| 叶 飞,江 南,陆 彬,梁世杰.微电网储能系统多目标运行优化策略研究[J].电力需求侧管理,2025,27(6):85-91 |
| 微电网储能系统多目标运行优化策略研究 |
| Research on multi-objective operation optimization strategies for microgrid energy storage systems |
| 投稿时间：2025-06-25 修订日期：2025-09-10 |
| DOI：10. 3969 / j. issn. 1009-1831. 2025. 06. 013 |
| 中文关键词: 微电网 储能系统 多目标 优化 经济性 碳排 |
| 英文关键词: microgrid energy storage systems multi-objective optimization economic carbon emission |
| 基金项目:国网江苏综合能源服务有限公司科技项目“微电网数智管控系统关键技术研究”（ZHNY2023RD01N）。 |
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| 中文摘要: |
| 针对用户真实应用需求的问题，构建了一种微电网储能系统运行优化策略，并通过实际用户数据验证其有效性和适用性。首先，建立了储能系统的基本模型，为储能系统运行优化策略奠定应用基础。然后，从经济目标、碳排目标和新能源消纳目标3个方面提出了一种微电网储能系统多目标运行优化策略，并利用商业版优化器Gurobi求解器提高优化效率。最后，利用我国华东某省A市的实际微电网数据验证了所提优化策略的有效性和适用性，结果表明：所构建的储能系统模型能够准确反映实际储能系统运行约束，所提优化策略较用户现有策略在电费支出成本方面平均降低13.467 6%；通过设置多目标优化的权重因子实现了运行方式的多样化，增强了储能系统运行策略的场景适应性；此外，该策略还可辅助制定用户侧微电网的余电上网政策。 |
| 英文摘要: |
| 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. |
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