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Potential of district cooling in hot and humid climates

Author

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  • Dominković, D.F.
  • Bin Abdul Rashid, K.A.
  • Romagnoli, A.
  • Pedersen, A.S.
  • Leong, K.C.
  • Krajačić, G.
  • Duić, N.

Abstract

Efficiently utilizing energy that is currently being wasted can significantly increase energy efficiency of the system, as well as reduce the carbon footprint. In hot climates with large cooling demands, excess waste heat can be utilized via absorption chillers to generate cold. Moreover, cold from liquefied natural gas gasification process can further provide energy source for meeting the cold demand. In order to connect the large sources of waste heat and cold energy with customers demanding the cold, a significant investment in district cooling grid is a necessity. In order to deal with the mentioned issue, an existing energy balance model was complemented with Matlab algorithms in order to model the whole energy system, including the detailed representation of the district cooling grid. Singapore was chosen for a case study and several different scenarios were developed for the year 2050, with the main indicators being total primary energy supply, total CO2 emissions and total socio-economic costs. The most beneficial scenario for the year 2050 had 19.5% lower primary energy demand, 38.4% lower total socio-economic costs and 41.5% lower CO2 emissions compared to the business-as-usual scenario for the year 2050, although significant investment in the district cooling grid was included in the calculations.

Suggested Citation

  • Dominković, D.F. & Bin Abdul Rashid, K.A. & Romagnoli, A. & Pedersen, A.S. & Leong, K.C. & Krajačić, G. & Duić, N., 2017. "Potential of district cooling in hot and humid climates," Applied Energy, Elsevier, vol. 208(C), pages 49-61.
    • Handle: RePEc:eee:appene:v:208:y:2017:i:c:p:49-61
    DOI: 10.1016/j.apenergy.2017.09.052
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    1. Broadstock, David C. & Wang, Xiangnan, 2024. "District cooling services: A bibliometric review and topic classification of existing research," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PB).
    2. Diego Viesi & Gregorio Borelli & Silvia Ricciuti & Giovanni Pernigotto & Md Shahriar Mahbub, 2024. "Modeling the Optimal Transition of an Urban Neighborhood towards an Energy Community and a Positive Energy District," Energies, MDPI, vol. 17(16), pages 1-30, August.
    3. Zhou, Yuekuan & Zheng, Siqian & Hensen, Jan L.M., 2024. "Machine learning-based digital district heating/cooling with renewable integrations and advanced low-carbon transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).

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