Multi‐Objective Optimal Scheduling Strategy for Virtual Power Plant Considering Temporal Coupling Characteristics of Resources

To overcome the challenges in traditional virtual power plant (VPP) scheduling—difficulties in coordinating economic and ecological benefits and limited intra‐day adaptability due to resource temporal coupling, like energy storage—this paper proposes a two‐stage multi‐objective optimal scheduling strategy. In the day‐ahead stage, a multi‐objective optimization model is established and an improved bi‐directional multi‐objective optimal scheduling strategy. In the day‐ahead stage, a multi‐objective optimization model is establishedhe optimal compromise solution. Additionally, a spectral decomposition method based on a value sensitivity matrix is introduced to identify key regulation intervals for energy storage. In the intra‐day stage, this high‐value period information guides the adaptive adjustment of the rolling optimization window. Simulation results show that the proposed strategy effectively balances renewable energy consumption, carbon emission reduction and operational economics. Compared to traditional fixed‐window methods, the proposed approach optimizes the balance between intra‐day operational revenue and computational burden, leading to superior energy storage regulation during critical demand response periods and thereby alleviating the conflict between global and local scheduling.