NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

An Investigation into Spectral 

Parameters as they Impact CPV 

Module Performance 

Matthew Muller, Bill Marion, Sarah Kurtz, 

and Jose Rodriguez

Presented at the 6th International Conference on Concentrating 

Photovoltaic Systems (CPV-6)

Freiburg, Germany

April 7-10, 2010

Conference Paper

NREL/CP-5200-47959

March 2011
NOTICE

The submitted manuscript has been offered by an employee of the Alliance for Sustainable Energy, LLC 

(Alliance), a contractor of the US Government under Contract No. DE-AC36-08GO28308. Accordingly, the US 

Government and Alliance retain a nonexclusive royalty-free license to publish or reproduce the published form of 

this contribution, or allow others to do so, for US Government purposes.

This report was prepared as an account of work sponsored by an agency of the United States government. 

Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, 

express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of 

any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately 

owned rights. Reference herein to any specific commercial product, process, or service by trade name, 

trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, 

or favoring by the United States government or any agency thereof. The views and opinions of authors 

expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.

Available electronically at http://www.osti.gov/bridge

Available for a processing fee to U.S. Department of Energy

and its contractors, in paper, from:

U.S. Department of Energy

Office of Scientific and Technical Information

P.O. Box 62

Oak Ridge, TN 37831-0062

phone: 865.576.8401

fax: 865.576.5728

email: mailto:reports@adonis.osti.gov

Available for sale to the public, in paper, from:

U.S. Department of Commerce

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22161

phone: 800.553.6847

fax: 703.605.6900

email: orders@ntis.fedworld.gov

online ordering: http://www.ntis.gov/help/ordermethods.aspx

Cover Photos: (left to right) PIX 16416, PIX 17423, PIX 16560, PIX 17613, PIX 17436, PIX 17721

Printed on paper containing at least 50% wastepaper, including 10% post consumer waste.
1 

An Investigation into Spectral Parameters as they Impact 

CPV Module Performance 

Matthew Muller, Bill Marion, Sarah Kurtz, Jose Rodriguez

National Renewable Energy Laboratory, Golden, CO, 80401 USA, matthew.muller@nrel.gov

Abstract: The CPV industry is well aware that performance of triple junction cells depends on spectral conditions but there 

is a lack of data quantifying this spectral dependence at the module level. This paper explores the impact of precipitable water 

vapor, aerosol optical depth (AOD), and optical air mass on multiple CPV module technologies on-sun in Golden, CO.

Keywords: Solar Concentrators, Spectral, Precipitable Water Vapor, Aerosol Optical Depth

PACS: 80, 88 

INTRODUCTION

It is well known that triple junction CPV cell 

performance depends on spectral conditions, and, 

therefore, atmospheric conditions such as aerosol optical 

depth (AOD), precipitable water vapor (PWV), and 

optical air mass (AM) can have an impact on cell and 

module performance [1]. Although improved models are 

needed to accurately predict energy production for CPV 

systems, it is not clear what, if any, spectral parameters 

should be included in these models. As spectral data is 

not as readily available, one must justify requiring this 

data by a significant increase in model accuracy. To 

further compound this issue, CPV system technology 

varies significantly, and it is probable that spectral data 

could improve energy predictions for one technology but 

have little impact on alternate technology.

This paper explores the impact of AOD (at 500 nm) 

PWV, and optical AM on module performance, in 

conjunction with the SMARTS spectral model [2], cell 

quantum efficiency (QE) data (InGaP/InGaAs/Ge 

optimized for a G173/AM 1.5 spectrum), and spectral 

measurements from a PG S-100 Direct Normal Spectral 

Radiometer. Integrating the QE data with both modeled 

and measured spectra provides a means to predict 

changes in module current as AOD, PWV, and optical 

AM change.

This paper proceeds as follows: First, spectral 

sensitivity in CPV modules is examined by analyzing 

short circuit current, Isc, divided by direct normal 

irradiance, DNI, as spectral parameters change. Percent 

changes in Isc/DNI are presented from both a modeling 

perspective and using module data. CPV module 

performance is then presented through metrics such as 

the PVUSA rating, (ASTM E-2527 [3]), performance 

ratio, PR, [4], and DNI weighted module efficiency. 

Deviations in annual energy production are predicted 

based on module level spectral sensitivity as quantified 

through an optical AM based correction factor. Finally 

conclusions are drawn and future work is discussed.

SPECTRAL SENSITIVITY OF ISC/DNI

It is useful to begin an analysis of the spectral 

sensitivity of CPV modules by briefly mentioning 

characteristics of an InGaP/InGaAs/Ge cell in relation to 

the solar spectrum and key spectral parameters. The 

InGaP (top junction) is generally responsive in the range 

of 350-700 nm, the InGaAs (middle junction) from 500-

1000 nm, and the Ge (bottom junction) from 800-1800 

nm. The three junctions are wired in series and therefore 

the cell operates based on the junction that is generating 

the least current. Manufactures typically design cells 

such that the top and middle junctions alternate in the 

role of limiting current while the bottom junction always 

has surplus current. The top and middle junctions are 

grown to be current matched for the G173/AM1.5 direct 

reference spectrum, a design point that is suggested for 

optimum energy production at many CPV-appropriate 

sites [5].

The spectrum is very dynamic, and changes in optical 

AM, PWV, and AOD all impact CPV performance. 

Optical AM, the relative path length through the 

atmosphere, varies from 1 to 5 when CPV produces the 

majority of its energy. As optical AM increases, there is 

an increased Rayleigh scattering of blue light and the top 

junction predominantly loses current. PWV, cm of 

condensed water vapor in the vertical direction, absorbs 

solar radiation in bands around 720, 820, 940, 1100, 

1380, 1870, and 3200 nm. Absorption bands 

corresponding to the bottom junction are the strongest, 

but because the bottom junction does not limit this 

device’s performance, PWV has little effect on cell 

performance. However, absorption by water vapor does 

decrease the measured DNI. Consequently, as PWV 

increases, DNI decreases more than power, resulting in 
2 

an increase in efficiency. It is interesting to note that 

changes in PWV have stronger impact in dry climates 

due to the fact that an increase from 0 to 0.4cm of PWV 

decreases solar radiation by 10% while an increase from 

0.4 cm to 4 cm is required to decrease radiation another 

10% [6]. AOD measurements quantify the number of 

particles/aerosols in the vertical direction that result in 

radiation attenuation in the range of 400 to 2000 nm. 

The rate of attenuation decreases with wavelength but is 

a complex function of quantity and size distribution of 

particles [6]. An increase in AOD typically results in 

the top junction decreasing in current more than the 

middle junction.

SMARTS is a readily available tool that can be used 

to predict the effects of changing optical AM, PWV, and 

AOD on triple junction cell performance. SMARTS 

v2.9.5 is used in its default settings with the exception of 

a pressure of 840 mB and altitude of 1.6km for Golden, 

CO. Figure 1 displays a family of curves that predicts 

how Isc/DNI changes as optical AM and PWV vary with 

AOD fixed at 0.084. Figure 2 predicts how Isc/DNI 

changes as optical AM and AOD vary with PWV fixed 

at 1.4cm. The curves in Figures 1 and 2 were generated 

by integrating each SMARTS spectrum with QE data 

from the top two junctions of a G173 optimized cell. 

The lesser of the two currents is then taken and a percent 

change was calculated based on the current predicted for 

optical AM1.5, PWV of 1.4 cm, and an AOD of 0.084. 

This procedure neglects the effective transfer of 

photocurrent to lower junctions when excess 

photocarriers recombine radiatively in an upper junction.

Figures 1 and 2 give an indication of how much 

Isc/DNI can change as the spectral conditions swing

from one extreme to the other, but this is somewhat 

misleading as many CPV sites are unlikely to see these

entire ranges. In Golden, Colorado (from 5/1/2009 to 

2/1/2010) the bulk of PWV measurements have fallen in 

the range of 0.3 to 2.5 cm, while AOD has 

approximately ranged from 0.02 to 0.2.

FIGURE 1. As PWV increases, Isc/DNI increases. Isc/DNI 

increase is stronger on the right side of the AM response peak

(top junction limited). If PWV increases from 0 to 0.4 cm, 

Isc/DNI increases 10% while an increase in PWV from 0.4 to 4 

cm results in a similar 10-12% increase in Isc/DNI.

FIGURE 2. When to the right of the AM response peak (top

junction limited), Isc/DNI increases as AOD decreases. When 

to the left of the AM response peak (middle junction limited), 

AOD change has little impact on cell performance.

Figure 3 plots measurements taken for a module from 

May, 2009 to February, 2010 in Golden, CO. The 

dashed lines in Fig. 3 are the upper and lower boundaries 

of change in Isc/DNI as predicted by SMARTS 

integrated with cell QE data adjusted to 57C, (average 

cell temperature for data set in Fig. 3).

FIGURE 3. Module data taken at Golden, CO. Dashed lines 

represent predicted boundaries for data using SMARTS, cell 

QE @ 57C, and measured ranges for PWV, and AOD. 

Although modeling provides a reasonable 

approximation for the boundaries to the 9-month data 

set, using the same approach to predict changes to 

Isc/DNI over the course of a sunny day is more 

problematic. Figure 4 presents Isc/DNI and AOD data 

for 8/20/2009 with an overlay of predictions using 

SMARTS spectra integrated with cell QE information.

The data for the module shows that the Isc/DNI is rising 

in the early morning, peaks at about AM2.5, and then 

declines until AM1 or solar noon. If spectral conditions 

and other meteorological conditions remained constant, 

it would be expected that the Isc/DNI would retrace the 

same curve for the second half of the day. In fig.4, there 

is a shift of the curve near solar noon and at the same 

time AOD increases from 0.1 to 0.15. Although it is not 
3 

shown on fig.4, the cell temperature has also climbed 

approximately 5-10C and about 2 hours before noon the 

PWV dropped from 1.8 to 1.5 cm. The solid line 

represents the predicted Isc/DNI for the morning 

conditions and the dashed line for afternoon conditions. 

The 5-10C cell temperature change has been neglected in 

the prediction and potentially could introduce a 1% 

change in Isc/DNI.

FIGURE 4. Isc/DNI (left axis) and AOD (right axis) data for 

8/20/2009. The solid line is predicted change in Isc/DNI for 

morning spectral conditions while the dashed line represents 

afternoon spectral conditions.

The data and predictions in fig.4 trigger many 

questions. First, why is the module Isc/DNI peaking 

near AM2.5 while the cell prediction suggests it should 

occur between AM1.4 and AM1.7. In further exploring 

this question, 28 individual sunny days were examined 

across the entire data set presented in fig.3. For these 28 

days Isc/DNI peaked in a range from AM2.4 to AM3.4, 

with most days also presenting a shift in the AM peak as 

spectral conditions varied from morning to afternoon. 

The magnitude of this range (1AM unit) is consistent 

with the magnitude between the peaks of the dashed 

lines shown in fig.3 (AM1.2 to AM2.2). Across the 28 

days, there is also a rightward shift of about 1.2 AM 

units from the modeled to the measured peak. It is 

speculated that module optics disproportionately filter 

red light, hence the module optical AM peak is to the 

right of the predicted peak for bare cells. Discussions 

with the manufacturer confirm they have seen a similar 

red shift and have since modified optics in the latest 

production module.

Module optics may explain some of the lack of 

alignment from left to right between data and prediction, 

but the model completely fails to explain the increase in 

the Isc/DNI around solar noon as AOD increases. 

Looking closer at the module data for the time around 

solar noon, Isc is increasing while DNI is decreasing. 

This suggests that the increase in AOD is decreasing 

DNI, (dominating over the PWV decrease), in the blue 

region but not reducing Isc as the cell is middle junction 

limited. At the same time the PWV decrease can allow 

more light to the middle cell due to bands at 720, 820, 

and 940 nm. This possible increase in light to the middle 

cell is additive with an increase in Isc as cell temperature 

rises. 

Data from a spectral radiometer provides another 

means to predict Isc/DNI for 8/20/2009. Rather than 

integrating the cell QE with SMARTS spectra it can be 

integrated with the measured spectra. Isc/DNI data is 

again plotted in fig.5 but this time against predictions 

using the measured spectra.

FIGURE 5. Module data plotted with Isc/DNI predicted using 

measured spectra, cell QE, and measured DNI

Calculations for Isc/DNI using the measured spectra 

clearly predict the direction of the shift at solar noon but 

with a decreased magnitude. The decreased magnitude 

could be due to neglected changes in cell temperature 

and effects from the module optics. The calculation 

using SMARTS spectra could have failed to predict the 

shift at noon for several reasons. First, the model 

requires various spectral inputs of which only PWV and 

AOD were varied. As with any model, the outputs are 

only as good as the inputs. In this case the differences in 

PWV and AOD seen in the morning and afternoon are in 

the same range as the uncertainties for these 

measurements. To compound this further, AOD

modeling is very complex and model uncertainty or 

model choice can introduce error comparable to the 2% 

shift in Isc/DNI near solar noon.

MODULE PERFORMANCE

CPV modules are built to generate energy and 

therefore studying the spectral sensitivity of Isc is only a 

step in the process. Although studying Isc provides a 

means to avoid the voltage temperature dependence, care 

must be taken in applying Isc results to module 

efficiency. It has been documented in the literature that 

a decrease in Isc/DNI has less than a 1:1 relationship 

with efficiency [7]. This reduced effect on efficiency is 

due to fill factor, FF, increasing as current mismatch 

increases in multi-junction cells. This relationship has 

Morning

Afternoon
4 

been documented at the cell level and is confirmed for 

three distinct modules shown in fig.6. The data in fig.6 

comes from modules that use both refractive and 

reflective optics and the number of cells in series varies 

widely.

FIGURE 6. (top) %change in ISC/DNI for 3 distinct modules 

(bottom) %change in FF for the same modules. ISC/DNI 

peaks as FF reaches a minimum. Data has been shifted in the 

vertical direction for readability.

To quantify the impact of spectral variations, a 

simple approach is used to examine module 

performance. Monthly data sets are analyzed for three 

modules that have been on-sun for multiple months in 

Golden, CO. Table 1 presents the PVUSA rating, the 

performance ratio, and the DNI weighted efficiency, 

(ΣηDNI/ΣDNI), for each month. The variation in these 

monthly metrics provides some gauge of performance 

spectral sensitivity. Golden, CO can have summer 

months with averages of 1.9 cm PWV and 0.135 AOD, 

while winter averages are near 0.45 cm PWV and 0.075 

AOD. Keep in mind that these modules have not been 

on sun long enough to rule out degradation mechanisms. 

There is approximately a 5-10% variation from the high 

months to the low months for the metrics given in table 

1. This is lower than the 15% variation in Isc/DNI 

shown in fig.3 and is consistent with the relationship 

between Isc/DNI and FF as shown in fig.6.

TABLE 1. Monthly PVUSA power rating (Watts), DNI 

weighted efficiency (η), and performance ratio (P.R.) for 3 

distinct module technologies on-sun at Golden, CO

ENERGY COMPARISON

An effort was made to model the efficiency of the 

three modules in Table 1 based on PWV, optical AM, 

AOD, and cell temperature. With more information 

needed to calculate cell temperature for two of the 

modules, and the uncertainties in the PWV and AOD

measurements, a simple approach was taken using only 

the optical AM correction in equation 1.

Rated Efficiency –a|AM-b| (1) 

This simple optical AM correction was fit to nine 

months of data for each of the modules and then used in 

an annual energy comparison. Table 2 contains the AM 

corrections for the three modules, but assumes each 

module has the same baseline efficiency. This provides 

a means to compare energy production differences from 

spectral sensitivity alone.

TABLE 2. Annual energy comparison using optical AM based 

spectral sensitivity from 3 module data sets

2009 M1

25% -

1.6|AM-1.7| 

M2

25% -

1|AM-2.8| 

M3

25% -

0.95|AM-2.5| 

Fixed

25% 

Q1 

kwh 

118.1 120.7 121.7 127.4

Q2 

kwh 

113.7 113.2 114.5 120.5

Q3

kwh 

130.4 130.0 131.5 138.3

Q4

kwh

97.8 102.0 102.6 107.3

total

kwh

460.05 465.95 470.3 493.5

The energies shown in Table 2 were calculated using the 

optical AM corrections, the 2009 one-minute average 

DNI from NREL, and a module area of one square 

meter. The results show that there is approximately a 

7% difference in energy produced from a fixed 

efficiency and module 1 and about 2.2% difference 

between module 1 and 3.

May Jun Jul Aug Sep Oct Nov Dec Jan

M

1 

Watts 69.5 71.3 72.0 72.5 72.0 69.9 68.7 70.9 67.9

η 23.7 24.4 24.8 25.0 24.9 24.4 23.5 21.7 22.3

P.R. .928 .958 .972 .982 .975 .957 .922 .853 .873

M

2 

Watts 77.2 78.4 78.9 79.3 82.1 79.1 78.1 81.1 75.9

η 18.8 19.4 19.2 19.0 19.4 19.6 19.0 19.3 19.3

P.R. .944 .974 .961 .951 .972 .981 .952 .965 .968

M

3 

Watts 111 110 110 107 107 105 104 106 99.5

η 22.1 21.9 21.8 24.5 21.4 21.4 20.8 20.5 20.5

P.R. .987 .979 .973 .960 .958 .957 .930 .919 .919
5 

CONCLUSIONS

This work examines the spectral sensitivity of CPV 

modules with triple-junction cells on-sun for multiple 

months in Golden, CO. The SMARTS model is used 

with QE data, optical AM, PWV, and AOD

measurements to predict variation in Isc/DNI over 9 

months. Ultimately this approach provides a reasonable 

estimate of long term variation in Isc/DNI. When 

SMARTS is used in the same approach to predict intraday changes the results are less useful. This is likely due 

to small intra-day changes in PWV and AOD being 

within the measurement uncertainty of these parameters 

coupled with uncertainties and assumptions made when 

applying the SMARTS model. At the same time, using 

measured spectra rather than SMARTS spectra, intra-day 

modeling indicated the correct movements in Isc/DNI 

but with a reduced magnitude. 

In examining CPV module performance, data show 

clearly defined AM response peaks for Isc/DNI and 

efficiency while confirming that FF is minimized when 

Isc/DNI peaks. The module metrics, PVUSA power 

ratings, Performance Ratio, and DNI weighted 

efficiency, all show monthly variations on the order of 5-

10%, while Isc/DNI shows variation of 15%. In a simple 

approach, the overall spectral sensitivity of 3 modules is 

quantified by fitting an AM correction factor to 9 months 

of data and then applying these correction factors in a 

yearly energy prediction. Ultimately, the AM correction 

factor results in a 7% drop in annual energy over a fixedefficiency module, and 2% difference between CPV 

modules with the differing spectral sensitivity.

Future work calls for continuing to gather module 

data as a measure against degradation and to verify that 

the spectral sensitivities suggested in this report are 

repeatable. As more spectral radiometer data becomes 

available, this will be used to further investigate the 

spectral parameters discussed in this report. Eventually, 

it is hoped that this work can be expanded to compare 

CPV module data to system level data.

ACKNOWLEDGMENTS

This work was completed under contract # DEAC36-08GO28308. 

REFERENCES

1. C. Gueymard, “Daily Spectral Effects on Concentrating PV 

Solar Cells as Affected by Realistic Aerosol Optical Depth 

and Other Atmospheric Conditions”, SPIE Conf, San 

Diego, CA (2009)

2. C. Gueymard, SMARTS (Simple Model of Atmospheric 

Radiative Transfer of Sunshine, Version 2.9.5 (2008)

3. E-2527-06, “Standard Test Method for Rating Electrical 

Performance of Concentrator Terrestrial Photovoltaic 

Modules and Systems Under Natural Sunlight”, ASTM 

Int., West Conshohocken, PA (2006)

4. B. Marion, J. Adelstein, K. Boyle, H. Hayden, B. 

Hammond, T. Fletcher, B. Canada, D. Narang, A. Kimber, 

L. Mitchell, G. Rich, T. Townsend, “Performance 

Parameters for Grid-Connected PV Systems”, IEEE (2005)

5. W. McMahon, S. Kurtz, K. Emery, M. Young, “Criteria 

for the Design of GaInP/GaAs/Ge Triple-Junction Cells to 

Optimize Their Performance Outdoors”, Golden, CO: 

National Renewable Energy Laboratory Report No. CP520-33554. 

6. M. Iqbal, An Introduction to Solar Radiation, New York: 

Academic Press, 1983, pp. 107-139. 

7. P. Faine, S. Kurtz, C. Riordian, and J. Olson, “The 

Influence of Spectral Solar Irradiance Variations on the 

Performance of Selected Single-Junction and Multijunction 

Solar Cells”, Solar Cells, 31, 259-278 (1991)
F1147-E(10/2008) 

REPORT DOCUMENTATION PAGE Form Approved

OMB No. 0704-0188

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, 

gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this 

collection of information, including suggestions for reducing the burden, to Department of Defense, Executive Services and Communications Directorate (0704-0188). Respondents 

should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a 

currently valid OMB control number.

PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION.

1. REPORT DATE (DD-MM-YYYY)

March 2011

2. REPORT TYPE

Conference Paper

3. DATES COVERED (From - To)

 

4. TITLE AND SUBTITLE

An Investigation into Spectral Parameters as they Impact CPV 

Module Performance

5a. CONTRACT NUMBER

DE-AC36-08GO28308

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

M. Muller, B. Marion, S. Kurtz, and J. Rodriguez

5d. PROJECT NUMBER

NREL/CP-5200-47959

5e. TASK NUMBER

PVA9.1410

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Renewable Energy Laboratory

1617 Cole Blvd.

Golden, CO 80401-3393

8. PERFORMING ORGANIZATION

REPORT NUMBER

NREL/CP-5200-47959

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)

NREL

11. SPONSORING/MONITORING

AGENCY REPORT NUMBER

12. DISTRIBUTION AVAILABILITY STATEMENT

National Technical Information Service

U.S. Department of Commerce

5285 Port Royal Road

Springfield, VA 22161

13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

The CPV industry is well aware that performance of triple junction cells depends on spectral conditions but there is a 

lack of data quantifying this spectral dependence at the module level. This paper explores the impact of precipitable 

water vapor, aerosol optical depth (AOD), and optical air mass on multiple CPV module technologies on-sun in 

Golden, CO.

15. SUBJECT TERMS

Solar Concentrators; Spectral; Precipitable Water Vapor; Aerosol Optical Depth

16. SECURITY CLASSIFICATION OF: 17. LIMITATION 

OF ABSTRACT

UL

18. NUMBER 

OF PAGES

19a. NAME OF RESPONSIBLE PERSON

a. REPORT

Unclassified

b. ABSTRACT

Unclassified

c. THIS PAGE

Unclassified 19b. TELEPHONE NUMBER (Include area code)

Standard Form 298 (Rev. 8/98)

Prescribed by ANSI Std. Z39.18