Popular Open Source Libraries for Power System Analysis

2 minute read


๐Ÿ† A ranked list of awesome open-source libraries and tools for Power Pystem Analysis

This curated list contains more than 30 open-source projects grouped into 11 categories. All projects are ranked by a project-popular score, which is calculated based on various metrics automatically collected from GitHub and different package managers.

Selected Contents


LTB andes

Python toolbox / library for power system transient dynamics simulation with symbolic modeling and numerical analysis ๐Ÿ”ฅ


Julia package to run Dynamic Power System simulations. Part of the Scalable Integrated Infrastructure Planning Initiative at the National Renewable Energy Lab.

Steady State


An easy to use open source tool for power system modeling, analysis and optimization with a high degree of automation.


PyPSA stands for Python for Power System Analysis. The aim of this project is to provide an open-source python environment for state-of-the-art energy system modelling. Here you find links to projects and research related to the PyPSA environment.

Power System Co-Simulation

Large-scale Testbed (LTB)

The Large-scale Testbed (LTB) is a tightly integrated, closed-loop platform for rapid prototyping of power systems.

Project Popularity Score

  • Has homepage link & description: + 1
  • Has an existing GitHub repository: + 1
  • Has a license: + 1
  • Has a commonly used license (e.g. MIT): + 1
  • Has multiple releases: + 1
  • Has stable releases based on semantic version: + 1
  • Has a release that is less than 6 months old: + 1
  • Repo was update in the last 3 months: + 1
  • Is older than 6 months: + 1
  • Metrics from GitHub & package mangers:
    • Number of stars: + log(COUNT / 2)
    • Number of contributors: + log(COUNT / 2) - 1
    • Number of commits: + log(COUNT / 2) - 1
    • Number of forks: + log(COUNT / 2)
    • Number of monthly downloads: + log(COUNT / 2) - 1
    • Number of dependent projects: + log(COUNT / 1.5)
    • Number of watchers: + log(COUNT / 2) - 1
    • Number of closed issues: + log(COUNT / 2) - 1

NOTE: This calculation is just chosen by EXPERIENCE. There is NO scientific proof that this really reflects the QUALITY of a project.

Project Data Collection

The data collection can be deficient for the projects that are not majorly hosted in GitHub.