Rapid adoption of electric vehicles (EVs) requires the development of a highly flexible charging network. The design and management of the charging infrastructure for EV-dominated transportation systems are intertwined with power grid operations both economically and technically. High penetration of EVs in the future can increase the charging loads and cause a wide range of operational issues in power distribution networks (PDNs). This paper aims to design an EV charging network with an embedded PDN layout to account for energy dispatch and underlying traffic flows in urban transportation networks supporting electric mobility in the near future. A mixed-integer bilevel model is proposed with the EV charging facility location and PDN energy decisions in the upper level and user equilibrium traffic assignment in the lower level considering an uncertain charging demand. The objective is to minimize the cost of PDN operations, charging facility deployments, and transportation. The proposed problem is solved using a column and constraint generation (C&CG ) algorithm, while a macroscopic fundamental diagram concept is implemented to estimate the arc travel times. The methodology is applied to a hypothetical and two real-world case study networks, and the solutions are compared to a Benders decomposition benchmark. The east-coast analysis results indicate a 77.3% reduction in the computational time. Additionally, the benchmark technique obtains an optimality gap of 1.15%, while the C&CG algorithm yields a 0.61% gap. The numerical experiments show the robustness of the proposed methodology. Besides, a series of sensitivity analyses has been conducted to study the impact of input parameters on the proposed methodology and draw managerial insights.

中文翻译：

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具有用户均衡决策的电气化移动联合配电和充电网络设计

电动汽车 (EV) 的快速普及需要开发高度灵活的充电网络。电动汽车主导的交通系统充电基础设施的设计和管理在经济和技术上都与电网运营交织在一起。未来电动汽车的高普及率会增加充电负载，并在配电网络 (PDN) 中引起广泛的运营问题。本文旨在设计一个具有嵌入式 PDN 布局的 EV 充电网络，以考虑在不久的将来支持电动汽车的城市交通网络中的能源调度和基础交通流。考虑到不确定的充电需求，提出了混合整数双层模型，上层为电动汽车充电设施位置和 PDN 能源决策，下层为用户均衡流量分配。目标是最大限度地降低 PDN 运营、充电设施部署和运输的成本。使用列和约束生成 (C&CG) 算法解决了所提出的问题，同时实施了宏观基本图概念来估计电弧行程时间。该方法应用于一个假设的和两个真实世界的案例研究网络，并将解决方案与 Benders 分解基准进行比较。东海岸分析结果表明计算时间减少了 77.3%。此外，基准技术获得了 1.15% 的最优性差距，而 C& CG 算法产生 0.61% 的差距。数值实验表明了所提出方法的稳健性。此外，还进行了一系列敏感性分析，以研究输入参数对拟议方法的影响并得出管理见解。