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Planning of static and dynamic charging facilities for electric vehicles in electrified transportation networks

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  • Zhou, Ze
  • Liu, Zhitao
  • Su, Hongye
  • Zhang, Liyan

Abstract

Dynamic wireless charging technology can promote the popularization of electric vehicles due to its role in alleviating range anxiety. It is necessary to consider it as a significant charging method in future urban scenarios. The development of EVs and charging facilities has brought about the electrification of transportation, forming a power-traffic system. To maximize the comprehensive performance of the system, this paper proposes a planning strategy for the coexistence of static and dynamic charging facilities. First, considering the response of vehicle users in the transportation network to the planned results, we design a bi-level optimization framework. Then, in the lower-level optimization model, taking into account the charging demands of different vehicles, we construct a multi-class user equilibrium model and adopt a modified adaptive path generation algorithm and a variable update factor algorithm to solve the traffic assignment problem. In the upper level, we utilize an evaluation indicator to indicate the comprehensive performance of the system, and the evaluation indicator is minimized under constraints on the capacities, charging prices, and usage prices of hybrid charging facilities. Then, an algorithm based on the surrogate model is introduced to solve the bi-level programming. Finally, the case studies demonstrate the advantages of hybrid charging facilities as well as the feasibility and effectiveness of the solution algorithm.

Suggested Citation

  • Zhou, Ze & Liu, Zhitao & Su, Hongye & Zhang, Liyan, 2023. "Planning of static and dynamic charging facilities for electric vehicles in electrified transportation networks," Energy, Elsevier, vol. 263(PE).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pe:s0360544222029590
    DOI: 10.1016/j.energy.2022.126073
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    References listed on IDEAS

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    Cited by:

    1. Peng, Qiao & Li, Wei & Fowler, Michael & Chen, Tao & Jiang, Wei & Liu, Kailong, 2024. "Battery calendar degradation trajectory prediction: Data-driven implementation and knowledge inspiration," Energy, Elsevier, vol. 294(C).
    2. Ji, Jinhua & Wang, Linhong & Yang, Menglin & Bie, Yiming & Hao, Mingjie, 2024. "Optimal deployment of dynamic wireless charging facilities for electric bus route considering stochastic travel times," Energy, Elsevier, vol. 289(C).

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