On the impact of solar spectral irradiance on the yield of differentPV technologies

Daniela Dirnberger n, Gina Blackburn, Björn Müller, Christian ReiseFraunhofer ISE, Fraunhofer Institute for Solar Energy Systems, Heidenhofstrasse 2, 79110 Freiburg, Germany

a r t i c l e i n f o

Article history:Received 22 April 2014Received in revised form20 September 2014Accepted 25 September 2014

Keywords:Spectral irradianceEnergy ratingThin film photovoltaicsSpectrumUncertainty

a b s t r a c t

This article presents results from investigating the impact of varying spectral irradiance on theperformance of different PV technologies. Relative gains or losses were quantified for five typical PVtechnologies with different band gaps and, consequently, different spectral responses using spectralirradiance measured from 01.06.2010 to 31.12.2013 in Freiburg im Breisgau, Germany. With the spectrallyresolved data, the effectively available annual irradiation per technology was calculated using thespectral mismatch factor, and compared to the total broadband irradiation measured by a pyranometer.The process used to calculate the spectral impact produces a result that can directly be used to estimatethe effectively available irradiation for yield prediction or energy rating. The annual spectral impact wasþ3.4% for amorphous silicon, þ2.4% for cadmium telluride, þ1.4% for crystalline silicon, þ1.1% for high-efficiency crystalline silicon and þ0.6% for small-band-gap CIGS. Technologies with a large band gapexhibited spectral gains in summer and spectral losses in winter, and vice versa for small-band-gaptechnologies. The results are discussed and interpreted with consideration to uncertainties and resultspublished so far. Furthermore, it was investigated in how far average photon energy (APE) can be used asa quantitative indicator for the spectral impact. In summary, it was found that using APE does notpresent actual advantages over using the spectral mismatch factor, and should rather be used forqualitative than for quantitative evaluations.

& 2014 Elsevier B.V. All rights reserved.

1. Introduction

PV modules are rated according to their power at StandardTesting Conditions (STC: 1000 W/m2, 25 1C, spectral distributionaccording to IEC 60904-3); however, the energy conversion duringtheir lifetime mostly occurs at conditions differing from STC.Under such conditions, other characteristics such as temperaturebehavior, low light behavior, angular response and spectralresponse influence the energy conversion. The goal of rating PVmodules according to their energy rather than their STC power hasbeen pursued for several decades [1–3], and is now the purpose ofthe series of standards IEC 61853.

As will be outlined below in more detail, the influence ofirradiance and temperature on PV power is well-known. In contrast,data quantifying the influence of the varying solar spectral irradianceon PV performance and yield on a general basis remains elusive,despite a number of studies having been conducted world-wide:Minemoto, Nakada and Nagae [4–11] investigated the generalinfluence of the spectrum on the performance ratio of PV modulesin Kusatsu City, Japan. They used data collected continuously over

several years, and used the average photon energy (APE) as anindicator for spectral irradiance. Also in Japan, Ishii et al. analyzedspectral influences during round robin measurements at severallocations and over a period of several months [12–15] using thespectral mismatch factor and APE as indicators. Gottschalg and Betts[16–19] collected spectral irradiance data over several years inLoughborough, UK and used APE [16], the useful fraction UF [17] orweighted useful fraction WUF [18] as indicators. Zinsser et al.presented results of a 1-year measurement period in Stuttgart,southern Germany, analyzing the spectral impact on energy produc-tion of different PV technologies by means of the integrated electricalcharge Q [20]. In Oldenburg, northern Germany, Behrendt et al.measured the spectrum on clear sky days [21], mainly for compar-ison with modeled data. Cornaro and Andreotti used APE to char-acterize the measured spectral irradiance conditions in Rome, Italy,and to relate spectral conditions to performance of PV modules [22].Perez-Lopez et al. presented data measured at several clear sky daysduring the year in Madrid, Spain in [23]. A more detailed study onthe influence of spectral irradiance on the energy yield of differentPV technologies was conducted by [24]. They used modeled spectralirradiance for 4 sites in Europe, and in addition measured thespectrum for two thereof (Madrid and Jaen, Spain, 1 year). Theycalculated the monthly and annual influence based on the spectralmismatch factor according to IEC 60904-7. Nofuentes et al. analyzed

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http://dx.doi.org/10.1016/j.solmat.2014.09.0340927-0248/& 2014 Elsevier B.V. All rights reserved.

n Corresponding author. Tel.: þ49 761 4588 5758; fax: þ49 761 4588 9000.E-mail address: daniela.dirnberger@ise.fraunhofer.de (D. Dirnberger).

Solar Energy Materials & Solar Cells 132 (2015) 431–442

the relationship between spectral mismatch factor, APE and ambientconditions like humidity and temperature in [25].

In summary, results are not easy to compare, as they focus ondifferent regions and different time-scales for the energetic influence(instantaneous, monthly, annual), use different indicators and usuallydo not consider measurement uncertainty. They agree, however, inone important aspect: the influence of varying spectral irradiance onthe performance of PV devices generally depends on its spectralresponse. A second overall conclusion is that the spectral impact isdependent on location in terms of latitude, longitude, climate, ruralor urban environment, albedo etc. This has not been demonstratedquantitatively, but the relation between APE or spectral mismatchfactor as indicator for the spectral distribution and solar zenith angle,water-vapor and aerosol content, and cloudiness shown e.g. in[21,25] support this assumption sufficiently. Furthermore, the roundrobin measurements of spectral irradiance by Ishii et al. in [12–14]showed that the extreme values for spectral influence appeared forthe combination of most southern location and summer; and mostnorthern location and winter, respectively.

The objective of this article is to contribute to existing knowl-edge by analyzing spectrally resolved irradiance data collected inthe period from 01.06.2010 to 31.12.2013 in Freiburg im Breisgau,Germany. The article is focused on determining the annual andmonthly energetic spectral impact on the yield of different PVtechnologies, and discusses additionally whether APE is suitablefor estimating the energetic spectral impact. A method is proposedwhich principally allows for application of the resulting spectralgains or losses to the pyranometer-measured irradiation; and thusto estimate the effectively available irradiation for a specific PVtechnology as it is necessary for energy rating or yield assessmentprocedures. Finally, in order to help close the existing gap betweenmere presentation of results for one location and the need formore comprehensive conclusions, the spectral impact results forFreiburg are discussed and interpreted with consideration to theiruncertainty and in comparison with other results from literature.

2. Background

While PV modules are sold based on the power rating at STCconditions, the energy they produce under real operating condi-tions is crucial for the return-on-investment. Therefore, whenassessing the suitability of different modules or module technol-ogies for investment decisions, their characteristics with regard totemperature, broadband irradiance, spectral irradiance and angleof incidence (AOI) are important. Energy ratings and yield predic-tions for PV systems combine real operating conditions withspecific module characteristics in order to provide an assessmentof the expected energy production. Energy rating aims at compar-ing the performance of different modules at realistic, but standar-dized conditions, whereas yield predictions give informationabout the yield that can be expected from a specific PV systemat a specific site (see [26] for closer definitions and furtherinformation.) Both ways to evaluate PV performance require firstinput data on the weather and climate conditions (mainly irra-diance and temperature) as well as on module characteristics, andsecond a simulation procedure to combine them.

Energy rating procedures are going to be standardized in theseries of standards IEC 61853. Part 1 [27] deals with power rating ofPV modules, i.e. the determination of electrical parameters of PVmodules at different irradiance levels and temperatures. Part 2 iscurrently a working draft and will cover measurement of spectralresponse, AOI effects and determination of nominal operating celltemperature (NOCT). Part 3 and 4, which will include standard datasets for weather data and methods for energy rating are still in theearly stages of development.

Concerning the availability of necessary input data for simulationpurposes, measuring irradiance and temperature dependence of PVmodules are state-of-the-art procedures [28], and high-quality dataon global and diffuse broadband irradiance (see e.g. [29]) andtemperature are also readily available with sufficient spatial andtemporal resolution. The influence of temperature and broadbandirradiance has been analyzed excessively, and state-of-the-art meth-ods are able to predict a module's expected energy production withgood accuracy regarding these influencing factors [30–36].

Angular losses have been analyzed in numerous publications andmodels to account for these losses (see e.g. [37]) as well as thenecessary meteorological data (direct and diffuse irradiance) aregenerally available. Information on module-specific reflection behavior(angular responsivity) is not readily available, as standards onmeasure-ment of module-specific AOI effects are only on the way (IEC 61853-2),but it has been demonstrated recently that differences betweenstandard and anti-reflection coated/textured glass can be measured[38,39]. When analyzing module performance data, it was found to bedifficult to differ between optical influences and other influences, eventhough differences of several percent in output between anti-reflectioncoated glass and standard glass were observed [40,41].

With regard to spectral influences, the situation is vice-versa:Methods to measure a module's spectral response exist [42,43]and are applied as state-of-the-art procedures evenwithout a validinternational standard (national standards exist, though); whereasdata on the spectral irradiance are rare, and models to take theinfluence of spectral effects under all sky conditions into accountare not readily available. This problem is discussed in more detailin [26], but is also evident from the discussions in several studieswhere the energy delivery and performance of different PVtechnologies is compared based on measurements on exposedmodules, such as [44–47]. Only Gottschalg et al. discuss theinfluence of spectral irradiance on a quantitative basis in [44].

The poor availability of spectrally resolved irradiance data leadsto the situation that spectral effects are either not considered atall, or just by fixed loss or gain factors (possibly technology-specific) that do, however, not take the location into account[26,48]. These factors are essentially what is called “energeticspectral impact” in this article. They express that the broadbandirradiance (or: “irradiation”, when referring to sums of irradiance)as available from solar resource databases is not necessarily equalto the irradiance that can actually be used by a PV module. Solarresource databases offer broadband irradiance data which includeenergy irradiated at almost all wavelengths, whereas PV modulescan only use irradiance in specific wavelength regions (Pyran-ometers are typically sensitive in a wavelength range of 280–2800 nm). Therefore, what should be used for energy ratings oryield predictions for PV is the “spectrally effective irradiance”rather than the total broadband irradiance.

Effective irradiance is the irradiance that actually contributes toshort circuit current generation in a PV device. It is important to keepin mind that the effective irradiance is also influenced by thepotentially different angular responsivites of PV modules and pyr-anometers, therefore the expression “spectrally effective” is used. Forenergy rating or yield predictions, losses due to reflection have to beconsidered in addition to the spectral impact. The separation ofspectral and angular effects might not always be possible.

The explanations above demonstrate that not considering thespectral impact presents an issue particularly because the spectralimpact is sensitive to the PV technology under scrutiny, and will thusintroduce a bias in the predicted energy yield depending on thetechnology. Without spectrally resolved irradiance data for arbitrarylocations, or reliable and generally applicable models for estimatingspectral effects for different module technologies, the only possible datasources are local spectral irradiance measurements. Based on thesemeasurements, energetic losses or gains for different technologies can

D. Dirnberger et al. / Solar Energy Materials & Solar Cells 132 (2015) 431–442432

be computed with respect to different periods of time (monthly and/orannual as e.g. in [15,20,24] or this article). Another option would be todetermine the spectral impact from measurement of short-circuitcurrent from different PV module technologies. The disadvantage tothis method is, however, that influences due to temperature, irradianceand reflection have to be removed. Especially when a pyranometer isused as a reference device, the different angular responsivity of flat PVpanels and pyranometers with glass domes will affect the results [49].However, if the spectral impact compared to total broadband irradianceis of interest, there is no real alternative to the pyranometer.

As an indicator for spectral irradiance and spectral impact, thisarticle will use the spectral mismatch factor MM according toIEC6094-3 [50], as it is widely used in the PV community. Incontrary to UF, APE and Q, it directly indicates whether a specificspectral distribution causes gains or losses compared to thereference situation. It relates instantaneous values of a PV mod-ule's power to its STC value, thus allowing for easy calculation ofthe energetic impact over a certain period of time (see Section 3.3).

3. Methodology

3.1. Experimental set up

In this study, the energetic impact of spectral irradiance wasinvestigated for the location of Freiburg im Breisgau, Germany(47.99731 N, 7.85251 E). Spectral irradiance was measured withtwo spectroradiometers, EKO MS-710 and MS-712, whose com-bined wavelength range is 335 nm to 1700 nm. The wavelengthinterval is 0.73 nm from 335–1100 nm (MS710), and 1.56 nm from900–1700 nm (MS712); the spectral resolution (FWHM) is 5 nmand 10 nm, respectively. The data from the two instruments arecombined by the manufacturer's software. The wavelength accu-racy is better than 1.5 nm. The instruments are suitable forcontinuous outdoor exposure and capable of measuring the solarspectral irradiance in all conditions. The integrating time is in arange of 100 ms (high irradiance conditions) to 5 s (low irradianceconditions). The instruments are installed on the roof of Fraunho-fer ISE, facing due south with a 301 tilt. The surrounding area ismostly urban, with the Black Forest directly to the east. Data was

taken in the period from 01.06.2010 to 31.12.2013 in varying timeincrements (mostly every 1 min, partly in 30 s or 5 min incre-ments). For the analysis presented in the following, the data wasresampled to 5-min averages. Due to recalibration in summer 2012and other outage periods, data is lacking for several periods.

The instruments were calibrated by the manufacturer at the startof the measurement period, and recalibrated in summer 2012. Thecalibration is traceable to NIST. Check-ups to the calibration wereperformed at Fraunhofer ISE several times before the recalibration bythe manufacturer. Measurement uncertainty with regard to linearity,angular responsivity and calibration was investigated and comparedwith manufacturer indications. Details on the estimation of measure-ment uncertainty are presented in [51].

Apart from the measured spectral irradiance, pyranometermeasurements and spectral response data for several PV technol-ogies were used in order to calculate the energetic gains or losseson the total solar resource. Pyranometer measurements (5-minaverages resampled from 10 s measurements) were used asindication of global broadband irradiance for two reasons: first,the limited spectral sensitivity of the spectroradiometer wouldlead to an offset towards lower irradiances, as data is lacking forwavelengths from 1700–4000 nm. Second, the absolute calibrationof a pyranometer is subject to smaller uncertainty. Data for thewhole period were taken from two pyranometers (both of themCMP11 Kipp & Zonen installed near the spectroradiometers, sametilt and orientation, one until 01.04.2011, the other afterwards).

The spectral response data used are examples for typical singlejunction PV technologies (Fig. 1): amorphous silicon (a-Si), Cad-miumtelluride (CdTe) crystalline silicon (c-Si), high-efficiency crys-talline silicon, and chalcopyrites (Cu(InxGa1�x)(SySe1�y)2, referredto as CIGS in the following). Especially the CIGS spectral response isan example that was selected because of its particularly small bandgap—there are other CIGS PV modules with larger band gaps and aspectral response rather similar to crystalline silicon.

3.2. Data filtering and calculation of data availability

Before calculation of the spectral impact as described in thefollowing section, spectral irradiance data was validated, filtered

Fig. 1. Typical, normalized spectral response data for single junction PV technologies, as used for calculation of spectral mismatch factors. The data was derived frommeasurements at Fraunhofer ISE.

D. Dirnberger et al. / Solar Energy Materials & Solar Cells 132 (2015) 431–442 433

and matched with pyranometer data. This was done with in thefollowing steps:

– Validating all measured spectra (i.e. removing spectra withobvious errors) and removing spectra with total irradianceo25 W/m2

– Calculation of the spectral mismatch factor (MM) according toEq (2a) for each available spectrum (instantaneous values) andall spectral response data as shown in Figs. 1 and 4

– Resampling of calculated instantaneous MM values to 5-minaverages (irrespective of number of available instantaneousvalues within the 5-min interval)

– Removing all values obtained at an AOI4601 to limit theinfluence of angular responsivity errors (see also uncertaintyestimation in [51])

– Concatenation of MM data with pyranometer data according tothe time steps

As the data is intended for calculation of the energetic spectralimpact of specific periods of time, the data availability is veryimportant. Spectral irradiance data were not available for severalweeks during the full period of data acquisition due to differentreasons, and removing data at AOI4601 further reduced thenumber of data points. More important than the pure number ofdata points is however, that the available data in total arerepresentative with regard to energy, i.e. that the spectral irra-diance is available for high irradiance conditions. This was eval-uated by calculating the “energetic availability” as given in Eq. (1)

energetic availability¼ ∑Ni ¼ 1Gpyr;i

∑Tt ¼ 1Gpyr;t

ð1Þ

where Gpyr is the irradiance measured with the pyranometer, N isthe number of data points where both pyranometer data andspectral irradiance data are available, and T is the number of allpyranometer data points in the respective period of time.

The availability of pyranometer data in the total time frame isbetter than 99%, so that no further influence from missingpyranometer data needs to be considered.

Table 1 summarizes the energetic availability per month, andshows that the results presented in Section 4 are representativefrom an energetic point of view. An energetic availability value of80% means, that spectral irradiance data were available for a totalof 5-min averages that make up 80% of the total irradiation in therespective period of time.

3.3. Calculation of spectral impact

The spectral impact is the energetic gain or loss in effectiveirradiance available to a specific PV technology compared to totalbroadband irradiation measured by a pyranometer, and comparedto the theoretical case of permanent reference spectral irradiance.It is calculated using the spectral mismatch factor according to IEC60904-7 [50] as given in Eq. (2a):

MM¼R ba SRModule λ

� �EmeasðλÞdλ

R ba SRModuleðλÞEref ðλÞdλ

�R ba SRref ðλÞEref ðλÞdλ

R ba SRref ðλÞEmeasðλÞdλ

ð2aÞ

where SRModuleðλÞ and SRref ðλÞ is the relative spectral response ofPV module and pyranometer, respectively, EmeasðλÞ is the measuredrelative spectral irradiance under actual condition, and Eref ðλÞ isthe reference spectral irradiance according to IEC 60904-3 [52].The wavelength limits a and b are determined by the spectro-radiometer wavelength range (a¼335 nm, b¼1700 nm) here, eventhough they should cover the full range of spectral sensitivity ofreference device and PV module (see also Section 5).

MM essentially expresses how much more or less irradiance thedevice under test sees at current conditions compared to thereference device's indication. MM can be rewritten as the ratio ofISC (short circuit current) of the module under investigation, and ISC ofthe reference device (see Eq. (2b)). In the case described here, thereference device is a pyranometer, which technically does not have ashort circuit current. For better comparability with the usual applica-tion of theMM between different PV devices, the pyranometer signalcan be thought of as the short circuit current of a theoretical PVdevice with SR λ

� �being unity for all wavelengths. Note that strictly

speaking, the spectral response of a pyranometer is not exactly unity,but this is a reasonable approximation.

MM¼ ISC;Module@Emeas λ� �

=ISC;ref @Emeas λ� �

ISC; Module@Eref λð Þ=ISC;ref @Eref λ� �

¼ difference@measured conditionsdifference@reference conditions

ð2bÞ

where ISC,Module and ISC,ref is the short circuit current of the module,and the fictive reference device with spectral response being unity,respectively.

if MM41 : Spectral Gain compared to STC

if MMo1 : Spectral Loss compared to STC

From Eq. (2b), one can easily see that an MM value smaller thanone indicates power loss compared to what the reference deviceindicates, whereas a value larger than one indicates power gain(note this refers solely to spectral differences). Consequently, thespectrally effective irradiance for the module is the irradiancemeasured by the pyranometer times MM (Eq. (3)). This approach isessentially equal to approaches in [15,24].

Gspec� ef f ;Module ¼MMGPyr ð3Þ

The spectral gain or loss (referred to as spectral impact ΔSpectrum inthe following) is then calculated as given in Eq. (4). For instantaneousor single 5-min average values, the impact can be calculated directlyfrom the MM: A value of e.g. 1.02 indicates that, at that moment, theeffective irradiance for the module is 2% larger than indicated by thereference device. If, however, the impact on an annual or monthlybasis is to be determined, the MM must be weighted with thebroadband irradiance G (Eq. (4)) as high MM occur mainly at timesof low irradiance (see also Fig. 2 on page 9). The energetic impact oflarge MM values is therefore usually limited: 2% at 200W/m2 has a

Table 1Spectral irradiance data availability per month, weighted with irradiated energymeasured by pyranometer (¼energetic availability). The rightmost column indi-cates how many kWh in total were available with spectral irradiance data.

Energeticavailability (%)

2010 (%) 2011 (%) 2012 (%) 2013 (%) kWh total

Jan – 54 83 63 76Feb – 69 73 11 110Mar – 66 87 0 222Apr – 50 87 1 199May – 72 85 85 399Jun 69 0 78 52 336Jul 64 0 22 71 302Aug 84 47 0 86 351Sep 87 89 0 84 360Okt 88 90 81 91 318Nov 71 81 89 89 141Dec 61 86 0 79 80

D. Dirnberger et al. / Solar Energy Materials & Solar Cells 132 (2015) 431–442434

smaller impact on the sum of irradiance than 2% at 1000W/m2.

ΔSpectrum;Period ¼∑Nt ¼ iGspec:ef f ;Module;i�∑N

t ¼ iGPyr;i

∑Nt ¼ iGPyr;i

¼ ∑Nt ¼ iMMiGPyr;i

∑Nt ¼ iGPyr;i

�1;

ð4Þwhere N is the number of available 5-min averages within the periodunder investigation (e.g. month, year, etc.)

For comparison with other published results, in addition to MMthe indicator APE was calculated. Eq. (5) gives the calculationaccording to [16] in the wavelength range of 350–1050 nm, forcomparability with [4–11,25]:

APE¼R 1050 nm350 nm EðλÞdλ

qR 1050 nm350 nm Φ λ

� �dλ

ð5Þ

where EðλÞ is the spectral irradiance, q is the elementary charge,and Φ λ

� �the photon flux.

Note that the wavelength range considered for the calculationof the APE strongly influences the value: The APE calculated fromthe reference spectrum in the range of 350–1050 nm is 1.88 eV, inthe range of 300–1600 nm it is 1.59 eV.

3.4. Estimation of an average annual spectral impact

In Section 4, it will be shown that the spectral impact calculatedaccording to Eq. (4) varies during the year—which was to be expecteddue to changing sun positions and seasonal climatic influences, and alsofrom previously published results (see Section 1). As a consequence, theannual impact calculated for individual years might be misleading ifdata of several weeks or months is missing. Missing data will bias theannual impact depending on which months are lacking. Therefore, inthis article, the annual impact is not calculated in the straightforwardway following Eq. (4), but using an approach being able to deal withmissing data as given in Eq. (6). It uses averaged monthly spectralimpact values obtained for the full 3.5-year measurement period.

ΔSpectrum;Year ¼∑12i ¼ 1GiΔ i

∑12i ¼ 1Gi

ð6Þ

where Gi is the respective monthly sum of irradiance, Δ i the averagemonthly spectral impact, and i the month.

The calculation of Δ i is done according to Eq. (4), but usingdata from all years binned to months (e.g. data from all years'Januaries) instead of averaging the spectral impact values deter-mined for the single months (e.g. the sum of spectral impactvalues of January 2011, January 2012, January 2013 divided by thenumber of investigated Januaries). This is necessary, as the straightforward method of averaging single months can be misleading interms of data availability. Months with very low data availabilitywould have to be excluded as they would influence the resultdisproportionately (a value standing for just 20% of the energy inone month would be weighted equally strong as a value repre-senting 80%). Binning data to months over several years maintainsthe correct weighting of all data points, and uses all available data.Additionally, this automatically includes year-to-year variations, asdata throughout all years are used (e. g. the January averageincludes data from 2011, 2012 and 2013). Note that with 100% dataavailability, Eqs. (4) and (6) would yield the same result.

4. Results

4.1. Instantaneous impact on performance of different PVtechnologies

The instantaneous impact of spectral irradiance on the perfor-mance of different PV technologies is shown in Fig. 2 in the form ofthe spectral mismatch factor. The pattern is different depending on

the technology: the largest MM values occur for large-band-gapamorphous silicon, followed by CdTe. Particularly high values occurat irradiance values smaller than 200W/m2, but even at 1000 W/m2, MM as high as 1.1 (a-Si) and 1.05 (CdTe) were observed. Theinfluence is smaller for standard and high-efficiency crystallinesilicon. In contrast to the other technologies, a large portion of theMM values at high irradiances are below 1 for small-band-gap CIGS.APE shows a pattern similar to amorphous silicon.

The data indicate that it is important to consider spectralinfluences when analyzing the performance of PV modules orsystems on an instantaneous basis—a fact that has been dis-cussed previously, but has rarely been demonstrated by showingcomprehensive data. It is known that spectral influences can beminimized by using spectrally matched reference cells orsensors [53], but e.g. for analyzing system monitoring databased on pyranometer measurements, being aware of consider-able spectral influences is crucial. Pyranometer measurements(or satellite derived irradiance data) are furthermore necessaryfor direct comparison of the performance of different PVtechnologies.

Keep in mind that the data presented here refer to a pyran-ometer as reference, and cannot be transferred to situations with acrystalline silicon cell as reference device [36].

4.2. Monthly spectral impact for different technologies

The spectral impact determined for all single months within theperiod under investigation is plotted in Fig. 3. Months with energeticavailability significantly less than 50% (Table 1) are not shown. Thespectral impact varies systematically over the year, with clear trendsvisible through all years depending on the technology. For a-Si, thetechnology with the largest band gap, the spectral impact variesmost. In the winter months, losses of up to 3% occurred whereas insummer, gains of up to þ6% were observed. For CdTe, the trend issimilar but less pronounced: In the winter months, there are gainsaround þ1% (the data is scattered), in summer up to þ4%. C-Si doesnot exhibit a clear seasonal trend; the spectral gain is roughly þ1% toþ2% throughout the year. High-efficiency c-Si, which is moresensitive in the infrared region, reverses the trend observed for thelarge-band-gap materials: The minimum spectral impact is þ1% insummer, and þ2% to þ3% in winter. For the small band gap CIGSsample, this trend is even more pronounced: spectral gains reachfrom 0% in summer to þ3% in winter. The average spectral impactvalues (black circles) that are shown were calculated as outlined inSection 3.4. They form the basis for calculating the annual impactaccording to Eq. (6), and are additionally given as numbers in Table 2.Changes between years are a second order effect.

Additionally, Fig. 3 shows the monthly unweighted arithmeticaverages and the weighted averages for APE. The unweighted APEis approximately 1.9070.01 eV except for January, February andMarch, and December 2013. This indicates that, on average thespectrum is blue-shifted compared to the reference spectrum. This isin accordance with the determined spectral gains for a-Si and CdTe, i.e.the results from different evaluation approaches made with the samedata set are consistent. The weighted APE follows particularly thetrend observed for a-Si: roughly from March to November, theweighted APE is above 1.88 eV (spectral gains for a-Si), from Novemberto March, it is below 1.88 eV. In how far APE is useful for quantitative estimation of spectral impact is discussed in Section 4.5 in moredetail.

4.3. Annual spectral impact for different technologies

Table 2 presents the calculation of the annual spectral impactaccording to Eq. (6). As expected, for the technologies withspectral gains in summer and losses in winter, the further have a

D. Dirnberger et al. / Solar Energy Materials & Solar Cells 132 (2015) 431–442 435

stronger influence on the result. In total, there are spectral gainsfor all technologies. The larger the band gap, the larger the gains:A-Si gains þ3.4% in effective irradiance compared to pyranometermeasurements and the theoretical case of a constant spectrumequal to the reference spectrum; CIGS gains only þ0.6%.

As reference data, the pyranometer data from the measurementperiod 01.06.2010–31.12.2013 were used as average monthly irradia-tion values (see Table 2, column 2). Technically, the numbers can beapplied to other irradiation data, provided one feels comfortable thatthe spectral conditions are comparable to the ones in Freiburg.

4.4. Annual spectral impact for different crystalline silicontechnologies

In order to investigate the influence of small differences inspectral response, the annual spectral impact was calculated fora set of different crystalline silicon spectral response data(Fig. 4). The difference is very small, which indicates that, todate, it is not necessary to differentiate between PV moduletypes with similar spectral response with regard to annualspectral impact.

Fig. 2. Spectral mismatch factor for typical PV technologies and pyranometer plotted versus broadband irradiance measured with a pyranometer (note different y-axisscaling for a-Si). Boxes indicate 25th, 50th and 75th percentile of all values within 750 W/m2 of the respective irradiance level. Data are 5-min averages from the period01.06.2010 to 31.12.2013 for AOIo601 (77,835 data sets in total). APE was calculated for the wavelength range 350–1050 nm.

D. Dirnberger et al. / Solar Energy Materials & Solar Cells 132 (2015) 431–442436

4.5. APE as an indicator for spectral impact

APE is an indicator for spectral irradiance that is often used inpublications, and the question whether it is a “useful” indicatorhas been discussed. Minemoto et al. [6] and Kataoka et al. [54]analyzed whether APE is able to represent a unique spectraldistribution based on different approaches, and concluded thatthis was case. APE was found to be a good indicator for

evaluating performance of PV modules. Nofuentes et al.observed that APE and MM correlate [25]. Ishii et al. establisheda relationship between MM and APE in [15], which they found tobe valid for 4 sites in Japan. They also concluded this relationcould be used to quantify the effect of spectral irradiance,although the spectral impact determined directly using MMdiffered by more than 2%-points from the derived results usingthe APE-MM relation.

Table 2Calculation of annual spectral impact based on the monthly and annual sums of irradiance of a reference year and the determined average monthly spectral impact.

i Gi [kWh] average monthly irradiation from reference period Δ i [%] Average, relative monthly spectral impact (also see Fig. 3)

a-Si (%) CdTe (%) c-Si (%) high eff. c-Si (%) CIGS (%)

1 38 �2.0 1.0 1.9 2.4 2.62 65 �1.3 0.1 1.0 1.4 1.63 122 0.1 0.6 0.7 0.8 0.94 141 3.5 1.9 1.2 0.9 0.45 166 4.2 2.3 1.5 0.9 0.36 166 5.1 2.8 1.4 0.8 0.07 184 5.3 3.4 1.5 0.8 0.08 168 5.3 3.5 1.6 0.9 0.19 136 4.3 3.1 1.5 1.0 0.410 91 2.8 3.0 1.9 1.7 1.311 43 0.8 2.3 2.1 2.2 2.112 35 �2.2 1.8 2.4 3.0 3.3

3.4 2.4 1.4 1.1 0.6 ΔSpectrum;Year

Year 1353 kWh 45.6 kWh 33.1 kWh 19.5 kWh 14.7 kWh 8.0 kWh ∑12i ¼ 1GiΔ i

Fig. 3. Monthly spectral impact for typical PV technologies, along with average APE. APE data are shown as weighted and unweighted averages, (For interpretation of thereferences to color in this figure, the reader is referred to the web version of this article.)

D. Dirnberger et al. / Solar Energy Materials & Solar Cells 132 (2015) 431–442 437

In order to compare data from Freiburg to these publishedresults, calculatedMM values were plotted versus APE in Fig. 5. It isobvious that the relation between MM and APE is not bijective (i.e.,the APE calculated from two different spectral distributions can beequal, but two different MM values occur; and vice versa). Notethat the different data populations are not specific to differentseasons; the principle pattern of the data is visible for all monthsthroughout the year. The same pattern of the data can also be seenin figures included in [15], despite different axis scaling andAPE calculated in a range of 350–1700 nm. The lacking bijectiverelation was principally also mentioned in [25]. These observationsform a contradiction to [6], where the authors propose that theAPE characterizes an individual spectral distribution “well”. Theirreasoning was as follows: If the spectral distributions creating oneAPE have sufficiently small standard deviation in all defined 50 nmintervals, the distributions can be looked at as equal. However, thedata presented in this article indicate that MM is more sensitive tosmall changes in spectrum than APE, so that several spectraldistributions can be represented by the same APE, but producedifferent MM values. Particularly, a spectral distribution withAPE¼1.88 eV as for the reference spectrum does not necessarilyproduce MM¼1, which means no energetic spectral impact. As aconsequence, the conclusion should be that APE is not a goodquantitative indicator for instantaneous spectral impact.

To investigate this also for monthly and annual spectral impact,monthly spectral impacts were plotted versus the monthlyweighted averages of APE in Fig. 6 (same data as in Fig. 3; it wasobvious there that the unweighted average is not useful to indicatequantitative spectral impact). The weighted APE is clearly corre-lated with the spectral gains and losses for the different moduletechnologies. The lacking bijective relation between MM and APEdoes not seem to be able to influence results when working withirradiance-weighted data. Using irradiance weighted data is con-sidered a more sound approach than using a least-square fittingprocedure as in [15], which grants more impact to the number ofoccurrence of a certain APE value than to the energetic relevance.Fig. 6 also includes regression lines, the parameters of which canbe used to determine the technology-specific annual spectralimpact from the annual weighted APE (1.8899 eV). The corre-sponding spectral impact values can be read from Fig. 6: the values

of þ3.5% (a-Si), þ2.6% (CdTe), þ1.6% (c-Si), þ1.2% (high-eff. c-Si)and þ0.7% (CIGS) agree well with the calculated values presentedin Table 2. The regression lines show once more that the spectralimpact is largest for a-Si.

In conclusion, similar to what was shown in [15], APE can beused as a derived indicator for the quantitative spectral impact,provided additional analyses are made which involve location-specific calculation of MM. As a consequence, it should be discussedwhether indicator for energetic spectral impact other than MM areneccessary. The fact that calculation of APE does not require spectralresponse data and that the calculation is somewhat simpler mightbe considered as an advantage. Given the fact that doing extensivecalculations using spectral irradiance data should not present aproblem nowadays, that spectral response data are widely available,and results need to be reported per PV technology anyway, wethink that using spectral impact directly calculated from MM isfavorable over using derived results using APE.

Nevertheless, APE is useful for qualitative comparison of spec-tral conditions, as ‘blue’ or ‘red-shift’ in the spectral distribution issimply indicated by the APE value being higher or smaller than APEat reference conditions. This qualitative comparison for differentlocations seems possible even if not the same wavelength rangesare used for calculation of APE, as the reference APE also dependson the wavelength range used for calculation. To date, it has notbeen investigated in how far the wavelength range for calculationof APE influences the conclusions that can be drawn from theresults.

5. Uncertainty estimation

In order to interpret the presented spectral impact valuescorrectly, their uncertainty must be considered. Factors thatcontribute to uncertainty are:

– Uncertainty of the sum of irradiance, which sums up from theuncertainty of the individual irradiance data points. Thisuncertainty depends on conditions at time of measurementas well as the calibration of the pyranometer.

Fig. 4. Spectral response data of different crystalline silicon PV modules, as measured by Fraunhofer ISE.

D. Dirnberger et al. / Solar Energy Materials & Solar Cells 132 (2015) 431–442438

– Uncertainty of the mismatch factor due to uncertainty of inputdata such as spectral responses and the measured relativespectral irradiance. The uncertainty of the latter again dependson conditions at time of measurement (irradiance, tempera-ture, angle-of-incidence, …) as well as the calibration.

– Uncertainty of the mismatch factor due to available wavelengthrange: As the spectroradiometer wavelength range from 335–1700 nm is smaller than that of the pyranometer (280–2800 nm), changes in the spectrum between 280–335 nm and1700–2800 nm are not considered. This can cause uncertaintyof statistical nature.

A more detailed uncertainty estimation considering the abovementioned points is presented in [51] for reasons of length andfocus. The determined standard uncertainty (k¼1) depends on thespectral response of the PV technology under scrutiny, and rangesfrom roughly 2% (a-Si) to roughly 1% (c-Si) [51]. The uncertaintylimits around the result (e.g. for a-Si: 3.472%¼1.4%…5.4%)indicate the range of values within which the true value for thespectral impact lies with a probability of 68%. Even though theuncertainty values are large compared to the magnitude of thespectral impact values themselves, they are considered rathertypical.

Fig. 5. Spectral mismatch factor for typical PV technologies and pyranometer plotted versus average photon energy (APE). Note different axis scaling for a-Si. Boxes indicate25th, 50th and 75th percentile of all values within 70.005 eV of the respective APE level. Data are 5-min averages from the period 01.06.2010 to 31.12.2013 for AOIo601(77,835 data sets in total). APE was calculated for the wavelength range 350–1050 nm.

D. Dirnberger et al. / Solar Energy Materials & Solar Cells 132 (2015) 431–442 439

6. Discussion and conclusions

For the period from 01.06.2010 to 31.12.2013, annual andmonthly spectral impact values were calculated based on measuredspectra and typical spectral response data. Devices with strongersensitivity at shorter wavelengths exhibited spectral gains of severalpercent (þ3.4% for a-Si, þ2.4% for CdTe) and a clear seasonal trendtowards spectral gains in summer and spectral losses in winter. Forc-Si, there was no clear seasonal trend, and the determined annualspectral impact was þ1.4%. High-efficiency c-Si with enhancedsensitivity in the longer wavelength and the investigated CIGSsample with sensitivity up to 1300 nm demonstrated a seasonaltrend the opposite of what was observed for large-band-gapmaterials: For these small-band-gap technologies, there was verylittle spectral impact in the summer months, and spectral gains inwinter. Over the full year, the impact was þ1.1% (high eff. Silicon)and þ0.6% (CIGS) respectively.

To put the results into broader perspective, annual spectralimpact values from literature are summarized in Table 3. Eventhough there are partly deviations of several percentage points,the most differences are within the above mentioned uncertaintyvalues (assuming all results have approximately the same uncer-tainty). As furthermore for most locations only one result isavailable, interpretation of the results is difficult.

Nevertheless, there is very good agreement between the resultsof this article and research results presented in [20], where Zinsseret al. analyzed the spectral impact based on measured spectra forone year in Stuttgart, Germany. As Freiburg and Stuttgart arerelatively close to each other, influences on the spectral irradiancedue to the airmass and weather are considered small enough to beable to compare the results. Zinsser et al. used the integratedelectrical charge Q as indicator, which makes the results directlycomparable to the spectral impact as calculated here. The annualeffect determined in [20] on the energy produced was þ2.5% foramorphous silicon, þ2.1% for cadmium telluride, þ1.5% forcrystalline silicon, and þ1.8% for a CIGS technology with sensitiv-ity from roughly 350–1100 nm. The spectral gain is also lined upaccording to the band gap of the investigated technologies, andthe seasonal trend shown based on APE is also equal.

The work by Alonso-Abella et al. presented in [24] agrees onlyqualitatively. They investigated the spectral impact based on

modeled data for four locations (Stuttgart, Madrid, Jaen andTamarasset), reporting the spectral losses for Stuttgart for alltechnologies, and a much lower difference between the impactfor different technologies. One reason for the obvious quantitativedifference between results from measured and modeled data mightbe the fact that spectral models are limited in their ability torepresent cloudy conditions [55]. The observed seasonal behavior ishowever in accordance to Zinsser's results and the ones presentedherein: For small-band-gap technologies, there are gains in summerand losses in winter. Similar differences (�2–3%) of measurement-and model-based spectral impact values were also reported in [24],however for mismatch values calculated with a crystalline siliconreference device.

Publications from other parts of the world further confirm thefindings in this article qualitatively: Results from Gottschalg andBetts based on the ‘weighted useful fraction’ (which is not fullycomparable to the spectral impact calculated here) indicated anannual effect on the energy production of amorphous silicon of þ4%to þ5% [18]. In a recent publication, they state that c-Si and CIGS donot significantly gain over the full year due to spectrum [44]. Inearlier works, they found that seasonal variations of the ‘usefulfraction’ around the annual average to be �9% to þ6% for amor-phous silicon, �6% to þ4% for CdTe and �1.5% to þ1.5% for CIGS[17], which is in accordance to the results presented in this article.

Minemoto, Nakada and Nagae [4–11] investigated the generalinfluence of the spectrum on the performance ratio of PV modules

Table 3Reported annual spectral impact for different locations and PV technologies. Resultsare based on measured spectral irradiance unless otherwise indicated (sim.).

Location Latitude a-si c-si CIGS Reference

Stuttgart 48.8 2.5 1.5 – [20]Stuttgart (sim.) 48.8 �0.4 �0.6 –0.7 [24]Freiburg 48.0 3.4 1.4 0.6 Section 4Sapporo 43.0 1.3 0.2 – [15]Madrid (sim.) 40.4 1.4 �0.6 �1.0 [24]Jaen (sim.) 37.7 2.0 �0.5 �1.0 [24]Gifu 35.4 1.3 0.2 – [15]Tosu 33.4 1.3 0.4 – [15]Okinoerabu 27.2 8.8 0.2 – [15]Tamanrasset (sim.) 22.8 2.1 �1.0 �1.3 [24]

Fig. 6. The monthly weighted APE correlates with the monthly spectral impact. Using the annual weighted average APE of 1.8899 eV, derived results for the technology-specific, annual energetic impact can be read from the figure (a-Si: þ3.5%, CdTe: þ2.6%, c-Si: þ1.6%, high-eff. c-Si: þ1.2%, CIGS: þ0.7%.).

D. Dirnberger et al. / Solar Energy Materials & Solar Cells 132 (2015) 431–442440

in Kusatsu City, Japan. They used data collected continuously overseveral years, and found that the performance of a-Si modules wasaffected by the spectrum to much larger extent than that of c-Si;particularly that higher APE (blue shifted spectrum) favored a-Si.Ishii et al. found in [15] that the annual spectral gain for a-Si was ina range of 1.9% to 8.8%, and for c-Si in a range of 0.2% to 1% for4 different locations in Japan.

In summary, we conclude that it is reasonable and evenneccessary to consider the technology-specific spectral impact inenergy rating or yield prediction procedures. Otherwise, theperformance of technologies with larger band gap might beunderestimated by up to several percent. However, the questionas to how large this underestimation exactly is can to date not beanswered significantly due to prevailing uncertainty and variety inpresented results. The correlation between band gap and spectralimpact was observed by different research groups and differentlocations, but the absolute levels of spectral impact for differenttechnologies vary. Additionally, detailed analyses focusing on thetotal annual impact are still rare [15,20,24], and can so far not givereasons for differences. As a consequence of the lacking overallpersuasive conclusion on spectral impact values, it remains withinthe expertise of the scientists in charge of energy rating or yieldprediction, how the spectral impact is accounted for quantitativelywith the specific simulation program in use.

It should be kept in mind that the spectral impact is moreimportant for energy rating than for yield predictions. For energyrating, input for irradiance is a definition for all PV technologies,and thus without uncertainty. The technology-specific spectralimpact is therefore a factor that systematically influences theresult, and has the potential to introduce a bias of severalpercentage points, as is shown by the data in Table 3. This isespecially the case when comparing PV technologies with differ-ent band gap. For yield predictions, the solar resource assessmenthas to be done specific to a location, leaving the spectral impact tobe just one of several contributions to uncertainty (e.g. theestimation of irradiation from long-term data [56].)

Perspectively, the following points seem necessary in order tomove toward a significant and location—specific consideration ofspectral impact: Still more ground-measured, spectrally-resolveddata in different locations is necessary, as well as exchange andcomparison of data and methods. Data analysis should include thefocus on monthly and annual impact in a way that allows for directconclusion of the percentage of spectral gains and losses, e.g. aspresented in this article. Agreed-on indicators and methods shouldbe defined for the community to use as a standard. For consistencywith other PV measurement tasks, MM is considered most useful.Establishing a set of reference spectral response data for commonuse in the community would be desirable. Furthermore, moreeffort regarding the estimation of uncertainty is necessary in orderto minimize uncertainty, and to compare different methods andtheir results. Ground-measured data are valuable not only fordirect evaluation. One the one hand, they can also be used fordevelopment of simple models or measurement methods toevaluate the influence of spectral irradiance based on spectrally-selective measurements of broadband irradiance [53,57]; on theother hand, they can serve as validation for more complicatedtasks such as deriving spectral irradiance from satellite data[21,58]. Given the fact that ground measurements are complicatedand expensive, it seems crucial to develop such methods to solvethe problem of limited spatial resolution of spectral impact data.

Acknowledgment

The authors would like to acknowledge Anton Driesse andWolfgang Heydenreich for helpful and interesting discussions. The

work of Gina Blackburn was supported by the Fulbright Commis-sion in the form of a Student Research Grant.

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