This work implements multiple ML models including GNN to identify a subset of critical lines to be monitored in OPF models, leading to size-reduced OPF models.
This set of codes/data implements our NAPS paper “Reduced Optimal Power Flow Using Graph Neural Network”. DNN, CNN and GNN are used to reduce constraints mainly line thermal limit constraints for OPF.
Python Environment:
Codes are implemented in Python, using Jupyter Notebook. ML model uses Tensorflow and Spektral (GNN) libraries. Optimization is implemented using Pyomo in python. A solver is required for pyomo to run.
To run the program, you may need to have the data files replaced in the proper folder.
Test Power Systems
The following test power system was used in this work:
- IEEE 73-bus system: the original data of this test system are described in this reference: “The IEEE Reliability Test System-1996. A report prepared by the Reliability Test System Task Force of the Application of Probability Methods Subcommittee” and link is here.
Citation:
If you use any of our codes/data for your work, please cite the following papers as your reference:
Thuan Pham and Xingpeng Li, “Reduced Optimal Power Flow Using Graph Neural Network”, North American Power Symposium, Salt Lake City, UT, USA, Oct. 2022.
Paper website: https://rpglab.github.io/papers/ThuanP-GNN-ROPF/
Contributions:
Thuan Pham developed this set of programs/data. Xingpeng Li supervised this work.
Contact:
Dr. Xingpeng Li
University of Houston
Email: xli83@central.uh.edu
Website: https://rpglab.github.io/
License:
This work is licensed under the terms of the Creative Commons Attribution 4.0 (CC BY 4.0) license.
Disclaimer:
The author doesn’t make any warranty for the accuracy, completeness, or usefulness of any information disclosed; and the author assumes no liability or responsibility for any errors or omissions for the information (data/code/results etc) disclosed.