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This work aims to study electric vehicle (EV) charging scheduling strategies that can reduce the impact of charging load on the distribution network while meeting the power demands of EVs. We use real-world EV data from Tianjin, China to analyze the characteristics of EV charging behavior, and employ Gaussian Mixture Model to describe the charging behavior characteristics. A charging load simulation model based on Monte Carlo method is proposed. The model uses the charging behavior characteristics as input, and calculates charging load by time-series simulation. Then we use the IEEE-33 bus system to simulate the influence of charging load on distribution network under different scenarios of the EV ownership proportion. Finally, we propose an EV charging scheduling optimization model to reduce the grid peak load on the distribution network.

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An EV Charging Scheduling Strategy Considering User Demand of Distribution Networks

  • 会议论文

  • 发表日期：

    2025年

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  • Xudong Peng,
  • Shuo Wang,
  • Dingsong Cui,
  • Ziyi Yang

摘要

This work aims to study electric vehicle (EV) charging scheduling strategies that can reduce the impact of charging load on the distribution network while meeting the power demands of EVs. We use real-world EV data from Tianjin, China to analyze the characteristics of EV charging behavior, and employ Gaussian Mixture Model to describe the charging behavior characteristics. A charging load simulation model based on Monte Carlo method is proposed. The model uses the charging behavior characteristics as input, and calculates charging load by time-series simulation. Then we use the IEEE-33 bus system to simulate the influence of charging load on distribution network under different scenarios of the EV ownership proportion. Finally, we propose an EV charging scheduling optimization model to reduce the grid peak load on the distribution network.