Exploring electricity generation alternatives for Canadian Arctic communities using a multi-objective genetic algorithm approach

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dc.contributor.authorQuitoras, Marvin R.
dc.contributor.authorCampana, Pietro E.
dc.contributor.authorCrawford, Curran
dc.date.accessioned2020-05-13T23:32:20Z
dc.date.available2020-05-13T23:32:20Z
dc.date.copyright2020en_US
dc.date.issued2020
dc.description.abstractIndigenous 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.en_US
dc.description.embargo2022-04-15
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.description.sponsorshipFunding for this work was provided by Polar Knowledge Canada and the Marine Environmental Observation, Prediction and Response Network. Authors would also like to acknowledge support from the Government of Northwest Territories and Northwest Territories Power Corporation by providing actual electrical load and wind data which served as vital inputs for the project. Marvin Quitoras acknowledges the financial support from Mitacs Canada.en_US
dc.identifier.citationQuitoras, M. R., Campana, P. E., & Crawford, C. (2020). Exploring electricity generation alternatives for Canadian Arctic communities using a multi-objective genetic algorithm approach. Energy Conversion and Management, 210, 1-19. https://doi.org/10.1016/j.enconman.2020.112471en_US
dc.identifier.urihttps://doi.org/10.1016/j.enconman.2020.112471
dc.identifier.urihttp://hdl.handle.net/1828/11748
dc.language.isoenen_US
dc.publisherEnergy Conversion and Managementen_US
dc.subjectArctic environment
dc.subjectEnergy model
dc.subjectMicrogrid
dc.subjectRenewable energy
dc.subjectOptimization
dc.subjectGenetic algorithm
dc.subject.departmentDepartment of Mechanical Engineering
dc.titleExploring electricity generation alternatives for Canadian Arctic communities using a multi-objective genetic algorithm approachen_US
dc.typePostprinten_US

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