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.