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Introduction to Solar Resource AssessmentsCarsten Hoyer-KlickGerman Aerospace CenterInstitute of Technical Thermodynamics ...
Transfer of Solar Radiation through theAtmosphere                              Radiation at the top of atmosphere         ...
Direct Normal Irradiation (DNI)            direct                                     Direct-Normal- Irradiation (DNI)   ...
Diffuse Horizontal Radiation                          diffuse      diffuse                                    Folie 4
Global Horizontal Irradiation (GHI)                 direct                           diffuse       diffuse                ...
Solar Spectrum and Atmospheric influence                     1 Planck curve T=5780 K at mean sun-                       ea...
Atmospheric Extinction Processes                                            Reflected back                                ...
Rayleigh and Mie Scattering        Rayleigh scattering                         Mie scattering                             ...
Rayleigh and Mie Scattering Rayleigh scattering            Mie scttering                                   Mie Scattering,...
Scattering RegimesDimensionless size parameter Χ=2πr/λ as a function of wavelength (λ) of the incident radiationand partic...
How two derive irradiance datafrom satellites                                 The Meteosat                                ...
Das Meteosat System – Image recording                           • The satellite rotates at 100 rpm                        ...
How two derive irradiance datafrom satellites                                 The Meteosat                                ...
How two derive irradiance datafrom satellites                                 The Meteosat                                ...
How two derive irradiance datafrom satellites                                 The Meteosat                                ...
Clear sky Model input data  Aerosol optical thickness  GACP Resolution 4°x5°, monthly climatology  MATCH Resolution 1.9°x1...
Remote Sensing of Aerosols  Usually split channel / dark target approach:       A dark target is searched in a long-wave c...
Chemistry Transport Models for   Aerosol DataModeling of aerosol uptake, transport, chemical change and deposition in a nu...
Chemistry Transport Models –Emission data bases  Emission data  bases, e.g.  SOx emissions                               F...
Chemistry Transport Models - Dust  Mineral dust mobilisation is initiated by strong winds blowing over bare  ground and er...
Uncertainty in Aerosols                                      All graphs are for July                                      ...
Calculation of solar radiation fromremote sensing                                    METEOSAT            Gext             ...
Cloud Transmission for GHI                             Folie 23
Cloud Transmission for DNI                                                                                           Sun-s...
Comparing ground and satellite data:time scales                                        12:45   13:00   13:15      13:30   ...
Comparing ground and satellite data:“sensor size”      solar thermal      power plant                   satellite pixel   ...
Comparison with ground measurements and accuracy                               general difficulties: point versus area and...
Comparison with ground measurements and accuracy                                 general difficulties: point versus area a...
Satellite data and nearestneighbour stations                             Satellite derived                             dat...
Inter annual variability  Strong inter annual and regional   1999  variations                                 deviation   ...
Long-term variability of solar irradiance  7 to 10 years of measurement to get long-term mean within 5%                   ...
Ground measurements vs.satellite derived dataGround measurements                         Satellite dataAdvantages         ...
Combining Ground andSatellite Assessments  Satellite data        Long term average        Year to year variability        ...
Matching Ground and Satellite DataWhy do ground and satellite data not match?Due to uncertainties in:   Atmospheric Parame...
Procedure for Matching Ground                       and Satellite Data                         Satellite          Ground  ...
Benchmarking of Time Series Products  First order measures:  Bias, root mean square error, standard  deviation  Exact matc...
Validation of the MFG data base DNI           Bias     RMSE     Correl.                                 Coeff overall     ...
Using Solar Resource Data for Policy Analysis                                           Folie 38
Policy questions  How potential is there for a certain technology?  Are there prime areas to develop a technology?        ...
1st Question: How much Potential?  Is the technology feasible, are there enough resources?  Is there sufficient area to de...
Assessing Potentials - Outline             Theoretical Potential           The Amount of solar energy                on th...
Assessing Potentials – CSP Sample                                    DNI map of the region     Theoretical Potential   The...
Assessment of Potentials,e.g. Large Solar                            Folie 43
Assessment of Potentials,e.g. Large Solar                            + Industry,                              settlements ...
Assessment of Potentials,e.g. Large Solar                            + Industry,                              settlements ...
Assessment of Potentials,e.g. Large Solar                            + Industry,                              settlements ...
Assessment of Potentials,e.g. Large Solar                            + Industry,                              settlements ...
Assessment of Potentials,e.g. Large Solar                            + Industry,                              settlements ...
Assessment of Potentials,e.g. Large Solar                            + Industry,                              settlements ...
Assessment of Potentials,e.g. Large Solar                            + Industry,                              settlements ...
Assessment of Potentials,e.g. Large Solar                            + Industry,                              settlements ...
Assessment of Potentials,e.g. Large Solar                            + Industry,                              settlements ...
Assessment of Potentials,e.g. Large Solar                            + Industry,                              settlements ...
Solar Radiation at Usable Areas                                  Folie 54
CSP Potential                  200000                  175000                  150000     Area [km²]                  1250...
Costal Potential (<20m a.s.l.)e.g. for sea water desalination                                  Folie 56
Coastal Potential in Egypt                                       Coastal Potential - Egypt (20 m a. s. l.)                ...
Rooftop PotentialAssuming:• 5% usage of populated area• 10% PV efficiencyPV Rooftop potential approx. 5 TWh               ...
Potential of Concentrating Solar Powerin Jordan                                DNI Classes• Demand 2050: 53 TWh/y         ...
CSP power generation potential in Jordan                                           Folie 60
2nd Question:Which areas the most interesting?  Where are resources available?  Are they close enough to the demand center...
Identification of technology specific hot spotsGeneral approach   Resource         Land resource                          ...
New Approach for Site Ranking  Prerequisite: GIS data for resources and infrastructure  Idea, giving Points to:        Lev...
Determination of weights - DNI                                 Folie 64
Determination of weights –Population and Infrastructure       Population               Streets                            ...
Determination of weights –Electricity Network       Substations                                                       Tran...
Final Ranking                                                                                                             ...
Identification of CSP Hot Spotsin JordanExclusion map                                                                     ...
Thank you for your attention!         Questions & AnswersEither now or carsten.hoyer-klick@dlr.de                         ...
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Introduction to solar resouce assessments

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Solar Med Atlas Workshop December 4th, 2012

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  1. 1. Introduction to Solar Resource AssessmentsCarsten Hoyer-KlickGerman Aerospace CenterInstitute of Technical Thermodynamics Folie 1
  2. 2. Transfer of Solar Radiation through theAtmosphere Radiation at the top of atmosphere Absorption (ca. 1%) Ozone.……….….... Rayleigh scattering and absorption (ca. 15%) Air molecules..…… Scatter and Absorption ( ca. 15%, max. 100%) Aerosol…….………..…...…… Clouds………….……….. Reflection, Scatter, Absorption (max. 100%) Water Vapor…….……...……… Absorption (ca. 15%) Direct normal irradiance at ground Folie 2
  3. 3. Direct Normal Irradiation (DNI) direct  Direct-Normal- Irradiation (DNI) Folie 3
  4. 4. Diffuse Horizontal Radiation diffuse diffuse Folie 4
  5. 5. Global Horizontal Irradiation (GHI) direct diffuse diffuse Global-Horizontal-Irradiation (GHI) Folie 5
  6. 6. Solar Spectrum and Atmospheric influence 1 Planck curve T=5780 K at mean sun- earth distance 2 extraterrestrial solar spectrum with additional 3 absorption by 03 4 scattering by 02 und N 5 scattering by aerosols 6 absorption by H2O vapor 7 absorption by aerosols Folie 6 Introduction to Solar Resources > Carsten Hoyer-Klick > Feb. 28th 2012> Sultan Qaboos University
  7. 7. Atmospheric Extinction Processes Reflected back to space Scattered in any direction Absorbed Direct transmitted to surface Folie 7 Introduction to Solar Resources > Carsten Hoyer-Klick > Feb. 28th 2012> Sultan Qaboos University
  8. 8. Rayleigh and Mie Scattering Rayleigh scattering Mie scattering particle size ≥ wavelengthparticle size << wavelength~ λ-4 ~ λ-1.3 directionality:directionality: (1 + cos² α) very strong forward scattering Large variability by non-uniform particles Folie 8 Introduction to Solar Resources > Carsten Hoyer-Klick > Feb. 28th 2012> Sultan Qaboos University
  9. 9. Rayleigh and Mie Scattering Rayleigh scattering Mie scttering Mie Scattering, larger particles Direction of incident light Folie 9 Introduction to Solar Resources > Carsten Hoyer-Klick > Feb. 28th 2012> Sultan Qaboos University
  10. 10. Scattering RegimesDimensionless size parameter Χ=2πr/λ as a function of wavelength (λ) of the incident radiationand particle radius r. Folie 10 Introduction to Solar Resources > Carsten Hoyer-Klick > Feb. 28th 2012> Sultan Qaboos University
  11. 11. How two derive irradiance datafrom satellites The Meteosat satellite is located in a geostationary orbit The satellite scans the earth line by line every half hour Folie 11
  12. 12. Das Meteosat System – Image recording • The satellite rotates at 100 rpm • Line by line scanning of the earth from south to north • Pixels by sampling of the analog sensor signal • Field of view of the sensor e.g. in Europe 3 x 4 km due to geometric distortion Scan Folie 12
  13. 13. How two derive irradiance datafrom 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 … Folie 13
  14. 14. How two derive irradiance datafrom 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 Folie 14
  15. 15. How two derive irradiance datafrom 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 Folie 15
  16. 16. Clear sky Model input data Aerosol optical thickness GACP Resolution 4°x5°, monthly climatology MATCH Resolution 1.9°x1.9°, daily climatology Water Vapor: NCAR/NCEP Reanalysis Resolution 1.125°x1.125°, daily values Ozone: TOMS sensor Resolution 1.25°x1.25°, monthly values Folie 16
  17. 17. Remote Sensing of Aerosols Usually split channel / dark target approach: A dark target is searched in a long-wave channel The reflectivity is observed in a short wave channel (usually aerosol backscatter increases with frequency) The difference is translated into a AOD Problems: Dark targets a land: Forrests, lakes – only a very few available almost none in deserts Folie 17
  18. 18. Chemistry Transport Models for Aerosol DataModeling of aerosol uptake, transport, chemical change and deposition in a numerical model Input: Surface properties Emission data bases Numerical weather models (especially wind fields, rain). Modeling: Aerosol uptake Aerosol transport with wind Aerosol outfall Aerosol chemistry, change in properties with time Output: Mass concentrations converted to optical properties Folie 18
  19. 19. Chemistry Transport Models –Emission data bases Emission data bases, e.g. SOx emissions Folie 19
  20. 20. Chemistry Transport Models - Dust Mineral dust mobilisation is initiated by strong winds blowing over bare ground and erodible particles. Medium range sand-sized particles above 60 μm particle diameter are lifted up, but also fall quickly down again to the ground. The momentum of the landing particles results in the loosening of small and fast moving particles which are known as sandblasting particles. Dust particles are mixed into higher atmospheric layers by turbulent processes and transported in the atmospheric flow over larger distances. Folie 20
  21. 21. Uncertainty in Aerosols All graphs are for July Scales are the same! (0 – 1.5) Large differences in Aerosol values and GADS NASA GISS v1 / GACP distribution Toms NASA GISS v2 1990 Linke TurbidityGOCART AeroCom MATCH Folie 21
  22. 22. Calculation of solar radiation fromremote sensing METEOSAT Gext image Atmospheric Model Images Ground Corrected image of one albedoGdirect, clear Gdiffuse, clear month cloud-index (    min ) n + (  max   min )Gclear sky clear sky index k *  f (n)  1  n Methods used: • Heliosat-2 for the · visible channel • IR brightnessGsurface temperature as indicator for high cirrus clouds (T < -30°C, DNI = 0) Folie 22
  23. 23. Cloud Transmission for GHI Folie 23
  24. 24. Cloud Transmission for DNI Sun-satellite angle 60-80 1.2 1.2 -26 °C -16 °C 1 1 -6 °C 4 °C 14 °C 0.8 0.8 -30°C - -20°Ccloud transmission -20°C - -10°C 0.6 0.6 -10 °C - 0°C 0°C - 10 °C >10°C 0.4 0.4 0.2 0.2 0 0 -0.2 -0.2 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 cloud index Complex functions: Simple function τ = e-10*ci Different exp. function for various viewing angles and brightness temperatures Folie 24
  25. 25. 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) Folie 25
  26. 26. Comparing ground and satellite data:“sensor size” solar thermal power plant satellite pixel (200MW  2x2 ( 3x4 km²) km² ground measurement instrument (2x2 cm²) Folie 26
  27. 27. Comparison with ground measurements and accuracy general difficulties: point versus area and time integrated versus area integrated 1200 ground 1000 satellite 800 W/m² 600 400 200 0DNI time serie for 1.11.2001, Almería 0 6 12 Folie 27 18 24Partially cloudy conditions, cumulus humilis hour of day
  28. 28. Comparison with ground measurements and accuracy general difficulties: point versus area and time integrated versus area integrated 1200 ground 1000 satellite 800 W/m² 600 400 200 0DNI time serie for 30.4.2000, Almería 0 6 12 Folie 28 18 24overcast conditions, strato cumulus hour of day
  29. 29. Satellite data and nearestneighbour stations Satellite derived data fit better to a selected site than ground measurements from a site farther than 25 km away. Folie 29
  30. 30. 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 Folie 30
  31. 31. Long-term variability of solar irradiance 7 to 10 years of measurement to get long-term mean within 5% Folie 31
  32. 32. 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 at high time resolution- no possibility to gain data of the past Folie 32
  33. 33. Combining Ground andSatellite Assessments Satellite data Long term average Year to year variability Regional assessment Ground data Site specific High temporal resolution possible (up to 1 min to model transient effects) Good distribution function Folie 33
  34. 34. Matching Ground and Satellite DataWhy do ground and satellite data not match?Due to uncertainties in: Atmospheric Parameters, most prominent Aerosols Cloud transmission: The cloud index is a combination of cloud fraction and transparency. A semi transparent cloud can be distinguished well from a fractional cloud cover. Parameterization may depend on prevailing cloud types in the region. Folie 34
  35. 35. Procedure for Matching Ground and Satellite Data Satellite Ground assessment measurements Comparison Recalculation cloud correction Separation of clear sky with alternative cloud and cloud conditions transmission tablesclear sky correction Recalculation Selection with alternative of best cloud atmospheric input transmission table Transfer MSG to MFG Selection Recalculation of of best atmospheric input long term time series Best fit satellite data Folie 35
  36. 36. Benchmarking of Time Series Products First order measures: Bias, root mean square error, standard deviation Exact match of data pairs in time Sometime this match is not necessary (e.g. system layout with historical data) Second order measures: Based on Kolmogrov-Smirnov Test Match of distribution functions Folie 36
  37. 37. Validation of the MFG data base DNI Bias RMSE Correl. Coeff overall -3.78% 27.52% 0.86 PSA 0.65 30.25% 0.89 SedeBoqer -6.86% 29.01% 0.84 Tamanrasset -5.83% 25.22% 0.84 Folie 37
  38. 38. Using Solar Resource Data for Policy Analysis Folie 38
  39. 39. Policy questions How potential is there for a certain technology? Are there prime areas to develop a technology? Folie 39
  40. 40. 1st Question: How much Potential? Is the technology feasible, are there enough resources? Is there sufficient area to depoly the technology? To which share of the (national) demand can they contribute? Folie 40
  41. 41. Assessing Potentials - Outline Theoretical Potential The Amount of solar energy on the whole area Technical potential Limited to suitable areas Economic Potential Limited to economic sites Folie 41
  42. 42. Assessing Potentials – CSP Sample DNI map of the region Theoretical Potential The Amount of solar energy on the whole area Technical potential DNI map minus Limited to suitable areas excluded areas Economic Potential Technical potential only Limited to for DNI e.g. above 2000 economic kWh/m² sites Folie 42
  43. 43. Assessment of Potentials,e.g. Large Solar Folie 43
  44. 44. Assessment of Potentials,e.g. Large Solar + Industry, settlements Folie 44
  45. 45. Assessment of Potentials,e.g. Large Solar + Industry, settlements + Hydrology Folie 45
  46. 46. Assessment of Potentials,e.g. Large Solar + Industry, settlements + Hydrology + Geomorphology Folie 46
  47. 47. Assessment of Potentials,e.g. Large Solar + Industry, settlements + Hydrology + Geomorphology + Protected Areas Folie 47
  48. 48. Assessment of Potentials,e.g. Large Solar + Industry, settlements + Hydrology + Geomorphology + Protected Areas + Wood Folie 48
  49. 49. Assessment of Potentials,e.g. Large Solar + Industry, settlements + Hydrology + Geomorphology + Protected Areas + Wood + Agriculture Folie 49
  50. 50. Assessment of Potentials,e.g. Large Solar + Industry, settlements + Hydrology + Geomorphology + Protected Areas + Wood + Agriculture + Slope Folie 50
  51. 51. Assessment of Potentials,e.g. Large Solar + Industry, settlements + Hydrology + Geomorphology + Protected Areas + Wood + Agriculture + Power lines Folie 51
  52. 52. Assessment of Potentials,e.g. Large Solar + Industry, settlements + Hydrology + Geomorphology + Protected Areas + Wood + Agriculture + Power lines + Gas pipelines Folie 52
  53. 53. Assessment of Potentials,e.g. Large Solar + Industry, settlements + Hydrology + Geomorphology + Protected Areas + Wood + Agriculture + Power lines + Gas pipelines + Oil pipelines Folie 53
  54. 54. Solar Radiation at Usable Areas Folie 54
  55. 55. CSP Potential 200000 175000 150000 Area [km²] 125000 Area with certain 100000 75000 level of solar radiation 50000 25000 0 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 > 2800 Electricity Potential [TWh/y] DNI [kWh/m²a] 25000 20000 15000 10000 Technical potential 5000calc. with power plant model 0 00 00 00 00 00 00 00 00 00 00 00 00 28 18 19 20 21 22 23 24 25 26 27 28 > Folie 55
  56. 56. Costal Potential (<20m a.s.l.)e.g. for sea water desalination Folie 56
  57. 57. Coastal Potential in Egypt Coastal Potential - Egypt (20 m a. s. l.) 180 Electricity Potential [TWh/y] 160 140 120 100 2008 power demand 80 60 40 20 0 00 00 00 00 00 00 00 00 00 00 > 0 00 0 18 19 20 21 22 23 24 25 26 27 28 28 DNI [kWh/m²a] Folie 57
  58. 58. Rooftop PotentialAssuming:• 5% usage of populated area• 10% PV efficiencyPV Rooftop potential approx. 5 TWh Folie 58
  59. 59. Potential of Concentrating Solar Powerin Jordan DNI Classes• Demand 2050: 53 TWh/y [kWh/m²/y]• Potential CSP: 5884 TWh/y < 1900 1,900 2,000 2,100 2,200 2,300 2,400 2,500 2,600 2,700 Exclusion Criteria Urban areas Population density Hydrography Land cover Protected areas Topography Folie 59
  60. 60. CSP power generation potential in Jordan Folie 60
  61. 61. 2nd Question:Which areas the most interesting? Where are resources available? Are they close enough to the demand centers and infrastructure? Which resource are available close to demand centers and intfrastructure? Can I optimize my spatial planning according to the resource distribution? Folie 61
  62. 62. Identification of technology specific hot spotsGeneral approach Resource Land resource Site ranking Hot spots assessment assessment - DNI - Spatial data - Distance to - Available area - GHI - Technology infrastructure - Max. installable capacity - Wind speed specific land - Resource - Representative hourly exclusion criteria availability resource profiles Folie 62
  63. 63. New Approach for Site Ranking Prerequisite: GIS data for resources and infrastructure Idea, giving Points to: Level of available resource Distance to the electricity grid Distance to settlements Distance to infrastructure Ranking based on the sum of points. Folie 63
  64. 64. Determination of weights - DNI Folie 64
  65. 65. Determination of weights –Population and Infrastructure Population Streets Folie 65
  66. 66. Determination of weights –Electricity Network Substations Transmission lines Folie 66 Using Solar Resource Data for Policy Analysis > Carsten Hoyer-Klick > Solar Med Atlas Stakeholder Workshop, Cairo, Nov. 1st, 2011
  67. 67. Final Ranking Folie 67 Using Solar Resource Data for Policy Analysis > Carsten Hoyer-Klick > Solar Med Atlas Stakeholder Workshop, Cairo, Nov. 1st, 2011
  68. 68. Identification of CSP Hot Spotsin JordanExclusion map Hot spots Folie 68 Using Solar Resource Data for Policy Analysis > Carsten Hoyer-Klick > Solar Med Atlas Stakeholder Workshop, Cairo, Nov. 1st, 2011
  69. 69. Thank you for your attention! Questions & AnswersEither now or carsten.hoyer-klick@dlr.de Folie 69

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