To help you get started, we've collected the most common ways that pypsa is being used within popular public projects.

PyPSA / PyPSA / test / test_pf_against_pypower.py View on Github

columns = 'bus0, bus1, r, x, b, rateA, rateB, rateC, ratio, angle, status, angmin, angmax, p0, q0, p1, q1'.split(", ")
    results_df['branch'] = pd.DataFrame(data=results["branch"],columns=columns)

    #buses
    columns = ["bus","type","Pd","Qd","Gs","Bs","area","v_mag_pu","v_ang","v_nom","zone","Vmax","Vmin"]
    results_df['bus'] = pd.DataFrame(data=results["bus"],columns=columns,index=results["bus"][:,0])

    #generators
    columns = "bus, p, q, q_max, q_min, Vg, mBase, status, p_max, p_min, Pc1, Pc2, Qc1min, Qc1max, Qc2min, Qc2max, ramp_agc, ramp_10, ramp_30, ramp_q, apf".split(", ")
    results_df['gen'] = pd.DataFrame(data=results["gen"],columns=columns)



    #now compute in PyPSA

    network = pypsa.Network()
    network.import_from_pypower_ppc(ppc)

    #PYPOWER uses PI model for transformers, whereas PyPSA defaults to
    #T since version 0.8.0
    network.transformers.model = "pi"

    network.pf()

    #compare branch flows
    for c in network.iterate_components(network.passive_branch_components):
        for si in ["p0","p1","q0","q1"]:
            si_pypsa = getattr(c.pnl,si).loc["now"].values
            si_pypower = results_df['branch'][si][c.df.original_index].values
            np.testing.assert_array_almost_equal(si_pypsa,si_pypower)

View more ways to use pypsa

PyPSA - Python for Power System Analysis

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

PyPSA is an open source toolbox for simulating and optimising modern power and energy 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 Department of Digital Transformation in Energy Systems at the Technical University of Berlin. Previous versions were developed by the Energy System Modelling group at the Institute for Automation and Applied Informatics at the Karlsruhe Institute of Technology funded by the Helmholtz Association, and 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.

Functionality

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 and investment periods 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 and links 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 (e.g. resistive Power-to-Heat (P2H), Power-to-Gas (P2G), battery electric vehicles (BEVs), Fischer-Tropsch, direct air capture (DAC))
• basic components out of which more complicated assets can be built, such as Combined Heat and Power (CHP) units and heat pumps.

Documentation

• Documentation

  • Quick start

  • Examples

  • Known users of PyPSA

Installation

pip:

pip install pypsa

conda/mamba:

conda install -c conda-forge pypsa

Additionally, install a solver (see here).

Usage

import pypsa

# create a new network
n = pypsa.Network()
n.add("Bus", "mybus")
n.add("Load", "myload", bus="mybus", p_set=100)
n.add("Generator", "mygen", bus="mybus", p_nom=100, marginal_cost=20)

# load an example network
n = pypsa.examples.ac_dc_meshed()

# run the optimisation
n.optimize()

# plot results
n.generators_t.p.plot()
n.plot()

# get statistics
n.statistics()
n.statistics.energy_balance()

There are more extensive examples available as Jupyter notebooks. They are also available as Python scripts in examples/notebooks/ directory.

Screenshots

PyPSA-Eur optimising capacities of generation, storage and transmission lines (9% line volume expansion allowed) for a 95% reduction in CO2 emissions in Europe compared to 1990 levels

SciGRID model simulating the German power system for 2015.

Dependencies

PyPSA is written and tested to be compatible with Python 3.9 and above. The last release supporting Python 2.7 was PyPSA 0.15.0.

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
• networkx for some network calculations
• matplotlib for static plotting
• linopy for preparing optimisation problems (currently only linear and mixed integer linear optimisation)
• cartopy for plotting the baselayer map
• pytest for unit testing
• logging for managing messages

Find the full list of dependencies in the dependency graph.

The optimisation uses interface libraries like linopy which are independent of the preferred solver. You can use e.g. one of the free solvers HiGHS, GLPK and CLP/CBC or the commercial solver Gurobi for which free academic licenses are available.

Contributing and Support

We strongly welcome anyone interested in contributing to this project. If you have any ideas, suggestions or encounter problems, feel invited to file issues or make pull requests on GitHub.

• In case of code-related questions, please post on stack overflow.
• For non-programming related and more general questions please refer to the mailing list.
• To discuss with other PyPSA users, organise projects, share news, and get in touch with the community you can use the discord server.
• For bugs and feature requests, please use the PyPSA Github Issues page.
• For troubleshooting, please check the troubleshooting in the documentation.

Detailed guidelines can be found in the Contributing section of our documentation.

Code of Conduct

Please respect our code of conduct.

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. The release-specific DOIs can be found linked from the overall PyPSA Zenodo DOI for Version 0.17.1 and onwards:

or from the overall PyPSA Zenodo DOI for Versions up to 0.17.0:

Licence

Copyright 2015-2024 PyPSA Developers

PyPSA is licensed under the open source MIT License.