PyPSA stands for "Python for Power System Analysis". It is pronounced "pipes-ah".

PyPSA is a free software toolbox for simulating and optimising modern power systems that include features such as conventional generators with unit commitment, variable wind and solar generation, storage units, coupling to other energy sectors, and mixed alternating and direct current networks. PyPSA is designed to scale well with large networks and long time series.

This project is maintained by the Energy System Modelling group at the Institute for Automation and Applied Informatics at the Karlsruhe Institute of Technology. The group is funded by the Helmholtz Association until 2024. Previous versions were developed by the Renewable Energy Group at FIAS to carry out simulations for the CoNDyNet project, financed by the German Federal Ministry for Education and Research (BMBF) as part of the Stromnetze Research Initiative.

See the installation instructions.

You can also see/download the code directly from github.

Documentation as a website

Quick start

Examples

PyPSA can calculate:

• static power flow (using both the full non-linear network equations and the linearised network equations)
• linear optimal power flow (least-cost optimisation of power plant and storage dispatch within network constraints, using the linear network equations, over several snapshots)
• security-constrained linear optimal power flow
• total electricity/energy system least-cost investment optimisation (using linear network equations, over several snapshots simultaneously for optimisation of generation and storage dispatch and investment in the capacities of generation, storage, transmission and other infrastructure)

It has models for:

• meshed multiply-connected AC and DC networks, with controllable converters between AC and DC networks
• standard types for lines and transformers following the implementation in pandapower
• conventional dispatchable generators with unit commitment
• generators with time-varying power availability, such as wind and solar generators
• storage units with efficiency losses
• simple hydroelectricity with inflow and spillage
• coupling with other energy carriers
• basic components out of which more complicated assets can be built, such as Combined Heat and Power (CHP) units, heat pumps, resistive Power-to-Heat (P2H), Power-to-Gas (P2G), battery electric vehicles (BEVs), Fischer-Tropsch, direct air capture (DAC), etc.; each of these is demonstrated in the examples

Other complementary libraries:

• pandapower for more detailed modelling of distribution grids, short-circuit calculations, unbalanced load flow and more
• PowerDynamics.jl for dynamic modelling of power grids at time scales where differential equations are relevant

There are extensive examples available as Jupyter notebooks.