Gaussian processes in power systems: Techniques, applications, and future works

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 GPs 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. • A structured overview of Gaussian Process (GP) fundamentals and software ecosystems is provided. • Applications of GP in power systems, including load and renewable energy forecasting, power system static and dynamic modeling, risk assessment, and decision-making, are systematically reviewed. • Key challenges in applying GP to power systems, including issues of scalability and robustness, are thoroughly discussed. • Future research directions are outlined to facilitate the integration of GP with advanced data driven methods in energy systems.