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Exploring electricity generation alternatives for Canadian Arctic communities using a multi-objective genetic algorithm approach
University of Victoria, Victoria, Canada.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-1351-9245
University of Victoria, Victoria, Canada.
2020 (English)In: Energy Conversion and Management, ISSN 0196-8904, E-ISSN 1879-2227, Vol. 210, article id 112471Article in journal (Refereed) Published
Abstract [en]

Indigenous peoples in the Northern communities of Canada are experiencing some of the worst catastrophic effects of climate change, given the Arctic region is warming twice as fast as the rest of the world. Paradoxically, this increasing temperature can be attributed to fossil fuel-based power generation on which the North is almost totally reliant. At the moment, diesel is the primary source of electricity for majority of Arctic communities. In addition to greenhouse gas and other airborne pollutants, this situation exposes risk of oil spills during fuel transport and storage. Moreover, shipping fuel is expensive and ice roads are harder to maintain as temperatures rise. As a result, Northern governments are burdened by rising fuel prices and increased supply volatility. In an effort to reduce diesel dependence, the multi-objective microgrid optimization model was built in this work to handle the complex trade-offs of designing energy system for an Arctic environment and other remote communities. The tool uses a genetic algorithm to simultaneously minimize levelised cost of energy and fuel consumption of the microgrid system through dynamic simulations. Component submodel simulation results were validated against an industry and academic accepted energy modeling tool. Compared to previous energy modeling platforms, proposed method is novel in considering Pareto front trade-offs between conflicting design objectives to better support practitioners and policy makers. The functionality of the method was demonstrated with a case study on Sachs Harbour, in the Northernmost region of the Northwest Territories. The algorithm selected a fully hybrid wind-solar-battery-diesel system as the most suited technically, economically and environmentally for the community. The robustness of the results was assessed by performing system failure analysis of the model results. Overall, the modeling framework can help decision makers in identifying trade-offs in energy policy to transition the Canadian Arctic and other remote communities towards more sustainable and clean sources of energy.

Place, publisher, year, edition, pages
Elsevier Ltd , 2020. Vol. 210, article id 112471
Keywords [en]
Arctic environment, Energy model, Genetic algorithm, Microgrid, Optimization, Renewable energy
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-47384DOI: 10.1016/j.enconman.2020.112471Scopus ID: 2-s2.0-85081220973OAI: oai:DiVA.org:mdh-47384DiVA, id: diva2:1415576
Available from: 2020-03-19 Created: 2020-03-19 Last updated: 2020-03-19Bibliographically approved

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Campana, Pietro Elia

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