matpowercaseframes 1.0.8

Parse MATPOWER case into pandas DataFrame.

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)

Since matpower itself suggests that we should use loadcase instead of parsing, we can use an engine using (require matlab or octave),

from matpower import start_instance
from matpowercaseframes import CaseFrames

m = start_instance()

case_name = f"case16am.m"
cf_16am_lc = CaseFrames(case_name, load_case_engine=m)
cf_16am_lc.branch  # see that the branch is already in p.u., converted by `loadcase`

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_mpc()  # identical with 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

1. 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 would be very glad if you cited any relevant publication under Mr. Sarjiya's name as thanks (but you are not responsible for citing). You can find his publications in the Semantic Scholar or IEEE.

2. This repository is working flawlessly with matpower-pip. If you use matpower-pip, make sure to cite using the below citation:

   M. Yasirroni, Sarjiya, and L. M. Putranto, "matpower-pip: A Python Package for Easy Access to MATPOWER Power System Simulation Package," [Online]. Available: https://github.com/yasirroni/matpower-pip.

   M. Yasirroni, Sarjiya, and L. M. Putranto, "matpower-pip". Zenodo, Jun. 13, 2024. doi: 10.5281/zenodo.11626845.

   @misc{matpower-pip,
     author       = {Yasirroni, M. and Sarjiya and Putranto, L. M.},
     title        = {matpower-pip: A Python Package for Easy Access to MATPOWER Power System Simulation Package},
     year         = {2023},
     howpublished = {\url{https://github.com/yasirroni/matpower-pip}},
   }
   
   @software{yasirroni_2024_11626845,
     author       = {Yasirroni, Muhammad and
                       Sarjiya, Sarjiya and
                       Putranto, Lesnanto Multa},
     title        = {matpower-pip},
     month        = jun,
     year         = 2024,
     publisher    = {Zenodo},
     version      = {8.0.0.2.1.8},
     doi          = {10.5281/zenodo.11626845},
     url          = {\url{https://doi.org/10.5281/zenodo.11626845}},
   }
   

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