Data-Driven Stochastic AC-OPF using Gaussian Processes

TL;DR

This paper introduces a machine learning-based data-driven approach using Gaussian processes to efficiently solve the complex, nonlinear stochastic AC-OPF problem, demonstrating superior empirical performance on IEEE test cases.

Contribution

It develops a novel Gaussian process regression method for approximating AC power flow equations under uncertainty, improving computational efficiency and accuracy over existing methods.

Findings

Full GP CC-OPF outperforms state-of-the-art sample-based methods.

The sparse and hybrid GP framework enhances robustness and reduces complexity.

Empirical tests on IEEE cases validate the approach's effectiveness.

Abstract

The thesis focuses on developing a data-driven algorithm, based on machine learning, to solve the stochastic alternating current (AC) chance-constrained (CC) Optimal Power Flow (OPF) problem. Although the AC CC-OPF problem has been successful in academic circles, it is highly nonlinear and computationally demanding, which limits its practical impact. The proposed approach aims to address this limitation and demonstrate its empirical efficiency through applications to multiple IEEE test cases. To solve the non-convex and computationally challenging CC AC-OPF problem, the proposed approach relies on a machine learning Gaussian process regression (GPR) model. The full Gaussian process (GP) approach is capable of learning a simple yet non-convex data-driven approximation to the AC power flow equations that can incorporate uncertain inputs. The proposed approach uses various approximations for GP-uncertainty propagation. The full GP CC-OPF approach exhibits highly competitive and promising results, outperforming the state-of-the-art sample-based chance constraint approaches. To further improve the robustness and complexity/accuracy trade-off of the full GP CC-OPF, a fast data-driven setup is proposed. This setup relies on the sparse and hybrid Gaussian processes (GP) framework to model the power flow equations with input uncertainty.