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Review of methods for the calculation of cell temperature in high concentration photovoltaic modules for electrical characterization

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  • Rodrigo, P.
  • Fernández, E.F.
  • Almonacid, F.
  • Pérez-Higueras, P.J.

Abstract

The solar cell operation temperature is an important input in models for the electrical characterization of high concentration photovoltaic cells and modules. However, the direct measurement of this temperature is difficult in these kinds of devices. Because of this, in recent years, the scientific community has proposed different methods for indirectly calculating the cell temperature in high concentration photovoltaic modules from atmospheric parameters and/or easily measurable parameters on the module. In this paper, a comprehensive review of existing methods for cell temperature calculation in high concentration photovoltaic modules for electrical characterization is presented. The different methods are summarized and a comparative analysis is done. Required inputs, advantages and technical difficulties of each method are highlighted. Also, an experimental campaign has been carried out at Jaén, south of Spain, in order to quantify the accuracy of the methods. Results show that methods based on direct measurements on the module are somewhat more accurate than methods only based on atmospheric parameters. The choice of the most suitable method for a specific application will depend on the availability of module information, on the required accuracy and on technical issues.

Suggested Citation

  • Rodrigo, P. & Fernández, E.F. & Almonacid, F. & Pérez-Higueras, P.J., 2014. "Review of methods for the calculation of cell temperature in high concentration photovoltaic modules for electrical characterization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 478-488.
    • Handle: RePEc:eee:rensus:v:38:y:2014:i:c:p:478-488
    DOI: 10.1016/j.rser.2014.06.008
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    1. Almonacid, Florencia & Fernandez, Eduardo F. & Mellit, Adel & Kalogirou, Soteris, 2017. "Review of techniques based on artificial neural networks for the electrical characterization of concentrator photovoltaic technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 938-953.
    2. Fernández, Eduardo F. & Talavera, D.L. & Almonacid, Florencia M. & Smestad, Greg P., 2016. "Investigating the impact of weather variables on the energy yield and cost of energy of grid-connected solar concentrator systems," Energy, Elsevier, vol. 106(C), pages 790-801.
    3. Fernández, Eduardo F. & Pérez-Higueras, P. & Almonacid, F. & Ruiz-Arias, J.A. & Rodrigo, P. & Fernandez, J.I. & Luque-Heredia, I., 2015. "Model for estimating the energy yield of a high concentrator photovoltaic system," Energy, Elsevier, vol. 87(C), pages 77-85.
    4. Almonacid, F. & Fernández, E.F. & Mallick, T.K. & Pérez-Higueras, P.J., 2015. "High concentrator photovoltaic module simulation by neuronal networks using spectrally corrected direct normal irradiance and cell temperature," Energy, Elsevier, vol. 84(C), pages 336-343.
    5. Abou-Ziyan, Hosny & Ibrahim, Mohammed & Abdel-Hameed, Hala, 2020. "Performance modeling and analysis of high-concentration multi-junction photovoltaics using advanced hybrid cooling systems," Applied Energy, Elsevier, vol. 269(C).
    6. Rodrigo, P. & Velázquez, Ramiro & Fernández, Eduardo F. & Almonacid, F. & Pérez-Higueras, P.J., 2016. "Analysis of electrical mismatches in high-concentrator photovoltaic power plants with distributed inverter configurations," Energy, Elsevier, vol. 107(C), pages 374-387.
    7. Li, Guiqiang & Xuan, Qingdong & Pei, Gang & Su, Yuehong & Ji, Jie, 2018. "Effect of non-uniform illumination and temperature distribution on concentrating solar cell - A review," Energy, Elsevier, vol. 144(C), pages 1119-1136.
    8. Rodrigo, P.M. & Talavera, D.L. & Fernández, E.F. & Almonacid, F.M. & Pérez-Higueras, P.J., 2019. "Optimum capacity of the inverters in concentrator photovoltaic power plants with emphasis on shading impact," Energy, Elsevier, vol. 187(C).
    9. Manuel Angel Gadeo-Martos & Antonio Jesús Yuste-Delgado & Florencia Almonacid Cruz & Jose-Angel Fernandez-Prieto & Joaquin Canada-Bago, 2019. "Modeling a High Concentrator Photovoltaic Module Using Fuzzy Rule-Based Systems," Energies, MDPI, vol. 12(3), pages 1-22, February.
    10. Almonacid, Florencia & Rodrigo, Pedro & Fernández, Eduardo F., 2016. "Determination of the current–voltage characteristics of concentrator systems by using different adapted conventional techniques," Energy, Elsevier, vol. 101(C), pages 146-160.
    11. Rodrigo, P. & Gutiérrez, S. & Velázquez, Ramiro & Fernández, Eduardo F. & Almonacid, F. & Pérez-Higueras, P.J., 2015. "A methodology for the electrical characterization of shaded high concentrator photovoltaic modules," Energy, Elsevier, vol. 89(C), pages 768-777.
    12. Carlo Renno, 2018. "Experimental and Theoretical Analysis of a Linear Focus CPV/T System for Cogeneration Purposes," Energies, MDPI, vol. 11(11), pages 1-15, October.
    13. Rodrigo, Pedro M. & Velázquez, Ramiro & Fernández, Eduardo F. & Almonacid, Florencia M. & Lay-Ekuakille, Aimé, 2018. "A method for the outdoor thermal characterisation of high-concentrator photovoltaic modules alternative to the IEC 62670-3 standard," Energy, Elsevier, vol. 148(C), pages 159-168.
    14. Pérez-Higueras, Pedro & Ferrer-Rodríguez, Juan P. & Almonacid, Florencia & Fernández, Eduardo F., 2018. "Efficiency and acceptance angle of High Concentrator Photovoltaic modules: Current status and indoor measurements," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 143-153.
    15. Fernández, Eduardo F. & Almonacid, Florencia & Soria-Moya, Alberto & Terrados, Julio, 2015. "Experimental analysis of the spectral factor for quantifying the spectral influence on concentrator photovoltaic systems under real operating conditions," Energy, Elsevier, vol. 90(P2), pages 1878-1886.
    16. Amanlou, Yasaman & Hashjin, Teymour Tavakoli & Ghobadian, Barat & Najafi, G. & Mamat, R., 2016. "A comprehensive review of Uniform Solar Illumination at Low Concentration Photovoltaic (LCPV) Systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1430-1441.
    17. Yaser I. Alamin & Mensah K. Anaty & José Domingo Álvarez Hervás & Khalid Bouziane & Manuel Pérez García & Reda Yaagoubi & María del Mar Castilla & Merouan Belkasmi & Mohammed Aggour, 2020. "Very Short-Term Power Forecasting of High Concentrator Photovoltaic Power Facility by Implementing Artificial Neural Network," Energies, MDPI, vol. 13(13), pages 1-16, July.

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