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Vattenfall Perspective on Wind in Forest

Jens MadsenPrincipal R&D Engineer, Ph.D

Vattenfall Research & Development AB

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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

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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

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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)

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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

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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

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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

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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

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1.5

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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

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Case 2: Velocity magnitude

Velocity magnitude, case 2

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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

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Case 2: Turbulence intensity

Turbulence Intensity, case 2

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1.5

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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

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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