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Overview of Field Experience - Degradation Rates & Lifetimes

Solar Power International Anaheim, CA

Dirk Jordan, Sarah Kurtz 9/14/2015

NREL/PR-5J00-65040

2

Outline

Definitions

Degradation rates • Study type (using nameplate rating vs. multiple

measurements)

• Climate

• Module vs. system

Degradation curves - non-linearity

Failure modes

3

Merriam-Webster: “The quality or state of being reliable”

Degradation no sudden failure

“Quality over time”

PV: high reliability application

Failure (sudden) Degradation Failure

Lifetime distribution

Accelerated tests

• Tracked systems • Warranty returns • Maintenance

records

Need to agree

What is reliability?

Field data

4

> 30 system studies of 20+ years field exposure

Geographic distribution of degradation rates (Rd)

20

25

30

35

Year

s1995 2000 2005 2010 2015

Ref year

Fiel

d ex

posu

re (y

ears

)

Algeria

Brazil

Canada

China

Germany

Greece

India

Italy

Korea

Libya

Saudia Arabia

Spain

Sweden

Switzerland

Tunisia

USA

Publication year

Studies of systems > 20+ years in field

Increased interest in recent years in long-term performance & degradation

5

Presidential election 1948: Harry Truman defeated Thomas Dewey

6

It is difficult to obtain sample w/o bias. Presidential election 1948: Harry Truman defeated Thomas Dewey

Chicago Tribune conducted poll by telephone. 1948 telephone existed only in affluent homes, which voted overwhelmingly for Dewey. The sample was biased because the telephone was used!

7

0.0

0.2

0.4

0.6

0.8

1.0

N

0 1 2 3 4 5

Median(Midpoint Rd+/Pmax+)

Nor

mal

ized

Freq

uenc

y

Jordan et al., “Degradation Rates – An Analytical Review”, Progress in PV, 2011

0

50

100

150

200

250

Freq

uenc

y

Degradation Rate (%/year)

Median: 0.5 %/yearAverage: 0.8 %/year# reported rates = 2128

Degradation rate (%/year)

2011 2015

Literature survey

• Few studies with > 1000 modules • Aggregated distribution is dominated by particular module, system, mounting, method etc. • Counteract overrepresentation analyze in different ways • Median per study & system second peak disappears • High quality data (multiple measurements, calibrations etc.) second peak disappears

Sampling bias is present – representative of the population?

8

1 2 continuous discrete

17%20%

15%48%

7%

10%9%

74%

Percent of studies Percent of degradation rates

Measurements

Majority of Rd are determined by single measurement.

9

Majority of Rd are determined by single measurement.

60

70

80

90

100

NP

(%)

0 5 10 15 20 25

Year

How are Rd determined with 1 measurement?

Perc

enta

ge o

f N

amep

late

Rat

ing

1 2 continuous discrete

17%20%

15%48%

7%

10%9%

74%

Percent of studies Percent of degradation rates

Measurements

10

Majority of Rd are determined by single measurement.

60

70

80

90

100

NP

(%)

0 5 10 15 20 25

Year

How are Rd determined with 1 measurement?

Perc

enta

ge o

f N

amep

late

Rat

ing

1 2 continuous discrete

17%20%

15%48%

7%

10%9%

74%

Percent of studies Percent of degradation rates

Measurements

11

Majority of Rd are determined by single measurement.

60

70

80

90

100

NP

(%)

0 5 10 15 20 25

Year

How are Rd determined with 1 measurement?

Perc

enta

ge o

f N

amep

late

Rat

ing

Did it start at 100%? Distribution?

1 2 continuous discrete

17%20%

15%48%

7%

10%9%

74%

Percent of studies Percent of degradation rates

Measurements

12

0.2

0.4

0.6

0.8

1

STC

diff

1980

1985

1990

1995

2000

2005

2010

2015

Year

M

Measurement

Indoor IV

Outdoor IV

0.2

0.4

0.6

0.8

1

1980

1985

1990

1995

2000

2005

2010

2015

Ti ( )

Climate

Desert

Hot & Humid

Moderate

Snow

Continent

Africa

Asia

Europe

North America

South America

100

60

80

40

20

RD using nameplate rating may have significant error. Pe

rcen

tage

of N

amep

late

ratin

g

Studies using nameplate rating Historical deviation from nameplate rating

• Indoor IV seem closer to nameplate rating (though limited data)

• Outdoor seem farther away trending towards nameplate rating. Outdoor data may include light-induced degradation (LID)

13

Med

ian(

Rd

+/P

max

+)

-1

0

1

2

3

4

5

6

2000

s

2010

s

1980

s

1990

s

2000

s

2010

s

2000

s

2010

s

1990

s

2000

s

2010

s

1990

s

2000

s

2010

s

1980

s

1990

s

2000

s

2010

s

µc-Si a-Si HIT CdTe CIGS x-Si

D d h ithi M d l T 3

Rd+

/Pm

ax+

-1

0

1

2

3

4

5

6

2000

s

2010

s

1980

s

1990

s

2000

s

2010

s

2000

s

2010

s

1990

s

2000

s

2010

s

1990

s

2000

s

2010

s

1980

s

1990

s

2000

s

2010

sµc-Si a-Si HIT CdTe CIGS x-Si

D d h ithi M d l T 3

Degr

adat

ion

rate

(%/y

ear)

All degradation rates Median per study & system

Measurements

Technology

1

2+

µc-Si

a-Si

HIT

CdTe

CIGS

x-Si

Significant difference between all Rd & median per study & system a-Si Rd > 1 %/year Hetero interface (HIT) Rd similar to a-Si than x-Si CIGS around 0.5 %/year

Reducing sampling bias x-Si median 0.5 - 0.6, mean 0.8 %/year

14

Rd+

/Pm

ax+

-1

0

1

2

3

4

1 2+ 1 2+ 1 2+ 1 2+

Desert Hot & Humid Moderate Snow

D t Pt 2 ithi Cli t 3

Measurements1

2+

Desert

Hot & Humid

Moderate

Snow

Climate

Rd+

/Pm

ax+

-1

0

1

2

3

4

1 2+ 1 2+ 1 2+ 1 2+

Desert Hot & Humid Moderate Snow

D t Pt 2 ithi Cli t 3

Degr

adat

ion

rate

(%/y

ear)

Climate category: simplified Köppen-Geiger

All degradation rates Median per study

1-measurement studies show statistically significant higher rates Climate statistically not significant … but…

Hotter climates appear to show higher Rd

15

Rd+

/Pm

ax+

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Solarex Siemens Solarex Siemens Solarex Siemens Siemens Solarex

Rack Roof Rack Roof Rack Roof Rack

Desert Hot & Humid Moderate Desert Moderate

1 2+

Manufacturer

Siemens

Solarex

Degr

adat

ion

rate

(%/y

ear)

Measurements

Climate

Mounting

Manufacturer

2.5x

Some modules more susceptible to hotter climates & mounting.

Show low median Rd across climates Desert (1-measurement) shows higher Rd 1-axis tracker & 2.5x concentration

Solarex:

Siemens: Roof mounting shows higher Rd even for moderate climate Hot & Humid shows higher Rd than Solarex

16

System vs. module degradation?

Performance and Aging of a 20-Year-Old Silicon PV System, Jordan et al., J. PV, 2015.

East

West

20 year old mono-Si system at NREL

Siemens M55 modules Size: 7.4kW

17

• The worst string appears to determine the subarray and array performance

• System degradation matches module degradation if modules degrade similarly, System degradation > average module degradation when large spread

System & module degrade similarly

18

0.2

0.4

0.6

0.8

Cum

ulat

ive

Prob

abili

ty

0 1 2 3

Degradation (%/year)

Modules, all (1552) Systems, all (385) Modules, median (61) Systems, median (71)

Cum

ulat

ive

prob

abili

ty

Degradation rate (%/year)

At median systems & modules degrade similarly

High –quality, x-Si data

At median modules degrade a little less than systems but in general very similar For less well-performing products more significant gap between modules & systems

19

60

70

80

90

100

Pmax

_sca

led

1990

1995

2000

2005

2010

2015

2020

Year2

90

80

70

60

70

80

90

100

Dat

a_sc

aled

1990

1995

2000

2005

2010

2015

Year

0

20

40

60

80

100

Dat

a

1990

1995

2000

2005

2010

2015

2020

Year

0

20

40

60

80

100

Dat

a_sc

aled

1985

1990

1995

2000

2005

2010

2015

Year

Perc

enta

ge o

f Ini

tial M

easu

rem

ent

Modules measured: 191 Modules measured: 8

Modules measured: 288

Modules measured: 54

Year

Northern CaliforniaColoradoSwitzerlandItaly 60

80

70

Appears linear for most modules, worst modules non-linear

1st measurement taken after 11 years

Studies that measured sample modules several times in 20+ years

Distribution skews towards low end worse performing modules show some non-linearity Central tendency & better modules fairly linear

ARCO ARCO

ARCO Siemens

20 years

30 years

20

( )( ) ( )

( )( )∑

∑

=

=

+−⋅

+−

+−⋅

+==

N

nn

nD

N

nnn

rRoductionInitial

rValuesidual

rRateTaxtMOCostInitial

oductionEnergyLifetimeTotalCostCycleLifeTotalkWhLCOE

1

1

11Pr

1Re

11cos&

Pr)/($)1(

80

85

90

95

100

Y

0 5 10 15 20 25

Year

Linear, 0.5 %/year Exponential 2-step Linear, 0.16 %/year

Nam

epla

te ra

ting

(%)

Year

Simulation assumption for LCOE impact

Parameter Distribution Mean Range Unit Initial production

Normal 159,437 ± 2% kWhr/year

Initial cost Triangular 2.6 2.4 – 2.8 $/W Discount rate Uniform 7.5 6 – 9 % Residual value Uniform 5000 0 – 10,000 $ O&M cost Triangular 20 17 – 25 $/kW/year O&M escalation

Triangular 2 0 – 4 %

Hypothetical commercial PV system near NREL: Size: 100 kW, tilt: 40º, azimuth: 180º PVWatts Tax rate: 30%

Input degradation curves

21

LCO

E M

ean

15.5

16.0

16.5

17.0

17.5

18.0

18.5

19.0

19.5

20.0

0 20 40 60 80 0

Discount rate

Initial cost

OM escalation

OM_total

Production_0

Residual value

1

2

3

4

5

6

LCO

E (¢

/kW

h)

Percentile

20.0

Mean: 17.3

Sensitivity analysis: Spider plots

See most important factors, can read the impact directly of axis. Different trends: ⇓ Productioninitial⇑ LCOE ; ⇓Costinitial ⇓LCOE

Most important factor: Discount rate ≈ 2.5 ȼ/kWh; Costinitial ≈ 2 ȼ/kWh; Productioninitial ≈ 1.5 ȼ/kWh; O&M < 0.5 ȼ/kWh

22

LCO

E M

ean

15.5

16.0

16.5

17.0

17.5

18.0

18.5

19.0

19.5

20.0

0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80

Percentile2

Discount rate

Initial cost

OM escalation

OM_total

Production_0

Residual value

1

2

3

4

5

6

LCO

E (¢

/kW

h)

Percentile

20.0

Mean: 17.3 Mean: 18.4 Mean: 18.2 Mean: 16.7

Sensitivity analysis: Spider plots

Degradation curve has significant impact on LCOE

See most important factors, can read the impact directly of axis. Different trends: ⇓ Productioninitial⇑ LCOE ; ⇓Costinitial ⇓LCOE

Most important factor: Discount rate ≈ 2.5 ȼ/kWh; Costinitial ≈ 2 ȼ/kWh; Productioninitial ≈ 1.5 ȼ/kWh; O&M < 0.5 ȼ/kWh

Degradation curve type ≈ 1.75 ȼ/kWh

23

Discoloration & delamination most common

0 25 50 75 100

Percentage of Studies

Discoloration/Browning

Delamination

Corrosion

Glass breakage

J-box

Cell breakage

Cracked backsheet

Hot spot

Soling

Mismatch

LID

Discoloration/Browning Delamination

Corrosion Glass breakage

J-box Cell breakage

Cracked backsheet Hot spot

Soiling Mismatch

LID

Majority of studies report discoloration & delamination

Pareto chart of failure modes in literature (2012)

24

Summary

1-measurement studies show significantly higher Rd because of nameplate rating deviation

Increased Rd in hotter climates & mounting for some products

x-Si has median in 0.5 – 0.6 %/year range, mean in 0.8 %/year range

Non-linearities have significant financial impact, need more accurately measure curves instead of relying solely on rates.

25

Tornado in Germany, May 2015

Roofs have substantial damage but modules appear for the most part undamaged.

Lifetime goal?

26

Acknowledgments

Thank you for your attention National 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