a turbine hub height wind speed consensus forecasting ......3/2/2011 a turbine hub height wind speed...
TRANSCRIPT
3/2/2011
A Turbine Hub Height Wind
Speed Consensus Forecasting System
NCAR’s Wind Energy Prediction System
Developed for Xcel Energy
Bill Myers
Seth Linden
National Center for Atmospheric Research
Xcel Energy Service Areas
Wind Farms (50+)
~3000 Turbines (growing)
~ 3.75 GW (Wind)
~10% Wind
3.4 million customers (electric)
Annual revenue $11B Copyright 2010 University Corporation for Atmospheric Research
NCAR Wind Energy Prediction System Xcel Energy Configuration
Copyright 2010 University Corporation for Atmospheric Research 3
WRF RTFDDA System
NCEP Data NAM GFS RUC MOS
Observations
Wind Farm Data Nacelle wind speed Generator power
Node power Met tower Availability
Ensemble RTFDDA System
Environment Canada
GEM Global GEM Regional
Operator GUI
Meteorologist GUI
WRF Model Output
Wind to Energy Conversion Subsystem
Dynamic, Integrated Forecast System
(DICast®)
CSV Data
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Xcel DICast® System
Overall Xcel system goal:
Generate accurate forecasts of power
Approach taken by NCAR :
First generate accurate forecasts of wind speed at
the hub height of each turbine
Then derive power from individual turbines from
wind speed
As a consensus point forecast system, DICast was a
logical choice to generate the HH wind speed
forecasts
DICast tries to predict Nacelle wind speed sensor
value
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Xcel DICast ® System Diagram
Wind Speed
Forecasts
GFS DMOS
NAM DMOS
RUC DMOS
Data
Ingest
Integrator
RT-FDDA WRF DMOS
Post
Processing
Ensemble Mean
. . GEM DMOS
DMOS means Dynamic Model Output Statistics
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Xcel DICast® System
Hub Height Wind Speed not explicitly
predicted by NWP models
Like other DICast variables (POP, etc)
Predictors relevant to HH Wind Speed
must be derived from NWP data
All attempt to directly predict HH Wspd
Use 10m Wspd and P-level Wspds
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Hub Height Wind Speed Predictors
1 : Interpolate between 10m and first P-
level above winds
2 : Extrapolate* up from 10m winds
3 : Extrapolate* down from next P-level
above winds
etc
* log-based extrapolation with roughness
dependent on land-use
Hub Hgt
First P-level above
First P-level below
10m wind level
Second P-level above
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Dynamic MOS
• Linear regression-based statistical method
• Similar to NWS MOS, but regressions built
dynamically
• New equations generated each week
• System learns based on recent data
• Can be applied to any NWP forecast model fairly
easily
• Tuned forecasts can be generated quickly for new
sites
• Uses “default equations” if statistical model fails
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NAM-DMOS Performance
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Xcel DICast ® System Diagram
Wind Speed
Forecasts
GFS DMOS
NAM DMOS
RUC DMOS
Data
Ingest
Integrator
RT-FDDA WRF DMOS
Post
Processing
Ensemble Mean
. . GEM DMOS
DMOS means Dynamic Model Output Statistics
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Forecast Integrator Objectives
• Discovers the “best” combination of forecast modules for a given forecast time and location.
• Computationally simple and robust.
• Can easily adapt to the addition of new modules or removal of obsolete modules.
• System learns as weights are updated daily • Weights nudged in direction of gradient in weight space
To combine forecasts from a set of models:
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Forecast Integrator Forecast error as function of W1 & W2
W2
W1 W1(i)
W2(i)
Integration Step
0
1
1 0
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Integrator Performance
Copyright 2010 University Corporation for Atmospheric Research
3/2/2011
Conclusions DICast reduces forecast errors with multiple
optimization steps
DMOS provides optimized forecasts from
individual NWP models
Integrated forecasts better than forecasts
from each individual NWP model
Summer predictions more difficult than
fall/winter
High resolution modeling by itself is not the
optimal solution to wind energy forecasting
DICast is a robust technology providing
documented forecast improvements