Research on Multi-Objective Optimization Configuration and Dispatching Methods for Microgrid Clusters

To address the issues of intensified source–load mismatch and peak-shaving pressure in microgrids under conditions of high-penetration distributed photovoltaic integration, this paper proposes a multi-objective optimization configuration method for microgrid clusters that integrates energy storage systems (ESSs) with demand response mechanisms. A mixed-integer linear operational model is constructed to characterize charge–discharge exclusivity and state-of-charge constraints. Based on this, a three-dimensional objective function encompassing economic efficiency, low carbon emissions, and reliability is established, and the Pareto-optimal solution set is solved using the NSGA-II algorithm. Through case studies in typical county-level scenarios, operational characteristics are compared between scenarios without ESSs and those with coordinated optimization, revealing the law of marginal returns for energy storage capacity and the mechanism by which source–grid–load–storage coordination mitigates the “duck curve.” The results demonstrate that the proposed method enables full accommodation of renewable energy, enhances peak-shaving capability by over 32%, reduces the cost per kilowatt-hour by approximately 11%, and identifies the economically optimal capacity point. This research provides a quantitative basis for energy storage planning and multi-objective decision-making in county-level microgrid clusters.