| 引用本文: | 苏粟,李玉璟,贾泽瑞,杨锦,夏明超,陈奇芳.基于GPS轨迹挖掘的电动出租车充电站规划[J].电力自动化设备,2022,42(10): |
| SU Su,LI Yujing,JIA Zerui,YANG Jin,XIA Mingchao,CHEN Qifang.Electric taxi charging station planning based on GPS trajectory mining[J].Electric Power Automation Equipment,2022,42(10): |
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| 摘要: |
| 针对现有充电站的规划布局不能充分考虑电动汽车动态充电需求分布和用户充电排队问题的不足，提出了一种基于全球定位系统(GPS)轨迹挖掘的电动出租车充电站规划方法。通过对出租车的GPS轨迹数据和城市交通态势数据进行数据处理，挖掘城市居民打车需求的起屹点(OD)分布特征；设计电动出租车的充电仿真算法，模拟实际场景中电动出租车的接单行为、行驶行为及充电行为，建立电动出租车的充电需求时空分布预测模型；在此基础上，综合考虑充电站建设运行成本、电动出租车到站时间成本及充电等待时间成本，建立充电站规划模型。通过实际算例验证了所提规划模型的有效性，并进一步分析了充电站的建设成本系数、电动出租车的时间成本折算系数、权重系数大小等参数对规划结果的灵敏度。结果表明：居民打车需求的OD分布决定了电动出租车的充电需求时空分布情况；在进行充电站规划时，充电机数量受充电站数量的变化影响较大，且电动出租车时间成本的变化对总成本的影响相对较明显。 |
| 关键词: 电动出租车 充电站 GPS 轨迹挖掘 充电需求 规划 |
| DOI：10.16081/j.epae.202205005 |
| 分类号:U469.72 |
| 基金项目:国家自然科学基金资助项目(51677004) |
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| Electric taxi charging station planning based on GPS trajectory mining |
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SU Su1, LI Yujing1, JIA Zerui1, YANG Jin2, XIA Mingchao1, CHEN Qifang1
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1.School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China;2.Jincheng Power Supply Company of State Grid Shanxi Electric Power Company, Jincheng 048000, China
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| Abstract: |
| Aiming at the shortages of the existing charging stations’ planning and layout that can not fully consider the dynamic charging demand distribution of electric vehicles and the charging queue problem of users, an electric taxi charging station planning method based on GPS(Global Position System) trajectory mining is proposed. By preprocessing the taxis’ GPS trajectory data and urban traffic situation data, the OD(Origin Destination) distribution characteristics of urban residents’ taxi demand are mined. The charging simulation algorithm of electric taxis is designed to simulate the order behavior, driving behavior and charging behavior of electric taxis in the actual scenes, and the space-time distribution prediction model of electric taxis’ charging demand is established. On this basis, the planning model of charging stations is established by comprehensively considering the construction and operation cost of charging stations, the arrival time cost and charging waiting time cost of electric taxis’. The effectiveness of the proposed planning model is verified by a practical example, and the sensitivity of parameters such as the construction cost coefficient of charging stations, the time cost conversion coefficient of electric taxis and the weight coefficient to the planning results is further analyzed. The results show that the OD distribution of residents’ taxi demand determines the space-time distribution of electric taxis’ charging demand. In the planning of charging stations, the number of chargers is greatly affected by the change in the number of charging stations, and the change of electric taxis’ time cost has a relatively obvious impact on the total cost. |
| Key words: electric taxis charging stations GPS trajectory mining charging demand planning |
