o:2395 GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow en 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. 1552099 https://doi.org/10.1016/j.simpa.2023.100489 2023-06-21T12:44:02.270Z 44 yes 46 Mile Mitrović Center for Energy Science and Technology, Skolkovo Institute of Science and Technology person 0000-0001-6691-2772 Ognjen Kundačina The Institute for Artificial Intelligence Research and Development of Serbia 0000-0003-0198-3363 Aleksandr Lukashevich Center for Energy Science and Technology, Skolkovo Institute of Science and Technology person 0000-0002-4986-9941 Semen Budennyy Sber AI Lab and Artificial Intelligence Research Institute person 0000-0001-6916-2030 Petr Vorobev Center for Energy Science and Technology, Skolkovo Institute of Science and Technology person 0000-0002-1622-5058 Vladimir Terzija Center for Energy Science and Technology, Skolkovo Institute of Science and Technology person 0000-0002-6538-6982 Yury Maximov Los Alamos National Laboratory person 0000-0003-4397-6479 Deepjyoti Deka Los Alamos National Laboratory person 0000-0003-3928-3936 application/pdf 275773 https://unilib.phaidrabg.rs/o:2395 no yes 1 70 92000001 71A08 Software Impacts 16 Elsevier 2023