cronian.run_co_optimization
Run the sector-coupled co-optimization in a centralized manner.
By default, this script runs the optimization using the yaml configurations, timeseries_data, price_timeseries csv files provided in the tests/data directory. Generators and prosumers are each defined in their respective yaml configuration file. The timeseries_data csv file contains the timeseries data such as VRE generators’ availability factors, demand profiles, minimum_state_of_charge and availability of EVs doing vehicle-to-grid (V2G), etc. The price_timeseries csv file contains the prices of non-electricity energy carriers such methane, hydrogen, biomass, etc. Finally, a general_config.yaml file is also needed where general parameters such as number of timesteps for which to run the simulation, etc., are defined.
NOTE: If you want to add a prosumer with a more detailed model than the one build by this package, explicitly build its model and add it to the simulation. See explicit_heat.py as an example. This design choice gives the user more flexibility in building its own explicit prosumers to be added to the simulation.
Attributes
Functions
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Solve the sector-coupled optimization model in a centralized manner. |
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Parse command line arguments. |
Module Contents
- cronian.run_co_optimization.main(configurations_folder: pathlib.Path, timeseries_data: pandas.DataFrame, price_timeseries: pandas.DataFrame, explicit_prosumer_configuration: dict | None, explicit_prosumer_timeseries_data: pandas.DataFrame | None, number_of_timesteps: int, storage_model: str, results_folder: pathlib.Path, include_base_load: bool = False, solver_name: str = 'gurobi', solver_options: dict | None = None) None
Solve the sector-coupled optimization model in a centralized manner.
- Args:
configurations_folder: Path to folder containing configuration files. explicit_prosumer_configuration: Configuration file of explicit prosumer. timeseries_data: Timeseries data containing the availability factors for
VRE generators and EVs doing V2G, demand profiles for prosumers, …
price_timeseries: Dataframe containing the prices of energy carriers. explicit_prosumer_timeseries_data: The csv file containing timeseries
data for the explicit prosumer.
number_of_timesteps: Number of timesteps to run the optimization for. storage_model: Type of storage model to use: simple or complex. results_folder: Path to the folder where the results will be saved. include_base_load: if True, base load from passive consumers is
included in the optimization model.
solver_name: Name of the solver to use. solver_options: Solver options to be passed to the solver.
- cronian.run_co_optimization.parse_command_line_args() argparse.Namespace
Parse command line arguments.
- cronian.run_co_optimization.args