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Direct spectral distribution characterisation using the Average Photon Energy for improved photovoltaic performance modelling

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  • Daxini, Rajiv
  • Sun, Yanyi
  • Wilson, Robin
  • Wu, Yupeng

Abstract

Accurate photovoltaic (PV) performance modelling is crucial for increasing the penetration of PV energy into the grid, analysing returns on investment, and optimising system design prior to investment and construction. Performance models usually correct an output value known at reference conditions for the effects of environmental and system variables at arbitrary conditions. Traditional approaches to correct for the effect of the solar spectrum on performance are based on proxy variables that represent spectral influences, such as absolute air mass (AMa) and clearness index (Kt). A new methodology to account for the spectral influence on PV performance is proposed in this study. The proposed methodology is used to derive a novel spectral correction function based on the average energy of photons contained within the measured solar spectral distribution. The Average Photon Energy (APE) parameter contains information on the combined effects of multiple proxy variables and is not limited by climatic conditions such as cloud cover, as is the case with most traditional models. The APE parameter is shown to be capable of explaining almost 90% of the variability in PV spectral efficiency, compared to around 65% for AMa. The derived APE function is validated and shown to offer an increase of 30% in predictive accuracy for the spectral efficiency compared with the traditional AMa function, and a 17% improvement relative to the AMa-Kt function.

Suggested Citation

  • Daxini, Rajiv & Sun, Yanyi & Wilson, Robin & Wu, Yupeng, 2022. "Direct spectral distribution characterisation using the Average Photon Energy for improved photovoltaic performance modelling," Renewable Energy, Elsevier, vol. 201(P1), pages 1176-1188.
    • Handle: RePEc:eee:renene:v:201:y:2022:i:p1:p:1176-1188
    DOI: 10.1016/j.renene.2022.11.001
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    1. Chantana, Jakapan & Imai, Yurie & Kawano, Yu & Hishikawa, Yoshihiro & Nishioka, Kensuke & Minemoto, Takashi, 2020. "Impact of average photon energy on spectral gain and loss of various-type PV technologies at different locations," Renewable Energy, Elsevier, vol. 145(C), pages 1317-1324.
    2. Meral, Mehmet Emin & Dinçer, Furkan, 2011. "A review of the factors affecting operation and efficiency of photovoltaic based electricity generation systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(5), pages 2176-2184, June.
    3. Ma, Tao & Yang, Hongxing & Lu, Lin, 2014. "Solar photovoltaic system modeling and performance prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 36(C), pages 304-315.
    4. Neves, Luciano A. & Leite, Gabriel C. & MacKenzie, Roderick C.I. & Ferreira, Rafael A.M. & Porto, Matheus P., 2021. "A methodology to simulate solar cells electrical response using optical-electrical mathematical models and real solar spectra," Renewable Energy, Elsevier, vol. 164(C), pages 968-977.
    5. Tsuji, Masaki & Chantana, Jakapan & Nakayama, Koichi & Kawano, Yu & Hishikawa, Yoshihiro & Minemoto, Takashi, 2020. "Utilization of spectral mismatch correction factor for estimation of precise outdoor performance under different average photon energies," Renewable Energy, Elsevier, vol. 157(C), pages 173-181.
    6. Ryan, Lisa & Dillon, Joseph & Monaca, Sarah La & Byrne, Julie & O'Malley, Mark, 2016. "Assessing the system and investor value of utility-scale solar PV," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 506-517.
    7. Piliougine, Michel & Sánchez-Friera, Paula & Petrone, Giovanni & Sánchez-Pacheco, Francisco José & Spagnuolo, Giovanni & Sidrach-de-Cardona, Mariano, 2022. "New model to study the outdoor degradation of thin–film photovoltaic modules," Renewable Energy, Elsevier, vol. 193(C), pages 857-869.
    8. Piliougine, Michel & Elizondo, David & Mora-López, Llanos & Sidrach-de-Cardona, Mariano, 2013. "Multilayer perceptron applied to the estimation of the influence of the solar spectral distribution on thin-film photovoltaic modules," Applied Energy, Elsevier, vol. 112(C), pages 610-617.
    9. Nofuentes, G. & García-Domingo, B. & Muñoz, J.V. & Chenlo, F., 2014. "Analysis of the dependence of the spectral factor of some PV technologies on the solar spectrum distribution," Applied Energy, Elsevier, vol. 113(C), pages 302-309.
    10. Mercaldo, Lucia Vittoria & Addonizio, Maria Luisa & Noce, Marco Della & Veneri, Paola Delli & Scognamiglio, Alessandra & Privato, Carlo, 2009. "Thin film silicon photovoltaics: Architectural perspectives and technological issues," Applied Energy, Elsevier, vol. 86(10), pages 1836-1844, October.
    11. Mellit, A. & Pavan, A. Massi & Lughi, V., 2021. "Deep learning neural networks for short-term photovoltaic power forecasting," Renewable Energy, Elsevier, vol. 172(C), pages 276-288.
    12. Eseye, Abinet Tesfaye & Zhang, Jianhua & Zheng, Dehua, 2018. "Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information," Renewable Energy, Elsevier, vol. 118(C), pages 357-367.
    13. Tzoumanikas, P. & Nikitidou, E. & Bais, A.F. & Kazantzidis, A., 2016. "The effect of clouds on surface solar irradiance, based on data from an all-sky imaging system," Renewable Energy, Elsevier, vol. 95(C), pages 314-322.
    14. Koster, Daniel & Minette, Frank & Braun, Christian & O'Nagy, Oliver, 2019. "Short-term and regionalized photovoltaic power forecasting, enhanced by reference systems, on the example of Luxembourg," Renewable Energy, Elsevier, vol. 132(C), pages 455-470.
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    1. Daxini, Rajiv & Wilson, Robin & Wu, Yupeng, 2023. "Modelling the spectral influence on photovoltaic device performance using the average photon energy and the depth of a water absorption band for improved forecasting," Energy, Elsevier, vol. 284(C).
    2. Daxini, Rajiv & Wu, Yupeng, 2024. "Review of methods to account for the solar spectral influence on photovoltaic device performance," Energy, Elsevier, vol. 286(C).

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