better wind resource estimation through detailed forest characterization
DESCRIPTION
Better Wind Resource Estimation through Detailed Forest Characterization. Jens Madsen & Adrien Corre Vattenfall R&D. EWEA 2011 (Session: Siting Challenges) Bruxelles, March 14.17, 2011. >50% Forest Dairy farming Meadows / pastures Tundra Intensive farming. - PowerPoint PPT PresentationTRANSCRIPT
Better Wind Resource Estimation through Detailed Forest Characterization
Jens Madsen & Adrien CorreVattenfall R&D
EWEA 2011 (Session: Siting Challenges)
Bruxelles, March 14.17, 2011
2 | EWEA 2011 | Jens Madsen | March 2011
Locating Wind Farms in Forested Areas …
What is the problem?• Forests induce high wind shear and turbulence• Reasonable wind speeds only at higher hub heights
So, build somewhere else then …• Sweden has 60-65% forest coverage• Onshore projects in/near forested areas is the rule
rather than the exception
>50% Forest
Dairy farming
Meadows / pastures
Tundra
Intensive farming
3 | EWEA 2011 | Jens Madsen | March 2011
CFD and Forest Flows
• CFD as a Wind Resource Assessment Tool
- Overcomes shortcomings of linearized flow models … including forested areas
- Technical Risk Mitigation: Mapping of severe conditions (turbulence, shear, inflow angle)
- Economical Risk Mitigation: Potential to significantly reduce wind resource uncertainty
• CFD approach to Forest Canopy Modeling
- Canopy represented by porous zone: drag resistance & turbulence modulation
- Applies first principles
4 | EWEA 2011 | Jens Madsen | March 2011
CFD Forest Canopy Model (by Katul et al.)
• Inside a forest canopy, (z<H) momentum sinks are applied
- This drag resistance depends on Leaf Area Density (LAD or α) [m2/m3]
- Leaf Area Index (LAI) is the corresponding integral forest density [-]
G.Katul et al. : ”One- and Two-Equation Models for Canopy Turbulence”
Boundary-Layer Meteorology, Vol.113, pp.81-109, 2004
H
z
dzLAI0
5 | EWEA 2011 | Jens Madsen | March 2011
Model Parameter Sensitivity – Uniform Forest, Flat Terrain
• Preliminary sanity check
using 2D model
• LAD profiles mostly impact
wind speeds within canopy
• Correct tree height H is much
more important than forest
density
• In particular true at typical
hub heights
• Highest sensitivity to model
parameters occur near step
changes in height and density
(forest edges, clearings)
6 | EWEA 2011 | Jens Madsen | March 2011
MM-1MM-2
Model Validation – Risø/DTU Forest Edge Experiments
7 | EWEA 2011 | Jens Madsen | March 2011
Risø/DTU Forest Edge Experiments
LAD data backed out from CFD (courtesy of Andrey Sogachev)Seasonal variation in forest density
8 | EWEA 2011 | Jens Madsen | March 2011
Model Validation – MM-2 (inside forest)
• Canopy models are sufficiently good …- … considering the poor parameters we feed into them (GIGO principle applies)
• Implement advanced forest characterization techniques- The idealized, homogeneous forest does not exist
- Spatial distribution of forest height (and density)
- What is the impact of a considering a more realistic, heterogeneous forest layout?
9 | EWEA 2011 | Jens Madsen | March 2011
Forest Characterization – Classical approaches
Spatial Layout• Classification from satellite/aerial images• Forest perimeters identified • Digitized vegetation map
Tree Height• Assessed through site inspection• Extensive lumping is necessary
10 | EWEA 2011 | Jens Madsen | March 2011
• Direct methods:
- Foliage samples, destructive testing
• Indirect methods:
- Measure Fraction of transmitted radiance
- Hemispherical image analysis (LAI)
Forest Characterization – Canopy density (LAD/LAI)
Z
LAD
hemispherical photography
• Tedious methods!• Limited point-wise sampling• Calibration of density level
11 | EWEA 2011 | Jens Madsen | March 2011
LIDAR Airborne Forest Imaging (cont’d)
• Technology used in Forest Inventory Management
- Laser beam is reflected either by vegetation or ground
- Scans 500-800 meter wide section per flight leg
- <10 cm accuracy (depending on flight height)
• Data acquired (resolutions up tp 1x1 m2)
- Digital Terrain Model (DTM)
- Digital forest model
• Spatial variation of tree heights and density profiles inferred from
point cloud percentile values, e.g. zP-90, zP-75, zP-50, and zP-25
12 | EWEA 2011 | Jens Madsen | March 2011
Int.
Time
LIDAR Airborne Forest Imaging
13 | EWEA 2011 | Jens Madsen | March 2011
LIDAR Point Sky Rendering (colors by veg.height)
14 | EWEA 2011 | Jens Madsen | March 2011
Detailed forest layout: wind speed distribution
Forest Layout Wind Speed @ 10m agl.
Lake with associated speed-up
15 | EWEA 2011 | Jens Madsen | March 2011
Detailed forest layout: CFD vs. met mast data
• Use of detailed forest layouts yield good agreement between CFD and on-site measurements for this Vattenfall site in Southern Sweden
- Instrumented telecom mast (93m) and LIDAR campaign (Vestas)
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Met Mast
LIDAR
CFD
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0 0,5 1 1,5 2 2,5
Met Mast
LIDAR
CFD
16 | EWEA 2011 | Jens Madsen | March 2011
Conclusions
• General on wind power in forest- Forested areas are problematic but inevitable sites (in some geographies)
- Extensive measurement campaigns (tall masts, SODAR and/or LIDAR)
- Use taller hub heights than you would normally do
• Wind resource assessment in forested areas- The CFD canopy models perform well
- Detailed forest characterization provides more accurate results than modeling based
on idealized, homogeneous forest
- Correct tree height distribution is the key information (canopy density, less so)
- Beware of information overkill.
• Investigate trade-off between forest data resolution and accuracy
17 | EWEA 2011 | Jens Madsen | March 2011
Thanks for your attention
Made possible throguh the collaboration with:
Risø/DTU, Vestas Technology R&D, and ETS Montreal