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EV charging load profile identification and seasonal difference analysis via charging sessions data of charging stations

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  • Huang, Wenxin
  • Wang, Jianguo
  • Wang, Jianping
  • Zeng, Haiyan
  • Zhou, Mi
  • Cao, Jinxin

Abstract

The popularity of electric vehicles (EVs) brings environmental benefits, but their hard-to-estimate stochastic charging behaviors places additional diversity on grid load management. This paper proposes a procedure to identify typical charging load profiles (CLPs) via large scale charging session data from charging stations (CSs). The daily CLPs are computed from charging sessions, and a comprehensive similarity metric based on the weighting of Euclidean and Pearson correlation coefficient is proposed to achieve better clustering. The clustering is performed using the Clustering LARge Applications (CLARA) algorithm to accommodate large sample scenarios. Subsequently, hierarchical clustering of CSs is performed based on possible CLPs，and their CLPs are estimated by Monte Carlo simulation. The performance of the proposed method is tested and evaluated with over 340,000 charging sessions from 109 CSs in Wuhan at central China, and seasonal differences in CLPs are explored. The results show that the method of mining typical CLPs from charging sessions is effective, 17 typical CLPs are identified in different seasons, which provide effective information on the fluctuation and magnitude of daily power demand, the charging power demand also shows significant seasonal differences, and good accuracy is achieved by dividing the CSs into different groups for load estimation.

Suggested Citation

  • Huang, Wenxin & Wang, Jianguo & Wang, Jianping & Zeng, Haiyan & Zhou, Mi & Cao, Jinxin, 2024. "EV charging load profile identification and seasonal difference analysis via charging sessions data of charging stations," Energy, Elsevier, vol. 288(C).
    • Handle: RePEc:eee:energy:v:288:y:2024:i:c:s0360544223031651
    DOI: 10.1016/j.energy.2023.129771
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    References listed on IDEAS

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    Cited by:

    1. Zhong, Zewei & Hu, Wuyang & Zhao, Xiaoli, 2024. "Rethinking electric vehicle smart charging and greenhouse gas emissions: Renewable energy growth, fuel switching, and efficiency improvement," Applied Energy, Elsevier, vol. 361(C).
    2. Tian, Chenlu & Liu, Yechun & Zhang, Guiqing & Yang, Yalong & Yan, Yi & Li, Chengdong, 2024. "Transfer learning based hybrid model for power demand prediction of large-scale electric vehicles," Energy, Elsevier, vol. 300(C).
    3. P, Balakumar & Ramu, Senthil Kumar & T, Vinopraba, 2024. "Optimizing electric vehicle charging in distribution networks: A dynamic pricing approach using internet of things and Bi-directional LSTM model," Energy, Elsevier, vol. 294(C).

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