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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),

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goes based renewable energy solar prediction products for decision makers

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)

project summary
Project Summary

Use GOES-derived products to compute parameters related to the solar irradiance at the surface:

Short-Term (< 3 hr) Predictionof the Solar Irradiance (direct/diffuse components and total), based on observed cloud cover and retrievals of optical & geometric properties.

Mid- to Long-Term (> 3 hrs to Several Days) Predictionof Cloud-Cover Likelihood, based on cloud statistics conditioned on model-predicted meteorological regimes.

overarching motivation
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.

slide4

Part 1: Short-Term Prediction

Broken Clouds Cause Significant Variability of Available Sunlight at a Solar Power Array

Solar Irradiance Measurements

Golden, CO July 3, 2004

Figure courtesy of Tom Stoffel (NREL)

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 capability for this behavior.

methods and observations part 1
Methods and Observations Part 1

GOES Imagery

Apply advection Method

Compute direct and diffuse radiation at surface

Compare the predicted irradiance with surface measurements

test location
Test Location

CSU Atmos. & CIRA

Solar Power Plant

Christman Field

Water Treatment Facility

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.

slide7

Christman Field Observations

Solar

Radiation

Temp.

& Dewpoint

Wind

Direction

Relative

Humidity

Wind

Speed

Precip.

5 min resolution, ~9 year dataset

ats web camera observations
ATS Web Camera Observations

N

Provides qualitative information explaining solar irradiance measurements, and validation of cloud shadows passing over the solar array.

clear day examples
Clear Day Examples

Pointed, spatial and webcam observations at test location

Christman Field

Solar Radiation [W/m2]

Satellite

Web Cam

Pyranometer

cloudy day examples
Cloudy Day Examples

Pointed, spatial and webcam observations at test location

Christman Field

Solar Radiation [W/m2]

Satellite

Web Cam

Pyranometer

slide12

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.

cloud climatology production
Cloud Climatology Production

Regional GOES Database:

Chose area and time period, then sector out area from archive images

CLOUD ALOGORITHM

Can tailor algorithm to meet requirements

-GOES East and West -All imager channels -12 years (1998-2009) -Aligned and QC

Colorado Vis 5/4/07 16UTC

Cloud/No Cloud images (CNC)

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.

GOES West Vis 5/4/07 16UTC

Divide CNCs into Regimes and/or time sets for conditional probabilities

CNC 5/4/07 16UTC

For each regime/time, combine CNCs into cloud climatologies.

slide14

Example of a Cloud Climatology Base on Wind

Christman

Field

COLORADO

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

regime based forecasting

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

Regime-Based Forecasting

1. Start with Solar Irradiance Time Series

comparison of surface and satellite data over christman field
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.

16

summary
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.