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A New Methodology for Building-Up a Robust Model for Heliostat Field Flux Characterization

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  • Nicolás C. Cruz

    (Department of Informatics, Agrifood Campus of International Excellence-Center for Research in Solar Energy (ceiA3-CIESOL), University of Almería, Sacramento Road s/n, La Cañada, 04120 Almería, Spain)

  • José D. Álvarez

    (Department of Informatics, Agrifood Campus of International Excellence-Center for Research in Solar Energy (ceiA3-CIESOL), University of Almería, Sacramento Road s/n, La Cañada, 04120 Almería, Spain)

  • Juana L. Redondo

    (Department of Informatics, Agrifood Campus of International Excellence-Center for Research in Solar Energy (ceiA3-CIESOL), University of Almería, Sacramento Road s/n, La Cañada, 04120 Almería, Spain)

  • Jesús Fernández-Reche

    (Solar Platform of Almería-Center for Research in Energy, Environment and Technology (CIEMAT), P.O. Box 22, Tabernas, Almería E-04200, Spain)

  • Manuel Berenguel

    (Department of Informatics, Agrifood Campus of International Excellence-Center for Research in Solar Energy (ceiA3-CIESOL), University of Almería, Sacramento Road s/n, La Cañada, 04120 Almería, Spain)

  • Rafael Monterreal

    (Solar Platform of Almería-Center for Research in Energy, Environment and Technology (CIEMAT), P.O. Box 22, Tabernas, Almería E-04200, Spain)

  • Pilar M. Ortigosa

    (Department of Informatics, Agrifood Campus of International Excellence-Center for Research in Solar Energy (ceiA3-CIESOL), University of Almería, Sacramento Road s/n, La Cañada, 04120 Almería, Spain)

Abstract

The heliostat field of solar central receiver systems (SCRS) is formed by hundreds, even thousands, of working heliostats. Their adequate configuration and control define a currently active research line. For instance, automatic aiming methodologies of existing heliostat fields are being widely studied. In general, control techniques require a model of the system to be controlled in order to obtain an estimation of its states. However, this kind of information may not be available or may be hard to obtain for every plant to be studied. In this work, an innovative methodology for data-based analytical heliostat field characterization is proposed and described. It formalizes the way in which the behavior of a whole field can be derived from the study of its more descriptive parts. By successfully applying this procedure, the instantaneous behavior of a field could be expressed by a reduced set of expressions that can be seen as a field descriptor. It is not intended to replace real experimentation but to enhance researchers’ autonomy to build their own reliable and portable synthetic datasets at preliminary stages of their work. The methodology proposed in this paper is successfully applied to a virtual field. Only 30 heliostats out of 541 were studied to characterize the whole field. For the validation set, the average difference in power between the flux maps directly fitted from the measured information and the estimated ones is only of 0.67% (just 0.10946 kW/m 2 of root-mean-square error, on average, between them). According to these results, a consistent field descriptor can be built by applying the proposed methodology, which is hence ready for use.

Suggested Citation

  • Nicolás C. Cruz & José D. Álvarez & Juana L. Redondo & Jesús Fernández-Reche & Manuel Berenguel & Rafael Monterreal & Pilar M. Ortigosa, 2017. "A New Methodology for Building-Up a Robust Model for Heliostat Field Flux Characterization," Energies, MDPI, vol. 10(5), pages 1-17, May.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:5:p:730-:d:99281
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    References listed on IDEAS

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    1. Khan, Jibran & Arsalan, Mudassar H., 2016. "Solar power technologies for sustainable electricity generation – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 414-425.
    2. Spiros Alexopoulos & Bernhard Hoffschmidt, 2017. "Advances in solar tower technology," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 6(1), January.
    3. Zhang, Maolong & Yang, Lijun & Xu, Chao & Du, Xiaoze, 2016. "An efficient code to optimize the heliostat field and comparisons between the biomimetic spiral and staggered layout," Renewable Energy, Elsevier, vol. 87(P1), pages 720-730.
    4. Behar, Omar & Khellaf, Abdallah & Mohammedi, Kamal, 2013. "A review of studies on central receiver solar thermal power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 12-39.
    5. Siala, F.M.F & Elayeb, M.E, 2001. "Mathematical formulation of a graphical method for a no-blocking heliostat field layout," Renewable Energy, Elsevier, vol. 23(1), pages 77-92.
    6. Zhang, H.L. & Baeyens, J. & Degrève, J. & Cacères, G., 2013. "Concentrated solar power plants: Review and design methodology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 466-481.
    7. Ellabban, Omar & Abu-Rub, Haitham & Blaabjerg, Frede, 2014. "Renewable energy resources: Current status, future prospects and their enabling technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 748-764.
    8. Piroozmand, Pasha & Boroushaki, Mehrdad, 2016. "A computational method for optimal design of the multi-tower heliostat field considering heliostats interactions," Energy, Elsevier, vol. 106(C), pages 240-252.
    9. Collado, Francisco J. & Guallar, Jesús, 2013. "A review of optimized design layouts for solar power tower plants with campo code," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 142-154.
    10. Collado, Francisco J. & Guallar, Jesús, 2012. "Campo: Generation of regular heliostat fields," Renewable Energy, Elsevier, vol. 46(C), pages 49-59.
    11. Avila-Marin, Antonio L. & Fernandez-Reche, Jesus & Tellez, Felix M., 2013. "Evaluation of the potential of central receiver solar power plants: Configuration, optimization and trends," Applied Energy, Elsevier, vol. 112(C), pages 274-288.
    12. Besarati, Saeb M. & Yogi Goswami, D., 2014. "A computationally efficient method for the design of the heliostat field for solar power tower plant," Renewable Energy, Elsevier, vol. 69(C), pages 226-232.
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