Adaptive and Collaborative Hierarchical Optimization Strategies for a Multi-Microgrid System Considering EV and Storage

The disordered nature of electric vehicle (EV) charging and user electricity consumption behaviors has intensified the strain on the grid. Meanwhile, energy storage technologies and microgrid interconnections still lack effective supply–consumption regulations and cost–benefit optimization mechanisms. Therefore, the system’s operational efficiency holds significant potential for improvement. This paper proposes hierarchical optimization strategies for the multi-microgrid system to address these issues. In the lower layer, for the charging states of EVs in a single microgrid, an improved simulation method to enhance accuracy and a recursion mechanism of an energy storage margin band to facilitate intelligent EV-to-grid interaction are proposed. Additionally, in conjunction with demand management, an adaptive optimization method and a Pareto decision method are proposed to achieve optimal peak shaving and valley filling for both the EVs and load, yielding a 38.5% reduction in the total electricity procurement costs. The upper layer is built upon the EV–load management strategies of microgrids in the lower layers and evolves into a distributed interconnection structure. Furthermore, a dynamic optimization mechanism based on state mapping and a collaborative optimization method are proposed to improve storage benefits and energy synergies, achieving a 22.1% reduction in the total operating cost. The results provided demonstrate that the proposed strategy optimizes the operation of the multi-microgrid system, effectively enhancing the overall operational efficiency and economic performance.