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<p>In response to the adverse impact of uncertainty in wind and photovoltaic energy output on microgrid operations, this paper introduces an Enhanced Whale Optimization Algorithm(EWOA) to optimize the energy storage capacity configuration of microgrids. The objective is to ensure stable microgrid operation and enhance system economy. Firstly, a microgrid framework incorporating wind-photovoltaic systems and a method for the characterization of wind-photovoltaic uncertainty are proposed. Secondly, daily operating cost is utilized as an objective function for microgrid configuration optimization, with constraints such as electrical power balance, unit operation, and energy storage device capacity provided. Additionally, to address the issue of the Whale Optimization Algorithm(WOA) easily converging to local optima during microgrid optimization, convergence factors, greedy selection strategies, and golden sine strategies are introduced to enhance the algorithm. Finally, the effectiveness of this method in optimizing microgrid operation is verified through simulation. Simulation results indicate that compared to the original WOA, the proposed algorithm reduces daily operating costs by 68% and 62% under deterministic and uncertain wind-photovoltaic power scenarios, respectively.</p>

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Optimizing Energy Storage Capacity Allocation for Microgrid considering Uncertainties in Wind and Photovoltaic Generation

  • 期刊论文

  • 发表日期：

    2025年7月14日

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  • Yingchun Wang,
  • Suzhan Xue,
  • Yang Liu,
  • Dongsheng Yang,
  • Shujuan Yang

摘要

In response to the adverse impact of uncertainty in wind and photovoltaic energy output on microgrid operations, this paper introduces an Enhanced Whale Optimization Algorithm(EWOA) to optimize the energy storage capacity configuration of microgrids. The objective is to ensure stable microgrid operation and enhance system economy. Firstly, a microgrid framework incorporating wind-photovoltaic systems and a method for the characterization of wind-photovoltaic uncertainty are proposed. Secondly, daily operating cost is utilized as an objective function for microgrid configuration optimization, with constraints such as electrical power balance, unit operation, and energy storage device capacity provided. Additionally, to address the issue of the Whale Optimization Algorithm(WOA) easily converging to local optima during microgrid optimization, convergence factors, greedy selection strategies, and golden sine strategies are introduced to enhance the algorithm. Finally, the effectiveness of this method in optimizing microgrid operation is verified through simulation. Simulation results indicate that compared to the original WOA, the proposed algorithm reduces daily operating costs by 68% and 62% under deterministic and uncertain wind-photovoltaic power scenarios, respectively.