Welcome to the documentation of eDisGo!


The python package eDisGo serves as a toolbox to evaluate flexibility measures as an economic alternative to conventional grid expansion in medium and low voltage grids.

The toolbox currently includes:

  • Data import from external data sources
    • ding0 tool for synthetic medium and low voltage grid topologies for the whole of Germany
    • OpenEnergy DataBase (oedb) for feed-in time series of fluctuating renewables and scenarios for future power plant park of Germany
    • demandlib for electrical load time series
  • Static, non-linear power flow analysis using PyPSA for grid issue identification
  • Automatic grid reinforcement methodology solving overloading and voltage issues to determine grid expansion needs and costs based on measures most commonly taken by German distribution grid operators
  • Multiperiod optimal power flow based on julia package PowerModels.jl optimizing storage positioning and/or operation as well as generator dispatch with regard to minimizing grid expansion costs
  • Temporal complexity reduction
  • Heuristic for grid-supportive generator curtailment
  • Heuristic grid-supportive battery storage integration

Currently, a method to optimize the flexibility that can be provided by electric vehicles through controlled charging and V2G is implemented. Prospectively, demand side management and reactive power management will be included.

See Getting started for the first steps. A deeper guide is provided in Usage details. Methodologies are explained in detail in Features in detail. For those of you who want to contribute see Notes to developers and the API reference.

eDisGo was initially developed in the open_eGo research project as part of a grid planning tool that can be used to determine the optimal grid and storage expansion of the German power grid over all voltage levels and has been used in two publications of the project: