Transcript Wind Energy Prediction Challenges of Opportunities William P. Mahoney III Branko Kosovic Research Applications Laboratory National Center for Atmospheric Research November 13, 2012 CONFIDENTIAL AND PROPRIETARY Any use.

Wind Energy Prediction
Challenges of Opportunities
William P. Mahoney III
Branko Kosovic
Research Applications Laboratory
National Center for Atmospheric Research
November 13, 2012
CONFIDENTIAL AND PROPRIETARY
Any use of this material without specific permission of UCAR-NCAR is strictly prohibited
Weather Related Wind Industry Issues
Financing
• Wind energy resource estimates at wind farm sites are
over-estimated on average
Maintenance
Costs
• Wind turbines are failing faster than predicted (up to
40% earlier)
Variability is
Costly
• Wind power variability complicates power integration
and load balancing across the grid – requires reserves
Prediction Errors
Costly
Extreme Weather
Impacts
• Wind energy prediction has typical errors of 10-15%
(flat terrain) to 15-25% (complex terrain)
• Wind turbines are not designed to handle extreme
weather conditions (shear, ice, snow, high wind, etc.).
More representative weather datasets are needed for
turbine design
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NCAR’s Renewable Energy Research
Activities
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Overarching Science Challenge
•
Boundary layer meteorology (0 to 200 m above ground)
is not well understood nor is this layer well measured
•
The wind energy industry greatly under appreciates the
complexity of the airflow in this layer
•
The wind industry has historically assumed less
turbulence and more wind with height above the ground
Image source: Wind Measure International
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Current Meteorological Shortfalls
for Wind Energy
• Lack of measurements between 10 and 200 m above
ground
• Weather models not optimized for wind (or solar) energy
prediction
• Need improved data assimilation techniques to take
advantage of wind farm and other local observations
• Dearth of vertical observations offshore
• Lack of understanding of complex flows near the Earth’s
surface
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Few Observations at Wind Turbine
Hub Height
Wind profiler
radar
Surface sensors
A lot of critical details are missed between observations!
Surface observations alone are not sufficient.
Satellite
s
Satellites
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Representative Winds
GE 1.5 MW
Wind Turbine
60-80 m
80 meter hub height
77 m blade diameter
Assessments &
Forecasts
Standard surface
weather station with
a 10 meter high
wind sensor.
observation
10 m
Satellites
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Low-Level Jets of High Wind (U.S.
Midwest)
Lidar (laser radar)
measured wind
velocity
toward lidar
10 ms-1 ribbon of high speed air
Height
(km)
Distance (km)
Courtesy, Robert Banta, NOAA
Low-level jet streams can damage wind generators
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Wind Variability at Turbine Height
Can be Substantial
Courtesy Ned Patton, NCAR
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Xcel Energy Wind Prediction
Project
Forecast Requirements
• 15-30 minute forecasts (predict rapid energy ramps)
• 1-3 hour forecasts (anticipate power adjustments)
• 24 hour forecasts
(energy trading and planning)
• 3-5 day forecasts
(long term trading & resource planning)
3.4 million customers
Annual revenue $11B
Xcel Energy needed power forecasts for 55 “electrical connection
nodes” representing 94 wind farms with 3283 turbines totaling 4000
MW of power generation capability.
But ….
The required forecasts need to be for POWER, not wind speed!
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Optimizing Prediction by Blending
Technologies
Each technology has its own ‘sweet spot’ with respect to prediction skill.
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Xcel Energy Wind Prediction
System
Predicting wind energy
ramp events using a
rapid cycle, highresolution weather
model and Doppler
radar data.
Animation of the Variational Doppler Radar Analysis System
(VDRAS) covering eastern Colorado wind farms. Wind vectors and
Doppler radar reflectivity are shown.
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Wind Shear vs. Turbine Efficiency
Knowledge of the vertical wind profile is important for
wind power conversion
Cold Front Wind Ramp
Shows several lower
atmosphere
stability
Low-level
Jet Stream
Wind Ramp
(vertical wind shear)
intensities
More shear = less power
SOARS student T. Aguilar (2010) – Texas Tech
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Xcel Energy Wind Prediction
Project
NCAR technology decreased
Xcel Energy’s wind energy
prediction error by ~40%!
Xcel Energy Customer Savings
2009
2010:
2011:
$5.87M (annualized)
$6.26M (additional over 2009)
$2.06M (additional over 2010)
$14.4M (over first 3 years)
Actual power (dots) and 20 hour power prediction
for Northern States Power
Also: saved > 238,000 tons CO2 (2011)
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Potential Value of Improved
Forecasts
Western, eastern, and Electrical
Reliability Council of Texas (ERCOT)
wind integration study areas, DOE
2010
Source DOE/NREL
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Research in Complex Flows
Global
Regional
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Grid Cell Size [m] 10
107
Domain Size [m]
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Adopted from Mike Robinson (DOE/NREL)
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Research in Complex Flows
Global
Regional
Local
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Grid Cell Size [m] 10
107
Domain Size [m]
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Adopted from Mike Robinson (DOE/NREL)
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Research in Complex Flows
Global
Regional
Local
Turbine
Blade
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Grid Cell Size [m] 10
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Domain Size [m]
100
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Adopted from Mike Robinson (DOE/NREL)
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Research in Complex Flows
Global
Regional
Local
Turbine
Blade
Grid Cell Size [m]
Domain Size [m]
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Adopted from Mike Robinson (DOE/NREL)
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Influence of Stability on
Boundary Layer Character
Horizontal slices of vertical velocity
Near neutral
Zi/L~-10
Strongly Unstable
Zi/L~-100
Atmospheric stability affects flow structures and turbulence
in atmospheric boundary layers
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Complex Flows – Terrain Effects
Turbulence Intensity
Flow and turbulence exhibit intermittency
downwind from the hill
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Complex Flows – Offshore Wind
For offshore
applications it
is important to
capture wind
and wave
interactions
Moving waves
Peter Sullivan, NCAR
Waves generate their own wind field that persists to hub height
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Complex Flows – Turbine Arrays
Vorticity Magnitude
Numerical simulations including effects of operating
turbines are aiding us in studying the interaction between
flow and wind turbine arrays
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Complex Flows
Turbine Wake Effects
Wake effects impact turbine efficiency and reduce lifecycle
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Fully-Coupled CFD/CSD for
Turbine/Platform Interaction with the
Atmosphere and Ocean
Objective: To create a state-of-the-art
High- Performance Computing “Cyber
Wind Facility” for the renewable energy
industry and researchers.
Courtesy Jim Brasseur, Penn State
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Required Atmospheric Science
Research to Support Wind Energy
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Need multi-year field experiments (on and off-shore)
Boundary layer meteorology (complex flow)
Cloud physics & precipitation processes (icing, snow, etc.)
Turbulence characteristics and prediction
Computational science (improve model efficiency)
Land surface condition prediction
Ocean dynamics (waves, currents)
Aerodynamic studies related to turbine design
Multi-scale modeling (global to mm scales)
Future climate modeling (effects on wind resources)
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NCAR’s Wind Energy
Partnerships
• NCAR started the American Meteorological Society
(AMS) Energy Committee
• Member of:
o
o
o
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American Wind Energy Association (AWEA)
Utility Variable-Generation Integration Group (UVIG)
Wind Energy Alliance
Center for Research & Education in Wind (CREW)
North American Wind Energy Academy
• Collaborating with:
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o
o
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National Renewable Energy Lab (NREL)
Lawrence Livermore (LLNL)
Technical University of Denmark (RISO)
NOAA/NWS/ESRL
Universities (University of Colorado, Penn State, Texas
Tech, Colorado State, U. of Wyoming, etc.)
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In Closing…
Predicting Wind Power's Growth -- an Art
That Needs More Science
By PETER BEHR of
Published: April 28, 2010
“More data, better weather and atmospheric models, and more
powerful computer runs are the paths to the next generation of
forecasting systems. Public-private partnerships between private
forecasting firms and the National Oceanic and Atmospheric
Administration and the National Center for Atmospheric Research are
crucial, industry officials say.”
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Thank You
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Copyright 2009 University Corporation for Atmospheric
Sampling of Renewable Energy
Funding Sources
NSF
DOE Offshore Wind
Penn State (DOE)
Xcel Energy
Vestas
ITRI (Taiwan)
NASA
NREL (DOE)
CREW (CU)
DOE Solar
Boundary layer meteorology, vertical lidar
Model state estimation for BL characterization
Full-coupled Cyber Wind Facility
Wind energy prediction
Analog post-processing techniques
Typhoon modeling for turbine design
Wind resource assessment
Inter-annual variability – wind/solar resources
Modeling stochastic variability in wind power
Advancements in solar energy prediction
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Industry Power Curve
Source: GE
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NCAR’s New Solar Energy
Partnerships
DOE/EERE Award
19 October 2012
3-Year Performance Period
Value: $4.5M (Federal)
$6.2M (w/cost share)
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