Invited Presentation - 2013 IAHR World Congress, Chengdu, China
Development and Application of Advanced Weather Prediction Technologies for the Wind Energy Industry...
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Photo by Bob Henson (UCAR) Challenges & Opportunities for Renewable Energy Prediction U02 – Breakthroughs in Understanding and Developing Renewable Energy AGU Conference 2010 William P. Mahoney, Program Director National Center for Atmospheric Research Boulder, CO Copyright 2010 University Corporation for Atmospheric Research 1
Transcript of Development and Application of Advanced Weather Prediction Technologies for the Wind Energy Industry...
Photo by Bob Henson (UCAR)
Challenges & Opportunities for
Renewable Energy Prediction
U02 – Breakthroughs in Understanding and Developing Renewable Energy
AGU Conference 2010
William P. Mahoney, Program DirectorNational Center for Atmospheric Research
Boulder, CO
Copyright 2010 University Corporation for Atmospheric Research1
Outline
Challenges
Wind Energy Prediction Approaches
NCAR‟s Wind Energy Research & Development
„Breakthroughs‟ or Findings to Date
Future needed breakthroughs
2NCAR
Overarching Challenge
Boundary layer meteorology (0-200 m) is not well understood nor is this layer well measured
Wind energy industry greatly under appreciates the complexity of the airflow in this layer
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Courtesy: Ned Patton, NCAR
Wind Measure International
Impact to Wind Energy Stakeholders
Uncertainty in available wind resources constrains the marketplace – slows growth
Lack of adequate performance in wind energy forecasts – leads to high integration costs
Wind generators fail early (shortened lifecycle) – leads to costly operations/maintenace
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Wind Prediction Challenges
Scale Interactions are Critical
Global Scales
Continental Scales
Regional Scales
Local Scales
Long Island
Urban Scales
Copyright 2010 University Corporation for Atmospheric Research5
GE 1.5 MW Wind Turbine
80 meter hub height77 m blade diameter
60-80 m
10 mobservation
Standard surfaceweather station with a 10 meter highwind sensor.
Assessments & Forecasts
Wind Prediction ChallengesLocal Effects & Phenomenon Must be Addressed
Local Topography
Surface Roughness
Land Use
Vegetation Characteristics
Urbanization
Atmospheric Gravity Waves
Low-level jets
Convection currents
Icing
Turbine wakes
Data ‘void’ boundary layerCopyright 2010 University Corporation for Atmospheric Research
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Wind Energy Prediction Challenges
End Users Want Energy Predicted Not Wind!
Converting wind to energy depends on:
Knowledge of turbine availability
Extracting hub height winds from NWP
Accurate power curves
Knowledge of wind characteristics
• wind shear (blade tip to tip)
• turbulence
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Approaches to Wind Energy Prediction
Numerical Weather Prediction (NWP)
Statistical Approaches
Use of Data Assimilation
Ensemble Methods
Combined Methods
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Typical Wind Energy Prediction Process Diagram
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Wind Farm Data
Numerical Weather Prediction (NWP)
StatisticalCorrections
Hub Height Conversion
Data Fusion
Wind-to-Power Curve CalculationNumerical Weather
Prediction (NWP)
Numerical Weather Prediction (NWP)
StatisticalCorrections
Wind Energy
Prediction
Simplified diagram!
Copyright 2010 University Corporation for Atmospheric Research
Data Assimilation
Weather Observations
Optimizing Prediction Methods by Blending Technologies
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Forecast Time
3 hrs 6 hrs 12 hrs 24 hrs 7 days 10 days
Persistence0-2 hrs
1 hr
Rapid Cycle Models0-2 hrs
4DDA+NWP3-12 hrs
3DVAR+NWP12 hrs to days
Climatology> 10 days
Copyright 2010 University Corporation for Atmospheric Research
Xcel Energy Project A Sampling of Results
Wind Farm in Northeast ColoradoPhoto by Carlye Calvin, UCAR
Copyright 2010 University Corporation for Atmospheric Research
Xcel Energy Service Areas
Wind Farms (50+)
2623 Turbines (growing)
3736 MW+ (wind)
~10% Wind
3.4 million customers (electric)
Annual revenue $11B
Copyright 2010 University Corporation for Atmospheric Research
NCAR Wind Energy Prediction SystemXcel Energy Configuration
Copyright 2010 University Corporation for Atmospheric Research14
WRF RTFDDA System
NCEP DataNAMGFSRUCMOS
Observations
Wind Farm DataNacelle wind speedGenerator power
Node powerMet towerAvailability
Ensemble RTFDDASystem
SupplementalWind Farm Data
Met towersWind profiler
Surface StationsWindcube Lidar
Operator GUI
Meteorologist GUI
WRF Model Output
Wind to Energy Conversion Subsystem
Dynamic, Integrated Forecast System
(DICast®)
CSV Data
VDRAS(nowcasting)
Summary of Preliminary Findings
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Significant Variability Exists Across Individual Wind Farms
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Predicting wind variabilityin complex terrain requires careful matching of NWP grid configuration.
Diff =10 ms-1
~250 turbines
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0
Time
Prediction of Wind Profiles (Shear) Important
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Increasing NWP
resolution results in
dramatic variations
of vertical profiles of
wind speeds.
Imposes a tough
challenge – which
profiles, if any, are
best?
Need more Obs!
30 kmspacing
123 m spacing
Calculating Turbine Height Winds from NWP Models Requires Special Care
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Weather models have varying vertical grid structures. Multiple approaches should be used to determine hub height winds.
NCAR uses an ensemble approach tuned for each model, forecast lead time, and location
NWP Output
Hub Hgt
First P-level above
First P-level below
10m wind level
Second P-level above
Blending Output from Multiple Forecast Systems Improves Results
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Integrating and dynamically weighting multiple forecasts results in a more accurate forecast than any single forecast input.
Wind speed error reductions of 5-30% have been found.
Statistical Post-Processing is Critical
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• Errors highly depend on
diurnal cycles, station
locations, forecasts
ranges, and weather
regimes.
• Applying statistical bias
correction (KF/AN/ANKF)
improves the forecasts
by 5-30%!
• Impact of analog
approaches last much
longer (> 10 days)
Courtesy: Luca Del MonacheNCAR/RAL
Wind Shear vs. Turbine Efficiency
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Low-level Jet Wind RampCold Front Wind Ramp
Knowledge of the wind profiler is importantfor wind to power conversion
(Lundquist and Wharton, 2009)
(T. Aguilar, 2010)
Shear Across Blades ImpactsTurbine Efficiency
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Courtesy, Julie Lundquist
Tip-to-tip shear can reduceBlade efficiency by upTo 20%!
Power curves need tobe adjusted accordingly
Value of Utilizing Wind Turbine Data(in WRF-RTFDDA)
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W/O Farm DATA
With Farm DATA
Gain:17 % in RMS20% in MAE11% in Bias
0-3 hour Wind Energy Predictions
Copyright 2010 University Corporation for Atmospheric Research
Value of Sharing Wind Turbine Data (in WRF-RTFDDA)
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W/O Farm DATA
With Farm DATA
Gain:6 % in RMS8% in MAE4% in Bias
3-6 hour Wind Energy Predictions
Copyright 2010 University Corporation for Atmospheric Research
NWP Ensemble Provides Important Information for Power Generation Decision Making
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Geographical View of PredictedWind Variability
24 hour animation
NCAR is working with Xcel Energy to identify the best methods for communicating uncertainty in wind energy prediction
Wind Energy Ramp Event Nowcasting
Causes of Wind Ramp Events
Cold Fronts
Warm Fronts
Thunderstorm Outflows
Sea Breezes
Microbursts
Gravity Waves
Eroding Surface Inversion
Momentum mixing
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Wind Energy Ramp Event8/03/09 771mw up-ramp from 20:10 - 22:10 followed by a 738mw down-ramp from 22:40 - 00:50
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Ponnequin
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Logan/Peetz Table
Colorado Green/Twin Buttes
Small convective cells
Larger front
2 hours
Copyright 2010 University Corporation for Atmospheric Research
800 MW Ramp
NCAR Auto-Nowcasting System
Wind Energy Nowcasting
Gust frontsapproaching„wind farm‟
Wind rampevent isimminent
Copyright 2010 University Corporation for Atmospheric Research
„Breakthrough‟ Summary
Ensemble approaches improves wind energy forecasts
Assimilation of nacelle wind data improves NWPperformance
Shear significantly impacts turbine efficiency
Assimilation of radar radial velocity data into rapid cycling NWP improves ramp forecasts
Additional boundary layer observations will improve wind energy forecasts
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Predicting Wind Power's Growth -- an Art
That Needs More ScienceBy 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.”
THANK YOU
31Copyright 2010 University Corporation for Atmospheric Research
William P. Mahoney [email protected]
Dr. Sue Ellen [email protected]
