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Bi-Level Planning of Electric Vehicle Charging Stations Considering Spatial–Temporal Distribution Characteristics of Charging Loads in Uncertain Environments

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  • Haiqing Gan

    (Jiangsu Power Grid Company Ltd., Nanjing 210024, China)

  • Wenjun Ruan

    (Jiangsu Power Grid Company Ltd., Nanjing 210024, China)

  • Mingshen Wang

    (The Electric Power Research Institute, Jiangsu Power Grid Company Ltd., Nanjing 211103, China)

  • Yi Pan

    (The Electric Power Research Institute, Jiangsu Power Grid Company Ltd., Nanjing 211103, China)

  • Huiyu Miu

    (The Electric Power Research Institute, Jiangsu Power Grid Company Ltd., Nanjing 211103, China)

  • Xiaodong Yuan

    (The Electric Power Research Institute, Jiangsu Power Grid Company Ltd., Nanjing 211103, China)

Abstract

With the increase in the number of distributed energy resources (DERs) and electric vehicles (EVs), it is particularly important to solve the problem of EV charging station siting and capacity determination under the distribution network considering a large proportion of DERs. This paper proposes a bi-level planning model for EV charging stations that takes into account the characteristics of the spatial–temporal distribution of charging loads under an uncertain environment. First, the Origin–Destination (OD) matrix analysis method and the real-time Dijkstra dynamic path search algorithm are introduced and combined with the Larin Hypercube Sampling (LHS) method to establish the EV charging load prediction model considering the spatial and temporal distribution characteristics. Second, the upper objective function with the objective of minimizing the cost of EV charging station planning and user charging behavior is constructed, while the lower objective function with the objective of minimizing the cost of distribution network operation and carbon emission cost considering the uncertainty of wind power and photovoltaics is constructed. The constraints of the lower-layer objective function are transformed into the upper-layer objective function through Karush–Kuhn–Tucker (KKT) conditions, the optimal location and capacity of charging stations are finally determined, and the model of EV charging station siting and capacity determination is established. Finally, the validity of the model was verified by planning the coupled IEEE 33-node distribution network with the traffic road map of a city in southeastern South Dakota, USA.

Suggested Citation

  • Haiqing Gan & Wenjun Ruan & Mingshen Wang & Yi Pan & Huiyu Miu & Xiaodong Yuan, 2024. "Bi-Level Planning of Electric Vehicle Charging Stations Considering Spatial–Temporal Distribution Characteristics of Charging Loads in Uncertain Environments," Energies, MDPI, vol. 17(12), pages 1-30, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:12:p:3004-:d:1417274
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    References listed on IDEAS

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    1. Luyun Wang & Bo Zhou, 2023. "Optimal Planning of Electric Vehicle Fast-Charging Stations Considering Uncertain Charging Demands via Dantzig–Wolfe Decomposition," Sustainability, MDPI, vol. 15(8), pages 1-23, April.
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    4. Zhouquan Wu & Pradeep Krishna Bhat & Bo Chen, 2023. "Optimal Configuration of Extreme Fast Charging Stations Integrated with Energy Storage System and Photovoltaic Panels in Distribution Networks," Energies, MDPI, vol. 16(5), pages 1-20, March.
    5. Woo, Hyeon & Son, Yongju & Cho, Jintae & Kim, Sung-Yul & Choi, Sungyun, 2023. "Optimal expansion planning of electric vehicle fast charging stations," Applied Energy, Elsevier, vol. 342(C).
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    Cited by:

    1. Guo, Hongxia & Chen, Lingxuan & Wang, Zhaocai & Li, Lin, 2025. "Day-ahead prediction of electric vehicle charging demand based on quadratic decomposition and dual attention mechanisms," Applied Energy, Elsevier, vol. 381(C).

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