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With the increasing penetration of renewable energy sources, voltage fluctuations and power losses have become critical operational challenges in distribution networks, highlighting the need for optimal siting and sizing of energy storage systems (ESSs). This paper formulates a multi-objective optimization model that simultaneously minimizes the total economic cost, bus voltage deviations, and network line losses, and introduces the Multi-Objective Improved Bat Optimization (MO-IBO) algorithm to solve it. The proposed MO-IBO integrates sinusoidal chaotic mapping, a chaotic inertia weight strategy, and Lévy-flight perturbations to enhance global search capability and maintain a well-distributed and diverse Pareto front. Case studies on the IEEE 33-bus radial distribution system demonstrate that the MO-IBO algorithm outperforms the conventional IBO and PSO algorithms in terms of convergence speed, computational efficiency, and overall optimization performance. The obtained energy storage planning schemes effectively reduce network losses, improve voltage profiles, and lower total system cost, thereby demonstrating the effectiveness and engineering applicability of the proposed approach. This work provides a practical decision-support tool for energy storage planning in distribution networks with high renewable penetration.

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A Multi-Objective Storage Planning Method for Distribution Networks Based on the MO-IBO Algorithm

  • 会议论文

  • 发表日期：

    2026年

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  • Minglei Jiang,
  • Bo Zhao,
  • Dachi Zhang,
  • Shengyao Shi,
  • Kerui Ma,
  • Zhipeng Zhang,
  • Bowen Wang,
  • Xin She,
  • Xin Li

摘要

With the increasing penetration of renewable energy sources, voltage fluctuations and power losses have become critical operational challenges in distribution networks, highlighting the need for optimal siting and sizing of energy storage systems (ESSs). This paper formulates a multi-objective optimization model that simultaneously minimizes the total economic cost, bus voltage deviations, and network line losses, and introduces the Multi-Objective Improved Bat Optimization (MO-IBO) algorithm to solve it. The proposed MO-IBO integrates sinusoidal chaotic mapping, a chaotic inertia weight strategy, and Lévy-flight perturbations to enhance global search capability and maintain a well-distributed and diverse Pareto front. Case studies on the IEEE 33-bus radial distribution system demonstrate that the MO-IBO algorithm outperforms the conventional IBO and PSO algorithms in terms of convergence speed, computational efficiency, and overall optimization performance. The obtained energy storage planning schemes effectively reduce network losses, improve voltage profiles, and lower total system cost, thereby demonstrating the effectiveness and engineering applicability of the proposed approach. This work provides a practical decision-support tool for energy storage planning in distribution networks with high renewable penetration.