gpn feasibility study pv-14 - green power nano · feasibility study greenpowernano anti-soiling...

Feasibility study GreenPower Nano anti-soiling coating for PV-cells: a case study in the Netherlands

Authors: Vincent Alexander Franken

Roel Cornelis Boekel

E-mail: [email protected]

Telephone: 06 52 463 543

Company: Nanonow V.O.F.

Address: 1074ET, Rustenburgerstraat 10HS

Amsterdam, The Netherlands

Date: 1 March 2016

� Nanonow & GreenPower Nano 2016 2

Table of Contents Abstract ................................................................................................................................................... 3

I. Introduction ..................................................................................................................................... 3

II. Problem statement............................................................................................................................ 4

II – A: Why cleaning solar panels is needed ....................................................................................... 4

III. Method .............................................................................................................................................. 7

III – A: Technical Feasibility .............................................................................................................. 7

III – B: Financial Feasibility ............................................................................................................... 7

IV. Technical Feasibility ......................................................................................................................... 8

V. Financial Feasibility ......................................................................................................................... 15

V - A Explaining the model ............................................................................................................ 15

V - B Assumptions .......................................................................................................................... 15

V – C Results Financial Feasibility model ...................................................................................... 18

VI. Conclusions & recommendations .................................................................................................... 21

V - I General conclusions ................................................................................................................. 21

V - II Future research ....................................................................................................................... 21

References ............................................................................................................................................. 23


Feasibility study GreenPowerNano anti-soiling coating for PV-cells: case study in the Netherlands

V.A. Franken1, R.C. Boekel2

Nanonow, 1074 ET Amsterdam, The Netherlands

Abstract

As a result of particles and contaminations adhering to solar panel surfaces, exploiters and owners of

PV solar cells face a reduction in efficiency. In this project the technical and financial feasibility of the

GreenPower Nano PV-14 coating as a solution for this problem is assessed. The hydrophobic character

of this strong silicon based coating results in the self cleaning effect that allows the PV solar cell to

generate more electricity. Both the technical and financial feasibility are tested in this study by

methodologically analyzing the PV-14 coating in the Netherlands. This study gives a very good

indication for the successful application of this coating.

I. Introduction

anotechnology is a branch of small scale enabling technologies that offer potential innovations

across many industries. Nanonow believes in the great potential of nanotechnology. Knowledge of

molecular interactions, computational modeling power and nano-scale production capabilities have led

to a huge amount of knowledge spread all over the world. This research shows promising and

tremendous opportunities. According to Nanonow, now is the time to apply nano-inventions.

Nanotechnology is a field of technology on a very small scale, approximately 1 - 100 nanometer.

Nanotechnologies can be used to build materials and systems that have innovative, special properties

and functions because of their small size. This research project aims to assess the technical

functionalities and application of the GreenPower Nano nanotechnology based PV-14 coating in the

Netherlands.

Nanonow helps companies to innovate using nanotechnologies by performing technology scouting

services, analyzing the technical and financial feasibility of opportunities and assisting in the

implementation/innovation project. Together Roel Boekel & Vincent Franken have assisted several

Dutch companies in finding and assessing innovative nanotechnology opportunities in the 2 years

preceding this research project.

1BSc Science, Business & Innovation, Master candidate Finance, VU University, Nanotechnology consultant at Nanonow, [email protected] 2BSc Science, Business & Innovation, Master candidate System Engineering, Delft University of Technology, Nanotechnology consultant at Nanonow, [email protected]

N


GreenPower Nano is a specialist in developing and delivering surface technology solutions. The

company has a range of unique products in the Netherlands and are serving clients all over the world.

Through the GreenPower Nano Technology Group, containing members from several Dutch industries,

the company has a central role in developing and executing surface based nanotechnology innovation

projects in the Netherlands.

II. Problem statement

II – A: Why cleaning solar panels is needed

The solar cell manufacturer produces silicon solar cells behind glass. This type of glass gets dirty which

inhibits the efficiency of the solar cell over longer periods of time. Filthiness of the glass surface is a

result of contamination of limestone, salt (at sea), smog, trees and bird feces [1]. As a result of particles

and contaminations adhering to the surface, exploiters and owners of PV solar cells face a reduction in

efficiency and have to clean their solar cells often. Consequently, exploiters and owners of solar panels

have to spend on maintenance and the cleaning of the panels to maintain the efficiency of the solar

panels at a constant level.

GreenPower Nano proposes a solution for the reduced solar panel efficiency and to overcome

unnecessary maintenance costs. By means of a silicon based surface coating that bonds covalently to

the glass’ surface [2] a hydrophobic layer is deposed. The claimed improvement is obtained by

dispositioning the surface coating and its hydrophobic character results in a self cleaning effect. This

self cleaning effect results from the hydrophobic surface and its related surface tension. As a result,

droplets of water flow from the surface and take contaminants with them on the way down. This

phenomenon has been proven multiple times in different studies [3]–[5].

Figure 1 Schematic visualization of the self-cleaning effect .


II – B: Introduction to the soiling problem

In the past years many studies have been conducted to measure the influence of soiling on PV solar cell

performance. The main outcomes of this research show that rain is an effective cleaner for heavy

contamination in areas with a lot of dust, sand and pollen-like deserts and dry farmland. In these area’s,

without rain, the loss of power generated is the highest and up till 17,4% per month. However, in case

of high precipitating areas like Switzerland and the Netherlands research found that rain cleans the solar

cells. Based on studies in similar areas [6]–[8], the average contamination remains between 1.1 and

6.9% after one year and up to 7.7 to 10% for periods longer than five years.

A TNO report in the Netherlands used data from the greenhouse industry. The report shows that for

glass windows with a tilt angle larger than 20q, rain is suitable to clean the glass and a 3 to 4% efficiency

loss is expected. Panels with an angle smaller than 20q are much more challenging, in this case

contaminants like algae, bird feces and dust adhere to the surface. Outcomes of the report show an

average efficiency loss of 2.2 to 7.71% in a period of 1 year with an average electricity price of 18ct.

TNO concludes that, in 2003 for normal consumers, cleaning solar panels is financially interesting if

the reduction is more than 3,5% per year and advises to clean the solar panels in spring to optimize

output. For businesses and large users this percentage must be larger than 6% due to the lower energy

price (6,5ct/kWh) [9]. Currently the price for green energy fed into the Dutch energy grid works with a

so-called feed in tariff. A price of about 13ct (may change based on the subsidy application date) is

guaranteed for every kWh back into the grid [10]. Based on TNO’s study, in the subsidy case, cleaning

the solar cell should be executed every 1.4-1.5 years.

Table 1 Studies showing the average solar cell efficiency losses due to soiling.

Study Location period Reduction Comments

Zorrilla-

Casanova

et al. [6]

Spain 1 year 4.4% Without rain losses over 20%, on average a

loss of 4,4% per year when including rainy

periods. Mitigation strategy: cleaning

schedule.

Pavan

et al. [7]

Italy 1 year 1.1-6.9% With rain a reduction between 1.1% for PV

cells on grass and 6.9% for cells located on

sandy soils.

Haeberlin

et al. [8]

Switzerland 5 years 8-10% 5 years of testing, solar cells where close to

railway lines.

Hammond

et al.

Arizona,

US

16 months

to 5 years

2.3-7.7% Differing outputs and tested angles, the

soiling remains stable over time.


Eliminir

et al. [11]

Egypt 1 month 17,4% Measurements at a 45q tilt angle, in a desert-

like environment without rain.

TNO [9] Netherlands 1 year 2.2-7.1% The lowest case of 2.2% showed

malfunctioning of the cell, TNO tested with

multiple types of solar cells with differing

tilt angles.

II – B: Solution needed

The client, a Dutch company who imports solar panels in from a production facility in Turkey, wishes

to reduce maintenance costs and increase the efficiency of the solar cells that suffer from surface

contamination. On average, the manufacturer produces 1 million solar panels with an average surface

of 1,65 m2 per year. The solar cells are silicon based with an average efficiency of 18-20%. Currently

the client does not provide any protection against soiling and advices his clients to clean the solar cells

by themselves.

The client has asked Green Power Nano to demonstrate the ability of their self-cleaning hydrophobic

coatings for application on the solar cells. To substantiate the functionality and economic performance

of the coatings, GreenPower Nano has asked Nanonow to provide further information of the

performance based on scientific literature and studies.


III. Method

This chapter describes the research method and the data generating process used to scientifically support

the successful adoption of GreenPower Nano coatings.

III – A: Technical Feasibility

For the technical feasibility an engineering design method will be used as described in Dym & Little

(2004) [12]. This method is used to construct engineering projects from problem formulation towards a

solution. The method provides tools to understand and solve an engineering problem. For this report the

method is used to test the functionality of hydrophobic self-cleaning nano-coatings on PV solar cells.

The method’s tools will be used to assess whether the coating is able to stand up to it’s claimed self-

cleaning ability, and consequently increase the solar cells’ efficiency and reduce the operation costs.’.

The method will consist of three parts:

x Generation of objectives for solving contamination of solar cells.

x Generation of constraints corresponding to the objectives, which are needed for the solution for

the contamination problem.

x Testing and evaluating if a coating with hydrophobic and self-cleaning properties satisfies the

objectives.

If the outcomes of the research show that some objectives and constraints are not met by the coatings’

specifications because:

x the specifications do not satisfy the constraints and/or;

x there is a lack of information to support a reliable confirmation of the constraints;

A mitigation strategy must be arranged and further tests will be required. At the end of this report these

actions will be ordered on a timeline that can be used as a tool for the client to improve the functionality

of the coating technology.

III – B: Financial Feasibility

From a finance perspective perhaps the most important criterion for selecting innovation projects is the

financial viability at the front end of an innovation project. This chapter will describe the financial

method that will be used to calculate the financial feasibility of coating solar panels in the Netherlands.

The second part of the research project focuses on the financial feasibility by using four real options.

Using the real options approach, the net present value of several potential paths can be determined.

Normally a Decision Tree would generate one figure with potential paths over time. Using new

information input, decisions can be made. For interpretation reasons, the model will represent the four

real options separately in the Excel model.


IV. Technical Feasibility

This chapter’s aim is to structure the technical feasibility of a silicon based nanostructured coating as a

soil contamination mitigation technique for PV cells. First the technical feasibility is structured based

on the requirements and objectives that a solution for the contamination of dust, soil, bird feces and

algae on the PV cell surface must have. Outcomes of the technical feasibility study must show whether

there is enough ground provided in previous research to make claims about efficiency loss due to soiling,

and if a nanostructured coating has the ability to reduce the soiling effect significantly.

Requirements and objectives that can not be measured or proven require further testing. For these

objectives and requirements suggestions for tests will be provided in the so-called “innovation

blueprint”. Outcomes of the technical feasibility and the proposed activities in the innovation blueprint

will be further examined from an economical and financial perspective. This will be done by making

assumptions on the claimed (by GreenPower Nano) and literature based efficiency results of such a

coating, and weighing these benefits with the current costs of cleaning, maintenance and loss of energy.

Product and customer requirements

The first step in the technical feasibility is the formulation of requirements and objectives which a

solution for the soiling problem must, should and could have. To generate requirements and objectives

a method is used described in Dym & Little. The problem formulation and input of the customer

requirements objectives are structured using an objective tree. The objective tree is visualized in figure

2 on page 10. Main objectives and secondary objectives can be obtained from the objective tree. The

objectives, metrics and corresponding constraints are shown in table 2.

Table 2 Objectives, corresponding metrics and constraints for a self cleaning coating.

Objective Metric Constraint

Good functionality in hot climate Δ Temperature 20 – 60 qC

Good functionality in warm climate Δ Temperature 15 – 50 qC

Good functionality in median climate Δ Temperature -10 – 40 qC Good functionality in low precipitation Precipitation mm/year <250 mm/year Good functionality in median precipitation Precipitation mm/year 250-1000 mm/year Good functionality in high precipitation Precipitation mm/year 1000-5000 mm/year Low dust, salt and particle adhesion Surface energy <… dynes/cm Low bird feces adhesion Surface energy <… dynes/cm Low algae growth Surface energy <25 dynes/cm [13] Long lifetime Years > 5 years


Low glancing of light Refractive index n <1.250-1.329 [14] Low glancing of light through time Change in refracting index n <1.250-1.329

Low application cost Cost/m2

Low maintenance cost Cost/area/year Lower than current cost

Large increase of solar panel efficiency kWh/year

Larger than without

coating. Low toxicity MAC value Depends on products used Environmentally friendly Degradation time Biodegradable

From these objectives it becomes clear that any solution should show a stable performance in differing

climates and temperatures. The solutions’ performance should be independent of precipitation rates.

The solution must have low adhesive strength towards dust, salt, particles, bird feces and algae. It also

should have a long lifetime, very low reflectivity, must be low cost and should increase the solar panel’s

efficiency. Furthermore, the solution must be (bio)degradable and have a low to no toxicity to the

biosphere or human health. The objectives are the main input for testing any possible solution and in

this case the solution proposed is the hydrophobic nanostructured SiO2 coating of GreenPower Nano.

� Nanonow & GreenPower Nano, 2016 10

Objectives

Figure 2 Objective tree with the main objective to prevent soil contamination on c-Si solar panels. The dark yellow boxes show secondary objectives that contribute to the main objectives. Light yellow boxes show metrics to measure how an objective can be achieved.

IV Proposing the use of specialized coatings to prevent soil contamination

Table 2 shows differing test outcomes with a large reduction range within a broad timeframe. However, it

can be concluded that the soiling level negatively influences the power generation of solar cells up to 17,4%

in arid regions and from 1.1% to 10% loss in Mediterranean climate and grassland climate. This gives

ground to study mitigation strategies and techniques to decrease soil formation on the surface of solar cells.

Cuddihy [15] has introduced this topic in 1980 to overcome the soiling problem. He proposes specific

surface characteristics that could overcome the soiling problem:

1. Hard (less susceptible to embedding particles or being damaged by them)

2. Smooth (less likely to trap particles)

3. Hydrophobic (less attractive to ionic species, adsorption of solids, and retention of water)

4. Low-surface energy (lower chemical reactions)

5. Chemically clean (especially of materials classified as potentially ‘‘sticky’’)

The use of nanostructured surface coatings have been proposed and discussed multiple times in literature as

a possible mitigation technique for soil contamination on solar cells [16]–[20]. The nanostructured materials

enable hydrophobic abilities and can therefore show the earlier introduced self-cleaning effect. The self-

cleaning capability is a result of surface with a very high water contact angle. Surfaces with a water contact

angle of |150q are called super-hydrophobic and surfaces with a water contact angle of <120q are called

hydrophobic [16]. Figure … shows how this relates to the surface.

Figure 1 Relation between hydrophobic and hydrophilic surfaces

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In the study of Yong et al., the functionality of a self cleaning coating on a solar cell has been studied. The

self cleaning effect follows from the super-hydrophobic material and low contact angle hysteresis (CA)

[20]. In this study it was shown that a hydrophobic coating on a plastic surface, when soiled, showed a

71,8% recovery of the solar cells current density while the untreated solar cell showed only a 13.6% recovery

after cleaning. Although this is a lab scale test and is not fully representative, as soil does not form that fast

on the solar cell surface and an elastomer surface is used. It shows that with a hydrophobic coating, cleaned

with water droplets, a surface cleaning rate is established that is 5 times larger than with an untreated surface.

This is supported by outcomes of a study conducted by Power et al., showing that glass coated with silica

nanoparticles deposited via a simple sol-gel has significantly higher self-cleaning abilities than without a

coating [17]. Results are depicted in figure 3 and show that on an untreated glass surface more than 65% of

the dirt and contamination sticks to the surface.

Figure 2 Results of the study by Power et al. (A) showing contaminated surfaces with 1 being the uncoated glass plate and 2 being the coated glass plate. (B) shows the result after applying water droplets of 1ml with a tilt angle of 5q [17].

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IV Testing the objectives for hydrophobic silicon based nano-coatings

It has been shown that soil contamination does impact the efficiency up to 17,4% for arid regions. Previous

studies have shown that a hydrophobic coating has a significant effect on the self-cleaning abilities of self

cleaning coatings at differing tilt angles and that 5 to 6 times more dirt, dust or soil is washed away than

without a coating. Based on these findings it can be said that a (super)hydrophobic coating has the ability

to improve the efficiency of solar cells. However, objectives generated in chapter IV-B are not fully

answered by providing this information. Furthermore, the requirements generated by Cuddihy are not

answered by the use of nano-silica based hydrophobic and self-cleaning coatings, like the coating provided

by GreenPower Nano.

Table 3 Testing the coatings' performance based on the earlier established objectives and corresponding constrains

Objective

Good functionality in hot climate Active material polysilazane has high temperature

resistance [21]

Good functionality in warm climate Active material polysilazane has high temperature

resistance

Good functionality in median climate Active material polysilazane has high temperature

resistance

Good functionality in low precipitation In general, polysilazanes show hydrophobic behavior, and

creates strong interconnected network. The water contact

angle is unknown. Good functionality in median precipitation No scientific research on this topic found. Good functionality in high precipitation No scientific research on this topic found.

Low dust, salt and particle adhesion Hydrophobic surface expected low surface energy.

Silazane comparable product shows surface energy lower

than 17,8 dyne/cm [22] Low bird feces adhesion Hydrophobic surface expected low surface energy. Low algae growth Hydrophobic surface expected low surface energy.

Long lifetime Coating manufactures states 10 years, Green Power Nano

expects 3 to 5 years. Active ingredient Polysilazane creates

strong bonds with surface [23]

Low glancing of light Unknown for GPN coating, nanosilica has in studies a

tuned refractive index of 1.18 to 1.42 [24] Low glancing of light through time Same as above.

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Low application cost Unknown, depends on application time. Low maintenance cost The coating itself

Large increase of solar panel efficiency On average, based on earlier described information

between 2 and 8% Low toxicity Solvent is harmful for health. Environmentally friendly Solvent is non-biodegradable (maximum of 5%) [MSDS]

Table 3 shows that the coating of GreenPower Nano corresponds to the overall requirements of a preferred

self-cleaning coating. The active nanostructured polysilazanes have a strong ability to bind with the surface,

show hydrophobic behavior, do have a long durability and are expected to increase the efficiency of solar

cells with 2% on the short term to 8% in the long run when compared to uncoated solar cells. However, not

all information can be fully quantified. The water contact angle, refractive index and surface tension are still

unknown. Furthermore, the application costs are not yet quantifiable and the carrier of the active materials

does not show the most preferred safety conditions. It must be added that the safety matter of the carrier

material is fully covered in extensive material handling guidelines and safety data sheets.

Based on these outcomes it can be concluded that further testing is required to obtain the level of

hydrophobicity/surface tension and the refractive index. This information could be beneficial as the lower

the refractive index, the better the light throughput, and thus the higher the solar panel efficiency. Knowing

the surface tension is beneficial for obtaining the adhesive strength of the coating, the lower the surface

tension the lower the interaction with contaminants corresponding with a better self-cleaning effect.

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V. Financial Feasibility

This chapter shows the methods that are used to determine the financial feasibility of the usage of

GreenPower Nano coatings on PV-cells. Before a company invests in new innovation projects, an important

part of the technology evaluation is the financial viability. There are numerous ways to quantify the

innovation linked with the development and adoption of new technologies. Often these techniques are based

on financial valuation tools. This research combines the Discounted Cash Flow method with a Decision

Tree based real options approach.

V - A Explaining the model

The Discounted Cash Flow (DCF) method determines the net present value of an investment by discounting

the free cash flows in the future. Using the most likely scenario of cash flows, the financial viability of the

innovation project can be determined. The perceived amount of risk of a project can be incorporated in the

calculation by increasing the discount factor, which represents an estimation of the risk premium.

For the purpose of this research project the real options approach (ROA) is combined with a Decision Tree

Analysis (DTA). The ROA applies the Black and Scholes option valuation model to the innovation projects,

analyzing the project as a financial option in which decisions can be made based on new information during

the implementation. The DTA also uses the tree structure to evaluate scenarios and is often mentioned to

enable managerial flexibility and differing scenarios during the innovation process. Both ROA and DTA

are based on discounting payoffs and investment with appropriate discount rates. Rather than simply

discounting one line of most likely payoffs (DCF), the DTA model constructs a tree consisting of events

and decisions.

In the Excel sheet “Financial Feasibility calculation GreenPower Nano PV-14” it is possible to alter all the

assumptions in the first Excel page and see the effect on the business cases in the other Excel pages. The

lock on the other Excel pages can be removed easily if fundamental changes in the model must be made.

V - B Assumptions

The fundament of the financial model are the assumptions. During the technical feasibility study information

was gathered about the nanotechnology anti-soiling technology and scientific, government and industry

sources have been used to support the range of possible values for each assumption.

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The values of the assumptions have been discussed with both an expert responsible for innovation at

Sungevity Solar in the Netherlands and a solar coating specialist at the Energy Research Center (ECN).

During the telephone conversations the assumptions have been tested for validity in the opinion of the

expert. The experts contact information can be obtained from the authors for verification purposes.

In the excel sheet “Financial Feasibility calculation GreenPower Nano PV-14” assumptions are the basis

for calculating the financial feasibility. In this chapter every assumption will be described shortly.

Table 4: Assumptions used in financial feasibility model

Assumptions Description and values Number of panels

In order to make it possible to observe differences between small solar users, large solar users, and the total production as a distributor, it is possible to fill in any amount of solar panels in the model. This allows calculations ranging from individual solar panel sets for consumers as well as analyzing the options for large scale energy production through solar fields.

Price GPN coating per panel

The price of the PV-14 coating is given by the company and is 10 euros per PV-cell of 1,00 by 1,65 meters.

Coating lifetime

Given the strong covalent and nitrogen bonds to the glass surface, the lifetime of the coating is at least 5 years. Producers claim more than 10 years and even up to the lifetime of the panel itself. For conservative reasons the model uses 5 years of full coverage with an additional 5 years in heavily decreasing functionality (Year 6: 60% functional, to 20% in Year 10).

Costs per clean for industry user (coated)

For industry users of solar panels, it is relatively easy to clean the coating. Research has found that this could be as low as 0,40 cents per panel. To be conservative, this research has set the default value at 0,80 cents. The optimal frequency of cleaning coated solar panels can be calculated in different ways. Following the observations from interviews with experts and the coatings functionality, cleaning the solar panels once in every four years should give an advantage over not cleaning the coating (TNO).

Costs per clean for industry user (not coated)

When solar panels are not coated they will be more difficult to clean. Therefore, the price of cleaning per coating has been assumed 0,20 euro cents higher than with coating. This will result in 1 euro cleaning costs per solar panel for industry scale users. Without a coating the frequency of cleaning the panels for optimal output should be higher, at least once every two years.

Costs per clean for consumers (coated)

For consumers the average price of cleaning the solar panels will be higher. This is primarily due to the relatively difficult reachability of solar panels, often on rooftops. When reached they will cost about the

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same time per panel as in the industry case, but the start-up costs will be higher. Therefore, we conservatively assessed that a roof consisting of 10 panels will cost for example about 50 euros on average. It is hard for consumers to earn back these high costs, so determining whether the investment of cleaning panels every two years is worth it depends on each unique situation.’

Costs per clean for consumers (not coated)

The costs of cleaning the solar panels, when reached, will only be slightly higher than the costs for cleaning not coated solar panels. The total extra costs are assumed to amount to about 20 cents per panel, including extra time, water and soap usage.’

Efficiency loss on average per month (without coating)

Average conservative measurements from scientific literature: Ranges from 0,1% per month to 0,4% loss per month. With a decreasing slope the maximum loss ranges from 5 to 17% efficiency loss due to soil per year.

Efficiency loss on average per month (with coating)

The coating is especially designed to lose soil easily. Tests have shown that the efficiency loss due to soil is at least five times smaller than without coating. The model keeps this ratio between not-coated/coated solar panels constant.

kWh price industry The energy price for industry is hard to determine. It fluctuates heavily with the oil price. The SDE+ program by the Dutch government gives an annual price for delivering back energy to the network. We have set the expected average price in the future 10 years on 0,14 cents per kWh. This value might strongly differ in the future.

kWh price consumers For the price of consumer electricity, a random energy supplier was selected and their variable energy price in February 2016 is 0,20 cents. This value might strongly differ in the future.

Discount rate

The risk free rate is the required rate of return. For the calculation of this renewable investment we assumed a correction for the current inflation rate and the risk free interest rate should be no higher than 3%.

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V – C Results Financial Feasibility model

Using the assumptions described in the previous section several scenario models have been described. By

altering some of the key assumptions, a conservative, normal and optimistic scenario has be calculated. This

report aims to find scientific ground for the adoption of the PV-14 coating, therefore all scenarios are based

on scientifically realistic assumptions.

Using the results of future worldwide tests more narrow predictions can be made. The three primary

assumptions that will be altered for this test are: The average annual output of a solar panel in kilowatt-

hours in the Netherlands, efficiency loss due to soil per month without coating and potential remaining

efficiency loss due to soil per month with coating.

Although the financial models in Excel also cover scenarios including cleaning, we assume for the most

likely scenario that consumers don’t clean due to the high start-up costs in comparison with the efficiency

gains. For industry it might be worth analyzing the optimal cleaning frequency in both the coating and non-

coating scenario.

By altering some of the key assumptions, a low case scenario, conservative scenario, and high case scenario

can be calculated. Most assumptions have been kept constant in this calculation. In the future it might be

worthwhile to also alter the other assumptions in table… for individual business cases.

Table 5 : Assumptions that were kept constant in the financial feasibility calculations Assumptions Value Number of panels 100.000 Price GPN coating per panel €5,00 Costs per clean for industry user (coated) €0,40 Costs per clean for industry user (not coated) €0,50 Costs per clean for consumers (coated) €1,80 Costs per clean for consumers (not coated) €2,00 Frequency per year cleaning with coating 0,25 Frequency per year cleaning without coating 0,50 kWh price industry €0,14 kWh price consumers €0,20 Discount rate 3%

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Low case scenario The low case scenario is the minimal scenario at which a discounted profit should be made. Given the

assumptions described in table… and table … the investment will be earned back at least two times within

10 years for both consumer and industry buyers (table X).

Table 6.1 : Assumptions changed for low case scenario

Assumptions low case Values Solar generation per year (kWh) 180 kWh Efficiency loss per month without a coating & Downwards slope towards maximum 8,6% 0,25% Efficiency loss per month with coating (%) 0,05%

Table 6.2 : Net present value in low case scenario Type of users Investment in PV-14 coating NPV of efficiency gains Net present value of coating for consumers - €500.000 €1.600.000

Net present value of coating for industry - €500.000 €1.100.000

Conservative scenario The middle case scenario is the most likely scenario based on the findings in this research project. Given

the conservative nature of the assumptions in Table X this middle case scenario is named conservative

scenario. The conservative scenario is a calculation that should at least break even after using the discount

rate for both the industry and consumer application. Both the ‘average’ and… In this case the net present

value (including the time value of money) results in a return of 14 euro’s per solar panel given the short

expected life on the 10 euro’s invested.

Table 7.1 : Assumptions changed for conservative scenario

Assumptions conservative case Values Solar generation per year (kWh) 210 kWh Efficiency loss per month without a coating & Downwards slope towards maximum 8,6% 0,30% Efficiency loss per month with coating (%) 0,06%

Table 7.2 : Net present value inconservative scenario Type of users Investment in PV-14 coating NPV of efficiency gains Net present value of coating for consumers - €500.000 €2.300.000


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High case scenario The high case scenario gives a very high return for both the industry and consumer users. Given the costs

of 5 euros per panel, the savings due to efficiency gains can go up to 30 euros within 10 years.

Table 8.1 : Assumptions changed for high case scenario

Assumptions high case scenario Values Solar generation per year (kWh) 240 kWh Efficiency loss per month without a coating & Downwards slope towards maximum 8,6% 0,35%

Efficiency loss per month with coating (%) 0,07%

Table 8.2 : Net present value high case scenario Type of users Investment in PV-14 coating NPV of efficiency gains Net present value of coating for consumers - €500.000 €3.000.000


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VI. Conclusions & recommendations

V - I General conclusions

The goal of this feasibility study was to find scientific prove for the effectivity and profitability of the

GreenPower Nano PV-14 coating. The technical and financial feasibility of the coating as a solution for the

soil efficiency loss problem have been assessed. The hydrophobic character of this strong silicon based

coating results indeed in a theoretical very promising self cleaning effect and a strong bond to the glass

surface. The characteristics of the GPN coating have been tested on the requirements following from an

objective tree. Following this structured approach almost all requirements should be met by the coating. The

requirements that cannot be confirmed at this stage, are not rejected but can be tested in future research to

further increase the technical foundation of the product. The coating has the highest benefits in challenging

geographic regions like California and Egypt but also has many advantages in the Netherlands, specifically

for consumers that might have high start-up costs for letting their solar panels be cleaned.

The financial feasibility is based on an Excel model with industry tested assumptions. By calculating three

relatively conservative scenarios, the net present value of the investments have been calculated. All

scenarios result in a positive net present value for the buyer. The options ‘coat panels’ / ‘not coat panels’

and ‘clean’ / ‘not clean’ have been calculated and can be analyzed by potential buyers by using the Excel

financial feasibility model. This Excel model furthermore makes it possible to calculate the financial

feasibility of individual cases in the future by altering the assumptions and changing the input values.

V - II Future research & tests

From the technical feasibility part of this research project two main challenges have been reported. For

better understanding of the coatings’ surface properties and performance, the refractive index of the coating

and the hydrophobicity/surface tension should be measured.

The refractive index is interesting as SiO2 is also often used as an antireflective coating. This might further

improve the efficiency gains from the coating. If it can be shown that the surface coating shows better anti-

reflectivity, the value of the coating increases as the efficiency of the solar panels can increase. Measuring

this could be done by a simple test in which two or more solar cells are used. Half of the solar panels should

be coated and the other half uncoated. By measuring the power output in a short timeframe, say one week,

or one month, it can be measured if the output of the solar cells show differing behavior. For a more specific

measure of the refractive index, measurements with laser light are necessary. These are often complex

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measurements which require specific measurement material. It is therefore more relevant to measure the

difference in efficiency with the earlier described method at this stage.

Measuring the surface tension and especially the water contact angle requires specific lab scale measuring

equipment. Most commonly a Tensiometer is used, for simpler methods the Du Noüy-Padday method can

be used to measure the drop and calculate the surface energy. This can be done in-house or can be executed

in the labarotories of one of the partners.

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References

[1] M. R. Maghami, H. Hizam, C. Gomes, M. A. Radzi, M. I. Rezadad, and S. Hajighorbani, “Power loss due to soiling on solar panel: A review,” Renew. Sustain. Energy Rev., vol. 59, pp. 1307–1316, Jun. 2016.

[2] C. Vu and Clariant, “Advanced coating materials based on silazane,” 2008. [Online]. Available: http://www.solgel.fr/exposes_2008/can_vu.pdf. [Accessed: 28-Feb-2016].

[3] I. P. Parkin and R. G. Palgrave, “Self-cleaning coatings,” J. Mater. Chem., vol. 15, no. 17, p. 1689, 2005.

[4] R. Blossey, “Self-cleaning surfaces — virtual realities,” Nat. Mater., vol. 2, no. 5, pp. 301–306, 2003.

[5] G. Gu, H. Dang, Z. Zhang, and Z. Wu, “Fabrication and characterization of transparent superhydrophobic thin films based on silica nanoparticles,” Appl. Phys. A Mater. Sci. Process., vol. 83, no. 1, pp. 131–132, 2006.

[6] J. Zorrilla-Casanova, M. Piliougine, J. Carretero, P. Bernaola, P. Carpena, L. Mora-López, and M. Sidrach-de-Cardona, “Analysis of dust losses in photovoltaic modules,” in World renewable energy congress, 2011, pp. 2985–2992.

[7] A. Massi Pavan, A. Mellit, and D. De Pieri, “The effect of soiling on energy production for large-scale photovoltaic plants,” Sol. Energy, vol. 85, no. 5, pp. 1128–1136, May 2011.

[8] H. Haeberlin and J. D. Graf, “Gradual reduction of PV generator yield due to pollution,” Power [W], vol. 1200, p. 1400, 1998.

[9] H. P. Oversloot, M. Tijssen, M. N. Slappendel, and B. J. M. van Kampen, “Invloed vervuiling op de energie-opbrengst van PV-elementen. Schoonmaak zonnepanelen,” TNO, 2003.

[10] RVO, “Zon SDE+ 2016,” 2016. [Online]. Available: http://www.rvo.nl/subsidies-regelingen/zon-sde-2015. [Accessed: 28-Feb-2016].

[11] H. K. Elminir, A. E. Ghitas, R. H. Hamid, F. El-Hussainy, M. M. Beheary, and K. M. Abdel-Moneim, “Effect of dust on the transparent cover of solar collectors,” Energy Convers. Manag., vol. 47, no. 18–19, pp. 3192–3203, 2006.

[12] C. L. Dym, P. Little, E. J. Orwin, and R. E. Spjut, Engineering design: a project-based introduction. Wiley New York, 2004.

[13] S. K. Kim, Springer handbook of Marine biotechnology. 2015. [14] M. Shields, “PV Systems : Low Levels of Glare and Reflectance vs . Surrounding

Environment,” 2010. . [15] E. F. Cuddihy, “Theoretical considerations of soil retention,” Sol. energy Mater., vol. 3,

no. 1, pp. 21–33, 1980. [16] J. Zhu, C. Hsu, Z. Yu, S. Fan, and Y. Cui, “Nanodome Solar Cells with Efficient Light

Management and Self-Cleaning,” pp. 1979–1984, 2010. [17] B. A. C. Power, A. Barrett, J. Abubakar, L. J. Suarez, L. Ryan, D. Wencel, T. Sullivan,

and F. Regan, “Versatile Self-Cleaning Coating Production Through Sol – Gel Chemistry,” no. 1, 2016.

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[18] Y. Yuan, Y. Chen, W. L. Chen, and R. J. Hong, “ScienceDirect Preparation , durability and thermostability of hydrophobic antireflective coatings for solar glass covers,” Sol. ENERGY, vol. 118, pp. 222–231, 2015.

[19] T. Sarver, A. Al-qaraghuli, and L. L. Kazmerski, “A comprehensive review of the impact of dust on the use of solar energy : History , investigations , results , literature , and mitigation approaches,” Renew. Sustain. Energy Rev., vol. 22, pp. 698–733, 2013.

[20] Y.-B. Park, H. Im, M. Im, and Y.-K. Choi, “Self-cleaning effect of highly water-repellent microshell structures for solar cell applications,” J. Mater. Chem., vol. 21, no. 3, pp. 633–636, 2011.

[21] J. K. Fink, Reactive Polymers Fundamentals and Applications: A Concise Guide to Industrial Polymers: Second Edition. 2013.

[22] A. Lukacs and G. J. Knasiak, “Thermally stable, moisture curable polysilazanes and polysiloxazanes,” 2003.

[23] S. Brand, M. Mahn, and F. Osterod, “Polysilazane Jung und wirkungsvoll,” Farbe und Lack, vol. 116, no. 11, p. 25, 2010.

[24] G. Wu, J. Wang, J. Shen, T. Yang, Q. Zhang, B. Zhou, Z. Deng, B. Fan, D. Zhou, and F. Zhang, “A novel route to control refractive index of sol-gel derived nano-porous silica films used as broadband antireflective coatings,” Mater. Sci. Eng. B, vol. 78, no. 2–3, pp. 135–139, Dec. 2000.

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