Research on Spatiotemporal Behavior of Electric Vehicles Considering the Users' Bounded Rationality

被引:0
|
作者
Wu F. [1 ]
Yang J. [1 ]
Lin Y. [1 ]
Xu J. [1 ]
Sun Y. [1 ]
机构
[1] School of Electrical Engineering and Automation, Wuhan University, Wuhan
来源
Yang, Jun (JYang@whu.edu.cn) | 1600年 / China Machine Press卷 / 35期
关键词
Activity-based analysis; Bounded rationality; Cumulative prospect theory; Dynamic traffic assignment model; Electric vehicles spatiotemporal behavior;
D O I
10.19595/j.cnki.1000-6753.tces.190475
中图分类号
学科分类号
摘要
The spatiotemporal behavior of electric vehicles (EVs) includes spatiotemporal transfer behavior and charging behavior. The accurate modeling of the spatiotemporal behavior of EVs has become the key to the effective interaction between large-scale EVs and power grid. The idea of activity-based analysis to understand the travel behavior as the activity derived behavior was applied in the paper, and the transfer relationship between different activity chains was established. Based on the cumulative prospect theory, the bounded rationality psychology of users in the choice of travel mode, travel path and departure time was described. Considering the dynamic characteristics of traffic network and the charging characteristics of EVs, the spatiotemporal distribution characteristics of EVs on each activity chain were studied. Finally, the Dupius network, a typical traffic network, was used to study the spatiotemporal transfer and charging behavior characteristics of EVs with different users' psychologies, proportions of EVs and service capabilities of charging stations. The simulation results show that the proposed method can more reasonably describe the users' choice psychology and the spatiotemporal behavior, and it is found that the proportion of EVs and the service capacity of charging station have great effect on them. © 2020, Electrical Technology Press Co. Ltd. All right reserved.
引用
收藏
页码:1563 / 1574
页数:11
相关论文
共 29 条
  • [1] Yang X., Zhang Y., Jiang Y., Et al., Renewable energy accommodation-based strategy for electric vehicle considering dynamic interaction in microgrid, Transactions of China Electrotechnical Society, 33, 2, pp. 390-400, (2018)
  • [2] Huang X., Chen W., Xie Q., Et al., The influence of users' charging selection on charging schedule power grid, Transactions of China Electrotechnical Society, 33, 13, pp. 3002-3011, (2018)
  • [3] Zhang C., Ding M., Zhang J., A temporal and spatial distribution forecasting of private car charging load based on origin-destination matrix, Transactions of China Electrotechnical Society, 32, 1, pp. 78-87, (2017)
  • [4] Wang Y., Gu Y., Ding Z., Et al., Charging demand forecasting of electric vehicle based on empirical mode decomposition-fuzzy entropy and ensemble learning, Automation of Electric Power Systems, 44, 3, pp. 114-124, (2020)
  • [5] Mao M., Chen Q., Ding Y., Et al., Parameters optimization design for MMC-based EV fleet integrated into smart grid, Transactions of China Electrotechnical Society, 33, 16, pp. 3802-3810, (2018)
  • [6] Gomez J.C., Morcos M.M., Impact of EV battery chargers on the power quality of distribution systems, IEEE Transactions on Power Delivery, 18, 3, pp. 975-981, (2003)
  • [7] Yang T., Liu X., Wu Q., Et al., Research on impacts of electric vehicle charging station location on voltage stability, Power System Protection and Control, 46, 5, pp. 31-37, (2018)
  • [8] Ben-Akiva M., Lerman S.R., Discrete Choice Analysis: Theory and Application Analysis, (1985)
  • [9] Lu X., Eric I.P., Socio-demographics, activity participation and travel behavior, Transportation Research A, 33, 1, pp. 1-18, (1999)
  • [10] Bath C.R., A model of post home-arrival activity participation behavior, Transportation Research B, 32, 1, pp. 387-400, (1998)