IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i10p2427-d1651929.html
   My bibliography  Save this article

The Optimal Operation of Ice-Storage Air-Conditioning Systems by Considering Thermal Comfort and Demand Response

Author

Listed:
  • Chia-Sheng Tu

    (School of Marine Mechatronics, Xiamen Ocean Vocational College, Xiamen 361101, China)

  • Yon-Hon Tsai

    (Institute of Mechatronics Engineering, Cheng-Shiu University, Kaohsiung 833, Taiwan)

  • Ming-Tang Tsai

    (Department of Electrical Engineering, Cheng-Shiu University, Kaohsiung 833, Taiwan)

  • Chih-Liang Chen

    (Department of Electrical Engineering, Cheng-Shiu University, Kaohsiung 833, Taiwan)

Abstract

The purpose of this paper is to discuss the optimal operation of ice-storage air-conditioning systems by considering thermal comfort and demand response (DR) in order to obtain the maximum benefit. This paper first collects the indoor environment parameters and human body parameters to calculate the Predicted Mean Vote (PMV). By considering the DR strategy, the cooling load requirements, thermal comfort, and the various operation constraints, the dispatch model of the ice-storage air-conditioning systems is formulated to minimize the total bill. This paper takes an office building as a case study to analyze the cooling capacity in ice-melting mode and ice-storage mode. A dynamic programming model is used to solve the dispatch model of ice-storage air-conditioning systems, and analyzes the optimal operation cost of ice-storage air-conditioning systems under a two-section and three-section Time-of-Use (TOU) price. The ice-storage mode and ice-melting mode of the ice-storage air-conditioning system are used as the analysis benchmark, and then the energy-saving strategy, thermal comfort, and the demand response (DR) strategy are added for analysis and comparison. It is shown that the total electricity cost of the two-section TOU and three-section TOU was reduced by 18.67% and 333%, respectively, if the DR is considered in our study. This study analyzes the optimal operation of the ice-storage air-conditioning system from an overall perspective under various conditions such as different seasons, time schedules, ice storage and melting, etc. Through the implementation of this paper, the ability for enterprise operation and management control is improved for the participants to reduce peak demand, save on an electricity bill, and raise the ability of the market’s competition.

Suggested Citation

  • Chia-Sheng Tu & Yon-Hon Tsai & Ming-Tang Tsai & Chih-Liang Chen, 2025. "The Optimal Operation of Ice-Storage Air-Conditioning Systems by Considering Thermal Comfort and Demand Response," Energies, MDPI, vol. 18(10), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:10:p:2427-:d:1651929
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/10/2427/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/10/2427/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiong, Chengyan & Meng, Qinglong & Wei, Ying'an & Luo, Huilong & Lei, Yu & Liu, Jiao & Yan, Xiuying, 2023. "A demand response method for an active thermal energy storage air-conditioning system using improved transactive control: On-site experiments," Applied Energy, Elsevier, vol. 339(C).
    2. Xiong, Chengyan & Sun, Zhe & Meng, Qinglong & Li, Zeyang & Wei, Yingan & Zhao, Fan & Jiang, Le, 2022. "A simplified improved transactive control of air-conditioning demand response for determining room set-point temperature: Experimental studies," Applied Energy, Elsevier, vol. 323(C).
    3. Ye, Jin & Wang, Xianlian & Hua, Qingsong & Sun, Li, 2024. "Deep reinforcement learning based energy management of a hybrid electricity-heat-hydrogen energy system with demand response," Energy, Elsevier, vol. 305(C).
    4. Song, Zhaofang & Shi, Jing & Li, Shujian & Chen, Zexu & Jiao, Fengshun & Yang, Wangwang & Zhang, Zitong, 2022. "Data-driven and physical model-based evaluation method for the achievable demand response potential of residential consumers' air conditioning loads," Applied Energy, Elsevier, vol. 307(C).
    5. Chi-Chun Lo & Shang-Ho Tsai & Bor-Shyh Lin, 2016. "Ice Storage Air-Conditioning System Simulation with Dynamic Electricity Pricing: A Demand Response Study," Energies, MDPI, vol. 9(2), pages 1-16, February.
    6. Ali Dargahi & Khezr Sanjani & Morteza Nazari-Heris & Behnam Mohammadi-Ivatloo & Sajjad Tohidi & Mousa Marzband, 2020. "Scheduling of Air Conditioning and Thermal Energy Storage Systems Considering Demand Response Programs," Sustainability, MDPI, vol. 12(18), pages 1-13, September.
    7. Xu, Ruoyu & Liu, Xiaochen & Liu, Xiaohua & Zhang, Tao, 2024. "Quantifying the energy flexibility potential of a centralized air-conditioning system: A field test study of hub airports," Energy, Elsevier, vol. 298(C).
    8. Baumgärtner, Nils & Delorme, Roman & Hennen, Maike & Bardow, André, 2019. "Design of low-carbon utility systems: Exploiting time-dependent grid emissions for climate-friendly demand-side management," Applied Energy, Elsevier, vol. 247(C), pages 755-765.
    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. Xiong, Chengyan & Meng, Qinglong & Wei, Ying'an & Luo, Huilong & Lei, Yu & Liu, Jiao & Yan, Xiuying, 2023. "A demand response method for an active thermal energy storage air-conditioning system using improved transactive control: On-site experiments," Applied Energy, Elsevier, vol. 339(C).
    2. Ting Wang & Qiya Wang & Caiqing Zhang, 2021. "Research on the Optimal Operation of a Novel Renewable Multi-Energy Complementary System in Rural Areas," Sustainability, MDPI, vol. 13(4), pages 1-16, February.
    3. Hao, Ling & Wei, Mingshan & Xu, Fei & Yang, Xiaochen & Meng, Jia & Song, Panpan & Min, Yong, 2020. "Study of operation strategies for integrating ice-storage district cooling systems into power dispatch for large-scale hydropower utilization," Applied Energy, Elsevier, vol. 261(C).
    4. Meng, Qinglong & Wei, Ying'an & Fan, Jingjing & Li, Yanbo & Zhao, Fan & Lei, Yu & Sun, Hang & Jiang, Le & Yu, Lingli, 2024. "Peak regulation strategies for ground source heat pump demand response of based on load forecasting: A case study of rural building in China," Renewable Energy, Elsevier, vol. 224(C).
    5. Arlt, Marie-Louise & Chassin, David & Rivetta, Claudio & Sweeney, James, 2024. "Impact of real-time pricing and residential load automation on distribution systems," Energy Policy, Elsevier, vol. 184(C).
    6. Pei Cai & Youxue Jiang & He Wang & Liangyu Wu & Peng Cao & Yulong Zhang & Feng Yao, 2020. "Numerical Simulation on the Influence of the Longitudinal Fins on the Enhancement of a Shell-and-Tube Ice Storage Device," Sustainability, MDPI, vol. 12(6), pages 1-14, March.
    7. Wolf, Isabel & Holzapfel, Peter K.R. & Meschede, Henning & Finkbeiner, Matthias, 2023. "On the potential of temporally resolved GHG emission factors for load shifting: A case study on electrified steam generation," Applied Energy, Elsevier, vol. 348(C).
    8. Maximilian Hoffmann & Leander Kotzur & Detlef Stolten & Martin Robinius, 2020. "A Review on Time Series Aggregation Methods for Energy System Models," Energies, MDPI, vol. 13(3), pages 1-61, February.
    9. Huang, Sen & Lian, Jianming & Katipamula, Srinivas & Lutes, Robert, 2025. "Transactive control for grid-interactive efficient commercial buildings," Applied Energy, Elsevier, vol. 378(PA).
    10. Wagner, Lukas Peter & Reinpold, Lasse Matthias & Kilthau, Maximilian & Fay, Alexander, 2023. "A systematic review of modeling approaches for flexible energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    11. Fleschutz, Markus & Bohlayer, Markus & Braun, Marco & Henze, Gregor & Murphy, Michael D., 2021. "The effect of price-based demand response on carbon emissions in European electricity markets: The importance of adequate carbon prices," Applied Energy, Elsevier, vol. 295(C).
    12. Golmohamadi, Hessam, 2022. "Demand-side management in industrial sector: A review of heavy industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    13. Kanakadhurga, Dharmaraj & Prabaharan, Natarajan, 2024. "Smart home energy management using demand response with uncertainty analysis of electric vehicle in the presence of renewable energy sources," Applied Energy, Elsevier, vol. 364(C).
    14. Wang, Jiawei & Wang, Yi & Qiu, Dawei & Su, Hanguang & Strbac, Goran & Gao, Zhiwei, 2025. "Resilient energy management of a multi-energy building under low-temperature district heating: A deep reinforcement learning approach," Applied Energy, Elsevier, vol. 378(PA).
    15. Mateusz Andrychowicz, 2021. "RES and ES Integration in Combination with Distribution Grid Development Using MILP," Energies, MDPI, vol. 14(2), pages 1-19, January.
    16. Shukai Liu & Liang Dong & Ling Han & Jiajia Huan & Baihao Qiao, 2022. "Efficiency versus System Synergism: An Advanced Life Cycle Assessment for a Novel Decarbonized Grid System Innovation," Energies, MDPI, vol. 15(12), pages 1-15, June.
    17. Nilges, B. & Haese, A. & Reinert, C. & von der Assen, N., 2025. "Validity ranges of locational marginal emission factors for the environmental assessment of electric load shifting," Applied Energy, Elsevier, vol. 380(C).
    18. Fleschutz, Markus & Bohlayer, Markus & Braun, Marco & Murphy, Michael D., 2025. "The hidden cost of using time series aggregation for modeling low-carbon industrial energy systems: An investors’ perspective," Energy, Elsevier, vol. 318(C).
    19. Biéron, M. & Le Dréau, J. & Haas, B., 2023. "Assessment of the marginal technologies reacting to demand response events: A French case-study," Energy, Elsevier, vol. 275(C).
    20. Song, Yuguang & Xia, Mingchao & Chen, Qifang & Chen, Fangjian, 2023. "A data-model fusion dispatch strategy for the building energy flexibility based on the digital twin," Applied Energy, Elsevier, vol. 332(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:gam:jeners:v:18:y:2025:i:10:p:2427-:d:1651929. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.