A Model and Data Hybrid-Driven Method for Operational Reliability Evaluation of Power Systems Considering Endogenous Uncertainty

Abstract

Renewable energy sources are increasingly integrated into power systems, leading to significant variability in operations. This necessitates robust methods for assessing operational reliability. We propose a novel model–data hybrid approach that incorporates endogenous uncertainty into the reliability evaluation process. First, unlike traditional methods that treat uncertainties as external factors, this approach recognizes that operational decisions can significantly influence how uncertainties are resolved and impact reliability metrics. The proposed method integrates device reliability indices with operational decision variables. This allows us to evaluate the impact of endogenous uncertainty on operational reliability through a reliability-constrained stochastic unit commitment model. Additionally, a model–data hybrid algorithm is introduced for efficient solution of the formulated optimization problem. Case studies demonstrate the effectiveness of the proposed method. Results also show that endogenous uncertainty may cause a 10% error in power system reliability indices.