Christian A. Gueymard

๏  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

Part 2—Overview

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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.

Interannual DNI Variability (1)

Eugene data: http://solardat.uoregon.edu/

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.

Interannual DNI Variability (2)

http://rredc.nrel.gov/solar/new_data/variability

S. Wilcox and C.A. Gueymard, Spatial and temporal variability in the solar resource in the United States. ASES Conf., 2010.

C.A. Gueymard, Fixed or tracking solar collectors? Helping the decision process with the Solar Resource Enhancement Factor. SPIE Conf. #7046, 2008.

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.

Long-term DNI Variability (1)

Early brightening Dimming Brightening

GHI, 1937–2006 Potsdam, Germany

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”).

Long-term DNI Variability (2)

M. Wild et al., J. Geophys. Res. 114D, doi:10.1029/2008JD011382, 2009 M. Wild, J. Geophys. Res. 114D, doi:10.1029/2008JD011470, 2009

Trends in GHI (% per decade)

Good news in some areas, bad news in others!

How

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.

Long-term DNI Variability (3)

L.D. Riihimaki et al., J. Geophys. Res. 114D, doi:10.1029/2008JD010970, 2009

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!

Main Causes Consequences

Long-term DNI Variability (4)

• Cloud climatology • Emissions of black

carbon (BC) and other aerosols

• Humidity patterns

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.

Spatial DNI Variability

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.

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.

Daily Frequency Distributions

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Daily frequenciesAlice Springs, avg 7.36 kWh/m2

Bermuda, avg. 3.76 kWh/m2

Freq

uenc

y %

Daily DNI (kWh/m2)

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!

Typical Meteorological Year—TMY (1)

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

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.

Typical Meteorological Year—TMY (2)

Hourly frequencies of 1991–2005 NSRDB data used

to obtain TMY3 for Golden, CO. Compared to measurements,

note the NSRDB and TMY3 overestimations below

900 W/m2, and underestimations above.

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Golden, COSunup hourly frequencies

MeasuredNSRDBTMY3

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DNI bins (W/m2)

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.

Typical Meteorological Year—TMY (3)

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.

Local Measurements

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

Solar Resource Enhancement Factor (1)

C.A. Gueymard, Fixed or tracking solar collectors? Helping the decision process with the Solar Resource Enhancement Factor. SPIE Conf. #7046, 2008.

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

Solar Resource Enhancement Factor (2)

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…

Solar Resource Enhancement Factor (3)

Plots based on the SREF method

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.

Circumsolar Irradiance (1)

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.

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.

Circumsolar Irradiance (2)

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

Sun and Sky Radiance • The radiance of the sun’s disc is not constant

(“limb darkening” effect). • The circumsolar sky radiance decreases

exponentially with radial distance • The slope of this decrease increases with optical

depth (clear hazy thin cloud).

Circumsolar Irradiance (3)

Linear scale

Logarithmic scale

“Monument Valley” analogy

Logarithmic scale

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).

Circumsolar Irradiance (4)

• 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.

Spectral Irradiance (1)

C.A. Gueymard et al., Solar Energy 73, 443-467, 2002.

• 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.

Spectral Irradiance (2)

MJ: 41% eff., for HCPV

c-Si: 22% eff., for LCPV

4 kW, 3 suns

JX Crystals

Daily-average direct spectrum:

Daily Spectral Enhancement Factor: DSEF

Spectral Irradiance (3)

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.

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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).

Spectral Irradiance (4)

G. Peharz et al., Evaluation of satellite cirrus data for performance models of CPV modules. CPV-6 Conf., 2010.

 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.

Conclusions (2)

Thank you!