edisgo.network.overlying_grid

Module Contents

Classes

OverlyingGrid

Data container for requirements from the overlying grid.

Functions

distribute_overlying_grid_requirements(edisgo_obj)

Distributes overlying grid requirements to components in grid.
class edisgo.network.overlying_grid.OverlyingGrid(**kwargs)[source]

Data container for requirements from the overlying grid.

The requirements from the overlying grid are used as constraints for flexibilities.

renewables_curtailment

Curtailment of fluctuating renewables per time step in MW.

Type:

pandas.Series

storage_units_active_power

Aggregated dispatch of storage units per time step in MW.

Type:

pandas.Series

storage_units_soc

State of charge of storage units per time step in p.u.. The state of charge at time step t here constitutes the state of charge at the beginning of time step t.

Type:

pandas.Series

dsm_active_power

Aggregated demand side management utilisation per time step in MW.

Type:

pandas.Series

electromobility_active_power

Aggregated charging demand at all charging sites in grid per time step in MW.

Type:

pandas.Series

heat_pump_decentral_active_power

Aggregated demand of flexible decentral heat pumps per time step in MW.

Type:

pandas.Series

thermal_storage_units_decentral_soc

State of charge of decentral thermal storage units in p.u..

Type:

pandas.Series

heat_pump_central_active_power

Aggregated demand of flexible central heat pumps per time step in MW.

Type:

pandas.Series

thermal_storage_units_central_soc

State of charge of central thermal storage units per district heating area (in columns as string of integer, i.e. “130” instead of “130.0”) and time step (in index) in p.u.. The state of charge at time step t here constitutes the state of charge at the beginning of time step t.

Type:

pandas.DataFrame

feedin_district_heating

Other thermal feed-in into district heating per district heating area (in columns as string of integer, i.e. “130” instead of “130.0”) and time step (in index) in MW.

Type:

pandas.DataFrame

reduce_memory(attr_to_reduce=None, to_type='float32')[source]

Reduces size of time series data to save memory.

Parameters:

  • attr_to_reduce (list(str), optional) – List of attributes to reduce size for. Per default, all time series data are reduced.

  • to_type (str, optional) – Data type to convert time series data to. This is a tradeoff between precision and memory. Default: “float32”.

to_csv(directory, reduce_memory=False, **kwargs)[source]

Saves data in object to csv.

Parameters:

  • directory (str) – Directory to save data in.

  • reduce_memory (bool, optional) – If True, size of time series data is reduced using reduce_memory. Optional parameters of reduce_memory can be passed as kwargs to this function. Default: False.

  • kwargs – Kwargs may contain arguments of reduce_memory.

from_csv(data_path, dtype=None, from_zip_archive=False, **kwargs)[source]

Restores data in object from csv files.

Parameters:

  • data_path (str) – Path to directory to obtain data from. Must be a directory or zip archive.

  • dtype (str, optional) – Numerical data type for data to be loaded from csv. E.g. “float32”. Default: None.

  • from_zip_archive (bool, optional) – Set True if data is archived in a zip archive. Default: False.

resample(method: str = 'ffill', freq: str | pandas.Timedelta = '15min')[source]

Resamples all time series to a desired resolution.

See resample_timeseries for more information.

Parameters:
edisgo.network.overlying_grid.distribute_overlying_grid_requirements(edisgo_obj)[source]

Distributes overlying grid requirements to components in grid.

Overlying grid requirements for e.g. electromobility charging are distributed to all charging points where cars are parked, and for DSM to all DSM loads based on their available load increase and decrease at each time step.

Parameters:

edisgo_obj (EDisGo) – The eDisGo API object

Returns:

New EDisGo object with only the topology data and adjusted time series data.

Return type:

EDisGo