GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow
Abstract
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.
Keywords
Cite
@article{arxiv.2302.08454,
title = {GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow},
author = {Mile Mitrovic and Ognjen Kundacina and Aleksandr Lukashevich and Petr Vorobev and Vladimir Terzija and Yury Maximov and Deepjyoti Deka},
journal= {arXiv preprint arXiv:2302.08454},
year = {2023}
}
Comments
6 pages, 2 figures