<p>This study proposes an optimization strategy for energy storage planning to address the challenges of coordinating photovoltaic storage clusters. The strategy aims to improve system performance within current group control systems, considering multi-scenario collaborative control. To identify typical scenarios of photovoltaic output and load demand over a year, a clustering analysis is conducted on data from a representative year. This analysis helps to model the distribution networks behavior under varying climatic conditions. A bilevel coordinated planning model for distributed energy storage (DESS) is then developed, integrating both planning costs and operational costs across multiple timescales. The bilevel model, characterized by multiple objectives and high dimensionality, is resolved using a bilevel iterative multi-objective particle swarm optimization algorithm. To assess the solutions, the TOPSIS method with entropy weights is employed to identify the optimal planning option. Simulation on the IEEE 33-node system with high photovoltaic penetration shows that the proposed strategy significantly enhances the network’s ability to absorb photovoltaic energy compared to traditional single-layer planning. It also reduces the occurrence of excess photovoltaic generation, improving system stability and reliability. The novelty of this research lies in the integration of multi-scenario analysis, clustering, and bilevel optimization to enhance the coordination and efficiency of photovoltaic storage systems.</p>