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Models for the electrical characterization of high concentration photovoltaic cells and modules: A review

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

Abstract

High concentration photovoltaic technology promises the large-scale generation of clean-renewable energy with competitive costs. Like any other systems for electricity generation, it is important to know the electrical characteristics of the system. However, while there is a wide experience in modeling the behavior of traditional photovoltaic systems, not every model for flat-plate solar cells or modules is directly applicable to high concentration photovoltaic cells or modules because of the special features of these devices (use of multijunction cells, use of optics for high concentration, etc.). So, in recent years, the scientific community has devoted considerable efforts in developing models that reproduce the electrical behavior of high concentration cells and modules. These models allow calculating the main electrical parameters of the device from its operating conditions (irradiance, cell temperature, spectral distribution of the radiation, etc.). In this paper, a comprehensive review of existing models for the electrical characterization of high concentration photovoltaic cells and modules is presented with the aim of helping the photovoltaic professionals and researchers in the design, monitoring and energy prediction tasks.

Suggested Citation

  • Rodrigo, P. & Fernández, E.F. & Almonacid, F. & Pérez-Higueras, P.J., 2013. "Models for the electrical characterization of high concentration photovoltaic cells and modules: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 752-760.
    • Handle: RePEc:eee:rensus:v:26:y:2013:i:c:p:752-760
    DOI: 10.1016/j.rser.2013.06.019
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    1. Almonacid, F. & Pérez-Higueras, P. & Rodrigo, P. & Hontoria, L., 2013. "Generation of ambient temperature hourly time series for some Spanish locations by artificial neural networks," Renewable Energy, Elsevier, vol. 51(C), pages 285-291.
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    5. Almonacid, F. & Fernández, Eduardo F. & Rodrigo, P. & Pérez-Higueras, P.J. & Rus-Casas, C., 2013. "Estimating the maximum power of a High Concentrator Photovoltaic (HCPV) module using an Artificial Neural Network," Energy, Elsevier, vol. 53(C), pages 165-172.
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    Cited by:

    1. Peñaranda Chenche, Luz Elena & Hernandez Mendoza, Oscar Saul & Bandarra Filho, Enio Pedone, 2018. "Comparison of four methods for parameter estimation of mono- and multi-junction photovoltaic devices using experimental data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2823-2838.
    2. 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.
    3. 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.
    4. 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.
    5. Andrea Salimbeni & Mario Porru & Luca Massidda & Alfonso Damiano, 2020. "A Forecasting-Based Control Algorithm for Improving Energy Managment in High Concentrator Photovoltaic Power Plant Integrated with Energy Storage Systems," Energies, MDPI, vol. 13(18), pages 1-20, September.
    6. 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.
    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. Wolfgang Albrecht & Martin Steinrücke, 2020. "Continuous-time scheduling of production, distribution and sales in photovoltaic supply chains with declining prices," Flexible Services and Manufacturing Journal, Springer, vol. 32(3), pages 629-667, September.
    12. 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.
    13. Fernández, Eduardo F. & Almonacid, Florencia & Garcia-Loureiro, Antonio J., 2015. "Multi-junction solar cells electrical characterization by neuronal networks under different irradiance, spectrum and cell temperature," Energy, Elsevier, vol. 90(P1), pages 846-856.
    14. 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.
    15. Sharaf, Omar Z. & Orhan, Mehmet F., 2015. "Concentrated photovoltaic thermal (CPVT) solar collector systems: Part I – Fundamentals, design considerations and current technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1500-1565.
    16. 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.
    17. Yadav, Pankaj & Tripathi, Brijesh & Rathod, Siddharth & Kumar, Manoj, 2013. "Real-time analysis of low-concentration photovoltaic systems: A review towards development of sustainable energy technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 812-823.
    18. Benhammane, Mousaab & Notton, Gilles & Pichenot, Grégoire & Voarino, Philippe & Ouvrard, David, 2021. "Overview of electrical power models for concentrated photovoltaic systems and development of a new operational model with easily accessible inputs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    19. Fernández, Eduardo F. & Almonacid, Florencia, 2014. "Spectrally corrected direct normal irradiance based on artificial neural networks for high concentrator photovoltaic applications," Energy, Elsevier, vol. 74(C), pages 941-949.
    20. 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.
    21. Humada, Ali M. & Hojabri, Mojgan & Mekhilef, Saad & Hamada, Hussein M., 2016. "Solar cell parameters extraction based on single and double-diode models: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 494-509.

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