ru
ru
Source
Software Impacts
DATE OF PUBLICATION
03/15/2023
Authors
Semen Budennyy Mile Mitrovic Ognjen Kundacina Aleksandr Lukashevich Petr Vorobev Vladimir Terzija Yury Maximov Deepjyoti Deka
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GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow

Chance-constrained optimization, Gaussian Processes, Machine learning, 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.

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