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物联感知环境下电动汽车充电等待时间分布的预测方法
作者:
作者单位:

浙江工业大学机械工程学院,浙江省 杭州市 310014

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基金项目:

国家自然科学基金资助项目(61773347);浙江省公益技术研究项目(LGF20F030001)。


Prediction Method of Waiting Time Distribution for Electric Vehicle in Internet of Things Perception Environment
Author:
Affiliation:

College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China

Fund Project:

This work is supported by National Natural Science Foundation of China (No. 61773347) and Zhejiang Commonweal Technology Research Project (No. LGF20F030001).

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    摘要:

    已有的充电站等待时间研究往往忽略不同车辆充电需求的时变差异性,为全面描述充电等待时间的时变规律,文中提出了物联感知环境下电动汽车充电等待时间分布的预测方法。首先,确定充电站到达车辆数分布和充电时长分布,采用M/G/n排队模型模拟充电站排队等待系统,求解电动汽车充电等待时间分布函数。其次,建立用户选择充电站效用函数,利用Multi-logit模型对各个充电站内的充电需求进行预测。再者,根据充电需求预测值和网联充电站实时数据更新平均到达率、服务时间变异系数,提出电动汽车充电等待时间短时分布的预测算法。最后,以某城市某区域为例,验证了该分布的准确性,并通过路径选择分析了充电等待时间分布和可靠性对充电站的影响,为减少充电站排队拥堵和均衡电网负荷提供决策依据。

    Abstract:

    Existing studies on waiting time of charging stations often neglect the time-varying differences in charging requirements for different vehicles. A prediction method of charging waiting time distribution for electric vehicles (EVs) in Internet of Things (IOT) perception environment is proposed to comprehensively explore the time-varying law of charging waiting time. Firstly, on the basis of determining the quantity distribution of vehicles arriving at the charging stations and the charging duration distribution, the M/G/n queuing model is used to simulate the queuing system at charging stations and solve the distribution function of charging waiting time for EVs. Secondly, the utility function for user to select charging stations is established, the Multi-logit model is used to predict the charging demand in each charging station. Furthermore, the average arrival rate, service time and variation coefficient are updated according to the predicted value of charging demand and the real-time data of network-connected charging stations, and the short-time prediction of EVs charging waiting time is realized with the distribution function of charging waiting time. Finally, taking a certain area of a city as an example, the accuracy of this distribution is verified, and the influence of charging waiting time distribution and reliability on charging stations is analyzed, which provides a decision basis for reducing queuing congestion and balancing power system load.

    表 4 Table 4
    表 5 Table 5
    表 3 Table 3
    图1 电动汽车在充电站充电排队模型示意图Fig.1 Schematic diagram of charging queue model for electric vehicles at charging stations
    图2 区域内各充电站同一日到达车辆数对比Fig.2 Comparison of the number of vehicles arriving at each charging station on the same day in the region
    图3 不同到达率下等待时间分布与实际分布对比Fig.3 Comparison diagram of waiting time distribution and actual distribution with different arrival rates
    图4 预测分布不同分位与实际等待时间箱形图对比Fig.4 Comparison of predicted distribution with actual waiting time box chart
    图5 预测分布分位数与排队论平均等待时间对比Fig.5 Comparison of predicted distribution quantile and queuing theory average waiting time
    图6 不同充电站电动汽车总等待时间对比Fig.6 Comparison of total waiting time of different charging stations
    图7 不同充电站电动汽车充电负荷对比Fig.7 Comparison of charging load of different charging stations
    表 6 Table 6
    表 1 充电站到达车辆数泊松分布拟合K-S检验结果(部分)Table 1 Poisson distribution fitting for K-S test results of the number of vehicles arriving at charging stations (part)
    图 充电站位置示意图Fig. Diagram of charging station location
    图 部分电动汽车优化路径示意图Fig. Optimized path diagram of some Electric Vehicles
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引用本文

董红召,方雅秀,付凤杰.物联感知环境下电动汽车充电等待时间分布的预测方法[J/OL].电力系统自动化,http://doi.org/10.7500/AEPS20190523010.
DONG Hongzhao,FANG Yaxiu,FU Fengjie.Prediction Method of Waiting Time Distribution for Electric Vehicle in Internet of Things Perception Environment[J/OL].Automation of Electric Power Systems,http://doi.org/10.7500/AEPS20190523010.

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  • 收稿日期:2019-05-23
  • 最后修改日期:2020-03-10
  • 录用日期:2019-11-13
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