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Vattenfall Perspective on Wind in Forest

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Vattenfall Perspective on Wind in Forest. Jens Madsen Principal R&D Engineer, Ph.D Vattenfall Research & Development AB. Presentation Outline. Who are we? Short introduction to Vattenfall Why do we care about “wind in forest”? Our motivation What are we doing?

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Vattenfall Perspective on Wind in Forest

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  1. Vattenfall Perspective on Wind in Forest Jens Madsen Principal R&D Engineer, Ph.D Vattenfall Research & Development 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

  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 MW Offshore 370 MW Production 2,2 TWh Wind Power Assets

  4. 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

  5. Foto: Hans Blomberg 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

  6. Ryningsnäs Foto: Hans Blomberg

  7. 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

  8. Ryningsnäs – Wind Shear & Turbulence • Large wind shear observed (up to: α = 0.6) • High turbulence levels (typically TI=20..25% at hub height)

  9. Wind Shear – seen with the naked eye 120-meter mast at Vattenfall site in Southern Sweden

  10. Ryningsnäs – Load variations in blade root • Clear advantages of higher hub heights • Higher energy production • Lower turbulence • Less variations in WT loads

  11. CFD school Porous zone with drag resistance Turbulence modulation Applies first principles Forest Canopy Models • WAsP school • Increase roughness class • Add zero-plane displacement • Applies empirical information

  12. 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:

  13. Forest Canopy Model of Katul et al.

  14. Uniform Sitka Spruce Pine Forest Forest Characterization Could Matter … Dalpé & Masson, EWEC-2007

  15. 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.

  16. Case 1: Velocity magnitude

  17. Case 1: Turbulence Intensity

  18. Case 2: Velocity magnitude

  19. Case 2: Turbulence intensity

  20. 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

  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

  22. 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 !!

  23. Extra Slides

  24. EUropean Forest

  25. Ryningsnäs - Turbulence

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