IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v271y2023ics0360544223003584.html
   My bibliography  Save this article

Hierarchical control based on a hybrid nonlinear predictive strategy for a solar-powered absorption machine facility

Author

Listed:
  • Pataro, Igor M.L.
  • Gil, Juan D.
  • Guzmán, José L.
  • Berenguel, Manuel
  • Lemos, João M.

Abstract

This work presents a hierarchical framework to control a solar thermal facility aiming to provide the required operating conditions for an absorption chiller machine. The CIESOL thermal plant, located at the University of Almería (Spain), is proposed as a case study in which all subsystems and valves are considered in a trustworthy simulation environment. Herein, the thermal plant dynamic representation is completed by presenting the absorption chiller modeling, in which three distinct models, namely, a first principle lumped parameters, an AutoRegressive with eXogenous input (ARX), and a Nonlinear ARX (NLARX) models, are validated. Accordingly, by using these models, the hierarchical control based on a hybrid nonlinear predictive controller is formulated. Dwell-time and stress zone are embedded in the operating condition constraints of the hybrid control component of the upper layer to enhance robustness and performance. Moreover, a lower layer based on Proportional–Integral (PI) controllers is also designed to overcome the valve’s nonlinear dynamics and improve disturbance rejection on the regulatory layer. The results demonstrate that the hierarchical structure controls the solar plant around 115 min more in the operating time of the solar-powered absorption chiller, with less usage of fossil fuels sources compared to a conventional operation of the system.

Suggested Citation

  • Pataro, Igor M.L. & Gil, Juan D. & Guzmán, José L. & Berenguel, Manuel & Lemos, João M., 2023. "Hierarchical control based on a hybrid nonlinear predictive strategy for a solar-powered absorption machine facility," Energy, Elsevier, vol. 271(C).
  • Handle: RePEc:eee:energy:v:271:y:2023:i:c:s0360544223003584
    DOI: 10.1016/j.energy.2023.126964
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223003584
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2023.126964?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. Abanda, F.H. & Byers, L., 2016. "An investigation of the impact of building orientation on energy consumption in a domestic building using emerging BIM (Building Information Modelling)," Energy, Elsevier, vol. 97(C), pages 517-527.
    2. José Vinagre Díaz, Juan & Richard Wilby, Mark & Belén Rodríguez González, Ana, 2013. "Setting up GHG-based energy efficiency targets in buildings: The Ecolabel," Energy Policy, Elsevier, vol. 59(C), pages 633-642.
    3. 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.
    4. Duan, Haiyan & Chen, Siyan & Song, Junnian, 2022. "Characterizing regional building energy consumption under joint climatic and socioeconomic impacts," Energy, Elsevier, vol. 245(C).
    5. Hu, Tianxiang & Shen, Yongting & Kwan, Trevor Hocksun & Pei, Gang, 2022. "Absorption chiller waste heat utilization to the desiccant dehumidifier system for enhanced cooling – Energy and exergy analysis," Energy, Elsevier, vol. 239(PA).
    6. Omrany, Hossein & Ghaffarianhoseini, Ali & Ghaffarianhoseini, Amirhosein & Raahemifar, Kaamran & Tookey, John, 2016. "Application of passive wall systems for improving the energy efficiency in buildings: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 1252-1269.
    7. Cuce, Erdem & Cuce, Pinar Mert, 2016. "Vacuum glazing for highly insulating windows: Recent developments and future prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1345-1357.
    8. Erasmo Cadenas & Wilfrido Rivera & Rafael Campos-Amezcua & Christopher Heard, 2016. "Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model," Energies, MDPI, vol. 9(2), pages 1-15, February.
    9. Pataro, Igor M.L. & Gil, Juan D. & Americano da Costa, Marcus V. & Guzmán, José L. & Berenguel, Manuel, 2022. "A nonlinear control approach for hybrid solar thermal plants based on operational conditions," Renewable Energy, Elsevier, vol. 183(C), pages 114-129.
    10. Zina Boussaada & Octavian Curea & Ahmed Remaci & Haritza Camblong & Najiba Mrabet Bellaaj, 2018. "A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation," Energies, MDPI, vol. 11(3), pages 1-21, March.
    11. Gil, Juan D. & Topa, A. & Álvarez, J.D. & Torres, J.L. & Pérez, M., 2022. "A review from design to control of solar systems for supplying heat in industrial process applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    Full references (including those not matched with items on IDEAS)

    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. Daniele Ferretti & Elena Michelini, 2021. "The Effect of Density on the Delicate Balance between Structural Requirements and Environmental Issues for AAC Blocks: An Experimental Investigation," Sustainability, MDPI, vol. 13(23), pages 1-21, November.
    2. Yu, Bendong & He, Wei & Li, Niansi & Wang, Liping & Cai, Jingyong & Chen, Hongbing & Ji, Jie & Xu, Gang, 2017. "Experimental and numerical performance analysis of a TC-Trombe wall," Applied Energy, Elsevier, vol. 206(C), pages 70-82.
    3. Gu, Tao & Li, Niansi & Li, Yulin & Che, Lei & Yu, Bendong & Liu, Huifang, 2023. "A novel Trombe wall with photo-thermal synergistically catalytic purification blinds: Material and experimental performance study," Energy, Elsevier, vol. 278(PB).
    4. Qian, Xiaoyan & Dai, Jie & Jiang, Weimin & Cai, Helen & Ye, Xixi & Shahab Vafadaran, Mohammad, 2024. "Economic viability and investment returns of innovative geothermal tri-generation systems: A comparative study," Renewable Energy, Elsevier, vol. 226(C).
    5. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    6. Karel Struhala & Miroslav Čekon & Richard Slávik, 2018. "Life Cycle Assessment of Solar Façade Concepts Based on Transparent Insulation Materials," Sustainability, MDPI, vol. 10(11), pages 1-16, November.
    7. Ascione, Fabrizio & De Masi, Rosa Francesca & de Rossi, Filippo & Ruggiero, Silvia & Vanoli, Giuseppe Peter, 2016. "Optimization of building envelope design for nZEBs in Mediterranean climate: Performance analysis of residential case study," Applied Energy, Elsevier, vol. 183(C), pages 938-957.
    8. Michaux, Ghislain & Greffet, Rémy & Salagnac, Patrick & Ridoret, Jean-Baptiste, 2019. "Modelling of an airflow window and numerical investigation of its thermal performances by comparison to conventional double and triple-glazed windows," Applied Energy, Elsevier, vol. 242(C), pages 27-45.
    9. Yiqi Chu & Chengcai Li & Yefang Wang & Jing Li & Jian Li, 2016. "A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction," Energies, MDPI, vol. 9(11), pages 1-20, October.
    10. Cheng-Yu Ho & Ke-Sheng Cheng & Chi-Hang Ang, 2023. "Utilizing the Random Forest Method for Short-Term Wind Speed Forecasting in the Coastal Area of Central Taiwan," Energies, MDPI, vol. 16(3), pages 1-18, January.
    11. Neeraj Bokde & Andrés Feijóo & Daniel Villanueva & Kishore Kulat, 2018. "A Novel and Alternative Approach for Direct and Indirect Wind-Power Prediction Methods," Energies, MDPI, vol. 11(11), pages 1-19, October.
    12. Anshuman Satapathy & Niranjan Nayak & Tanmoy Parida, 2022. "Real-Time Power Quality Enhancement in a Hybrid Micro-Grid Using Nonlinear Autoregressive Neural Network," Energies, MDPI, vol. 15(23), pages 1-35, November.
    13. Oksana Mandrikova & Yuryi Polozov & Nataly Zhukova & Yulia Shichkina, 2022. "Approximation and Analysis of Natural Data Based on NARX Neural Networks Involving Wavelet Filtering," Mathematics, MDPI, vol. 10(22), pages 1-16, November.
    14. Zhang, Lili & Hou, Yuyao & Liu, Zu’an & Du, Junfei & Xu, Long & Zhang, Guomin & Shi, Long, 2020. "Trombe wall for a residential building in Sichuan-Tibet alpine valley – A case study," Renewable Energy, Elsevier, vol. 156(C), pages 31-46.
    15. Chi, Fang'ai & Zhang, Jianxun & Li, Gaomei & Zhu, Zongzhou & Bart, Dewancker, 2019. "An investigation of the impact of Building Azimuth on energy consumption in sizhai traditional dwellings," Energy, Elsevier, vol. 180(C), pages 594-614.
    16. Shen, Meng & Li, Xiang & Lu, Yujie & Cui, Qingbin & Wei, Yi-Ming, 2021. "Personality-based normative feedback intervention for energy conservation," Energy Economics, Elsevier, vol. 104(C).
    17. Guilherme Henrique Alves & Geraldo Caixeta Guimarães & Fabricio Augusto Matheus Moura, 2023. "Battery Storage Systems Control Strategies with Intelligent Algorithms in Microgrids with Dynamic Pricing," Energies, MDPI, vol. 16(14), pages 1-30, July.
    18. George M. Stavrakakis & Dimitris Al. Katsaprakakis & Markos Damasiotis, 2021. "Basic Principles, Most Common Computational Tools, and Capabilities for Building Energy and Urban Microclimate Simulations," Energies, MDPI, vol. 14(20), pages 1-41, October.
    19. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.
    20. Jee-Heon Kim & Nam-Chul Seong & Wonchang Choi, 2020. "Forecasting the Energy Consumption of an Actual Air Handling Unit and Absorption Chiller Using ANN Models," Energies, MDPI, vol. 13(17), pages 1-12, August.

    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:energy:v:271:y:2023:i:c:s0360544223003584. 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/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.