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<p>To address the volatility and intermittency issues in multi-microgrid (MMG) systems caused by high renewable energy penetration, as well as the high costs associated with traditional distributed energy storage, Shared Energy Storage (SES) is employed to mitigate net load fluctuations and enhance power supply reliability. Regarding the power and capacity configuration of the SES system, a multi-objective optimization model is established aiming to minimize both the energy storage investment costs and the net load fluctuations of the microgrids. To overcome the tendency of the traditional Non-dominated Sorting Genetic Algorithm II (NSGA-II) to fall into local optima, an improved SA-NSGA-II algorithm is proposed. This algorithm incorporates a simulated annealing mechanism and introduces a novel variance-based crowding distance calculation method. Furthermore, dynamic mutation and crossover rates are adopted to enhance optimization performance, thereby yielding superior power and capacity configuration schemes. Finally, a simulation analysis based on a three-microgrid case study is conducted to validate the effectiveness and superiority of the proposed model and algorithm.</p>

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Research on Multi-Objective Optimal Allocation of Multi-Microgrid-Shared Energy Storage Based on SA-NSGA-II Algorithm

  • 期刊论文

  • 发表日期：

    2026年4月11日

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  • Hairui Xu,
  • Wei Bao,
  • Yanan Yang,
  • Yafei Zhu

摘要

To address the volatility and intermittency issues in multi-microgrid (MMG) systems caused by high renewable energy penetration, as well as the high costs associated with traditional distributed energy storage, Shared Energy Storage (SES) is employed to mitigate net load fluctuations and enhance power supply reliability. Regarding the power and capacity configuration of the SES system, a multi-objective optimization model is established aiming to minimize both the energy storage investment costs and the net load fluctuations of the microgrids. To overcome the tendency of the traditional Non-dominated Sorting Genetic Algorithm II (NSGA-II) to fall into local optima, an improved SA-NSGA-II algorithm is proposed. This algorithm incorporates a simulated annealing mechanism and introduces a novel variance-based crowding distance calculation method. Furthermore, dynamic mutation and crossover rates are adopted to enhance optimization performance, thereby yielding superior power and capacity configuration schemes. Finally, a simulation analysis based on a three-microgrid case study is conducted to validate the effectiveness and superiority of the proposed model and algorithm.