CSP Training course - Lesson 5 : Assessing the Solar Resource for CSP plants

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

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

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

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

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
λnI0
⋅
(W·m-2 ·µm-1)
λ (µm)
Blackbody @ 5777 K
Extraterrestrial solar spectrum
Visible
http://rredc.nrel.gov/solar/standards/am0/wehrli1985.new.html
UV IR
THE SUN AS A BLACKBODY

Rayleigh
diffusion Mie diffusion
Beam
irradiance
Diffuse
irradiance
Albedo
irradiance
Beam
irradiance
INTERACTION BETWEEN SOLAR RADIATION AND THE
EARTH’S ATMOSPHERE

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
·µm
Extraterrestre
5777 K
In
Idh
IT
http://rredc.nrel.gov/solar/standards/am0/wehrli1985.new.html

(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

SOLAR RADIATION CHARACTERISTICS
CYCLES
 Daily
 Day – night
 Modulation of solar radiation
during the day

 Seasonal
 Modulation of solar radiation
during the year

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

GEOGRAPHY
 Cloudless sky: Solar radiation depends mainly on
latitude.

SOLAR RADIATION CAHRACTERISITICS
RANDOM COMPONENT
 Solar radiation is modulated by meteorological
conditions – CLOUDS
 Local climatic characteristics have to be taken into
account!

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 Solar
W/m
2
Global irradiance
Diffuse irradiance
Beam irradiance
Solar radiation measurement
Sunshine duration
Campbell – Stokes heliograph
Pyranometer
Shaded Pyranometer
Pyrheliometer

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

 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

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)

QUALITY CONTROL OF SOLAR RADIATION DATA
 Physically Possible Limits
 Extremely Rare Limits
 Comparisons vs other measurements
 Comparisons vs model
 Visual inspection

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

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

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.

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

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

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

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

SOLAR RADIATION ESTIMATION FROM
SATELLITE IMAGES

SOLAR RADIATION ESTIMATION FROM
SATELLITE IMAGES
 Energy balance
tase0 EEII ++=
( )aseg EII
A
I −−
−
= 0
1
1

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

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.

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.

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.

METHODOLOGY
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.

METHODOLOGY
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.

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/

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.

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?)

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!)

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

THANKS FOR YOUR ATTENTION!

CSP Training course - Lesson 5 : Assessing the Solar Resource for CSP plants

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CSP Training course - Lesson 5 : Assessing the Solar Resource for CSP plants