Photovoltaic Degradation Risk 

Preprint 

Dirk C. Jordan and Sarah R. Kurtz 

To be presented at the 2012 World Renewable Energy Forum 

Denver, Colorado 

May 13–17, 2012 

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. 

Conference Paper 

NREL/CP-5200-53712 

April 2012 

Contract No. DE-AC36-08GO28308 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

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PHOTOVOLTAIC DEGRADATION RISK 

Dirk C. Jordan
 

Sarah R. Kurtz
 

National Renewable Energy Laboratory
 

1617 Cole Boulevard 

Golden, CO 80401
 

dirk.jordan@nrel.gov
 

sarah.kurtz@nrel.gov
 

ABSTRACT 

The ability to accurately predict power delivery over the 

course of time is of vital importance to the growth of the 

photovoltaic (PV) industry. Important cost drivers include 

the efficiency with which sunlight is converted into power, 

how this relationship changes over time, and the uncertainty 

in this prediction. An accurate quantification of power 

decline over time, also known as degradation rate, is 

essential to all stakeholders - utility companies, integrators, 

investors, and researchers alike. In this paper we use a 

statistical approach based on historical data to quantify 

degradation rates, discern trends and quantify risks related 

to measurement uncertainties, number of measurements and 

methodologies. 

1. INTRODUCTION 

To sustain the commercial success of photovoltaic (PV) 

technologies it becomes vital to know how power output 

decreases with time. An accurate quantification of power 

decline over time, also known as degradation rate (Rd), is 

essential to all stakeholders - utility companies, integrators, 

investors, and researchers alike. Financially, degradation of 

a PV module or system is equally important, because a 

higher degradation rate translates directly into less power 

produced and, therefore, reduces future cash flows. [1] 

Technically, degradation mechanisms are important to 

understand because they may eventually lead to failure. The 

identification of the underlying degradation mechanism 

through experiments and modeling can lead directly to 

lifetime improvements. Outdoor field testing has played a 

vital role in quantifying long-term behavior and lifetime for 

at least two reasons: it is the typical operating environment 

for PV systems, and it is thus far the only way to correlate 

indoor accelerated testing to outdoor results to forecast field 

performance. We will discuss in this paper the connection of 

outdoor field measurement uncertainties to financial risk as 

the primary motivation. Several excellent treatments of 

uncertainty analysis of PV testing have been done in the 

past, specifically for outdoor [2, 3, 4, 5] and indoor I-V 

characterization. [6] We will expand on this work and 

discuss the impact of the number of measurements on 

uncertainty. Finally, we will investigate uncertainties 

associated with continuous data metrics such as the 

performance ratio and PVUSA method. Specifically, we 

will demonstrate the influence of data filtering which is a 

necessary step in the assessment of degradation rate 

uncertainties. 

2. HISTORICAL DEGRADATION RATES 

An extensive search resulted in more than 2000 PV 

degradation rates (Rd) quoted in publications and locations 

worldwide. Figure 1 shows a summary histogram of 

degradation rates and provides an update of a more detailed 

previous report. [7] The summarized rates are long-term 

degradation rates and usually do not include short-term, 

light-induced degradation. A decrease in performance is 

defined as a positive degradation rate. Conversely, a 

negative rate indicates an improvement. While this 

histogram needs to be updated frequently as new 

information becomes available, some general insights can 

be drawn from it. The distribution is skewed toward high 

degradation rates with a mean of 0.8%/year and a median of 

0.5%/year. The majority of these reported rates, 80% of all 

data, are below a rate of 1%/year. 

1 
 

 

 

 

 

 

0 

50 

100 

150 

200 

250 

F

r

e

q 

u 

e 

n 

c

y 

Degradation Rate (%/year) 

Median: 0.5 %/year 

Average: 0.8 %/year 

# reported rates = 2074 

Fig. 1: Histogram of historical degradation rates. 

Crystalline Si technologies (x-Si), mono-crystalline (monoSi) and multi-crystalline Si (multi-Si), showed very little 

change between the older and newer data, but each of the 

thin-film technologies improved significantly during the last 

decade. Furthermore, Figure 4 compares the field exposure 

for each of these studies compared to a typical module 

manufacturer warranty. [8] As module durability increased 

during the last three decades, module warranties increased 

accordingly, but only in the last six years have there been 

studies that meet or exceed a typical module warranty. 

Offering a warranty that is longer than available field 

observations can create a state of elevated risk. 

Fig. 2: Geographical distribution of degradation rates. 

Figure 2 shows the geographical distribution of the same 

rates of Fig. 1. The size of the circle indicates the number of 

reported rates at a given location. The number of cited rates 

is large enough to allow grouping for installation before and 

after the year 2000, and by technology as shown in Fig. 3. 

0 

5 

10 

15 

20 

25 

30 

35 

1985 1990 1995 2000 2005 2010 

Outdoor Exposure Length

(years) 

Publication Date 

Degradation Rate Publication 

Typical Module Warranty 

Fig. 4: Outdoor field exposure in years versus date of 

publication. Module warranty from one manufacturer is 

shown as comparison. 

3. DEGRADATION RATE UNCERTAINTY AND RISK 

It is important to distinguish the terms risk and uncertainty. 

Uncertainty in this paper will be used as the measurement 

uncertainty of a physical parameter while risk indicates an 

uncertain state that could lead to a loss, specifically to a 

financial loss. This section will discuss the connection 

between these two terms. 

Figure 5 shows the evolution with time of the performance 

of two modules deployed at NREL. It is immediately 

apparent that the two modules exhibit very different 

behavior. Module 1 has been deployed for nearly 10 years 

while Module 2 has been exposed for only 3 years. Module 

2 shows strong seasonal fluctuations whereas Module 1 

exhibits almost no seasonality. Surprisingly, both modules 

have the same degradation rate of 0.8%/year. The 

uncertainties, however, are very different, changing the 

warranty default risk significantly. 

Fig. 3: Historical degradation rates partitioned by 

technology and date of installation. The number in each 

category indicates the number of data points. The 95% 

confidence interval is denoted by the diamonds with the 

mean as the crossbar. 

2 
 

 

 

 

 

40 4. NUMBER OF MEASUREMENTS 

Further insight can be gained by investigating the historical 

degradation rates with respect to the number of 

30

DC Power (W) 20 

10 

0 

Module 1 

Module 2 

Module 1: Rd = (0.8 ± 0.2) %/year 

Module 2: Rd = (0.8 ± 1.0) %/year 

0 50 100 

Time (Months) 

Fig. 5: DC power data from 2 modules located at NREL 

using PVUSA method. [9] 

The lack of certainty has significant impact when the power 

production is calculated after 25 years according to equation 

(1). 

(1) Power (Yearn) = Power (Year1) · (1-Rd)

n 

A Monte Carlo simulation, shown in Fig. 6, illustrates this 

point. Typical maximum manufacturer warranties are 80% 

power production after 25 years of field use, which is 

indicated by a dashed green line. For the module with the 

smaller uncertainty the probability to default on the 

warranty (the integrated probability below the warranty line) 

measurements taken to determine degradation rates. 

Fig. 7: Percentage pie chart indicating the number of 

measurements taken to determine degradation rates. 

47% 

22% 

19% 

13% Continuous 

Discrete 

2 

1 

Number of measurements 

Figure 7 indicates that almost half the studies use 

continuous data metrics such as the performance ratio (PR) 

[10], PVUSA or take I-V curves in very short time intervals. 

[11] The discrete category consists of studies that take more 

than two measurements but do not acquire data 

continuously. It is noteworthy, however that a high 

percentage of references take only one or two measurements 

to report degradation rates. This situation is often is 24% after 25 years. However, the module with the larger 

uncertainty exhibits almost a 60% chance to default on the 

warranty despite the fact that both modules have the same 

degradation rate. This probabilistic approach demonstrates 

clearly how increased uncertainty in determining physical 

parameters can have a dramatic impact on risk in the future. 

encountered when baseline measurements were never taken 

or no longer exist today. Thus, modern measurements need 

to be compared to the original manufacturer’s standard test 

condition (STC) ratings. [12] This approach can add 

significant uncertainty to the measured degradation rates. 

[13, 14] To demonstrate the risk resulting from few The remainder of the paper will investigate various sources 

of uncertainty. 

0.00 

0.02 

0.04 

0.06 

0.08 

0.10 

0.12 

Probability 

Power Production after 25 Years (%) 

Rd=(0.8±0.2) %/year 

Rd=(0.8±1.0) %/year 

Fig. 6: Monte Carlo simulation for the standard-conditions 

power production of the two modules after 25 years 

measurements, I-V data taken on cloudless days every two 

months for a module deployed at NREL are shown in Fig. 8. 

I-V curves were irradiance and temperature corrected 

according to IEC 60891 method 1. [15] 

0 

20 

40 

60 

80 

DC Power (W) 

Time (Months) 

Rd = (0.38±0.03) %/year 

Fig. 8: Quarterly I-V data of a mono-Si module deployed at 

compared to a typical warranty of 80% indicated by the NREL. 

green dashed line. 

3 
 

 

 

 

The degradation rate using all available data is determined 

to be (0.38±0.03) %/year. Subsequently, a specific number 

of data points are taken randomly from this dataset and the 

degradation rate calculated. The resulting degradation rate 

will depend on the number of data points chosen and the 

time span between them, as illustrated by taking two data 

points (squares) and three data points (triangles), 

respectively (Fig. 8). The degradation rate may deviate 

substantially from the nominal (0.38±0.03) %/year 

particularly when the time span between taken data points is 

short. To evaluate the one data point situation a 3% 

uncertainty was added to the nominal rating in accordance 

with the Solar America Board for Codes and Standards 

recommendation. [16] As this procedure is repeated the 

resulting distribution of degradation rates for a specific time 

interval and a specific number of data points can be 

determined. The standard deviation of these distributions is 

a measure of the uncertainty and is shown in Fig. 9 as a 

function of the number of data points and number of years 

between the data points. 

(a) 

(b) 

Fig. 9: Contour plot of the standard deviation of the 

calculated Rd as a function of the number of years and data 

points from the data set in Fig. 8. No uncertainty was 

assumed for the temperature correction factor (a); a 20% 

relative uncertainty in the correction factor was assumed in 

(b). 

As the number of data points and the number of years 

increases, the calculated degradation rate approaches the 

rate of Fig 8. For fewer data points and short time spans the 

Rd distribution and therefore the probability increases 

significantly that the obtained degradation rate may be 

misleading (a). The situation is exacerbated when the 

temperature and irradiance correction is not ideal and 

includes a relative uncertainty (b). 

5. Rd METRICS AND TOTAL UNCERTAINTY 

Not only the number of measurements but also the 

methodology used for quantifying degradation rates can 

affect uncertainty. No standard exists today to determine 

long-term degradation rates, therefore a variety of different 

metrics are used. Examining again the historical data, 

degradation rate methodologies can be grouped into 4 broad 

categories, Indoor and Outdoor I-V, PVUSA and 

Performance Ratio (PR). Figure 10 shows a percentage 

breakdown of the different methodologies. 

31% 

13% 22% 

34% 

Outdoor IV 

Indoor IV 

PVUSA 

PR 

Fig. 10: Pie chart of the number of references deploying the 

indicated methods to determine degradation rates. 

One important aspect for the continuous data metrics 

PVUSA and PR that is not necessarily applicable to the 

other metrics is data filtering. Data filtering refers here to 

any examination or treatment of the data prior to assessing 

long-term trends. Outdoor I-V curves are typically taken 

around solar noon on clear days which essentially reflects a 

type of data filtering by choosing sunny days. Indoor I-V 

curves are typically taken under the same STC conditions. 

After the continuous raw data are collected the question 

arises as to which subset will be taken to assess long-term 

performance. Outliers, for instance caused by maintenance 

events or snow covering the panels may need to be excluded 

from the assessment. Performance metrics can be 

determined in different sampling intervals; for example, in 

monthly, weekly or daily increments. It has been shown that 

reducing the sampling interval from monthly to weekly 

increases the number of outliers, but with additional timeseries modeling the calculated uncertainty may be reduced. 

[17] Additionally, utilizing only sunny days has been shown 

to reduce the uncertainty for the PVUSA methodology. [18] 

4 
 

 

 

 

 

 

 

 

 

 

 

However, whether other metrics benefit in a similar way is 

not clear and some of the benefit may depend on what 

specific metric is utilized. 

To answer some of these questions, 4 different performance 

metrics were investigated on 7 different systems at NREL. 

The metrics included PVUSA, PR, the median DC Power 

over plane-of-array irradiance (DC/POA) uncorrected and 

temperature-corrected and the systems consisted of 3 x-Si 

and 4 thin-film arrays. The temperature correction was 

preferably done using the median of several module 

temperature (Tmod) measurements and corrected to 45o C 

using temperature coefficients derived from the continuous 

data. However, not all systems contained consistent multiple 

module temperature measurements in which case ambient 

temperature (Tamb) was used and corrected to 20o C, which 

is approximately equivalent to 45o C Tmod for the climate 

in Colorado. Since the data quality differed for DC Power 

and AC Power data for some systems all calculations are 

based on DC Power data such that direct comparison was 

possible. However, the following section should also be 

applicable if only AC Power data are used. All four metrics 

were investigated in monthly and daily intervals except the 

shorter interval for the PVUSA methodology was weekly. 

The impact of using only sunny days versus using all 

available days was investigated. Sunny days were 

determined by the clearness index of 0.5 which is the ratio 

of measured global irradiance over the extraterrestrial beam 

irradiance on a similarly tilted surface. [19] Filtering by 

irradiance was done by (1) keeping the upper limit fixed at 

1200 W/m2

 with a variable lower limit and (2) keeping both 

limits flexible resulting in 200W/m2

 irradiance bands. 

Lastly, different irradiance-level cutoffs ranging from 0 to 

800 W/m2

 were investigated. The filtering criteria are 

summarized in Fig. 11. To investigate the fairly large 

decision tree of Fig. 11, degradation rates and their 

statistical uncertainties were determined through automation 

resulting in 300-500 degradation rates per system. 

Filtering 

Metric PVUSA Performance Ratio DC/POA DC/POA Temp-corr 

Interval 

Sunny only 

Monthly Weekly Monthly Daily 

Yes No 

Irradiance Limit Flexible LL & UL Fixed UL & Flexible LL 

. 

. 

. 

. 

. 

. 

. 

. 

. 

. 

. 

. 

. 

. 

. 

. 

. 

. 

. 

. 

. 

Tamb (20C) Tmod (45C) 

Irradiance Level . 200 W/m2 0 W/m2 400 W/m . 2 600 W/m2 800 W/m2 

Fig. 11: Decision tree for data filtering. 

To assess the impact of the filtering on the degradation rate, 

a most likely or nominal degradation rate was needed. 

Since a true degradation rate cannot be determined [20] the 

nominal Rd was calculated as the median of 5 methods that 

included time series modeling and measurement of quarterly 

field I-V curves. [21] 

Because performance at low irradiance may differ 

substantially from technology to technology, especially for 

thin-film systems only degradation rates at high irradiance 

(800W/m2

) were further analyzed. Therefore, the group 

“Lower” in the Irradiance limit (H limit) category in Fig. 12 

refers to the flexible lower limit and constitutes an actual 

filtering interval from 800-1200W/m2

. The group “Upper” 

refers to a flexible upper limit in addition to a flexible lower 

limit and an actual filtering interval from 700-900W/m2

. 

Red dashed lines of 0.2%/year deviation from the nominal 

degradation rate are added as reference lines to the eye. The 

difference to the nominal degradation rate for each system 

and each data filtering category is shown for 4 thin-film 

systems (top) and 3 x-Si systems (bottom). Because of the 

lack of knowledge of a true degradation rate the salient 

feature of this figure is how the different metrics compare 

under the same filtering conditions. 

It appears that the temperature corrected DC/POA ratio falls 

mostly within or very close to the tight 0.2%/year interval. 

For the thin-film systems even the uncorrected DC/POA 

ratio and the PR fall within that same interval. This may be 

due to the 4 thin-film systems having on average been 

fielded longer than the 3 x-Si systems. For the x-Si systems 

the uncorrected DC/POA ratio and the PR show more 

scatter around the 0.2%/year interval. The PVUSA 

methodology shows large fluctuations depending on the 

filtering conditions. A possible reason is that the PVUSA 

methodology uses a two regression approach. In the first 

step, the raw data are normalized using POA, ambient 

temperature and wind speed in typically monthly 

increments. The second step is to graph the adjusted data in 

a time series and use a second regression to assess long-term 

behavior. Typically, the two regressions use a standard least 

square approach based on minimization of the squared 

residuals making it susceptible to outliers. It may be 

possible to improve the methodology by using for example 

a robust regression approach making it less susceptible to 

outliers. Another difficulty in the analysis of Fig. 12 is that 

filtering that gives the lowest uncertainty depends on the 

data set, since large data sets may still retain substantial data 

even after heavy filtering, whereas sparse datasets can’t 

tolerate much filtering. In general, this clearly shows that 

different performance metrics have different sensitivities to 

data filtering and thus different contributions to uncertainty. 

5 
 

 

 

 

 

Fig. 12: Difference to nominal degradation rate in %/year 

for different metrics and data filtering methods. Four thinfilm systems are shown on top and 3 x-Si shown at the 

bottom. Intervals of 0.2%/year interval are indicated by the 

red dashed lines and range bars for each category are also 

shown. 

7. ACKNOWLEDGMENTS 

This work was supported by the U.S. Department of Energy 

under Contract No. DE-AC36-08-GO28308 with the 

National Renewable Energy Laboratory. 

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6. CONCLUSION 

Historical degradation rates show that thin-film technologies 

narrowed the gap to x-Si technologies during the last 

decade. It was shown that uncertainty in the degradation rate 

determination directly impacts the warranty default risk. 

One third of the surveyed historical degradation rates have 

been determined based upon 1 or 2 measurements, which 

can significantly increase the probability of a misleading 

degradation rate. Continuous data are typically pre-treated, 

i.e. filtered prior to long-term performance assessment. The 

choice of filtering results in calculation of different 

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7