Review on Source-storage Coordinated Optimization Operation and Control Considering New Energy Uncertainty

Abstract

With the increasing proportion of renewable energy sources, the output characteristics of wind and solar power in power systems have become increasingly stochastic. This phenomenon not only affects clean energy utilization efficiency but also poses significant challenges to the safety and stability of traditional power grids. In these conditions, energy storage devices have become essential for reducing power fluctuations from renewable sources, and they are crucial for source-storage joint dispatching and dynamic control. Through literature review and comprehensive analysis, this study investigates characterization methods and quantification approaches for renewable energy uncertainty within source-storage coordination frameworks, explores optimization model design philosophies and related strategies, and evaluates current mainstream control technology trends. The uncertainty assessment system, established using parameters such as prediction deviations and fluctuation amplitudes, demonstrates greater scientific rigor than conventional coarse scenario segmentation methods. Multi-objective function-based collaborative planning approaches are gradually replacing single-objective modeling paradigms. Regarding real-time response performance improvement techniques, model predictive control (MPC) integrated with reinforcement learning algorithms has become a new research focus. This study aims to clarify academic frameworks in relevant fields and provide theoretical foundations and technical support for practical applications of source-storage coordination mechanisms in high-renewable-energy environments.