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

Bendong Tan, Tong Su, Yu Weng, Ketian Ye, Parikshit Pareek, Petr Vorobev, Hung Nguyen, Junbo Zhao and Deepjyoti Deka

Applied Energy, 2026, vol. 402, issue PC, No S0306261925017258

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 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.

Keywords: Gaussian process; Power system dynamics learning; Power system optimization and control; Renewable energy and load forecasting; Risk assessment; Static power flow learning; Uncertainty quantification (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1016/j.apenergy.2025.126995

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