Data sources

An eDisGo study combines two kinds of input data: the grid topology (where the lines, transformers, loads and generators are) and scenario / time-series data (how much they consume or feed in, and which flexibilities exist). This page explains where each comes from and how to access it.

Grid topology — ding0

eDisGo works on synthetic medium- and low-voltage grids generated by ding0. ding0 produces a complete, realistic MV/LV grid for each German MV grid district; a grid is identified by that district number (e.g. 32377).

A ding0 grid is a directory of CSV files (buses.csv, lines.csv, loads.csv, generators.csv, transformers.csv, transformers_hvmv.csv, storage_units.csv, switches.csv, network.csv; the CSVs may also sit in a topology/ subfolder). It is loaded by passing the directory to the EDisGo constructor:

from edisgo import EDisGo

edisgo = EDisGo(ding0_grid="path/to/ding0_grid", legacy_ding0_grids=False)

  • legacy_ding0_grids defaults to True. Set it to False for the current (egon-data compatible) grid format; True is only for older ding0 grids. Omitting it on a current grid triggers the legacy code path (different column renames) and mis-imports the grid.

Internally the constructor calls import_ding0_grid(), which you can also invoke directly on an existing EDisGo object.

Where to get ding0 grids:

Scenario & time-series data — OpenEnergy Platform / egon-data

Everything scenario-dependent — future generator parks, load profiles, heat-pump COP and heat demand, demand-side-management potential, home-battery and electric-vehicle data — is loaded from the egon-data dataset hosted on the OpenEnergy Platform (OEP). The relevant import methods are import_generators(), import_electromobility(), import_heat_pumps(), import_dsm(), import_home_batteries() and set_time_series_active_power_predefined(). Most of these select a scenario with the scenario argument — "eGon2035" or "eGon100RE". Two differ: import_generators() takes generator_scenario ("eGon2035"/"eGon100RE" for current ding0 grids, or "nep2035"/"ego100" for legacy grids), and set_time_series_active_power_predefined() selects its sources per component type (e.g. fluctuating_generators_ts="oedb"). In eDisGo’s API the OEP data source is referred to as "oedb" (OpenEnergy DataBase) for historical reasons.

OEP database (standard)

The OEP is the default data source. For most import methods, when the engine argument is omitted a default OEP engine is created automatically:

edisgo.set_time_series_active_power_predefined(
    fluctuating_generators_ts="oedb",  # load feed-in profiles from the OEP
)

Note

import_electromobility() is an exception: with data_source="oedb" it does not create a default engine, so you must pass an explicit engine.

OEP token. To authenticate, place a file named OEP_TOKEN.txt containing your personal access token in the directory edisgo/config/ (or set the environment variable OEP_TOKEN). A token can be requested at https://openenergyplatform.org/. Using a token is optional but recommended, as anonymous access is rate-limited.

Custom PostgreSQL database (egon-data)

You can also connect to your own egon-data PostgreSQL database via an SSH tunnel. Connecting through engine() always uses an SSH tunnel (ssh=True with a complete ssh-tunnel section); for a tunnel-less local database you have to build a SQLAlchemy engine yourself. The database keys follow the egon-data CLI-flag style and must be prefixed with --. Create a YAML configuration file:

egon-data:
  --database-host: mydb.example.org
  --database-port: 5432
  --database-name: egon_data
  --database-user: my_user
  --database-password: my_password

ssh-tunnel:                 # required when ssh=True
  ssh-host: my.ssh.server
  ssh-user: ubuntu
  ssh-pkey: ~/.ssh/id_rsa
  pgres-host: localhost

then create an engine with engine() and pass it to any import method:

from edisgo.io.db import engine

eng = engine(path="path/to/database_config.yaml", ssh=True)
edisgo.import_heat_pumps(scenario="eGon2035", engine=eng)

Access type

Description

Authentication

OEP (default)

Official egon-data source on the OpenEnergy Platform

OEP token (recommended)

Custom PostgreSQL

Your own egon-data database via an SSH tunnel

YAML config file with -- credentials + ssh-tunnel section

Load profiles — demandlib

Where no measured or scenario profile is available, standard load profiles for the residential, commercial, agricultural and industrial sectors are generated with the oemof demandlib. This is selected with conventional_loads_ts="demandlib" in set_time_series_active_power_predefined().

Electric-vehicle data — SimBEV / TracBEV

When not taken from the OEP, electric-vehicle data can be imported from a directory with import_electromobility() using data_source="directory" and the charging_processes_dir / potential_charging_points_dir arguments. The data is produced by SimBEV (charging processes: standing times, charging demand per vehicle) and TracBEV (potential charging-point locations). The import is tied to specific tool versions — SimBEV commit 3083c5a and TracBEV commit 14d864c; data from other versions is not guaranteed to parse. See Electromobility and the Electromobility example notebook.

Configuration data

Defaults for grid expansion (equipment, costs, allowed voltage deviations, load factors), worst-case assumptions and database tables live in the package configuration files. They are documented in Configuration data and the equipment data in Equipment data.