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A Multi-Objective Collaborative Planning Strategy for Integrated Power Distribution and Electric Vehicle Charging Systems

机译:配电和电动汽车充电系统的多目标协同规划策略

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摘要

An elaborately designed integrated power distribution and electric vehicle (EV) charging system will not only reduce the investment and operation cost of the system concerned, but also promote the popularization of environmentally friendly EVs. In this context, a multi-objective collaborative planning strategy is presented to deal with the optimal planning issue in integrated power distribution and EV charging systems. In the developed model, the overall annual cost of investment and energy losses is minimized simultaneously with the maximization of the annual traffic flow captured by fast charging stations (FCSs). Additionally, the user equilibrium based traffic assignment model (UETAM) is integrated to address the maximal traffic flow capturing problem. Subsequently, a decomposition based multi-objective evolutionary algorithm (MOEA/D) is employed to seek the non-dominated solutions, i.e., the Pareto frontier. Finally, collaborative planning results of two coupled distribution and transportation systems are presented to illustrate the performance of the proposed model and solution method.
机译:精心设计的集成配电和电动汽车(EV)充电系统,不仅可以减少相关系统的投资和运营成本,而且可以促进环保型EV的普及。在这种情况下,提出了一种多目标协作计划策略,以解决集成配电和EV充电系统中的最佳计划问题。在已开发的模型中,同时将快速充电站(FCS)捕获的年度交通流量最大化的同时,将总的年度投资成本和能源损失降至最低。此外,集成了基于用户平衡的流量分配模型(UETAM),以解决最大的流量捕获问题。随后,采用基于分解的多目标进化算法(MOEA / D)来寻找非主导解,即帕累托边界。最后,给出了两个耦合的配送和运输系统的协同计划结果,以说明所提出的模型和求解方法的性能。

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