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

TL;DR

This paper introduces GP CC-OPF, an open-source Python tool that uses Gaussian Process regression to efficiently solve chance-constrained optimal power flow problems under high renewable energy uncertainty.

Contribution

It presents a novel data-driven Gaussian Process approach for CC-OPF, balancing complexity and accuracy in power grid optimization with uncertainty.

Findings

Effective handling of high renewable uncertainty in power grids

Open-source Python implementation available

Improved computational efficiency over traditional methods

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.