Plug-in electric vehicles are widely acknowledged as an effective tool for numerous environmental and economic concerns. In this article, a novel model for the planning of fast-charging stations is established based on a data-driven distributionally robust optimization approach, which aims to minimize the expected planning cost for both transportation network and distribution network. $\phi $ -divergence, a statistical measure, is utilized to establish the serviceability constraints. On the other hand, a modified capacitated flow refueling location model is employed to develop the location constraints. In addition, ac power flow constraints are developed to model the operation of DN with the penetrations of PEVs. Finally, a case study is illustrated to validate the proposed planning model.

中文翻译：

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使用基于 ϕ-divergence 的数据驱动分布式鲁棒优化方法规划 PEV 快速充电站

插电式电动汽车被广泛认为是解决众多环境和经济问题的有效工具。在本文中，基于数据驱动的分布式鲁棒优化方法建立了一种新的快速充电站规划模型，旨在最小化运输网络和配电网络的预期规划成本。$\phi $ -divergence，一种统计量度，用于建立适用性约束。另一方面，采用改进的有能力流量加油位置模型来开发位置约束。此外，还开发了交流功率流约束来模拟 DN 的运行与 PEV 的渗透。最后，通过一个案例研究来验证所提出的规划模型。