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.

中文翻译：

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抗洪电动汽车充电站优化规划

本研究是首次尝试将抗洪能力整合到电动汽车 (EV) 充电站规划过程中。考虑洪水泛滥程度的优化布局不是完全避开洪水易发地区，而是可以最大限度地减少洪水危害的影响，同时最大限度地提高电动汽车充电站网络的社会经济效益。在这项研究中，提出了一种非支配排序遗传算法-III (NSGA-III) 和理想解决方案相似度排序技术 (TOPSIS) 的集成框架，以通过最大化充电便利性来优化充电站位置，最大限度地减少洪水灾害的影响，并最大限度地减少现有充电站的影响。NSGA-III被应用于解决充电站的多目标选址优化问题。随后使用 TOPSIS 从 NSGA-III 生成的可行候选方案中确定最佳解决方案。在威基基地区进行的案例研究表明，所提出的优化框架可以有效地处理洪水灾害的影响与充电站网络的充电服务之间的权衡。本研究为处理气候变化下电动汽车充电站规划中多个相互冲突的目标的最佳实践提供了新的见解。