edisgo.network.electromobility¶
Module Contents¶
Classes¶
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 SimBEV 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 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
- property potential_charging_parks_gdf¶
GeoDataFrame with all TracBEV 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
- 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¶
Dict with all SimBEV config 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 a 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
- 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()orintegrate_component_based_on_geolocation()method.- Returns
Mapping DataFrame to map the charging park ID to the internal eDisGo ID.
- Return type
- property simulated_days¶
Number of simulated days in SimBEV.
- Returns
Number of simulated days
- Return type
- property eta_charging_points¶
Charging point efficiency.
- Returns
Charging point efficiency in p.u..
- Return type
- 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)[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
- 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)
- to_csv(directory, attributes=None)[source]¶
Exports electromobility data to csv files.
The following attributes can be exported:
‘charging_processes_df’ : Attribute
charging_processes_dfis saved to charging_processes.csv.‘potential_charging_parks_gdf’ : Attribute
potential_charging_parks_gdfis saved to potential_charging_parks.csv.‘integrated_charging_parks_df’ : Attribute
integrated_charging_parks_dfis saved to integrated_charging_parks.csv.‘simbev_config_df’ : Attribute
simbev_config_dfis saved to simbev_config.csv.‘flexibility_bands’ : The three flexibility bands in attribute
flexibility_bandsare saved to flexibility_band_upper_power.csv, flexibility_band_lower_energy.csv, and flexibility_band_upper_energy.csv.