Fifth session of the 2nd Concentrated Solar Power Training dedicated to solar resource assessment. * DNI Variability, Frequency Distributions * Typical Meteorological Years * DNI measurements: broadband vs. spectral, and their limitations * What is circumsolar radiation and why should we care in CSP/CPV? * How much diffuse irradiance can be used in concentrators? * How to measure and model the circumsolar irradiance? * Spectral irradiance standards and their use for PV/CPV rating * The AM1.5 direct standard spectrum: Why did it change? Why AM1.5? * Use of the SMARTS radiative code to evaluate clear-sky spectral irradiances * Sources of measured spectral irradiance data * Spectral effects on silicon and multijunction cells and their dependence on climate

1. Christian A. Gueymard

2. Part 2—Overview
๏ Interannual and long-term variability in DNI
๏ Spatial variability in DNI
๏ Daily frequency distributions
๏ Typical Meteorological Year (TMY), use and abuse
๏ Resource assessment for large projects: local
measurements are important!
๏ Solar Resource Enhancement Factor (SREF)
๏ Circumsolar irradiance
๏ Spectral irradiance & SMARTS
๏ Conclusions
For more information:
http://www.SolarConsultingServices.com

3. Interannual DNI Variability (1)
There are good years and bad years in everything, particularly in DNI, due to:
Climate cycles (El Niño, La Niña…), changes in release of natural aerosols,
increase or decrease in pollution, volcanic eruptions, climate change…
For GHI, it might take only 2–3 years of measurement to be within ±5% of the long-
term mean. For DNI, it takes much longer, up to 5–15 years.
Short measurement periods (e.g. 1 year) are not sufficient for serious DNI resource
assessment!
Special techniques must be used to
correct long-term modeled data using
short-term measured data.
Eugene data: http://solardat.uoregon.edu/

4. Interannual DNI Variability (2)
Interannual variability in DNI is much higher (at least double) than that in
GHI. This variability is higher in cloudier climates (low Kn), but still
significant in clearer regions (high Kn), which are targeted by CSP/CPV.
Plots and maps provide this variability in terms of Coefficient of Variation
(COV): COV = St. Dev. / Mean
This is significant at only a 66%
probability level. For a “bankable”
95% probability, double the COV
results.
C.A. Gueymard, Fixed or tracking solar collectors?
Helping the decision process with the Solar Resource
Enhancement Factor. SPIE Conf. #7046, 2008.
S. Wilcox and C.A. Gueymard, Spatial and temporal
variability in the solar resource in the United States.
ASES Conf., 2010.
http://rredc.nrel.gov/solar/new_data/variability

5. Long-term DNI Variability (1)
Only the past DNI resource can be known with some (relative) degree of
certainty. But the goal of CSP/CPV resource assessment is to obtain
projections of 20–30 years into the future. Q: How can this be done if there are
unknown “forcings” that result in long-term trends?
Only a handful of stations in the world have measured radiation for more than
50 years. Long-term trends in GHI and DNI have been detected. Periods of
“Brightening” and “Dimming” are now documented.
GHI, 1937–2006
Potsdam,
Germany
Early brightening Dimming Brightening

6. Long-term DNI Variability (2)
Long-term trends do not affect the world equally.
Current results indicate a brightening in most of the
NH, and a dimming in the tropical regions of the
NH and SH. India and China are directly affected,
most probably because of the current increase in
coal burning and pollution (“Asian Brown Cloud”).
Trends in GHI
(% per decade)
Good news in some
areas,
bad news in others!
M. Wild et al., J. Geophys. Res. 114D, doi:10.1029/2008JD011382, 2009
M. Wild, J. Geophys. Res. 114D, doi:10.1029/2008JD011470, 2009

7. How
Long-term DNI Variability (3)
Most long-term variability results are for GHI. One difficulty is to transform
these results into DNI variability. There are regions where DNI varies more
than GHI, others where the reverse occurs.
How DNI will vary during the next 20–30 years
depends on many unknowns:
• Air quality regulations and Kyoto-type accords
• Climate change evolution
• Possible geoengineering (forced dimming)
• Volcanic eruptions, etc.
So nobody has a definite answer!
L.D. Riihimaki et al., J. Geophys. Res. 114D, doi:10.1029/2008JD010970, 2009

8. Long-term DNI Variability (4)
Main Causes Consequences
• Cloud climatology
• Emissions of black
carbon (BC) and other
aerosols
• Humidity patterns

9. Spatial DNI Variability
Spatial variability is important for two reasons:
• In regions of low spatial variability, use of low-res resource maps (e.g.,
100x100 km) might be OK, at least for preliminary design. Conversely, in
regions of high spatial variability, only hi-res maps (10x10 km or better)
should be used.
• If variability is high, measured data from only nearby weather stations
should be trusted.
5x5 matrix
10x10 km grid cells
S. Wilcox and C.A. Gueymard, Spatial and temporal
variability in the solar resource in the United States.
ASES Conf., 2010.

10. Daily Frequency Distributions
Most generally, daily frequency distributions are highly skewed. This
suggests a log-normal probability distribution, for instance. At high-DNI
sites, the most “typical” days of the year (modal value) provide much more
direct energy than the average (mean value) days of the year. This is
reversed at cloudy sites. Hence, the mean daily DNI should not be the
only indicator to use when evaluating the potential of the solar resource.
14
Daily frequencies
Alice Springs, avg 7.36 kWh/m2
12
Bermuda, avg. 3.76 kWh/m2
10
Frequency %
8
6
4
2
0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
2
Daily DNI (kWh/m )

11. Typical Meteorological Year—TMY (1)
For decades, TMYs have been used by engineers to simulate solar systems or
building energy performance. TMYs conveniently replace ≈30 years of data
with a single “typical” year. Models of solar system power output prediction
(e.g., PVWatts, http://www.nrel.gov/rredc/pvwatts/) or of performance and
economic estimates to help decision making (e.g., Solar Advisor Model,
https://www.nrel.gov/analysis/sam/) still rely heavily on TMY-type data.
To define each of the 12 months of a synthetic year, TMYs use weighting
factors to select the most “typical” year among a long series of available data
(including modeled irradiance). In the U.S., three different series of TMY files
have been produced. The weight they all used for DNI is relatively small.
It should not be construed that TMY3 is more advanced or better than TMY2!
TMY TMY2 TMY3
Period 1952–1975 1961-1990 (i) 1976–2005
(ii) 1991–2005
GHI weight 12/24 5/20 5/20
DNI weight 0 5/20 5/20
# Stations 222 239 (i) 239
(ii) 950

12. Typical Meteorological Year—TMY (2)
Q: Are TMY data appropriate for CSP/CPV applications?
TMYs have some potential drawbacks:
• DNI in TMY data is 99% modeled. At clear sites, the TMY hourly distributions
usually show discrepancies above 500 W/m2, compared to measured data. This is
due to the use of climatological monthly values (rather than discrete daily values) for
the aerosol data.
• Hourly values are used. This may not be ideal for non-linear systems with
thresholds above 150 W/m2 (see why in Pt. 1 of this webinar).
• “Non-typical” low-DNI years are excluded from the data pool. Using TMYs for risk
assessment is… risky.
Golden, CO
20 Sunup hourly frequencies
Measured
Hourly frequencies of 16 NSRDB
1991–2005 NSRDB data used TMY3
Frequency %
to obtain TMY3 for Golden, CO. 12
Compared to measurements,
note the NSRDB and TMY3 8
overestimations below
900 W/m2, and 4
underestimations above.
0
0 100 200 300 400 500 600 700 800 900 1000 1100
2
DNI bins (W/m )

13. Typical Meteorological Year—TMY (3)
To obtain “bankable” data, the use of TMYs is inappropriate. The risk of “bad
years” cannot be assessed correctly. TMY may seriously overestimate the P90
exceedance probability. Example: For Boulder, the total annual DNI from TMY2
happens to correspond to P50, but this is far from being a general rule.

14. Local Measurements
An essential part of CSP/CPV
resource assessment!
Two types of weather stations,
depending on radiometer
technology
Minimum measurement period
recommended: 1 year
Performance and prices vary…
[Ask us for more details and
custom solutions!]
These short-term
measurements should then be
used to correct long-term
satellite-based modeled data
using appropriate statistical
methods.

15. Solar Resource Enhancement Factor (1)
Q: What is the average annual resource of CSP/CPV compared to that for
other solar technologies?
For each type of concentrator, the Solar Resource Enhancement Factor
can help decide
C.A. Gueymard, Fixed or tracking solar collectors? Helping
the decision process with the Solar Resource Enhancement
Factor. SPIE Conf. #7046, 2008.

16. Solar Resource Enhancement Factor (2)
Latitude is not a good predictor for the
solar resource.
Based on the 1961–1990 NSRDB
(excluding Alaska), the minimum U.S.
resource (measured by KT or Kn) is
found at Quillayute (northern
Washington state), whereas the
maximum is found at Daggett,
California.
KT = GHI/ETHI
Kn = DNI/ETNI

17. Solar Resource Enhancement Factor (3)
Know your competition!
Flat-plate PV collectors on 2-axis trackers have a sizeable resource
advantage over CSP/CPV.
With recent smart 2-axis
trackers, the annual resource
for planar collectors may
increase another 5–15%
(depending on cloudiness).
This is severe competition…
Plots based on the SREF method

18. Circumsolar Irradiance (1)
Definition
Scattering is typically very strong around the sun, so the sky looks bright. This is
diffuse radiation that behaves like direct radiation, and can thus be concentrated.
Measurement
Circumsolar irradiance (CSI) is difficult to measure, but is possible with a
specially modified NIP. T.H. Jeys and L.L. Vant-Hull, Solar Energy 18, 343-348, 1976.
Routine measurements of DNI actually include CSI within 2.5–2.9° of the
sun center. Such data slightly overestimate the true DNI that can be used
by CSP/CPV since their concentration ratio is high and the subtended
cone is smaller (usually <1°).
The CS radiance (intensity) can be measured only with specialized
equipment. The only known current instrument to be designed for this is
SAM, which scans from the sun center to 8° from it.

19. Circumsolar Irradiance (2)
Modeling
The clear-sky CSI (up to 10°) can be modeled with SMARTS, if the
atmospheric input data is available. Below 3°, the CS effect is found
negligible under very clear conditions, but can represent up to 5% of DNI
under very hazy conditions.
C.A. Gueymard, Spectral circumsolar radiation contribution to CPV. Proc. CPV-6 Conf., Freiburg, 2010.
C.A. Gueymard, Solar Energy 71, 325-346, 2001.
http://www.solarconsultingservices.com/smarts.php
Under thin cirrus clouds, the CS effect
becomes important, but its modeling is
then difficult.
A large collection of SAM
measurements would be needed to
develop simple empirical models.
We are now trying to make such a
research project possible, in collabo-
ration with SAM’s manufacturer, as
well as U.S. and European institutions.

20. Circumsolar Irradiance (3)
Sun and Sky Radiance
• The radiance of the sun’s disc is not constant
(“limb darkening” effect). Linear scale
• The circumsolar sky radiance decreases
exponentially with radial distance
• The slope of this decrease increases with optical
depth (clear hazy thin cloud).
“Monument Valley”
analogy
Logarithmic scale Logarithmic scale

21. Circumsolar Irradiance (4)
Characteristics of CS irradiance
• The CS effect is more pronounced at shorter wavelengths, since it is
caused by scattering
• The CS/DNI fraction increases linearly with the opening angle
• It is also a function of air mass and optical depth (aerosol or cloud)
• More results to be presented at the CPV-7 conference (2011).

22. Spectral Irradiance (1)
• The direct spectrum “red shifts” when air mass (AM) increases or when aerosol
turbidity (AOD) increases
• Below 700 nm, atmospheric extinction is dominated by scattering
• Above 700 nm, it is dominated by absorption (water vapor, CO2…)
• Reference AM1.5 spectra have been standardized by ASTM: E891 (1987) and
G173 (2003). The latter was specially designed for CPV.
C.A. Gueymard et al., Solar Energy 73, 443-467, 2002.

23. Spectral Irradiance (2)
• Routine spectral measurements are difficult and costly
• Spectral modeling is possible with various existing codes, e.g., SMARTS
• SMARTS was used to develop ASTM G173 and other standards (IEC)
• SMARTS is commonly used tool to evaluate spectral effects in PV and
CPV, and offers compatibility with current standards
• All PV cells have a strong spectral selectivity. SMARTS can be used to
evaluate spectral mismatch correction factors, or the output of multijunction
(MJ) cells under variable spectral conditions.
MJ: 41% eff., for HCPV
c-Si: 22% eff., for LCPV
4 kW, 3 suns
JX Crystals

24. Spectral Irradiance (3)
t2 t2
Daily-average direct spectrum: Ednλ = ∑ Enλ (t)En (t) / ∑ En (t)
t1 t1
4000 4000
−1 −1
Daily Spectral Enhancement Factor: DSEF = [Edn ∫E dnλ Sλ dλ ]/[Esn ∫E snλ Sλ dλ ]
280 280
€
C.A. Gueymard, Daily spectral effects on concentrating PV solar cells as affected by realistic aerosol optical
depth and other atmospheric conditions. SPIE Conf. #7410, 2009.
A.L. Dobbin et al., How important is the resolution of atmospheric data in calculations of spectral irradiance
€
and energy yield for (III-V) triple-junction cells? CPV-6 Conf., 2010.

25. Spectral Irradiance (4)
It is found that, for any type of solar cell, the spectral effect is a strong
function of AOD.
One goal of the current R&D is to “fine tune” MJ cells by optimizing their
bandgap combinations as a function of the regional “average” spectrum.
This might result in significant increases in the annual energy output.
Cirrus clouds appear to affect the performance of CPV modules, but it
is unclear if it’s because of spectral or circumsolar effect (or both).
G. Peharz et al., Evaluation of satellite cirrus data for
performance models of CPV modules. CPV-6 Conf.,
2010.

26. Conclusions (2)
 The DNI solar resource is highly variable and difficult to model using past
data. Projecting it 20–30 years into the future is even more difficult.
 Local radiation measurements are still the best source of data, and are
necessary to derive the bankable data needed for big projects. However,
the type of radiometer should be selected properly, its limitations known,
and appropriate maintenance provided.
 If local DNI measurements are available for only a short period (less than 5
years), they should be used in conjunction with long-term modeled data to
obtain “locally adjusted” time series spanning at least 10 years.
 The use of TMY data is not recommended, particularly for a non-linear
operation (startup threshold). In that case, sub-hourly time series are ideal.
 Circumsolar and spectral effects have second-order importance, but should
still be studied for better simulation, and possible fine tuning of CPV cells.
 The benefit of a larger circumsolar contribution to LCPV systems cannot be
evaluated yet.
 Because of the lack of high-quality measured DNI data in the public
domain, the science of resource assessment progresses only slowly.

27. Thank you!