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Remote community integrated energy system optimization including building enclosure improvements and quantitative energy trilemma metrics

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journal contribution
posted on 04.05.2020, 10:48 by Marvin Quitoras, Pietro Campana, Paul Rowley, Curran Crawford
Design strategies for sustainable energy systems in remote communities require holistic approaches, as policy, technological development and complex energy systems operation are inherently intertwined. The present work takes a multi-domain perspective in which various energy solution philosophies co-exist. In particular, a multi-objective energy system model has been developed to determine the optimal configuration of integrated electrical and thermal energy systems for Sachs Harbour, the Northernmost community in the Northwest Territories of Canada. From the four scenarios implemented in the model, the Pareto front curves show that the fuel consumption can vary from 0 – 700,000 L/yr while the cost of energy is in the range of 0.5 – 2.7 CND $/kWh. Further, a comparative dynamic simulation has been carried out to analyze the impacts of using electric baseboard heaters versus air-source heat pumps. The results indicate that load fluctuations caused by the variations of the heat pumps’ coefficients of performance negatively impact the operation of the energy system. These demand fluctuations result in a larger battery storage requirement, along with an increase in overall energy system costs. Building enclosure improvements alone were found to reduce space heating loads by up to 40%. Finally, nine solutions of interest from the Pareto front were quantified and tested in the energy trilemma index model. From the multiple viable configurations, the proposed solution was estimated to have a weighted average trilemma score of 73.3. Overall, the use of such innovative modeling approaches in real-world applications can support policy makers to make informed decisions in balancing trade-offs from various energy solution viewpoints.


Polar Knowledge Canada and the Marine Environmental Observation, Prediction and Response Network (MEOPAR)



  • Mechanical, Electrical and Manufacturing Engineering

Research Unit

  • Centre for Renewable Energy Systems Technology (CREST)

Published in

Applied Energy






AM (Accepted Manuscript)

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© Elsevier Ltd

Publisher statement

This paper was accepted for publication in the journal Applied Energy and the definitive published version is available at https://doi.org/10.1016/j.apenergy.2020.115017.

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Dr Paul Rowley. Deposit date: 3 May 2020

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