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Battery Control for Node Capacity Increase for Electric Vehicle Charging Support

Author

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  • Md Wakil Ahmad

    (Centro de Sistemas de Energia, INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal
    These authors contributed equally to this work.)

  • Alexandre Lucas

    (Centro de Sistemas de Energia, INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal
    These authors contributed equally to this work.)

  • Salvador Moreira Paes Carvalhosa

    (Centro de Sistemas de Energia, INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal)

Abstract

The integration of electric vehicles (EVs) into the power grid poses significant challenges and opportunities for energy management systems. This is especially concerning for parking lots or private building condominiums in which refurbishing is not possible or is costly. This paper presents a real-time monitoring approach to EV charging dynamics with battery storage support over a 24 h period. By simulating EV demand, state of charge (SOC), and charging and discharging events, we provide insights into the operational strategies for energy storage systems to ensure maximum charging simultaneity factor through internal power enhancement. The study uses a time-series analysis of EV demand, contrasting it with the battery’s SOC, to dynamically adjust charging and discharging actions within the constraints of the upstream infrastructure capacity. The model incorporates parameters such as maximum power capacity, energy storage capacity, and charging efficiencies, to reflect realistic conditions. Results indicate that real-time SOC monitoring, coupled with adaptive charging strategies, can mitigate peak demands and enhance the system’s responsiveness to fluctuating loads. This paper emphasizes the critical role of real-time data analysis in the effective management of energy resources in existing parking lots and lays the groundwork for developing intelligent grid-supportive frameworks in the context of growing EV adoption.

Suggested Citation

  • Md Wakil Ahmad & Alexandre Lucas & Salvador Moreira Paes Carvalhosa, 2024. "Battery Control for Node Capacity Increase for Electric Vehicle Charging Support," Energies, MDPI, vol. 17(22), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5554-:d:1515606
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    References listed on IDEAS

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    1. Shahid Hussain & Subhasis Thakur & Saurabh Shukla & John G. Breslin & Qasim Jan & Faisal Khan & Ibrar Ahmad & Mousa Marzband & Michael G. Madden, 2022. "A Heuristic Charging Cost Optimization Algorithm for Residential Charging of Electric Vehicles," Energies, MDPI, vol. 15(4), pages 1-18, February.
    2. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2022. "Modelling and optimal energy management for battery energy storage systems in renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
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