Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processes

TL;DR

This paper introduces a fast, data-driven method using hybrid sparse Gaussian processes to model AC-OPF under uncertainty, significantly improving speed and accuracy for power grid optimization.

Contribution

It proposes a novel hybrid sparse Gaussian process framework for AC-OPF that enhances computational efficiency and modeling accuracy under uncertainty.

Findings

Up to two times faster solutions compared to existing methods.

More accurate power flow modeling under uncertainty.

Effective application demonstrated on IEEE test cases.

Abstract

The alternating current (AC) chance-constrained optimal power flow (CC-OPF) problem addresses the economic efficiency of electricity generation and delivery under generation uncertainty. The latter is intrinsic to modern power grids because of the high amount of renewables. Despite its academic success, the AC CC-OPF problem is highly nonlinear and computationally demanding, which limits its practical impact. For improving the AC-OPF problem complexity/accuracy trade-off, the paper proposes a fast data-driven setup that uses the sparse and hybrid Gaussian processes (GP) framework to model the power flow equations with input uncertainty. We advocate the efficiency of the proposed approach by a numerical study over multiple IEEE test cases showing up to two times faster and more accurate solutions compared to the state-of-the-art methods.