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Complex terrain wind resource estimation with the wind-atlas method: Predictionerrors using linearized and nonlinear CFD micro-scale models

Troen, Ib; Bechmann, Andreas; Kelly, Mark C.; Sørensen, Niels N.; Réthoré, Pierre-Elouan; Cavar,Dalibor; Ejsing Jørgensen, Hans

Publication date:2014

Document VersionPeer reviewed version

Link back to DTU Orbit

Citation (APA):Troen, I. (Author), Bechmann, A. (Author), Kelly, M. C. (Author), Sørensen, N. N. (Author), Réthoré, P-E.(Author), Cavar, D. (Author), & Ejsing Jørgensen, H. (Author). (2014). Complex terrain wind resource estimationwith the wind-atlas method: Prediction errors using linearized and nonlinear CFD micro-scale models.Sound/Visual production (digital), European Wind Energy Association (EWEA).

https://orbit.dtu.dk/en/publications/8a21d377-c9f5-459c-bbde-5bcaeda31e4f

Complex terrain wind resource estimation with the wind-atlas method: Prediction errors using linearized and nonlinear CFD microscale models.

Ib Troen, Andreas Bechmann, Mark C. Kelly, Niels N. Sørensen, Pierre-Elouan Réthoré, Dalibor Cavar, Hans E. Jørgensen

DTU Wind Energy, Technical University of Denmark

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Outline

• Wind Atlas Analysis, Wind Atlas Application (WAsP)

• The linear model (BZ)

• Known deficiencies in complex terrain and possible remedies

• The fully nonlinear RANS model (Ellipsys3D)

• Form drag issue

• Validation

• Delta-RIX revisited

• Conclusions

EWEA 2014, Barcelona Spain



Sector wise histograms of measured winds

Wind measuring site Maps, location

Orographic flow model (BZ)

Roughness change model (IBL)

Wind Atlas data set

Roughness change factors

Effective upstream roughness, land fraction

Stability correction

Corrected histograms

”up” transformation: histograms of Geo-wind

”down” transformation: to std z over std z0s

Weibull fitting

Height

Orographic correction factors, turning


Wind Atlas Analysis (IBZ)



Target site Maps, location

Orographic flow model (BZ)

Roughness change model (IBL)

Roughness change factors


Interpolation and stability correction

Target Weibull parameters

Upstream Weibull parameters

Height

Orographic correction factors, turning


Wind Atlas data set

Ressource data: mean, power…

WT data Wind Atlas Application (IBZ)



Sector wise histograms of measured winds

Wind measuring site Maps, target area

3D grid generation

Terrain flow model (Ellipsys)

Wind Atlas data set


Stability correction

Corrected histograms

”up” transformation: histograms of Geo-wind

”down” transformation: to std z over std z0s

Weibull fitting

Height, location

Terrain flow corrections result volume

Terrain flow corrections


Wind Atlas Analysis (CFD)



Ressource data: mean, power…

Maps, target area

3D grid generation

Terrain flow model (Ellipsys)


Interpolation and stability correction

Upstream Weibull parameters

Height, location

Terrain flow corrections result volume

Terrain flow corrections

Wind Atlas data set

Target Weibull parameters

WT data


Wind Atlas Application (CFD)



BZ model

• Linearized spectral flow model for neutral boundary layer.

• Spectral expansion in terms of

Fourier-Bessel series in polar

coordinates with ”zooming” radial

grid distances

• Calculates flow perturbation only at the central point.

• Very fast for calculating flow perturbation at single points in the terrain, and vertical profiles.

• Integrated in WAsP.

7 29 June 2015



Linear model forcing term

Figure adapted from Wood and Mason 1993:

The pressure force induced by neutral, turbulent flow over hills QJRMS, vol 119, pp 1233-1267




Ruggedness IndeX (RIX)

from: Bowen, A.J. and Niels G. Mortensen (2004):

WAsP prediction errors due to site orography Risø-R-995(EN)




Ellipsys3D

• Multiblock finite volume discretization of the incompressible Reynolds Averaged Navier-Stokes (RANS) equations in general curvilinear coordinates

• Neutral atmospheric stability in WAsP-CFD configuration

• Fully dynamic model with nonlinear terms, energy-dissipation two equation turbulence closure

• One simulation (steady state) for each of 36 upstream wind directions




Ellipsys surface grid


Diameter = 30 km



Volume grid




Result 3D volume for each of 36 directions




Formdrag – small scales: roughness




Form drag – larger scales


In CFD physics, but B.C.s, not in IBZ, link to RIX ?



Validation

• 9 complex sites, each with several masts

• Levels from 10m (1 site with 5 masts) to 100m (1 site, 2 masts)

• Mast distances from ~1 km to ~15km

• Sites in Europe, Americas, A-Asia




Site example with (2km by 2km) CFD tile




Horizontal 10m-20m




Horizontal 30m-40m




Horizontal 50m-60m




Horizontal 80m-100m




All upwards extrapolations




All horizontal predictions by distance




Delta-RIX revisited


,



Finer scale features - example


BZ CFD

Slopefix Delta-RIX



Conclusions

• Wind Atlas method in WAsP has been extended to allow for more advanced flow modelling (via Cloud).

• Validation of system against available complex terrain data show significant improvement of CFD over linearized flow model.

• CFD can give additional siting information (TKI, tilt)

• For the specific use of predictions from well exposed locations to well exposed locations the delta-RIX correction show good skill in reducing the error variance of the linear model, but correction ”aliasing” to larger scales could be an issue. Slanted extrapolations ? Combination with moderate ”landfill” ?

• The delta-RIX method depends, however, on some empirical fitting of parameters, but is otherwise very convenient.

• More (complex) site data would be very desirable for further validation.

• Large scale field experiments in complex terrain are needed to help improve physical models and to guide the simpler ones (NEWA project).


Thanks for your attention !


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