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How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
How to get bankable meteo data
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How to get bankable meteo data

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To download, head to - http://solarreference.com/solar-resource-assessment-how-to-get-bankable-meteo-data/ …

To download, head to - 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

More quality resources at http://solarreference.com

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  • 1. How to get bankable meteo data?DLR solar resource assessmentRobert 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 Levelized Electricity Costs (LEC) Levelized Electricity Costs in €/kWh on the LEC Annual electricity generation in GW Mean system efficiency and Mean system efficiency Irradiation is a crucial parameter for site selection and plant design and Annual electricity generation economics of plant Solar field size (aperture) in 1000 m² Slide 3 > Solar resource assessment at DLR Why solar resource assessment?
  • 4. Characteristics of solar irradiation dataWhat 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 Slide 4 > Solar resource assessment at DLR Characteristics of solar irradiation data
  • 5. Ground measurements vs. satellite derived dataGround measurements Satellite dataAdvantages 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 costsDisadvantages- 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 Slide 5 > Solar resource assessment at DLR Characteristics of solar irradiation data
  • 6. Inter annual variability Strong inter annual and regional 1999 variations deviation to mean 2000 kWh/m²a 2001Average of the direct normal 2002irradiance from 1999-2003 2003 Slide 6 > Solar resource assessment at DLR Characteristics of solar irradiation data
  • 7. Long-term variability of solar irradiance7 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 Slide 7 > Solar resource assessment at DLR Characteristics of solar irradiation data
  • 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) Spain linear regression: Australia -0.93 W/(m² year) with stratospheric aerosol Slide 8 > Solar resource assessment at DLR Characteristics of solar irradiation data
  • 9. How to get bankable Meteo Data? Quality checked ground Derivation of irradiation from satellite measurements to gain data to get highly accurate data ƒ spatial distribution and ƒ long-term time series Validation of the satellite data with accurate ground dataResult: accurate hourly time series, irradiation maps and long-term annual mean Slide 9 > Solar resource assessment at DLR DLR solar resource assessment
  • 10. Instrumentation for unattended abroad sites: Rotating Shadowband Pyranometer (RSP) sh adpyra nom ow Sensor: Si photodiode eter ba sen nd sor Advantages: + fairly acquisition costs + small maintenance costs + low susceptibility for soiling + low power supply Disadvantage: diffus horizontal - special correction for good accuracyGlobal HorizontaI Irradiation (GHI) necessary (established by DLR)measurement Slide 10 > Solar resource assessment at DLR Ground measurements
  • 11. Precise sensors (also for calibration of RSP): Thermal sensors: pyranometer and pyrheliometer,GHI DNI precise 2-axis tracking 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 Slide 11 > Solar resource assessment at DLR Ground measurements
  • 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) Slide 12 > Solar resource assessment at DLR Satellite data
  • 13. Radiative Transfer in the Atmosphere 1400 Extraterrestrial 1200 O2 and CO2Direct Normal Irradiation 1000 Ozone 800 Rayleigh (W/m²) 600 Water Vapor 400 Aerosol 200 Clouds 0 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 00:00 Hour of Day Slide 13 > Solar resource assessment at DLR Satellite data
  • 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 Slide 14 > Solar resource assessment at DLR Satellite data
  • 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 … Slide 15 > Solar resource assessment at DLR Satellite data
  • 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 Slide 16 > Solar resource assessment at DLR Satellite data
  • 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 Slide 17 > Solar resource assessment at DLR Satellite data
  • 18. How two derive irradiance data from satellites Atmospheric transmission is calculated from global data sets elevation Slide 18 > Solar resource assessment at DLR Satellite data
  • 19. How two derive irradiance data from satellites Atmospheric transmission is calculated from global data sets elevation ozone Slide 19 > Solar resource assessment at DLR Satellite data
  • 20. How two derive irradiance data from satellites Atmospheric transmission is calculated from global data sets elevation ozone water vapor Slide 20 > Solar resource assessment at DLR Satellite data
  • 21. How two derive irradiance data from satellites Atmospheric transmission is calculated from global data sets elevation ozone water vapor aerosols Slide 21 > Solar resource assessment at DLR Satellite data
  • 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 Slide 22 > Solar resource assessment at DLR Satellite data
  • 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 Slide 23 > Solar resource assessment at DLR Satellite data
  • 24. How two derive irradiance data from satellites Finally the irradiance is calculated for every hour Slide 24 > Solar resource assessment at DLR Satellite data
  • 25. Example for hourly time series for Plataforma Solar de Almería (Spain) 1200 ground 1000 satellite 800W/m² 600 400 200 0 13 14 15 16 17 18 day in march, 2001 Slide 25 > Solar resource assessment at DLR Validation of the data
  • 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 Satellite measurements are Hi-res satellite pixel in Europe 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 satellite pixel (200MW ≈ 2x2 (∼ 3x4 km²) 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 1000 sate llite 800 W/m² 600 400 200 0DNI time serie for 1.11.2001, Almería 0 6 12 18 24 Slide 28 > Solar resource assessment at DLRPartially cloudy conditions, cumulus humilis hour of day
  • 29. Comparison with ground measurements and accuracy general difficulties: point versus area and time integrated versus area integrated 1200 ground 1000 sate llite 800 W/m² 600 400 200 0DNI time serie for 30.4.2000, Almería 0 6 12 18 24 Slide 29 > Solar resource assessment at DLRovercast conditions, strato cumulus hour of day
  • 30. Validation in Tabouk, Saudi Arabia all-sky clear-sky All Sky Clear sky only Validation of the data
  • 31. Validation resultsRMSE (Root Mean Square Error)Decreasing deviation between ground and satellite deriveddata with increasing duration of the integration timeBias Site bias Measurement Network Saudi Arabia +4.3%Variing within different sites Several Sites in Spain -6.4% to +0.7% Morocco -2.0% Algeria -4.8% Slide 31 > Solar resource assessment at DLR Validation of the data
  • 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 Slide 32 > Solar resource assessment at DLR Validation of the data
  • 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 5 Korrekturfaktor 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 Slide 35 > Solar resource assessment at DLR Summary
  • 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 Slide 36 > Solar resource assessment at DLR Acknowledgements
  • 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. Slide 38 > Solar resource assessment at DLR Validation of the data
  • 39. Derivation of the Cloud-Index from satellite images VIS-channel IR-channel (0.5 – 0.9µm) (10.5 –12.5 µm) Original-imageMeteosat-7 Reference-image Derived cloud information (Cloud-Index)
  • 40. Time series of direct normal irradiance aerosol opt. thickness linear regression: Spain 2.16 W/(m² year) 1.56 W/(m² year) aerosol optical thickness PinatuboEl Chichon with without stratospheric aerosol with without stratospheric aerosol linear regression: Pinatubo Australia - 0.93 W/(m² year)El Chichon - 1.22 W/(m² year) Slide 40 > Solar resource assessment at DLR

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