This is the data from PV module energy rating standard IEC 61853-3 intercomparison. Details can be found in: M. R. Vogt, S. Riechelmann, A. M. Gracia-Amillo, A. Driesse, A. Kokka, K. Maham, P. Kärhä, R. Kenny, C. Schinke, K. Bothe, J. C. Blakesley, E. Music, F. Plag, G. Friesen, G. Corbellini, N. Riedel-Lyngskær, R. Valckenborg, M. Schweiger, W. Herrmann, „PV module energy rating standard IEC 61853-3 intercomparison and best practice guidelines for implementation and validation”, accepted IEEE JPV. DOI (identifier) 10.1109/JPHOTOV.2021.3135258

The IEC 61853 standard series “Photovoltaic (PV) module performance testing and energy rating” aims to provide a standardized measure for PV module performance, namely the Climate Specific Energy Rating (CSER). An algorithm to calculate CSER is specified in part 3 based on laboratory measurements defined in parts 1 and 2 as well as the climate data set given in part 4. To test the comparability and clarity of the algorithm in part 3, we share the same input data, obtained by measuring a standard photovoltaic module, among different research organizations. Each participant then uses their individual implementations of the algorithm to calculate the resulting CSER values. The initial blind comparison reveals differences of 0.133 (14.7%) in CSER between the ten different implementations of the algorithm. Despite the differences in CSER, an analysis of intermediate results revealed differences of less than 1% at each step of the calculation chain among at least three participants. Thereby, we identify the extrapolation of the power table, the handling of the differences in the wavelength bands between measurement and climate data set, and several coding errors as the three biggest sources for the differences. After discussing the results and comparing different approaches, all participants rework their implementations individually and compare the results two more times. In the third intercomparison, the differences are less than 0.029 (3.2%) in CSER. When excluding the remaining three outliers, the largest absolute difference between the other seven participants is 0.0037 (0.38%). Based on our findings we identified four recommendations for improvement of the standard series.

In the current market, the specific annual energy yield (kWh/kWp) of a PV system is gaining in importance due to its direct link to the financial returns for possible investors who typically demand an accuracy of 5% in this prediction. This paper focuses on the energy prediction of photovoltaic modules themselves, as there have been significant advances achieved with module technologies which affect the device physics in a way that might force the revisiting of device modelling. The paper reports the results of a round robin based evaluation of European modelling methodologies. The results indicate that the error in predicting energy yield for the same module at different locations was within 5% for most of the methodologies. However, this error increased significantly if the nominal nameplate rating is used in the characterization stage. For similar modules at the same location the uncertainties were much larger due to module-module variations.

The energy delivery of PV devices for a specific location is determined by the interaction of the electrical performance of the PV device and the site specific environmental and meteorological conditions. The spectral response curve of a PV device is a determining factor for its energy yield performance with regard to spectral effects. For a given spectral irradiance distribution the generated photocurrent results out of the product of both curves. Deviations to AM 1.5 of measured real sun spectra can be either subject to a blue shift when the composition of the spectrum shows a higher intensity in the low wavelength range or a red shift when a higher intensity for high wavelength range is observed. Accordingly, PV devices with a narrow band of spectral response will benefit from a blue shift of spectral irradiance. Global solar irradiance is commonly measured with spectrally neutral pyranometers. Therefore, the effective irradiance for photocurrent generation of a PV device can be higher or lower depending on the spectral composition of solar irradiance. This relation is described by the spectral mismatch correction factor (MMF), which is a direct measure for spectral gains (MMF > 1) or losses (MMF < 1). We analyzed spectral shifts in the solar spectral irradiance using the average photon energy (APE) and quantify the impact on the performance of eight PV technologies in Arizona and Italy using the spectral mismatch factor.