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Optimal Planning of Electric Vehicle Fast-Charging Stations Considering Uncertain Charging Demands via Dantzig–Wolfe Decomposition

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  • Luyun Wang

    (College of Economics and Management, Southwest University, Chongqing 400715, China
    School of Economics, Chongqing Financial and Economic College, Chongqing 401320, China)

  • Bo Zhou

    (College of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400074, China)

Abstract

This study investigates the planning problem of fast-charging stations for electric vehicles with the consideration of uncertain charging demands. This research aims to determine where to build fast-charging stations and how many charging piles to be installed in each fast-charging station. Based on the multicommodity flow model, a chance-constrained programming model is established to address this planning problem. A scenario-based approach as well as a big-M coefficients generation algorithm are applied to reformulate the programming model into tractable one, then the Dantzig–Wolfe decomposition method is leveraged to find its optimal solution. Finally, a numerical experiment is conducted in a 25-node network to assess the efficiency of the proposed model and solution approach.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6588-:d:1122652
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    References listed on IDEAS

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    1. Jiang, Ziyue & Han, Jingzuo & Li, Yetong & Chen, Xinyu & Peng, Tianduo & Xiong, Jianliang & Shu, Zhan, 2023. "Charging station layout planning for electric vehicles based on power system flexibility requirements," Energy, Elsevier, vol. 283(C).
    2. 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.

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