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Wind Farm Wake Effects Estimations by a Mosaic Tile Wake Model.

Wind Farm Wake Effects Estimations by a Mosaic Tile Wake Model. Ole Steen Rathmann * , Søren Ott * , Mark Kelly * * Risoe-DTU , Roskilde DENMARK. In Memory of Sten Frandsen.

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Wind Farm Wake Effects Estimations by a Mosaic Tile Wake Model.

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  1. Wind Farm Wake Effects Estimations by a Mosaic Tile Wake Model. Ole Steen Rathmann*, Søren Ott*, Mark Kelly* * Risoe-DTU, Roskilde DENMARK

  2. In Memory of Sten Frandsen Sten Frandsen achieved in 2007 the Danish Doctoral degree (highest academic degree in Danmark) on turbulence in wind farms. He was a forerunner in describing wind farm wake effects.He was a key-person in establishing the EU Upwind project and other cooperation projects. He was a great inspiration to colleagues and co-workers in in various international projects. 1951-October 2010 As a IPCC-member: Nobel Peace Price 2007 laureate Wind Farm Wake Effects Estimations by a Mosaic Tile Wake Model

  3. OUTLINE Introduction FUGA CFD-modelling of wakes: Basic features FUGA CFD-modelling: predictions vs. selected wind farm data FUGA-Light Wake parameterization Wake-surface and wake-turbine interaction The new “mosaic tile” concept Model predictions vs. wind farm data Downwind recovery and stability impact Conclusion Wind Farm Wake Effects Estimations by a Mosaic Tile Wake Model

  4. Introduction ___________________________________________________________________________________________ 1) S. Frandsen et al., Analytical Modeling of Wind speed Deficit in Large Offshore Wind Farms, Wind Energy 9, 39-53 (2006). • Existing simple parameterized as well as CFD wake models tend to underestimate the over-all speed- and power deficits in large wind farms. • Existing models also often fail to catch the details in the increase of speed deficit with downwind distance 1). • Task of this work: • a fast wake model, able to represent the over-all speed- and power-deficit as well as the variation within a farm with reasonable accuracy; • to be applicable in engineering wind resource software. Wind Farm Wake Effects Estimations by a Mosaic Tile Wake Model

  5. Acknowledgments Work funded by: EU-projects TopFarm and Upwind (WP8) The Offshore Wind Accelerator (OWA) Wake Effects project. OWA is a Research and Development collaboration which aims to significantly reduce the costs of offshore wind power. The OWA partners are The Carbon Trust, Dong Energy, E-on, Mainstream Renewable Power, RWE Innogy, SSE Renewables, Scottish Power Renewables, Statoil and Statkraft. Danish PSO-project WindShadow. Thanks are due to Dong Energy and Vattenfall for permission to utilize off-shore wind farm data. 5 Wind Farm Wake Effects Estimations by a Mosaic Tile Wake Model March 2011

  6. FUGA2): Linearised CFD modelling for wakes (1) ____________________________________________________________________________________ 2) S.Ott: “Linearised CFD Models for Wakes”. Risoe-R-1772(EN). Risoe-DTU (2011). Linearised RANS equations (momentum+continuity) ‘Simple closure’: BL-domain defined by surface roughness Z0 and inversion layer height Zi. Turbine rotor represented by an actuator disk Fast, mixed-spectralsolver using pre-calculated look-up tables (LUTs) No computational grid No numerical diffusion No spurious mean pressure gradients No adjustable model parameters Integration with WAsP: import of wind climate and turbine data. 105 times faster than conventional CFD! Wind Farm Wake Effects Estimations by a Mosaic Tile Wake Model

  7. FUGA-Modelling of wakes (2) • A few rotor-diameters downwind of a wind turbine: • non-linear effects vanish; and • speed deficits of individual wakes scale with Ct and U0. • Effects of different wakes may be superimposed • Accurate in the far wake even if inaccurate in the near wake • Local reduced wind speed at each turbine used to estimate the individual turbine thrust coefficient Ct. • By a downwind marching procedure the entire wind farm is covered Wind Farm Wake Effects Estimations by a Mosaic Tile Wake Model

  8. CFD-model: Comparison to selected WF data Wind farm data: U=8m/s +/- 0.5 m/s FUGA was used to model selected characteristic flow cases from the Danish off-shore wind farm Horns Rev Compares very well with data in view of the somewhat crude assumptions applied. Wind Farm Wake Effects Estimations by a Mosaic Tile Wake Model

  9. ”FUGA-Light” Wakeparametrization: (1) The “near-field” close WT : classical tulip-like stream-line expansion. A parameterization is picked from the FUGA CFD-model Characteristic length scale derived from most important part of FUGA momentum equation controlling the momentum diffusion: => All spatial dimensions scaled by Lν : Wind Farm Wake Effects Estimations by a Mosaic Tile Wake Model

  10. ”FUGA-Light” Wakeparametrization (2) Appliedx#-shift in wakeexpansionrule: • Presentlylimited to off-shore: Z0=0.2mm • Cross-windreduced-speedprofile at Z=H: • EcellentGaussianrepresentation: • Downwind evolution: wakeexpandshorizontallyappr. as ½-power-law, including a shift in x#: Wind Farm Wake Effects Estimations by a Mosaic Tile Wake Model

  11. ”FUGA-Light” Wakeparametrization (3)Surface Interactions FUGA-Light uses the approximation that vertical wake profile is equal to the horizontal one – thereby omitting a direct description of wake interaction with sea/ground surface. Indirectly, the surface interaction and the effect of the height-depending diffusivity are described by the Q# dependence on downwind distance: Q#(x#). Wind Farm Wake Effects Estimations by a Mosaic Tile Wake Model

  12. Influence of Near-Turbine flow Effect on wake of “tulip-flow” expansion at originating turbine described by additional x#-offset to achieve: Interaction of a wake with the “tulip-flow” at a downwind turbine “j” described by a discrete x#-shift, causing an “sudden” wake expansion: (Foverlap : the fraction of turbine rotor “j” covered by the wake in question.) Wind Farm Wake Effects Estimations by a Mosaic Tile Wake Model

  13. New “Mosaic tile concept” Vertical plane cut, perpendicular to wind direction, indicating the turbine rotor divided in a number of ’tiles’. 3) S.Frandsen, H.E.Jørgensen, R.Barthelmie, O.Rathmann et al.: ”The Making of a second-generation wind farm efficiency model-complex ”., EWEC 2008. paper 49. The mosaic-tile model: Redefinition of the original concept 3) Rotor area divided in a “tiles-mosaic”, each with a sampling point Mean reduced wind speed over the rotor area calculated as a weighted mean of reduced wind speeds in each tile Wind Farm Wake Effects Estimations by a Mosaic Tile Wake Model

  14. Wind farm data: Horns Rev 222° +/-7.5° (15°) 8, 10 m/s +/- 0.5 m/s 270° +/-7.5° (15°) 8, 10 m/s +/- 0.5 m/s Turbines: 2MW, DR = 80m, Hhub = 70m Layout: sr = sf = 7 Wind Farm Wake Effects Estimations by a Mosaic Tile Wake Model

  15. Model predictions vs. Horns Rev data FUGA and Fuga-Lightprelimin. results 8 m/s 10 m/s 270° 222° Wind Farm Wake Effects Estimations by a Mosaic Tile Wake Model

  16. Wind farm data: Nysted 263° +/-7.5° (15°) 8, 10 m/s +/- 0.5 m/s 277° +/-7.5° (15°) 8, 10 m/s +/- 0.5 m/s Turbines: 2.33 MW, DR = 82.4m, Hhub = 68.8m Layout: sr = 10.6, sf = 5.9 Wind Farm Wake Effects Estimations by a Mosaic Tile Wake Model

  17. FUGA model predictions vs. Nysted data FUGA and Fuga-Lightprelimin. results 8 m/s 10 m/s 278° 263° Wind Farm Wake Effects Estimations by a Mosaic Tile Wake Model

  18. Downwind Speed Recovery Normalized wind speed through the wind farm and behind the wind farm compared to measurements at Horns rev. Full curves are canopy-CFD-model predictions. _______________________________________________________________________________________ 4)R.J.Barthelmie et al., ” Flow and wakes in large wind farms: Final report for UpWind WP8”. Risø-R1765(EN) (2011). FUGA – and thusalsoFuga-light - predicts a much ”slower” speed recoverythan standard wake models. For HR rec.distance is about 16 km; somewhatslowerthanobserved4) Wind Farm Wake Effects Estimations by a Mosaic Tile Wake Model

  19. Stabilityimpactonwakeeffects Horns Rev row 4, 5-8 m/s, 270 °+/-10° _______________________________________________________________________________________ 5) Alfredo Peña, private communication, Risoe-DTU (2011). From wind farm data5) it is clear the stability has an effect: In FUGA – and in Fuga-Light – stabilitymaybeincluded via the diffusivity: Wind Farm Wake Effects Estimations by a Mosaic Tile Wake Model

  20. Conclusions and Future Development • FUGA model predictions compare encouragingly well with wind farm data • Deviations in downwind speed- and power deficits should be analyzed – and possible model improvements implemented • Downwind speed recovery distance seems realistic, but may have to be improved • Inclusion of stability impact possible • FUGA-Light, in a future version: • parameterization to be improved to match FUGA better • the non-Gaussian vertical profile should be taken into account • should be verified also for on-shore roughnesses by analyzing single-wake profiles from FUGA for such conditions • FUGA-Light seems suitable for inclusion in engineering wind resource estimation software. Wind Farm Wake Effects Estimations by a Mosaic Tile Wake Model

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