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

The Modeling of the 

Effects of Soiling, Its 

Mechanisms, and the 

Corresponding Abrasion

PV Module Reliability Workshop

Lin Simpson, Matthew Muller, Michael Deceglie, 

David Miller, Helio Moutinho

Lakewood, Colorado

February 24, 2016

NREL/PR-5K00-66822
2

 Background

 Project Description and Objectives

 Focus Areas

– Soiling Rate Model

– Mechanisms

– Standards

 Summary

 Acknowledgements

Overview
3

Background

• Problem: 

o Natural soiling has reduced the energy output of PV systems since the 

inception of the technology. 

o Soiling is a complex problem that increases uncertainty and drives up 

LCOE through lost energy production, increased O&M costs, and higher 

finance rates. 

– Overall, soiling may be adding ¢1/kWh to LCOE in the United States (may be 

worse in some other parts of the world) depending on the site. 

o In NREL’s comprehensive review of solar energy soiling,1 the issues have 

been discussed in the literature for more than 70 years, and yet “the 

fundamental properties of dust and its effect on energy transfer are still 

not fully understood, nor is there a clear solution to the problem.” 

• Goal:

o NREL’s team will work with the PV community to go beyond the past 

work to try and understand the processes involved so that the effects of 

soiling can be predicted for different environmental conditions.

o Provide the PV industry with the tools and knowledge necessary to 

devise cost effective mitigation. 

1Sarver et. al., Renewable and Sustainable Energy Reviews, 2013, vol. 22, issue C, pages 698-733 
4

Project Description and Overview

 3 year long competitively selected SuNLaMP project:

– Addressing Soiling: From Interface Chemistry to Practicality

 Soiling reduces PV energy output & increases LCOE two ways:

– Indirectly through increased performance uncertainty  higher finance rates

– Directly through reduced power output

 Working with stakeholders and PV community from the outset

– Focus on addressing 3 main problems to decrease LCOE

4 

• Predictive soiling loss models: predict

annualized (perhaps seasonal) losses at new PV

plant sites. (reduce performance uncertainty)

• Quantify the different soiling mechanisms:

develop guidelines of the appropriate properties

PV module surfaces and coatings might need to

reduce soiling. (increased power output)

• Standards: develop durability standards for PV

module coatings.

o Perhaps artificial soiling standard
5

 Slide on previous attempts to predict soiling

rates
6

Site Specific Soiling Loss Rates

 Past efforts are narrowly applicable physics based models.

 This effort focuses on empirical modeling that attempts to include all 

possible predictive variables and predicts annualized and seasonal

losses.

 Annual empirical metric more achievable and provides significant 

value in 30 year performance predictions, seasonal metric provides 

guidance in O&M planning.

 Industry driven: PV manufactures and power plant owners are 

sharing high quality data from over 200 sites to enable this effort to 

be successful. 

Picture of initial data sites 

used for identifying the key 

model parameters. The sites 

are representative of the 

different climatic zones 

within the US and should 

provide the requisite 

information needed for 

robust models.
7

Soiling Rate Model Parameters • Examples of production data information

o Location

o Module model

o Mounting info

o System layout

o Calculated soiling loss

o Met/Soiling data

o Detailed site description

o Maintenance/cleaning logs

o Inverter power

o Wind speed

o Ambient temperature

o Irradiance (GHI)

• Lessons learned so far?

Creating automated means to process the data, provide further quality checks, and determine 

daily/monthly/annualized soiling rates. The processed data from the 64 sites will be used for 

model development. The data processing will be as automated as possible so information from 

additional sites can be added with minimal amounts of effort.

• Other data and possible sources:

o NSRDB1: irradiance

o PRISM2: precipitation, temperature

o NASA MERRA3: wind, temperature,

humidity, pwv

o In-house: highways, airports, railways,

industrial sites, urban environments

o NLCDB4: land cover, agriculture

1http://nsrdb.nrel.gov/

2http://www.prism.oregonstate.edu/

3http://gmao.gsfc.nasa.gov/research/merra/

4http://www.mrlc.gov/
8

Modeling Methods

 Focus on small but high quality data sets. Supplement the data with potential

predictive variables quantified from EPA particulate maps, rainfall maps, NOAA, and

USGS (guided by PVQAT 12). Once supplementation is complete, multi–variate

analysis techniques will be used to determine which predictive variables are

relevant (p<0.05).

 Based on Pareto chart of the predictive variables expand data sets to over 100

sites.

 Apply neural network analysis, cluster

algorithms and other data mining

techniques to determine possible

nonlinearities/complex relationships.

 Empirical coefficients will be derived

for all models using a learning data set

and then the “best” model(s) will be

determined by the root mean square

error (RMSE) statistics based on

application against a validation data

set.

e.g.,: National Land

cover database:

– 16-classes of

land cover

– 30m

resolution

www.mrlc.gov
9

control Siemens M55 module 28 years in field

• White light optical profilometry of module glass

• Provides information over large lateral length scales

on module glass and AR coatings

• Scratches and large scale (~mm) hillocks were

observed on control sample.

• The small-scale roughness of control and aged

modules appears qualitatively different.

• SEM and EDS show thin salt layer may be present on

glass surface

. 

Weathered PV Module Glass Analysis
10

1

10

100

0 1 2 3 4 5

AFM-Based Characterization

Tampa

FL

Sacramento

CA

Chandler

AZ

Controlled

RSM roughness (nm)

-600

-500

-400

-300

-200

-100

0 1 2 3 4 5

Average CPD (mV)

Multiple measurement

Average

Tampa

FL

Sacramento

CA

Chandler

AZ

Controlled

-38

-36

-34

-32

-30

-28

-26

-24

-22

-20

0 1 2 3 4 5

Average Phase (Degree)

Multiple measurement

Average

Tampa

FL

Sacramento

CA

Chandler

AZ

Controlled

AFM Roughness KPFM Electrostatic Potential

• Roughness: Tempe>Chandler>Sacramento

• Phase lag and viscosity/softness: 

Sacramento>Chandler ≈ Tempe

• Contact Potential Difference: 

Sacramento>Chandler>Tempe

AFM-based techniques:

• 2D and real 3D data and roughness.

• Phase imaging/lag: elasticity, adhesion &friction.

• Lateral force: inhomogeneity not from topography.

• Force-distance curves: adhesion (e.g. capillary).

• EFM/KPFM: electrostatic interaction.

• Electrostatic/Kelvin Probe Force Microscopy
11

Mechanism Investigations • Conclusions from initial literature survey

o Early work attempted to address mechanism issues more

– E.g.,

o A lot of work identifies soiling issues, but do not evaluate/identify exact adhesion mechanism

– E.g.,

• To go beyond just “observing” soiling we must systematically evaluate each adhesion

mechanism involved at each step.

Glass

• If gravity or wind brings dust to

the surface, what makes it stick?

• Kaz’s recent paper binding

energy

• What role does humidity play?

• Reduces electrostatics?

• Enables capillary?

• What is relative strength?

• What is the effect of surface

properties and dust composition?

• Surface roughness?

• Surface Energy?

• Conductivity?

• Is the roughness due to

weathering from chemical

etching of the glass or surface

deposits?

Sample Observations

• Dust that has been on the surface for a

while tends to be harder to remove.

• Humidity and dew cycle seem to

increase soiling.

• Soiling is often not uniform on module.

Physisorption goes to 

chemisorption

After outside 

weathering
12

Mechanism Investigations

• To address these mechanism questions, quantify

impact of individual mechanisms, e.g.,:

o Study effects of substrate roughness, relative humidity (RH),

probe/substrate conductivity, surface energy, and surface

contamination on:

– Van der Waals forces

– Electrostatic adhesion

– Capillary forces

– Hydrogen bonding

o Identify potential sources of salt; leaching from the module

glass, airborne salt, and leaching from deposited soiling

materials like alumina-silicate or clay particles.

– Accelerated testing of glass and dust

• With individual mechanisms quantified, then evaluate

complex or multiple step soiling mechanisms

o e.g., cementation

e.g., use Atomic Force Microscopy Based Measurements

and Analysis

force-distance (f-z) curves, lateral force microscopy 

(LFM), phase imaging (PI), electrostatic force 

microscopy (EFM).
13

 Working through literature to develop an investigation plan for each combination of mechanisms and surface

properties

– Physics/chemistry based interactions or bonding

• Atoms and Electrons

• Probably most interested in psuedo-covalent bonding

 Quantify at both the atomic and macroscopic scales

 Evaluate both “model” and field samples to ensure all appropriate mechanisms are characterized

– Leverage “standards” work

 Summary of what we believe/know now  Identify the specific mechanisms associated with soiling to find appropriate mitigation processes for a given region

– e.g., develop optimized cleaning schedules and techniques for a given region based on the type of environmental factors that may cause irreversible soiling.

 Techno-economic based preventative approaches 

– e.g., efficacy of anti-reflection/anti-soiling coatings.

 Bridge the gap between the surface science and actual field operation

– Provide the community with an understanding of how to select between different surface properties 

• e.g., hydrophobic, hydrophilic and photocatalytic, for different locations and to optimize optical, self-cleaning, anti-soiling, and durability characteristics.

Soiling Mechanisms
14

Coatings Standards: Overview

 Determine repeatable indoor abrasion test procedures suitable for PV

surfaces

 Deploy coating samples at various test sites, acquire aged PV modules

with standard solar glass.

 Correlate degradation of veteran PV modules with indoor abrasion test

procedures,

– Test variations in accelerated protocols to improve correlation.

– Incorporate best accelerated test protocols into draft IEC standards. (Validation efforts

will need to be ongoing as samples are maintained in the field more than 3 years)

 Submitted draft standard to IEC.

Images of (a) pristine PV module 

surface, relative to degradation 

(b)-(d) due to weathering and 

cleaning. Proc. SPIE, 2010, 7773-

02 
15

Indoor Abrasion Test:

Summary and Approach Based on the Literature

 Relevant existing methods (popular use & literature):

– Falling sand test, e.g., ASTM D968 & DIN 52348

– Forced sand impingement , e.g., MIL-STD-810G or ASTM G76.

– Machine abrasion (linear or rotary), e.g., BS EN1096-2, ASTM D4060, or ASTM D2486

– Abrasive media in tumbler or shaker, e.g., DUR-5.2.9 and DUR-5.2.5

15 

Schematic of abrasive media “Bayer” 

shaker from DUR-5.2.5

Photo of BYK Abrasion Tester 

PB-8100. www.byk.com

Schematic of forced sand impingement 

test. From Klimm et. al., Proc. Euro. 

Weathering Symp. 2015.

 Limitations:

– Existing methods (samples & procedure) are not tailored to PV industry

– Existing methods typically too severe for coated glass, e.g., result in frosted glass.

 Recommend develop: falling sand, forced sand impingent, linear machine abrasion

methods.
16

Adapt/Define Accelerated Abrasion Test(s)

Potential Candidates

• Falling sand tests

o ASTM D968 & DIN 52348 used in industry

o Moderate impact velocity, e.g., ~5.7 m/s for 1.65 m fall distance

o 3 kg of sand overly damaging to coatings even substrate (frosted

glass)

o Modify orifice diameter, sample angle, fall distance, abrasive

medium... to PV industry

• Forced sand impingement tests:

o MIL-STD-810G or ASTM G76.

o High impact velocity (18-29 m/s) to account for most severe

storms and locations.

o Test equipment, labs commercially available.

• Machine abrasion:

o BS EN1096-2 (for window glass), ASTM D4060 (Taber Abraser),

ASTM D2486 (for wall paints)

o Use rubberized pad, bristle, or felt tip in conjunction with water

and/or grit.

o Rotary or linear actuation of abrasive.

o EN1096 most often referenced/used, even though known as not

industry relevant .
17

Specimens for Field and Indoor Study

• Presently acquiring a set of commercial specimens, to be used for the abrasion

test standard development and soiling mechanisms study.

• 3” square coupons in most cases...12” if necessary for manufacturer processing

• Field results will be

compared to indoor results 

to validate modes and 

magnitude of abrasion 

damage 

• Field samples will also be 

examined to quantify 

accumulated soil facilitating 

modeling of soiling 

mechanisms and validating 

indoor soiling tests. 

• Also ASU is developing 

“artificial soiling” techniques 

that may be the start of a 

standard 

VENDOR SUBSTRATE

AR

FUNCTIONLIZATION

AS

FUNCTIONLIZATION STATUS

1 PMMA N/A N/A at NREL

2 glass graded index N/A in fabrication

2 glass graded index new in fabrication

2 glass (reference) N/A N/A at vendor

3 glass 1/4λ,dielectric N/A fabricated

4 glass 1/4λ,polymer hydrophillic in hydrophobic in fabrication

5 glass graded index hydrophobic in fabrication

5 glass graded index olephobic in fabrication

5 glass graded index hydrophillic in hydrophobic in fabrication

4, 5, 7 glass (reference) N/A N/A at NREL

6 glass graded index N/A in fabrication

6 glass graded index N/A in fabrication

6 glass (reference) N/A N/A at vendor

7 glass (tempered) N/A N/A at NREL

Table of specimens to be examined in the study.
18

Coupon Deployment

• Sacramento, high soiling agricultural location

• ASU, Tempe Arizona, U.S. dry desert

• Mumbai India, IIT, urban with high soiling rate and monsoon season

• K.A.CARE Northeast Saudi Arabia dry and high frequency sandstorm location

• DEWA in Dubai, both represent desert climate with dew cycles and sandstorms

Easily deployable rack if partner 

cant provide rack Coupon identification

Coupon holder

For this task, the deployment sites are being selected based on having very harsh weathering 

conditions, including high amounts of dust/sandstorms, humidity level, and high 

temperatures. This should help identify coating abrasion/durability issues quickly to help 

guide the development of the standards.
19

Summary

 Overall, soiling may be impacting PV installations by 4% to 10% or more (LCOE ¢0.3/kWh to

¢1/kWh, respectively) depending on the site from lost energy production, which could be

eliminated with an “ideal” solution.

 The need to address soiling to remove roadblocks in U.S. PV markets has created intense

interest that is best addressed by pooling knowledge from around the world.

19 
20

Acknowledgements/Thanks • NREL Staff

o Sarah Kurtz

o Lin Simpson

o Matt Muller

o Mowafak Al-Jassim

o Michael Deceglie

o David Miller

o Helio Montinho

o Craig Perkins

o Paul Ndione

o C.S. Jiang

o Bobbie To

o Greg Perrin

o Paul Basore

o Nancy Haegel

o Jen Liebold

o Jina Martingano

o Jane Fisher

• Collaborators

o Mike Van Iseghem: EDF

o Richard Perkins: SunPower

o Zoe Defreitas: SunPower

o Mike Anderson: SunPower

o Mark Lewis: First Solar

o Markus Gloeckler: First Solar

o Mark Grammatico: First Solar

o John Previtali: Wells Fargo

o Brenor Brophy: ENKI

o Tim Narum: 3M

o Dennis DeCourcy: 3M

o Dirk Weiss: 3M

• DOE

o Inna Kozinsky

o Geoffrey Kinsey

o Rebecca Jones-Albertus

• Partners

o G. TamizhMani: ASU

o Alan Sellinger: CSM

• Collaborators

o Nicoleta Voicu: DSM

o Yi Li: DSM

o Ian Bennett: DSM

o Milica Mrcarica: DSM

o Hugo Schoot: DSM

o Hassan Qasem: DEWA

o Pedro Banda: DEWA

o Russell Jones: KACARE

o Ben Figgs: Qatar
21

Backup Slides

21
22

(QPI) FY16Q2 Field test description

Individual unit holds

20 coupon replicates,

Ready to attach to rack

1828 mm

127 mm No cleaning Dry brush clean

monthly

Sponge/squeegee

/water bucket clean

monthly

Handheld pressurized

water spray monthly

Example demonstrates testing 4 different coating types and 4 different cleaning/treatment

methods (20 coating replicates allows returning coupons to NREL in various years and for

potential breakage). Possibility of testing 20 unique coatings results in 1828 mm by 5072 mm rack
23

(QPI) FY16Q1 Geographic distribution of 64 sites

Site locations: SunPower Sun Edison

Initial data are from sites that are representative of the different climatic zones within the US 

and should provide the requisite information needed for robust models.
24

(QPI) FY16Q1 SunPower data

• 15 minute production data from 44

unique sites

o Inverter power

o Wind speed

o Ambient temperature

o Irradiance (GHI)

• Pre-screened by SunPower for quality

o Assessing internally also

• Data spans 3–12 years

o (ending in fall 2014)

• SunPower calculated daily soiling on

16 sites
25

(QPI) FY16Q1 SunEdison soiling station data

Images from Sun Edison 

commissioning reports

• Hourly data from 20 unique sites

o DHI, DNI, DHI

o Humidity

o Pressure

o Wind speed/direction

o Precipitation

o Clean/dirty reference cell Voc and Iisc

Need different pictures
26

 Obtained an initial set of PV modules that had

been in the field for 17-18 years and compared

with controls that had not been deployed.

– Using to evaluate what soiling/degradation mechanisms

are present and what characterization needs to be done.

Initial Studies of Soiling

Sample ID Sample 

Description Location Model Module 

Type

Years 

Exposed Mounting

#1

Tempered glass

Tempe, AZ Siemens, M55 Aged 18 Open rack

#2 Tempe, AZ Siemens, M55 Control 0 N/A

#3 Sacramento, CA Siemens, M55 Aged 18 Open rack

#4 Sacramento, CA Siemens, M55 Control 0 N/A

#5 Chandler, AZ

ASE Americas, 

ASE-300-

DGF/50

Aged 17 Rooftop
27

XPS analysis of module glass: initial results

Q: Why perform chemical analyses of module front surfaces?

A: Mechanical, optical, and other properties such as propensity for water

adsorption all depend on glass composition.1

1. Hayashi et al., Surface Science 507-510 (2002) 872;

Cleaver et al., KONA Particle and Powder Journal 22 (2004) 9

sample Atomic Concentration (%)

N O F Na Mg Al Si K Ca Fe

Siemens 

control 1.0 67.9 0.3 1.4 0.8 2.7 23.4 1.1 0.8 0.6

Siemens 18 

yr AZ 1.2 68.1 0.5 0.7 1.9 4.5 20.0 0.6 1.1 1.4

Siemens 18 

yr CA 1.4 68.5 0.4 0.7 1.0 3.9 21.1 0.3 0.7 2.0

ASE 17yr AZ 1.1 67.9 0.2 0.3 2.8 5.7 18.3 0.6 1.3 1.9

ASE, in 

fracture 0.1 68.3 0.2 3.7 --- 0.5 25.1 --- 1.9 0.2

Depth profile, Siemens control

XPS concentrations, DI/UV/O3-cleaned samples

Controls and aged samples

show different compositions.

Surface and bulk also have 

different compositions.

Sputter depth profiling of these 

insulating samples appears 

possible and should provide 

additional information about 

subsurface in select cases.

Note high amounts of carbon at 

surface of soiled glass.
28

SEM with No Conductive Coatings Applied for Analysis 

For most semiconductor samples charging is not a problem for SEM analysis. However, 

for the glass samples in the Soiling project significant charging in high-vacuum analysis 

is observed (left). Using low vacuum decreases the charging without the need to 

contaminate the surface with a conductive thin film.

High-Vacuum Low-Vacuum

SEM - Development of procedure to analyze high-resistivity glass samples in lowvacuum mode with no secondary conductive coatings applied. 
29

SEM has a large field of view and is suitable to study distribution and size of soiling 

particles on glass, as well as to study small features of the glass surface.

SEM images from samples of three solar panels operated outdoors for about 18 years 

in three different locations (Tempe, AZ, Sacramento, CA, and Chandler, AZ, 

respectively) showing different topographies and some apparent damage due to 

weather exposure. It is also clear that the type of damage is location dependent.

SEM (uncoated) of Weathered Glass
30

AFM 3D images (left) 

and linescan on the 

panel from Tempe 

(top) and the control 

sample. While larger 

scale roughness is 

similar, the AFM is 

clearly able to 

quantify the increase 

in small scale 

roughness of the 

Tempe sample due 

to weathering.

The 3D (x,y,z) data collection and high-magnification capabilities of the AFM allows for 

acquisition of 2D and real 3D images as well as measurements such as quantification of 

surface roughness. This allows for the study of changes on the surface topography of 

glass and AR and soiling layers before and after soiling. 

AFM Comparison of Weathered Glass
31

 Physics/chemistry 

based interactions or 

bonding – Atoms and Electrons – Probably most interested 

in psuedo-covalent 

bonding

 Quantify at both the 

atomic and 

macroscopic scales

 Evaluate both 

“model” and field 

samples to ensure all 

appropriate 

mechanisms are 

characterized – Leverage “standards” 

work

Different Approaches
32

 Create and evaluate different surfaces and quantify

interactions

 Quantify different adhesion with different tools

– e.g., EFM/KPFM to measure electrostatic and Vandeer Waals with silica tips

e.g., Model Surfaces to Evaluate Mechanisms

Starts with glass.

Can have many different layers and 

interactions. 

Evaluate range (e.g., superhydrophilic

to superhydrophobic) of surfaces 

properties.
33

C O Fe Na M

g 

Al Si S Cl K Ca

81.4 11.7 - 1.3 0.

2 

- 1.3 1.5 1.0 0.8 0.7

21.0 50.5 2.2 1.7 0.

5

1.9 21.2 - - - 0.9

Concentration of the sample from Tempe at two different spots (P – particle, M – Matrix). 

Large difference in composition between the 

matrix (SiO2) and analyzed particle.

Complimentary to XPS, EDS allows for the analysis of the composition with high spatial 

resolution. This makes it possible to analyze the glass substrate and individual soiling 

particles.

EDS Identifies Soiling Contaminants