การวิเคราะห์พฤติกรรมการอัดประจุรถยนต์ไฟฟ้าและผลกระทบต่อการจัดการโหลด : กรณีศึกษาแพลตฟอร์ม PEA Volta

Surapong  Keanmanee; Narin  Koolnapadol; Chatchawal               Nimrotham; Apichart  Sungthong

Authors

Surapong Keanmanee Department of Electrical and Energy Engineering, Faculty of Industrial Technology, Rajabhat Rajanagarindra University
Narin Koolnapadol Department of Modern Automotive Engineering, Faculty of Industrial Technology, Rajabhat Rajanagarindra University
Chatchawal Nimrotham Department of Modern Automotive Engineering, Faculty of Industrial Technology, Rajabhat Rajanagarindra University
Apichart Sungthong Department of Applied Physics, Faculty of Science and Technology, Rajabhat Rajanagarindra University

Abstract

This study aims to analyze the Electric Vehicle (EV) charging behavior of Provincial Electricity Authority (PEA) customers based on real-world data comprising 48,752 charging sessions collected from 124 DC Fast Charger stations installed by P.G. Electric Group Co., Ltd. and integrated with the PEA Volta platform. The analysis covers the 14 month period from January 2024 to February 2025.The results reveal that users predominantly choose to charge during Off-Peak hours (22:00–09:00) accounting for 62.2% of total sessions, compared to 37.8% during Peak hours, yielding an Off-Peak to Peak session ratio of approximately 1.65:1. The average energy delivered per session during Off-Peak periods was 57.2% higher than during Peak periods (29.4 kWh versus 18.7 kWh). Full charging to 100% state of charge (SoC) occurred in 41.7% of Off-Peak sessions, in contrast to only 12.3% of Peak sessions. Charging activity was heavily concentrated in the Central and Eastern regions, which together accounted for 81.5% of all sessions. The CCS Combo II connector was the most frequently used standard, representing 64.1% of total charging events. The findings indicate that the Time-of-Use (TOU) tariff structure effectively shifted charging demand away from peak periods, resulting in an estimated reduction of Peak Charging Demand by approximately 24–28%. These results provide valuable insights for strategic planning of charging infrastructure expansion, implementation of dynamic pricing mechanisms, and development of smart charging systems to support the continued growth of electric vehicles within the distribution network. Note: The total dataset of 48,752 charging sessions was used for system-level statistical analysis and contextual framing, while a representative sub-sample of 86 sessions across 12 provinces was used for behavioral pattern analysis presented in the results section.

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