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Optimization of photovoltaic battery swapping station based on weather/traffic forecasts and speed variable charging

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  • Feng, Jiawei
  • Hou, Shengya
  • Yu, Lijun
  • Dimov, Nikolay
  • Zheng, Pei
  • Wang, Chunping

Abstract

An economic model of integrated Photovoltaic - Battery Swapping Station (PV-BSS) is developed in this work. Speed-variable charging taking into account battery degradation models of modern lithium-ion batteries is combined with weather and road traffic forecasts for the first time to maximize the economic and environmental impacts of this emerging technology. Annual net revenue is used as an objective function, and the power balance, charging rate limit, and consumer demand are set as model constraints. Particle swarm optimization (PSO) algorithm complemented with non-linear programming features is applied to optimize the operation of the integrated PV-BSS. The extensive model developed in this work let us propose an optimized speed variable charging method with clear economic and environmental advantages over the other methods considered in the literature so far. A case study with specific values of input parameters fed into the model is performed to verify the applicability of the model and the reliability of its predictions. The case study confirms that the proposed speed-variable charging method dispatches the PV-generated energy in an intelligent manner and shifts the energy of the grid from the peak to the valley. The model is easily extendable, allows implementation of additional metrics of interest and could be an important enabler of the upcoming smart grid revolution.

Suggested Citation

  • Feng, Jiawei & Hou, Shengya & Yu, Lijun & Dimov, Nikolay & Zheng, Pei & Wang, Chunping, 2020. "Optimization of photovoltaic battery swapping station based on weather/traffic forecasts and speed variable charging," Applied Energy, Elsevier, vol. 264(C).
    • Handle: RePEc:eee:appene:v:264:y:2020:i:c:s0306261920302208
    DOI: 10.1016/j.apenergy.2020.114708
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    References listed on IDEAS

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    Cited by:

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    2. Adu-Gyamfi, Gibbson & Song, Huaming & Nketiah, Emmanuel & Obuobi, Bright & Wu, Qin & Cudjoe, Dan, 2024. "Refueling convenience and range satisfaction in electric mobility: Investigating consumer willingness to use battery swap services for electric vehicles," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    3. Cui, Dingsong & Wang, Zhenpo & Liu, Peng & Wang, Shuo & Dorrell, David G. & Li, Xiaohui & Zhan, Weipeng, 2023. "Operation optimization approaches of electric vehicle battery swapping and charging station: A literature review," Energy, Elsevier, vol. 263(PE).
    4. Mazzeo, Domenico & Herdem, Münür Sacit & Matera, Nicoletta & Bonini, Matteo & Wen, John Z. & Nathwani, Jatin & Oliveti, Giuseppe, 2021. "Artificial intelligence application for the performance prediction of a clean energy community," Energy, Elsevier, vol. 232(C).
    5. Wang, Ziqi & Hou, Sizu, 2023. "A real-time strategy for vehicle-to-station recommendation in battery swapping mode," Energy, Elsevier, vol. 272(C).
    6. Gönül, Ömer & Duman, A. Can & Güler, Önder, 2021. "Electric vehicles and charging infrastructure in Turkey: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    7. Nayak, Dhyaan Sandeep & Misra, Shamik, 2024. "An operational scheduling framework for Electric Vehicle Battery Swapping Station under demand uncertainty," Energy, Elsevier, vol. 290(C).

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