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考虑路网电网交互和用户心理的电动汽车充电负荷预测
作者:
作者单位:

武汉大学电气与自动化学院

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基金项目:

国家自然科学基金资助项目、教育部人文社会科学研究项目、国家重点研发计划项目


Prediction of Electric Vehicle Charging Load Considering Road Network-Power Grid Interaction and User’s Psychology
Author:
Affiliation:

Electrical Engineering and Automation College of Wuhan University

Fund Project:

National Natural Science Foundation of China,MOE (Ministry of Education in China) Project of Humanities and Social Sciences,National Key R&D Plan of China

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    摘要:

    本文提出了一种考虑路网-电网信息交互和用户心理的电动汽车充电负荷预测框架,首先以出行链和OD矩阵得到电动汽车出行目的地;然后,考虑行驶、排队时间和充电电价,提出基于后悔理论的充电站选择模型;接着,基于跟驰模型对车辆在路网中的行驶过程进行微观交通分析,建立基于电价驱动的路网-电网交互式负荷预测框架;最后,采用蒙特卡洛方法模拟电动汽车的出行和充电情况,以预测电动汽车充电负荷时空分布。通过在北京市三环路网和相应电网上的仿真,验证了所提出的电动汽车充电负荷预测框架的有效性。结果也表明路网和电网通过电价相互作用,使得电动私家车和出租车的充电负荷在时间与空间上分布差异明显。

    Abstract:

    In this paper, a framework of EV (electric vehicle) charging load forecasting is proposed, which considers user psychology and information interaction of road network and power network. Firstly, the destination of electric vehicle is obtained by travel chain and OD matrix. Secondly, considering driving time, queuing time and charging price, a model of choosing charging station based on Regret Theory is proposed. Thirdly, based on the car following model, the micro traffic analysis of vehicle driving process in the road network is carried out. And the framework of charging load forecasting considering the interaction of road network and power network driven by charging price is established. Finally, the Monte Carlo method was used to simulate the travel and charging of EVs, so as to predict the time-space distribution of charging load of EVs. Through the simulation on Beijing Third Ring Road Network and the corresponding power grid, the effectiveness of the proposed EV charging load prediction framework is verified. The results also showed that the road network and power grid through charging price make the charging load distribution of electric private cars and taxis significantly different in time and space.

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引用本文

龙雪梅,杨军,吴赋章,等.考虑路网电网交互和用户心理的电动汽车充电负荷预测[J/OL].电力系统自动化,http://doi.org/10.7500/AEPS20191011008.
LONG Xuemei,YANG Jun,WU Fuzhang,et al.Prediction of Electric Vehicle Charging Load Considering Road Network-Power Grid Interaction and User’s Psychology[J/OL].Automation of Electric Power Systems,http://doi.org/10.7500/AEPS20191011008.

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  • 收稿日期:2019-10-11
  • 最后修改日期:2020-05-05
  • 录用日期:2020-03-26
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