model_timeseries

Generate timeseries for eTraGo and pypsa-eur-sec

Call order * generate_model_data_eGon2035() / generate_model_data_eGon100RE()

  • generate_model_data()

    • generate_model_data_grid_district()

      • load_evs_trips()

      • data_preprocessing()

      • generate_load_time_series()

      • write_model_data_to_db()

Notes

# TODO REWORK Share of EV with access to private charging infrastructure (flex_share) for use cases work and home are not supported by simBEV v0.1.2 and are applied here (after simulation). Applying those fixed shares post-simulation introduces small errors compared to application during simBEV’s trip generation.

Values (cf. flex_share in scenario parameters egon.data.datasets.scenario_parameters.parameters.mobility()) were linearly extrapolated based upon https://nationale-leitstelle.de/wp-content/pdf/broschuere-lis-2025-2030-final.pdf (p.92):

  • eGon2035: home=0.8, work=1.0

  • eGon100RE: home=1.0, work=1.0

data_preprocessing(scenario_data: DataFrame, ev_data_df: DataFrame) → DataFrame[source]

Filter SimBEV data to match region requirements. Duplicates profiles if necessary. Pre-calculates necessary parameters for the load time series.

Parameters:

  • scenario_data (pd.Dataframe) – EV per grid district

  • ev_data_df (pd.Dataframe) – Trip data

Returns:

pd.Dataframe – Trip data

delete_model_data_from_db()[source]

Delete all eMob MIT data from eTraGo PF tables

generate_load_time_series(ev_data_df: DataFrame, run_config: DataFrame, scenario_data: DataFrame) → DataFrame[source]

Calculate the load time series from the given trip data. A dumb charging strategy is assumed where each EV starts charging immediately after plugging it in. Simultaneously the flexible charging capacity is calculated.

Parameters:

  • ev_data_df (pd.DataFrame) – Full trip data

  • run_config (pd.DataFrame) – simBEV metadata: run config

  • scenario_data (pd.Dataframe) – EV per grid district

Returns:

pd.DataFrame – time series of the load and the flex potential

generate_model_data_bunch(scenario_name: str, bunch: range) → None[source]

Generates timeseries from simBEV trip data for a bunch of MV grid districts.

Parameters:

  • scenario_name (str) – Scenario name

  • bunch (list) – Bunch of grid districts to generate data for, e.g. [1,2,..,100]. Note: bunch is NOT a list of grid districts but is used for slicing the ordered list (by bus_id) of grid districts! This is used for parallelization. See egon.data.datasets.emobility.motorized_individual_travel.MotorizedIndividualTravel.generate_model_data_tasks()

generate_model_data_eGon100RE_remaining()[source]

Generates timeseries for eGon100RE scenario for grid districts which has not been processed in the parallel tasks before.

generate_model_data_eGon2035_remaining()[source]

Generates timeseries for eGon2035 scenario for grid districts which has not been processed in the parallel tasks before.

generate_model_data_grid_district(scenario_name: str, evs_grid_district: DataFrame, bat_cap_dict: dict, run_config: DataFrame) → tuple[source]

Generates timeseries from simBEV trip data for MV grid district

Parameters:

  • scenario_name (str) – Scenario name

  • evs_grid_district (pd.DataFrame) – EV data for grid district

  • bat_cap_dict (dict) – Battery capacity per EV type

  • run_config (pd.DataFrame) – simBEV metadata: run config

Returns:

pd.DataFrame – Model data for grid district

generate_model_data_status2019_remaining()[source]

Generates timeseries for status2019 scenario for grid districts which has not been processed in the parallel tasks before.

generate_model_data_status2023_remaining()[source]

Generates timeseries for status2023 scenario for grid districts which has not been processed in the parallel tasks before.

generate_static_params(ev_data_df: DataFrame, load_time_series_df: DataFrame, evs_grid_district_df: DataFrame) → dict[source]

Calculate static parameters from trip data.

  • cumulative initial SoC

  • cumulative battery capacity

  • simultaneous plugged in charging capacity

Parameters:

ev_data_df (pd.DataFrame) – Fill trip data

Returns:

dict – Static parameters

load_evs_trips(scenario_name: str, evs_ids: list, charging_events_only: bool = False, flex_only_at_charging_events: bool = True) → DataFrame[source]

Load trips for EVs

Parameters:

  • scenario_name (str) – Scenario name

  • evs_ids (list of int) – IDs of EV to load the trips for

  • charging_events_only (bool) – Load only events where charging takes place

  • flex_only_at_charging_events (bool) – Flexibility only at charging events. If False, flexibility is provided by plugged-in EVs even if no charging takes place.

Returns:

pd.DataFrame – Trip data

load_grid_district_ids() → Series[source]

Load bus IDs of all grid districts

write_model_data_to_db(static_params_dict: dict, load_time_series_df: DataFrame, bus_id: int, scenario_name: str, run_config: DataFrame, bat_cap: DataFrame) → None[source]

Write all results for grid district to database

Parameters:

  • static_params_dict (dict) – Static model params

  • load_time_series_df (pd.DataFrame) – Load time series for grid district

  • bus_id (int) – ID of grid district

  • scenario_name (str) – Scenario name

  • run_config (pd.DataFrame) – simBEV metadata: run config

  • bat_cap (pd.DataFrame) – Battery capacities per EV type

Returns:

None