IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v128y2018ipap250-264.html
   My bibliography  Save this article

Generation of the site-adapted clearest-sky year of direct normal irradiance for solar concentrating technologies

Author

Listed:
  • Royo, Alberto
  • García, Ignacio
  • Torres, José Luis

Abstract

Concentrating photovoltaic and thermoelectric solar facilities base their operation on collecting the direct component of solar radiation. Given that the direct beam that reaches the Earth's surface varies greatly in time and space, it is common to assist the bankability of projects with a solar resource assessment. Sun-tracking collector plants are typically examined via a time series analysis of measured weather data and test reference years. Such analysis, which considers the eventual presence of clouds, may be complemented with the use of the synthetic clear-sky year assuring the maximum theoretical availability of direct normal irradiance at a site. This work introduces for the first time the concept of site-adapted clearest-sky year (CSY) and provides a methodology for its generation. Three methods to build the CSY and one algorithm to detect clear-sky moments are proposed.

Suggested Citation

  • Royo, Alberto & García, Ignacio & Torres, José Luis, 2018. "Generation of the site-adapted clearest-sky year of direct normal irradiance for solar concentrating technologies," Renewable Energy, Elsevier, vol. 128(PA), pages 250-264.
    • Handle: RePEc:eee:renene:v:128:y:2018:i:pa:p:250-264
    DOI: 10.1016/j.renene.2018.04.088
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148118305135
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2018.04.088?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. Kambezidis, H.D. & Psiloglou, B.E. & Karagiannis, D. & Dumka, U.C. & Kaskaoutis, D.G., 2016. "Recent improvements of the Meteorological Radiation Model for solar irradiance estimates under all-sky conditions," Renewable Energy, Elsevier, vol. 93(C), pages 142-158.
    2. Kambezidis, H.D. & Psiloglou, B.E. & Karagiannis, D. & Dumka, U.C. & Kaskaoutis, D.G., 2017. "Meteorological Radiation Model (MRM v6.1): Improvements in diffuse radiation estimates and a new approach for implementation of cloud products," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 616-637.
    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. Alonso-Montesinos, J. & Polo, Jesús & Ballestrín, Jesús & Batlles, F.J. & Portillo, C., 2019. "Impact of DNI forecasting on CSP tower plant power production," Renewable Energy, Elsevier, vol. 138(C), pages 368-377.
    2. Vasallo, Manuel Jesús & Cojocaru, Emilian Gelu & Gegúndez, Manuel Emilio & Marín, Diego, 2021. "Application of data-based solar field models to optimal generation scheduling in concentrating solar power plants," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 1130-1149.

    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. Feiyan Chen & Zhigao Zhou & Aiwen Lin & Jiqiang Niu & Wenmin Qin & Zhong Yang, 2019. "Evaluation of Direct Horizontal Irradiance in China Using a Physically-Based Model and Machine Learning Methods," Energies, MDPI, vol. 12(1), pages 1-19, January.
    2. 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.
    3. Su, Gang & Zhang, Shuangyang & Hu, Mengru & Yao, Wanxiang & Li, Ziwei & Xi, Yue, 2022. "The modified layer-by-layer weakening solar radiation models based on relative humidity and air quality index," Energy, Elsevier, vol. 239(PE).
    4. Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Ma, Xin & Bai, Hua, 2019. "Evaluation and development of empirical models for estimating daily and monthly mean daily diffuse horizontal solar radiation for different climatic regions of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 168-186.
    5. Ruiz-Arias, José A., 2021. "Aerosol transmittance for clear-sky solar irradiance models: Review and validation of an accurate universal parameterization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    6. Tingting Zhu & Yiren Guo & Cong Wang & Chao Ni, 2020. "Inter-Hour Forecast of Solar Radiation Based on the Structural Equation Model and Ensemble Model," Energies, MDPI, vol. 13(17), pages 1-15, September.
    7. Torres, José Luis & García, Ignacio, 2021. "Analytical expressions for estimating sky diffuse irradiance and illuminance on tilted planes for the CIE Standard General Skies," Renewable Energy, Elsevier, vol. 174(C), pages 320-335.
    8. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    9. Feng, Lan & Lin, Aiwen & Wang, Lunche & Qin, Wenmin & Gong, Wei, 2018. "Evaluation of sunshine-based models for predicting diffuse solar radiation in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 168-182.
    10. Sun, Xixi & Bright, Jamie M. & Gueymard, Christian A. & Bai, Xinyu & Acord, Brendan & Wang, Peng, 2021. "Worldwide performance assessment of 95 direct and diffuse clear-sky irradiance models using principal component analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    11. Marzo, A. & Trigo-Gonzalez, M. & Alonso-Montesinos, J. & Martínez-Durbán, M. & López, G. & Ferrada, P. & Fuentealba, E. & Cortés, M. & Batlles, F.J., 2017. "Daily global solar radiation estimation in desert areas using daily extreme temperatures and extraterrestrial radiation," Renewable Energy, Elsevier, vol. 113(C), pages 303-311.
    12. Moldovan, Camelia Liliana & Păltănea, Radu & Visa, Ion, 2020. "Improvement of clear sky models for direct solar irradiance considering turbidity factor variable during the day," Renewable Energy, Elsevier, vol. 161(C), pages 559-569.
    13. Chang, Kai & Zhang, Qingyuan, 2019. "Improvement of the hourly global solar model and solar radiation for air-conditioning design in China," Renewable Energy, Elsevier, vol. 138(C), pages 1232-1238.
    14. Bey, M. & Hamidat, A. & Benyoucef, B. & Nacer, T., 2016. "Viability study of the use of grid connected photovoltaic system in agriculture: Case of Algerian dairy farms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 333-345.
    15. Qin, Wenmin & Wang, Lunche & Lin, Aiwen & Zhang, Ming & Xia, Xiangao & Hu, Bo & Niu, Zigeng, 2018. "Comparison of deterministic and data-driven models for solar radiation estimation in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 579-594.
    16. Zhou, Zhigao & Wang, Lunche & Lin, Aiwen & Zhang, Ming & Niu, Zigeng, 2018. "Innovative trend analysis of solar radiation in China during 1962–2015," Renewable Energy, Elsevier, vol. 119(C), pages 675-689.
    17. Wang, Lunche & Lu, Yunbo & Zou, Ling & Feng, Lan & Wei, Jing & Qin, Wenmin & Niu, Zigeng, 2019. "Prediction of diffuse solar radiation based on multiple variables in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 151-216.
    18. Ruiz-Arias, José A., 2022. "Spectral integration of clear-sky atmospheric transmittance: Review and worldwide performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    19. Paredes, Paula & Trigo, Isabel & de Bruin, Henk & Simões, Nuno & Pereira, Luis S., 2021. "Daily grass reference evapotranspiration with Meteosat Second Generation shortwave radiation and reference ET products," Agricultural Water Management, Elsevier, vol. 248(C).
    20. Lou, Siwei & Huang, Yu & Li, Danny H.W. & Xia, Dawei & Zhou, Xiaoqing & Zhao, Yang, 2020. "A novel method for fast sky conditions identification from global solar radiation measurements," Renewable Energy, Elsevier, vol. 161(C), pages 77-90.

    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:128:y:2018:i:pa:p:250-264. 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.