Welcome to the documentation of eDisGo!
eDisGo (electric Distribution Grid optimization) is a Python toolbox to analyse and plan medium- and low-voltage distribution grids. Its purpose is to evaluate flexibility measures (controlled charging, heat-pump and storage operation, demand side management, reactive power) as an economic alternative to — or in combination with — conventional grid reinforcement.
What eDisGo can do
Import grid data and scenarios from external sources:
ding0 — synthetic medium- and low-voltage grid topologies for all of Germany.
OpenEnergy DataBase (oedb) / egon-data — generator parks, load, heat-pump, DSM, storage and electric-vehicle data for future scenarios.
demandlib — standard electrical load profiles.
SimBEV / TracBEV — electric-vehicle charging demand and potential charging-point locations.
Power flow analysis — non-linear AC power flow via PyPSA to find voltage and loading problems.
Automatic grid reinforcement — solves overloading and voltage issues with the measures German distribution grid operators commonly use, and reports the resulting grid-expansion costs.
Flexibility & optimisation — represents electric vehicles, heat pumps, battery storage and demand side management as flexibilities and schedules them with a multi-period optimal power flow (PowerModels.jl) to minimise grid expansion. See Flexibility & optimisation.
Spatial and temporal complexity reduction for large grids.
How to read this documentation
The documentation is organised by how deep you want to go:
Getting Started — install eDisGo and run your first analysis.
User Guide — task-oriented guide to the data model and every step of a study (data import, time series, analysis, reinforcement, results).
Methodology & Physics — the engineering and physics behind each method, function by function, including the flexibility & optimisation chapters.
Tutorials — runnable Jupyter notebooks.
Reference — conventions, units, configuration and equipment data, and the full auto-generated API reference.
eDisGo was initially developed in the open_eGo research project as part of a grid-planning tool spanning all voltage levels, documented in two project publications:
Publications using eDisGo
eDisGo has since been used for the grid analysis and reinforcement-cost calculations in the following peer-reviewed studies:
Challenges of top-down flexibility deployment for grid expansion across all voltage levels — Büttner et al., Environmental Research: Energy, 2025.
Analyzing the Impact of Dynamic Tariff Adoption and Regulatory Options on Distribution Grids with an Open-Source Framework — Semmelmann et al., ACM e-Energy, 2025.
On the Integration of Electric Vehicles Into German Distribution Grids Through Smart Charging — Heider et al., IEEE Transactions on Industry Applications, 2024 (conference version: SEST 2022).
Grid Reinforcement Costs with Increasing Penetrations of Distributed Energy Resources — Heider et al., IEEE PowerTech Belgrade, 2023.
On the impact of heat pump installations and peak blocking strategies on grid expansion costs — Semmelmann et al., IEEE ISGT Europe, 2023.
Assessing the impacts of market-oriented electric vehicle charging on German distribution grids — Schachler et al., CIRED 2021.
Distribution System Planning with Battery Storage using Multiperiod Optimal Power Flow — Pedersen et al., 2021.
The synthetic distribution grids analysed in these studies are generated with ding0; an example dataset is published on Zenodo (Amme et al., 2023).
Getting Started
User Guide
Methodology & Physics
Tutorials
Reference
Experimental & Legacy
Project