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Research on electric vehicle cluster model based on scenes simulation

机译:基于场景仿真的电动汽车集群模型研究

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In recent years, the development of electric vehicles (EVs) has received more attention. As a mobile energy storage unit, electric vehicle can realize energy exchange with the power grid. However, the character of random charging and discharging makes it difficult for the integration of EVs. Conventional charging, fast charging and battery replacement are the most commonly used charging modes for EVs. First, a charging model is proposed considering a variety of scenes of the random charging of EVs in this paper. The method of random sampling is used to establish a statistical model of timing distribution of EVs load. The proposed model can well reflect the charging cluster characteristics of a large scale of EVs connected to gird widely and randomly. Then, considering the discharging characteristics of EVs in the specified time period, a charging and discharging cluster model is established based on the method of Latin hypercube sampling. The agglomeration model can reflect the timing distribution characteristics of EVs in a 24-hour cycle. Finally, the model verifies the effect of peak load shifting of EVs.
机译:近年来,电动汽车(EV)的发展受到了越来越多的关注。电动汽车作为一种移动式储能单元,可以实现与电网的能量交换。然而,随机充电和放电的特性使得EV的集成变得困难。常规充电,快速充电和电池更换是电动汽车最常用的充电模式。首先,本文提出了一种考虑电动汽车随机充电场景的充电模型。采用随机抽样的方法建立电动汽车负荷时序分布的统计模型。所提出的模型可以很好地反映与电动汽车广泛随机连接的大型电动汽车的充电集群特性。然后,考虑到特定时间段内电动汽车的放电特性,基于拉丁超立方采样的方法建立了充放电簇模型。集聚模型可以反映电动汽车在24小时周期内的时间分布特征。最后,该模型验证了电动汽车的峰值负载转移的影响。

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