GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow

• Published in: Software impacts 2023-05, Vol.16, p.100489, Article 100489
• Main Authors: Mitrovic, Mile, Kundacina, Ognjen, Lukashevich, Aleksandr, Budennyy, Semen, Vorobev, Petr, Terzija, Vladimir, Maximov, Yury, Deka, Deepjyoti
• Format: Article
• Language: English
• Subjects: CasADi; Chance-constrained optimization; Gaussian Processes; Machine learning; Optimal power flow; Python
• ISSN: 2665-9638; 2665-9638
• DOI: 10.1016/j.simpa.2023.100489

The Gaussian Process (GP) based Chance-Constrained Optimal Power Flow (CC-OPF) is an open-source Python code developed for solving economic dispatch (ED) problem in modern power grids. In recent years, integrating a significant amount of renewables into a power grid causes high fluctuations and thus brings a lot of uncertainty to power grid operations. This fact makes the conventional model-based CC-OPF problem non-convex and computationally complex to solve. The developed tool presents a novel data-driven approach based on the GP regression model for solving the CC-OPF problem with a trade-off between complexity and accuracy. The proposed approach and developed software can help system operators to effectively perform ED optimization in the presence of large uncertainties in the power grid. •GP CC-OPF hybrid approach for chance-constrained OPF is proposed.•A sparse Gaussian process model is considered for the trade-off between accuracy and complexity.•The proposed approach does not require information about the topology and parameters of the electrical grid.•GP CC-OPF can help the power system operator to plan generation dispatch under injection uncertainties.