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Solar Energy 83 (2009) 1977–1984

A model to determine financial indicators for organic solar cells

Colin Powell *, Timothy Bender, Yuri Lawryshyn

Department of Chemical Engineering and Applied Chemistry, Faculty of Engineering and Applied Science, University of Toronto,

200 College Street, Toronto, Ont., Canada M5S 3E5

Received 16 March 2009; received in revised form 29 June 2009; accepted 23 July 2009Available online 19 August 2009

Communicated by: Associate Editor Sam-Shajin Sun

Abstract

Organic solar cells are an emerging photovoltaic technology that is inexpensive and easy to manufacture, despite low efficiency andstability. A model, named TEEOS (Technical and Economic Evaluator for Organic Solar), is presented that evaluates organic solar cellsfor various solar energy applications in different geographic locations, in terms of two financial indicators, payback period and net pres-ent value (NPV). TEEOS uses SMARTS2 software to estimate broadband (280–4000 nm) spectral irradiance data and with the use of acloud modification factor, predicts hourly irradiation in the absence of actual broadband irradiance data, which is scarce for most urbanlocations. By using the avoided cost of electricity, annual savings are calculated which produce the financial indicators. It is hoped thatthese financial indicators can help guide certain technical decisions regarding the direction of research for organic solar cells, for example,increasing efficiency or increasing the absorptive wavelength range. A sample calculation using solar hats is shown to be uneconomical,but a good example of large-scale organic PV production.� 2009 Elsevier Ltd. All rights reserved.

Keywords: Organic photovoltaics; Payback period; Cost of production; Large-scale organic solar cells

1. Introduction

Organic or polymer solar cells, made entirely from plas-tic and other organic materials, provide significant advan-tages over traditional PV technologies, such as crystallineSi and thin-film cells, but still have significant headway tomake in a number of fields. Organic PV cells are ‘‘one ofthe future key technologies opening up completely newapplications and markets for photovoltaics” (Brabec,2004). The main advantages of organic PV fall into twocategories: inexpensive synthesis and ease of manufacture(Gnes et al., 2007). Their low cost can be attributed to sim-ple and established methods for synthesis and their easymanufacture using existing printing press technologies.Also, the amount of organic compounds used and the

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doi:10.1016/j.solener.2009.07.009

* Corresponding author. Tel.: +1 416 978 6924x219.E-mail address: colin.powell@utoronto.ca (C. Powell).

energy needed to produce and manufacture the organiccells can be small, implying that ecological damage canbe minimal. However, the cost and environmental impactof organic PV depends on the process used for their man-ufacture. Krebs (2009) states that the ideal manufacturingprocess would result in a polymer solar cell that has a‘‘low environmental impact and a high degree of recyclabil-ity” with a minimal number of production steps. An exten-sive review on printing and coating techniques forproducing organic PV is available in Krebs (2009). How-ever, while organic PV has the potential to have low pro-cess costs compared to other PV technologies (Brabec,2004; Gnes et al., 2007; Krebs et al., 2009; Scharberet al., 2006; Bundgaard and Krebs, 2007; Krebs, 2005;Jørgensen et al., 2008; Shaheen et al., 2005), a main stum-bling block to commercialization is low efficiency and shortlifetime due to low stability (Brabec, 2004; Gnes et al.,2007; Krebs et al., 2009; Bundgaard and Krebs, 2007;Krebs, 2005; Jørgensen et al., 2008).

1978 C. Powell et al. / Solar Energy 83 (2009) 1977–1984

The power conversion efficiency of an organic PV cell,defined as the ratio of the energy that is available for con-sumption to the energy available from the sun, currently sitsaround 5% (Gnes et al., 2007; Krebs et al., 2009; Scharberet al., 2006; Bundgaard and Krebs, 2007; Krebs, 2005;Jørgensen et al., 2008; Shaheen et al., 2005; Gratzel, 2007;Kim et al., 2007; Koster et al., 2006) for small-area devices.The upper bound on the efficiency for organic PV has beenstated at approximately 20% (Forrest, 2005; Curtrightet al., in press), but a more reasonable value is likely around10% (Scharber et al., 2006; Kim et al., 2007; Koster et al.,2006). Sargent (2008) notes that higher overall solar powerconversion can be achieved with tandem cells of differentband gaps, but this is especially true of polymers with a lowerband gap (Scharber et al., 2006; Bundgaard and Krebs, 2007;Koster et al., 2006). Indeed, the highest reported energy con-version efficiency of 6.5% was reported using polymers ofdifferent band gaps in tandem (Kim et al., 2007). While thevast majority of these tandems only consist of semi-conduc-tors in the visible range, Bundgaard and Krebs (2007) havecompleted a thorough review of low band gap polymers withrespect to organic photovoltaics, outlining different materi-als, performance, and design considerations.

Krebs et al. (2009) have provided the first cost structureof the production of organic solar cells. Specifically, theauthors provide a breakdown of the different steps usedin the production of organic PV as well as material andlabour costs. While their experimental tests are only basedon small individual modules, they concede that there existsgreat potential for cost reduction by using cheaper labour,increasing throughput, and most importantly, reducingmaterial costs. A recent survey of solar energy expertshas shown that organic polymer PV will have one of the

Actual UVB Irradiance

Data

Actual Weather

Conditions

Cloud Modification Factor (CMF)

ModelledUVB

Irradiance Data

TechnoCharact

Electricit

EstimIrradi

ModelledBroadband

Spectral Irradiance

Fig. 1. Flow chart of

best cost structures (price per peak watt) compared to arange of cell types, such as crystalline Si, thin-film andother excitonic and novel technologies (Curtright et al.,in press).

The model developed in this study, named TEEOS(Technical and Economic Evaluator for Organic Solar),will provide a methodology to determine economic feasibil-ity for organic solar cells, as well as provide informationregarding the economics of certain research decisions.The model is highly dependent on available data: weather,spectral irradiance, and electricity prices. If actual broad-band irradiance data is available, the analysis is simpler;but this data is not available for most locales, necessitatingthe approach outlined here. This paper will discuss themethodology behind the model and provide a short samplecalculation. Future papers will discuss results and differentscenarios that arise from this model being applied to cer-tain locations and cells and incorporate different cost struc-tures outlined by other researchers.

2. Methodology

Fig. 1 shows a flow chart that outlines the inputs andoutputs of various stages of TEEOS. The model is dividedinto three sections. The first compares actual irradiancedata to modeled clear-sky irradiance data in the UVBrange and, using actual weather conditions, produces acloud modification factor (CMF) to model the effect ofclouds on spectral irradiance. The second step combinesthis CMF and the modeled broadband spectral irradiancedata to produce an estimate of the hourly irradiance forthe chosen location. The third section uses hourly electric-ity prices, hourly irradiance values, and a variety of cell

logical eristics

y Prices

Financial Indicators

ated ance

TEEOS process.

C. Powell et al. / Solar Energy 83 (2009) 1977–1984 1979

characteristics to produce financial indicators in order toevaluate the specific solar cell.

The model can be simplified if actual broadband spectralirradiance data is available for the chosen location; the firsttwo steps, used in the absence of spectral irradiance data,can be eliminated. This model is applicable to any location,provided there is sufficient spectral irradiance, weather, andelectricity price data.

2.1. Weather conditions and irradiance data

Hourly weather conditions and spectral irradiance dataare obtained from government meteorological agencies, aswell as universities and research centres. While it is a safeassumption that clear-sky spectral irradiance data will beconstant throughout the lifetime of the organic cell, theweather conditions will not be constant. It is possible touse past data to generate weather conditions using weathergenerators. See (Wilks and Wilby (1999) for a review ofstochastic weather models.

There are a variety of descriptors used by meteorologi-cal agencies to identify the actual weather conditionsoccurring at a given time in a given location. For simplic-ity, the model initially divided these into four groups:cloudy, mostly cloudy, clear and mainly clear. All precipi-tation events, including rain, snow, drizzle, and thunder-storms, as well as visibility-reducing events, such as hazeand fog, are included in the cloudy group. Due to ambigu-ity, the groups are consolidated so that the clear group alsoincludes the mostly cloudy and mainly clear weather condi-tions, leaving just two weather conditions to consider:cloudy and clear.

The spectral irradiance data consists of irradiance dataover a certain wavelength range at different times through-out each day. If data is available that covers the absorptivewavelength range of the PV cell in question, then the modelsimplifies greatly.

2.2. Modeled irradiance data

To accurately measure the economic feasibility oforganic PV, one needs accurate solar radiation data acrossa large spectrum. There are in fact very few locations thatrecord spectral irradiance data outside the UV and visibleranges. This makes the case for modeling programs thatestimate the daily solar irradiation; they make up for a lackof consistent and robust spatial and temporal irradiationmeasurements and can accurately predict broadband spec-tral irradiance, such as IR.

There are two main types of spectral irradiance models:sophisticated rigorous code, such as LOWTRAN andMODTRAN, and simple transmittance parameterizedmodels, such as SMARTS2, the Simple Model of theAtmospheric Radiative Transfer of Sunshine developedby Christian Gueymard. SMARTS2 will be used in thispaper to model spectral irradiance. This model is moreappropriate for engineering applications, where low dura-

tion and complexity of execution is desired. It uses spectraltransmittance functions for the most significant scatteringand absorption processes that take place as the sun’s rayspass through the atmosphere. The model is described inmore detail in other papers by Gueymard (2004, 2005,2008).

One limitation of SMARTS2 is that it produces onlyclear-sky spectral irradiance data. Solar radiation is scat-tered as it passes through clouds, so it is necessary to useother means to determine the effect clouds have on the irra-diation, such as developing a cloud modification factor(CMF) to simulate a cloudy day. For example, Burrows(Burrows, 1997) completed a study predicting UV radia-tion at the ground in the presence of environmental factors,such as cloud, in Toronto, Canada. Herman et al. (1980)and Raschke et al. (2004) describe methods for determiningthe cloud effects on radiation budgets (the net radiation inversus out of the atmosphere) in the infrared. Unfortu-nately, these methods both deal with global radiation nearthe top of the atmosphere, not at ground level. There is alack of information regarding the precise effect of cloudson solar radiation of different wavelengths being absorbedat ground level. This information is necessary for predict-ing irradiation data at locations where no monitoringequipment exists and is of particular importance to theorganic PV industry because of the need for wide-rangespectral irradiance data. Because of this insufficient data,we have made the assumption here that the effect of cloudson the UV spectrum is the same across the full spectrumof light and have applied this in the development of ourCMF.

SMARTS2 is used to determine the broadband (280–4000 nm) spectral irradiance. It uses a series of inputs, suchas latitude and atmospheric conditions to calculate irradi-ance across a chosen wavelength range.

In order to obtain the total irradiance (W m�2), it is nec-essary to integrate the hourly spectral irradiance data overthe chosen range of wavelengths according to the followingequation:

I ½h; d� ¼Z k2

k2

I ½h; d;k�dk ð1Þ

where: I[h,d] = irradiance at each hour, h and day, d [W m�2],k2 = upper range of chosen wavelength [nm], k1 = lowerrange of chosen wavelength [nm], I[h,d,k] = spectral irradi-ance at each wavelength, k, each hour, h, and day, d [W m�2

nm�1], and dk = wavelength step size [nm].The upper and lower ranges of the wavelengths corre-

spond to the optical absorption range of the solar cell beingevaluated. Within the SMARTS2 model, different step sizesare used for different wavelength ranges: 0.5 nm in the UV(280–400 nm), 1 nm in the visible and part of the near infra-red (400–1702 nm), and 5 nm beyond, up to 4000 nm. Thetrapezoidal rule is used to estimate the integral.

Eq. (1) produces a matrix of clear-sky irradiance valuesfor each hour over the course of a given year. It is necessary

1980 C. Powell et al. / Solar Energy 83 (2009) 1977–1984

to account for days when the weather conditions are notclear, because this is only clear-sky data; this can be doneby developing a cloud modification factor (CMF).

2.3. Cloud modification factor (CMF)

A CMF is used to adjust modeled irradiance data to bet-ter reflect actual weather conditions in absence of actualbroad-spectrum irradiance data. CMF is defined in TEEOSas: (Staiger, 2008).

CMF ¼ Irradiation under a specific weather condition

Irradiation under clear-sky

ð2Þ

As discussed above, the irradiance under clear skies isobtained using the SMARTS2 simulation. The irradiancefor non-clear skies is obtained from the inputs in SectionOne of TEEOS. The actual and modeled UV irradianceand weather condition for each hour are arranged in aspreadsheet and then sorted by the two weather conditions,clear and cloudy. For each weather condition, monthlyCMFs are obtained by plotting the actual hourly irradianceon the y-axis and modeled hourly irradiance on the x-axis;an example is shown in Fig. 2. The slope, which is themonthly CMF, is determined using robust regressionbecause of the high possibility of outliers. See Hollandand Welsch (1977)) and (Street et al. (1988) for more dis-cussion on robust regression.

It is expected that for the clear condition, the CMFs willbe close to 1 because the modeled (clear-sky) data would besimilar to the actual ‘‘clear” data. Similarly, it is expectedthat the CMFs for cloudy conditions are lower than 1

Fig. 2. CMFs for January 2006 and 2007 in Toronto, Canada. The markersstraight lines have a slope equal to the robust slope, showing the trend.

because clouds absorb some of the UV radiation (Calboet al., 2005).

The main limitation in the CMF calculation is a lack ofbroadband spectral irradiance data for a given geographiclocation. Due to the lack of sufficient data, an assumptionis made here that the effect of clouds on the UV spectrum isat least similar to the effect on the rest of the spectrum. Thisassumption should be tested in a sensitivity analysis.

2.4. Modified solar irradiance

The ‘‘modified” hourly solar irradiance is calculated asfollows:

I ½h; d�modified ¼ I ½h; d�modeled � CMF ð3Þ

Eq. (3) produces a matrix of hourly irradiance data thatbetter represents the effect of weather on the modeled irra-diance. The CMF used is dependent on the hourly weathercondition for that time.

2.5. Electricity pricing data

Electricity prices are used in the model to determine theavoided cost of electricity and form the basis of the calcu-lation of annual savings.

There are two main components to non-commercial retailelectricity rates: fixed recurring charges and volumetricenergy charges (Hydro, 2008). The fixed charges generallyconsist of charges for the delivery of electricity from the gen-erator to the supplier and then to the customer, as well asgeneral charges for administration. The volumetric charges,which are proportional to the amount of electricity used,

represent hourly data points for that month and weather condition. The

C. Powell et al. / Solar Energy 83 (2009) 1977–1984 1981

include the actual price for electricity, as well as othercharges, including transmission and distribution charges.These charges vary widely depending on jurisdiction, elec-tricity generating source and cost structure of the generatingcompany. In Ontario, Canada, for example, the hourlyOntario energy price (HOEP) is the market price for electric-ity and is directly related to the price for domestic customers(Independent Electricity System Operator, 2008a). In gen-eral, there are three rate structures for electricity prices: ratesthat are set by a local utility and do not change regularly;rates that change depending on time and quantity of electric-ity used; and wholesale prices (Independent Electricity Sys-tem Operator, 2008b). In this model, wholesale rates areused because it is expected that this is the direction that util-ities will move in order to better reflect the cost of energy.

It is evident that an organic PV installation will not pro-duce more electricity than the demand of the customer, butsupplement the power from the grid. Because the customeris still connected to the grid, the fixed charges on the elec-tricity bill will still have to be paid. Therefore, the fixedcharges will not be included in the avoided cost of the elec-tricity. However, other variable charges, mentioned above,must be added to the base electricity price in this model.

Forward electricity prices typically follow models suchas the one-factor mean reversion jump diffusion model.This model takes into account the mean reverting natureof electricity prices, but can also predict the jumps thatoften occur. Cartea and Figueroa(2005) outlined a methodfor predicting spot electricity prices, as well as the forwardprice curve. While using this model to predict electricityprices over the lifetime of the solar cell would be useful,it only predicts daily prices, not hourly. Hourly forwardelectricity prices would provide more accuracy for themodel, even over the long lifetime of the cell. Therefore,since the calibration for such models is taken from histor-ical data, hourly electricity prices in this model are assumedto be constant over the lifetime of the cell.

2.6. Technological characteristics

There are four main cell characteristics that are neces-sary for the model presented here: absorptive wavelengthrange, power conversion efficiency, cell lifetime, and thecost of producing the cell.

2.6.1. Wavelength rangeThe wavelength range of an organic solar cell is charac-

teristic of the polymer material used to absorb photonsfrom the sun. The absorptive wavelength range gives alower and upper wavelength value in which the materialcan absorb photons and convert them to electrons. Bundg-aard and Krebs (2007) have created a useful table that cal-culates the integrated photon flux and current density from280 nm up to certain wavelengths from 500–1500 nm. Anincident photon to current efficiency (IPCE) or quantumefficiency versus wavelength graph can provide an accurate

estimate of the wavelength range of certain materials inorder to be used in this model.

2.6.2. Power conversion efficiency

The power conversion efficiency of an organic solar cell isthe percentage of power converted from absorbed light toelectrical energy when the solar cell is connected to an elec-trical circuit. While power conversion efficiencies of inor-ganic solar cells have easily surpassed 20%, organic solarcells are still less than 7%. There has been exceptional pro-gress made in the past number of years, but the maximumreported power conversion efficiency is 6.5% (Kim et al.,2007). Most laboratory cells have reported efficiencies below5%. For large-scale applications, however, much smaller effi-ciencies are seen, almost 15–20 times poorer than laboratorymodels under the same conditions (Krebs et al., 2009). Hop-pe and Sariciftci (2006) have compiled a list of some of thebest power conversion efficiencies of polymer solar cells.

2.6.3. Cell lifetime

The lifetime of an organic solar cell depends on theextent of chemical, physical and mechanical degradationof the materials and hardware used in the cell. The lifetimeof a cell is defined as the time that a cell has a relativelyconsistent current or power conversion efficiency. The bestlifetime for an organic/polymer cell is estimated at 20,000 h(Bundgaard and Krebs, 2007), but more realistic lifetimesof cells with small cells have reached 10,000 h, or just over1 year, in standard conditions (Jørgensen et al., 2008).

2.6.4. Cost of productionThe processes used to make dyes and other technologies

used in the copier/printer industry are similar to those usedto make organic solar cells. The cost of producing anorganic solar cell has not been extensively studied, exceptby Krebs et al. (2009). As mentioned earlier, a veryexhaustive costing of the various processes and materialsused in the sequence of producing the organic solar cellsis detailed. The authors also provide some much neededinsight into the large-scale production of organic solarcells, which has also been widely ignored in literature. Itis concluded that materials cost are the dominant cost fac-tor and should be first reduced to make organic PV com-petitive with other solar technologies.

2.7. Ancillary costs

Ancillary costs are made up of costs including installa-tion, maintenance, and other equipment necessary for usein a solar module to generate electricity, such as an inverter,power monitor and electrical meter. These are all dependenton the size and complexity of the solar installation.

2.8. Financial indicators

The initial cost of a traditional solar electricity system isgenerally higher than the typical electrical system for

Table 1Technological characteristics of the organic PV cells attached to solar hats.

Wavelength range 350–950 nm (Hagemann et al., 2008)Power conversion efficiency 0.013% (Krebs et al., 2009)Cell lifetime �3 Months (Krebs et al., 2009)Cost of production 0.68–4.538 €/module (Krebs et al., 2009)Active area 75 cm2 (Krebs et al., 2009)

1982 C. Powell et al. / Solar Energy 83 (2009) 1977–1984

domestic use. However, a solar electricity system providessignificant savings on electricity bills compared to a typicalgrid-connected system because the only costs involved areinitial set-up costs. This savings can be used to calculatea simple payback period for a solar array.

The annual savings is calculated as follows:

Annual savings ðASÞ ¼ Aarray

X365

d¼1

X23

h¼0

g

� I ½h; d�modified � C½h; d� ð4Þ

where Aarray is the area of the array [m2], g is the efficiencyof the solar cell, I[h,d]modified is the modified irradiancefrom Eq. (3) [kWh m�2], and C[h,d] is the cost of electricityat hour, h and day, d [$/kWh].

Using this methodology, the annual savings representsthe avoided cost of electricity that one would not have tobuy from the grid because of the solar electricity system.

The simple payback period is defined as the following:

Simple payback period ðyÞ ¼ Initial investment

Annual savingsð5Þ

The initial investment in this case represents the initialcost of the solar array. The annual savings are calculatedfrom the avoided cost of purchasing electricity from a util-ity. Payback period, however, does not take into accountthe time value of money. A more accurate measure of valueused for economic analysis is net present value (NPV),defined as:

NPV ¼Xn

i¼0

F i

ð1þ rÞið6Þ

where Fi is the net cash flow at time i, the time period (typ-ically years; year zero is present time), r is the discount ratefor each cash flow, and n is the total number of timeperiods.

This indicator takes into account the lifetime of the celland money that is saved even after the payback period isreached. The detailed NPV equation used is:

NPV ¼ �ICþXn¼1

i

AS

ð1þ rÞið7Þ

The cash flow at time zero is the initial cost (IC) of pur-chasing and installing the system and is negative. The sub-sequent cash flows represent the annual savings (AS) thataccrue by making the switch from the grid to organic solarcells to produce electricity; these are positive. A positiveNPV shows that the initial investment will be paid back,with a profit, at the end of the lifetime of the project.

An additional indicator that is used in the analysis isinternal rate of return (IRR). It is often used by firms tomake decisions on whether or not to go ahead with aninvestment. The IRR is the rate of interest that makesthe NPV of a project equal to zero, as such:

NPV ¼Xn

i¼0

F i

ð1þ rÞi¼ 0 ð8Þ

where the r represents the IRR and the remaining symbolsare defined as such in Eq. (6).

3. Sample calculation

A simple calculation is provided in this section in orderto provide the reader an example from the model. A moredetailed analysis using the model will be shown in a com-panion paper.

The first major demonstration of large area polymersolar cells, big enough to power a small rechargeable bat-tery, was performed by Krebs et al. (2009). Using circularpolymer solar modules, their team made over 1000 ‘‘SolarHats” intended to power an FM radio and other batterypowered devices at a music festival in Denmark (Krebset al., 2009). The data from that paper will be used to cal-culate the financial indicators for the use of the solar hatsor a similar sized device in Toronto, Canada.

3.1. Data

A polymer solar cell with active layers of P3MHOCT/ZnO and P3MHOCT/PCBM/ZnO, a PET-ITO (indium-tin-oxide) substrate, and PEDOT:PSS and silver electrodeswere used to make the solar hats using screen printing asthe process technology (Krebs et al., 2009).

Table 1 shows the technological characteristics for thecells used in the solar hats.

The wavelength range is an estimate taken from theabsorption spectra of a similar solar module in Hagemannet al. (2008). The weather is taken from Environment Can-ada (Canada’s National Climate and Weather DataArchive, 2008).

3.2. Results

Due to the short lifetime of the cells, the financial anal-ysis was carried out using 2006 weather data for threemonths, from June 1 to August 31, the peak months ofsolar irradiation in Toronto, Canada, also coinciding withthe time the experiment in Krebs et al. (2009) was con-ducted at the Roksilde music festival in Roksilde, Den-mark. The lowest estimated price for the cost of themodule in Table 1 was used. The very low efficiency andsmall active solar area mean that in Toronto, these mod-

C. Powell et al. / Solar Energy 83 (2009) 1977–1984 1983

ules would produce only 0.47 W-h of electricity. By usingsolar energy to recharge the battery in the radio insteadof electricity purchased from the grid in Toronto, the userwould save just over one-half cent (CAD) in that time per-iod. The payback period or NPV are not relevant financialindicators in this case due to the absence of full costs forthe FM radios and materials used to connect the cellsand the radios, not to mention the poor savings achievedin general. The authors claim that ‘‘due the poor perfor-mance of the modules, it is not meaningful to work out acost of electricity” (Krebs et al., 2009). Justifiably, thisexperiment was conducted not to prove the economics oforganic PV, but to prove that organic PV is indeedscalable.

An interesting exercise would be to use the maximumreported efficiency and cell lifetime of an organic solar cellto see if current technology, if ever available in the same cell,has the potential to be economically viable. It must be notedthat this exercise is purely for calculation purposes and thatsuch a cell does not exist. Indeed, the cell that has the highestefficiency is not the same as the cell with the longest lifetimeand each of these cells were produced on a very small scale,unsuitable for modern electronic devices.

Using 6.5% (Kim et al., 2007) as the efficiency and20,000 h (Bundgaard and Krebs, 2007) as the lifetime, aswell as using the low estimate for the cost of a moduleand active solar area of 0.0075 m2 (Krebs et al., 2009),one module would provide approximately 1334 W-h overthe 20,000 h lifetime, as well as saving the user just under$0.125 (CAD) in Toronto electricity charges (using 2006weather Canada’s National Climate and Weather DataArchive, 2008 and electricity price data (Independent Elec-tricity System Operator, 2008a). Even without the inclusionof the cost of the connection diodes and the battery itself(for whatever application), the payback period is well overthe lifetime of the cell at about 18 years. While the powergenerated was sufficient to power an FM radio, the shortlifetime and very low efficiency of the cell make it uneco-nomical at this time for domestic power generation.

In order for this particular cell to be economical, theprice must be reduced: mainly through material cost reduc-tion, the use of cheaper manpower, and, in general, morepractice with up-scaling this relatively new technology(Krebs et al., 2009).

3.3. Optimization scenarios

At the current efficiency and lifetime, one house wouldneed just under 13 million of these solar modules to meetthe power needs of a typical Canadian household [Green-peace link], with replacements every three months. Thiswould require just under 25,000 m2 of exposure area(ignoring connection areas) and cost well over $80 millionCAD to purchase using a per module cost. In practice, thecost would be less due to the scale-up of production, butnonetheless this particular cell is not feasible in its currentform for domestic power generation.

To power a typical Canadian household with a roofexposure area of 10 m2, the efficiency of these cells wouldneed to be over 130% not to mention requiring close to1340 modules (ignoring connection areas).

Even at best cost estimates for optimized large-scaleorganic PV production (€1/m2), this optimization is futileas the number of cells that need to be purchased is enor-mous, making payback periods very large.

From these results, there is a dire need for more work onlarge-scale organic solar cell development.

4. Conclusion

The TEEOS model presented here can provide usefulfinancial indicators for all PV technologies, but is inher-ently flexible in order to incorporate the unique character-istics of organic PV. The economic feasibility of tandemorganic cells that use combinations of different materialsto absorb certain wavelength ranges should be determinedusing the methodology outlined here. It can allow the userto determine the best cost structure for certain locations.Indeed, the cost structure for certain organic PV could beso favorable that it may be feasible to manufacture cellsfor different weather conditions and different localesaround the world. Future papers will use the TEEOSmodel in greater detail and show the viability of certainorganic solar cells that have been developed for variousapplications. It is hoped that it will be possible to use thisresults from the model to guide research for organic solarcells, specifically regarding efficiency and spectral range.

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