中国电力 ›› 2020, Vol. 53 ›› Issue (4): 122-130.DOI: 10.11930/j.issn.1004-9649.201807075

• 电动汽车 • 上一篇    下一篇

考虑用户满意度的电动汽车时空双尺度有序充电引导策略

蒋怡静1, 于艾清1, 黄敏丽2   

  1. 1. 上海电力大学 电气工程学院,上海 200090;
    2. 上海勘测设计研究院有限公司,上海 200434
  • 收稿日期:2018-08-01 修回日期:2020-01-13 发布日期:2020-04-05
  • 作者简介:蒋怡静(1994-),女,硕士研究生,从事电动汽车充电调度研究,E-mail:jiang_yijing@mail.shiep.edu.cn;于艾清(1981-),女,通信作者,博士,副教授,从事电力系统调度与优化、电力电子系统智能控制技术研究,E-mail:yuaiqing@shiep.edu.cn;黄敏丽(1994-),女,硕士,从事电动汽车有序充电、智能优化算法研究,E-mail:huangminli@mail.shiep.edu.cn
  • 基金资助:
    上海市绿色能源并网工程技术研究中心资助项目(13DZ2251900)

Coordinated Charging Guiding Strategy for Electric Vehicles in Temporal-spatial Dimension Considering User Satisfaction Degree

JIANG Yijing1, YU Aiqing1, HUANG Minli2   

  1. 1. College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China;
    2. Shanghai Investigation, Design & Research Institute, Shanghai 200434, China
  • Received:2018-08-01 Revised:2020-01-13 Published:2020-04-05
  • Supported by:
    This work is supported by Foundation of Shanghai Engineering Research Center of Green Energy Grid-connected Technology (No.13DZ2251900)

摘要: 规模化电动汽车的充电行为在时间和空间上具有随机性和不确定性,针对该特性,以多网融合的车联网平台系统为基础,构建有序充电引导模型架构。为了体现接入系统的用户充电时间选择的多样性,根据用户意愿将电动汽车在时间层分为接受系统调度集群和不接受系统调度集群,建立不同尺度下的用户满意度函数来充分调动电动汽车用户参与性,在此基础上提出有序充电引导调度策略。在时间层,通过引导车辆的充电时间调节负荷曲线;在空间层,规划各车辆的充电站选择。以包含4座充电站的IEEE-33节点配网为例,利用优化软件LINGO11对模型进行仿真。算例结果表明,所提引导策略具有好的控制效果,并且在保证用户满意度时,能改善电网负荷、充电站状况。

关键词: 电动汽车, 充电引导, 时空双尺度, 车联网平台, 用户满意度

Abstract: The charging behavior of large-scale electric vehicles (EVs) is random and uncertain in temporal and spatial dimensions. An architecture for coordinated charging guiding model is thus built based on the internet of vehicles system with integration of multi-networks. In order to reflect the diversity of charging time choices for users accessing the system, the EVs are divided into receiving/not receiving system scheduling clusters according to the user’s charging wishes. Then, the satisfaction function under different dimensions is established to mobilize the participation of EV users, and a coordinated charging guidance strategy is proposed on this basis. In temporal level, the load curve is adjusted via directing the charging time of EV users. In spatial level, the selection of charging station is planned for various vehicles. Based on an IEEE 33-node distribution grid with integration of four charging stations, a model simulation is made using the optimization software LINGO11. The simulation results proves that the proposed guidance strategy has good controlling effects, and can improve the load curve of grid and the equipment utilization of charging stations.

Key words: electric vehicles, charging guidance, temporal-spatial dimension, internet of vehicles, user satisfaction degree