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# Concentrated Solar Power Course - Session 5 - Solar Resource Assessment

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In this session there will be a complete review of technologies and techniques to assess the solar resource of a site and its suitability for a CSP project.

- Understanding the solar resource for csp plants

- Solar radiation measurement and estimation

- Statistical characterisation of the solar resource. Typical meteorological years

- Solar resource assessment for csp plants

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### Concentrated Solar Power Course - Session 5 - Solar Resource Assessment

1. 1. By Manuel A. Silva Pérez silva@esi.us.es May 5, 2010 Concentrated Solar Thermal Power Technnology Training Session 5 – SOLAR RESOURCE ASSESSMENT FOR CSP PLANTS http://www.leonardo-energy.org/csp-training-course-5-lessons
2. 2. SOLAR RESOURCE ASSESSMENT FOR CSP PLANTS Manuel A. Silva Pérez Group of Thermodynamics and Renewable Energy ETSI – University of Seville http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
3. 3. CONTENTS  Understanding the solar resource for CSP plants  Solar radiation measurement and estimation  Solar radiation databases  Statistical characterization of the solar resource. Typical meteorological years  Solar resource assessment for CSP plants http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
4. 4. UNDERSTANDING THE SOLAR RESOURCE FOR CSP PLANTS  The Sun as an energy source Mass: 1,99 x 1030 kg Diameter: 1,392 x 109 m Area: 6,087 x 1018 m2 Volume: 1,412 x 1027 m3 Average density: 1,41 x 103 kg/m3 Angular diameter: 31’ 59,3’’ Average distance to earth: 1,496 x 1011 m = 1 AU Equivalent Temperature: 5777 K Power: 3,86 x 1026 W Irradiance: 6,35 x 107 W/m2 http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
5. 5. 0,0 0,5 1,0 1,5 2,0 2,5 3,0 0 500 1000 1500 2000 2500 0,0 0,5 1,0 1,5 2,0 2,5 3,0 0 500 1000 1500 2000 2500 nI0  (W·m-2 ·m-1)  (m) Blackbody @ 5777 K Extraterrestrial solar spectrum Visible http://rredc.nrel.gov/solar/standards/am0/wehrli1985.new.html UV IR THE SUN AS A BLACKBODY http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
7. 7. INTERACTION BETWEEN SOLAR RADIATION AND THE EARTH’S ATMOSPHERE 0 500 1000 1500 2000 0,3 1,3 2,3 3,3 Longitud de onda (micras) W/m 2 ·m Extraterrestre 5777 K In Idh IT http://rredc.nrel.gov/solar/standards/am0/wehrli1985.new.html http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
8. 8. (Cloudless sky) Absorption % 8 100% Air molecules 1 1 to 5 0.1 a 10 5 Dust, aerosols Moisture 0.5 to 10 2 to 10 Diffuse % Reflection to space % Beam 83% to 56% 11% to 23% 5% a 15% INTERACTION BETWEEN SOLAR RADIATION AND THE EARTH’S ATMOSPHERE http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
9. 9. SOLAR RADIATION CHARACTERISTICS CYCLES  Daily  Day – night  Modulation of solar radiation during the day   Seasonal  Modulation of solar radiation during the year http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
10. 10. SOLAR RADIATION CHARACTERISTICS LOW DENSITY  Maximum value < 1367 W/m2  Large areas required for solar energy applications  Concentration increases energy power density.  Only the direct (beam) component can be concentrated http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
11. 11. SOLAR RADIATION CHARACTERISTICS GEOGRAPHY  Cloudless sky: Solar radiation depends mainly on latitude. http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
12. 12. SOLAR RADIATION CAHRACTERISITICS RANDOM COMPONENT  Solar radiation is modulated by meteorological conditions – CLOUDS  Local climatic characteristics have to be taken into account! http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
13. 13. Meteorological Station at the Seville Engineering School (since 1984) Solar radiation measurement
14. 14. 0 4 8 12 16 20 24 Hora Solar 0 200 400 600 800 1000 0 4 8 12 16 20 24 Hora Solar W/m 2 0 200 400 600 800 1000 0 4 8 12 16 20 24 Hora Solar W/m 2 0 200 400 600 800 1000 0 4 8 12 16 20 24 Hora Solar W/m 2 Global irradiance Diffuse irradiance Beam irradiance Solar radiation measurement Sunshine duration Campbell – Stokes heliograph Pyranometer Shaded Pyranometer Pyrheliometer http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
15. 15. Measurement of Solar Radiation  Broad-band global solar irradiance: Pyranometer  Diffuse radiation is measured with a pyranometer and a shading device (disc, shadow ring, or band) that excludes direct solar radiation  Response decreases approximately as the cosine of the angle of incidence.  Measures energy incident on a flat surface, usually horizontal http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
16. 16.  Easy to model  Sensitive to attenuation  It is the main component under clear sky Measurement  Precise calibration (absolute – cavity- radiometer)  Requires continuous tracking 5.7 º Eppley Labs pyrheliometer (NIP) & tracker DIRECT NORMAL (BEAM) IRRADIANCE MEASUREMENT http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
17. 17. QUALITY CONTROL OF SOLAR RADIATION DATA  Different procedures, all based on data filtering by:  Comparison with physical constraints, other measurements, models.  Visual inspection by experienced staff  An example follows (see also http://rredc.nrel.gov/solar/pubs/qc_tnd/ for a different, more exhaustive procedure) http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
18. 18. QUALITY CONTROL OF SOLAR RADIATION DATA  Physically Possible Limits  Extremely Rare Limits  Comparisons vs other measurements  Comparisons vs model  Visual inspection http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
19. 19. FILTER 5: VISUAL INSPECTION 0 200 400 600 800 1000 1200 1400 -8 -6 -4 -2 0 2 4 6 8 hora solar irradianciasW/m2 IDmedida ig id http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
20. 20. TIME OFFSET  Incorrect time stamp 0 100 200 300 400 500 600 700 800 900 -8 -6 -4 -2 0 2 4 6 8 Ig horas sol t1 torto tocaso t2 dm dt 0 100 200 300 400 500 600 700 800 900 -8 -6 -4 -2 0 2 4 6 8 Ig horas sol Igcorregida torto tocaso t2 t2't1' t1 http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
21. 21. CLASSICAL ESTIMATION OF SOLAR RADIATION  Models depend on the variable to estimate and on the available data and their characteristics:  Estimation of daily or monthly global horizontal or direct normal irradiation from sunshine duration  Estimation of hourly values from daily values of global horizontal irradiation  Estimation of global irradiation on tilted surfaces  Estimation of the beam component from global horizontal irradiation  Etc. http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
22. 22. ESTIMATION OF DAILY OR MONTHLY GLOBAL HORIZONTAL IRRADIATION FROM SUNSHINE DURATION  Angstrom – type formulas H/H0 = a + b (s/s0)  Where  H is the monthly average daily global irradiation on a horizontal surface  H0 is the monthly average daily extraterrestrial irradiation on a horizontal surface  s is the monthly average daily number of hours of bright sunshine,  s0 is the monthly average daily maximum number of hours f possible sunshine  a and b are regression constants http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
23. 23. ESTIMATION OF DIRECT NORMAL IRRADIATION FROM SUNSHINE DURATION 0 100 200 300 400 500 600 700 800 900 1000 -8 -6 -4 -2 0 2 4 6 8 hora solar / h Ebn/W·m-2 http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
24. 24. Daily or hourly global horizontal irradiation values 0.0 0.2 0.4 0.6 0.8 1.0 0 0.2 0.4 0.6 0.8 1 Kt Kd Daily or hourly Diffuse values Hb,0 = Hg,0 - Hg,0 Decomposition models (estimation of beam and diffuse components from global horizontal) KT = Kd = Hg,0 Ho Hd,0 Hg,0
25. 25. KD – KT MODELS Modelos Kt-Kd diarios 0 0.2 0.4 0.6 0.8 1 1.2 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Kt Kd Collares Muneer Liu-Jordan GTER00-05 Ruth and Chant GTERD00-05 http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
26. 26. SOLAR RADIATION ESTIMATION FROM SATELLITE IMAGES http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
27. 27. SOLAR RADIATION ESTIMATION FROM SATELLITE IMAGES  Energy balance tase0 EEII   aseg EII A I    0 1 1 http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
28. 28. THE SATELLITE METEOROLOGICAL SATELLITES  In meteorology studies frequent and high density observations on the Earth's surface are required.  Conventional systems do not provide a global cover.  An important tool to analyse the distribution of the climatic system are the METEOROLOGICAL SATELLITES. These can be:  Polar  Geostationary: In Europe, the system o geostationary meteorological satellites is METEOSAT http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
29. 29. METHODOLOGY ADVANTAGES  The geostationary satellites show simultaneously wide areas.  The information of these satellites is always referred to the same window.  It is possible to analyse past climate using satellite images of previous years.  The utilisation of the same detector to evaluate the radiation in different places. http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
30. 30. METHODOLOGY DISADVANTAGES  The range of the brilliance values of cloud cover (90-255) and of the soils (30-100) overlap.  The digital conversion results in imprecision for low values of brilliance.  The image information is related to an instant, while the radiation data is estimated in a hourly or daily period.  The spectral response of the detector is not in the same range of that of conventional pyranometers. http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
31. 31. METHODOLOGY PHYSICAL AND STATISTICAL MODELS  The purpose of all models is the estimation of the solar global irradiation on every pixel of the image.  The existing models are classified in: physical and statistical depending of the nature of the apporach to evaluate the interaction between the solar radiation and the atmosphere.  Both types of models show similar error ranges. http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
32. 32. METHODOLOGY PHYSICAL AND STATISTICAL MODELS STATISTICAL MODELS  Based on relationships (usually statistical regressions) between pyranometric data and the digital count of the satellite.  This relation is used to calculate the global radiation from the digital count of the satellite.  Simple and easy to apply.  They do not need meteorological measurements.  The main limitations are:  The needed of ground data.  The lack of universality. http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
33. 33. METHODOLOGY PHYSICAL AND STATISTICAL MODELS PHYSICAL MODELS  Based on the physics of the atmosphere. They consider:  The absorption and scatter coefficients of the atmospheric components.  The albedo of the clouds and their absorption coefficients.  The ground albedo.  Physical models do not need ground data and are universal models.  Need atmospheric measurements. http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
34. 34. DATA BASES AND TOOLS  EUROPE  HELIOCLIM1 Y HELIOCLIM.  http://www.helioclim.net/index.html  http://www.soda-is.com/eng/index.html  ESRA (European Solar Radiation Atlas).  http://www.helioclim.net/esra/  PVGIS (Photovoltaic Gis)  http://re.jrc.cec.eu.int/pvgis/pv/  SOLEMI (Solar Energy Mining)  http://www.solemi.de/home.html  USA  National Solar Radiation Database  http://rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/tmy3  NASA  http://eosweb.larc.nasa.gov/sse/  WORLD  METEONORM.  http://www.meteotest.ch/en/mn_home?w=ber  WRDC (World Radiation Data Centre)  http://wrdc-mgo.nrel.gov/
35. 35. http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
36. 36. THE NATIONAL SOLAR RADIATION DATABASE. TMY3  The TMY3s are data sets of hourly values of solar radiation and meteorological elements for a 1-year period. Their intended use is for computer simulations of solar energy conversion systems and building systems to facilitate performance comparisons of different system types, configurations, and locations in the United States and its territories. Because they represent typical rather than extreme conditions, they are not suited for designing systems to meet the worst-case conditions occurring at a location.  rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/tmy3. http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
37. 37. STATISTICAL CHARACTERIZATION OF THE SOLAR RESOURCE  The statistical characterization of solar radiation requires long series of MEASURED data  Sunshine hours – good availability  Global horizontal (GH) – good availability  Direct Normal (DNI) – poor availability  The statistical distribution of solar radiation depends on the aggregation periods  Monthly and yearly values of global irradiation have normal distribution  The distribution of yearly values of DNI is not normal (Weibul?) http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
38. 38. SOLAR RESOURCE ASSESSMENT FOR CSP PLANTS 1. Estimate the solar resource from readily available information (expertise required!) 1 Surface measurements 1 On site 2 Nearby 2 Satellite estimates 3 Sunshine hours 4 Qualitative information 2. Set up a measurement station 1. Datalogger 2. Pyrheliometer 3. Pyranometer (global and diffuse) 4. Meteo (wind, temperature, RH) 3. Maintain the station (frequent cleaning!) http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
39. 39. SOLAR RESOURCE ASSESSMENT FOR CSP PLANTS 5. Perfom quality control of measured data 6. Compare estimates with measurements and assess solar resource (DNI, Global)  After 1 year of on-site measurements  1 year is not significant:  long term estimates should prevail  Analysis must be made by experts 7. Elaborate design year(s) from measured data  Time series -1 year- of hourly or n-minute values  Typical  P50  Pxx http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants
40. 40. THANKS FOR YOUR ATTENTION! http://www.leonardo-energy.org/csp-training-course- lesson-5-assessing-solar-resource-csp-plants