Research on Charging Strategy Optimization of Electric Vehicle based on AGA

Bowen Xu
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引用次数: 1

Abstract

Because the charging load of electric vehicles is random in time and space, a large number of disorderly charging of electric vehicles will lead to the peak load of distribution network exceeding the limit of equipment, which will bring adverse effects on the operation of power grid. In order to smooth the daily load curve of distribution network, this paper establishes a solution model of intelligent charging control strategy for large-scale electric vehicle considering the charging demand constraints of electric vehicle users, and uses adaptive genetic algorithm (AGA) to solve the model. Taking IEEE33 bus distribution network as an example, based on Monte Carlo stochastic simulation of large-scale electric vehicle gridconnected scene, the impact of electric vehicle load on distribution network under two control modes of disorderly charging and intelligent charging is studied comparatively, and the effectiveness of this method is
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基于AGA的电动汽车充电策略优化研究
由于电动汽车的充电负荷在时间和空间上具有随机性,大量的电动汽车无序充电将导致配电网的峰值负荷超过设备的极限,给电网的运行带来不利影响。为了平滑配电网日负荷曲线,考虑电动汽车用户的充电需求约束,建立了大型电动汽车智能充电控制策略的求解模型,并采用自适应遗传算法(AGA)对模型进行求解。以IEEE33总线配电网为例,基于蒙特卡罗随机模拟大规模电动汽车并网场景,比较研究了无序充电和智能充电两种控制模式下电动汽车负荷对配电网的影响,并验证了该方法的有效性
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