IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v255y2022ics0360544222013810.html
   My bibliography  Save this article

Strategical district cooling system operation in hub airport terminals, a research focusing on COVID-19 pandemic impact

Author

Listed:
  • Yan, Biao
  • Yang, Wansheng
  • He, Fuquan
  • Huang, Kehua
  • Zeng, Wenhao
  • Zhang, Wenlong
  • Ye, Haiseng

Abstract

Part load ratio is often observed in real operations of airport terminal cooling system. This phenomenon is more obvious during the COVID-19 pandemic, as sudden flight restrictions impacting cooling demand are widely adopted in hub airport terminals. This research aims to propose optimal strategies of multi-chiller in airport terminals based on cooling load characteristics modeling, to tackle the aforementioned issues. Numerical experiments based on a real-world Chinese airport terminal are conducted to validate the proposed method. The results show that an average cooling load drop of 30% is observed from scenario of normal flight before COVID-19 to scenario of COVID-19 Period flight, and the average cooling load drop reaches to 44% from scenario of busy flight before COVID-19 to scenario of COVID-19 Period flight. The results also reflect that cooling load presents synchronous trend with passenger flow, but presents asynchronous trend with outdoor temperature. The influence of outdoor temperature on cooling demand delays due to building envelops. It indicates that simple superimposition according to passenger flow change for chiller operation number is reliable, efficient and effective, but is not suitable for outdoor temperature change. The findings are helpful to develop optimal strategies for further real-time control of multi-chiller.

Suggested Citation

  • Yan, Biao & Yang, Wansheng & He, Fuquan & Huang, Kehua & Zeng, Wenhao & Zhang, Wenlong & Ye, Haiseng, 2022. "Strategical district cooling system operation in hub airport terminals, a research focusing on COVID-19 pandemic impact," Energy, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:energy:v:255:y:2022:i:c:s0360544222013810
    DOI: 10.1016/j.energy.2022.124478
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222013810
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.124478?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. Catrini, P. & Panno, D. & Cardona, F. & Piacentino, A., 2020. "Characterization of cooling loads in the wine industry and novel seasonal indicator for reliable assessment of energy saving through retrofit of chillers," Applied Energy, Elsevier, vol. 266(C).
    2. Zhang, Wei & Hong, Wenpeng & Jin, Xu, 2022. "Research on performance and control strategy of multi-cold source district cooling system," Energy, Elsevier, vol. 239(PB).
    3. Zabala, Laura & Febres, Jesus & Sterling, Raymond & López, Susana & Keane, Marcus, 2020. "Virtual testbed for model predictive control development in district cooling systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 129(C).
    4. Fathollahzadeh, Mohammad Hassan & Tabares-Velasco, Paulo Cesar, 2021. "Electric demand minimization of existing district chiller plants with rigid or flexible thermal demand," Applied Energy, Elsevier, vol. 289(C).
    5. Wang, Yang & Zhang, Shanhong & Chow, David & Kuckelkorn, Jens M., 2021. "Evaluation and optimization of district energy network performance: Present and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    6. Fan, Yi & Zhao, Xudong & Li, Jing & Li, Guiqiang & Myers, Steve & Cheng, Yuanda & Badiei, Ali & Yu, Min & Golizadeh Akhlaghi, Yousef & Shittu, Samson & Ma, Xiaoli, 2020. "Economic and environmental analysis of a novel rural house heating and cooling system using a solar-assisted vapour injection heat pump," Applied Energy, Elsevier, vol. 275(C).
    7. Ho, W.T. & Yu, F.W., 2021. "Improved model and optimization for the energy performance of chiller system with diverse component staging," Energy, Elsevier, vol. 217(C).
    8. Quirosa, Gonzalo & Torres, Miguel & Chacartegui, Ricardo, 2022. "Analysis of the integration of photovoltaic excess into a 5th generation district heating and cooling system for network energy storage," Energy, Elsevier, vol. 239(PC).
    9. Menapace, Andrea & Thellufsen, Jakob Zinck & Pernigotto, Giovanni & Roberti, Francesca & Gasparella, Andrea & Righetti, Maurizio & Baratieri, Marco & Lund, Henrik, 2020. "The design of 100 % renewable smart urb an energy systems: The case of Bozen-Bolzano," Energy, Elsevier, vol. 207(C).
    10. Pourfattah, Farzad & Sabzpooshani, Majid, 2021. "On the thermal management of a power electronics system: Optimization of the cooling system using genetic algorithm and response surface method," Energy, Elsevier, vol. 232(C).
    11. Cox, Sam J. & Kim, Dongsu & Cho, Heejin & Mago, Pedro, 2019. "Real time optimal control of district cooling system with thermal energy storage using neural networks," Applied Energy, Elsevier, vol. 238(C), pages 466-480.
    12. Du, Yan & Zandi, Helia & Kotevska, Olivera & Kurte, Kuldeep & Munk, Jeffery & Amasyali, Kadir & Mckee, Evan & Li, Fangxing, 2021. "Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning," Applied Energy, Elsevier, vol. 281(C).
    13. Zhuang, Chaoqun & Wang, Shengwei & Shan, Kui, 2020. "A risk-based robust optimal chiller sequencing control strategy for energy-efficient operation considering measurement uncertainties," Applied Energy, Elsevier, vol. 280(C).
    14. Ji, Ying & Xu, Peng & Duan, Pengfei & Lu, Xing, 2016. "Estimating hourly cooling load in commercial buildings using a thermal network model and electricity submetering data," Applied Energy, Elsevier, vol. 169(C), pages 309-323.
    15. Shi, Shaofei & Wang, Yufei & Wang, Youlei & Feng, Xiao, 2022. "A new optimization method for cooling systems considering low-temperature waste heat utilization in a polysilicon industry," Energy, Elsevier, vol. 238(PA).
    16. Lund, Henrik & Østergaard, Poul Alberg & Nielsen, Tore Bach & Werner, Sven & Thorsen, Jan Eric & Gudmundsson, Oddgeir & Arabkoohsar, Ahmad & Mathiesen, Brian Vad, 2021. "Perspectives on fourth and fifth generation district heating," Energy, Elsevier, vol. 227(C).
    17. Pieper, Henrik & Krupenski, Igor & Brix Markussen, Wiebke & Ommen, Torben & Siirde, Andres & Volkova, Anna, 2021. "Method of linear approximation of COP for heat pumps and chillers based on thermodynamic modelling and off-design operation," Energy, Elsevier, vol. 230(C).
    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. Yan, Biao & Yang, Wansheng & He, Fuquan & Zeng, Wenhao, 2023. "Occupant behavior impact in buildings and the artificial intelligence-based techniques and data-driven approach solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(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. Calise, Francesco & Cappiello, Francesco Liberato & Cimmino, Luca & Dentice d’Accadia, Massimo & Vicidomini, Maria, 2023. "A comparative thermoeconomic analysis of fourth generation and fifth generation district heating and cooling networks," Energy, Elsevier, vol. 284(C).
    2. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    3. Mengting Jiang & Camilo Rindt & David M. J. Smeulders, 2022. "Optimal Planning of Future District Heating Systems—A Review," Energies, MDPI, vol. 15(19), pages 1-38, September.
    4. Østergaard, Dorte Skaarup & Smith, Kevin Michael & Tunzi, Michele & Svendsen, Svend, 2022. "Low-temperature operation of heating systems to enable 4th generation district heating: A review," Energy, Elsevier, vol. 248(C).
    5. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    6. F. Marta L. Di Lascio & Andrea Menapace & Roberta Pappadà, 2024. "A spatially‐weighted AMH copula‐based dissimilarity measure for clustering variables: An application to urban thermal efficiency," Environmetrics, John Wiley & Sons, Ltd., vol. 35(1), February.
    7. Lund, Henrik & Thellufsen, Jakob Zinck & Sorknæs, Peter & Mathiesen, Brian Vad & Chang, Miguel & Madsen, Poul Thøis & Kany, Mikkel Strunge & Skov, Iva Ridjan, 2022. "Smart energy Denmark. A consistent and detailed strategy for a fully decarbonized society," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    8. He, Zhaoyu & Guo, Weimin & Zhang, Peng, 2022. "Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    9. Wirtz, Marco, 2023. "nPro: A web-based planning tool for designing district energy systems and thermal networks," Energy, Elsevier, vol. 268(C).
    10. Barth, Florian & Schüppler, Simon & Menberg, Kathrin & Blum, Philipp, 2023. "Estimating cooling capacities from aerial images using convolutional neural networks," Applied Energy, Elsevier, vol. 349(C).
    11. Shen, Boyang & Chen, Yu & Li, Chuanyue & Wang, Sheng & Chen, Xiaoyuan, 2021. "Superconducting fault current limiter (SFCL): Experiment and the simulation from finite-element method (FEM) to power/energy system software," Energy, Elsevier, vol. 234(C).
    12. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    13. Billerbeck, Anna & Breitschopf, Barbara & Winkler, Jenny & Bürger, Veit & Köhler, Benjamin & Bacquet, Alexandre & Popovski, Eftim & Fallahnejad, Mostafa & Kranzl, Lukas & Ragwitz, Mario, 2023. "Policy frameworks for district heating: A comprehensive overview and analysis of regulations and support measures across Europe," Energy Policy, Elsevier, vol. 173(C).
    14. Yang, Weijia & Huang, Yuping & Zhao, Daiqing, 2023. "A coupled hydraulic–thermal dynamic model for the steam network in a heat–electricity integrated energy system," Energy, Elsevier, vol. 263(PC).
    15. Zeyue Sun & Mohsen Eskandari & Chaoran Zheng & Ming Li, 2022. "Handling Computation Hardness and Time Complexity Issue of Battery Energy Storage Scheduling in Microgrids by Deep Reinforcement Learning," Energies, MDPI, vol. 16(1), pages 1-20, December.
    16. Zhang, Bin & Hu, Weihao & Ghias, Amer M.Y.M. & Xu, Xiao & Chen, Zhe, 2022. "Multi-agent deep reinforcement learning-based coordination control for grid-aware multi-buildings," Applied Energy, Elsevier, vol. 328(C).
    17. M. Usman Saleem & Mustafa Shakir & M. Rehan Usman & M. Hamza Tahir Bajwa & Noman Shabbir & Payam Shams Ghahfarokhi & Kamran Daniel, 2023. "Integrating Smart Energy Management System with Internet of Things and Cloud Computing for Efficient Demand Side Management in Smart Grids," Energies, MDPI, vol. 16(12), pages 1-21, June.
    18. Charalampos Rafail Lazaridis & Iakovos Michailidis & Georgios Karatzinis & Panagiotis Michailidis & Elias Kosmatopoulos, 2024. "Evaluating Reinforcement Learning Algorithms in Residential Energy Saving and Comfort Management," Energies, MDPI, vol. 17(3), pages 1-33, January.
    19. Sun, Chunhua & Liu, Yiting & Cao, Shanshan & Chen, Jiali & Xia, Guoqiang & Wu, Xiangdong, 2022. "Identification of control regularity of heating stations based on cross-correlation function dynamic time delay method," Energy, Elsevier, vol. 246(C).
    20. Brumana, Giovanni & Franchini, Giuseppe & Ghirardi, Elisa & Perdichizzi, Antonio, 2022. "Techno-economic optimization of hybrid power generation systems: A renewables community case study," Energy, Elsevier, vol. 246(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:energy:v:255:y:2022:i:c:s0360544222013810. 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.