GOES-Based Renewable Energy (Solar) Prediction Products for Decision Makers

1. 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)

2. 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.

3. 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.

4. 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.

5. Methods and Observations Part 1 GOES Imagery Apply advection Method Compute direct and diffuse radiation at surface Compare the predicted irradiance with surface measurements

6. 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.

7. Christman Field Observations Solar Radiation Temp. & Dewpoint Wind Direction Relative Humidity Wind Speed Precip. 5 min resolution, ~9 year dataset

8. ATS Web Camera Observations N Provides qualitative information explaining solar irradiance measurements, and validation of cloud shadows passing over the solar array.

9. Clear Day Examples Pointed, spatial and webcam observations at test location Christman Field Solar Radiation [W/m2] Satellite Web Cam Pyranometer

10. Cloudy Day Examples Pointed, spatial and webcam observations at test location Christman Field Solar Radiation [W/m2] Satellite Web Cam Pyranometer

11. Hourly and monthly variability in solar energy at test location

12. 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.

13. 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.

14. 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

15. 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

16. 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

17. 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.