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

Mile Mitrovic, Ognjen Kundacina, Aleksandr Lukashevich, Petr Vorobev, Vladimir Terzija +2 more
2/16/2023
stat.MLcs.LG

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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.

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Cite this paper

@article{mitrovic2023ccopf,
  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},
  year   = {2023},
  eprint = {2302.08454},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.08454v1}
}

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