Optimal Planning of Renewable Energy and Storage Considering Capacity Credit Constraint

The increasing integration of renewable energy poses the dual challenges of renewable curtailment and supply shortage due to its temporal variability and high uncertainty. Optimal planning of renewable and storage is critical for ensuring power supply reliability and enhancing renewable energy accommodation. This paper develops a co-planning model of renewable and storage considering capacity credit (CC) constraints. A refined multi-time-scale CC assessment method based on improved scenario selection is proposed to quantify power supply capability of renewable and storage, thereby accounting for their capacity value and establishing system adequacy constraints. To improve computational efficiency, an accelerated operational simulation algorithm and an iterative solving approach are introduced. Through case studies on the RTS-GMLC system and a practical power system in Northeast China, the proposed method improves computational efficiency by 14 times compared to year-round operation without affecting accuracy, while effectively ensuring supply reliability under extreme weather conditions and maintaining economic efficiency.