PYPOWER is a power flow and Optimal Power Flow (OPF) solver. It is a port of MATPOWER to the Python programming language. Current features include:

• DC and AC (Newton's method & Fast Decoupled) power flow and
• DC and AC optimal power flow (OPF)

PYPOWER depends upon:

• Python 2.6-3.5 and
• SciPy 0.9 or later.

It can be installed using pip:

$ pip install PYPOWER

Alternatively, download and unpack the tarball and install:

$ tar zxf PYPOWER-4.x.y.tar.gz
$ python setup.py install

Installing PYPOWER creates pf and opf commands. To list the command options:

$ pf -h

PYPOWER includes a selection of test cases. For example, to run a power flow on the IEEE 14 bus test case:

$ pf -c case14

Alternatively, the path to a PYPOWER case data file can be specified:

$ pf /path/to/case14.py

The opf command has the same calling syntax. For example, to solve an OPF for the IEEE Reliability Test System and write the solved case to file:

$ opf -c case24_ieee_rts --solvedcase=rtsout.py

For further information please refer to https://rwl.github.io/PYPOWER/ and the API documentation.

Questions and comments regarding PYPOWER should be directed to the mailing list:

pypower@googlegroups.com

Copyright (c) 1996-2015, Power System Engineering Research Center (PSERC) Copyright (c) 2010-2015 Richard Lincoln

The code in PYPOWER is distributed under the 3-clause BSD license below. The PYPOWER case files distributed with PYPOWER are not covered by the BSD license. In most cases, the data has either been included with permission or has been converted from data available from a public source.

While not required by the terms of the license, we do request that publications derived from the use of MATPOWER explicitly acknowledge that fact by citing:

R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J. Thomas, "MATPOWER: Steady-State Operations, Planning and Analysis Tools for Power Systems Research and Education," Power Systems, IEEE Transactions on, vol. 26, no. 1, pp. 12–19, Feb. 2011.

• MATPOWER from PSERC (Cornell)
• MatDyn by Stijn Cole
• PSAT by Federico Milano
• OpenDSS from EPRI
• GridLAB-D from PNNL
• PyCIM