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Introduction to solar resouce assessments
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Introduction to solar resouce assessments

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Solar Med Atlas Workshop December 4th, 2012

Solar Med Atlas Workshop December 4th, 2012

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  • 1. Introduction to Solar Resource AssessmentsCarsten Hoyer-KlickGerman Aerospace CenterInstitute of Technical Thermodynamics Folie 1
  • 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. Direct Normal Irradiation (DNI) direct  Direct-Normal- Irradiation (DNI) Folie 3
  • 4. Diffuse Horizontal Radiation diffuse diffuse Folie 4
  • 5. Global Horizontal Irradiation (GHI) direct diffuse diffuse Global-Horizontal-Irradiation (GHI) Folie 5
  • 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Chemistry Transport Models –Emission data bases Emission data bases, e.g. SOx emissions Folie 19
  • 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. 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. 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. Cloud Transmission for GHI Folie 23
  • 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. 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. 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. 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. 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. 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. 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. Long-term variability of solar irradiance 7 to 10 years of measurement to get long-term mean within 5% Folie 31
  • 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. 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. 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. 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. 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. 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. Using Solar Resource Data for Policy Analysis Folie 38
  • 39. Policy questions How potential is there for a certain technology? Are there prime areas to develop a technology? Folie 39
  • 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. 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. 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. Assessment of Potentials,e.g. Large Solar Folie 43
  • 44. Assessment of Potentials,e.g. Large Solar + Industry, settlements Folie 44
  • 45. Assessment of Potentials,e.g. Large Solar + Industry, settlements + Hydrology Folie 45
  • 46. Assessment of Potentials,e.g. Large Solar + Industry, settlements + Hydrology + Geomorphology Folie 46
  • 47. Assessment of Potentials,e.g. Large Solar + Industry, settlements + Hydrology + Geomorphology + Protected Areas Folie 47
  • 48. Assessment of Potentials,e.g. Large Solar + Industry, settlements + Hydrology + Geomorphology + Protected Areas + Wood Folie 48
  • 49. Assessment of Potentials,e.g. Large Solar + Industry, settlements + Hydrology + Geomorphology + Protected Areas + Wood + Agriculture Folie 49
  • 50. Assessment of Potentials,e.g. Large Solar + Industry, settlements + Hydrology + Geomorphology + Protected Areas + Wood + Agriculture + Slope Folie 50
  • 51. Assessment of Potentials,e.g. Large Solar + Industry, settlements + Hydrology + Geomorphology + Protected Areas + Wood + Agriculture + Power lines Folie 51
  • 52. Assessment of Potentials,e.g. Large Solar + Industry, settlements + Hydrology + Geomorphology + Protected Areas + Wood + Agriculture + Power lines + Gas pipelines Folie 52
  • 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. Solar Radiation at Usable Areas Folie 54
  • 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. Costal Potential (<20m a.s.l.)e.g. for sea water desalination Folie 56
  • 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. Rooftop PotentialAssuming:• 5% usage of populated area• 10% PV efficiencyPV Rooftop potential approx. 5 TWh Folie 58
  • 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. CSP power generation potential in Jordan Folie 60
  • 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. 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. 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. Determination of weights - DNI Folie 64
  • 65. Determination of weights –Population and Infrastructure Population Streets Folie 65
  • 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. Final Ranking Folie 67 Using Solar Resource Data for Policy Analysis > Carsten Hoyer-Klick > Solar Med Atlas Stakeholder Workshop, Cairo, Nov. 1st, 2011
  • 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. Thank you for your attention! Questions & AnswersEither now or carsten.hoyer-klick@dlr.de Folie 69