cronian.DERs.storage
====================

.. py:module:: cronian.DERs.storage

.. autoapi-nested-parse::

   Model of a storage asset that can buffer an arbitrary energy carrier.



Functions
---------

.. autoapisummary::

   cronian.DERs.storage.add_storage_asset_to_model
   cronian.DERs.storage.add_simple_model_of_storage_asset
   cronian.DERs.storage.add_complex_model_of_storage_asset
   cronian.DERs.storage.add_storage_asset_minimum_state_of_charge_constraint
   cronian.DERs.storage.add_storage_asset_availability_constraints


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

.. py:function:: add_storage_asset_to_model(model: pyomo.environ.AbstractModel, prosumer: dict, asset_name: str, timeseries_data: pandas.DataFrame, number_of_timesteps, storage_model: str)

   Add a storage asset to the optimization model of the given prosumer.

   Example of storage assets: battery, heat storage, hydrogen storage, etc.

   Args:
       model: Pyomo model to which the prosumer's storage will be added.
       prosumer: Dictionary containing prosumer details.
       asset_name: Name of the storage asset, e.g., battery.
       timeseries_data: Timeseries data containing the availability factors for
           VRE generators and EVs doing V2G, demand profiles for prosumers, ...
       number_of_timesteps: Number of timesteps to run the optimization for.
       storage_model: Type of storage model to use: `simple` or `complex`.

   Returns:
       Pyomo AbstractModel with the storage asset added to it.

   Raises:
       ValueError: If an invalid storage model is specified.


.. py:function:: add_simple_model_of_storage_asset(model: pyomo.environ.AbstractModel, prosumer: dict, asset_name: str, timeseries_data: pandas.DataFrame, number_of_timesteps) -> pyomo.environ.AbstractModel

   Add a simple model of the storage asset to the optimization model.

   NOTE: The `simple_storage` model does not strictly/explicitly restrict the
   simultaneous charge and discharge of the storage. Hence, under negative electricity
   prices and excess wind generation, storage may charge and discharge at the same time.

   Args:
       model: Pyomo model to which the prosumer's storage will be added.
       prosumer: Dictionary containing prosumer details.
       asset_name: Name of the storage asset, e.g., battery.
       timeseries_data: Timeseries data containing the availability factors for
           VRE generators and EVs doing V2G, demand profiles for prosumers, ...
       number_of_timesteps: Number of timesteps to run the optimization for.

   Returns:
       Pyomo AbstractModel with the storage asset added to it.

   Raises:
       KeyError: If a required asset parameter is missing


.. py:function:: add_complex_model_of_storage_asset(model: pyomo.environ.AbstractModel, prosumer: dict, asset_name: str, timeseries_data: pandas.DataFrame, number_of_timesteps) -> pyomo.environ.AbstractModel

   Add a complex model of the storage asset to the optimization model.

   NOTE: The `complex_storage` model strictly/explicitly restricts the simultaneous
   charge and discharge of the storage using binary variables.

   Args:
       model: Pyomo model to which the prosumer's storage will be added.
       prosumer: Dictionary containing prosumer details.
       asset_name: Name of the storage asset, e.g., battery.
       timeseries_data: Timeseries data containing the availability factors for
           VRE generators and EVs doing V2G, demand profiles for prosumers, ...
       number_of_timesteps: Number of timesteps to run the optimization for.

   Returns:
       Pyomo AbstractModel with the storage asset added to it.


.. py:function:: add_storage_asset_minimum_state_of_charge_constraint(model: pyomo.environ.AbstractModel, prosumer: dict, asset_name: str, timeseries_data: pandas.DataFrame, number_of_timesteps) -> pyomo.environ.AbstractModel

   Add minimum state-of-charge constraint for the storage asset.

   Args:
       model: Pyomo model to which the prosumer's storage will be added.
       prosumer: Dictionary containing prosumer details.
       asset_name: Name of the storage asset, e.g., battery.
       timeseries_data: Timeseries data containing the availability factors for
           VRE generators and EVs doing V2G, demand profiles for prosumers, ...
       number_of_timesteps: Number of timesteps to run the optimization for.

   Returns:
       Pyomo AbstractModel with the storage asset's min SOC constraints added.


.. py:function:: add_storage_asset_availability_constraints(model: pyomo.environ.AbstractModel, prosumer: dict, asset_name: str, timeseries_data: pandas.DataFrame, number_of_timesteps) -> pyomo.environ.AbstractModel

   Add availability constraints for the storage asset.

   Args:
       model: Pyomo model to which the prosumer's storage will be added.
       prosumer: Dictionary containing prosumer details.
       asset_name: Name of the storage asset, e.g., battery.
       timeseries_data: Timeseries data containing the availability factors for
           VRE generators and EVs doing V2G, demand profiles for prosumers, ...
       number_of_timesteps: Number of timesteps to run the optimization for.

   Returns:
       Pyomo AbstractModel with the storage's availability constraints added.