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© Vattenfall AB
Vattenfall Perspective on Wind in Forest
Jens MadsenPrincipal R&D Engineer, Ph.D
Vattenfall Research & Development AB
© Vattenfall AB 2
Presentation Outline
• Who are we?– Short introduction to Vattenfall
• Why do we care about “wind in forest”?– Our motivation
• What are we doing?– Overview of forest-related activities (measurements, CFD, …)
• Where do we want to go?
Acknowledgements: Adrien Corre, Jan-Åke Dahlberg, Rasmus Bernsdorff
© Vattenfall AB 3
Vattenfall AB at a glance
The Vattenfall Group• wholly owned by the Swedish State• Europe’s 5th largest producer of electricity• Europe’s largest producer of heat
Key Figures • Net sales: € 21,2 billion • Electricity generation: 183.4TWh
– Wind power is only 1-2%
• Heat generation: 36,2TWh• More than 43,000 employees
Vattenfall Wind Power• Largest Nordic generator of wind power • World’s 2nd largest offshore wind
owner/operator
Onshore 530 MWOffshore 370 MW
Production 2,2 TWh
Onshore 530 MWOffshore 370 MW
Production 2,2 TWh
Wind Power Assets
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Welcome to our world – it’s full of trees !
• Aggressive growth in wind power portfolio– Majority of onshore projects (Sweden, UK,
Denmark) are in areas affected by forest
• Sweden has 60-65% forest cover – About 18% of all forest in Europe– Forest coverage in comparison:
• Denmark: 11%• United Kingdom: 12% (Scotland 15%)• Germany: 31%• European average: 35-45%
• Need to understand wind conditions in forest– 35 met masts and 20 SODAR systems in
operation (mostly in southern Sweden) – High turbulence and wind shear confirmed– A matter of techno-economical risk mitigation
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Ryningsnäs – Forest Test Site
Improve knowledge on wind power in forest– Wind measurements using SODAR and met mast
(96m, 5booms / 140m, 7 booms)
– Two Nordex turbines (2.5MW) with hub heights 80m and 100m
Foto: Hans Blomberg
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Ryningsnäs
Foto: Hans Blomberg
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Ryningsnäs – wind resources
• Site wind resources much poorer than expected– 6m/s mean wind speed (measured @ 100m-agl / 88m over zero plane)– MIUU windmapping of Sweden (meso-scale) predicted 7.2m/s– Translates to an AEP of 7TWh, much lower than expected 12TWh– The 100m hub WT produces 35% more than the 80m hub turbine
0
200
400
600
800
1000
1200
1400
0 1 2 3 4 5 6 7 8 9 10 11 12
Vindhastighet i navhöjd
Effe
kt k
W
p100_ave
p80_ave (WS-100m)
Ca 35% högre produktion med 100m navhöjd
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Ryningsnäs – Wind Shear & Turbulence
• Large wind shear observed (up to: α = 0.6)
• High turbulence levels (typically TI=20..25% at hub height)
0
20
40
60
80
100
120
140
160
0 1 2 3 4 5 6 7 8
Vindhastighet
Hö
jd
Alfa = 0,41
Alfa = 0,2
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Wind Shear – seen with the naked eye
120-meter mast at Vattenfall site in Southern Sweden
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Ryningsnäs – Load variations in blade root
• Clear advantages of higher hub heights– Higher energy production
– Lower turbulence
– Less variations in WT loads
Flap moment standard deviation
0
100
200
300
400
500
600
700
0 2 4 6 8 10 12
Wind speed m/s
f1_100_ave
f2_100_ave
f3_100_ave
f1_80_ave
f2_80_ave
f3_80_ave
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Forest Canopy Models
CFD school• Porous zone with drag resistance• Turbulence modulation• Applies first principles
WAsP school• Increase roughness class • Add zero-plane displacement• Applies empirical information
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CFD Forest Test Model
U*=0.58
K=0.42 (Von Karman constant)
Z0=0.005
Inlet profile:
TKE inlet:
Dissipation rate inlet:
With:
Where:
k − ε constant:
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Forest Canopy Model of Katul et al.
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Forest Characterization Could Matter …
Dalpé & Masson, EWEC-2007
Pine Forest
Uniform Sitka Spruce
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Two cases considered
• CASE 1– Comparison forest constant
resistance with LAI = 4.2 vs forest with LAI = 8.6
– Determine the impact of forest density.
• CASE 2– Comparison forest constant
resistance forest (LAI = 2.03) vs profiled resistance with LAI = 2.03 (jack pine forest)
– Investigate impact of forest density profile.
Case 2: Leaf area density distribution
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1 1.2
α
z/h
Constant distribution LAI=4.2
Spruce forest, LAI=4.2
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Case 1: Velocity magnitude
Velocity magnitude, case 1
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0 2 4 6 8 10 12
Velocity (m/s)
z/h
start forest constant resistance LAI=4.2 start forest constant resistance LAI=8.6
middle forest constant resistance LAI=4.2 middle forest constant resistance LAI=8.6
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Case 1: Turbulence Intensity
Turbulence intensity, case 1
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0 0.2 0.4 0.6 0.8 1 1.2 1.4
TI
z/h
start forest LAI=4.2 constant resistance
start forest LAI=8.6 constant resistance
middle forest LAI=4.2 constant resistance
middle forest LAI=8.6 constant resistance
© Vattenfall AB 18
Case 2: Velocity magnitude
Velocity magnitude, case 2
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0 1 2 3 4 5 6 7 8 9 10
Velocity magnitude (m/s)
z/h
Start forest constant resistance LAI=4.2
Start forest profiled resistance LAI=4.2
Middle forest constant resistance LAI=4.2
Middle forest profiledt resistance LAI=4.2
© Vattenfall AB 19
Case 2: Turbulence intensity
Turbulence Intensity, case 2
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0 0.2 0.4 0.6 0.8 1 1.2 1.4
TI
z/h
Start forest constant resistance LAI = 2.03
Start forest profiled resistance LAI = 2.03
Middle forest constant resistance LAI = 2.03
Middle forest profiled resistance LAI = 2.03
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Thoughts on CFD canopy modeling
• The idealized, homogeneous forest does not exist– What is the impact of a heterogeneous forest layout?– Main difference in canopy model flow predictions in zones with
changes in roughness and density
• Conclusion– Canopy models are sufficiently good …– … considering the poor parameters we feed into them
• Spatial distribution of forest height and density– From a practical standpoint, there is no sense in continuing to tweak
models until better inputs become available– Implement advanced forest characterization techniques
© Vattenfall AB 21
LIDAR Airborne Forest Imaging
• Technology used in Forest Inventory Management– Laser beam is reflected either by canopy or ground– Scans 500-800 meter wide section per flight leg– 10 cm accuracy (height)
• Data provided– Digital Terrain Model (DTM)– Forest parameters
• Mean tree height (± 5%)
• Density parameters (such as LAI)
– Detailed input for CFD forest canopy models
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Final remarks
• Other activities– Forest model validation studies
– Noise dispersion in the forest
• Validation of Nord2000 model
– Wake effects in forest
• How does the severe wind shear and turbulence impact wake dynamics
Thanks for listening !!
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Extra Slides
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EUropean Forest
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Ryningsnäs - Turbulence
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