# Popular Open Source Libraries for Power System Analysis

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🏆 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

## Dynamic

### LTB andes

Python toolbox / library for power system transient dynamics simulation with symbolic modeling and numerical analysis 🔥

### PowerSimulationsDynamics.jl

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

### pandapower

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

### PyPSA

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.

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