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Tuesday, 11 March 2014
16:30 - 18:00 How does the wind blow behind wind turbines and in wind farms?
Science & Research  


Room: Llevant
Session description

Wind turbines operate under highly fluctuating wind conditions. It is thus important to achieve a profound understanding of the characteristic features of the micro scale meteorological conditions. Current research activities focus not only on the inflow conditions and their impact on wind turbines, but also on the wake structures and the wind conditions within a wind farm.

Learning objectives

  • Get a better understanding micro scale wind conditions
  • Learn about new advanced measuring techniques
  • See the possibilities of numerical methods to simulate complex wind conditions
  • Learn about the impact of wind on turbine components
Lead Session Chair:
Joachim Peinke, Uni Oldenburg, Germany

Co-chair(s):
Jakob Mann, DTU Wind Energy
Matthieu Boquet University of Colorado at Boulder, United States
Co-authors:
Julie Lundquist (1) F P Eugene Takle (1) Michael Rhodes (1) Matthieu Boquet (1) Branko Kosovic (1) Dan Rajewski (2) Samantha Irvin (2) Russell Doorenbos (2) Jiwan Rana (1)
(1) University of Colorado at Boulder, Boulder, United States (2) Iowa State University, Ames, United States (3) Leosphere, Orsay, France (4) National Center for Atmospheric Research, Boulder, United States

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Presenter's biography

Biographies are supplied directly by presenters at EWEA 2014 and are published here unedited

Prof. Julie Lundquist leads an interdisciplinary research group in the Dept. of Atmospheric and Oceanic Sciences, University of Colorado, with a joint appointment at the National Renewable Energy Laboratory. Her research group uses observational and computational approaches to understand atmospheric influences on turbine productivity, turbine wake dynamics, and downwind impacts of wind energy. Before joining CU-Boulder, Dr. Lundquist designed and led wind energy projects at Lawrence Livermore National Laboratory. Her Ph.D. is in Astrophysical, Planetary, and Atmospheric Science from CU-Boulder. She studied English and Physics as an undergraduate at Trinity University.

Abstract

Lidar observations of interacting wind turbine wakes in an onshore wind farm

Introduction

As wind energy deployment grows, questions arise regarding how wind power plants affect the local environment. The 2010 and 2011 field campaigns of the Crop-Wind Energy Experiment (CWEX) (Rajewski et al., 2013a; Rhodes and Lundquist, 2013, Rajewski et al. 2013b) quantified the effects of one row of turbines on the local environment. In the 2013 CWEX field campaign presented here, multiple remote sensing instruments quantified the spatial variability of winds and turbulence through the complex flow of multiple rows of a ~ 150 MW onshore wind plant.

Approach

The previous CWEX experiments utilized profiling lidar systems (Leosphere/Renewable NRG Systems WINDCUBE™ v1 systems) to quantify wake wind speed deficits and wake turbulence enhancements in the near-wake region. In CWEX-11, one lidar was located ~ 2 rotor diameters D south (typically upwind) of one row of turbines to quantify inflow conditions. Another lidar located ~ 3D north (typically downwind) provided quantification of the wake impacts during southerly flow conditions. Wake wind speed deficits were largest during wind speeds just slower than rated speed, when the turbine thrust coefficient was at maximum value (Rhodes and Lundquist, 2013). Analysis of surface flux measurements suggest that turbines significantly enhance nocturnal surface carbon dioxide and sensible heat fluxes downwind (Rajewski et al., 2013b).

The CWEX-13 field campaign expanded these observations to consider far-wake impacts and the impacts of multiple rows of turbines. The field campaign took place between late June and early September 2013 in a 150 MW wind farm in central Iowa, the same wind farm studied in the previous CWEX campaigns. In the region of interest in CWEX-13, turbines of nominally 80m hub height and 80 m rotor diameter D were located in rows separated by 20 D. Three WINDCUBE™ v1 profiling lidar systems (provided by the University of Colorado at Boulder and the National Center for Atmospheric Research) were located a) south of the first turbine row, b) 9D north of the first turbine row, and c) 5 D north of a second row of turbines. Complementary instrumentation included a Radiometrics MP-3000A microwave radiometer (University of Colorado at Boulder) to quantify atmospheric stability (Friedrich et al., 2012) and several surface flux stations (Iowa State University). Finally, for a portion of the CWEX-13 campaign (31 July – 6 September), a WINDCUBE™ 200S from LEOSPHERE was co-located with the northernmost WINDCUBE™ v1 lidar to enable instrument intercomparisons as well as horizontal scans of the turbine wakes and vertical scans of the wake from one individual turbine.


Main body of abstract

The experimental design of CWEX-13 relied upon frequent southeasterly-southwesterly winds to enable “upwind” and “downwind” measurements around the two rows of turbines of focus. Climatology for this region (Rajewski et al. 2013a) supports such a field design. Wind roses ( , , ) from the 40m, 120m, and 220m altitudes from the southernmost (undisturbed) lidar show that wind conditions were predominantly southeasterly-southwesterly, with infrequent northerly and northwesterly wind cases. The wind roses suggest slight veering of the wind direction with height, likely due to nocturnal veering during stable conditions associated with the nocturnal low-level jet (Blackadar, 1957; Banta et al., 2002; Kelley et al., 2006). The CWEX team is exploring this observed veering to quantify if this veering is indeed most strongly associated with strong stability conditions or if there is a large-scale mesoscale veering due to the enhanced roughness of the wind farm.

The frequent southerly conditions enabled the WINDCUBE™ 200S from LEOSPHERE, when operating in plan-position-indicator (“horizontal slice”) mode, to observe multiple wakes propagating from either row of turbines depending on the scanning elevation of the lidar, similar to the study of Smalikho et al. (2013) and Aitken et al. (2013). Because the 200S lidar was located on the ground, the horizontal scans were taken at a range of elevation angles, documenting velocities at different altitudes depending on the distance from the lidar. An example () of these wakes as observed with a scan from ~ 2 UTC under southwesterly flow conditions (darker blue represents faster flow towards the lidar). The scan of 50 degrees in azimuth at a scanning rate of 0.5 degrees per second required 100 seconds of scan time. The elevation angle of this scan collects data at an elevation of approximately 110m at the row of turbines visualized here, the southerly row of turbines. (At this elevation angle, at the range of the northerly row of turbines, the lidar scan is at 20 m above the surface, underneath that row of turbines, so the northernmost row of turbines is not visible in this scan.) The degradation or “recovery” of the wake with downwind distance is clearly visible, but quantification of wake expansion rates requires quantitative analysis that considers the change of altitude with range. The quantitative approach of Aitken et al. (2013), previously applied to scanning lidar measurements of wakes from a single turbine, will be applied to these data. Goals include measuring interactions between turbine wakes, quantifying variation of wake characteristics with atmospheric stability, and quantifying wake meandering.

Many of the science goals of CWEX-13 focus on the effect of atmospheric stability on turbine wake evolution. As a result, the field program design incorporated multiple measurements for quantifying atmospheric stability. The primary means for quantifying atmospheric stability is via measurement of the Obukhov length from sonic anemometers mounted on 10 m surface flux stations. In addition to these measurements, this campaign involved the use of a microwave radiometer (Radiometrics MP-3000A) to quantify the vertical variation of temperature and moisture. The Brunt-Vaisala frequency can be calculated from such temperature profile data (Friedrich et al., 2012), and will be compared to Obukhov lengths estimated by surface-based sonic anemometer data.

Example time-height cross-sections of temperature and moisture exhibit the strong diurnal cycle characteristic of the summertime Midwestern United States (). Surface-driven cooling over the night (0000-1200 UTC) is evident, as well as strong heating of the surface during the day. During this 24-hour period, winds are consistently southerly-southwesterly (, bottom panel). During the night, a nocturnal low-level jet developed, with maximum observed wind speeds of 17 m s-1 just 200 m above the surface (, top panel). Numerous other case studies of nocturnal low-level jets are available in the CWEX-13 dataset.



Conclusion

The CWEX-13 field campaign employed a suite of remote sensing and in situ instruments to explore the dynamics and thermodynamics of the complex wind flow through multiple rows of a ~ 150 MW onshore wind plant in the Midwestern United States. Turbines of nominally 80m hub height and 80 m rotor diameter D were located in rows separated by 20 D in the area of observation in CWEX-13. WINDCUBE™ v1 lidar wind profiles at three locations within the wind plant (upwind, 9 rotor diameters D past a first row, and 5 D past a second row) provided observation of wind speed deficits and turbulence enhancements within the turbine wakes during southerly flow conditions. Scanning lidar (WINDCUBE™ 200S from LEOSPHERE) measurements of line-of-sight velocity enabled assessments of wake locations for comparison with the profiling lidar measurements, as well as data for quantification of wake characteristics. Retrievals of temperature and moisture profiles from a microwave radiometer (Radiometrics MP-3000A) quantify atmospheric stability via the Brunt-Vaisala frequency. Finally, surface flux stations equipped with sonic anemometers at several locations within the wind plant document atmospheric stability as well as surface fluxes of heat, momentum, and moisture to identify the impact of multiple rows of turbines on surface-atmosphere exchange processes.

Initial analysis from CWEX-13 suggest that the dataset includes numerous observations of nocturnal low-level jets interacting with the wind farm. Furthermore, these data may be used to validate wind plant representations in numerical weather prediction models, such as the Fitch et al. (2012) Weather Research and Forecasting (WRF) wind farm parameterization. The detailed observations of multiple wakes from two rows of turbines may prove useful for comparison to more explicit turbine-resolving models such as available in OpenFOAM (Churchfield et al. 2012), the Large-Eddy Simulation capability of WRF (Mirocha et al. 2013), and others (Lu and Porté-Agel, 2001; WindBlade: http://ees.lanl.gov/ees16/windblade.shtml). Evaluation of the turbine power data in conjunction with these meteorological data, as in Barthelmie et al. (2009) and Hansen et al. (2012), will enable insight into the impacts of atmospheric stability on power performance in this region (Wharton and Lundquist, 2012; Vanderwende and Lundquist, 2012).



Learning objectives
- Visualize how remote sensing technology can enable assessment of the wind resource as it is modified in the complex flow within a wind plant experiencing a range of atmospheric stability conditions
- Recognize the role that atmospheric stability plays in determining the wind resource
- Explore how turbine wakes are affected by atmospheric inflow conditions



References
Aitken, M., L., M. E. Rhodes, and J. K. Lundquist. 2012. Performance of a wind-profiling lidar in the region of wind turbine rotor disks. Journal of Atmospheric and Oceanic Technology 29, 347-355. http://dx.doi.org/10.1175/JTECH-D-11-00033.1

Aitken, M. L., J. K. Lundquist, Y. L. Pichugina, and R. M. Banta. 2013. Quantifying wind turbine wake characteristics from scanning remote sensor data. In review at J. Atmos. Ocean. Tech.

Banta, R. M., R. K. Newsom, J. K. Lundquist, Y. L. Pichugina, R. L. Coulter, and L. Mahrt. 2002. Nocturnal low-level jet characteristics over Kansas during CASES-99. Boundary-Layer Meteorology 105 (2), 221-252.

Barthelmie, R.J., Pryor, S.C., Frandsen, S.T., Hansen, K.S., Schepers, J.G., Rados, K., Schlez, W., Neubert, A., Jensen, L.E. and Neckelmann, S. 2009. Quantifying the impact of wind turbine wakes on power output at offshore wind farms. Journal of Atmospheric and Oceanic Technology, 27, 1302–1317. doi: 10.1175/2010JTECHA1398.1

Blackadar, A. K. 1957. Boundary-Layer Wind Maxima and their Significance for the Growth of Nocturnal Inversions. Bulletin of the American Meteorological Society, 38, 283-290.

Fitch, A. C., J. B. Olson, J. K. Lundquist, J. Dudhia, A. K. Gupta, J. Michalakes, and I. Barstad. 2012. Local and Mesoscale Impacts of Wind Farms as Parameterized in a Mesoscale NWP Model. Monthly Weather Review. Vol. 140, No. 9, 3017-3038. http://dx.doi.org/10.1175/MWR-D-11-00352.1

Fitch, A., J. K. Lundquist, and J. B. Olson. 2013. Mesoscale Influences of Wind Farms throughout a diurnal cycle. Monthly Weather Review, 141, 2173-2198. doi: http://dx.doi.org/10.1175/MWR-D-12-00185.1

Friedrich, K. J. K. Lundquist, E. Kalina, M. Aitken, and R. Marshall. 2012. Stability and Turbulence in the Atmospheric Boundary Layer: An Intercomparison of Remote Sensing and Tower Observations. Geophys. Res. Lett., Vol. 39, No. 3, L03801, doi:10.1029/2011GL050413.

Hansen, K. S., R. J. Barthelmie, L. E. Jensen, and A. Sommer. 2012. The impact of turbulence intensity and atmospheric stability on power deficits due to wind turbine wakes at Horns Rev wind farm. Wind Energy, 15, 183-196.

Kelley, N.D., B. J. Jonkman, and G. N. Scott. 2006. The Great Plains Turbulence Environment: Its Origins, Impact and Simulation. AWEA’s 2006 WindPower Conference Pittsburgh, Pennsylvania June 4–7, 2006. Available at http://www.nrel.gov/docs/fy07osti/40176.pdf

Lu, H. and F. Porte-Agel. 2011. Large-eddy simulation of a very large wind farm in a stable atmospheric boundary layer. Phys. Fluids, 23, 065 101, doi:10.1063/1.3589857.

Mirocha, J., B. Kosovic, M. Aitken, and J. K. Lundquist. 2013. Implementation of a generalized actuator disk wind turbine model into WRF for large-eddy simulation applications. In review at Journal of Renewable and Sustainable Energy.

Rajewski, D., G. Takle, J. K. Lundquist, M. E. Rhodes, S. Oncley, T. Horst. 2013a. Crop Wind Energy Experiment (CWEX): Observations of Surface-Layer, Boundary Layer, and Mesoscale Interactions with a Wind Farm. Bull Amer Meteor Soc 94: 655–672.

Rajewski, D., E. S. Takle, J. K. Lundquist, J. H. Prueger, R. Pfeiffer, J. L. Hatfield, K. K. Spoth, and R. K. Doorenbos. 2013b. Changes in fluxes of heat, H2O, and CO2 caused by a large wind farm. In review at Agricultural & Forest Meteorology.

Rhodes, M. E., and J. K. Lundquist. 2013. The Effect of Wind Turbine Wakes on Summertime Midwest Atmospheric Wind Profiles. Boundary-Layer Meteorology 149, 85-103. doi:10.1007/s10546-013-9834-x

Smalikho, I. N., V. A. Banakh, Y. L. Pichugina, W. A. Brewer, R. M. Banta, J. K. Lundquist, and N. D. Kelley. 2013. Lidar investigation of atmosphere effect on a wind turbine wake. In press at J. Atmos. Ocean. Tech. http://dx.doi.org/10.1175/JTECH-D-12-00108.1

Vanderwende, B. and J. K. Lundquist. 2012. The modification of wind turbine performance by statistically distinct atmospheric regimes. Environmental Research Letters 7 (2012) 034035 doi:10.1088/1748-9326/7/3/034035

Wharton, S. and J. K. Lundquist. 2012. Atmospheric Stability Affects Wind Turbine Power Collection. Environ. Res. Lett. 7 014005 doi:10.1088/1748-9326/7/1/014005