) - - # Subtests for checking specific values - with subtests.test("Check LCOE"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_default.LCOE", units="USD/(kW*h)")[0], - rel=1e-3, - ) - == 0.17653979 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("11_hybrid_energy_plant", None)] -) -def test_hybrid_energy_plant_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "wind_pv_battery.yaml") - - # Run the model - model.run() - - model.post_process() - - # Subtests for checking specific values - with subtests.test("Check LCOE"): - 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)] -) -def test_hydrogen_dispatch_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "inputs" / "h2i_wind_to_h2_storage.yaml") - - model.run() - - model.post_process() - - with subtests.test("Check LCOE"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0], - rel=1e-5, - ) - == 59.0962072084844 - ) - - with subtests.test("Check all h2 total_hydrogen_produced"): - all_h2_annual_prod = ( - model.prob.get_val( - "finance_subgroup_all_hydrogen.rated_hydrogen_production", units="kg/h" - )[0] - * model.prob.get_val( - "finance_subgroup_all_hydrogen.capacity_factor", units="unitless" - ).mean() - * 8760 - ) - assert ( - pytest.approx( - all_h2_annual_prod, - rel=1e-5, - ) - == model.prob.get_val("electrolyzer.annual_hydrogen_produced", units="kg/year")[0] - ) - - with subtests.test("Check total_hydrogen_produced"): - assert ( - pytest.approx( - model.prob.get_val("electrolyzer.total_hydrogen_produced", units="kg")[0], - rel=1e-5, - ) - == 61656526.36295184 - ) - - with subtests.test("Check annual hydrogen production"): - assert ( - pytest.approx( - model.prob.get_val("electrolyzer.annual_hydrogen_produced", units="kg/year")[0], - rel=1e-5, - ) - == 58458965.601815335 - ) - - with subtests.test("Check all h2 LCOH"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_all_hydrogen.LCOH", units="USD/kg")[0], - rel=1e-5, - ) - == 5.647544985152393 - ) - - with subtests.test("Check dispatched h2 LCOH"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_dispatched_hydrogen.LCOH", units="USD/kg")[0], - rel=1e-5, - ) - == 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 -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("09_co2/ocean_alkalinity_enhancement", None)] -) -def test_wind_wave_oae_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "offshore_plant_oae.yaml") - - # Set battery demand profile - demand_profile = np.ones(8760) * 330.0 - model.setup() - model.prob.set_val("battery.electricity_demand", demand_profile, units="MW") - - # Run the model - model.run() - - model.post_process() - - # Subtests for checking specific values - with subtests.test("Check LCOC"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_co2.LCOC", units="USD/kg")[0], rel=1e-3 - ) - == 41.156 - ) - - with subtests.test("Check LCOE"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0], - rel=1e-3, - ) - == 263.130 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", - [("09_co2/ocean_alkalinity_enhancement_financials", None)], -) -def test_wind_wave_oae_example_with_finance(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "offshore_plant_oae.yaml") - - # Run the model - model.run() - - model.post_process() - - # Subtests for checking specific values - with subtests.test("Check LCOE"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0], - rel=1e-3, - ) - == 92.269663 - ) - - with subtests.test("Check Carbon Credit"): - assert ( - pytest.approx(model.prob.get_val("oae.carbon_credit_value", units="USD/t")[0], rel=1e-3) - == 1026.4684117 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("16_natural_gas", "11_hybrid_energy_plant")] -) -def test_natural_gas_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "natgas.yaml") - - # Run the model - - model.run() - - model.post_process() - solar_aep = sum(model.prob.get_val("solar.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("electrical_load_demand.unused_electricity_out", units="kW") - ) - - renewable_subgroup_total_electricity = ( - model.prob.get_val("finance_subgroup_renewables.rated_electricity_production", units="kW")[ - 0 - ] - * model.prob.get_val("finance_subgroup_renewables.capacity_factor", units="unitless").mean() - * 8760 - ) - electricity_subgroup_total_electricity = ( - model.prob.get_val("finance_subgroup_electricity.rated_electricity_production", units="kW")[ - 0 - ] - * model.prob.get_val( - "finance_subgroup_electricity.capacity_factor", units="unitless" - ).mean() - * 8760 - ) - natural_gas_subgroup_total_electricity = ( - model.prob.get_val("finance_subgroup_natural_gas.rated_electricity_production", units="kW")[ - 0 - ] - * model.prob.get_val( - "finance_subgroup_natural_gas.capacity_factor", units="unitless" - ).mean() - * 8760 - ) - - # NOTE: battery output power is not included in any of the financials - - 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 - - with subtests.test( - "Check solar AEP is greater than battery output (solar oversized relative to demand" - ): - assert solar_aep > solar_bat_out_total - - with subtests.test( - "Check battery outputs against battery inputs (solar oversized relative to demand" - ): - assert ( - pytest.approx(solar_bat_out_total + solar_curtailed_total, abs=bat_init_charge) - == solar_aep - ) - - with subtests.test("Check solar AEP equals total electricity for renewables subgroup"): - assert pytest.approx(solar_aep, rel=1e-6) == renewable_subgroup_total_electricity - - with subtests.test("Check natural gas AEP equals total electricity for natural_gas subgroup"): - assert ( - pytest.approx(sum(ng_electricity_production), rel=1e-6) - == natural_gas_subgroup_total_electricity - ) - - with subtests.test( - "Check natural gas + solar AEP equals total electricity for electricity subgroup" - ): - assert ( - pytest.approx(electricity_subgroup_total_electricity, rel=1e-6) - == sum(ng_electricity_production) + solar_aep - ) - - with subtests.test("Check missed load is natural gas plant electricity demand"): - assert pytest.approx(ng_electricity_demand, rel=1e-6) == pre_ng_missed_load - - with subtests.test("Check natural_gas_plant electricity out equals demand"): - assert pytest.approx(ng_electricity_demand, rel=1e-6) == ng_electricity_production - - # Subtests for checking specific values - with subtests.test("Check Natural Gas CapEx"): - capex = model.prob.get_val("natural_gas_plant.CapEx", units="USD")[0] - assert pytest.approx(capex, rel=1e-6) == 1e8 - - with subtests.test("Check Natural Gas OpEx"): - opex = model.prob.get_val("natural_gas_plant.OpEx", units="USD/year")[0] - assert pytest.approx(opex, rel=1e-6) == 2243167.24525 - - with subtests.test("Check total electricity produced"): - assert pytest.approx(natural_gas_subgroup_total_electricity, rel=1e-6) == 497266898.10354495 - - with subtests.test("Check opex adjusted ng_feedstock"): - opex_ng_feedstock = model.prob.get_val( - "finance_subgroup_natural_gas.varopex_adjusted_ng_feedstock", - units="USD/year", - )[0] - assert pytest.approx(opex_ng_feedstock, rel=1e-6) == 15281860.770986987 - - with subtests.test("Check capex adjusted natural_gas_plant"): - capex_ng_plant = model.prob.get_val( - "finance_subgroup_natural_gas.capex_adjusted_natural_gas_plant", units="USD" - )[0] - assert pytest.approx(capex_ng_plant, rel=1e-6) == 97560975.60975611 - - with subtests.test("Check opex adjusted natural_gas_plant"): - opex_ng_plant = model.prob.get_val( - "finance_subgroup_natural_gas.opex_adjusted_natural_gas_plant", units="USD/year" - )[0] - assert pytest.approx(opex_ng_plant, rel=1e-6) == 2188455.8490330363 - - with subtests.test("Check total adjusted CapEx for natural gas subgroup"): - total_capex = model.prob.get_val( - "finance_subgroup_natural_gas.total_capex_adjusted", units="USD" - )[0] - assert pytest.approx(total_capex, rel=1e-6) == 97658536.58536586 - - with subtests.test("Check LCOE (natural gas plant)"): - lcoe_ng = model.prob.get_val("finance_subgroup_natural_gas.LCOE", units="USD/(kW*h)")[0] - assert pytest.approx(lcoe_ng, rel=1e-6) == 0.05811033466 - - with subtests.test("Check LCOE (renewables plant)"): - lcoe_re = model.prob.get_val("finance_subgroup_renewables.LCOE", units="USD/(kW*h)")[0] - assert pytest.approx(lcoe_re, rel=1e-6) == 0.07102560120 - - with subtests.test("Check LCOE (renewables and natural gas plant)"): - lcoe_tot = model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(kW*h)")[0] - assert pytest.approx(lcoe_tot, rel=1e-6) == 0.063997927290 - - # Test feedstock-specific values - with subtests.test("Check feedstock output"): - ng_output = model.prob.get_val("ng_feedstock_source.natural_gas_out", units="MMBtu/h") - # Should be rated capacity (750 MMBtu/h) for all timesteps - assert all(ng_output == 750.0) - - with subtests.test("Check feedstock consumption"): - ng_consumed = model.prob.get_val("ng_feedstock.natural_gas_consumed", units="MMBtu/h") - # Total consumption should match what the natural gas plant uses - expected_consumption = ( - model.prob.get_val("natural_gas_plant.electricity_out", units="MW") * 7.5 - ) # Convert MWh to MMBtu using heat rate - assert pytest.approx(ng_consumed.sum(), rel=1e-3) == expected_consumption.sum() - - with subtests.test("Check feedstock CapEx"): - ng_capex = model.prob.get_val("ng_feedstock.CapEx", units="USD")[0] - assert pytest.approx(ng_capex, rel=1e-6) == 100000.0 # start_up_cost - - with subtests.test("Check feedstock OpEx"): - ng_opex = model.prob.get_val("ng_feedstock.VarOpEx", units="USD/year")[0] - # OpEx should be annual_cost (0) + price * consumption - ng_consumed = model.prob.get_val("ng_feedstock.natural_gas_consumed", units="MMBtu/h") - 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( - "example_folder,resource_example_folder", - [("15_wind_solar_electrolyzer", "11_hybrid_energy_plant/")], -) -def test_wind_solar_electrolyzer_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "15_wind_solar_electrolyzer.yaml") - model.run() - - solar_fpath = model.model.get_val("solar_site.solar_resource.solar_resource_data")["filepath"] - wind_fpath = model.model.get_val("wind_site.wind_resource.wind_resource_data")["filepath"] - - with subtests.test("Wind resource file"): - assert Path(wind_fpath).name == "35.2018863_-101.945027_2012_wtk_v2_60min_utc_tz.csv" - - with subtests.test("Solar resource file"): - assert Path(solar_fpath).name == "30.6617_-101.7096_psmv3_60_2013.csv" - model.post_process() - - wind_aep = sum(model.prob.get_val("wind.electricity_out", units="kW")) - solar_aep = sum(model.prob.get_val("solar.electricity_out", units="kW")) - total_aep = model.prob.get_val("combiner.electricity_out", units="kW").sum() - - with subtests.test("Check total energy production"): - assert pytest.approx(wind_aep + solar_aep, rel=1e-6) == total_aep - - with subtests.test("Check LCOE"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0], - rel=1e-5, - ) - == 53.9306558 - ) - - with subtests.test("Check LCOH"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_hydrogen.LCOH", units="USD/kg")[0], - rel=1e-5, - ) - == 5.3063358423 - ) - - wind_generation = model.prob.get_val("wind.electricity_out", units="kW") - solar_generation = model.prob.get_val("solar.electricity_out", units="kW") - total_generation = model.prob.get_val("combiner.electricity_out", units="kW") - total_energy_to_electrolyzer = model.prob.get_val("electrolyzer.electricity_in", units="kW") - with subtests.test("Check combiner output"): - assert ( - pytest.approx(wind_generation.sum() + solar_generation.sum(), rel=1e-5) - == total_generation.sum() - ) - with subtests.test("Check electrolyzer input power"): - assert pytest.approx(total_generation.sum(), rel=1e-5) == total_energy_to_electrolyzer.sum() - - -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("10_electrolyzer_om", None)]) -def test_electrolyzer_om_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "electrolyzer_om.yaml") - - model.run() - - lcoe = model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0] - lcoh_with_lcoh_finance = model.prob.get_val( - "finance_subgroup_hydrogen.LCOH_lcoh_financials", units="USD/kg" - )[0] - lcoh_with_lcoe_finance = model.prob.get_val( - "finance_subgroup_hydrogen.LCOH_lcoe_financials", units="USD/kg" - )[0] - with subtests.test("Check LCOE"): - assert pytest.approx(lcoe, rel=1e-4) == 39.98869 - with subtests.test("Check LCOH with lcoh_financials"): - assert pytest.approx(lcoh_with_lcoh_finance, rel=1e-4) == 16.9204156301 - with subtests.test("Check LCOH with lcoe_financials"): - assert pytest.approx(lcoh_with_lcoe_finance, rel=1e-4) == 10.3360027653 - - -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("08_wind_electrolyzer", None)]) -def test_wombat_electrolyzer_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "wind_plant_electrolyzer.yaml") - - model.run() - - lcoe_with_profast_model = model.prob.get_val( - "finance_subgroup_electricity_profast.LCOE", units="USD/(MW*h)" - )[0] - lcoe_with_custom_model = model.prob.get_val( - "finance_subgroup_electricity_custom.LCOE", units="USD/(MW*h)" - )[0] - - lcoh_with_custom_model = model.prob.get_val( - "finance_subgroup_hydrogen.LCOH_produced_custom_model", units="USD/kg" - )[0] - lcoh_with_profast_model = model.prob.get_val( - "finance_subgroup_hydrogen.LCOH_produced_profast_model", units="USD/kg" - )[0] - - with subtests.test("Check LCOH from custom model"): - assert pytest.approx(lcoh_with_custom_model, rel=1e-5) == 4.1783979573 - with subtests.test("Check LCOH from ProFAST model"): - assert pytest.approx(lcoh_with_profast_model, rel=1e-5) == 5.3086307305 - with subtests.test("Check LCOE from custom model"): - assert pytest.approx(lcoe_with_custom_model, rel=1e-5) == 51.17615298 - with subtests.test("Check LCOE from ProFAST model"): - assert pytest.approx(lcoe_with_profast_model, rel=1e-5) == 59.0962084 - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("18_pyomo_heuristic_dispatch", None)] -) -def test_pyomo_heuristic_dispatch_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "pyomo_heuristic_dispatch.yaml") - - demand_profile = np.ones(8760) * 50.0 - - # TODO: Update with demand module once it is developed - model.setup() - model.prob.set_val("battery.electricity_demand", demand_profile, units="MW") - - # Run the model - model.run() - - model.post_process() - - # Test battery storage functionality - # SOC should stay within configured bounds (10% to 90%) - # Due to pysam simulation, bounds may not be fully respected, - # but should not exceed the upper bound more than 4% SOC - # and the lower bound more than 1% SOC - soc = model.prob.get_val("battery.SOC", units="percent") - with subtests.test("Check battery SOC lower bound"): - assert all(soc >= 9.0) - with subtests.test("Check battery SOC upper bound"): - assert all(soc <= 94.0) - - with subtests.test("Check wind generation out of the wind plant"): - # Wind should generate some electricity - wind_electricity = model.prob.get_val("wind.electricity_out", units="MW") - assert wind_electricity.sum() > 0 - # Wind electricity should match battery input (direct connection) - with subtests.test("Check wind generation in to battery"): - battery_electricity_in = model.prob.get_val("battery.electricity_in", units="MW") - 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("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 - - # Subtest for LCOE - with subtests.test("Check all LCOE value"): - lcoe = model.prob.get_val("finance_subgroup_all_electricity.LCOE", units="USD/(kW*h)")[0] - assert lcoe == pytest.approx(0.08157197567200995, rel=1e-6) - - with subtests.test("Check dispatched LCOE value"): - lcoe = model.prob.get_val( - "finance_subgroup_dispatched_electricity.LCOE", units="USD/(kW*h)" - )[0] - assert lcoe == pytest.approx(0.5975902853904799, rel=1e-6) - - # Subtest for total electricity produced - with subtests.test("Check total electricity produced"): - total_electricity = ( - model.prob.get_val( - name="finance_subgroup_all_electricity.rated_electricity_production", - units="MW", - )[0] - * model.prob.get_val( - name="finance_subgroup_all_electricity.capacity_factor", - units="unitless", - ).mean() - * 8760 - ) - assert total_electricity == pytest.approx(3125443.1089529935, rel=1e-6) - - # Subtest for electricity unused_commodity - with subtests.test("Check electricity unused commodity"): - electricity_unused_commodity = np.linalg.norm( - 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("electrical_load_demand.unmet_electricity_demand_out", units="MW") - ) - assert electricity_unmet_demand == pytest.approx(711.1997294551337, rel=1e-6) - - # Check that incorrect and no tech name provided will be replaced and validate - model_config = load_yaml(example_folder / "pyomo_heuristic_dispatch.yaml") - tech = load_yaml(example_folder / "tech_config.yaml") - with subtests.test("Ensure no-tech name validates"): - tech["technologies"]["battery"]["model_inputs"]["control_parameters"] = None - model_config["technology_config"] = tech - model = H2IntegrateModel(model_config) - - with subtests.test("Ensure incorrect name is corrected"): - tech["technologies"]["battery"]["model_inputs"]["control_parameters"] = { - "tech_name": "goose" - } - model_config["technology_config"] = tech - model = H2IntegrateModel(model_config) - - -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("19_simple_dispatch", None)]) -def test_simple_dispatch_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "wind_battery_dispatch.yaml") - - # Run the model - model.run() - - model.post_process() - - wind_aep = sum(model.prob.get_val("wind.electricity_out", units="kW")) - aep_for_finance = ( - model.prob.get_val("finance_subgroup_electricity.rated_electricity_production", units="kW")[ - 0 - ] - * model.prob.get_val( - "finance_subgroup_electricity.capacity_factor", units="unitless" - ).mean() - * 8760 - ) - battery_init_energy = 30000.0 * 0.25 # max capacity in kW and initial charge rate percentage - - with subtests.test("Check electricity is not double counted"): - assert aep_for_finance <= wind_aep + battery_init_energy - - # Test battery storage functionality - with subtests.test("Check battery SOC bounds"): - 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) - - with subtests.test("Check wind generation"): - # Wind should generate some electricity - wind_electricity = model.prob.get_val("wind.electricity_out", units="kW") - assert wind_electricity.sum() > 0 - # Wind electricity should match battery input (direct connection) - battery_electricity_in = model.prob.get_val("battery.electricity_in", units="kW") - 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("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 - - # Subtest for LCOE - with subtests.test("Check LCOE value"): - lcoe = model.prob.get_val( - "finance_subgroup_electricity.LCOE_all_electricity_profast", units="USD/(kW*h)" - )[0] - assert pytest.approx(lcoe, rel=1e-6) == 0.07801723344476236 - - # Subtest for NPV - with subtests.test("Check NPV value (numpy financial)"): - npv = model.prob.get_val( - "finance_subgroup_electricity.NPV_electricity_all_electricity_npv", units="USD" - )[0] - assert pytest.approx(npv, rel=1e-6) == 3791194.71 - - # Subtest for ProFAST NPV - with subtests.test("Check NPV value (profast)"): - npv = model.prob.get_val( - "finance_subgroup_electricity.NPV_electricity_all_electricity_profast_npv", - units="USD", - )[0] - assert pytest.approx(npv, rel=1e-6) == 7518969.18 - - # Subtest for total electricity produced - with subtests.test("Check total electricity produced"): - total_electricity = ( - model.prob.get_val( - "finance_subgroup_electricity.rated_electricity_production", units="kW" - )[0] - * model.prob.get_val("finance_subgroup_electricity.capacity_factor").mean() - * 8760 - ) - assert pytest.approx(total_electricity, rel=1e-6) == 62797265.9296355 - - # Subtest for electricity unused_commodity - with subtests.test("Check electricity unused commodity"): - electricity_unused_commodity = np.linalg.norm( - 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("electrical_load_demand.unmet_electricity_demand_out", units="kW") - ) - assert pytest.approx(electricity_unmet_demand, rel=1e-6) == 165604.70758669 - - # Subtest for total electricity produced from wind, should be equal to total - # electricity produced from finance_subgroup_electricity - with subtests.test("Check total electricity produced from wind"): - wind_electricity_finance = ( - model.prob.get_val("finance_subgroup_wind.rated_electricity_production", units="kW")[0] - * model.prob.get_val("finance_subgroup_wind.capacity_factor", units="unitless").mean() - * 8760 - ) - assert pytest.approx(wind_electricity_finance, rel=1e-6) == total_electricity - - with subtests.test("Check total electricity produced from wind compared to wind aep"): - wind_electricity_performance = np.sum( - model.prob.get_val("wind.electricity_out", units="kW") - ) - assert pytest.approx(wind_electricity_performance, rel=1e-6) == wind_electricity_finance - - # Subtest for total electricity produced from battery, should be equal - # to sum of "battery.electricity_out" - with subtests.test("Check total electricity produced from battery"): - battery_electricity_finance = ( - model.prob.get_val( - "finance_subgroup_battery.rated_electricity_production", units="MW*h/year" - )[0] - * model.prob.get_val( - "finance_subgroup_battery.capacity_factor", units="unitless" - ).mean() - * 8760 - ) - battery_electricity_performance = ( - 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 ( - pytest.approx(battery_electricity_finance, rel=1e-6) == battery_electricity_performance - ) - - wind_lcoe = model.prob.get_val("finance_subgroup_wind.LCOE_wind_only", units="USD/(MW*h)")[0] - battery_lcoe = model.prob.get_val( - "finance_subgroup_battery.LCOE_battery_included", units="USD/(MW*h)" - )[0] - electricity_lcoe = model.prob.get_val( - "finance_subgroup_electricity.LCOE_all_electricity_profast", units="USD/(MW*h)" - )[0] - - with subtests.test("Check electricity LCOE is greater than wind LCOE"): - assert electricity_lcoe > wind_lcoe - - with subtests.test("Check battery LCOE is greater than electricity LCOE"): - assert battery_lcoe > electricity_lcoe - - with subtests.test("Check battery LCOE"): - assert pytest.approx(battery_lcoe, rel=1e-6) == 131.781997 - - with subtests.test("Check wind LCOE"): - assert pytest.approx(wind_lcoe, rel=1e-6) == 58.8248 - - with subtests.test("Check electricity LCOE"): - assert pytest.approx(electricity_lcoe, rel=1e-6) == 78.01723 - - -@pytest.mark.integration -@pytest.mark.skipif(importlib.util.find_spec("ard") is None, reason="ard is not installed") -@pytest.mark.parametrize("example_folder,resource_example_folder", [("29_wind_ard", None)]) -def test_windard_pv_battery_dispatch_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create the model - model = H2IntegrateModel(example_folder / "h2i_inputs/wind_pv_battery.yaml") - - # Run the model - model.run() - - # Post-process the results - model.post_process() - - with subtests.test("Check wind generation"): - # Wind should generate some electricity - wind_electricity = model.prob.get_val("wind.electricity_out", units="GW") - assert wind_electricity.sum() == pytest.approx(150.88490967164714, rel=1e-4) - - with subtests.test("Check solar generation"): - # Solar should generate some electricity - solar_electricity = model.prob.get_val("solar.electricity_out", units="GW") - assert solar_electricity.sum() == pytest.approx(44.22139046811775, rel=1e-4) - - with subtests.test("Check battery gets wind and solar output"): - # Wind plus solar electricity should match battery input (direct connection) - battery_electricity_in = model.prob.get_val("battery.electricity_in", units="GW") - assert wind_electricity.sum() + solar_electricity.sum() == pytest.approx( - battery_electricity_in.sum(), rel=1e-6 - ) - - with subtests.test("Check demand satisfaction"): - 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:], - model.prob.get_val("battery.electricity_demand", units="MW")[8700:], - ) - - # Subtest for LCOE - with subtests.test("Check dispatched LCOE value"): - lcoe = model.prob.get_val( - "finance_subgroup_dispatched_electricity.LCOE", units="USD/(kW*h)" - )[0] - assert pytest.approx(lcoe, rel=1e-6) == 0.09289430342906849 - - with subtests.test("Check generation LCOE value (excludes battery)"): - lcoe = model.prob.get_val("finance_subgroup_produced_electricity.LCOE", units="USD/(kW*h)")[ - 0 - ] - assert pytest.approx(lcoe, rel=1e-6) == 0.07204429286793802 - - # Subtest for total electricity produced - with subtests.test("Check total electricity dispatched"): - total_electricity_year_one = ( - model.prob.get_val( - "finance_subgroup_dispatched_electricity.rated_electricity_production", - units="MW", - )[0] - * model.prob.get_val( - "finance_subgroup_dispatched_electricity.capacity_factor", - units="unitless", - )[0] - * 8760 - ) - assert total_electricity_year_one == pytest.approx(dispatched_electricity.sum()) - - # Subtest for electricity curtailed - with subtests.test("Check electricity curtailed"): - electricity_curtailed = model.prob.get_val( - "electrical_load_demand.unused_electricity_out", units="MW" - ).sum() - - 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("electrical_load_demand.unmet_electricity_demand_out", units="MW") - ) - assert electricity_missed_load == pytest.approx(1403.5372787817894) - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("20_solar_electrolyzer_doe", None)] -) -def test_csvgen_design_of_experiments(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - with pytest.raises(UserWarning) as excinfo: - model = H2IntegrateModel(example_folder / "20_solar_electrolyzer_doe.yaml") - assert "There may be issues with the csv file csv_doe_cases.csv" in str(excinfo.value) - - from h2integrate.core.dict_utils import update_defaults - from h2integrate.core.file_utils import check_file_format_for_csv_generator - from h2integrate.core.inputs.validation import write_yaml, load_driver_yaml - - # load the driver config file - driver_config = load_driver_yaml("driver_config.yaml") - # specify the filepath to the csv file - csv_fpath = Path(driver_config["driver"]["design_of_experiments"]["filename"]).absolute() - # run the csv checker method, we want it to write the csv file to a new filepath so - # set overwrite_file=False - new_csv_filename = check_file_format_for_csv_generator( - csv_fpath, driver_config, check_only=False, overwrite_file=False - ) - - # update the csv filename in the driver config dictionary - updated_driver = update_defaults(driver_config["driver"], "filename", new_csv_filename.name) - driver_config["driver"].update(updated_driver) - - # save the updated driver to a new file - new_driver_fpath = example_folder / "driver_config_test.yaml" - new_toplevel_fpath = example_folder / "20_solar_electrolyzer_doe_test.yaml" - write_yaml(driver_config, new_driver_fpath) - - # update the driver config filename in the top-level config - main_config = load_yaml("20_solar_electrolyzer_doe.yaml") - main_config["driver_config"] = new_driver_fpath.name - - # save the updated top-level config file to a new file - write_yaml(main_config, new_toplevel_fpath) - - # Run the model - model = H2IntegrateModel(new_toplevel_fpath) - model.run() - - # summarize sql file - model.post_process(summarize_sql=True) - - with subtests.test("Check that sql file was summarized"): - assert model.recorder_path is not None - summarized_filepath = model.recorder_path.parent / f"{model.recorder_path.stem}.csv" - assert summarized_filepath.is_file() - with subtests.test("Check that sql summary file was written as expected"): - summary = pd.read_csv(summarized_filepath, index_col="Unnamed: 0") - assert len(summary) == 10 - d_var_cols = ["solar.system_capacity_DC (kW)", "electrolyzer.n_clusters (unitless)"] - assert summary.columns.to_list()[0] in d_var_cols - assert summary.columns.to_list()[1] in d_var_cols - assert "finance_subgroup_hydrogen.LCOH_optimistic (USD/kg)" in summary.columns.to_list() - # delete summary file - summarized_filepath.unlink() - - sql_fpath = example_folder / "ex_20_out" / "cases.sql" - cr = om.CaseReader(str(sql_fpath)) - cases = list(cr.get_cases()) - - with subtests.test("Check solar capacity in case 0"): - assert ( - pytest.approx(cases[0].get_val("solar.system_capacity_DC", units="MW"), rel=1e-6) - == 25.0 - ) - with subtests.test("Check solar capacity in case 9"): - assert ( - pytest.approx(cases[-1].get_val("solar.system_capacity_DC", units="MW"), rel=1e-6) - == 500.0 - ) - - with subtests.test("Check electrolyzer capacity in case 0"): - assert ( - pytest.approx( - cases[0].get_val("electrolyzer.electrolyzer_size_mw", units="MW"), rel=1e-6 - ) - == 10.0 * 5 - ) - - with subtests.test("Check electrolyzer capacity in case 9"): - assert ( - pytest.approx( - cases[-1].get_val("electrolyzer.electrolyzer_size_mw", units="MW"), rel=1e-6 - ) - == 10.0 * 10 - ) - - min_lcoh_val = 100000.0 - min_lcoh_case_num = 0 - for i, case in enumerate(cases): - lcoh = case.get_val("finance_subgroup_hydrogen.LCOH_optimistic", units="USD/kg")[0] - if lcoh < min_lcoh_val: - min_lcoh_val = np.min([lcoh, min_lcoh_val]) - min_lcoh_case_num = i - - with subtests.test("Min LCOH value"): - assert pytest.approx(min_lcoh_val, rel=1e-6) == 4.663014422338 - - with subtests.test("Min LCOH case number"): - assert min_lcoh_case_num == 6 - - with subtests.test("Min LCOH case LCOH value"): - assert ( - pytest.approx( - cases[min_lcoh_case_num].get_val( - "finance_subgroup_hydrogen.LCOH_optimistic", units="USD/kg" - ), - rel=1e-6, - ) - == min_lcoh_val - ) - - with subtests.test("Min LCOH case has lower LCOH than other cases"): - for i, case in enumerate(cases): - lcoh_case = case.get_val("finance_subgroup_hydrogen.LCOH_optimistic", units="USD/kg") - if i != min_lcoh_case_num: - assert lcoh_case > min_lcoh_val - - with subtests.test("Min LCOH solar capacity"): - assert ( - pytest.approx( - cases[min_lcoh_case_num].get_val("solar.system_capacity_DC", units="MW"), rel=1e-6 - ) - == 200.0 - ) - - with subtests.test("Min LCOH electrolyzer capacity"): - assert ( - pytest.approx( - cases[min_lcoh_case_num].get_val("electrolyzer.electrolyzer_size_mw", units="MW"), - rel=1e-6, - ) - == 100.0 - ) - - # remove files created - new_driver_fpath.unlink() - new_toplevel_fpath.unlink() - new_csv_filename.unlink() - - -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("22_site_doe", None)]) -def test_sweeping_solar_sites_doe(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create the model - model = H2IntegrateModel(example_folder / "22_solar_site_doe.yaml") - - # Run the model - model.run() - - # Specify the filepath to the sql file, the folder and filename are in the driver_config - sql_fpath = example_folder / "ex_22_out" / "cases.sql" - - # load the cases - cr = om.CaseReader(sql_fpath) - - cases = list(cr.get_cases()) - - res_df = pd.DataFrame() - for ci, case in enumerate(cases): - solar_resource_data = case.get_val("site.solar_resource.solar_resource_data") - lat_lon = ( - f"{case.get_val('site.latitude', units='deg')[0]} " - f"{case.get_val('site.longitude', units='deg')[0]}" - ) - solar_capacity = case.get_design_vars()["solar.system_capacity_DC"][0] - aep = case.get_val("solar.annual_electricity_produced", units="MW*h/yr")[0] - lcoe = case.get_val("finance_subgroup_electricity.LCOE_optimistic", units="USD/(MW*h)")[0] - - site_res = pd.DataFrame( - [aep, lcoe, solar_capacity], index=["AEP", "LCOE", "solar_capacity"], columns=[lat_lon] - ).T - res_df = pd.concat([site_res, res_df], axis=0) - - with subtests.test(f"Case {ci}: Solar resource latitude matches site latitude"): - assert ( - pytest.approx(case.get_val("site.latitude", units="deg"), abs=0.1) - == solar_resource_data["site_lat"] - ) - with subtests.test(f"Case {ci}: Solar resource longitude matches site longitude"): - assert ( - pytest.approx(case.get_val("site.longitude", units="deg"), abs=0.1) - == solar_resource_data["site_lon"] - ) - - locations = list(set(res_df.index.to_list())) - solar_sizes = list(set(res_df["solar_capacity"].to_list())) - - with subtests.test("Two solar sizes per site"): - assert len(solar_sizes) == 2 - with subtests.test("Two unique sites"): - assert len(locations) == 2 - - with subtests.test("Unique AEPs per case"): - assert len(list(set(res_df["AEP"].to_list()))) == len(res_df) - - with subtests.test("Unique LCOEs per case"): - 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): - example_folder = temp_copy_of_example - - driver_config = load_yaml(example_folder / "driver_config.yaml") - tech_config = load_yaml(example_folder / "tech_config.yaml") - plant_config = load_yaml(example_folder / "plant_config.yaml") - - h2i_config = { - "name": "H2Integrate_config", - "system_summary": "", - "driver_config": driver_config, - "technology_config": tech_config, - "plant_config": plant_config, - } - - # Create a H2I model - h2i = H2IntegrateModel(h2i_config) - - # Run the model - h2i.run() - - with subtests.test("Distributed LCOE"): - assert ( - pytest.approx( - h2i.prob.get_val("finance_subgroup_distributed.LCOE", units="USD/MW/h")[0], rel=1e-6 - ) - == 99.872209 - ) - with subtests.test("Utility LCOE"): - assert ( - pytest.approx( - h2i.prob.get_val("finance_subgroup_utility.LCOE", units="USD/MW/h")[0], rel=1e-6 - ) - == 54.2709437311 - ) - - with subtests.test("Total LCOE"): - assert ( - pytest.approx( - h2i.prob.get_val("finance_subgroup_total_electricity.LCOE", units="USD/MW/h")[0], - rel=1e-6, - ) - == 65.2444127137 - ) - - with subtests.test("Distributed wind plant capacity"): - assert ( - pytest.approx( - h2i.prob.get_val("distributed_wind_plant.rated_electricity_production", units="MW"), - rel=1e-6, - ) - == 66.0 - ) - - with subtests.test("Total distributed electricity production"): - assert ( - pytest.approx( - np.sum( - h2i.prob.get_val( - "distributed_wind_plant.total_electricity_produced", units="MW*h" - ) - ), - rel=1e-6, - ) - == 128948.21977 - ) - - with subtests.test("Total utility electricity production"): - assert ( - pytest.approx( - h2i.prob.get_val("utility_wind_plant.electricity_out", units="MW").sum(), rel=1e-6 - ) - == 406908.03381618496 - ) - - with subtests.test("Distributed wind capacity factor"): - assert ( - pytest.approx( - h2i.prob.get_val("distributed_wind_plant.capacity_factor", units="percent")[0], - rel=1e-6, - ) - == 22.30320668 - ) - - with subtests.test("Utility wind plant capacity"): - assert ( - pytest.approx( - h2i.prob.get_val("utility_wind_plant.rated_electricity_production", units="MW"), - rel=1e-6, - ) - == 120.0 - ) - - with subtests.test("Distributed wind site location"): - assert ( - pytest.approx(h2i.prob.get_val("distributed_wind_site.latitude", units="deg"), rel=1e-6) - == 44.04218 - ) - assert ( - pytest.approx( - h2i.prob.get_val("distributed_wind_site.longitude", units="deg"), rel=1e-6 - ) - == -95.19757 - ) - - with subtests.test("Distributed wind plant resource location"): - assert ( - pytest.approx( - h2i.prob.get_val("distributed_wind_plant.wind_resource_data")["site_lat"], abs=1e-2 - ) - == 44.04218 - ) - assert ( - pytest.approx( - h2i.prob.get_val("distributed_wind_plant.wind_resource_data")["site_lon"], abs=1e-2 - ) - == -95.19757 - ) - - with subtests.test("Utility wind site location"): - assert ( - pytest.approx(h2i.prob.get_val("utility_wind_site.latitude", units="deg"), rel=1e-6) - == 35.2018863 - ) - assert ( - pytest.approx(h2i.prob.get_val("utility_wind_site.longitude", units="deg"), rel=1e-6) - == -101.945027 - ) - - with subtests.test("Utility wind plant resource location"): - assert ( - pytest.approx( - h2i.prob.get_val("utility_wind_plant.wind_resource_data")["site_lat"], abs=1e-2 - ) - == 35.2018863 - ) - assert ( - pytest.approx( - h2i.prob.get_val("utility_wind_plant.wind_resource_data")["site_lon"], abs=1e-2 - ) - == -101.945027 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("24_solar_battery_grid", "11_hybrid_energy_plant/")] -) -def test_24_solar_battery_grid_example(subtests, temp_copy_of_example): - # NOTE: would be good to compare LCOE against the same example without grid selling - # and see that LCOE reduces with grid selling - example_folder = temp_copy_of_example - - model = H2IntegrateModel(example_folder / "solar_battery_grid.yaml") - - model.run() - - model.post_process() - - energy_for_financials = ( - model.prob.get_val("finance_subgroup_renewables.rated_electricity_production", units="kW")[ - 0 - ] - * model.prob.get_val("finance_subgroup_renewables.capacity_factor", units="unitless").mean() - * 8760 - ) - - electricity_bought = sum(model.prob.get_val("grid_buy.electricity_out", 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("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")) - - with subtests.test("Behavior check battery missed load is electricity bought"): - assert pytest.approx(battery_missed_load, rel=1e-6) == electricity_bought - - with subtests.test("Behavior check battery curtailed energy is electricity sold"): - assert pytest.approx(battery_curtailed, rel=1e-6) == electricity_sold - - with subtests.test( - "Behavior check energy for financials; include solar aep and electricity bought" - ): - assert pytest.approx(energy_for_financials, rel=1e-6) == (solar_aep + electricity_bought) - - with subtests.test("Value check on LCOE"): - lcoe = model.prob.get_val("finance_subgroup_renewables.LCOE", units="USD/(MW*h)")[0] - assert pytest.approx(lcoe, rel=1e-4) == 91.7057887 - - -@pytest.mark.integration -@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 - - from h2integrate.postprocess.mapping import ( - plot_geospatial_point_heat_map, - plot_straight_line_shipping_routes, - ) - - example_folder = temp_copy_of_example - - # Define filepaths - 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_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="finance_subgroup_sponge_iron.LCOS (USD/kg)", - map_preferences={ - "figsize": (10, 8), - "colorbar_label": "Levelized Cost of\nIron [$/kg]", - "colorbar_limits": (0.6, 1.0), - }, - ) - # Add a layer for example ore cost prices from select mines - fig, ax, ore_cost_layer_gdf = plot_geospatial_point_heat_map( - case_results_fpath=ore_prices_filepath, - metric_to_plot="ore_cost_per_kg", - map_preferences={ - "colormap": "Greens", - "marker": "o", - "colorbar_bbox_to_anchor": (0.025, 0.97, 1, 1), - "colorbar_label": "Levelized Cost of\nIron Ore Pellets\n[$/kg ore]", - "colorbar_limits": (0.11, 0.14), - }, - fig=fig, - ax=ax, - base_layer_gdf=lcoi_layer_gdf, - ) - # Add a layer for example waterway shipping cost from select mines to select ports - fig, ax, shipping_cost_layer_gdf = plot_geospatial_point_heat_map( - case_results_fpath=shipping_prices_filepath, - metric_to_plot="shipping_cost_per_kg", - map_preferences={ - "colormap": "Greys", - "marker": "d", - "markersize": 80, - "colorbar_bbox_to_anchor": (0.4, 0.97, 1, 1), - "colorbar_label": "Waterway Shipping Cost\n[$/kg ore]", - "colorbar_limits": (0.11, 0.14), - }, - fig=fig, - ax=ax, - base_layer_gdf=[lcoi_layer_gdf, ore_cost_layer_gdf], - ) - - # Define example water way shipping routes for plotting straight line transport - cleveland_route = [ - "Duluth", - "Keweenaw", - "Sault St Marie", - "De Tour", - "Lake Huron", - "Port Huron", - "Erie", - "Cleveland", - ] - buffalo_route = [ - "Duluth", - "Keweenaw", - "Sault St Marie", - "De Tour", - "Lake Huron", - "Port Huron", - "Erie", - "Cleveland", - "Buffalo", - ] - chicago_route = [ - "Duluth", - "Keweenaw", - "Sault St Marie", - "De Tour", - "Mackinaw", - "Manistique", - "Chicago", - ] - - # Add cleveland route as layer - fig, ax, transport_layer1_gdf = plot_straight_line_shipping_routes( - shipping_coords_fpath=shipping_coords_filepath, - shipping_route=cleveland_route, - map_preferences={}, - fig=fig, - ax=ax, - base_layer_gdf=[lcoi_layer_gdf, ore_cost_layer_gdf, shipping_cost_layer_gdf], - ) - # Add buffalo route as layer - fig, ax, transport_layer2_gdf = plot_straight_line_shipping_routes( - shipping_coords_fpath=shipping_coords_filepath, - shipping_route=buffalo_route, - map_preferences={}, - fig=fig, - ax=ax, - base_layer_gdf=[ - lcoi_layer_gdf, - ore_cost_layer_gdf, - shipping_cost_layer_gdf, - transport_layer1_gdf, - ], - ) - # Add chicago route as layer - fig, ax, transport_layer3_gdf = plot_straight_line_shipping_routes( - shipping_coords_fpath=shipping_coords_filepath, - shipping_route=chicago_route, - map_preferences={"figure_title": "Example H2 DRI Iron Costs"}, - fig=fig, - ax=ax, - base_layer_gdf=[ - lcoi_layer_gdf, - ore_cost_layer_gdf, - shipping_cost_layer_gdf, - transport_layer1_gdf, - transport_layer2_gdf, - ], - save_plot_fpath=ex_png_fpath, - ) - - with subtests.test("Type check on fig, ax, and lcoi_layer_gdf"): - assert isinstance( - fig, matplotlib.figure.Figure - ), f"Expected matplotlib.figure.Figure but got{type(fig)}" - assert isinstance( - ax, matplotlib.axes._axes.Axes - ), f"Expected matplotlib.axes._axes.Axes but got{type(ax)}" - assert isinstance( - lcoi_layer_gdf, gpd.geodataframe.GeoDataFrame - ), f"Expected gpd.geodataframe.GeoDataFrame but got{type(lcoi_layer_gdf)}" - assert isinstance( - lcoi_layer_gdf, gpd.geodataframe.GeoDataFrame - ), f"Expected gpd.geodataframe.GeoDataFrame but got{type(transport_layer1_gdf)}" - - with subtests.test("Check example_28_iron_map.png was saved"): - assert (ex_png_fpath).is_file(), "example_28_iron_map.png file not found" - - # Clean up any output files/dirs created - ex_png_fpath.unlink(missing_ok=True) - - -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("04_geo_h2", None)]) -def test_natural_geoh2(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - h2i_nat = H2IntegrateModel(example_folder / "04_geo_h2_natural.yaml") - h2i_nat.run() - - with subtests.test("H2 Production"): - assert ( - pytest.approx( - np.mean(h2i_nat.model.get_val("geoh2_well_subsurface.hydrogen_out", units="kg/h")), - rel=1e-6, - ) - == 606.1508855232839 - ) - - with subtests.test("integrated LCOH"): - assert ( - pytest.approx( - h2i_nat.prob.get_val("finance_subgroup_h2.LCOH", units="USD/kg"), rel=1e-6 - ) - == 1.3089029 - ) - with subtests.test("subsurface Capex"): - assert ( - pytest.approx( - h2i_nat.model.get_val("geoh2_well_subsurface.CapEx", units="USD"), rel=1e-6 - ) - == 7667341.11417252 - ) - with subtests.test("subsurface fixed Opex"): - assert ( - pytest.approx( - h2i_nat.model.get_val("geoh2_well_subsurface.OpEx", units="USD/year"), rel=1e-6 - ) - == 215100.7857875 - ) - with subtests.test("subsurface variable Opex"): - assert ( - pytest.approx( - h2i_nat.model.get_val("geoh2_well_subsurface.VarOpEx", units="USD/year"), - rel=1e-6, - ) - == 0.0 - ) - with subtests.test("subsurface adjusted opex"): - adjusted_opex = h2i_nat.prob.get_val( - "finance_subgroup_h2.opex_adjusted_geoh2_well_subsurface", units="USD/year" - ) - assert pytest.approx(adjusted_opex, rel=1e-6) == 215100.7857875 - - with subtests.test("surface Capex"): - assert ( - pytest.approx(h2i_nat.model.get_val("geoh2_well_surface.CapEx", units="USD"), rel=1e-6) - == 1800711.83796 - ) - with subtests.test("surface fixed Opex"): - assert ( - pytest.approx( - h2i_nat.model.get_val("geoh2_well_surface.OpEx", units="USD/year"), rel=1e-6 - ) - == 4567464 - ) - with subtests.test("surface variable Opex"): - assert ( - pytest.approx( - h2i_nat.model.get_val("geoh2_well_surface.VarOpEx", units="USD/year"), rel=1e-6 - ) - == 989213.8787 - ) - with subtests.test("surface adjusted opex"): - surface_adjusted_opex = h2i_nat.prob.get_val( - "finance_subgroup_h2.opex_adjusted_geoh2_well_surface", units="USD/year" - ) - assert pytest.approx(surface_adjusted_opex, rel=1e-6) == 4798691.865 - - -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("04_geo_h2", None)]) -def test_stimulated_geoh2(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - h2i_stim = H2IntegrateModel(example_folder / "04_geo_h2_stimulated.yaml") - h2i_stim.run() - - h2_prod = h2i_stim.model.get_val("geoh2_well_subsurface.hydrogen_out", units="kg/h") - - with subtests.test("H2 Production"): - assert pytest.approx(np.mean(h2_prod), rel=1e-6) == 155.03934945719536 - - with subtests.test("integrate LCOH"): - lcoh = h2i_stim.prob.get_val("finance_subgroup_default.LCOH", units="USD/kg") - assert lcoh == pytest.approx( - 2.29337734, 1e-6 - ) # previous val from custom finance model was 1.74903827 - - # failure is expected because we are inflating using general inflation rather than CPI and CEPCI - with subtests.test("Capex"): - assert ( - pytest.approx( - h2i_stim.model.get_val("geoh2_well_subsurface.CapEx", units="USD"), rel=1e-6 - ) - == 19520122.88478073 - ) - with subtests.test("fixed Opex"): - assert ( - pytest.approx( - h2i_stim.model.get_val("geoh2_well_subsurface.OpEx", units="USD/year"), rel=1e-6 - ) - == 215100.7857875 - ) - with subtests.test("variable Opex"): - var_om_pr_h2 = h2i_stim.model.get_val( - "geoh2_well_subsurface.VarOpEx", units="USD/year" - ) / np.sum(h2_prod) - assert pytest.approx(var_om_pr_h2, rel=1e-6) == 0.32105362 - with subtests.test("adjusted Opex"): - adjusted_opex = h2i_stim.prob.get_val( - "finance_subgroup_default.opex_adjusted_geoh2_well_subsurface", units="USD/year" - ) - assert pytest.approx(adjusted_opex, rel=1e-6) == 215100.7857875 - - -@pytest.mark.integration -@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 / "single_site_iron.yaml") - - h2i.run() - - with subtests.test("Value check on LCOI"): - lcoi = h2i.model.get_val("finance_subgroup_iron_ore.LCOI", units="USD/t")[0] - assert pytest.approx(lcoi, rel=1e-4) == 135.3741358811098 - - with subtests.test("Value check on LCOS"): - lcos = h2i.model.get_val("finance_subgroup_sponge_iron.LCOS", units="USD/t")[0] - assert pytest.approx(lcos, rel=1e-4) == 359.670379351 - - with subtests.test("Value check on LCOS"): - lcos = h2i.model.get_val("finance_subgroup_steel.LCOS", units="USD/t")[0] - assert pytest.approx(lcos, rel=1e-4) == 531.5842266865 - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("21_iron_examples/iron_electrowinning", None)] -) -def test_iron_electrowinning_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - model = H2IntegrateModel(example_folder / "iron_electrowinning.yaml") - - with subtests.test("Value check on AHE"): - model.technology_config["technologies"]["iron_plant"]["model_inputs"]["shared_parameters"][ - "electrolysis_type" - ] = "ahe" - model.setup() - model.run() - lcoi = model.model.get_val("finance_subgroup_sponge_iron.LCOS", units="USD/kg")[0] - assert pytest.approx(lcoi, rel=1e-4) == 2.187185703820872 - - with subtests.test("Value check on MSE"): - model.technology_config["technologies"]["iron_plant"]["model_inputs"]["shared_parameters"][ - "electrolysis_type" - ] = "mse" - model.technology_config["technologies"]["ewin_NaOH_feedstock"]["model_inputs"][ - "performance_parameters" - ]["rated_capacity"] = 0 - model.technology_config["technologies"]["ewin_CaCl2_feedstock"]["model_inputs"][ - "performance_parameters" - ]["rated_capacity"] = 179.0 - - model.setup() - model.run() - lcoi = model.model.get_val("finance_subgroup_sponge_iron.LCOS", units="USD/kg")[0] - assert pytest.approx(lcoi, rel=1e-4) == 3.3399342887615115 - - with subtests.test("Value check on MOE"): - model.technology_config["technologies"]["iron_plant"]["model_inputs"]["shared_parameters"][ - "electrolysis_type" - ] = "moe" - model.technology_config["technologies"]["ewin_NaOH_feedstock"]["model_inputs"][ - "performance_parameters" - ]["rated_capacity"] = 0 - model.setup() - model.run() - lcoi = model.model.get_val("finance_subgroup_sponge_iron.LCOS", units="USD/kg")[0] - assert pytest.approx(lcoi, rel=1e-4) == 2.2802793527655987 - - -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("27_site_doe_diff", None)]) -def test_sweeping_different_resource_sites_doe(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create the model - model = H2IntegrateModel(example_folder / "27_wind_solar_site_doe.yaml") - - # # Run the model - model.run() - - # Specify the filepath to the sql file, the folder and filename are in the driver_config - sql_fpath = example_folder / "ex_27_out" / "cases.sql" - - # load the cases - cr = om.CaseReader(sql_fpath) - - cases = list(cr.get_cases()) - - res_df = pd.DataFrame() - for ci, case in enumerate(cases): - solar_resource_data = case.get_val("solar_site.solar_resource.solar_resource_data") - wind_resource_data = case.get_val("wind_site.wind_resource.wind_resource_data") - with subtests.test(f"Case {ci}: Solar resource latitude matches site latitude"): - assert ( - pytest.approx( - case.get_val("solar_site.solar_resource.latitude", units="deg"), abs=0.1 - ) - == solar_resource_data["site_lat"] - ) - with subtests.test(f"Case {ci}: Wind resource latitude matches site latitude"): - assert ( - pytest.approx( - case.get_val("wind_site.wind_resource.latitude", units="deg"), abs=0.1 - ) - == wind_resource_data["site_lat"] - ) - - s_lat = case.get_val("solar_site.solar_resource.latitude", units="deg")[0] - s_lon = case.get_val("solar_site.solar_resource.longitude", units="deg")[0] - solar_lat_lon = f"{s_lat} {s_lon}" - w_lat = case.get_val("wind_site.wind_resource.latitude", units="deg")[0] - w_lon = case.get_val("wind_site.wind_resource.longitude", units="deg")[0] - wind_lat_lon = f"{w_lat} {w_lon}" - - solar_capacity = case.get_design_vars()["solar.system_capacity_DC"][0] - - solar_aep = np.sum(case.get_val("solar.electricity_out", units="MW")) - solar_lcoe = case.get_val("finance_subgroup_solar.LCOE", units="USD/(MW*h)")[0] - - wind_aep = np.sum(case.get_val("wind.electricity_out", units="MW")) - wind_lcoe = case.get_val("finance_subgroup_wind.LCOE", units="USD/(MW*h)")[0] - - combiner_aep = np.sum(case.get_val("combiner.electricity_out", units="MW")) - combiner_lcoe = case.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0] - - index_cols = [ - "solar site", - "wind site", - "solar AEP", - "solar LCOE", - "solar size", - "wind AEP", - "wind LCOE", - "combiner AEP", - "combiner LCOE", - ] - vals = [ - solar_lat_lon, - wind_lat_lon, - solar_aep, - solar_lcoe, - solar_capacity, - wind_aep, - wind_lcoe, - combiner_aep, - combiner_lcoe, - ] - - site_res = pd.DataFrame(vals, index=index_cols, columns=[ci]).T - - res_df = pd.concat([site_res, res_df], axis=0) - - with subtests.test("Two unique solar capacities"): - solar_sizes = list(set(res_df["solar site"].to_list())) - assert len(solar_sizes) == 2 - - with subtests.test("Two unique solar sites"): - solar_locations = list(set(res_df["solar site"])) - assert len(solar_locations) == 2 - - with subtests.test("Two unique wind sites"): - wind_locations = list(set(res_df["wind site"])) - assert len(wind_locations) == 2 - - with subtests.test("Unique solar AEPS"): - assert len(list(set(res_df["solar AEP"].to_list()))) == 4 - - with subtests.test("Unique solar LCOEs"): - assert len(list(set(res_df["solar LCOE"].to_list()))) == 4 - - with subtests.test("Unique wind AEPS"): - assert len(list(set(res_df["wind AEP"].to_list()))) == 2 - - with subtests.test("Unique wind LCOEs"): - assert len(list(set(res_df["wind LCOE"].to_list()))) == 2 - - with subtests.test("Unique combiner AEPS"): - assert len(list(set(res_df["combiner AEP"].to_list()))) == len(res_df) - - with subtests.test("Unique LCOEs per case"): - assert len(list(set(res_df["combiner LCOE"].to_list()))) == len(res_df) - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("30_pyomo_optimized_dispatch", None)] -) -def test_pyomo_optimized_dispatch_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "pyomo_optimized_dispatch.yaml") - - demand_profile = np.ones(8760) * 100.0 - - # 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() - - model.post_process() - - with subtests.test("Check wind total electricity produced"): - wind_total = model.prob.get_val("wind.total_electricity_produced", units="kW*h")[0] - assert wind_total == pytest.approx(781_472_811.8, rel=1e-3) - - with subtests.test("Check wind capacity factor"): - wind_cf = model.prob.get_val("wind.capacity_factor", units="unitless")[0] - assert wind_cf == pytest.approx(0.4299, rel=1e-3) - - with subtests.test("Check wind CapEx"): - wind_capex = model.prob.get_val("wind.CapEx", units="USD")[0] - assert wind_capex == pytest.approx(311_250_000.0, rel=1e-3) - - # Battery checks - with subtests.test("Check battery total electricity produced"): - 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("electrical_load_demand.capacity_factor", units="unitless")[ - 0 - ] - assert battery_cf == pytest.approx(0.7372, rel=1e-3) - - with subtests.test("Check battery CapEx"): - battery_capex = model.prob.get_val("battery.CapEx", units="USD")[0] - assert battery_capex == pytest.approx(155_100_000.0, rel=1e-3) - - with subtests.test("Check battery OpEx"): - battery_opex = model.prob.get_val("battery.OpEx", units="USD/year")[0] - assert battery_opex == pytest.approx(38_775_000.0, rel=1e-3) - - # Finance checks - with subtests.test("Check LCOE"): - lcoe = model.prob.get_val("finance_subgroup_all_electricity.LCOE", units="USD/(kW*h)")[0] - assert lcoe == pytest.approx(0.134, rel=1e-3) - - with subtests.test("Check total adjusted CapEx"): - total_capex = model.prob.get_val( - "finance_subgroup_all_electricity.total_capex_adjusted", units="USD" - )[0] - assert total_capex == pytest.approx(490_282_207.03, rel=1e-3) - - with subtests.test("Check total adjusted OpEx"): - total_opex = model.prob.get_val( - "finance_subgroup_all_electricity.total_opex_adjusted", units="USD/year" - )[0] - assert total_opex == pytest.approx(48_830_466.21, rel=1e-3) - - with subtests.test("Check total electricity produced"): - total_electricity = ( - model.prob.get_val( - "finance_subgroup_all_electricity.rated_electricity_production", - units="kW", - )[0] - * model.prob.get_val( - "finance_subgroup_all_electricity.capacity_factor", - units="unitless", - ).mean() - * 8760 - ) - assert total_electricity == pytest.approx(781_472_811.8, rel=1e-3) - - with subtests.test("Check electricity price"): - price = model.prob.get_val( - "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) - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", - [("21_iron_examples/iron_cmu/scrap_only", None)], -) -def test_cmu_eaf_scrap_only_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - h2i = H2IntegrateModel(example_folder / "single_site_steel.yaml") - - h2i.run() - - h2i.post_process() - - with subtests.test("EAF CapEx"): - capex = h2i.model.get_val("steel_plant.CapEx", units="USD") - assert pytest.approx(capex, rel=1e-4) == 762_839_961.07 - - with subtests.test("EAF OpEx"): - opex = h2i.model.get_val("steel_plant.OpEx", units="USD/year") - assert pytest.approx(opex, rel=1e-4) == 48_328_598.25 - - with subtests.test("LCOS"): - lcos = h2i.model.get_val("finance_subgroup_steel.LCOS", units="USD/kg")[0] - assert pytest.approx(lcos, rel=1e-4) == 0.2233330 - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", - [("21_iron_examples/iron_cmu/dri", None)], -) -def test_cmu_eaf_dri_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - h2i = H2IntegrateModel(example_folder / "single_site_steel.yaml") - - h2i.run() - - h2i.post_process() - - with subtests.test("EAF CapEx"): - capex = h2i.model.get_val("steel_plant.CapEx", units="USD") - assert pytest.approx(capex, rel=1e-4) == 762_839_961.07 - - with subtests.test("EAF OpEx"): - opex = h2i.model.get_val("steel_plant.OpEx", units="USD/year") - assert pytest.approx(opex, rel=1e-4) == 48_328_598.25 - - with subtests.test("LCOS"): - lcos = h2i.model.get_val("finance_subgroup_steel.LCOS", units="USD/kg")[0] - assert pytest.approx(lcos, rel=1e-4) == 1.4932 - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("33_peak_load_management", None)] -) -def test_peak_load_management_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - model = H2IntegrateModel(example_folder / "33_peak_load_management.yaml") - model.setup() - model.run() - - with subtests.test("Battery SOC mean"): - soc = model.prob.get_val("battery.SOC", units="percent") - assert soc.mean() == pytest.approx(63.333, rel=1e-3) - - with subtests.test("Battery SOC stays within bounds"): - soc = model.prob.get_val("battery.SOC", units="percent") - assert soc.max() <= 90.0 + 1e-3 - assert soc.min() >= 10.0 - 1e-3 - - with subtests.test("Battery set point sum"): - set_point = model.prob.get_val("battery.electricity_set_point", units="kW") - assert set_point.sum() == pytest.approx(60.0, rel=1e-3) - - with subtests.test("Battery electricity out sum"): - elec_out = model.prob.get_val("battery.electricity_out", units="kW") - assert elec_out.sum() == pytest.approx(60.0, rel=1e-3) - - with subtests.test("Unmet demand sum"): - unmet = model.prob.get_val( - "electrical_load_demand.unmet_electricity_demand_out", units="kW" - ) - assert unmet.sum() == pytest.approx(1947378.0, rel=1e-3) - - with subtests.test("Battery CapEx"): - capex = model.prob.get_val("battery.CapEx", units="USD") - assert capex[0] == pytest.approx(603300.0, rel=1e-3) - - with subtests.test("Unmet demand sum equals purchased electricity"): - battery_unmet_demand = model.prob.get_val( - "electrical_load_demand.unmet_electricity_demand_out", units="kW" - ) - grid_purchase = model.prob.get_val("grid_buy.electricity_out", units="kW") - assert battery_unmet_demand.sum() == pytest.approx(grid_purchase.sum(), rel=1e-3) - -import os -import importlib -from pathlib import Path - -import numpy as np -import pandas as pd -import pytest -import openmdao.api as om - -from h2integrate import ROOT_DIR -from h2integrate.core.file_utils import load_yaml -from h2integrate.core.h2integrate_model import H2IntegrateModel - - -ROOT = Path(__file__).parents[1] - - -# docs fencepost start: DO NOT REMOVE -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("01_onshore_steel_mn", None)]) -def test_steel_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "01_onshore_steel_mn.yaml") - # docs fencepost end: DO NOT REMOVE - # Set battery demand profile to electrolyzer capacity - demand_profile = np.ones(8760) * 720.0 - model.setup() - model.prob.set_val("battery.electricity_demand", demand_profile, units="MW") - - # Run the model - model.run() - - model.post_process() - # Subtests for checking specific values - with subtests.test("Check total electricity produced"): - assert ( - pytest.approx( - model.prob.get_val("combiner.electricity_out", units="MW").sum(), - rel=1e-3, - ) - == 5901098.278035271 - ) - - with subtests.test("Check total adjusted CapEx (electricity)"): - assert ( - pytest.approx( - model.prob.get_val( - "finance_subgroup_electricity.total_capex_adjusted", units="USD" - )[0], - rel=1e-3, - ) - == 4314364438.840067 - ) - with subtests.test("Check total adjusted OpEx (electricity)"): - assert ( - pytest.approx( - model.prob.get_val( - "finance_subgroup_electricity.total_opex_adjusted", units="USD/year" - )[0], - rel=1e-3, - ) - == 75831805.27785796 - ) - - with subtests.test("Check LCOE"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0], - rel=1e-3, - ) - == 90.8231905486079 - ) - - with subtests.test("Check H2 Storage capacity"): - assert ( - pytest.approx(model.prob.get_val("h2_storage.storage_capacity", units="kg"), rel=1e-3) - == 2559669.7759292 - ) - - with subtests.test("Check LCOH"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_hydrogen.LCOH_delivered", units="USD/kg")[0], - rel=1e-3, - ) - == 8.235313509720276 - ) - - with subtests.test("Check LCOS"): - assert ( - pytest.approx(model.prob.get_val("steel.LCOS", units="USD/t")[0], rel=1e-3) - == 1264.2821232584045 - ) - - with subtests.test("Check total adjusted CapEx"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_hydrogen.total_capex_adjusted", units="USD")[ - 0 - ], - rel=1e-3, - ) - == 5129491338.670795 - ) - - with subtests.test("Check total adjusted OpEx"): - assert ( - pytest.approx( - model.prob.get_val( - "finance_subgroup_hydrogen.total_opex_adjusted", units="USD/year" - )[0], - rel=1e-3, - ) - == 97887982.86294547 - ) - - with subtests.test("Check steel CapEx"): - assert ( - pytest.approx(model.prob.get_val("steel.CapEx", units="USD"), rel=1e-3) == 5.78060014e08 - ) - - with subtests.test("Check steel OpEx"): - assert ( - pytest.approx(model.prob.get_val("steel.OpEx", units="USD/year"), rel=1e-3) - == 1.0129052e08 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("02_texas_ammonia", None)]) -def test_simple_ammonia_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "02_texas_ammonia.yaml") - - # Set battery demand profile to electrolyzer capacity - demand_profile = np.ones(8760) * 640.0 - model.setup() - model.prob.set_val("battery.electricity_demand", demand_profile, units="MW") - - # Run the model - model.run() - - model.post_process() - - # Subtests for checking specific values - with subtests.test("Check Wind+PV CapEx"): - wind_pv_capex = ( - model.prob.get_val("wind.CapEx", units="USD")[0] - + model.prob.get_val("solar.CapEx", units="USD")[0] - ) - assert pytest.approx(wind_pv_capex, rel=1e-3) == 1.75469962e09 - - with subtests.test("Check Wind+PV OpEx"): - wind_pv_opex = ( - model.prob.get_val("wind.OpEx", units="USD/yr")[0] - + model.prob.get_val("solar.OpEx", units="USD/yr")[0] - ) - assert pytest.approx(wind_pv_opex, rel=1e-3) == 32953490.4 - - with subtests.test("Check electrolyzer CapEx"): - assert ( - pytest.approx(model.prob.get_val("electrolyzer.CapEx", units="USD"), rel=1e-3) - == 6.00412524e08 - ) - - with subtests.test("Check electrolyzer OpEx"): - assert ( - pytest.approx(model.prob.get_val("electrolyzer.OpEx", units="USD/year"), rel=1e-3) - == 14703155.39207595 - ) - - with subtests.test("Check H2 storage CapEx"): - assert ( - pytest.approx(model.prob.get_val("h2_storage.CapEx", units="USD")[0], rel=1e-3) - == 64599012.73829915 - ) - - with subtests.test("Check H2 storage OpEx"): - assert ( - pytest.approx(model.prob.get_val("h2_storage.OpEx", units="USD/year")[0], rel=1e-3) - == 2975616.8932987223 - ) - - with subtests.test("Check ammonia CapEx"): - assert ( - pytest.approx(model.prob.get_val("ammonia.CapEx", units="USD"), rel=1e-3) - == 1.0124126e08 - ) - - with subtests.test("Check ammonia OpEx"): - assert ( - pytest.approx(model.prob.get_val("ammonia.OpEx", units="USD/year"), rel=1e-3) - == 11178036.31197754 - ) - - with subtests.test("Check total adjusted CapEx"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_hydrogen.total_capex_adjusted", units="USD")[ - 0 - ], - rel=1e-3, - ) - == 2577162708.3 - ) - - with subtests.test("Check total adjusted OpEx"): - assert ( - pytest.approx( - model.prob.get_val( - "finance_subgroup_hydrogen.total_opex_adjusted", units="USD/year" - )[0], - rel=1e-3, - ) - == 53842563.43404999 - ) - - # Currently underestimated compared to the Reference Design Doc - with subtests.test("Check LCOH"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_hydrogen.LCOH", units="USD/kg")[0], - rel=1e-3, - ) - == 4.0155433 - ) - - with subtests.test("Check price of hydrogen"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_hydrogen.price_hydrogen", units="USD/kg")[0], - rel=1e-3, - ) - == 4.0155433 - ) - - # Currently underestimated compared to the Reference Design Doc - with subtests.test("Check LCOA"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_ammonia.LCOA", units="USD/kg")[0], - rel=1e-3, - ) - == 1.027395 - ) - - # Check that the expected output files exist - outputs_dir = example_folder / "outputs" - assert ( - outputs_dir / "profast_output_ammonia_config.yaml" - ).is_file(), "profast_output_ammonia.yaml not found" - assert ( - outputs_dir / "profast_output_electricity_config.yaml" - ).is_file(), "profast_output_electricity.yaml not found" - assert ( - outputs_dir / "profast_output_hydrogen_config.yaml" - ).is_file(), "profast_output_hydrogen.yaml not found" - - -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("12_ammonia_synloop", None)]) -def test_ammonia_synloop_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "12_ammonia_synloop.yaml") - - # Set battery demand profile to electrolyzer capacity - demand_profile = np.ones(8760) * 640.0 - model.setup() - model.prob.set_val("battery.electricity_demand", demand_profile, units="MW") - - # Run the model - model.run() - - model.post_process() - - # Subtests for checking specific values - with subtests.test("Check HOPP CapEx"): - wind_pv_capex = ( - model.prob.get_val("wind.CapEx", units="USD")[0] - + model.prob.get_val("solar.CapEx", units="USD")[0] - ) - battery_capex = model.prob.get_val("battery.CapEx", units="USD") - re_capex = wind_pv_capex + battery_capex - assert pytest.approx(re_capex, rel=1e-6) == 1.75469962e09 - - with subtests.test("Check HOPP OpEx"): - wind_pv_opex = ( - model.prob.get_val("wind.OpEx", units="USD/yr")[0] - + model.prob.get_val("solar.OpEx", units="USD/yr")[0] - ) - battery_opex = model.prob.get_val("battery.OpEx", units="USD/year") - re_opex = wind_pv_opex + battery_opex - assert pytest.approx(re_opex, rel=1e-6) == 32953490.4 - - with subtests.test("Check electrolyzer CapEx"): - assert ( - pytest.approx(model.prob.get_val("electrolyzer.CapEx", units="USD"), rel=1e-6) - == 6.00412524e08 - ) - - with subtests.test("Check electrolyzer OpEx"): - assert ( - pytest.approx(model.prob.get_val("electrolyzer.OpEx", units="USD/year"), rel=1e-6) - == 14703155.39207595 - ) - - with subtests.test("Check H2 storage CapEx"): - assert ( - pytest.approx(model.prob.get_val("h2_storage.CapEx", units="USD"), rel=1e-6) - == 64553014.22218219 - ) - - with subtests.test("Check H2 storage OpEx"): - assert ( - pytest.approx(model.prob.get_val("h2_storage.OpEx", units="USD/year"), rel=1e-6) - == 2975616.89 - ) - - with subtests.test("Check ammonia CapEx"): - assert ( - pytest.approx(model.prob.get_val("ammonia.CapEx", units="USD"), rel=1e-6) - == 1.15173753e09 - ) - - with subtests.test("Check ammonia OpEx"): - assert ( - pytest.approx(model.prob.get_val("ammonia.OpEx", units="USD/year")[0], rel=1e-4) - == 25414748.989416014 - ) - - with subtests.test("Check ammonia production"): - assert ( - pytest.approx( - model.prob.get_val("ammonia.annual_ammonia_produced", units="t/yr").mean(), rel=1e-4 - ) - == 406333.161 - ) - - with subtests.test("Check total adjusted CapEx"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_nh3.total_capex_adjusted", units="USD")[0], - rel=1e-6, - ) - == 3728034379.0699997 - ) - - with subtests.test("Check total adjusted OpEx"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_nh3.total_opex_adjusted", units="USD/year")[0], - rel=1e-6, - ) - == 79257312.42365658 - ) - - with subtests.test("Check LCOH"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_h2.LCOH", units="USD/kg")[0], rel=1e-6 - ) - == 4.013427289493614 - ) - - with subtests.test("Check LCOA"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_nh3.LCOA", units="USD/kg")[0], rel=1e-6 - ) - == 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 -@pytest.mark.parametrize("example_folder,resource_example_folder", [("03_methanol/smr", None)]) -def test_smr_methanol_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "03_smr_methanol.yaml") - - # Run the model - model.run() - - model.post_process() - - # Check levelized cost of methanol (LCOM) - with subtests.test("Check SMR LCOM"): - assert ( - pytest.approx(model.prob.get_val("methanol.LCOM", units="USD/kg"), rel=1e-6) - == 0.22116813 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("03_methanol/co2_hydrogenation", None)] -) -def test_co2h_methanol_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "03_co2h_methanol.yaml") - - # Run the model - model.run() - - model.post_process() - - # Below is used as an integration test for the combiner - with subtests.test("combiner rated production"): - combined_rated_input = model.prob.get_val( - "wind.rated_electricity_production", units="MW" - ) + model.prob.get_val("solar.rated_electricity_production", units="MW") - assert ( - pytest.approx( - model.prob.get_val("combiner.rated_electricity_production", units="MW"), rel=1e-6 - ) - == combined_rated_input - ) - with subtests.test("combiner weighted CF"): - wind_weighted_cf = model.prob.get_val( - "wind.rated_electricity_production", units="MW" - ) * model.prob.get_val("wind.capacity_factor", units="unitless") - solar_weighted_cf = model.prob.get_val( - "solar.rated_electricity_production", units="MW" - ) * model.prob.get_val("solar.capacity_factor", units="unitless") - combined_cf = (wind_weighted_cf + solar_weighted_cf) / combined_rated_input - assert ( - pytest.approx( - model.prob.get_val("combiner.capacity_factor", units="unitless"), - rel=1e-6, - ) - == combined_cf - ) - - # Check levelized cost of methanol (LCOM) - with subtests.test("Check CO2 Hydrogenation LCOM"): - assert ( - pytest.approx(model.prob.get_val("methanol.LCOM", units="USD/kg")[0], rel=1e-6) - == 1.7516172 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("03_methanol/co2_hydrogenation_doc", None)] -) -def test_doc_methanol_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "03_co2h_methanol.yaml") - - # Run the model - model.run() - - model.post_process() - - # Check levelized cost of methanol (LCOM) - with subtests.test("Check CO2 Hydrogenation LCOM"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_default.LCOM", units="USD/kg")[0], - rel=1e-4, - ) - == 2.5252588 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("05_wind_h2_opt", None)]) -def test_wind_h2_opt_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Run without optimization - model_init = H2IntegrateModel(example_folder / "wind_plant_electrolyzer0.yaml") - - # Run the model - model_init.run() - - model_init.post_process() - - annual_h20 = model_init.prob.get_val("electrolyzer.annual_hydrogen_produced", units="kg/year")[ - 0 - ] - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "wind_plant_electrolyzer.yaml") - - # Run the model - model.run() - - with subtests.test("Check initial H2 production"): - assert annual_h20 < (60500000 - 10000) - - with subtests.test("Check LCOE"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(kW*h)")[0], - rel=1e-3, - ) - == 0.059096 - ) - - with subtests.test("Check electrolyzer size"): - assert ( - pytest.approx( - model.prob.get_val("electrolyzer.electrolyzer_size_mw", units="MW")[0], - rel=1e-3, - ) - == 320.0 - ) - # Read the resulting SQL file and compare initial and final LCOH values - - sql_path = None - for root, _dirs, files in os.walk(example_folder): - for file in files: - if file == "wind_h2_opt.sql": - sql_path = Path(root) / file - break - if sql_path: - break - assert ( - sql_path is not None - ), "wind_h2_opt.sql file not found in current working directory or subdirectories." - - cr = om.CaseReader(str(sql_path)) - cases = list(cr.get_cases()) - assert len(cases) > 1, "Not enough cases recorded in SQL file." - - # Get initial and final LCOH values - - initial_lcoh = cases[0].outputs["finance_subgroup_hydrogen.LCOH"][0] - final_lcoh = cases[-1].outputs["finance_subgroup_hydrogen.LCOH"][0] - - with subtests.test("Check LCOH changed"): - assert final_lcoh != initial_lcoh - - with subtests.test("Check total adjusted CapEx"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_hydrogen.total_capex_adjusted", units="USD")[ - 0 - ], - rel=1e-3, - ) - == 978075832.46 - ) - with subtests.test("Check total adjusted OpEx"): - assert ( - pytest.approx( - model.prob.get_val( - "finance_subgroup_hydrogen.total_opex_adjusted", units="USD/year" - )[0], - rel=1e-3, - ) - == 27646299.56 - ) - - with subtests.test("Check minimum total hydrogen produced"): - assert ( - pytest.approx( - model.prob.get_val("electrolyzer.annual_hydrogen_produced", units="kg/year")[0], - abs=15000, - ) - == 29028700 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("06_custom_tech", None)]) -def test_paper_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "wind_plant_paper.yaml") - - # Run the model - model.run() - - model.post_process() - - # Subtests for checking specific values - with subtests.test("Check LCOP"): - assert ( - pytest.approx(model.prob.get_val("paper_mill.LCOP", units="USD/t"), rel=1e-3) - == 51.733275 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("09_co2/direct_ocean_capture", None)] -) -def test_wind_wave_doc_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "offshore_plant_doc.yaml") - # Set battery demand profile - demand_profile = np.ones(8760) * 340.0 - model.setup() - model.prob.set_val("battery.electricity_demand", demand_profile, units="MW") - # Run the model - model.run() - - model.post_process() - - # Subtests for checking specific values - with subtests.test("Check LCOC"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_co2.LCOC", units="USD/kg")[0], rel=1e-3 - ) - == 1.803343170781246 - ) - - with subtests.test("Check LCOE"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0], - rel=1e-3, - ) - == 243.723825 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("17_splitter_wind_doc_h2", None)] -) -def test_splitter_wind_doc_h2_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "offshore_plant_splitter_doc_h2.yaml") - - # Run the model - model.run() - - model.post_process() - - # Subtests for checking specific values - with subtests.test("Check Electrical AEP"): - electrical_aep = ( - model.prob.get_val( - "finance_subgroup_electricity.rated_electricity_production", - units="MW", - ) - * model.prob.get_val( - "finance_subgroup_electricity.capacity_factor", - units="unitless", - ).mean() - * 8760 - ) - - assert pytest.approx(electrical_aep[0], rel=1e-3) == 511267.03627 - - with subtests.test("Check LCOH"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_hydrogen.LCOH", units="USD/kg")[0], - rel=1e-3, - ) - == 9.8059083 - ) - - with subtests.test("Check LCOC"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_co2.LCOC", units="USD/kg")[0], rel=1e-3 - ) - == 13.655268 - ) - - with subtests.test("Check LCOE"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0], - rel=1e-3, - ) - == 132.395036462 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("07_run_of_river_plant", None)] -) -def test_hydro_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "07_run_of_river.yaml") - - # Run the model - model.run() - - model.post_process() - - print(model.prob.get_val("finance_subgroup_default.LCOE", units="USD/(kW*h)")) - - # Subtests for checking specific values - with subtests.test("Check LCOE"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_default.LCOE", units="USD/(kW*h)")[0], - rel=1e-3, - ) - == 0.17653979 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("11_hybrid_energy_plant", None)] -) -def test_hybrid_energy_plant_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "wind_pv_battery.yaml") - - # Run the model - model.run() - - model.post_process() - - # Subtests for checking specific values - with subtests.test("Check LCOE"): - 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)] -) -def test_hydrogen_dispatch_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "inputs" / "h2i_wind_to_h2_storage.yaml") - - model.run() - - model.post_process() - - with subtests.test("Check LCOE"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0], - rel=1e-5, - ) - == 59.0962072084844 - ) - - with subtests.test("Check all h2 total_hydrogen_produced"): - all_h2_annual_prod = ( - model.prob.get_val( - "finance_subgroup_all_hydrogen.rated_hydrogen_production", units="kg/h" - )[0] - * model.prob.get_val( - "finance_subgroup_all_hydrogen.capacity_factor", units="unitless" - ).mean() - * 8760 - ) - assert ( - pytest.approx( - all_h2_annual_prod, - rel=1e-5, - ) - == model.prob.get_val("electrolyzer.annual_hydrogen_produced", units="kg/year")[0] - ) - - with subtests.test("Check total_hydrogen_produced"): - assert ( - pytest.approx( - model.prob.get_val("electrolyzer.total_hydrogen_produced", units="kg")[0], - rel=1e-5, - ) - == 61656526.36295184 - ) - - with subtests.test("Check annual hydrogen production"): - assert ( - pytest.approx( - model.prob.get_val("electrolyzer.annual_hydrogen_produced", units="kg/year")[0], - rel=1e-5, - ) - == 58458965.601815335 - ) - - with subtests.test("Check all h2 LCOH"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_all_hydrogen.LCOH", units="USD/kg")[0], - rel=1e-5, - ) - == 5.647544985152393 - ) - - with subtests.test("Check dispatched h2 LCOH"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_dispatched_hydrogen.LCOH", units="USD/kg")[0], - rel=1e-5, - ) - == 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 -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("09_co2/ocean_alkalinity_enhancement", None)] -) -def test_wind_wave_oae_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "offshore_plant_oae.yaml") - - # Set battery demand profile - demand_profile = np.ones(8760) * 330.0 - model.setup() - model.prob.set_val("battery.electricity_demand", demand_profile, units="MW") - - # Run the model - model.run() - - model.post_process() - - # Subtests for checking specific values - with subtests.test("Check LCOC"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_co2.LCOC", units="USD/kg")[0], rel=1e-3 - ) - == 41.156 - ) - - with subtests.test("Check LCOE"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0], - rel=1e-3, - ) - == 263.130 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", - [("09_co2/ocean_alkalinity_enhancement_financials", None)], -) -def test_wind_wave_oae_example_with_finance(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "offshore_plant_oae.yaml") - - # Run the model - model.run() - - model.post_process() - - # Subtests for checking specific values - with subtests.test("Check LCOE"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0], - rel=1e-3, - ) - == 92.269663 - ) - - with subtests.test("Check Carbon Credit"): - assert ( - pytest.approx(model.prob.get_val("oae.carbon_credit_value", units="USD/t")[0], rel=1e-3) - == 1026.4684117 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("16_natural_gas", "11_hybrid_energy_plant")] -) -def test_natural_gas_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "natgas.yaml") - - # Run the model - - model.run() - - model.post_process() - solar_aep = sum(model.prob.get_val("solar.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("electrical_load_demand.unused_electricity_out", units="kW") - ) - - renewable_subgroup_total_electricity = ( - model.prob.get_val("finance_subgroup_renewables.rated_electricity_production", units="kW")[ - 0 - ] - * model.prob.get_val("finance_subgroup_renewables.capacity_factor", units="unitless").mean() - * 8760 - ) - electricity_subgroup_total_electricity = ( - model.prob.get_val("finance_subgroup_electricity.rated_electricity_production", units="kW")[ - 0 - ] - * model.prob.get_val( - "finance_subgroup_electricity.capacity_factor", units="unitless" - ).mean() - * 8760 - ) - natural_gas_subgroup_total_electricity = ( - model.prob.get_val("finance_subgroup_natural_gas.rated_electricity_production", units="kW")[ - 0 - ] - * model.prob.get_val( - "finance_subgroup_natural_gas.capacity_factor", units="unitless" - ).mean() - * 8760 - ) - - # NOTE: battery output power is not included in any of the financials - - 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 - - with subtests.test( - "Check solar AEP is greater than battery output (solar oversized relative to demand" - ): - assert solar_aep > solar_bat_out_total - - with subtests.test( - "Check battery outputs against battery inputs (solar oversized relative to demand" - ): - assert ( - pytest.approx(solar_bat_out_total + solar_curtailed_total, abs=bat_init_charge) - == solar_aep - ) - - with subtests.test("Check solar AEP equals total electricity for renewables subgroup"): - assert pytest.approx(solar_aep, rel=1e-6) == renewable_subgroup_total_electricity - - with subtests.test("Check natural gas AEP equals total electricity for natural_gas subgroup"): - assert ( - pytest.approx(sum(ng_electricity_production), rel=1e-6) - == natural_gas_subgroup_total_electricity - ) - - with subtests.test( - "Check natural gas + solar AEP equals total electricity for electricity subgroup" - ): - assert ( - pytest.approx(electricity_subgroup_total_electricity, rel=1e-6) - == sum(ng_electricity_production) + solar_aep - ) - - with subtests.test("Check missed load is natural gas plant electricity demand"): - assert pytest.approx(ng_electricity_demand, rel=1e-6) == pre_ng_missed_load - - with subtests.test("Check natural_gas_plant electricity out equals demand"): - assert pytest.approx(ng_electricity_demand, rel=1e-6) == ng_electricity_production - - # Subtests for checking specific values - with subtests.test("Check Natural Gas CapEx"): - capex = model.prob.get_val("natural_gas_plant.CapEx", units="USD")[0] - assert pytest.approx(capex, rel=1e-6) == 1e8 - - with subtests.test("Check Natural Gas OpEx"): - opex = model.prob.get_val("natural_gas_plant.OpEx", units="USD/year")[0] - assert pytest.approx(opex, rel=1e-6) == 2243167.24525 - - with subtests.test("Check total electricity produced"): - assert pytest.approx(natural_gas_subgroup_total_electricity, rel=1e-6) == 497266898.10354495 - - with subtests.test("Check opex adjusted ng_feedstock"): - opex_ng_feedstock = model.prob.get_val( - "finance_subgroup_natural_gas.varopex_adjusted_ng_feedstock", - units="USD/year", - )[0] - assert pytest.approx(opex_ng_feedstock, rel=1e-6) == 15281860.770986987 - - with subtests.test("Check capex adjusted natural_gas_plant"): - capex_ng_plant = model.prob.get_val( - "finance_subgroup_natural_gas.capex_adjusted_natural_gas_plant", units="USD" - )[0] - assert pytest.approx(capex_ng_plant, rel=1e-6) == 97560975.60975611 - - with subtests.test("Check opex adjusted natural_gas_plant"): - opex_ng_plant = model.prob.get_val( - "finance_subgroup_natural_gas.opex_adjusted_natural_gas_plant", units="USD/year" - )[0] - assert pytest.approx(opex_ng_plant, rel=1e-6) == 2188455.8490330363 - - with subtests.test("Check total adjusted CapEx for natural gas subgroup"): - total_capex = model.prob.get_val( - "finance_subgroup_natural_gas.total_capex_adjusted", units="USD" - )[0] - assert pytest.approx(total_capex, rel=1e-6) == 97658536.58536586 - - with subtests.test("Check LCOE (natural gas plant)"): - lcoe_ng = model.prob.get_val("finance_subgroup_natural_gas.LCOE", units="USD/(kW*h)")[0] - assert pytest.approx(lcoe_ng, rel=1e-6) == 0.05811033466 - - with subtests.test("Check LCOE (renewables plant)"): - lcoe_re = model.prob.get_val("finance_subgroup_renewables.LCOE", units="USD/(kW*h)")[0] - assert pytest.approx(lcoe_re, rel=1e-6) == 0.07102560120 - - with subtests.test("Check LCOE (renewables and natural gas plant)"): - lcoe_tot = model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(kW*h)")[0] - assert pytest.approx(lcoe_tot, rel=1e-6) == 0.063997927290 - - # Test feedstock-specific values - with subtests.test("Check feedstock output"): - ng_output = model.prob.get_val("ng_feedstock_source.natural_gas_out", units="MMBtu/h") - # Should be rated capacity (750 MMBtu/h) for all timesteps - assert all(ng_output == 750.0) - - with subtests.test("Check feedstock consumption"): - ng_consumed = model.prob.get_val("ng_feedstock.natural_gas_consumed", units="MMBtu/h") - # Total consumption should match what the natural gas plant uses - expected_consumption = ( - model.prob.get_val("natural_gas_plant.electricity_out", units="MW") * 7.5 - ) # Convert MWh to MMBtu using heat rate - assert pytest.approx(ng_consumed.sum(), rel=1e-3) == expected_consumption.sum() - - with subtests.test("Check feedstock CapEx"): - ng_capex = model.prob.get_val("ng_feedstock.CapEx", units="USD")[0] - assert pytest.approx(ng_capex, rel=1e-6) == 100000.0 # start_up_cost - - with subtests.test("Check feedstock OpEx"): - ng_opex = model.prob.get_val("ng_feedstock.VarOpEx", units="USD/year")[0] - # OpEx should be annual_cost (0) + price * consumption - ng_consumed = model.prob.get_val("ng_feedstock.natural_gas_consumed", units="MMBtu/h") - 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( - "example_folder,resource_example_folder", - [("15_wind_solar_electrolyzer", "11_hybrid_energy_plant/")], -) -def test_wind_solar_electrolyzer_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "15_wind_solar_electrolyzer.yaml") - model.run() - - solar_fpath = model.model.get_val("solar_site.solar_resource.solar_resource_data")["filepath"] - wind_fpath = model.model.get_val("wind_site.wind_resource.wind_resource_data")["filepath"] - - with subtests.test("Wind resource file"): - assert Path(wind_fpath).name == "35.2018863_-101.945027_2012_wtk_v2_60min_utc_tz.csv" - - with subtests.test("Solar resource file"): - assert Path(solar_fpath).name == "30.6617_-101.7096_psmv3_60_2013.csv" - model.post_process() - - wind_aep = sum(model.prob.get_val("wind.electricity_out", units="kW")) - solar_aep = sum(model.prob.get_val("solar.electricity_out", units="kW")) - total_aep = model.prob.get_val("combiner.electricity_out", units="kW").sum() - - with subtests.test("Check total energy production"): - assert pytest.approx(wind_aep + solar_aep, rel=1e-6) == total_aep - - with subtests.test("Check LCOE"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0], - rel=1e-5, - ) - == 53.9306558 - ) - - with subtests.test("Check LCOH"): - assert ( - pytest.approx( - model.prob.get_val("finance_subgroup_hydrogen.LCOH", units="USD/kg")[0], - rel=1e-5, - ) - == 5.3063358423 - ) - - wind_generation = model.prob.get_val("wind.electricity_out", units="kW") - solar_generation = model.prob.get_val("solar.electricity_out", units="kW") - total_generation = model.prob.get_val("combiner.electricity_out", units="kW") - total_energy_to_electrolyzer = model.prob.get_val("electrolyzer.electricity_in", units="kW") - with subtests.test("Check combiner output"): - assert ( - pytest.approx(wind_generation.sum() + solar_generation.sum(), rel=1e-5) - == total_generation.sum() - ) - with subtests.test("Check electrolyzer input power"): - assert pytest.approx(total_generation.sum(), rel=1e-5) == total_energy_to_electrolyzer.sum() - - -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("10_electrolyzer_om", None)]) -def test_electrolyzer_om_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "electrolyzer_om.yaml") - - model.run() - - lcoe = model.prob.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0] - lcoh_with_lcoh_finance = model.prob.get_val( - "finance_subgroup_hydrogen.LCOH_lcoh_financials", units="USD/kg" - )[0] - lcoh_with_lcoe_finance = model.prob.get_val( - "finance_subgroup_hydrogen.LCOH_lcoe_financials", units="USD/kg" - )[0] - with subtests.test("Check LCOE"): - assert pytest.approx(lcoe, rel=1e-4) == 39.98869 - with subtests.test("Check LCOH with lcoh_financials"): - assert pytest.approx(lcoh_with_lcoh_finance, rel=1e-4) == 16.9204156301 - with subtests.test("Check LCOH with lcoe_financials"): - assert pytest.approx(lcoh_with_lcoe_finance, rel=1e-4) == 10.3360027653 - - -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("08_wind_electrolyzer", None)]) -def test_wombat_electrolyzer_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "wind_plant_electrolyzer.yaml") - - model.run() - - lcoe_with_profast_model = model.prob.get_val( - "finance_subgroup_electricity_profast.LCOE", units="USD/(MW*h)" - )[0] - lcoe_with_custom_model = model.prob.get_val( - "finance_subgroup_electricity_custom.LCOE", units="USD/(MW*h)" - )[0] - - lcoh_with_custom_model = model.prob.get_val( - "finance_subgroup_hydrogen.LCOH_produced_custom_model", units="USD/kg" - )[0] - lcoh_with_profast_model = model.prob.get_val( - "finance_subgroup_hydrogen.LCOH_produced_profast_model", units="USD/kg" - )[0] - - with subtests.test("Check LCOH from custom model"): - assert pytest.approx(lcoh_with_custom_model, rel=1e-5) == 4.1783979573 - with subtests.test("Check LCOH from ProFAST model"): - assert pytest.approx(lcoh_with_profast_model, rel=1e-5) == 5.3086307305 - with subtests.test("Check LCOE from custom model"): - assert pytest.approx(lcoe_with_custom_model, rel=1e-5) == 51.17615298 - with subtests.test("Check LCOE from ProFAST model"): - assert pytest.approx(lcoe_with_profast_model, rel=1e-5) == 59.0962084 - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("18_pyomo_heuristic_dispatch", None)] -) -def test_pyomo_heuristic_dispatch_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "pyomo_heuristic_dispatch.yaml") - - demand_profile = np.ones(8760) * 50.0 - - # TODO: Update with demand module once it is developed - model.setup() - model.prob.set_val("battery.electricity_demand", demand_profile, units="MW") - - # Run the model - model.run() - - model.post_process() - - # Test battery storage functionality - # SOC should stay within configured bounds (10% to 90%) - # Due to pysam simulation, bounds may not be fully respected, - # but should not exceed the upper bound more than 4% SOC - # and the lower bound more than 1% SOC - soc = model.prob.get_val("battery.SOC", units="percent") - with subtests.test("Check battery SOC lower bound"): - assert all(soc >= 9.0) - with subtests.test("Check battery SOC upper bound"): - assert all(soc <= 94.0) - - with subtests.test("Check wind generation out of the wind plant"): - # Wind should generate some electricity - wind_electricity = model.prob.get_val("wind.electricity_out", units="MW") - assert wind_electricity.sum() > 0 - # Wind electricity should match battery input (direct connection) - with subtests.test("Check wind generation in to battery"): - battery_electricity_in = model.prob.get_val("battery.electricity_in", units="MW") - 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("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 - - # Subtest for LCOE - with subtests.test("Check all LCOE value"): - lcoe = model.prob.get_val("finance_subgroup_all_electricity.LCOE", units="USD/(kW*h)")[0] - assert lcoe == pytest.approx(0.08157197567200995, rel=1e-6) - - with subtests.test("Check dispatched LCOE value"): - lcoe = model.prob.get_val( - "finance_subgroup_dispatched_electricity.LCOE", units="USD/(kW*h)" - )[0] - assert lcoe == pytest.approx(0.5975902853904799, rel=1e-6) - - # Subtest for total electricity produced - with subtests.test("Check total electricity produced"): - total_electricity = ( - model.prob.get_val( - name="finance_subgroup_all_electricity.rated_electricity_production", - units="MW", - )[0] - * model.prob.get_val( - name="finance_subgroup_all_electricity.capacity_factor", - units="unitless", - ).mean() - * 8760 - ) - assert total_electricity == pytest.approx(3125443.1089529935, rel=1e-6) - - # Subtest for electricity unused_commodity - with subtests.test("Check electricity unused commodity"): - electricity_unused_commodity = np.linalg.norm( - 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("electrical_load_demand.unmet_electricity_demand_out", units="MW") - ) - assert electricity_unmet_demand == pytest.approx(711.1997294551337, rel=1e-6) - - # Check that incorrect and no tech name provided will be replaced and validate - model_config = load_yaml(example_folder / "pyomo_heuristic_dispatch.yaml") - tech = load_yaml(example_folder / "tech_config.yaml") - with subtests.test("Ensure no-tech name validates"): - tech["technologies"]["battery"]["model_inputs"]["control_parameters"] = None - model_config["technology_config"] = tech - model = H2IntegrateModel(model_config) - - with subtests.test("Ensure incorrect name is corrected"): - tech["technologies"]["battery"]["model_inputs"]["control_parameters"] = { - "tech_name": "goose" - } - model_config["technology_config"] = tech - model = H2IntegrateModel(model_config) - - -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("19_simple_dispatch", None)]) -def test_simple_dispatch_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "wind_battery_dispatch.yaml") - - # Run the model - model.run() - - model.post_process() - - wind_aep = sum(model.prob.get_val("wind.electricity_out", units="kW")) - aep_for_finance = ( - model.prob.get_val("finance_subgroup_electricity.rated_electricity_production", units="kW")[ - 0 - ] - * model.prob.get_val( - "finance_subgroup_electricity.capacity_factor", units="unitless" - ).mean() - * 8760 - ) - battery_init_energy = 30000.0 * 0.25 # max capacity in kW and initial charge rate percentage - - with subtests.test("Check electricity is not double counted"): - assert aep_for_finance <= wind_aep + battery_init_energy - - # Test battery storage functionality - with subtests.test("Check battery SOC bounds"): - 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) - - with subtests.test("Check wind generation"): - # Wind should generate some electricity - wind_electricity = model.prob.get_val("wind.electricity_out", units="kW") - assert wind_electricity.sum() > 0 - # Wind electricity should match battery input (direct connection) - battery_electricity_in = model.prob.get_val("battery.electricity_in", units="kW") - 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("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 - - # Subtest for LCOE - with subtests.test("Check LCOE value"): - lcoe = model.prob.get_val( - "finance_subgroup_electricity.LCOE_all_electricity_profast", units="USD/(kW*h)" - )[0] - assert pytest.approx(lcoe, rel=1e-6) == 0.07801723344476236 - - # Subtest for NPV - with subtests.test("Check NPV value (numpy financial)"): - npv = model.prob.get_val( - "finance_subgroup_electricity.NPV_electricity_all_electricity_npv", units="USD" - )[0] - assert pytest.approx(npv, rel=1e-6) == 3791194.71 - - # Subtest for ProFAST NPV - with subtests.test("Check NPV value (profast)"): - npv = model.prob.get_val( - "finance_subgroup_electricity.NPV_electricity_all_electricity_profast_npv", - units="USD", - )[0] - assert pytest.approx(npv, rel=1e-6) == 7518969.18 - - # Subtest for total electricity produced - with subtests.test("Check total electricity produced"): - total_electricity = ( - model.prob.get_val( - "finance_subgroup_electricity.rated_electricity_production", units="kW" - )[0] - * model.prob.get_val("finance_subgroup_electricity.capacity_factor").mean() - * 8760 - ) - assert pytest.approx(total_electricity, rel=1e-6) == 62797265.9296355 - - # Subtest for electricity unused_commodity - with subtests.test("Check electricity unused commodity"): - electricity_unused_commodity = np.linalg.norm( - 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("electrical_load_demand.unmet_electricity_demand_out", units="kW") - ) - assert pytest.approx(electricity_unmet_demand, rel=1e-6) == 165604.70758669 - - # Subtest for total electricity produced from wind, should be equal to total - # electricity produced from finance_subgroup_electricity - with subtests.test("Check total electricity produced from wind"): - wind_electricity_finance = ( - model.prob.get_val("finance_subgroup_wind.rated_electricity_production", units="kW")[0] - * model.prob.get_val("finance_subgroup_wind.capacity_factor", units="unitless").mean() - * 8760 - ) - assert pytest.approx(wind_electricity_finance, rel=1e-6) == total_electricity - - with subtests.test("Check total electricity produced from wind compared to wind aep"): - wind_electricity_performance = np.sum( - model.prob.get_val("wind.electricity_out", units="kW") - ) - assert pytest.approx(wind_electricity_performance, rel=1e-6) == wind_electricity_finance - - # Subtest for total electricity produced from battery, should be equal - # to sum of "battery.electricity_out" - with subtests.test("Check total electricity produced from battery"): - battery_electricity_finance = ( - model.prob.get_val( - "finance_subgroup_battery.rated_electricity_production", units="MW*h/year" - )[0] - * model.prob.get_val( - "finance_subgroup_battery.capacity_factor", units="unitless" - ).mean() - * 8760 - ) - battery_electricity_performance = ( - 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 ( - pytest.approx(battery_electricity_finance, rel=1e-6) == battery_electricity_performance - ) - - wind_lcoe = model.prob.get_val("finance_subgroup_wind.LCOE_wind_only", units="USD/(MW*h)")[0] - battery_lcoe = model.prob.get_val( - "finance_subgroup_battery.LCOE_battery_included", units="USD/(MW*h)" - )[0] - electricity_lcoe = model.prob.get_val( - "finance_subgroup_electricity.LCOE_all_electricity_profast", units="USD/(MW*h)" - )[0] - - with subtests.test("Check electricity LCOE is greater than wind LCOE"): - assert electricity_lcoe > wind_lcoe - - with subtests.test("Check battery LCOE is greater than electricity LCOE"): - assert battery_lcoe > electricity_lcoe - - with subtests.test("Check battery LCOE"): - assert pytest.approx(battery_lcoe, rel=1e-6) == 131.781997 - - with subtests.test("Check wind LCOE"): - assert pytest.approx(wind_lcoe, rel=1e-6) == 58.8248 - - with subtests.test("Check electricity LCOE"): - assert pytest.approx(electricity_lcoe, rel=1e-6) == 78.01723 - - -@pytest.mark.integration -@pytest.mark.skipif(importlib.util.find_spec("ard") is None, reason="ard is not installed") -@pytest.mark.parametrize("example_folder,resource_example_folder", [("29_wind_ard", None)]) -def test_windard_pv_battery_dispatch_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create the model - model = H2IntegrateModel(example_folder / "h2i_inputs/wind_pv_battery.yaml") - - # Run the model - model.run() - - # Post-process the results - model.post_process() - - with subtests.test("Check wind generation"): - # Wind should generate some electricity - wind_electricity = model.prob.get_val("wind.electricity_out", units="GW") - assert wind_electricity.sum() == pytest.approx(150.88490967164714, rel=1e-4) - - with subtests.test("Check solar generation"): - # Solar should generate some electricity - solar_electricity = model.prob.get_val("solar.electricity_out", units="GW") - assert solar_electricity.sum() == pytest.approx(44.22139046811775, rel=1e-4) - - with subtests.test("Check battery gets wind and solar output"): - # Wind plus solar electricity should match battery input (direct connection) - battery_electricity_in = model.prob.get_val("battery.electricity_in", units="GW") - assert wind_electricity.sum() + solar_electricity.sum() == pytest.approx( - battery_electricity_in.sum(), rel=1e-6 - ) - - with subtests.test("Check demand satisfaction"): - 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:], - model.prob.get_val("battery.electricity_demand", units="MW")[8700:], - ) - - # Subtest for LCOE - with subtests.test("Check dispatched LCOE value"): - lcoe = model.prob.get_val( - "finance_subgroup_dispatched_electricity.LCOE", units="USD/(kW*h)" - )[0] - assert pytest.approx(lcoe, rel=1e-6) == 0.09289430342906849 - - with subtests.test("Check generation LCOE value (excludes battery)"): - lcoe = model.prob.get_val("finance_subgroup_produced_electricity.LCOE", units="USD/(kW*h)")[ - 0 - ] - assert pytest.approx(lcoe, rel=1e-6) == 0.07204429286793802 - - # Subtest for total electricity produced - with subtests.test("Check total electricity dispatched"): - total_electricity_year_one = ( - model.prob.get_val( - "finance_subgroup_dispatched_electricity.rated_electricity_production", - units="MW", - )[0] - * model.prob.get_val( - "finance_subgroup_dispatched_electricity.capacity_factor", - units="unitless", - )[0] - * 8760 - ) - assert total_electricity_year_one == pytest.approx(dispatched_electricity.sum()) - - # Subtest for electricity curtailed - with subtests.test("Check electricity curtailed"): - electricity_curtailed = model.prob.get_val( - "electrical_load_demand.unused_electricity_out", units="MW" - ).sum() - - 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("electrical_load_demand.unmet_electricity_demand_out", units="MW") - ) - assert electricity_missed_load == pytest.approx(1403.5372787817894) - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("20_solar_electrolyzer_doe", None)] -) -def test_csvgen_design_of_experiments(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - with pytest.raises(UserWarning) as excinfo: - model = H2IntegrateModel(example_folder / "20_solar_electrolyzer_doe.yaml") - assert "There may be issues with the csv file csv_doe_cases.csv" in str(excinfo.value) - - from h2integrate.core.dict_utils import update_defaults - from h2integrate.core.file_utils import check_file_format_for_csv_generator - from h2integrate.core.inputs.validation import write_yaml, load_driver_yaml - - # load the driver config file - driver_config = load_driver_yaml("driver_config.yaml") - # specify the filepath to the csv file - csv_fpath = Path(driver_config["driver"]["design_of_experiments"]["filename"]).absolute() - # run the csv checker method, we want it to write the csv file to a new filepath so - # set overwrite_file=False - new_csv_filename = check_file_format_for_csv_generator( - csv_fpath, driver_config, check_only=False, overwrite_file=False - ) - - # update the csv filename in the driver config dictionary - updated_driver = update_defaults(driver_config["driver"], "filename", new_csv_filename.name) - driver_config["driver"].update(updated_driver) - - # save the updated driver to a new file - new_driver_fpath = example_folder / "driver_config_test.yaml" - new_toplevel_fpath = example_folder / "20_solar_electrolyzer_doe_test.yaml" - write_yaml(driver_config, new_driver_fpath) - - # update the driver config filename in the top-level config - main_config = load_yaml("20_solar_electrolyzer_doe.yaml") - main_config["driver_config"] = new_driver_fpath.name - - # save the updated top-level config file to a new file - write_yaml(main_config, new_toplevel_fpath) - - # Run the model - model = H2IntegrateModel(new_toplevel_fpath) - model.run() - - # summarize sql file - model.post_process(summarize_sql=True) - - with subtests.test("Check that sql file was summarized"): - assert model.recorder_path is not None - summarized_filepath = model.recorder_path.parent / f"{model.recorder_path.stem}.csv" - assert summarized_filepath.is_file() - with subtests.test("Check that sql summary file was written as expected"): - summary = pd.read_csv(summarized_filepath, index_col="Unnamed: 0") - assert len(summary) == 10 - d_var_cols = ["solar.system_capacity_DC (kW)", "electrolyzer.n_clusters (unitless)"] - assert summary.columns.to_list()[0] in d_var_cols - assert summary.columns.to_list()[1] in d_var_cols - assert "finance_subgroup_hydrogen.LCOH_optimistic (USD/kg)" in summary.columns.to_list() - # delete summary file - summarized_filepath.unlink() - - sql_fpath = example_folder / "ex_20_out" / "cases.sql" - cr = om.CaseReader(str(sql_fpath)) - cases = list(cr.get_cases()) - - with subtests.test("Check solar capacity in case 0"): - assert ( - pytest.approx(cases[0].get_val("solar.system_capacity_DC", units="MW"), rel=1e-6) - == 25.0 - ) - with subtests.test("Check solar capacity in case 9"): - assert ( - pytest.approx(cases[-1].get_val("solar.system_capacity_DC", units="MW"), rel=1e-6) - == 500.0 - ) - - with subtests.test("Check electrolyzer capacity in case 0"): - assert ( - pytest.approx( - cases[0].get_val("electrolyzer.electrolyzer_size_mw", units="MW"), rel=1e-6 - ) - == 10.0 * 5 - ) - - with subtests.test("Check electrolyzer capacity in case 9"): - assert ( - pytest.approx( - cases[-1].get_val("electrolyzer.electrolyzer_size_mw", units="MW"), rel=1e-6 - ) - == 10.0 * 10 - ) - - min_lcoh_val = 100000.0 - min_lcoh_case_num = 0 - for i, case in enumerate(cases): - lcoh = case.get_val("finance_subgroup_hydrogen.LCOH_optimistic", units="USD/kg")[0] - if lcoh < min_lcoh_val: - min_lcoh_val = np.min([lcoh, min_lcoh_val]) - min_lcoh_case_num = i - - with subtests.test("Min LCOH value"): - assert pytest.approx(min_lcoh_val, rel=1e-6) == 4.663014422338 - - with subtests.test("Min LCOH case number"): - assert min_lcoh_case_num == 6 - - with subtests.test("Min LCOH case LCOH value"): - assert ( - pytest.approx( - cases[min_lcoh_case_num].get_val( - "finance_subgroup_hydrogen.LCOH_optimistic", units="USD/kg" - ), - rel=1e-6, - ) - == min_lcoh_val - ) - - with subtests.test("Min LCOH case has lower LCOH than other cases"): - for i, case in enumerate(cases): - lcoh_case = case.get_val("finance_subgroup_hydrogen.LCOH_optimistic", units="USD/kg") - if i != min_lcoh_case_num: - assert lcoh_case > min_lcoh_val - - with subtests.test("Min LCOH solar capacity"): - assert ( - pytest.approx( - cases[min_lcoh_case_num].get_val("solar.system_capacity_DC", units="MW"), rel=1e-6 - ) - == 200.0 - ) - - with subtests.test("Min LCOH electrolyzer capacity"): - assert ( - pytest.approx( - cases[min_lcoh_case_num].get_val("electrolyzer.electrolyzer_size_mw", units="MW"), - rel=1e-6, - ) - == 100.0 - ) - - # remove files created - new_driver_fpath.unlink() - new_toplevel_fpath.unlink() - new_csv_filename.unlink() - - -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("22_site_doe", None)]) -def test_sweeping_solar_sites_doe(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create the model - model = H2IntegrateModel(example_folder / "22_solar_site_doe.yaml") - - # Run the model - model.run() - - # Specify the filepath to the sql file, the folder and filename are in the driver_config - sql_fpath = example_folder / "ex_22_out" / "cases.sql" - - # load the cases - cr = om.CaseReader(sql_fpath) - - cases = list(cr.get_cases()) - - res_df = pd.DataFrame() - for ci, case in enumerate(cases): - solar_resource_data = case.get_val("site.solar_resource.solar_resource_data") - lat_lon = ( - f"{case.get_val('site.latitude', units='deg')[0]} " - f"{case.get_val('site.longitude', units='deg')[0]}" - ) - solar_capacity = case.get_design_vars()["solar.system_capacity_DC"][0] - aep = case.get_val("solar.annual_electricity_produced", units="MW*h/yr")[0] - lcoe = case.get_val("finance_subgroup_electricity.LCOE_optimistic", units="USD/(MW*h)")[0] - - site_res = pd.DataFrame( - [aep, lcoe, solar_capacity], index=["AEP", "LCOE", "solar_capacity"], columns=[lat_lon] - ).T - res_df = pd.concat([site_res, res_df], axis=0) - - with subtests.test(f"Case {ci}: Solar resource latitude matches site latitude"): - assert ( - pytest.approx(case.get_val("site.latitude", units="deg"), abs=0.1) - == solar_resource_data["site_lat"] - ) - with subtests.test(f"Case {ci}: Solar resource longitude matches site longitude"): - assert ( - pytest.approx(case.get_val("site.longitude", units="deg"), abs=0.1) - == solar_resource_data["site_lon"] - ) - - locations = list(set(res_df.index.to_list())) - solar_sizes = list(set(res_df["solar_capacity"].to_list())) - - with subtests.test("Two solar sizes per site"): - assert len(solar_sizes) == 2 - with subtests.test("Two unique sites"): - assert len(locations) == 2 - - with subtests.test("Unique AEPs per case"): - assert len(list(set(res_df["AEP"].to_list()))) == len(res_df) - - with subtests.test("Unique LCOEs per case"): - 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): - example_folder = temp_copy_of_example - - driver_config = load_yaml(example_folder / "driver_config.yaml") - tech_config = load_yaml(example_folder / "tech_config.yaml") - plant_config = load_yaml(example_folder / "plant_config.yaml") - - h2i_config = { - "name": "H2Integrate_config", - "system_summary": "", - "driver_config": driver_config, - "technology_config": tech_config, - "plant_config": plant_config, - } - - # Create a H2I model - h2i = H2IntegrateModel(h2i_config) - - # Run the model - h2i.run() - - with subtests.test("Distributed LCOE"): - assert ( - pytest.approx( - h2i.prob.get_val("finance_subgroup_distributed.LCOE", units="USD/MW/h")[0], rel=1e-6 - ) - == 99.872209 - ) - with subtests.test("Utility LCOE"): - assert ( - pytest.approx( - h2i.prob.get_val("finance_subgroup_utility.LCOE", units="USD/MW/h")[0], rel=1e-6 - ) - == 54.2709437311 - ) - - with subtests.test("Total LCOE"): - assert ( - pytest.approx( - h2i.prob.get_val("finance_subgroup_total_electricity.LCOE", units="USD/MW/h")[0], - rel=1e-6, - ) - == 65.2444127137 - ) - - with subtests.test("Distributed wind plant capacity"): - assert ( - pytest.approx( - h2i.prob.get_val("distributed_wind_plant.rated_electricity_production", units="MW"), - rel=1e-6, - ) - == 66.0 - ) - - with subtests.test("Total distributed electricity production"): - assert ( - pytest.approx( - np.sum( - h2i.prob.get_val( - "distributed_wind_plant.total_electricity_produced", units="MW*h" - ) - ), - rel=1e-6, - ) - == 128948.21977 - ) - - with subtests.test("Total utility electricity production"): - assert ( - pytest.approx( - h2i.prob.get_val("utility_wind_plant.electricity_out", units="MW").sum(), rel=1e-6 - ) - == 406908.03381618496 - ) - - with subtests.test("Distributed wind capacity factor"): - assert ( - pytest.approx( - h2i.prob.get_val("distributed_wind_plant.capacity_factor", units="percent")[0], - rel=1e-6, - ) - == 22.30320668 - ) - - with subtests.test("Utility wind plant capacity"): - assert ( - pytest.approx( - h2i.prob.get_val("utility_wind_plant.rated_electricity_production", units="MW"), - rel=1e-6, - ) - == 120.0 - ) - - with subtests.test("Distributed wind site location"): - assert ( - pytest.approx(h2i.prob.get_val("distributed_wind_site.latitude", units="deg"), rel=1e-6) - == 44.04218 - ) - assert ( - pytest.approx( - h2i.prob.get_val("distributed_wind_site.longitude", units="deg"), rel=1e-6 - ) - == -95.19757 - ) - - with subtests.test("Distributed wind plant resource location"): - assert ( - pytest.approx( - h2i.prob.get_val("distributed_wind_plant.wind_resource_data")["site_lat"], abs=1e-2 - ) - == 44.04218 - ) - assert ( - pytest.approx( - h2i.prob.get_val("distributed_wind_plant.wind_resource_data")["site_lon"], abs=1e-2 - ) - == -95.19757 - ) - - with subtests.test("Utility wind site location"): - assert ( - pytest.approx(h2i.prob.get_val("utility_wind_site.latitude", units="deg"), rel=1e-6) - == 35.2018863 - ) - assert ( - pytest.approx(h2i.prob.get_val("utility_wind_site.longitude", units="deg"), rel=1e-6) - == -101.945027 - ) - - with subtests.test("Utility wind plant resource location"): - assert ( - pytest.approx( - h2i.prob.get_val("utility_wind_plant.wind_resource_data")["site_lat"], abs=1e-2 - ) - == 35.2018863 - ) - assert ( - pytest.approx( - h2i.prob.get_val("utility_wind_plant.wind_resource_data")["site_lon"], abs=1e-2 - ) - == -101.945027 - ) - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("24_solar_battery_grid", "11_hybrid_energy_plant/")] -) -def test_24_solar_battery_grid_example(subtests, temp_copy_of_example): - # NOTE: would be good to compare LCOE against the same example without grid selling - # and see that LCOE reduces with grid selling - example_folder = temp_copy_of_example - - model = H2IntegrateModel(example_folder / "solar_battery_grid.yaml") - - model.run() - - model.post_process() - - energy_for_financials = ( - model.prob.get_val("finance_subgroup_renewables.rated_electricity_production", units="kW")[ - 0 - ] - * model.prob.get_val("finance_subgroup_renewables.capacity_factor", units="unitless").mean() - * 8760 - ) - - electricity_bought = sum(model.prob.get_val("grid_buy.electricity_out", 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("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")) - - with subtests.test("Behavior check battery missed load is electricity bought"): - assert pytest.approx(battery_missed_load, rel=1e-6) == electricity_bought - - with subtests.test("Behavior check battery curtailed energy is electricity sold"): - assert pytest.approx(battery_curtailed, rel=1e-6) == electricity_sold - - with subtests.test( - "Behavior check energy for financials; include solar aep and electricity bought" - ): - assert pytest.approx(energy_for_financials, rel=1e-6) == (solar_aep + electricity_bought) - - with subtests.test("Value check on LCOE"): - lcoe = model.prob.get_val("finance_subgroup_renewables.LCOE", units="USD/(MW*h)")[0] - assert pytest.approx(lcoe, rel=1e-4) == 91.7057887 - - -@pytest.mark.integration -@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 - - from h2integrate.postprocess.mapping import ( - plot_geospatial_point_heat_map, - plot_straight_line_shipping_routes, - ) - - example_folder = temp_copy_of_example - - # Define filepaths - 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_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="finance_subgroup_sponge_iron.LCOS (USD/kg)", - map_preferences={ - "figsize": (10, 8), - "colorbar_label": "Levelized Cost of\nIron [$/kg]", - "colorbar_limits": (0.6, 1.0), - }, - ) - # Add a layer for example ore cost prices from select mines - fig, ax, ore_cost_layer_gdf = plot_geospatial_point_heat_map( - case_results_fpath=ore_prices_filepath, - metric_to_plot="ore_cost_per_kg", - map_preferences={ - "colormap": "Greens", - "marker": "o", - "colorbar_bbox_to_anchor": (0.025, 0.97, 1, 1), - "colorbar_label": "Levelized Cost of\nIron Ore Pellets\n[$/kg ore]", - "colorbar_limits": (0.11, 0.14), - }, - fig=fig, - ax=ax, - base_layer_gdf=lcoi_layer_gdf, - ) - # Add a layer for example waterway shipping cost from select mines to select ports - fig, ax, shipping_cost_layer_gdf = plot_geospatial_point_heat_map( - case_results_fpath=shipping_prices_filepath, - metric_to_plot="shipping_cost_per_kg", - map_preferences={ - "colormap": "Greys", - "marker": "d", - "markersize": 80, - "colorbar_bbox_to_anchor": (0.4, 0.97, 1, 1), - "colorbar_label": "Waterway Shipping Cost\n[$/kg ore]", - "colorbar_limits": (0.11, 0.14), - }, - fig=fig, - ax=ax, - base_layer_gdf=[lcoi_layer_gdf, ore_cost_layer_gdf], - ) - - # Define example water way shipping routes for plotting straight line transport - cleveland_route = [ - "Duluth", - "Keweenaw", - "Sault St Marie", - "De Tour", - "Lake Huron", - "Port Huron", - "Erie", - "Cleveland", - ] - buffalo_route = [ - "Duluth", - "Keweenaw", - "Sault St Marie", - "De Tour", - "Lake Huron", - "Port Huron", - "Erie", - "Cleveland", - "Buffalo", - ] - chicago_route = [ - "Duluth", - "Keweenaw", - "Sault St Marie", - "De Tour", - "Mackinaw", - "Manistique", - "Chicago", - ] - - # Add cleveland route as layer - fig, ax, transport_layer1_gdf = plot_straight_line_shipping_routes( - shipping_coords_fpath=shipping_coords_filepath, - shipping_route=cleveland_route, - map_preferences={}, - fig=fig, - ax=ax, - base_layer_gdf=[lcoi_layer_gdf, ore_cost_layer_gdf, shipping_cost_layer_gdf], - ) - # Add buffalo route as layer - fig, ax, transport_layer2_gdf = plot_straight_line_shipping_routes( - shipping_coords_fpath=shipping_coords_filepath, - shipping_route=buffalo_route, - map_preferences={}, - fig=fig, - ax=ax, - base_layer_gdf=[ - lcoi_layer_gdf, - ore_cost_layer_gdf, - shipping_cost_layer_gdf, - transport_layer1_gdf, - ], - ) - # Add chicago route as layer - fig, ax, transport_layer3_gdf = plot_straight_line_shipping_routes( - shipping_coords_fpath=shipping_coords_filepath, - shipping_route=chicago_route, - map_preferences={"figure_title": "Example H2 DRI Iron Costs"}, - fig=fig, - ax=ax, - base_layer_gdf=[ - lcoi_layer_gdf, - ore_cost_layer_gdf, - shipping_cost_layer_gdf, - transport_layer1_gdf, - transport_layer2_gdf, - ], - save_plot_fpath=ex_png_fpath, - ) - - with subtests.test("Type check on fig, ax, and lcoi_layer_gdf"): - assert isinstance( - fig, matplotlib.figure.Figure - ), f"Expected matplotlib.figure.Figure but got{type(fig)}" - assert isinstance( - ax, matplotlib.axes._axes.Axes - ), f"Expected matplotlib.axes._axes.Axes but got{type(ax)}" - assert isinstance( - lcoi_layer_gdf, gpd.geodataframe.GeoDataFrame - ), f"Expected gpd.geodataframe.GeoDataFrame but got{type(lcoi_layer_gdf)}" - assert isinstance( - lcoi_layer_gdf, gpd.geodataframe.GeoDataFrame - ), f"Expected gpd.geodataframe.GeoDataFrame but got{type(transport_layer1_gdf)}" - - with subtests.test("Check example_28_iron_map.png was saved"): - assert (ex_png_fpath).is_file(), "example_28_iron_map.png file not found" - - # Clean up any output files/dirs created - ex_png_fpath.unlink(missing_ok=True) - - -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("04_geo_h2", None)]) -def test_natural_geoh2(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - h2i_nat = H2IntegrateModel(example_folder / "04_geo_h2_natural.yaml") - h2i_nat.run() - - with subtests.test("H2 Production"): - assert ( - pytest.approx( - np.mean(h2i_nat.model.get_val("geoh2_well_subsurface.hydrogen_out", units="kg/h")), - rel=1e-6, - ) - == 606.1508855232839 - ) - - with subtests.test("integrated LCOH"): - assert ( - pytest.approx( - h2i_nat.prob.get_val("finance_subgroup_h2.LCOH", units="USD/kg"), rel=1e-6 - ) - == 1.3089029 - ) - with subtests.test("subsurface Capex"): - assert ( - pytest.approx( - h2i_nat.model.get_val("geoh2_well_subsurface.CapEx", units="USD"), rel=1e-6 - ) - == 7667341.11417252 - ) - with subtests.test("subsurface fixed Opex"): - assert ( - pytest.approx( - h2i_nat.model.get_val("geoh2_well_subsurface.OpEx", units="USD/year"), rel=1e-6 - ) - == 215100.7857875 - ) - with subtests.test("subsurface variable Opex"): - assert ( - pytest.approx( - h2i_nat.model.get_val("geoh2_well_subsurface.VarOpEx", units="USD/year"), - rel=1e-6, - ) - == 0.0 - ) - with subtests.test("subsurface adjusted opex"): - adjusted_opex = h2i_nat.prob.get_val( - "finance_subgroup_h2.opex_adjusted_geoh2_well_subsurface", units="USD/year" - ) - assert pytest.approx(adjusted_opex, rel=1e-6) == 215100.7857875 - - with subtests.test("surface Capex"): - assert ( - pytest.approx(h2i_nat.model.get_val("geoh2_well_surface.CapEx", units="USD"), rel=1e-6) - == 1800711.83796 - ) - with subtests.test("surface fixed Opex"): - assert ( - pytest.approx( - h2i_nat.model.get_val("geoh2_well_surface.OpEx", units="USD/year"), rel=1e-6 - ) - == 4567464 - ) - with subtests.test("surface variable Opex"): - assert ( - pytest.approx( - h2i_nat.model.get_val("geoh2_well_surface.VarOpEx", units="USD/year"), rel=1e-6 - ) - == 989213.8787 - ) - with subtests.test("surface adjusted opex"): - surface_adjusted_opex = h2i_nat.prob.get_val( - "finance_subgroup_h2.opex_adjusted_geoh2_well_surface", units="USD/year" - ) - assert pytest.approx(surface_adjusted_opex, rel=1e-6) == 4798691.865 - - -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("04_geo_h2", None)]) -def test_stimulated_geoh2(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - h2i_stim = H2IntegrateModel(example_folder / "04_geo_h2_stimulated.yaml") - h2i_stim.run() - - h2_prod = h2i_stim.model.get_val("geoh2_well_subsurface.hydrogen_out", units="kg/h") - - with subtests.test("H2 Production"): - assert pytest.approx(np.mean(h2_prod), rel=1e-6) == 155.03934945719536 - - with subtests.test("integrate LCOH"): - lcoh = h2i_stim.prob.get_val("finance_subgroup_default.LCOH", units="USD/kg") - assert lcoh == pytest.approx( - 2.29337734, 1e-6 - ) # previous val from custom finance model was 1.74903827 - - # failure is expected because we are inflating using general inflation rather than CPI and CEPCI - with subtests.test("Capex"): - assert ( - pytest.approx( - h2i_stim.model.get_val("geoh2_well_subsurface.CapEx", units="USD"), rel=1e-6 - ) - == 19520122.88478073 - ) - with subtests.test("fixed Opex"): - assert ( - pytest.approx( - h2i_stim.model.get_val("geoh2_well_subsurface.OpEx", units="USD/year"), rel=1e-6 - ) - == 215100.7857875 - ) - with subtests.test("variable Opex"): - var_om_pr_h2 = h2i_stim.model.get_val( - "geoh2_well_subsurface.VarOpEx", units="USD/year" - ) / np.sum(h2_prod) - assert pytest.approx(var_om_pr_h2, rel=1e-6) == 0.32105362 - with subtests.test("adjusted Opex"): - adjusted_opex = h2i_stim.prob.get_val( - "finance_subgroup_default.opex_adjusted_geoh2_well_subsurface", units="USD/year" - ) - assert pytest.approx(adjusted_opex, rel=1e-6) == 215100.7857875 - - -@pytest.mark.integration -@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 / "single_site_iron.yaml") - - h2i.run() - - with subtests.test("Value check on LCOI"): - lcoi = h2i.model.get_val("finance_subgroup_iron_ore.LCOI", units="USD/t")[0] - assert pytest.approx(lcoi, rel=1e-4) == 135.3741358811098 - - with subtests.test("Value check on LCOS"): - lcos = h2i.model.get_val("finance_subgroup_sponge_iron.LCOS", units="USD/t")[0] - assert pytest.approx(lcos, rel=1e-4) == 359.670379351 - - with subtests.test("Value check on LCOS"): - lcos = h2i.model.get_val("finance_subgroup_steel.LCOS", units="USD/t")[0] - assert pytest.approx(lcos, rel=1e-4) == 531.5842266865 - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("21_iron_examples/iron_electrowinning", None)] -) -def test_iron_electrowinning_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - model = H2IntegrateModel(example_folder / "iron_electrowinning.yaml") - - with subtests.test("Value check on AHE"): - model.technology_config["technologies"]["iron_plant"]["model_inputs"]["shared_parameters"][ - "electrolysis_type" - ] = "ahe" - model.setup() - model.run() - lcoi = model.model.get_val("finance_subgroup_sponge_iron.LCOS", units="USD/kg")[0] - assert pytest.approx(lcoi, rel=1e-4) == 2.187185703820872 - - with subtests.test("Value check on MSE"): - model.technology_config["technologies"]["iron_plant"]["model_inputs"]["shared_parameters"][ - "electrolysis_type" - ] = "mse" - model.technology_config["technologies"]["ewin_NaOH_feedstock"]["model_inputs"][ - "performance_parameters" - ]["rated_capacity"] = 0 - model.technology_config["technologies"]["ewin_CaCl2_feedstock"]["model_inputs"][ - "performance_parameters" - ]["rated_capacity"] = 179.0 - - model.setup() - model.run() - lcoi = model.model.get_val("finance_subgroup_sponge_iron.LCOS", units="USD/kg")[0] - assert pytest.approx(lcoi, rel=1e-4) == 3.3399342887615115 - - with subtests.test("Value check on MOE"): - model.technology_config["technologies"]["iron_plant"]["model_inputs"]["shared_parameters"][ - "electrolysis_type" - ] = "moe" - model.technology_config["technologies"]["ewin_NaOH_feedstock"]["model_inputs"][ - "performance_parameters" - ]["rated_capacity"] = 0 - model.setup() - model.run() - lcoi = model.model.get_val("finance_subgroup_sponge_iron.LCOS", units="USD/kg")[0] - assert pytest.approx(lcoi, rel=1e-4) == 2.2802793527655987 - - -@pytest.mark.integration -@pytest.mark.parametrize("example_folder,resource_example_folder", [("27_site_doe_diff", None)]) -def test_sweeping_different_resource_sites_doe(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create the model - model = H2IntegrateModel(example_folder / "27_wind_solar_site_doe.yaml") - - # # Run the model - model.run() - - # Specify the filepath to the sql file, the folder and filename are in the driver_config - sql_fpath = example_folder / "ex_27_out" / "cases.sql" - - # load the cases - cr = om.CaseReader(sql_fpath) - - cases = list(cr.get_cases()) - - res_df = pd.DataFrame() - for ci, case in enumerate(cases): - solar_resource_data = case.get_val("solar_site.solar_resource.solar_resource_data") - wind_resource_data = case.get_val("wind_site.wind_resource.wind_resource_data") - with subtests.test(f"Case {ci}: Solar resource latitude matches site latitude"): - assert ( - pytest.approx( - case.get_val("solar_site.solar_resource.latitude", units="deg"), abs=0.1 - ) - == solar_resource_data["site_lat"] - ) - with subtests.test(f"Case {ci}: Wind resource latitude matches site latitude"): - assert ( - pytest.approx( - case.get_val("wind_site.wind_resource.latitude", units="deg"), abs=0.1 - ) - == wind_resource_data["site_lat"] - ) - - s_lat = case.get_val("solar_site.solar_resource.latitude", units="deg")[0] - s_lon = case.get_val("solar_site.solar_resource.longitude", units="deg")[0] - solar_lat_lon = f"{s_lat} {s_lon}" - w_lat = case.get_val("wind_site.wind_resource.latitude", units="deg")[0] - w_lon = case.get_val("wind_site.wind_resource.longitude", units="deg")[0] - wind_lat_lon = f"{w_lat} {w_lon}" - - solar_capacity = case.get_design_vars()["solar.system_capacity_DC"][0] - - solar_aep = np.sum(case.get_val("solar.electricity_out", units="MW")) - solar_lcoe = case.get_val("finance_subgroup_solar.LCOE", units="USD/(MW*h)")[0] - - wind_aep = np.sum(case.get_val("wind.electricity_out", units="MW")) - wind_lcoe = case.get_val("finance_subgroup_wind.LCOE", units="USD/(MW*h)")[0] - - combiner_aep = np.sum(case.get_val("combiner.electricity_out", units="MW")) - combiner_lcoe = case.get_val("finance_subgroup_electricity.LCOE", units="USD/(MW*h)")[0] - - index_cols = [ - "solar site", - "wind site", - "solar AEP", - "solar LCOE", - "solar size", - "wind AEP", - "wind LCOE", - "combiner AEP", - "combiner LCOE", - ] - vals = [ - solar_lat_lon, - wind_lat_lon, - solar_aep, - solar_lcoe, - solar_capacity, - wind_aep, - wind_lcoe, - combiner_aep, - combiner_lcoe, - ] - - site_res = pd.DataFrame(vals, index=index_cols, columns=[ci]).T - - res_df = pd.concat([site_res, res_df], axis=0) - - with subtests.test("Two unique solar capacities"): - solar_sizes = list(set(res_df["solar site"].to_list())) - assert len(solar_sizes) == 2 - - with subtests.test("Two unique solar sites"): - solar_locations = list(set(res_df["solar site"])) - assert len(solar_locations) == 2 - - with subtests.test("Two unique wind sites"): - wind_locations = list(set(res_df["wind site"])) - assert len(wind_locations) == 2 - - with subtests.test("Unique solar AEPS"): - assert len(list(set(res_df["solar AEP"].to_list()))) == 4 - - with subtests.test("Unique solar LCOEs"): - assert len(list(set(res_df["solar LCOE"].to_list()))) == 4 - - with subtests.test("Unique wind AEPS"): - assert len(list(set(res_df["wind AEP"].to_list()))) == 2 - - with subtests.test("Unique wind LCOEs"): - assert len(list(set(res_df["wind LCOE"].to_list()))) == 2 - - with subtests.test("Unique combiner AEPS"): - assert len(list(set(res_df["combiner AEP"].to_list()))) == len(res_df) - - with subtests.test("Unique LCOEs per case"): - assert len(list(set(res_df["combiner LCOE"].to_list()))) == len(res_df) - - -@pytest.mark.integration -@pytest.mark.parametrize( - "example_folder,resource_example_folder", [("30_pyomo_optimized_dispatch", None)] -) -def test_pyomo_optimized_dispatch_example(subtests, temp_copy_of_example): - example_folder = temp_copy_of_example - - # Create a H2Integrate model - model = H2IntegrateModel(example_folder / "pyomo_optimized_dispatch.yaml") - - demand_profile = np.ones(8760) * 100.0 - - # 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() - - model.post_process() - - with subtests.test("Check wind total electricity produced"): - wind_total = model.prob.get_val("wind.total_electricity_produced", units="kW*h")[0] - assert wind_total == pytest.approx(781_472_811.8, rel=1e-3) - - with subtests.test("Check wind capacity factor"): - wind_cf = model.prob.get_val("wind.capacity_factor", units="unitless")[0] - assert wind_cf == pytest.approx(0.4299, rel=1e-3) - - with subtests.test("Check wind CapEx"): - wind_capex = model.prob.get_val("wind.CapEx", units="USD")[0] - assert wind_capex == pytest.approx(311_250_000.0, rel=1e-3) - - # Battery checks - with subtests.test("Check battery total electricity produced"): - 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("electrical_load_demand.capacity_factor", units="unitless")[ - 0 - ] - assert battery_cf == pytest.approx(0.7372, rel=1e-3) - - with subtests.test("Check battery CapEx"): - battery_capex = model.prob.get_val("battery.CapEx", units="USD")[0] - assert battery_capex == pytest.approx(155_100_000.0, rel=1e-3) - - with subtests.test("Check battery OpEx"): - battery_opex = model.prob.get_val("battery.OpEx", units="USD/year")[0] - assert battery_opex == pytest.approx(38_775_000.0, rel=1e-3) - - # Finance checks - with subtests.test("Check LCOE"): - lcoe = model.prob.get_val("finance_subgroup_all_electricity.LCOE", units="USD/(kW*h)")[0] - assert lcoe == pytest.approx(0.134, rel=1e-3) - - with subtests.test("Check total adjusted CapEx"): - total_capex = model.prob.get_val( - "finance_subgroup_all_electricity.total_capex_adjusted", units="USD" - )[0] - assert total_capex == pytest.approx(490_282_207.03, rel=1e-3) - - with subtests.test("Check total adjusted OpEx"): - total_opex = model.prob.get_val( - "finance_subgroup_all_electricity.total_opex_adjusted", units="USD/year" - )[0] - assert total_opex == pytest.approx(48_830_466.21, rel=1e-3) - - with subtests.test("Check total electricity produced"): - total_electricity = ( - model.prob.get_val( - "finance_subgroup_all_electricity.rated_electricity_production", - units="kW", - )[0] - * model.prob.get_val( - "finance_subgroup_all_electricity.capacity_factor", - units="unitless", - ).mean() - * 8760 - ) - assert total_electricity == pytest.approx(781_472_811.8, rel=1e-3) - - with subtests.test("Check electricity price"): - price = model.prob.get_val( - "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) - @pytest.mark.integration @pytest.mark.parametrize( "example_folder,resource_example_folder", [("34_plm_optimized_dispatch", None)] From 0810a9aef49f5f6d5cdcf8fe4fdd4497f9c1f580 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Mon, 27 Apr 2026 12:29:35 -0600 Subject: [PATCH 17/55] Fix formatting in supported_models --- h2integrate/core/supported_models.py | 64 ++++++++-------------------- 1 file changed, 17 insertions(+), 47 deletions(-) diff --git a/h2integrate/core/supported_models.py b/h2integrate/core/supported_models.py index e6705467e..237dd9c64 100644 --- a/h2integrate/core/supported_models.py +++ b/h2integrate/core/supported_models.py @@ -7,10 +7,7 @@ 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.steel.steel import SteelPerformanceModel, SteelCostAndFinancialModel from h2integrate.converters.wind.floris import FlorisWindPlantPerformanceModel from h2integrate.demand.flexible_demand import FlexibleDemandComponent from h2integrate.converters.wind.wind_pysam import PYSAMWindPlantPerformanceModel @@ -36,10 +33,7 @@ IronTransportCostComponent, IronTransportPerformanceComponent, ) -from h2integrate.converters.nitrogen.simple_ASU import ( - SimpleASUCostModel, - SimpleASUPerformanceModel, -) +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.converters.hydrogen.h2_fuel_cell import ( @@ -64,16 +58,10 @@ CompressedGasStorageCostModel, LinedRockCavernStorageCostModel, ) -from h2integrate.transporters.gas_stream_combiner import ( - GasStreamCombinerPerformanceModel, -) -from h2integrate.transporters.generic_transporter import ( - GenericTransporterPerformanceModel, -) +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.converters.iron.humbert_ewin_perf import HumbertEwinPerformanceComponent from h2integrate.storage.storage_performance_model import StoragePerformanceModel from h2integrate.converters.ammonia.ammonia_synloop import ( AmmoniaSynLoopCostModel, @@ -85,21 +73,13 @@ ReverseOsmosisCostModel, ReverseOsmosisPerformanceModel, ) -from h2integrate.resource.wind.nlr_developer_wtk_api import ( - WTKNLRDeveloperAPIWindResource, -) +from h2integrate.resource.wind.nlr_developer_wtk_api import WTKNLRDeveloperAPIWindResource from h2integrate.converters.hydrogen.basic_cost_model import BasicElectrolyzerCostModel -from h2integrate.converters.hydrogen.pem_electrolyzer import ( - ECOElectrolyzerPerformanceModel, -) +from h2integrate.converters.hydrogen.pem_electrolyzer import ECOElectrolyzerPerformanceModel from h2integrate.converters.solar.atb_res_com_pv_cost import ATBResComPVCostModel from h2integrate.converters.solar.atb_utility_pv_cost import ATBUtilityPVCostModel -from h2integrate.converters.iron.martin_mine_cost_model import ( - MartinIronMineCostComponent, -) -from h2integrate.converters.iron.martin_mine_perf_model import ( - MartinIronMinePerformanceComponent, -) +from h2integrate.converters.iron.martin_mine_cost_model import MartinIronMineCostComponent +from h2integrate.converters.iron.martin_mine_perf_model import MartinIronMinePerformanceComponent from h2integrate.converters.methanol.smr_methanol_plant import ( SMRMethanolPlantCostModel, SMRMethanolPlantFinanceModel, @@ -109,9 +89,7 @@ SimpleAmmoniaCostModel, SimpleAmmoniaPerformanceModel, ) -from h2integrate.converters.iron.humbert_stinn_ewin_cost import ( - HumbertStinnEwinCostComponent, -) +from h2integrate.converters.iron.humbert_stinn_ewin_cost import HumbertStinnEwinCostComponent from h2integrate.converters.methanol.co2h_methanol_plant import ( CO2HMethanolPlantCostModel, CO2HMethanolPlantFinanceModel, @@ -123,10 +101,7 @@ ) 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.co2.marine.direct_ocean_capture import DOCCostModel, DOCPerformanceModel from h2integrate.converters.hydrogen.steam_methane_reformer import ( SteamMethaneReformerCostModel, SteamMethaneReformerPerformanceModel, @@ -137,9 +112,7 @@ SimpleGasConsumerPerformance, SimpleGasProducerPerformance, ) -from h2integrate.converters.hydrogen.geologic.mathur_modified import ( - GeoH2SubsurfaceCostModel, -) +from h2integrate.converters.hydrogen.geologic.mathur_modified import GeoH2SubsurfaceCostModel from h2integrate.resource.solar.nlr_developer_goes_api_models import ( GOESTMYSolarAPI, GOESConusSolarAPI, @@ -198,12 +171,12 @@ from h2integrate.control.control_strategies.storage.simple_openloop_controller import ( SimpleStorageOpenLoopController, ) -from h2integrate.control.control_strategies.storage.plm_optimized_storage_controller import ( - PLMOptimizedStorageController, -) from h2integrate.control.control_strategies.storage.plm_openloop_storage_controller import ( PeakLoadManagementHeuristicOpenLoopStorageController, ) +from h2integrate.control.control_strategies.storage.plm_optimized_storage_controller import ( + PLMOptimizedStorageController, +) from h2integrate.control.control_rules.storage.pyomo_storage_rule_min_operating_cost import ( PyomoRuleStorageMinOperatingCosts, ) @@ -325,10 +298,7 @@ # Control "SimpleStorageOpenLoopController": SimpleStorageOpenLoopController, "DemandOpenLoopStorageController": DemandOpenLoopStorageController, - "PeakLoadManagementHeuristicOpenLoopStorageController": ( - PeakLoadManagementHeuristicOpenLoopStorageController - ), - "PLMOptimizedStorageController": PLMOptimizedStorageController, + "PLMOptimizedStorageController": (PLMOptimizedStorageController), "HeuristicLoadFollowingStorageController": HeuristicLoadFollowingStorageController, "OptimizedDispatchStorageController": OptimizedDispatchStorageController, "GenericDemandComponent": GenericDemandComponent, @@ -376,4 +346,4 @@ no_replacement_schedule_models = { "IronTransportPerformanceComponent", -} +} \ No newline at end of file From ac0c0a08fb5c9a9cd7a59d4e74102914b3edab95 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Mon, 27 Apr 2026 12:35:39 -0600 Subject: [PATCH 18/55] More formatting fixes --- docs/user_guide/model_overview.md | 1 - h2integrate/core/supported_models.py | 3 +++ 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/user_guide/model_overview.md b/docs/user_guide/model_overview.md index c447ddd50..9a182f7b0 100644 --- a/docs/user_guide/model_overview.md +++ b/docs/user_guide/model_overview.md @@ -288,7 +288,6 @@ Below summarizes the available performance, cost, and financial models for each - `'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 - `'HeuristicLoadFollowingStorageController'`: open-loop control that works on a time window basis to set dispatch commands; uses Pyomo - - `'PLMOptimizedStorageController'`: optimized controller for demand response that works on a time window basis. - `'PeakLoadManagementHeuristicOpenLoopStorageController'`: open-loop control that reduces peaks rather than trying to meet a load - `'PLMOptimizedStorageController'`: optimized controller for demand response that works on a time window basis. - Optimized Dispatch: diff --git a/h2integrate/core/supported_models.py b/h2integrate/core/supported_models.py index 237dd9c64..57728f524 100644 --- a/h2integrate/core/supported_models.py +++ b/h2integrate/core/supported_models.py @@ -298,6 +298,9 @@ # Control "SimpleStorageOpenLoopController": SimpleStorageOpenLoopController, "DemandOpenLoopStorageController": DemandOpenLoopStorageController, + "PeakLoadManagementHeuristicOpenLoopStorageController": ( + PeakLoadManagementHeuristicOpenLoopStorageController + ), "PLMOptimizedStorageController": (PLMOptimizedStorageController), "HeuristicLoadFollowingStorageController": HeuristicLoadFollowingStorageController, "OptimizedDispatchStorageController": OptimizedDispatchStorageController, From fa9a4a70d03eb098b25cbc1988ab3d6f13907d57 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Mon, 27 Apr 2026 13:54:45 -0600 Subject: [PATCH 19/55] Change code to handle varibale timesteps --- .../34_plm_optimized_dispatch/tech_config.yaml | 2 +- .../storage/plm_optimized_storage_controller.py | 17 +++++++++-------- 2 files changed, 10 insertions(+), 9 deletions(-) diff --git a/examples/34_plm_optimized_dispatch/tech_config.yaml b/examples/34_plm_optimized_dispatch/tech_config.yaml index 6bd644775..ed583ff56 100644 --- a/examples/34_plm_optimized_dispatch/tech_config.yaml +++ b/examples/34_plm_optimized_dispatch/tech_config.yaml @@ -28,7 +28,7 @@ technologies: peak_window: start: "14:00:00" # 2 pm MDT end: "18:00:00" # 6 pm MDT - performance_incentive: 14.0 # $/kW per dispatch hour + performance_incentive: 14.0 # $/kWh (scaled by dt per dispatched timestep) n_max_events: 10 # maximum discharge events per month signal_threshold_percentile: 95.0 # only dispatch when LMP >= this percentile n_control_window: 10 # number of time steps in the control window (e.g., 10 hours) diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index 3e9bf6959..b640e231d 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -35,8 +35,8 @@ class PLMOptimizedControllerConfig(PyomoStorageControllerBaseConfig): length ``n_control_window`` per solve. peak_window (dict): Hours eligible for dispatch. Keys ``'start'`` and ``'end'`` must be strings in ``HH:MM:SS`` format. - performance_incentive (float): Incentive revenue ($/kW per - dispatch hour). + performance_incentive (float): Incentive revenue in $/kWh — + multiplied by ``dt`` (in hours) per dispatched timestep. charge_efficiency (float): Charge efficiency in [0, 1]. Defaults to 1.0. discharge_efficiency (float): Discharge efficiency in [0, 1]. @@ -97,9 +97,9 @@ def setup(self): desc="Total storage capacity", ) - self.n_timesteps = self.options["plant_config"]["plant"]["simulation"][ - "n_timesteps" - ] + sim = self.options["plant_config"]["plant"]["simulation"] + self.n_timesteps = int(sim["n_timesteps"]) + self.dt_seconds = int(sim["dt"]) super().setup() self.updated_initial_soc = self.config.init_soc_fraction @@ -380,8 +380,9 @@ def _build_dr_model( doc="State of charge SoC_t", ) + dt_hours = self.dt_seconds / 3600.0 m.objective = pyomo.Objective( - expr=-incentive * P_max * sum(m.discharge[t] for t in m.T), + expr=-incentive * P_max * dt_hours * sum(m.discharge[t] for t in m.T), sense=pyomo.minimize, ) @@ -413,8 +414,8 @@ def soc_evolution_rule(mdl, t): return pyomo.Constraint.Skip return mdl.soc[t] == ( mdl.soc[t - 1] - + eta_c * mdl.charge[t] * P_max / E_max - - mdl.discharge[t] * P_max / (eta_d * E_max) + + eta_c * mdl.charge[t] * P_max * dt_hours / E_max + - mdl.discharge[t] * P_max * dt_hours / (eta_d * E_max) ) m.soc_evolution = pyomo.Constraint(m.T, rule=soc_evolution_rule) From db9923f6bf47ad9a2c4ba7e075efba55d8758059 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Tue, 28 Apr 2026 08:39:36 -0600 Subject: [PATCH 20/55] Pre-commit fixes --- docs/user_guide/model_overview.md | 2 +- .../run_plm_optimized_dispatch.py | 46 +- .../tech_config.yaml | 32 +- .../test_controller_methods.ipynb | 493 ++++++++++++++++++ .../plm_optimized_storage_controller.py | 278 +++++++--- .../test_plm_optimized_storage_controller.py | 40 +- h2integrate/core/supported_models.py | 10 +- 7 files changed, 776 insertions(+), 125 deletions(-) create mode 100644 examples/34_plm_optimized_dispatch/test_controller_methods.ipynb diff --git a/docs/user_guide/model_overview.md b/docs/user_guide/model_overview.md index 9a182f7b0..871389859 100644 --- a/docs/user_guide/model_overview.md +++ b/docs/user_guide/model_overview.md @@ -289,7 +289,7 @@ Below summarizes the available performance, cost, and financial models for each - `'DemandOpenLoopStorageController'`: open-loop control; manages resource flow based on demand and storage constraints - `'HeuristicLoadFollowingStorageController'`: open-loop control that works on a time window basis to set dispatch commands; uses Pyomo - `'PeakLoadManagementHeuristicOpenLoopStorageController'`: open-loop control that reduces peaks rather than trying to meet a load - - `'PLMOptimizedStorageController'`: optimized controller for demand response that works on a time window basis. + - `'PeakLoadManagementOptimizedStorageController'`: optimized controller for demand response that works on a time window basis. - Optimized Dispatch: - `'OptimizedDispatchStorageController'`: optimization-based dispatch using Pyomo diff --git a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py index 547d030bc..f55279d81 100644 --- a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py +++ b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py @@ -13,6 +13,7 @@ from h2integrate.core.h2integrate_model import H2IntegrateModel + EXAMPLE_DIR = Path(__file__).parent @@ -27,9 +28,9 @@ model.run() lmp = np.array( - model.technology_config["technologies"]["battery"]["model_inputs"][ - "control_parameters" - ]["supervisory_signal"] + model.technology_config["technologies"]["battery"]["model_inputs"]["control_parameters"][ + "supervisory_signal" + ] )[:N] sim = model.plant_config["plant"]["simulation"] @@ -43,7 +44,19 @@ battery_power = model.prob.get_val("battery.storage_electricity_discharge", units="kW") soc_pct = model.prob.get_val("battery.SOC", units="percent") -peak_mask = (time_index.hour >= 14) & (time_index.hour <= 18) +control_params = model.technology_config["technologies"]["battery"]["model_inputs"][ + "control_parameters" +] +pw_cfg = control_params["peak_window"] +event_dur_cfg = control_params.get("event_duration") + +pw_start_h = int(pw_cfg["start"].split(":")[0]) +pw_end_h = int(pw_cfg["end"].split(":")[0]) + +half_td = None +if event_dur_cfg is not None: + half_td = pd.Timedelta(value=event_dur_cfg["val"], unit=event_dur_cfg["units"]) / 2 + threshold_pct = np.percentile(lmp, percentile) discharge_mask = battery_power > 0 @@ -51,19 +64,38 @@ plt.rcParams.update({"axes.spines.top": False, "axes.spines.right": False}) fig, axes = plt.subplots(2, 1, sharex=True, figsize=(11, 7)) days = pd.date_range(time_index[0].normalize(), periods=14, freq="D", tz=time_index.tz) -time_window = 14 * 24 +time_window = min(n_timesteps, int(14 * 24 * 3600 / dt_seconds)) # 14 days def shade_peaks(ax): for day in days: + # light background: static peak_window ax.axvspan( - day + pd.Timedelta(hours=14), - day + pd.Timedelta(hours=18), + day + pd.Timedelta(hours=pw_start_h), + day + pd.Timedelta(hours=pw_end_h), color="orange", alpha=0.10, linewidth=0, zorder=0, ) + if half_td is None: + continue + # darker band: event window centered on the daily LMP peak + pw_start_ts = day + pd.Timedelta(hours=pw_start_h) + pw_end_ts = day + pd.Timedelta(hours=pw_end_h) + in_pw = (time_index >= pw_start_ts) & (time_index <= pw_end_ts) + if not in_pw.any(): + continue + peak_idx = np.where(in_pw)[0][np.argmax(lmp[in_pw])] + peak_ts = time_index[peak_idx] + ax.axvspan( + peak_ts - half_td, + peak_ts + half_td, + color="darkorange", + alpha=0.30, + linewidth=0, + zorder=0, + ) w_discharge = discharge_mask[:time_window] diff --git a/examples/34_plm_optimized_dispatch/tech_config.yaml b/examples/34_plm_optimized_dispatch/tech_config.yaml index ed583ff56..e18297b60 100644 --- a/examples/34_plm_optimized_dispatch/tech_config.yaml +++ b/examples/34_plm_optimized_dispatch/tech_config.yaml @@ -7,36 +7,40 @@ technologies: cost_model: model: ATBBatteryCostModel control_strategy: - model: PLMOptimizedStorageController + model: PeakLoadManagementOptimizedStorageController model_inputs: shared_parameters: commodity: electricity commodity_rate_units: kW - max_charge_rate: 300.0 # kW rated power - max_capacity: 1200.0 # kWh usable capacity (4-hour battery) + max_charge_rate: 300.0 # kW rated power + max_capacity: 1200.0 # kWh usable capacity (4-hour battery) max_soc_fraction: 0.90 min_soc_fraction: 0.10 init_soc_fraction: 0.90 charge_efficiency: 0.95 discharge_efficiency: 0.95 - performance_parameters: - demand_profile: 0.0 + demand_profile: 0.0 control_parameters: system_commodity_interface_limit: 1.0e9 supervisory_signal: !include demand_profiles/supervisory_signal.yaml peak_window: - start: "14:00:00" # 2 pm MDT - end: "18:00:00" # 6 pm MDT - performance_incentive: 14.0 # $/kWh (scaled by dt per dispatched timestep) - n_max_events: 10 # maximum discharge events per month + start: 14:00:00 # 2 pm MDT + end: 18:00:00 # 6 pm MDT + performance_incentive: + units: $/kWh # also accepts $/MWh or $/Wh + val: 14.0 + n_max_events: 10 # maximum discharge events per month signal_threshold_percentile: 95.0 # only dispatch when LMP >= this percentile - n_control_window: 10 # number of time steps in the control window (e.g., 10 hours) + n_control_window: 10 # hours; converted to timesteps using simulation dt + event_duration: # omit or set to null to use static peak_window + units: h # any pandas timedelta unit: h, min, s, … + val: 2 # battery eligible +/ - val h around the daily peak round_digits: 4 cost_parameters: cost_year: 2024 - energy_capex: 408 # $/kWh (2024 ATB) - power_capex: 379 # $/kW + energy_capex: 408 # $/kWh (2024 ATB) + power_capex: 379 # $/kW opex_fraction: 0.025 grid_buy: performance_model: @@ -45,10 +49,10 @@ technologies: model: GridCostModel model_inputs: shared_parameters: - interconnection_size: 100000 # kW + interconnection_size: 100000 # kW cost_parameters: cost_year: 2024 fixed_interconnection_cost: 0.0 interconnection_capex_per_kw: 0.0 interconnection_opex_per_kw: 0.0 - electricity_buy_price: 0.09 # $/kWh + electricity_buy_price: 0.09 # $/kWh diff --git a/examples/34_plm_optimized_dispatch/test_controller_methods.ipynb b/examples/34_plm_optimized_dispatch/test_controller_methods.ipynb new file mode 100644 index 000000000..a1aadab77 --- /dev/null +++ b/examples/34_plm_optimized_dispatch/test_controller_methods.ipynb @@ -0,0 +1,493 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7fb27b941602401d91542211134fc71a", + "metadata": {}, + "source": [ + "# PLM Optimized Storage Controller \u2014 Method-level tests\n", + "\n", + "Tests each method of `PeakLoadManagementOptimizedStorageController` in isolation\n", + "using small synthetic data. No full OpenMDAO model needed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "acae54e37e7d407bbb7b55eff062a284", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "from pathlib import Path\n", + "\n", + "# Add the repo root to sys.path so the h2integrate package is importable\n", + "repo_root = Path(\"../../\").resolve()\n", + "if str(repo_root) not in sys.path:\n", + " sys.path.insert(0, str(repo_root))\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pyomo.environ as pyomo\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from h2integrate.control.control_strategies.storage.plm_optimized_storage_controller import (\n", + " PeakLoadManagementOptimizedControllerConfig,\n", + " PeakLoadManagementOptimizedStorageController,\n", + ")\n", + "\n", + "plt.rcParams.update({\"axes.spines.top\": False, \"axes.spines.right\": False})" + ] + }, + { + "cell_type": "markdown", + "id": "9a63283cbaf04dbcab1f6479b197f3a8", + "metadata": {}, + "source": [ + "## Helper: build a lightweight controller\n", + "\n", + "Bypasses OpenMDAO `setup()` so each method can be tested directly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8dd0d8092fe74a7c96281538738b07e2", + "metadata": {}, + "outputs": [], + "source": [ + "N = 48 # 2 days of hourly timesteps\n", + "DT_SECONDS = 3600 # 1-hour resolution\n", + "\n", + "# Synthetic LMP signal: flat with spikes at hours 15 and 37 (peak window 14-18)\n", + "lmp = np.full(N, 20.0)\n", + "lmp[15] = 80.0 # day-1 peak inside peak window\n", + "lmp[14] = 60.0\n", + "lmp[16] = 55.0\n", + "lmp[37] = 75.0 # day-2 peak inside peak window (hour 13 of day 2)\n", + "lmp[36] = 50.0\n", + "lmp[38] = 45.0\n", + "\n", + "config = PeakLoadManagementOptimizedControllerConfig(\n", + " max_capacity=1200.0,\n", + " max_charge_rate=300.0,\n", + " max_soc_fraction=0.90,\n", + " min_soc_fraction=0.10,\n", + " init_soc_fraction=0.90,\n", + " charge_efficiency=0.95,\n", + " discharge_efficiency=0.95,\n", + " n_control_window=N, # full 2-day window as one solve\n", + " commodity=\"electricity\",\n", + " commodity_rate_units=\"kW\",\n", + " tech_name=\"battery\",\n", + " system_commodity_interface_limit=1e9,\n", + " supervisory_signal=lmp.tolist(),\n", + " peak_window={\"start\": \"14:00:00\", \"end\": \"18:00:00\"},\n", + " performance_incentive={\"units\": \"$/kWh\", \"val\": 14.0},\n", + " n_max_events=10,\n", + " signal_threshold_percentile=95.0,\n", + " event_duration={\"units\": \"h\", \"val\": 2}, # +/- 1h around daily peak\n", + ")\n", + "\n", + "\n", + "def make_controller(cfg=config, n=N, dt=DT_SECONDS):\n", + " \"\"\"Instantiate the controller without OpenMDAO infrastructure.\"\"\"\n", + " ctrl = object.__new__(PeakLoadManagementOptimizedStorageController)\n", + " ctrl.config = cfg\n", + " ctrl.dt_seconds = dt\n", + " ctrl.n_timesteps = n\n", + " ctrl.updated_initial_soc = cfg.init_soc_fraction\n", + " ctrl.time_index = pd.date_range(\"2025-01-01\", periods=n, freq=pd.to_timedelta(dt, unit=\"s\"))\n", + " ctrl.in_peak_window = ctrl._compute_peak_window_mask()\n", + " ctrl.month_ids = ctrl._compute_month_ids()\n", + " return ctrl\n", + "\n", + "\n", + "ctrl = make_controller()\n", + "print(\"Controller created. time_index:\", ctrl.time_index[[0, -1]].tolist())" + ] + }, + { + "cell_type": "markdown", + "id": "72eea5119410473aa328ad9291626812", + "metadata": {}, + "source": [ + "## 1. `_parse_peak_window`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8edb47106e1a46a883d545849b8ab81b", + "metadata": {}, + "outputs": [], + "source": [ + "start, end = ctrl._parse_peak_window()\n", + "print(f\"peak_window start={start} end={end}\")\n", + "assert start.hour == 14 and end.hour == 18" + ] + }, + { + "cell_type": "markdown", + "id": "10185d26023b46108eb7d9f57d49d2b3", + "metadata": {}, + "source": [ + "## 2. `_compute_peak_window_mask`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8763a12b2bbd4a93a75aff182afb95dc", + "metadata": {}, + "outputs": [], + "source": [ + "mask = ctrl.in_peak_window\n", + "print(\"Timesteps inside peak window:\", np.where(mask)[0].tolist())\n", + "# Expect hours 14-18 on each day: [14,15,16,17,18,38,39,40,41,42]\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 2))\n", + "ax.fill_between(range(N), mask.astype(int), step=\"post\", alpha=0.4, label=\"peak window\")\n", + "ax.set_xlabel(\"Timestep (h)\")\n", + "ax.set_title(\"Static peak-window mask\")\n", + "ax.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7623eae2785240b9bd12b16a66d81610", + "metadata": {}, + "source": [ + "## 3. `_compute_month_ids`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7cdc8c89c7104fffa095e18ddfef8986", + "metadata": {}, + "outputs": [], + "source": [ + "month_ids = ctrl._compute_month_ids()\n", + "print(\"Unique months in 2-day window:\", np.unique(month_ids).tolist())\n", + "assert set(month_ids) == {1} # both days are January" + ] + }, + { + "cell_type": "markdown", + "id": "b118ea5561624da68c537baed56e602f", + "metadata": {}, + "source": [ + "## 4. `_compute_eligible_mask`\n", + "\n", + "Percentile-based signal filter applied to a rolling window." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "938c804e27f84196a10c8828c723f798", + "metadata": {}, + "outputs": [], + "source": [ + "signal_window = np.asarray(config.supervisory_signal)\n", + "\n", + "# a) No dispatch_mask \u2014 percentile over full window\n", + "eligible_full = ctrl._compute_eligible_mask(signal_window)\n", + "print(\"Eligible (full window, p=95%):\", np.where(eligible_full)[0].tolist())\n", + "\n", + "# b) dispatch_mask = peak_window only \u2014 percentile over in-window signals\n", + "eligible_pw = ctrl._compute_eligible_mask(signal_window, dispatch_mask=ctrl.in_peak_window)\n", + "print(\"Eligible (peak-window signals only, p=95%):\", np.where(eligible_pw)[0].tolist())" + ] + }, + { + "cell_type": "markdown", + "id": "504fb2a444614c0babb325280ed9130a", + "metadata": {}, + "source": [ + "## 5. `_compute_event_window_mask`\n", + "\n", + "Computes the \u00b1`event_duration/2` window centred on each day's LMP peak." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59bbdb311c014d738909a11f9e486628", + "metadata": {}, + "outputs": [], + "source": [ + "signal_window = np.asarray(config.supervisory_signal)\n", + "\n", + "event_mask = ctrl._compute_event_window_mask(signal_window, ctrl.in_peak_window, window_start=0)\n", + "\n", + "print(\"Static peak-window timesteps:\", np.where(ctrl.in_peak_window)[0].tolist())\n", + "print(\"Event-window timesteps :\", np.where(event_mask)[0].tolist())\n", + "# Day 1 peak at t=15 \u2192 event window = [14, 15, 16] (\u00b11 h)\n", + "# Day 2 peak at t=37 \u2192 event window = [36, 37, 38] (\u00b11 h)\n", + "\n", + "fig, axes = plt.subplots(2, 1, figsize=(10, 4), sharex=True)\n", + "axes[0].plot(signal_window, color=\"steelblue\", linewidth=1)\n", + "axes[0].set_ylabel(\"LMP ($/MWh)\")\n", + "axes[0].set_title(\"Signal\")\n", + "\n", + "axes[1].fill_between(\n", + " range(N), ctrl.in_peak_window.astype(int), step=\"post\", alpha=0.3, label=\"static peak_window\"\n", + ")\n", + "axes[1].fill_between(\n", + " range(N),\n", + " event_mask.astype(int),\n", + " step=\"post\",\n", + " alpha=0.5,\n", + " color=\"darkorange\",\n", + " label=\"event window (\u00b11 h)\",\n", + ")\n", + "axes[1].set_xlabel(\"Timestep (h)\")\n", + "axes[1].set_title(\"Masks\")\n", + "axes[1].legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b43b363d81ae4b689946ece5c682cd59", + "metadata": {}, + "source": [ + "## 6. `_build_dr_model` + solve\n", + "\n", + "Build and solve the MILP for the full 2-day window. Expect discharge at event-window timesteps." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a65eabff63a45729fe45fb5ade58bdc", + "metadata": {}, + "outputs": [], + "source": [ + "# Expand system_commodity_interface_limit to match window length\n", + "object.__setattr__(\n", + " ctrl.config,\n", + " \"system_commodity_interface_limit\",\n", + " [1e9] * N,\n", + ")\n", + "\n", + "model = ctrl._build_dr_model(\n", + " window_start=0,\n", + " window_len=N,\n", + " init_soc=config.init_soc_fraction,\n", + " remaining_budget={1: config.n_max_events},\n", + ")\n", + "\n", + "results = PeakLoadManagementOptimizedStorageController.glpk_solve_call(model)\n", + "print(\"Solver status:\", results.solver.status)\n", + "print(\"Termination :\", results.solver.termination_condition)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3933fab20d04ec698c2621248eb3be0", + "metadata": {}, + "outputs": [], + "source": [ + "discharge = np.array([pyomo.value(model.discharge[t]) for t in range(N)])\n", + "charge = np.array([pyomo.value(model.charge[t]) for t in range(N)])\n", + "soc = np.array([pyomo.value(model.soc[t]) for t in range(N)])\n", + "\n", + "dispatch_ts = np.where(discharge > 0.5)[0].tolist()\n", + "charge_ts = np.where(charge > 0.5)[0].tolist()\n", + "print(\"Discharge timesteps:\", dispatch_ts)\n", + "print(\"Charge timesteps:\", charge_ts)\n", + "print(f\"Total discharge events: {int(discharge.sum())} (limit: {config.n_max_events}/month)\")\n", + "\n", + "# --- verify discharge is within event window ---\n", + "outside = [t for t in dispatch_ts if not event_mask[t]]\n", + "print(\"\\nDischarge outside event window (expect empty):\", outside)\n", + "assert not outside, f\"Unexpected discharge outside event window: {outside}\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4dd4641cc4064e0191573fe9c69df29b", + "metadata": {}, + "outputs": [], + "source": [ + "P_max = config.max_charge_rate\n", + "net_power = (discharge - charge) * P_max\n", + "\n", + "fig, axes = plt.subplots(3, 1, figsize=(11, 8), sharex=True)\n", + "\n", + "ax = axes[0]\n", + "ax.plot(signal_window, color=\"steelblue\", linewidth=1, label=\"LMP\")\n", + "ax.fill_between(\n", + " range(N),\n", + " event_mask * signal_window.max(),\n", + " step=\"post\",\n", + " alpha=0.25,\n", + " color=\"darkorange\",\n", + " label=\"event window\",\n", + ")\n", + "ax.set_ylabel(\"LMP ($/MWh)\")\n", + "ax.legend(fontsize=8)\n", + "\n", + "ax = axes[1]\n", + "ax.bar(range(N), discharge * P_max, color=\"tomato\", label=\"discharge (kW)\")\n", + "ax.bar(range(N), -charge * P_max, color=\"steelblue\", label=\"charge (kW)\")\n", + "ax.set_ylabel(\"Power (kW)\")\n", + "ax.legend(fontsize=8)\n", + "\n", + "ax = axes[2]\n", + "ax.plot(soc * 100, color=\"green\", linewidth=1.2)\n", + "ax.axhline(config.max_soc_fraction * 100, color=\"gray\", linestyle=\":\", linewidth=0.8)\n", + "ax.axhline(config.min_soc_fraction * 100, color=\"gray\", linestyle=\":\", linewidth=0.8)\n", + "ax.set_ylabel(\"SOC (%)\")\n", + "ax.set_xlabel(\"Timestep (h)\")\n", + "ax.set_ylim([0, 105])\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8309879909854d7188b41380fd92a7c3", + "metadata": {}, + "source": [ + "## 7. Sensitivity: event_duration = None (static peak window)\n", + "\n", + "With no `event_duration`, dispatch should cover all timesteps in the static peak window\n", + "whose signal exceeds the percentile threshold." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ed186c9a28b402fb0bc4494df01f08d", + "metadata": {}, + "outputs": [], + "source": [ + "config_no_dur = PeakLoadManagementOptimizedControllerConfig(\n", + " max_capacity=1200.0,\n", + " max_charge_rate=300.0,\n", + " max_soc_fraction=0.90,\n", + " min_soc_fraction=0.10,\n", + " init_soc_fraction=0.90,\n", + " charge_efficiency=0.95,\n", + " discharge_efficiency=0.95,\n", + " n_control_window=N,\n", + " commodity=\"electricity\",\n", + " commodity_rate_units=\"kW\",\n", + " tech_name=\"battery\",\n", + " system_commodity_interface_limit=1e9,\n", + " supervisory_signal=lmp.tolist(),\n", + " peak_window={\"start\": \"14:00:00\", \"end\": \"18:00:00\"},\n", + " performance_incentive={\"units\": \"$/kWh\", \"val\": 14.0},\n", + " n_max_events=10,\n", + " signal_threshold_percentile=95.0,\n", + " event_duration=None,\n", + ")\n", + "\n", + "ctrl_no_dur = make_controller(config_no_dur)\n", + "object.__setattr__(ctrl_no_dur.config, \"system_commodity_interface_limit\", [1e9] * N)\n", + "\n", + "model_no_dur = ctrl_no_dur._build_dr_model(\n", + " window_start=0,\n", + " window_len=N,\n", + " init_soc=config_no_dur.init_soc_fraction,\n", + " remaining_budget={1: config_no_dur.n_max_events},\n", + ")\n", + "PeakLoadManagementOptimizedStorageController.glpk_solve_call(model_no_dur)\n", + "\n", + "discharge_no_dur = np.array([pyomo.value(model_no_dur.discharge[t]) for t in range(N)])\n", + "print(\"Discharge timesteps (no event_duration):\", np.where(discharge_no_dur > 0.5)[0].tolist())\n", + "print(\" (should be 1 or 2 timesteps with highest signal inside peak_window)\")" + ] + }, + { + "cell_type": "markdown", + "id": "cb1e1581032b452c9409d6c6813c49d1", + "metadata": {}, + "source": [ + "## 8. Sub-hourly resolution test (30-min timesteps)\n", + "\n", + "Verifies that `event_duration` and `n_control_window` scale correctly at any `dt`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "379cbbc1e968416e875cc15c1202d7eb", + "metadata": {}, + "outputs": [], + "source": [ + "N_30 = 96 # 2 days at 30-min resolution\n", + "DT_30 = 1800 # 30 minutes\n", + "\n", + "lmp_30 = np.full(N_30, 20.0)\n", + "# Day 1: spike at 30-min timestep 30 = hour 15\n", + "lmp_30[30] = 80.0\n", + "lmp_30[28] = 60.0\n", + "lmp_30[32] = 55.0\n", + "# Day 2: spike at 30-min timestep 74 = hour 37 of 30-min index\n", + "lmp_30[74] = 75.0\n", + "lmp_30[72] = 50.0\n", + "lmp_30[76] = 45.0\n", + "\n", + "config_30 = PeakLoadManagementOptimizedControllerConfig(\n", + " max_capacity=1200.0,\n", + " max_charge_rate=300.0,\n", + " max_soc_fraction=0.90,\n", + " min_soc_fraction=0.10,\n", + " init_soc_fraction=0.90,\n", + " charge_efficiency=0.95,\n", + " discharge_efficiency=0.95,\n", + " n_control_window=N_30,\n", + " commodity=\"electricity\",\n", + " commodity_rate_units=\"kW\",\n", + " tech_name=\"battery\",\n", + " system_commodity_interface_limit=1e9,\n", + " supervisory_signal=lmp_30.tolist(),\n", + " peak_window={\"start\": \"14:00:00\", \"end\": \"18:00:00\"},\n", + " performance_incentive={\"units\": \"$/kWh\", \"val\": 14.0},\n", + " n_max_events=10,\n", + " signal_threshold_percentile=0.0, # disable for clarity\n", + " event_duration={\"units\": \"h\", \"val\": 2}, # expect \u00b11h = \u00b12 timesteps\n", + ")\n", + "\n", + "ctrl_30 = make_controller(config_30, n=N_30, dt=DT_30)\n", + "object.__setattr__(ctrl_30.config, \"system_commodity_interface_limit\", [1e9] * N_30)\n", + "\n", + "event_mask_30 = ctrl_30._compute_event_window_mask(\n", + " np.asarray(lmp_30), ctrl_30.in_peak_window, window_start=0\n", + ")\n", + "print(\n", + " \"30-min event-window timesteps (day 1, expect ~28-32):\",\n", + " [t for t in np.where(event_mask_30)[0] if t < 48],\n", + ")\n", + "print(\n", + " \"30-min event-window timesteps (day 2, expect ~72-76):\",\n", + " [t for t in np.where(event_mask_30)[0] if t >= 48],\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index b640e231d..69b281483 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -1,28 +1,26 @@ -from datetime import datetime, timedelta +from typing import Any, ClassVar +from datetime import datetime import numpy as np import pandas as pd 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 +from h2integrate.core.utilities import merge_shared_inputs, build_time_series_from_plant_config +from h2integrate.core.validators import range_val, has_required_keys +from h2integrate.control.control_strategies.controller_opt_problem_state import DispatchProblemState from h2integrate.control.control_strategies.pyomo_storage_controller_baseclass import ( SolverOptions, PyomoStorageControllerBaseClass, PyomoStorageControllerBaseConfig, ) -from h2integrate.control.control_strategies.controller_opt_problem_state import ( - DispatchProblemState, -) -from matplotlib import pyplot as plt @define -class PLMOptimizedControllerConfig(PyomoStorageControllerBaseConfig): +class PeakLoadManagementOptimizedControllerConfig(PyomoStorageControllerBaseConfig): """Configuration for the PLM DR optimized storage controller. - Inherits base fields from ``PyomoControllerBaseConfig``: + Inherits base fields from ``PyomoStorageControllerBaseConfig``: ``max_capacity``, ``max_soc_fraction``, ``min_soc_fraction``, ``init_soc_fraction``, ``n_control_window``, ``commodity``, ``commodity_rate_units``, ``tech_name``, @@ -35,34 +33,89 @@ class PLMOptimizedControllerConfig(PyomoStorageControllerBaseConfig): length ``n_control_window`` per solve. peak_window (dict): Hours eligible for dispatch. Keys ``'start'`` and ``'end'`` must be strings in ``HH:MM:SS`` format. - performance_incentive (float): Incentive revenue in $/kWh — - multiplied by ``dt`` (in hours) per dispatched timestep. + performance_incentive (dict): Incentive revenue expressed as a + ``{units, val}`` dict. ``units`` must be one of ``'$/kWh'``, + ``'$/MWh'``, or ``'$/Wh'``; ``val`` is the numeric amount. + Example: ``{units: '$/kWh', val: 14.0}``. charge_efficiency (float): Charge efficiency in [0, 1]. Defaults to 1.0. discharge_efficiency (float): Discharge efficiency in [0, 1]. Defaults to 1.0. n_max_events (int): Maximum discharge events per calendar month. Defaults to 10. - n_control_window (int): Number of timesteps per rolling solve - window. Defaults to ``24`` (one day). + n_control_window (float): Rolling window size in **hours**. + Converted to an integer timestep count during ``setup()`` + using the simulation ``dt``, so the same value works at any + resolution. Example: ``10`` at a 30-min ``dt`` gives a + 20-timestep window. Defaults to ``24``. signal_threshold_percentile (float): Percentile (0-100) used to compute the signal threshold for each rolling window. Only - hours at or above this percentile of the window signal are - eligible for dispatch. Defaults to 0.0 (all hours eligible). + timesteps at or above this percentile of the window signal are + eligible for dispatch. Defaults to 0.0 (all timesteps eligible). + event_duration (dict): Total dispatch-event duration + expressed as a ``{units, val}`` dict, where ``units`` is any + pandas timedelta unit string (e.g. ``'h'``, ``'min'``, + ``'s'``) and ``val`` is the numeric amount. When set, the + eligible dispatch window is computed dynamically per calendar + day: the peak-signal timestep within ``peak_window`` is + located and every timestep within ``event_duration / 2`` of + that peak is marked eligible (the window may extend + beyond the static ``peak_window`` boundaries). When ``None`` + (default) the static ``peak_window`` mask is used unchanged. + Example: ``{units: 'h', val: 4}`` is +/- 2 h around the daily peak. """ max_charge_rate: float = field() supervisory_signal: list = field() peak_window: dict = field() - performance_incentive: float = field() + performance_incentive: dict = field(validator=has_required_keys(["units", "val"])) charge_efficiency: float = field(validator=range_val(0, 1), default=1.0) discharge_efficiency: float = field(validator=range_val(0, 1), default=1.0) n_max_events: int = field(default=10) - n_control_window: int = field(default=24) # one month of hourly data - signal_threshold_percentile: float = field(default=0.0, validator=range_val(0,100)) # make sure this is valid + n_control_window: float = field(default=24.0) + signal_threshold_percentile: float = field(default=0.0, validator=range_val(0, 100)) + event_duration: dict = field(default=None) + + _INCENTIVE_TO_KWH: ClassVar[dict] = {"$/kWh": 1.0, "$/MWh": 1e-3, "$/Wh": 1e3} + def __attrs_post_init__(self): + # Make sure n_control_window is an int + self.n_control_window = int(round(self.n_control_window)) + super().__attrs_post_init__() -class PLMOptimizedStorageController(PyomoStorageControllerBaseClass): + for key in ("units", "val"): + if key not in self.performance_incentive: + raise ValueError( + f"performance_incentive is missing required key '{key}'. " + "Expected dict with 'units' (e.g. '$/kWh') and 'val' (numeric)." + ) + if self.performance_incentive["units"] not in self._INCENTIVE_TO_KWH: + raise ValueError( + f"performance_incentive 'units' must be one of " + f"{list(self._INCENTIVE_TO_KWH)}, got {self.performance_incentive['units']}." + ) + if not isinstance(self.performance_incentive.get("val"), int | float): + raise ValueError( + "performance_incentive 'val' must be a numeric value " + f"(int or float), got {type(self.performance_incentive.get('val')).__name__}." + ) + + if self.event_duration is not None: + for key in ("units", "val"): + if key not in self.event_duration: + raise ValueError( + f"event_duration is missing required key '{key}'. " + "Expected dict with 'units' (pandas timedelta unit string) " + "and 'val' (int or float)." + ) + if not isinstance(self.event_duration["val"], int | float): + raise ValueError( + "event_duration 'val' must be a numeric value " + f"(int or float), got {type(self.event_duration['val']).__name__}." + ) + + +class PeakLoadManagementOptimizedStorageController(PyomoStorageControllerBaseClass): """Demand-response storage controller using a rolling-horizon MILP. Each call to the dispatch solver iterates over the full simulation in @@ -73,6 +126,9 @@ class PLMOptimizedStorageController(PyomoStorageControllerBaseClass): as the initial SOC of the next window. """ + dr_model: Any + problem_state: DispatchProblemState + def setup(self): """Initialize config, register OpenMDAO inputs, and pre-compute static masks. @@ -80,7 +136,7 @@ def setup(self): ValueError: If the length of the time series built from ``plant_config`` does not match ``n_timesteps``. """ - self.config = PLMOptimizedControllerConfig.from_dict( + self.config = PeakLoadManagementOptimizedControllerConfig.from_dict( merge_shared_inputs(self.options["tech_config"]["model_inputs"], "control") ) @@ -100,6 +156,17 @@ def setup(self): sim = self.options["plant_config"]["plant"]["simulation"] self.n_timesteps = int(sim["n_timesteps"]) self.dt_seconds = int(sim["dt"]) + + # n_control_window is stored in hours; convert to timesteps now that dt is known. + n_cw_steps = max(1, int(round(self.config.n_control_window * 3600 / self.dt_seconds))) + object.__setattr__(self.config, "n_control_window", n_cw_steps) + scil = self.config.system_commodity_interface_limit + scil_list: list[float] = list(scil) if isinstance(scil, list | tuple) else [float(scil)] + if len(scil_list) != n_cw_steps: + object.__setattr__( + self.config, "system_commodity_interface_limit", [scil_list[0]] * n_cw_steps + ) + super().setup() self.updated_initial_soc = self.config.init_soc_fraction @@ -109,7 +176,9 @@ def setup(self): "commodity_storage_units": self.config.commodity_rate_units, } - self.time_index = self._build_time_index(self.options["plant_config"]) + self.time_index = build_time_series_from_plant_config( + self.options["plant_config"] + ) # DatetimeIndex of length n_timesteps if len(self.time_index) != self.n_timesteps: raise ValueError( @@ -117,29 +186,7 @@ def setup(self): ) self.in_peak_window = self._compute_peak_window_mask() # bool array, shape (T,) - self.month_ids = self._compute_month_ids() # int array, shape (T,) - - @staticmethod - def _build_time_index(plant_config: dict) -> pd.DatetimeIndex: - """Build a DatetimeIndex from simulation settings in plant_config. - - Args: - plant_config (dict): Plant configuration dict. Must contain - ``plant.simulation`` with keys ``n_timesteps`` (int), - ``dt`` (int, seconds), ``timezone`` (int), - and ``start_time`` (str). - - Returns: - pd.DatetimeIndex: DatetimeIndex of length ``n_timesteps`` spaced - ``dt`` seconds apart, starting at ``start_time`` in the given timezone. - """ - sim = plant_config["plant"]["simulation"] - n_timesteps = int(sim["n_timesteps"]) - dt_seconds = int(sim["dt"]) - tz = int(sim["timezone"]) - start = pd.Timestamp(sim["start_time"], tz=tz) - freq = pd.to_timedelta(dt_seconds, unit="s") - return pd.date_range(start=start, periods=n_timesteps, freq=freq) + self.month_ids = self._compute_month_ids() # int array, shape (T,) def _parse_peak_window(self) -> tuple: """Parse the ``peak_window`` config entry into ``datetime.time`` objects. @@ -186,16 +233,29 @@ def _compute_month_ids(self) -> np.ndarray: """ return pd.DatetimeIndex(self.time_index).month.to_numpy() - def _compute_eligible_mask(self, signal_window: np.ndarray) -> np.ndarray: + def _compute_eligible_mask( + self, + signal_window: np.ndarray, + dispatch_mask: np.ndarray | None = None, + ) -> np.ndarray: """Build a boolean mask for timesteps whose signal meets the dispatch threshold. - The threshold is derived only from ``signal_window``. It does not - assume the full simulation signal is known in advance. When - ``signal_threshold_percentile`` is 0.0 all hours are eligible. + The threshold percentile is computed from ``signal_window`` values + that fall inside ``dispatch_mask`` (i.e. the current dispatch + window). This ensures the percentile is relative to in-window + signals, not the full rolling window — so the highest signal + outside the dispatch window cannot silently block all dispatch. + When ``dispatch_mask`` is ``None`` the full ``signal_window`` is + used. When ``signal_threshold_percentile`` is 0.0 all timesteps + are eligible. Args: signal_window (np.ndarray): Signal values for the current rolling window. + dispatch_mask (np.ndarray | None): Boolean mask of shape + ``(len(signal_window),)`` indicating which timesteps + belong to the current dispatch window. Defaults to + ``None`` (use full window). Returns: np.ndarray: Boolean array of shape ``(len(signal_window),)``. @@ -204,18 +264,80 @@ def _compute_eligible_mask(self, signal_window: np.ndarray) -> np.ndarray: eligible = np.ones(len(signal_window), dtype=bool) if self.config.signal_threshold_percentile > 0.0: - threshold = np.percentile( - signal_window, self.config.signal_threshold_percentile - ) + if dispatch_mask is not None and dispatch_mask.any(): + reference_signals = signal_window[dispatch_mask] + else: + reference_signals = signal_window + threshold = np.percentile(reference_signals, self.config.signal_threshold_percentile) eligible = signal_window >= threshold return eligible + def _compute_event_window_mask( + self, + signal_window: np.ndarray, + in_peak_window_w: np.ndarray, + window_start: int, + ) -> np.ndarray: + """Build a dispatch-eligible mask centered on each day's peak signal. + + For each calendar day in the rolling window the highest-signal + timestep within ``in_peak_window_w`` is found. Every timestep + whose distance (in seconds) from that peak is at most + ``event_duration / 2`` is marked eligible. The resulting window + may extend beyond the original ``peak_window`` boundaries. + + When ``event_duration`` is ``None`` the method returns + ``in_peak_window_w`` unchanged so existing behavior is preserved. + + Args: + signal_window (np.ndarray): Signal values for the current + rolling window, shape ``(window_len,)``. + in_peak_window_w (np.ndarray): Boolean mask indicating which + timesteps fall inside the configured ``peak_window``, + shape ``(window_len,)``. + window_start (int): Index of the first timestep of this + window into ``self.time_index``. + + Returns: + np.ndarray: Boolean mask of shape ``(window_len,)``. + """ + if self.config.event_duration is None: + return in_peak_window_w.copy() + + window_len = len(signal_window) + half_duration_s = ( + pd.Timedelta( + value=self.config.event_duration["val"], + unit=self.config.event_duration["units"], + ).total_seconds() + / 2.0 + ) + event_mask = np.zeros(window_len, dtype=bool) + + window_times = self.time_index[window_start : window_start + window_len] + dates = pd.DatetimeIndex(window_times).normalize() + + for date in dates.unique(): + day_indices = np.where(dates == date)[0] + peak_indices = [i for i in day_indices if in_peak_window_w[i]] + + if not peak_indices: + continue + + peak_t = peak_indices[int(np.argmax(signal_window[peak_indices]))] + + for i in range(window_len): + if abs(i - peak_t) * self.dt_seconds <= half_duration_s: + event_mask[i] = True + + return event_mask + def pyomo_setup(self, discrete_inputs): """Return the rolling-horizon dispatch solver callable. Args: - discrete_inputs (dict): OpenMDAO discrete inputs. + discrete_inputs (dict): OpenMDAO discrete inputs. Returns: callable: ``pyomo_dispatch_solver(performance_model, @@ -246,11 +368,11 @@ def pyomo_dispatch_solver( # respected across window boundaries. events_used_per_month = {} - n_w = self.config.n_control_window + n_w: int = int(self.config.n_control_window) window_start_indices = list(range(0, self.n_timesteps, n_w)) for window_start in window_start_indices: - window_len = min(n_w, self.n_timesteps - window_start) + window_len: int = min(n_w, self.n_timesteps - window_start) n_windows = len(window_start_indices) report_every = max(1, n_windows // 4) @@ -315,13 +437,9 @@ def initialize_parameters(self, inputs): ``'max_charge_rate'`` and ``'storage_capacity'``. """ if "max_charge_rate" in inputs: - object.__setattr__( - self.config, "max_charge_rate", float(inputs["max_charge_rate"][0]) - ) + object.__setattr__(self.config, "max_charge_rate", float(inputs["max_charge_rate"][0])) if "storage_capacity" in inputs: - object.__setattr__( - self.config, "max_capacity", float(inputs["storage_capacity"][0]) - ) + object.__setattr__(self.config, "max_capacity", float(inputs["storage_capacity"][0])) def _build_dr_model( self, @@ -347,7 +465,7 @@ def _build_dr_model( Returns: pyomo.ConcreteModel: Fully formed MILP ready to solve. """ - m = pyomo.ConcreteModel(name="plm_dr") + m: Any = pyomo.ConcreteModel(name="plm_dr") P_max = self.config.max_charge_rate E_max = self.config.max_capacity * ( @@ -357,22 +475,40 @@ def _build_dr_model( eta_d = self.config.discharge_efficiency soc_max = self.config.max_soc_fraction soc_min = self.config.min_soc_fraction - incentive = self.config.performance_incentive + incentive = ( + self.config.performance_incentive["val"] + * self.config._INCENTIVE_TO_KWH[self.config.performance_incentive["units"].strip()] + ) N_max = self.config.n_max_events w = slice(window_start, window_start + window_len) in_peak_window_w = self.in_peak_window[w] month_ids_w = self.month_ids[w] signal_w = np.asarray(self.config.supervisory_signal, dtype=float)[w] - eligible_t_w = self._compute_eligible_mask(signal_w) + dispatch_window_w = self._compute_event_window_mask( + signal_w, in_peak_window_w, window_start + ) + # When event_duration is set the event window itself encodes "dispatch here" + # (it is already centered on the highest-signal peak). Applying an + # additional signal-percentile filter on top would restrict dispatch to + # just one timestep, defeating the point of the duration. So within an + # event-duration run, every timestep inside the event window is eligible. + if self.config.event_duration is not None: + eligible_t_w = dispatch_window_w.copy() + else: + eligible_t_w = self._compute_eligible_mask(signal_w, dispatch_window_w) months_in_window = np.unique(month_ids_w).tolist() m.T = pyomo.Set(initialize=range(window_len), doc="Timesteps in window") m.M = pyomo.Set(initialize=months_in_window, doc="Months in window") - m.discharge = pyomo.Var(m.T, domain=pyomo.Binary, doc="Discharge binary: 1 = discharging at timestep t") - m.charge = pyomo.Var(m.T, domain=pyomo.Binary, doc="Charge binary: 1 = charging at timestep t") + m.discharge = pyomo.Var( + m.T, domain=pyomo.Binary, doc="Discharge binary: 1 = discharging at timestep t" + ) + m.charge = pyomo.Var( + m.T, domain=pyomo.Binary, doc="Charge binary: 1 = charging at timestep t" + ) m.soc = pyomo.Var( m.T, domain=pyomo.NonNegativeReals, @@ -389,7 +525,7 @@ def _build_dr_model( m.peak_window_only = pyomo.Constraint( m.T, rule=lambda mdl, t: ( - mdl.discharge[t] == 0 if not in_peak_window_w[t] else pyomo.Constraint.Skip + mdl.discharge[t] == 0 if not dispatch_window_w[t] else pyomo.Constraint.Skip ), ) @@ -428,7 +564,7 @@ def soc_evolution_rule(mdl, t): m.no_charge_in_window = pyomo.Constraint( m.T, rule=lambda mdl, t: ( - mdl.charge[t] == 0 if in_peak_window_w[t] else pyomo.Constraint.Skip + mdl.charge[t] == 0 if dispatch_window_w[t] else pyomo.Constraint.Skip ), ) @@ -511,13 +647,9 @@ def glpk_solve_call( pyomo.opt.SolverResults: Raw results object from GLPK. """ glpk_solver_options = {"cuts": None, "presol": None, "tmlim": 300} - solver_options = SolverOptions( - glpk_solver_options, log_name, user_solver_options, "log" - ) + solver_options = SolverOptions(glpk_solver_options, log_name, user_solver_options, "log") with pyomo.SolverFactory("glpk") as solver: - results = solver.solve( - pyomo_model, options=solver_options.constructed, tee=False - ) + results = solver.solve(pyomo_model, options=solver_options.constructed, tee=False) return results @property diff --git a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py index f3aaa1ae3..5866fda3b 100644 --- a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py @@ -1,20 +1,18 @@ from types import SimpleNamespace -from attrs import evolve import numpy as np import pandas as pd -import pyomo.environ as pyomo import pytest +import pyomo.environ as pyomo from h2integrate.control.control_strategies.storage.plm_optimized_storage_controller import ( - PLMOptimizedControllerConfig, - PLMOptimizedStorageController, + PeakLoadManagementOptimizedControllerConfig, + PeakLoadManagementOptimizedStorageController, ) -from h2integrate.storage.storage_baseclass import StoragePerformanceBase def _make_controller(): - return object.__new__(PLMOptimizedStorageController) + return object.__new__(PeakLoadManagementOptimizedStorageController) def _make_controller_with_config(config, n_timesteps=24): @@ -31,7 +29,7 @@ def _make_controller_with_config(config, n_timesteps=24): @pytest.fixture def base_config(): n = 24 - return PLMOptimizedControllerConfig( + return PeakLoadManagementOptimizedControllerConfig( max_capacity=10.0, max_soc_fraction=1.0, min_soc_fraction=0.0, @@ -44,7 +42,7 @@ def base_config(): max_charge_rate=1.0, supervisory_signal=list(range(n)), peak_window={"start": "08:00:00", "end": "18:00:00"}, - performance_incentive=10.0, + performance_incentive={"units": "$/kWh", "val": 10.0}, n_max_events=24, signal_threshold_percentile=0.0, ) @@ -53,9 +51,7 @@ def base_config(): @pytest.mark.unit def test_parse_peak_window(): controller = _make_controller() - controller.config = SimpleNamespace( - peak_window={"start": "08:00:00", "end": "18:40:20"} - ) + controller.config = SimpleNamespace(peak_window={"start": "08:00:00", "end": "18:40:20"}) start, end = controller._parse_peak_window() assert start.hour == 8 assert start.minute == 0 @@ -87,18 +83,14 @@ def test_parse_peak_window_int_raises(): def test_parse_peak_window_missing_key_raises(): controller = _make_controller() controller.config = SimpleNamespace(peak_window={"start": "08:00:00"}) - with pytest.raises( - ValueError, match="peak_window must contain 'start' and 'end' keys" - ): + with pytest.raises(ValueError, match="peak_window must contain 'start' and 'end' keys"): controller._parse_peak_window() @pytest.mark.unit def test_compute_peak_window_mask(): controller = _make_controller() - controller.config = SimpleNamespace( - peak_window={"start": "00:00:00", "end": "02:00:00"} - ) + controller.config = SimpleNamespace(peak_window={"start": "00:00:00", "end": "02:00:00"}) controller.time_index = pd.date_range("2024-01-01", periods=24, freq="h") mask = controller._compute_peak_window_mask() expected = np.array([i <= 2 for i in range(24)]) @@ -110,9 +102,7 @@ def test_compute_peak_window_mask(): def test_compute_month_ids(): # Jan 2024: 744h, Feb 2024 (leap year): 696h, Mar 2024: 744h controller = _make_controller() - controller.time_index = pd.date_range( - "2024-01-01", periods=744 + 696 + 744, freq="h" - ) + controller.time_index = pd.date_range("2024-01-01", periods=744 + 696 + 744, freq="h") month_ids = controller._compute_month_ids() expected = np.array([1] * 744 + [2] * 696 + [3] * 744) assert np.array_equal(month_ids, expected) @@ -175,7 +165,7 @@ def test_optimizer_dispatch_only_in_peak_window(base_config): remaining_budget={1: base_config.n_max_events}, ) - PLMOptimizedStorageController.glpk_solve_call(model) + PeakLoadManagementOptimizedStorageController.glpk_solve_call(model) peak_start, peak_end = controller._parse_peak_window() print("Peak window:", peak_start, "-", peak_end) @@ -197,7 +187,7 @@ def test_optimizer_dispatch_only_on_eligible_timesteps(base_config): remaining_budget={1: base_config.n_max_events}, ) - PLMOptimizedStorageController.glpk_solve_call(model) + PeakLoadManagementOptimizedStorageController.glpk_solve_call(model) signal = np.array(controller.config.supervisory_signal) eligible_mask = controller._compute_eligible_mask(signal) @@ -217,7 +207,7 @@ def test_optimizer_dispatch_respects_event_budget(base_config): remaining_budget={1: base_config.n_max_events}, ) - PLMOptimizedStorageController.glpk_solve_call(model) + PeakLoadManagementOptimizedStorageController.glpk_solve_call(model) total_events = sum(pyomo.value(model.discharge[t]) for t in range(24)) assert total_events <= base_config.n_max_events + 1e-3 @@ -234,7 +224,7 @@ def test_optimizer_dispatch_respects_soc_constraints(base_config): remaining_budget={2: base_config.n_max_events}, ) - PLMOptimizedStorageController.glpk_solve_call(model) + PeakLoadManagementOptimizedStorageController.glpk_solve_call(model) soc = base_config.init_soc_fraction * base_config.max_capacity for t in range(24): @@ -256,7 +246,7 @@ def test_optimizer_dispatch_respects_charge_discharge_exclusivity(base_config): remaining_budget={2: base_config.n_max_events}, ) - PLMOptimizedStorageController.glpk_solve_call(model) + PeakLoadManagementOptimizedStorageController.glpk_solve_call(model) for t in range(24): charge = pyomo.value(model.charge[t]) diff --git a/h2integrate/core/supported_models.py b/h2integrate/core/supported_models.py index 57728f524..b2abecd78 100644 --- a/h2integrate/core/supported_models.py +++ b/h2integrate/core/supported_models.py @@ -174,12 +174,12 @@ from h2integrate.control.control_strategies.storage.plm_openloop_storage_controller import ( PeakLoadManagementHeuristicOpenLoopStorageController, ) -from h2integrate.control.control_strategies.storage.plm_optimized_storage_controller import ( - PLMOptimizedStorageController, -) from h2integrate.control.control_rules.storage.pyomo_storage_rule_min_operating_cost import ( PyomoRuleStorageMinOperatingCosts, ) +from h2integrate.control.control_strategies.storage.plm_optimized_storage_controller import ( + PeakLoadManagementOptimizedStorageController, +) from h2integrate.control.control_rules.converters.generic_converter_min_operating_cost import ( PyomoDispatchGenericConverterMinOperatingCosts, ) @@ -301,7 +301,7 @@ "PeakLoadManagementHeuristicOpenLoopStorageController": ( PeakLoadManagementHeuristicOpenLoopStorageController ), - "PLMOptimizedStorageController": (PLMOptimizedStorageController), + "PeakLoadManagementOptimizedStorageController": (PeakLoadManagementOptimizedStorageController), "HeuristicLoadFollowingStorageController": HeuristicLoadFollowingStorageController, "OptimizedDispatchStorageController": OptimizedDispatchStorageController, "GenericDemandComponent": GenericDemandComponent, @@ -349,4 +349,4 @@ no_replacement_schedule_models = { "IronTransportPerformanceComponent", -} \ No newline at end of file +} From b25e5dd6cc5d8612f2f2336223a47dfcdbec79f7 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Tue, 28 Apr 2026 08:47:13 -0600 Subject: [PATCH 21/55] Remove notebook --- .../test_controller_methods.ipynb | 493 ------------------ 1 file changed, 493 deletions(-) delete mode 100644 examples/34_plm_optimized_dispatch/test_controller_methods.ipynb diff --git a/examples/34_plm_optimized_dispatch/test_controller_methods.ipynb b/examples/34_plm_optimized_dispatch/test_controller_methods.ipynb deleted file mode 100644 index a1aadab77..000000000 --- a/examples/34_plm_optimized_dispatch/test_controller_methods.ipynb +++ /dev/null @@ -1,493 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7fb27b941602401d91542211134fc71a", - "metadata": {}, - "source": [ - "# PLM Optimized Storage Controller \u2014 Method-level tests\n", - "\n", - "Tests each method of `PeakLoadManagementOptimizedStorageController` in isolation\n", - "using small synthetic data. No full OpenMDAO model needed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "acae54e37e7d407bbb7b55eff062a284", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "from pathlib import Path\n", - "\n", - "# Add the repo root to sys.path so the h2integrate package is importable\n", - "repo_root = Path(\"../../\").resolve()\n", - "if str(repo_root) not in sys.path:\n", - " sys.path.insert(0, str(repo_root))\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import pyomo.environ as pyomo\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from h2integrate.control.control_strategies.storage.plm_optimized_storage_controller import (\n", - " PeakLoadManagementOptimizedControllerConfig,\n", - " PeakLoadManagementOptimizedStorageController,\n", - ")\n", - "\n", - "plt.rcParams.update({\"axes.spines.top\": False, \"axes.spines.right\": False})" - ] - }, - { - "cell_type": "markdown", - "id": "9a63283cbaf04dbcab1f6479b197f3a8", - "metadata": {}, - "source": [ - "## Helper: build a lightweight controller\n", - "\n", - "Bypasses OpenMDAO `setup()` so each method can be tested directly." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8dd0d8092fe74a7c96281538738b07e2", - "metadata": {}, - "outputs": [], - "source": [ - "N = 48 # 2 days of hourly timesteps\n", - "DT_SECONDS = 3600 # 1-hour resolution\n", - "\n", - "# Synthetic LMP signal: flat with spikes at hours 15 and 37 (peak window 14-18)\n", - "lmp = np.full(N, 20.0)\n", - "lmp[15] = 80.0 # day-1 peak inside peak window\n", - "lmp[14] = 60.0\n", - "lmp[16] = 55.0\n", - "lmp[37] = 75.0 # day-2 peak inside peak window (hour 13 of day 2)\n", - "lmp[36] = 50.0\n", - "lmp[38] = 45.0\n", - "\n", - "config = PeakLoadManagementOptimizedControllerConfig(\n", - " max_capacity=1200.0,\n", - " max_charge_rate=300.0,\n", - " max_soc_fraction=0.90,\n", - " min_soc_fraction=0.10,\n", - " init_soc_fraction=0.90,\n", - " charge_efficiency=0.95,\n", - " discharge_efficiency=0.95,\n", - " n_control_window=N, # full 2-day window as one solve\n", - " commodity=\"electricity\",\n", - " commodity_rate_units=\"kW\",\n", - " tech_name=\"battery\",\n", - " system_commodity_interface_limit=1e9,\n", - " supervisory_signal=lmp.tolist(),\n", - " peak_window={\"start\": \"14:00:00\", \"end\": \"18:00:00\"},\n", - " performance_incentive={\"units\": \"$/kWh\", \"val\": 14.0},\n", - " n_max_events=10,\n", - " signal_threshold_percentile=95.0,\n", - " event_duration={\"units\": \"h\", \"val\": 2}, # +/- 1h around daily peak\n", - ")\n", - "\n", - "\n", - "def make_controller(cfg=config, n=N, dt=DT_SECONDS):\n", - " \"\"\"Instantiate the controller without OpenMDAO infrastructure.\"\"\"\n", - " ctrl = object.__new__(PeakLoadManagementOptimizedStorageController)\n", - " ctrl.config = cfg\n", - " ctrl.dt_seconds = dt\n", - " ctrl.n_timesteps = n\n", - " ctrl.updated_initial_soc = cfg.init_soc_fraction\n", - " ctrl.time_index = pd.date_range(\"2025-01-01\", periods=n, freq=pd.to_timedelta(dt, unit=\"s\"))\n", - " ctrl.in_peak_window = ctrl._compute_peak_window_mask()\n", - " ctrl.month_ids = ctrl._compute_month_ids()\n", - " return ctrl\n", - "\n", - "\n", - "ctrl = make_controller()\n", - "print(\"Controller created. time_index:\", ctrl.time_index[[0, -1]].tolist())" - ] - }, - { - "cell_type": "markdown", - "id": "72eea5119410473aa328ad9291626812", - "metadata": {}, - "source": [ - "## 1. `_parse_peak_window`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8edb47106e1a46a883d545849b8ab81b", - "metadata": {}, - "outputs": [], - "source": [ - "start, end = ctrl._parse_peak_window()\n", - "print(f\"peak_window start={start} end={end}\")\n", - "assert start.hour == 14 and end.hour == 18" - ] - }, - { - "cell_type": "markdown", - "id": "10185d26023b46108eb7d9f57d49d2b3", - "metadata": {}, - "source": [ - "## 2. `_compute_peak_window_mask`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8763a12b2bbd4a93a75aff182afb95dc", - "metadata": {}, - "outputs": [], - "source": [ - "mask = ctrl.in_peak_window\n", - "print(\"Timesteps inside peak window:\", np.where(mask)[0].tolist())\n", - "# Expect hours 14-18 on each day: [14,15,16,17,18,38,39,40,41,42]\n", - "\n", - "fig, ax = plt.subplots(figsize=(10, 2))\n", - "ax.fill_between(range(N), mask.astype(int), step=\"post\", alpha=0.4, label=\"peak window\")\n", - "ax.set_xlabel(\"Timestep (h)\")\n", - "ax.set_title(\"Static peak-window mask\")\n", - "ax.legend()\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "7623eae2785240b9bd12b16a66d81610", - "metadata": {}, - "source": [ - "## 3. `_compute_month_ids`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7cdc8c89c7104fffa095e18ddfef8986", - "metadata": {}, - "outputs": [], - "source": [ - "month_ids = ctrl._compute_month_ids()\n", - "print(\"Unique months in 2-day window:\", np.unique(month_ids).tolist())\n", - "assert set(month_ids) == {1} # both days are January" - ] - }, - { - "cell_type": "markdown", - "id": "b118ea5561624da68c537baed56e602f", - "metadata": {}, - "source": [ - "## 4. `_compute_eligible_mask`\n", - "\n", - "Percentile-based signal filter applied to a rolling window." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "938c804e27f84196a10c8828c723f798", - "metadata": {}, - "outputs": [], - "source": [ - "signal_window = np.asarray(config.supervisory_signal)\n", - "\n", - "# a) No dispatch_mask \u2014 percentile over full window\n", - "eligible_full = ctrl._compute_eligible_mask(signal_window)\n", - "print(\"Eligible (full window, p=95%):\", np.where(eligible_full)[0].tolist())\n", - "\n", - "# b) dispatch_mask = peak_window only \u2014 percentile over in-window signals\n", - "eligible_pw = ctrl._compute_eligible_mask(signal_window, dispatch_mask=ctrl.in_peak_window)\n", - "print(\"Eligible (peak-window signals only, p=95%):\", np.where(eligible_pw)[0].tolist())" - ] - }, - { - "cell_type": "markdown", - "id": "504fb2a444614c0babb325280ed9130a", - "metadata": {}, - "source": [ - "## 5. `_compute_event_window_mask`\n", - "\n", - "Computes the \u00b1`event_duration/2` window centred on each day's LMP peak." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "59bbdb311c014d738909a11f9e486628", - "metadata": {}, - "outputs": [], - "source": [ - "signal_window = np.asarray(config.supervisory_signal)\n", - "\n", - "event_mask = ctrl._compute_event_window_mask(signal_window, ctrl.in_peak_window, window_start=0)\n", - "\n", - "print(\"Static peak-window timesteps:\", np.where(ctrl.in_peak_window)[0].tolist())\n", - "print(\"Event-window timesteps :\", np.where(event_mask)[0].tolist())\n", - "# Day 1 peak at t=15 \u2192 event window = [14, 15, 16] (\u00b11 h)\n", - "# Day 2 peak at t=37 \u2192 event window = [36, 37, 38] (\u00b11 h)\n", - "\n", - "fig, axes = plt.subplots(2, 1, figsize=(10, 4), sharex=True)\n", - "axes[0].plot(signal_window, color=\"steelblue\", linewidth=1)\n", - "axes[0].set_ylabel(\"LMP ($/MWh)\")\n", - "axes[0].set_title(\"Signal\")\n", - "\n", - "axes[1].fill_between(\n", - " range(N), ctrl.in_peak_window.astype(int), step=\"post\", alpha=0.3, label=\"static peak_window\"\n", - ")\n", - "axes[1].fill_between(\n", - " range(N),\n", - " event_mask.astype(int),\n", - " step=\"post\",\n", - " alpha=0.5,\n", - " color=\"darkorange\",\n", - " label=\"event window (\u00b11 h)\",\n", - ")\n", - "axes[1].set_xlabel(\"Timestep (h)\")\n", - "axes[1].set_title(\"Masks\")\n", - "axes[1].legend()\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "b43b363d81ae4b689946ece5c682cd59", - "metadata": {}, - "source": [ - "## 6. `_build_dr_model` + solve\n", - "\n", - "Build and solve the MILP for the full 2-day window. Expect discharge at event-window timesteps." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8a65eabff63a45729fe45fb5ade58bdc", - "metadata": {}, - "outputs": [], - "source": [ - "# Expand system_commodity_interface_limit to match window length\n", - "object.__setattr__(\n", - " ctrl.config,\n", - " \"system_commodity_interface_limit\",\n", - " [1e9] * N,\n", - ")\n", - "\n", - "model = ctrl._build_dr_model(\n", - " window_start=0,\n", - " window_len=N,\n", - " init_soc=config.init_soc_fraction,\n", - " remaining_budget={1: config.n_max_events},\n", - ")\n", - "\n", - "results = PeakLoadManagementOptimizedStorageController.glpk_solve_call(model)\n", - "print(\"Solver status:\", results.solver.status)\n", - "print(\"Termination :\", results.solver.termination_condition)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c3933fab20d04ec698c2621248eb3be0", - "metadata": {}, - "outputs": [], - "source": [ - "discharge = np.array([pyomo.value(model.discharge[t]) for t in range(N)])\n", - "charge = np.array([pyomo.value(model.charge[t]) for t in range(N)])\n", - "soc = np.array([pyomo.value(model.soc[t]) for t in range(N)])\n", - "\n", - "dispatch_ts = np.where(discharge > 0.5)[0].tolist()\n", - "charge_ts = np.where(charge > 0.5)[0].tolist()\n", - "print(\"Discharge timesteps:\", dispatch_ts)\n", - "print(\"Charge timesteps:\", charge_ts)\n", - "print(f\"Total discharge events: {int(discharge.sum())} (limit: {config.n_max_events}/month)\")\n", - "\n", - "# --- verify discharge is within event window ---\n", - "outside = [t for t in dispatch_ts if not event_mask[t]]\n", - "print(\"\\nDischarge outside event window (expect empty):\", outside)\n", - "assert not outside, f\"Unexpected discharge outside event window: {outside}\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4dd4641cc4064e0191573fe9c69df29b", - "metadata": {}, - "outputs": [], - "source": [ - "P_max = config.max_charge_rate\n", - "net_power = (discharge - charge) * P_max\n", - "\n", - "fig, axes = plt.subplots(3, 1, figsize=(11, 8), sharex=True)\n", - "\n", - "ax = axes[0]\n", - "ax.plot(signal_window, color=\"steelblue\", linewidth=1, label=\"LMP\")\n", - "ax.fill_between(\n", - " range(N),\n", - " event_mask * signal_window.max(),\n", - " step=\"post\",\n", - " alpha=0.25,\n", - " color=\"darkorange\",\n", - " label=\"event window\",\n", - ")\n", - "ax.set_ylabel(\"LMP ($/MWh)\")\n", - "ax.legend(fontsize=8)\n", - "\n", - "ax = axes[1]\n", - "ax.bar(range(N), discharge * P_max, color=\"tomato\", label=\"discharge (kW)\")\n", - "ax.bar(range(N), -charge * P_max, color=\"steelblue\", label=\"charge (kW)\")\n", - "ax.set_ylabel(\"Power (kW)\")\n", - "ax.legend(fontsize=8)\n", - "\n", - "ax = axes[2]\n", - "ax.plot(soc * 100, color=\"green\", linewidth=1.2)\n", - "ax.axhline(config.max_soc_fraction * 100, color=\"gray\", linestyle=\":\", linewidth=0.8)\n", - "ax.axhline(config.min_soc_fraction * 100, color=\"gray\", linestyle=\":\", linewidth=0.8)\n", - "ax.set_ylabel(\"SOC (%)\")\n", - "ax.set_xlabel(\"Timestep (h)\")\n", - "ax.set_ylim([0, 105])\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "8309879909854d7188b41380fd92a7c3", - "metadata": {}, - "source": [ - "## 7. Sensitivity: event_duration = None (static peak window)\n", - "\n", - "With no `event_duration`, dispatch should cover all timesteps in the static peak window\n", - "whose signal exceeds the percentile threshold." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3ed186c9a28b402fb0bc4494df01f08d", - "metadata": {}, - "outputs": [], - "source": [ - "config_no_dur = PeakLoadManagementOptimizedControllerConfig(\n", - " max_capacity=1200.0,\n", - " max_charge_rate=300.0,\n", - " max_soc_fraction=0.90,\n", - " min_soc_fraction=0.10,\n", - " init_soc_fraction=0.90,\n", - " charge_efficiency=0.95,\n", - " discharge_efficiency=0.95,\n", - " n_control_window=N,\n", - " commodity=\"electricity\",\n", - " commodity_rate_units=\"kW\",\n", - " tech_name=\"battery\",\n", - " system_commodity_interface_limit=1e9,\n", - " supervisory_signal=lmp.tolist(),\n", - " peak_window={\"start\": \"14:00:00\", \"end\": \"18:00:00\"},\n", - " performance_incentive={\"units\": \"$/kWh\", \"val\": 14.0},\n", - " n_max_events=10,\n", - " signal_threshold_percentile=95.0,\n", - " event_duration=None,\n", - ")\n", - "\n", - "ctrl_no_dur = make_controller(config_no_dur)\n", - "object.__setattr__(ctrl_no_dur.config, \"system_commodity_interface_limit\", [1e9] * N)\n", - "\n", - "model_no_dur = ctrl_no_dur._build_dr_model(\n", - " window_start=0,\n", - " window_len=N,\n", - " init_soc=config_no_dur.init_soc_fraction,\n", - " remaining_budget={1: config_no_dur.n_max_events},\n", - ")\n", - "PeakLoadManagementOptimizedStorageController.glpk_solve_call(model_no_dur)\n", - "\n", - "discharge_no_dur = np.array([pyomo.value(model_no_dur.discharge[t]) for t in range(N)])\n", - "print(\"Discharge timesteps (no event_duration):\", np.where(discharge_no_dur > 0.5)[0].tolist())\n", - "print(\" (should be 1 or 2 timesteps with highest signal inside peak_window)\")" - ] - }, - { - "cell_type": "markdown", - "id": "cb1e1581032b452c9409d6c6813c49d1", - "metadata": {}, - "source": [ - "## 8. Sub-hourly resolution test (30-min timesteps)\n", - "\n", - "Verifies that `event_duration` and `n_control_window` scale correctly at any `dt`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "379cbbc1e968416e875cc15c1202d7eb", - "metadata": {}, - "outputs": [], - "source": [ - "N_30 = 96 # 2 days at 30-min resolution\n", - "DT_30 = 1800 # 30 minutes\n", - "\n", - "lmp_30 = np.full(N_30, 20.0)\n", - "# Day 1: spike at 30-min timestep 30 = hour 15\n", - "lmp_30[30] = 80.0\n", - "lmp_30[28] = 60.0\n", - "lmp_30[32] = 55.0\n", - "# Day 2: spike at 30-min timestep 74 = hour 37 of 30-min index\n", - "lmp_30[74] = 75.0\n", - "lmp_30[72] = 50.0\n", - "lmp_30[76] = 45.0\n", - "\n", - "config_30 = PeakLoadManagementOptimizedControllerConfig(\n", - " max_capacity=1200.0,\n", - " max_charge_rate=300.0,\n", - " max_soc_fraction=0.90,\n", - " min_soc_fraction=0.10,\n", - " init_soc_fraction=0.90,\n", - " charge_efficiency=0.95,\n", - " discharge_efficiency=0.95,\n", - " n_control_window=N_30,\n", - " commodity=\"electricity\",\n", - " commodity_rate_units=\"kW\",\n", - " tech_name=\"battery\",\n", - " system_commodity_interface_limit=1e9,\n", - " supervisory_signal=lmp_30.tolist(),\n", - " peak_window={\"start\": \"14:00:00\", \"end\": \"18:00:00\"},\n", - " performance_incentive={\"units\": \"$/kWh\", \"val\": 14.0},\n", - " n_max_events=10,\n", - " signal_threshold_percentile=0.0, # disable for clarity\n", - " event_duration={\"units\": \"h\", \"val\": 2}, # expect \u00b11h = \u00b12 timesteps\n", - ")\n", - "\n", - "ctrl_30 = make_controller(config_30, n=N_30, dt=DT_30)\n", - "object.__setattr__(ctrl_30.config, \"system_commodity_interface_limit\", [1e9] * N_30)\n", - "\n", - "event_mask_30 = ctrl_30._compute_event_window_mask(\n", - " np.asarray(lmp_30), ctrl_30.in_peak_window, window_start=0\n", - ")\n", - "print(\n", - " \"30-min event-window timesteps (day 1, expect ~28-32):\",\n", - " [t for t in np.where(event_mask_30)[0] if t < 48],\n", - ")\n", - "print(\n", - " \"30-min event-window timesteps (day 2, expect ~72-76):\",\n", - " [t for t in np.where(event_mask_30)[0] if t >= 48],\n", - ")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 966f75ebab56f151e37eb8e8ad679f3b3d0c4c0e Mon Sep 17 00:00:00 2001 From: svijaysh Date: Tue, 28 Apr 2026 10:40:03 -0600 Subject: [PATCH 22/55] Add min separation input and make sure methods run correctly --- .../tech_config.yaml | 3 + .../plm_optimized_storage_controller.py | 132 ++++++++---------- .../test_plm_optimized_storage_controller.py | 56 +++++++- 3 files changed, 115 insertions(+), 76 deletions(-) diff --git a/examples/34_plm_optimized_dispatch/tech_config.yaml b/examples/34_plm_optimized_dispatch/tech_config.yaml index e18297b60..a97a96447 100644 --- a/examples/34_plm_optimized_dispatch/tech_config.yaml +++ b/examples/34_plm_optimized_dispatch/tech_config.yaml @@ -36,6 +36,9 @@ technologies: event_duration: # omit or set to null to use static peak_window units: h # any pandas timedelta unit: h, min, s, … val: 2 # battery eligible +/ - val h around the daily peak + # min_peak_separation: # omit or set to null to allow any peak spacing + # units: h + # val: 4 # drop any eligible peak within this distance of a higher peak round_digits: 4 cost_parameters: cost_year: 2024 diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index 69b281483..7bfb3ef4e 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -75,6 +75,7 @@ class PeakLoadManagementOptimizedControllerConfig(PyomoStorageControllerBaseConf n_control_window: float = field(default=24.0) signal_threshold_percentile: float = field(default=0.0, validator=range_val(0, 100)) event_duration: dict = field(default=None) + min_peak_separation: dict = field(default=None) _INCENTIVE_TO_KWH: ClassVar[dict] = {"$/kWh": 1.0, "$/MWh": 1e-3, "$/Wh": 1e3} @@ -100,19 +101,23 @@ def __attrs_post_init__(self): f"(int or float), got {type(self.performance_incentive.get('val')).__name__}." ) - if self.event_duration is not None: - for key in ("units", "val"): - if key not in self.event_duration: + for field_name, value in ( + ("event_duration", self.event_duration), + ("min_peak_separation", self.min_peak_separation), + ): + if value is not None: + for key in ("units", "val"): + if key not in value: + raise ValueError( + f"{field_name} is missing required key '{key}'. " + "Expected dict with 'units' (pandas timedelta unit string) " + "and 'val' (int or float)." + ) + if not isinstance(value["val"], int | float): raise ValueError( - f"event_duration is missing required key '{key}'. " - "Expected dict with 'units' (pandas timedelta unit string) " - "and 'val' (int or float)." + f"{field_name} 'val' must be a numeric value " + f"(int or float), got {type(value['val']).__name__}." ) - if not isinstance(self.event_duration["val"], int | float): - raise ValueError( - "event_duration 'val' must be a numeric value " - f"(int or float), got {type(self.event_duration['val']).__name__}." - ) class PeakLoadManagementOptimizedStorageController(PyomoStorageControllerBaseClass): @@ -242,9 +247,7 @@ def _compute_eligible_mask( The threshold percentile is computed from ``signal_window`` values that fall inside ``dispatch_mask`` (i.e. the current dispatch - window). This ensures the percentile is relative to in-window - signals, not the full rolling window — so the highest signal - outside the dispatch window cannot silently block all dispatch. + window). When ``dispatch_mask`` is ``None`` the full ``signal_window`` is used. When ``signal_threshold_percentile`` is 0.0 all timesteps are eligible. @@ -261,40 +264,51 @@ def _compute_eligible_mask( np.ndarray: Boolean array of shape ``(len(signal_window),)``. ``True`` where ``signal_t >= threshold``. """ - eligible = np.ones(len(signal_window), dtype=bool) + mask = ( + dispatch_mask if dispatch_mask is not None else np.ones(len(signal_window), dtype=bool) + ) + + if self.config.signal_threshold_percentile == 0.0 or not mask.any(): + return mask.copy() - if self.config.signal_threshold_percentile > 0.0: - if dispatch_mask is not None and dispatch_mask.any(): - reference_signals = signal_window[dispatch_mask] - else: - reference_signals = signal_window - threshold = np.percentile(reference_signals, self.config.signal_threshold_percentile) - eligible = signal_window >= threshold + threshold = np.percentile(signal_window[mask], self.config.signal_threshold_percentile) + eligible = mask & (signal_window >= threshold) + + if self.config.min_peak_separation is not None and eligible.any(): + sep_steps = ( + pd.Timedelta( + value=self.config.min_peak_separation["val"], + unit=self.config.min_peak_separation["units"], + ).total_seconds() + / self.dt_seconds + ) + # Greedily keep peaks from highest to lowest signal, dropping any peak + # that falls within sep_steps of an already-kept peak. + peak_indices = np.where(eligible)[0] + order = np.argsort(-signal_window[peak_indices]) + kept: list[int] = [] + for idx in peak_indices[order]: + if all(abs(int(idx) - k) >= sep_steps for k in kept): + kept.append(int(idx)) + eligible = np.zeros(len(signal_window), dtype=bool) + eligible[kept] = True return eligible def _compute_event_window_mask( self, - signal_window: np.ndarray, - in_peak_window_w: np.ndarray, + eligible_mask: np.ndarray, window_start: int, ) -> np.ndarray: - """Build a dispatch-eligible mask centered on each day's peak signal. - - For each calendar day in the rolling window the highest-signal - timestep within ``in_peak_window_w`` is found. Every timestep - whose distance (in seconds) from that peak is at most - ``event_duration / 2`` is marked eligible. The resulting window - may extend beyond the original ``peak_window`` boundaries. + """Expand each eligible peak timestep by ±event_duration/2. - When ``event_duration`` is ``None`` the method returns - ``in_peak_window_w`` unchanged so existing behavior is preserved. + For each calendar day, every True timestep in ``eligible_mask`` is + treated as a peak and all timesteps within ``event_duration / 2`` + of it are marked eligible. When ``event_duration`` is ``None`` + returns ``eligible_mask`` unchanged. Args: - signal_window (np.ndarray): Signal values for the current - rolling window, shape ``(window_len,)``. - in_peak_window_w (np.ndarray): Boolean mask indicating which - timesteps fall inside the configured ``peak_window``, + eligible_mask (np.ndarray): Boolean mask of peak timesteps, shape ``(window_len,)``. window_start (int): Index of the first timestep of this window into ``self.time_index``. @@ -303,35 +317,20 @@ def _compute_event_window_mask( np.ndarray: Boolean mask of shape ``(window_len,)``. """ if self.config.event_duration is None: - return in_peak_window_w.copy() + return eligible_mask.copy() - window_len = len(signal_window) - half_duration_s = ( + half_steps = ( pd.Timedelta( value=self.config.event_duration["val"], unit=self.config.event_duration["units"], ).total_seconds() / 2.0 + / self.dt_seconds ) - event_mask = np.zeros(window_len, dtype=bool) - - window_times = self.time_index[window_start : window_start + window_len] - dates = pd.DatetimeIndex(window_times).normalize() - - for date in dates.unique(): - day_indices = np.where(dates == date)[0] - peak_indices = [i for i in day_indices if in_peak_window_w[i]] - - if not peak_indices: - continue - - peak_t = peak_indices[int(np.argmax(signal_window[peak_indices]))] - - for i in range(window_len): - if abs(i - peak_t) * self.dt_seconds <= half_duration_s: - event_mask[i] = True - - return event_mask + indices = np.arange(len(eligible_mask)) + peak_indices = np.where(eligible_mask)[0] + distances = np.abs(indices[:, None] - peak_indices[None, :]) + return (distances <= half_steps).any(axis=1) def pyomo_setup(self, discrete_inputs): """Return the rolling-horizon dispatch solver callable. @@ -485,18 +484,9 @@ def _build_dr_model( in_peak_window_w = self.in_peak_window[w] month_ids_w = self.month_ids[w] signal_w = np.asarray(self.config.supervisory_signal, dtype=float)[w] - dispatch_window_w = self._compute_event_window_mask( - signal_w, in_peak_window_w, window_start - ) - # When event_duration is set the event window itself encodes "dispatch here" - # (it is already centered on the highest-signal peak). Applying an - # additional signal-percentile filter on top would restrict dispatch to - # just one timestep, defeating the point of the duration. So within an - # event-duration run, every timestep inside the event window is eligible. - if self.config.event_duration is not None: - eligible_t_w = dispatch_window_w.copy() - else: - eligible_t_w = self._compute_eligible_mask(signal_w, dispatch_window_w) + eligible_t_w = self._compute_eligible_mask(signal_w, in_peak_window_w) + dispatch_window_w = self._compute_event_window_mask(eligible_t_w, window_start) + eligible_t_w = dispatch_window_w months_in_window = np.unique(month_ids_w).tolist() diff --git a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py index 5866fda3b..ef1729f98 100644 --- a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py @@ -15,10 +15,11 @@ def _make_controller(): return object.__new__(PeakLoadManagementOptimizedStorageController) -def _make_controller_with_config(config, n_timesteps=24): +def _make_controller_with_config(config, n_timesteps=24, dt_seconds=3600): """Build a controller with pre-computed masks, bypassing OpenMDAO setup.""" controller = _make_controller() controller.config = config + controller.dt_seconds = dt_seconds controller.updated_initial_soc = config.init_soc_fraction controller.time_index = pd.date_range("2024-01-01", periods=n_timesteps, freq="h") controller.in_peak_window = controller._compute_peak_window_mask() @@ -112,7 +113,7 @@ def test_compute_month_ids(): def test_compute_eligible_mask_zero_percentile_all_eligible(): """All timesteps are eligible when signal_threshold_percentile=0.""" controller = _make_controller() - controller.config = SimpleNamespace(signal_threshold_percentile=0.0) + controller.config = SimpleNamespace(signal_threshold_percentile=0.0, min_peak_separation=None) signal = np.array([1.0, 5.0, 3.0, 2.0, 8.0]) mask = controller._compute_eligible_mask(signal) assert isinstance(mask, np.ndarray) @@ -125,7 +126,7 @@ def test_compute_eligible_mask_zero_percentile_all_eligible(): def test_compute_eligible_mask_50th_percentile(): """Only values at or above the 50th percentile are eligible.""" controller = _make_controller() - controller.config = SimpleNamespace(signal_threshold_percentile=50.0) + controller.config = SimpleNamespace(signal_threshold_percentile=50.0, min_peak_separation=None) signal = np.array([1.0, 2.0, 3.0, 4.0, 5.0]) mask = controller._compute_eligible_mask(signal) expected = signal >= np.percentile(signal, 50.0) @@ -136,7 +137,7 @@ def test_compute_eligible_mask_50th_percentile(): def test_compute_eligible_mask_100th_percentile_only_max_eligible(): """Only the maximum value(s) are eligible at percentile=100.""" controller = _make_controller() - controller.config = SimpleNamespace(signal_threshold_percentile=100.0) + controller.config = SimpleNamespace(signal_threshold_percentile=100.0, min_peak_separation=None) signal = np.array([1.0, 2.0, 10.0, 3.0, 10.0]) mask = controller._compute_eligible_mask(signal) expected = np.array([False, False, True, False, True]) @@ -147,7 +148,7 @@ def test_compute_eligible_mask_100th_percentile_only_max_eligible(): def test_compute_eligible_mask_uniform_signal_all_eligible(): """All timesteps are eligible when the signal is uniform, regardless of percentile.""" controller = _make_controller() - controller.config = SimpleNamespace(signal_threshold_percentile=75.0) + controller.config = SimpleNamespace(signal_threshold_percentile=75.0, min_peak_separation=None) signal = np.full(10, 5.0) mask = controller._compute_eligible_mask(signal) assert mask.all() @@ -235,6 +236,51 @@ def test_optimizer_dispatch_respects_soc_constraints(base_config): assert soc <= base_config.max_soc_fraction * base_config.max_capacity +@pytest.mark.unit +def test_compute_eligible_mask_min_peak_separation_drops_nearby_peak(): + """A lower peak within min_peak_separation of a higher peak is dropped.""" + controller = _make_controller() + controller.config = SimpleNamespace( + signal_threshold_percentile=0.0, + min_peak_separation={"units": "h", "val": 3}, + ) + controller.dt_seconds = 3600 + # t=5 (signal=10) and t=7 (signal=8) are 2h apart — below the 3h threshold + signal = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 1.0, 8.0, 1.0, 1.0]) + mask = controller._compute_eligible_mask(signal) + assert mask[5], "higher peak should be kept" + assert not mask[7], "lower peak within separation should be dropped" + + +@pytest.mark.unit +def test_compute_eligible_mask_min_peak_separation_keeps_far_peaks(): + """Peaks separated by more than min_peak_separation are both kept.""" + controller = _make_controller() + controller.config = SimpleNamespace( + signal_threshold_percentile=0.0, + min_peak_separation={"units": "h", "val": 3}, + ) + controller.dt_seconds = 3600 + # t=2 (signal=10) and t=6 (signal=8) are 4h apart — above the 3h threshold + signal = np.array([1.0, 1.0, 10.0, 1.0, 1.0, 1.0, 8.0, 1.0, 1.0, 1.0]) + mask = controller._compute_eligible_mask(signal) + assert mask[2], "first peak should be kept" + assert mask[6], "second peak far enough away should also be kept" + + +@pytest.mark.unit +def test_compute_eligible_mask_min_peak_separation_none_no_pruning(): + """min_peak_separation=None leaves all percentile-eligible peaks unchanged.""" + controller = _make_controller() + controller.config = SimpleNamespace( + signal_threshold_percentile=0.0, + min_peak_separation=None, + ) + signal = np.array([1.0, 5.0, 6.0, 5.0, 1.0]) + mask = controller._compute_eligible_mask(signal) + assert mask.all() + + @pytest.mark.regression def test_optimizer_dispatch_respects_charge_discharge_exclusivity(base_config): """Solver must never set charge and discharge to 1 in the same timestep.""" From c33d498f3ed7b02a2e86f8c39a712d4303afafac Mon Sep 17 00:00:00 2001 From: svijaysh Date: Tue, 28 Apr 2026 10:57:59 -0600 Subject: [PATCH 23/55] Fix bug in peak window start time definition in example --- .../storage/plm_optimized_storage_controller.py | 12 +----------- 1 file changed, 1 insertion(+), 11 deletions(-) diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index 7bfb3ef4e..a15fb0662 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -377,8 +377,7 @@ def pyomo_dispatch_solver( report_every = max(1, n_windows // 4) window_idx = window_start // n_w if window_idx % report_every == 0: - pct = round(window_start / self.n_timesteps * 100) - print(f"{pct}% done with PLM rolling dispatch") + round(window_start / self.n_timesteps * 100) month_ids_w = self.month_ids[window_start : window_start + window_len] remaining_budget = { @@ -420,10 +419,6 @@ def pyomo_dispatch_solver( storage_out[window_start + j] = storage_out_window[j] soc_out[window_start + j] = soc_window[j] - print( - f" Events per month: {dict(events_used_per_month)} " - f"(limit: {self.config.n_max_events}/month)" - ) return storage_out, soc_out return pyomo_dispatch_solver @@ -592,11 +587,6 @@ def solve_dispatch_model(self, start_time: int = 0, n_days: int = 0): f"init_soc={self.updated_initial_soc:.4f}, " f"window_len={len(list(self.dr_model.T))}" ) - if tc == TerminationCondition.maxTimeLimit: - print( - f" WARNING: solver hit time limit at window start={start_time} " - f"— using best solution found so far" - ) self.problem_state.store_problem_metrics( solver_results, From 6ddb79af6204326b8668c32e9472a6066a865258 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Tue, 28 Apr 2026 12:22:57 -0600 Subject: [PATCH 24/55] Change user defined param fromn_max)hrs to n_max_events --- examples/34_plm_optimized_dispatch/tech_config.yaml | 12 ++++++------ .../storage/plm_optimized_storage_controller.py | 4 +++- 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/examples/34_plm_optimized_dispatch/tech_config.yaml b/examples/34_plm_optimized_dispatch/tech_config.yaml index a97a96447..9eed083b4 100644 --- a/examples/34_plm_optimized_dispatch/tech_config.yaml +++ b/examples/34_plm_optimized_dispatch/tech_config.yaml @@ -13,7 +13,7 @@ technologies: commodity: electricity commodity_rate_units: kW max_charge_rate: 300.0 # kW rated power - max_capacity: 1200.0 # kWh usable capacity (4-hour battery) + max_capacity: 2000.0 # kWh usable capacity (4-hour battery) max_soc_fraction: 0.90 min_soc_fraction: 0.10 init_soc_fraction: 0.90 @@ -25,17 +25,17 @@ technologies: system_commodity_interface_limit: 1.0e9 supervisory_signal: !include demand_profiles/supervisory_signal.yaml peak_window: - start: 14:00:00 # 2 pm MDT - end: 18:00:00 # 6 pm MDT + start: 12:00:00 # 2 pm MDT + end: 20:00:00 # 6 pm MDT performance_incentive: units: $/kWh # also accepts $/MWh or $/Wh val: 14.0 n_max_events: 10 # maximum discharge events per month signal_threshold_percentile: 95.0 # only dispatch when LMP >= this percentile - n_control_window: 10 # hours; converted to timesteps using simulation dt + n_control_window: 24 # hours; converted to timesteps using simulation dt event_duration: # omit or set to null to use static peak_window - units: h # any pandas timedelta unit: h, min, s, … - val: 2 # battery eligible +/ - val h around the daily peak + units: h # any pandas timedelta unit: h, min, s, + val: 4 # battery eligible +/ - val h around the daily peak # min_peak_separation: # omit or set to null to allow any peak spacing # units: h # val: 4 # drop any eligible peak within this distance of a higher peak diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index a15fb0662..16c8cc011 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -402,7 +402,9 @@ def pyomo_dispatch_solver( ) for t in range(window_len): - if pyomo.value(self.dr_model.discharge[t]) > 0.5: + discharging = pyomo.value(self.dr_model.discharge[t]) > 0.5 + prev_discharging = t > 0 and pyomo.value(self.dr_model.discharge[t - 1]) > 0.5 + if discharging and not prev_discharging: # rising edge = new event m = int(month_ids_w[t]) events_used_per_month[m] = events_used_per_month.get(m, 0) + 1 From 4c949359deccaf0254315d9144dbe49f2af3b8ba Mon Sep 17 00:00:00 2001 From: svijaysh Date: Wed, 29 Apr 2026 14:32:24 -0600 Subject: [PATCH 25/55] Address reviewer comments --- docs/control/pyomo_controllers.md | 71 +++++--- .../tech_config.yaml | 4 +- .../plm_optimized_storage_controller.py | 171 ++++++++++-------- .../test_plm_optimized_storage_controller.py | 25 ++- 4 files changed, 157 insertions(+), 114 deletions(-) diff --git a/docs/control/pyomo_controllers.md b/docs/control/pyomo_controllers.md index 4fdcdf233..25ed05ec8 100644 --- a/docs/control/pyomo_controllers.md +++ b/docs/control/pyomo_controllers.md @@ -63,63 +63,76 @@ tech_to_dispatch_connections: [ This controller optimizes the dispatch of a Battery Energy Storage System (BESS) based on a pre-defined supervisory signal. This signal could be the Locational Marginal Price (LMP), a demand profile, or a $LMP\times demand$ product depending on the application. The objective is to maximize incentive payments to the battery, subject to constraints on the maximum number of dispatch events per month and on the battery state of charge. +The controller works at any simulation timestep resolution (`dt`). All time-based parameters ( `event_duration`, `min_peak_separation`) are specified in physical time units (hours, minutes, etc.) and are internally converted to timesteps using `dt`. + ## Definitions **Given:** -- $\lambda_t$ := `supervisory_signal`: price, demand, or price $\times$ demand time series at time $t$ -- $\mathcal{W}$ := `peak_window`: set of hours eligible for dispatch (e.g., 12:00--19:00) -- $\gamma$ := performance incentive (\$/kW per dispatch hour) -- $\bar{P}$ := `max_charge_rate` (kW): maximum charge and discharge rate, used as deemed capacity since the battery is assumed to always dispatch at full rated power +- $\lambda_t$ := `supervisory_signal`: price, demand, or price $\times$ demand time series at timestep $t$ +- $\Delta t$ := simulation timestep duration (hours), derived from `dt` in the plant config +- $\mathcal{W}$ := `peak_window`: set of timesteps eligible for dispatch (e.g., 12:00--20:00 each day) +- $\lambda_*$ := signal threshold = `signal_threshold_percentile`-th percentile of $\lambda_t$ over $\mathcal{W}$ +- $\mathcal{E}$ := eligible peak timesteps: $\{t \in \mathcal{W} : \lambda_t \geq \lambda_*\}$, respecting `min_peak_separation` +- $\mathcal{D}$ := dispatch window: union of $\pm\,\tfrac{\text{event\_duration}}{2}$ neighbourhoods around each peak in $\mathcal{E}$ (equals $\mathcal{E}$ when `event_duration` is `null`) +- $\gamma$ := `performance_incentive` (\$/kWh): incentive revenue per kWh discharged +- $\bar{P}$ := `max_charge_rate` (kW): maximum charge and discharge rate - $E_{\max} :=$ `max_capacity` $\times$ (`max_soc_fraction` $-$ `min_soc_fraction`): usable energy capacity (kWh) - $\eta_c$ := `charge_efficiency`, $\quad \eta_d$ := `discharge_efficiency` - $\overline{\text{SoC}}$ := `max_soc_fraction`, $\quad \underline{\text{SoC}}$ := `min_soc_fraction` -- `n_control_window` := Horizon length for optimization -- $\mathcal{T} := \{0, 1, \ldots, T\}$: hourly time steps over `n_control_window` -- $\mathcal{M}_m$ := set of hours in month $m$, for $m = 1, \ldots, 12$ +- `n_control_window` := rolling horizon length in hours; converted to $T = \lceil \text{n\_control\_window} / \Delta t \rceil$ timesteps +- $\mathcal{T} := \{0, 1, \ldots, T-1\}$: timesteps in the current rolling window +- $\mathcal{M}_m$ := set of timesteps in month $m$, for $m = 1, \ldots, 12$ +- $N_{\max}$ := `n_max_events`: maximum number of discharge events per calendar month +- $\tau$ := `steps_per_event` : number of timesteps per event (1 when `event_duration` is `null`) +- $B_m$ := remaining event budget for month $m$ = $N_{\max}$ minus events already dispatched in prior windows + +## Dispatch Window Construction + +Before the MILP is solved, the dispatch window $\mathcal{D}$ is built in two steps: + +**Step 1 : Peak selection:** Within $\mathcal{W}$, timesteps at or above the `signal_threshold_percentile` of $\lambda_t$ are marked eligible: $\mathcal{E} = \{t \in \mathcal{W} : \lambda_t \geq \lambda_*\}$. If `min_peak_separation` is set, only the first peak is chosen. + +**Step 2 : Event window expansion:** If `event_duration` is specified, each peak in $\mathcal{E}$ is expanded by $\pm\,\tfrac{\text{event\_duration}}{2}$ timesteps to form $\mathcal{D}$. If `event_duration` is `null`, $\mathcal{D} = \mathcal{E}$. ## Decision Variables -- $u_t \in \{0, 1\}$ := discharge binary: 1 if battery dispatches at hour $t$, 0 otherwise -- $v_t \in \{0, 1\}$ := charge binary: 1 if battery charges at hour $t$, 0 otherwise +- $u_t \in \{0, 1\}$ := discharge binary: 1 if the battery discharges at timestep $t$, 0 otherwise +- $v_t \in \{0, 1\}$ := charge binary: 1 if the battery charges at timestep $t$, 0 otherwise ## Optimization Problem -This optimization is executed for each window, during which the performance model is invoked and the initial conditions are set. +This optimization is executed for each rolling window. At each window boundary the terminal SoC is carried forward as the initial condition for the next window. ### Objective -Maximize total annual incentive revenue: +Maximize total incentive revenue over the window: $$ -\max_{u_t,\, v_t} \quad \gamma \cdot \bar{P} \sum_{t \in \mathcal{T}} u_t +\max_{u_t,\, v_t} \quad \gamma \cdot \bar{P} \cdot \Delta t \sum_{t \in \mathcal{T}} u_t $$ +The factor $\Delta t$ converts power (kW) to energy (kWh), so the objective is correctly scaled at any timestep resolution. + ### Constraints -- Dispatch only within peak window: +- Dispatch only within the event window $\mathcal{D}$: $$ -u_t = 0 \qquad \forall\, t \notin \mathcal{W} +u_t = 0 \qquad \forall\, t \notin \mathcal{D} $$ -- Dispatch only on high supervisory signal: +- Maximum $N_{\max}$ discharge events per month. Because `event_duration` fixes each event to exactly $\tau$ timesteps, the event cap translates directly into a timestep cap: $$ -u_t = 1 \implies \lambda_t \geq \lambda^*_m \qquad \forall\, t \in \mathcal{M}_m +\sum_{t \in \mathcal{M}_m \cap \mathcal{T}} u_t \leq B_m \cdot \tau \qquad \forall\, m $$ -where $\lambda^*_m$ is the threshold selecting the high LMP/peak load hours within month $m$. - -- Maximum $N_{max}$ events per month: - -$$ -\sum_{t \in \mathcal{M}_m} u_t \leq N_{\max} \qquad \forall\, m, \quad N_{\max}. -$$ +After each window is solved, events are counted via rising-edge detection (a new event begins whenever $u_t = 1$ and $u_{t-1} = 0$) and $B_m$ is decremented accordingly for subsequent windows. - SoC evolution with charge and discharge: $$ -\text{SoC}_{t+1} = \text{SoC}_t + \frac{\eta_c \cdot v_t \cdot \bar{P}}{E_{\max}} - \frac{u_t \cdot \bar{P}}{\eta_d \cdot E_{\max}} \qquad \forall\, t \in \mathcal{T} +\text{SoC}_{t+1} = \text{SoC}_t + \frac{\eta_c \cdot v_t \cdot \bar{P} \cdot \Delta t}{E_{\max}} - \frac{u_t \cdot \bar{P} \cdot \Delta t}{\eta_d \cdot E_{\max}} \qquad \forall\, t \in \mathcal{T} $$ - SoC bounds: @@ -134,17 +147,19 @@ $$ u_t + v_t \leq 1 \qquad \forall\, t \in \mathcal{T} $$ -- No charging during dispatch window (battery reserved for discharge): +- No charging during the dispatch window (battery reserved for discharge): $$ -v_t = 0 \qquad \forall\, t \in \mathcal{W} +v_t = 0 \qquad \forall\, t \in \mathcal{D} $$ - Binary variables: $$ -u_t \in \{0, 1\}, \quad v_t \in \{0, 1\}, \quad \text{SoC}_t \in [0, 1] \qquad \forall\, t, m +u_t \in \{0, 1\}, \quad v_t \in \{0, 1\}, \quad \text{SoC}_t \in [\underline{\text{SoC}},\, \overline{\text{SoC}}] \qquad \forall\, t $$ -Example 34 performs the optimization with a synthetic LMP signal. The look-ahead horizon is set to 10 hours. As this value increases, the computational complexity grows and the solver may take significantly longer to run or fail to converge. Care should be taken when choosing this parameter: a short horizon limits visibility, making it difficult for the optimizer to identify the best dispatch opportunities across the full month. See figure below for the results. + + +Example 34 performs the optimization with a real LMP signal. The look-ahead horizon (`n_control_window`) controls how many hours are optimized at once. Larger values improve solution quality but increase solve time. See the figure below for results. ![](./figures/plm_optimized_dispatch.png) diff --git a/examples/34_plm_optimized_dispatch/tech_config.yaml b/examples/34_plm_optimized_dispatch/tech_config.yaml index 9eed083b4..890c279d3 100644 --- a/examples/34_plm_optimized_dispatch/tech_config.yaml +++ b/examples/34_plm_optimized_dispatch/tech_config.yaml @@ -27,9 +27,7 @@ technologies: peak_window: start: 12:00:00 # 2 pm MDT end: 20:00:00 # 6 pm MDT - performance_incentive: - units: $/kWh # also accepts $/MWh or $/Wh - val: 14.0 + performance_incentive: 14.0 # $/kWh n_max_events: 10 # maximum discharge events per month signal_threshold_percentile: 95.0 # only dispatch when LMP >= this percentile n_control_window: 24 # hours; converted to timesteps using simulation dt diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index 16c8cc011..fcc1b116b 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -1,13 +1,14 @@ -from typing import Any, ClassVar +from typing import Any from datetime import datetime import numpy as np import pandas as pd import pyomo.environ as pyomo from attrs import field, define +from pyomo.opt import SolverStatus, TerminationCondition from h2integrate.core.utilities import merge_shared_inputs, build_time_series_from_plant_config -from h2integrate.core.validators import range_val, has_required_keys +from h2integrate.core.validators import range_val from h2integrate.control.control_strategies.controller_opt_problem_state import DispatchProblemState from h2integrate.control.control_strategies.pyomo_storage_controller_baseclass import ( SolverOptions, @@ -18,7 +19,7 @@ @define class PeakLoadManagementOptimizedControllerConfig(PyomoStorageControllerBaseConfig): - """Configuration for the PLM DR optimized storage controller. + """Configuration for the Peak Load Management optimized storage controller. Inherits base fields from ``PyomoStorageControllerBaseConfig``: ``max_capacity``, ``max_soc_fraction``, ``min_soc_fraction``, @@ -29,14 +30,11 @@ class PeakLoadManagementOptimizedControllerConfig(PyomoStorageControllerBaseConf Attributes: max_charge_rate (float): Maximum charge and discharge rate (kW). supervisory_signal (list[float]): Price, demand, or price*demand - forecast time series. The rolling solver uses one window of + forecast time series. The rolling horizon solver uses one window of length ``n_control_window`` per solve. peak_window (dict): Hours eligible for dispatch. Keys ``'start'`` and ``'end'`` must be strings in ``HH:MM:SS`` format. - performance_incentive (dict): Incentive revenue expressed as a - ``{units, val}`` dict. ``units`` must be one of ``'$/kWh'``, - ``'$/MWh'``, or ``'$/Wh'``; ``val`` is the numeric amount. - Example: ``{units: '$/kWh', val: 14.0}``. + performance_incentive (float): Incentive revenue in $/kWh. charge_efficiency (float): Charge efficiency in [0, 1]. Defaults to 1.0. discharge_efficiency (float): Discharge efficiency in [0, 1]. @@ -56,19 +54,23 @@ class PeakLoadManagementOptimizedControllerConfig(PyomoStorageControllerBaseConf expressed as a ``{units, val}`` dict, where ``units`` is any pandas timedelta unit string (e.g. ``'h'``, ``'min'``, ``'s'``) and ``val`` is the numeric amount. When set, the - eligible dispatch window is computed dynamically per calendar - day: the peak-signal timestep within ``peak_window`` is - located and every timestep within ``event_duration / 2`` of + peak-signal timestep within ``peak_window`` is located and + every timestep within ``event_duration / 2`` of that peak is marked eligible (the window may extend beyond the static ``peak_window`` boundaries). When ``None`` (default) the static ``peak_window`` mask is used unchanged. Example: ``{units: 'h', val: 4}`` is +/- 2 h around the daily peak. + min_peak_separation (dict): Minimum separation between eligible + peaks, expressed as a ``{units, val}`` dict like + ``event_duration``. When set, the eligible timesteps identified + by ``signal_threshold_percentile`` are treated as peaks and + the first peak is chosen as eligible. """ max_charge_rate: float = field() supervisory_signal: list = field() peak_window: dict = field() - performance_incentive: dict = field(validator=has_required_keys(["units", "val"])) + performance_incentive: float = field() charge_efficiency: float = field(validator=range_val(0, 1), default=1.0) discharge_efficiency: float = field(validator=range_val(0, 1), default=1.0) n_max_events: int = field(default=10) @@ -77,30 +79,11 @@ class PeakLoadManagementOptimizedControllerConfig(PyomoStorageControllerBaseConf event_duration: dict = field(default=None) min_peak_separation: dict = field(default=None) - _INCENTIVE_TO_KWH: ClassVar[dict] = {"$/kWh": 1.0, "$/MWh": 1e-3, "$/Wh": 1e3} - def __attrs_post_init__(self): # Make sure n_control_window is an int self.n_control_window = int(round(self.n_control_window)) super().__attrs_post_init__() - for key in ("units", "val"): - if key not in self.performance_incentive: - raise ValueError( - f"performance_incentive is missing required key '{key}'. " - "Expected dict with 'units' (e.g. '$/kWh') and 'val' (numeric)." - ) - if self.performance_incentive["units"] not in self._INCENTIVE_TO_KWH: - raise ValueError( - f"performance_incentive 'units' must be one of " - f"{list(self._INCENTIVE_TO_KWH)}, got {self.performance_incentive['units']}." - ) - if not isinstance(self.performance_incentive.get("val"), int | float): - raise ValueError( - "performance_incentive 'val' must be a numeric value " - f"(int or float), got {type(self.performance_incentive.get('val')).__name__}." - ) - for field_name, value in ( ("event_duration", self.event_duration), ("min_peak_separation", self.min_peak_separation), @@ -159,18 +142,12 @@ def setup(self): ) sim = self.options["plant_config"]["plant"]["simulation"] - self.n_timesteps = int(sim["n_timesteps"]) - self.dt_seconds = int(sim["dt"]) + self.n_timesteps = int(sim["n_timesteps"]) # number of "dt"s in the simulation + self.dt_seconds = int(sim["dt"]) # length of each timestep in seconds # n_control_window is stored in hours; convert to timesteps now that dt is known. n_cw_steps = max(1, int(round(self.config.n_control_window * 3600 / self.dt_seconds))) object.__setattr__(self.config, "n_control_window", n_cw_steps) - scil = self.config.system_commodity_interface_limit - scil_list: list[float] = list(scil) if isinstance(scil, list | tuple) else [float(scil)] - if len(scil_list) != n_cw_steps: - object.__setattr__( - self.config, "system_commodity_interface_limit", [scil_list[0]] * n_cw_steps - ) super().setup() @@ -191,7 +168,25 @@ def setup(self): ) self.in_peak_window = self._compute_peak_window_mask() # bool array, shape (T,) - self.month_ids = self._compute_month_ids() # int array, shape (T,) + self.month_ids = self._compute_month_ids() # int array, shape (T,) + + # Computes the number of timesteps in an event based on event_duration and dt_seconds, + # rounded to nearest int and at least 1. + if self.config.event_duration is not None: + self.steps_per_event: int = max( + 1, + int( + round( + pd.Timedelta( + value=self.config.event_duration["val"], + unit=self.config.event_duration["units"], + ).total_seconds() + / self.dt_seconds + ) + ), + ) + else: + self.steps_per_event = 1 def _parse_peak_window(self) -> tuple: """Parse the ``peak_window`` config entry into ``datetime.time`` objects. @@ -251,6 +246,8 @@ def _compute_eligible_mask( When ``dispatch_mask`` is ``None`` the full ``signal_window`` is used. When ``signal_threshold_percentile`` is 0.0 all timesteps are eligible. + If there are multiple timesteps above the threshold within a window and + ``min_peak_separation`` is set, only the first one is eligible. Args: signal_window (np.ndarray): Signal values for the current @@ -274,7 +271,7 @@ def _compute_eligible_mask( threshold = np.percentile(signal_window[mask], self.config.signal_threshold_percentile) eligible = mask & (signal_window >= threshold) - if self.config.min_peak_separation is not None and eligible.any(): + if self.config.min_peak_separation is not None: sep_steps = ( pd.Timedelta( value=self.config.min_peak_separation["val"], @@ -282,13 +279,14 @@ def _compute_eligible_mask( ).total_seconds() / self.dt_seconds ) - # Greedily keep peaks from highest to lowest signal, dropping any peak - # that falls within sep_steps of an already-kept peak. - peak_indices = np.where(eligible)[0] - order = np.argsort(-signal_window[peak_indices]) - kept: list[int] = [] - for idx in peak_indices[order]: - if all(abs(int(idx) - k) >= sep_steps for k in kept): + # Keep the first peak; drop any subsequent peaks + # within sep_steps of the last kept one. + # This is a greedy choice but subsequent iterations will + # use a smarter logic + kept = [] + for idx in np.where(eligible)[0]: + if not kept or int(idx) - kept[-1] >= sep_steps: + # Won't enter this condition if the peaks are too close together kept.append(int(idx)) eligible = np.zeros(len(signal_window), dtype=bool) eligible[kept] = True @@ -298,11 +296,10 @@ def _compute_eligible_mask( def _compute_event_window_mask( self, eligible_mask: np.ndarray, - window_start: int, ) -> np.ndarray: - """Expand each eligible peak timestep by ±event_duration/2. + """Expand each eligible peak timestep by +/-event_duration/2. - For each calendar day, every True timestep in ``eligible_mask`` is + Every True timestep in ``eligible_mask`` is treated as a peak and all timesteps within ``event_duration / 2`` of it are marked eligible. When ``event_duration`` is ``None`` returns ``eligible_mask`` unchanged. @@ -310,8 +307,6 @@ def _compute_event_window_mask( Args: eligible_mask (np.ndarray): Boolean mask of peak timesteps, shape ``(window_len,)``. - window_start (int): Index of the first timestep of this - window into ``self.time_index``. Returns: np.ndarray: Boolean mask of shape ``(window_len,)``. @@ -319,7 +314,7 @@ def _compute_event_window_mask( if self.config.event_duration is None: return eligible_mask.copy() - half_steps = ( + half_event_steps = ( pd.Timedelta( value=self.config.event_duration["val"], unit=self.config.event_duration["units"], @@ -327,10 +322,12 @@ def _compute_event_window_mask( / 2.0 / self.dt_seconds ) - indices = np.arange(len(eligible_mask)) peak_indices = np.where(eligible_mask)[0] - distances = np.abs(indices[:, None] - peak_indices[None, :]) - return (distances <= half_steps).any(axis=1) + event_mask = np.zeros(len(eligible_mask), dtype=bool) + for peak in peak_indices: + near_peak = np.abs(np.arange(len(eligible_mask)) - peak) <= half_event_steps + event_mask |= near_peak + return event_mask def pyomo_setup(self, discrete_inputs): """Return the rolling-horizon dispatch solver callable. @@ -368,26 +365,23 @@ def pyomo_dispatch_solver( events_used_per_month = {} n_w: int = int(self.config.n_control_window) + # Compute the starting index of each rolling window. window_start_indices = list(range(0, self.n_timesteps, n_w)) for window_start in window_start_indices: window_len: int = min(n_w, self.n_timesteps - window_start) - - n_windows = len(window_start_indices) - report_every = max(1, n_windows // 4) - window_idx = window_start // n_w - if window_idx % report_every == 0: - round(window_start / self.n_timesteps * 100) - month_ids_w = self.month_ids[window_start : window_start + window_len] + # Since this is a rolling horizon, we need to compute the remaining budget + # for the next optimization call remaining_budget = { int(m): max( 0, - self.config.n_max_events - events_used_per_month.get(int(m), 0), + self.config.n_max_events + - (events_used_per_month[int(m)] if int(m) in events_used_per_month else 0), ) for m in np.unique(month_ids_w) } - + # Construct the MILP for this window self.dr_model = self._build_dr_model( window_start=window_start, window_len=window_len, @@ -396,18 +390,23 @@ def pyomo_dispatch_solver( ) self.problem_state = DispatchProblemState() + # Solve the optimzation problem self.solve_dispatch_model( start_time=window_start, n_days=self.n_timesteps // 24, ) + # Count new discharge events to track the monthly cap. for t in range(window_len): discharging = pyomo.value(self.dr_model.discharge[t]) > 0.5 prev_discharging = t > 0 and pyomo.value(self.dr_model.discharge[t - 1]) > 0.5 - if discharging and not prev_discharging: # rising edge = new event - m = int(month_ids_w[t]) - events_used_per_month[m] = events_used_per_month.get(m, 0) + 1 + if discharging and not prev_discharging: + month = int(month_ids_w[t]) + if month not in events_used_per_month: + events_used_per_month[month] = 0 + events_used_per_month[month] += 1 + # Run the performance model for this window. storage_out_window, soc_window = performance_model( self.storage_dispatch_commands, **performance_model_kwargs, @@ -429,7 +428,7 @@ def initialize_parameters(self, inputs): """Sync OpenMDAO inputs into the config. Args: - inputs (dict): OpenMDAO inputs dict. Recognised keys are + inputs (dict): OpenMDAO inputs dict. Recognized keys are ``'max_charge_rate'`` and ``'storage_capacity'``. """ if "max_charge_rate" in inputs: @@ -471,25 +470,28 @@ def _build_dr_model( eta_d = self.config.discharge_efficiency soc_max = self.config.max_soc_fraction soc_min = self.config.min_soc_fraction - incentive = ( - self.config.performance_incentive["val"] - * self.config._INCENTIVE_TO_KWH[self.config.performance_incentive["units"].strip()] - ) + incentive = self.config.performance_incentive N_max = self.config.n_max_events + # Only the timesteps within the current window are relevant w = slice(window_start, window_start + window_len) in_peak_window_w = self.in_peak_window[w] month_ids_w = self.month_ids[w] signal_w = np.asarray(self.config.supervisory_signal, dtype=float)[w] + + # Eligible timesteps for discharge based on percentile eligible_t_w = self._compute_eligible_mask(signal_w, in_peak_window_w) - dispatch_window_w = self._compute_event_window_mask(eligible_t_w, window_start) + # Expand eligible timesteps into event windows based on event_duration + dispatch_window_w = self._compute_event_window_mask(eligible_t_w) eligible_t_w = dispatch_window_w months_in_window = np.unique(month_ids_w).tolist() + # Sets we need to iterate on in the constraints and objective m.T = pyomo.Set(initialize=range(window_len), doc="Timesteps in window") m.M = pyomo.Set(initialize=months_in_window, doc="Months in window") + # Things to solve for m.discharge = pyomo.Var( m.T, domain=pyomo.Binary, doc="Discharge binary: 1 = discharging at timestep t" ) @@ -503,12 +505,16 @@ def _build_dr_model( doc="State of charge SoC_t", ) + # Incentive revenue is earned for every kWh discharged during the dispatch window, + # so we need the energy at every timestep to be in Kwh dt_hours = self.dt_seconds / 3600.0 m.objective = pyomo.Objective( expr=-incentive * P_max * dt_hours * sum(m.discharge[t] for t in m.T), sense=pyomo.minimize, ) + ## Constraints + # Discharge can only occur during the dispatch window. m.peak_window_only = pyomo.Constraint( m.T, rule=lambda mdl, t: ( @@ -516,22 +522,30 @@ def _build_dr_model( ), ) + # Discharge can only occur at eligible timesteps. m.high_signal_only = pyomo.Constraint( m.T, rule=lambda mdl, t: mdl.discharge[t] <= int(eligible_t_w[t]), ) + # There is limit on the number of events, not discharge timesteps. + # However, we know the number of timesteps in the event from steps_per_event, + # so we can indirectly limit the number of discharge timesteps in each month + # by multiplying the event cap by steps_per_event. def max_events_rule(mdl, month): ts_in_month = [t for t in mdl.T if month_ids_w[t] == month] if not ts_in_month: return pyomo.Constraint.Skip - budget = remaining_budget.get(month, N_max) - return sum(mdl.discharge[t] for t in ts_in_month) <= budget + budget_steps = ( + remaining_budget[month] if month in remaining_budget else N_max + ) * self.steps_per_event + return sum(mdl.discharge[t] for t in ts_in_month) <= budget_steps m.max_events = pyomo.Constraint(m.M, rule=max_events_rule) m.soc_init = pyomo.Constraint(expr=m.soc[0] == init_soc) + # Dynamics of the battery SOC evolution. def soc_evolution_rule(mdl, t): if t == 0: return pyomo.Constraint.Skip @@ -543,11 +557,13 @@ def soc_evolution_rule(mdl, t): m.soc_evolution = pyomo.Constraint(m.T, rule=soc_evolution_rule) + # Can't charge and discharge at the same time. m.no_simultaneous = pyomo.Constraint( m.T, rule=lambda mdl, t: mdl.discharge[t] + mdl.charge[t] <= 1, ) + # Can't charge in the dispatch window m.no_charge_in_window = pyomo.Constraint( m.T, rule=lambda mdl, t: ( @@ -571,7 +587,6 @@ def solve_dispatch_model(self, start_time: int = 0, n_days: int = 0): RuntimeError: If GLPK returns a not OK status or an unacceptable termination condition. """ - from pyomo.opt import SolverStatus, TerminationCondition solver_results = self.glpk_solve_call(self.dr_model) diff --git a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py index ef1729f98..633b364c2 100644 --- a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py @@ -24,6 +24,21 @@ def _make_controller_with_config(config, n_timesteps=24, dt_seconds=3600): controller.time_index = pd.date_range("2024-01-01", periods=n_timesteps, freq="h") controller.in_peak_window = controller._compute_peak_window_mask() controller.month_ids = controller._compute_month_ids() + if config.event_duration is not None: + controller.steps_per_event = max( + 1, + int( + round( + pd.Timedelta( + value=config.event_duration["val"], + unit=config.event_duration["units"], + ).total_seconds() + / dt_seconds + ) + ), + ) + else: + controller.steps_per_event = 1 return controller @@ -43,7 +58,7 @@ def base_config(): max_charge_rate=1.0, supervisory_signal=list(range(n)), peak_window={"start": "08:00:00", "end": "18:00:00"}, - performance_incentive={"units": "$/kWh", "val": 10.0}, + performance_incentive=10.0, n_max_events=24, signal_threshold_percentile=0.0, ) @@ -238,18 +253,18 @@ def test_optimizer_dispatch_respects_soc_constraints(base_config): @pytest.mark.unit def test_compute_eligible_mask_min_peak_separation_drops_nearby_peak(): - """A lower peak within min_peak_separation of a higher peak is dropped.""" + """A later peak within min_peak_separation of an earlier peak is dropped.""" controller = _make_controller() controller.config = SimpleNamespace( signal_threshold_percentile=0.0, min_peak_separation={"units": "h", "val": 3}, ) controller.dt_seconds = 3600 - # t=5 (signal=10) and t=7 (signal=8) are 2h apart — below the 3h threshold + # t=5 and t=7 are 2h apart — below the 3h threshold; first one (t=5) is kept signal = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 1.0, 8.0, 1.0, 1.0]) mask = controller._compute_eligible_mask(signal) - assert mask[5], "higher peak should be kept" - assert not mask[7], "lower peak within separation should be dropped" + assert mask[5], "first peak should be kept" + assert not mask[7], "later peak within separation should be dropped" @pytest.mark.unit From aa7d94c3d16b97dfa991ffd727e8bb5ea5aab04b Mon Sep 17 00:00:00 2001 From: svijaysh Date: Wed, 29 Apr 2026 14:59:41 -0600 Subject: [PATCH 26/55] Make description in tech config more specific --- examples/34_plm_optimized_dispatch/tech_config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/34_plm_optimized_dispatch/tech_config.yaml b/examples/34_plm_optimized_dispatch/tech_config.yaml index 890c279d3..55062246a 100644 --- a/examples/34_plm_optimized_dispatch/tech_config.yaml +++ b/examples/34_plm_optimized_dispatch/tech_config.yaml @@ -1,5 +1,5 @@ name: technology_config -description: 300 kW / 1.2 MWh battery with PLM MILP-optimized dispatch controller +description: 300 kW / 1.2 MWh battery with Peak Load Management MILP-optimized dispatch controller technologies: battery: performance_model: @@ -13,7 +13,7 @@ technologies: commodity: electricity commodity_rate_units: kW max_charge_rate: 300.0 # kW rated power - max_capacity: 2000.0 # kWh usable capacity (4-hour battery) + max_capacity: 1200.0 # kWh usable capacity (4-hour battery) max_soc_fraction: 0.90 min_soc_fraction: 0.10 init_soc_fraction: 0.90 From e1bd57855bcdc5476b1bbf8345a98b33ee317e3e Mon Sep 17 00:00:00 2001 From: svijaysh Date: Wed, 29 Apr 2026 15:51:40 -0600 Subject: [PATCH 27/55] Move standard methds to the top and delete demand csv --- .../demand_profiles/demand_profile.yaml | 3361 ----------------- .../run_plm_optimized_dispatch.py | 11 +- .../tech_config.yaml | 4 +- .../plm_optimized_storage_controller.py | 258 +- 4 files changed, 145 insertions(+), 3489 deletions(-) delete mode 100644 examples/34_plm_optimized_dispatch/demand_profiles/demand_profile.yaml diff --git a/examples/34_plm_optimized_dispatch/demand_profiles/demand_profile.yaml b/examples/34_plm_optimized_dispatch/demand_profiles/demand_profile.yaml deleted file mode 100644 index 7f14e933f..000000000 --- a/examples/34_plm_optimized_dispatch/demand_profiles/demand_profile.yaml +++ /dev/null @@ -1,3361 +0,0 @@ -- 212.4377 -- 201.6801 -- 216.0036 -- 217.5245 -- 194.3917 -- 199.5826 -- 251.0227 -- 307.4701 -- 309.8656 -- 303.1756 -- 257.0352 -- 256.2223 -- 250.5282 -- 259.0179 -- 433.7401 -- 423.1257 -- 432.9500 -- 422.3289 -- 437.0276 -- 429.6006 -- 248.5211 -- 244.5526 -- 219.7803 -- 208.7638 -- 206.5734 -- 207.1829 -- 214.2585 -- 212.9236 -- 213.3019 -- 213.4466 -- 267.1332 -- 306.7487 -- 305.9021 -- 303.4898 -- 254.9278 -- 259.0318 -- 249.0884 -- 243.2787 -- 423.4042 -- 435.2047 -- 435.9460 -- 434.3452 -- 424.6759 -- 431.8573 -- 250.9335 -- 251.7495 -- 216.9714 -- 211.7888 -- 215.4313 -- 210.5406 -- 212.3130 -- 215.0503 -- 198.3428 -- 207.4426 -- 246.2370 -- 304.8890 -- 307.7989 -- 321.9595 -- 243.0734 -- 257.7462 -- 236.5370 -- 247.3209 -- 431.3020 -- 434.6898 -- 435.6898 -- 436.3468 -- 427.2102 -- 426.3012 -- 256.8638 -- 248.4696 -- 199.7945 -- 200.9337 -- 202.6444 -- 213.9773 -- 211.1394 -- 215.5239 -- 206.5820 -- 211.2683 -- 255.0047 -- 307.5252 -- 313.6542 -- 304.7046 -- 247.0956 -- 246.9461 -- 240.4333 -- 253.8958 -- 426.2448 -- 430.1000 -- 433.8460 -- 433.5722 -- 435.3231 -- 429.2121 -- 246.6136 -- 249.3623 -- 196.5013 -- 198.4231 -- 199.4184 -- 202.0220 -- 213.1982 -- 202.7562 -- 206.9747 -- 220.3938 -- 247.1499 -- 315.9001 -- 302.5311 -- 308.3565 -- 242.3998 -- 247.2877 -- 256.7225 -- 236.1814 -- 433.4754 -- 431.9019 -- 425.2468 -- 418.4315 -- 430.5770 -- 425.7641 -- 251.8614 -- 250.1748 -- 222.8142 -- 208.0852 -- 121.8120 -- 131.4342 -- 131.7600 -- 140.8735 -- 136.6809 -- 132.8550 -- 181.7064 -- 220.4899 -- 224.8820 -- 222.5874 -- 166.8815 -- 158.9865 -- 175.0812 -- 168.2222 -- 338.2335 -- 341.8754 -- 352.5081 -- 356.7050 -- 365.9738 -- 373.3109 -- 173.3153 -- 162.0837 -- 112.9436 -- 132.1417 -- 123.4965 -- 126.6771 -- 125.1032 -- 128.8737 -- 138.5278 -- 131.2564 -- 168.7309 -- 221.7148 -- 216.6025 -- 226.1095 -- 169.5697 -- 184.1434 -- 171.0422 -- 177.8619 -- 346.0056 -- 340.5204 -- 342.2791 -- 344.1982 -- 367.0278 -- 343.4289 -- 176.7079 -- 162.7766 -- 137.4526 -- 133.0796 -212.4377 -- 201.6801 -- 216.0036 -- 217.5245 -- 194.3917 -- 199.5826 -- 251.0227 -- 307.4701 -- 309.8656 -- 303.1756 -- 257.0352 -- 256.2223 -- 250.5282 -- 259.0179 -- 433.7401 -- 423.1257 -- 432.9500 -- 422.3289 -- 437.0276 -- 429.6006 -- 248.5211 -- 244.5526 -- 219.7803 -- 208.7638 -- 206.5734 -- 207.1829 -- 214.2585 -- 212.9236 -- 213.3019 -- 213.4466 -- 267.1332 -- 306.7487 -- 305.9021 -- 303.4898 -- 254.9278 -- 259.0318 -- 249.0884 -- 243.2787 -- 423.4042 -- 435.2047 -- 435.9460 -- 434.3452 -- 424.6759 -- 431.8573 -- 250.9335 -- 251.7495 -- 216.9714 -- 211.7888 -- 215.4313 -- 210.5406 -- 212.3130 -- 215.0503 -- 198.3428 -- 207.4426 -- 246.2370 -- 304.8890 -- 307.7989 -- 321.9595 -- 243.0734 -- 257.7462 -- 236.5370 -- 247.3209 -- 431.3020 -- 434.6898 -- 435.6898 -- 436.3468 -- 427.2102 -- 426.3012 -- 256.8638 -- 248.4696 -- 199.7945 -- 200.9337 -- 202.6444 -- 213.9773 -- 211.1394 -- 215.5239 -- 206.5820 -- 211.2683 -- 255.0047 -- 307.5252 -- 313.6542 -- 304.7046 -- 247.0956 -- 246.9461 -- 240.4333 -- 253.8958 -- 426.2448 -- 430.1000 -- 433.8460 -- 433.5722 -- 435.3231 -- 429.2121 -- 246.6136 -- 249.3623 -- 196.5013 -- 198.4231 -- 199.4184 -- 202.0220 -- 213.1982 -- 202.7562 -- 206.9747 -- 220.3938 -- 247.1499 -- 315.9001 -- 302.5311 -- 308.3565 -- 242.3998 -- 247.2877 -- 256.7225 -- 236.1814 -- 433.4754 -- 431.9019 -- 425.2468 -- 418.4315 -- 430.5770 -- 425.7641 -- 251.8614 -- 250.1748 -- 222.8142 -- 208.0852 -- 121.8120 -- 131.4342 -- 131.7600 -- 140.8735 -- 136.6809 -- 132.8550 -- 181.7064 -- 220.4899 -- 224.8820 -- 222.5874 -- 166.8815 -- 158.9865 -- 175.0812 -- 168.2222 -- 338.2335 -- 341.8754 -- 352.5081 -- 356.7050 -- 365.9738 -- 373.3109 -- 173.3153 -- 162.0837 -- 112.9436 -- 132.1417 -- 123.4965 -- 126.6771 -- 125.1032 -- 128.8737 -- 138.5278 -- 131.2564 -- 168.7309 -- 221.7148 -- 216.6025 -- 226.1095 -- 169.5697 -- 184.1434 -- 171.0422 -- 177.8619 -- 346.0056 -- 340.5204 -- 342.2791 -- 344.1982 -- 367.0278 -- 343.4289 -- 176.7079 -- 162.7766 -- 137.4526 -- 133.0796 -212.4377 -- 201.6801 -- 216.0036 -- 217.5245 -- 194.3917 -- 199.5826 -- 251.0227 -- 307.4701 -- 309.8656 -- 303.1756 -- 257.0352 -- 256.2223 -- 250.5282 -- 259.0179 -- 433.7401 -- 423.1257 -- 432.9500 -- 422.3289 -- 437.0276 -- 429.6006 -- 248.5211 -- 244.5526 -- 219.7803 -- 208.7638 -- 206.5734 -- 207.1829 -- 214.2585 -- 212.9236 -- 213.3019 -- 213.4466 -- 267.1332 -- 306.7487 -- 305.9021 -- 303.4898 -- 254.9278 -- 259.0318 -- 249.0884 -- 243.2787 -- 423.4042 -- 435.2047 -- 435.9460 -- 434.3452 -- 424.6759 -- 431.8573 -- 250.9335 -- 251.7495 -- 216.9714 -- 211.7888 -- 215.4313 -- 210.5406 -- 212.3130 -- 215.0503 -- 198.3428 -- 207.4426 -- 246.2370 -- 304.8890 -- 307.7989 -- 321.9595 -- 243.0734 -- 257.7462 -- 236.5370 -- 247.3209 -- 431.3020 -- 434.6898 -- 435.6898 -- 436.3468 -- 427.2102 -- 426.3012 -- 256.8638 -- 248.4696 -- 199.7945 -- 200.9337 -- 202.6444 -- 213.9773 -- 211.1394 -- 215.5239 -- 206.5820 -- 211.2683 -- 255.0047 -- 307.5252 -- 313.6542 -- 304.7046 -- 247.0956 -- 246.9461 -- 240.4333 -- 253.8958 -- 426.2448 -- 430.1000 -- 433.8460 -- 433.5722 -- 435.3231 -- 429.2121 -- 246.6136 -- 249.3623 -- 196.5013 -- 198.4231 -- 199.4184 -- 202.0220 -- 213.1982 -- 202.7562 -- 206.9747 -- 220.3938 -- 247.1499 -- 315.9001 -- 302.5311 -- 308.3565 -- 242.3998 -- 247.2877 -- 256.7225 -- 236.1814 -- 433.4754 -- 431.9019 -- 425.2468 -- 418.4315 -- 430.5770 -- 425.7641 -- 251.8614 -- 250.1748 -- 222.8142 -- 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253.8958 -- 426.2448 -- 430.1000 -- 433.8460 -- 433.5722 -- 435.3231 -- 429.2121 -- 246.6136 -- 249.3623 -- 196.5013 -- 198.4231 -- 199.4184 -- 202.0220 -- 213.1982 -- 202.7562 -- 206.9747 -- 220.3938 -- 247.1499 -- 315.9001 -- 302.5311 -- 308.3565 -- 242.3998 -- 247.2877 -- 256.7225 -- 236.1814 -- 433.4754 -- 431.9019 -- 425.2468 -- 418.4315 -- 430.5770 -- 425.7641 -- 251.8614 -- 250.1748 -- 222.8142 -- 208.0852 -- 121.8120 -- 131.4342 -- 131.7600 -- 140.8735 -- 136.6809 -- 132.8550 -- 181.7064 -- 220.4899 -- 224.8820 -- 222.5874 -- 166.8815 -- 158.9865 -- 175.0812 -- 168.2222 -- 338.2335 -- 341.8754 -- 352.5081 -- 356.7050 -- 365.9738 -- 373.3109 -- 173.3153 -- 162.0837 -- 112.9436 -- 132.1417 -- 123.4965 -- 126.6771 -- 125.1032 -- 128.8737 -- 138.5278 -- 131.2564 -- 168.7309 -- 221.7148 -- 216.6025 -- 226.1095 -- 169.5697 -- 184.1434 -- 171.0422 -- 177.8619 -- 346.0056 -- 340.5204 -- 342.2791 -- 344.1982 -- 367.0278 -- 343.4289 -- 176.7079 -- 162.7766 -- 137.4526 -- 133.0796 - diff --git a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py index f55279d81..1f3d3b6fb 100644 --- a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py +++ b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py @@ -50,8 +50,15 @@ pw_cfg = control_params["peak_window"] event_dur_cfg = control_params.get("event_duration") -pw_start_h = int(pw_cfg["start"].split(":")[0]) -pw_end_h = int(pw_cfg["end"].split(":")[0]) + +def _peak_window_to_hour(val) -> int: + if isinstance(val, int | float): + return int(val) // 3600 + return int(str(val).split(":")[0]) + + +pw_start_h = _peak_window_to_hour(pw_cfg["start"]) +pw_end_h = _peak_window_to_hour(pw_cfg["end"]) half_td = None if event_dur_cfg is not None: diff --git a/examples/34_plm_optimized_dispatch/tech_config.yaml b/examples/34_plm_optimized_dispatch/tech_config.yaml index 55062246a..5732b548f 100644 --- a/examples/34_plm_optimized_dispatch/tech_config.yaml +++ b/examples/34_plm_optimized_dispatch/tech_config.yaml @@ -1,5 +1,5 @@ name: technology_config -description: 300 kW / 1.2 MWh battery with Peak Load Management MILP-optimized dispatch controller +description: 300 kW / 2 MWh battery with Peak Load Management MILP-optimized dispatch controller technologies: battery: performance_model: @@ -13,7 +13,7 @@ technologies: commodity: electricity commodity_rate_units: kW max_charge_rate: 300.0 # kW rated power - max_capacity: 1200.0 # kWh usable capacity (4-hour battery) + max_capacity: 2000.0 # kWh usable capacity (4-hour battery) max_soc_fraction: 0.90 min_soc_fraction: 0.10 init_soc_fraction: 0.90 diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index fcc1b116b..0f6831a3d 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -188,26 +188,156 @@ def setup(self): else: self.steps_per_event = 1 + def initialize_parameters(self, inputs): + """Sync OpenMDAO inputs into the config. + + Args: + inputs (dict): OpenMDAO inputs dict. Recognized keys are + ``'max_charge_rate'`` and ``'storage_capacity'``. + """ + if "max_charge_rate" in inputs: + object.__setattr__(self.config, "max_charge_rate", float(inputs["max_charge_rate"][0])) + if "storage_capacity" in inputs: + object.__setattr__(self.config, "max_capacity", float(inputs["storage_capacity"][0])) + + def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): + """Build the DR dispatch solver and write it to discrete outputs. + + Args: + inputs (dict): OpenMDAO continuous inputs. + outputs (dict): OpenMDAO continuous outputs. + discrete_inputs (dict): OpenMDAO discrete inputs. + discrete_outputs (dict): OpenMDAO discrete outputs. The key + ``'pyomo_dispatch_solver'`` is set to the callable + returned by :meth:`pyomo_setup`. + """ + discrete_outputs["pyomo_dispatch_solver"] = self.pyomo_setup(discrete_inputs) + + def pyomo_setup(self, discrete_inputs): + """Return the rolling-horizon dispatch solver callable. + + Args: + discrete_inputs (dict): OpenMDAO discrete inputs. + + Returns: + callable: ``pyomo_dispatch_solver(performance_model, + performance_model_kwargs, inputs)`` that iterates over the + simulation in windows of ``n_control_window`` timesteps. + For each window it: + + 1. Builds a fresh MILP from the window's signal slice. + 2. Solves the MILP with GLPK. + 3. Calls ``performance_model`` with the resulting dispatch + commands. + 4. Carries the terminal SOC into the next window. + + Returns ``(storage_out, soc_out)`` - two ``np.ndarray`` of + length ``n_timesteps``. + """ + + def pyomo_dispatch_solver( + performance_model, + performance_model_kwargs, + inputs, + commodity_name=self.config.commodity, + ): + storage_out = np.zeros(self.n_timesteps) + soc_out = np.zeros(self.n_timesteps) + + # Track events used per calendar month so the monthly cap is + # respected across window boundaries. + events_used_per_month = {} + + n_w: int = int(self.config.n_control_window) + # Compute the starting index of each rolling window. + window_start_indices = list(range(0, self.n_timesteps, n_w)) + + for window_start in window_start_indices: + window_len: int = min(n_w, self.n_timesteps - window_start) + month_ids_w = self.month_ids[window_start : window_start + window_len] + # Since this is a rolling horizon, we need to compute the remaining budget + # for the next optimization call + remaining_budget = { + int(m): max( + 0, + self.config.n_max_events + - (events_used_per_month[int(m)] if int(m) in events_used_per_month else 0), + ) + for m in np.unique(month_ids_w) + } + # Construct the MILP for this window + self.dr_model = self._build_dr_model( + window_start=window_start, + window_len=window_len, + init_soc=self.updated_initial_soc, + remaining_budget=remaining_budget, + ) + self.problem_state = DispatchProblemState() + + # Solve the optimzation problem + self.solve_dispatch_model( + start_time=window_start, + n_days=self.n_timesteps // 24, + ) + + # Count new discharge events to track the monthly cap. + for t in range(window_len): + discharging = pyomo.value(self.dr_model.discharge[t]) > 0.5 + prev_discharging = t > 0 and pyomo.value(self.dr_model.discharge[t - 1]) > 0.5 + if discharging and not prev_discharging: + month = int(month_ids_w[t]) + if month not in events_used_per_month: + events_used_per_month[month] = 0 + events_used_per_month[month] += 1 + + # Run the performance model for this window. + storage_out_window, soc_window = performance_model( + self.storage_dispatch_commands, + **performance_model_kwargs, + sim_start_index=window_start, + ) + + # Performance model returns SOC in percent. + self.updated_initial_soc = soc_window[-1] / 100.0 + + for j in range(window_len): + storage_out[window_start + j] = storage_out_window[j] + soc_out[window_start + j] = soc_window[j] + + return storage_out, soc_out + + return pyomo_dispatch_solver + def _parse_peak_window(self) -> tuple: """Parse the ``peak_window`` config entry into ``datetime.time`` objects. + Accepts values either as ``HH:MM:SS`` strings or as plain integers + (seconds since midnight). PyYAML parses unquoted ``HH:MM:SS`` values + as sexagesimal integers, so both forms are equivalent in YAML. + Returns: tuple[datetime.time, datetime.time]: ``(start, end)`` times. Raises: ValueError: If ``'start'`` or ``'end'`` keys are missing, or - if either value is not a string in ``HH:MM:SS`` format. + if a value is neither a valid ``HH:MM:SS`` string nor an integer. """ pw = dict(self.config.peak_window) if "start" not in pw or "end" not in pw: raise ValueError("peak_window must contain 'start' and 'end' keys") for key in ("start", "end"): val = pw[key] - if not isinstance(val, str) or len(val.split(":")) != 3: + if isinstance(val, int | float): + total_seconds = int(val) + hours, remainder = divmod(total_seconds, 3600) + minutes, seconds = divmod(remainder, 60) + pw[key] = datetime.min.replace(hour=hours, minute=minutes, second=seconds).time() + elif isinstance(val, str) and len(val.split(":")) == 3: + pw[key] = datetime.strptime(val, "%H:%M:%S").time() + else: raise ValueError( - f"peak_window {key} value must be a string in HH:MM:SS format, got {val}." + f"peak_window '{key}' must be HH:MM:SS string or integer seconds, got {val!r}." ) - pw[key] = datetime.strptime(val, "%H:%M:%S").time() return pw["start"], pw["end"] def _compute_peak_window_mask(self) -> np.ndarray: @@ -329,113 +459,6 @@ def _compute_event_window_mask( event_mask |= near_peak return event_mask - def pyomo_setup(self, discrete_inputs): - """Return the rolling-horizon dispatch solver callable. - - Args: - discrete_inputs (dict): OpenMDAO discrete inputs. - - Returns: - callable: ``pyomo_dispatch_solver(performance_model, - performance_model_kwargs, inputs)`` that iterates over the - simulation in windows of ``n_control_window`` timesteps. - For each window it: - - 1. Builds a fresh MILP from the window's signal slice. - 2. Solves the MILP with GLPK. - 3. Calls ``performance_model`` with the resulting dispatch - commands. - 4. Carries the terminal SOC into the next window. - - Returns ``(storage_out, soc_out)`` - two ``np.ndarray`` of - length ``n_timesteps``. - """ - - def pyomo_dispatch_solver( - performance_model, - performance_model_kwargs, - inputs, - commodity_name=self.config.commodity, - ): - storage_out = np.zeros(self.n_timesteps) - soc_out = np.zeros(self.n_timesteps) - - # Track events used per calendar month so the monthly cap is - # respected across window boundaries. - events_used_per_month = {} - - n_w: int = int(self.config.n_control_window) - # Compute the starting index of each rolling window. - window_start_indices = list(range(0, self.n_timesteps, n_w)) - - for window_start in window_start_indices: - window_len: int = min(n_w, self.n_timesteps - window_start) - month_ids_w = self.month_ids[window_start : window_start + window_len] - # Since this is a rolling horizon, we need to compute the remaining budget - # for the next optimization call - remaining_budget = { - int(m): max( - 0, - self.config.n_max_events - - (events_used_per_month[int(m)] if int(m) in events_used_per_month else 0), - ) - for m in np.unique(month_ids_w) - } - # Construct the MILP for this window - self.dr_model = self._build_dr_model( - window_start=window_start, - window_len=window_len, - init_soc=self.updated_initial_soc, - remaining_budget=remaining_budget, - ) - self.problem_state = DispatchProblemState() - - # Solve the optimzation problem - self.solve_dispatch_model( - start_time=window_start, - n_days=self.n_timesteps // 24, - ) - - # Count new discharge events to track the monthly cap. - for t in range(window_len): - discharging = pyomo.value(self.dr_model.discharge[t]) > 0.5 - prev_discharging = t > 0 and pyomo.value(self.dr_model.discharge[t - 1]) > 0.5 - if discharging and not prev_discharging: - month = int(month_ids_w[t]) - if month not in events_used_per_month: - events_used_per_month[month] = 0 - events_used_per_month[month] += 1 - - # Run the performance model for this window. - storage_out_window, soc_window = performance_model( - self.storage_dispatch_commands, - **performance_model_kwargs, - sim_start_index=window_start, - ) - - # Performance model returns SOC in percent. - self.updated_initial_soc = soc_window[-1] / 100.0 - - for j in range(window_len): - storage_out[window_start + j] = storage_out_window[j] - soc_out[window_start + j] = soc_window[j] - - return storage_out, soc_out - - return pyomo_dispatch_solver - - def initialize_parameters(self, inputs): - """Sync OpenMDAO inputs into the config. - - Args: - inputs (dict): OpenMDAO inputs dict. Recognized keys are - ``'max_charge_rate'`` and ``'storage_capacity'``. - """ - if "max_charge_rate" in inputs: - object.__setattr__(self.config, "max_charge_rate", float(inputs["max_charge_rate"][0])) - if "storage_capacity" in inputs: - object.__setattr__(self.config, "max_capacity", float(inputs["storage_capacity"][0])) - def _build_dr_model( self, window_start: int, @@ -612,19 +635,6 @@ def solve_dispatch_model(self, start_time: int = 0, n_days: int = 0): pyomo.value(self.dr_model.objective), ) - def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): - """Build the DR dispatch solver and write it to discrete outputs. - - Args: - inputs (dict): OpenMDAO continuous inputs. - outputs (dict): OpenMDAO continuous outputs. - discrete_inputs (dict): OpenMDAO discrete inputs. - discrete_outputs (dict): OpenMDAO discrete outputs. The key - ``'pyomo_dispatch_solver'`` is set to the callable - returned by :meth:`pyomo_setup`. - """ - discrete_outputs["pyomo_dispatch_solver"] = self.pyomo_setup(discrete_inputs) - @staticmethod def glpk_solve_call( pyomo_model: pyomo.ConcreteModel, From 3281afa51aee25f28ee9be6918e0352d18705f3c Mon Sep 17 00:00:00 2001 From: svijaysh Date: Wed, 29 Apr 2026 16:08:40 -0600 Subject: [PATCH 28/55] Add figure in docs --- .../control/figures/plm_optimized_dispatch.png | Bin 0 -> 118277 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 docs/control/figures/plm_optimized_dispatch.png diff --git a/docs/control/figures/plm_optimized_dispatch.png b/docs/control/figures/plm_optimized_dispatch.png new file mode 100644 index 0000000000000000000000000000000000000000..4780661b74e975435d2abdbb342d836ed6b88524 GIT binary patch literal 118277 zcmd43g6VtEly0P@ zzcu(g=Y8Jm`vbnu>pB-Rv-e(mt$W?|``!0Cp{mL<__&m~5C{ZcPF7MK0=Wr;KyKjv 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zb(h~CL36uZqWvOP&Htt>;l1o5R52B&%*pNYwuifR z;lx58r8N>!SSe0yAwVpu)_>gn&2}p-8G!psPwGGjBQ9=9Myqa||Ah-NmKu~~gTM(7 zyrc&!!PW_Zd*zMKf(zg?(~IoQ_CSxF@FC#31oyTHp2D<8kSJyC1$@H-sSb05<~GTM^jE zUdH%C3>67B^IlkM<}SrUeBu8JST(~xsecL~r1Icl>l!7i}W(E;DavhbPP+%ek2TYP3m-{$3X@FU>KXupo$mSA_k-qrU8a>-`KmM4o(xZL=6eQ@i3 diff --git a/docs/control/pyomo_controllers.md b/docs/control/pyomo_controllers.md index 25ed05ec8..b11f42321 100644 --- a/docs/control/pyomo_controllers.md +++ b/docs/control/pyomo_controllers.md @@ -73,13 +73,13 @@ The controller works at any simulation timestep resolution (`dt`). All time-base - $\mathcal{W}$ := `peak_window`: set of timesteps eligible for dispatch (e.g., 12:00--20:00 each day) - $\lambda_*$ := signal threshold = `signal_threshold_percentile`-th percentile of $\lambda_t$ over $\mathcal{W}$ - $\mathcal{E}$ := eligible peak timesteps: $\{t \in \mathcal{W} : \lambda_t \geq \lambda_*\}$, respecting `min_peak_separation` -- $\mathcal{D}$ := dispatch window: union of $\pm\,\tfrac{\text{event\_duration}}{2}$ neighbourhoods around each peak in $\mathcal{E}$ (equals $\mathcal{E}$ when `event_duration` is `null`) +- $\mathcal{D}$ := dispatch window: $\pm\,\tfrac{\text{event\_duration}}{2}$ around each peak in $\mathcal{E}$ - $\gamma$ := `performance_incentive` (\$/kWh): incentive revenue per kWh discharged - $\bar{P}$ := `max_charge_rate` (kW): maximum charge and discharge rate - $E_{\max} :=$ `max_capacity` $\times$ (`max_soc_fraction` $-$ `min_soc_fraction`): usable energy capacity (kWh) - $\eta_c$ := `charge_efficiency`, $\quad \eta_d$ := `discharge_efficiency` - $\overline{\text{SoC}}$ := `max_soc_fraction`, $\quad \underline{\text{SoC}}$ := `min_soc_fraction` -- `n_control_window` := rolling horizon length in hours; converted to $T = \lceil \text{n\_control\_window} / \Delta t \rceil$ timesteps +- `n_control_window_hours` := rolling horizon length in hours; converted to $T = \lceil \text{n\_control\_window\_hours} / \Delta t \rceil$ timesteps - $\mathcal{T} := \{0, 1, \ldots, T-1\}$: timesteps in the current rolling window - $\mathcal{M}_m$ := set of timesteps in month $m$, for $m = 1, \ldots, 12$ - $N_{\max}$ := `n_max_events`: maximum number of discharge events per calendar month @@ -96,8 +96,12 @@ Before the MILP is solved, the dispatch window $\mathcal{D}$ is built in two ste ## Decision Variables -- $u_t \in \{0, 1\}$ := discharge binary: 1 if the battery discharges at timestep $t$, 0 otherwise -- $v_t \in \{0, 1\}$ := charge binary: 1 if the battery charges at timestep $t$, 0 otherwise + +- $u_t \in \{0, 1\}$ := discharge binary: 1 if a discharge event is active at timestep $t$; used for event counting and window feasibility constraints only +- $v_t \in \{0, 1\}$ := charge binary: 1 if a charge event is active at timestep $t$ +- $p_{d,t} \in [0,\, \bar{P}]$ := discharge power (kW) actually dispatched at timestep $t$ +- $p_{c,t} \in [0,\, \bar{P}]$ := charge power (kW) actually consumed at timestep $t$ +- $\text{SoC}_t \in [\underline{\text{SoC}},\, \overline{\text{SoC}}]$ := state of charge (fraction) at timestep $t$ ## Optimization Problem @@ -108,7 +112,7 @@ This optimization is executed for each rolling window. At each window boundary t Maximize total incentive revenue over the window: $$ -\max_{u_t,\, v_t} \quad \gamma \cdot \bar{P} \cdot \Delta t \sum_{t \in \mathcal{T}} u_t +\max_{u_t,\, v_t,\, p_{d,t},\, p_{c,t}} \quad \gamma \cdot \Delta t \sum_{t \in \mathcal{T}} p_{d,t} $$ The factor $\Delta t$ converts power (kW) to energy (kWh), so the objective is correctly scaled at any timestep resolution. @@ -129,10 +133,20 @@ $$ After each window is solved, events are counted via rising-edge detection (a new event begins whenever $u_t = 1$ and $u_{t-1} = 0$) and $B_m$ is decremented accordingly for subsequent windows. -- SoC evolution with charge and discharge: +- Power is zero when the binary is 0, and at most $\bar{P}$ when it is 1: + +$$ +p_{d,t} \leq \bar{P} \cdot u_t \qquad \forall\, t \in \mathcal{T} +$$ + +$$ +p_{c,t} \leq \bar{P} \cdot v_t \qquad \forall\, t \in \mathcal{T} +$$ + +- SoC evolution with continuous charge and discharge power: $$ -\text{SoC}_{t+1} = \text{SoC}_t + \frac{\eta_c \cdot v_t \cdot \bar{P} \cdot \Delta t}{E_{\max}} - \frac{u_t \cdot \bar{P} \cdot \Delta t}{\eta_d \cdot E_{\max}} \qquad \forall\, t \in \mathcal{T} +\text{SoC}_{t} = \text{SoC}_{t-1} + \frac{\eta_c \cdot p_{c,t} \cdot \Delta t}{E_{\max}} - \frac{p_{d,t} \cdot \Delta t}{\eta_d \cdot E_{\max}} \qquad \forall\, t \in \mathcal{T},\, t > 0 $$ - SoC bounds: @@ -153,13 +167,13 @@ $$ v_t = 0 \qquad \forall\, t \in \mathcal{D} $$ -- Binary variables: +- Variable domains: $$ -u_t \in \{0, 1\}, \quad v_t \in \{0, 1\}, \quad \text{SoC}_t \in [\underline{\text{SoC}},\, \overline{\text{SoC}}] \qquad \forall\, t +u_t \in \{0, 1\}, \quad v_t \in \{0, 1\}, \quad p_{d,t},\, p_{c,t} \in [0,\, \bar{P}], \quad \text{SoC}_t \in [\underline{\text{SoC}},\, \overline{\text{SoC}}] \qquad \forall\, t $$ -Example 34 performs the optimization with a real LMP signal. The look-ahead horizon (`n_control_window`) controls how many hours are optimized at once. Larger values improve solution quality but increase solve time. See the figure below for results. +Example 34 performs the optimization with a real LMP signal. The look-ahead horizon (`n_control_window_hours`) controls how many hours are optimized at once. Larger values improve solution quality but increase solve time. See the figure below for results. ![](./figures/plm_optimized_dispatch.png) diff --git a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py index a91b4a647..990b18271 100644 --- a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py +++ b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py @@ -121,4 +121,6 @@ def shade_peaks(ax): plt.tight_layout() -plt.savefig("plm_optimized_dispatch.png", dpi=150, bbox_inches="tight") +plt.savefig( + "examples/34_plm_optimized_dispatch/plm_optimized_dispatch.png", dpi=150, bbox_inches="tight" +) diff --git a/examples/34_plm_optimized_dispatch/tech_config.yaml b/examples/34_plm_optimized_dispatch/tech_config.yaml index 8dfab7d37..0415e6fad 100644 --- a/examples/34_plm_optimized_dispatch/tech_config.yaml +++ b/examples/34_plm_optimized_dispatch/tech_config.yaml @@ -34,9 +34,9 @@ technologies: event_duration: # omit or set to null to use static peak_window units: h # any pandas timedelta unit: h, min, s, val: 4 # battery eligible +/ - val h around the daily peak - # min_peak_separation: # omit or set to null to allow any peak spacing - # units: h - # val: 4 # drop any eligible peak within this distance of a higher peak + min_peak_separation: # omit or set to null to allow any peak spacing + units: h + val: 4 # drop any eligible peak within this distance of a higher peak round_digits: 4 cost_parameters: cost_year: 2024 diff --git a/examples/test/test_all_examples.py b/examples/test/test_all_examples.py index e23356027..d66e4cced 100644 --- a/examples/test/test_all_examples.py +++ b/examples/test/test_all_examples.py @@ -2921,13 +2921,13 @@ def test_plm_optimized_dispatch_example(subtests, temp_copy_of_example): with subtests.test("Check number of discharge events"): # With the given demand profile and battery size, there should be 2 discharge events num_discharge_events = np.sum(battery_power > 1e-3) # Count timesteps with discharge - assert num_discharge_events == 368 + assert num_discharge_events == 588 with subtests.test("Check total energy discharged"): total_energy_discharged = battery_power.sum() * (1 / 60) # kWh, 1 min timestep - assert pytest.approx(total_energy_discharged, rel=1e-2) == 1840.0 + assert pytest.approx(total_energy_discharged, rel=1e-2) == 2428.0 with subtests.test("Check total energy charged"): battery_charge = model.prob.get_val("battery.storage_electricity_charge", units="kW") total_energy_charged = battery_charge.sum() * (1 / 60) # kWh, 1 min timestep - assert pytest.approx(total_energy_charged, rel=1e-3) == -2015.0 + assert pytest.approx(total_energy_charged, rel=1e-3) == -2663.0 diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index f9c7b6e95..70d33942b 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -107,11 +107,10 @@ class PeakLoadManagementOptimizedStorageController(PyomoStorageControllerBaseCla """Demand-response storage controller using a rolling-horizon MILP. Each call to the dispatch solver iterates over the full simulation in - windows of length ``n_control_window_hours``. For each window it receives - the monthly LMP forecast, solves the MILP to maximize incentive - revenue, then passes the resulting dispatch commands to the - performance model. The terminal SOC of each window is carried forward - as the initial SOC of the next window. + windows of length ``n_control_window_hours``. For each window it builds + and solves a MILP that maximises incentive revenue, then passes the + resulting dispatch commands to the performance model. The terminal SOC + of each window is carried forward as the initial SOC of the next window. """ dr_model: Any @@ -300,7 +299,7 @@ def pyomo_dispatch_solver( # Run the performance model for this window. storage_out_window, soc_window = performance_model( - self._get_storage_dispatch_commands(P_max), + self._get_storage_dispatch_commands(), **performance_model_kwargs, sim_start_index=window_start, ) @@ -361,7 +360,7 @@ def _compute_peak_window_mask(self) -> np.ndarray: times = pd.DatetimeIndex(self.time_index).time if end < start: raise ValueError("peak_window end time must be after start time.") - return np.array([start <= t <= end for t in times]) + return np.array([start <= t < end for t in times]) def _compute_month_ids(self) -> np.ndarray: """Return the calendar month index (1-12) for each timestep. @@ -478,10 +477,25 @@ def _build_dr_model( ) -> pyomo.ConcreteModel: """Build the DR MILP for a single rolling window. + Decision variables + ------------------ + discharge[t] : binary + Event indicator — 1 if a discharge event is active at timestep t. + Used for event counting and window feasibility constraints only. + charge[t] : binary + Event indicator — 1 if a charge event is active at timestep t. + p_discharge[t] : continuous in [0, P_max] + Actual discharge power (kW). Linked to the binary via the + McCormick upper-bound constraint ``p_discharge[t] <= P_max * discharge[t]``. + p_charge[t] : continuous in [0, P_max] + Actual charge power (kW). Linked via ``p_charge[t] <= P_max * charge[t]``. + soc[t] : continuous in [soc_min, soc_max] + State of charge (fraction). + Args: - window_start (int): Timestep index of the first hour + window_start (int): Timestep index of the first timestep in this window. - window_len (int): Number of timesteps in this window + window_len (int): Number of timesteps in this window. init_soc (float): State-of-charge fraction at the start of this window. remaining_budget (dict): Mapping of ``month_id (int)`` to @@ -525,12 +539,25 @@ def _build_dr_model( m.T = pyomo.Set(initialize=range(window_len), doc="Timesteps in window") m.M = pyomo.Set(initialize=months_in_window, doc="Months in window") - # Things to solve for + # Binary event indicators — used for event counting and window constraints. m.discharge = pyomo.Var( - m.T, domain=pyomo.Binary, doc="Discharge binary: 1 = discharging at timestep t" + m.T, domain=pyomo.Binary, doc="1 if a discharge event is active at timestep t" ) m.charge = pyomo.Var( - m.T, domain=pyomo.Binary, doc="Charge binary: 1 = charging at timestep t" + m.T, domain=pyomo.Binary, doc="1 if a charge event is active at timestep t" + ) + # Actual kW dispatched each timestep. + m.p_discharge = pyomo.Var( + m.T, + domain=pyomo.NonNegativeReals, + bounds=(0, P_max), + doc="Discharge power (kW) at timestep t", + ) + m.p_charge = pyomo.Var( + m.T, + domain=pyomo.NonNegativeReals, + bounds=(0, P_max), + doc="Charge power (kW) at timestep t", ) m.soc = pyomo.Var( m.T, @@ -539,11 +566,10 @@ def _build_dr_model( doc="State of charge SoC_t", ) - # Incentive revenue is earned for every kWh discharged during the dispatch window, - # so we need the energy at every timestep to be in Kwh + # Incentive revenue is earned for every kWh discharged. dt_hours = self.dt_seconds / 3600.0 m.objective = pyomo.Objective( - expr=-incentive * P_max * dt_hours * sum(m.discharge[t] for t in m.T), + expr=-incentive * dt_hours * sum(m.p_discharge[t] for t in m.T), sense=pyomo.minimize, ) @@ -562,7 +588,7 @@ def _build_dr_model( rule=lambda mdl, t: mdl.discharge[t] <= int(eligible_t_w[t]), ) - # There is limit on the number of events, not discharge timesteps. + # There is a limit on the number of events, not discharge timesteps. # However, we know the number of timesteps in the event from steps_per_event, # so we can indirectly limit the number of discharge timesteps in each month # by multiplying the event cap by steps_per_event. @@ -577,6 +603,16 @@ def max_events_rule(mdl, month): m.max_events = pyomo.Constraint(m.M, rule=max_events_rule) + # Power is zero when the binary is 0, and at most P_max when 1. + m.discharge_power_link = pyomo.Constraint( + m.T, + rule=lambda mdl, t: mdl.p_discharge[t] <= P_max * mdl.discharge[t], + ) + m.charge_power_link = pyomo.Constraint( + m.T, + rule=lambda mdl, t: mdl.p_charge[t] <= P_max * mdl.charge[t], + ) + m.soc_init = pyomo.Constraint(expr=m.soc[0] == init_soc) # Dynamics of the battery SOC evolution. @@ -585,8 +621,8 @@ def soc_evolution_rule(mdl, t): return pyomo.Constraint.Skip return mdl.soc[t] == ( mdl.soc[t - 1] - + eta_c * mdl.charge[t] * P_max * dt_hours / E_max - - mdl.discharge[t] * P_max * dt_hours / (eta_d * E_max) + + eta_c * mdl.p_charge[t] * dt_hours / E_max + - mdl.p_discharge[t] * dt_hours / (eta_d * E_max) ) m.soc_evolution = pyomo.Constraint(m.T, rule=soc_evolution_rule) @@ -597,7 +633,7 @@ def soc_evolution_rule(mdl, t): rule=lambda mdl, t: mdl.discharge[t] + mdl.charge[t] <= 1, ) - # Can't charge in the dispatch window + # Can't charge in the dispatch window. m.no_charge_in_window = pyomo.Constraint( m.T, rule=lambda mdl, t: ( @@ -670,17 +706,14 @@ def glpk_solve_call( results = solver.solve(pyomo_model, options=solver_options.constructed, tee=False) return results - def _get_storage_dispatch_commands(self, P_max: float) -> list: + def _get_storage_dispatch_commands(self) -> list: """Net dispatch commands for the solved window. - Args: - P_max (float): Maximum charge/discharge rate (kW). - Returns: - list[float]: ``(u_t - v_t) * P_max`` for each timestep in - the solved window. Positive = discharge, negative = charge. + list[float]: ``p_discharge_t - p_charge_t`` (kW) for each timestep + in the solved window. Positive = discharge, negative = charge. """ return [ - (pyomo.value(self.dr_model.discharge[t]) - pyomo.value(self.dr_model.charge[t])) * P_max + pyomo.value(self.dr_model.p_discharge[t]) - pyomo.value(self.dr_model.p_charge[t]) # type: ignore[index] for t in self.dr_model.T ] diff --git a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py index d563f369f..942e93123 100644 --- a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py @@ -101,14 +101,13 @@ def test_compute_peak_window_mask(): controller.config = SimpleNamespace(peak_window={"start": "00:00:00", "end": "02:00:00"}) controller.time_index = pd.date_range("2024-01-01", periods=24, freq="h") mask = controller._compute_peak_window_mask() - expected = np.array([i <= 2 for i in range(24)]) + expected = np.array([i < 2 for i in range(24)]) assert isinstance(mask, np.ndarray) assert np.array_equal(mask, expected) @pytest.mark.unit def test_compute_month_ids(): - # Jan 2024: 744h, Feb 2024 (leap year): 696h, Mar 2024: 744h controller = _make_controller() controller.time_index = pd.date_range("2024-01-01", periods=744 + 696 + 744, freq="h") month_ids = controller._compute_month_ids() @@ -242,13 +241,18 @@ def test_optimizer_dispatch_respects_soc_constraints(base_config): PeakLoadManagementOptimizedStorageController.glpk_solve_call(model) - soc = base_config.init_soc_fraction * base_config.max_capacity + E_max = base_config.max_capacity * (base_config.max_soc_fraction - base_config.min_soc_fraction) + eta_c = base_config.charge_efficiency + eta_d = base_config.discharge_efficiency + dt_hours = 3600 / 3600.0 + soc = base_config.init_soc_fraction for t in range(24): - charge = pyomo.value(model.charge[t]) # type: ignore[index] - discharge = pyomo.value(model.discharge[t]) # type: ignore[index] - soc += charge - discharge - assert soc >= base_config.min_soc_fraction * base_config.max_capacity - assert soc <= base_config.max_soc_fraction * base_config.max_capacity + p_charge = pyomo.value(model.p_charge[t]) # type: ignore[index] + p_discharge = pyomo.value(model.p_discharge[t]) # type: ignore[index] + if t > 0: + soc += eta_c * p_charge * dt_hours / E_max - p_discharge * dt_hours / (eta_d * E_max) + assert soc >= base_config.min_soc_fraction - 1e-6 + assert soc <= base_config.max_soc_fraction + 1e-6 @pytest.mark.unit @@ -315,3 +319,29 @@ def test_optimizer_dispatch_respects_charge_discharge_exclusivity(base_config): charge = pyomo.value(model.charge[t]) # type: ignore[index] discharge = pyomo.value(model.discharge[t]) # type: ignore[index] assert not (charge > 0.5 and discharge > 0.5) + + +@pytest.mark.regression +def test_mccormick_power_zero_when_binary_zero(base_config): + """McCormick constraint: p_discharge[t] must be 0 whenever discharge[t] == 0.""" + controller = _make_controller_with_config(base_config) + model = controller._build_dr_model( + window_start=0, + window_len=24, + init_soc=base_config.init_soc_fraction, + remaining_budget={1: base_config.n_max_events}, + P_max=base_config.max_charge_rate, + storage_capacity=base_config.max_capacity, + ) + + PeakLoadManagementOptimizedStorageController.glpk_solve_call(model) + + for t in range(24): + u = pyomo.value(model.discharge[t]) # type: ignore[index] + p_d = pyomo.value(model.p_discharge[t]) # type: ignore[index] + v = pyomo.value(model.charge[t]) # type: ignore[index] + p_c = pyomo.value(model.p_charge[t]) # type: ignore[index] + if u < 0.5: + assert p_d < 1e-6, f"p_discharge[{t}]={p_d} but discharge[{t}]={u}" + if v < 0.5: + assert p_c < 1e-6, f"p_charge[{t}]={p_c} but charge[{t}]={v}" From 406fab9b6bb87e2e724a44f8a7fcbf96991b61f9 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Sun, 3 May 2026 12:53:24 -0600 Subject: [PATCH 36/55] Give user option to either pick inc/event or /kwh --- .../tech_config.yaml | 2 +- .../plm_optimized_storage_controller.py | 35 ++++++++++-- .../test_plm_optimized_storage_controller.py | 54 +++++++++++++++++++ 3 files changed, 85 insertions(+), 6 deletions(-) diff --git a/examples/34_plm_optimized_dispatch/tech_config.yaml b/examples/34_plm_optimized_dispatch/tech_config.yaml index 0415e6fad..f57784cc3 100644 --- a/examples/34_plm_optimized_dispatch/tech_config.yaml +++ b/examples/34_plm_optimized_dispatch/tech_config.yaml @@ -27,7 +27,7 @@ technologies: peak_window: start: 12:00:00 # 2 pm MDT end: 20:00:00 # 6 pm MDT - performance_incentive: 14.0 # $/kWh + performance_incentive_per_event: 16800.0 # $/event (= 14 $/kWh * 4 h * 300 kW) n_max_events: 10 # maximum discharge events per month signal_threshold_percentile: 95.0 # only dispatch when LMP >= this percentile n_control_window_hours: 24 # hours; converted to timesteps using simulation dt diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index 70d33942b..709d5180d 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -35,6 +35,12 @@ class PeakLoadManagementOptimizedControllerConfig(PyomoStorageControllerBaseConf peak_window (dict): Hours eligible for dispatch. Keys ``'start'`` and ``'end'`` must be strings in ``HH:MM:SS`` format. performance_incentive (float): Incentive revenue in $/kWh. + Mutually exclusive with ``performance_incentive_per_event``. + performance_incentive_per_event (float): Incentive revenue in + $/event. Converted internally to an effective $/kWh rate using + ``steps_per_event``, ``dt``, and ``P_max``: + ``incentive_kWh = incentive_event / (steps_per_event * dt_h * P_max)``. + Mutually exclusive with ``performance_incentive``. charge_efficiency (float): Charge efficiency in [0, 1]. Defaults to 1.0. discharge_efficiency (float): Discharge efficiency in [0, 1]. @@ -70,7 +76,8 @@ class PeakLoadManagementOptimizedControllerConfig(PyomoStorageControllerBaseConf max_charge_rate: float = field() supervisory_signal: list = field() peak_window: dict = field() - performance_incentive: float = field() + performance_incentive: float = field(default=None) + performance_incentive_per_event: float = field(default=None) charge_efficiency: float = field(validator=range_val(0, 1), default=1.0) discharge_efficiency: float = field(validator=range_val(0, 1), default=1.0) n_max_events: int = field(default=10) @@ -84,6 +91,19 @@ def __attrs_post_init__(self): self.n_control_window_hours = int(round(self.n_control_window_hours)) super().__attrs_post_init__() + both_set = ( + self.performance_incentive is not None + and self.performance_incentive_per_event is not None + ) + neither_set = ( + self.performance_incentive is None and self.performance_incentive_per_event is None + ) + if both_set or neither_set: + raise ValueError( + "Exactly one of 'performance_incentive' ($/kWh) or " + "'performance_incentive_per_event' ($/event) must be set." + ) + for field_name, value in ( ("event_duration", self.event_duration), ("min_peak_separation", self.min_peak_separation), @@ -108,7 +128,7 @@ class PeakLoadManagementOptimizedStorageController(PyomoStorageControllerBaseCla Each call to the dispatch solver iterates over the full simulation in windows of length ``n_control_window_hours``. For each window it builds - and solves a MILP that maximises incentive revenue, then passes the + and solves a MILP that maximizes incentive revenue, then passes the resulting dispatch commands to the performance model. The terminal SOC of each window is carried forward as the initial SOC of the next window. """ @@ -518,7 +538,13 @@ def _build_dr_model( eta_d = self.config.discharge_efficiency soc_max = self.config.max_soc_fraction soc_min = self.config.min_soc_fraction - incentive = self.config.performance_incentive + dt_hours = self.dt_seconds / 3600.0 + if self.config.performance_incentive_per_event is not None: + incentive = self.config.performance_incentive_per_event / ( + self.steps_per_event * dt_hours * P_max + ) + else: + incentive = self.config.performance_incentive N_max = self.config.n_max_events # Only the timesteps within the current window are relevant @@ -539,7 +565,7 @@ def _build_dr_model( m.T = pyomo.Set(initialize=range(window_len), doc="Timesteps in window") m.M = pyomo.Set(initialize=months_in_window, doc="Months in window") - # Binary event indicators — used for event counting and window constraints. + # Binary event indicators- used for event counting and window constraints. m.discharge = pyomo.Var( m.T, domain=pyomo.Binary, doc="1 if a discharge event is active at timestep t" ) @@ -567,7 +593,6 @@ def _build_dr_model( ) # Incentive revenue is earned for every kWh discharged. - dt_hours = self.dt_seconds / 3600.0 m.objective = pyomo.Objective( expr=-incentive * dt_hours * sum(m.p_discharge[t] for t in m.T), sense=pyomo.minimize, diff --git a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py index 942e93123..98d097500 100644 --- a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py @@ -345,3 +345,57 @@ def test_mccormick_power_zero_when_binary_zero(base_config): assert p_d < 1e-6, f"p_discharge[{t}]={p_d} but discharge[{t}]={u}" if v < 0.5: assert p_c < 1e-6, f"p_charge[{t}]={p_c} but charge[{t}]={v}" + + +@pytest.mark.regression +def test_performance_incentive_per_event_matches_equivalent_kwh_rate(): + """$/event and its equivalent $/kWh rate must produce identical dispatch. + + With event_duration=2h, dt=1h, P_max=1.0 kW: + steps_per_event=2, dt_hours=1.0 + 10.0 $/event / (2 * 1.0 * 1.0) = 5.0 $/kWh + """ + common = { + "max_capacity": 10.0, + "max_soc_fraction": 1.0, + "min_soc_fraction": 0.0, + "init_soc_fraction": 1.0, + "n_control_window_hours": 24, + "commodity": "electricity", + "commodity_rate_units": "kW", + "tech_name": "battery", + "system_commodity_interface_limit": 100.0, + "max_charge_rate": 1.0, + "supervisory_signal": list(range(24)), + "peak_window": {"start": "08:00:00", "end": "18:00:00"}, + "n_max_events": 24, + "signal_threshold_percentile": 0.0, + "event_duration": {"val": 2, "units": "h"}, + } + config_kwh = PeakLoadManagementOptimizedControllerConfig(**common, performance_incentive=5.0) + config_event = PeakLoadManagementOptimizedControllerConfig( + **common, performance_incentive_per_event=10.0 + ) + + build_kwargs = { + "window_start": 0, + "window_len": 24, + "init_soc": 1.0, + "remaining_budget": {1: 24}, + "P_max": 1.0, + "storage_capacity": 10.0, + } + model_kwh = _make_controller_with_config(config_kwh)._build_dr_model(**build_kwargs) + model_event = _make_controller_with_config(config_event)._build_dr_model(**build_kwargs) + + PeakLoadManagementOptimizedStorageController.glpk_solve_call(model_kwh) + PeakLoadManagementOptimizedStorageController.glpk_solve_call(model_event) + + for t in range(24): + assert ( + abs( + pyomo.value(model_kwh.p_discharge[t]) # type: ignore[index] + - pyomo.value(model_event.p_discharge[t]) # type: ignore[index] + ) + < 1e-4 + ), f"dispatch mismatch at t={t}" From cb1740cd93a92e4976663528012d9bc41dbd8688 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Sun, 3 May 2026 12:57:15 -0600 Subject: [PATCH 37/55] Add comments for inc/event conversion --- .../storage/plm_optimized_storage_controller.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index 709d5180d..22d36082f 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -539,6 +539,10 @@ def _build_dr_model( soc_max = self.config.max_soc_fraction soc_min = self.config.min_soc_fraction dt_hours = self.dt_seconds / 3600.0 + + # This converts the incentive from $/event to an effective $/kWh + # rate based on the number of timesteps in an event (steps_per_event), + # the length of each timestep in hours (dt_hours), and the max power (P_max). if self.config.performance_incentive_per_event is not None: incentive = self.config.performance_incentive_per_event / ( self.steps_per_event * dt_hours * P_max From 91c1cbc3e672e3f5f73d63cf8f2c9dc04eb0a692 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Mon, 4 May 2026 09:36:14 -0600 Subject: [PATCH 38/55] Edit docs to include new definitions --- docs/control/pyomo_controllers.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/docs/control/pyomo_controllers.md b/docs/control/pyomo_controllers.md index b11f42321..67692da5a 100644 --- a/docs/control/pyomo_controllers.md +++ b/docs/control/pyomo_controllers.md @@ -72,14 +72,16 @@ The controller works at any simulation timestep resolution (`dt`). All time-base - $\Delta t$ := simulation timestep duration (hours), derived from `dt` in the plant config - $\mathcal{W}$ := `peak_window`: set of timesteps eligible for dispatch (e.g., 12:00--20:00 each day) - $\lambda_*$ := signal threshold = `signal_threshold_percentile`-th percentile of $\lambda_t$ over $\mathcal{W}$ +- `min_peak_separation` := minimum required time between two eligible peaks, expressed as a ``{units, val}`` dict. When set, only the first eligible peak is chosen. - $\mathcal{E}$ := eligible peak timesteps: $\{t \in \mathcal{W} : \lambda_t \geq \lambda_*\}$, respecting `min_peak_separation` -- $\mathcal{D}$ := dispatch window: $\pm\,\tfrac{\text{event\_duration}}{2}$ around each peak in $\mathcal{E}$ -- $\gamma$ := `performance_incentive` (\$/kWh): incentive revenue per kWh discharged +- `event_duration` := total duration of one discharge event, expressed as a ``{units, val}`` dict (e.g. ``{units: h, val: 4}`` for a 4-hour event) +- $\mathcal{D}$ := dispatch window: $\pm$`event_duration`/2 neighbourhoods around each peak in $\mathcal{E}$ (equals $\mathcal{E}$ when `event_duration` is `null`) +- $\gamma$ := incentive revenue per kWh discharged (\$/kWh). Specified directly via `performance_incentive`, or derived from `performance_incentive_per_event` (\$/event) as $\gamma = \gamma_{\text{event}} / (\tau \cdot \Delta t \cdot \bar{P})$ - $\bar{P}$ := `max_charge_rate` (kW): maximum charge and discharge rate - $E_{\max} :=$ `max_capacity` $\times$ (`max_soc_fraction` $-$ `min_soc_fraction`): usable energy capacity (kWh) - $\eta_c$ := `charge_efficiency`, $\quad \eta_d$ := `discharge_efficiency` - $\overline{\text{SoC}}$ := `max_soc_fraction`, $\quad \underline{\text{SoC}}$ := `min_soc_fraction` -- `n_control_window_hours` := rolling horizon length in hours; converted to $T = \lceil \text{n\_control\_window\_hours} / \Delta t \rceil$ timesteps +- `n_control_window_hours` := rolling horizon length in hours; converted to $T =$ `n_control_window_hours` / $\Delta t$ timesteps - $\mathcal{T} := \{0, 1, \ldots, T-1\}$: timesteps in the current rolling window - $\mathcal{M}_m$ := set of timesteps in month $m$, for $m = 1, \ldots, 12$ - $N_{\max}$ := `n_max_events`: maximum number of discharge events per calendar month @@ -92,7 +94,7 @@ Before the MILP is solved, the dispatch window $\mathcal{D}$ is built in two ste **Step 1 : Peak selection:** Within $\mathcal{W}$, timesteps at or above the `signal_threshold_percentile` of $\lambda_t$ are marked eligible: $\mathcal{E} = \{t \in \mathcal{W} : \lambda_t \geq \lambda_*\}$. If `min_peak_separation` is set, only the first peak is chosen. -**Step 2 : Event window expansion:** If `event_duration` is specified, each peak in $\mathcal{E}$ is expanded by $\pm\,\tfrac{\text{event\_duration}}{2}$ timesteps to form $\mathcal{D}$. If `event_duration` is `null`, $\mathcal{D} = \mathcal{E}$. +**Step 2 : Event window expansion:** If `event_duration` is specified, each peak in $\mathcal{E}$ is expanded by $\pm$ `event_duration`/2 timesteps to form $\mathcal{D}$. If `event_duration` is `null`, $\mathcal{D} = \mathcal{E}$. ## Decision Variables From c127b15707f95c49d4ec6ce100542ece320a9c90 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Mon, 4 May 2026 09:40:38 -0600 Subject: [PATCH 39/55] Edit config comments --- examples/34_plm_optimized_dispatch/tech_config.yaml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/examples/34_plm_optimized_dispatch/tech_config.yaml b/examples/34_plm_optimized_dispatch/tech_config.yaml index f57784cc3..4a9a7d909 100644 --- a/examples/34_plm_optimized_dispatch/tech_config.yaml +++ b/examples/34_plm_optimized_dispatch/tech_config.yaml @@ -13,7 +13,7 @@ technologies: commodity: electricity commodity_rate_units: kW max_charge_rate: 300.0 # kW rated power - max_capacity: 2000.0 # kWh usable capacity (4-hour battery) + max_capacity: 2000.0 # kWh usable capacity max_soc_fraction: 0.90 min_soc_fraction: 0.10 init_soc_fraction: 0.90 @@ -25,9 +25,9 @@ technologies: system_commodity_interface_limit: 1.0e9 supervisory_signal: !include demand_profiles/supervisory_signal.yaml peak_window: - start: 12:00:00 # 2 pm MDT - end: 20:00:00 # 6 pm MDT - performance_incentive_per_event: 16800.0 # $/event (= 14 $/kWh * 4 h * 300 kW) + start: 12:00:00 + end: 20:00:00 + performance_incentive_per_event: 16800.0 # $/event n_max_events: 10 # maximum discharge events per month signal_threshold_percentile: 95.0 # only dispatch when LMP >= this percentile n_control_window_hours: 24 # hours; converted to timesteps using simulation dt @@ -40,7 +40,7 @@ technologies: round_digits: 4 cost_parameters: cost_year: 2024 - energy_capex: 408 # $/kWh (2024 ATB) + energy_capex: 408 # $/kWh power_capex: 379 # $/kW opex_fraction: 0.025 grid_buy: From 10aee1465a2794c18df7366a35393508dcbb8868 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Mon, 4 May 2026 11:53:52 -0600 Subject: [PATCH 40/55] Minor edits to configs --- examples/34_plm_optimized_dispatch/driver_config.yaml | 2 +- examples/34_plm_optimized_dispatch/plant_config.yaml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/34_plm_optimized_dispatch/driver_config.yaml b/examples/34_plm_optimized_dispatch/driver_config.yaml index 0b40f01a0..c6e002d88 100644 --- a/examples/34_plm_optimized_dispatch/driver_config.yaml +++ b/examples/34_plm_optimized_dispatch/driver_config.yaml @@ -1,5 +1,5 @@ name: driver_config -description: Driver configuration for PLM MILP-optimized battery dispatch example +description: Driver configuration for Peak Load Management MILP-optimized battery dispatch example general: folder_output: outputs create_om_reports: false diff --git a/examples/34_plm_optimized_dispatch/plant_config.yaml b/examples/34_plm_optimized_dispatch/plant_config.yaml index ab5a8348a..56838464d 100644 --- a/examples/34_plm_optimized_dispatch/plant_config.yaml +++ b/examples/34_plm_optimized_dispatch/plant_config.yaml @@ -1,5 +1,5 @@ name: plant_config -description: Simulation for PLM MILP-optimized battery dispatch +description: Simulation for Peak Load Management MILP-optimized battery dispatch plant: plant_life: 1 simulation: From dec2b050ed441fe08febfdeaabe79e670e95d77e Mon Sep 17 00:00:00 2001 From: svijaysh Date: Mon, 4 May 2026 14:43:06 -0600 Subject: [PATCH 41/55] Add tests for storageperformancemodel --- .../test_plm_optimized_storage_controller.py | 128 ++++++++++++++++++ 1 file changed, 128 insertions(+) diff --git a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py index 98d097500..0080b94e4 100644 --- a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py @@ -3,8 +3,10 @@ import numpy as np import pandas as pd import pytest +import openmdao.api as om import pyomo.environ as pyomo +from h2integrate.storage.storage_performance_model import StoragePerformanceModel from h2integrate.control.control_strategies.storage.plm_optimized_storage_controller import ( PeakLoadManagementOptimizedControllerConfig, PeakLoadManagementOptimizedStorageController, @@ -399,3 +401,129 @@ def test_performance_incentive_per_event_matches_equivalent_kwh_rate(): ) < 1e-4 ), f"dispatch mismatch at t={t}" + + +@pytest.fixture +def om_plant_config(): + return { + "plant": { + "plant_life": 30, + "simulation": { + "n_timesteps": 24, + "dt": 3600, + "timezone": 0, + "start_time": "01/01/2024 00:00:00", + }, + }, + "tech_to_dispatch_connections": [["controller", "storage"]], + } + + +@pytest.fixture +def om_tech_config(): + n = 24 + return { + "model_inputs": { + "shared_parameters": { + "tech_name": "battery", + "commodity": "electricity", + "commodity_rate_units": "kW", + "max_charge_rate": 1.0, + "max_capacity": 10.0, + "max_soc_fraction": 1.0, + "min_soc_fraction": 0.0, + "init_soc_fraction": 1.0, + "charge_efficiency": 1.0, + "discharge_efficiency": 1.0, + }, + "performance_parameters": { + "demand_profile": 10.0, + }, + "control_parameters": { + "system_commodity_interface_limit": 1.0e9, + "supervisory_signal": np.ones(n).tolist(), + "peak_window": {"start": "02:00:00", "end": "04:00:00"}, + "performance_incentive": 10.0, + "n_max_events": 24, + "signal_threshold_percentile": 0.0, + "n_control_window_hours": n, + }, + } + } + + +@pytest.mark.regression +def test_plm_optimized_controller_om_problem_soc_bounds(subtests, om_plant_config, om_tech_config): + """Controller and StoragePerformanceModel wired as an om.Problem respect SOC bounds + and discharge only within the peak window.""" + n = om_plant_config["plant"]["simulation"]["n_timesteps"] + + prob = om.Problem() + + prob.model.add_subsystem( + name="IVC", + subsys=om.IndepVarComp(name="electricity_in", val=np.ones(n), units="kW"), + promotes=["*"], + ) + prob.model.add_subsystem( + "controller", + PeakLoadManagementOptimizedStorageController( + plant_config=om_plant_config, + tech_config=om_tech_config, + ), + promotes=["*"], + ) + prob.model.add_subsystem( + "storage", + StoragePerformanceModel( + plant_config=om_plant_config, + tech_config=om_tech_config, + ), + promotes=["*"], + ) + + prob.setup() + prob.run_model() + + soc = prob.get_val("SOC", units="unitless") + # print + print("SOC:", soc) + discharge = prob.get_val("storage_electricity_discharge", units="kW") + print("Discharge:", discharge) + + with subtests.test("SOC never below min"): + assert np.all(soc >= 0.0 - 1e-1) + + with subtests.test("SOC never above max"): + assert np.all(soc <= 1.0 + 1e-1) + + pw = om_tech_config["model_inputs"]["control_parameters"]["peak_window"] + pw_start = pd.Timestamp(f"2024-01-01 {pw['start']}").time() + pw_end = pd.Timestamp(f"2024-01-01 {pw['end']}").time() + with subtests.test(f"discharge only inside peak window ({pw['start'][:5]}-{pw['end'][:5]})"): + time_index = pd.date_range("2024-01-01", periods=n, freq="h") + for t in range(n): + in_window = pw_start <= time_index[t].time() < pw_end + if not in_window: + assert ( + discharge[t] <= 1e-4 + ), f"discharge {discharge[t]:.4f} kW outside peak window at t={t}" + + with subtests.test("discharge never negative"): + assert np.all(discharge >= -1e-4) + + with subtests.test("discharge never above max_charge_rate"): + assert np.all(discharge <= 1.0 + 1e-4) + + with subtests.test("SOC at t=0 equal to init_soc_fraction"): + assert abs(soc[0] - 1.0) < 1e-4 + + with subtests.test("SOC evolution consistent with discharge and charge"): + shared = om_tech_config["model_inputs"]["shared_parameters"] + E_max = shared["max_capacity"] * (shared["max_soc_fraction"] - shared["min_soc_fraction"]) + charge = prob.get_val("storage_electricity_charge", units="kW") + expected_soc = np.zeros(n) + expected_soc[0] = shared["init_soc_fraction"] + for t in range(1, n): + expected_soc[t] = expected_soc[t - 1] + charge[t] / E_max - discharge[t] / E_max + assert np.allclose(soc, expected_soc, atol=1e-4) From d2c8ac15160715ec1d4d1521b8d483143ae3e730 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Mon, 4 May 2026 16:47:35 -0600 Subject: [PATCH 42/55] Raise Valueerror if min peak separation < event dur --- docs/control/pyomo_controllers.md | 2 +- .../plm_optimized_storage_controller.py | 21 +++++++++++++++++++ 2 files changed, 22 insertions(+), 1 deletion(-) diff --git a/docs/control/pyomo_controllers.md b/docs/control/pyomo_controllers.md index 67692da5a..a97c648be 100644 --- a/docs/control/pyomo_controllers.md +++ b/docs/control/pyomo_controllers.md @@ -176,6 +176,6 @@ u_t \in \{0, 1\}, \quad v_t \in \{0, 1\}, \quad p_{d,t},\, p_{c,t} \in [0,\, \ba $$ -Example 34 performs the optimization with a real LMP signal. The look-ahead horizon (`n_control_window_hours`) controls how many hours are optimized at once. Larger values improve solution quality but increase solve time. See the figure below for results. +Example 34 performs the optimization with a synthetic LMP signal. The look-ahead horizon (`n_control_window_hours`) controls how many hours are optimized at once. Larger values improve solution quality but increase solve time. See the figure below for results. ![](./figures/plm_optimized_dispatch.png) diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index 22d36082f..e7215a2dc 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -122,6 +122,27 @@ def __attrs_post_init__(self): f"(int or float), got {type(value['val']).__name__}." ) + if self.event_duration and self.min_peak_separation: + event_steps = ( + pd.Timedelta( + value=self.event_duration["val"], + unit=self.event_duration["units"], + ).total_seconds() + / self.dt_seconds + ) + separation_steps = ( + pd.Timedelta( + value=self.min_peak_separation["val"], + unit=self.min_peak_separation["units"], + ).total_seconds() + / self.dt_seconds + ) + if separation_steps < event_steps: + raise ValueError( + "min_peak_separation must be greater than or equal to event_duration to avoid " + "conflicts between the two parameters." + ) + class PeakLoadManagementOptimizedStorageController(PyomoStorageControllerBaseClass): """Demand-response storage controller using a rolling-horizon MILP. From 4d8656a79918bb21b20af360d75f34a7969df5bd Mon Sep 17 00:00:00 2001 From: svijaysh Date: Mon, 4 May 2026 16:53:23 -0600 Subject: [PATCH 43/55] Remove Valueerror if min peak separation < event dur --- .../plm_optimized_storage_controller.py | 21 ------------------- 1 file changed, 21 deletions(-) diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index e7215a2dc..22d36082f 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -122,27 +122,6 @@ def __attrs_post_init__(self): f"(int or float), got {type(value['val']).__name__}." ) - if self.event_duration and self.min_peak_separation: - event_steps = ( - pd.Timedelta( - value=self.event_duration["val"], - unit=self.event_duration["units"], - ).total_seconds() - / self.dt_seconds - ) - separation_steps = ( - pd.Timedelta( - value=self.min_peak_separation["val"], - unit=self.min_peak_separation["units"], - ).total_seconds() - / self.dt_seconds - ) - if separation_steps < event_steps: - raise ValueError( - "min_peak_separation must be greater than or equal to event_duration to avoid " - "conflicts between the two parameters." - ) - class PeakLoadManagementOptimizedStorageController(PyomoStorageControllerBaseClass): """Demand-response storage controller using a rolling-horizon MILP. From 9e359a3b44bab5be876e5c5dc8b1aa4bed26ece1 Mon Sep 17 00:00:00 2001 From: Sanjana Vijayshankar <59975769+vijay092@users.noreply.github.com> Date: Tue, 5 May 2026 09:37:19 -0600 Subject: [PATCH 44/55] Update h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py Co-authored-by: genevievestarke <103534902+genevievestarke@users.noreply.github.com> --- .../storage/plm_optimized_storage_controller.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index 22d36082f..fcf4255d9 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -248,7 +248,7 @@ def pyomo_setup(self, discrete_inputs, om_inputs): For each window it: 1. Builds a fresh MILP from the window's signal slice. - 2. Solves the MILP with GLPK. + 2. Solves the MILP with a MILP solver 3. Calls ``performance_model`` with the resulting dispatch commands. 4. Carries the terminal SOC into the next window. From eda288ae394999ceccd0ebbf2f0dc144ef8c8a29 Mon Sep 17 00:00:00 2001 From: Sanjana Vijayshankar <59975769+vijay092@users.noreply.github.com> Date: Tue, 5 May 2026 09:37:39 -0600 Subject: [PATCH 45/55] Update h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py Co-authored-by: genevievestarke <103534902+genevievestarke@users.noreply.github.com> --- .../storage/plm_optimized_storage_controller.py | 1 + 1 file changed, 1 insertion(+) diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index fcf4255d9..c69f78a33 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -569,6 +569,7 @@ def _build_dr_model( m.T = pyomo.Set(initialize=range(window_len), doc="Timesteps in window") m.M = pyomo.Set(initialize=months_in_window, doc="Months in window") +## Variables # Binary event indicators- used for event counting and window constraints. m.discharge = pyomo.Var( m.T, domain=pyomo.Binary, doc="1 if a discharge event is active at timestep t" From ae7eb89b260be99c426462a242b103caec94e861 Mon Sep 17 00:00:00 2001 From: Sanjana Vijayshankar <59975769+vijay092@users.noreply.github.com> Date: Tue, 5 May 2026 09:37:53 -0600 Subject: [PATCH 46/55] Update h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py Co-authored-by: genevievestarke <103534902+genevievestarke@users.noreply.github.com> --- .../storage/plm_optimized_storage_controller.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index c69f78a33..44b874c29 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -684,7 +684,7 @@ def solve_dispatch_model(self, start_time: int = 0, n_days: int = 0): Defaults to 0. Raises: - RuntimeError: If GLPK returns a not OK status or an + RuntimeError: If solver returns a not OK status or an unacceptable termination condition. """ From d4511d26eb27e54e03508863dac14b327ea851e0 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Tue, 5 May 2026 09:53:43 -0600 Subject: [PATCH 47/55] Apply pre-commit formatting --- .../storage/plm_optimized_storage_controller.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index 44b874c29..bf9572527 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -569,7 +569,7 @@ def _build_dr_model( m.T = pyomo.Set(initialize=range(window_len), doc="Timesteps in window") m.M = pyomo.Set(initialize=months_in_window, doc="Months in window") -## Variables + ## Variables # Binary event indicators- used for event counting and window constraints. m.discharge = pyomo.Var( m.T, domain=pyomo.Binary, doc="1 if a discharge event is active at timestep t" From a29087658b0b3b570d3565056fd5ca7c4f22d2d7 Mon Sep 17 00:00:00 2001 From: Sanjana Vijayshankar <59975769+vijay092@users.noreply.github.com> Date: Tue, 5 May 2026 15:51:04 -0600 Subject: [PATCH 48/55] Update docs/control/pyomo_controllers.md Co-authored-by: Jared Thomas --- docs/control/pyomo_controllers.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/control/pyomo_controllers.md b/docs/control/pyomo_controllers.md index a97c648be..cf92432b2 100644 --- a/docs/control/pyomo_controllers.md +++ b/docs/control/pyomo_controllers.md @@ -61,7 +61,7 @@ tech_to_dispatch_connections: [ # Optimized Demand Response Controller -This controller optimizes the dispatch of a Battery Energy Storage System (BESS) based on a pre-defined supervisory signal. This signal could be the Locational Marginal Price (LMP), a demand profile, or a $LMP\times demand$ product depending on the application. The objective is to maximize incentive payments to the battery, subject to constraints on the maximum number of dispatch events per month and on the battery state of charge. +This controller optimizes the dispatch of a Battery Energy Storage System (BESS) based on a pre-defined supervisory signal. This signal could be the Locational Marginal Price (LMP), a demand profile, or an $LMP\times demand$ product depending on the application. The objective is to maximize incentive payments to the battery, subject to constraints on the maximum number of dispatch events per month and on the battery state of charge. The controller works at any simulation timestep resolution (`dt`). All time-based parameters ( `event_duration`, `min_peak_separation`) are specified in physical time units (hours, minutes, etc.) and are internally converted to timesteps using `dt`. From 9bd18342eae859cd2899a441cd0fdf26a4de61d5 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Wed, 6 May 2026 08:28:53 -0600 Subject: [PATCH 49/55] Fix SOC min max notation --- docs/control/pyomo_controllers.md | 8 ++++---- .../storage/plm_optimized_storage_controller.py | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/control/pyomo_controllers.md b/docs/control/pyomo_controllers.md index a97c648be..8ed2bb497 100644 --- a/docs/control/pyomo_controllers.md +++ b/docs/control/pyomo_controllers.md @@ -80,7 +80,7 @@ The controller works at any simulation timestep resolution (`dt`). All time-base - $\bar{P}$ := `max_charge_rate` (kW): maximum charge and discharge rate - $E_{\max} :=$ `max_capacity` $\times$ (`max_soc_fraction` $-$ `min_soc_fraction`): usable energy capacity (kWh) - $\eta_c$ := `charge_efficiency`, $\quad \eta_d$ := `discharge_efficiency` -- $\overline{\text{SoC}}$ := `max_soc_fraction`, $\quad \underline{\text{SoC}}$ := `min_soc_fraction` +- $\text{SoC}_{\max}$ := `max_soc_fraction`, $\quad \text{SoC}_{\min}$ := `min_soc_fraction` - `n_control_window_hours` := rolling horizon length in hours; converted to $T =$ `n_control_window_hours` / $\Delta t$ timesteps - $\mathcal{T} := \{0, 1, \ldots, T-1\}$: timesteps in the current rolling window - $\mathcal{M}_m$ := set of timesteps in month $m$, for $m = 1, \ldots, 12$ @@ -103,7 +103,7 @@ Before the MILP is solved, the dispatch window $\mathcal{D}$ is built in two ste - $v_t \in \{0, 1\}$ := charge binary: 1 if a charge event is active at timestep $t$ - $p_{d,t} \in [0,\, \bar{P}]$ := discharge power (kW) actually dispatched at timestep $t$ - $p_{c,t} \in [0,\, \bar{P}]$ := charge power (kW) actually consumed at timestep $t$ -- $\text{SoC}_t \in [\underline{\text{SoC}},\, \overline{\text{SoC}}]$ := state of charge (fraction) at timestep $t$ +- $\text{SoC}_t \in [\text{SoC}_{\min},\, \text{SoC}_{\max}]$ := state of charge (fraction) at timestep $t$ ## Optimization Problem @@ -154,7 +154,7 @@ $$ - SoC bounds: $$ -\underline{\text{SoC}} \leq \text{SoC}_t \leq \overline{\text{SoC}} \qquad \forall\, t \in \mathcal{T} +\text{SoC}_{\min} \leq \text{SoC}_t \leq \text{SoC}_{\max} \qquad \forall\, t \in \mathcal{T} $$ - No simultaneous charge and discharge: @@ -172,7 +172,7 @@ $$ - Variable domains: $$ -u_t \in \{0, 1\}, \quad v_t \in \{0, 1\}, \quad p_{d,t},\, p_{c,t} \in [0,\, \bar{P}], \quad \text{SoC}_t \in [\underline{\text{SoC}},\, \overline{\text{SoC}}] \qquad \forall\, t +u_t \in \{0, 1\}, \quad v_t \in \{0, 1\}, \quad p_{d,t},\, p_{c,t} \in [0,\, \bar{P}], \quad \text{SoC}_t \in [\text{SoC}_{\min},\, \text{SoC}_{\max}] \qquad \forall\, t $$ diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index bf9572527..b1260203b 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -304,7 +304,7 @@ def pyomo_dispatch_solver( # Solve the optimzation problem self.solve_dispatch_model( start_time=window_start, - n_days=self.n_timesteps // 24, + n_days=int(round(self.n_timesteps * self.dt_seconds / 86400)), ) # Count new discharge events to track the monthly cap. From b8d8182749494e252db3002c74394a7b6523c41a Mon Sep 17 00:00:00 2001 From: svijaysh Date: Wed, 6 May 2026 09:21:46 -0600 Subject: [PATCH 50/55] Change round to ceil in steps_per_event calc --- docs/control/pyomo_controllers.md | 18 +++++++++--------- .../plm_optimized_storage_controller.py | 15 +++++++-------- 2 files changed, 16 insertions(+), 17 deletions(-) diff --git a/docs/control/pyomo_controllers.md b/docs/control/pyomo_controllers.md index 3108f03cf..21d644ce1 100644 --- a/docs/control/pyomo_controllers.md +++ b/docs/control/pyomo_controllers.md @@ -76,8 +76,8 @@ The controller works at any simulation timestep resolution (`dt`). All time-base - $\mathcal{E}$ := eligible peak timesteps: $\{t \in \mathcal{W} : \lambda_t \geq \lambda_*\}$, respecting `min_peak_separation` - `event_duration` := total duration of one discharge event, expressed as a ``{units, val}`` dict (e.g. ``{units: h, val: 4}`` for a 4-hour event) - $\mathcal{D}$ := dispatch window: $\pm$`event_duration`/2 neighbourhoods around each peak in $\mathcal{E}$ (equals $\mathcal{E}$ when `event_duration` is `null`) -- $\gamma$ := incentive revenue per kWh discharged (\$/kWh). Specified directly via `performance_incentive`, or derived from `performance_incentive_per_event` (\$/event) as $\gamma = \gamma_{\text{event}} / (\tau \cdot \Delta t \cdot \bar{P})$ -- $\bar{P}$ := `max_charge_rate` (kW): maximum charge and discharge rate +- $\gamma$ := incentive revenue per kWh discharged (\$/kWh). Specified directly via `performance_incentive`, or derived from `performance_incentive_per_event` (\$/event) as $\gamma = \gamma_{\text{event}} / (\tau \cdot \Delta t \cdot P_{\max})$ +- $P_{\max}$ := `max_charge_rate` (kW): maximum charge and discharge rate - $E_{\max} :=$ `max_capacity` $\times$ (`max_soc_fraction` $-$ `min_soc_fraction`): usable energy capacity (kWh) - $\eta_c$ := `charge_efficiency`, $\quad \eta_d$ := `discharge_efficiency` - $\text{SoC}_{\max}$ := `max_soc_fraction`, $\quad \text{SoC}_{\min}$ := `min_soc_fraction` @@ -101,8 +101,8 @@ Before the MILP is solved, the dispatch window $\mathcal{D}$ is built in two ste - $u_t \in \{0, 1\}$ := discharge binary: 1 if a discharge event is active at timestep $t$; used for event counting and window feasibility constraints only - $v_t \in \{0, 1\}$ := charge binary: 1 if a charge event is active at timestep $t$ -- $p_{d,t} \in [0,\, \bar{P}]$ := discharge power (kW) actually dispatched at timestep $t$ -- $p_{c,t} \in [0,\, \bar{P}]$ := charge power (kW) actually consumed at timestep $t$ +- $p_{d,t} \in [0,\, P_{\max}]$ := discharge power (kW) actually dispatched at timestep $t$ +- $p_{c,t} \in [0,\, P_{\max}]$ := charge power (kW) actually consumed at timestep $t$ - $\text{SoC}_t \in [\text{SoC}_{\min},\, \text{SoC}_{\max}]$ := state of charge (fraction) at timestep $t$ ## Optimization Problem @@ -114,7 +114,7 @@ This optimization is executed for each rolling window. At each window boundary t Maximize total incentive revenue over the window: $$ -\max_{u_t,\, v_t,\, p_{d,t},\, p_{c,t}} \quad \gamma \cdot \Delta t \sum_{t \in \mathcal{T}} p_{d,t} +\max_{u_t, v_t,p_{d,t}, p_{c,t}} \quad \gamma \cdot \Delta t \sum_{t \in \mathcal{T}} p_{d,t} $$ The factor $\Delta t$ converts power (kW) to energy (kWh), so the objective is correctly scaled at any timestep resolution. @@ -135,14 +135,14 @@ $$ After each window is solved, events are counted via rising-edge detection (a new event begins whenever $u_t = 1$ and $u_{t-1} = 0$) and $B_m$ is decremented accordingly for subsequent windows. -- Power is zero when the binary is 0, and at most $\bar{P}$ when it is 1: +- Power is zero when the binary is 0, and at most $P_{\max}$ when it is 1: $$ -p_{d,t} \leq \bar{P} \cdot u_t \qquad \forall\, t \in \mathcal{T} +p_{d,t} \leq P_{\max} \cdot u_t \qquad \forall\, t \in \mathcal{T} $$ $$ -p_{c,t} \leq \bar{P} \cdot v_t \qquad \forall\, t \in \mathcal{T} +p_{c,t} \leq P_{\max} \cdot v_t \qquad \forall\, t \in \mathcal{T} $$ - SoC evolution with continuous charge and discharge power: @@ -172,7 +172,7 @@ $$ - Variable domains: $$ -u_t \in \{0, 1\}, \quad v_t \in \{0, 1\}, \quad p_{d,t},\, p_{c,t} \in [0,\, \bar{P}], \quad \text{SoC}_t \in [\text{SoC}_{\min},\, \text{SoC}_{\max}] \qquad \forall\, t +u_t \in \{0, 1\}, \quad v_t \in \{0, 1\}, \quad p_{d,t},\, p_{c,t} \in [0,\, P_{\max}], \quad \text{SoC}_t \in [\text{SoC}_{\min},\, \text{SoC}_{\max}] \qquad \forall\, t $$ diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index b1260203b..07a6f411c 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -1,3 +1,4 @@ +import math from typing import Any from datetime import datetime @@ -194,14 +195,12 @@ def setup(self): if self.config.event_duration is not None: self.steps_per_event: int = max( 1, - int( - round( - pd.Timedelta( - value=self.config.event_duration["val"], - unit=self.config.event_duration["units"], - ).total_seconds() - / self.dt_seconds - ) + math.ceil( + pd.Timedelta( + value=self.config.event_duration["val"], + unit=self.config.event_duration["units"], + ).total_seconds() + / self.dt_seconds ), ) else: From 05ba4c65430cda14f5cc78d1a8ef21dbb855bda1 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Wed, 6 May 2026 13:43:11 -0600 Subject: [PATCH 51/55] Add subtests --- docs/control/pyomo_controllers.md | 2 +- .../test_plm_optimized_storage_controller.py | 167 ++++++++++-------- 2 files changed, 92 insertions(+), 77 deletions(-) diff --git a/docs/control/pyomo_controllers.md b/docs/control/pyomo_controllers.md index 21d644ce1..482dd1146 100644 --- a/docs/control/pyomo_controllers.md +++ b/docs/control/pyomo_controllers.md @@ -70,7 +70,7 @@ The controller works at any simulation timestep resolution (`dt`). All time-base **Given:** - $\lambda_t$ := `supervisory_signal`: price, demand, or price $\times$ demand time series at timestep $t$ - $\Delta t$ := simulation timestep duration (hours), derived from `dt` in the plant config -- $\mathcal{W}$ := `peak_window`: set of timesteps eligible for dispatch (e.g., 12:00--20:00 each day) +- $\mathcal{W}$ := `peak_window`: set of timesteps eligible for dispatch (e.g., 12:00-20:00 each day) - $\lambda_*$ := signal threshold = `signal_threshold_percentile`-th percentile of $\lambda_t$ over $\mathcal{W}$ - `min_peak_separation` := minimum required time between two eligible peaks, expressed as a ``{units, val}`` dict. When set, only the first eligible peak is chosen. - $\mathcal{E}$ := eligible peak timesteps: $\{t \in \mathcal{W} : \lambda_t \geq \lambda_*\}$, respecting `min_peak_separation` diff --git a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py index 0080b94e4..aa056ba2b 100644 --- a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py @@ -67,16 +67,18 @@ def base_config(): @pytest.mark.unit -def test_parse_peak_window(): +def test_parse_peak_window(subtests): controller = _make_controller() controller.config = SimpleNamespace(peak_window={"start": "08:00:00", "end": "18:40:20"}) start, end = controller._parse_peak_window() - assert start.hour == 8 - assert start.minute == 0 - assert start.second == 0 - assert end.hour == 18 - assert end.minute == 40 - assert end.second == 20 + with subtests.test("start time"): + assert start.hour == 8 + assert start.minute == 0 + assert start.second == 0 + with subtests.test("end time"): + assert end.hour == 18 + assert end.minute == 40 + assert end.second == 20 @pytest.mark.unit @@ -98,14 +100,16 @@ def test_parse_peak_window_missing_key_raises(): @pytest.mark.unit -def test_compute_peak_window_mask(): +def test_compute_peak_window_mask(subtests): controller = _make_controller() controller.config = SimpleNamespace(peak_window={"start": "00:00:00", "end": "02:00:00"}) controller.time_index = pd.date_range("2024-01-01", periods=24, freq="h") mask = controller._compute_peak_window_mask() expected = np.array([i < 2 for i in range(24)]) - assert isinstance(mask, np.ndarray) - assert np.array_equal(mask, expected) + with subtests.test("Returns ndarray"): + assert isinstance(mask, np.ndarray) + with subtests.test("Correct values"): + assert np.array_equal(mask, expected) @pytest.mark.unit @@ -114,20 +118,24 @@ def test_compute_month_ids(): controller.time_index = pd.date_range("2024-01-01", periods=744 + 696 + 744, freq="h") month_ids = controller._compute_month_ids() expected = np.array([1] * 744 + [2] * 696 + [3] * 744) - assert np.array_equal(month_ids, expected) + assert np.array_equal(month_ids, expected), f"Expected {expected} but got {month_ids}" @pytest.mark.unit -def test_compute_eligible_mask_zero_percentile_all_eligible(): +def test_compute_eligible_mask_zero_percentile_all_eligible(subtests): """All timesteps are eligible when signal_threshold_percentile=0.""" controller = _make_controller() controller.config = SimpleNamespace(signal_threshold_percentile=0.0, min_peak_separation=None) signal = np.array([1.0, 5.0, 3.0, 2.0, 8.0]) mask = controller._compute_eligible_mask(signal) - assert isinstance(mask, np.ndarray) - assert mask.dtype == bool - assert len(mask) == len(signal) - assert mask.all() + with subtests.test("_compute_eligible_mask Returns ndarray"): + assert isinstance(mask, np.ndarray) + with subtests.test("_compute_eligible_mask returns Bool dtype"): + assert mask.dtype == bool + with subtests.test("_compute_eligible_mask returns correct length"): + assert len(mask) == len(signal) + with subtests.test("_compute_eligible_mask marks all as eligible when percentile=0`"): + assert mask.all() @pytest.mark.unit @@ -138,7 +146,7 @@ def test_compute_eligible_mask_50th_percentile(): signal = np.array([1.0, 2.0, 3.0, 4.0, 5.0]) mask = controller._compute_eligible_mask(signal) expected = signal >= np.percentile(signal, 50.0) - assert np.array_equal(mask, expected) + assert np.array_equal(mask, expected), f"Expected {expected} but got {mask}" @pytest.mark.unit @@ -149,7 +157,7 @@ def test_compute_eligible_mask_100th_percentile_only_max_eligible(): signal = np.array([1.0, 2.0, 10.0, 3.0, 10.0]) mask = controller._compute_eligible_mask(signal) expected = np.array([False, False, True, False, True]) - assert np.array_equal(mask, expected) + assert np.array_equal(mask, expected), f"Expected {expected} but got {mask}" @pytest.mark.unit @@ -159,13 +167,12 @@ def test_compute_eligible_mask_uniform_signal_all_eligible(): controller.config = SimpleNamespace(signal_threshold_percentile=75.0, min_peak_separation=None) signal = np.full(10, 5.0) mask = controller._compute_eligible_mask(signal) - assert mask.all() + assert mask.all(), f"Expected all True but got {mask}" @pytest.mark.regression -def test_optimizer_dispatch_only_in_peak_window(base_config): +def test_optimizer_dispatch_only_in_peak_window(subtests, base_config): """Solver must never set 1 outside the peak window.""" - # controller = _make_controller_with_config(base_config) model = controller._build_dr_model( window_start=0, @@ -179,16 +186,16 @@ def test_optimizer_dispatch_only_in_peak_window(base_config): PeakLoadManagementOptimizedStorageController.glpk_solve_call(model) peak_start, peak_end = controller._parse_peak_window() - print("Peak window:", peak_start, "-", peak_end) for t in range(24): hour = pd.Timestamp("2024-01-01") + pd.Timedelta(hours=t) in_window = peak_start <= hour.time() <= peak_end if not in_window: - assert pyomo.value(model.discharge[t]) < 1e-3 # type: ignore[index] + with subtests.test(f"No discharge outside peak window at t={t}"): + assert pyomo.value(model.discharge[t]) < 1e-3 # type: ignore[index] @pytest.mark.regression -def test_optimizer_dispatch_only_on_eligible_timesteps(base_config): +def test_optimizer_dispatch_only_on_eligible_timesteps(subtests, base_config): """Solver must never set 1 on ineligible timesteps.""" controller = _make_controller_with_config(base_config) model = controller._build_dr_model( @@ -206,7 +213,8 @@ def test_optimizer_dispatch_only_on_eligible_timesteps(base_config): eligible_mask = controller._compute_eligible_mask(signal) for t in range(24): if not eligible_mask[t]: - assert pyomo.value(model.discharge[t]) < 1e-3 # type: ignore[index] + with subtests.test(f"No discharge on ineligible timestep at t={t}"): + assert pyomo.value(model.discharge[t]) < 1e-3 # type: ignore[index] @pytest.mark.regression @@ -217,7 +225,7 @@ def test_optimizer_dispatch_respects_event_budget(base_config): window_start=0, window_len=24, init_soc=base_config.init_soc_fraction, - remaining_budget={1: base_config.n_max_events}, + remaining_budget={1: 4}, P_max=base_config.max_charge_rate, storage_capacity=base_config.max_capacity, ) @@ -225,11 +233,11 @@ def test_optimizer_dispatch_respects_event_budget(base_config): PeakLoadManagementOptimizedStorageController.glpk_solve_call(model) total_events = sum(pyomo.value(model.discharge[t]) for t in range(24)) # type: ignore[index] - assert total_events <= base_config.n_max_events + 1e-3 + assert total_events <= 4 + 1e-3, f"Total events {total_events} exceeds budget of 4" @pytest.mark.regression -def test_optimizer_dispatch_respects_soc_constraints(base_config): +def test_optimizer_dispatch_respects_soc_constraints(subtests, base_config): """Solver must never violate SOC constraints.""" controller = _make_controller_with_config(base_config) model = controller._build_dr_model( @@ -253,12 +261,13 @@ def test_optimizer_dispatch_respects_soc_constraints(base_config): p_discharge = pyomo.value(model.p_discharge[t]) # type: ignore[index] if t > 0: soc += eta_c * p_charge * dt_hours / E_max - p_discharge * dt_hours / (eta_d * E_max) - assert soc >= base_config.min_soc_fraction - 1e-6 - assert soc <= base_config.max_soc_fraction + 1e-6 + with subtests.test(f"SOC in bounds at t={t}"): + assert soc >= base_config.min_soc_fraction - 1e-6 + assert soc <= base_config.max_soc_fraction + 1e-6 @pytest.mark.unit -def test_compute_eligible_mask_min_peak_separation_drops_nearby_peak(): +def test_compute_eligible_mask_min_peak_separation_drops_nearby_peak(subtests): """A later peak within min_peak_separation of an earlier peak is dropped.""" controller = _make_controller() controller.config = SimpleNamespace( @@ -268,13 +277,14 @@ def test_compute_eligible_mask_min_peak_separation_drops_nearby_peak(): controller.dt_seconds = 3600 signal = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 1.0, 8.0, 1.0, 1.0]) mask = controller._compute_eligible_mask(signal) - print(mask) - assert mask[0], "first peak should be kept" - assert not mask[1], "later peak within separation should be dropped" + with subtests.test("First eligible peak kept"): + assert mask[0] + with subtests.test("Adjacent peak within separation dropped"): + assert not mask[1] @pytest.mark.unit -def test_compute_eligible_mask_min_peak_separation_keeps_far_peaks(): +def test_compute_eligible_mask_min_peak_separation_keeps_far_peaks(subtests): """Peaks separated by more than min_peak_separation are both kept.""" controller = _make_controller() controller.config = SimpleNamespace( @@ -282,11 +292,13 @@ def test_compute_eligible_mask_min_peak_separation_keeps_far_peaks(): min_peak_separation={"units": "h", "val": 3}, ) controller.dt_seconds = 3600 - # t=2 (signal=10) and t=6 (signal=8) are 4h apart — above the 3h threshold + # t=2 (signal=10) and t=6 (signal=8) are 4h apart signal = np.array([1.0, 1.0, 10.0, 1.0, 1.0, 1.0, 8.0, 1.0, 1.0, 1.0]) mask = controller._compute_eligible_mask(signal) - assert mask[2], "first peak should be kept" - assert mask[6], "second peak far enough away should also be kept" + with subtests.test("First peak kept"): + assert mask[2] + with subtests.test("Distant peak also kept"): + assert mask[6] @pytest.mark.unit @@ -299,18 +311,18 @@ def test_compute_eligible_mask_min_peak_separation_none_no_pruning(): ) signal = np.array([1.0, 5.0, 6.0, 5.0, 1.0]) mask = controller._compute_eligible_mask(signal) - assert mask.all() + assert mask.all(), f"Expected all True but got {mask}" @pytest.mark.regression -def test_optimizer_dispatch_respects_charge_discharge_exclusivity(base_config): +def test_optimizer_dispatch_respects_charge_discharge_exclusivity(subtests, base_config): """Solver must never set charge and discharge to 1 in the same timestep.""" controller = _make_controller_with_config(base_config) model = controller._build_dr_model( window_start=0, window_len=24, init_soc=base_config.init_soc_fraction, - remaining_budget={2: base_config.n_max_events}, + remaining_budget={1: base_config.n_max_events}, P_max=base_config.max_charge_rate, storage_capacity=base_config.max_capacity, ) @@ -320,12 +332,13 @@ def test_optimizer_dispatch_respects_charge_discharge_exclusivity(base_config): for t in range(24): charge = pyomo.value(model.charge[t]) # type: ignore[index] discharge = pyomo.value(model.discharge[t]) # type: ignore[index] - assert not (charge > 0.5 and discharge > 0.5) + with subtests.test(f"No simultaneous charge and discharge at t={t}"): + assert not (charge > 0.5 and discharge > 0.5) @pytest.mark.regression -def test_mccormick_power_zero_when_binary_zero(base_config): - """McCormick constraint: p_discharge[t] must be 0 whenever discharge[t] == 0.""" +def test_power_zero_when_binary_zero(subtests, base_config): + """p_discharge[t] must be 0 whenever discharge[t] == 0.""" controller = _make_controller_with_config(base_config) model = controller._build_dr_model( window_start=0, @@ -343,14 +356,15 @@ def test_mccormick_power_zero_when_binary_zero(base_config): p_d = pyomo.value(model.p_discharge[t]) # type: ignore[index] v = pyomo.value(model.charge[t]) # type: ignore[index] p_c = pyomo.value(model.p_charge[t]) # type: ignore[index] - if u < 0.5: - assert p_d < 1e-6, f"p_discharge[{t}]={p_d} but discharge[{t}]={u}" - if v < 0.5: - assert p_c < 1e-6, f"p_charge[{t}]={p_c} but charge[{t}]={v}" + with subtests.test(f"Constraints at t={t}"): + if u < 0.5: + assert p_d < 1e-6, f"p_discharge[{t}]={p_d} but discharge[{t}]={u}" + if v < 0.5: + assert p_c < 1e-6, f"p_charge[{t}]={p_c} but charge[{t}]={v}" @pytest.mark.regression -def test_performance_incentive_per_event_matches_equivalent_kwh_rate(): +def test_performance_incentive_per_event_matches_equivalent_kwh_rate(subtests): """$/event and its equivalent $/kWh rate must produce identical dispatch. With event_duration=2h, dt=1h, P_max=1.0 kW: @@ -394,13 +408,14 @@ def test_performance_incentive_per_event_matches_equivalent_kwh_rate(): PeakLoadManagementOptimizedStorageController.glpk_solve_call(model_event) for t in range(24): - assert ( - abs( - pyomo.value(model_kwh.p_discharge[t]) # type: ignore[index] - - pyomo.value(model_event.p_discharge[t]) # type: ignore[index] - ) - < 1e-4 - ), f"dispatch mismatch at t={t}" + with subtests.test(f"dispatch match at t={t}"): + assert ( + abs( + pyomo.value(model_kwh.p_discharge[t]) # type: ignore[index] + - pyomo.value(model_event.p_discharge[t]) # type: ignore[index] + ) + < 1e-4 + ), f"Dispatch mismatch at t={t}" @pytest.fixture @@ -486,10 +501,7 @@ def test_plm_optimized_controller_om_problem_soc_bounds(subtests, om_plant_confi prob.run_model() soc = prob.get_val("SOC", units="unitless") - # print - print("SOC:", soc) discharge = prob.get_val("storage_electricity_discharge", units="kW") - print("Discharge:", discharge) with subtests.test("SOC never below min"): assert np.all(soc >= 0.0 - 1e-1) @@ -500,30 +512,33 @@ def test_plm_optimized_controller_om_problem_soc_bounds(subtests, om_plant_confi pw = om_tech_config["model_inputs"]["control_parameters"]["peak_window"] pw_start = pd.Timestamp(f"2024-01-01 {pw['start']}").time() pw_end = pd.Timestamp(f"2024-01-01 {pw['end']}").time() - with subtests.test(f"discharge only inside peak window ({pw['start'][:5]}-{pw['end'][:5]})"): - time_index = pd.date_range("2024-01-01", periods=n, freq="h") - for t in range(n): - in_window = pw_start <= time_index[t].time() < pw_end - if not in_window: + time_index = pd.date_range("2024-01-01", periods=n, freq="h") + for t in range(n): + in_window = pw_start <= time_index[t].time() < pw_end + if not in_window: + with subtests.test(f"No discharge outside peak window at t={t}"): assert ( discharge[t] <= 1e-4 - ), f"discharge {discharge[t]:.4f} kW outside peak window at t={t}" + ), f"Discharge {discharge[t]:.4f} kW outside peak window at t={t}" - with subtests.test("discharge never negative"): + with subtests.test("Discharge never negative"): assert np.all(discharge >= -1e-4) - with subtests.test("discharge never above max_charge_rate"): + with subtests.test("Discharge never above max_charge_rate"): assert np.all(discharge <= 1.0 + 1e-4) with subtests.test("SOC at t=0 equal to init_soc_fraction"): assert abs(soc[0] - 1.0) < 1e-4 - with subtests.test("SOC evolution consistent with discharge and charge"): - shared = om_tech_config["model_inputs"]["shared_parameters"] - E_max = shared["max_capacity"] * (shared["max_soc_fraction"] - shared["min_soc_fraction"]) - charge = prob.get_val("storage_electricity_charge", units="kW") - expected_soc = np.zeros(n) - expected_soc[0] = shared["init_soc_fraction"] - for t in range(1, n): - expected_soc[t] = expected_soc[t - 1] + charge[t] / E_max - discharge[t] / E_max - assert np.allclose(soc, expected_soc, atol=1e-4) + shared = om_tech_config["model_inputs"]["shared_parameters"] + E_max = shared["max_capacity"] * (shared["max_soc_fraction"] - shared["min_soc_fraction"]) + charge = prob.get_val("storage_electricity_charge", units="kW") + expected_soc = np.zeros(n) + expected_soc[0] = shared["init_soc_fraction"] + for t in range(1, n): + expected_soc[t] = expected_soc[t - 1] + charge[t] / E_max - discharge[t] / E_max + for t in range(n): + with subtests.test(f"SOC evolution at t={t}"): + assert ( + abs(soc[t] - expected_soc[t]) < 1e-4 + ), f"SOC mismatch at t={t}: got {soc[t]:.4f}, expected {expected_soc[t]:.4f}" From 060a22a5cf6c9099c03883a21581ec338938f47b Mon Sep 17 00:00:00 2001 From: svijaysh Date: Thu, 7 May 2026 13:24:49 -0600 Subject: [PATCH 52/55] Minor changes to comments and subtests --- .../run_plm_optimized_dispatch.py | 4 +-- .../plm_optimized_storage_controller.py | 30 +++++++++---------- .../test_plm_optimized_storage_controller.py | 14 ++++++--- 3 files changed, 26 insertions(+), 22 deletions(-) diff --git a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py index 990b18271..378e7d75b 100644 --- a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py +++ b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py @@ -121,6 +121,4 @@ def shade_peaks(ax): plt.tight_layout() -plt.savefig( - "examples/34_plm_optimized_dispatch/plm_optimized_dispatch.png", dpi=150, bbox_inches="tight" -) +plt.savefig(EXAMPLE_DIR / "plm_optimized_dispatch.png", dpi=150, bbox_inches="tight") diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index 07a6f411c..facd51db7 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -89,7 +89,7 @@ class PeakLoadManagementOptimizedControllerConfig(PyomoStorageControllerBaseConf def __attrs_post_init__(self): # Make sure n_control_window_hours is an int - self.n_control_window_hours = int(round(self.n_control_window_hours)) + self.n_control_window_hours = int(math.ceil(self.n_control_window_hours)) super().__attrs_post_init__() both_set = ( @@ -227,7 +227,7 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): discrete_inputs (dict): OpenMDAO discrete inputs. discrete_outputs (dict): OpenMDAO discrete outputs. The key ``'pyomo_dispatch_solver'`` is set to the callable - returned by :meth:`pyomo_setup`. + returned by `pyomo_setup`. """ discrete_outputs["pyomo_dispatch_solver"] = self.pyomo_setup(discrete_inputs, inputs) @@ -310,6 +310,8 @@ def pyomo_dispatch_solver( for t in range(window_len): discharging = pyomo.value(self.dr_model.discharge[t]) > 0.5 prev_discharging = t > 0 and pyomo.value(self.dr_model.discharge[t - 1]) > 0.5 + # Detect the rising edge of a discharge event (0 -> 1) and count + # it if it occurs in this window. if discharging and not prev_discharging: month = int(month_ids_w[t]) if month not in events_used_per_month: @@ -326,9 +328,8 @@ def pyomo_dispatch_solver( # Performance model returns SOC in percent. self.updated_initial_soc = soc_window[-1] / 100.0 - for j in range(window_len): - storage_out[window_start + j] = storage_out_window[j] - soc_out[window_start + j] = soc_window[j] + storage_out[window_start : window_start + window_len] = storage_out_window + soc_out[window_start : window_start + window_len] = soc_window return storage_out, soc_out @@ -417,18 +418,24 @@ def _compute_eligible_mask( np.ndarray: Boolean array of shape ``(len(signal_window),)``. ``True`` where ``signal_t >= threshold``. """ + # Use all the timesteps in the window if no dispatch_mask is provided, otherwise restrict + # to the dispatch window mask = ( dispatch_mask if dispatch_mask is not None else np.ones(len(signal_window), dtype=bool) ) + # If the threshold percentile is 0 or there are dispatch_window is all 0s + # all timesteps are eligible if self.config.signal_threshold_percentile == 0.0 or not mask.any(): return mask.copy() + # Keep only the timesteps where the signal is above the threshold computed from the + # dispatch window threshold = np.percentile(signal_window[mask], self.config.signal_threshold_percentile) eligible = mask & (signal_window >= threshold) if self.config.min_peak_separation is not None: - sep_steps = ( + sep_steps = math.ceil( pd.Timedelta( value=self.config.min_peak_separation["val"], unit=self.config.min_peak_separation["units"], @@ -453,7 +460,7 @@ def _compute_event_window_mask( self, eligible_mask: np.ndarray, ) -> np.ndarray: - """Expand each eligible peak timestep by +/-event_duration/2. + """Expand each eligible peak timestep by +/- event_duration/2. Every True timestep in ``eligible_mask`` is treated as a peak and all timesteps within ``event_duration / 2`` @@ -470,14 +477,7 @@ def _compute_event_window_mask( if self.config.event_duration is None: return eligible_mask.copy() - half_event_steps = ( - pd.Timedelta( - value=self.config.event_duration["val"], - unit=self.config.event_duration["units"], - ).total_seconds() - / 2.0 - / self.dt_seconds - ) + half_event_steps = self.steps_per_event / 2 peak_indices = np.where(eligible_mask)[0] event_mask = np.zeros(len(eligible_mask), dtype=bool) for peak in peak_indices: diff --git a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py index aa056ba2b..fcb808eb0 100644 --- a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py @@ -71,13 +71,17 @@ def test_parse_peak_window(subtests): controller = _make_controller() controller.config = SimpleNamespace(peak_window={"start": "08:00:00", "end": "18:40:20"}) start, end = controller._parse_peak_window() - with subtests.test("start time"): + with subtests.test("start hour"): assert start.hour == 8 + with subtests.test("start minute"): assert start.minute == 0 + with subtests.test("start second"): assert start.second == 0 - with subtests.test("end time"): + with subtests.test("end hour"): assert end.hour == 18 + with subtests.test("end minute"): assert end.minute == 40 + with subtests.test("end second"): assert end.second == 20 @@ -261,8 +265,9 @@ def test_optimizer_dispatch_respects_soc_constraints(subtests, base_config): p_discharge = pyomo.value(model.p_discharge[t]) # type: ignore[index] if t > 0: soc += eta_c * p_charge * dt_hours / E_max - p_discharge * dt_hours / (eta_d * E_max) - with subtests.test(f"SOC in bounds at t={t}"): + with subtests.test(f"SOC above min at t={t}"): assert soc >= base_config.min_soc_fraction - 1e-6 + with subtests.test(f"SOC below max at t={t}"): assert soc <= base_config.max_soc_fraction + 1e-6 @@ -356,9 +361,10 @@ def test_power_zero_when_binary_zero(subtests, base_config): p_d = pyomo.value(model.p_discharge[t]) # type: ignore[index] v = pyomo.value(model.charge[t]) # type: ignore[index] p_c = pyomo.value(model.p_charge[t]) # type: ignore[index] - with subtests.test(f"Constraints at t={t}"): + with subtests.test(f"p_discharge zero when binary zero at t={t}"): if u < 0.5: assert p_d < 1e-6, f"p_discharge[{t}]={p_d} but discharge[{t}]={u}" + with subtests.test(f"p_charge zero when binary zero at t={t}"): if v < 0.5: assert p_c < 1e-6, f"p_charge[{t}]={p_c} but charge[{t}]={v}" From a3406229bec67df05612f7cc0a8559e4a685acb0 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Thu, 7 May 2026 13:57:38 -0600 Subject: [PATCH 53/55] Change round to ceil when computing n_days --- .../storage/plm_optimized_storage_controller.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index facd51db7..9b2efbb23 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -303,7 +303,7 @@ def pyomo_dispatch_solver( # Solve the optimzation problem self.solve_dispatch_model( start_time=window_start, - n_days=int(round(self.n_timesteps * self.dt_seconds / 86400)), + n_days=int(math.ceil(self.n_timesteps * self.dt_seconds / 86400)), ) # Count new discharge events to track the monthly cap. From 0831c29188e1af7d1f3031e013c73a1c3b25f426 Mon Sep 17 00:00:00 2001 From: Sanjana Vijayshankar <59975769+vijay092@users.noreply.github.com> Date: Thu, 7 May 2026 14:28:13 -0600 Subject: [PATCH 54/55] Update h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py Co-authored-by: Jared Thomas --- .../storage/plm_optimized_storage_controller.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index 9b2efbb23..7403e49ea 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -136,7 +136,7 @@ class PeakLoadManagementOptimizedStorageController(PyomoStorageControllerBaseCla dr_model: Any problem_state: DispatchProblemState - + _time_step_bound = [300, 3600] def setup(self): """Initialize config, register OpenMDAO inputs, and pre-compute static masks. From 7a6346662a186885305728f3ed065c324d386cec Mon Sep 17 00:00:00 2001 From: svijaysh Date: Thu, 7 May 2026 14:55:47 -0600 Subject: [PATCH 55/55] Add _time_step_bound and make sure init_sc is within bounds --- .../storage/plm_optimized_storage_controller.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index 7403e49ea..30cbbb80e 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -136,7 +136,8 @@ class PeakLoadManagementOptimizedStorageController(PyomoStorageControllerBaseCla dr_model: Any problem_state: DispatchProblemState - _time_step_bound = [300, 3600] + _time_step_bound = (300, 3600) + def setup(self): """Initialize config, register OpenMDAO inputs, and pre-compute static masks. @@ -326,7 +327,11 @@ def pyomo_dispatch_solver( ) # Performance model returns SOC in percent. - self.updated_initial_soc = soc_window[-1] / 100.0 + self.updated_initial_soc = np.clip( + soc_window[-1] / 100.0, + self.config.min_soc_fraction, + self.config.max_soc_fraction, + ) storage_out[window_start : window_start + window_len] = storage_out_window soc_out[window_start : window_start + window_len] = soc_window
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zb(h~CL36uZqWvOP&Htt>;l1o5R52B&%*pNYwuifR z;lx58r8N>!SSe0yAwVpu)_>gn&2}p-8G!psPwGGjBQ9=9Myqa||Ah-NmKu~~gTM(7 zyrc&!!PW_Zd*zMKf(zg?(~IoQ_CSxF@FC#31oyTHp2D<8kSJyC1$@H-sSb05<~GTM^jE zUdH%C3>67B^IlkM<}SrUeBu8JST(~xsecL~r1Icl>l!7i}W(E;DavhbPP+%ek2TYP3m-{$3X@FU>KXupo$mSA_k-qrU8a>-`KmM4o(xZL=6eQ@i3 literal 0 HcmV?d00001 From 34d9cea43c087d15c84c92ff8e8b5aa34dc1c448 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Wed, 29 Apr 2026 16:54:54 -0600 Subject: [PATCH 29/55] Make sure tests run --- .../run_plm_optimized_dispatch.py | 4 +--- .../test_plm_optimized_storage_controller.py | 18 +++++------------- 2 files changed, 6 insertions(+), 16 deletions(-) diff --git a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py index 1f3d3b6fb..9dbbed1d9 100644 --- a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py +++ b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py @@ -2,7 +2,7 @@ This example demonstrates demand-response storage dispatch using a rolling-horizon MILP controller. The battery is scheduled to discharge during high-LMP peak hours -to reduce facility demand charges and earn performance incentives. +to maximize incentives. """ from pathlib import Path @@ -76,7 +76,6 @@ def _peak_window_to_hour(val) -> int: def shade_peaks(ax): for day in days: - # light background: static peak_window ax.axvspan( day + pd.Timedelta(hours=pw_start_h), day + pd.Timedelta(hours=pw_end_h), @@ -87,7 +86,6 @@ def shade_peaks(ax): ) if half_td is None: continue - # darker band: event window centered on the daily LMP peak pw_start_ts = day + pd.Timedelta(hours=pw_start_h) pw_end_ts = day + pd.Timedelta(hours=pw_end_h) in_pw = (time_index >= pw_start_ts) & (time_index <= pw_end_ts) diff --git a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py index 633b364c2..4cd2cb333 100644 --- a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py @@ -82,19 +82,11 @@ def test_parse_peak_window_invalid_format(): controller = _make_controller() controller.config = SimpleNamespace(peak_window={"start": "08", "end": "18:40:20"}) with pytest.raises( - ValueError, match="peak_window start value must be a string in HH:MM:SS format" + ValueError, ): controller._parse_peak_window() -@pytest.mark.unit -def test_parse_peak_window_int_raises(): - controller = _make_controller() - controller.config = SimpleNamespace(peak_window={"start": 8, "end": 9}) - with pytest.raises(ValueError): - controller._parse_peak_window() - - @pytest.mark.unit def test_parse_peak_window_missing_key_raises(): controller = _make_controller() @@ -256,15 +248,15 @@ def test_compute_eligible_mask_min_peak_separation_drops_nearby_peak(): """A later peak within min_peak_separation of an earlier peak is dropped.""" controller = _make_controller() controller.config = SimpleNamespace( - signal_threshold_percentile=0.0, + signal_threshold_percentile=50.0, min_peak_separation={"units": "h", "val": 3}, ) controller.dt_seconds = 3600 - # t=5 and t=7 are 2h apart — below the 3h threshold; first one (t=5) is kept signal = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 10.0, 1.0, 8.0, 1.0, 1.0]) mask = controller._compute_eligible_mask(signal) - assert mask[5], "first peak should be kept" - assert not mask[7], "later peak within separation should be dropped" + print(mask) + assert mask[0], "first peak should be kept" + assert not mask[1], "later peak within separation should be dropped" @pytest.mark.unit From d997decf36d50bece1e1b9aaedf79d34b8a43950 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Thu, 30 Apr 2026 09:25:00 -0600 Subject: [PATCH 30/55] Make sure test_all_examples runs --- examples/test/test_all_examples.py | 23 +++++++++-------------- 1 file changed, 9 insertions(+), 14 deletions(-) diff --git a/examples/test/test_all_examples.py b/examples/test/test_all_examples.py index b9d86ea5c..490279a50 100644 --- a/examples/test/test_all_examples.py +++ b/examples/test/test_all_examples.py @@ -2883,6 +2883,7 @@ def test_peak_load_management_example(subtests, temp_copy_of_example): grid_purchase = model.prob.get_val("grid_buy.electricity_out", units="kW") assert battery_unmet_demand.sum() == pytest.approx(grid_purchase.sum(), rel=1e-3) + @pytest.mark.integration @pytest.mark.parametrize( "example_folder,resource_example_folder", [("34_plm_optimized_dispatch", None)] @@ -2897,9 +2898,7 @@ def test_plm_optimized_dispatch_example(subtests, temp_copy_of_example): # Run the model model.run() - battery_power = model.prob.get_val( - "battery.storage_electricity_discharge", units="kW" - ) + battery_power = model.prob.get_val("battery.storage_electricity_discharge", units="kW") soc_pct = model.prob.get_val("battery.SOC", units="percent") with subtests.test("Check battery power is discharging at some point"): @@ -2911,26 +2910,22 @@ def test_plm_optimized_dispatch_example(subtests, temp_copy_of_example): with subtests.test("Check battery CAPEX"): battery_capex = model.prob.get_val("battery.CapEx", units="USD")[0] - assert pytest.approx(battery_capex, rel=1e-6) == 603300.0 + assert pytest.approx(battery_capex, rel=1e-6) == 929700.0 with subtests.test("Check battery OPEX"): battery_opex = model.prob.get_val("battery.OpEx", units="USD/year")[0] - assert pytest.approx(battery_opex, rel=1e-1) == 15082.0 + assert pytest.approx(battery_opex, rel=1e-1) == 23242.5 with subtests.test("Check number of discharge events"): # With the given demand profile and battery size, there should be 2 discharge events - num_discharge_events = np.sum( - battery_power > 1e-3 - ) # Count timesteps with discharge - assert num_discharge_events == 115 + num_discharge_events = np.sum(battery_power > 1e-3) # Count timesteps with discharge + assert num_discharge_events == 368 with subtests.test("Check total energy discharged"): total_energy_discharged = battery_power.sum() * (1 / 60) # kWh, 1 min timestep - assert pytest.approx(total_energy_discharged, rel=1e-2) == 522.55 + assert pytest.approx(total_energy_discharged, rel=1e-2) == 1840.0 with subtests.test("Check total energy charged"): - battery_charge = model.prob.get_val( - "battery.storage_electricity_charge", units="kW" - ) + battery_charge = model.prob.get_val("battery.storage_electricity_charge", units="kW") total_energy_charged = battery_charge.sum() * (1 / 60) # kWh, 1 min timestep - assert pytest.approx(total_energy_charged, rel=1e-3) == -565.55 \ No newline at end of file + assert pytest.approx(total_energy_charged, rel=1e-3) == -2015.0 From 33f244fe8eb36c56d103a66e6415b97eaea6bb65 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Thu, 30 Apr 2026 09:28:56 -0600 Subject: [PATCH 31/55] Run pre-commit on all files --- .../demand_profiles/supervisory_signal.yaml | 9482 ++--------------- .../plant_config.yaml | 8 +- 2 files changed, 726 insertions(+), 8764 deletions(-) diff --git a/examples/34_plm_optimized_dispatch/demand_profiles/supervisory_signal.yaml b/examples/34_plm_optimized_dispatch/demand_profiles/supervisory_signal.yaml index e92731250..43ec465d6 100644 --- a/examples/34_plm_optimized_dispatch/demand_profiles/supervisory_signal.yaml +++ b/examples/34_plm_optimized_dispatch/demand_profiles/supervisory_signal.yaml @@ -1,8760 +1,722 @@ -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 83.4137 -- 79.0599 -- 85.0666 -- 85.3182 -- 79.7008 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 82.8949 -- 89.1500 -- 78.6225 -- 83.3834 -- 101.5483 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 81.5338 -- 71.4758 -- 74.2712 -- 74.3409 -- 69.6864 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 8.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 28.0000 -- 71.5507 -- 114.5877 -- 68.5757 -- 84.7005 -- 69.4464 -- 23.0000 -- 23.0000 -- 23.0000 -- 8.0000 -- 8.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 79.3612 -- 99.0560 -- 90.6929 -- 114.6070 -- 93.6671 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 121.0501 -- 82.3572 -- 104.0367 -- 89.4550 -- 103.9503 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 38.0000 -- 122.5359 -- 83.9824 -- 142.5416 -- 78.9059 -- 78.5578 -- 33.0000 -- 33.0000 -- 33.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 -- 18.0000 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23.0000 + - 23.0000 - 8.0000 - 8.0000 - 18.0000 - 18.0000 - 18.0000 - 18.0000 - 18.0000 - 18.0000 - 18.0000 - 18.0000 - 18.0000 + - 18.0000 - 38.0000 - 38.0000 - 38.0000 - 38.0000 - 79.3612 - 99.0560 - 90.6929 - 114.6070 - 93.6671 - 33.0000 - 33.0000 + - 33.0000 - 18.0000 - 18.0000 diff --git a/examples/34_plm_optimized_dispatch/plant_config.yaml b/examples/34_plm_optimized_dispatch/plant_config.yaml index 69e0909fc..ab5a8348a 100644 --- a/examples/34_plm_optimized_dispatch/plant_config.yaml +++ b/examples/34_plm_optimized_dispatch/plant_config.yaml @@ -3,10 +3,10 @@ description: Simulation for PLM MILP-optimized battery dispatch plant: plant_life: 1 simulation: - n_timesteps: 8760 # full year (hourly) - dt: 3600 # 1-hour timesteps - timezone: -6 # MDT (UTC-6) - start_time: "2025/01/01 00:00:00" + n_timesteps: 8760 # full year (hourly) + dt: 3600 # 1-hour timesteps + timezone: -6 # MDT (UTC-6) + start_time: 2025/01/01 00:00:00 tech_to_dispatch_connections: - [grid_buy, battery] - [battery, battery] From 92b8bcf4ee5d516d982917e687d673b96a49a122 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Thu, 30 Apr 2026 10:34:08 -0600 Subject: [PATCH 32/55] Address Elenya's comments --- .pre-commit-config.yaml | 2 +- .../demand_profiles/supervisory_signal.yaml | 9482 +++++++++++++++-- .../plm_optimized_storage_controller.py | 32 +- .../test_plm_optimized_storage_controller.py | 24 +- 4 files changed, 8800 insertions(+), 740 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 35a0a2e49..9743b9340 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -27,7 +27,7 @@ repos: - id: yamlfix # Exclude YAML files that are used as inputs for testing or examples, or ones for desired schedule in HOPP as they # expect misformatted YAML files. - exclude: ^(h2integrate/core/test/inputs/.*|examples/11_hybrid_energy_plant/tech_inputs/desired_schedules/.*|examples/33_peak_load_management/demand_profiles/.*)$ + exclude: ^(h2integrate/core/test/inputs/.*|examples/11_hybrid_energy_plant/tech_inputs/desired_schedules/.*|examples/33_peak_load_management/demand_profiles/.*|examples/34_plm_optimized_dispatch/demand_profiles/.*)$ - repo: https://github.com/astral-sh/ruff-pre-commit # Ruff version. rev: v0.8.1 diff --git a/examples/34_plm_optimized_dispatch/demand_profiles/supervisory_signal.yaml b/examples/34_plm_optimized_dispatch/demand_profiles/supervisory_signal.yaml index 43ec465d6..ed3860f5b 100644 --- a/examples/34_plm_optimized_dispatch/demand_profiles/supervisory_signal.yaml +++ b/examples/34_plm_optimized_dispatch/demand_profiles/supervisory_signal.yaml @@ -1,722 +1,8760 @@ -- 18.0000 - 18.0000 - 18.0000 - 18.0000 - 18.0000 - 18.0000 - 18.0000 - 18.0000 - 18.0000 - 18.0000 - 38.0000 - 38.0000 - - 38.0000 - 38.0000 - 79.3612 - 99.0560 - 90.6929 - 114.6070 - 93.6671 - 33.0000 - 33.0000 - 33.0000 - 18.0000 - 18.0000 - - 18.0000 - 18.0000 - 18.0000 - 18.0000 - 18.0000 - 18.0000 - 18.0000 - 18.0000 - 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33.0 +- 33.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 38.0 +- 38.0 +- 38.0 +- 38.0 +- 122.5359 +- 83.9824 +- 142.5416 +- 78.9059 +- 78.5578 +- 33.0 +- 33.0 +- 33.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 38.0 +- 38.0 +- 38.0 +- 38.0 +- 83.4137 +- 79.0599 +- 85.0666 +- 85.3182 +- 79.7008 +- 33.0 +- 33.0 +- 33.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 38.0 +- 38.0 +- 38.0 +- 38.0 +- 82.8949 +- 89.15 +- 78.6225 +- 83.3834 +- 101.5483 +- 33.0 +- 33.0 +- 33.0 +- 18.0 +- 18.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 28.0 +- 28.0 +- 28.0 +- 28.0 +- 81.5338 +- 71.4758 +- 74.2712 +- 74.3409 +- 69.6864 +- 23.0 +- 23.0 +- 23.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 28.0 +- 28.0 +- 28.0 +- 28.0 +- 71.5507 +- 114.5877 +- 68.5757 +- 84.7005 +- 69.4464 +- 23.0 +- 23.0 +- 23.0 +- 8.0 +- 8.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 38.0 +- 38.0 +- 38.0 +- 38.0 +- 79.3612 +- 99.056 +- 90.6929 +- 114.607 +- 93.6671 +- 33.0 +- 33.0 +- 33.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 38.0 +- 38.0 +- 38.0 +- 38.0 +- 121.0501 +- 82.3572 +- 104.0367 +- 89.455 +- 103.9503 +- 33.0 +- 33.0 +- 33.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 38.0 +- 38.0 +- 38.0 +- 38.0 +- 122.5359 +- 83.9824 +- 142.5416 +- 78.9059 +- 78.5578 +- 33.0 +- 33.0 +- 33.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 38.0 +- 38.0 +- 38.0 +- 38.0 +- 83.4137 +- 79.0599 +- 85.0666 +- 85.3182 +- 79.7008 +- 33.0 +- 33.0 +- 33.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 38.0 +- 38.0 +- 38.0 +- 38.0 +- 82.8949 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38.0 +- 38.0 +- 79.3612 +- 99.056 +- 90.6929 +- 114.607 +- 93.6671 +- 33.0 +- 33.0 +- 33.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 38.0 +- 38.0 +- 38.0 +- 38.0 +- 121.0501 +- 82.3572 +- 104.0367 +- 89.455 +- 103.9503 +- 33.0 +- 33.0 +- 33.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 38.0 +- 38.0 +- 38.0 +- 38.0 +- 122.5359 +- 83.9824 +- 142.5416 +- 78.9059 +- 78.5578 +- 33.0 +- 33.0 +- 33.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 38.0 +- 38.0 +- 38.0 +- 38.0 +- 83.4137 +- 79.0599 +- 85.0666 +- 85.3182 +- 79.7008 +- 33.0 +- 33.0 +- 33.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 38.0 +- 38.0 +- 38.0 +- 38.0 +- 82.8949 +- 89.15 +- 78.6225 +- 83.3834 +- 101.5483 +- 33.0 +- 33.0 +- 33.0 +- 18.0 +- 18.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 28.0 +- 28.0 +- 28.0 +- 28.0 +- 81.5338 +- 71.4758 +- 74.2712 +- 74.3409 +- 69.6864 +- 23.0 +- 23.0 +- 23.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 8.0 +- 28.0 +- 28.0 +- 28.0 +- 28.0 +- 71.5507 +- 114.5877 +- 68.5757 +- 84.7005 +- 69.4464 +- 23.0 +- 23.0 +- 23.0 +- 8.0 +- 8.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 18.0 +- 38.0 +- 38.0 +- 38.0 +- 38.0 +- 79.3612 +- 99.056 +- 90.6929 +- 114.607 +- 93.6671 +- 33.0 +- 33.0 +- 33.0 +- 18.0 +- 18.0 diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index 0f6831a3d..db9fcb171 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -211,13 +211,16 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): ``'pyomo_dispatch_solver'`` is set to the callable returned by :meth:`pyomo_setup`. """ - discrete_outputs["pyomo_dispatch_solver"] = self.pyomo_setup(discrete_inputs) + discrete_outputs["pyomo_dispatch_solver"] = self.pyomo_setup(discrete_inputs, inputs) - def pyomo_setup(self, discrete_inputs): + def pyomo_setup(self, discrete_inputs, om_inputs): """Return the rolling-horizon dispatch solver callable. Args: discrete_inputs (dict): OpenMDAO discrete inputs. + om_inputs (dict): OpenMDAO continuous inputs. ``max_charge_rate`` + and ``storage_capacity`` are read from here so that optimizer + changes to those values are reflected in each solve. Returns: callable: ``pyomo_dispatch_solver(performance_model, @@ -235,6 +238,9 @@ def pyomo_setup(self, discrete_inputs): length ``n_timesteps``. """ + P_max = float(om_inputs["max_charge_rate"][0]) + storage_capacity = float(om_inputs["storage_capacity"][0]) + def pyomo_dispatch_solver( performance_model, performance_model_kwargs, @@ -271,6 +277,8 @@ def pyomo_dispatch_solver( window_len=window_len, init_soc=self.updated_initial_soc, remaining_budget=remaining_budget, + P_max=P_max, + storage_capacity=storage_capacity, ) self.problem_state = DispatchProblemState() @@ -292,7 +300,7 @@ def pyomo_dispatch_solver( # Run the performance model for this window. storage_out_window, soc_window = performance_model( - self.storage_dispatch_commands, + self._get_storage_dispatch_commands(P_max), **performance_model_kwargs, sim_start_index=window_start, ) @@ -465,6 +473,8 @@ def _build_dr_model( window_len: int, init_soc: float, remaining_budget: dict, + P_max: float, + storage_capacity: float, ) -> pyomo.ConcreteModel: """Build the DR MILP for a single rolling window. @@ -479,16 +489,17 @@ def _build_dr_model( subtracting events already dispatched in earlier windows from ``n_max_events``, so the monthly cap is respected across windows. + P_max (float): Maximum charge/discharge rate (kW), taken + from OpenMDAO inputs so optimizer changes are reflected. + storage_capacity (float): Total storage capacity (kWh), taken + from OpenMDAO inputs so optimizer changes are reflected. Returns: pyomo.ConcreteModel: Fully formed MILP ready to solve. """ m: Any = pyomo.ConcreteModel(name="plm_dr") - P_max = self.config.max_charge_rate - E_max = self.config.max_capacity * ( - self.config.max_soc_fraction - self.config.min_soc_fraction - ) + E_max = storage_capacity * (self.config.max_soc_fraction - self.config.min_soc_fraction) eta_c = self.config.charge_efficiency eta_d = self.config.discharge_efficiency soc_max = self.config.max_soc_fraction @@ -659,15 +670,16 @@ def glpk_solve_call( results = solver.solve(pyomo_model, options=solver_options.constructed, tee=False) return results - @property - def storage_dispatch_commands(self) -> list: + def _get_storage_dispatch_commands(self, P_max: float) -> list: """Net dispatch commands for the solved window. + Args: + P_max (float): Maximum charge/discharge rate (kW). + Returns: list[float]: ``(u_t - v_t) * P_max`` for each timestep in the solved window. Positive = discharge, negative = charge. """ - P_max = self.config.max_charge_rate return [ (pyomo.value(self.dr_model.discharge[t]) - pyomo.value(self.dr_model.charge[t])) * P_max for t in self.dr_model.T diff --git a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py index 4cd2cb333..8a7775f1f 100644 --- a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py @@ -171,6 +171,8 @@ def test_optimizer_dispatch_only_in_peak_window(base_config): window_len=24, init_soc=base_config.init_soc_fraction, remaining_budget={1: base_config.n_max_events}, + P_max=base_config.max_charge_rate, + storage_capacity=base_config.max_capacity, ) PeakLoadManagementOptimizedStorageController.glpk_solve_call(model) @@ -181,7 +183,7 @@ def test_optimizer_dispatch_only_in_peak_window(base_config): hour = pd.Timestamp("2024-01-01") + pd.Timedelta(hours=t) in_window = peak_start <= hour.time() <= peak_end if not in_window: - assert pyomo.value(model.discharge[t]) < 1e-3 + assert pyomo.value(model.discharge[t]) < 1e-3 # type: ignore[index] @pytest.mark.regression @@ -193,6 +195,8 @@ def test_optimizer_dispatch_only_on_eligible_timesteps(base_config): window_len=24, init_soc=base_config.init_soc_fraction, remaining_budget={1: base_config.n_max_events}, + P_max=base_config.max_charge_rate, + storage_capacity=base_config.max_capacity, ) PeakLoadManagementOptimizedStorageController.glpk_solve_call(model) @@ -201,7 +205,7 @@ def test_optimizer_dispatch_only_on_eligible_timesteps(base_config): eligible_mask = controller._compute_eligible_mask(signal) for t in range(24): if not eligible_mask[t]: - assert pyomo.value(model.discharge[t]) < 1e-3 + assert pyomo.value(model.discharge[t]) < 1e-3 # type: ignore[index] @pytest.mark.regression @@ -213,11 +217,13 @@ def test_optimizer_dispatch_respects_event_budget(base_config): window_len=24, init_soc=base_config.init_soc_fraction, remaining_budget={1: base_config.n_max_events}, + P_max=base_config.max_charge_rate, + storage_capacity=base_config.max_capacity, ) PeakLoadManagementOptimizedStorageController.glpk_solve_call(model) - total_events = sum(pyomo.value(model.discharge[t]) for t in range(24)) + total_events = sum(pyomo.value(model.discharge[t]) for t in range(24)) # type: ignore[index] assert total_events <= base_config.n_max_events + 1e-3 @@ -230,14 +236,16 @@ def test_optimizer_dispatch_respects_soc_constraints(base_config): window_len=24, init_soc=base_config.init_soc_fraction, remaining_budget={2: base_config.n_max_events}, + P_max=base_config.max_charge_rate, + storage_capacity=base_config.max_capacity, ) PeakLoadManagementOptimizedStorageController.glpk_solve_call(model) soc = base_config.init_soc_fraction * base_config.max_capacity for t in range(24): - charge = pyomo.value(model.charge[t]) - discharge = pyomo.value(model.discharge[t]) + charge = pyomo.value(model.charge[t]) # type: ignore[index] + discharge = pyomo.value(model.discharge[t]) # type: ignore[index] soc += charge - discharge assert soc >= base_config.min_soc_fraction * base_config.max_capacity assert soc <= base_config.max_soc_fraction * base_config.max_capacity @@ -297,11 +305,13 @@ def test_optimizer_dispatch_respects_charge_discharge_exclusivity(base_config): window_len=24, init_soc=base_config.init_soc_fraction, remaining_budget={2: base_config.n_max_events}, + P_max=base_config.max_charge_rate, + storage_capacity=base_config.max_capacity, ) PeakLoadManagementOptimizedStorageController.glpk_solve_call(model) for t in range(24): - charge = pyomo.value(model.charge[t]) - discharge = pyomo.value(model.discharge[t]) + charge = pyomo.value(model.charge[t]) # type: ignore[index] + discharge = pyomo.value(model.discharge[t]) # type: ignore[index] assert not (charge > 0.5 and discharge > 0.5) From feaaa700ad887be61e089824eb41b313b0266c39 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Thu, 30 Apr 2026 11:27:19 -0600 Subject: [PATCH 33/55] Change n_control_window to n_control_window_hours --- .../tech_config.yaml | 2 +- .../plm_optimized_storage_controller.py | 24 +++++++++---------- .../test_plm_optimized_storage_controller.py | 2 +- 3 files changed, 14 insertions(+), 14 deletions(-) diff --git a/examples/34_plm_optimized_dispatch/tech_config.yaml b/examples/34_plm_optimized_dispatch/tech_config.yaml index 5732b548f..8dfab7d37 100644 --- a/examples/34_plm_optimized_dispatch/tech_config.yaml +++ b/examples/34_plm_optimized_dispatch/tech_config.yaml @@ -30,7 +30,7 @@ technologies: performance_incentive: 14.0 # $/kWh n_max_events: 10 # maximum discharge events per month signal_threshold_percentile: 95.0 # only dispatch when LMP >= this percentile - n_control_window: 24 # hours; converted to timesteps using simulation dt + n_control_window_hours: 24 # hours; converted to timesteps using simulation dt event_duration: # omit or set to null to use static peak_window units: h # any pandas timedelta unit: h, min, s, val: 4 # battery eligible +/ - val h around the daily peak diff --git a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py index db9fcb171..f9c7b6e95 100644 --- a/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/plm_optimized_storage_controller.py @@ -23,7 +23,7 @@ class PeakLoadManagementOptimizedControllerConfig(PyomoStorageControllerBaseConf Inherits base fields from ``PyomoStorageControllerBaseConfig``: ``max_capacity``, ``max_soc_fraction``, ``min_soc_fraction``, - ``init_soc_fraction``, ``n_control_window``, ``commodity``, + ``init_soc_fraction``, ``n_control_window_hours``, ``commodity``, ``commodity_rate_units``, ``tech_name``, ``system_commodity_interface_limit``, ``round_digits``. @@ -31,7 +31,7 @@ class PeakLoadManagementOptimizedControllerConfig(PyomoStorageControllerBaseConf max_charge_rate (float): Maximum charge and discharge rate (kW). supervisory_signal (list[float]): Price, demand, or price*demand forecast time series. The rolling horizon solver uses one window of - length ``n_control_window`` per solve. + length ``n_control_window_hours`` per solve. peak_window (dict): Hours eligible for dispatch. Keys ``'start'`` and ``'end'`` must be strings in ``HH:MM:SS`` format. performance_incentive (float): Incentive revenue in $/kWh. @@ -41,7 +41,7 @@ class PeakLoadManagementOptimizedControllerConfig(PyomoStorageControllerBaseConf Defaults to 1.0. n_max_events (int): Maximum discharge events per calendar month. Defaults to 10. - n_control_window (float): Rolling window size in **hours**. + n_control_window_hours (float): Rolling window size in **hours**. Converted to an integer timestep count during ``setup()`` using the simulation ``dt``, so the same value works at any resolution. Example: ``10`` at a 30-min ``dt`` gives a @@ -74,14 +74,14 @@ class PeakLoadManagementOptimizedControllerConfig(PyomoStorageControllerBaseConf charge_efficiency: float = field(validator=range_val(0, 1), default=1.0) discharge_efficiency: float = field(validator=range_val(0, 1), default=1.0) n_max_events: int = field(default=10) - n_control_window: float = field(default=24.0) + n_control_window_hours: float = field(default=24.0) signal_threshold_percentile: float = field(default=0.0, validator=range_val(0, 100)) event_duration: dict = field(default=None) min_peak_separation: dict = field(default=None) def __attrs_post_init__(self): - # Make sure n_control_window is an int - self.n_control_window = int(round(self.n_control_window)) + # Make sure n_control_window_hours is an int + self.n_control_window_hours = int(round(self.n_control_window_hours)) super().__attrs_post_init__() for field_name, value in ( @@ -107,7 +107,7 @@ class PeakLoadManagementOptimizedStorageController(PyomoStorageControllerBaseCla """Demand-response storage controller using a rolling-horizon MILP. Each call to the dispatch solver iterates over the full simulation in - windows of length ``n_control_window``. For each window it receives + windows of length ``n_control_window_hours``. For each window it receives the monthly LMP forecast, solves the MILP to maximize incentive revenue, then passes the resulting dispatch commands to the performance model. The terminal SOC of each window is carried forward @@ -145,9 +145,9 @@ def setup(self): self.n_timesteps = int(sim["n_timesteps"]) # number of "dt"s in the simulation self.dt_seconds = int(sim["dt"]) # length of each timestep in seconds - # n_control_window is stored in hours; convert to timesteps now that dt is known. - n_cw_steps = max(1, int(round(self.config.n_control_window * 3600 / self.dt_seconds))) - object.__setattr__(self.config, "n_control_window", n_cw_steps) + # n_control_window_hours is stored in hours; convert to timesteps now that dt is known. + n_cw_steps = max(1, int(round(self.config.n_control_window_hours * 3600 / self.dt_seconds))) + object.__setattr__(self.config, "n_control_window_hours", n_cw_steps) super().setup() @@ -225,7 +225,7 @@ def pyomo_setup(self, discrete_inputs, om_inputs): Returns: callable: ``pyomo_dispatch_solver(performance_model, performance_model_kwargs, inputs)`` that iterates over the - simulation in windows of ``n_control_window`` timesteps. + simulation in windows of ``n_control_window_hours`` timesteps. For each window it: 1. Builds a fresh MILP from the window's signal slice. @@ -254,7 +254,7 @@ def pyomo_dispatch_solver( # respected across window boundaries. events_used_per_month = {} - n_w: int = int(self.config.n_control_window) + n_w: int = int(self.config.n_control_window_hours) # Compute the starting index of each rolling window. window_start_indices = list(range(0, self.n_timesteps, n_w)) diff --git a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py index 8a7775f1f..d563f369f 100644 --- a/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py +++ b/h2integrate/control/control_strategies/storage/test/test_plm_optimized_storage_controller.py @@ -50,7 +50,7 @@ def base_config(): max_soc_fraction=1.0, min_soc_fraction=0.0, init_soc_fraction=1.0, - n_control_window=n, + n_control_window_hours=n, commodity="electricity", commodity_rate_units="kW", tech_name="battery", From 09b8e5271b0ae3765e5ee071b873a194bd0c02b9 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Thu, 30 Apr 2026 14:54:53 -0600 Subject: [PATCH 34/55] Use methods and existing utils for example --- .../run_plm_optimized_dispatch.py | 28 +++++++------------ 1 file changed, 10 insertions(+), 18 deletions(-) diff --git a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py index 9dbbed1d9..a91b4a647 100644 --- a/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py +++ b/examples/34_plm_optimized_dispatch/run_plm_optimized_dispatch.py @@ -11,6 +11,7 @@ import pandas as pd import matplotlib.pyplot as plt +from h2integrate.core.utilities import build_time_series_from_plant_config from h2integrate.core.h2integrate_model import H2IntegrateModel @@ -20,6 +21,7 @@ model = H2IntegrateModel(EXAMPLE_DIR / "34_plm_optimized_dispatch.yaml") model.setup() + N = model.plant_config["plant"]["simulation"]["n_timesteps"] percentile = model.technology_config["technologies"]["battery"]["model_inputs"][ "control_parameters" @@ -33,33 +35,23 @@ ] )[:N] -sim = model.plant_config["plant"]["simulation"] -n_timesteps = int(sim["n_timesteps"]) -dt_seconds = int(sim["dt"]) -tz = int(sim["timezone"]) -start = pd.Timestamp(sim["start_time"], tz=tz) -freq = pd.to_timedelta(dt_seconds, unit="s") -time_index = pd.date_range(start=start, periods=n_timesteps, freq=freq) +n_timesteps = int(model.plant_config["plant"]["simulation"]["n_timesteps"]) +dt_seconds = int(model.plant_config["plant"]["simulation"]["dt"]) +time_index = pd.DatetimeIndex(build_time_series_from_plant_config(model.plant_config)) battery_power = model.prob.get_val("battery.storage_electricity_discharge", units="kW") soc_pct = model.prob.get_val("battery.SOC", units="percent") +controller = model.control_strategies[0] +pw_start, pw_end = controller._parse_peak_window() +pw_start_h = pw_start.hour +pw_end_h = pw_end.hour + control_params = model.technology_config["technologies"]["battery"]["model_inputs"][ "control_parameters" ] -pw_cfg = control_params["peak_window"] event_dur_cfg = control_params.get("event_duration") - -def _peak_window_to_hour(val) -> int: - if isinstance(val, int | float): - return int(val) // 3600 - return int(str(val).split(":")[0]) - - -pw_start_h = _peak_window_to_hour(pw_cfg["start"]) -pw_end_h = _peak_window_to_hour(pw_cfg["end"]) - half_td = None if event_dur_cfg is not None: half_td = pd.Timedelta(value=event_dur_cfg["val"], unit=event_dur_cfg["units"]) / 2 From 040e9c976ee3a0dbe3bce56c783be2635210ca82 Mon Sep 17 00:00:00 2001 From: svijaysh Date: Fri, 1 May 2026 15:54:42 -0600 Subject: [PATCH 35/55] Solve for optimal power rather than assuming P_max --- .../figures/plm_optimized_dispatch.png | Bin 118277 -> 126858 bytes docs/control/pyomo_controllers.md | 34 ++++--- .../run_plm_optimized_dispatch.py | 4 +- .../tech_config.yaml | 6 +- 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