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CSP Quasi-Dynamic Performance Model Development for All Project Life Cycle Stages and Considering Operation Modes. Validation Using One Year Data

Author

Listed:
  • Adrian Gonzalez Gonzalez

    (TSK, 33203 Gijón, Spain)

  • J. Valeriano Alvarez Cabal

    (Project Engineering Area, University of Oviedo, 33003 Oviedo, Spain)

  • Vicente Rodríguez Montequin

    (Project Engineering Area, University of Oviedo, 33003 Oviedo, Spain)

  • Joaquín Villanueva Balsera

    (Project Engineering Area, University of Oviedo, 33003 Oviedo, Spain)

  • Rogelio Peón Menéndez

    (TSK, 33203 Gijón, Spain)

Abstract

The energy production of concentrated solar power (CSP) plants not only depends on their design, but also of the weather conditions and the way they are operated. A performance model (PM) of a CSP plant is an essential tool to determine production costs, to optimize design and also to supervise the operation of the plant. The challenge is developing a PM that is both easy enough to be useful during the earlier stages of the project, and also useful for supervision of plant operation. This requires one to be able to describe the step between the different modes of operation and to fit the response to transient meteorological phenomena, not so relevant in terms of aggregate values, but crucial for the supervision. The quasi-dynamic performance model (QD-PM) can predict the net energy exported to the grid, as well as all the key operational variables. The QD-PM was implemented using Matlab-Simulink of Mathwoks (MA, USA) with a modular structure. Each module is developed using specific software and a state machine is used to simulate the sequence between the operation modes. The validation of the PM is made using one complete year of commercial operation of a 50 MWe CSP plant situated in Spain. The comparison between the actual data and the results of the model shows an excellent fit, being especially noteworthy as follows the transients between the different CSP operation modes. Then, QD-PM provides an accuracy better than the usual PM, and, almost, as good as that of a fully dynamic model but with a shorter simulation time. But, the main advantage of the QD-PM is that it can be use not only in the feasibility and design stages, but it can be used to supervise the operation of the plant.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2020:i:1:p:14-:d:466463
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    References listed on IDEAS

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    1. Lourdes A. Barcia & Rogelio Peón Menéndez & Juan Á. Martínez Esteban & Miguel A. José Prieto & Juan A. Martín Ramos & F. Javier De Cos Juez & Antonio Nevado Reviriego, 2015. "Dynamic Modeling of the Solar Field in Parabolic Trough Solar Power Plants," Energies, MDPI, vol. 8(12), pages 1-17, November.
    2. Meybodi, Mehdi Aghaei & Ramirez Santigosa, Lourdes & Beath, Andrew C., 2017. "A study on the impact of time resolution in solar data on the performance modelling of CSP plants," Renewable Energy, Elsevier, vol. 109(C), pages 551-563.
    3. Manzolini, Giampaolo & Giostri, Andrea & Saccilotto, Claudio & Silva, Paolo & Macchi, Ennio, 2011. "Development of an innovative code for the design of thermodynamic solar power plants part A: Code description and test case," Renewable Energy, Elsevier, vol. 36(7), pages 1993-2003.
    4. Desideri, Umberto & Campana, Pietro Elia, 2014. "Analysis and comparison between a concentrating solar and a photovoltaic power plant," Applied Energy, Elsevier, vol. 113(C), pages 422-433.
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    Cited by:

    1. 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.

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