Floors, Rogier

Gryning, Sven-Erik

Hahmann, Andrea N.

Kelly, Mark C.

Troen, Ib

Online Research Database In Technology

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Downloaded from orbit.dtu.dk on: Dec 18, 2017"Tall" wind profiles and related issuesKelly, Mark C.; Gryning, Sven-Erik; Troen, Ib; Hahmann, Andrea  N.; Floors, Rogier RalphPublished in:ProceedingsPublication date:2011Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Kelly, M. C., Gryning, S-E., Troen, I., Hahmann, A. N., & Floors, R. (2011). "Tall" wind profiles and relatedissues. In Proceedings European Wind Energy Association (EWEA).“Tall” wind profiles and related issues Mark Kelly*, Sven-Erik Gryning, Ib Troen, Andrea Hahmann, Rogier Floors Meteorology Group, Wind Energy Division, Risoe DTU Frederiksbergvej 399, P.O. Box 49--Building 125, Roskilde 4000 *corresponding author: mkel@risoe.dtu.dk ; tel.(+45) 4677 5020 , fax(+45)4677 5083  Summary  Recent progress in the theoretical extension and application of models for "tall" wind profiles, as well as re-evaluation of accepted atmospheric surface-layer parameterizations, have both clarified the need for various research-grade data to further evaluate and develop current wind models; they also highlight the need for scrutiny of commonly-used models' limits. We elucidate some current needs and progress in extended wind profile modelling within the context of various contemporary wind models.  The final emphasis is upon the use of measurements to drive tall-wind predictions, as well as to facilitate the development and mutual use of mesoscale and microscale models.    Introduction  The increase in wind turbine hub heights has led to a greater need to characterize the wind field well beyond the atmospheric surface layer, for reasons including estimation of production and loads. Typically wind siting engineers obtain wind speed and direction statistics at some height(s) below hub height, as well as a surface description.  But obtaining relevant long-term wind statistics for heights above 80–100 m based on measurements below these heights—in order to more reliably estimate the production for today’s tall turbines—one needs to account for phenomena other than surface effects.   Finite extent of the Atmospheric Boundary Layer The atmospheric boundary layer (ABL) is the turbulent portion of the atmosphere in contact with the surface, which extends to some spatially and temporally varying height above which the flow tends to be non-turbulent.[1] Typical ABL depths can vary from roughly 100 m (e.g. night, winter) to 1–2 km (e.g. sunny day over land).  A number of physical mechanisms and processes can influence the wind statistics and their vertical profile in the upper ABL; among these are the temperature profile characterizing the ABL “top” and environment above, shear instability, gravity waves, entrainment (clouds), low-level jets, baroclinic shear, and the time-varying finite extent of the ABL itself—which is influenced by the surface. For example, the influence of baroclinity, that is the geostrophic wind shear caused by large-scale horizontal temperature gradients (over tens of km), is seen in Figure 1.  The figure displays dimensionless wind profiles measured by tall tower and LIDAR at the Høvsøre test station on the west coast of Denmark for 6 months, grouped by baroclinity computed from corresponding runs of the mesoscale model WRF; note the different mean wind profiles and shears corresponding to each baroclinic class.    Figure 1: Observed mean wind profile at Høvsøre  (triangles from mast, circles are LIDAR), broken into baroclinity classes taken from corresponding WRF runs.  The ABL top and upper-ABL phenomena mentioned above can impact turbines in a number of ways.  Large (or negative) shear and greater shear variability tend to become more common at heights approaching the minimum ABL depth, as seen in Figure 2 for recent observations taken from 60 and 116.5 m at Høvsøre. At this coastal location the ABL depth can often drop below  150 m, particularly in winter; as a consequence, the shear exponent exceeds the accepted IEC-61400-1 value during most of the 4 days shown in the figure.  The wind also tends to turn in the upper ABL, affecting turbines via directional shear.  As seen in Figure 3, which shows the wind directions at Høvsøre for 60 m and 100 m during the last day of data shown in Figure 2, differences of 20–30° or more can persist for hours across a relatively small (40 m) rotor.  The presence of the ABL top further affects the amplitudes of turbulent fluctuations and fluxes, alters the length scales inherent in the wind profile and turbulence, as well as the three-dimensional turbulent structure (i.e. cross-spectra). Thus the ABL top generally changes the wind distribution and power density, as well as expected loads and fatigue.    Figure 2: Observed shear exponent from 60–116.5 m at Høvsøre for 19–22 Feb 2011.    Figure 3: Observed wind directions at 60 at Høvsøre, corresponding to final 24 Modelling issues  Because there are generally insufficient measurements taken above 100and due to the non-trivial combination of topabove, wind engineers are increasingly observations—in order to estimate the wind resocommonly used today in this context microscale computational fluid dynamics (CFD) models.  As of 2011 the CFD models used in the wind industry are averaged Navier-Stokes (RANS) models, which solve the mean fluid equations and parameterize the mean turbulence in a domain extending a few tens of kilometersRANS models account for the effects of staThese limitations having been stated, more importantly RANS models the ABL depth or its effects†.  RANS models used for wind generally horizontal pressure gradients which drive the turbulence parameterizations used by characteristic length scale (see e.g.complex terrain (or with modelled turbines                                                          † with one partial exception, which unfortunately does not fully treat surface heat fluxes, but an above-ABL temperature gradient [2]  m (magenta) and 100 m (blue) hours of Figure 2.  m for most -down processes at play in the upper ABL as outlined turning to computational models—with limited or no local urce for taller turbines.  The two methods most are mesoscale (numerical weather prediction) models and  generally limited to be Reynolds.  A small number of these bility, and even fewer incorporate measured windsfor wind do not Further, due in part to resource limitations (computational/time), do not include realistic variable forcing—ABL vary in both space and in time [1].wind-industry RANS are limited in effect to a single  [3]), which limits their ability to treat turbulence interacting with ).  Another feature lacking from CFD applied to wind   wind projects, -.  (yet) include in nature the  In addition, the is does include the interaction between scales larger and smaller than the domain, and such RANS models do not account for larger-scale effects such as baroclinic shear.   Mesoscale models, on the other hand, do a good job capturing phenomena such as baroclinic shear and the variable pressure gradients which drive ABL winds.  However, mesoscale models are limited to effective resolutions on the order of 5–20 km (~5–7 times the grid spacing [4]), and must parameterize all motions and physics at scales smaller than this.  Moreover mesoscale models are typically configured to employ planetary boundary layer (PBL) schemes to parameterize ABL- and surface fluxes, doing so in a reduced-dimensional manner (i.e. not 3-D). The schemes give ABL heights and behaviour which are not necessarily physically representative, but which are tuned to give reliable results for weather prediction; they possess restricted capability in reproducing statistics associated with the ABL top.  Common PBL schemes are not optimized for wind prediction, and can give unrepresentative stability and  surface interactions that affect wind profiles and distributions—and hence wind power estimates—more adversely than they affect other fields important for weather prediction. An example of this is given in Figure 4, which displays the distribution of shear exponent (from10-60m) predicted by the mesoscale model WRF using the most popular PBL scheme (YSU, [9]) at a resolution of 2 km, versus observations over a one month period at the Høvsøre test station on the western Danish coast.  The figure starkly demonstrates mesoscale predictions of shear exponents that appear to satisfy the IEC standard, in contrast to the observations which show significant violation of the standard.   Applied modelling for wind energy: profiles driven by observations  Given that a number of physical mechanisms contribute in a complex and highly variable (and interacting) way to the upper-ABL flow field, and given the inability of mesoscale and RANS wind models to reliably characterize these mechanisms and their statistical effect upon the modelled wind beyond the atmospheric surface layer, it is reasonable to build upon observation-driven modelling which produces appropriate wind statistics in the lower ABL.  By incorporating representative estimated statistics of relevant upper-ABL processes along with measured statistics below, we can produce wind statistics applicable for tall turbines.    Figure 4:  Distributions of shear exponent from WRF, using popular PBL scheme, vs. observed distribution at site on western Danish coast.   Surface-layer similarity theory for wind profiles has recently been adapted for long-term wind statistics [5], including a framework which adapts a modelaccounting for ABL depth.  A sample application of such a model can be found in Figure 5, which shows observed dimensionless wind profilethe Høvsøre mast for a 4-year periodthe profile modelled via conventional surface-layer theory using mean stability, and the “tall” model (green) where the effective mean boundary layer depth is taken into accountthe long-term mean stability based on sonic anemometer (heat and momentum flux) measurementsthe classic theory using a mean stability fails even in the surface layer, because longbaroclinity has not been accounted for here, but effective mean baroclinic shear has been derived where s is the dimensionless baroclinic shear the climatological values of roughness, ABL depth and matching scale {At Høvsøre the effective long-term mean dimensionless baroclinity is close to 1, and does not have a large effect.  The tall profile model presented aaccount the effect of terrain and roughness variationsneeded in order to obtain wind statistics.  wind speeds—and the vertical variation of these of the long-term variance (or anotherwind variance profile is necessitated, including physicrelevant variations in representative upper [6] the  at ,  plus . Note -term variations in stability dominate.  an expression for the wind profile including the after [5] and is given by  and angle brackets denote long-term mean values; z0m, heff, ℓeff} are given inbove is amenable for use with linearized flow models which  on the mean flow, but more information is In order to predict the 2-parameter Weibull distributiontwo parameters—it is necessary to have the profile  moment) of wind speed.  Thus a model for the climatological ally-based statistical dependences upon-ABL and surface quantities.  Figure 5:  Dimensionless mean wind profile at Høvsøre over 4year period.  Dotted line is standard similarity theory, dashes are adapted surface-layer theory, green line is adapted tall theory [5], black dots are observationsThe long-term   [5].  of   -.   That is, observed and/or estimated low-order statistics of stability (surface heat and momentum flux), ABL depth, and baroclinity allow observation-based modelling of the profiles of long-term wind speed and wind variance, which together allow calculation of the vertical profile of Weibull parameters.  This method is amenable to statistically adapted flow models which allow perturbations of the wind statisticdue to roughness and terrain variations, and has been implemented in WAsP (following For tall profiles, the shortcomingsa problem than near the surface, particularly because we are conabove classically estimated mixing depthsThere will be problems in areas where the heights are comparable to the terrain variation scales, and in complex terrain there can profile theory works best with measured and work is now underway to exploit estimated distributions of boundary layer depthdepth is not often measured and cannot be reliably obtained from mesoscale models. Barotends to become a greater issue above 150of its distribution for tall-wind parameterization.   Conclusions  Due to the complexity of multiple layer (ABL) and large-scale mechanisms operating aboveand mesoscale models to capture the relevant physics afusing these models alone without direct use of observations error in wind predictions above 80Figure 6: Long-term rms wind speed profile corresponding to Fig. An example of measured and modelled tall wind variance profiles is shown in Figure 6 standard-deviation of 10winds), and together with the longterm mean wind profile this the Weibull shape parametershown in Figure 7; this in turn allows estimation of profile of wind power density.   s [7]).    of linearized microscale flow models—such as WAsPsidering heights over 100 (~60-80 m) where flow recirculation is not an issuebe issues with obtaining representative surface fluxes. near-surface vertical fluxes (i.e. with a sonic anemometer m, and use of mesoscale models is allowing estimation  phenomena associated with the top of the atmospheric boundary , and the inability of both microscale CFD fecting the flow field near the ABL top, results in significant uncertainty and –100 m.  Thus we have a developed a “tall profile” model which    5. Figure 7: Long-term observed profile of Weibullparameter, corresponding to Figs. (as long-term -minute -leads to  (k) as —is less of  m, .  The tall-), , since ABL clinity  -k (shape) 5-6. uses measured wind statistics—plus statistics representative of the non-surface phenomena that impact the flow field in the upper-ABL—to give wind statistical profiles which allow better calculation of wind power at heights more than ~100 m above surface level.  The tall-profile formalism is made in conjunction with (implemented alongside) linearized flow models (WAsP) which calculate perturbed wind statistics based on measured winds and terrain descriptions.   Mesoscale models and their PBL schemes are currently being optimized for wind, but caution is presently advised for their use alone.  The tall wind model can benefit from mesoscale studies which provide estimates of e.g. baroclinity, but we remind the reader that downscaling is needed in order to use mesoscale results in either RANS or linearized microscale models [8].  Microscale RANS models offer improvements over linearized models in complex terrain and some RANS include atmospheric stability to various extents, but they need to include ABL depth and/or more realistic forcing in order to produce usable tall wind statistics for many locations.  There is a need for more long-term wind data above the atmospheric surface layer as well as near-surface fluxes, both for improvement and development of all three kinds of models (mesoscale, linearized ‘tall’ flow models, and CFD), as well as for model validation studies and direct use in tall turbine projects.  The use of LIDAR greatly helps this cause, and long-term (multi-year) LIDAR measurements are now mounting.  Research is underway to both couple meso- and microscale modelling as well as systematically connect results from each in a general way; in practice, mutual use of mesoscale and both kinds of microscale models is recommended for sites where doubts exist.    References  [1] Wyngaard J. Turbulence in the Atmosphere;  Cambridge University Press: UK, 2010.  [2] Montavon C.  Considerations about the role of atmospheric stability in CFD models.  Proceedings of the European Wind Energy Association (Conference), Brussels, Belgium, 2011.  [3] Hanjalic K.  Will RANS Survive LES?  A View of Perspectives.  J. Fluids Eng. (ASME) 2005; 127:831–839.   [4] Skamarock W C.  Evaluating Mesoscale NWP Models Using Kinetic Energy Spectra.  J. Atmos. Sci. 2004;  132:3019–3032.  [5] Kelly M, Gryning S-E. Long-term mean wind profiles based on similarity theory. Boundary-Layer Meteorol. 2010; 136:377-390.  [6] Gryning S-E, Batchvarova E, Brümmer B, Jørgensen H, Larsen S.  On the extension of the wind profile over homogeneous terrain beyond the surface boundary layer. Boundary-Layer Meteorol 2007; 124(2):371–379. [7]  Troen I, Petersen E L. European Wind Atlas.  Risø National Laboratory, Roskilde, 1989.  [8] Badger J, Larsén X G, Mortensen N G, Hahmann A, Hansen J C, Jørgensen H E.  A universal mesoscale to microscale modeling interface tool.  Proceedings of the European Wind Energy Conference, Warsaw, Poland 2010.   [9] Hong S-Y, Noh Y, Dudhia J.  A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes.  Mon.Wea.Rev. 2006;  134:2318–2341. 

"Tall" wind profiles and related issues

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