edisgo.io package¶

edisgo.io.ding0_import module¶

edisgo.io.ding0_import.import_ding0_grid(path, edisgo_obj)[source]¶

Import an eDisGo network topology from Ding0 data.

This import method is specifically designed to load network topology data in the format as Ding0 provides it via csv files.

Parameters

path (str) – Path to ding0 network csv files.
edisgo_obj (EDisGo) – The eDisGo data container object.

edisgo.io.ding0_import.remove_1m_end_lines(edisgo)[source]¶

Method to remove 1m end lines to reduce size of edisgo object.

Short lines inside houses are removed in this function, including the end node. Components that were originally connected to the end node are reconnected to the upstream node.

This function will become obsolete once it is changed in the ding0 export.

Parameters: edisgo (EDisGo) –
Returns: edisgo – EDisGo object where 1m end lines are removed from topology.
Return type: EDisGo

edisgo.io.electromobility_import module¶

edisgo.io.electromobility_import.import_electromobility(edisgo_obj, simbev_directory, tracbev_directory, **kwargs)[source]¶

Import electromobility data from SimBEV and TracBEV.

Parameters

edisgo_obj (EDisGo) –
simbev_directory (str or pathlib.PurePath) – SimBEV directory holding SimBEV data.
tracbev_directory (str or pathlib.PurePath) – TracBEV directory holding TracBEV data.
kwargs –
Kwargs may contain any further attributes you want to specify.

gc_to_car_rate_homefloat
Specifies the minimum rate between potential charging parks points for the use case “home” and the total number of cars. Default 0.5 .

gc_to_car_rate_workfloat
Specifies the minimum rate between potential charging parks points for the use case “work” and the total number of cars. Default 0.25 .

gc_to_car_rate_publicfloat
Specifies the minimum rate between potential charging parks points for the use case “public” and the total number of cars. Default 0.1 .

gc_to_car_rate_hpcfloat
Specifies the minimum rate between potential charging parks points for the use case “hpc” and the total number of cars. Default 0.005 .

mode_parking_timesstr
If the mode_parking_times is set to “frugal” only parking times with any charging demand are imported. Default “frugal”.

charging_processes_dirstr
Charging processes sub-directory. Default None.

simbev_config_filestr
Name of the simbev config file. Default “metadata_simbev_run.json”.

edisgo.io.electromobility_import.read_csvs_charging_processes(csv_path, mode='frugal', csv_dir=None)[source]¶

Reads all CSVs in a given path and returns a DataFrame with all SimBEV charging processes.

Parameters

csv_path (str) – Main path holding SimBEV output data
mode (str) – Returns all information if None. Returns only rows with charging demand greater than 0 if “frugal”. Default: “frugal”.
csv_dir (str) – Optional sub-directory holding charging processes CSVs under path. Default: None.

Returns

DataFrame with AGS, car ID, trip destination, charging use case (private or public), netto charging capacity, charging demand, charge start, charge end, potential charging park ID and charging point ID.

Return type

pandas.DataFrame

edisgo.io.electromobility_import.read_simbev_config_df(path, edisgo_obj, simbev_config_file='metadata_simbev_run.json')[source]¶

Get SimBEV config data.

Parameters

path (str) – Main path holding SimBEV output data.
edisgo_obj (EDisGo) –
simbev_config_file (str) – SimBEV config file name. Default: “metadata_simbev_run.json”.

Returns

DataFrame with used random seed, used threads, stepsize in minutes, year, scenarette, simulated days, maximum number of cars per AGS, completed standing times and time series per AGS and used ramp up data CSV.

Return type

pandas.DataFrame

edisgo.io.electromobility_import.read_gpkg_potential_charging_parks(path, edisgo_obj, **kwargs)[source]¶

Get GeoDataFrame with all TracBEV potential charging parks.

Parameters

path (str) – Main path holding SimBEV output data
edisgo_obj (EDisGo) –

Returns

GeoDataFrame with AGS, charging use case (home, work, public or hpc), user centric weight and geometry.

Return type

geopandas.geodataframe

edisgo.io.electromobility_import.distribute_charging_demand(edisgo_obj, **kwargs)[source]¶

Distribute charging demand from SimBEV onto potential charging parks from TracBEV.

Parameters

edisgo_obj (EDisGo) –
kwargs –
Kwargs may contain any further attributes you want to specify.

modestr
Distribution mode. If the mode is set to “user_friendly” only the simbev weights are used for the distribution. If the mode is “grid_friendly” also grid conditions are respected. Default “user_friendly”.

generators_weight_factorfloat
Weighting factor of the generators weight within an LV grid in comparison to the loads weight. Default 0.5.

distance_weightfloat
Weighting factor for the distance between a potential charging park and its nearest substation in comparison to the combination of the generators and load factors of the LV grids. Default 1 / 3.

user_friendly_weightfloat
Weighting factor of the user friendly weight in comparison to the grid friendly weight. Default 0.5.

edisgo.io.electromobility_import.get_weights_df(edisgo_obj, potential_charging_park_indices, **kwargs)[source]¶

Get weights per potential charging point for a given set of grid connection indices.

Parameters

edisgo_obj (EDisGo) –
potential_charging_park_indices (list) – List of potential charging parks indices
mode (str) – Only use user friendly weights (“user_friendly”) or combine with grid friendly weights (“grid_friendly”). Default: “user_friendly”.
user_friendly_weight (float) – Weight of user friendly weight if mode “grid_friendly”. Default: 0.5.
distance_weight (float) – Grid friendly weight is a combination of the installed capacity of generators and loads within a LV grid and the distance towards the nearest substation. This parameter sets the weight for the distance parameter. Default: 1/3.

Returns

DataFrame with numeric weights

Return type

pandas.DataFrame

edisgo.io.electromobility_import.normalize(weights_df)[source]¶

Normalize a given DataFrame so that its sum equals 1 and return a flattened Array.

Parameters: weights_df (pandas.DataFrame) – DataFrame with single numeric column
Returns: Array with normalized weights
Return type: Numpy 1-D array

edisgo.io.electromobility_import.combine_weights(potential_charging_park_indices, designated_charging_point_capacity_df, weights_df)[source]¶

Add designated charging capacity weights into the initial weights and normalize weights

Parameters

potential_charging_park_indices (list) – List of potential charging parks indices
designated_charging_point_capacity_df – pandas.DataFrame DataFrame with designated charging point capacity per potential charging park
weights_df (pandas.DataFrame) – DataFrame with initial user or combined weights

Returns

Array with normalized weights

Return type

Numpy 1-D array

edisgo.io.electromobility_import.weighted_random_choice(edisgo_obj, potential_charging_park_indices, car_id, destination, charging_point_id, normalized_weights, rng=None)[source]¶

Weighted random choice of a potential charging park. Setting the chosen values into charging_processes_df

Parameters

edisgo_obj (EDisGo) –
potential_charging_park_indices (list) – List of potential charging parks indices
car_id (int) – Car ID
destination (str) – Trip destination
charging_point_id (int) – Charging Point ID
normalized_weights (Numpy 1-D array) – Array with normalized weights
rng (Numpy random generator) – If None a random generator with seed=charging_point_id is initialized

Returns

Chosen Charging Park ID

Return type

int

edisgo.io.electromobility_import.distribute_private_charging_demand(edisgo_obj)[source]¶

Distributes all private charging processes. Each car gets its own private charging point if a charging process takes place.

Parameters: edisgo_obj (EDisGo) –

edisgo.io.electromobility_import.distribute_public_charging_demand(edisgo_obj, **kwargs)[source]¶

Distributes all public charging processes. For each process it is checked if a matching charging point exists to minimize the number of charging points.

Parameters: edisgo_obj (EDisGo) –

edisgo.io.electromobility_import.determine_grid_connection_capacity(total_charging_point_capacity, lower_limit=0.3, upper_limit=1.0, minimum_factor=0.45)[source]¶

edisgo.io.electromobility_import.integrate_charging_parks(edisgo_obj)[source]¶

Integrates all designated charging parks into the grid.

The charging time series at each charging park are not set in this function.

Parameters: edisgo_obj (EDisGo) –

edisgo.io.generators_import module¶

edisgo.io.generators_import.oedb(edisgo_object, generator_scenario, **kwargs)[source]¶

Gets generator park for specified scenario from oedb and integrates them into the grid.

The importer uses SQLAlchemy ORM objects. These are defined in ego.io.

For further information see also import_generators.

Parameters

edisgo_object (EDisGo) –
generator_scenario (str) – Scenario for which to retrieve generator data. Possible options are ‘nep2035’ and ‘ego100’.
remove_decommissioned (bool) – If True, removes generators from network that are not included in the imported dataset (=decommissioned). Default: True.
update_existing (bool) – If True, updates capacity of already existing generators to capacity specified in the imported dataset. Default: True.
p_target (dict or None) – Per default, no target capacity is specified and generators are expanded as specified in the respective scenario. However, you may want to use one of the scenarios but have slightly more or less generation capacity than given in the respective scenario. In that case you can specify the desired target capacity per technology type using this input parameter. The target capacity dictionary must have technology types (e.g. ‘wind’ or ‘solar’) as keys and corresponding target capacities in MW as values. If a target capacity is given that is smaller than the total capacity of all generators of that type in the future scenario, only some of the generators in the future scenario generator park are installed, until the target capacity is reached. If the given target capacity is greater than that of all generators of that type in the future scenario, then each generator capacity is scaled up to reach the target capacity. Be careful to not have much greater target capacities as this will lead to unplausible generation capacities being connected to the different voltage levels. Also be aware that only technologies specified in the dictionary are expanded. Other technologies are kept the same. Default: None.
allowed_number_of_comp_per_lv_bus (int) – Specifies, how many generators are at most allowed to be placed at the same LV bus. Default: 2.

edisgo.io.pypsa_io module¶

This module provides tools to convert eDisGo representation of the network topology to PyPSA data model. Call to_pypsa() to retrieve the PyPSA network container.

edisgo.io.pypsa_io.to_pypsa(edisgo_object, mode=None, timesteps=None, **kwargs)[source]¶

Convert grid to PyPSA.Network representation.

You can choose between translation of the MV and all underlying LV grids (mode=None (default)), the MV network only (mode=’mv’ or mode=’mvlv’) or a single LV network (mode=’lv’).

Parameters

edisgo_object (EDisGo) – EDisGo object containing grid topology and time series information.
mode (str) – Determines network levels that are translated to PyPSA.Network. See mode parameter in to_pypsa for more information.
timesteps (pandas.DatetimeIndex or pandas.Timestamp) – See timesteps parameter in to_pypsa for more information.

:param See other parameters in to_pypsa for more: :param information.:

Returns: PyPSA.Network representation.
Return type: PyPSA.Network

edisgo.io.pypsa_io.set_seed(edisgo_obj, pypsa_network)[source]¶

Set initial guess for the Newton-Raphson algorithm.

In PyPSA an initial guess for the Newton-Raphson algorithm used in the power flow analysis can be provided to speed up calculations. For PQ buses, which besides the slack bus, is the only bus type in edisgo, voltage magnitude and angle need to be guessed. If the power flow was already conducted for the required time steps and buses, the voltage magnitude and angle results from previously conducted power flows stored in pfa_v_mag_pu_seed and pfa_v_ang_seed are used as the initial guess. Always the latest power flow calculation is used and only results from power flow analyses including the MV level are considered, as analysing single LV grids is currently not in the focus of edisgo and does not require as much speeding up, as analysing single LV grids is usually already quite quick. If for some buses or time steps no power flow results are available, default values are used. For the voltage magnitude the default value is 1 and for the voltage angle 0.

Parameters

edisgo_obj (EDisGo) –
pypsa_network (pypsa.Network) – Pypsa network in which seed is set.

edisgo.io.pypsa_io.process_pfa_results(edisgo, pypsa, timesteps, dtype='float')[source]¶

Passing power flow results from PyPSA to Results.

Parameters

edisgo (EDisGo) –
pypsa (pypsa.Network) – The PyPSA Network container
timesteps (pandas.DatetimeIndex or pandas.Timestamp) – Time steps for which latest power flow analysis was conducted and for which to retrieve pypsa results.

Notes

P and Q are returned from the line ending/transformer side with highest apparent power S, exemplary written as

$S_{max} = max(\sqrt{P_0^2 + Q_0^2}, \sqrt{P_1^2 + Q_1^2}) \ P = P_0 P_1(S_{max}) \ Q = Q_0 Q_1(S_{max})$

edisgo.io.timeseries_import module¶

edisgo.io.timeseries_import.feedin_oedb(config_data, weather_cell_ids, timeindex)[source]¶

Import feed-in time series data for wind and solar power plants from the OpenEnergy DataBase.

Parameters

config_data (Config) – Configuration data from config files, relevant for information of which data base table to retrieve feed-in data from.
weather_cell_ids (list(int)) – List of weather cell id’s (integers) to obtain feed-in data for.
timeindex (pandas.DatetimeIndex) – Feed-in data is currently only provided for weather year 2011. If timeindex contains a different year, the data is reindexed.

Returns

DataFrame with hourly time series for active power feed-in per generator type (wind or solar, in column level 0) and weather cell (in column level 1), normalized to a capacity of 1 MW.

Return type

pandas.DataFrame

edisgo.io.timeseries_import.load_time_series_demandlib(config_data, timeindex)[source]¶

Get normalized sectoral electricity load time series using the demandlib.

Resulting electricity load profiles hold time series of hourly conventional electricity demand for the sectors residential, retail, agricultural and industrial. Time series are normalized to a consumption of 1 MWh per year.

Parameters

config_data (Config) – Configuration data from config files, relevant for industrial load profiles.
timeindex (pandas.DatetimeIndex) – Timesteps for which to generate load time series.

Returns

DataFrame with conventional electricity load time series for sectors residential, retail, agricultural and industrial. Index is a pandas.DatetimeIndex. Columns hold the sector type.

Return type

pandas.DataFrame

edisgo.io.timeseries_import.cop_oedb(config_data, weather_cell_ids=None, timeindex=None)[source]¶

Get COP (coefficient of performance) time series data from the OpenEnergy DataBase.

Parameters

config_data (Config) – Configuration data from config files, relevant for information of which data base table to retrieve COP data from.
weather_cell_ids (list(int)) – List of weather cell id’s (integers) to obtain COP data for.
timeindex (pandas.DatetimeIndex) – COP data is only provided for the weather year 2011. If timeindex contains a different year, the data is reindexed.

Returns

DataFrame with hourly COP time series in p.u. per weather cell.

Return type

pandas.DataFrame

edisgo.io.timeseries_import.heat_demand_oedb(config_data, building_ids, timeindex=None)[source]¶

Get heat demand time series data from the OpenEnergy DataBase.

Parameters

config_data (Config) – Configuration data from config files, relevant for information of which data base table to retrieve data from.
building_ids (list(int)) – List of building IDs to obtain heat demand for.
timeindex (pandas.DatetimeIndex) – Heat demand data is only provided for the weather year 2011. If timeindex contains a different year, the data is reindexed.

Returns

DataFrame with hourly heat demand time series in MW per building ID.

Return type

pandas.DataFrame