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.

翻译：摘要：基于高斯过程的概率约束最优潮流（GP CC-OPF）是一种开源的Python代码，用于解决现代电网中的经济调度（ED）问题。近年来，大量可再生能源接入电网导致系统波动剧烈，从而给电网运行带来显著的不确定性。这一现状使得传统的基于模型的概率约束最优潮流问题呈现非凸性，且求解复杂度极高。本文开发了一套基于高斯过程回归模型的新型数据驱动方法，在复杂性与精度之间实现权衡，用于求解概率约束最优潮流问题。所提出的方法与开发的软件能够帮助系统运营商在电网存在较大不确定性的情况下有效执行经济调度优化。