Grid Charging Demand Forecasting for Private Electric Vehicles Considering Regional Differences

WEN Xin; HUANG Xueliang; GAO Shan; LIU Yu; GU Yaru

doi:10.7500/AEPS20231207002

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Grid Charging Demand Forecasting for Private Electric Vehicles Considering Regional Differences

Basic Research | 更新时间：2025-06-16

- Grid Charging Demand Forecasting for Private Electric Vehicles Considering Regional Differences
- Automation of Electric Power Systems Vol. 49, Issue 7, Pages: 158-168(2025)
- 作者机构：
  
  东南大学电气工程学院，江苏省南京市 210096
- 作者简介：
- 基金信息：
  
  Jiangsu Provincial Science and Technology Innovation Special Fund for Carbon Peak and Carbon Neutrality(BE2022030-2);National Key R&D Program of China(2021YFB2501600)
- DOI：10.7500/AEPS20231207002
  CLC：
- Received：07 December 2023，
  
  Revised：2024-11-28，
  
  Published Online：06 February 2025，
  
  Published：10 April 2025
- 稿件说明：
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文欣,黄学良,高山等.考虑区域差异性的电动私家车网格化充电需求预测[J].电力系统自动化,2025,49(7):158-168.

WEN Xin,HUANG Xueliang,GAO Shan,et al.Grid Charging Demand Forecasting for Private Electric Vehicles Considering Regional Differences[J].Automation of Electric Power Systems,2025,49(7):158-168.
文欣,黄学良,高山等.考虑区域差异性的电动私家车网格化充电需求预测[J].电力系统自动化,2025,49(7):158-168. DOI： 10.7500/AEPS20231207002.

WEN Xin,HUANG Xueliang,GAO Shan,et al.Grid Charging Demand Forecasting for Private Electric Vehicles Considering Regional Differences[J].Automation of Electric Power Systems,2025,49(7):158-168. DOI： 10.7500/AEPS20231207002.

摘要

地理区域位置的不同与区域网格功能属性的差别都在一定程度上造成了各网格内部电动汽车充电负荷的差异较大。针对目前电动汽车充电需求预测研究中未能充分考虑到电动汽车动态充电负荷分布差异性的不足，文中提出考虑地理区域差异性与用户行程多样性的数据驱动的电动私家车网格化充电需求预测方法。首先，对电动私家车用户出行轨迹与城市交通网络等类型数据进行数据挖掘并建立数学模型，获取电动私家车用户多段行程起讫点信息与用户出行基本规律。其次，基于地理信息系统平台将各类兴趣点（POI）经纬度坐标映射至地理网络中，将地理区域网格内结合用户日常出行目的的各类POI数量集分类处理，并采取自然分级法对所研究的地理区域实施精确的网格划分，功能区网格包含工作区、商业区、生活区、住宅区和混合区5种类别，建立多个时段下各功能区间的起讫点信息概率矩阵。结合得到的电动私家车在各网格内的分布结果，建立了基于蒙特卡洛法的电动汽车充电负荷预测模型，获取电动汽车在网格间转移的电量连续变化状况。基于中国苏州市实际电动汽车历史数据，以该市某区域为应用环境，完成各功能区域内的电动私家车充电需求预测仿真。仿真结果验证了区域网格划分的合理性及充电需求预测的准确性。

Abstract

To a certain extent

the difference in the location of geographical regions and functional attributes of regional grids result in the great difference in electric vehicle charging load within each grid. Aiming at the insufficient consideration of the difference in dynamic charging load distribution of electric vehicles in current research on electric vehicle charging demand forecasting

this paper proposes a data-driven grid charging demand forecasting method for private electric vehicles considering the differences of geographical regions and the diversity of user trips. Firstly

data mining on the travel tracks of private electric vehicle users

the urban traffic network

and other data types is conducted. Mathematical models are constructed to obtain the origin-destination information of multi-stage trips and the basic travel patterns of private electric vehicle users. Secondly

the latitude and longitude coordinates of each point of interest (POI) are mapped to the geographic network based on the geographic information system platform. Various POI quantity sets combined with users’ daily travel purposes in the geographical region grid are classified. The natural classification method is adopted to implement accurate grid division of the studied geographical region. The functional-area grid includes five categories: the work area

the business area

the living area

the residential area

and the mixed area. An origin-destination information probability matrix for each functional area is established during multiple periods. Combined with the obtained distribution results of private electric vehicles in each grid

this paper establishes an electric vehicle charging load forecasting model based on the Monte Carlo method to capture the continuous changes of electric vehicle electricity amount transferred between grids. Based on the actual historical data of electric vehicles in Suzhou

China

and taking a region of Suzhou as the application environment

the simulation of charging demand forecasting for private electric vehicles in each functional region is completed. The simulation results verify the rationality of regional grid division and the accuracy of charging demand forecasting.

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references

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