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Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
Solar resource measurements and sattelite data
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Solar resource measurements and sattelite data

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  • 1. Solar Resource Measurements and Satellite Data 4th Sfera Summer School 2013 Hornberg Castle, Germany Dr. Norbert Geuder CSP Services – Almería – Cologne
  • 2. Outline 1) Introduction 2) Fundamentals on Solar Irradiation 3) Variability of Irradiation 4) Irradiation Sensors 5) Sensor calibration and measurement accuracy enhancement 6) Satellite Based Assessment 7) Outlook / Summary 4th Sfera Summer School, Hornberg Castle, 2013 -2
  • 3. General Procedure at Solar Resource Assessment Irradiation map: spatial distribution Geographical data: land use, etc. + GIS analysis Selection of promising sites Meteorological Station: Accurate irradiation data Long-term time series (>10 years) Plant design, inter-annual variability, uncertainty analysis, financing, … 4th Sfera Summer School, Hornberg Castle, 2013 -3
  • 4. Solar Resource Assessment for CSP Plants • Direct Beam Irradiation data required for CSP Applications not global irradiation – this is quite a difference! • Usually not available in suitable sunny regions in the world • Accessible via measurements or derived from satellite data Restrictions: – Measurements: expensive, long duration, not for the past – Satellite data: high uncertainty (≈ 10 % or more) • High accuracy required for DNI with long-term performance 4th Sfera Summer School, Hornberg Castle, 2013 -4
  • 5. Impact of Solar Resource Uncertainty on CSP Plant rentability DNI uncertainty expected long-term value (100%) (e.g. ±10 %) Annual DNI Electricity production Earnings Losses Annual expenses (redemption, O&M, …) Irradiation uncertainty decides over project realization ! With thoroughly performed measurements an accuracy of approximately 2 % is achievable. 4th Sfera Summer School, Hornberg Castle, 2013 -5
  • 6. Fundamentals on Solar Irradiation 4th Sfera Summer School, Hornberg Castle, 2013 -6
  • 7. Solar Constant 1367 W/m² Calculation: L W  1367 2 4r 2 m Luminosity of the sun: L = 3.86 x 1026 W Astronomical unit: r = 149.60×109 m Annual Variation of Solar Constant 1420 Irradiance Outside Atmosphere (W/m²) Mean solar irradiance (flux density in W/m²) at normal incidence outside the atmosphere at the mean sun-earth distance r. 1410 1400 1390 1380 1370 1360 1350 1340 1330 1320 0 90 180 270 360 Day of Year 4th Sfera Summer School, Hornberg Castle, 2013 -7
  • 8. Path of Solar Radiation through the atmosphere Radiation at the top of atmosphere Ozone.……….….... Absorption (ca. 1%) Air molecules..…… Rayleigh scattering and absorption (ca. 15%) Aerosol…….………..…...…… Scatter and Absorption ( ca. 15%, max. 100%) Clouds………….……….. Reflection, Scatter, Absorption (max. 100%) Water Vapor…….……...……… Direct normal irradiance at ground Absorption (ca. 15%) Source: DLR 4th Sfera Summer School, Hornberg Castle, 2013 -8
  • 9. Radiative Transfer in the Atmosphere 1400 Extraterrestrial O2 and CO2 1000 Ozone 800 Rayleigh 600 Water Vapor 400 Aerosol 200 Clouds Hour of Day 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 Source: DLR 4th Sfera Summer School, Hornberg Castle, 2013 -9
  • 10. Air Mass AM = 0 (outside of atmosphere) Source: DLR 4th Sfera Summer School, Hornberg Castle, 2013 - 10
  • 11. Solar Spectrum and Atmospheric Influence 1 Planck curve T=5780 K at mean sunearth distance 2 extraterrestrial solar spectrum 3 absorption by 03 4 scattering by 02 und N2 5 scattering by aerosols 6 absorption by H2O vapor 7 absorption by aerosols UV radiation: 0.01 - 0.39 µm, ~ 7 % Visible Spectrum: 0.39 – 0.75 µm, ~ 46 % Near infrared: 0.75 – 2.5 µm, ~ 47 % www.volker-quaschning.de/articles/fundamentals1/index.php 4th Sfera Summer School, Hornberg Castle, 2013 - 11
  • 12. Characteristics of solar irradiation data • Component: – DNI (Direct-Normal Irradiation) – DHI (Diffus-Horizontal Irradiation) – GHI (Global-Horizontal Irradiation) • Source: – ground measurements: • precise thermal sensors (thermopiles) • Rotating Shadowband Irradiometers (RSI) – satellite data • Properties of irradiation: – spatial variability – inter-annual variability – long-term drifts 4th Sfera Summer School, Hornberg Castle, 2013 - 12
  • 13. Direct, Diffuse and Global Irradiance When measuring solar irradiance, the following components are of particular interest: Zenith angle θ, solar elevation   Direct normal irradiance (DNI) (also: beam irradiance)  Diffuse horizontal irradiance (DHI) (also: diffuse sky radiation)  Global horizontal irradiance (GHI) (also: total solar irradiance)  GHI = DHI + DNI * sin () 4th Sfera Summer School, Hornberg Castle, 2013 - 13
  • 14. Direct Normal Irradiation (DNI) DNI = BHI / sin  with: BHI = Beam Horizontal Irradiation direct Example: BHI = 600W/m²  = 50°  DNI = 848W/m² DNI > BHI  Direct-Normal- Irradiation (DNI) 4th Sfera Summer School, Hornberg Castle, 2013 - 14
  • 15. Global Horizontal Irradiation (GHI) GHI = BHI + DIF Example: direct diffuse BHI = 600W/m² DIF = 150W/m²  GHI = 750W/m² diffuse Global-Horizontal-Irradiation (GHI) 4th Sfera Summer School, Hornberg Castle, 2013 - 15
  • 16. Variability of irradiation
  • 17. Long-term variability of solar irradiance GHI from Potsdam, Germany 4th Sfera Summer School, Hornberg Castle, 2013 - 17
  • 18. Long-term variability of solar irradiance Source: DLR 7 to 10 years of measurement to get long-term mean within 5% 4th Sfera Summer School, Hornberg Castle, 2013 - 18
  • 19. Inter-annual variability 1999 deviation to mean 2000 kWh/m²a Average of the direct normal irradiance from 1999 to 2003 Strong inter-annual and regional variations 2001 2002 Source: DLR 2003 4th Sfera Summer School, Hornberg Castle, 2013 - 19
  • 20. Irradiation sensors 4th Sfera Summer School, Hornberg Castle, 2013 - 20
  • 21. Absolute Cavity Radiometer Principle of Measurements: - - Possibility to measure absolute irradiance values. All other irradiance measurement devices need to be calibrated using an absolute cavity radiometer Its principle of operation is based on the substitution of radiative power by electrical (heating) power - Measurement in intervals with minimal length of 45 s. Constant irradiation required for measurement campain - Tracking device required - No continuous measurement (!) ftp.pmodwrc.ch/pub/pmo6-cc/user_guide_11.pdf Valid for calibration purposes 4th Sfera Summer School, Hornberg Castle, 2013 - 21
  • 22. Suitable equipment for irradiance measurements for Concentrating Solar Power (CSP) Thermal sensors Semiconductor sensor Rotating Shadowband Irradiometer, RSI (photodiode) Pyranometer, pyrheliometer (thermopiles) 4th Sfera Summer School, Hornberg Castle, 2013 - 22
  • 23. Thermopile Sensors Shading assembly with shading ball CMP21 Pyranometer (GHI, DHI shaded) with ventilation unit CVF3 CHP1 Pyrheliometer (DNI) Sun sensor Solys 2 sun tracker 4th Sfera Summer School, Hornberg Castle, 2013 - 23
  • 24. Thermopile Sensors – Pyrheliometer Principle of Measurement: - Pyrheliometer = radiometer suitable to measure direct normal irradiance - Highly transparent window 97 – 98 % transmission of solar radiation - Housing geomerty with 200 mm absorber tube restricting acceptance angle to 5° - Sensing element with black coating and built-in termopile device - www.kippzonen.com/?product/18172/CHP+1.aspx Pt-100 temperature sensor for temperature corrections 4th Sfera Summer School, Hornberg Castle, 2013 - 24
  • 25. Pyrheliometer Specifications Kipp&Zonen CHP 1 Specifications: Spectral range: 200 to 4000 nm Sensitivity: 7 to 14 µV/W/m² (mV/kW/m²) Response time: < 5 s Expected daily uncertainty: ± 1 % Full opening view angle: 5° ± 0.2° Required tracking accuracy: ± 0.5° from ideal www.kippzonen.com/?product/18172/CHP+1.aspx 4th Sfera Summer School, Hornberg Castle, 2013 - 25
  • 26. Thermopile Sensors – Pyranometer Principle of Measurements: - Pyrheliometer = radiometer suitable to measure short-wave (0.2 - 4 µm) global or diffuse radiation - Highly transparent glass dome 97 – 98 % transmission of solar radiation - Full view on 2π hemisphere (horizontal levelling required) - Sensing element with black coating and built-in termopile - www.kippzonen.com/?product/18172/CHP+1.aspx Pt-100 temperature sensor for temperature corrections 4th Sfera Summer School, Hornberg Castle, 2013 - 26
  • 27. Thermopile Sensors – Pyranometer Kipp&Zonen CMP21 Specifications: Spectral range: 285 to 2800 nm Sensitivity: 7 to 14 µV/W/m² (mV/kW/m²) Response time: 5 s www.kippzonen.com/?product/1491/CMP+21.aspx 4th Sfera Summer School, Hornberg Castle, 2013 - 27
  • 28. Rotating Shadowband Pyranometer (RSP) Licor silicon photodiode Rotating shadowband Housing of mechanics 4th Sfera Summer School, Hornberg Castle, 2013 - 28
  • 29. RSI – Principle of Measurement Simplified sensor signal during shadow band rotation: once per minute, rotation lasts about 1.5 seconds Source: Solar Millennium AG 4th Sfera Summer School, Hornberg Castle, 2013 - 29
  • 30. Licor Li-200 Pyranometer Sensor Specifications: Sensitivity: Typically 90 µA per 1000 W/m² Response time: 10 µs. Spectral range: 0.4 – 1.1 µm Calibration: Calibrated against an Eppley Precision Spectral Pyranometer under natural daylight conditions. Typical error under these conditions is ±3% up to ±5%. www.licor.com/env/Products/Sensors/200/li200_description.jsp 4th Sfera Summer School, Hornberg Castle, 2013 - 30
  • 31. Precise thermal sensors: Pyrheliometer and Pyranometer on sun tracker Advantages: + high accuracy (1 to 2%) -GHI -DNI -DHI + separate sensors for GHI, DNI and DHI (cross-check through redundancy) Disadvantages: - high acquisition costs - high maintenance costs - high susceptibility for soiling - high power demand (grid connection required) 4th Sfera Summer School, Hornberg Castle, 2013 - 31
  • 32. Sensor with photo diode: Rotating Shadowband Irradiometer, RSI Advantages: + fair acquisition costs + low maintenance + low susceptibility for soiling + low power demand (PV-Panel) Disadvantage: - reduced accuracy due to systematic deviations of the photodiode sensor response: primordial DNI: ≈ 6 to 10 % (or even higher) 4th Sfera Summer School, Hornberg Castle, 2013 - 32
  • 33. Measurement uncertainty Precise instruments (HP) versus RSI Error source: Pyrheliometer: RSI:  Calibration < ±1.1% ±3% (... ±5%)  Temperature < ±0.5% 0% ... ±5%  Linearity < ±0.2% ±1%  Stability < ±0.5%/a < ±2%/a  Spectral dependence <±0.1%  Sensor soiling -0.7% per day 0% ... ±8% -0.07% per day systematic errors can be corrected!! 4th Sfera Summer School, Hornberg Castle, 2013 - 33
  • 34. Choice of Measurement Equipment Which equipment is suitable for measurements in Solar Resource Assessment? ? High Precision sensors (thermopiles) Rotating Shadowband Irradiometer: RSI 4th Sfera Summer School, Hornberg Castle, 2013 - 34
  • 35. Objectives for Irradiance Measurements Solar Resource Assessment Power Plant Monitoring • at remote site • no qualified staff • always qualified staff on site • no electric grid • electric power available • often dusty and arid areas 4th Sfera Summer School, Hornberg Castle, 2013 - 35
  • 36. Pyrheliometer soiling in southern Spain Plataforma Solar de Almería 4th Sfera Summer School, Hornberg Castle, 2013 - 36
  • 37. Comparison of sensor soiling University of Almería 4th Sfera Summer School, Hornberg Castle, 2013 - 37
  • 38. Soiling characteristics of pyrheliometers and RSI‘s Solar Irradiation direct  sunlight glass plate tube with  200 mm  length diffusor disk  over  photodiode absorber Pyrheliometer RSI  sensor head 4th Sfera Summer School, Hornberg Castle, 2013 - 38
  • 39. Choice of the adequate equipment For Solar Resource Assessment • at remote sites and • daily maintenance not feasible an RSI is the premium choice for DNI measurements. However: • Proper calibration • Corrections of systematic signal response • regular maintenance inspections (2 to 4 weeks) are indispensable for reliable measurements. 4th Sfera Summer School, Hornberg Castle, 2013 - 39
  • 40. Sensor calibration and measurement accuracy enhancement 4th Sfera Summer School, Hornberg Castle, 2013 - 40
  • 41. Sensor Calibration – Fundamentals I - The World Standard Group (WSG) is an assembly of highly precise absolute cavity radiometers. www.pmodwrc.ch/pmod.php?topic=wrc - The measured mean value (World Radiometric Reference) is the measurement standard representing the SI unit of irradiance with an estimated accuracy of 0.3 %. - All other short wave irradiation measurement systems are calibrated against this single value. Precision measurement at the WorldRadiation Center (WRC) 4th Sfera Summer School, Hornberg Castle, 2013 - 41
  • 42. RSI sensor calibration by DLR on PSA 2-monthly calibration of each RSI against high-precision instruments at Plataforma Solar de Almería (PSA) (recommended every 2 years) 4th Sfera Summer School, Hornberg Castle, 2013 - 42
  • 43. RSI sensor calibration duration Variations of the Calibration Constant with calibration duration DNI 4th Sfera Summer School, Hornberg Castle, 2013 - 43
  • 44. Variability of the Correction Factors (CF) Variability of correction factors (CF): radiation components need to be corrected with separate CFs. 4th Sfera Summer School, Hornberg Castle, 2013 - 44
  • 45. Recalibration of RSIs Drift of Calibration Factor within 2 to 4 years 4th Sfera Summer School, Hornberg Castle, 2013 - 45
  • 46. Correction of raw RSI measurement values Origin of systematic errors of RSI response • Temperature dependence of semiconductor sensor • Spectrally varying irradiation – different for irradiation components (direct beam / diffuse) – depending on Air Mass • Angle of incidence • Pre-calibration of the sensor head (from the manufacturer) 4th Sfera Summer School, Hornberg Castle, 2013 - 46
  • 47. Spectral correction of diffuse irradiation raw values variation with air mass + altitude corrected Π spec  DNI  GHI DHI 2 4th Sfera Summer School, Hornberg Castle, 2013 - 47
  • 48. Dependence of response on solar elevation BHIref / BHIRSI so called „cat-ear effect“ solar elevation angle in degree Correction applied only on direct beam portion of the global response 4th Sfera Summer School, Hornberg Castle, 2013 - 48
  • 49. BHIref / BHIRSI Dependence of response on solar elevation corrected solar elevation angle in degree 4th Sfera Summer School, Hornberg Castle, 2013 - 49
  • 50. Reachable accuracy for DNI with RSIs Accuracy of RSI measurements as derived from a comparison of the data from 23 RSIs RMSD = 13 W/m² with precise thermopile measurements within the course of a whole year GHI DHI DNI raw cor raw cor raw cor reference (CHP 1) average MB -10.3 ± 4.0 0.3 ± 1.3 -17.3 ± 1.6 -0.4 ± 0.7 24.6 ± 10.5 1.0 ± 0.5 1.0 ± 3.9 W/m² RMSD 14.2 7.6 18.9 4.5 33.3 13.0 5.3 W/m² Annual sum up to -2.5 < ±1 up to -15 < ±3.5 up to +7 < ±1 up to 1.3 % RSP unit 10 min time resolution 4th Sfera Summer School, Hornberg Castle, 2013 - 50
  • 51. Transferability of the results? The reachable accuracy of the measured beam irradiance data for the client at his prospected sites depends on 2 crucial points: • Stability of the sensor sensitivity • Transferability of the results to other sites • Regular inspections and data controlling 4th Sfera Summer School, Hornberg Castle, 2013 - 51
  • 52. Transferability to other sites and climates Parallel measurement campaigns in UAE: • Comparision of 6 RSIs to high-precision thermal sensors • Measurement periods >3 weeks • in summer and winter 4th Sfera Summer School, Hornberg Castle, 2013 - 52
  • 53. Relative deviation of DNI sum within measurement campaign only summer: DNI < 730 W/m² summer + winter: DNI until 1000 W/m² 4th Sfera Summer School, Hornberg Castle, 2013 - 53
  • 54. Satellite Based Assessment 4th Sfera Summer School, Hornberg Castle, 2013 - 54
  • 55. Satellite Derived Data www.solemi.de/method.html Principle of Measurements: Analyze satellite data in two steps: 1. Atmosphere: Gather satellite information of atmospheric composition (ozone, water vapor and aerosols) and apply the ‘clear sky model’ to calculate the fractions of direct and diffuse irradiance 1400 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) 2. Clouds: Calculate the cloud index as the difference between actual reflectivity of the earth as it is seen by the satellite and a reference image which only includes reflectance of the ground Extraterrestrial 1200 Hour of Day 4th Sfera Summer School, Hornberg Castle, 2013 - 55
  • 56. How to 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 Scan Source: DLR 4th Sfera Summer School, Hornberg Castle, 2013 - 56
  • 57. How to derive irradiance data from satellites Scan in visible spectrum Derivation of a cloud index from the two channels Scan in infra-red spectrum Source: DLR 4th Sfera Summer School, Hornberg Castle, 2013 - 57
  • 58. Different Cloud Transmission for GHI and DNI  Global Irradiation  Direct Irradiation Sun-satellite angle 60-80 1.2 1.2 -26 °C -16 °C -6 °C 4 °C 14 °C -30°C - -20°C -20°C - -10°C -10 °C - 0°C 0°C - 10 °C >10°C 1 cloud transmission 1 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0 Different exponential functions for varying viewing angles and brightness temperatures 0 -0.2 -0.2 0 0.2 0.4 0.6 cloud index 0.8 1 1.2 -0.2 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 Source: DLR 4th Sfera Summer School, Hornberg Castle, 2013 - 58
  • 59. Clear sky Model input data  Aerosol optical thickness  GACP Resolution 4°x5°, monthly data  MATCH Resolution 1.9°x1.9°, daily data  Water Vapor: NCAR/NCEP Reanalysis Resolution 1.125°x1.125°, daily values  Ozone: TOMS sensor Resolution 1.25°x1.25°, monthly values Source: DLR 4th Sfera Summer School, Hornberg Castle, 2013 - 59
  • 60. Uncertainty in Aerosols GADS Toms GOCART NASA GISS v1 / GACP All graphs are for July Scales are the same! (0 – 1.5) Large differences in Aerosol values and distribution NASA GISS v2 1990 AeroCom Linke Turbidity Source: DLR 4th Sfera Summer School, Hornberg Castle, 2013 - 60
  • 61. Comparing ground and satellite data: “sensor size” solar thermal power plant (200MW  2x2 km² satellite pixel ( 3x4 km²) ground measurement instrument (2x2 cm²) Source: DLR 4th Sfera Summer School, Hornberg Castle, 2013 - 61
  • 62. Comparing ground and satellite data: accuracy general difficulties: • point versus area • time integrated versus area integrated 1200 ground 1000 satellite W/m² 800 600 400 200 0 0 6 12 hour of day 18 24 Source: DLR 4th Sfera Summer School, Hornberg Castle, 2013 - 62
  • 63. Comparing ground and satellite data: time scales 12:45 13:00 13:15 Hourly average 13:30 13:45 Meteosat image 14:00 14:15 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) Source: DLR 4th Sfera Summer School, Hornberg Castle, 2013 - 63
  • 64. Results of the satellite-based solar assessment Digital maps: e.g. annual sum of direct normal irradiation in 2002 in the Mediterranean Region The original digital maps can be navigated and zoomed with Geographical Informations Systems like ArcView or Idrisi. data produced by (DLR, 2004) for MED-CSP Temporal resolution of input data: 1 hour Spatial resolution of digital map: 1 km x 1 km per Pixel Long term analysis: up to 20 years of data Source: DLR 4th Sfera Summer School, Hornberg Castle, 2013 - 64
  • 65. Results of the satellite-based solar assessment Time series: for single sites, e.g. hourly, monthly or annual Hourly DNI [Wh/m²] for one site in Spain Annual sums of DNI [kWh/m²] for one site in Spain Monthly sums of DNI [kWh/m²] for one site in Spain Hourly monthly mean of DNI in Wh/m², Solar Village 2000 hour Source: DLR 4th Sfera Summer School, Hornberg Castle, 2013 - 65
  • 66. 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. Source: DLR 4th Sfera Summer School, Hornberg Castle, 2013 - 66
  • 67. Ground measurements vs. satellite derived data Ground measurements Satellite data Advantages Advantages + high accuracy (depending on sensors) + high time resolution + spatial 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 - possible sensor failures - low accuracy at high time resolution - no possibility to gain data of the past 4th Sfera Summer School, Hornberg Castle, 2013 - 67
  • 68. Combining Ground and Satellite Assessments • Satellite data – Long-term average – Year to year variability – Regional assessment • Ground data – High Precision (if measurements taken thoroughly) – High temporal resolution possible (up to 1 min to model transient effects) – Good distribution function – Site specific 4th Sfera Summer School, Hornberg Castle, 2013 - 68
  • 69. Procedure for Matching Ground and Satellite Data Ground measurements Recalculation Separation of clear sky and cloud conditions clear sky correction Comparison with alternative cloud transmission tables Recalculation Selection with alternative atmospheric input Selection of best atmospheric input of best cloud transmission table cloud correction Satellite assessment Transfer MSG to MFG Recalculation of long term time series Best fit satellite data 4th Sfera Summer School, Hornberg Castle, 2013 - 69
  • 70. What you should care for in good Solar Resource Assessment 4th Sfera Summer School, Hornberg Castle, 2013 - 70
  • 71. Procedure to Follow for Proper Solar Resource Assessment • Find a good location: close to site, safe, suitable for collocation of Weather Station • Clarify the ground property conditions • Check/define the budget for: instrumentation, maintenance and measurement related services • Select the appropriate measurement equipment and provider (based on budget considerations, local conditions on site and maintenance possibilities) • Find local maintenance personnel • Prepare the measurement site according to the supplier’s specifications (foundations, fencing, etc.) • Installation and commissioning of the measurement equipment • Steady monitoring of the measurement data, duration minimum 1 year 4th Sfera Summer School, Hornberg Castle, 2013 - 71
  • 72. Procedure to Follow for Proper Solar Resource Assessment • Documenting the selection of instruments • Choosing a renowned company or institution to conduct or assist the measurement campaign • Documenting sensor calibration with proper calibration certificates • Meticolously documenting the instrument installation and alignment • Performing and documenting regular sensor cleaning, maintenance and verification of alignment • Cautiously and continuously checking data for errors and outliers • Flagging suspect data, and applying corrections if possible, during and after the measurement campaign • Stating and justifying the uncertainty estimate in a detailed report after the measurement campaign. 4th Sfera Summer School, Hornberg Castle, 2013 - 72
  • 73. Usual Expert Service for Solar Resource Assessment Expert office On site Daily data retrieval via modem (GSM/GPRS) Data collection and processing: • •  Installation & commissioning  Operational supervision and control  Equipment monitoring with inspection visits on site Client quality and functionality check •  Delivery of hardware accuracy enhancement (correction) graphical visualization Daily, monthly, annual report with good quality data to client (via e-mail) 4th Sfera Summer School, Hornberg Castle, 2013 - 73
  • 74. Quality Control of Measurement Data  Are values physically possible ? Measurement values must met physical limits  Are they reasonable? e.g. comparison to a clear sky model (Bird) or in kd-kt-space  Are they consistent? Comparison of redundant information  Visual inspection by an expert 4th Sfera Summer School, Hornberg Castle, 2013 - 74
  • 75. Summary • Knowledge of accurate irradiation data is indispensable for CSP projects (→ proper plant design, financial calculation, efficient plant operation) • site selection, pre‐feasibility with satellite data • colocation of a measurement station, taking care on thorough operation • match long‐term satellite data with good quality measurement data from ground • monitor the operating plant efficiency thoroughly with high‐precision data 4th Sfera Summer School, Hornberg Castle, 2013 - 75
  • 76. Thank you very much for your attention! 4th Sfera Summer School, Hornberg Castle, 2013 - 76

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