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Optimal planning of flood‐resilient electric vehicle charging stations
This study is the first attempt to integrate flood resilience into the electric vehicle (EV) charging station planning process. Instead of fully avoiding flood‐prone areas, an optimized placement considering the magnitude of flood inundations can minimize the impact of flood hazards and simultaneously maximize the socio‐economic benefit of EV charging station networks. In this study, an integrated framework of the non‐dominated sorting genetic algorithm‐III (NSGA‐III) and the technique for order of preference by similarity to ideal solution (TOPSIS) is proposed to optimize the charging station locations by maximizing the charging convenience, minimizing the impact of flood hazards, and minimizing the impact of existing charging stations. The NSGA‐III is applied to solve the multi‐objective location optimization of charging stations. TOPSIS is subsequently used to determine the best solution from the feasible candidates generated by the NSGA‐III. A case study conducted in the Waikiki area demonstrates that the proposed optimization framework can effectively deal with the trade‐off between the impact of flood hazards and the charging service of a charging station network. This study provides new insights into best practices for dealing with multiple conflicting objectives in EV charging station planning under climate change.
Optimal planning of flood‐resilient electric vehicle charging stations
This study is the first attempt to integrate flood resilience into the electric vehicle (EV) charging station planning process. Instead of fully avoiding flood‐prone areas, an optimized placement considering the magnitude of flood inundations can minimize the impact of flood hazards and simultaneously maximize the socio‐economic benefit of EV charging station networks. In this study, an integrated framework of the non‐dominated sorting genetic algorithm‐III (NSGA‐III) and the technique for order of preference by similarity to ideal solution (TOPSIS) is proposed to optimize the charging station locations by maximizing the charging convenience, minimizing the impact of flood hazards, and minimizing the impact of existing charging stations. The NSGA‐III is applied to solve the multi‐objective location optimization of charging stations. TOPSIS is subsequently used to determine the best solution from the feasible candidates generated by the NSGA‐III. A case study conducted in the Waikiki area demonstrates that the proposed optimization framework can effectively deal with the trade‐off between the impact of flood hazards and the charging service of a charging station network. This study provides new insights into best practices for dealing with multiple conflicting objectives in EV charging station planning under climate change.
Optimal planning of flood‐resilient electric vehicle charging stations
Zhang, Qian (author) / Yu, Hao (author) / Zhang, Guohui (author) / Ma, Tianwei (author)
Computer‐Aided Civil and Infrastructure Engineering ; 38 ; 489-507
2023-03-01
19 pages
Article (Journal)
Electronic Resource
English
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