Seasonal Load Statistics of EV Charging and Battery Swapping Stations Based on Gaussian Mixture Model for Charging Strategy Optimization in Electric Power Distribution Systems

Shengcong Wu, Hang Li and Hang Wang ()
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Shengcong Wu: Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology, Wuhan 430068, China
Hang Li: State Grid Wuhan Electric Power Supply Company, Wuhan 430013, China
Hang Wang: Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment, Hubei University of Technology, Wuhan 430068, China

Energies, 2025, vol. 18, issue 20, 1-24

Abstract: The rapidly growing demand of electric vehicle (EV) charging is one of the main challenges to modern electrical distribution systems. Accurate modelling of the EV charging load is crucial for charging load prediction and optimization. However, previous methods based on the charging behaviors of private EVs are hard to collect user’s private data. In this study, charging load data from 962 charging and battery swapping stations (CBSSs), classified into dedicated charging stations, public charging stations, and battery swapping stations, collected during 2021–2022, are analyzed to investigate seasonal variations in the charging coincidence factor. A data-driven probabilistic model of charging load, based on the Gaussian Mixture Model, is developed to address various scenarios, including new station construction, capacity expansions, and optimized charging strategies. This model is applicable to different types of CBSSs. A real-world 10 kV feeder system is employed as a case study to validate the model, and a delayed charging strategy is proposed. The results demonstrate that the proposed model accurately estimates charging load peaks after new construction and expansion in 2023, with an error rate under 3%. Furthermore, the delayed charging strategy achieved a 24.79% reduction in maximum load and a 31.96% decrease in the peak–valley difference. Its implementation in the real-world feeder significantly alleviated nighttime overloading in 2024.

Keywords: data-driven; EV charging and battery swapping station load; gaussian mixture model; charging strategy optimization; electrical distribution system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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