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Bilevel optimal coordination of active distribution network and charging stations considering EV drivers' willingness

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  • Zhang, Kaizhe
  • Xu, Yinliang
  • Sun, Hongbin

Abstract

With the popularity of electric vehicles (EVs) in urban areas, the effective utilization of their charging flexibility in active distribution networks (ADNs) has drawn wide attentions. This paper proposes a bilevel model for the collaborative operation of ADNs with multiple charging stations (CSs) considering EV drivers' willingness. In the upper level, the distribution system operator (DSO) minimizes the total operational cost of ADNs and sets the optimal energy and reserve prices to trade with CSs under loads and market uncertainties. In the lower level, the aggregated model of EVs including drivers' response willingness is firstly established. Then, CSs minimize their own costs by adjusting bidding quantities and setting optimal incentive prices for EV drivers. Further, the developed bilevel model considering responsivities of EV drivers and various uncertainties is transformed into a tractable single level problem via the deterministic reformulation, Karush-Kuhn–Tucker (KKT) conditions and linearization method. Finally, numerical studies show that the proposed method can facilitate the DSO, CSs, EV drivers to reduce 3.7%, 20%, 9.6% total costs on average and improve the network security of ADNs under various uncertainties. Moreover, the scalability of the proposed approach is verified in the IEEE 123-bus DN with the computation time less than 600 s, which satisfies the computational efficiency requirement for the ADN day-ahead optimal scheduling.

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  • Zhang, Kaizhe & Xu, Yinliang & Sun, Hongbin, 2024. "Bilevel optimal coordination of active distribution network and charging stations considering EV drivers' willingness," Applied Energy, Elsevier, vol. 360(C).
    • Handle: RePEc:eee:appene:v:360:y:2024:i:c:s0306261924001739
    DOI: 10.1016/j.apenergy.2024.122790
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    References listed on IDEAS

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    Cited by:

    1. Liu, Ke & Liu, Yanli, 2024. "Incentive-willingness-decision framework: Unit discharge triangle-based maximum stable V2G capability evaluation," Applied Energy, Elsevier, vol. 374(C).
    2. Wang, Jun & Zhao, Yuanlong & Li, Hongyan & Xu, Xiao, 2025. "Enhanced photovoltaic-powered distribution network resilience aided by electric vehicle emergency response with willingness and reward," Applied Energy, Elsevier, vol. 389(C).
    3. Tahir, Mustafa & Hu, Sideng & Khan, Tahir & Zhu, Haoqi, 2024. "Multi-energy station design for future electric vehicles: A synergistic approach starting from scratch," Applied Energy, Elsevier, vol. 372(C).
    4. Yang Gao & Xiaohong Zhang & Qingyuan Yan & Yanxue Li, 2025. "Demand Response Strategies for Electric Vehicle Charging and Discharging Behavior Based on Road–Electric Grid Interaction and User Psychology," Sustainability, MDPI, vol. 17(6), pages 1-27, March.
    5. Wang, Yunqi & Wang, Hao & Razzaghi, Reza & Jalili, Mahdi & Liebman, Ariel, 2024. "Multi-objective coordinated EV charging strategy in distribution networks using an improved augmented epsilon-constrained method," Applied Energy, Elsevier, vol. 369(C).
    6. Shiqian Wang & Bo Liu & Qiuyan Li & Ding Han & Jianshu Zhou & Yue Xiang, 2025. "EV Charging Behavior Analysis and Load Prediction via Order Data of Charging Stations," Sustainability, MDPI, vol. 17(5), pages 1-16, February.
    7. Zhu, Jie & Xu, Yinliang & Tai, Nengling & Sun, Hongbin, 2025. "Joint chance-constrained energy-reserve co-optimization for distribution networks with flexible resource aggregators," Applied Energy, Elsevier, vol. 388(C).

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