4.8 Article

Siting and sizing of fast charging stations in highway network with budget constraint

Journal

APPLIED ENERGY
Volume 228, Issue -, Pages 1255-1271

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2018.07.025

Keywords

Charging station; Plug-in electric vehicles; Heuristic; Location problem

Funding

  1. National Key R&D Program of China [2016YFB0901900]
  2. National Natural Science Foundation of China [71771215, 71471175, 61773390]
  3. National Natural Science Fund for Distinguished Young Scholars of China [61525304]
  4. Natural Science Fund for Distinguished Young Scholars of Hunan Province [2018JJ1035, 2017JJ1001]

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An appropriate infrastructure of fast charging stations is critical to customers for adopting plug-in electric vehicles. In this paper, we investigated the siting and sizing problem of fast charging stations in a highway network, where the budget constraint and the service capacities of charging spots are considered. The utility theory is employed to analyze drivers' charging strategies, and an iterative approach is developed to calculate the equilibrium distribution of vehicle flows in the network when the charging infrastructure is insufficient. Based on the assignment rules of the drivers for each charging station, a mixed integer programming model is developed to formulate the problem. Three algorithms, including the genetic algorithm, a problem specified heuristic and an algorithm by combining the heuristic and genetic algorithm, are employed to maximize the overall valid plug-in electric vehicles flows in the network. A practical case based on the highway network in Hebei, China, is investigated, through which the proposed approach is illustrated and the critical factors impacting the deployment of charging infrastructures are analyzed. The analysis results indicate that improving the endurance range may be one of the most efficient ways to expend the adaption of plug-in electric vehicles in a highway network. Random networks and practical networks with different sizes are used to test the robustness and efficiency of the proposed algorithms.

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