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| Original file line number | Diff line number | Diff line change |
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| # Renewable Energy Case Study: Wind + PEM and Wind + Battery Plants | ||
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| This directory contains the files required for the renewable energy case studies, which use flowsheets in which any of the following | ||
| technologies (modeled as unit models) may be present: Wind, PV, Battery, PEM Electrolysis, Hydrogen Storage Tank and Hydrogen Turbine. | ||
| The RE case studies focused on a Wind + PEM plant and a Wind + Battery plant, while the the industry-partnership case study looked at a PV + Battery + PEM + Hydrogen. | ||
| Each of these three case studies has different approaches to modeling prices or the grid. | ||
|
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| ## Wind + PEM case: | ||
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| 1. Price-taker Design Optimization: `dispatches/case_studies/renewables_case/run_pricetaker_wind_PEM.py` | ||
| 2. Market Surrogate Design Optimization: `dispatches/case_studies/renewables_case/RE_surrogate_optimization_steadystate.py` | ||
| 3. Double Loop Simulation for Validation: `dispatches/case_studies/renewables_case/run_double_loop_PEM.py` | ||
| 4. Market Surrogate Design and Validation Plotting: `dispatches/case_studies/renewables_case/SurrogateDesignResults.ipynb` | ||
|
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| ## Wind + Battery case: | ||
| 1. Price-taker Design Optimization: `dispatches/case_studies/renewables_case/run_pricetaker_battery_ratio_size.py` | ||
| 2. Double Loop Simulation: `dispatches/case_studies/renewables_case/run_double_loop_battery.py` | ||
| 3. Parametrized Bidder Double Loop Simulation: `dispatches/case_studies/renewables_case/run_double_loop_battery_parametrized.py` | ||
|
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| ## PV + Battery + Hydrogen case: | ||
| 1. Price-taker Design Optimization: `dispatches/case_studies/renewables_case/solar_battery_hydrogen.py` | ||
|
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| ## Software and Hardware | ||
| All the models are run on a Red Hat Enterprise Linux Server version 7.9 (Maipo). The versions for the solvers used are given below: | ||
| - Xpress: Version 8.13.0 | ||
| - IPOPT: Version 3.13.2 |
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1,593 changes: 933 additions & 660 deletions
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dispatches/case_studies/renewables_case/SurrogateDesignResults.ipynb
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dispatches/case_studies/renewables_case/battery_parametrized_bidder.py
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| ################################################################################# | ||
| # DISPATCHES was produced under the DOE Design Integration and Synthesis Platform | ||
| # to Advance Tightly Coupled Hybrid Energy Systems program (DISPATCHES), and is | ||
| # copyright (c) 2020-2023 by the software owners: The Regents of the University | ||
| # of California, through Lawrence Berkeley National Laboratory, National | ||
| # Technology & Engineering Solutions of Sandia, LLC, Alliance for Sustainable | ||
| # Energy, LLC, Battelle Energy Alliance, LLC, University of Notre Dame du Lac, et | ||
| # al. All rights reserved. | ||
| # | ||
| # Please see the files COPYRIGHT.md and LICENSE.md for full copyright and license | ||
| # information, respectively. Both files are also available online at the URL: | ||
| # "https://github.com/gmlc-dispatches/dispatches". | ||
| ################################################################################# | ||
| import numpy as np | ||
| from idaes.apps.grid_integration.bidder import convert_marginal_costs_to_actual_costs, tx_utils | ||
| from dispatches.workflow.parametrized_bidder import ParametrizedBidder | ||
|
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| class FixedParametrizedBidder(ParametrizedBidder): | ||
|
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| """ | ||
| Template class for bidders that use fixed parameters. | ||
| The functions for computing the day ahead and real time bids do not use any information from Prescient, | ||
| and only depend on internal system information such as wind capacity factors, and on bid parameters. | ||
| """ | ||
|
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| def __init__( | ||
| self, | ||
| bidding_model_object, | ||
| day_ahead_horizon, | ||
| real_time_horizon, | ||
| solver, | ||
| forecaster, | ||
| storage_marginal_cost, | ||
| storage_mw | ||
| ): | ||
| super().__init__(bidding_model_object, | ||
| day_ahead_horizon, | ||
| real_time_horizon, | ||
| solver, | ||
| forecaster) | ||
| self.wind_marginal_cost = 0 | ||
| self.wind_mw = self.bidding_model_object._wind_pmax_mw | ||
| self.storage_marginal_cost = storage_marginal_cost | ||
| self.storage_mw = storage_mw | ||
|
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| def compute_day_ahead_bids(self, date, hour=0): | ||
| """ | ||
| Day ahead bid has two parts: | ||
| 1. the part of the DA wind energy that is able to be stored in the battery is at the higher "storage_marginal_cost" | ||
| 2. the remainder of the wind energy is bid at the wind marginal cost of $0/MWh | ||
| For each time period in the day ahead horizon, the marginal cost bid is assembled as the two parts. | ||
| Then the marginal costs are converted to actual costs, as expected by Prescient. | ||
| """ | ||
| gen = self.generator | ||
| forecast = self.forecaster.forecast_day_ahead_capacity_factor(date, hour, gen, self.day_ahead_horizon) | ||
|
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| full_bids = {} | ||
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| for t_idx in range(self.day_ahead_horizon): | ||
| da_wind = forecast[t_idx] * self.wind_mw | ||
| p_max = max(da_wind, self.storage_mw) | ||
| bids = [(0, 0), (max(0, da_wind - self.storage_mw), 0), (p_max, self.storage_marginal_cost)] | ||
| cost_curve = convert_marginal_costs_to_actual_costs(bids) | ||
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| temp_curve = { | ||
| "data_type": "cost_curve", | ||
| "cost_curve_type": "piecewise", | ||
| "values": cost_curve, | ||
| } | ||
| tx_utils.validate_and_clean_cost_curve( | ||
| curve=temp_curve, | ||
| curve_type="cost_curve", | ||
| p_min=0, | ||
| p_max=max([p[0] for p in cost_curve]), | ||
| gen_name=gen, | ||
| t=t_idx, | ||
| ) | ||
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| t = t_idx + hour | ||
| full_bids[t] = {} | ||
| full_bids[t][gen] = {} | ||
| full_bids[t][gen]["p_cost"] = cost_curve | ||
| full_bids[t][gen]["p_min"] = 0 | ||
| full_bids[t][gen]["p_max"] = p_max | ||
| full_bids[t][gen]["startup_capacity"] = p_max | ||
| full_bids[t][gen]["shutdown_capacity"] = p_max | ||
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| self._record_bids(full_bids, date, hour, Market="Day-ahead") | ||
| return full_bids | ||
|
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| def compute_real_time_bids( | ||
| self, date, hour, realized_day_ahead_prices, realized_day_ahead_dispatches | ||
| ): | ||
| """ | ||
| Real time bid has two parts: | ||
| 1. the part of the RT wind energy that is able to be stored in the battery is at the higher "storage_marginal_cost" | ||
| 2. the remainder of the wind energy is bid at the wind marginal cost of $0/MWh | ||
| For each time period in the day ahead horizon, the marginal cost bid is assembled as the two parts. | ||
| Then the marginal costs are converted to actual costs, as expected by Prescient. | ||
| """ | ||
| gen = self.generator | ||
| forecast = self.forecaster.forecast_real_time_capacity_factor(date, hour, gen, self.day_ahead_horizon) | ||
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| full_bids = {} | ||
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| for t_idx in range(self.real_time_horizon): | ||
| rt_wind = forecast[t_idx] * self.wind_mw | ||
| p_max = max(rt_wind, self.storage_mw) | ||
| bids = [(0, 0), (max(0, rt_wind - self.storage_mw), 0), (p_max, self.storage_marginal_cost)] | ||
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| t = t_idx + hour | ||
| full_bids[t] = {} | ||
| full_bids[t][gen] = {} | ||
| full_bids[t][gen]["p_cost"] = convert_marginal_costs_to_actual_costs(bids) | ||
| full_bids[t][gen]["p_min"] = 0 | ||
| full_bids[t][gen]["p_max"] = p_max | ||
| full_bids[t][gen]["startup_capacity"] = p_max | ||
| full_bids[t][gen]["shutdown_capacity"] = p_max | ||
|
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| self._record_bids(full_bids, date, hour, Market="Real-time") | ||
| return full_bids |
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