IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v198y2022icp492-504.html
   My bibliography  Save this article

Stable deep Koopman model predictive control for solar parabolic-trough collector field

Author

Listed:
  • Gholaminejad, Tahereh
  • Khaki-Sedigh, Ali

Abstract

Concentrated Solar Power plants (CSP) have the energy storage capability to generate electricity when sunlight is scarce. However, due to the highly non-linear dynamics of these systems, a simple linear controller will not be able to overcome the variable dynamics and multiple disturbance sources affecting it. In this paper, a deep Model Predictive Control (MPC) based on the Koopman operator is proposed and applied to control the Heat Transfer Fluid (HTF) temperature of a distributed-parameter model of the ACUREX solar collector field located at Almería, Spain. The Koopman operator is an infinite-dimensional linear operator that fully captures a system's non-linear dynamics through the linear evolution of functions of the state-space. However, one of the major problems is identifying a Koopman linear model for a non-linear system. Koopman eigenfunctions are involved in converting a non-linear model to a Koopman-based linear model. In this paper, a deep Long Short-Term Memory (LSTM) autoencoder is designed to calculate Koopman eigenfunctions of the solar collector field. The Koopman linear model is then used to design a linear MPC with terminal components to ensure closed-loop stability guarantees. Simulation results are utilized to show the satisfactory tracking performance of the proposed approach.

Suggested Citation

  • Gholaminejad, Tahereh & Khaki-Sedigh, Ali, 2022. "Stable deep Koopman model predictive control for solar parabolic-trough collector field," Renewable Energy, Elsevier, vol. 198(C), pages 492-504.
    • Handle: RePEc:eee:renene:v:198:y:2022:i:c:p:492-504
    DOI: 10.1016/j.renene.2022.08.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148122011788
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2022.08.012?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. Ruiz-Moreno, Sara & Frejo, José Ramón D. & Camacho, Eduardo F., 2021. "Model predictive control based on deep learning for solar parabolic-trough plants," Renewable Energy, Elsevier, vol. 180(C), pages 193-202.
    2. Masero, Eva & Maestre, José M. & Camacho, Eduardo F., 2022. "Market-based clustering of model predictive controllers for maximizing collected energy by parabolic-trough solar collector fields," Applied Energy, Elsevier, vol. 306(PA).
    3. Eduardo F. Camacho & Antonio J. Gallego & Adolfo J. Sanchez & Manuel Berenguel, 2018. "Incremental State-Space Model Predictive Control of a Fresnel Solar Collector Field," Energies, MDPI, vol. 12(1), pages 1-23, December.
    4. Bethany Lusch & J. Nathan Kutz & Steven L. Brunton, 2018. "Deep learning for universal linear embeddings of nonlinear dynamics," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    5. Lourdes A. Barcia & Rogelio Peon & Juan Díaz & A.M. Pernía & Juan Ángel Martínez, 2017. "Heat Transfer Fluid Temperature Control in a Thermoelectric Solar Power Plant," Energies, MDPI, vol. 10(8), pages 1-11, July.
    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. Chanfreut, Paula & Maestre, José M. & Gallego, Antonio J. & Annaswamy, Anuradha M. & Camacho, Eduardo F., 2023. "Clustering-based model predictive control of solar parabolic trough plants," Renewable Energy, Elsevier, vol. 216(C).

    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. Sánchez-Amores, Ana & Martinez-Piazuelo, Juan & Maestre, José M. & Ocampo-Martinez, Carlos & Camacho, Eduardo F. & Quijano, Nicanor, 2023. "Coalitional model predictive control of parabolic-trough solar collector fields with population-dynamics assistance," Applied Energy, Elsevier, vol. 334(C).
    2. Velarde, Pablo & Gallego, Antonio J. & Bordons, Carlos & Camacho, Eduardo F., 2023. "Scenario-based model predictive control for energy scheduling in a parabolic trough concentrating solar plant with thermal storage," Renewable Energy, Elsevier, vol. 206(C), pages 1228-1238.
    3. Konstantin Avchaciov & Marina P. Antoch & Ekaterina L. Andrianova & Andrei E. Tarkhov & Leonid I. Menshikov & Olga Burmistrova & Andrei V. Gudkov & Peter O. Fedichev, 2022. "Unsupervised learning of aging principles from longitudinal data," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    4. Gong, Xun & Wang, Xiaozhe & Cao, Bo, 2023. "On data-driven modeling and control in modern power grids stability: Survey and perspective," Applied Energy, Elsevier, vol. 350(C).
    5. Rijwan Khan, 2023. "Deep Learning System and It’s Automatic Testing: An Approach," Annals of Data Science, Springer, vol. 10(4), pages 1019-1033, August.
    6. Lizárraga-Morazán, Juan Ramón & Picón-Núñez, Martín, 2023. "Optimal sizing and control strategy of low temperature solar thermal utility systems," Energy, Elsevier, vol. 263(PC).
    7. Jose Ramón Rogada & Lourdes A. Barcia & Juan Angel Martinez & Mario Menendez & Francisco Javier De Cos Juez, 2017. "Comparative Modeling of a Parabolic Trough Collectors Solar Power Plant with MARS Models," Energies, MDPI, vol. 11(1), pages 1-15, December.
    8. Wang, Jiaxing & Li, Yiguo & Zhang, Junli, 2023. "Coordinated control of concentrated solar power systems with indirect molten salt storage considering operation mode switching: Using switching model predictive control," Energy, Elsevier, vol. 268(C).
    9. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
    10. Mandal, Ankit & Tiwari, Yash & Panigrahi, Prasanta K. & Pal, Mayukha, 2022. "Physics aware analytics for accurate state prediction of dynamical systems," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    11. Mattia Cenedese & Joar Axås & Bastian Bäuerlein & Kerstin Avila & George Haller, 2022. "Data-driven modeling and prediction of non-linearizable dynamics via spectral submanifolds," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    12. Gary Ampuño & Juan Lata-Garcia & Francisco Jurado, 2020. "Evaluation of Energy Efficiency and the Reduction of Atmospheric Emissions by Generating Electricity from a Solar Thermal Power Generation Plant," Energies, MDPI, vol. 13(3), pages 1-20, February.
    13. Zhao Chen & Yang Liu & Hao Sun, 2021. "Physics-informed learning of governing equations from scarce data," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    14. Eduardo F. Camacho & Antonio J. Gallego & Juan M. Escaño & Adolfo J. Sánchez, 2019. "Hybrid Nonlinear MPC of a Solar Cooling Plant," Energies, MDPI, vol. 12(14), pages 1-22, July.
    15. Joaquim Fernando Pinto da Costa & Manuel Cabral, 2022. "Statistical Methods with Applications in Data Mining: A Review of the Most Recent Works," Mathematics, MDPI, vol. 10(6), pages 1-22, March.
    16. Cardoso, Ana Sofia & Renna, Francesco & Moreno-Llorca, Ricardo & Alcaraz-Segura, Domingo & Tabik, Siham & Ladle, Richard J. & Vaz, Ana Sofia, 2022. "Classifying the content of social media images to support cultural ecosystem service assessments using deep learning models," Ecosystem Services, Elsevier, vol. 54(C).

    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:198:y:2022:i:c:p:492-504. 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.