基于用户画像的电动汽车充电负荷预测方法

doi:10.13229/j.cnki.jdxbgxb.20211130

Abstract

Abstract:

In order to reasonably evaluate the impact of various factors on the charging load， this paper introduced the concept of user portrait， and generated a typical user portrait that could describe the charging behavior of users through the construction and extraction of the characteristics of vehicle charging behavior data. At the same time， it is found that the shape of load curve can be adjusted by adjusting the proportion of different types of users. Through practical examples， the effects of user behavior characteristics and attribute characteristics on key grid indicators such as charging load form， peak valley time and load rate are comprehensively analyzed， so as to reasonably guide users to charge in order， provide basis for power grid planning and capacity expansion considering electric vehicle charging load.

Key words: vehicle engineering, charging load forecasting, user portrait, distribution network

CLC Number:

TM92

Xue-jin HUANG,Jin-xing ZHONG,Jing-yu LU,Ji ZHAO,Wei XIAO,Xin-mei YUAN. Electric vehicle charging load forecasting method based on user portrait[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(8): 2193-2200.

Figures/Tables 9

Table 1

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Table 2

GMM model parameter table after fitting"

特征	聚类中心（均值）	方差
充电开始时间	$μ = [2.38,10.66,15.5,20.85]$	$σ 2 = [2.70,3.68,2.93,2.32$ ］
充电额定功率	$μ = [65,45.90,23.30,57.17]$	$σ 2 = [0,25.13,119.46,16.44]$
充电时间间隔	$μ = [0.60,2.10]$	$σ 2 = [0.13,0.97]$
开始-结束SOC	$μ = [[22.14,81.73], [78.18,98.01], [43.95,67.79], [37.36,98.13]]$	$σ 2 = [[[68.89,14.76], [14.76,15.75]], [[122.99, - 1.53], [- 1.53,4.53]], [[349.90,236.88], [236.88,356.62]], [[195.30,3.51], [3.51,3.87]]]$

Table 2

Table 3

Table 4

Simulation results of different user ratios"

场景	用户比例	用户规模/个	日峰谷差 $Δ P V$ /kW	日负荷率 $φ$ /%
1	2∶1∶1∶1	4000	6081.14	36.28
2	1∶2∶1∶1	4000	6728.78	33.23
3	1∶1∶2∶1	4000	7504.26	29.86
4	0∶0∶0∶1	4000	4581.07	46.10

Table 4

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字段名称	数据类型	字段定义
时间	timestamp	采样时间
BCUBattSOC	float	车辆SOC
BCUBattCrrt	float	电流值
BCUBattU	float	电压值
charge_segment	int	充电片段序号

用户	行为特征	属性特征
用户1	充电开始时间：早充电额定功率：快充、慢充充电时间间隔：1天开始-结束SOC：低SOC-80%	电池容量：30 kW·h
用户2	充电开始时间：午充电额定功率：快充充电时间间隔：1天开始-结束SOC：中SOC-充满	电池容量：30 kW·h
用户3	充电开始时间：晚充电额定功率：慢充充电时间间隔：2天开始-结束SOC：中SOC-80%、中SOC-充满	电池容量：45 kW·h
用户4	充电开始时间：早、午、晚、凌晨充电额定功率：快充、慢充充电时间间隔：半天开始-结束SOC：低SOC-80%、低SOC-充满	电池容量：20 kW·h

Electric vehicle charging load forecasting method based on user portrait

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