An operational optimization strategy for microgrid energy storage systems is developed to meet real-world user application requirements, and its effectiveness and applicability are validated using actual user data. First, a fundamental model of the energy storage system is established, providing a theoretical foundation for the operational optimization strategy. Subsequently, a multi-objective operational optimization strategy for the microgrid energy storage system is developed, focusing on economic objectives, carbon emission reduction targets, and renewable energy integration goals. The commercial optimization solver Gurobi is employed to enhance computational efficiency. Finally, the proposed optimization strategy is validated using real-world microgrid data from City A in a province of East China.The results demonstrate that the constructed energy storage system model accurately captures the operational constraints of the actual system. Compared to the user’s existing strategy, the proposed optimization strategy achieves an average reduction of 13.4676% in electricity cost expenditure. By dynamically adjusting weight factors for multi- objective optimization, the strategy enables diversified operational modes, significantly enhancing the scenario adaptability of the energy storage system’s operational strategy. Furthermore, the strategy provides decision-making support for formulating policies related to surplus electricity feed-in from us-er-side microgrids. The main innovation lies in the strategy’s user-centric design, which achieves operational flexibility through multi-objective weight allocation, improves scenario adaptability, and offers a novel approach for the practical implementation of microgrid energy storage systems.