29th European Photovoltaic Solar Energy Conference and Exhibition

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1 HIGH QUALITY MEASUREMENTS OF THE SOLAR SPECTRUM FOR SIMULATION OF MULTI-JUNCTION PHOTOVOLTAIC CELL YIELDS Matthew Norton *1, 2, Vasiliki, Paraskeva 2, Roberto Galleano 1, George Makrides 2, Robert P. Kenny 1, George E Georghiou 2 1 European Commission, DG JRC, Institute for Energy and Transport, TP 450, Ispra (VA), Italy 2 FOSS Research Centre for Sustainable Energy, Photovoltaic Technology Laboratory, Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos St, Nicosia 1678, Cyprus * Corresponding author: Matthew S. H. Norton (Phone , Fax , matthew.norton@jrc.ec.europa.eu) (Vasiliki Paraskeva, Phone , Fax , paraskeva.vasiliki@ucy.ac.cy) (Roberto Galleano, Phone , Fax , roberto.galleano@ec.europa.eu) George Makrides (Tel: , Fax , makrides.georgios@ucy.ac.cy) (Robert P. Kenny, Phone , Fax , robert.kenny@jrc.ec.europa.eu) (George E. Georghiou, Phone , Fax , geg@ucy.ac.cy ABSTRACT: Photovoltaic cells that incorporate several active junctions in electrical series are demonstrating everhigher laboratory efficiencies. However, their sensitivity to variations in the solar spectrum can reduce their operating efficiency in the field. To assess how these variations can affect energy yield requires a detailed and reliable spectral irradiance dataset covering a typical meteorological year for any potential deployment site. This paper describes an attempt to produce such a dataset through a combination of measurements and simulation. A dedicated measurement system has been assembled to collect environmental data alongside direct normal spectral irradiance measurements utilising charge-coupled device array spectroradiometers. The calibration and validation procedures for this system are presented, and the final calibration uncertainty of the system is judged to be ±5.4% (k=2). The SMARTS atmospheric radiative transfer model is used to provide another means of measurement validation. Furthermore, SMARTS is then used to generate long-term irradiance data covering several months. A comparison of the measurements and simulations has shown that SMARTS is a useful tool for measurement validation. However, the present environmental inputs to the model produce long-term data that underestimates the occurrence of very bluerich spectra compared to measurements. Keywords: Multijunction solar cell, solar radiation, calibration, modelling, energy performance 1 INTRODUCTION As high-efficiency multi-junction photovoltaic (PV) cells continue to set conversion efficiency records [1], there is a growing interest in understanding how these high laboratory efficiencies will translate into increases in energy yield under real operating conditions. Multijunction PV cells operate with greater overall efficiency than single-junction designs by utilising a wider range of the solar spectrum. However, connecting junctions with different spectral responses in electrical series creates devices with output levels that are more sensitive to changes in the spectral content of the light. The prediction of the operational output of such cells at the design stage would allow producers to optimise the current ratios that are generated to minimise the mismatch losses between the different junctions. However, this would require a complete and reliable dataset of spectra for a typical meteorological year at the site where the cells are to be deployed. Often such data are incomplete or simply do not exist. In addition, the measurement acquisition poses a number of technical challenges that impact the level of uncertainty in the final data. Supporting the assessment of multi-junction cell performances in a location of prime interest for concentrator photovoltaic systems forms the basis of the PV-TUNE project being conducted at the University of Cyprus (UCY), in Nicosia, Cyprus [2]. As part of this project, an attempt is underway to create a high-quality dataset of spectrally resolved direct normal irradiance (DNI) representative of a typical meteorological year in Cyprus. To produce this DNI dataset, a methodology has been adopted that combines a field measurement campaign with simulations performed using an atmospheric radiative transfer model fed by real meteorological site data. The goal is to describe a set of inputs to the chosen model that will generate an entire year of spectral irradiance data that will be, on average, equivalent to the spectra seen during a typical meteorological year. Ultimately, this dataset will be used to perform simulations of multi-junction cell outputs [3]. 2 APPROACH 2.1 Apparatus To acquire long-term measurements of direct normal spectral irradiance, a dedicated setup has been installed at the UCY PV Technology Laboratory, in Nicosia, Cyprus. A charge-coupled device (CCD) based spectroradiometer system has been installed outdoors to capture the spectrum of direct sunlight at 5-minute intervals. Measurements are taken automatically between 6am and 8pm every day. This system comprises two spectroradiometer units covering the wavelength range of 300 to 1700 nm, placed inside a temperature-controlled cabinet. A silicon detector recorded the range nm, and an InGaAs detector was used from 900 to 1700 nm. These devices contain no moving parts and are thus capable of taking measurements of the whole spectrum within a few milliseconds. Irradiation is directed into the spectroradiometers via a fibre optic cable. In this case the input irradiation is collected through a cosine receptor with a 180 field of view fixed to the other end of the fibre optic. To collect direct normal irradiation data, this input device is located at the base of a custom-made collimating tube. The collimator is mounted on a high-accuracy two-axis tracker. In addition, a Kipp & Zonen CH1 pyrheliometer 2002

2 was installed alongside the collimating tube for the purpose of providing a reference irradiance intensity reading. The custom collimator was designed with acceptance geometries identical to the pyrheliometer to ensure a fair comparison of the measurements. Measurements taken using these two units were controlled using custom LabVIEW software running on a dedicated computer. The complete arrangement is illustrated in the schematic in Figure 1. In addition to these measurements, a number of meteorological conditions to support atmospheric modelling were recorded, including ambient temperature, relative humidity and atmospheric pressure. pm solar time, when the sun was highest in the sky at the test location. Cosine correctors (to reduce angular sensitivity) attached to the ends of the fibre optics were mounted side-by-side on the tracker. The Set 1 spectroradiometers were then used to take a measurement of the global normal spectral irradiance. This measurement was then used to create a standard file with which to calibrate Set 2 within a 5-minute period. The GNI was monitored to ensure that the irradiance varied by less than 2% over the period. With this approach a calibration uncertainty, uc, of ±5.4% (k=2) was achieved, consistent with previously reported uncertainties for array spectroradiometers [5, 6]. The complete uncertainty budget is presented in Table I. Table I: Average uncertainty budget for the outdoor calibration transfer over the range of sensitivity. Uncertainty Component ± uc [%] Set 1 calibration 1.40 Set 1 calibration drift 0.92 Set 1 noise 0.25 Set 1 sensor linearity 1.73 Set 2 noise 0.25 Irradiance stability 1.15 Alignment 0.08 Combined Standard Uncertainty 2.70 k=1 Expanded Uncertainty 5.40 k=2 Figure 1: Measurement setup used to collect spectrally resolved direct normal solar irradiance data. 2.2 Calibration A twin set of spectroradiometers (Set 1) with identical technical specifications was used to calibrate and verify the outdoor spectroradiometer system (Set 2). Set 1 was calibrated at the European Joint Research Centre (JRC), in Ispra, Italy. The calibration was performed on an optical bench setup incorporating a National Physical Laboratory UK (NPLUK) -calibrated FEL-type standard lamp directly traceable to SI units [4]. The calibration transfer uncertainty for these spectroradiometers with this particular setup is calculated to be ±2.8% with a coverage factor of 95% (k=2). Spectroradiometer Set 1 is scheduled to be periodically calibrated, and has a calibration history extending back over the past 3 years. Spectroradiometer Set 1 was then used to perform a calibration transfer to Set 2. Initially this was performed indoors using a halogen lamp under controlled laboratory conditions. Following this, both sets were installed outdoors for a side-by-side verification of the calibration transfer. The comparison showed a poor agreement when these units were used outdoors. This was attributed to the arrangement of the fibre optic cable for the outdoor system. The primary concern was that excessive rotation or flexing of the cable at the terminals when relocating the units could distort the signal. A satisfactory calibration was finally achieved by performing an in-situ calibration transfer to the outdoor spectroradiometers using the sun as the reference radiation source. The transfer was performed using global normal irradiance (GNI) measurements under steady conditions on a clear day at between 11:30 am and 12:00 Table I includes contributions from the uncertainty in alignment of the cosine receptors and the random noise levels in both spectroradiometer sets. It also includes the linearity of the response of Set 1 with varying intensity levels, as array spectroradiometers are known to exhibit nonlinear behaviour [7]. It is noteworthy that in this case one of the largest uncertainty components is the calibration drift of Set 1. This is based on long-term observations of these particular spectroradiometers, which have indicated a drift in sensitivity of up to 0.8% per month, and is a typical value for such devices. Despite the known sensitivity of these devices to detector temperature, this was neglected due to the combination of software compensation to remove the effect and the temperature control of the operating environment. One benefit of performing the calibration under real operating conditions is that the irradiance intensity is of the order of magnitude expected for the majority of measurements. This will thereby reduce the contribution to the final measurement uncertainty arising from nonlinearities with light intensity. 2.3 Verification through comparison A verification of the calibration transfer was performed by comparing measurements from both spectroradiometer systems, and showed excellent agreement. Figure 2 shows an example of such a comparison: in this case a problem was identified in the region between 900 and 1000 nm and the calibration was repeated. This result suggests that by performing the calibration in situ, the problems associated with handling the fibre optic cable had been overcome. 2003

3 Figure 2: Measurement verification using both spectroradiometer sets. Here an example where a calibration error was detected and subsequently corrected is shown. Following the validation procedure, the collimator was attached to the cosine corrector of the fibre optic of Set 2. Although a small amount of flexure in the fibre optic is encountered as the collimator tracks the sun across the sky, the cable was arranged to minimise this. The integrated broadband irradiance was then also crosschecked with the pyrheliometer measurements. For this particular case with a spectroradiometer sensitivity limited to 1700 nm, the pyrheliometer can be expected to detect up to a further 60 Wm -2 under good conditions. Whilst the best available option to verify the measurements of the Set 2 spectroradiometers was through a comparison with Set 1, this approach still has limitations. If the calibration of Set 1 drifts excessively, or if the two spectroradiometers exhibit a similar inherent nonlinearity of performance (e.g. with intensity), it will not be possible to detect these errors by comparing measurements. Therefore, to provide further confidence in the calibration, an atmospheric transmission model was used as an additional tool in the verification process. 2.4 Verification against SMARTS Performing computational simulations of the terrestrial solar spectrum can provide another means to validate real measurements. SMARTS (Simple Model of the Atmospheric Radiative Transfer of Sunshine) is a spectral model to predict the direct beam, diffuse and global irradiance incident on the Earth s surface [8, 9]. It can be used to simulate the spectral or broadband irradiance that would be measured by a radiometer, such as a spectroradiometer, a pyranometer, or a pyrheliometer. This model, version 2.9.5, was chosen due to its popularity within in the photovoltaic community and its affordability compared with larger packages such as MODTRAN. As part of the verification process, a good day of spectral measurements was selected for a comparison against SMARTS, characterised by steady, clear sky conditions. SMARTS was then used to simulate each spectral measurement over the day using the environmental conditions occurring at that moment. For each simulation the program was supplied with: the site atmospheric pressure (hpa); the relative humidity (%); the instantaneous ambient temperature ( C); the average temperature over the day; and the monthly averaged aerosol optical depth (AOD) obtained at 500 nm (dimensionless). The AERONET data network was used to obtain the monthly-averaged aerosol optical depth values. A level 2 dataset was acquired from the CUT- TEPAK site situated within a few km of the test site [10]. SMARTS determined the precipitable water content using the relative humidity and ambient temperature inputs. To calculate the direct normal irradiation at a specified time, date and location, SMARTS also requires a number of inputs describing the apparatus and test site. For the purposes of the simulation, a collimator design of 1 slope angle and 2.5 half acceptance angle was entered to match the dimensions of the collimator attached to the end of the fibre optic cables. For a satisfactory agreement, both the broadband irradiance integral and the spectral irradiance distribution would need to match within the limits of the calibration uncertainty. To facilitate the comparison of the spectral irradiance distributions, the average photon energy (APE), was calculated for each spectrum. This has been shown to be a reliable metric with which to classify spectral irradiance measurements at a given test site [11]. The APE is calculated by dividing the total energy in a spectrum by the total number of photons it contains, as shown in equation 1. APE [ev] 1 E d (1) q d Here q [ev] is the electron charge and Eλ [Wm 2 ] is the spectral irradiance at wavelength λ. Φλ is the photon flux density at wavelength λ, calculated using the Plank- Einstein relation hc/λ [J] as in the formula in equation 2. E [photons m 2 s 1 ] hc / 3 RESULTS & ANALYSIS 3.1 Verification of calibration Shortly after the calibration transfer was performed, a clear-sky day was chosen for verifying the measurements against SMARTS. In this procedure, the environmental data for the day were fed into the program to generate a series of spectral irradiance measurements for every 5 minutes from sunrise to sunset. Figure 3 shows the result of this analysis for the 17 th August, performed shortly after a periodic recalibration. These plots show an agreement between the broadband integrated irradiance of the simulation and measurements within ±6%. The spectroradiometer integral also remained between -4% and -6% of the pyrheliometer measurement over the day. This is consistent with expectations considering the sensitivity ranges of the two devices as mentioned before. Agreement of the spectral distributions of the simulations and measurements was also observed in the morning and midday, although at the start and end of the day deviations of up to 0.04 ev are seen. To investigate the cause of this deviation, the measured and simulated spectra are plotted for 12:00 and 16:30 on the same day, shown in Figure 4. The change in APE arises from increased attenuation in the wavelength range from 400 to 700 nm compared to the simulation. This could be attributed to changes in atmospheric turbidity over the day. These results provided confidence that the calibration of the units gave reasonable results under the operating conditions investigated. Nonetheless, Figures 3 and 4 also (2) 2004

4 illustrate that the changing atmospheric conditions over the course of a day is enough to introduce disagreement with a model that uses daily average values of AOD and temperature. Moreover, this result was achieved using a custom aerosol scattering model for this data. This highlights the fact that due to the wide range of possible inputs for the model, this verification step can only indicate what a reasonable spectral measurement would look like under the given measurement conditions. has been analysed to verify the integrity of the data, and used to develop a preliminary set of inputs to the SMARTS model that will generate the closest match to the observed spectral resource over this period. Amongst the available input options for SMARTS is the choice of the aerosol model to apply in the calculation procedure. This dataset was evaluated using Maritime and Desert aerosol models from the predefined models available, along with a custom user-defined model. The performance of the model was judged by comparing the measured and simulated broadband integrals and APE values at instances where the total direct normal irradiance exceeded 400 Wm -2. This threshold was selected as it excluded spectral irradiances measured under highly variable conditions that contributed only 10% of the total energy resource. Figure 5 illustrates a period from the October dataset where several days of clear skies were modelled using a custom aerosol model and monthly averaged AOD. Whilst the first four days show an excellent match for both criteria, the spectrum changes on the fifth day and the irradiation intensity on the seventh day. This again illustrates that the use of monthly average values in the simulation is not sufficient to pick up daily or hourly variations in atmospheric conditions. Figure 3: (top) plot of measured broadband irradiance compared with the SMARTS simulation, and (bottom) plot of the measured APE compared with the same simulation. Figure 5: Comparison of the integrated irradiance levels (top) and the APE values (bottom) over one week, showing the broad agreement between simulation and measurement. Figure 4: Comparison of the modelled and simulated spectra at 12:00 and 16:30 on the same day, confirming the close agreement at 12:00. The difference in the region of nm explains the higher APE for the simulation at 16: Resource modelling Presently, twelve months of environmental and spectral data have been collected at the test site in Nicosia over the period to This As Figure 5 demonstrates, the large variability between measurements and simulations arising from atmospheric changes makes such direct comparisons between long-term measurement and simulation uninformative. Plotting histograms of the APE distribution over the whole period provides a better way to evaluate the overall spectral match between simulation and measurement, as shown in Figure 6. Here the spectral distributions characterised by APE value for the month of August are plotted. This result shows that for simulation and measurement there was a broad agreement in the distribution of spectral irradiance, with the same mode spectral distribution for both datasets. 2005

5 response. This analysis indicates a conversion efficiency peak at a spectral distribution corresponding to an APE of 1.56 ev. This is not the most frequently occurring APE value for this dataset, suggesting that there is room to improve the energy yield of this device by tailoring the spectral response to a more blue spectrum. Figure 6: Histogram showing the spectral irradiance distribution occurrence for both the simulated and the measured datasets for the month of August. 4 DISCUSSION Along with the aerosol optical depth itself, one of the most significant choices that affected the output of the SMARTS simulation was that of the aerosol model. Among the predefined aerosol models available in SMARTS it appears that the most appropriate fit for the measurement site is the maritime aerosol model of the IAMAP preliminary standard atmosphere [12]. However, the best match with observations at this site has been obtained when a custom aerosol model was defined for each month. The main observation that has arisen from the simulations to date is that the SMARTS program yields a narrower range of APE values than are measured, and that this holds for all the months simulated. The simulated spectra are skewed towards an overall more blue spectral dataset, with more short-wavelength irradiance. Despite this, there are fewer occurrences of very blue-shifted spectra than measured as characterised by APE values greater than 1.60 ev. This perhaps arises because the averaged AOD values used mask the occurrence of very clear sky days. At this stage, more data is needed to provide better average input values for each month, as the overall aim is to produce a simulated dataset that best represents the average conditions at the test site for a complete year. Once sufficient information has been gathered to describe the most frequently occurring spectral distributions over a year, this information could be used to analyse the performance of multi-junction photovoltaic cells. To demonstrate the potential application of this information in assessing the output of a triple-junction solar cell, an analysis is performed using the measured dataset. Here the APE for each measured spectrum was calculated, and then used to group all the measurements in APE steps of 0.02 ev. For each APE group, an average spectrum GAPE was then calculated. These average spectra were then convolved with the spectral response (SR) of a typical triple-junction cell to produce a corresponding current output Ijx for each junction, j1-j3, according to the formula in equation 3. The result of this analysis is illustrated with reference to Figure 7. Here, the spectral responses of the top two junctions of the cell dictate the change in apparent (3) Figure 7: Current yield simulation as a function of average photon energy, showing that for this particular multi-junction PV cell, the peak efficiency occurs at a spectral distribution characterised by an APE of 1.56 ev. 5 CONCLUSIONS This work has presented the experiences gained in the attempts to produce a high-quality spectral resource dataset for a single location. At present a calibration uncertainty of ±5.4% (k=2) for the measurement setup has been achieved by performing an in-situ calibration transfer. The subsequent verification procedures have confirmed the satisfactory calibration transfer to the outdoor spectroradiometers. This has illustrated how a method that combines measurement comparison and spectrally resolved irradiance simulations can be used to provide confidence in spectrally resolved solar irradiance measurements. The APE has shown itself to be a useful metric when assessing large datasets of spectral irradiance measurements. A similar APE value for simulated and measured spectra has been shown to identify similar spectral distributions. In combination with the broadband integrated irradiance values, this analysis forms a useful method for the cross-comparison of spectral irradiance measurements. To date, attempts to reconcile the long-term measurements with a simulated spectral dataset for the same location has found that the SMARTS model tends to produce a simulated dataset that is skewed towards blue-rich spectral distributions. At the same time, simulations also underestimate the occurrence of both very blue-shifted and very red-shifted spectra. This suggests that at present, this approach to generating a synthetic typical meteorological year of spectral data is not yet an adequate substitute for real spectral measurements. To produce a synthetic spectral dataset equivalent to a year s spectral irradiance resource at the site in Nicosia, further measurements will be required to improve the quality of the input data. Nonetheless, the data collected to date can already be used to provide indications of the performance levels that could be expected of multijunction PV cell used at this location. 2006

6 6 REFERENCES [1] K. Schneider, C. Darnaud-Dufour, P.-D. Berger, World Record Solar Cell with 44.7% Efficiency, no. 22. Freiburg, Germany, pp. 1 6, [2] PV-TUNE Project Webpage. [Online]. Available: [Accessed: 15-Sep-2014]. [3] A. Dobbin, M. Norton, G. E. Georghiou, M. Lumb, and T. N. D. Tibbits, Energy Harvest Predictions for a Spectrally Tuned Multiple Quantum Well Device Utilising Measured and Modelled Solar Spectra, in CPV-7 conference, [4] V. Paraskeva, M. Norton, M. Hadjipanayi, and G. E. Georghiou, Calibration of Spectroradiometers for Outdoor Direct Solar Spectral Irradiance Measurements, in 28th European Photovoltaic Solar Energy Conference and Exhibition, 2013, pp [5] M. E. Schaepman and S. Dangel, Solid laboratory calibration of a nonimaging spectroradiometer., Appl. Opt., vol. 39, pp , [6] D. R. Myers, I. Reda, S. Wilcox, A. Andreas, Optical radiation measurements for photovoltaic applications: instrumentation uncertainty and performance, International Symposium on Optical Science and Technology, SPIE's 49th Annual Meeting, 2-6 August 2004, Denver, Colorado [7] S. G. R. Salim, N. P. Fox, E. Theocharous, T. Sun, and K. T. V Grattan, Temperature and nonlinearity corrections for a photodiode array spectrometer used in the field., Appl. Opt., vol. 50, pp , [8] C. Gueymard, Parameterized transmittance model for direct beam and circumsolar spectral irradiance, Sol. Energy, vol. 71, no. 5, pp , [9] Gueymard, C., "SMARTS, A Simple Model of the Atmospheric Radiative Transfer of Sunshine: Algorithms and Performance Assessment". Professional Paper FSEC-PF Florida Solar Energy Center, 1679 Clearlake Road, Cocoa, FL 32922, [10]B. Holben, D. Tanre, and A. Smirnov, An emerging ground-based aerosol climatology: Aerosol optical depth from AERONET, J. Geophys. Res., [11] S. G. R. Salim, N. P. Fox, E. Theocharous, T. Sun, and K. T. V Grattan, Temperature and nonlinearity corrections for a photodiode array spectrometer used in the field., Appl. Opt., vol. 50, pp , [12]WMO, A preliminary cloudless standard atmosphere for radiation computation. pp. WCP 112, WMO/TD No. 24, ACKNOWLEDGEMENTS This work has been co-financed by the European Regional Development Fund and by Republic of Cyprus in the framework of the project Spectrally Tuned Solar Cells for Improved Energy Harvesting with grant number ΤΕΧΝΟΛΟΓΙΑ/ΕΝΕΡΓ/0311(ΒΙΕ)/