Electric taxi charging station planning based on GPS trajectory mining

被引：0
作者：
Su S. [1 ]
Li Y. [1 ]
Jia Z. [1 ]
Yang J. [2 ]
Xia M. [1 ]
Chen Q. [1 ]
机构：
[1] School of Electrical Engineering, Beijing Jiaotong University, Beijing
[2] Jincheng Power Supply Company of State Grid Shanxi Electric Power Company, Jincheng
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2022年 / 42卷 / 10期
基金：
中国国家自然科学基金;
关键词：
charging demand; charging stations; electric taxis; GPS; planning; trajectory mining;
D O I：
10.16081/j.epae.202205005
中图分类号：
学科分类号：
摘要：
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. © 2022 Electric Power Automation Equipment Press. All rights reserved.
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页码：255 / 263
页数：8
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共 24 条
  • [1] SHI Xiaoqing, LI Xiaonuo, YANG Jianxin, Research on carbon reduction potential of electric vehicles for low-carbon transportation and its influencing factors［J］, Environmental Science, 34, 1, pp. 385-394, (2013)
  • [2] NANAKI E A, KORONEOS C J., Climate change mitigation and deployment of electric vehicles in urban areas［J］, Renewable Energy, 99, pp. 1153-1160, (2016)
  • [3] ZENG Menglong, WEI Gang, ZHU Lan, Et al., Planning of electric vehicle charging-swapping-storage integrated station in AC／DC distribution network［J］, Automation of Electric Power Systems, 45, 18, pp. 52-60, (2021)
  • [4] DING Danjun, DAI Kang, ZHANG Xinsong, Et al., Network planning for electric vehicle charging based on fuzzy multi-objective optimization, Power System Protection and Control, 46, 3, pp. 43-50, (2018)
  • [5] WU Yu, WANG Yufei, ZHANG Yu, Et al., Siting and sizing method of electric vehicle charging station based on improved immune clonal selection algorithm［J］, Automation of Electric Power Systems, 45, 7, pp. 95-103, (2021)
  • [6] TIAN Feng, CHEN Huaili, Research on planning of electric vehicle charging station considering user choice preference［J］, Computer Engineering and Applications, 58, 15, pp. 294-301, (2022)
  • [7] LUO Qingyu, TIAN Wanli, JIA Hongfei, Location and capacity model of electric vehicle charging station considering commuting demand［J］, Journal of Jilin University（Engineering and Technology Edition）, 49, 5, pp. 1471-1477, (2019)
  • [8] DONG Xiaohong, MU Yunfei, YU Li, Et al., Freeway FCS planning and correction considering power-flow constraints of distribution network［J］, Electric Power Automation Equipment, 37, 6, pp. 124-131, (2017)
  • [9] ZANG Haixiang, FU Yuting, CHEN Ming, Et al., Dynamic planning of EV charging stations based on improved adaptive genetic algorithm［J］, Electric Power Automation Equipment, 40, 1, pp. 163-170, (2020)
  • [10] MENG Xuyao, ZHANG Weige, BAO Yan, Et al., Optimal configuration of charging facility for electric vehicle fast charging station considering charging power［J］, Electric Power Automation Equipment, 38, 7, pp. 28-34, (2018)
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