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Building-mix optimization in district cooling system implementation

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  • Chow, T. T.
  • Chan, Apple L. S.
  • Song, C. L.

Abstract

A district-cooling system (DCS) has been applied in a number of countries where chilled water from a central plant is delivered through a distribution network to groups of buildings in an urban district. Because of the expected considerable investment and lengthy payback period, well-planned and optimized system design and operation are crucial areas leading to the success of the implementation. Much saving can be achieved when the plant serves a group of buildings with diversifying daily cooling-load patterns. Among various design factors and solution schemes, one important planning decision is therefore to determine the desirable mix of building types, within the district of interest, to be served by the DCS. An approach to determine this optimal mix through the use of genetic algorithm (GA) was described in this paper. The thermal-load modeling technique and the objective function for optimization were derived. The case studies showed that the method was effective to give optimal or near-optimal solutions.

Suggested Citation

  • Chow, T. T. & Chan, Apple L. S. & Song, C. L., 2004. "Building-mix optimization in district cooling system implementation," Applied Energy, Elsevier, vol. 77(1), pages 1-13, January.
    • Handle: RePEc:eee:appene:v:77:y:2004:i:1:p:1-13
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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.
    2. An, Jingjing & Yan, Da & Hong, Tianzhen & Sun, Kaiyu, 2017. "A novel stochastic modeling method to simulate cooling loads in residential districts," Applied Energy, Elsevier, vol. 206(C), pages 134-149.
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    4. 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).
    5. 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).
    6. Manfren, Massimiliano & Caputo, Paola & Costa, Gaia, 2011. "Paradigm shift in urban energy systems through distributed generation: Methods and models," Applied Energy, Elsevier, vol. 88(4), pages 1032-1048, April.
    7. Ma, Zhenjun & Wang, Shengwei, 2009. "Building energy research in Hong Kong: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1870-1883, October.
    8. Li, Yu & Rezgui, Yacine & Zhu, Hanxing, 2017. "District heating and cooling optimization and enhancement – Towards integration of renewables, storage and smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 281-294.
    9. 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.
    10. 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.
    11. 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.
    12. Luerssen, Christoph & Gandhi, Oktoviano & Reindl, Thomas & Sekhar, Chandra & Cheong, David, 2019. "Levelised Cost of Storage (LCOS) for solar-PV-powered cooling in the tropics," Applied Energy, Elsevier, vol. 242(C), pages 640-654.
    13. Luerssen, Christoph & Gandhi, Oktoviano & Reindl, Thomas & Sekhar, Chandra & Cheong, David, 2020. "Life cycle cost analysis (LCCA) of PV-powered cooling systems with thermal energy and battery storage for off-grid applications," Applied Energy, Elsevier, vol. 273(C).
    14. 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.
    15. Wu, Qiong & Ren, Hongbo & Gao, Weijun & Weng, Peifen & Ren, Jianxing, 2018. "Coupling optimization of urban spatial structure and neighborhood-scale distributed energy systems," Energy, Elsevier, vol. 144(C), pages 472-481.
    16. Chinese, Damiana & Meneghetti, Antonella, 2005. "Optimisation models for decision support in the development of biomass-based industrial district-heating networks in Italy," Applied Energy, Elsevier, vol. 82(3), pages 228-254, November.
    17. Luerssen, Christoph & Verbois, Hadrien & Gandhi, Oktoviano & Reindl, Thomas & Sekhar, Chandra & Cheong, David, 2021. "Global sensitivity and uncertainty analysis of the levelised cost of storage (LCOS) for solar-PV-powered cooling," Applied Energy, Elsevier, vol. 286(C).
    18. 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.
    19. Ge, Gaoming & Xiao, Fu & Xu, Xinhua, 2011. "Model-based optimal control of a dedicated outdoor air-chilled ceiling system using liquid desiccant and membrane-based total heat recovery," Applied Energy, Elsevier, vol. 88(11), pages 4180-4190.
    20. Best, Robert E. & Flager, Forest & Lepech, Michael D., 2015. "Modeling and optimization of building mix and energy supply technology for urban districts," Applied Energy, Elsevier, vol. 159(C), pages 161-177.
    21. Zheng Li & Ruoyao Tang & Hanbin Qiu & Linwei Ma, 2023. "Smart Energy Urban Agglomerations in China: The Driving Mechanism, Basic Concepts, and Indicator Evaluation," Sustainability, MDPI, vol. 15(15), pages 1-23, August.

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