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Applying district-cooling technology in Hong Kong

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  • Chow, T. T.
  • Au, W. H.
  • Yau, Raymond
  • Cheng, Vincent
  • Chan, Apple
  • Fong, K. F.

Abstract

Land reclamation has been a long-term government policy of expanding the Hong Kong urban areas along the waterfront. These flat pieces of land are ideal sites for the application of district-cooling technology. At a central refrigeration plant, chilled water is generated and supplied to a district to support the air-conditioning systems in buildings. Because of the large-scale production, together with the convenience of bringing in seawater for condenser cooling, the chiller plant is higher in efficiency than those in individual buildings. The customers can also use the building space of their own more effectively. In this paper, the technical requirements and the cooling scheme options in the context of the subtropical urban environment are discussed. A government-commissioned feasibility study of a proposed district-cooling site in Hong Kong, with an estimated 200 MW cooling-plant capacity is then described. The proposed system and the methodology in predicting the thermal demand and the energy consumptions are introduced.

Suggested Citation

  • Chow, T. T. & Au, W. H. & Yau, Raymond & Cheng, Vincent & Chan, Apple & Fong, K. F., 2004. "Applying district-cooling technology in Hong Kong," Applied Energy, Elsevier, vol. 79(3), pages 275-289, November.
    • Handle: RePEc:eee:appene:v:79:y:2004:i:3:p:275-289
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    References listed on IDEAS

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    Cited by:

    1. Yan, Chengchu & Gang, Wenjie & Niu, Xiaofeng & Peng, Xujian & Wang, Shengwei, 2017. "Quantitative evaluation of the impact of building load characteristics on energy performance of district cooling systems," Applied Energy, Elsevier, vol. 205(C), pages 635-643.
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    3. Gang, Wenjie & Augenbroe, Godfried & Wang, Shengwei & Fan, Cheng & Xiao, Fu, 2016. "An uncertainty-based design optimization method for district cooling systems," Energy, Elsevier, vol. 102(C), pages 516-527.
    4. Jannatabadi, Mohsen & Rahbari, Hamid Reza & Arabkoohsar, Ahmad, 2021. "District cooling systems in Iranian energy matrix, a techno-economic analysis of a reliable solution for a serious challenge," Energy, Elsevier, vol. 214(C).
    5. Neri, Manfredi & Guelpa, Elisa & Verda, Vittorio, 2022. "Design and connection optimization of a district cooling network: Mixed integer programming and heuristic approach," Applied Energy, Elsevier, vol. 306(PA).
    6. Shu, Haiwen & Duanmu, Lin & Zhang, Chaohui & Zhu, Yingxin, 2010. "Study on the decision-making of district cooling and heating systems by means of value engineering," Renewable Energy, Elsevier, vol. 35(9), pages 1929-1939.
    7. Happle, Gabriel & Fonseca, Jimeno A. & Schlueter, Arno, 2020. "Impacts of diversity in commercial building occupancy profiles on district energy demand and supply," Applied Energy, Elsevier, vol. 277(C).
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    9. Colmenar-Santos, Antonio & Rosales-Asensio, Enrique & Borge-Diez, David & Collado-Fernández, Eduardo, 2016. "Evaluation of the cost of using power plant reject heat in low-temperature district heating and cooling networks," Applied Energy, Elsevier, vol. 162(C), pages 892-907.
    10. Gang, Wenjie & Wang, Shengwei & Gao, Diance & Xiao, Fu, 2015. "Performance assessment of district cooling systems for a new development district at planning stage," Applied Energy, Elsevier, vol. 140(C), pages 33-43.
    11. Valerie Eveloy & Dereje S. Ayou, 2019. "Sustainable District Cooling Systems: Status, Challenges, and Future Opportunities, with Emphasis on Cooling-Dominated Regions," Energies, MDPI, vol. 12(2), pages 1-64, January.
    12. Chan, Apple L.S. & Chow, Tin-Tai & Fong, Square K.F. & Lin, John Z., 2006. "Performance evaluation of district cooling plant with ice storage," Energy, Elsevier, vol. 31(14), pages 2750-2762.
    13. Si, Pengfei & Li, Angui & Rong, Xiangyang & Feng, Ya & Yang, Zhengwu & Gao, Qinglong, 2015. "New optimized model for water temperature calculation of river-water source heat pump and its application in simulation of energy consumption," Renewable Energy, Elsevier, vol. 84(C), pages 65-73.
    14. Osorio, Andrés F. & Arias-Gaviria, Jessica & Devis-Morales, Andrea & Acevedo, Diego & Velasquez, Héctor Iván & Arango-Aramburo, Santiago, 2016. "Beyond electricity: The potential of ocean thermal energy and ocean technology ecoparks in small tropical islands," Energy Policy, Elsevier, vol. 98(C), pages 713-724.
    15. Zhen, Li & Lin, D.M. & Shu, H.W. & Jiang, Shuang & Zhu, Y.X., 2007. "District cooling and heating with seawater as heat source and sink in Dalian, China," Renewable Energy, Elsevier, vol. 32(15), pages 2603-2616.
    16. Pan, Wei & Qin, Hao & Zhao, Yisong, 2017. "Challenges for energy and carbon modeling of high-rise buildings: The case of public housing in Hong Kong," Resources, Conservation & Recycling, Elsevier, vol. 123(C), pages 208-218.
    17. Rismanchi, B., 2017. "District energy network (DEN), current global status and future development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 571-579.
    18. Chua, K.J. & Chou, S.K. & Yang, W.M. & Yan, J., 2013. "Achieving better energy-efficient air conditioning – A review of technologies and strategies," Applied Energy, Elsevier, vol. 104(C), pages 87-104.
    19. Čož, T. Duh & Kitanovski, A. & Poredoš, A., 2017. "Exergoeconomic optimization of a district cooling network," Energy, Elsevier, vol. 135(C), pages 342-351.
    20. Gang, Wenjie & Wang, Shengwei & Xiao, Fu & Gao, Dian-ce, 2016. "District cooling systems: Technology integration, system optimization, challenges and opportunities for applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 253-264.
    21. Chen, Qun & Wang, Yi-Fei & Xu, Yun-Chao, 2015. "A thermal resistance-based method for the optimal design of central variable water/air volume chiller systems," Applied Energy, Elsevier, vol. 139(C), pages 119-130.
    22. Su, Lingqi & Nie, Ting & On Ho, Chi & Yang, Zheng & Calvez, Philippe & Jain, Rishee K. & Schwegler, Ben, 2022. "Optimizing pipe network design and central plant positioning of district heating and cooling System: A Graph-Based Multi-Objective genetic algorithm approach," Applied Energy, Elsevier, vol. 325(C).

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