IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v313y2024ics0360544224036363.html
   My bibliography  Save this article

Development and implementation of supply-based feedback controls for energy-efficient and grid-interactive cooling management over entire building daily cycle

Author

Listed:
  • Dai, Mingkun
  • Li, Hangxin
  • Wang, Shengwei

Abstract

Air-conditioning systems account for a large portion of energy usage, making their effective control vital for overall building energy performance. Conventional process control strategies utilized in building air-conditioning systems adopt “demand-based” feedback control. However, they fail to properly manage the cooling distribution when the cooling supply is insufficient. Reconfigurable feedback control for supply-based cooling management has shown its effectiveness in addressing this problem. However, when and how buildings can benefit from supply-based cooling management in their entire daily cycle are not investigated, and the practical implementation of supply-based cooling management in building automation systems is still a major challenge. This study, therefore, proposes the strategies implementing the reconfigurable feedback control for supply-based cooling management in the entire building daily cycle, including demand limiting, morning start and soft stop. A hardware-in-the-loop test platform, involving typical digital controllers commonly used in practice, is developed for validation tests. Test results show that the reconfigurable supply-based feedback control method can be deployed conveniently in today's practical building automation systems. 9.1 % and 13.3 % of energy savings can be achieved during the morning start and soft stop periods respectively. The power demand can be reduced by 30.8 % during the demand limiting period.

Suggested Citation

  • Dai, Mingkun & Li, Hangxin & Wang, Shengwei, 2024. "Development and implementation of supply-based feedback controls for energy-efficient and grid-interactive cooling management over entire building daily cycle," Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:energy:v:313:y:2024:i:c:s0360544224036363
    DOI: 10.1016/j.energy.2024.133858
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224036363
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.133858?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. Tang, Hong & Wang, Shengwei & Li, Hangxin, 2021. "Flexibility categorization, sources, capabilities and technologies for energy-flexible and grid-responsive buildings: State-of-the-art and future perspective," Energy, Elsevier, vol. 219(C).
    2. Wang, Shengwei & Tang, Rui, 2017. "Supply-based feedback control strategy of air-conditioning systems for direct load control of buildings responding to urgent requests of smart grids," Applied Energy, Elsevier, vol. 201(C), pages 419-432.
    3. Shan, Kui & Wang, Jiayuan & Hu, Maomao & Gao, Dian-ce, 2019. "A model-based control strategy to recover cooling energy from thermal mass in commercial buildings," Energy, Elsevier, vol. 172(C), pages 958-967.
    4. Xue, Xue & Wang, Shengwei & Yan, Chengchu & Cui, Borui, 2015. "A fast chiller power demand response control strategy for buildings connected to smart grid," Applied Energy, Elsevier, vol. 137(C), pages 77-87.
    5. Dai, Mingkun & Li, Hangxin & Wang, Shengwei, 2023. "A reinforcement learning-enabled iterative learning control strategy of air-conditioning systems for building energy saving by shortening the morning start period," Applied Energy, Elsevier, vol. 334(C).
    6. Song, Kwonsik & Jang, Youjin & Park, Moonseo & Lee, Hyun-Soo & Ahn, Joseph, 2020. "Energy efficiency of end-user groups for personalized HVAC control in multi-zone buildings," Energy, Elsevier, vol. 206(C).
    7. Wang, Jingjie & Qiu, Rujia & Xu, Bin & Wu, Hongbin & Tang, Longjiang & Zhang, Mingxing & Ding, Ming, 2023. "Aggregated large-scale air-conditioning load: Modeling and response capability evaluation of virtual generator units," Energy, Elsevier, vol. 276(C).
    8. Tang, Rui & Wang, Shengwei & Shan, Kui & Cheung, Howard, 2018. "Optimal control strategy of central air-conditioning systems of buildings at morning start period for enhanced energy efficiency and peak demand limiting," Energy, Elsevier, vol. 151(C), pages 771-781.
    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. Cui, Can & Xue, Jing, 2024. "Energy and comfort aware operation of multi-zone HVAC system through preference-inspired deep reinforcement learning," Energy, Elsevier, vol. 292(C).
    2. Chen, Yuxin & Sun, Yongjun & Yang, Jinling & Tan, Jiaqi & Liu, Yang & Gao, Dian-ce, 2024. "Demand response with PCM-based pipe-embedded wall in commercial buildings: Combined passive and active energy storage in envelopes," Energy, Elsevier, vol. 308(C).
    3. Jing Zhao & Yu Shan, 2020. "A Fuzzy Control Strategy Using the Load Forecast for Air Conditioning System," Energies, MDPI, vol. 13(3), pages 1-17, January.
    4. Ran, Fengming & Gao, Dian-ce & Zhang, Xu & Chen, Shuyue, 2020. "A virtual sensor based self-adjusting control for HVAC fast demand response in commercial buildings towards smart grid applications," Applied Energy, Elsevier, vol. 269(C).
    5. Wang, Huilong & Wang, Shengwei & Tang, Rui, 2019. "Development of grid-responsive buildings: Opportunities, challenges, capabilities and applications of HVAC systems in non-residential buildings in providing ancillary services by fast demand responses," Applied Energy, Elsevier, vol. 250(C), pages 697-712.
    6. Kang, Jing & Wang, Shengwei, 2018. "Robust optimal design of distributed energy systems based on life-cycle performance analysis using a probabilistic approach considering uncertainties of design inputs and equipment degradations," Applied Energy, Elsevier, vol. 231(C), pages 615-627.
    7. Tang, Hong & Wang, Shengwei, 2022. "A model-based predictive dispatch strategy for unlocking and optimizing the building energy flexibilities of multiple resources in electricity markets of multiple services," Applied Energy, Elsevier, vol. 305(C).
    8. Liu, Mingzhe & Ooka, Ryozo & Choi, Wonjun & Ikeda, Shintaro, 2019. "Experimental and numerical investigation of energy saving potential of centralized and decentralized pumping systems," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    9. Tang, Rui & Wang, Shengwei, 2019. "Model predictive control for thermal energy storage and thermal comfort optimization of building demand response in smart grids," Applied Energy, Elsevier, vol. 242(C), pages 873-882.
    10. Tang, Rui & Li, Hangxin & Wang, Shengwei, 2019. "A game theory-based decentralized control strategy for power demand management of building cluster using thermal mass and energy storage," Applied Energy, Elsevier, vol. 242(C), pages 809-820.
    11. Tang, Rui & Wang, Shengwei & Li, Hangxin, 2019. "Game theory based interactive demand side management responding to dynamic pricing in price-based demand response of smart grids," Applied Energy, Elsevier, vol. 250(C), pages 118-130.
    12. Tina, Giuseppe Marco & Aneli, Stefano & Gagliano, Antonio, 2022. "Technical and economic analysis of the provision of ancillary services through the flexibility of HVAC system in shopping centers," Energy, Elsevier, vol. 258(C).
    13. Tang, Hong & Wang, Shengwei & Li, Hangxin, 2021. "Flexibility categorization, sources, capabilities and technologies for energy-flexible and grid-responsive buildings: State-of-the-art and future perspective," Energy, Elsevier, vol. 219(C).
    14. Fu, Yangyang & O'Neill, Zheng & Wen, Jin & Pertzborn, Amanda & Bushby, Steven T., 2022. "Utilizing commercial heating, ventilating, and air conditioning systems to provide grid services: A review," Applied Energy, Elsevier, vol. 307(C).
    15. Fu, Yangyang & Han, Xu & Baker, Kyri & Zuo, Wangda, 2020. "Assessments of data centers for provision of frequency regulation," Applied Energy, Elsevier, vol. 277(C).
    16. Morovat, Navid & Athienitis, Andreas K. & Candanedo, José Agustín & Nouanegue, Hervé Frank, 2024. "Heuristic model predictive control implementation to activate energy flexibility in a fully electric school building," Energy, Elsevier, vol. 296(C).
    17. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    18. Pang, Simian & Xu, Qingshan & Yang, Yongbiao & Cheng, Aoxue & Shi, Zhengkun & Shi, Yun, 2024. "Robust decomposition and tracking strategy for demand response enhanced virtual power plants," Applied Energy, Elsevier, vol. 373(C).
    19. Hawks, M.A. & Cho, S., 2024. "Review and analysis of current solutions and trends for zero energy building (ZEB) thermal systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    20. Li, Xueqin & Zheng, Zhuoji & Luo, Beier & Shi, Daqian & Han, Xianfeng, 2024. "The impact of electricity sales side reform on energy technology innovation: An analysis based on SCP paradigm," Energy Economics, Elsevier, vol. 136(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:313:y:2024:i:c:s0360544224036363. 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.