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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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?
4
Sub-hour 1-3 hours day ahead seasonal lifecycle
Sky imagery
Satellite motion vectors
Numerical Weather Prediction Models
Regional Climate Models
integrator
5
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
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
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