Forecasting Wind Energy Variability using Statistical Downscaling Techniques
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Forecasting Wind Energy Variability using Statistical Downscaling Techniques
Peter CoppinEuropean Wind Energy Conference March 2009
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CSIRO. Forecasting Wind Energy Variability
Acknowledgements
Division of Marine and Atmospheric Research• Robert Davy (principal researcher)• Milton Woods• Chris Russell• Peter Coppin
Funded by the Australian Government – Department of Resources Energy and Tourism (DRET)
• Australia Wind Energy Forecasting System (supplied by ANEMOS)
In collaboration with• Australian Bureau of Meteorology• European Union Framework 7 “SAFEWIND” Project partners
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CSIRO. Forecasting Wind Energy Variability
SE states 35,000MW max demand
Core wind generating area
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CSIRO. Forecasting Wind Energy Variability
Typical winter / early spring conditions
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CSIRO. Forecasting Wind Energy Variability
IR Satellite image – 1225 hr on Sept 11 2004 showing organised convection
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CSIRO. Forecasting Wind Energy Variability
Example of problem – high variability in South Australia
High wind variability at moderate wind speeds
• large swings in aggregate wind power
• wind farm aggregation can amplify the absolute magnitude of power changes
• variation can exceed available spinning reserve response capability
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Generation in 6 regions
Regional Generation at 12:30
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CSIRO. Forecasting Wind Energy Variability
Steps to forecasting wind variability
• Create a numerical index to describe severity
• Calculate index for observations
• Formally correlate observed variability with weather patterns derived from Numerical Weather Prediction (NWP) products via EOF analysis
• Produce prediction of variability based on combination of important patterns – using Random forest techniques – express in terms of wind speed and wind energy production ramp rate at suitable forecast time horizon
• Check predictability with observations (separate data segment)
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CSIRO. Forecasting Wind Energy Variability
Modelling wind variability
Initial proof-of principle based on analysis fields (i.e. not forecast mode)
• NCEP Global Tropospheric Analyses ( ds083.2 )• six hourly meteorological fields at 1.0° resolution [1999 - ]
• Surface wind speed measurements: • 6 locations across South eastern Australia• Used for calculating variability index and validation
Forecast mode trial
• Bureau of Meteorology W-LAPS model• Six hourly fields at 1° and 0.1° grids (data set length?)• 12 hours ahead
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CSIRO. Forecasting Wind Energy Variability
Index of wind variability
• from Woods, Davy & Coppin, 2007• Variability index is six hour running standard deviation of 2 hour
high-pass filtered (10min) raw data-5
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Original dataFluctuationsVariability index
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CSIRO. Forecasting Wind Energy Variability
Available (and relevant) variables for EOF analysis
• NCEP Global Tropospheric Analyses ( ds083.2 )• six hourly meteorological fields at 1.0° resolution [1999 - ]
Geopotential height Air temperature Relative humidityU wind speedV wind speedVertical velocityAbsolute vorticityHeight of planetary boundary layerPrecipitable waterCloud waterLifting indexConvective inhibitionConvective available potential energyetc
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CSIRO. Developing a prediction of wind variability
EOF example : Temperature-2
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CSIRO. Forecasting Wind Energy Variability
Quantitative modelling
Meteorological inputs(transformed using EOF)
Empirical model(random forest)
Sum of variability indexat 6 sites
Model period: 2003-4
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CSIRO. Forecasting Wind Energy Variability
Winter – important patterns explaining variability
Height of planetary boundary layer – EOF1 Absolute vorticity – EOF3
Velocity component V – EOF2
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CSIRO. Forecasting Wind Energy Variability
NCEP Results – winter / early spring
• Prediction of variability index (using analysis NWP products)
Important predictors:EOF number
Height of planetary boundary layer 1Absolute vorticity 3V wind speed 2U wind speed 1
R2=0.74
Aggregate 6 locations in SE Australia
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CSIRO. Forecasting Wind Energy Variability
NCEP Results – winter / early spring
• Prediction of variability index (using analysis NWP products)
Important predictors:EOF number
Height of planetary boundary layer 1Absolute vorticity 3V wind speed 2U wind speed 1
R2=0.74
Single siteAggregate 6 locations in SE Australia
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CSIRO. Forecasting Wind Energy Variability
Summer – important patterns explaining variability
Geopotential height – EOF2
Convective available potential energy – EOF1Cloud water – EOF1
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CSIRO. Forecasting Wind Energy Variability
NCEP Results - summer
• Prediction of variability index (using analysis NWP products)
Important predictors:EOF number
Cloud water 1Geopotential height 2CAPE 1V wind speed 2
R2=0.66
Aggregate 6 locations in SE Australia
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CSIRO. Forecasting Wind Energy Variability
NCEP Results - summer
• Prediction of variability index (using analysis NWP products)
Important predictors:EOF number
Cloud water 1Geopotential height 2CAPE 1V wind speed 2
R2=0.66
Single siteAggregate 6 locations in SE Australia
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CSIRO. Developing a prediction of wind variability
Modelling of 6-hour mean ramp rate • aggregate power from 6 locations vs fitted variability index
Mean wind power ramp rate modelling
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CSIRO. Forecasting Wind Energy Variability
Short-term wind power ramp rate modelling
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Modelling of 10 min ramp rate
•aggregate power from 6 locations (normalised)
•relationship of 6 hourly maximum ramp rate (left) and instantaneous ramp rate (right) to variability index
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CSIRO. Forecasting Wind Energy Variability
W-LAPS forecast mode trial
• EOF analysis uses both 1° (100km) and 0.1° (10km) fields
• Correlations performed on 12-hour ahead forecast products
• Single site results available
• Example EOF model fit:
Variable Grid domain EOF number
Vertical velocity (omega) Inner 1
Mean sea level pressure Inner 3
24 hour precipitation Inner 1
Latent heat flux Outer 2
Atmospheric boundary layer height
Outer 2
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CSIRO. Forecasting Wind Energy Variability
W-LAPS Results – 12 hours ahead
• Forecast of variability index (using analysis NWP products)• Single site
Winter Summer
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CSIRO. Forecasting Wind Energy Variability
W-LAPS Results – 12 hours ahead
• Forecast of variability index (using analysis NWP products)• Aggregate of several sites in one state
Winter Summer
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CSIRO. Forecasting Wind Energy Variability
Further work
• Implement working system• ready for coding into ANEMOS system as a module
• Further validate forecast-mode models for aggregate variability• requires additional surface data
• Quantify value to system operators of variability information• cost benefit analysis
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Contact UsPhone: 1300 363 400 or +61 3 9545 2176
Email: [email protected] Web: www.csiro.au
Thank you
Centre for Australian Weather and Climate ResearchA partnership between CSIRO and the Bureau of MeteorologyRobert Davy
Phone: 02 6246 5604Email: [email protected]: www.csiro.au/weru