@inproceedings{f0d83f50026844acb22ec541a1bda2ba,
title = "An EV Charging Scheduling Strategy Considering User Demand of Distribution Networks",
abstract = "This work aims to study electric vehicle (EV) charging scheduling strategies that can reduce the impact of charging load on the distribution network while meeting the power demands of EVs. We use real-world EV data from Tianjin, China to analyze the characteristics of EV charging behavior, and employ Gaussian Mixture Model to describe the charging behavior characteristics. A charging load simulation model based on Monte Carlo method is proposed. The model uses the charging behavior characteristics as input, and calculates charging load by time-series simulation. Then we use the IEEE-33 bus system to simulate the influence of charging load on distribution network under different scenarios of the EV ownership proportion. Finally, we propose an EV charging scheduling optimization model to reduce the grid peak load on the distribution network.",
keywords = "Charging Scheduling, Distribution Network, Electric Vehicles",
author = "Xudong Peng and Shuo Wang and Dingsong Cui and Ziyi Yang",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; 2nd CCF Intelligent Vehicles Symposium, CIVS 2024 ; Conference date: 19-10-2024 Through 20-10-2024",
year = "2025",
doi = "10.1007/978-981-95-0848-8\_19",
language = "English",
isbn = "9789819508471",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "229--242",
editor = "Huiyun Li and Peng Sun and Daxin Tian and Zhengguo Sheng and Victor Leung and Huaxia Xia and Yong Hong",
booktitle = "Intelligent Vehicles - 2nd CCF Intelligent Vehicles Symposium, CIVS 2024, Revised Selected Papers",
address = "Germany",
}