IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v8y2015i6p5159-5181d50569.html
   My bibliography  Save this article

Estimating PV Module Performance over Large Geographical Regions: The Role of Irradiance, Air Temperature, Wind Speed and Solar Spectrum

Author

Listed:
  • Thomas Huld

    (European Commission, Joint Research Centre, Via Fermi 2749, Ispra I-21027, Italy)

  • Ana M. Gracia Amillo

    (European Commission, Joint Research Centre, Via Fermi 2749, Ispra I-21027, Italy)

Abstract

We present a study of how photovoltaic (PV) module performance varies on continental scale. Mathematical models have been used to take into account shallow-angle reflectivity, spectral sensitivity, dependence of module efficiency on irradiance and module temperature as well as how the module temperature depends on irradiance, ambient temperature and wind speed. Spectrally resolved irradiance data retrieved from satellite images are combined with temperature and wind speed data from global computational weather forecast data to produce maps of PV performance for Eurasia and Africa. Results show that module reflectivity causes a fairly small drop of 2\%–4\% in PV performance. Spectral effects may modify the performance by up to \(\pm 6\)\%, depending on location and module type. The strongest effect is seen in the dependence on irradiance and module temperature, which may range from \(-\)20\% to +5\% at different locations.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:6:p:5159-5181:d:50569
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/8/6/5159/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/8/6/5159/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. Thomas Huld & Irene Pinedo Pascua, 2015. "Spatial Downscaling of 2-Meter Air Temperature Using Operational Forecast Data," Energies, MDPI, vol. 8(4), pages 1-31, March.
    Full references (including those not matched with items on IDEAS)

    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 & Wu, Yupeng, 2024. "Review of methods to account for the solar spectral influence on photovoltaic device performance," Energy, Elsevier, vol. 286(C).
    2. 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).
    3. 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.
    4. 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.
    5. 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.
    6. 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).
    7. 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.
    8. 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.
    9. Psiloglou, B.E. & Kambezidis, H.D. & Kaskaoutis, D.G. & Karagiannis, D. & Polo, J.M., 2020. "Comparison between MRM simulations, CAMS and PVGIS databases with measured solar radiation components at the Methoni station, Greece," Renewable Energy, Elsevier, vol. 146(C), pages 1372-1391.
    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. Thopil, George Alex & Sachse, Christiaan Eddie & Lalk, Jörg & Thopil, Miriam Sara, 2020. "Techno-economic performance comparison of crystalline and thin film PV panels under varying meteorological conditions: A high solar resource southern hemisphere case," Applied Energy, Elsevier, vol. 275(C).
    12. Liukkonen, M. & Hiltunen, T., 2014. "Adaptive monitoring of emissions in energy boilers using self-organizing maps: An application to a biomass-fired CFB (circulating fluidized bed)," Energy, Elsevier, vol. 73(C), pages 443-452.
    13. 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.
    14. 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.
    15. 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).
    16. Ren, Tao & Modest, Michael F. & Fateev, Alexander & Sutton, Gavin & Zhao, Weijie & Rusu, Florin, 2019. "Machine learning applied to retrieval of temperature and concentration distributions from infrared emission measurements," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    17. Yilmaz, Saban & Ozcalik, Hasan Riza & Kesler, Selami & Dincer, Furkan & Yelmen, Bekir, 2015. "The analysis of different PV power systems for the determination of optimal PV panels and system installation—A case study in Kahramanmaras, Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1015-1024.
    18. García-Domingo, B. & Piliougine, M. & Elizondo, D. & Aguilera, J., 2015. "CPV module electric characterisation by artificial neural networks," Renewable Energy, Elsevier, vol. 78(C), pages 173-181.
    19. Polo, J. & Fernandez-Neira, W.G. & Alonso-García, M.C., 2017. "On the use of reference modules as irradiance sensor for monitoring and modelling rooftop PV systems," Renewable Energy, Elsevier, vol. 106(C), pages 186-191.
    20. 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.

    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:gam:jeners:v:8:y:2015:i:6:p:5159-5181:d:50569. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.