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<p>This paper proposes an improved dynamic parameters MOPSO method for analyzing the optimal size and placement of two capacitor banks for standard IEEE 33-bus and IEEE 69-bus distribution feeders. The novelty of this work lies in the application of the MOPSO algorithm to optimize capacitor bank placement, considering multiple objectives such as energy loss reduction, voltage stability, and cost savings, which differentiates it from conventional approaches. MOPSO distinguishes itself from conventional methods by incorporating advanced mechanisms to improve population diversity and prevent early convergence, which results in more accurate optimization outcomes. This improvement is critical for the future of low-carbon technologies in power distribution systems, as it helps reduce energy losses, operational costs, and improve voltage profiles. The performance of the proposed MOPSO is compared with several state-of-the-art optimization approaches, including NSGA-II, MOGWO, and MODE, under different scenarios. Results demonstrate the superiority of MOPSO, showing more than a 25% reduction in energy losses, around a 20% reduction in operating costs, and improved voltage profiles, with better computational efficiency and solution diversity. These results confirm the advantages of the proposed MOPSO for more sustainable and efficient power distribution systems.</p>

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Optimizing capacitor bank placement in distribution networks using a multi-objective particle swarm optimization approach for energy efficiency and cost reduction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月10日

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  • Mehrdad Ahmadi Kamarposhti,
  • Raymond Ghandour,
  • Mahmoud Abdel-Aty,
  • Mohamed Hafez,
  • Mohanad Alfiras,
  • Shawkat Alkhazaleh,
  • Ilhami Colak,
  • Ahmed Solyman

摘要

This paper proposes an improved dynamic parameters MOPSO method for analyzing the optimal size and placement of two capacitor banks for standard IEEE 33-bus and IEEE 69-bus distribution feeders. The novelty of this work lies in the application of the MOPSO algorithm to optimize capacitor bank placement, considering multiple objectives such as energy loss reduction, voltage stability, and cost savings, which differentiates it from conventional approaches. MOPSO distinguishes itself from conventional methods by incorporating advanced mechanisms to improve population diversity and prevent early convergence, which results in more accurate optimization outcomes. This improvement is critical for the future of low-carbon technologies in power distribution systems, as it helps reduce energy losses, operational costs, and improve voltage profiles. The performance of the proposed MOPSO is compared with several state-of-the-art optimization approaches, including NSGA-II, MOGWO, and MODE, under different scenarios. Results demonstrate the superiority of MOPSO, showing more than a 25% reduction in energy losses, around a 20% reduction in operating costs, and improved voltage profiles, with better computational efficiency and solution diversity. These results confirm the advantages of the proposed MOPSO for more sustainable and efficient power distribution systems.