Empirical analysis of electric vehicle charging load forecasting based on Monte Carlo simulation model

Kun Wei; Guang Tian; Yang Yang; Xufeng Zhang; Yuanying Chi; Yi Zheng

doi:10.1016/j.gloei.2025.12.003

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Empirical analysis of electric vehicle charging load forecasting based on Monte Carlo simulation model

Advanced Energy Storage Technologies | 更新时间：2026-03-30

- Empirical analysis of electric vehicle charging load forecasting based on Monte Carlo simulation model
- Empirical analysis of electric vehicle charging load forecasting based on Monte Carlo simulation model
- 全球能源互联网(英文) 2026年9卷第1期
- 作者机构：
  
  1. State Grid Hebei Electric Power Co., Ltd.,Shijiazhuang,PR China,050000
  2. School of Economics and Management, Beijing University of Technology,Beijing,PR China,100124
- 作者简介：
- 基金信息：
- DOI：10.1016/j.gloei.2025.12.003
  中图分类号：
- 纸质出版：2026
- 稿件说明：
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Kun Wei, Guang Tian, Yang Yang, 等. Empirical analysis of electric vehicle charging load forecasting based on Monte Carlo simulation model[J]. 全球能源互联网(英文), 2026,9(1).

Kun Wei, Guang Tian, Yang Yang, et al. Empirical analysis of electric vehicle charging load forecasting based on Monte Carlo simulation model[J]. Global Energy Interconnection, 2026, 9(1).
Kun Wei, Guang Tian, Yang Yang, 等. Empirical analysis of electric vehicle charging load forecasting based on Monte Carlo simulation model[J]. 全球能源互联网(英文), 2026,9(1). DOI： 10.1016/j.gloei.2025.12.003.

Kun Wei, Guang Tian, Yang Yang, et al. Empirical analysis of electric vehicle charging load forecasting based on Monte Carlo simulation model[J]. Global Energy Interconnection, 2026, 9(1). DOI： 10.1016/j.gloei.2025.12.003.

摘要

Abstract

With the rapid proliferation of electric vehicles

their charging loads pose new challenges to power grid stability and operational effi-ciency. To address this

this study employs a Monte Carlo simulation model to analyze the charging load characteristics of six battery electric vehicle categories in Hebei Province

leveraging multi-source probabilistic distribution data under typical operational scenarios.The findings reveal that electric vehicle charging loads are primarily concentrated during midday and nighttime periods

with significant load fluctuations exerting substantial pressure on the grid. In response

this paper proposes strategic interventions including optimized charging infrastructure planning

time-of-use electricity pricing mechanisms

and smart charging technologies to balance grid loads.The results provide a theoretical foundation for electric vehicle load forecasting

smart grid dispatching

and vehicle-grid integration

thereby enhancing grid operational efficiency and sustainability.

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