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Contract No. DE-AC36-08GO28308

Technical Report

NREL/CP-6A20-72212

November 2018

Evaluating Energy Impacts and Costs 

from PV Component Failures

Preprint

Janine Freeman,

1 Geoffrey T. Klise,2 Andy Walker,1

and Olga Lavrova2

1 National Renewable Energy Laboratory

2 Sandia National Laboratories

Presented at the World Conference on 

Photovoltaic Energy Conversion (WCPEC-7)

Waikoloa, Hawaii

June 10–15, 2018
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

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

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Technical Report

NREL/CP-6A20-72212

November 2018

Evaluating Energy Impacts and Costs 

from PV Component Failures

Preprint

Janine Freeman,

1 Geoffrey T. Klise,2 Andy Walker,1

and Olga Lavrova2

1 National Renewable Energy Laboratory

2 Sandia National Laboratories

Suggested Citation 

Freeman, Janine, Geoffrey T. Klise, Andy Walker, and Olga Labrova. 2018. Evaluating 

Energy Impacts and Costs from PV Component Failures: Preprint. Golden, CO: National 

Renewable Energy Laboratory. NREL/CP-6A20-72212. 

https://www.nrel.gov/docs/fy19osti/72212.pdf.
NOTICE

This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable 

Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding 

provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy 

Technologies Office. The views expressed herein do not necessarily represent the views of the DOE or the U.S. 

Government.

Energy Laboratory (NREL) at www.nrel.gov/publications.

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NREL 46526.

NREL prints on paper that contains recycled content.
1

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Evaluating Energy Impacts and Costs from PV Component Failures

Janine M. Freeman1, Geoffrey T. Klise2, Andy Walker1, Olga Lavrova2

1

National Renewable Energy Laboratory, Golden, CO, 80401, USA

2

Sandia National Laboratories, Albuquerque, NM, 87185, USA

Abstract — The PV Reliability Performance Model (PV-RPM) 

tool is used to simulate the cost and energy impacts of component 

faults and failures for a hypothetical PV system. This model, 

originally developed by Sandia National Laboratories, is a new 

feature in the National Renewable Energy Laboratory’s System 

Advisor Model (SAM), which performs stochastic analysis 

sampling of probability distributions for faults, failures, and 

repairs. One PV system was created for this analysis to be 

representative of a portfolio of maintenance data maintained by 

Sandia National Laboratories. Fault, failure, and repair 

distributions from this system are varied from current baseline 

conditions to simulate different reliability and repair scenarios 

and resulting energy and cost impacts. Results highlight ways to 

express the uncertainty around PV system performance when 

considering the probabilistic behavior of components in the 

system, and ways the PV-RPM model can be used to explore 

different failure and repair scenarios. 

I. INTRODUCTION

Sandia National Laboratories (SNL) has been collecting 

maintenance data from operational PV systems to work with 

industry partners to better understand and characterize how 

component events are impacting system performance [1]. SNL 

worked with the National Renewable Energy Laboratory

(NREL) using the SNL reliability dataset in the development of

O&M cost budgets for new PV systems in the PV O&M Cost 

Model [2], and in the PV Reliability Performance Model (PVRPM) in the System Advisor Model (SAM) to perform 

probabilistic simulations of lifetime performance based on fault 

and failure distributions [3,4,5]. These tools allow for many 

different types of planning scenarios for new or existing PV 

systems, considering different component failure rates and 

maintenance scheduling scenarios, and modeled scenarios,

using an existing system’s fault/failure and repair distributions 

to evaluate the uncertainty in energy production based on how 

the system is currently performing. The PV-RPM tool allows 

one to analyze not only the reduction in power due to a failed 

component, but also the cost to repair the component, which are 

then used as inputs in a SAM simulation so that the effect of 

both the reduced power and the repair cost are included in the 

calculated levelized cost of energy (LCOE).

We used the PV-RPM tool to conduct hypothetical reliability 

scenario analysis utilizing existing fault/failure and repair 

distributions from the SNL reliability dataset for a fictitious 305 

kWDC system. This system is meant to represent a system within 

Portfolio D, which consists exclusively of distributed 

generation (DG) as compared to other data portfolios managed 

by SNL (Table 1). We use this system to establish a baseline in 

terms of estimated performance (energy, O&M costs, and 

LCOE) considering a selection of existing failure and repair 

rates. We then vary fault and failure distributions and different 

repair scenarios to compare to the baseline scenario.

II. PORTFOLIO AND DATASET

The SNL portfolio has a total capacity of 780 MWDC with a 

total of 144 systems. Currently, 56% (109 out of the 189 PV 

systems) have maintenance data recorded against specific 

components. This represents around 510 MWDC out of the total 

780 MWDC in the portfolio. The data collection range varies for 

each portfolio as shown in Table I.

Fig. 1 presents the total number of both faults and failures 

collected by SNL within all portfolios. The data is sorted by the 

component with the most events in the database, to those that 

have the fewest catalogued events.

The 305 kWDC system used in this analysis is representative

of Portfolio D, which is characterized in Fig. 2 as a function of 

the percentage of events (both faults and failures) that cover the 

data collection timeframe of 2013 to 2017. An “event” is any 

Fig. 1. Summary of number of events (faults and failures) across 

all portfolios

TABLE I

CURRENT PORTFOLIO SUMMARY

Portfolio Comm. year

Data 

collection 

range

# of 

systems MWDC

% 

DG

% 

utility 

scale

A 2003 2003-2008 1 3.5 0 100

B 2008-2009 2012-2014 2 1.75 100 0

C 2008-2016 2015-2016 180 578 3.4 96

D 2010-2017 2013-2017 61 25.6 100 0

Comm. – commissioning
2

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fault or failure that triggers a trouble ticket in the computerized 

maintenance management software. Inverters make up about 

half of all events within Portfolio D, followed by grid issues 

(external to the PV system). Combiner issues are next, then 

followed by environmental impacts, AC meter, string and 

finally weather station outages.

Previous work [1] analyzed the failure information in these 

datasets to fit statistical distributions to the failure and repair 

times of components in these datasets.

III. BASELINE SCENARIO

To conduct the analysis at hand, we first established a 

baseline scenario, representing current reliability and repair 

information, for comparison against our hypothetical reliability 

scenarios. In SAM, we created a fictitious system design 

representing a 305 kWDC ground mount system from Portfolio 

D of the maintenance data. We populated this simulation with 

system cost data corresponding to a commercial system in the 

“U.S. Solar Photovoltaic System Cost Benchmark: Q1 2017”, 

with a total installed cost of $1.84/WDC and an assumed inverter 

replacement at 10¢/WDC in year 15 [6]. For the financial model, 

we used the Single Owner model within SAM, using mostly its

default inputs, and examining the system over a 20-year 

lifetime. 1

We then set up a stochastic component reliability analysis in 

the PV-RPM tool within SAM to run on our representative PV 

system. We modeled failures for only the top three components 

with the most failures from Fig. 2: inverters, DC combiners, and 

the grid. All other components were assumed not to fail, which 

will artificially lower final LCOEs. For each component, we 

selected representative failure distributions and their 

corresponding repair distributions from Appendix A of [1]. 

Three common failure modes were selected for the inverters: 

fan failures, power cycling failures, and insulated gate bipolar 

transistor (IGBT) failures. Only two failure modes were 

available for the grid and one for DC combiners, so all of those 

 1 The SAM file and PV-RPM scripts used for this analysis will 

be posted on the SAM website at https://sam.nrel.gov/pvrpm

failure distributions were used for their respective components. 

Table II shows the selected failure and repair distributions for 

each component. Note that although sub-components are 

replaced, none of these failure distributions model the end-oflife failure and total replacement of the entire inverter, so the 

assumed total inverter replacement in year 15 remained in our 

simulation, consistent with the inverter lifetime estimate used 

in [6].

Finally, repair costs, repair labor time, and labor rates for 

each failure type were taken from the spreadsheet O&M cost 

model associated with [7] and entered into the PV-RPM tool 

within SAM. These costs and repair times are shown with their 

corresponding failure mode in Table II.

With our baseline scenario completed with a system design, 

system costs, representative failure and repair distributions, and 

associated repair cost information, we ran 50 stochastic 

realizations of this hypothetical scenario to establish our 

baseline LCOE range.

In addition to our baseline scenario, we ran the baseline 

system in a deterministic manner assuming no component 

failures whatsoever throughout the lifetime of the system. This 

demonstrates the lower limit for the improvement that our 

assumed reliability and repair scenarios can provide. Assuming 

a 20-year system lifetime, the no-failure LCOE of this system 

is 9.50¢/kWh. 

IV. RELIABILITY AND REPAIR SCENARIOS

After establishing our baseline scenario, we simulated 

several different reliability and repair scenarios. These 

scenarios fall into three different categories: 1) improved 

reliability scenarios, where we extend the time to failure as a 

proxy for improved component reliability, 2) improved repair 

scenarios, where we shorten the time to repair, and 3) an 

improved inverter lifetime scenario, where we lengthen the 

assumed lifetime of the inverter, versus improving reliability of 

the non-fatal failure modes we chose to model. 

For the improved reliability scenarios, we first explored an 

“attainable” improvement where it takes components 20% 

longer to fail. We accomplished this, after sampling the original 

failure distributions, by multiplying the sampled failure time for 

each component by 1.2 (applied evenly across all failure 

modes), thereby shifting the entire sample set 20% to the right. 

We did this for each component type individually, leaving the 

other components using their baseline failure distributions, as

well as for all three components simultaneously, to allow us to 

explore the relative impact of different component failures. We 

also explored a more ambitious reliability improvement, where 

we doubled the time it took components to fail, again for each 

component individually and then for all three simultaneously.

For the improved repair scenarios, we performed a similar 

shift, but in the opposite direction. We explored a regime where 

time to repair is cut in half by multiplying each sampled repair 

time by 0.5. This could represent some combination of faster 

failure identification and shorter lead times on repairs.

Fig. 2. Component event percentages for Portfolio D

String, 2%

Combiner, 

14%

Inverter, 50%

Weather 

Station, 2%

AC Meter, 

3%

Grid, 25%

Environmental 

Impacts, 4%

Portfolio D
3

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TABLE II

FAILURE AND REPAIR DISTRIBUTIONS AND ASSOCIATED COSTS

Failure Mode Failure 

Distribution

Repair 

Distribution Repair Labor Hrs Material Cost Total Cost

DC Combiner Failure: 

Unknown Cause

Weibull

Shape: 0.51

Scale: 1200000

Lognormal-n

Mean: -0.98

Std: 2.07

Replace MC 

connector lead to 

combiner

0.1 $20 $22.40

Inverter Failure: Fans

Weibull

Shape: 12.34901

Scale: 273.5486

Lognormal

Mean: 3.86093

Std: 11.9357

Replace inverter fan 

motor 1 $120 $144.04

Inverter Failure: Power 

Cycling

Weibull

Shape: 3.09197

Scale: 418.1346

Lognormal

Mean: 4.73865

Std: 4.41415

Reboot for unknown 

error 0.25 $0 $6.01

Inverter Failure: IGBT 

Matrix

Weibull

Shape: 111.0869

Scale: 1693.973

Lognormal

Mean: 1.5026

Std: 2.17808

Replace IGBT matrix 4 $20,000 $20,096.16

Grid Failure: Unknown

Lognormal-n

Mean: 3.62

Std: 1.7

Weibull

Shape: 1.07

Scale: 0.16

Utility side 0 $0 $0.00

Grid Failure: Recloser Trip

Weibull

Shape: 1.36296

Scale: 332.93

Lognormal-n

Mean: -1.72747

Std: 1.16951

Utility side 0 $0 $0.00

For the improved inverter lifetime scenario, we ran the 

baseline scenario, with all its component and repair 

distributions exactly the same as before, except we removed the 

cost of replacing the inverter in year 15. This assumes that the 

smaller, non-fatal inverter sub-component failures still occur 

throughout the system lifetime, but the fatal failure of the entire 

inverter requiring full replacement is pushed past the end of the 

assumed system lifetime, in this case 20 years. This allows us 

to compare the effect of improving inverter lifetime to 

improving the occurrence of the three non-fatal failures that we 

examined for inverters (fans, power cycling, and IGBT).

In total, we ran 12 different reliability and repair 

combinations and 1 inverter lifetime scenario stochastically in 

PV-RPM in SAM, with 50 stochastic realizations for each 

reliability/repair scenario. The PV-RPM model in SAM 

calculates a variety of output statistics [4], but in this analysis 

we will focus on the LCOE of the system, documented in [8], 

since this metric accounts for both system performance and 

system costs. We present the average LCOE for each batch of 

stochastic simulations as the main metric, but because the 

simulations are stochastic, we also provide some information 

on the range of LCOEs calculated across those 50 simulations. 

In this analysis, we use the 90th and 10th percentiles to illustrate 

the range of stochastic results. We determine each percentile 

empirically by sorting the LCOE results of all 50 simulations 

and choosing the points corresponding to the desired percentile. 

Together, these percentiles show the LCOE range containing 

80% of the simulations. 

IV. ANALYSIS & RESULTS

Fig. 3 presents a summary of the reliability and repair 

scenarios we examined. The average LCOE and 90th and 10th

percentile LCOEs for the baseline scenario are plotted as dashdot lines, labelled on the right. The purple diamonds show the 

average LCOE of the scenarios where time to failure is 

increased by 20%, the green circles show the average LCOE of

the scenarios where time to failure is doubled, and the blue 

circles show the average LCOE of the scenarios where time to 

repair is cut in half. The different series are grouped together 

along the x-axis according to which component distribution was 

varied from the baseline in that simulation: the DC combiner 

only, the inverter only, the grid only, or all three components 

combined. The error bars around each point show the 10th

percentile of the stochastic simulations on the lower end and the 

90th percentile of the stochastic simulations on the upper end. 

The dashed lines toward the bottom of the plot, labelled on the 

left side, show 1) the average LCOE of the 50 simulations 

where the baseline scenario was run without any inverter 

replacement costs, and 2) the LCOE of the baseline system with 

no component failures whatsoever.

As one can see in Fig. 3, varying the failure or repair times 

for the DC combiners yields almost no improvement compared 

to the baseline scenario. This is an expected result: since we 

assumed two strings per combiner in our system, a failed DC 

combiner has the smallest effect on the power production of the 

system, versus an inverter or the grid which prevents power 

delivery from a much larger portion of the system. As you can 

see from Table II, the cost of repairing this DC combiner failure 

is also fairly low, so that the repair costs likewise have a small 

impact on LCOE.

Failures of the grid, on the other hand, prevent power delivery 

from the entire system. Even though they have a large effect 

when they are happening, the grid failure and repair scenarios 

only move the LCOE by a maximum of 0.05 ¢/kWh in this 

hypothetical scenario.
4

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Fig. 3. Effect on LCOE of increased time to failure or decreased time to repair for the DC combiner, Inverter, Grid, or all three components 

combined, with baseline scenario lines for reference

This results jointly from the facts that 1) grid failures do not 

cost the system operator anything to fix, so reducing the number 

of grid failures doesn’t affect LCOE from a repair costs 

perspective, and 2) although grid failures are one of the top 

three most frequent failure types, their base repair distributions 

from representative systems in Portfolio D indicate that they are 

not of a very long duration, so they do not have a large effect 

on the power production of the system. The latter conclusion is 

further supported by the fact that reducing grid repair time has 

a smaller effect than reducing the number of grid failures. 

The inverter reliability and repair scenarios have the largest 

effect of any single component on the system, showing average 

LCOE reductions ranging from 0.04 - 0.09 ¢/kWh in this 

hypothetical scenario. Interestingly, for this component, 

shortening repair time by 50% has a larger effect than 

increasing time to failure by 20%, and almost as large of an 

effect as doubling time to failure. However, despite the fact that 

the average number of inverter failures is halved in the doubledtime-to-failure scenario (decreasing from 28 to 14 failures over 

the lifetime), this scenario still represents only about a 1% 

reduction in LCOE for this combination of failure modes. This 

is partially due to the fact that the IGBT failure- the most 

expensive to fix- was rarely triggered in these scenarios due to 

the formulation of the PV-RPM model; only the earliest failure 

mode occurs (in this case, either the fan or power cycling 

failure), then all failure modes are re-sampled. Future work 

allowing multiple failure modes to occur in the model before all 

are reset could lead to very different results. However, in this 

hypothetical scenario, even eliminating all failures from the 

system only results in an LCOE reduction of 0.28 ¢/kWh, or 

~3%. So, doubling the time to failure for inverters alone bridges 

about 1/3 of the gap in LCOE attributable to component failures 

in this scenario.

As expected, applying the improved reliability and repair 

scenarios to all three components has a larger effect on LCOE 

than applying them only to a single component. However, Fig. 

3 demonstrates that it is not a purely additive effect; this is 

expected because of where the failures occur in time, and the 

fact that the ability of the upstream components to deliver 

power is still dependent on the operational state of the 

downstream components. For example, if a DC combiner is 

operational, but the grid is not, it negates the operational state 

of the DC combiner, not allowing that improved reliability to 

pass through to LCOE.

A very interesting result is that in this scenario, simply 

improving the lifetime of the inverter so that it does not need to 

be fully replaced during the lifetime of the system has a bigger 

effect than doubling the time to failure for all the non-fatal 
5

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component failures considered here, and almost as large of an 

effect as removing all component failures for this system. This 

suggests a continued need for research to improve total inverter 

lifetime, along with some examination of the interaction that 

these non-fatal failures have with that total lifetime.

Again, these numbers, and the LCOE gap, are specific to this 

analysis and could change if one considered additional or 

different components or failure/repair distributions. This points 

strongly to the need for more comprehensive failure data 

collection, to help the industry better understand actual failure 

distributions for a larger collection of systems and failure 

modes. However, a total possible LCOE reduction of 0.28 

¢/kWh in this scenario is within reason- the U.S. Department of 

Energy’s SunShot 2030 Goals aim for a 0.7 ¢/kWh reduction in 

LCOE due to lowered O&M costs considering all failure modes 

for all components, not just these six failure modes for three 

components [9]. Further research into component reliability 

will allow industry to identify the best paths forward to achieve 

the full 0.7 ¢/kWh LCOE reduction via O&M costs.

IV. CONCLUSIONS

This paper demonstrates an example of the type of analysis 

that one can conduct using the PV-RPM model in SAM. 

Consistent with current industry experience, improvements in 

inverter non-fatal failures has the largest effect on LCOE of the 

three components that were examined here (DC combiners, 

inverters, and the grid) using the distributions identified in this 

analysis. However, improving the overall inverter lifetime to 

last beyond the LCOE analysis period had a larger effect than

improved reliability or repair times for any component, or all 

three components combined, pointing to the need for further 

research and development on inverter lifetime reliability.

The overall improvement to LCOE achieved by any of the 

examined reliability or repair distributions was fairly small in 

this hypothetical analysis (about 1%), but this is to be expected 

given that the total possible reduction in LCOE by eliminating 

all system failures is only 3% in this analysis. This is not 

entirely inconsistent with other literature on the subject [9], but 

is nonetheless specific to this analysis. Assuming different 

failure/repair distributions or repair costs, modeling more 

system component failures, or modifying the PV-RPM model 

to better represent partial failures has the potential to change 

these results dramatically. This indicates that further data 

collection and analysis on system and component reliability is 

needed to achieve SunShot O&M cost reduction goals in O&M. 

Future work should explore different combinations of failure 

and repair distributions, more components, and possible 

improvements to PV-RPM in SAM.

System operators can utilize the new PV-RPM feature in 

SAM using their own datasets to develop and analyze O&M 

scenarios, and adjust component event probabilities based on 

assumed or known behavior. Models can provide data-driven

support for O&M cost reduction strategies and can be adjusted 

to account for changes in reliability.

ACKNOWLEDGEMENT

This work was authored in part by Alliance for Sustainable 

Energy, LLC, the manager and operator of the National 

Renewable Energy Laboratory for the U.S. Department of 

Energy (DOE) under Contract No. DE-AC36-08GO28308. 

Funding provided by U.S. Department of Energy Office of 

Energy Efficiency and Renewable Energy Solar Energy 

Technologies Office. The views expressed in the article do not 

necessarily represent the views of the DOE or the U.S. 

Government. The U.S. Government retains and the publisher, 

by accepting the article for publication, acknowledges that the 

U.S. Government retains a nonexclusive, paid-up, irrevocable, 

worldwide license to publish or reproduce the published form 

of this work, or allow others to do so, for U.S. Government 

purposes.

Sandia National Laboratories is a multimission laboratory 

managed and operated by National Technology and 

Engineering Solutions of Sandia, LLC, a wholly owned 

subsidiary of Honeywell International, Inc., for the U.S. 

Department of Energy’s National Nuclear Security 

Administration under contract DE-NA0003525. 

REFERENCES

[1] G. Klise, O. Lavrova, and R. Gooding, “PV Component Fault and 

Failure Compilation and Analysis.” 2018 unpublished report, 

Sandia National Laboratories, Albuquerque, NM.

[2] SunSpec Alliance, “PV O&M Cost Model user guide, draft v0.9”, 

2017. Available at: https://sunspec.org/wpcontent/uploads/2016/03/SunSpecPVOMCostModelUserManual

-Beta.pdf. For an on-line version of cost model, see 

apsuite.sunspec.org 

[3] G. Klise, O. Lavrova, and J. Freeman, “Validation of PV-RPM 

Code in the System Advisor Model,” SAND2017-3676, Sandia 

National Laboratories, Albuquerque, NM, April 2.

[4] G. Klise, J. Freeman, O. Lavrova, and R. Gooding, “PV-RPM 

v2.0 beta – SAM Implementation Beta Test: User Instructions.” 

June 2017.

[5] PV-Reliability Performance Model – System Advisor Model. 

Available at: https://sam.nrel.gov/pvrpm

[6] R. Fu, D. Feldman, R. Margolis, M. Woodhouse, and K. Ardani, 

“U.S. Solar Photovoltaic System Cost Benchmark: Q1 2017”, 

NREL/TP-6A20-68925, National Renewable Energy Laboratory, 

Golden, CO, September 2017. 

[7] NREL/Sandia/SunSpec Alliance SuNLaMP PV O&M Working 

Group, “Best Practices in Photovoltaic System Operations and 

Maintenance, 2nd Edition”, NREL/TP-7A40-67553, National 

Renewable Energy Laboratory, Golden, CO, December 2016. 

[8] System Advisor Model Version 2015.9.7 User Documentation, 

Levelized cost. National Renewable Energy Laboratory, Golden 

CO, 2017.

[9] U.S. Department of Energy Office of Energy Efficiency and 

Renewable Energy Solar Energy Technologies Office, “SunShot 

2030 Fact Sheet”, DOE/EE-1501, August 2017.