Title

Geographic Information System-assisted optimal design of renewable powered electric vehicle charging stations in high-density cities

RIS ID

138919

Publication Details

Huang, P., Ma, Z., Xiao, L. & Sun, Y. (2019). Geographic Information System-assisted optimal design of renewable powered electric vehicle charging stations in high-density cities. Applied Energy, 255 113855-1-113855-12.

Abstract

The crowded urban environment and busy traffic lead to heavy roadside pollutions in high-density cities, thereby causing health damages to city pedestrians. Electric vehicle (EV) is considered as a promising solution to such street-level air pollutions. Currently, in high-density cities, the number of public charging stations is limited, and they are far from enough to form a complete charging network with a high coverage ratio that can provide easy and convenient charging services for EV users. Concerns and worries on being unable to find a charging port when needed become a major hurdle to EV practical applications. Meanwhile, greener and cheaper renewable energy is recommended to replace fossil fuel-based grid energy that is commonly used in existing charging stations. Thus, this study proposes a novel Geographic Information System (GIS) assisted optimal design method for renewable powered EV charging stations in high-density cities. By selecting the optimal locations and optimal number of the renewable powered charging stations with the considerations of the existing charging stations and renewable potentials, the proposed method is able to minimize the life cycle cost of the charging stations while satisfying a user defined area coverage ratio. Using Hong Kong as an example, case studies have been conducted to verify the proposed design method. The design method can be used in practice to help high-density cities build their public charging networks with cost-effectiveness, which will promote EV practical applications and thus alleviate the roadside air pollutions in high-density cities.

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Link to publisher version (DOI)

http://dx.doi.org/10.1016/j.apenergy.2019.113855