motorized_individual_travel

• db_classes
• ev_allocation
• helpers
• model_timeseries
• tests

Main module for preparation of model data (static and timeseries) for motorized individual travel (MIT).

Contents of this module

• Creation of DB tables

• Download and preprocessing of vehicle registration data from KBA and BMVI

• Calculate number of electric vehicles and allocate on different spatial levels.

• Extract and write pre-generated trips to DB

class MotorizedIndividualTravel(dependencies)

Bases: Dataset

Class to set up static and timeseries data for motorized individual travel (MIT).

For more information see data documentation on Motorized individual travel.

Dependencies

• DataBundle

• MvGridDistricts

• ScenarioParameters

• EtragoSetup

• ZensusMvGridDistricts

• ZensusVg250

• StorageEtrago

• HtsEtragoTable

• ChpEtrago

• DsmPotential

• HeatEtrago

• Egon_etrago_gen

• OpenCycleGasTurbineEtrago

• HydrogenStoreEtrago

• HydrogenPowerLinkEtrago

• HydrogenMethaneLinkEtrago

• GasAreaseGon100RE

• CH4Production

• CH4Storages

Resulting Tables

• EgonEvPool is created and filled

• EgonEvTrip is created and filled

• EgonEvCountRegistrationDistrict is created and filled

• EgonEvCountMunicipality is created and filled

• EgonEvCountMvGridDistrict is created and filled

• EgonEvMvGridDistrict is created and filled

• EgonEvMetadata is created and filled

Configuration

The config of this dataset can be found in datasets.yml in section emobility_mit.

name: str = 'MotorizedIndividualTravel'

sources: DatasetSources = DatasetSources(tables={}, files={'trips_status2019': 'mit_trip_data/eGon2035_RS7_min2k_2022-06-01_175429_simbev_run.tar.gz', 'trips_status2023': 'mit_trip_data/eGon2035_RS7_min2k_2022-06-01_175429_simbev_run.tar.gz', 'trips_eGon2035': 'mit_trip_data/eGon2035_RS7_min2k_2022-06-01_175429_simbev_run.tar.gz', 'trips_eGon100RE': 'mit_trip_data/eGon100RE_RS7_min2k_2022-06-01_175444_simbev_run.tar.gz', 'original_data': {'original_data': {'sources': {'RS7': {'url': 'https://www.bmv.de/SharedDocs/DE/Anlage/G/regiostar-referenzdateien.xlsx?__blob=publicationFile', 'file': 'regiostar-referenzdateien.xlsx', 'file_processed': 'regiostar-referenzdateien_preprocessed.csv', 'sheet': 'ReferenzGebietsstand2020'}, 'KBA': {'url': 'https://www.kba.de/SharedDocs/Downloads/DE/Statistik/Fahrzeuge/FZ1/fz1_2021.xlsx?__blob=publicationFile&v=2', 'file': 'fz1_2021.xlsx', 'file_processed': 'fz1_2021_preprocessed.csv', 'sheet': 'FZ1.1', 'columns': 'D, J:N', 'skiprows': 8}, 'trips': {'status2019': {'file': 'eGon2035_RS7_min2k_2022-06-01_175429_simbev_run.tar.gz', 'file_metadata': 'metadata_simbev_run.json'}, 'status2023': {'file': 'eGon2035_RS7_min2k_2022-06-01_175429_simbev_run.tar.gz', 'file_metadata': 'metadata_simbev_run.json'}, 'eGon2035': {'file': 'eGon2035_RS7_min2k_2022-06-01_175429_simbev_run.tar.gz', 'file_metadata': 'metadata_simbev_run.json'}, 'eGon100RE': {'file': 'eGon100RE_RS7_min2k_2022-06-01_175444_simbev_run.tar.gz', 'file_metadata': 'metadata_simbev_run.json'}}}}, 'scenario': {'variation': {'status2019': 'status2019', 'status2023': 'status2023', 'eGon2035': 'NEP C 2035', 'eGon100RE': 'Reference 2050'}, 'lowflex': {'create_lowflex_scenario': True, 'names': {'eGon2035': 'eGon2035_lowflex', 'eGon100RE': 'eGon100RE_lowflex'}}}, 'model_timeseries': {'reduce_memory': True, 'export_results_to_csv': True, 'parallel_tasks': 10}, 'demand_timeseries_mvgd': {'parallel_tasks': 10}}}, urls={'KBA': 'https://www.kba.de/SharedDocs/Downloads/DE/Statistik/Fahrzeuge/FZ1/fz1_2021.xlsx?__blob=publicationFile&v=2', 'RS7': 'https://www.bmv.de/SharedDocs/DE/Anlage/G/regiostar-referenzdateien.xlsx?__blob=publicationFile'})

The sources used by the datasets. Could be tables, files and urls

targets: DatasetTargets = DatasetTargets(tables={'ev_pool': 'emobility.egon_ev_pool', 'ev_trip': 'emobility.egon_ev_trip', 'ev_count_reg_district': 'emobility.egon_ev_count_registration_district', 'ev_count_municipality': 'emobility.egon_ev_count_municipality', 'ev_count_mv_grid': 'emobility.egon_ev_count_mv_grid_district', 'ev_mv_grid': 'emobility.egon_ev_mv_grid_district', 'ev_metadata': 'emobility.egon_ev_metadata'}, files={'KBA_download': 'motorized_individual_travel/fz1_2021.xlsx', 'KBA_processed': 'motorized_individual_travel/fz1_2021_preprocessed.csv', 'RS7_download': 'motorized_individual_travel/regiostar-referenzdateien.xlsx', 'RS7_processed': 'motorized_individual_travel/regiostar-referenzdateien_preprocessed.csv'})

The targets created by the datasets. Could be tables and files

version: str = '0.0.11'

adapt_numpy_float64(numpy_float64)

adapt_numpy_int64(numpy_int64)

create_tables()

Create tables for electric vehicles

Returns:

None

download_and_preprocess()

Downloads and preprocesses data from KBA and BMVI

Returns:

• pandas.DataFrame – Vehicle registration data for registration district

• pandas.DataFrame – RegioStaR7 data

extract_trip_file()

Extract trip file from data bundle

write_evs_trips_to_db()

Write EVs and trips generated by simBEV from data bundle to database table

write_metadata_to_db()

Write used SimBEV metadata per scenario to database.