prepare_vehicle_charging_demand¶
Inputs¶
- input_dirstr
raw/time_series/vehicle_charging
: Path of directory where csv-files containing electric vehicle charging demand profiles are placed- scalars_filestr
raw/scalars/demands.csv
: Path incl. file name of demand scalars including ‘bev_car_share’, the share of electricity demand for charging passenger cars- output_filestr
results/_resources/ts_load_electricity_vehicles.csv
: Path incl. file name of prepared time series- logfilestr
results/_resources/ts_load_electricity_vehicles.log
: path to logfile
Outputs¶
- pd.DataFrame
Normalized electric vehicle charging demand profiles for regions “B” and “BB” for all years provided in input_dir.
Description¶
This script prepares electric vehicle charging demand profiles for the regions Berlin and Brandenburg. The profiles for passenger cars have been created before with simBEV (https://github.com/rl-institut/simbev), other vehicles are taken into consideration with constant profiles. The charging strategy of simBEV data is “greedy”, i.e. batteries are charged with maximum power until they are fully charged or removed. This script applies a charging strategy we refer to as “balanced” for the profiles “home” and “work” during specific hours (see global variables). To apply this, charging strategy values between [HOME_START, HOME_END] and [WORK_START, WORK_END] respectively are replaced by the average of all these values. We assume that this is a more realistic picture of the future than a charging strategy “greedy”.