the global wind atlas: investigating country wind resources … · 2018-12-31 · head of section,...
TRANSCRIPT
Introduction to Global Wind Atlas Webinar
The Global Wind Atlas: Investigating Country Wind Resources
Presented by Jake BadgerHead of Section, Wind Resource Assessment ModellingDTU Wind Energy
DTU Wind Energy, Technical University of Denmark 20 November 2018
Global Wind Atlas model chain
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DTU Wind Energy, Technical University of Denmark 20 November 2018
The Team
• DTU Wind Energy
J Badger, B Hansen, N Davis, B Olsen, A Hahmann, N Mortensen, J Hansen• Nazka Mapps
I Bauwens, I Dautashvil, D Codrescu, A Vaidya
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• World in a Box
D Heathfield, M Onninen
Vortex
O Lacave, P Casso, G Lizcano, A BoschWorld Bank
O Knight, T P Nguyen, S Krohn, A v Loon
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Context of the Global Wind Atlas
www.globalwindatlas.info www.globalsolaratlas.info www.energydata.info
DTU Wind Energy, Technical University of Denmark 20 November 2018
PhilippinesWind speed at 100 m
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DTU Wind Energy, Technical University of Denmark 20 November 2018
Philippines example features: flow between high terrain
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DTU Wind Energy, Technical University of Denmark 20 November 2018
Elevation
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Roughness
DTU Wind Energy, Technical University of Denmark 20 November 2018
Map and Satellite imagery
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DTU Wind Energy, Technical University of Denmark 20 November 2018
Capacity Factor
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DTU Wind Energy, Technical University of Denmark 20 November 2018
Capacity Factor and ruggedness index
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DTU Wind Energy, Technical University of Denmark 20 November 2018
Validation GWA2
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60 wind measurement stations 7 countries (UK, CN, MX, EG, CV, ZA, DK)
Mean error = -3 %Mean absolute error = 9 %Standard deviation = 11%
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GWA derived mean wind speed v observed wind speed
GW
A d
eriv
ed w
ind
spee
d m
/s
Obs. wind speed m/s
DTU Wind Energy, Technical University of Denmark 20 November 2018
Validation
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DTU Wind Energy, Technical University of Denmark 20 November 2018
Example: Observed vs Predicted wind climate 80 m a.s.l. Site: Chanka, Zambia
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Sergio Roldan, Jean-Marc Bernier, Shant Dokouzian;
Commissioning Report for Chanka, Zambia; DNV GL, 2017
Observed
Mean wind speed 6.46 m/s
Mean power density 206 W/m2
Predicted by GWA3 model chain
Mean wind speed 6.53 m/s
Mean power density 210 W/m2
Site reporting and resource assessment source:
DTU Wind Energy, Technical University of Denmark 20 November 2018
•understand physical phenomena that give wind resources–mesoscale effects–microscale effects–wind speeds and wind roses
•quantify the resource –wind power density–capacity factors
•estimate level of uncertainty–ruggedness index–integrated validation results for the future
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Summary
With the Global Wind Atlas you can
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