The impact of electric vehicle scale on grid load: analysis based on the load peak-valley difference minimization model

Qiushi Li; Yi Yuan; Xiaomin Lu; Xiaoqi Zhu

doi:10.1117/12.3023938

9 April 2024 The impact of electric vehicle scale on grid load: analysis based on the load peak-valley difference minimization model

Qiushi Li, Yi Yuan, Xiaomin Lu, Xiaoqi Zhu

Author Affiliations +

Proceedings Volume 12989, Third International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2023); 1298916 (2024) https://doi.org/10.1117/12.3023938
Event: Third International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2023), 2023, Xi'an, China

Abstract

Electric vehicles have been experiencing rapid growth in China in recent years, and this trend is expected to impact the grid load. Using Jiangsu Province in China as an example, this paper predicts that the market penetration rate of new energy vehicles will reach 50% in China, and the number of electric vehicle owners in Jiangsu Province will exceed 6 million by the year 2030. Further analysis reveals that the increasing adoption of new energy sources and electric vehicles leads to a rise in centralized EV charging, which amplifies the disparity between peak and off-peak electricity demand. Additionally, this paper introduces an objective function centered around peak-value electricity demand and a peak-valley time-sharing tariff strategy with the aim of minimizing the peak-to-off-peak load difference in the grid. It is found that, in order to reduce the peak-to-off-peak load difference in the grid, there is a need to increase the electricity price during peak hours to some extent.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qiushi Li, Yi Yuan, Xiaomin Lu, and Xiaoqi Zhu "The impact of electric vehicle scale on grid load: analysis based on the load peak-valley difference minimization model", Proc. SPIE 12989, Third International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2023), 1298916 (9 April 2024); https://doi.org/10.1117/12.3023938

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