Optimization of wind turbine location in urban environment
Making energy clean, reliable and readily available is essential for fighting climate change and to supply an ever-rising global power demand. The aim of the study is to identify optimal locations for a wind turbine to be joined to a small-scale hybrid system at the main campus of UiT – The Arctic U...
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UiT The Arctic University of Norway
2017
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Online Access: | https://hdl.handle.net/10037/11620 |
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ftunivtroemsoe:oai:munin.uit.no:10037/11620 2023-05-15T15:55:39+02:00 Optimization of wind turbine location in urban environment Hågbo, Trond-Ola 2017-06-01 https://hdl.handle.net/10037/11620 eng eng UiT The Arctic University of Norway UiT Norges arktiske universitet https://hdl.handle.net/10037/11620 openAccess Copyright 2017 The Author(s) VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430 VDP::Mathematics and natural science: 400::Physics: 430 VDP::Teknologi: 500::Miljøteknologi: 610 VDP::Technology: 500::Environmental engineering: 610 Renewable energy Wind turbine Computational fluid dynamics Geographical information system EOM-3901 Master thesis Mastergradsoppgave 2017 ftunivtroemsoe 2021-06-25T17:55:25Z Making energy clean, reliable and readily available is essential for fighting climate change and to supply an ever-rising global power demand. The aim of the study is to identify optimal locations for a wind turbine to be joined to a small-scale hybrid system at the main campus of UiT – The Arctic University of Norway. To identify feasible areas for maximizing electric power production, techniques originating from two quite different disciplines are utilized: Geographical Information System and Computational Fluid Dynamics. Local weather data and detailed 3D-models are used as inputs to the wind simulations. Two optimal wind turbine locations are proposed with the following UTM-33N coordinates: (654053 – 7735418) at Realfagsbygget and (653410 - 7736185) at Grønnåsen. To further study the power production capability at Grønnåsen wind sensors were installed in the Avinor mast, 200 m east of the suggested optimal location. Here the average wind speed and power density at 21 m above ground level was calculated to be 4.22 m/s and 135.4 W/m^2 corresponding to the period of mid-February to mid-May 2017. For both the suggested optimal locations of a wind turbine, more weather data is necessary to accurately estimate the annual wind speed and power density averages. Master Thesis Climate change Arctic University of Norway UiT The Arctic University of Norway University of Tromsø: Munin Open Research Archive Arctic Norway Grønnåsen ENVELOPE(13.288,13.288,65.615,65.615) |
institution |
Open Polar |
collection |
University of Tromsø: Munin Open Research Archive |
op_collection_id |
ftunivtroemsoe |
language |
English |
topic |
VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430 VDP::Mathematics and natural science: 400::Physics: 430 VDP::Teknologi: 500::Miljøteknologi: 610 VDP::Technology: 500::Environmental engineering: 610 Renewable energy Wind turbine Computational fluid dynamics Geographical information system EOM-3901 |
spellingShingle |
VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430 VDP::Mathematics and natural science: 400::Physics: 430 VDP::Teknologi: 500::Miljøteknologi: 610 VDP::Technology: 500::Environmental engineering: 610 Renewable energy Wind turbine Computational fluid dynamics Geographical information system EOM-3901 Hågbo, Trond-Ola Optimization of wind turbine location in urban environment |
topic_facet |
VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430 VDP::Mathematics and natural science: 400::Physics: 430 VDP::Teknologi: 500::Miljøteknologi: 610 VDP::Technology: 500::Environmental engineering: 610 Renewable energy Wind turbine Computational fluid dynamics Geographical information system EOM-3901 |
description |
Making energy clean, reliable and readily available is essential for fighting climate change and to supply an ever-rising global power demand. The aim of the study is to identify optimal locations for a wind turbine to be joined to a small-scale hybrid system at the main campus of UiT – The Arctic University of Norway. To identify feasible areas for maximizing electric power production, techniques originating from two quite different disciplines are utilized: Geographical Information System and Computational Fluid Dynamics. Local weather data and detailed 3D-models are used as inputs to the wind simulations. Two optimal wind turbine locations are proposed with the following UTM-33N coordinates: (654053 – 7735418) at Realfagsbygget and (653410 - 7736185) at Grønnåsen. To further study the power production capability at Grønnåsen wind sensors were installed in the Avinor mast, 200 m east of the suggested optimal location. Here the average wind speed and power density at 21 m above ground level was calculated to be 4.22 m/s and 135.4 W/m^2 corresponding to the period of mid-February to mid-May 2017. For both the suggested optimal locations of a wind turbine, more weather data is necessary to accurately estimate the annual wind speed and power density averages. |
format |
Master Thesis |
author |
Hågbo, Trond-Ola |
author_facet |
Hågbo, Trond-Ola |
author_sort |
Hågbo, Trond-Ola |
title |
Optimization of wind turbine location in urban environment |
title_short |
Optimization of wind turbine location in urban environment |
title_full |
Optimization of wind turbine location in urban environment |
title_fullStr |
Optimization of wind turbine location in urban environment |
title_full_unstemmed |
Optimization of wind turbine location in urban environment |
title_sort |
optimization of wind turbine location in urban environment |
publisher |
UiT The Arctic University of Norway |
publishDate |
2017 |
url |
https://hdl.handle.net/10037/11620 |
long_lat |
ENVELOPE(13.288,13.288,65.615,65.615) |
geographic |
Arctic Norway Grønnåsen |
geographic_facet |
Arctic Norway Grønnåsen |
genre |
Climate change Arctic University of Norway UiT The Arctic University of Norway |
genre_facet |
Climate change Arctic University of Norway UiT The Arctic University of Norway |
op_relation |
https://hdl.handle.net/10037/11620 |
op_rights |
openAccess Copyright 2017 The Author(s) |
_version_ |
1766391150990589952 |