:zap: PyOPF: Optimal Power Flow Modeling in Python

PyOPF is Optimal Power Flow (OPF) modeling framework in Python. This modeling is basically based on Pyomo, which is a solver-agnostic optimization modeling package in Python. PyOPF generally can take PGLib based input and formulate various OPF problems including AC-OPF, DC-OPF.

Installation

pip install opf

• Dependencies
  • python>=3.8
  • pyomo>=6.5.0
  • numpy>=1.22.3
  • ipopt>=1.0.3

Formulations

1. :o: AC-OPF (AC Optimal Power Flow): python model = opf.build_model('acopf')

   • AC-OPF with a polar bus voltage variable representations.
   • The detailed formulation can be found in PGLib.
   • PyOPF takes the the input files from PGLib, which is basically based on MATPOWER format.
   • Uses various solvers supported in Pyomo including IPOPT and Gurobi to solve problem instances.

2. :o: DC-OPF (DC Optimal Power Flow) python model = opf.build_model('dcopf') # base DC-OPF model model = opf.build_model('dcopf-ptdf') # DC-OPF model using PTDF

   • Linear approximation to AC-OPF.
   • Also support PTDF (power transfer distribution factor) based formulation.
   • Only use active power generations and bus voltage angles (for base DC-OPF) as variables.
   • Like AC-OPF, PGLib m-files can be taken as input.

Warmstarting

• PyOPF fully supports primal and dual warmstarting for IPOPT. Documentation is to be added. python model.setup_warmstart(warmstart_solution_dict)

Examples

Running AC-OPF from PGLib.

• Before solving the AC-OPF, you should install IPOPT, which is a canonical solver for AC-OPF, as follows: conda install -c conda-forge ipopt

• Running the following AC-OPF problem ```python import opf

  build abstract model for AC-OPF

  model = opf.build_model('acopf')

  load pglib input model file

  network = opf.parsefile("./data/pglibopfcase5pjm.m")

  create the model instance (concrete model)

  model.instantiate(network)

  solve the problem

  result = model.solve(solveroption={'printlevel' : 5, 'linear_solver': 'ma27'}, tee=True)

  check the optimal objective value

  print('obj value', result['obj_cost'])

  check the (primal) optimal solution

  print('primal solution', result['sol']['primal']) ```

Citation

• If you exploit this repository in your research, please cite using the following BibTeX:

@software{ PyOPF_2023, author = {Park, Seonho}, doi = {10.5281/zenodo.7772531}, license = {MIT}, month = {10}, title = {{PyOPF: Optimal Power Flow Modeling in Python}}, version = {0.3.1}, year = {2023} }