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轨迹数据驱动的电动汽车充电需求及V2G可调控容量估计
作者:
作者单位:

四川大学电气工程学院,四川省成都市 610065

摘要:

电动汽车(EV)充电需求估计是研究电动汽车与电网互动(V2G)的重要前提。为此,提出一种行驶轨迹数据驱动的EV充电需求预测模型,并进一步考虑用户多维效益,构建用户选择参与V2G响应的用户决策模型,分析区域V2G响应能力的调控潜力。首先,对行车轨迹大数据集进行清洗与挖掘,基于动态能耗理论构建了EV充电需求时空分布预估模型。其次,基于社会行为学理论并综合考虑用电需求效用、经济效用、环保效用以及社会效用,构建了EV用户选择参与V2G响应的概率选择模型。该模型不仅考虑了EV用户的异质性,而且体现了用户决策的交互影响。最后,建立V2G可响应容量调度模型,分析V2G响应资源对区域负荷的调节效果。结果表明,所提模型不仅能有效估计某城市区域的EV充电需求时空分布特性,而且能挖掘该区域选择参与V2G响应的EV潜在用户数量,为研究V2G响应资源对区域负荷的调控潜力提供了支撑。

关键词:

基金项目:

国家自然科学基金资助项目(52111530067);四川省科技计划资助项目(2020YFSY0037)。

通信作者:

作者简介:

周椿奇(1995—),男,硕士研究生,主要研究方向:智能电网与电动汽车交互。E-mail:1249787972@qq.com
向月(1987—),男,通信作者,副教授,博士生导师,主要研究方向:智能电网与电动汽车交互。E-mail:xiang@scu.edu.cn
童话(1997—),男,硕士研究生,主要研究方向:电动汽车并网运营。E-mail:529165561@qq.com


Trajectory-data-driven Estimation of Electric Vehicle Charging Demand and Vechicle-to-Grid Regulable Capacity
Author:
Affiliation:

College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Abstract:

Electric vehicle (EV) charging demand estimation is an important precondition for studying the vehicle-to-grid (V2G) interaction. Therefore, this paper proposes a charging demand prediction model of EVs driven by driving trajectory data, constructs a decision-making model of users to choose to participate in V2G response by further considering the multi-dimensional benefits of users, and analyzes the regulation potential of regional V2G response capabilities. Firstly, the big data set of driving trajectory is cleaned and mined, and a prediction model for the spatio-temporal distribution of EV charging demand is constructed based on the dynamic energy consumption theory. Secondly, based on the social behavior theory and considering the electricity demand utility, economic utility, environmental protection utility and social utility, the probabilistic selection model of EV users participating in V2G response is constructed. The model not only considers the heterogeneity of EV users, but also reflects the interactive influence of user decisions. Finally, a V2G responsive capacity regulation model is established to analyze the adjustment effect of V2G responsive resources on the regional load. The results show that the proposed model can not only effectively estimate the spatio-temporal distribution characteristics of EV charging demand in a certain urban area, but also obtain the number of potential EV users who choose to participate in V2G response in this area, which provides support for studying the regulation potential of V2G responsive resources on the regional load.

Keywords:

Foundation:
This work is supported by National Natural Science Foundation of China (No. 52111530067) and Science and Technology Program of Sichuan Province (No. 2020YFSY0037).
引用本文
[1]周椿奇,向月,童话,等.轨迹数据驱动的电动汽车充电需求及V2G可调控容量估计[J].电力系统自动化,2022,46(12):46-55. DOI:10.7500/AEPS20211227005.
ZHOU Chunqi, XIANG Yue, TONG Hua, et al. Trajectory-data-driven Estimation of Electric Vehicle Charging Demand and Vechicle-to-Grid Regulable Capacity[J]. Automation of Electric Power Systems, 2022, 46(12):46-55. DOI:10.7500/AEPS20211227005.
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  • 收稿日期:2021-12-27
  • 最后修改日期:2022-05-09
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  • 在线发布日期: 2022-06-27
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