MATPOWER Case Frames

Parse MATPOWER case into pandas DataFrame.

Unlike the tutorial on matpower-pip, this package supports parsing MATPOWER case using re instead of Oct2Py and Octave. After that, you can further parse the data into any format supported by your solver.

Installation

pip install matpowercaseframes

Usage

The main utility of matpowercaseframes is to help read matpower data in user-friendly format as follows,

from matpowercaseframes import CaseFrames

case_path = 'case9.m'
cf = CaseFrames(case_path)

print(cf.gencost)

If you have matpower installed via pip install matpower (did not require matpower[octave]), you can easily navigate matpower case using:

import os
from matpower import path_matpower # require `pip install matpower`
from matpowercaseframes import CaseFrames

case_name = 'case9.m'
case_path = os.path.join(path_matpower, 'data', case_name)
cf = CaseFrames(case_path)

print(cf.gencost)

Furthermore, matpowercaseframes also support generating data that is acceptable by matpower via matpower-pip package (require matlab or octave),

from matpowercaseframes import CaseFrames

case_path = 'case9.m'
cf = CaseFrames(case_path)
mpc = cf.to_dict()

m = start_instance()
m.runpf(mpc)

To save all DataFrame to a single xlsx file, use:

from matpowercaseframes import CaseFrames

case_path = 'case9.m'
cf = CaseFrames(case_path)

cf.to_excel('PATH/TO/DIR/case9.xlsx')

If you use matpower[octave], CaseFrames also support oct2py.io.Struct as input using:

from matpower import start_instance
from matpowercaseframes import CaseFrames

m = start_instance()

# support mpc before runpf
mpc = m.loadcase('case9', verbose=False)
cf = CaseFrames(mpc)
print(cf.gencost)

# support mpc after runpf
mpc = m.runpf(mpc, verbose=False)
cf = CaseFrames(mpc)
print(cf.gencost)

m.exit()

Acknowledgment

This repository was supported by the Faculty of Engineering, Universitas Gadjah Mada under the supervision of Mr. Sarjiya. If you use this package for your research, we are very glad if you cite any relevant publication under Mr. Sarjiya's name as thanks (but you are not responsible to cite). You can find his publications in the semantic scholar](https://www.semanticscholar.org/author/Sarjiya/2267414) or IEEE.

This package is a fork and simplification from psst MATPOWER parser, thus we greatly thank psst developers and contributors.