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Comparative Analysis and Optimal Operation of an On-Grid and Off-Grid Solar Photovoltaic-Based Electric Vehicle Charging Station

Author

Listed:
  • Andu Dukpa

    (Faculty of Engineering, University of New Brunswick, 15 Dineen Drive, Fredericton, NB E3B 5A3, Canada)

  • Boguslaw Butrylo

    (Faculty of Electrical Engineering, Bialystok University of Technology, ul. Wiejska 45D, 15-351 Bialystok, Poland)

  • Bala Venkatesh

    (Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria St., Toronto, ON M5B 2K3, Canada)

Abstract

One of the key strategies for decarbonization and green transportation is using electric vehicles (EVs). However, challenges like limited charging infrastructure, EV battery characteristics, and grid integration complexities persist. This study proposes a mixed-integer linear programming (MILP) approach to optimize a grid-connected solar PV-based commercial EV charging station (SPEVCS) with a battery energy storage system (BESS) for profit maximization. The MILP model efficiently manages SPEVCS operations, considering solar power fluctuations, EV charging patterns, and BESS usage. By coordinating charging schedules, grid stability is reinforced, and excess solar power can be lucratively managed. Comparing grid-connected and off-grid SPEVCS scenarios highlights grid integration benefits. Solar power mismatches with optimal charging periods pose a challenge, addressed here by BESS utilization and import/export of deficit/surplus power from/to the grid. The proposed framework incorporates solar power forecasts and probabilistic EV arrival predictions, enhancing decision accuracy. This approach fosters viable commercial EV charging, promotes green transportation, and reinforces grid resilience.

Suggested Citation

  • Andu Dukpa & Boguslaw Butrylo & Bala Venkatesh, 2023. "Comparative Analysis and Optimal Operation of an On-Grid and Off-Grid Solar Photovoltaic-Based Electric Vehicle Charging Station," Energies, MDPI, vol. 16(24), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:8086-:d:1301177
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    References listed on IDEAS

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    1. Yunyan Li & Yuansheng Huang & Meimei Zhang, 2018. "Short-Term Load Forecasting for Electric Vehicle Charging Station Based on Niche Immunity Lion Algorithm and Convolutional Neural Network," Energies, MDPI, vol. 11(5), pages 1-18, May.
    2. Andu Dukpa & Boguslaw Butrylo, 2022. "MILP-Based Profit Maximization of Electric Vehicle Charging Station Based on Solar and EV Arrival Forecasts," Energies, MDPI, vol. 15(15), pages 1-14, August.
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