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<p>Modern power grids are increasingly integrating sustainable technologies, such as distributed generation and electric vehicles. This evolution poses significant challenges for distribution systems, including frequency fluctuations, voltage deviations, and line overloading. In light of these issues, this paper proposes a methodology for optimizing the power scheduling of a battery energy storage system, with the objectives of minimizing active power losses, smoothing the substation load curve, and enhancing voltage profiles. The approach incorporates the Analytic Hierarchy Process to prioritize objectives and employs Particle Swarm Optimization, supported by an initialization procedure based on the load curve. Simulation results demonstrate that the proposed method achieves improvements of at least 9% in power losses, 13% in peak load, 29% in the voltage deviation index, and 2.76% in the daily average substation power factor. These findings underscore the practical applicability of the methodology in enhancing grid reliability and reducing operational costs for modern distribution systems.</p>

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Optimization of battery energy storage system power scheduling for loss reduction, load smoothing and voltage regulation in electrical distribution system

  • 期刊论文

  • 发表日期：

    2025年12月11日

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  • John Jefferson Antunes Saldanha,
  • Ademir Nied,
  • Rüdiger Kutzner,
  • Rodrigo Trentini,
  • Cesar Manchein

摘要

Modern power grids are increasingly integrating sustainable technologies, such as distributed generation and electric vehicles. This evolution poses significant challenges for distribution systems, including frequency fluctuations, voltage deviations, and line overloading. In light of these issues, this paper proposes a methodology for optimizing the power scheduling of a battery energy storage system, with the objectives of minimizing active power losses, smoothing the substation load curve, and enhancing voltage profiles. The approach incorporates the Analytic Hierarchy Process to prioritize objectives and employs Particle Swarm Optimization, supported by an initialization procedure based on the load curve. Simulation results demonstrate that the proposed method achieves improvements of at least 9% in power losses, 13% in peak load, 29% in the voltage deviation index, and 2.76% in the daily average substation power factor. These findings underscore the practical applicability of the methodology in enhancing grid reliability and reducing operational costs for modern distribution systems.