摘要
由于电动汽车充电需求的时空分布不均匀,不同地区和时间段的充电需求波动较大,导致电动汽车充电控制效果差.为此,提出了基于遗传算法的电动汽车有序充电控制方法,以影响充电负荷变化的相关因素为基础,计算起始与结束时刻负荷的概率密度函数,完成电动汽车充电负荷的预测与估算.将不同类型的电动汽车得到的充电负荷矩阵进行叠加处理,以获取行驶里程的概率函数,并以负荷波动方差值最小、负荷峰谷差最小为目标函数,建立双层有序充电控制模型,基于遗传算法求解该模型,从而获取最优有序充电控制策略.实验结果显示,本文方法可以有效降低负荷峰谷差,且负荷波动率为0.1%,具有较好的电动汽车有序充电控制效果.
The uneven spatiotemporal distribution of charging demand for electric vehicles results in poor charging control effects for electric vehicles.Therefore,a genetic algorithm based control method for ordered charging of electric vehicles is proposed.Based on the relevant factors that affect changes in charging load,this method calculates the probability density functions of the load at the beginning and end times,and completes the prediction and estimation of electric vehicle charging load.The charging load matrices obtained from different types of electric vehicles is overlayed to obtain a probability function of driving distance.With the objective function of minimizing load fluctuation variance and load peak valley difference,a double-layer ordered charging control model is established.Based on genetic algorithm.The model is solved to obtain the optimal ordered charging control strategy.The experimental results show that the method proposed in this paper can effectively reduce the peak valley load difference,with a load fluctuation rate of 0.1%,and has a good control effect on ordered charging of electric vehicles.
作者
韩丽
潘林燕
孙明
HAN Li;PAN Linyan;SUN Ming(School of Intelligent Manufacturing,Anhui Wenda University of Information Engineering,Hefei 230032,China)
出处
《西安文理学院学报(自然科学版)》
2025年第2期9-15,共7页
Journal of Xi’an University(Natural Science Edition)
关键词
遗传算法
电动汽车
有序充电控制
负荷波动
genetic algorithm
electric vehicles
ordered charging control
load fluctuations
