edisgo.tools package

edisgo.tools.config module

This file is part of eDisGo, a python package for distribution network analysis and optimization.

It is developed in the project open_eGo: https://openegoproject.wordpress.com

eDisGo lives on github: https://github.com/openego/edisgo/ The documentation is available on RTD: http://edisgo.readthedocs.io

Based on code by oemof developing group

This module provides a highlevel layer for reading and writing config files.

class edisgo.tools.config.Config(**kwargs)[source]

Bases: object

Container for all configurations.

Parameters

config_path (None or str or dict) –

Path to the config directory. Options are:

  • None If config_path is None configs are loaded from the edisgo default config directory ($HOME$/.edisgo). If the directory does not exist it is created. If config files don’t exist the default config files are copied into the directory.

  • str If config_path is a string configs will be loaded from the directory specified by config_path. If the directory does not exist it is created. If config files don’t exist the default config files are copied into the directory.

  • dict A dictionary can be used to specify different paths to the different config files. The dictionary must have the following keys: * ‘config_db_tables’ * ‘config_grid’ * ‘config_grid_expansion’ * ‘config_timeseries’

    Values of the dictionary are paths to the corresponding config file. In contrast to the other two options the directories and config files must exist and are not automatically created.

Default: None.

Notes

The Config object can be used like a dictionary. See example on how to use it.

Examples

Create Config object from default config files

>>> from edisgo.tools.config import Config
>>> config = Config()

Get reactive power factor for generators in the MV network

>>> config['reactive_power_factor']['mv_gen']
edisgo.tools.config.load_config(filename, config_dir=None, copy_default_config=True)[source]

Loads the specified config file.

Parameters
  • filename (str) – Config file name, e.g. ‘config_grid.cfg’.

  • config_dir (str, optional) – Path to config file. If None uses default edisgo config directory specified in config file ‘config_system.cfg’ in section ‘user_dirs’ by subsections ‘root_dir’ and ‘config_dir’. Default: None.

  • copy_default_config (Boolean) – If True copies a default config file into config_dir if the specified config file does not exist. Default: True.

edisgo.tools.config.get(section, key)[source]

Returns the value of a given key of a given section of the main config file.

Parameters
  • section (str) –

  • key (str) –

Returns

The value which will be casted to float, int or boolean. If no cast is successful, the raw string is returned.

Return type

float or int or Boolean or str

edisgo.tools.config.get_default_config_path()[source]

Returns the basic edisgo config path. If it does not yet exist it creates it and copies all default config files into it.

Returns

Path to default edisgo config directory specified in config file ‘config_system.cfg’ in section ‘user_dirs’ by subsections ‘root_dir’ and ‘config_dir’.

Return type

str

edisgo.tools.config.make_directory(directory)[source]

Makes directory if it does not exist.

Parameters

directory (str) – Directory path

edisgo.tools.edisgo_run module

edisgo.tools.edisgo_run.setup_logging(logfilename=None, logfile_loglevel='debug', console_loglevel='info', **logging_kwargs)[source]
edisgo.tools.edisgo_run.run_edisgo_basic(ding0_path, generator_scenario=None, analysis='worst-case', *edisgo_grid)[source]

Determine network expansion costs for given ding0 grid and scenario.

Parameters
  • ding0_path (str) – Path to ding0 network csv data.

  • analysis (str) – Either ‘worst-case’ or ‘timeseries’.

  • generator_scenario (None or str) – If provided defines which scenario of future generator park to use and invokes import of these generators. Possible options are ‘nep2035’ and ‘ego100’.

  • edisgo_grid (EDisGo (optional)) – If an EDisGo object is provided it is used instead of creating a new object using parameters ding0_path and analysis.

Returns

  • edisgo_grid (EDisGo)

  • costs (pandas.DataFrame) – Costs of network expansion

  • grid_issues (dict) – Log for remaining grid issues after network expansion. For grids resulting in an error this gives the error message.

edisgo.tools.edisgo_run.run_edisgo_twice(run_args)[source]

Run network analysis twice on same network: once w/ and once w/o new generators

ToDo: adapt to refactored code!

First run without connection of new generators approves sufficient network hosting capacity. Otherwise, network is reinforced. Second run assessment network extension needs in terms of RES integration

Parameters

run_args (list) – Optional parameters for run_edisgo_basic().

Returns

  • all_costs_before_geno_import (pandas.Dataframe) – Grid extension cost before network connection of new generators

  • all_grid_issues_before_geno_import (dict) – Remaining overloading or over-voltage issues in network

  • all_costs (pandas.Dataframe) – Grid extension cost due to network connection of new generators

  • all_grid_issues (dict) – Remaining overloading or over-voltage issues in network

edisgo.tools.edisgo_run.run_edisgo_pool(ding0_file_list, run_args_opt=[None, 'worst-case'], workers=2, worker_lifetime=1)[source]

Use python multiprocessing toolbox for parallelization

Several grids are analyzed in parallel.

Parameters
  • ding0_file_list (list) – Ding0 network data file names

  • run_args_opt (list) – eDisGo options, see run_edisgo_basic() and run_edisgo_twice(), has to contain generator_scenario and analysis as entries

  • workers (int) – Number of parallel process

  • worker_lifetime (int) – Bunch of grids sequentially analyzed by a worker

Returns

  • all_costs_before_geno_import (list) – Grid extension cost before network connection of new generators

  • all_grid_issues_before_geno_import (list) – Remaining overloading or over-voltage issues in network

  • all_costs (list) – Grid extension cost due to network connection of new generators

  • all_grid_issues (list) – Remaining overloading or over-voltage issues in network

edisgo.tools.edisgo_run.run_edisgo_pool_flexible(ding0_id_list, func, func_arguments, workers=2, worker_lifetime=1)[source]

Use python multiprocessing toolbox for parallelization

Several grids are analyzed in parallel based on your custom function that defines the specific application of eDisGo.

Parameters
  • ding0_id_list (list of int) – List of ding0 network data IDs (also known as HV/MV substation IDs)

  • func (any function) – Your custom function that shall be parallelized

  • func_arguments (tuple) – Arguments to custom function func

  • workers (int) – Number of parallel process

  • worker_lifetime (int) – Bunch of grids sequentially analyzed by a worker

Notes

Please note, the following requirements for the custom function which is to be executed in parallel

  1. It must return an instance of the type EDisGo.

  2. The first positional argument is the MV network district id (as int). It is prepended to the tuple of arguments func_arguments

Returns

containers – Dict of EDisGo instances keyed by its ID

Return type

dict of EDisGo

edisgo.tools.edisgo_run.edisgo_run()[source]

edisgo.tools.geo module

edisgo.tools.geo.proj2equidistant(srid)[source]

Transforms to equidistant projection (epsg:3035).

Parameters

srid (int) – Spatial reference identifier of geometry to transform.

Return type

functools.partial()

edisgo.tools.geo.proj2equidistant_reverse(srid)[source]

Transforms back from equidistant projection to given projection.

Parameters

srid (int) – Spatial reference identifier of geometry to transform.

Return type

functools.partial()

edisgo.tools.geo.proj_by_srids(srid1, srid2)[source]

Transforms from specified projection to other specified projection.

Parameters
  • srid1 (int) – Spatial reference identifier of geometry to transform.

  • srid2 (int) – Spatial reference identifier of destination CRS.

Return type

functools.partial()

Notes

Projections often used are conformal projection (epsg:4326), equidistant projection (epsg:3035) and spherical mercator projection (epsg:3857).

edisgo.tools.geo.calc_geo_lines_in_buffer(grid_topology, bus, grid, buffer_radius=2000, buffer_radius_inc=1000)[source]

Determines lines that are at least partly within buffer around given bus.

If there are no lines, the buffer specified in buffer_radius is successively extended by buffer_radius_inc until lines are found.

Parameters
  • grid_topology (Topology) –

  • bus (pandas.Series) – Data of origin bus the buffer is created around. Series has same rows as columns of buses_df.

  • grid (Grid) – Grid whose lines are searched.

  • buffer_radius (float, optional) – Radius in m used to find connection targets. Default: 2000.

  • buffer_radius_inc (float, optional) – Radius in m which is incrementally added to buffer_radius as long as no target is found. Default: 1000.

Returns

List of lines in buffer (meaning close to the bus) sorted by the lines’ representatives.

Return type

list(str)

edisgo.tools.geo.calc_geo_dist_vincenty(grid_topology, bus_source, bus_target, branch_detour_factor=1.3)[source]

Calculates the geodesic distance between two buses in km.

The detour factor in config_grid is incorporated in the geodesic distance.

Parameters
  • grid_topology (Topology) –

  • bus_source (str) – Name of source bus as in index of buses_df.

  • bus_target (str) – Name of target bus as in index of buses_df.

  • branch_detour_factor (float) – Detour factor to consider that two buses can usually not be connected directly. Default: 1.3.

Returns

Distance in km.

Return type

float

edisgo.tools.geo.find_nearest_bus(point, bus_target)[source]

Finds the nearest bus in bus_target to a given point.

Parameters
  • point (shapely.Point) – Point to find nearest bus for.

  • bus_target (pandas.DataFrame) – Dataframe with candidate buses and their positions given in ‘x’ and ‘y’ columns. The dataframe has the same format as buses_df.

Returns

Tuple that contains the name of the nearest bus and its distance.

Return type

tuple(str, float)

edisgo.tools.geo.find_nearest_conn_objects(grid_topology, bus, lines, conn_diff_tolerance=0.0001)[source]

Searches all lines for the nearest possible connection object per line.

It picks out 1 object out of 3 possible objects: 2 line-adjacent buses and 1 potentially created branch tee on the line (using perpendicular projection). The resulting stack (list) is sorted ascending by distance from bus.

Parameters
  • grid_topology (Topology) –

  • bus (pandas.Series) – Data of bus to connect. Series has same rows as columns of buses_df.

  • lines (list(str)) – List of line representatives from index of lines_df.

  • conn_diff_tolerance (float, optional) – Threshold which is used to determine if 2 objects are at the same position. Default: 0.0001.

Returns

List of connection objects. Each object is represented by dict with representative, shapely object and distance to node.

Return type

list(dict)

edisgo.tools.geopandas_helper module

class edisgo.tools.geopandas_helper.GeoPandasGridContainer(crs, grid_id, grid, buses_gdf, generators_gdf, loads_gdf, storage_units_gdf, transformers_gdf, lines_gdf)[source]

Bases: object

Grids geo data for all components with information about their geolocation.

Parameters
edisgo.tools.geopandas_helper.to_geopandas(grid_obj)[source]

Translates all DataFrames with geolocations within a Grid class to GeoDataFrames.

Parameters

grid_obj (Grid) – Grid object to transform.

Returns

Data container with the grids geo data for all components with information about their geolocation.

Return type

GeoPandasGridContainer

edisgo.tools.networkx_helper module

edisgo.tools.networkx_helper.translate_df_to_graph(buses_df, lines_df, transformers_df=None)[source]

Translate DataFrames to networkx Graph Object.

Parameters
  • buses_df (pandas.DataFrame) – Dataframe with all buses to use as Graph nodes. For more information about the Dataframe see buses_df.

  • lines_df (pandas.DataFrame) – Dataframe with all lines to use as Graph branches. For more information about the Dataframe see lines_df

  • transformers_df (pandas.DataFrame, optional) – Dataframe with all transformers to use as additional Graph nodes. For more information about the Dataframe see transformers_df

Returns

Graph representation of the grid as networkx Ordered Graph, where lines are represented by edges in the graph, and buses and transformers are represented by nodes.

Return type

networkx.Graph

edisgo.tools.plots module

edisgo.tools.plots.histogram(data, **kwargs)[source]

Function to create histogram, e.g. for voltages or currents.

Parameters
  • data (pandas.DataFrame) – Data to be plotted, e.g. voltage or current (v_res or i_res from network.results.Results). Index of the dataframe must be a pandas.DatetimeIndex.

  • timeindex (pandas.Timestamp or list(pandas.Timestamp) or None, optional) – Specifies time steps histogram is plotted for. If timeindex is None all time steps provided in data are used. Default: None.

  • directory (str or None, optional) – Path to directory the plot is saved to. Is created if it does not exist. Default: None.

  • filename (str or None, optional) – Filename the plot is saved as. File format is specified by ending. If filename is None, the plot is shown. Default: None.

  • color (str or None, optional) – Color used in plot. If None it defaults to blue. Default: None.

  • alpha (float, optional) – Transparency of the plot. Must be a number between 0 and 1, where 0 is see through and 1 is opaque. Default: 1.

  • title (str or None, optional) – Plot title. Default: None.

  • x_label (str, optional) – Label for x-axis. Default: “”.

  • y_label (str, optional) – Label for y-axis. Default: “”.

  • normed (bool, optional) – Defines if histogram is normed. Default: False.

  • x_limits (tuple or None, optional) – Tuple with x-axis limits. First entry is the minimum and second entry the maximum value. Default: None.

  • y_limits (tuple or None, optional) – Tuple with y-axis limits. First entry is the minimum and second entry the maximum value. Default: None.

  • fig_size (str or tuple, optional) –

    Size of the figure in inches or a string with the following options:
    • ’a4portrait’

    • ’a4landscape’

    • ’a5portrait’

    • ’a5landscape’

    Default: ‘a5landscape’.

  • binwidth (float) – Width of bins. Default: None.

edisgo.tools.plots.add_basemap(ax, zoom=12)[source]

Adds map to a plot.

edisgo.tools.plots.get_grid_district_polygon(config, subst_id=None, projection=4326)[source]

Get MV network district polygon from oedb for plotting.

edisgo.tools.plots.mv_grid_topology(edisgo_obj, timestep=None, line_color=None, node_color=None, line_load=None, grid_expansion_costs=None, filename=None, arrows=False, grid_district_geom=True, background_map=True, voltage=None, limits_cb_lines=None, limits_cb_nodes=None, xlim=None, ylim=None, lines_cmap='inferno_r', title='', scaling_factor_line_width=None, curtailment_df=None, **kwargs)[source]

Plot line loading as color on lines.

Displays line loading relative to nominal capacity.

Parameters
  • edisgo_obj (EDisGo) –

  • timestep (pandas.Timestamp) – Time step to plot analysis results for. If timestep is None maximum line load and if given, maximum voltage deviation, is used. In that case arrows cannot be drawn. Default: None.

  • line_color (str or None) –

    Defines whereby to choose line colors (and implicitly size). Possible options are:

    • ’loading’ Line color is set according to loading of the line. Loading of MV lines must be provided by parameter line_load.

    • ’expansion_costs’ Line color is set according to investment costs of the line. This option also effects node colors and sizes by plotting investment in stations and setting node_color to ‘storage_integration’ in order to plot storage size of integrated storage units. Grid expansion costs must be provided by parameter grid_expansion_costs.

    • None (default) Lines are plotted in black. Is also the fallback option in case of wrong input.

  • node_color (str or None) –

    Defines whereby to choose node colors (and implicitly size). Possible options are:

    • ’technology’ Node color as well as size is set according to type of node (generator, MV station, etc.).

    • ’voltage’ Node color is set according to maximum voltage at each node. Voltages of nodes in MV network must be provided by parameter voltage.

    • ’voltage_deviation’ Node color is set according to voltage deviation from 1 p.u.. Voltages of nodes in MV network must be provided by parameter voltage.

    • ’storage_integration’ Only storage units are plotted. Size of node corresponds to size of storage.

    • None (default) Nodes are not plotted. Is also the fallback option in case of wrong input.

    • ’curtailment’ Plots curtailment per node. Size of node corresponds to share of curtailed power for the given time span. When this option is chosen a dataframe with curtailed power per time step and node needs to be provided in parameter curtailment_df.

    • ’charging_park’ Plots nodes with charging stations in red.

  • line_load (pandas.DataFrame or None) – Dataframe with current results from power flow analysis in A. Index of the dataframe is a pandas.DatetimeIndex, columns are the line representatives. Only needs to be provided when parameter line_color is set to ‘loading’. Default: None.

  • grid_expansion_costs (pandas.DataFrame or None) – Dataframe with network expansion costs in kEUR. See grid_expansion_costs in Results for more information. Only needs to be provided when parameter line_color is set to ‘expansion_costs’. Default: None.

  • filename (str) – Filename to save plot under. If not provided, figure is shown directly. Default: None.

  • arrows (Boolean) – If True draws arrows on lines in the direction of the power flow. Does only work when line_color option ‘loading’ is used and a time step is given. Default: False.

  • grid_district_geom (Boolean) – If True network district polygon is plotted in the background. This also requires the geopandas package to be installed. Default: True.

  • background_map (Boolean) – If True map is drawn in the background. This also requires the contextily package to be installed. Default: True.

  • voltage (pandas.DataFrame) – Dataframe with voltage results from power flow analysis in p.u.. Index of the dataframe is a pandas.DatetimeIndex, columns are the bus representatives. Only needs to be provided when parameter node_color is set to ‘voltage’. Default: None.

  • limits_cb_lines (tuple) – Tuple with limits for colorbar of line color. First entry is the minimum and second entry the maximum value. Only needs to be provided when parameter line_color is not None. Default: None.

  • limits_cb_nodes (tuple) – Tuple with limits for colorbar of nodes. First entry is the minimum and second entry the maximum value. Only needs to be provided when parameter node_color is not None. Default: None.

  • xlim (tuple) – Limits of x-axis. Default: None.

  • ylim (tuple) – Limits of y-axis. Default: None.

  • lines_cmap (str) – Colormap to use for lines in case line_color is ‘loading’ or ‘expansion_costs’. Default: ‘inferno_r’.

  • title (str) – Title of the plot. Default: ‘’.

  • scaling_factor_line_width (float or None) – If provided line width is set according to the nominal apparent power of the lines. If line width is None a default line width of 2 is used for each line. Default: None.

  • curtailment_df (pandas.DataFrame) – Dataframe with curtailed power per time step and node. Columns of the dataframe correspond to buses and index to the time step. Only needs to be provided if node_color is set to ‘curtailment’.

  • legend_loc (str) – Location of legend. See matplotlib legend location options for more information. Default: ‘upper left’.

edisgo.tools.plots.color_map_color(value, vmin, vmax, cmap_name='coolwarm')[source]

Get matching color for a value on a matplotlib color map.

Parameters
  • value (float or int) – Value to get color for

  • vmin (float or int) – Minimum value on color map

  • vmax (float or int) – Maximum value on color map

  • cmap_name (str) – Name of color map to use

Returns

Color name in hex format

Return type

str

edisgo.tools.plots.draw_plotly(edisgo_obj, G=None, line_color='relative_loading', node_color='voltage_deviation', grid=False)[source]

Draw a plotly html figure

Parameters
  • edisgo_obj (EDisGo) –

  • G (networkx.Graph, optional) – Graph representation of the grid as networkx Ordered Graph, where lines are represented by edges in the graph, and buses and transformers are represented by nodes. If no graph is given the mv grid graph of the edisgo object is used.

  • line_color (str) –

    Defines whereby to choose line colors (and implicitly size). Possible options are:

    • ’loading’ Line color is set according to loading of the line.

    • ’relative_loading’ (Default) Line color is set according to relative loading of the line.

    • ’reinforce’ Line color is set according to investment costs of the line.

  • node_color (str or None) –

    Defines whereby to choose node colors (and implicitly size). Possible options are:

    • ’adjacencies’ Node color as well as size is set according to the number of direct neighbors.

    • ’voltage_deviation’ (default) Node color is set according to voltage deviation from 1 p.u..

  • grid (Grid or bool) – Grid to use as root node. If a grid is given the transforer station is used as root. If False the root is set to the coordinates x=0 and y=0. Else the coordinates from the hv-mv-station of the mv grid are used. Default: False

Returns

Plotly figure with branches and nodes.

Return type

plotlyplotly.graph_objects.Figure

edisgo.tools.plots.chosen_graph(edisgo_obj, selected_grid)[source]

Get the matching networkx graph from a chosen grid.

Parameters
  • edisgo_obj (EDisGo) –

  • selected_grid (str) – Grid name. Can be either ‘Grid’ to select the MV grid with all LV grids or the name of the MV grid to select only the MV grid or the name of one of the LV grids of the eDisGo object to select a specific LV grid.

Returns

Tuple with the first entry being the networkx graph of the selected grid and the second entry the grid to use as root node. See draw_plotly() for more information.

Return type

(networkx.Graph, Grid or bool)

edisgo.tools.plots.dash_plot(edisgo_objects, line_plot_modes=None, node_plot_modes=None)[source]

Generates a jupyter dash app from given eDisGo object(s).

TODO: The app doesn’t display two seperate colorbars for line and bus values atm

Parameters
  • edisgo_objects (EDisGo or dict[str, EDisGo]) – eDisGo objects to show in plotly dash app. In the case of multiple edisgo objects pass a dictionary with the eDisGo objects as values and the respective eDisGo object names as keys.

  • line_plot_modes (list(str), optional) – List of line plot modes to display in plotly dash app. See draw_plotly() for more information. If None is passed the modes ‘reinforce’, ‘loading’ and ‘relative_loading’ will be used. Default: None

  • node_plot_modes (list(str), optional) – List of line plot modes to display in plotly dash app. See draw_plotly() for more information. If None is passed the modes ‘adjacencies’ and ‘voltage_deviation’ will be used. Default: None

Returns

Jupyter dash app.

Return type

JupyterDash

edisgo.tools.powermodels_io module

edisgo.tools.powermodels_io.to_powermodels(pypsa_net)[source]

Convert pypsa network to network dictionary format, using the pypower structure as an intermediate steps

powermodels network dictionary: https://lanl-ansi.github.io/PowerModels.jl/stable/network-data/

pypower caseformat: https://github.com/rwl/PYPOWER/blob/master/pypower/caseformat.py

Parameters

pypsa_net

Returns

edisgo.tools.powermodels_io.convert_storage_series(timeseries)[source]
edisgo.tools.powermodels_io.add_storage_from_edisgo(edisgo_obj, psa_net, pm_dict)[source]

Read static storage data (position and capacity) from eDisGo and export to Powermodels dict

edisgo.tools.powermodels_io.pypsa2ppc(psa_net)[source]

Converter from pypsa data structure to pypower data structure

adapted from pandapower’s pd2ppc converter

https://github.com/e2nIEE/pandapower/blob/911f300a96ee0ac062d82f7684083168ff052586/pandapower/pd2ppc.py

edisgo.tools.powermodels_io.ppc2pm(ppc, psa_net)[source]

converter from pypower datastructure to powermodels dictionary,

adapted from pandapower to powermodels converter: https://github.com/e2nIEE/pandapower/blob/develop/pandapower/converter/powermodels/to_pm.py

Parameters

ppc

Returns

edisgo.tools.preprocess_pypsa_opf_structure module

edisgo.tools.preprocess_pypsa_opf_structure.preprocess_pypsa_opf_structure(edisgo_grid, psa_network, hvmv_trafo=False)[source]

Prepares pypsa network for OPF problem.

  • adds line costs

  • adds HV side of HV/MV transformer to network

  • moves slack to HV side of HV/MV transformer

Parameters
  • edisgo_grid (EDisGo) –

  • psa_network (pypsa.Network) –

  • hvmv_trafo (Boolean) – If True, HV side of HV/MV transformer is added to buses and Slack generator is moved to HV side.

edisgo.tools.preprocess_pypsa_opf_structure.aggregate_fluct_generators(psa_network)[source]

Aggregates fluctuating generators of same type at the same node.

Iterates over all generator buses. If multiple fluctuating generators are attached, they are aggregated by type.

Parameters

psa_network (pypsa.Network) –

edisgo.tools.tools module

edisgo.tools.tools.select_worstcase_snapshots(edisgo_obj)[source]

Select two worst-case snapshots from time series

Two time steps in a time series represent worst-case snapshots. These are

  1. Maximum Residual Load: refers to the point in the time series where the

    (load - generation) achieves its maximum.

  2. Minimum Residual Load: refers to the point in the time series where the

    (load - generation) achieves its minimum.

These two points are identified based on the generation and load time series. In case load or feed-in case don’t exist None is returned.

Parameters

edisgo_obj (EDisGo) –

Returns

Dictionary with keys ‘min_residual_load’ and ‘max_residual_load’. Values are corresponding worst-case snapshots of type pandas.Timestamp.

Return type

dict

edisgo.tools.tools.calculate_relative_line_load(edisgo_obj, lines=None, timesteps=None)[source]

Calculates relative line loading for specified lines and time steps.

Line loading is calculated by dividing the current at the given time step by the allowed current.

Parameters
  • edisgo_obj (EDisGo) –

  • lines (list(str) or None, optional) – Line names/representatives of lines to calculate line loading for. If None, line loading is calculated for all lines in the network. Default: None.

  • timesteps (pandas.Timestamp or list(pandas.Timestamp) or None, optional) – Specifies time steps to calculate line loading for. If timesteps is None, all time steps power flow analysis was conducted for are used. Default: None.

Returns

Dataframe with relative line loading (unitless). Index of the dataframe is a pandas.DatetimeIndex, columns are the line representatives.

Return type

pandas.DataFrame

edisgo.tools.tools.calculate_line_reactance(line_inductance_per_km, line_length, num_parallel)[source]

Calculates line reactance in Ohm.

Parameters
  • line_inductance_per_km (float or array-like) – Line inductance in mH/km.

  • line_length (float) – Length of line in km.

  • num_parallel (int) – Number of parallel lines.

Returns

Reactance in Ohm

Return type

float

edisgo.tools.tools.calculate_line_resistance(line_resistance_per_km, line_length, num_parallel)[source]

Calculates line resistance in Ohm.

Parameters
  • line_resistance_per_km (float or array-like) – Line resistance in Ohm/km.

  • line_length (float) – Length of line in km.

  • num_parallel (int) – Number of parallel lines.

Returns

Resistance in Ohm

Return type

float

edisgo.tools.tools.calculate_apparent_power(nominal_voltage, current, num_parallel)[source]

Calculates apparent power in MVA from given voltage and current.

Parameters
  • nominal_voltage (float or array-like) – Nominal voltage in kV.

  • current (float or array-like) – Current in kA.

  • num_parallel (int or array-like) – Number of parallel lines.

Returns

Apparent power in MVA.

Return type

float

edisgo.tools.tools.drop_duplicated_indices(dataframe, keep='first')[source]

Drop rows of duplicate indices in dataframe.

Parameters
  • dataframe (pandas.DataFrame) – handled dataframe

  • keep (str) – indicator of row to be kept, ‘first’, ‘last’ or False, see pandas.DataFrame.drop_duplicates() method

edisgo.tools.tools.drop_duplicated_columns(df, keep='first')[source]

Drop columns of dataframe that appear more than once.

Parameters
  • df (pandas.DataFrame) – Dataframe of which columns are dropped.

  • keep (str) – Indicator of whether to keep first (‘first’), last (‘last’) or none (False) of the duplicated columns. See drop_duplicates() method of pandas.DataFrame.

edisgo.tools.tools.select_cable(edisgo_obj, level, apparent_power)[source]

Selects suitable cable type and quantity using given apparent power.

Cable is selected to be able to carry the given apparent_power, no load factor is considered. Overhead lines are not considered in choosing a suitable cable.

Parameters
  • edisgo_obj (EDisGo) –

  • level (str) – Grid level to get suitable cable for. Possible options are ‘mv’ or ‘lv’.

  • apparent_power (float) – Apparent power the cable must carry in MVA.

Returns

  • pandas.Series – Series with attributes of selected cable as in equipment data and cable type as series name.

  • int – Number of necessary parallel cables.

edisgo.tools.tools.assign_feeder(edisgo_obj, mode='mv_feeder')[source]

Assigns MV or LV feeder to each bus and line, depending on the mode.

The feeder name is written to a new column mv_feeder or lv_feeder in Topology’s buses_df and lines_df. The MV respectively LV feeder name corresponds to the name of the first bus in the respective feeder.

Parameters
  • edisgo_obj (EDisGo) –

  • mode (str) – Specifies whether to assign MV or LV feeder. Valid options are ‘mv_feeder’ or ‘lv_feeder’. Default: ‘mv_feeder’.

edisgo.tools.tools.get_path_length_to_station(edisgo_obj)[source]

Determines path length from each bus to HV-MV station.

The path length is written to a new column path_length_to_station in buses_df dataframe of Topology class.

Parameters

edisgo_obj (EDisGo) –

Returns

Series with bus name in index and path length to station as value.

Return type

pandas.Series

edisgo.tools.tools.assign_voltage_level_to_component(df, buses_df)[source]

Adds column with specification of voltage level component is in.

The voltage level (‘mv’ or ‘lv’) is determined based on the nominal voltage of the bus the component is connected to. If the nominal voltage is smaller than 1 kV, voltage level ‘lv’ is assigned, otherwise ‘mv’ is assigned.

Parameters
  • df (pandas.DataFrame) – Dataframe with component names in the index. Only required column is column ‘bus’, giving the name of the bus the component is connected to.

  • buses_df (pandas.DataFrame) – Dataframe with bus information. Bus names are in the index. Only required column is column ‘v_nom’, giving the nominal voltage of the voltage level the bus is in.

Returns

Same dataframe as given in parameter df with new column ‘voltage_level’ specifying the voltage level the component is in (either ‘mv’ or ‘lv’).

Return type

pandas.DataFrame

edisgo.tools.tools.get_weather_cells_intersecting_with_grid_district(edisgo_obj)[source]

Get all weather cells that intersect with the grid district.

Parameters

edisgo_obj (EDisGo) –

Returns

Set with weather cell IDs

Return type

set

edisgo.tools.tools.get_directory_size(start_dir)[source]

Calculates the size of all files within the start path.

Walks through all files and sub-directories within a given directory and calculate the sum of size of all files in the directory. See: https://stackoverflow.com/a/1392549/13491957

Parameters

start_dir (str) – Start path.

Returns

Size of the directory.

Return type

int

edisgo.tools.tools.get_files_recursive(path, files=None)[source]

Recursive function to get all files in a given path and its sub directories.

Parameters
  • path (str) – Directory to start from.

  • files (list, optional) – List of files to start with. Default: None.

Module contents

edisgo.tools.session_scope()[source]

Function to ensure that sessions are closed properly.