# Introduction¶

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.

## What PyPSA does and does not do (yet)¶

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

Functionality that may be added in the future:

- Multi-year investment optimisation
- Distributed active power slack
- Interactive web-based GUI with SVG
- OPF with the full non-linear network equations
- Port to Julia

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

## Example scripts as Jupyter notebooks¶

There are extensive examples
available as Jupyter notebooks. They are also described in the
Examples and are available as Python scripts in `examples/`

.

## Screenshots¶

Results from a PyPSA simulation can be converted into an interactive online animation using PyPSA-animation, see the PyPSA-Eur-30 example.

Another showcase for PyPSA is the SciGRID example which demonstrates interactive plots generated with the plotly library.

Optimised capacities of generation and storage for a 95% reduction in CO2 emissions in Europe compare to 1990 levels:

## Target user group¶

PyPSA is intended for researchers, planners and utilities who need a fast, easy-to-use and transparent tool for power system analysis. PyPSA is free software and can be arbitrarily extended.

## Other comparable software¶

For a full list see Comparable Software.

PyPSA is not as fully featured as other power system simulation tools such as the Matlab-based free software PSAT or the commercial package DIgSILENT PowerFactory.

However for power flow and optimal power flow over several time snapshots with variable renewable energy sources and/or storage and/or mixed AC-DC systems, it offers the flexibility of Python and the transparency of free software.

Another Python power system tool is PYPOWER, which is based on the Matlab-based MATPOWER. In contrast to PYPOWER, PyPSA has an easier-to-use data model (pandas DataFrames instead of numpy arrays), support for time-varying data inputs and support for multiply-connected networks using both AC and DC. PyPSA uses some of the sparse-matrix constructs from PYPOWER.

## What PyPSA uses under the hood¶

PyPSA is written and tested to be compatible with both Python 2.7 and Python 3.6.

It leans heavily on the following Python packages:

- pandas for storing data about components and time series
- numpy and scipy for calculations, such as linear algebra and sparse matrix calculations
- pyomo for preparing optimisation problems (currently only linear)
- plotly for interactive plotting
- matplotlib for static plotting
- networkx for some network calculations
- py.test for unit testing
- logging for managing messages

The optimisation uses pyomo so that it is independent of the preferred solver (you can use e.g. the free software GLPK or the commercial software Gurobi).

The time-expensive calculations, such as solving sparse linear equations, are carried out using the scipy.sparse libraries.

## Mailing list¶

PyPSA has a Google Group forum / mailing list.

## Citing PyPSA¶

If you use PyPSA for your research, we would appreciate it if you would cite the following paper:

- T. Brown, J. Hörsch, D. Schlachtberger, PyPSA: Python for Power System Analysis, 2018, Journal of Open Research Software, 6(1), arXiv:1707.09913, DOI:10.5334/jors.188

Please use the following BibTeX:

```
@article{PyPSA,
author = {T. Brown and J. H\"orsch and D. Schlachtberger},
title = {{PyPSA: Python for Power System Analysis}},
journal = {Journal of Open Research Software},
volume = {6},
issue = {1},
number = {4},
year = {2018},
eprint = {1707.09913},
url = {https://doi.org/10.5334/jors.188},
doi = {10.5334/jors.188}
}
```

If you want to cite a specific PyPSA version, each release of PyPSA is stored on Zenodo with a release-specific DOI. This can be found linked from the overall PyPSA Zenodo DOI: