Atmospheric Stability Impacts on Wind
Turbine PerformanceJulie K. Lundquist, Ph.D.
Professor, Dept. of Atmospheric and Oceanic SciencesUniversity of Colorado at Boulder
Fellow, Renewable and Sustainable Energy InstituteWind 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 PerformanceJulie K. Lundquist, Ph.D.
Professor, Dept. of Atmospheric and Oceanic SciencesUniversity of Colorado at Boulder
Fellow, Renewable and Sustainable Energy InstituteWind 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
Hub Heightsrangefrom 60-120m
49 m blade:Rotor diameter~ 100m
The diurnal cycle of atmospheric stability strongly influences winds in the turbine rotor disk
Wind Speed [ms-1]
Potential Temperature [K]
Hei
ght a
bove
su
rface
[m]
Hei
ght a
bove
su
rface
[m]
1800 LST 2200 LST0200 LST0800 LST
1800 LST 2200 LST0200 LST0800 LST
Poulos, Blumen,Fritts, Lundquist, et al.,2002
Radiosonde profiles demonstrate that the cooling of the surface overnight is accompanied by dramatic accelerations in the winds
This wind farm provides a unique and valuable dataset
Characteristics:
Presence of both marine and terrestrial BL over hilly terrain
Little directional wind shear
Strongly channeled flow
Large dataset:
On-site met towers + SODAR
Turbine power and nacelle wind speeds available
Four seasons of data; strong seasonality and diurnal signal
NIGHT
DAY
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:
Leading edge turbines: nacelle U and power, 10 min. avgs, 80m hubs
Vertical profile of
cup anemometers
Doppler Sound Detection and Ranging (SODAR)
'' vw θ
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?
vw θ ′′
Obukhov length, L (off-site)
θv : virtual potential temperaturek : von Karman constantg : gravity
: sensible heat flux u* : friction velocity =
''
3*
v
v
wgk
uL
θ
θ
⋅⋅
⋅−=
(3) Turbulence kinetic energy, TKE
u' 2: variance of wind speed
)'''(5.0 222 wvuTKE ++=
4/122)''''( wvwu +
(2) Turbulence intensity, IU
σU : standard dev. of mean horz. wind speed (U) at 80 m
)(zUI U
Uσ
=
(1) Wind shear exponent, α
U : mean horz. wind speed at height z or zR
α)()(R
R zzUzU =
Estimates of stability from a typical cup anemometer fail to agree with more sophisticated measures
55%
Summer data
80m
80m40m to 120m
surface
Averaged rotor disk profiles reflect atmospheric stability
Stable Neutral Convectivez/L > 0.1 - 0.1 < z/L < 0.1 z/L < - 0.1
α > 0.2 0.1 < α < 0.2 α < 0.1
IU < 10% 10% < IU < 20% IU > 20%
TKE <0.6
0.6 < TKE < 1.0 TKE > 1.0
Summer
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.
stable
neutral
convective
A typical summer power curve based on equivalent wind speed still exhibits
significant variabilityCapacity factor, CF (%)
Pactual : actual power yield of the individual turbine
Prated : maximum power yield of the turbine as determined by the manufacturer
100×=rated
actual
PPCF
At 8 m s-1 the CF ranges from 35% to 70%!
Stratification of power curves reveal stability-related influences on power output
Lawrence Livermore National Laboratory
STABLENEUTRALCONVECTIVEMANUFACTURER
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 varied by over 20% due to atmospheric stability.
“Underperformance” may be due to inaccurate assessments ofavailable power due to failure to account for variability of wind and turbulence across rotor disk due to atmospheric stability variations.
Power is a function of atmospheric stability
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?
Lawrence Livermore National Laboratory
Julie K. LundquistUniversity of Colorado at Boulder
Voice: 303/492-8932
http://atoc.colorado.edu/~jlundqui
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,
stable
Equivalent wind speed, UequivTI
A : rotor area, Ueff(z) : mean wind speed at height z, r : radius ofrotor area, H : hub-height
Ueff(z) calculated for each height within the rotor disk:
U(80m)
U(70m)
U(60m)
U(50m)
U(40m)
U(120m)
U(110m)
U(100m)
U(90m)
Rotor swept area
Ueff (z) = U(z)3(1+ 3IU2)3
accounting for the additional energy (turbulence) in the instantaneous wind speed(following Wagner et al. 2009)
dzzHzHrzUA
UrH
rHeffequivTI
2/1222 )2)((2∫+
−
−+−=
Hub-height wind speed often fails to represent momentum experienced by the
entire rotor disk
stable
neutral
convective
Hub-height winds are often maximum winds across the rotordisk!
Stable UequivTI > U80m
Neutral UequivTI = U80m
Convective UequivTI < U80m
Nacelle-based “equivalent” wind speed produces the most accurate power curve
Ongoing work: forecasting “ramping events” to predict sudden changes in power
100
0 Time (fraction of day) on ramp 1
100% power increase in 30 minutes
Forecasts capture the ramping event
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-surface-atmosphere model (CSM)
WRF-LES: a higher-resolution, turbulence-resolving model (UC Berkeley, CU-Boulder/NREL)
U (m
s-1
) at 1
00 m
WRF-LES forecasted wind speed in comparison toactual 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
Project Plan: Deploy NOAA’s High Resolution Doppler Lidar at NREL’sNational Wind Technology Center (Fall/Winter 2010) to characterize inflow and wake from the Siemens 2.3 MW turbine; model with WRF and WRF-LES
Kelley et al., 2006: streamwisevelocity and velocity variance from HRDL
TWICS field plan (as of 4/2010)
135m met tower, 6+ sonics, T profiles
NOAA HRDL LIDAR
2 NREL SODAR
CU WindCube LIDAR
Two-week deployment during windy season
Plan subject to change
Mean wind direction (292)
Lundquist (CUB, NREL), Kelley (NREL), Banta/Pichugina (NOAA), Mirocha (LLNL)
Typical output data from the HRDL reveals rapid intermittent turbulence bursting events
HRDL observations enable high-time resolution characterization of atmospheric turbulencesuch 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
Lawrence Livermore National Laboratory
D03, mesoscale, Δx = 1.33kmMYJ36 hrs
20 05 14 23 08Local Time
800
600
400
200
0Hei
ght a
bove
sur
face
(m)
800
600
400
200
0Hei
ght a
bove
sur
face
(m)
20 05 14 23 08Local Time
D06, LES, Δx = 49mTKE SFS closure36 hrs
Win
d S
peed
[m/s
]
10.0
5.0
0.0
LES shows increasedvariability in early LLJ development with quiescentperiod early and delayed onset of stronger winds
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
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