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Solar Resource Assessment - How to get bankable meteo data

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Available for download at http://solarreference.com/solar-resource-assessment-how-to-get-bankable-meteo-data/ …

Available for download at http://solarreference.com/solar-resource-assessment-how-to-get-bankable-meteo-data/

This presentation from DLR (German Aerospace Center) explains.

1. Solar radiation data characteristics
2. How radiation data is gathered from ground measurements and derived from satellite data
3. Comparison of the two, and some important factors to be weighed in when deciding what to use

This presentation can also be downloaded at NREL

Published in: Technology, Business

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  • 1. How to get bankable meteo data? DLR solar resource assessment Robert Pitz-Paal, Norbert Geuder, Carsten Hoyer-Klick, Christoph Schillings
  • 2. Solar resource assessment for solar power plants Why solar resource assessment ? Characteristics of solar irradiation data DLR solar resource assessment: ground measurements satellite data How to get bankable meteo data? Summary Slide 2 > Solar resource assessment at DLR
  • 3. Why is exact knowledge of the solar resource so important? High impact of the annual 50 MW Parabolic Trough solar thermal power plant Irradiation is a crucial parameter for site selection and plant design and economics of plant Levelized Electricity Costs (LEC) Annual electricity generation in GW on the LEC Mean system efficiency Annual electricity generation Mean system efficiency and Levelized Electricity Costs in €/kWh irradiation Solar field size (aperture) in 1000 m² Why solar resource assessment? Slide 3 > Solar resource assessment at DLR
  • 4. Characteristics of solar irradiation data What kind of irradiation data is needed? Type: DNI (Direct-Normal Irradiation) GHI (Global-Horizontal Irradiation) DHI (Diffus-Horizontal Irradiation) Source: ground measurements satellite data Properties of irradiation: spatial variability inter annual variability long-term drifts Characteristics of solar irradiation data Slide 4 > Solar resource assessment at DLR
  • 5. Ground measurements vs. satellite derived data Ground measurements Satellite data Advantages Advantages + high accuracy (depending on sensors) + spatial resolution + high time resolution + long-term data (more than 20 years) + effectively no failures + no soiling + no ground site necessary + low costs Disadvantages - high costs for installation and O&M Disadvantages - soiling of the sensors - lower time resolution - sometimes sensor failure - low accuracy in particular at high time - no possibility to gain data of the past resolution Characteristics of solar irradiation data Slide 5 > Solar resource assessment at DLR
  • 6. Inter annual variability Strong inter annual and regional variations 1999 deviation to mean 2000 kWh/m²a 2001 Average of the direct normal irradiance from 1999-2003 2002 2003 Characteristics of solar irradiation data Slide 6 > Solar resource assessment at DLR
  • 7. Long-term variability of solar irradiance 7 to 10 years of measurement to get long-term mean within 5% If you have only one year of measured data, banks have to assume, that this it is here Characteristics of solar irradiation data Slide 7 > Solar resource assessment at DLR
  • 8. Time series of annual direct normal irradiance Long-term tendency may exist and differs from site to site linear regression: +2.16 W/(m² year) linear regression: -0.93 W/(m² year) Spain Australia with stratospheric aerosol Characteristics of solar irradiation data Slide 8 > Solar resource assessment at DLR
  • 9. How to get bankable Meteo Data? Quality checked ground measurements to gain highly accurate data Derivation of irradiation from satellite data to get ƒ spatial distribution and ƒ long-term time series Validation of the satellite data with accurate ground data Result: accurate hourly time series, irradiation maps and long-term annual mean DLR solar resource assessment Slide 9 > Solar resource assessment at DLR
  • 10. Instrumentation for unattended abroad sites: Rotating Shadowband Pyranometer (RSP) pyra nom eter sen sor sh ad ow Sensor: Si photodiode ba nd Advantages: + fairly acquisition costs + small maintenance costs + low susceptibility for soiling + low power supply Disadvantage: diffus horizontal Global HorizontaI Irradiation (GHI) measurement - special correction for good accuracy necessary Ground measurements (established by DLR) Slide 10 > Solar resource assessment at DLR
  • 11. Precise sensors (also for calibration of RSP): GHI Thermal sensors: pyranometer and pyrheliometer, precise 2-axis tracking DNI DHI Advantage: + high accuracy + separate GHI, DNI and DHI sensors (cross-check through redundant measurements) Disadvantages: - high acquisition and O&M costs - high susceptibility for soiling - high power supply Ground measurements Slide 11 > Solar resource assessment at DLR
  • 12. Satellite data: SOLEMI – Solar Energy Mining Meteosat Prime Meteosat East SOLEMI is a service for high resolution and high quality data Coverage: Meteosat Prime up to 22 years, Meteosat East 10 years (in 2008) Satellite data Slide 12 > Solar resource assessment at DLR
  • 13. Radiative Transfer in the Atmosphere 1400 Extraterrestrial O2 and CO2 1000 Ozone 800 Rayleigh 600 Water Vapor 400 Aerosol 200 Clouds 00:00 22:00 20:00 18:00 16:00 14:00 12:00 10:00 08:00 06:00 04:00 02:00 0 00:00 Direct Normal Irradiation (W/m²) 1200 Hour of Day Satellite data Slide 13 > Solar resource assessment at DLR
  • 14. How two derive irradiance data from satellites The Meteosat satellite is located in a geostationary orbit The satellite scans the earth line by line every half hour Satellite data Slide 14 > Solar resource assessment at DLR
  • 15. How two derive irradiance data from satellites The Meteosat satellite is located in a geostationary orbit The satellite scans the earth line by line every half hour The earth is scanned in the visible … Satellite data Slide 15 > Solar resource assessment at DLR
  • 16. How two derive irradiance data from satellites The Meteosat satellite is located in a geostationary orbit The satellite scans the earth line by line every half hour The earth is scanned in the visible and infra red spectrum Satellite data Slide 16 > Solar resource assessment at DLR
  • 17. How two derive irradiance data from satellites The Meteosat satellite is located in a geostationary orbit The satellite scans the earth line by line every half hour The earth is scanned in the visible and infra red spectrum A cloud index is composed from the two channels Satellite data Slide 17 > Solar resource assessment at DLR
  • 18. How two derive irradiance data from satellites Atmospheric transmission is calculated from global data sets elevation Satellite data Slide 18 > Solar resource assessment at DLR
  • 19. How two derive irradiance data from satellites Atmospheric transmission is calculated from global data sets elevation ozone Satellite data Slide 19 > Solar resource assessment at DLR
  • 20. How two derive irradiance data from satellites Atmospheric transmission is calculated from global data sets elevation ozone water vapor Satellite data Slide 20 > Solar resource assessment at DLR
  • 21. How two derive irradiance data from satellites Atmospheric transmission is calculated from global data sets elevation ozone water vapor aerosols Satellite data Slide 21 > Solar resource assessment at DLR
  • 22. How two derive irradiance data from satellites Atmospheric transmission is calculated from global data sets elevation ozone water vapor aerosols The cloud index is added for cloud transmission Satellite data Slide 22 > Solar resource assessment at DLR
  • 23. How two derive irradiance data from satellites Atmospheric transmission is calculated from global data sets elevation ozone water vapor aerosols The cloud index is added for cloud transmission All components are cut out to the region of interest Satellite data Slide 23 > Solar resource assessment at DLR
  • 24. How two derive irradiance data from satellites Finally the irradiance is calculated for every hour Satellite data Slide 24 > Solar resource assessment at DLR
  • 25. Example for hourly time series for Plataforma Solar de Almería (Spain) 1200 ground 1000 satellite W/m² 800 600 400 200 0 13 14 15 16 17 18 day in march, 2001 Validation of the data Slide 25 > Solar resource assessment at DLR
  • 26. Comparing ground and satellite data: time scales 12:45 13:00 13:15 13:30 13:45 14:00 14:15 Hourly average Meteosat image Measurement Ground measurements are typically pin point measurements which are temporally integrated Hi-res satellite pixel in Europe Satellite measurements are instantaneous spatial averages Hourly values are calculated from temporal and spatial averaging (cloud movement) Slide 26 > Solar resource assessment at DLR
  • 27. Comparing ground and satellite data: “sensor size” solar thermal power plant (200MW ≈ 2x2 km² satellite pixel (∼ 3x4 km²) ground measurement instrument (∼2x2 cm²) Slide 27 > Solar resource assessment at DLR
  • 28. Comparison with ground measurements and accuracy general difficulties: point versus area and time integrated versus area integrated 1200 ground sate llite 1000 W/m² 800 600 400 200 DNI time serie for 1.11.2001, Almería Partially cloudy conditions, cumulus humilis 0 0 6 12 18 Slide 28 > Solar resource assessment at DLR hour of day 24
  • 29. Comparison with ground measurements and accuracy general difficulties: point versus area and time integrated versus area integrated 1200 ground sate llite 1000 W/m² 800 600 400 200 DNI time serie for 30.4.2000, Almería overcast conditions, strato cumulus 0 0 6 12 18 Slide 29 > Solar resource assessment at DLR hour of day 24
  • 30. Validation in Tabouk, Saudi Arabia clear-sky all-sky Clear sky only All Sky Validation of the data
  • 31. Validation results RMSE (Root Mean Square Error) Decreasing deviation between ground and satellite derived data with increasing duration of the integration time Site Bias Variing within different sites bias Measurement Network Saudi Arabia Several Sites in Spain Morocco Algeria +4.3% -6.4% to +0.7% -2.0% -4.8% Validation of the data Slide 31 > Solar resource assessment at DLR
  • 32. Monthly ground and satellite derived irradiation data hourly monthly mean (DNI_ground) in Wh/m², Solar Village 2000 hourly monthly mean (DNI_satellite) in Wh/m², Solar Village 2000 Validation of the data Slide 32 > Solar resource assessment at DLR
  • 33. Adjusting Ground and Satellite Data Simple Method: Scaling with the Bias E.g. with a Bias of -5%, every value is multiplied with 1.05 + Very easy to apply Modification of frequency distribution at the extreme end a factor > 1 may produce unrealistic high values a factor < 1 may omit high values Suitable for average values Slide 33 > Solar resource assessment at DLR
  • 34. Adjusting Ground and Satellite Data Advanced Method: Error analysis and correction functions Analysis of the Deviations: At clear sky? (e.g. due to incorrect atmospheric data) During cloud situations? (e.g. incorrect cloud modelling) Development of a correction function dependent e.g. on the cloud index. 7 c(x) 6 Korrekturfaktor 5 4 3 2 1 0 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 Wolkenindex Bias before Correction Bias after Slide 34 > Solar resource assessment at DLR
  • 35. Summary Ground measurements are accurate but expensive and in suitable regions mostly rare (especially DNI) New ground measurements do not deliver time series for the past Satellite data offers spatial resolution and long-term time series of more than 20 years into the past Combination of ground and satellite date yields: irradiation maps, long term annual means and time series – all with good accuracy Realistic long term meteo data helps to avoid risk surcharges of banks due to conservative assumptions and increase the financial viability of your project Summary Slide 35 > Solar resource assessment at DLR
  • 36. Thank you for your attention ! Acknowledgements Richard Meyer, Institute for Physics of the Atmosphere, DLR Oberpfaffenhofen Sina Lohmann, Institute for Physics of the atmosphere, DLR Oberpfaffenhofen Antonie Zelenka, MeteoSwiss, Zürich Volker Quaschning, FHTW Berlin Acknowledgements Slide 36 > Solar resource assessment at DLR
  • 37. Slide 37 > Solar resource assessment at DLR
  • 38. Satellite data and nearest neighbour stations Satellite derived data fit better to a selected site than ground measurements from a site farther than 25 km away. Validation of the data Slide 38 > Solar resource assessment at DLR
  • 39. Derivation of the Cloud-Index from satellite images VIS-channel (0.5 – 0.9µm) IR-channel (10.5 –12.5 µm) Original-image Meteosat-7 Reference-image Derived cloud information (Cloud-Index)
  • 40. Time series of direct normal irradiance linear regression: 2.16 W/(m² year) 1.56 W/(m² year) aerosol opt. thickness Pinatubo El Chichon with without stratospheric aerosol Pinatubo El Chichon aerosol optical thickness Spain with without stratospheric aerosol Australia linear regression: - 0.93 W/(m² year) - 1.22 W/(m² year) Slide 40 > Solar resource assessment at DLR