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Simulation of a CSP Solar Steam Generator, Using Machine Learning

Author

Listed:
  • Adrian Gonzalez Gonzalez

    (TSK, 33203 Gijón, Spain)

  • Jose Valeriano Alvarez Cabal

    (Department of Mines Explotation and Prospecting, Project Engineering Area, University of Oviedo, 33004 Oviedo, Spain)

  • Miguel Angel Vigil Berrocal

    (Department of Mines Explotation and Prospecting, Project Engineering Area, University of Oviedo, 33004 Oviedo, Spain)

  • Rogelio Peón Menéndez

    (TSK, 33203 Gijón, Spain)

  • Adrian Riesgo Fernández

    (TSK, 33203 Gijón, Spain)

Abstract

Developing an accurate concentrated solar power (CSP) performance model requires significant effort and time. The power block (PB) is the most complex system, and its modeling is clearly the most complicated and time-demanding part. Nonetheless, PB layouts are quite similar throughout CSP plants, meaning that there are enough historical process data available from commercial plants to use machine learning techniques. These algorithms allowed the development of a very accurate black-box PB model in a very short amount of time. This PB model could be easily integrated as a block into the PM. The machine learning technique selected was SVR (support vector regression). The PB model was trained using a complete year of data from a commercial CSP plant situated in southern Spain. With a very limited set of inputs, the PB model results were very accurate, according to their validation against a new complete year of data. The model not only fit well on an aggregate basis, but also in the transients between operation modes. To validate applicability, the same model methodology is used with a data from a very different CSP Plant, located in the MENA region and with more than double nominal electric power, obtaining an excellent fitting in the validation.

Suggested Citation

  • Adrian Gonzalez Gonzalez & Jose Valeriano Alvarez Cabal & Miguel Angel Vigil Berrocal & Rogelio Peón Menéndez & Adrian Riesgo Fernández, 2021. "Simulation of a CSP Solar Steam Generator, Using Machine Learning," Energies, MDPI, vol. 14(12), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3613-:d:576794
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    References listed on IDEAS

    as
    1. Siva Reddy, V. & Kaushik, S.C. & Ranjan, K.R. & Tyagi, S.K., 2013. "State-of-the-art of solar thermal power plants—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 258-273.
    2. Zhang, Yabei & Smith, Steven J. & Kyle, G. Page & Stackhouse Jr., Paul W., 2010. "Modeling the potential for thermal concentrating solar power technologies," Energy Policy, Elsevier, vol. 38(12), pages 7884-7897, December.
    3. Adrian Gonzalez Gonzalez & J. Valeriano Alvarez Cabal & Vicente Rodríguez Montequin & Joaquín Villanueva Balsera & Rogelio Peón Menéndez, 2020. "CSP Quasi-Dynamic Performance Model Development for All Project Life Cycle Stages and Considering Operation Modes. Validation Using One Year Data," Energies, MDPI, vol. 14(1), pages 1-22, December.
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    Cited by:

    1. Gul, Eid & Baldinelli, Giorgio & Wang, Jinwen & Bartocci, Pietro & Shamim, Tariq, 2025. "Artificial intelligence based forecasting and optimization model for concentrated solar power system with thermal energy storage," Applied Energy, Elsevier, vol. 382(C).

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