Reviewing two decades of energy system analysis with bibliometrics

Acknowledgements Dominik Franjo Dominkovi´c was funded by the CITIES project nr. DSF1305-00027B and Cool-Data project nr. 0177-00066B, both funded by the Danish Innovationsfonden. Fabian Scheller kindly acknowledges the financial support of the European Union’s research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 713683 (COFUNDfellowsDTU). The authors also thank three anonymous reviewers for their helpful comments on an earlier version of this paper. The usual disclaimer applies.Peer reviewedPostprin

Reviewing two decades of energy system analysis with bibliometrics

Acknowledgements Dominik Franjo Dominkovi´c was funded by the CITIES project nr. DSF1305-00027B and Cool-Data project nr. 0177-00066B, both funded by the Danish Innovationsfonden. Fabian Scheller kindly acknowledges the financial support of the European Union’s research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 713683 (COFUNDfellowsDTU). The authors also thank three anonymous reviewers for their helpful comments on an earlier version of this paper. The usual disclaimer applies.Peer reviewedPostprin

Modelling smart energy systems in tropical regions

A large majority of energy systems models of smart urban energy systems are modelling moderate climate with seasonal variations, such as the European ones. The climate in the tropical region is dominated by very high stable temperatures and high humidity and lacks the moderate climate's seasonality. Furthermore, the smart energy system models tend to focus on CO2 emissions only and lack integrated air pollution modelling of other air pollutants. In this study, an integrated urban energy system for a tropical climate was modelled, including modelling the interactions between power, cooling, gas, mobility and water desalination sectors. Five different large scale storages were modelled, too. The developed linear optimization model further included endogenous decisions about the share of district versus individual cooling, implementation of energy efficiency solutions and implementation of demand response measures in buildings and industry. Six scenarios for the year 2030 were developed in order to present a stepwise increase in energy system integration in a transition to a smart urban energy system in Singapore. The economically best performing scenario had 48% lower socio-economic costs, 68% lower CO2e emissions, 15% higher particulate matter emissions and 2% larger primary energy consumption compared to a business-as-usual case.</p