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Photovoltaic field emulation including dynamic and partial shadow conditions

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  • Di Piazza, Maria Carmela
  • Vitale, Gianpaolo

Abstract

In this paper the development of a new laboratory prototype for the emulation of a photovoltaic (PV) field is presented. The proposed system is based on a DC/DC step-down converter topology and allows to obtain the solar array I-V curves, taking into account the environmental changes in solar irradiance and cell temperature. The DC/DC converter control strategy is deduced by using a comprehensive mathematical model of the PV field whose parameters are obtained from the knowledge of: (a) maximum power point data, measured when the PV plant power converter is running, (b) open circuit voltage and short-circuit current, measured off-line. This approach allows the most accurate representation of the PV source. Computer simulations and experimental results demonstrate that the proposed circuit acts as a highly accurate and efficient laboratory simulator of the photovoltaic array electrical characteristics both in steady state and transient conditions. Partial shading and fluctuating conditions can be reproduced too. Moreover the dynamic behaviour of the proposed laboratory emulator is suitable to its effective connection to power electronic interface to the utility or to load through a DC/DC boost converter.

Suggested Citation

  • Di Piazza, Maria Carmela & Vitale, Gianpaolo, 2010. "Photovoltaic field emulation including dynamic and partial shadow conditions," Applied Energy, Elsevier, vol. 87(3), pages 814-823, March.
    • Handle: RePEc:eee:appene:v:87:y:2010:i:3:p:814-823
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    1. Masa-Bote, Daniel & Caamaño-Martín, Estefanía, 2014. "Methodology for estimating building integrated photovoltaics electricity production under shadowing conditions and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 492-500.
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    8. Wang, Derek D. & Sueyoshi, Toshiyuki, 2017. "Assessment of large commercial rooftop photovoltaic system installations: Evidence from California," Applied Energy, Elsevier, vol. 188(C), pages 45-55.
    9. Sacco, Adriano & Rolle, Lidia & Scaltrito, Luciano & Tresso, Elena & Pirri, Candido Fabrizio, 2013. "Characterization of photovoltaic modules for low-power indoor application," Applied Energy, Elsevier, vol. 102(C), pages 1295-1302.
    10. Ayop, Razman & Tan, Chee Wei, 2017. "A comprehensive review on photovoltaic emulator," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 430-452.
    11. Chen, Cheng-Chuan & Chang, Hong-Chan & Kuo, Cheng-Chien & Lin, Chien-Chin, 2013. "Programmable energy source emulator for photovoltaic panels considering partial shadow effect," Energy, Elsevier, vol. 54(C), pages 174-183.
    12. Ram, J. Prasanth & Manghani, Himanshu & Pillai, Dhanup S. & Babu, T. Sudhakar & Miyatake, Masafumi & Rajasekar, N., 2018. "Analysis on solar PV emulators: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 149-160.
    13. Prieto-Araujo, E. & Olivella-Rosell, P. & Cheah-Mañe, M. & Villafafila-Robles, R. & Gomis-Bellmunt, O., 2015. "Renewable energy emulation concepts for microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 325-345.
    14. Ahmed, Jubaer & Salam, Zainal, 2014. "A Maximum Power Point Tracking (MPPT) for PV system using Cuckoo Search with partial shading capability," Applied Energy, Elsevier, vol. 119(C), pages 118-130.
    15. Kalantar, M. & Mousavi G., S.M., 2010. "Posicast control within feedback structure for a DC-DC single ended primary inductor converter in renewable energy applications," Applied Energy, Elsevier, vol. 87(10), pages 3110-3114, October.
    16. Bonanno, F. & Capizzi, G. & Graditi, G. & Napoli, C. & Tina, G.M., 2012. "A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module," Applied Energy, Elsevier, vol. 97(C), pages 956-961.
    17. Aurel Gontean & Septimiu Lica & Szilard Bularka & Roland Szabo & Dan Lascu, 2017. "A Novel High Accuracy PV Cell Model Including Self Heating and Parameter Variation," Energies, MDPI, vol. 11(1), pages 1-21, December.
    18. Akram M. Abdurraqeeb & Abdullrahman A. Al-Shamma’a & Abdulaziz Alkuhayli & Abdullah M. Noman & Khaled E. Addoweesh, 2022. "RST Digital Robust Control for DC/DC Buck Converter Feeding Constant Power Load," Mathematics, MDPI, vol. 10(10), pages 1-15, May.
    19. Ahmed, Jubaer & Salam, Zainal, 2015. "A critical evaluation on maximum power point tracking methods for partial shading in PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 933-953.
    20. Abu Eldahab, Yasser E. & Saad, Naggar H. & Zekry, Abdalhalim, 2017. "Enhancing the tracking techniques for the global maximum power point under partial shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1173-1183.
    21. Li, Guiqiang & Jin, Yi & Akram, M.W. & Chen, Xiao & Ji, Jie, 2018. "Application of bio-inspired algorithms in maximum power point tracking for PV systems under partial shading conditions – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 840-873.
    22. Papaioannou, Ioulia T. & Purvins, Arturs, 2012. "Mathematical and graphical approach for maximum power point modelling," Applied Energy, Elsevier, vol. 91(1), pages 59-66.
    23. d'Alessandro, Vincenzo & Di Napoli, Fabio & Guerriero, Pierluigi & Daliento, Santolo, 2015. "An automated high-granularity tool for a fast evaluation of the yield of PV plants accounting for shading effects," Renewable Energy, Elsevier, vol. 83(C), pages 294-304.

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