Transcript Short- and Long-term Cloud Forecasting for Solar Energy

GOES-Based Renewable Energy
(Solar) Prediction Products for
Decision Makers
Steven Miller (CIRA), Duli Chand (CIRA), Cindy Combs (CIRA),
Dan Lindsey (NOAA/NESDIS), Renate Brummer (CIRA)
In collaboration with
Andy Heidinger (NOAA/NESDIS), Don Hillger (NOAA/NESDIS),
Istvan Laszlo (NOAA/NESDIS), Manajit Sengupta (NREL),
William Straka (UW-Madison/SSEC/CIMSS)
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Project Summary
Use GOES-derived products to compute parameters
related to the solar irradiance at the surface:
1. Short-Term (< 3 hr) Prediction of the Solar Irradiance
(direct/diffuse components and total), based on observed
cloud cover and retrievals of optical & geometric properties.
2. Mid- to Long-Term (> 3 hrs to Several Days) Prediction of
Cloud-Cover Likelihood, based on cloud statistics
conditioned on model-predicted meteorological regimes.
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Overarching Motivation
• Harnessing the power of the sun as a “renewable” energy
source is a national priority and nascent interest of NOAA.
• Operational facilities require improved guidance on solar
availability to mitigate risks associated with:
– the high cost of developing utility-scale power plants
– site selection that balances availability and transmission
– the inherent volatility of resource availability
– power storage issues related to this resource
– predictive requirements at multiple timescales
• The proposed research leverages GOES observations of cloud
cover to advance current techniques in the prediction of
available solar energy via higher resolution, physically based
methods.
Part 1: Short-Term Prediction
Broken Clouds Cause Significant Variability of Available
Sunlight at a Solar Power Array
Solar Irradiance Measurements
Figure courtesy of Tom Stoffel (NREL)
Golden, CO July 3, 2004
Direct
(Beam)
Global (Total, with
direct beam
weighted by solar
zenith angle)
Diffuse
(Sky)
 The presence of clouds results in abrupt and significant changes in
available sunlight with respect to clear sky conditions. The first part of
our proposal aims to develop a GOES-based short-term predictive
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capability for this behavior.
Methods and Observations Part 1
GOES Imagery
Apply advection Method
Compute direct and
diffuse radiation at surface
Compare the predicted
irradiance with surface
measurements
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Test Location
Solar Power
Plant
Water Treatment Facility
Christman Field
CSU Atmos.
& CIRA
CSU Engineering Res. Center
The Colorado State University Foothills Campus will be used as a validation
site. A 15 acre, ~2 MW solar power plant was built by Xcel Energy in 2009 and
operated by Renewable Ventures.
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Christman Field Observations
Solar
Radiation
Temp.
& Dewpoint
Wind
Direction
Relative
Humidity
Wind
Speed
Precip.
5 min resolution, ~9 year dataset
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ATS Web Camera Observations
N
 Provides qualitative information explaining solar irradiance measurements,
and validation of cloud shadows passing over the solar array.
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Clear Day Examples
Solar Radiation [W/m2]
Pointed, spatial and webcam observations at test location
Christman Field
Pyranometer
Web Cam
Satellite
Cloudy Day Examples
Solar Radiation [W/m2]
Pointed, spatial and webcam observations at test location
Christman Field
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Pyranometer
Web Cam
Satellite
Hourly and monthly variability in solar energy at test location
Part 2: Mid- to Long-Term Prediction
(>3 hr to several days)
Model predictions of cloud cover vary considerably
with scheme and may not capture important local
effects. The second part of this project offers a
predictive capability for cloud cover that is
independent of a model’s ability to represent cloud
formation, morphology, and processes.
We will leverage our archive of satellite
data and experience with satellite cloud
climatologies to achieve this goal.
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Cloud Climatology Production
Regional GOES Database:
-GOES East and West
-All imager channels
-12 years (1998-2009)
-Aligned and QC
Chose area and time
period, then sector out
area from archive
images
CLOUD
ALOGORITHM
Can tailor algorithm
to meet
requirements
Colorado Vis 5/4/07 16UTC
Cloud/No Cloud
images (CNC)
GOES West Vis 5/4/07 16UTC
Divide CNCs
into Regimes
and/or time sets
for conditional
probabilities
Determine Categories (Regimes)
Can be based on a variety of
parameters, like time of day, wind
direction, pressure difference, hours
before/after an event, etc.
CNC 5/4/07 16UTC
For each regime/time, combine CNCs
into cloud climatologies.
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Example of a Cloud Climatology Base on Wind
COLORADO
Christman
Field
X
Boulder
Cloud Cover (%) at 2100 UTC (3 PM MST)
Westerly Boundary Layer (Sfc700mb) Wind Regime for Boulder, CO
All months of June compiled over 1999-2009
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Regime-Based Forecasting
1. Start with Solar Irradiance Time Series
2. Use PC Analysis to Identify Regimes
PM Broken Cloud
Clear All Day
Broken Cloud All Day
…
Overcast All Day
Etc.
3. Relate Solar Irradiance
Regimes to Meteorological
Parameter & Flow Regimes
5. Determine most likely
cloud cover and Solar
Irradiance Regime for
the Forecast.
4. Relate Forecast to Characteristic
Meteorological Regimes
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Comparison of Surface and Satellite Data
over Christman Field
Shows expected result of solar reflectance from GOES
being indirectly proportional to solar irradiance (ground).
Note: while solar irradiance is 5 minute data, the satellite
is hourly and at 4 km resolution.
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Summary
• Although work on this GIMPAP project
began at CIRA very recently, we have
made some early progress.
• Short term forecast work, more
deterministic in nature, is focusing on
areas where strong ground truth exists.
• Medium to long-term forecast work, more
statistical in nature, leverages long time
series of satellite and surface obs.
• Work complements and leverages NREL
directed research in solar forecasting
capabilities.
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