District Cooling Versus Individual Cooling in Urban Energy Systems: The Impact of District Energy Share in Cities on the Optimal Storage Sizing

Dominik Dominković*, Goran Krajačić

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

The energy transition of future urban energy systems is still the subject of an ongoing debate. District energy supply can play an important role in reducing the total socio-economic costs of energy systems and primary energy supply. Although lots of research was done on integrated modelling including district heating, there is a lack of research on integrated energy modelling including district cooling. This paper addressed the latter gap using linear continuous optimization model of the whole energy system, using Singapore for a case study. Results showed that optimal district cooling share was 30% of the total cooling energy demand for both developed scenarios, one that took into account spatial constraints for photovoltaics installation and the other one that did not. In the scenario that took into account existing spatial constraints for installations, optimal capacities of methane and thermal energy storage types were much larger than capacities of grid battery storage, battery storage in vehicles and hydrogen storage. Grid battery storage correlated with photovoltaics capacity installed in the energy system. Furthermore, it was shown that successful representation of long-term storage solutions in urban energy models reduced the total socio-economic costs of the energy system for 4.1%.
Original languageEnglish
Article number407
JournalEnergies
Volume12
Issue number3
Number of pages21
ISSN1996-1073
DOIs
Publication statusPublished - 2019

Bibliographical note

This is an open-access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0)

Keywords

  • District cooling
  • Energy storage
  • Linear programming
  • Tropical climate
  • Integrated energy modelling
  • Energy system optimization
  • Temporal resolution
  • Energy planning
  • Variable renewable energy sources

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