NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by 

Technology and Clim

in PV Module Degr

27th European PV Solar Energy Confe

Frankfurt, GermanyDirk Jordan, John Wohlgemuth, Sarah 

9/27/2012

NREL/PR-5200-56690
 Introduction

 I‐V parameter degradation analys

 I‐V parameter distribution Climatic trends

 Conclusion

Outline
Introduction‐ Degradation Ra

Rd (Module 1) = 0.8 %/year

Rd (Module 2) = 0.8 %/year

Photo by Warren Gretz, NREL/PIX 03877

NREL – Outdoor Test Facility
2 modules: Different observation lengths, seasonalit

05

1015202530354045

0 50 

DC Power (W)

Time (Month

MoMo

Rd (Module 1) = 0.8 %/year

Rd (Module 2) = 0.8 %/year

Photo by Warren Gretz, NREL/PIX 03877

NREL – Outdoor Test Facility

Introduction‐ Degradation Ra
NREL – Outdoor Test Facility

2 modules: Different observation lengths, seasonalit

05

1015202530354045

0 50 

DC Power (W)

Time (Month

MoMo

Rd (Module 1) = (0.8 ±0.2) %/year

Rd (Module 2) = (0.8 ±1.0) %/year

Same degradation rate, different Unce

Photo by Warren Gretz, NREL/PIX 03877

Introduction‐ Degradation Ra
0.000.020.040.060.080.100.12

Probability

Power Product

Rd=(0.8±0.2) %Rd=(0.8±1.0) %

RD Uncertainty Impact on Wa

Maximum module warranty ‐ 2010

Probability to defau

Module 1: Probabil

  Module 2: Probabil

n n  1  R1)Year(Power)Year(Power d

Need to determine Rd accurately

Source: Photon International, Feb 2010

Monte Carlo Simula
Degradation Rates – Literature 

Most modules degrade by ca. 0.5 %/ye

Number of Degradation rates (Rd)

from literature: 2128

Jordan et al., “Degradation Rates – An Anal

Technology, age, packaging, geographic location

ca. 80% below 1%/year

> 100 publications fr

Circle size = numb

points from a give

0

50

100150200250

Frequency

Degradation Rate (%/year)

Median: 0.5 %/yearAverage: 0.8 %/year

# reported rates = 2128
Literature Degradation Ra

Date of installation: Pre‐ & Post‐2000

Red diamonds: mean & 95% confidence interval

Thin‐film technologies narrowed gap to c‐Si in la

I‐V parameter information for ca. 50% of 

> 50% of Rd taken by 

Information on shor

open‐circuit voltage 

Imax, Vmax

No standard to 
Degradation Rate (%/year)

IV Parameter

Pmax Imax Vmax

N=209

Degradation Rate (%/year)

IV Paramete

Pmax Imax 

Statistically degradation in current not in 

Mono‐Si Multi‐Si

Pmax Degradation Partiti

Important for proper inverter sizing

N=209 
I‐V Parameters by Technol

Mono‐Si Multi‐Si

639

IV Parameter

Pmax Isc Voc FF

632 591 584

Degradation Rate (%/year)

-1

01234

Data

Pmax Rd+ Isc Rd+ Voc Rd+ FF Rd+

Label

476

IV Parameter

Pmax Isc Voc FF

475 473 397

Degradation Rate (%/year)
I‐V Parameters by Technol

Mono‐Si Multi‐Si

639

IV Parameter

Pmax Isc Voc FF

632 591 584

Degradation Rate (%/year)

-1

01234

Data

Pmax Rd+ Isc Rd+ Voc Rd+ FF Rd+

Label

476

IV Parameter

Pmax Isc Voc FF

475 473 397

Degradation Rate (%/year)

0.00.51.01.52.02.5

0 10 20

Current(A)

Voltage (V)

10/18/2000 ‐ 12 pm10/15/2010 ‐ 12 pm

Smith et al., 

Energy Foru

Pmax correlated to Isc loss Pmax correlated to FF loss

9/12 modules 3/12 modules

NREL study – May 2012 – World Renewable Energy Forum
I‐V Parameters by Technol

Mono‐Si Multi‐Si 

639

IV Parameter

Pmax Isc Voc FF

632 591 584

Degradation Rate (%/year) 42

Pm

Degradation Rate (%/year)

Pm

-1

01234

Data

Pmax Rd+ Isc Rd+ Voc Rd+ FF Rd+

Label

476

IV Parameter

Pmax Isc Voc FF

475 473 397

Degradation Rate (%/year)

0.00.51.01.52.02.5

0 10 20

Current(A)

Voltage (V)

10/18/2000 ‐ 12 pm10/15/2010 ‐ 12 pm

Smith et al., 

Energy Foru

Pmax correlated to Isc loss

9/12 modules 3/12 modules

Pmax correlated to FF loss

NREL study – May 2012 – World Renewable Energy Forum
I‐V Parameters by Technol

Mono‐Si Multi‐Si 

Degradation for c‐Si Isc; Thin‐film 

639

IV Parameter

Pmax Isc Voc FF

632 591 584

Degradation Rate (%/year) 42

Pm

Degradation Rate (%/year)

Pm

-1

01234

Data

Pmax Rd+ Isc Rd+ Voc Rd+ FF Rd+

Label

476

IV Parameter

Pmax Isc Voc FF

475 473 397

Degradation Rate (%/year)

0.00.51.01.52.02.5

0 10 20

Current(A)

Voltage (V)

10/18/2000 ‐ 12 pm10/15/2010 ‐ 12 pm

Smith et al., 

Energy Foru

NREL study – May 2012 – World Renewable Energy Forum

Pmax correlated to Isc loss Pmax correlated to FF loss
Degradation Rate (%/year)

-1 0 1 2 3 4

Climate Analysis

Based on Köppen‐Geiger Classification
Degradation Rate (%/year)

-101234

Climate Analysis

• Pmax most strongly correl• Isc losses: discoloration, so

light‐induced degradation

• Majority of studies report • No standard reporting of f

mechanisms, C. Packard ta

Discoloration/Browning

Delamination

Corrosion

Glass breakage

J‐box

Cell breakage

Cracked backsheet

Hot spot

Soiling

Mismatch

LID

Based on Köppen‐Geiger Classification
Degradation Rate (%/year)

-101234

Climate Analysis

Pmax correlated with Isc loss for most c

Discoloration/Browning

Delamination

Corrosion

Glass breakage

J‐box

Cell breakage

Cracked backsheet

Hot spot

Soiling

Mismatch

LID

Based on Köppen‐Geiger Classification

• Pmax most strongly correl• Isc losses: discoloration, so

light‐induced degradation

• Majority of studies report • No standard reporting of f

mechanisms, C. Packard ta
NonHot &

Humid

Pmax Rd+

Pmax Rd+

Pmax Rd+

I‐V parameters for c‐Si

Pmax degradation mostly correlated with Isc loss2 parallel lines – 1 measurement studies

Tail f

show
Hot &

Humid

NonHot &

Humid

Pmax Rd+ Pmax Rd+

Pmax Rd+

Pmax Rd+

Pmax Rd+

Pmax Rd+

I‐V parameters for c‐Si

Hot & Humid shows mixture of degrad

Pmax degradation mostly correlated with Isc loss2 parallel lines – 1 measurement studies

Tail f

show
Hot & Humid by Decad

Field failure mechanism may change with fie

-0.5

0

0.5

1

1.5

2

Pmax Isc Voc FF

I-V Parameter

-0.5

0

0.5

1

1.5

2

Pmax Isc Voc FF

I-V Parameter

-0.5

0

0.5

1

1.5

2

Pma

Decade 1 

Decade 2 

Very little FF degradation till in first 2 decades

Much hig

studies l
Thin‐film show high FF Rd in humid climates

c‐Si – Thin‐film Comparis

Different lonperformance 

based on clim

First Solar re‐0.7%/year f

‐0.5%/year f

First 

Different long‐term recommendation based 

Rd (%/year)

Strevel et a

Internation

Hot & Humid Climate
Module I‐V Distribution

Color: I‐V parameterSize: Number of module

Coefficient of Variation (CoV): Standard 

Data

Distribution width

Coefficient of Variation (%)

Years
Data

Module I‐V Distribution

Color: I‐V parameterSize: Number of module

Coefficient of Variation (CoV): Standard 

Distribution width

Coefficient of Variation (%)

Years
Module I‐V Distribution

Color: I‐V parameterSize: Number of module

Coefficient of Variation (CoV): Standard 

Data

Distribution width

Coefficient of Variation (%)

Years
Module I‐V Distribution

Color: I‐V parameterSize: Number of module

Coefficient of Variation (CoV): Standard 

Data

Distribution width

Coefficient of Variation (%)

Years
Module I‐V Distribution

Color: I‐V parameterSize: Number of module

Coefficient of Variation (CoV): Standard 

Data

-4-3-2-1

012

Skewness

-5 0 5 10 15 

Yea

Distribution width Distribution sy

Coefficient of Variation (%)

Distribution Skewness

Years Yea
Module I‐V Distribution

Color: I‐V parameterSize: Number of module

Coefficient of Variation (CoV): Standard 

Data

-4-3-2-1

012

Skewness

-5 0 5 10 15 

Yea

Distribution width Distribution sy

Coefficient of Variation (%)

Distribution Skewness

Years Yea
Module I‐V Distribution

Color: I‐V parameterSize: Number of module

Coefficient of Variation (CoV): Standard 

Data

-4-3-2-1

012

Skewness

-5 0 5 10 15 

Yea

Distribution width Distribution sy

Coefficient of Variation (%)

Distribution Skewness

Years Yea
Module I‐V Distribution

Color: I‐V parameterSize: Number of module

Coefficient of Variation (CoV): Standard 

Data

-4-3-2-1

012

Skewness

-5 0 5 10 15 

Yea

Distribution width Distribution sy

Coefficient of Variation (%)

Distribution Skewness

Years Yea
Module I‐V Distribution

Color: I‐V parameterSize: Number of module

Coefficient of Variation (CoV): Standard 

Data

-4-3-2-1

012

Skewness

-5 0 5 10 15 

Yea

Distribution width Distribution sy

Coefficient of Variation (%)

Distribution Skewness

Years Yea

Distributions widen and skew with field 
Israel – 1995 – 7 years

I‐V Distributions
Israel – 1995 – 7 years Japan – 2003 – 10 years

I‐V Distributions
Israel – 1995 – 7 years Japan – 2003 – 10 years Sweden – 2006 – 25 years

I‐V Distributions
Israel – 1995 – 7 years Japan – 2003 – 10 years Sweden – 2006 – 25 years

I‐V Distributions
Israel – 1995 – 7 years Japan – 2003 – 10 years Sweden – 2006 – 25 years

I‐V Distributions

MA – 2010 – 30 years
Israel – 1995 – 7 years Japan – 2003 – 10 years Sweden – 2006 – 25 years

I‐V Distributions

Spain – 2011 – 12 years

MA – 2010 – 30 years
Israel – 1995 – 7 years Japan – 2003 – 10 years Sweden – 2006 – 25 years

I‐V Distributions

Spain – 2011 – 12 years

MA – 2010 – 30 years

Switzerland – 2010 ‐20 years
Israel – 1995 – 7 years Japan – 2003 – 10 years Sweden – 2006 – 25 years

I‐V Distributions

Spain – 2011 – 12 years

MA – 2010 – 30 years

Switzerland – 2010 ‐20 years CO – 2012 – 15 years
Israel – 1995 – 7 years Japan – 2003 – 10 years Sweden – 2006 – 25 years

I‐V Distributions

Spain – 2011 – 12 years

MA – 2010 – 30 years Italy – 2010 –

23 & 30 years

Switzerland – 2010 ‐20 years CO – 2012 – 15 years
Israel – 1995 – 7 years Japan – 2003 – 10 years Sweden – 2006 – 25 years

I‐V Distributions

Spain – 2011 – 12 years

MA – 2010 – 30 years Italy – 2010 –

23 & 30 years

Switzerland – 2010 ‐20 years CO – 2012 – 15 years
Israel – 1995 – 7 years Japan – 2003 – 10 years Sweden – 2006 – 25 years

I‐V Distributions

Spain – 2011 – 12 years

MA – 2010 – 30 years Italy – 2010 –

23 & 30 years

Switzerland – 2010 ‐20 years CO – 2012 – 15 years
Israel – 1995 – 7 years Japan – 2003 – 10 years Sweden – 2006 – 25 years

I‐V Distributions

Spain – 2011 – 12 years

MA – 2010 – 30 years Italy – 2010 –

23 & 30 years

Switzerland – 2010 ‐20 years CO – 2012 – 15 years

0

50

100150200250

Frequency

Degradation Rate (%/year)
Israel – 1995 – 7 years Japan – 2003 – 10 years Sweden – 2006 – 25 years

I‐V Distributions

Spain – 2011 – 12 years

MA – 2010 – 30 years Italy – 2010 –

23 & 30 years

Switzerland – 2010 ‐20 years CO – 2012 – 15 years

0

50

100150200250

Frequency

Degradation Rate (%/year)

Module Parameter Distributions similar to Rd 
Module Failure Mechanis

Extreme value theory: situation where many independent processes c

Situation often modeled by (Maximum or Minimum) Extreme Value D

Also known as the “Weakest link distribution”

WohReliaMon

Module may be modeled by Extreme Value 
Extreme Weather

Extreme events can be modeled by Extreme Value Dist

Photosfrom University Corporation for Atmospheric Research

Hurricane Katrina 2005

Hailstone Nebraska 2003Drought ‐ Current Tornado 1995 

Photosfrom University Corporation for Atmospheric Research

500 year flood Midwest 1993

Emil Gumbel:"It seems that the rivers know the theory. It only remains to conv

of the validity of this analysis."

President Truman’s 

(1950):"However big floodalways be a bigger 

one theory of extre

suggests it is true."
Count

132140148156164172180188196

System Performance

10 normal distributions, take the smallest value

Repeat 2000 times ‐‐>Smallest Extreme Value Distribution

Very few studies investigate that many modules

‐‐> take 50 random sample
100200300400

Count

132140148156164172180188196

10 normal distributions, take the smallest value

Repeat 2000 times ‐‐>Smallest Extreme Value Distribution

Very few studies investigate that many modules

‐‐> take 50 random sample

System Performance
100200300400

Count

132140148156164172180188196

10 normal distributions, take the smallest value

Repeat 2000 times ‐‐>Smallest Extreme Value Distribution

Very few studies investigate that many modules

‐‐> take 50 random sample

132140148156

System Performance
100200300400

Count

132140148156164172180188196

10 normal distributions, take the smallest value

Repeat 2000 times ‐‐>Smallest Extreme Value Distribution

Very few studies investigate that many modules

‐‐> take 50 random sample

132140148156

System Performance
100200300400

Count

132140148156164172180188196

10 normal distributions, take the smallest value

Repeat 2000 times ‐‐>Smallest Extreme Value Distribution

Very few studies investigate that many modules

‐‐> take 50 random sample

132140148156

Outlier may be indicative of skewed dist

Takes sufficient field exposure & sample si

System Performance
0

50

100150200250300

Frequency

System Degradation Rate (%/year)

CoV=1, Skewness=0CoV=2, Skewness=‐1.1CoV=8, Skewness=0CoV=8, Skewness=‐2.6

Why do we care?

Monte Carlo Simulation:

Assumption: modules 0.5%/year degradation over 25 years

20kW array, 200W modules, 100 modules, 10 strings, 10 modules/
0

50

100150200250300

Frequency

System Degradation Rate (%/year)

CoV=1, Skewness=0CoV=2, Skewness=‐1.1CoV=8, Skewness=0CoV=8, Skewness=‐2.6

Why do we care?

Monte Carlo Simulation:

Assumption: modules 0.5%/year degradation over 25 years

20kW array, 200W modules, 100 modules, 10 strings, 10 modules/

Distribution of module degradation affects system 
Summary

1. Individual trends

 c‐Si Pmax decline is most strongly correlated with Isc , 

 Hot & humid climate show mixture with higher FF deg

 Thin‐film are characterized by much higher FF degradat

have a lot of field data)

 Module distributions widen and skew with increasing f

→ can have significant impact on system performance

2. Big Picture

 IEC 61215: Infant mortality test ↔ No lifetime test, Inte

Quality Assurance Task Force

 Can help in prioritizing failure mechanisms & used to d

accelerated tests for lifetime in c‐Si.
Acknowledgments

Thank you to 

Chris DelineJosh SteinNREL T&M teaRest of the NR

Thank you for your attentionVielen Dank für Ihre AufmerksamNational Renewable Energy Laboratory

15013 Denver West Parkway, MS 3411

Golden, CO 80401, USA

dirk.jordan@nrel.gov

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

Energy under Contract No. DE‐AC36‐08‐GO28308

with the National Renewable Energy Laboratory