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An Accurate Model for Bifacial Photovoltaic Panels

Author

Listed:
  • Emad M. Ahmed

    (Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia)

  • Mokhtar Aly

    (Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Bellavista 7, Santiago 8420524, Chile)

  • Manar Mostafa

    (Department of Electrical Engineering, Aswan University, Aswan 81542, Egypt)

  • Hegazy Rezk

    (Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam bin Abdulaziz University, Wadi Alddawasir 11991, Saudi Arabia
    Electrical Engineering Department, Faculty of Engineering, Minia University, Minya 61517, Egypt)

  • Hammad Alnuman

    (Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia)

  • Waleed Alhosaini

    (Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia)

Abstract

Recently, there has been increasing concerns over bifacial PV (BPV) modules over the conventional monofacial PV (MPV) modules owing to their potential to add extra electrical energy from their rear-side irradiance. However, adding the rear-side irradiance to the front-side irradiance results in the increased nonlinearity of the BPV modules compared to MPV modules. Such nonlinearity makes the conventional methods unable to accurately extract the BPV module parameters. In this context, the precise determination of the BPV module parameters is a crucial issue for establishing energy yield estimations and for the proper planning of BPV installations as well. This paper proposes a new model for the BPV modules based on the MPV modeling, in which a new parameter is added to the MPV model to adjust the value of the model series resistance in order to provide a generic model for BPV modules in both monofacial and bifacial operating regions. Moreover, a new determination method for optimizing BPV model parameters using the recently developed enhanced version of the success-history-based adaptive differential evolution (SHADE) algorithm with linear population size reduction, known as the LSHADE method, is applied. The determination process of the model parameters is adapted using a two-stage optimization scheme to model the full operating range of BPV modules. The accuracy of the obtained parameters using the proposed model is compared with the conventional single-diode and double-diode models of the BPV. The obtained results using the proposed model of the BPV module show the performance superiority and accuracy of the LSHADE method over the existing methods in the literature. Furthermore, the LSHADE method provides the successful and accurate extraction of the global optimized parameters to model MPV and BPV modules. Therefore, the proposed method can provide an accurate model for the whole operating range of BPV that would be beneficial for further studies of their economic and technical feasibility for wide installation plans.

Suggested Citation

  • Emad M. Ahmed & Mokhtar Aly & Manar Mostafa & Hegazy Rezk & Hammad Alnuman & Waleed Alhosaini, 2022. "An Accurate Model for Bifacial Photovoltaic Panels," Sustainability, MDPI, vol. 15(1), pages 1-27, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:509-:d:1017670
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    References listed on IDEAS

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    1. Tina, Giuseppe Marco & Bontempo Scavo, Fausto & Merlo, Leonardo & Bizzarri, Fabrizio, 2021. "Comparative analysis of monofacial and bifacial photovoltaic modules for floating power plants," Applied Energy, Elsevier, vol. 281(C).
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    Cited by:

    1. Furkan Dincer & Emre Ozer, 2025. "Numerical Analysis of Bifacial Photovoltaic Systems Under Different Snow Climatic Conditions," Sustainability, MDPI, vol. 17(14), pages 1-24, July.
    2. Salma Riad & Naoual Bekkioui & Merlin Simo-Tagne & Ndukwu Macmanus Chinenye & Hamid Ez-Zahraouy, 2025. "Artificial Intelligence Prediction Analysis of Daily Power Photovoltaic Bifacial Modules in Two Moroccan Cities," Sustainability, MDPI, vol. 17(15), pages 1-22, July.

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