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Coordinated Interaction Strategy of User-Side EV Charging Piles for Distribution Network Power Stability

Author

Listed:
  • Juan Zhan

    (Shenzhen Power Supply Bureau Co., Ltd., Shenzhen 515100, China)

  • Mei Huang

    (Shenzhen Power Supply Bureau Co., Ltd., Shenzhen 515100, China)

  • Xiaojia Sun

    (Shenzhen Power Supply Bureau Co., Ltd., Shenzhen 515100, China)

  • Zuowei Chen

    (Shenzhen Power Supply Bureau Co., Ltd., Shenzhen 515100, China)

  • Zhihan Zhang

    (Shenzhen Power Supply Bureau Co., Ltd., Shenzhen 515100, China)

  • Yang Li

    (Shenzhen Power Supply Bureau Co., Ltd., Shenzhen 515100, China)

  • Yubo Zhang

    (Shenzhen Power Supply Bureau Co., Ltd., Shenzhen 515100, China)

  • Qian Ai

    (Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

Abstract

In response to the challenges of imbalanced economic efficiency of charging stations caused by disorderly charging of large-scale electric vehicles (EVs), rising electricity expenditure of users, and increased risk of stable operation of the power grid, this study designs a user-side vehicle pile resource interaction strategy considering source load clustering to enhance the economy and safety of electric vehicle energy management. Firstly, by constructing a dynamic traffic flow distribution network coupling architecture, a bidirectional interaction model between charging facilities and transportation/power systems is established to analyze the dynamic correlation between charging demand and road network status. Next, an EV charging and discharging electricity price response model is established to quantify the load regulation potential under different scenarios. Secondly, by combining urban transportation big data and prediction networks, high-precision inference of the spatiotemporal distribution of charging loads can be achieved. Then, a multidimensional optimization objective function covering operator revenue, user economy, and grid power quality is constructed, and a collaborative decision-making model is established. Finally, the IEEE69 node system is validated through joint simulation with actual urban areas, and the non-dominated sorting genetic algorithm II (NSGA-II) based on reference points is used for the solution. The results show that the optimization strategy proposed by NSGA-II can increase the operating revenue of charging stations by 33.43% while reducing user energy costs and grid voltage deviations by 18.9% and 68.89%, respectively.

Suggested Citation

  • Juan Zhan & Mei Huang & Xiaojia Sun & Zuowei Chen & Zhihan Zhang & Yang Li & Yubo Zhang & Qian Ai, 2025. "Coordinated Interaction Strategy of User-Side EV Charging Piles for Distribution Network Power Stability," Energies, MDPI, vol. 18(8), pages 1-22, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:8:p:1944-:d:1632157
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    References listed on IDEAS

    as
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    3. Yunzheng Ran & Honghua Liao & Huijun Liang & Luoping Lu & Jianwei Zhong, 2024. "Optimal Scheduling Strategies for EV Charging and Discharging in a Coupled Power–Transportation Network with V2G Scheduling and Dynamic Pricing," Energies, MDPI, vol. 17(23), pages 1-17, December.
    4. Li, Yujing & Zhang, Zhisheng & Xing, Qiang, 2025. "Real-time online charging control of electric vehicle charging station based on a multi-agent deep reinforcement learning," Energy, Elsevier, vol. 319(C).
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