• DocumentCode
    154934
  • Title

    A stochastic flow capturing location and allocation model for siting electric vehicle charging stations

  • Author

    Jingzi Tan ; Wei-Hua Lin

  • Author_Institution
    Analytics Anal. &Optimization, IBM, Chicago, IL, USA
  • fYear
    2014
  • fDate
    8-11 Oct. 2014
  • Firstpage
    2811
  • Lastpage
    2816
  • Abstract
    With the move of electric vehicle (EV) initiatives in many countries, there is a growing demand for fast-charging stations for recharging EVs. In this paper, we consider the problem of siting these EV charging stations in a transportation network with demand uncertainty. The demand for service considered is the passing flows in the network, i.e., the drive-by customers. We started with formulating the problem as a deterministic flow capturing location-allocation problem and then extended it into a stochastic model. Our results show that the stochastic model more realistically capture the actual coverage of the demand. We also developed a backup flow capturing model for providing secondary or multiple facilities coverage to ensure stability in service coverage and reduce the “range anxiety.” Test cases with different flow composition and cost parameters are examined.
  • Keywords
    electric vehicles; stochastic processes; EV charging stations; allocation model; backup flow capturing model; demand uncertainty; deterministic flow capturing location-allocation problem; electric vehicle charging stations; service coverage; stochastic flow capturing location; stochastic model; transportation network; Buildings; Charging stations; Electric vehicles; Linear programming; Resource management; Stochastic processes;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on
  • Conference_Location
    Qingdao
  • Type

    conf

  • DOI
    10.1109/ITSC.2014.6958140
  • Filename
    6958140