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Merge pull request #386 from openego/features/home_storage_operation
Add home storage operation strategy
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,255 @@ | ||
| import logging | ||
| import math | ||
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| import pandas as pd | ||
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| logger = logging.getLogger(__name__) | ||
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| def _reference_operation( | ||
| df, | ||
| soe_init, | ||
| soe_max, | ||
| storage_p_nom, | ||
| freq, | ||
| efficiency_store, | ||
| efficiency_dispatch, | ||
| ): | ||
| """ | ||
| Reference operation of storage system where it is directly charged when PV feed-in | ||
| is higher than electricity demand of the building. | ||
| Battery model handles generation positive, demand negative. | ||
| Parameters | ||
| ----------- | ||
| df : :pandas:`pandas.DataFrame<DataFrame>` | ||
| Dataframe with time index and the buildings residual electricity demand | ||
| (PV generation minus electricity demand) in column "feedin_minus_demand". | ||
| soe_init : float | ||
| Initial state of energy of storage device in MWh. | ||
| soe_max : float | ||
| Maximum energy level of storage device in MWh. | ||
| storage_p_nom : float | ||
| Nominal charging power of storage device in MW. | ||
| freq : float | ||
| Frequency of provided time series. Set to one, in case of hourly time series or | ||
| 0.5 in case of half-hourly time series. | ||
| efficiency_store : float | ||
| Efficiency of storage system in case of charging. | ||
| efficiency_dispatch : float | ||
| Efficiency of storage system in case of discharging. | ||
| Returns | ||
| --------- | ||
| :pandas:`pandas.DataFrame<DataFrame>` | ||
| Dataframe provided through parameter `df` extended by columns "storage_power", | ||
| holding the charging (negative values) and discharging (positive values) power | ||
| of the storage unit in MW, and "storage_soe" holding the storage unit's state of | ||
| energy in MWh. | ||
| """ | ||
| lst_storage_power = [] | ||
| lst_storage_soe = [] | ||
| storage_soe = soe_init | ||
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| for i, d in df.iterrows(): | ||
| # If the house were to feed electricity into the grid, charge the storage first. | ||
| # No electricity exchange with grid as long as charger power is not exceeded. | ||
| if (d.feedin_minus_demand > 0.0) & (storage_soe < soe_max): | ||
| # Check if energy produced exceeds charger power | ||
| if d.feedin_minus_demand < storage_p_nom: | ||
| storage_power = -d.feedin_minus_demand | ||
| # If it does, feed the rest to the grid | ||
| else: | ||
| storage_power = -storage_p_nom | ||
| storage_soe = storage_soe + (-storage_power * efficiency_store * freq) | ||
| # If the storage is overcharged, feed the 'rest' to the grid | ||
| if storage_soe > soe_max: | ||
| storage_power = storage_power + (storage_soe - soe_max) / ( | ||
| efficiency_store * freq | ||
| ) | ||
| storage_soe = soe_max | ||
|
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||
| # If the house needs electricity from the grid, discharge the storage first. | ||
| # In this case d.feedin_minus_demand is negative! | ||
| # No electricity exchange with grid as long as demand does not exceed charging | ||
| # power | ||
| elif (d.feedin_minus_demand < 0.0) & (storage_soe > 0.0): | ||
| # Check if energy demand exceeds charger power | ||
| if d.feedin_minus_demand / efficiency_dispatch < (storage_p_nom * -1): | ||
| storage_soe = storage_soe - (storage_p_nom * freq) | ||
| storage_power = storage_p_nom * efficiency_dispatch | ||
| else: | ||
| storage_soe = storage_soe + ( | ||
| d.feedin_minus_demand / efficiency_dispatch * freq | ||
| ) | ||
| storage_power = -d.feedin_minus_demand | ||
| # If the storage is undercharged, take the 'rest' from the grid | ||
| if storage_soe < 0.0: | ||
| # since storage_soe is negative in this case it can be taken as | ||
| # demand | ||
| storage_power = storage_power + storage_soe * efficiency_dispatch / freq | ||
| storage_soe = 0.0 | ||
|
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| # If the storage is full or empty, the demand is not affected | ||
| else: | ||
| storage_power = 0.0 | ||
| lst_storage_power.append(storage_power) | ||
| lst_storage_soe.append(storage_soe) | ||
|
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| df["storage_power"] = lst_storage_power | ||
| df["storage_soe"] = lst_storage_soe | ||
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| return df.round(6) | ||
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| def apply_reference_operation( | ||
| edisgo_obj, storage_units_names=None, soe_init=0.0, freq=1 | ||
| ): | ||
| """ | ||
| Applies reference storage operation to specified home storage units. | ||
| In the reference storage operation, the home storage system is directly charged when | ||
| PV feed-in is higher than electricity demand of the building until the storage | ||
| is fully charged. The storage is directly discharged, in case electricity demand | ||
| of the building is higher than the PV feed-in, until it is fully discharged. | ||
| The battery model handles generation positive and demand negative. | ||
| To determine the PV feed-in and electricity demand of the building that the home | ||
| storage is located in (including demand from heat pumps | ||
| and electric vehicles), this function matches the storage units to PV plants and | ||
| building electricity demand using the building ID. | ||
| In case there is no electricity load or no PV system, the storage operation is set | ||
| to zero. | ||
| The resulting storage units' active power time series are written to | ||
| :attr:`~.network.timeseries.TimeSeries.loads_active_power`. | ||
| Further, reactive power time series are set up using function | ||
| :attr:`~.edisgo.EDisGo.set_time_series_reactive_power_control` with default values. | ||
| The state of energy time series that are calculated within this function are not | ||
| written anywhere, but are returned by this function. | ||
| Parameters | ||
| ---------- | ||
| edisgo_obj : :class:`~.EDisGo` | ||
| EDisGo object to obtain storage units and PV feed-in and electricity demand | ||
| in same building from. | ||
| storage_units_names : list(str) or None | ||
| Names of storage units as in | ||
| :attr:`~.network.topology.Topology.storage_units_df` to set time for. If None, | ||
| time series are set for all storage units in | ||
| :attr:`~.network.topology.Topology.storage_units_df`. | ||
| soe_init : float | ||
| Initial state of energy of storage device in MWh. Default: 0 MWh. | ||
| freq : float | ||
| Frequency of provided time series. Set to one, in case of hourly time series or | ||
| 0.5 in case of half-hourly time series. Default: 1. | ||
| Returns | ||
| -------- | ||
| :pandas:`pandas.DataFrame<DataFrame>` | ||
| Dataframe with time index and state of energy in MWh of each storage in columns. | ||
| Column names correspond to storage name as in | ||
| :attr:`~.network.topology.Topology.storage_units_df`. | ||
| Notes | ||
| ------ | ||
| This function requires that the storage parameters `building_id`, | ||
| `efficiency_store`, `efficiency_dispatch` and `max_hours` are set in | ||
| :attr:`~.network.topology.Topology.storage_units_df` for all storage units | ||
| specified in parameter `storage_units_names`. | ||
| """ | ||
| if storage_units_names is None: | ||
| storage_units_names = edisgo_obj.topology.storage_units_df.index | ||
|
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| storage_units = edisgo_obj.topology.storage_units_df.loc[storage_units_names] | ||
| soe_df = pd.DataFrame(index=edisgo_obj.timeseries.timeindex) | ||
|
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| for stor_name, stor_data in storage_units.iterrows(): | ||
| # get corresponding PV systems and electric loads | ||
| building_id = stor_data["building_id"] | ||
| pv_gens = edisgo_obj.topology.generators_df.loc[ | ||
| edisgo_obj.topology.generators_df.building_id == building_id | ||
| ].index | ||
| loads = edisgo_obj.topology.loads_df.loc[ | ||
| edisgo_obj.topology.loads_df.building_id == building_id | ||
| ].index | ||
| if len(loads) == 0 or len(pv_gens) == 0: | ||
| if len(loads) == 0: | ||
| logger.warning( | ||
| f"Storage unit {stor_name} in building {building_id} has no load. " | ||
| f"Storage operation is therefore set to zero." | ||
| ) | ||
| if len(pv_gens) == 0: | ||
| logger.warning( | ||
| f"Storage unit {stor_name} in building {building_id} has no PV " | ||
| f"system. Storage operation is therefore set to zero." | ||
| ) | ||
| edisgo_obj.set_time_series_manual( | ||
| storage_units_p=pd.DataFrame( | ||
| columns=[stor_name], | ||
| index=soe_df.index, | ||
| data=0.0, | ||
| ) | ||
| ) | ||
| else: | ||
| # check storage values | ||
| if math.isnan(stor_data.max_hours) is True: | ||
| raise ValueError( | ||
| f"Parameter max_hours for storage unit {stor_name} is not a " | ||
| f"number. It needs to be set in Topology.storage_units_df." | ||
| ) | ||
| if math.isnan(stor_data.efficiency_store) is True: | ||
| raise ValueError( | ||
| f"Parameter efficiency_store for storage unit {stor_name} is not a " | ||
| f"number. It needs to be set in Topology.storage_units_df." | ||
| ) | ||
| if math.isnan(stor_data.efficiency_dispatch) is True: | ||
| raise ValueError( | ||
| f"Parameter efficiency_dispatch for storage unit {stor_name} is " | ||
| f"not a number. It needs to be set in Topology.storage_units_df." | ||
| ) | ||
| pv_feedin = edisgo_obj.timeseries.generators_active_power[pv_gens].sum( | ||
| axis=1 | ||
| ) | ||
| house_demand = edisgo_obj.timeseries.loads_active_power[loads].sum(axis=1) | ||
| # apply operation strategy | ||
| storage_ts = _reference_operation( | ||
| df=pd.DataFrame( | ||
| columns=["feedin_minus_demand"], data=pv_feedin - house_demand | ||
| ), | ||
| soe_init=soe_init, | ||
| soe_max=stor_data.p_nom * stor_data.max_hours, | ||
| storage_p_nom=stor_data.p_nom, | ||
| freq=freq, | ||
| efficiency_store=stor_data.efficiency_store, | ||
| efficiency_dispatch=stor_data.efficiency_dispatch, | ||
| ) | ||
| # add storage time series to storage_units_active_power dataframe | ||
| edisgo_obj.set_time_series_manual( | ||
| storage_units_p=pd.DataFrame( | ||
| columns=[stor_name], | ||
| index=storage_ts.index, | ||
| data=storage_ts.storage_power.values, | ||
| ) | ||
| ) | ||
| soe_df = pd.concat( | ||
| [soe_df, storage_ts.storage_soe.to_frame(stor_name)], axis=1 | ||
| ) | ||
|
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| edisgo_obj.set_time_series_reactive_power_control( | ||
| generators_parametrisation=None, | ||
| loads_parametrisation=None, | ||
| storage_units_parametrisation=pd.DataFrame( | ||
| { | ||
| "components": [storage_units_names], | ||
| "mode": ["default"], | ||
| "power_factor": ["default"], | ||
| }, | ||
| index=[1], | ||
| ), | ||
| ) | ||
|
|
||
| return soe_df |
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