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Multi-objective simultaneous optimal planning of electrical vehicle fast charging stations and DGs in distribution system

机译:配电系统中电动汽车快速充电站和分布式发电的多目标同时优化规划

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摘要

The large-scale construction of fast charging stations (FCSs) for electrical vehicles (EVs) is helpful in promoting the EV. It creates a significant challenge for the distribution system operator to determine the optimal planning, especially the siting and sizing of FCSs in the electrical distribution system. Inappropriate planning of fast EV charging stations (EVCSs) cause a negative impact on the distribution system. This paper presented a multi-objective optimization problem to obtain the simultaneous placement and sizing of FCSs and distributed generations (DGs) with the constraints such as the number of EVs in all zones and possible number of FCSs based on the road and electrical network in the proposed system. The problem is formulated as a mixed integer non-linear problem (MINLP) to optimize the loss of EV user, network power loss (NPL), FCS development cost and improve the voltage profile of the electrical distribution system. Non-dominated sorting genetic algorithm II (NSGA-II) is used for solving the MINLP. The performance of the proposed technique is evaluated by the 118-bus electrical distribution system.
机译:电动汽车(EV)的快速充电站(FCS)的大规模建设有助于推广电动汽车。这给配电系统运营商确定最佳计划提出了巨大挑战,尤其是配电系统中FCS的选址和选型。快速电动汽车充电站(EVCS)的规划不当会对配电系统造成负面影响。本文提出了一个多目标优化问题,以获取FCS和分布式发电(DG)的同时放置和大小确定,其中包括基于区域中的道路和电网的所有区域中的EV数量以及可能的FCS数量等约束。建议的系统。该问题被公式化为混合整数非线性问题(MINLP),以优化EV用户的损失,网络功率损失(NPL),FCS开发成本并改善配电系统的电压曲线。非支配排序遗传算法II(NSGA-II)用于求解MINLP。 118总线配电系统评估了所提出技术的性能。

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