Stochastic Simulation of Electric Vehicle Charging Be‍havior and Charging-Simultaneity-Factor Calculation Method Based on Gaussian Mixture Model

doi:10.3969/j.issn.1008-0198.2025.06.003

Hunan Electric Power ›› 2025, Vol. 45 ›› Issue (6): 19-25.doi: 10.3969/j.issn.1008-0198.2025.06.003

• Expert Column:Research on Modeling and Optimization of Largescale New Power System Planning Considering Complex Security Boundaries • Previous Articles Next Articles

Stochastic Simulation of Electric Vehicle Charging Be‍havior and Charging-Simultaneity-Factor Calculation Method Based on Gaussian Mixture Model

SUN Chongbo¹, YUAN Kai¹, SONG Yi¹, GAO Zhi¹, DING Yudi¹, DU Mengke²

1. State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China;
2. State Grid Beijing Chaoyang Power Supply Company, Beijing 100031, China

Received:2025-08-19 Revised:2025-10-10 Online:2025-12-25 Published:2026-01-13

Abstract

Abstract: To address the expansion planning of electric vehicle(EV) charging stations and improve the utilization of charging piles, a method for calculating the simultaneity factor is proposed. A Gaussian mixture model(GMM) and a Monte Carlo algorithm-based queuing model is used to simulate user charging behavior, thereby calculating the optimal vehicle-to-pile ratio and simultaneity factor. The influence of multi-scenario factors on the simultaneity factor is investigated, and a charging facility design framework for different scenarios in residential and commercial areas is established. By analyzing charging power characteristic curves of private vehicles, the charging simultaneity factor for diverse scenarios is measured. Case study results demonstrate the effectiveness and feasibility of the proposed method, providing valuable insights for the planning and construction of EV charging stations.

Key words: electric vehicle(EV), charging infrastructure, Gaussian mixture model(GMM), Monte Carlo, charging simultaneity factor

CLC Number:

TM73

SUN Chongbo, YUAN Kai, SONG Yi, GAO Zhi, DING Yudi, DU Mengke. Stochastic Simulation of Electric Vehicle Charging Be‍havior and Charging-Simultaneity-Factor Calculation Method Based on Gaussian Mixture Model[J]. Hunan Electric Power, 2025, 45(6): 19-25.

References

[1] 国务院新闻办.国务院新闻办发布会介绍2023年工业和信息化发展情况[EB/OL].(2024-01-19)[2025-08-10]. https://www.gov.cn/zhengce/202401/content_6927371.htm.
[2] 徐佳夫,王旭红,李浩,等. 无序充电模式下电动汽车对住宅区配电网谐波影响研究[J]. 电力科学与技术学报,2019,34(2):61-67.
[3] 周原冰,龚乃玮,王皓界,等. 中国电动汽车发展及车网互动对新型储能配置的影响[J]. 中国电力,2024,57(10):1-11.
[4] 国务院办公厅. 国务院办公厅关于印发新能源汽车产业发展规划(2021—2035年)的通知[EB/OL]. (2020-11-02)[2025-07-18]. https://www.gov.cn/gongbao/content/2020/content_5560291.htm.
[5] 魏大钧,孙波,张承慧. 电动汽车车载充电机接入住宅区配电网谐波研究[J]. 电力自动化设备,2014,34(2):13-18.
[6] 查鹏程,甘雅丽,高海祐,等. 电动汽车充电站接入配电网的电能质量评估[J]. 电测与仪表,2022,59(6):69-75.
[7] 高赐威,张亮. 电动汽车充电对电网影响的综述[J]. 电网技术,2011,35(2):127-131.
[8] 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.
[9] DOKUR E,ERDOGAN N,KUCUKSARI S. EV fleet charging load forecasting based on multiple decomposition with CEEMDAN and swarm decomposition[J]. IEEE Access,2022,10:62330-62340.
[10] 张琳娟,李文峰,许长清,等. 计及充电排队与实时SOC的电动汽车充电负荷时空分布预测[J]. 电力建设,2025,46(8):54-66.
[11] XING Q,CHEN Z,ZHANG Z Q,et al.Charging demand forecasting model for electric vehicles based on online ride-hailing trip data[J]. IEEE Access,2019,7:137390-137409.
[12] 范宏,李嘉晖,郭琦. 考虑用户充电决策行为的电动汽车充电引导策略[J]. 电力工程技术,2023,42(4):23-30.
[13] 杨晓宇. 计及电动汽车充放电时空特性的车网源协同优化模型[D]. 北京:华北电力大学,2023.
[14] 张宇,何奇,王海亮,等. 基于改进出行链的电动汽车充电负荷预测及其对配电网的影响[J]. 电网与清洁能源,2025,41(3):67-77.
[15] XIANG Y,JIANG Z,GU C,et al.Electric vehicle charging in smartgrid:a spatial-temporal simulation method[J]. Energy,2019,189:116221-116232.
[16] 张美霞,孙铨杰,杨秀. 考虑多源信息实时交互和用户后悔心理的电动汽车充电负荷预测[J]. 电网技术,2022,46(2):632-645.
[17] 刘志强,张谦,朱熠,等. 计及车-路-站-网融合的电动汽车充电负荷时空分布预测[J]. 电力系统自动化,2022,46(12):36-45.
[18] 牛牧童,廖凯,杨健维,等. 考虑季节特性的多时间尺度电动汽车负荷预测模型[J]. 电力系统保护与控制,2022,50(5):74-85.
[19] LIU D H,LI Z,JIANG J Y,et al.Electric vehicle load forecast based on Monte Carlo algorithm[C] //2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference(ITAIC).Chongqing,China. IEEE,2020:1760-1763.
[20] 董晓红,孔华志,丁飞,等. 考虑电池老化的电动汽车中长期充电负荷预测方法[J]. 电力系统自动化,2024,48(13):109-119.
[21] 罗卓伟,胡泽春,宋永华,等. 电动汽车充电负荷计算方法[J]. 电力系统自动化,2011,35(14):36-42.
[22] 杜漾腾,辛洁晴,王简,等. 计入空间可转移性的社区共享充电桩总负荷预测[J]. 电力系统及其自动化学报,2025,37(6):33-42,54.
[23] 周衍涛,戴军,苑惠丽,等. 城市电动汽车充电设施需求预测与规划布局研究[J]. 电力系统保护与控制,2021,49(24):177-187.
[24] 中华人民共和国住房和城乡建设部. 民用建筑电气设计标准(共二册):GB 51348—2019[S]. 北京:中国建筑工业出版社,2020.
[25] 中国建筑标准设计研究院.电动汽车充电基础设施设计与安装:18D705-2[M]. 北京:中国计划出版社,2018.
[26] 何晨可,朱继忠,刘云,等. 计及碳减排的电动汽车充换储一体站与主动配电网协调规划[J]. 电工技术学报,2022,37(1):92-111.
[27] 罗平,杨泽喆,张嘉昊,等. 考虑多场景充电需求预测的电动汽车充电站规划[J]. 高电压技术,2025,51(1):368-378.
[28] 住房和城乡建设部. 电动汽车充电基础设施设计与安装:18D705-2[S]. 北京:中国计划出版社,2018.

Stochastic Simulation of Electric Vehicle Charging Be‍havior and Charging-Simultaneity-Factor Calculation Method Based on Gaussian Mixture Model

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 1

Recommended Articles

Metrics

Comments