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Emerging Parameters Extraction Method of PV Modules Based on the Survival Strategies of Flying Foxes Optimization (FFO)

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  • Radouane Aalloul

    (Laboratory of Engineering and Materials (LIMAT), Faculty of Sciences, Ben M’Sick Hassan II University of Casablanca, Casablanca 20000, Morocco
    Laboratory of Agricultural Engineering, Energy National Institute of Agricultural Research Settat, Settat 26000, Morocco)

  • Abdellah Elaissaoui

    (Laboratory of Agricultural Engineering, Energy National Institute of Agricultural Research Settat, Settat 26000, Morocco)

  • Mourad Benlattar

    (Matter Physics Laboratory (LPM), Faculty of Sciences, Ben M’Sick Hassan II University of Casablanca, Casablanca 20000, Morocco)

  • Rhma Adhiri

    (Laboratory of Engineering and Materials (LIMAT), Faculty of Sciences, Ben M’Sick Hassan II University of Casablanca, Casablanca 20000, Morocco)

Abstract

Nowadays, the world is encountering multiple challenges of energy security, economic recovery, and the effect of global warming. Investing in new fossil fuels only locks in uneconomic practices, sustains existing risks and increases the threats of climate change. In contrast, renewable energies, such as photovoltaic energy, constitute one of the most promising technologies in combating global increase in temperatures. Given its simplicity and low maintenance costs, photovoltaic energy is the most effective alternative to address the issues above. However, the standard test conditions (STCs) of PV modules are, in most cases, different from the real working conditions of a solar module. For instance, high levels of incident irradiation in an arid climate may cause the temperature of a module to rise by many degrees above the STC temperature of 25 °C, lowering the module’s performance. To effectively simulate and control PV systems for a given location, it has become paramount to develop a robust and accurate model that considers how PV modules behave. This study seeks to introduce an emerging metaheuristic optimization algorithm to estimate the unknown parameters of PV modules. The strategies deployed by flying foxes in the event of high temperatures have given birth to the development of a new metaheuristic algorithm called FFO. Contrary to previous methods, this new modeling procedure makes it possible to calculate all the parameters, regardless of temperature or irradiance. Four PV modules, having different technologies, were tested to evaluate the accuracy of the algorithm in question. The effectiveness of FFO is then contrasted with other well-known metaheuristics where single and double diode models are deployed. The results show that the FFO optimizer represents a substantial and compelling substitute for PV module extraction methods.

Suggested Citation

  • Radouane Aalloul & Abdellah Elaissaoui & Mourad Benlattar & Rhma Adhiri, 2023. "Emerging Parameters Extraction Method of PV Modules Based on the Survival Strategies of Flying Foxes Optimization (FFO)," Energies, MDPI, vol. 16(8), pages 1-24, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3531-:d:1127250
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    References listed on IDEAS

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    1. Chennoufi, Khalid & Ferfra, Mohammed & Mokhlis, Mohcine, 2021. "An accurate modelling of Photovoltaic modules based on two-diode model," Renewable Energy, Elsevier, vol. 167(C), pages 294-305.
    2. Tsafarakis, Stelios & Zervoudakis, Konstantinos & Andronikidis, Andreas & Altsitsiadis, Efthymios, 2020. "Fuzzy self-tuning differential evolution for optimal product line design," European Journal of Operational Research, Elsevier, vol. 287(3), pages 1161-1169.
    3. Wang, Gang & Zhao, Ke & Shi, Jiangtao & Chen, Wei & Zhang, Haiyang & Yang, Xinsheng & Zhao, Yong, 2017. "An iterative approach for modeling photovoltaic modules without implicit equations," Applied Energy, Elsevier, vol. 202(C), pages 189-198.
    4. Piliougine, M. & Guejia-Burbano, R.A. & Petrone, G. & Sánchez-Pacheco, F.J. & Mora-López, L. & Sidrach-de-Cardona, M., 2021. "Parameters extraction of single diode model for degraded photovoltaic modules," Renewable Energy, Elsevier, vol. 164(C), pages 674-686.
    5. Sandrolini, L. & Artioli, M. & Reggiani, U., 2010. "Numerical method for the extraction of photovoltaic module double-diode model parameters through cluster analysis," Applied Energy, Elsevier, vol. 87(2), pages 442-451, February.
    6. Benkercha, Rabah & Moulahoum, Samir & Taghezouit, Bilal, 2019. "Extraction of the PV modules parameters with MPP estimation using the modified flower algorithm," Renewable Energy, Elsevier, vol. 143(C), pages 1698-1709.
    7. Zhou, Wei & Yang, Hongxing & Fang, Zhaohong, 2007. "A novel model for photovoltaic array performance prediction," Applied Energy, Elsevier, vol. 84(12), pages 1187-1198, December.
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