High-Confidence Prediction of Energy Production
From High-Efficiency Photovoltaic Systems
by Doug Rose, Oliver Koehler,
Ben Bourne, David Kavulak,
and Lauren Nelson
2010
EXECUTIVE SUMMARY
This paper discusses four interrelated topics regarding energy production from photovoltaic systems and
prediction of that energy production. Together these topics support high kWh/kWp performance of
SunPower modules and accurate prediction of that performance across a wide range of applications.
The first topic is an analysis of attributes which influence energy production from SunPower modules
relative to other technologies. Differences which contribute to high kWh/kWp include reduced operating
temperature resulting from high efficiency and infrared reflectance and transmittance, a power temperature
coefficient of -0.38%/°C, no light-induced degradation, and increased efficiency at high air-mass; these
benefits are partially offset by the effect of a low module series resistance. Low yearly degradation is
expected to further increase the kWh/kWp advantage. Second, a meta-analysis of 10 independent
field studies was conducted and the methodology for the analysis is discussed; the results support the
physics-based estimate of kWh/kWp advantage vs. standard c-Si and thin films. Third, a description
of the SunPower approach to simulation for energy prediction is provided, with results showing a
standard deviation in the error of the energy prediction across a broad range of mounting conditions
and locations to be l.3%. Lastly, some of the commercial energy prediction models are discussed.
EXECUTIVE SUMMARY
2
INTRODUCTION
SECTION 1
The value of a photovoltaic (PV) system is largely determined by its energy production over the life of the system,
and the level of confidence in the prediction of that production has a large impact on the rates and availability
of financing for the system. This paper provides an analysis of key attributes of modules which influence energy
production, a meta-analysis of independent field tests, and the methodology and error analysis for the energyprediction tool used by SunPower. Together, they suggest high performance of SunPower systems and low-error
prediction of that performance.
Understanding the physics of energy production
for a technology can be used to improve
prediction accuracy by:
1. identifying attributes capable of affecting field
performance which are not captured by standard
module response characterization or limited field
testing
2. Identifying errors in characterization or test
3. Identifying the need to redo characterization and
adjust or discard previous field data as the product
changes. An analysis of the characteristics which
influence energy production, with a focus on factors
which impact high efficiency modules relative to
other technologies, is provided in section 2.
Field testing is an essential element of accurate
prediction. In addition to the use of field results
to improve the absolute value of prediction from
simulation, comparison studies can be used to help
customers and companies with technology options
optimize their projects. Section 3 provides data from
the independent field tests which included SunPower
modules, with discussion of these tests and field test
analysis in general.
Sections 2 and 3 focus on energy production per
rated peak Watt (kWh/kWp), but readers should be
aware that nominal efficiency has the largest impact
on energy production, with common module efficiency
ranging from 4 to 20% (i.e., 12% ±67%). The range
of climates and mounting approaches has a similar
impact. In contrast, variation in kWh/kWp is typically
< ±5% across a broad range of module technologies
in identical installed conditions (not including modules
with substandard quality or degradation) [1].
The main tool used by SunPower for accurate prediction
of energy is a proprietary PV simulation tool (PVSim).
At the core of the PVSim tool is the Sandia Performance
Model [2] which provides the robust approach to
modeling module response under a broad range
of ambient conditions. A description of PVSim, the
validation approach used by SunPower, and validation
results are provided in section 4. Discussion of some
other models is provided in section 5.
1. S. Ransome, Proceedings of the 35th Photovoltaic Specialists Conference, 22 June 2010, Hawaii.; 2. King D.L., Boyson W.E., Kratochvil J.A., (2004).
Photovoltaic Array Performance Model. SANDIA REPORT (SAND2004-3535). Unlimited Release.
INTRODUCTION
SECTION 1
3
PHYSICS OF ENERGY PRODUCTION
SECTION 2
SunPower Technology
SunPower cells use silicon wafers and an all-backcontact architecture with some proprietary features and
processes. The result is the world’s highest efficiency
cells for use in flat-plate modules, with a production
average efficiency of 22.4% and demonstration of
24.2% with a 155cm2 cell [3]. High efficiency has an
obvious, and high, impact on the energy produced.
This section, however, analyzes 10 key attributes
which could influence the kWh/kWp performance of
SunPower modules relative to other technologies.
Response to Incident Irradiance Level
Nominal efficiency is measured at an irradiance of
1000 W/m2. In an ideal cell, the efficiency will increase
nearly logarithmically as the irradiance increases from
0 to 1000 W/m2 because short circuit current (Isc) is
roughly linear with irradiance and open-circuit voltage
(Voc) is roughly logarithmic with irradiance. Deviation
in response to the irradiance level compared to an
ideal module is primarily driven by series resistance
(Rs) at mid-irradiance (~250-750 W/m2), and shunt
resistance (Rsh) at low irradiance.
As can be seen in Figure 2, SunPower modules have
low series resistance, which leads to a lower kWh/
kWp production (looking only at this one factor)
compared to modules with a high series resistance. The
impact is small when compared to a good standard
c-Si module, but can be significant when compared
to a module with very high Rs (e.g., some thin film
modules). A numerical estimation of this effect, along
with other attributes, is provided in section 2.11.
It should be noted, however, that a key approach to
improve the 1-sun efficiency of modules which have high
Rs will be to reduce Rs. This will reduce the kWh/kWp
for those technologies. Field measurements of kWh/
kWp performance before the product improvement
should thus not be used without adjustment. The fact
that energy production will increase less than efficiency
when Rs is reduced should also be factored into cost/
benefit calculations of manufacturers considering
changes.
Series Resistance
Figure 1 demonstrates the effect of high Rs on the
response of a module as a function of irradiance.
High series resistance (Rs) in a module reduces the
conversion efficiency at all irradiance levels, but reduces
it more at high irradiance (e.g., 1 sun) compared to low
irradiance (e.g., 0.4 suns) since the loss is proportional
to the square of the current (i2). As a result (and as
can be seen in Figure 1), a module with very high Rs
can have an efficiency that is higher at 0.5 suns than
it is at 1 sun, which would increase the kWh/kWp
for the module compared to an ideal module because
the energy-weighted average irradiance on a module
over the course of a year is less than 1-sun.
Figure 1: Efficiency as a function of irradiance for two
hypothetical modules; black curve for a module with
typical Rs, red curve for a module with very high Rs.
3. P. Cousins, et al., Proceedings of the 35th Photovoltaic Specialists Conference, 22 June 2010, Honolulu, Hawaii.
PHYSICS OF ENERGY PRODUCTION
SECTION 2
4
SECTION 2
Shunt resistance
Temperature Coefficient
Low shunt resistance (Rsh) in a module noticeably
reduces module efficiency at low illumination levels.
Most modules now have a high enough Rsh such
that differences in energy production as a result of
differences in Rsh among ―average― modules are
small. However, an Rsh low enough to significantly
lower energy production has been noted in multiple
instances, including quality problems in c-Si modules
[1], low Rsh in a particular model from a CIGS
company [6], and from degradation of Rsh after field
exposure in a thin-film module [7].
The change in efficiency with temperature is represented
by the power temperature coefficient. Module
parameterization allows the effect of temperature to be
included in simulation models, but the main effects can
be understood with simple theory. While temperature
also influences the Isc and fill factor (FF), the efficiency
loss is dominated by the loss in voltage unless there is
a cell issue which suppresses efficiency at 25―C but
not at higher temperatures. The rate of the reduction
in voltage is a function of the Voc of the cell vs. the
band-gap – high Voc/Eg gives a smaller temperature
coefficient and thus a smaller energy loss in operation.
SunPower cells and modules have high Rsh as a result
of the structure and process control needed to achieve
high efficiency. Additionally, screening for moderatelylow shunting was instituted in 2008. Some advantage
relative to ―average― modules can be seen in the early
turn-on that has been noted of SunPower arrays, and
in the low-irradiance output curves, but we have not
quantified this advantage as a separate effect from the
overall impact of irradiance level.
Figure 2: Efficiency as a function of irradiance data from
five PV technologies from Sandia Performance Model
database. The ―SPR-318-AR― is a SunPower module.
Table I: Cell Voc and temperature coefficient of
power. Traditional Si values are average for the
10 largest producers of traditional silicon modules
as calculated from data reported by Photon
International in Feb. 2010.
Voc
Power Tcoef
SunPower back-contact
0.691
-0.38%/°C
Traditional c-Si and mc-Si
0.625
5.1%
An operating temperature (irradiance-weighted over
the year) of 45―C is in the range seen in warm climates.
When a constant temperature coefficient is used, the
power loss is calculated by multiplying the coefficient
times the ―T to the STC value of 25―C. Using the values
in Table I and an irradiance-weighted temperature
of 45―C, the loss for the SunPower module would be
7.6%, and the loss for the traditional silicon would be
9.4%, thus giving the SunPower module a 1.8% kWh/
kWp advantage relative to standard c-Si from this
attribute.
6. Y. Tsuno, et al., as presented at the 25th European Photovoltaic Solar Energy Conference, Valencia, Spain, 6-9 Sept. 2010.; 7. D. Cunningham, et al.,
Proceedings of the 35th Photovoltaic Specialists Conference, 22 June 2010, Honolulu, Hawaii.
PHYSICS OF ENERGY PRODUCTION
SECTION 2
5
SECTION 2
Temperature of operation
Determining differences in operating temperatures
among technology types is difficult, with many papers
written on the topic. The temperature differences can
be broken into temperature difference at open circuit
plus additional difference in operating temperature
from the impact of differences in efficiency.
Effect of efficiency on operating temperature
Measurement of NOCT (Normal Operating Cell
Temperature) is done at open circuit (despite the use
of the word ―operating‖) [4]. The temperature in actual
operation in the field will be reduced because a portion
of the incident energy is delivered out of the module as
electrical energy. Advanced simulation programs (such
as PVSim and SAM) adjust the operating temperature
due to this energy delivered out of the module, but,
remarkably, simple models and programs such as the
CEC rating system do not make this adjustment.
Reflectance of SunPower modules
Since absorption equals the incident energy minus
that reflected (R) or transmitted (T), high R+T results
in a lower NOCT. Previously we surmised the lower
NOCT of SunPower modules was the result of higher
reflectance and transmittance in the infra-red relative
to standard c-Si modules, with this effect more than
compensating for the higher absorbance in the visible
portion of the spectrum. Experiments were done to test
this hypothesis and are described here.
Figure 3 compares the sum of reflected and transmitted
light as a function of wavelength for a coupon with a
SunPower cell verses a coupon utilizing a traditional
c-Si cell. Fully encapsulated cells with identical glass
and backsheet were measured using a PerkinElmer
spectrometer with integrating sphere in both reflection
Higher efficiency modules deliver more energy out
as electricity instead of heat. Comparing a 19.4%
efficiency SunPower module to a 14.4% standard
c-Si module, ~5% less of the absorbed energy must
be dissipated as heat; for a climate with an energyweighted operating temperature of 25⁰C above
ambient, this would result in roughly a 1.4⁰C lower
operating temperature.
Temperature in open-circuit condition
Testing at multiple laboratories has indicated that
SunPower modules have a 45⁰C NOCT, which is
1-2⁰C lower than typical for traditional c-Si modules.
For modules of similar construction, the largest source
of differences in NOCT is the difference of absorbed
energy; this is explored further in the next section.
Figure 3: Percentage of light reflected + transmitted (i.e.,
not absorbed) by complete solar modules as a function
of wavelength. Data is a weighted average of multiplespot measurements by spectrophotometry.
4. M. Garcia, et al., ―Estimation of photovoltaic module yearly temperature and performance based on Nominal Operation Cell Temperature calculations‖,
Renewable Energy 29 (2004) 1997-2010.
PHYSICS OF ENERGY PRODUCTION
SECTION 2
6
SECTION 2
and transmission geometries. This allows for the
determination of the total amount of energy absorbed
within all layers. The results shown in Figure 3 are an
average of selected regions across the surface of the
cells in order to incorporate optical differences due to
the cell, metallic fingers, and busbars. Measurements
across the white spaces in between cells was ignored
and assumed to be equivalent between the two
systems.
Higher absorption (and thus more energy to
dissipate) is seen in the SunPower cell over the range
of wavelengths to which the cell responds. Lower
reflection from the SunPower cell in this region is the
result of better texture and light trapping, as well as
the lack of reflecting busbars and gridlines. The region
of response extends out to approximately 1150nm for
most c-Si cells but continues out to almost 1200nm for
SunPower cells.
Lower absorption can be seen in the SunPower cell
in the infra-red (IR) regions of the spectrum. Higher
reflection of IR in the SunPower cell is the result of
a photon reflector at the rear surface instead of the
absorbing interface in a traditional cell, partially offset
by the lower front surface reflection in the SunPower
cell. Higher IR transmission in the SunPower cell is the
result of some light passages which are result of the
architecture of the cell, while the traditional cell has
full metal coverage. Peaks observed in the IR region,
such as those at ~1215nm, 1420nm, and 1740nm are
vibrational absorption bands from the polymers used
in the encapsulant or the backsheet.
In total across the AM 1.5 spectrum, our calculations
show that SunPower cells absorb 88.7% of the incident
energy versus 89.9% for traditional cells, resulting
in 1.3% less energy that must be dissipated as heat
inthe SunPower modules, This would result in roughly a
0.3⁰C lower temperature in the case of a ΔT to ambient
of 25⁰C.
Measurement of temperature in operation
The total operating temperature advantage comes
from a combination of the lower NOCT and the higher
amount of energy delivered out as electricity, for a total
of possibly ~2⁰C. Figure 4, which has temperature data
taken on a hot day by ASU-PTL, supports the expected
lower temperature of operation of SunPower modules
Figure 4: Temperature of SunPower module and
standard c-Si modules in operation as measured by
ASU-PTL.
Spectrum (Air Mass) response
Nominal efficiency measurements are taken using the
AM1.5 spectrum. The energy-weighted average air
mass (AM) over the course of the year is greater than
1.5, with early morning and late day being at very
high air mass (and thus a much higher red/blue ratio
compared to the AM1.5 spectrum).
PHYSICS OF ENERGY PRODUCTION
SECTION 2
7
SECTION 2
Figure 5 shows the response to the range of spectrum
(plotted as a function of air mass) as shown from the
Sandia model database for SunPower modules and
four other technologies. Both the SunPower and the
standard silicon modules have higher efficiency at AM
> 1.5, which provides a relative increase in kWh/
kWp. The high efficiency at AM > 1.5 is the result of a
single junction with a low band-gap which gives good
red response and no multi-junction current-matching
loss. In contrast, modules with higher band gap and
those with multiple junctions will have reduced kWh/
kWp from spectrum effects, as can be seen in Figure
5. Some analyses have indicated that the loss for a-Si
from this effect is larger than the gain that a-Si gets from
an extremely low effective temperature coefficient.
the field as a result of the formation of a boron-oxygen
defect. SunPower cells use n-type silicon and thus
have no LID.
The typical value of LID for traditional silicon is ~1.5%,
but lower values have been reported and a few
manufacturers adjust their reported flash test data to
account for the expected loss. There were reports of
LID > 10% from some manufacturers resulting from
the use of poor quality poly-Si, particularly during the
shortage of poly-Si in 2007-2008.
The fast degradation of a-Si with exposure to sunlight
and some initial degradation of other thin film modules
are sometimes also called LID. At minimum, this
degradation can complicate system design, inverter
matching, and field test analysis.
Angle of incidence
According to the reported values in the Sandia Model
database, c-Si has advantage in performance at low
angle of incidence (AOI) relative to some thin film
modules.
Figure 5: Relative response to air mass plotted
from data in the Sandia Performance Model
database. Higher air mass has a spectrum with
a higher red/blue ratio.
Light-induced degradation (LID)
Traditional silicon cells use p-type silicon and thus
experience a loss in efficiency in the first few hours in
SunPower modules use stippled front glass as is
common with c-Si, and have a better cell texture and
front-surface passivation which results in a slightly
better off-angle response compared to ―standard― c-Si.
A larger impact is the benefit of anti-reflection coated
(ARC) glass. A portion of SunPower’s modules now use
ARC glass, which results in improved performance at
low angle compared to standard glass. This provides
energy production benefits up to 2.5% beyond the
benefits to the module’s rated power. The highest
kWh/kWp benefit occurs with modules in low-angle
fixed tilt. Data is provided in section 4.3.
5. Sample NPV calculation is for example purposes only and is based on the following assumptions: $0.12 current rate of electricity, 3 % annual rate escalation,
$0.01 REC value, $0.05 performance based solar incentive over 5 years, 8% discount rate, 25 project life cycle.
PHYSICS OF ENERGY PRODUCTION
SECTION 2
8
SECTION 2
Light-induced degradation (LID)
A yearly degradation of 0.3% instead of 0.7% would
lead to a 4.5% higher energy production over the 25
year warranted life of the module. Field testing from
NREL and other locations is showing a lower-thanaverage degradation of SunPower modules. Data will
be presented at a later time.
On average thin films have a higher degradation, but
the range is broad, from positive change from some
modules to consistent > 2%/yr loss for others.
Failure rate and module and system life
Typically performance simulations are performed
assuming the same failure rate of modules and
the same system life. However, failures prior to the
designed life increase costs and reduce availability
(and thus energy production) from the system. The
impact of system life is evident, with design life above
or below the standard warranty of 25 years having a
large impact.
SunPower has devoted considerable resources to
achieving superior reliability and product life as
reported in this conference [5].
Other factors
Other factors which influence system output which are
beyond the scope of this paper, but are included in
our performance modeling, include tracking (which
can increase kWh/kWp by 20-35%), meteorological
inputs, inverter performance and match to the system,
soiling, shading and tolerance to shading, module
mismatch loss, DC and AC wiring loss, inverter AC-
capacity clipping loss, day and night transformer
efficiency loss, auxiliary load loss, and annual
availability. Difference in actual vs. rated efficiencies
are discussed in section 3.
Conclusions
The performance advantage of SunPower modules
due to the above discussed attributes depends on
the location and type of installation as well as the ―
average‖ module selected as the benchmark. Rough
estimates of the values for the kWh/kWp advantage
are shown in Table II. Non-ARC glass was used
for all the technologies. Due to the large range of ―
average c-Si‖, and the large range of mounting types
and climates, the estimates are only offered as a
benchmark for analysis of field test results. The values
for the ―average‖ CdTe and a-Si have even greater
uncertainty, due to both the variation within each of
those technologies and the amount of technical data
we have available, but the estimates should still provide
a useful benchmark and insights for the analysis of
field test results.
Additional potential sources of kWh/kWp advantage
of SunPower modules over the life of a system beyond
the values shown in Table II include:
• Relative kWh/kWp benefit resulting from a serious
problem with the comparison module (e.g.,
a shunting problem as discussed in section 2.2.2)
• The benefit from lower degradation than average
as discussed in section 2.8.
• The benefit from a reduced likelihood of failure
as discussed in section 2.9.
5. D. DeGraaff, et al., ―Qualification, Manufacturing, and Reliability Testing Methodologies for Depolying High-Reliability Solar Modules,‖ these proceedings.
PHYSICS OF ENERGY PRODUCTION
SECTION 2
9
SECTION 2
Table II: Approximate kWh/kWp of SunPower
modules compared to ―average― c-Si in hot and
cool climates, and compared to CdTe and a-Si in a
temperate climate. Additional sources of potential
advantage are listed in the paragraph above.
Avg. c-Si
c-Si
CdTe
a-Si
hot
cool
“average”
“average”
Irradiance level
-0.4%
-0.7%
-2%
-4%
Temp. coefficient
2.4%
1.2%
-1.9%
-4%
Operating Temp.
1.3%
1.0%
2%
1.5%
LID
1.3%
1.5%
0
3%
0
0
1.5%
8%
Angle of incidence
0.1%
0.3%
1%
1%
Total
5.0%
3.3%
0.5%
5.1%
SunPower vs
Climate:
Spectrum
PHYSICS OF ENERGY PRODUCTION
SECTION 2
10
EVALUATION OF INDEPENDENT FIELD TESTS
SECTION 3
There is substantial interest in independent field studies because they can provide vital evidence about the relative
performance among PV technologies, and because they offer information to improve the absolute value of
prediction of energy in different climates and mounting conditions. The value of a comparative independent test
of various PV technologies is that it takes away many of the system-level variables that make it difficult to compare
performance between different sites. In addition, numerous papers have shown that PV modeling programs differ
in their predictions of PV technologies and have been prone to over-predict thin film performance [8].
There are 10 different independent tests, shown in Table III, that include SunPower modules and which have made
data available either directly through a website or through published papers. The data from these independent
field tests will be evaluated in order to estimate the relative kWh/kWp performance between SunPower’s high
efficiency all-back contact technology and other PV technologies.
Table III: Field Tests Including SunPower Modules
Institute/Test Site
Location
Time
Years
#
Models
SunTechnics Study
Landshut, DE
4/05-4/06
1.0
2
Leicestshire, UK
4/07-4/08
1.0
3
ISAAC-TISO
Lugano, CH
3/06-7/07
1.3
14
IPE, Univ. Stuttgart
Stutgartt, DE
6/06-on-going
4.1
13
IPE, Univ. Cyprus
Nicosia, Cyprus
6/06-on-going
4.1
13
Cairo, Egypt
8/08-6/09
.09
13
Wakanai, Japan
4/07-9/07
0.5
8
Landwirtshaftliche
Lehranstalten,
Bayreuth
Bayreuth , DE
1/09-12/09
1.0
6
Gelsenkirchen FHS
Bochot, DE
6/09-10/09
.04
4
1.8
6
CREST
IPE, Egypt
Hokaido Elec.
(NEDO)
Desert Knowledge Alice Springs, AU 10/08-on-going
tests except SunTechnics, CREST, and Gelsenkirchen
compare both crystalline and thin film technologies
together Seven of the studies compare more than 6
different PV module types Only 4 of the sites have
data over 1 year and only 3 have more than two
years of data; together they represent 16 years of
comparative field data All studies measure modules
installed on rack-mounted arrays facing south
Discussion of Field Testing Issues
The 10 comparative field test sites provide a diversity
of locations, climates, test duration, number of different
manufacturer models included, and test and analysis
rigor. The data sources and studies are referenced
at the end of this paper (sources [9] to [18]). Key
observations about the studies are: All of the field
Two things should be considered when evaluating
the results of comparative field tests. First, there is a
wide range of outcomes from the many comparative
studies that have been published. For any technology,
one can find studies that show both good and poor
outcomes, which underscores the need for rigorous
scrutiny of field test data. The range of outcomes
stems from the difficulty in experimental design,
execution, analysis, and communication of results,
as well as variations in products. Some of the key
difficulties in testing and interpretation are described
below. A methodology that mitigates some of the
sources of variation is developed in Section 3.3.
Second, the typical uncertainty of yield measurements
8. S. Ransome, ―Errors and uncertainties in kWh/kWp modeling, predictions and measurements,― Proceedings of the 19th International Photovoltaic Science
and Engineering Conference 3 Nov, 2009, Jeju, Korea, 1.; 9. D. Rose, et al., ―Mass Production of PV Modules with 18% Total-Area Efficiency and High Energy
Delivery Per Peak Watt,― Proceedings of the 4th WCPVEC, 7-12 May 2006, Waikoloa, Hawaii; 18. Desert Knowledge Australia Solar Centre Interactive Site,
http://www.dkasolarcentre.com.au/.
E VA L U AT I O N O F I N D E P E N D E N T F I E L D T E S T S
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is about +/-5%, even for well designed and executed
studies. This margin typically exceeds the performance
differences that one seeks to measure, meaning
performance differences cannot be definitively
measured [13] [19].
Field Test Issue #1 - Degradation
• The degradation of energy performance over time
can bias the results of a field test. There are two
types of degradation that should be considered:
Initial stabilization of thin film technologies - There
is typically a -10% to -35% degradation for a-Si
over the first few months of field exposure [20].
CIS and CdTe thin films can have an initial, but
typically much lower, loss; the amount of loss
and rates can vary between manufactures and
technology generations. Initial degradation can
cause thin films to have a kWh/kWp advantage
in the first year of a comparative test which is not
representative of energy production over the life of
the system.
• Year to year degradation - Different technologies
can have different annual degradation rates, and
other factors such as module construction can
influence a module’s long-term degradation. As
discussed in section 2.9, this can have a strong
influence on long-term energy generation and
any interpretation of a comparative test should
consider how long-term degradation will change
the relative performance estimates.
Our analysis of data taken by Rodziewicz, et al. [21],
shown in Table IV illustrates how degradation can
influence the results of a comparative test. It can be
seen that in this study, amorphous silicon modules
in the test started out with 7-23% higher kWh/kWp
compared to the mc-Si modules, but underperformed
by 7-10% in year 5. We used the mc-Si module as
the benchmark because its performance ratio was
stable over the test period, while the c-Si module
varied from year to year (e.g., the c-Si module had
a performance ratio of approximately 85% in year 3
and 90% in year 5).
Table IV: kWh/kWp of a-Si relative to mc-Si modules
in a 5 year field test [21], shown here to illustrate the
inability to use 1 year data with any technology with
severe degradation. As with all tests, these results
should not be taken as representative of all modules
within a technology type.
Year 1
Year 2
Year 5
a-Si single junction
+23%
+3%
1%
a-Si triple junction
+7%
-3%
-10%
To mitigate the bias in performance comparisons for
a technology with large or unknown degradation,
longer-term test data should be used where possible
or the first year data adjusted accordingly. To be
conservative in our meta-analysis, however, we used
1st year data as is for all technologies. In general,
however, when averaging the results of several tests,
weighting the average by the field test duration can
also help reduce the degradation bias since the goal
is to accurately predict energy production over the life
of the system.
13. B. Zinsser, et al., ―Annual Energy Yield of 13 Photovoltaic Technologies in Germany and Cyprus,― Institut für Physikalishe Elektronik (ipe) and Department
of Electrical and Computer Engineering, University of Cyprus, Proceedings of the 35th IEEE PVSC, June 2010.; 19. S. Ransome, SRCL ―Are kWh/kWp Values
Really the Best Way to Differentiate Between PV Technologies?― Proceedings of the 22nd EU PVSEC, September 2009, Hamburg Germany; 20. S. Ransome,
SRCL, “A detailed comparison of measured outdoor performance vs. simulation program predictions for different PV technologies”, 25th PV Symposium Bad
Staffelstein Germany, Mar 2010.; 21. T. Rodziewicz, et al., ―Long-Term Analysis of Energy Gained by Different PV Modules in 2001-2006―, Proceedings of the
22nd European Photovoltaic Solar Energy Conference, 3-7 September 2007, Milan, Italy.
E VA L U AT I O N O F I N D E P E N D E N T F I E L D T E S T S
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Field Test Issue #2 – Defining kWp baseline and
managing module sample uncertainty
In order to compare technologies in a field test,
kWh/kWp or yield measurements are normally used.
To calculate these, every study must define how to
measure the kWp size of the array. How this is defined
can bias the results of the field test either up or down.
Most tests have used nameplate or rated power as the
kWp baseline measure because it is easy to determine
and typically sets the purchase price of a module.
However, many studies have commented on the
uncertainties that the use of rated power introduces
into a field test [13] [19]. Using rated power can give
a high kWh/kWp result if the modules placed in the
tests actually have a nominal power higher than rated
(e.g., if the modules are 205W with a nameplate of
200W). If their nominal power is lower than rated,
the kWh/kWp result will be lower. The question then
is whether the modules introduced into a test are ―
representative‖ of what a customer receives. Given that
a typical product may have a power tolerance of +/3-5%, there is a given statistical sampling uncertainty
introduced into the test from the start. This uncertainty
can be exacerbated as manufacturers often seek to
place modules into comparative tests that flash high
relative to their rated power.
An example of how using rated power can bias a test’s
results is illustrated in the SunPower modules that were
used in the IPE, (Stuttgart and Nicosia) and ISAACTISO tests. Figure 6 shows the deviation between
rated power and manufacturers’ factory measurements
and IPE’s independent measurements. The SunPower
modules placed into the IPE field tests are 4 to 5%
above the rated power. In contrast, in the ISAACTISO test, the SunPower modules are 2.6% below the
rated power based on ISAAC-TISO’s independent
measurement. It is easy to see how using rated
power as the baseline for comparative kWh/kWp
measurements can introduce variation that reduces the
usefulness of the results.
Many studies try to avoid this issue by using
either manufacturer factory measurements or by
independently measuring all test modules separately.
Using manufacturer flash test data is beneficial because
customers today commonly receive flash test data from
manufactures and often specify expected delivered
Wp power based on module flash. Manufactures
also seek to keep a stable measurement calibration
over time across all products and manufacturing
sites, whereas rating policies can vary over time and
between products. Providing performance relative to
manufacturer flash may provide customers the most
relevant kWp baseline for decision making.
Figure 6: Deviation of manufacturer and IPE
measurements from rated power for modules
placed into the IPE Stuttgart and Nicosia field
tests [12].
12. B. Zinsser, et al., ―Rating of Annual Energy Yield More Sensitive to Reference Power than Module Technology,‖ Institut für Physikalishe Elektronik (ipe) and
Department of Electrical and Computer Engineering, University of Cyprus, Proceedings of the 22nd EUPVSEC, September, 2009, Hamburg, Germany.
E VA L U AT I O N O F I N D E P E N D E N T F I E L D T E S T S
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Many studies try to avoid this issue by using
either manufacturer factory measurements or by
independently measuring all test modules separately.
Using manufacturer flash test data is beneficial because
customers today commonly receive flash test data from
manufactures and often specify expected delivered
Wp power based on module flash. Manufactures
also seek to keep a stable measurement calibration
over time across all products and manufacturing
sites, whereas rating policies can vary over time and
between products. Providing performance relative to
manufacturer flash may provide customers the most
relevant kWp baseline for decision making.
Independent measurements are also beneficial,
because they provide one measurement standard.
However, both of these strategies introduce other
uncertainties. Factory measurements based on third
party calibrations can differ by +/-3% between
manufacturers [13]. Independent measurements can
be prone to mis-measurement of the starting values,
particularly differing technologies are measured
– i.e. thin films or high capacitance modules like
high efficiency SunPower for modules. Experienced
labs that do independent measurements will tend to
measure the most accurately.
When dealing with a module which has a starting
efficiency well above the rated power in order
to accommodate expected degradation, neither
using rated power (which would give an artificiallyhigh kWh/kWp), nor using the starting efficiency
(which would give an artificially low kWh/kWp),
is appropriate. In these cases, a model should be
used to better predict lifetime energy production, or,
failing that, only data after the degradation period
should be used.
The conclusion then is to minimize rating and sampling
bias by using factory flash test data or independent
measurements along with the consideration of the test
data only after periods of strong degradation. This
reduces the uncertainty of performance measurements
among crystalline technologies, while not unfairly
penalizing the relative performance of thin film
technologies. However, this does require thin films that
have strong initial degradation to be independently
measured after the degradation period to establish a
kWp baseline. If this is not practical, or if the kWh
yield measurements for the first year or period of
strong degradation cannot be omitted from results,
using a combination of the rated power for thin film
technologies and flash test data or independent
measurements for c-Si technologies will still provide an
improved analysis (though it acts to the detriment of the
c-Si modules).
Of the 10 sites being evaluated here all base their
performance estimates on rated power except two,
SunTechnics and CREST. However, 3 studies, the
ISAAC-TISO, IPE Stuttgart and IPE Nicosia do provide
data that enables the calculation of kWp based on
measured or manufacturer flash test data. These three
latter studies will enable a more accurate relative
performance estimate.
Field Test Issue #3 – Sample Size
As explained in other subsections and in this section,
no one study should be taken as representative for
all manufacturers within a technology, or even of the
manufacturer’s model tested. There can be significant
variation within thin film technologies and even within
―traditional silicon‖ modules. For instance, the field tests
that have been evaluated show varying results among
the standard crystalline panels. The IPE, Egypt site for
instance has variation between crystalline panels of up
to 14% and the CREST study up to 9%.
E VA L U AT I O N O F I N D E P E N D E N T F I E L D T E S T S
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In order to reduce the bias caused by this variability it
is important to analyze several field tests and to have
a good sample of each technology in each of the tests.
Table V shows the break-down and number of modules
per technology per test and the standard deviation of
resulting performance of the conventional crystalline
modules relative to the SunPower technology. The studies
with more modules generally have a smaller standard
deviation between the results and therefore can provide
a better representation of the technology’s performance.
Where this is not true – as in the case of the IPE test in
Egypt - the high standard deviation of results may be an
indicator of other test issues.
Field Test Issue #4 – Non-Representative Results
Table V: Number of modules per PV technology per
• Improper grounding
field test site, and the standard deviation of energy
performance of the conventional crystalline modules
relative to the SunPower technology.
# of
TF
Std
Dev
c-Si
# of
HE c-Si
# of
c-Si
CREST
1
2
SAAC-TISO
2
9
3
1.75
IPE, Uni Stuttgart
2
7
4
1.58
IPE, Uni Cyprus
2
7
4
1.95
IPE, Egypt
2
7
4
5.28
Hokaido Elec.
(NEDO)
2
4
2
0.70
Landwirtshaftliche
Lehranstalten,
Bayreuth
2
1
3
NA
Gelsenkirchen FHS
3
1
Institute/Test Site
3.72
NA
Before evaluating data from a comparative test site, the
test set-up, execution and results should be reviewed
for non-representative results. Many things can bias
performance such as:
• Defective or damaged modules
• Inverter loading or faulty performance
• Monitoring issues
• Differential shading
• Differential ventilation due to array position for
roof-top systems
A few examples illustrate non-representative results that
can result from test issues. One study [22] published
18 months of data for four ~2.6 kWp systems from
different PV technologies. The results showed the kWh/
kWp of the mc-Si array was 63% higher than the CdTe
array. However, the results were not representative
because the CdTe arrays was ―severely affected― by
shading from a control room and significant inverter
and monitoring equipment issues.
One of the comparative studies evaluated for this study
also suffered from test issues that in this case made
the SunPower performance data non-representative.
Data from the Desert Knowledge site in Australia
shows that there is an anomaly in the daily output
curves for the SunPower array as shown in Figure 7.
Root cause for this test issue still has to be confirmed,
but it is believed to be a drop-out of the DC sensor at
low current due to thicker insulation of the wires used
in the SunPower array. The impact of this irregularity
is large enough to make the data non-representative.
22. R. Eke, S. Oktik, ―Comparison of 18 Month kWh/kWp Energy Output of Four Photovoltaic Systems with Four Different Module Technologies, Proceedings of
the 22nd European Photovoltaic Solar Energy Conference, 3-7 September 2007, Milan, Italy.
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15
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For some of the other field tests evaluated in this study,
the performance of certain comparison modules
is so poor that it indicates a likely module defect or
measurement issue. In these instances these data
points have been removed from the analysis. These
are specified in Section 3.3 below.
Separating out the performance data for the first
three months of these tests is unfortunately not
feasible with the data available in these papers.
2. kWp Baseline: Only tests will be evaluated that
can enable a comparison based on independent
or factory measurements for crystalline modules.
For Thin Film modules, the kWp baseline will still
be rated power.
3. Sample Size: Only tests that have more than 10
modules/manufacturer and at least two modules
in each of the major technology categories. High
efficiency c-Si, c-Si, and thin films will be used.
These criteria limit the field tests evaluated to only
the ISAAC-TISO, IPE, Stuttgart and IPE Cyprus tests.
Although the dataset is smaller, the resulting accuracy
is potentially higher.
Figure 7: Typical daily irradiance for June 09 for
SunPower and another crystalline technology
at the Desert Knowledge Site, Alice Springs,
Australia [18].
Field Test Evaluation Methodology
Two approaches will be taken to evaluate the
performance data. ―Approach 1‖ simply quantifies
the average performance of SunPower compared to
different PV Technologies. This approach maximizes
the amount of data evaluated.
―Approach 2‖ seeks to reduce the common field test
issues that have been discussed in Section 3.2 above
by following these criteria:
1. Degradation: Only tests greater than 1 year in
duration will be evaluated and the results will
be weighted by the duration of field exposure.
Both approaches do not include non-representative
results. For this reason the data from the Desert
Knowledge field test data will not be included and
the following three poor performing data points have
been removed from the studies listed.
• IPE, Nicosia – the BP Solar array performed 16%
lower than the SunPower array and has been
omitted
• IPE, Egypt – the CdTe array performed 25% lower
than the SunPower array and has been omitted
• Gelsenkirchen – the IBC array performed 19%
lower than the SunPower array and has been
omitted
Results of Field Test Evaluation
Tables VI and VII show the estimated relative
performance of SunPower modules versus HIT,
Standard c-Si, CdTe, a-Si and CIS according to the
two approaches discussed above.
E VA L U AT I O N O F I N D E P E N D E N T F I E L D T E S T S
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16
SECTION 3
Table VI: SunPower Relative kWh/kWp Performance
Table VII: SunPower Relative kWh/kWp Performance
using analysis approach 1 (+ means higher kWh/
kWp of the SunPower modules compared to the
module listed).
(Approach 2)
Institute/Test Site
SunTechnics Study
c-Si
CdTe
3.1%
1%
a-Si
CIS
CREST
0.7% 6.5% -10%
ISAAC-TISO
-5.6% 0.2% -5.7% 1.0%
IPE, Univ Stuttgart
7.7% 7.0% 6.8% 11.8% 0.9%
IPE, Univ Cyprus
3.9% 7.5% 7.6% 10.3% 3.0%
IPE, Egypt
-1.3% 0.6%
8.7%
Hokaido Elec.
(NEDO)
-0.1% 2.9%
13.4%
Landwirtshaftliche
Lehranstalten,
Bayreuth
0.4% 1.3% -1.4% 6.2%
c-Si
CdTe
1.8%
2.5%
3.5%
6.2%
2.3%
Approach 1 shows a 5.3% higher performance
for SunPower modules compared to standard c-Si
modules, which is slightly higher than the ~3-5%
advantage predicted in section 2.11 based on product
attributes. The SunPower advantage is 1-2% vs. HIT,
CdTe and CIS technologies, and 6.2% vs. a-Si.
a-Si
CIS
ISAAC-TISO
-2.8% 1.3% -3.1% 4.9%
-1.8%
IPE, Univ Stuttgart
6.1% 3.5% 2.0% 7.0%
-4.3%
IPE, Univ Cyprus
2.2% 3.4% 2.6% 4.7%
0.0%
Average – All Tests 3.2% 3.2% 1.6%
Gelsenkirchen FHS 2.4%
1.0% 5.3% 1.8%
kWh/kWp Relative to SunPower
HIT
kWh/kWp Relative to SunPower
HIT
Average – All Tests
Institute/Test Site
5.8% -2.1%
The results from Approach 2 generally show a slightly
lower performance advantage than the results from
Approach 1. While the performance advantage versus
CdTe and a-Si has not changed much; HIT, c-Si and
especially CIS results are different.
The difference in the results between the broader
Approach 1 and the more conservative, but smaller
data set, Approach 2 demonstrates that addressing
field test issues can have an impact. The largest
change in the results was that Approach 1 showed a
SunPower advantage of 2.3% vs. CIS, but Approach
2 showed a 2.1% disadvantage. This was primarily
the result of the downward adjustment in the SunPower
kWh/kWp from using initial measured power instead
of rated power (which is a ~5% reduction since the
SunPower modules in the IPE tests had initial power
~5% higher than rated), while the rated power of the
CIS modules was still used, despite those modules also
having initial power ~4% higher than rated.
E VA L U AT I O N O F I N D E P E N D E N T F I E L D T E S T S
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17
SUNPOWER PERFORMANCE MODELING
SECTION 4
SunPower developed and maintains a custom energy production simulation tool. Although there are ―off-the-shelf‖
tools available, SunPower has a high demand for accuracy because of the energy production guarantees we
offer. With PVSim, we can adapt for new module and mounting products, use measured data for validation, and
use the model to optimize system design.
The diagram in Figure 8 illustrates the flow of
information into and out of PVSim.
System
Design
W eather
Data
Site
Location
Balance of
System Losses
External
Loss Factors
PVSim
W eb UI
PVSim Engine
Geom etry :
So lar Po sitio n
Mo d u le Orien tatio n
In cid en ce A n g le
Sh ad in g
Irradiance :
B eam /D N I
D iffu se
Plan e -o f-A rray
PV M odel :
C ell T em p eratu re
En erg y C o n versio n
In verter
System Power &
Energy Yield
Figure 8: PVSim Data Flow diagram.
PVSim represents an evolution of SunPower’s legacy
simulation tool, PVGrid [30]. During the past three
years, SunPower has overhauled the suite of models
in the tool, and completed each model revision or
replacement with a verification and/or validation
process. Upgrades include:
• Replaced and validated geometry, solar position
and shading models with generalized models
which apply to any fixed-tilt or tracking system
of arbitrary configuration, including SunPower’s
proprietary backtracking algorithm for its tracking
products.
• Implementation of the most current Perez models
for calculating beam irradiance (sources [23] to
[26]) and diffuse irradiance on a tilted plane [27].
• Revised beam and diffuse angle-of-incidence
modifier functions based on analytical evaluation
of the anisotropic sky dome and the glass-laminate
optical reflective properties.
• Replacement of the single-diode power conversion
circuit model with the Sandia Performance [2] and
dynamic inverter [28] models.
Geometry Models
PVSim makes use of a generalized geometry model
which allows users to define any mounting system—
fixed tilt or tracking—for calculating the orientation of
the PV surface. Based on standard user inputs—tilt,
azimuth, ground/roof slope, row spacing, rotation
constraints, etc.—PVSim calculates the solar position,
absolute orientation of the module surface, and
the angle of incidence between the sun beam and
the module surface normal. Figure 9 demonstrates
a validation of the geometry algorithms and
implementation in PVSim against a similar model
developed independently by Richard Perez, and under
a range of increasingly complex tracking scenarios
for SunPower’s T20 tracker.
22. R. Perez, et al., ―Dynamic global to direct irradiance conversion models.‖ ASHRAE Transactions Vol. 98, Part 1, 3578, pp 354-369 (1992).; 26. P. Ineichen,
―Comparison and validation of three global-to-beam irradiance models against ground measurements‖ (Center of Energy, University of Geneva). Accepted for
publication in Solar Energy, July 2007.; 27. R. Perez et al., ―A new simplified version of the Perez diffuse irradiance model for tilted surfaces.‖ Solar Energy,
39(3):221–31. (1987).; 28. D.L. King, et al., ―Performance Model for Grid-Connected Photovoltaic Inverters.‖ SANDIA REPORT (SAND2007-5036). Unlimited
Release. (2007).; 30. PVGrid. Copyright to Howard Wenger, 1995-2004.
SUNPOWER PERFORMANCE MODELING
SECTION 4
18
SECTION 4
SunPower trackers are all equipped with a proprietary
backtracking algorithm which reduces shading during
the morning and evening periods of operation. The
PVSim tracking models are synchronized with the
tracking software in our installed systems, as is shown
in Figure 10 (for an arbitrary validation test case).
Figure 9: Angle of incidence between direct
beam irradiance on the module surface of
a T20 tracking system oriented at several
azimuth angles, for July 17 in Olivenza, Spain.
PVSim outputs are evaluated against outputs
from Richard Perez’ independently-developed
geometry model.
Figure 10: Tracker rotation angles for a T20
tracker in Olivenza, Spain on October 17th.
Here, angles calculated by PVSim are compared
to angles calculated by SunPower’s tracker
controller. In this case, a 40° maximum rotation
angle was a constraint.
SunPower’s revision of the geometry models included
an overhaul of the existing shading model, which
explicitly models inter-array shading. Shadow patterns
can be complex at the interior of a large array, so
we used tools such as Google SketchUp to validate
the revised algorithms under a broad range of system
configurations and locations. Figure 11 shows an
example validation test for a non-backtracking T20
system.
Figure 11: Illustrations of shading on a T20
system. These images were created using Google
Sketch-Up, and used as visualization aids for
geometry model and shading validation.
SUNPOWER PERFORMANCE MODELING
SECTION 4
19
SECTION 4
Irradiance Models
In 2008 SunPower worked closely with Richard Perez to implement his most current irradiance models. The
Perez direct normal irradiance (DNI) model was validated against DNI measurements made by Sandia National
Laboratories (Figures 12 and 13), and the Perez tilted-plane, or plane-of-array (POA) irradiance model was
validated against three cases: 2-axis tracking plane-of-array data from Albuquerque, NM (Sandia National Labs),
measured POA data from three SunPower T0 tracking sites in Germany, and measured POA data from several
SunPower flat, fixed PowerGuard systems.
Figure 12: Measured vs. PVSim-calculated beam
irradiance before implementation of Perez model
for Albuquerque, NM, January 1 – December
31, 2006 as measured by the Sandia National
Laboratories.
Figure 13: Measured vs. PVSim-calculated beam
irradiance after implementation of Perez model.
For the beam component,
To complete the validation of the irradiance models in PVSim using plane-of-array irradiance measurements from
a reference cell, it was necessary to develop a higher-fidelity model for predicting the transmission of diffuse
irradiance through the glass laminate. A typical angle-of-incidence response function, shown for a standard glass
surface with and without an anti-reflective coating in Figure 14, may be integrated over the visible sky dome to
evaluate the amount of diffuse irradiance which penetrates the glass surface at a given tilt angle relative to the
ground (Figure 15).
SUNPOWER PERFORMANCE MODELING
SECTION 4
20
SECTION 4
To complete the validation of the irradiance models in PVSim using plane-of-array irradiance measurements from
a reference cell, it was necessary to develop a higher-fidelity model for predicting the transmission of diffuse
irradiance through the glass laminate. A typical angle-of-incidence response function, shown for a standard glass
surface with and without an anti-reflective coating in Figure 14, may be integrated over the visible sky dome to
evaluate the amount of diffuse irradiance which penetrates the glass surface at a given tilt angle relative to the
ground (Figure 15).
Figure 14: Direct normal irradiance transmission,
or angle-of-incidence response, of a SunPower
module with and without anti-reflective coating.
Figure 15: Diffuse irradiance transmission, or
angle-of-incidence response, of a SunPower
module with and without anti-reflective coating.
SUNPOWER PERFORMANCE MODELING
SECTION 4
21
SECTION 4
The extensive work done to overhaul the beam and diffuse irradiance models in PVSim, along with the improved
geometry models, resulted in a significant improvement in both mean bias error (MBE) and root mean square error
(RMSE) for both high-beam and high-diffuse climates. An example of this improvement is shown in for a T0 system
in Muehlhausen, Germany (Figures 16 and 17).
Figure 14: Direct normal irradiance transmission,
or angle-of-incidence response, of a SunPower
module with and without anti-reflective coating.
Figure 15: Diffuse irradiance transmission, or
angle-of-incidence response, of a SunPower
module with and without anti-reflective coating.
Figure 16: Measured vs. PVSim-calculated POA
irradiance before beam and POA model changes
in PVSim for a T0 system in Muehlhausen, Germany.
Figure 17: Measured vs. PVSim-calculated POA
irradiance before and after beam and POA model
changes in PVSim for a T0 system in Muehlhausen,
Germany, Jan. 1 – Dec. 31, 2006. MBE was significantly
improved from -32.6 W/m2 to -1.2 W/m2.
SUNPOWER PERFORMANCE MODELING
SECTION 4
22
SECTION 4
PVSim Accuracy
Mounting Type
Location
Accuracy (%)
PowerGuard
Las Vegas, NV
-0.27
PowerGuard
Los Angeles, CA
2.85
PowerGuard
Parsippany, NJ
3.40
PowerGuard
Los Angeles, CA
1.53
PowerGuard
Mountain View, CA
0.50
T5
Redlands, CA
-0.50
T10
Fresno, CA
0.17
T10
Margate City, NJ
0.70
T10
St. Helena, CA
4.48
T0
Bavaria, Germany
1.17
T0
Bavaria, Germany
0.34
T0
Bavaria,
3.28
T0
Las Vegas, NV
1.04
T0
Las Vegas, NV
1.97
T0
Las Vegas, NV
-1.86
T0
Skillman, NJ
3.12
T0
Santa Clara, CA
0.40
T20
Las Vegas, NV
1.51
T20
Las Vegas, NV
-0.17
T20
Las Vegas, NV
0.80
T20
Lake, CA
1.40
Germany
Average – All Sites
1.20
Standard Deviation – All Sites
1.30
was greater than the PVSim yield estimate (―underprediction―), while a result less than 0% indicates
that the PVSim yield estimate was greater than actual
system energy production (―over-prediction―).
After validating each of the discussed model changes
and incorporating the Sandia PV Performance and
inverter models, PVSim was benchmarked against
measured system yields from a range of module
technologies, system types and climates. The results
of this benchmarking are shown in Table VII. PVSim
1.1, which provides our most current and accurate
estimates of system performance, under-predicts
actual system performance, on average, by 1.15%.
Table VII: PVSim accuracy of annual yield estimates,
based on the production of 21 installed systems across
a range of climate zones and the suite of standard
SunPower mounting systems: PowerGuard, T5, T10,
T0 Tracker and T20 Tracker. A result greater than 0%
indicates that the actual system energy production
SUNPOWER PERFORMANCE MODELING
SECTION 4
23
THIRD PARTY PERFORMANCE MODELS
SECTION 5
Common energy prediction models
There are several publicly available PV energy
calculators of varying sophistication and accuracy for
use by the industry. PVSyst (University of Geneva), the
most widely used of these tools, continues to use the
single-diode circuit model for its conversion calculation
and, in most cases, does not rely on test data for
validating the values in its databases. Figures 18 and 19
demonstrate how the efficiency response of a module
— as measured by Sandia National Lab — may be
misrepresented by PVSyst and another publicallyavailable model, PVSol (Valentin Software). Other
studies have found similar modeling inconsistencies
for thin-film products [29]. Without validating against
controlled outdoor measurements, these modeling
constraints may
Figure 18: Module efficiency vs. irradiance for a
SunPower 305W module, as characterized by PVSyst,
by PVSol and by the Sandia Performance Model.
incorrectly predict module response to irradiance
and spectral variations, leading to potentially large
prediction error. The use of manufacturer-supplied
parameters in tools which do not use module
characterization has been identified as one source of
error. It was reported that the values for module Rs
were higher than the actual value for some modules. If
the modules had the high Rs used as input to the model,
they would have been lower efficiency than their rated
power. The use of incorrectly-high Rs values along with
the rated power results in significant over-prediction of
energy production for those modules [31].
Figure 19: Module efficiency vs. irradiance for a
standard c-Si module, as characterized by PVSyst, by
PVSol and by the Sandia Performance Model.
29. D. W. Cunningham et al., Proceedings of the 35th Photovoltaic Specialists Conference, 22 June 2010, Honolulu, Hawaii; 31. J. Wohlgemuth, as presented
at the 19th International Photovoltaic Science and Engineering Conference, Jeju, Korea, 9-13 Nov. 2009
T H I R D PA R T Y P E R F O R M A N C E M O D E L S
SECTION 5
24
SECTION 5
Other limitations of Third-Party Models
Most publicly available simulation tools also lack modeling capabilities for predicting energy from SunPower
systems. SAM (Solar Advisor Model, NREL), PVWatts (NREL) and PVSol all lack both a backtracking model for
tracking products, and an explicit shading model for calculating row-to-row shading. These modeling constraints
inevitably result in lower simulation accuracy and, in the case of the backtracking algorithm, incorrect modeling
of SunPower’s system performance.
T H I R D PA R T Y P E R F O R M A N C E M O D E L S
SECTION 5
25
CONCLUSIONS
SECTION 6
Analysis of the attributes of SunPower’s high efficiency modules led to a prediction of higher kWh/kWp
performance compared to other technologies. The key positive effects compared to traditional c-Si of lower
temperature coefficient, lower operating temperature, and no LID were only partially offset by the effect of a
low series resistance. The kWh/kWp advantage compared to traditional c-Si was estimated to be 3% in a cool
climate and 5% in a hot climate, but is dependent on the ―standard‖ module selected for the comparison. Rough
estimates of the advantage compare to CdTe and a-Si were 0.5% and 5%, respectively. Low yearly degradation
and low failure rates are expected to increase the kWh/kWp advantage of SunPower modules over the life of
systems.
The effect of series resistance was used to illustrate the point that changes in products can affect kWh/kWp. If
a product exhibits high kWh/kWp now because of high Rs, improvement of the module efficiency through a
reduction in the Rs will improve efficiency more than energy production, thus lowering the kWh/kWp for that
module.
Meta-analysis of 10 independent tests which included SunPower modules indicated higher kWh/kWp for
SunPower as follows: ~3-5% vs. c-Si, ~1% vs. CdTe, ~6% vs. a-Si, ~1% vs. HIT, and ± 2% vs. CIGS.
SunPower’s energy simulation tool was described, along with validation of the components of the model.
Comparison of actual production to predicted values for 21 systems across a large range of location and
installation type showed a 1.2% higher energy production compared to the predicted amount, and a standard
deviation of error of prediction of 1.3%.
CONCLUSIONS
SECTION 6
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© February 2011 SunPower Corporation. All rights reserved.
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