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Multi-objective simultaneous optimal planning of electric vehiclefast charging stations and DGs in distribution system

机译:电动汽车快速充电站和分配系统DGS的多目标同步规划

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The large-scale construction of fast charging stations(FCSs)for electric vehicles(EVs)is helpful inpromoting 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 multiobjective 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 algorithmⅡ(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:用于电动车辆的快速充电站(FCS)的大规模建造有助于EV.IT为分配系统运营商创造了一个重​​大挑战,以确定最佳规划,特别是电气中FCSS的选址和尺寸分发系统。快速EV充电站的不适当规划(EVCS)对分配系统产生负面影响。本文提出了一种多目标优化问题,以获得FCSS和分布代(DGS)的同时放置和尺寸所有区域中的EVS数量和基于所提出的系统中的道路和电气网络的可能数量的FCS。问题被制定为混合整数非线性问题(MINLP),以优化EV用户的丢失,网络功率损耗( NPL),FCS开发成本和改善配电系统的电压曲线。统治分类遗传算法Ⅱ(NSGA-Ⅱ)用于求解MI NLP的性能由118母线配电系统评估。

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