Robust Optimization Strategy for Reserve Capacity via Source-Load-Storage Integration

Due to the influence of external power supply environments and the frequent occurrence of extreme weather events, the power supply security of receiving-end power systems faces significant challenges, necessitating source-load-storage resources to provide more stable and reliable reserve capabilities. To address this issue, we propose a robust optimization dispatching strategy for source-load-storage resources that accommodates diverse system regulation demands. First, adjustable thermal power units are classified into cold, hot, and spinning reserve states based on response delay and regulation speed, and the adjustable characteristics of multiple resources, including various types of thermal units, electric vehicles, and distributed battery energy storage, are analyzed. Next, a robust optimization model for reserve capacity distribution of adjustable resources is developed, minimizing load shedding based on representative scenarios obtained through clustering. An iterative solution method is constructed to determine the optimal number of representative scenarios and the robust optimization strategy for reserve capacity. Finally, case studies validate the adaptability and effectiveness of the proposed robust optimization dispatching strategy in diverse regulation scenarios, supporting the adequate operation of power systems.