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

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1 Downloaded from orbit.dtu.dk on: Jan 05, 2016 Complex terrain wind resource estimation with the wind-atlas method: Prediction errors 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 Version Author final version (often known as postprint) Link to publication Citation (APA): Troen, I., Bechmann, A., Kelly, M. C., Sørensen, N. N., Réthoré, P-E., Cavar, D., & Ejsing Jørgensen, H. (2014). Complex terrain wind resource estimation with the wind-atlas method: Prediction errors using linearized and nonlinear CFD micro-scale models European Wind Energy Association (EWEA). [Sound/Visual production (digital)]. European Wind Energy Conference & Exhibition 2014, Barcelona, Spain, 10/03/2014 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal? If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

2 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

3 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

4 Wind Atlas Analysis (IBZ) Wind measuring site Maps, location Height Sector wise histograms of measured winds Orographic flow model (BZ) Roughness change model (IBL) Wind Atlas data set Orographic correction factors, turning 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

5 Wind Atlas Application (IBZ) WT data Target site Maps, location Height Ressource data: mean, power Orographic flow model (BZ) Roughness change model (IBL) Wind Atlas data set Orographic correction factors, turning Roughness change factors Effective upstream roughness, land fraction Interpolation and stability correction Target Weibull parameters Upstream Weibull parameters

6 Wind Atlas Analysis (CFD) Wind measuring site Maps, target area Height, location Sector wise histograms of measured winds 3D grid generation Terrain flow model (Ellipsys) Terrain flow corrections Corrected histograms Terrain flow corrections result volume Wind Atlas data set Effective upstream roughness, land fraction Stability correction up transformation: histograms of Geo-wind down transformation: to std z over std z0s Weibull fitting

7 Wind Atlas Application (CFD) WT data Maps, target area Height, location Ressource data: mean, power 3D grid generation Terrain flow model (Ellipsys) Terrain flow corrections result volume Wind Atlas data set Terrain flow corrections Effective upstream roughness, land fraction Interpolation and stability correction Target Weibull parameters Upstream Weibull parameters

8 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 June 2015

9 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

10 Ruggedness IndeX (RIX) from: Bowen, A.J. and Niels G. Mortensen (2004): WAsP prediction errors due to site orography Risø-R-995(EN)

11 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, energydissipation two equation turbulence closure One simulation (steady state) for each of 36 upstream wind directions

12 Ellipsys surface grid Diameter = 30 km

13 Volume grid

14 Result 3D volume for each of 36 directions

15 Formdrag small scales: roughness

16 Form drag larger scales In CFD physics, but B.C.s, not in IBZ, link to RIX?

17 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

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

19 Horizontal 10m-20m

20 Horizontal 30m-40m

21 Horizontal 50m-60m

22 Horizontal 80m-100m

23 All upwards extrapolations

24 All horizontal predictions by distance

25 , Delta-RIX revisited

26 Finer scale features - example BZ CFD Slopefix Delta-RIX

27 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).

28 Thanks for your attention!