edisgo.network.electromobility

Module Contents

Classes

Electromobility

Data container for all electromobility data.
class edisgo.network.electromobility.Electromobility(**kwargs)[source]

Data container for all electromobility data.

This class holds data on charging processes (how long cars are parking at a charging station, how much they need to charge, etc.) necessary to apply different charging strategies, as well as information on potential charging sites and integrated charging parks.

property charging_processes_df

DataFrame with all charging processes.

Returns:

DataFrame with AGS, car ID, trip destination, charging use case, netto charging capacity, charging demand, charge start, charge end, grid connection point and charging point ID. The columns are:

agsint

8-digit AGS (Amtlicher Gemeindeschlüssel, eng. Community Identification Number). Leading zeros are missing.

car_idint

Car ID to differentiate charging processes from different cars.

destinationstr

SimBEV driving destination.

use_casestr

SimBEV use case. Can be “hpc”, “home”, “public” or “work”.

nominal_charging_capacity_kWfloat

Vehicle charging capacity in kW.

grid_charging_capacity_kWfloat

Grid-sided charging capacity including charging infrastructure losses (nominal_charging_capacity_kW / eta_cp) in kW.

chargingdemand_kWhfloat

Charging demand in kWh.

park_time_timestepsint

Number of parking time steps.

park_start_timestepsint

Time step the parking event starts.

park_end_timestepsint

Time step the parking event ends.

charging_park_idint

Designated charging park ID from potential_charging_parks_gdf. Is NaN if the charging demand is not yet distributed.

charging_point_idint

Designated charging point ID. Is used to differentiate between multiple charging points at one charging park.

Return type:

pandas.DataFrame

property potential_charging_parks_gdf

GeoDataFrame with all potential charging parks.

Returns:

GeoDataFrame with ID as index, AGS, charging use case (home, work, public or hpc), user-centric weight and geometry. Columns are:

indexint

Charging park ID.

use_casestr

TracBEV use case. Can be “hpc”, “home”, “public” or “work”.

user_centric_weightflaot

User centric weight used in distribution of charging demand. Weight is determined by TracBEV but normalized from 0 .. 1.

geometryGeoSeries

Geolocation of charging parks.

Return type:

geopandas.GeoDataFrame

property potential_charging_parks

Potential charging parks within the AGS.

Returns:

List of potential charging parks within the AGS.

Return type:

list(PotentialChargingParks)

property simbev_config_df

Dictionary containing configuration data.

Returns:

DataFrame with used regio type, charging point efficiency, stepsize in minutes, start date, end date, minimum SoC for hpc, grid timeseries setting, grid timeseries by use case setting and the number of simulated days. Columns are:

regio_typestr

RegioStaR 7 ID used in SimBEV.

eta_cpfloat or int

Charging point efficiency used in SimBEV.

stepsizeint

Stepsize in minutes the driving profile is simulated for in SimBEV.

start_datedatetime64

Start date of the SimBEV simulation.

end_datedatetime64

End date of the SimBEV simulation.

soc_minfloat

Minimum SoC when an HPC event is initialized in SimBEV.

grid_timeseriesbool

Setting whether a grid timeseries is generated within the SimBEV simulation.

grid_timeseries_by_usecasebool

Setting whether a grid timeseries by use case is generated within the SimBEV simulation.

daysint

Timedelta between the end_date and start_date in days.

Return type:

pandas.DataFrame

property integrated_charging_parks_df

Mapping DataFrame to map the charging park ID to the internal eDisGo ID.

The eDisGo ID is determined when integrating components using add_component() or integrate_component_based_on_geolocation() method.

Returns:

Mapping DataFrame to map the charging park ID to the internal eDisGo ID.

Return type:

pandas.DataFrame

property stepsize

Stepsize in minutes used in SimBEV.

Returns:

Stepsize in minutes

Return type:

int

property simulated_days

Number of simulated days in SimBEV.

Returns:

Number of simulated days

Return type:

int

property eta_charging_points

Charging point efficiency.

Returns:

Charging point efficiency in p.u..

Return type:

float

property flexibility_bands

Dictionary with flexibility bands (lower and upper energy band as well as upper power band).

Parameters:

flex_dict (dict(str, pandas.DataFrame)) – Keys are ‘upper_power’, ‘lower_energy’ and ‘upper_energy’. Values are dataframes containing the corresponding band per each charging point. Columns of the dataframe are the charging point names as in loads_df. Index is a time index.

Returns:

See input parameter flex_dict for more information on the dictionary.

Return type:

dict(str, pandas.DataFrame)

get_flexibility_bands(edisgo_obj, use_case, resample=True, tol=1e-06)[source]

Method to determine flexibility bands (lower and upper energy band as well as upper power band).

Besides being returned by this function, flexibility bands are written to flexibility_bands.

Parameters:

  • edisgo_obj (EDisGo)

  • use_case (str or list(str)) – Charging point use case(s) to determine flexibility bands for.

  • resample (bool (optional)) – If True, flexibility bands are resampled to the same frequency as time series data in TimeSeries object. If False, original frequency is kept. Default: True.

  • tol (float) – Tolerance to reduce or increase flexibility band values by to fix possible rounding errors that may lead to failing integrity checks and infeasibility when used to optimise charging. See fix_flexibility_bands_rounding_errors for more information. To avoid this behaviour, set tol to 0.0. Default: 1e-6.

Returns:

Keys are ‘upper_power’, ‘lower_energy’ and ‘upper_energy’. Values are dataframes containing the corresponding band for each charging point of the specified use case. Columns of the dataframe are the charging point names as in loads_df. Index is a time index.

Return type:

dict(str, pandas.DataFrame)

fix_flexibility_bands_rounding_errors(tol=1e-06)[source]

Fixes possible rounding errors that may lead to failing integrity checks.

Due to rounding errors it may occur, that e.g. the upper energy band is lower than the lower energy band. This does in some cases lead to infeasibilities when used to optimise charging processes.

This function increases or reduces a flexibility band by the specified tolerance in case an integrity check fails as follows:

  • If there are cases where the upper power band is not sufficient to meet the charged upper energy, the upper power band is increased for all charging points and all time steps.

  • If there are cases where the lower energy band is larger than the upper energy band, the lower energy band is reduced for all charging points and all time steps.

  • If there are cases where upper power band is not sufficient to meet charged lower energy, the upper power band is increased for all charging points and all time steps.

Parameters:

tol (float) – Tolerance to reduce or increase values by to fix rounding errors. Default: 1e-6.

resample(freq: str = '15min')[source]

Resamples flexibility bands.

Parameters:

freq (str or pandas.Timedelta, optional) – Frequency that time series is resampled to. Offset aliases can be found here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases. Default: ‘15min’.

check_integrity()[source]

Method to check the integrity of the Electromobility object.

Raises an error in case any of the checks fails.

Currently only checks integrity of flexibility bands.

to_csv(directory, attributes=None)[source]

Exports electromobility data to csv files.

The following attributes can be exported:

  • ‘charging_processes_df’ : Attribute charging_processes_df is saved to charging_processes.csv.

  • ‘potential_charging_parks_gdf’ : Attribute potential_charging_parks_gdf is saved to potential_charging_parks.csv.

  • ‘integrated_charging_parks_df’ : Attribute integrated_charging_parks_df is saved to integrated_charging_parks.csv.

  • ‘simbev_config_df’ : Attribute simbev_config_df is saved to simbev_config.csv.

  • ‘flexibility_bands’ : The three flexibility bands in attribute flexibility_bands are saved to flexibility_band_upper_power.csv, flexibility_band_lower_energy.csv, and flexibility_band_upper_energy.csv.

Parameters:

  • directory (str) – Path to save electromobility data to.

  • attributes (list(str) or None) – List of attributes to export. See above for attributes that can be exported. If None, all specified attributes are exported. Default: None.

from_csv(data_path, edisgo_obj, from_zip_archive=False)[source]

Restores electromobility from csv files.

Parameters:

  • data_path (str) – Path to electromobility csv files.

  • edisgo_obj (EDisGo)

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