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With the rapid proliferation of ultra-fast charging technology (up to 5C) and the increasing demand of electric vehicle (EV) users for “fast and convenient” energy replenishment, simultaneous high-power charging of multiple EVs may easily cause local overload and voltage violations in the distribution network. To address this challenge, this paper proposes an instantaneous power dynamic allocation algorithm for EV charging stations with the objective of supporting grid regulation. The algorithm constructs a feature framework based on the spatiotemporal coupling among EV users, charging stations, and the distribution grid, incorporating user demand, station-level operational constraints, and grid security limits. An improved Mixed-Integer Programming (MIP) optimization model is developed to maximize the satisfaction of EV charging requirements while considering charging pile power limits, feeder load capacity, and charging/discharging exclusivity constraints. Simulation studies on 50 EVs over a 6-h scheduling horizon in a typical urban distribution area demonstrate that the proposed allocation mechanism can effectively optimize charging station power output, significantly enhance user demand satisfaction and resource utilization, and simultaneously ensure the safe and stable operation of the distribution grid, thereby providing effective support for flexible grid regulation.

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Research on Dynamic Instantaneous Power Allocation Algorithm for Electric Vehicle Charging Stations Oriented to Grid Regulation Demand

  • 会议论文

  • 发表日期：

    2026年

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  • Bin Zhu,
  • Di Wu,
  • Zhi Li,
  • Yongyu Zhang,
  • Lizheng Yi

摘要

With the rapid proliferation of ultra-fast charging technology (up to 5C) and the increasing demand of electric vehicle (EV) users for “fast and convenient” energy replenishment, simultaneous high-power charging of multiple EVs may easily cause local overload and voltage violations in the distribution network. To address this challenge, this paper proposes an instantaneous power dynamic allocation algorithm for EV charging stations with the objective of supporting grid regulation. The algorithm constructs a feature framework based on the spatiotemporal coupling among EV users, charging stations, and the distribution grid, incorporating user demand, station-level operational constraints, and grid security limits. An improved Mixed-Integer Programming (MIP) optimization model is developed to maximize the satisfaction of EV charging requirements while considering charging pile power limits, feeder load capacity, and charging/discharging exclusivity constraints. Simulation studies on 50 EVs over a 6-h scheduling horizon in a typical urban distribution area demonstrate that the proposed allocation mechanism can effectively optimize charging station power output, significantly enhance user demand satisfaction and resource utilization, and simultaneously ensure the safe and stable operation of the distribution grid, thereby providing effective support for flexible grid regulation.