陈小闽, 王钢, 汪隆君. 基于车联网框架的电动汽车有序充电策略[J]. 现代电力, 2018, 35(4): 1-7.
引用本文: 陈小闽, 王钢, 汪隆君. 基于车联网框架的电动汽车有序充电策略[J]. 现代电力, 2018, 35(4): 1-7.
CHEN Xiaomin, WANG Gang, WANG Longjun. Ordered Charging Strategy of Electric Vehicle Based on Vehicle Networking Framework[J]. Modern Electric Power, 2018, 35(4): 1-7.
Citation: CHEN Xiaomin, WANG Gang, WANG Longjun. Ordered Charging Strategy of Electric Vehicle Based on Vehicle Networking Framework[J]. Modern Electric Power, 2018, 35(4): 1-7.

基于车联网框架的电动汽车有序充电策略

Ordered Charging Strategy of Electric Vehicle Based on Vehicle Networking Framework

  • 摘要: 电动汽车作为可进行需求响应的移动负荷,其对电网的影响力随着规模的扩大日益凸显。为了发挥其作为可控负荷的潜力,促进电网与用户间的互动,提出基于车联网APP和电动汽车电池管理系统的车联网系统结构,该系统可实现电池状态数据的监测与传输;以平抑当前日负荷为优化目标,以避免在用电高峰时期充电和不形成充电高峰为约束构建二次规划模型,得到每一推送时间段推送量理论值;并根据用户响应概率分布修正该理论值,进而得到每一推送时间段实际推送值;进一步提出基于车联网框架的有序充电策略;最后通过蒙特卡洛模拟法对该方案进行验证,结果表明以用户响应行为大数据为基础的电动汽车车联网系统通过引导电动汽车有序充电,达到降低日负荷峰谷差的目的。

     

    Abstract: As the mobile load for demand response, the influence of the electric vehicle on the power grid is becoming more and more obvious with the expanding scale. To make good use of its potential as a controllable load and to promote the interaction between the grid and users, a vehicle networking system based on vehicle networking APP and electric vehicle battery management system is proposed. The system can monitor and transmit battery status data. By restraining the current daily load as the optimization objective, the quadratic programming model is constructed to obtain the theoretical value of each push amount with the constraints of avoiding charging in the peak charging period and not forming charging peak. The theoretical value is corrected according to the probability distribution of the user response, and then the actual push value of each push time is obtained. Furthermore, an orderly charging strategy based on the framework of vehicle networking is proposed. Finally, the Monte Carlo simulation method is used to verify the proposed scheme. The results show that the electric vehicle networking system based on user response behavior and large data can reduce the peak and valley difference of daily load by guiding the orderly charging of electric vehicles.

     

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