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Predicting and Managing EV Charging Demand on Electrical Grids: A Simulation-Based Approach

Author

Listed:
  • Pramote Jaruwatanachai

    (Technology of Information System Management Division, Faculty of Engineering, Mahidol University, Nakhon Pathom 73170, Thailand)

  • Yod Sukamongkol

    (Energy Engineering Department, Faculty of Engineering, Ramkhamhaeng University, Bangkok 10240, Thailand)

  • Taweesak Samanchuen

    (Technology of Information System Management Division, Faculty of Engineering, Mahidol University, Nakhon Pathom 73170, Thailand)

Abstract

Electric vehicles (EVs) are becoming increasingly popular, and it is important for utilities to understand their charging characteristics to accurately estimate the demand on the electrical grid. In this work, we developed simulation models for different EV charging scenarios in the home sector. We used them to predict maximum demand based on the increasing penetration of EV consumers. We comprehensively reviewed the literature on EV charging technologies, battery capacity, charging situations, and the impact of EV loads. Our results suggest a method for visualizing the impact of EV charging loads by considering factors such as state of charge, arrival time, charging duration, rate of charge, maximum charging power, and involvement rate. This method can be used to model load profiles and determine the number of chargers needed to meet EV user demand. We also explored the use of a time-of-use (TOU) tariff as a demand response strategy, which encourages EV owners to charge their vehicles off-peak in order to avoid higher demand charges. Our simulation results show the effects of various charging conditions on load profiles and indicate that the current TOU price strategy can accommodate a 20% growth in EV consumers, while the alternative TOU price strategy can handle up to a 30% penetration level.

Suggested Citation

  • Pramote Jaruwatanachai & Yod Sukamongkol & Taweesak Samanchuen, 2023. "Predicting and Managing EV Charging Demand on Electrical Grids: A Simulation-Based Approach," Energies, MDPI, vol. 16(8), pages 1-22, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3562-:d:1128245
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    References listed on IDEAS

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    Cited by:

    1. Wenping Zhang & Yiming Wang & Po Xu & Donghui Li & Baosong Liu, 2023. "Evaluation and Improvement of Backup Capacity for Household Electric Vehicle Uninterruptible Power Supply (EV-UPS) Systems," Energies, MDPI, vol. 16(12), pages 1-20, June.
    2. Wardah Afzal & Li-Ye Zhao & Guang-Zhi Chen & Yu Xue, 2023. "Hybrid Wind/PV E-Bike Charging Station: Comparison of Onshore and Offshore Systems," Sustainability, MDPI, vol. 15(20), pages 1-19, October.
    3. Ahmad Almaghrebi & Kevin James & Fares Al Juheshi & Mahmoud Alahmad, 2024. "Insights into Household Electric Vehicle Charging Behavior: Analysis and Predictive Modeling," Energies, MDPI, vol. 17(4), pages 1-20, February.

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