SOPF

This repo implements four different Stochastic Optimal Power Flow (SOPF) models in AMPL.

Model 1, R-SOPF, a relaxed SOPF model that assums infinite network capacity.
Model 2, N-SOPF, a normal SOPF model that enforces base-case network constraints only.
Model 3, E-SOPF, a N-1 SOPF model that enforces both base-case network constraints and contingency-case network constraints.
Model 4, E-SOPFwNR, a N-1 SOPF w. CTS model that implements corrective transmission switching in post-contingency cases beyong the N-1 SOPF model.

Simulations:

The test case used here is a modified IEEE RTS-96 reliability test system (24-bus). The reference is: “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.
Though only tested on this single system here, these codes can work on any other systems.
For the most complex model (E-SOPFwNR), the code takes 130 seconds (~2 minutes) on a laptop: Intel(R) Core(TM) i7-8850H CPU @ 2.60GHz, 32 GB RAM, Windows 10.

The following paper provides more details about these four models:

Xingpeng Li and Qianxue Xia, “Stochastic Optimal Power Flow with Network Reconfiguration: Congestion Management and Facilitating Grid Integration of Renewables”, IEEE PES T&D Conference & Exposition, (Virtually), Chicago, IL, USA, Oct. 2020. (DOI: 10.1109/TD39804.2020.9299954)

Citation:

If you use these codes for your work, please cite the following paper:

Xingpeng Li and Qianxue Xia, “Stochastic Optimal Power Flow with Network Reconfiguration: Congestion Management and Facilitating Grid Integration of Renewables”, IEEE PES T&D Conference & Exposition, (Virtually), Chicago, IL, USA, Oct. 2020.

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.