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<p>With the widespread application of renewable energy in microgrids, collaborative optimization scheduling of microgrid clusters has become a key issue in improving energy utilization efficiency and operational economy. To solve this problem, this paper proposes a microgrid cluster optimization scheduling method based on the sparrow search algorithm. Firstly, construct a microgrid cluster model that includes wind turbines, photovoltaics, energy storage batteries, diesel generators, and hydrogen fuel cells. Secondly, the economic optimum is defined as the objective function, and combined with the constraints of equipment and system operation, the sparrow search algorithm is proposed to solve the optimization model of the microgrid cluster. Finally, this method is used to simulate an IEEE 9-node system with 4 microgrids. The simulation results showed that this method has significant advantages over traditional particle swarm optimization algorithms in reducing total costs and new energy utilization efficiency, verifying the feasibility of this method in microgrid cluster optimization scheduling.</p>

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Research on microgrid cluster optimization method based on sparrow search algorithm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月14日

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  • Hongzhi Su,
  • Pengtao Mu,
  • Shenglin Xu,
  • Lingrao Wang,
  • Liying Yu,
  • Bo Zhang

摘要

With the widespread application of renewable energy in microgrids, collaborative optimization scheduling of microgrid clusters has become a key issue in improving energy utilization efficiency and operational economy. To solve this problem, this paper proposes a microgrid cluster optimization scheduling method based on the sparrow search algorithm. Firstly, construct a microgrid cluster model that includes wind turbines, photovoltaics, energy storage batteries, diesel generators, and hydrogen fuel cells. Secondly, the economic optimum is defined as the objective function, and combined with the constraints of equipment and system operation, the sparrow search algorithm is proposed to solve the optimization model of the microgrid cluster. Finally, this method is used to simulate an IEEE 9-node system with 4 microgrids. The simulation results showed that this method has significant advantages over traditional particle swarm optimization algorithms in reducing total costs and new energy utilization efficiency, verifying the feasibility of this method in microgrid cluster optimization scheduling.