Exploring electricity generation alternatives for Canadian Arctic communities using a multi-objective genetic algorithm approach
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 gener...
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Energy Conversion and Management
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ftuvicpubl:oai:dspace.library.uvic.ca:1828/11748 2023-05-15T14:25:04+02:00 Exploring electricity generation alternatives for Canadian Arctic communities using a multi-objective genetic algorithm approach Quitoras, Marvin R. Campana, Pietro E. Crawford, Curran 2020 application/pdf http://hdl.handle.net/1828/11748 https://doi.org/10.1016/j.enconman.2020.112471 en eng Energy Conversion and Management Quitoras, 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.2020.112471 https://doi.org/10.1016/j.enconman.2020.112471 http://hdl.handle.net/1828/11748 Arctic environment Energy model Microgrid Renewable energy Optimization Genetic algorithm Postprint 2020 ftuvicpubl https://doi.org/10.1016/j.enconman.2020.112471 2022-05-19T06:12:19Z 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 ... Other/Unknown Material Arctic Arctic Climate change Northwest Territories Sachs Harbour University of Victoria (Canada): UVicDSpace Arctic Canada Handle The ENVELOPE(161.983,161.983,-78.000,-78.000) Northwest Territories Sachs Harbour ENVELOPE(-125.280,-125.280,71.975,71.975) Energy Conversion and Management 210 112471 |
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Open Polar |
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University of Victoria (Canada): UVicDSpace |
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ftuvicpubl |
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English |
topic |
Arctic environment Energy model Microgrid Renewable energy Optimization Genetic algorithm |
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Arctic environment Energy model Microgrid Renewable energy Optimization Genetic algorithm Quitoras, Marvin R. Campana, Pietro E. Crawford, Curran Exploring electricity generation alternatives for Canadian Arctic communities using a multi-objective genetic algorithm approach |
topic_facet |
Arctic environment Energy model Microgrid Renewable energy Optimization Genetic algorithm |
description |
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 ... |
format |
Other/Unknown Material |
author |
Quitoras, Marvin R. Campana, Pietro E. Crawford, Curran |
author_facet |
Quitoras, Marvin R. Campana, Pietro E. Crawford, Curran |
author_sort |
Quitoras, Marvin R. |
title |
Exploring electricity generation alternatives for Canadian Arctic communities using a multi-objective genetic algorithm approach |
title_short |
Exploring electricity generation alternatives for Canadian Arctic communities using a multi-objective genetic algorithm approach |
title_full |
Exploring electricity generation alternatives for Canadian Arctic communities using a multi-objective genetic algorithm approach |
title_fullStr |
Exploring electricity generation alternatives for Canadian Arctic communities using a multi-objective genetic algorithm approach |
title_full_unstemmed |
Exploring electricity generation alternatives for Canadian Arctic communities using a multi-objective genetic algorithm approach |
title_sort |
exploring electricity generation alternatives for canadian arctic communities using a multi-objective genetic algorithm approach |
publisher |
Energy Conversion and Management |
publishDate |
2020 |
url |
http://hdl.handle.net/1828/11748 https://doi.org/10.1016/j.enconman.2020.112471 |
long_lat |
ENVELOPE(161.983,161.983,-78.000,-78.000) ENVELOPE(-125.280,-125.280,71.975,71.975) |
geographic |
Arctic Canada Handle The Northwest Territories Sachs Harbour |
geographic_facet |
Arctic Canada Handle The Northwest Territories Sachs Harbour |
genre |
Arctic Arctic Climate change Northwest Territories Sachs Harbour |
genre_facet |
Arctic Arctic Climate change Northwest Territories Sachs Harbour |
op_relation |
Quitoras, 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.2020.112471 https://doi.org/10.1016/j.enconman.2020.112471 http://hdl.handle.net/1828/11748 |
op_doi |
https://doi.org/10.1016/j.enconman.2020.112471 |
container_title |
Energy Conversion and Management |
container_volume |
210 |
container_start_page |
112471 |
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1766297505243332608 |