# PyPSA/PyPSA

PyPSA: Python for Power System Analysis

Repository: https://github.com/PyPSA/PyPSA
Canonical: https://ross.abutalabs.com/products/pypsa
Homepage: https://docs.pypsa.org
Language: Python
License: MIT
License Family: permissive
Topics: python, optimisation, energy-system, power-systems-analysis, optimal-power-flow, powerflow, energy, energy-systems, power-flow, power-systems, renewable-energy, electrical-engineering, capacity-expansion-planning, clean-energy, climate-change, renewables, electricity, energy-system-model, energy-system-modelling, modelling-framework
Last push: 2026-08-26T18:49:38+00:00

## Health v2 (maintenance only)
Score: 99/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 98, longevity 100
- inputs: {"age_days": 3887, "days_push": 7, "days_rel": 14, "gap_med": 13, "n_releases_24m": 34}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2113, forks 690 (observed 2026-08-28T04:06:14.568408+00:00)

## What it is
PyPSA is an open-source Python framework for optimising and simulating power and energy systems, covering conventional generators with unit commitment, wind and solar generation, storage, sector coupling, and linearised DC/AC power flow. It is built to scale to large networks with long time series and is aimed at researchers, planners, and utilities with basic coding skills.

## Use cases
- model national or regional electricity systems with renewables in python
- solve optimal power flow for a transmission network
- run capacity expansion planning for least-cost energy scenarios
- simulate dispatch of wind, solar, hydro, and storage over a year of time series
- compute linearised DC or AC power flows on large grids
- model coupling of electricity with heat, gas, or mobility sectors
- study unit commitment of conventional power plants

## When to choose
- you need a transparent, open-source alternative to proprietary energy system modelling tools
- you want scriptable, reproducible scenario analysis integrated with the scientific Python stack
- you need to handle large networks with long time series efficiently
- you are doing academic research or planning on renewable integration, storage, or grid expansion

## When to avoid
- you need detailed dynamic or transient grid simulation rather than optimisation and static power flow
- you require a graphical point-and-click tool with no programming
- you need certified commercial planning software for regulatory studies
- your problem is unrelated to power or energy systems

## Facets
- artifact type: framework
- maturity: stable
- function: simulation, math
- domain: simulation, mathematics
- platform: python, cross-platform
- tags: energy-systems, power-systems, optimal-power-flow, power-flow, capacity-expansion-planning, unit-commitment, renewable-energy, sector-coupling, electrical-engineering, optimization, energy-modelling, linopy, scientific-computing, pandas, electricity-grid

## Member repositories
- PyPSA/PyPSA (main) score 99

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:14.568408+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:53:39.271936+00:00, confidence not recorded.
  - readme: https://github.com/PyPSA/PyPSA (fetched 2026-08-28T04:06:14.568408+00:00, sha ff43efe40b99)
  - registry_pypi: https://pypi.org/pypi/pypsa/json (fetched 2026-08-29T10:34:10.114232+00:00, sha 503428ce774b)
- Data as of 2026-08-30T08:39:29.467469+00:00.