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Sizing battery energy storage and PV system in an extreme fast charging station considering uncertainties and battery degradation

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  • Rehman, Waqas ur
  • Bo, Rui
  • Mehdipourpicha, Hossein
  • Kimball, Jonathan W.

Abstract

This paper presents mixed integer linear programming (MILP) formulations to obtain optimal sizing for a battery energy storage system (BESS) and solar generation system in an extreme fast charging station (XFCS) to reduce the annualized total cost. The proposed model characterizes a typical year with eight representative scenarios and obtains the optimal energy management for the station and BESS operation to exploit the energy arbitrage for each scenario. Contrasting extant literature, this paper proposes a constant power constant voltage (CPCV) based improved probabilistic approach to model the XFCS charging demand for weekdays and weekends. This paper also accounts for the monthly and annual demand charges based on realistic utility tariffs. Furthermore, BESS life degradation is considered in the model to ensure no replacement is needed during the considered planning horizon. Different from the literature, this paper offers pragmatic MILP formulations to tally BESS charge/discharge cycles using the cumulative charge/discharge energy concept. McCormick relaxations and the Big-M method are utilized to relax the bi-linear terms in the BESS operational constraints. Finally, a robust optimization-based MILP model is proposed and leveraged to account for uncertainties in electricity price, solar generation, and XFCS demand. Case studies were performed to signify the efficacy of the proposed formulations.

Suggested Citation

  • Rehman, Waqas ur & Bo, Rui & Mehdipourpicha, Hossein & Kimball, Jonathan W., 2022. "Sizing battery energy storage and PV system in an extreme fast charging station considering uncertainties and battery degradation," Applied Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:appene:v:313:y:2022:i:c:s030626192200201x
    DOI: 10.1016/j.apenergy.2022.118745
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    3. Asrin Seyedzahedi & Salah Bahramara, 2023. "Facilitating Investment in Photovoltaic Systems in Iran Considering Time-of-Use Feed-in-Tariff and Carbon Market," Energies, MDPI, vol. 16(3), pages 1-20, January.
    4. Sohani, Ali & Cornaro, Cristina & Shahverdian, Mohammad Hassan & Moser, David & Pierro, Marco & Olabi, Abdul Ghani & Karimi, Nader & Nižetić, Sandro & Li, Larry K.B. & Doranehgard, Mohammad Hossein, 2023. "Techno-economic evaluation of a hybrid photovoltaic system with hot/cold water storage for poly-generation in a residential building," Applied Energy, Elsevier, vol. 331(C).
    5. Houssem R. E. H. Bouchekara & Yusuf A. Sha’aban & Mohammad S. Shahriar & Saad M. Abdullah & Makbul A. Ramli, 2023. "Sizing of Hybrid PV/Battery/Wind/Diesel Microgrid System Using an Improved Decomposition Multi-Objective Evolutionary Algorithm Considering Uncertainties and Battery Degradation," Sustainability, MDPI, vol. 15(14), pages 1-38, July.
    6. Àlex Alonso-Travesset & Diederik Coppitters & Helena Martín & Jordi de la Hoz, 2023. "Economic and Regulatory Uncertainty in Renewable Energy System Design: A Review," Energies, MDPI, vol. 16(2), pages 1-30, January.
    7. Zhou, Siyu & Han, Yang & Mahmoud, Karar & Darwish, Mohamed M.F. & Lehtonen, Matti & Yang, Ping & Zalhaf, Amr S., 2023. "A novel unified planning model for distributed generation and electric vehicle charging station considering multi-uncertainties and battery degradation," Applied Energy, Elsevier, vol. 348(C).
    8. Irina Picioroaga & Madalina Luca & Andrei Tudose & Dorian Sidea & Mircea Eremia & Constantin Bulac, 2023. "Resilience-Driven Optimal Sizing of Energy Storage Systems in Remote Microgrids," Sustainability, MDPI, vol. 15(22), pages 1-16, November.

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