User Charging Behavior Analysis and Charging Facility Planning Practice Based on Multi-source Data Fusion

Yujie SHENG; Qinglai GUO; Mengjie LIU; Jian LAN; Hongtai ZENG; Fang WANG

doi:10.7500/AEPS20220130011

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User Charging Behavior Analysis and Charging Facility Planning Practice Based on Multi-source Data Fusion

Special Topic on Key Technologies of Urban Power Grid and Transportation Integration for New Power Systems | 更新时间：2025-06-16

- User Charging Behavior Analysis and Charging Facility Planning Practice Based on Multi-source Data Fusion
- Automation of Electric Power Systems Vol. 46, Issue 12, Pages: 151-162(2022)
- 作者机构：
  
  1.清华大学电机工程与应用电子技术系，北京市 100084
  2.电力系统及大型发电设备安全控制和仿真国家重点实验室（清华大学），北京市 100084
  3.上海市新能源汽车公共数据采集与监测研究中心，上海市 201805
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(52061635103)
- DOI：10.7500/AEPS20220130011
  CLC：
- Received：30 January 2022，
  
  Revised：2022-04-15，
  
  Published Online：05 May 2022，
  
  Published：25 June 2022
- 稿件说明：
移动端阅览
盛裕杰,郭庆来,刘梦洁等.多源数据融合的用户充电行为分析与充电设施规划实践[J].电力系统自动化,2022,46(12):151-162.

SHENG Yujie,GUO Qinglai,LIU Mengjie,et al.User Charging Behavior Analysis and Charging Facility Planning Practice Based on Multi-source Data Fusion[J].Automation of Electric Power Systems,2022,46(12):151-162.
盛裕杰,郭庆来,刘梦洁等.多源数据融合的用户充电行为分析与充电设施规划实践[J].电力系统自动化,2022,46(12):151-162. DOI： 10.7500/AEPS20220130011.

SHENG Yujie,GUO Qinglai,LIU Mengjie,et al.User Charging Behavior Analysis and Charging Facility Planning Practice Based on Multi-source Data Fusion[J].Automation of Electric Power Systems,2022,46(12):151-162. DOI： 10.7500/AEPS20220130011.

摘要

作为中国新基建战略的重要组成，电动汽车充电设施在近年获得了快速发展，但面临供需不平衡的难题。因此，亟须通过真实数据分析用户需求与行为特征，构建高效的公共充电设施规划体系，服务电动汽车发展。文中以上海市为实践案例，融合营运车辆轨迹数据、充电站数据、乘用车出行统计数据、交通路况数据、兴趣点检索数据等多源真实数据集开展了实践性大数据挖掘分析，旨在为现实充电站规划提供可行架构：从宏观角度对车辆用户群体的出行-充电行为与城市时空特征的关联进行了分析；从微观角度对车辆用户个体的行为偏好进行了建模，基于决策树模型实现了用户充电需求预测，并基于Huff吸引力模型描述了用户的充电站选择行为。在此基础上，对海量用户行为进行重构与仿真，建立了城市能源-交通融合网络仿真架构，从总体供需情况与个体服务质量等多维度对现有充电设施的建设进行了评估，提出了计及多重目标的未来充电设施扩展规划架构，为电动汽车充电设施规划提供了数据支持和决策依据。

Abstract

As a key element of the new infrastructure strategy of China

the charging facilities of electric vehicles have developed rapidly in recent years. However

the development of charging facilities faces the problem of imbalance between supply and demand. Therefore

it is necessary to analyze the user demands and behavior characteristics through real data and build an effective public charging facility planning system to serve the development of electric vehicles. To provide a feasible framework for the planning of real-world charging stations

by taking Shanghai as a practical case

this paper carries out practical big data mining analysis

which integrates multi-source real-world data such as service vehicle trajectory data

charging station data

passenger vehicle travel statistical data

traffic road condition data

and interest point retrieval data. From the macro-perspective

the correlation between the traveling-charging behavior of vehicle users and the city temporal-spatial characteristics is analyzed. From the micro-perspective

the behavioral preferences of individual vehicle users are modeled

where the user charging demand is predicted by the decision tree model

and the user's charging station selection behavior is described by the Huff attraction model. On this basis

the massive user behavior is reconstructed and simulated

and a simulation framework of the urban integrated energy-traffic network is established. The construction of existing charging facilities is evaluated from multiple dimensions such as the overall supply and demand and the individual service quality. Finally

a future charging facility expansion planning framework considering multiple objectives is proposed

which provides data support and decision-making basis for the charging facility planning of electric vehicles.

关键词

Keywords

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