solar forecasting: short-term to day ahead€¦ · demonstrate a state-of-the-science solar power...
TRANSCRIPT
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NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.
Solar Forecasting: Short-term to Day Ahead
Presenter: Dr. Manajit Sengupta
Dr. Sue Haupt, NCAR
April 2016
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What do we really need to forecast?
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(a) Clouds (Ice and water droplets)
– Scatter solar radiation – Ice clouds are more forward scattering that water clouds. – Smaller droplets scatter more.
• (b) water vapor – Important for cloud formation – Absorb solar radiation.
• (c) Winds
– Vertical winds for cloud formation – Cloud level winds for advection
• (d) Aerosols (mineral dust, soot etc.)
– Most impact in clear sky situations. – Absorb and scatter solar radiation (depends on aerosol type)
•
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What time-scales do we forecast for?
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Sub-hour 1-3 hours day ahead seasonal lifecycle
Sky imagery
Satellite motion vectors
Numerical Weather Prediction Models
Regional Climate Models
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What time-scales do we forecast for?
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Sub-hour 1-3 hours day ahead seasonal lifecycle
Sky imagery
Satellite motion vectors
Numerical Weather Prediction Models
Regional Climate Models
integrator
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GOALS • Demonstrate a state-of-the-science solar power
forecasting system through applying cutting edge research
• Test the system with appropriate metrics in several geographically-diverse, high penetration solar utilities and ISO/TSOs
• Disseminate the research results widely to raise the bar on solar power forecasting technology
A Public-Private-Academic Partnership for Solar Power Forecasting
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A Public-Private-Academic Partnership for Solar Power Forecasting
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Weather Monitoring Observation Modelling Forecasting Dissemination &
Communication Perception
Interpretation
Uses / Decision Making
Outcomes Economic & social values
Value Chain: What is the value of solar power forecasting?
Clouds
Aerosols
Clear Sky
SURFRAD
Satellites
Total Sky Imagers
Pyranometers
WRF-Solar
HRRR
StatCast
TSICast
CIRACast
MADCast
DICast
NowCast
Production Cost
Changes
Unit Allocations
Area Forecast
Point Forecast
Reserve Estimates Reserve
Analysis
Projected Power
Production Day Ahead
Planning
Real Time Operation
Actual Power
Production
Load Balancing
Uncertainty Quant
Power Conversion
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Seamless Scaled Approach to Solar Power Forecasting
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= Physical approach
Cloud Prediction:
Adapted from Ravela, 2008 Auligne, 2014
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LIPA – 32 MW Xcel – 90 MW
DeSoto Plant – 25 MW HECO– 43 MW
SMUD – 100 + 50 MW
SCE – 350 Comm + 325Q + 1000 Dist MW
Operationalization
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SunCast
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Nowcasting - Overview 1. StatCast – regimes and data
2. TSICast - Total Sky Imaging
3. CIRACast – Satellite-based Cloud Advection
4. MADCast Multi-sensor Advective Diffusive WRF Nowcasting 5. WRF-SolarNowcasting
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StatCast
• Forecast Clear Sky Index • Separate into:
• Clear • Partly Cloudy • Cloudy
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Regime-Dependent StatCast
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StatCast Results
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Total Sky Imager Forecast
θ1 θ2
6/19/12
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Sky imagers Deployed in Colorado • San Luis Valley near
mountains in CO
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CIRACast Satellite Based Cloud Advection
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Satellite Advection Forecasting: Background
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Attention to Details
X
Imagine we are viewing this cloud from the satellite
PV Array
Without account for sensor/sun geometry, the placement of cloud shadows can be 10’s of km in error
Speed Directional Both
TYPES OF WIND SHEAR
Advection of complex cloud layers requires proper account for wind shear
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Satellite Advection Forecasting: Comparisons
Desert Rock, NV Jan-Dec 2014, 0-1 hr forecast MAE: 9.6% (57.8 W/m2)
Table Mountain, CO Jan-Dec 2014, 0-1 hr forecast MAE: 21.8% (132.3 W/m2)
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AIRS IASI MODIS
GOES Sounder GOES Imager
Multi-sensor
MADCast: Multi-sensor Advective Diffusive foreCast
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WRF-Solar: CLOUD-RADIATION-AEROSOL INTERACTION
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WRF-Solar: CLOUD-RADIATION-AEROSOL INTERACTION
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WRF-Solar versus Standard WRF
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5- 95% 25-75% An-En Mean
Uncertainty Quantification: Analog Ensemble Approach
Station SMUD 67, forecast initialized at 12 UTC, 15 July 2014
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Final Forecasting Metrics
Model-Model Comparison Economic Value
Base
• Mean Absolute Error • Root Mean Square Error • Distribution (Statistical Moments and Quantiles) • Categorical Statistics for Events
• Operating Reserves Analysis
• Production Cost
Enha
nced
• Maximum Absolute Error • Pearson's Correlation Coefficient • Kolmogorov-Smirnov Integral • Statistical Tests for Mean and Variance • OVER Metric • Renyi Entropy • Brier Score incl. decomposition for probability forecasts • Receiver Operating Characteristic (ROC) Curve • Calibration Diagram • Probability Interval Evaluation • Frequency of Superior Performance • Performance Diagram for Events • Taylor Diagram for Errors
• Cost of Ramp Forecasting
Accuracy Variability Events Uncertainty/Probability Synthesis Tools
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NowCasting Component Ranks
Updated Figure 16. Frequency of Superior Performance based on MAE improvement over persistence for StatCast (orange), CIRACast (grey), MADCast (blue), and WRF-SolarNow (yellow). Results are for Partly Cloudy sky condition for the 0-1hr forecast (top left), 1-3hr (top right), and 1-6hr (bottom left).
StatCast CIRACast MADCast WRF-SolarNow
PRELIMINARY RESULTS Each component has a “sweet spot” when it can contribute skill to nowcast. It is now a matter of building this information into
the NowCast integrator
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GEM
GFS
NAM
HRRR
1
2
3
4
Legend Rank
NWP Component Rank: Based on MAE In
it Ti
me
Init
Tim
e In
it Ti
me
Lead Time
PRELIMINARY RESULTS • GEM strongest component • HRRR provides good skill at
short lead times • GFS and NAM provide fair to
good skill at longer lead times
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Software Dissemination
• WRF-Solar o New radiation scheme o New cloud physics
parameterization o Improved GODDARD
parameterization for equation of time
o High frequency output o Fast radiation scheme (NREL) o Shallow convection scheme (PSU) o Satellite data assimilation o I/O Parallelization documentation
and scripts o Climatological Aerosol information
Black – already released Brown – will release
StatCast Power Conversion scripts &
software MADCast MET enhancements
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• Thank You!
• Contact: • Manajit Sengupta [email protected]
Contributors: National Center for Atmospheric Research, Colorado State University, Penn State University, Brookhaven National Lab