session 5 solar power

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By Manuel A. Silva Pé[email protected]

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

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 WIrradiance: 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

n I 0

(W·m-2 ·m-1)

(m)

Blackbody @ 5777 K

Extraterrestrial solar spectrumVisible

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

E ARTH’S ATMOSPHERE



























INTERACTION BETWEEN SOLAR RADIATION

AND THE E ARTH’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




























(Cloudless sky)

Absorption

%

8

100%

Air molecules

1

1 to 5

0.1 a 10

5 Dust, aerosols

Moisture0.5 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 E ARTH’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

R ANDOM 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

8001000

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)


http://rredc.nrel.gov/solar/pubs/qc_tnd/
























http://rredc.nrel.gov/solar/pubs/qc_tnd/



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

i r r a d i a n c i a s W / m 2

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

1orto

ocaso

2

dmdt

0

100

200

300

400

500

600

700

800

900

-8 -6 -4 -2 0 2 4 6 8

Ig

horas solIgcorregida

orto ocaso

22'1'

1





























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 globalhorizontal 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 ahorizontal 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

E b n

/ W · m - 2


Decomposition models (estimation of beam and diffuse



























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

K d

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

K d

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

t a se0 E E I I

a se g E I I

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/



http://www.helioclim.net/esra/

http://re.jrc.cec.eu.int/pvgis/pv/

http://www.solemi.de/home.html


http://eosweb.larc.nasa.gov/sse/



http://eosweb.larc.nasa.gov/sse/




http://www.solemi.de/home.html

http://re.jrc.cec.eu.int/pvgis/pv/

http://www.helioclim.net/esra/


































THE N ATIONAL SOLAR R ADIATION D ATABASE.

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 itsterritories. 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://localhost/var/AppData/Local/Temp/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 availableinformation (expertise required!)

1 Surface measurements1 On site

2 Nearby

2 Satellite estimates

3 Sunshine hours

4 Qualitative information

2. Set up a measurement station1. 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!
























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