Atmospheric Stability Impacts on Wind Turbine Performance Julie K. Lundquist, Ph.D. Professor, Dept. of Atmospheric and Oceanic Sciences University of Colorado at Boulder Fellow, Renewable and Sustainable Energy Institute Wind Energy R&D Workshop at NCAR May 11-12, 2010
Upcoming AMS events relevant to the renewable energy community Summer AMS Joint meeting, 2-6 Aug 2010, in Keystone, CO: Boundary Layers and Turbulence Meeting Urban Symposium Agricultural and Forest Meteorology Meeting Several renewable-energy joint sessions Short course on Wind energy applications, supported by atmospheric boundary layer theory, observations and modeling on Sunday 1 August, Keystone, CO
Atmospheric Stability Impacts on Wind Turbine Performance Julie K. Lundquist, Ph.D. Professor, Dept. of Atmospheric and Oceanic Sciences University of Colorado at Boulder Fellow, Renewable and Sustainable Energy Institute Wind Energy R&D Workshop at NCAR May 11-12, 2010
Contributions gratefully acknowledged from Lawrence Livermore National Laboratory: Sonia Wharton Research funding LDRD 06-ERD-026 Dept of Energy Wind and Hydropower Technology Office for research funding: Renewable Systems Interconnect Support Tall Turbines Ibderdrola Renewables, Inc. for provision of this unique and extensive dataset Justin Sharp Mike Zulauf Jerry Crescenti
Widespread impression of wind farm underperformance 20% by 2030 depends on sufficient capacity factor, not just installations Impression that many US parks underperforming can undermine public perception, financing, etc. With support from IRI, we investigate the role of atmospheric variability in one wind farm s performance
We analyze data from an operating wind farm with modern tall turbines Lundquist and Wharton, 2009, IEA Experts Meeting on SODAR and LIDAR; Wharton, Lundquist, Sharp, Crescenti, and Zulauf, 2009, AGU Fall Meeting; Wharton and Lundquist, 2010, DOE Technical report and in preparation for Wind Energy
Can atmospheric conditions explain wind turbine performance? 1. Background on atmospheric dynamics, wind turbine power curves 2. Overview of unique dataset 3. Approaches to quantifying atmospheric stability 4. Can stability/turbulence variations explain wind park underperformance?
Modern wind turbines span heights ~ 200m, penetrating a complex atmosphere Siemens 3.0 MW turbine 49 m blade: Rotor diameter ~ 100m Hub Heights range from 60-120m
The diurnal cycle of atmospheric stability strongly influences winds in the turbine rotor disk Radiosonde profiles demonstrate that the cooling of the surface overnight is accompanied by dramatic accelerations in the winds 1800 LST 2200 LST 0200 LST 0800 LST 1800 LST 2200 LST 0200 LST 0800 LST Height above surface [m] Height above surface [m] Wind Speed [ms -1 ] Potential Temperature [K] Poulos, Blumen, Fritts, Lundquist, et al., 2002
This wind farm provides a unique and valuable dataset Characteristics: Presence of both marine and terrestrial BL over hilly terrain Little directional wind shear NIGHT Strongly channeled flow Large dataset: DAY On-site met towers + SODAR Turbine power and nacelle wind speeds available Four seasons of data; strong seasonality and diurnal signal
The data surpass those typically available at wind farms Meteorological data: 2 met towers w/ cup anemometers (u, v) at 5 heights (30, 40, 50, 60, 80 m), 10 min. avgs; (T, p measurements unusable) SODAR observations (u, v, w) for 19 heights (20 m to 200 m, 10 m resolution), 10 min. avgs. Nearby research station with a sonic anemometer (u, v, w, ), 30 min. avgs. Turbine data: w' θv ' Leading edge turbines: nacelle U and power, 10 min. avgs, 80m hubs Doppler Sound Detection and Ranging (SODAR) Vertical profile of cup anemometers sonic anemometer
Wind speeds vary with seasons; summer winds exhibit strong wind shear
Wind speeds exhibit a strong daily cycle in spring and summer
Seasonal variability in winds is reflected in turbine capacity factor: most power generated on summer/spring nights
In absence of temperature measurements, how can stability be assessed? (1) Wind shear exponent, α z α U ( z) = U ( ) R z R U : mean horz. wind speed at height z or z R (2) Turbulence intensity, I U σu IU = U (z) σ U : standard dev. of mean horz. wind speed (U) at 80 m (3) Turbulence kinetic energy, TKE 2 2 TKE = 0.5( u' + v' + w' u' 2 : variance of wind speed 2 ) Obukhov length, L (off-site) 3 θv u * L = ' ' k g wθ θ v : virtual potential temperature k : von Karman constant g : gravity w θ v : sensible heat flux 2 2 u * : friction velocity = v ( u ' w' + v' w' ) 1/ 4
Estimates of stability from a typical cup anemometer fail to agree with more sophisticated measures surface 80m 55% 40m to 120m 80m Summer data
Averaged rotor disk profiles reflect atmospheric stability Stable Neutral Convective z/l > 0.1-0.1 < z/l < 0.1 z/l < - 0.1 Summer α > 0.2 0.1 < α < 0.2 α < 0.1 stable I U < 10% 10% < I U < 20% I U > 20% TKE < 0.6 0.6 < TKE < 1.0 TKE > 1.0 neutral Stable conditions: high wind shear, low turbulence, and possible nocturnal low-level jets Neutral conditions: minimal wind shear Convective conditions have lowest wind speeds, very little wind shear in swept-area, and are highly turbulent. convective
A typical summer power curve based on equivalent wind speed still exhibits significant variability At 8 m s -1 the CF ranges from 35% to 70%! Capacity factor, CF (%) CF = P P actual rated 100 P actual : actual power yield of the individual turbine P rated : maximum power yield of the turbine as determined by the manufacturer
Stratification of power curves reveal stability-related influences on power output STABLE NEUTRAL CONVECTIVE MANUFACTURER Lawrence Livermore National Laboratory
Even stronger variation seen in another leading-edge turbine Lawrence Livermore National Laboratory
In fact, all leading edge turbines show that power generated is dependent on stability Lawrence Livermore National Laboratory
In summary: Atmospheric stability, through the mechanisms of turbulence and wind shear, governs the generation of power at these tall turbines. Power is a function of atmospheric stability Power varied by over 20% due to atmospheric stability. Underperformance may be due to inaccurate assessments of available power due to failure to account for variability of wind and turbulence across rotor disk due to atmospheric stability variations. 63% capacity (stable) vs. 41% (convective)
Wind farm underperformance can in part be explained due to incomplete resource assessment Resource assessment instrumentation should be upgraded: SODAR stability parameters segregate wind farm data into stable, neutra and convective periods in agreement with research-grade observations Cup anemometer data inaccurately estimate stability regimes SODAR performs poorly during precipitation, however role for LIDAR? Because of complex wind profile shapes, power curves should be a function of wind speed and turbulence over entire rotor disk (UequivTI) (as in Wagner et al., 2009) Power output correlates well with atmospheric stability: Enhanced turbine performance during stable conditions Reduced turbine performance during convective conditions
Questions? Julie K. Lundquist University of Colorado at Boulder Julie.Lundquist@colorado.edu Voice: 303/492-8932 http://atoc.colorado.edu/~jlundqui Lawrence Livermore National Laboratory
Which velocity should be used to determine power curve? Available measurements: Nacelle anemometers (cup anemometers) in immediate wake Hub-height met mast up to 7 km away from each turbine SODAR measurements/profiles up to 5 km away from each turbine Industry-standard power curves require met masts Met masts cannot capture shear across turbine rotor disk SODAR, which can capture shear, too far from turbines
Because hub-height often fails to indicate the true rotor wind speed, we calculate an equivalent wind speed by integrating across rotor disk, stabl e U(120m) U(110m) U(100m) Equivalent wind speed, U equivti A : rotor area, U eff (z) : mean wind speed at height z, r : radius of rotor area, H : hub-height U eff (z) calculated for each height within the rotor disk: U(90m) 3 Rotor swept area U(80m) U eff (z) = U(z) 3 (1+ 3I 2 U ) U(70m) U(60m) U(50m) U(40m) U equivti 2 = A H + r U H r eff ( z)( r H + 2Hz z accounting for the additional energy (turbulence) in the instantaneous wind speed (following Wagner et al. 2009) 2 2 2 ) 1/ 2 dz
Hub-height wind speed often fails to represent momentum experienced by the entire rotor disk stable Stable U equivti > U 80m neutral Neutral U equivti = U 80m convective Convective U equivti < U 80m Hub-height winds are often maximum winds across the rotor disk!
Nacelle-based equivalent wind speed produces the most accurate power curve
Ongoing work: forecasting ramping events to predict sudden changes in power Ramping events complicate integration of wind-generated power into power grids Large ramping events at this farm have been identified in the data and are currently being modeled by CU- Boulder/NREL, LLNL, CSM, and UC- Berkeley: WRF: 1 km resolution mesoscale model with increased vertical resolution in the lowest 200 m (CU-Boulder/NREL, LLNL) PF.WRF: coupled subsurface-surfaceatmosphere model (CSM) WRF-LES: a higher-resolution, turbulence-resolving model (UC Berkeley, CU-Boulder/NREL) 100 U (m s -1 ) at 100 m 100% power increase in 30 minutes 0 Time (fraction of day) on ramp 1 Forecasts capture the ramping event WRF-LES forecasted wind speed in comparison to actual wind speed. The two runs show that higher vertical resolution improves the forecast. From Marjanovic, Chow, Lundquist, 2010
TWICS: Turbine Wake and Inflow Characterization Study Although large wind turbines are designed to IEC standards, turbines regularly experience extreme wind inflow events outside of the limits defined by those standards: Need wind, turbulence, and stability measurements across entire rotor disk Downwind turbines experience wakes with decreased winds and increased turbulence Need detailed wake measurements along with inflow meteorology to understand atmospheric effects on wakes and on downwind turbines Background: Wuβow, Sitzki, & Hahn, 2007, CFD simulation using ANSYS FLUENT 6.3 LES
Characterizing turbine inflow and turbine wakes with Doppler LIDAR at a modern 2.3 MW turbine: couple models (left) with observations (right) Wuβow, Sitzki, & Hahn, 2007, CFD simulation using ANSYS FLUENT 6.3 LES Kelley et al., 2006: streamwise velocity and velocity variance from HRDL Project Plan: Deploy NOAA s High Resolution Doppler Lidar at NREL s National Wind Technology Center (Fall/Winter 2010) to characterize inflow and wake from the Siemens 2.3 MW turbine; model with WRF and WRF-LES
TWICS field plan (as of 4/2010) Lundquist (CUB, NREL), Kelley (NREL), Banta/Pichugina (NOAA), Mirocha (LLNL) 135m met tower, 6+ sonics, T profiles Mean wind direction (292) NOAA HRDL LIDAR 2 NREL SODAR CU WindCube LIDAR Two-week deployment during windy season Plan subject to change
Typical output data from the HRDL reveals rapid intermittent turbulence bursting events HRDL observations enable high-time resolution characterization of atmospheric turbulence such as would be needed for anticipating turbine response (Banta et al., 2006; Pichugina et al., 2008)
Observations will be compared with nested WRF/WRF-LES simulations D1: x = 2430m D2: x = 810m D3: x = 270m D4: x = 90m D5: x = 30m D6: x = 10m LES domains (D3+) use nonlinear backscatter with anisotropy model to capture stability effects Challenges include complex topography and appropriate spin-up for LES turbulence
At a different site, we see LES winds differ from mesoscale winds in both timing and intensity due to fundamentally different physics Height above surface (m) 800 600 400 200 0 20 05 14 23 08 Local Time LES shows increased variability in early LLJ development with quiescent period early and delayed onset of stronger winds Lawrence Livermore National Laboratory D03, mesoscale, Δx = 1.33km MYJ 36 hrs D06, LES, Δx = 49m TKE SFS closure 36 hrs Height above surface (m) 800 600 400 200 0 20 05 14 23 08 Local Time Wind Speed [m/s] 10.0 5.0 0.0
Meteorological insight can drive solutions to technical challenges inhibiting expansion of domestic wind energy New instrumentation can provide wind and turbulence profiles throughout turbine rotor disk Resource assessment instrumentation can be upgraded: Explain wind farm over/underperformance Increase accuracy of resource assessment Coupling of innovative observations with high-resolution atmospheric modeling provides insight to turbine inflow conditions and wake behavior Important for downwind turbines Important for downwind microclimates, especially crops upcoming experiment with Iowa State University in June/July 2010