IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v38y2012i1p31-39.html
   My bibliography  Save this article

Very high fluxes for concentrating photovoltaics: Considerations from simple experiments and modeling

Author

Listed:
  • Vossier, Alexis
  • Chemisana, Daniel
  • Flamant, Gilles
  • Dollet, Alain

Abstract

Among commercial photovoltaic technologies, concentrating photovoltaics (CPV) has the highest solar energy-to-electricity conversion efficiency; however, CPV electricity costs are still higher than thin film or silicon PV costs, mainly because of the additional components needed (optics, tracker) and the very high price of III–V multi-junction solar cells. To date, most commercial CPV systems operated at maximum concentrations of about 500 suns; but even at this concentration level, multi-junction cells retain a significant contribution to the total cost of the system. Further increasing the concentration ratio seems an interesting route for decreasing CPV electricity costs since the efficiency of concentrator cells theoretically increases with increasing illumination levels whilst the part of the solar cells in the total system cost decreases.

Suggested Citation

  • Vossier, Alexis & Chemisana, Daniel & Flamant, Gilles & Dollet, Alain, 2012. "Very high fluxes for concentrating photovoltaics: Considerations from simple experiments and modeling," Renewable Energy, Elsevier, vol. 38(1), pages 31-39.
    • Handle: RePEc:eee:renene:v:38:y:2012:i:1:p:31-39
    DOI: 10.1016/j.renene.2011.06.036
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148111003739
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2011.06.036?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. Unknown, 2006. "Front Materials," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 21(3), pages 1-4.
    2. Unknown, 2006. "Front Materials," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 21(1), pages 1-4.
    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. Gulbakhar Dosymbetova & Saad Mekhilef & Ahmet Saymbetov & Madiyar Nurgaliyev & Ainur Kapparova & Sergey Manakov & Sayat Orynbassar & Nurzhigit Kuttybay & Yeldos Svanbayev & Isroil Yuldoshev & Batyrbek, 2022. "Modeling and Simulation of Silicon Solar Cells under Low Concentration Conditions," Energies, MDPI, vol. 15(24), pages 1-17, December.
    2. Siaw, Fei-Lu & Chong, Kok-Keong & Wong, Chee-Woon, 2014. "A comprehensive study of dense-array concentrator photovoltaic system using non-imaging planar concentrator," Renewable Energy, Elsevier, vol. 62(C), pages 542-555.
    3. Zhang, J.J. & Qu, Z.G. & Zhang, J.F., 2022. "Diode model of nonuniform irradiation treatment to predict multiscale solar-electrical conversion for the concentrating plasmonic photovoltaic system," Applied Energy, Elsevier, vol. 324(C).
    4. Chemisana, D. & Lamnatou, Chr., 2014. "Photovoltaic-green roofs: An experimental evaluation of system performance," Applied Energy, Elsevier, vol. 119(C), pages 246-256.
    5. 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.

    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. Yan Hu & Lijia Xu & Peng Huang & Xiong Luo & Peng Wang & Zhiliang Kang, 2021. "Reliable Identification of Oolong Tea Species: Nondestructive Testing Classification Based on Fluorescence Hyperspectral Technology and Machine Learning," Agriculture, MDPI, vol. 11(11), pages 1-19, November.
    2. Sigal Blumenfeld, 2013. "Location of hazardous materials plants in Israel," Journal of Risk Research, Taylor & Francis Journals, vol. 16(7), pages 921-935, August.

    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:renene:v:38:y:2012:i:1:p:31-39. 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/renewable-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.