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Mathematical Modeling of the Mojave Solar Plants

Author

Listed:
  • Antonio J. Gallego

    (Departamento de Ingeniería de Sistemas y Automática, Universidad de Sevilla, Camino de los Descubrimientos s/n, 41092 Sevilla, Spain)

  • Manuel Macías

    (Atlantica Yield, Albert Einstein s/n, 41092 Sevilla, Spain)

  • Fernando de Castilla

    (Atlantica Yield, Albert Einstein s/n, 41092 Sevilla, Spain)

  • Eduardo F. Camacho

    (Departamento de Ingeniería de Sistemas y Automática, Universidad de Sevilla, Camino de los Descubrimientos s/n, 41092 Sevilla, Spain)

Abstract

Competitiveness of solar energy is one of current main research topics. Overall efficiency of solar plants can be improved by using advanced control strategies. To design and tuning properly advanced control strategies, a mathematical model of the plant is needed. The model has to fulfill two important points: (1) It has to reproduce accurately the dynamics of the real system; and (2) since the model is used to test advanced control strategies, its computational burden has to be as low as possible. This trade-off is essential to optimize the tuning process of the controller and minimize the commissioning time. In this paper, the modeling of the large-scale commercial solar trough plants Mojave Beta and Mojave Alpha is presented. These two models were used to test advanced control strategies to operate the plants.

Suggested Citation

  • Antonio J. Gallego & Manuel Macías & Fernando de Castilla & Eduardo F. Camacho, 2019. "Mathematical Modeling of the Mojave Solar Plants," Energies, MDPI, vol. 12(21), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4197-:d:283234
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    References listed on IDEAS

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    Cited by:

    1. Ruiz-Moreno, Sara & Sanchez, Adolfo J. & Gallego, Antonio J. & Camacho, Eduardo F., 2022. "A deep learning-based strategy for fault detection and isolation in parabolic-trough collectors," Renewable Energy, Elsevier, vol. 186(C), pages 691-703.

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