Considering the complexity and diversity of the users' travel habits,Markov chain is used to describe the changes in the state of charge (SOC) of power battery during the electric vehicle users' travel in a whole day.The realtime charging behavior is simulated,and then according to the probability of travel time corresponding to different destinations,the regional distribution of electric vehicles during different periods of a day is determined.By taking into account the influence of traffic time consumption coefficient on the driving process,the regional charging demand on different types of day is also predicted.The charging load of electric vehicles with different permeabilities,different types of day and different charging thresholds is calculated by Monte Carlo simulation method,and the results show that the proposed method can simulate the users' travel habits accurately and reflect the space-time distribution characteristics of charging demand.Moreover,it is indicated that the traffic situation and the charging threshold of battery have certain effect on the charging demand from electric vehicles.%充分考虑用户出行习惯的复杂性、多样性,提出了采用马尔可夫链描述电动汽车用户一天出行过程中动力电池荷电状态的变化情况.模拟实时充电行为,然后根据电动汽车的出行时间对应各出行目的地的概率,确定电动汽车一天各时段的区域分布情况,同时考虑交通耗时系数对电动汽车行驶过程的影响,从而预测不同类型日各区域电动汽车的负荷需求情况.采用蒙特卡洛模拟方法对不同渗透率、不同类型日和不同充电阈值等情景下的电动汽车充电负荷进行计算.结果表明,该方法可以较准确地模拟用户的出行规律,反映充电需求的时空分布特点;同时反映出交通状况、电池充电阈值对电动汽车充电需求存在一定的影响.
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