cronian.run_co_optimization
===========================

.. py:module:: cronian.run_co_optimization

.. autoapi-nested-parse::

   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
----------

.. autoapisummary::

   cronian.run_co_optimization.args


Functions
---------

.. autoapisummary::

   cronian.run_co_optimization.main
   cronian.run_co_optimization.parse_command_line_args


Module Contents
---------------

.. py:function:: 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.


.. py:function:: parse_command_line_args() -> argparse.Namespace

   Parse command line arguments.


.. py:data:: args