IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v286y2024ics0360544223028554.html
   My bibliography  Save this article

Review of methods to account for the solar spectral influence on photovoltaic device performance

Author

Listed:
  • Daxini, Rajiv
  • Wu, Yupeng

Abstract

The accuracy of photovoltaic (PV) performance forecasts is essential for improving grid penetration, fault detection, and financing of new installations. Failing to account for the spectral influence on PV performance can lead to weekly errors of up to 14% even for relatively stable technologies such as polycrystalline silicon. There exist a range models, known as spectral correction functions (SCFs), to account for the spectral influence on PV performance forecasts. These SCFs use different methods to characterise both the shift in PV performance due to the spectrum, and the solar spectrum itself. This review analyses the merits and limitations of seven commonly used spectral characterisation indices — five proxy variables (air mass, clearness index, precipitable water, aerosol, diffuse solar radiation ratio) and two variables extracted from the spectral distribution (average photon energy, depth of a water absorption band). The same analytical approach is adopted to review a further four indices (mismatch factor and its variants, (weighted) useful fraction, normalised short-circuit current) that are commonly used to characterise the variation in PV performance due to the solar spectrum. A review of ten SCFs that are based on these indices is undertaken to analyse the current state of the art of spectral correction modelling. The results of the review show that whereas some proxy-variable methods offer a simple and convenient way to account for the spectral influence in PV performance forecasts, they are surpassed in terms of accuracy by SCFs based on parameters derived directly from the spectrum, such as the average photon energy and the depth of spectral absorption bands. A decision-making framework is proposed to guide PV performance modellers in their choice of spectral correction model. The framework considers system specifications, climate, data availability, etc. The results of this work may be applied in, for example, software packages for PV performance forecasting to enable more accurate case-specific power forecasts. In future work, a standardised comparison of all SCFs and their respective indices is necessary to quantify the differences between a wider range of models than is currently available in the literature and substantiate the proposed framework.

Suggested Citation

  • Daxini, Rajiv & Wu, Yupeng, 2024. "Review of methods to account for the solar spectral influence on photovoltaic device performance," Energy, Elsevier, vol. 286(C).
    • Handle: RePEc:eee:energy:v:286:y:2024:i:c:s0360544223028554
    DOI: 10.1016/j.energy.2023.129461
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223028554
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.129461?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Eke, R. & Betts, T.R. & Gottschalg, R.,, 2017. "Spectral irradiance effects on the outdoor performance of photovoltaic modules," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 429-434.
    2. 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).
    3. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    4. Ana Maria Gracia Amillo & Thomas Huld & Paraskevi Vourlioti & Richard Müller & Matthew Norton, 2015. "Application of Satellite-Based Spectrally-Resolved Solar Radiation Data to PV Performance Studies," Energies, MDPI, vol. 8(5), pages 1-34, April.
    5. Almonacid, Florencia & Fernández, Eduardo F. & Almonacid-Cruz, Bernardo & Rodrigo, Pedro M., 2018. "Spectral-matching-ratio modelling based on ANNs and atmospheric parameters for the electrical characterization of multi-junction concentrator PV systems," Energy, Elsevier, vol. 156(C), pages 409-417.
    6. Conde, Luis A. & Angulo, José R. & Sevillano-Bendezú, Miguel Á. & Nofuentes, Gustavo & Töfflinger, Jan A. & de la Casa, Juan, 2021. "Spectral effects on the energy yield of various photovoltaic technologies in Lima (Peru)," Energy, Elsevier, vol. 223(C).
    7. Dixon, Robert K. & McGowan, Elizabeth & Onysko, Ganna & Scheer, Richard M., 2010. "US energy conservation and efficiency policies: Challenges and opportunities," Energy Policy, Elsevier, vol. 38(11), pages 6398-6408, November.
    8. Chantana, Jakapan & Mano, Hiroyuki & Horio, Yuhei & Hishikawa, Yoshihiro & Minemoto, Takashi, 2017. "Spectral mismatch correction factor indicated by average photon energy for precise outdoor performance measurements of different-type photovoltaic modules," Renewable Energy, Elsevier, vol. 114(PB), pages 567-573.
    9. Alonso-Abella, M. & Chenlo, F. & Nofuentes, G. & Torres-Ramírez, M., 2014. "Analysis of spectral effects on the energy yield of different PV (photovoltaic) technologies: The case of four specific sites," Energy, Elsevier, vol. 67(C), pages 435-443.
    10. Seera, Manjeevan & Tan, Choo Jun & Chong, Kok-Keong & Lim, Chee Peng, 2021. "Performance analyses of various commercial photovoltaic modules based on local spectral irradiances in Malaysia using genetic algorithm," Energy, Elsevier, vol. 223(C).
    11. Kinsey, Geoffrey S. & Riedel-Lyngskær, Nicholas C. & Miguel, Alonso-Abella & Boyd, Matthew & Braga, Marília & Shou, Chunhui & Cordero, Raul R. & Duck, Benjamin C. & Fell, Christopher J. & Feron, Sarah, 2022. "Impact of measured spectrum variation on solar photovoltaic efficiencies worldwide," Renewable Energy, Elsevier, vol. 196(C), pages 995-1016.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. Michael L. Roderick & Graham D. Farquhar, 2012. "Hazy, cool and well fed?," Nature Climate Change, Nature, vol. 2(2), pages 76-77, February.
    17. Gómez-Amo, J.L. & Freile-Aranda, M.D. & Camarasa, J. & Estellés, V. & Utrillas, M.P. & Martínez-Lozano, J.A., 2019. "Empirical estimates of the radiative impact of an unusually extreme dust and wildfire episode on the performance of a photovoltaic plant in Western Mediterranean," Applied Energy, Elsevier, vol. 235(C), pages 1226-1234.
    18. Barbieri, Florian & Rajakaruna, Sumedha & Ghosh, Arindam, 2017. "Very short-term photovoltaic power forecasting with cloud modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 242-263.
    19. Peng, Jinqing & Lu, Lin & Wang, Meng, 2019. "A new model to evaluate solar spectrum impacts on the short circuit current of solar photovoltaic modules," Energy, Elsevier, vol. 169(C), pages 29-37.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hong, Wenpeng & Mu, Yuhan & Lan, Jingrui & Jin, Xu & Wang, Xinzhi & Li, Haoran, 2024. "Improving vapor condensation via copper foam in capillary-fed photovoltaic membrane distillation," Energy, Elsevier, vol. 296(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Nofuentes, Gustavo & de la Casa, Juan & Solís-Alemán, Ernesto M. & Fernández, Eduardo F., 2017. "Spectral impact on PV performance in mid-latitude sunny inland sites: Experimental vs. modelled results," Energy, Elsevier, vol. 141(C), pages 1857-1868.
    3. Kinsey, Geoffrey S. & Riedel-Lyngskær, Nicholas C. & Miguel, Alonso-Abella & Boyd, Matthew & Braga, Marília & Shou, Chunhui & Cordero, Raul R. & Duck, Benjamin C. & Fell, Christopher J. & Feron, Sarah, 2022. "Impact of measured spectrum variation on solar photovoltaic efficiencies worldwide," Renewable Energy, Elsevier, vol. 196(C), pages 995-1016.
    4. Wang, Meng & Peng, Jinqing & Luo, Yimo & Shen, Zhicheng & Yang, Hongxing, 2021. "Comparison of different simplistic prediction models for forecasting PV power output: Assessment with experimental measurements," Energy, Elsevier, vol. 224(C).
    5. Thomas Huld & Ana M. Gracia Amillo, 2015. "Estimating PV Module Performance over Large Geographical Regions: The Role of Irradiance, Air Temperature, Wind Speed and Solar Spectrum," Energies, MDPI, vol. 8(6), pages 1-23, June.
    6. Sharma, Manoj Kumar & Bhattacharya, Jishnu, 2022. "Dependence of spectral factor on angle of incidence for monocrystalline silicon based photovoltaic solar panel," Renewable Energy, Elsevier, vol. 184(C), pages 820-829.
    7. Torres-Ramírez, M. & Elizondo, D. & García-Domingo, B. & Nofuentes, G. & Talavera, D.L., 2015. "Modelling the spectral irradiance distribution in sunny inland locations using an ANN-based methodology," Energy, Elsevier, vol. 86(C), pages 323-334.
    8. Polo, Jesús & Alonso-Abella, Miguel & Martín-Chivelet, Nuria & Alonso-Montesinos, Joaquín & López, Gabriel & Marzo, Aitor & Nofuentes, Gustavo & Vela-Barrionuevo, Nieves, 2020. "Typical Meteorological Year methodologies applied to solar spectral irradiance for PV applications," Energy, Elsevier, vol. 190(C).
    9. Neves, Guilherme & Vilela, Waldeir & Pereira, Enio & Yamasoe, Marcia & Nofuentes, Gustavo, 2021. "Spectral impact on PV in low-latitude sites: The case of southeastern Brazil," Renewable Energy, Elsevier, vol. 164(C), pages 1306-1319.
    10. Torres-Ramírez, M. & Nofuentes, G. & Silva, J.P. & Silvestre, S. & Muñoz, J.V., 2014. "Study on analytical modelling approaches to the performance of thin film PV modules in sunny inland climates," Energy, Elsevier, vol. 73(C), pages 731-740.
    11. Senturk, Ali, 2020. "Investigation of datasheet provided temperature coefficients of photovoltaic modules under various sky profiles at the field by applying a new validation procedure," Renewable Energy, Elsevier, vol. 152(C), pages 644-652.
    12. Wang, Shuhao & Peng, Jinqing & Wang, Meng & Xue, Peng & Luo, Yimo & Ma, Tao & Zhao, Yifan, 2023. "Evaluation of the energy conversion performance of different photovoltaic materials with measured solar spectral irradiance," Renewable Energy, Elsevier, vol. 219(P1).
    13. Espinoza, R. & Muñoz-Cerón, E. & Aguilera, J. & de la Casa, J., 2019. "Feasibility evaluation of residential photovoltaic self-consumption projects in Peru," Renewable Energy, Elsevier, vol. 136(C), pages 414-427.
    14. 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.
    15. Nguyen, Thi Ngoc & Müsgens, Felix, 2022. "What drives the accuracy of PV output forecasts?," Applied Energy, Elsevier, vol. 323(C).
    16. Kumar, Manish & Chandel, S.S. & Kumar, Arun, 2020. "Performance analysis of a 10 MWp utility scale grid-connected canal-top photovoltaic power plant under Indian climatic conditions," Energy, Elsevier, vol. 204(C).
    17. Zhang, Yanyun & Xue, Peng & Zhao, Yifan & Zhang, Qianqian & Bai, Gongxun & Peng, Jinqing & Li, Bojia, 2024. "Spectra measurement and clustering analysis of global horizontal irradiance for solar energy application," Renewable Energy, Elsevier, vol. 222(C).
    18. Ana Maria Gracia Amillo & Thomas Huld & Paraskevi Vourlioti & Richard Müller & Matthew Norton, 2015. "Application of Satellite-Based Spectrally-Resolved Solar Radiation Data to PV Performance Studies," Energies, MDPI, vol. 8(5), pages 1-34, April.
    19. Sylvain Cros & Jordi Badosa & André Szantaï & Martial Haeffelin, 2020. "Reliability Predictors for Solar Irradiance Satellite-Based Forecast," Energies, MDPI, vol. 13(21), pages 1-21, October.
    20. Nie, Yuhao & Li, Xiatong & Paletta, Quentin & Aragon, Max & Scott, Andea & Brandt, Adam, 2024. "Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:286:y:2024:i:c:s0360544223028554. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.