• Energy Research

Abstract Latest trends in the photovoltaic sector see the use of innovative photovoltaic technologies with extended spectral responsivity ranging from 300 to 1200 nm for non-concentrating terrestrial applications, and to 1800 nm for concentrating PV and space applications. As a consequence, an update of the IEC 60904-9 standard is ongoing with a definition of new spectral ranges for the assessment of the spectral match. This poses new challenges to laboratories and research centers on whether or not they still are able to accurately measure the spectral mismatch of their sun simulator in the newly-defined spectral regions. Prior to that, there is a need to understand if the commercially available spectroradiometers are ready to extend their measurement range as prescribed by the forthcoming new standard. This paper analyses two options for an extension of the spectrum characterisation of solar simulators to 300–1200 nm and compares them in terms of spectral match of global normal irradiance (GNI) spectra acquired under natural sunlight by eight spectroradiometers during the 6th European Spectroradiometer Intercomparison. The acquired spectra are also compared in terms of an index of consistency of the spread of the measured spectra with the estimated measurement uncertainty, hereafter named as performance statistics E n . Results show that all investigated laboratories assure the equivalence of the spectral match classification well below the 25% limit corresponding to class-A simulators. When considering the more stringent class-A+ corresponding to a 12.5% limit, one of the two considered options that rearranges the 300–1200 nm spectral range into 6 bands appears to still assure the equivalence of the class A+ limits among considered instruments. The E n performance index analysis highlights some inconsistencies with the estimated measurement uncertainty or instrument drifts from the expected performance, and the need of further improvements in calibration, set up and measurement procedures.

In this work, the seasonality and performance loss rates of eleven grid-connected photovoltaic (PV) systems of different technologies were evaluated through seasonal adjustment. The classical seasonal decomposition (CSD) and X-12-ARIMA statistical techniques were applied on monthly DC performance ratio, RP, time series, constructed from field measurements over the systems' first five years of operation. The results have shown that the RP of crystalline silicon (c-Si) technologies was higher during winter. This was also the case for the copper–indium gallium-diselenide (CIGS) and cadmium telluride (CdTe) technologies but with lower seasonal amplitude. The amorphous silicon (a-Si) technology exhibited a different seasonal profile, with high RP during summer and autumn and low during winter. In addition, the trends extracted from the application of CSD and X-12-ARIMA on three-year, four-year and five-year RP time series were used to estimate linear performance loss rates. A comparison between standard linear regression (LR), CSD and X-12-ARIMA has shown that CSD and X-12-ARIMA resulted in higher rates overall for c-Si, 1.07 and 0.93%/year respectively, but with significantly less uncertainty than LR. Lastly, it was shown that X-12-ARIMA provided statistical inference in the presence of outliers and produced model residuals that were uncorrelated, in contrast to CSD.

Abstract This paper provides a review of methodologies for measuring the degradation rate, RD, of photovoltaic (PV) technologies, as reported in the literature. As presented in this paper, each method yields different results with varying uncertainty depending on the measuring equipment, the data qualification and filtering criteria, the performance metric and the statistical method of estimation of the trend. This imposes the risk of overestimating or underestimating the true degradation rate and, subsequently, the effective lifetime of a PV module/array/system and proves the need for defining a standardized methodology. Through a literature search, four major statistical analysis methods were recognized for calculating degradation rates: (1) Linear Regression (LR), (2) Classical Seasonal Decomposition (CSD), (3) AutoRegressive Integrated Moving Average (ARIMA) and, (4) LOcally wEighted Scatterplot Smoothing (LOESS), with LR being the most common. These analyses were applied on the following performance metrics: (1) electrical parameters from IV curves recorded under outdoor or simulated indoor conditions and corrected to STC, (2) regression models such as the Photovoltaics for Utility Scale Applications (PVUSA) and Sandia models, (3) normalized ratings such as Performance Ratio, RP, and PMPP/GI and, (4) scaled ratings such as PMPP/Pmax, PAC/Pmax and kWh/kWp. The degradation rate results have shown that the IV method produced the lowest RD and LR produced results with large variation and the largest uncertainty. The ARIMA and LOESS methods, albeit less popular, produced results with low variation and uncertainty and with good agreement between them. Most importantly, this review showed that the RD is not only technology and site dependent, but also methodology dependent.

Abstract The effect of temperature on different grid-connected photovoltaic (PV) technologies installed in Cyprus was analyzed in this study. Initially, the performance losses due to the temperature effect on the annual energy yield of each technology were investigated using measurements of module temperature and the manufacturer provided maximum power point (MPP) temperature coefficients, γPMPP. The same methodology was also applied using outdoor evaluated γPMPP coefficients for comparison. When using the manufacturer’s temperature coefficient, the results showed that over the evaluation period the highest average thermal losses in annual dc energy yield were 8% for mono-crystalline silicon (mono-c-Si) and 9% for multi-crystalline silicon (multi-c-Si) technologies while for thin-film technologies, the average losses were 5%. Similar losses were found when using the outdoor evaluated temperature coefficients. Additionally, temperature effects on the seasonal performance of the different technologies were evident on the monthly average performance ratio (PR). For the amorphous silicon (a-Si) technologies, a performance increase from spring until early autumn was observed and was attributed to thermal annealing. The effect of thermal annealing on the performance was evident by filtering dc MPP power measurements at high irradiance (greater than 800 W/m2) and restricting the values at geometric air mass (AM) in the range 1 ≤ geometric AM ≤ 1.5. The extracted dc MPP power was corrected for irradiance and temperature at standard test conditions (STC) using the manufacturer provided γPMPP over a period of two years. Subsequently, the effect of thermal annealing was further investigated by extracting dc MPP power measurements at geometric AM in the range 1.4 ≤ geometric AM ≤ 1.6 in order to minimize the spectral influences on the performance of a-Si technologies. An increase in power for all the a-Si technologies was obvious during the warm summer season and was recorded over the period of March until September for both years.

In this work, the performance loss rates of eleven grid-connected photovoltaic (PV) systems of different technologies were evaluated by applying linear regression (LR) and trend extraction methods to Performance Ratio, R P , time series. In particular, model-based methods such as Classical Seasonal Decomposition (CSD), Holt-Winters (HW) exponential smoothing and Autoregressive Integrated Moving Average (ARIMA), as well as non-parametric filtering methods such as LOcally wEighted Scatterplot Smoothing (LOESS) were used to extract the trend from monthly R P time series of the first five years of operation of each PV system. The results showed that applying LR on the time series produced the lowest performance loss rates for most systems, but with significant autocorrelations in the residuals, signifying statistical inaccuracy. The application of CSD and HW significantly reduced the residual autocorrelations as the seasonal component was extracted from the time series, resulting in comparable results for eight out of eleven PV systems, with a mean absolute percentage error (MAPE) of 6.22 % between the performance loss rates calculated from each method. Finally, the optimal use of multiplicative ARIMA resulted in Gaussian white noise (GWN) residuals and the most accurate statistical model of the R P time series. ARIMA produced higher performance loss rates than LR for all technologies, except the amorphous Silicon (a-Si) system. The LOESS non-parametric method produced directly comparable results to multiplicative ARIMA, with a MAPE of −2.04 % between the performance loss rates calculated from each method, whereas LR, CSD and HW showed higher deviation from ARIMA, with MAPE of 25.14 %, −13.71 % and −6.39 %, respectively.

Abstract This review investigates the potential of photovoltaics in southern Europe and Middle East/North Africa in terms of PV status and policies/initiatives for PV market development in the region. In some Sunbelt countries, PV has become a competitive alternative for electricity generation resulting from a combination of high solar resource, decreasing PV system costs and high fuel prices. The PV levelized cost of electricity has fallen in some regions to 0.08 €/kWh whilst retail electricity prices are almost three times higher in some cases attesting to the ideal conditions in the region for PV uptake. In grid parity regions of south Europe, incentives such as feed-in-tariffs have served their purpose. Net metering and self-consumption are proving to be good solutions for driving effectively the PV market as evidenced for the case of Cyprus. In MENA countries, the renewables policy landscape, although still at an exploratory phase, is rapidly developing providing the backbone for a large number of new PV projects. This reflects the region's commitment to meet its ambitious national targets regarding PV. Overall, the timing for the whole region is excellent to achieve energy sustainability, once better policies are adopted and challenges regarding grid integration and reliability are addressed.

Grid-connected photovoltaic (PV) systems have become a significant constituent of the power supply mix. A challenge faced by both users and suppliers of PV systems is that of defining and computing a reliable metric of annual degradation rate while in service. This paper defines a new measure to calculate the degradation rate of PV systems from the PV field measured performance ratio (PR). At first, the PR time series is processed by conventional principal component analysis, which yields seasonality as the dominant data feature. The environment, operating conditions, uncertainty, and hardware used for monitoring influence the outdoor measurements unpredictably. These influences are viewed as perturbations that render the dominant feature obtained by PCA unsuitable to be used in a degradation rate definition. Robust principal component analysis (RPCA) is proposed to alleviate these effects. The new measure is defined as the area enclosed by the time series of the corrected by the RPCA annual monthly PR values. The degradation rates obtained for different technologies are compared with those obtained in previous studies. The results have shown that the degradation rates estimated by RPCA were in good agreement with previous investigations and provided increased confidence due to mitigation of uncertainty.