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<p>The adoption of electric vehicles significantly contributes to reducing air pollution and reducing dependency on fossil fuels. However, integrating electric vehicles into power distribution systems poses challenges such as increased grid load, voltage instability and power losses. This study introduces a novel energy management methodology based on binary particle swarm optimization, designed specifically for the optimal placement and charging/discharging strategy of plug-in electric vehicles in distribution networks. The core novelty lies in utilizing the loss sensitivity factor to reduce the solution space, thereby enhancing the optimization efficiency. The method is applied to both a standard IEEE 33-bus test system and a real-time TN 84-bus distribution system in Madurai city. The algorithm operates by identifying nodes with the highest sensitivity to power losses and iteratively determining the most cost-effective and loss-minimizing locations for plug-in electric vehicle (PEV) integration. Simulation results demonstrate that the proposed BPSO-based integration framework reduces real power losses by 6.39% in the IEEE 33-bus system and 5.77% in the TN 84-bus system, outperforming binary genetic algorithm and simple bacterial foraging algorithm. This approach ensures minimal daily operational costs and enhances overall grid performance, making it a viable strategy for smart energy management in modern distribution systems.</p>

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A novel method for energy management with optimization in plug-in electric vehicle integration into the grids : An experimental study

  • 期刊论文

  • 发表日期：

    2025年7月8日

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  • Arun Mozhi Subbukalai,
  • Charles Raja Sathia Samuel,
  • Vijaya Kumar Nambi Mahadevan,
  • Arockia Edwin Xavier Santiago,
  • Suresh Muthusamy,
  • Surya Kavitha Tirugatla

摘要

The adoption of electric vehicles significantly contributes to reducing air pollution and reducing dependency on fossil fuels. However, integrating electric vehicles into power distribution systems poses challenges such as increased grid load, voltage instability and power losses. This study introduces a novel energy management methodology based on binary particle swarm optimization, designed specifically for the optimal placement and charging/discharging strategy of plug-in electric vehicles in distribution networks. The core novelty lies in utilizing the loss sensitivity factor to reduce the solution space, thereby enhancing the optimization efficiency. The method is applied to both a standard IEEE 33-bus test system and a real-time TN 84-bus distribution system in Madurai city. The algorithm operates by identifying nodes with the highest sensitivity to power losses and iteratively determining the most cost-effective and loss-minimizing locations for plug-in electric vehicle (PEV) integration. Simulation results demonstrate that the proposed BPSO-based integration framework reduces real power losses by 6.39% in the IEEE 33-bus system and 5.77% in the TN 84-bus system, outperforming binary genetic algorithm and simple bacterial foraging algorithm. This approach ensures minimal daily operational costs and enhances overall grid performance, making it a viable strategy for smart energy management in modern distribution systems.