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

• URL: http://arxiv.org/abs/2302.08454v1
• Date: Thu, 16 Feb 2023 17:59:06 GMT
• Title: GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow
• Authors: Mile Mitrovic, Ognjen Kundacina, Aleksandr Lukashevich, Petr Vorobev, Vladimir Terzija, Yury Maximov, Deepjyoti Deka
• Abstract summary: The Gaussian Process (GP) based Chance-Constrained Optimal Flow (CC-OPF) is an open-source Python code for economic dispatch (ED) problem in power grids. The developed tool presents a novel data-driven approach based on the CC-OP model for solving the large regression problem with a trade-off between complexity and accuracy.
• Score: 54.94701604030199
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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