Solar resource monitoring and forecasting using satellite data


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Solar resource monitoring and forecasting using satellite data

  1. 1. Solar resource monitoring and forecasting using satellite data Green Power Labs Inc. And Applied Geomatics Research Group
  2. 2. Presentation contents: • Satellite data and solar climatology • Rationale for using geostationary satellites for monitoring solar radiation • Example of satellite mapping technology applied in Atlantic Canada (logic, sequence of steps, groundtruthing) • GPLI developed software (SolarSatData) • Next Steps and Commercial applications
  3. 3. NASA Satellite-based solar climatology NASA Surface meteorology and Solar Energy dataset Data period: the monthly average amount of the total solar radiation incident on a horizontal surface at the surface of the earth for a given month will be averaged for that month over the 22-year period (1983 - 2005). World Climate Research Program and International Satellite Cloud Climatology Project.
  4. 4. Long term Satellite climatology and landscape High resolution map created for an international solar power producer based on long-term satellite based climatology and landscape analysis. With easy to use GIS tools, our client was able to quickly and easily locate five prime sites for a PV plant. The plant is now under construction.
  5. 5. Motivation for using satellite data •Interest to satellite 1.2 data is triggered by lack of observations 1 •Environment Canada 0.8 operated only 2 Complete% stations 0.6 •Halifax Citadel and Kentville 0.4 Citadel •No new data since Kentville 0.2 2002 0 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2001 2003 2005 2007 UP: Solar data collection intensity by Environment Canada In the Province of Nova Scotia 1970 till present. LEFT: GOES satellite coverage.
  6. 6. Geostationary satellites •Positioned at an exact height above the Earth •Rotate around the Earth at the same speed as the earth rotates around its axis, so remain stationary above a point on the Earth •Can view the whole Earth disk below them •Can scan the same area very frequently •They are many (e.g. Meteosat, GOES- EAST, GOES-WEST, GMS, IODC, GOMS)
  7. 7. Solar climatology from satellites •Lack of spatially and temporally continuous data •25 km interpolation bottleneck •Cano et al. (1986) describe a method for the determination of the global solar radiation from meteorological satellite data. •Perez et al. (2003) calculate satellite-derived irradiances for R. Perez: “80,000 radiometers models that use the visible covering US at 10 km grid would satellite channel as main input not achieve an accuracy better for cloud index determination. than 13% for points located •Avoid satellite data calibration between stations” •A small number of high accuracy ground stations are needed for satellite model ground truth and real time calibration
  8. 8. Project objectives • Use GOES-East visible spectrum images (1 km nadir resolution) • Develop methodology for solar modeling • Develop mathematical algorithms • Test results against a number of field stations • Design software to function within GIS • Test on a large dataset • Create maps for Atlantic Canada
  9. 9. Northern portion of GOES East image (above the equator) and … Maritime Canada study area
  10. 10. The process Clear sky model Dynamic range Brightest Hour of day observed Darkest High resolution data 1x1 km, 30 min Ghi Hour of day
  11. 11. GOES image being analyzed Prince Edward New Brunswick Island Julian Day 85 (March 26) 2007 at 15:15 UTC
  12. 12. Minimum pixel brightness 25 day window around Day 85
  13. 13. Maximum pixel brightness 25 day window around Day 85
  14. 14. Global Insolation for the analyzed image Calculated for 15:15 UTC on Day 85 (25 day window)
  15. 15. Solar radiation for the studied day 85 Wh/m2 2750 750 Calculated for all daylight hours of Day 85 (25 day window)
  16. 16. Daily Solar Radiation averaged for a month Each of these results represent the combination of approximately 744 GOES images (~24 images/day x 31 days/month) Wh/m2 3100 2200 Calculated for all daylight hours in March 2007 (25 day window)
  17. 17. Resulting Satellite-based radiation Maps High-resolution satellite- based solar resource maps for Nova Scotia (Canada) Shows spatial pattern and temporal variability
  18. 18. Groundtruthing Calculated Irradiance values have been compared to solar radiation measurements collected by the AGRG’s meteorological stations. The stations are measuring a set of meteorological parameters (i.e., air temperature, relative humidity, wind speed/direction, barometric pressure, solar radiation, rainfall, soil temperature, and soil moisture). 14 Station locations are shown on a colorized hillshade of the Annapolis Valley. Validation results for the Stations circled in red will be shown on following slides.
  19. 19. Groundtruthing The SP LITE sensor measures the solar energy received from the entire hemisphere. It is ideal for measuring available energy for use in solar energy applications, plant growth, thermal convection and evapotranspiration.
  20. 20. Comparison of modelled and observed irradiance. R2 = 0.94 R2 = 0.81 RMSE <15% Observations taken at meteorological stations in the Annapolis Valley, Canada
  21. 21. 5 Linke Turbidity 4.5 4 3.5 Optical thickness of the Linke turbidity Halifax 3 atmosphere due to the absorption Kejimkujik 2.5 Sable Island by the water vapor and the 2 Howland absorption by the aerosol particles. 1.5 1 It summarizes the attenuation of 0.5 the direct beam solar radiation. 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Important for CSP Month Insolation LT=3 LT=6
  22. 22. Effect of snow masking algorithm Snow Normal processing Insolation Snow processing January 2007
  23. 23. Modeled hourly values of solar radiation compared to observed at meteorological stations in the Annapolis Valley for Julian Day 85, 2007 The meteorological stations shown here are Stations 10, 30, and 70 – three stations across the Annapolis Valley transect
  24. 24. Methodology is applicable to any areas with satellite coverage
  25. 25. Solar Mapping Toolset for ArcGIS: Managing and Processing GOES Satellite Data GPLI developed a toolset for automated download , clipping and processing of GOES images into maps of solar radiation in ArcGIS 9.2
  26. 26. ArcGIS plugins This toolset functions as a plugin for ArcGIS 9.2
  27. 27. The Next Steps: Solar System Performance Monitoring • Site specific detailed information on available solar resource collected every 30 minutes • Close monitoring of solar technologies to maintain performance and maximize energy output • Effective management Point and area monitoring of heating and cooling cycles based on micro climate data
  28. 28. The Next Steps: Forecasting Solar Resource Effective energy management strategies require forecasting of energy output from solar technologies. Energy traders Utilities Power Producers Building Owners
  29. 29. Thank you! Contact Information: 1 Research Drive Dartmouth Nova Scotia Canada B2Y 4M9 1-902-466-6475