concentrated solar power course - session 5 - solar resource assessment
DESCRIPTION
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 - Solar radiation databases - Statistical characterisation of the solar resource. Typical meteorological years - Solar resource assessment for csp plantsTRANSCRIPT
By Manuel A. Silva Pé[email protected]
May 5, 2010
Concentrated Solar Thermal PowerTechnnology Training
Session 5 – SOLAR RESOURCE ASSESSMENT FOR CSP PLANTS
http://www.leonardo-energy.org/csp-training-course-5-lessons
SOLAR RESOURCE ASSESSMENT FOR CSP PLANTS
Manuel A. Silva PérezGroup of Thermodynamics and Renewable EnergyETSI – University of Seville
http://www.leonardo-energy.org/csp-training-course-lesson-5-assessing-solar-resource-csp-plants
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
UNDERSTANDING THE SOLAR RESOURCE
FOR CSP PLANTS
The Sun as an energy source
Mass:1,99
x 1030 kgDiameter:
1,392 x 109 mArea:
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
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
nI 0
(W·m-2 ·m-1)
(m)
Blackbody @ 5777 KExtraterrestrial 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
Rayleighdiffusion Mie diffusion
Beam irradiance
Diffuse irradiance
Albedo irradiance
Beam irradiance
INTERACTION BETWEEN SOLAR RADIATION AND THE EARTH’S ATMOSPHERE
http://www.leonardo-energy.org/csp-training-course-lesson-5-assessing-solar-resource-csp-plants
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
m
Extraterrestre
5777 K
In
Idh
IT
http://rredc.nrel.gov/solar/standards/am0/wehrli1985.new.htmlhttp://www.leonardo-energy.org/csp-training-course-lesson-5-assessing-solar-resource-csp-plants
(Cloudless sky)
Absorption%
8
100%
Air molecules1
1 to 5
0.1 a 10
5Dust, 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
SOLAR RADIATION CHARACTERISTICSCYCLES
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
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
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
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
Meteorological Station at the Seville Engineering School (since 1984)
Solar radiation measurement
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 SolarW
/m2
Global irradiance
Diffuse irradiance
Beam irradiance
Solar radiation measurement
Sunshine duration
Campbell – Stokes heliograph
Pyranometer
Shaded Pyranometer
Pyrheliometerhttp://www.leonardo-energy.org/csp-training-course-lesson-5-assessing-solar-resource-csp-plants
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
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
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
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
FILTER 5: VISUAL INSPECTION
0
200
400
600
800
1000
1200
1400
-8 -6 -4 -2 0 2 4 6 8
hora solar
irra
dia
nci
as W
/m2
IDmedida
ig
id
http://www.leonardo-energy.org/csp-training-course-lesson-5-assessing-solar-resource-csp-plants
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
t1torto
tocaso
t2
dmdt
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
t2t2't1'
t1
http://www.leonardo-energy.org/csp-training-course-lesson-5-assessing-solar-resource-csp-plants
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
ESTIMATION OF DAILY OR MONTHLY GLOBAL HORIZONTAL IRRADIATION FROM SUNSHINE DURATION
Angstrom – type formulasH/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
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
Eb
n /
W·m
-2
http://www.leonardo-energy.org/csp-training-course-lesson-5-assessing-solar-resource-csp-plants
Daily or hourly global horizontal irradiation values
0.00.20.40.60.81.0
0 0.2 0.4 0.6 0.8 1Kt
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
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
SOLAR RADIATION ESTIMATION FROM SATELLITE IMAGES
http://www.leonardo-energy.org/csp-training-course-lesson-5-assessing-solar-resource-csp-plants
SOLAR RADIATION ESTIMATION FROM SATELLITE IMAGES
Energy balance
tase0 EEII
aseg EIIA
I
011
http://www.leonardo-energy.org/csp-training-course-lesson-5-assessing-solar-resource-csp-plants
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-lesso
n-5-assessing-solar-resource-csp-plants
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
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
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
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
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
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/
http://www.leonardo-energy.org/csp-training-course-lesson-5-assessing-solar-resource-csp-plants
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
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
SOLAR RESOURCE ASSESSMENT FOR CSP PLANTS
1. Estimate the solar resource from readily available information (expertise required!)
1 Surface measurements1 On site2 Nearby
2 Satellite estimates3 Sunshine hours4 Qualitative information
2. Set up a measurement station 1. Datalogger2. Pyrheliometer3. 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
SOLAR RESOURCE ASSESSMENT FOR CSP PLANTS
5. Perfom quality control of measured data6. 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
THANKS FOR YOUR ATTENTION!
http://www.leonardo-energy.org/csp-training-course-lesson-5-assessing-solar-resource-csp-plants