Gaussian Processes in Power Systems: Techniques, Applications, and Future Works

TL;DR

This paper reviews how Gaussian processes are used in power systems for modeling, risk assessment, and control, highlighting their benefits and outlining future research challenges in the context of increasing renewable integration.

Contribution

It provides a comprehensive overview of Gaussian process techniques applied to power systems, including recent applications, challenges, and future research directions.

Findings

Gaussian processes improve uncertainty quantification in power system analysis.

GP-based methods enhance risk assessment and decision-making.

The paper identifies key challenges and future research opportunities in applying GPs to power systems.

Abstract

The increasing integration of renewable energy sources (RESs) and distributed energy resources (DERs) has significantly heightened operational complexity and uncertainty in modern power systems. Concurrently, the widespread deployment of smart meters, phasor measurement units (PMUs) and other sensors has generated vast spatiotemporal data streams, enabling advanced data-driven analytics and decision-making in grid operations. In this context, Gaussian processes (GPs) have emerged as a powerful probabilistic framework, offering uncertainty quantification, non-parametric modeling, and predictive capabilities to enhance power system analysis and control. This paper presents a comprehensive review of GP techniques and their applications in power system operation and control. GP applications are reviewed across three key domains: GP-based modeling, risk assessment, and optimization and control. These areas serve as representative examples of how GP can be utilized in power systems. Furthermore, critical challenges in GP applications are discussed, and potential research directions are outlined to facilitate future power system operations.