Software Impacts 16 Elsevier 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. application/pdf 275773 bytes GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow https://unilib.phaidrabg.rs/o:2395 doi:10.1016/j.simpa.2023.100489 eng 2023 Mitrović, Mile Kundačina, Ognjen Lukashevich, Aleksandr Budennyy, Semen Vorobev, Petr Terzija, Vladimir Maximov, Yury Deka, Deepjyoti All rights reserved info:eu-repo/semantics/article