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Modeling Electric Vehicle Charging Demand with the Effect of Increasing EVSEs: A Discrete Event Simulation-Based Model

Author

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  • Neil Stephen Lopez

    (Mechanical Engineering Department, De La Salle University, Manila 0922, Philippines)

  • Adrian Allana

    (Mechanical Engineering Department, De La Salle University, Manila 0922, Philippines)

  • Jose Bienvenido Manuel Biona

    (Mechanical Engineering Department, De La Salle University, Manila 0922, Philippines
    Enrique Razon Logistics Institute, De La Salle University, Manila 0922, Philippines)

Abstract

Electric vehicle (EV) use is growing at a steady rate globally. Many countries are planning to ban internal combustion engines by 2030. One of the key issues needed to be addressed before the full-scale deployment of EVs is ensuring energy security. Various studies have developed models to simulate and study hourly electricity demand from EV charging. In this study, we present an improved model based on discrete event simulation, which allows for modeling characteristics of individual EV users, including the availability of electric vehicle supply equipment (EVSE) outside homes and the charging threshold of each EV user. The model is illustrated by simulating 1000 random electric vehicles generated using data from an actual survey. The results agree with previous studies that daily charging demands do not significantly vary. However, the results show a significant shift in charging schedule during weekends. Moreover, the simulation demonstrated that the charging peak demand can be reduced by as much as 11% if EVSEs are made more available outside homes. Interestingly, a behavioral solution, such as requiring users to fully utilize their EV’s battery capacity, is more effective in reducing the peak demand (14–17%). Finally, the study concludes by discussing a few potential implications on electric vehicle charging policy.

Suggested Citation

  • Neil Stephen Lopez & Adrian Allana & Jose Bienvenido Manuel Biona, 2021. "Modeling Electric Vehicle Charging Demand with the Effect of Increasing EVSEs: A Discrete Event Simulation-Based Model," Energies, MDPI, vol. 14(13), pages 1-15, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3734-:d:579704
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    References listed on IDEAS

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    1. Anil K. Madhusudhanan & Xiaoxiang Na & David Cebon, 2021. "A Computationally Efficient Framework for Modelling Energy Consumption of ICE and Electric Vehicles," Energies, MDPI, vol. 14(7), pages 1-15, April.
    2. Foley, Aoife & Tyther, Barry & Calnan, Patrick & Ó Gallachóir, Brian, 2013. "Impacts of Electric Vehicle charging under electricity market operations," Applied Energy, Elsevier, vol. 101(C), pages 93-102.
    3. Harris, Chioke B. & Webber, Michael E., 2014. "An empirically-validated methodology to simulate electricity demand for electric vehicle charging," Applied Energy, Elsevier, vol. 126(C), pages 172-181.
    4. Arias, Mariz B. & Kim, Myungchin & Bae, Sungwoo, 2017. "Prediction of electric vehicle charging-power demand in realistic urban traffic networks," Applied Energy, Elsevier, vol. 195(C), pages 738-753.
    5. Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
    6. Morrissey, Patrick & Weldon, Peter & O’Mahony, Margaret, 2016. "Future standard and fast charging infrastructure planning: An analysis of electric vehicle charging behaviour," Energy Policy, Elsevier, vol. 89(C), pages 257-270.
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    Cited by:

    1. Young-Eun Jeon & Suk-Bok Kang & Jung-In Seo, 2022. "Hybrid Predictive Modeling for Charging Demand Prediction of Electric Vehicles," Sustainability, MDPI, vol. 14(9), pages 1-15, April.
    2. Yongzhong Wu & Siyi Zhuge & Guoxin Han & Wei Xie, 2022. "Economics of Battery Swapping for Electric Vehicles—Simulation-Based Analysis," Energies, MDPI, vol. 15(5), pages 1-18, February.
    3. Graham Town & Seyedfoad Taghizadeh & Sara Deilami, 2022. "Review of Fast Charging for Electrified Transport: Demand, Technology, Systems, and Planning," Energies, MDPI, vol. 15(4), pages 1-30, February.

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