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MILP-Based Profit Maximization of Electric Vehicle Charging Station Based on Solar and EV Arrival Forecasts

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  • Andu Dukpa

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

  • Boguslaw Butrylo

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

Abstract

Electric vehicles (EVs) will be dominating the modes of transport in the future. Current limitations discouraging the use of EVs are mainly due to the characteristics of the EV battery and lack of easy access to charging stations. Charging schedules of EVs are usually uncoordinated, whereas coordinated charging offers several advantages, including grid stability. For a solar photovoltaic (PV)-based charging station (CS), optimal utilization of solar power results in an increased revenue and efficient utilization of related equipment. The solar PV and the arrival of EVs for charging are both highly stochastic. This work considers the solar PV forecast and the probability of EV arrival to optimize the operation of an off-grid, solar PV-based commercial CS with a battery energy storage system (BESS) to realize maximum profit. BESS supports the sale of power when the solar PV generation is low and subsequently captures energy from the solar PV when the generation is high. Due to contrasting characteristics of the solar PV and EV charging pattern, strategies to maximize the profit are proposed. One such strategy is to optimally size the BESS to gain maximum profit. A mixed integer linear programming (MILP) method is used to determine the optimal solution.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5760-:d:883403
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    References listed on IDEAS

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    Cited by:

    1. João Fausto L. de Oliveira & Paulo S. G. de Mattos Neto & Hugo Valadares Siqueira & Domingos S. de O. Santos & Aranildo R. Lima & Francisco Madeiro & Douglas A. P. Dantas & Mariana de Morais Cavalcant, 2023. "Forecasting Methods for Photovoltaic Energy in the Scenario of Battery Energy Storage Systems: A Comprehensive Review," Energies, MDPI, vol. 16(18), pages 1-20, September.
    2. Xiang Liao & Beibei Qian & Zhiqiang Jiang & Bo Fu & Hui He, 2023. "Integrated Energy Station Optimal Dispatching Using a Novel Many-Objective Optimization Algorithm Based on Multiple Update Strategies," Energies, MDPI, vol. 16(13), pages 1-26, July.
    3. Kenji Araki & Yasuyuki Ota & Anju Maeda & Minoru Kumano & Kensuke Nishioka, 2023. "Solar Electric Vehicles as Energy Sources in Disaster Zones: Physical and Social Factors," Energies, MDPI, vol. 16(8), pages 1-25, April.
    4. 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.
    5. Stavros Poniris & Anastasios I. Dounis, 2022. "Electric Vehicle Charging Schedules in Workplace Parking Lots Based on Evolutionary Optimization Algorithm," Energies, MDPI, vol. 16(1), pages 1-16, December.
    6. Giovanni Gino Zanvettor & Marco Casini & Antonio Giannitrapani & Simone Paoletti & Antonio Vicino, 2022. "Optimal Management of Energy Communities Hosting a Fleet of Electric Vehicles," Energies, MDPI, vol. 15(22), pages 1-16, November.
    7. Syed Muhammad Ahsan & Hassan Abbas Khan & Sarmad Sohaib & Anas M. Hashmi, 2023. "Optimized Power Dispatch for Smart Building and Electric Vehicles with V2V, V2B and V2G Operations," Energies, MDPI, vol. 16(13), pages 1-15, June.

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