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  • 1.
    Bian, Caiyun
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Academy of Chinese Energy Strategy, China University of Petroleum-Beijing, China.
    Li, Hailong
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Wallin, Fredrik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Avelin, Anders
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Lin, L.
    Academy of Chinese Energy Strategy, China University of Petroleum-Beijing, Beijing, 102249, China.
    Yu, Z.
    Department of Energy and Petroleum EngineeringUniversity of Stavanger, Norway.
    Finding the optimal location for public charging stations - A GIS-based MILP approach2019In: Energy Procedia, Elsevier Ltd , 2019, Vol. 158, p. 6582-6588Conference paper (Refereed)
    Abstract [en]

    Electric Vehicles (EVs) have achieved a significant development because of the continuous technology revolution and policy supports in recent years, which leads to a larger demand of charging stations. Strategies about how to find the optimal location for charging facilities are urgently needed in order to further assist the development of EVs. This paper focus on the return of investments on EV charging stations and proposes a Mixed Integer Linear Programming (MILP) model based on Geographic Information System (GIS) to identify the optimal location of charging stations in cities. Traffic flow data and land-use classifications are used as important inputs, and six important constraints are included in the MILP model with the objective function of maximizing the total profits of new charging stations. The effectiveness of the proposed method is then demonstrated by implementing a case study in Västerås, Sweden.

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