Representative day selection using statistical bootstrapping for accelerating annual distribution simulations

Palmintier, Bryan; Bugbee, Bruce; Gotseff, Peter

doi:10.1109/isgt.2017.8086066

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…One paper demonstrated the possibility of using statistics-based methods for selecting the optimal subset of the representative days to capture the variability of solar generation and power load in the distribution system [21]. They significantly reduced the number of days that need to be represented, from 365 to 40.…”

Section: Introductionmentioning

confidence: 99%

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

Dominković

Krajačić

2019

Energies

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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%.

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Section: Introductionmentioning

confidence: 99%