标题： 固定与移动电动汽车充电站的联合规划模型，考虑灵活容量策略 显示英文标题

标题： A Joint Planning Model for Fixed and Mobile Electric Vehicle Charging Stations Considering Flexible Capacity Strategy

摘要： 电动汽车（EV）的广泛采用显著增加了交通系统和电力系统的负荷，对其稳定运行构成了挑战。 为满足电动汽车充电日益增长的需求，固定充电站（FCSs）和移动充电站（MCSs）已被引入，作为电网与交通网络之间的关键接口。 认识到这些领域协同规划的重要性，本文提出了一种两阶段的FCSs和MCSs联合规划模型，利用改进的交替方向乘子法（ADMM）算法。 所提出的模型的主要目标是将大规模电动汽车接入可能带来的负面影响转化为积极成果，从而通过多方利益相关者的协作提高社会福利。 在第一阶段，我们开发了一个评估FCS位置的框架，结合了电动汽车承载能力和电压稳定性的评估。 第二阶段引入了FCSs和MCSs的联合规划模型，旨在最小化电动汽车充电系统的总体社会成本，同时保持可靠的电力供应。 为了解决规划问题，我们采用了混合整数线性规划、排队论和序列二次规划的组合方法。 改进的ADMM算法通过引入耦合约束，一致地将选址和容量决策结合起来，并支持一个分布式优化框架，协调电动汽车用户、MCS运营商和配电系统运营商的利益。 此外，提出了一种灵活的容量规划策略，考虑电动汽车充电站的多时段发展潜力，以降低FCS建设的复杂性和投资需求。 最后，通过对比实验的案例研究证明了所提出模型和求解方法的有效性。 显示英文摘要

摘要： The widespread adoption of electric vehicles (EVs) has significantly increased demand on both transportation and power systems, posing challenges to their stable operation. To support the growing need for EV charging, both fixed charging stations (FCSs) and mobile charging stations (MCSs) have been introduced, serving as key interfaces between the power grid and traffic network. Recognizing the importance of collaborative planning across these sectors, this paper presents a two-stage joint planning model for FCSs and MCSs, utilizing an improved alternating direction method of multipliers (ADMM) algorithm. The primary goal of the proposed model is to transform the potential negative impacts of large-scale EV integration into positive outcomes, thereby enhancing social welfare through collaboration among multiple stakeholders. In the first stage, we develop a framework for evaluating FCS locations, incorporating assessments of EV hosting capacity and voltage stability. The second stage introduces a joint planning model for FCSs and MCSs, aiming to minimize the overall social costs of the EV charging system while maintaining a reliable power supply. To solve the planning problem, we employ a combination of mixed-integer linear programming, queueing theory, and sequential quadratic programming. The improved ADMM algorithm couples the siting and sizing decisions consistently by introducing coupling constraints, and supports a distributed optimization framework that coordinates the interests of EV users, MCS operators, and distribution system operators. Additionally, a flexible capacity planning strategy that accounts for the multi-period development potential of EVCS is proposed to reduce both the complexity and the investment required for FCS construction. Finally, a case study with comparative experiments demonstrates the effectiveness of the proposed models and solution methods.