• Energy Research

<p>The IEC 61853 standard series aims to provide a standardized measure for PV module energy rating, namely the Climate Specific Energy Rating (CSER). For this purpose, it defines procedures for the experimental determination of input data and algorithms for calculating the CSER. However, some steps leave room for interpretation regarding the specific implementation. To analyze the impact of these ambiguities, the comparability of results and the clarity of the algorithm for calculating the CSER in part 3 of the standard, an intercomparison is performed among research organizations with 10 different implementations of the algorithm. We share the same input data, obtained by measurement of a commercial crystalline silicon PV module, among the participating 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. After several comparison phases, a best practice approach is defined, which reduces the difference by a factor of 210 to below 0.001 (0.1%) in CSER for two independent PV modules. The best practice presented in this paper establishes clear guidelines for the numerical treatment of the spectral correction and power matrix extrapolation, where the methods in the standard are not clearly defined. Additionally, we provide input data and results for the PV community to test their implementations of the standard’s algorithm. To identify the source of the deviations, we introduce a climate data diagnostic set. Based on our experiences, we give recommendations for the future development of the standard.</p>

<p>The IEC 61853 standard series aims to provide a standardized measure for PV module energy rating, namely the Climate Specific Energy Rating (CSER). For this purpose, it defines procedures for the experimental determination of input data and algorithms for calculating the CSER. However, some steps leave room for interpretation regarding the specific implementation. To analyze the impact of these ambiguities, the comparability of results and the clarity of the algorithm for calculating the CSER in part 3 of the standard, an intercomparison is performed among research organizations with 10 different implementations of the algorithm. We share the same input data, obtained by measurement of a commercial crystalline silicon PV module, among the participating 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. After several comparison phases, a best practice approach is defined, which reduces the difference by a factor of 210 to below 0.001 (0.1%) in CSER for two independent PV modules. The best practice presented in this paper establishes clear guidelines for the numerical treatment of the spectral correction and power matrix extrapolation, where the methods in the standard are not clearly defined. Additionally, we provide input data and results for the PV community to test their implementations of the standard’s algorithm. To identify the source of the deviations, we introduce a climate data diagnostic set. Based on our experiences, we give recommendations for the future development of the standard.</p>

Abstract In this work, we present a simplified model that describes the energy performance of solar photovoltaic (PV) modules under real operating conditions. The model, validated against three full years of data collected from our outdoor test field for amorphous (a-Si) and crystalline (c-Si) silicon PV modules, agrees well in describing the energy performance of both technologies, including their peculiar counter-cyclical seasonal oscillations. The model focuses on clear-sky conditions and on four main loss/gain mechanism: (1) temperature, (2) spectral-effects, (3) reflection, and (4) irradiance, with the addition for a-Si of the Staebler–Wronsky effect. From the device side, the model requires a limited characterisation of the device under test: (a) power rating, (b) temperature coefficients, (c) spectral, (d) angle-of-incidence response and (e) irradiance dependence. Compared to approaches that are more conventional, our model, rather than focusing on the instantaneous power, concentrates on the large picture and directly attempts at providing a description of the daily performance ratio of the device allowing us to introduce a number of simplifications and, most notably, work with much smaller data sets. Input for our simulations are daily aggregate meteorological, and solar data weighted on the irradiance profile. For the a-Si device, slight discrepancies on the long term are attributed to an intrinsic degradation of the module’s energy performance, which is not observed for c-Si. Moreover, for the devices investigated in this work we show that by neglecting the irradiance dependence parameter the model can further be simplified without loss of accuracy.

Abstract In this work, we present a simplified model that describes the energy performance of solar photovoltaic (PV) modules under real operating conditions. The model, validated against three full years of data collected from our outdoor test field for amorphous (a-Si) and crystalline (c-Si) silicon PV modules, agrees well in describing the energy performance of both technologies, including their peculiar counter-cyclical seasonal oscillations. The model focuses on clear-sky conditions and on four main loss/gain mechanism: (1) temperature, (2) spectral-effects, (3) reflection, and (4) irradiance, with the addition for a-Si of the Staebler–Wronsky effect. From the device side, the model requires a limited characterisation of the device under test: (a) power rating, (b) temperature coefficients, (c) spectral, (d) angle-of-incidence response and (e) irradiance dependence. Compared to approaches that are more conventional, our model, rather than focusing on the instantaneous power, concentrates on the large picture and directly attempts at providing a description of the daily performance ratio of the device allowing us to introduce a number of simplifications and, most notably, work with much smaller data sets. Input for our simulations are daily aggregate meteorological, and solar data weighted on the irradiance profile. For the a-Si device, slight discrepancies on the long term are attributed to an intrinsic degradation of the module’s energy performance, which is not observed for c-Si. Moreover, for the devices investigated in this work we show that by neglecting the irradiance dependence parameter the model can further be simplified without loss of accuracy.

AbstractThis work presents the results of a high‐efficiency (HE) photovoltaic (PV) module round‐robin intercomparison between five Asian and European ISO/IEC 17025 accredited laboratories and one industrial laboratory based in Europe. The scope of the round‐robin was to examine the measurements comparability for this PV technology with respect to ISO/IEC 17025 laboratory conformity assessment and also to examine the accuracy of step‐like methods towards transient errors against already validated methods. The devices under test were four types of HE c‐Si PV modules with efficiencies varying between 16.5% and 19.0%. The results indicate that a satisfactory agreement was achieved with maximum deviations of 1.59% in Pmax, 1.13% in Isc, and 0.64% in Voc for all devices under test. The weighted standard deviations in Pmax per device type, which can be seen as a conservative estimate of interlaboratory agreement for HE c‐Si PV, ranged within 0.82% to 2.23% (k = 2). The accuracy of step‐like methods towards transient errors was evaluated by comparing a second series of results at fixed Isc for each module under test, eliminating the influence of the effective irradiance measurement. This work suggests that the contribution of capacitive errors was in the range (0.47 ± 0.19) % (k = 2). A spectral mismatch sensitivity analysis showed that an accurate measurement of the spectral irradiance and of the involved spectral responsivities together with the punctual correction for the spectral mismatch can reduce the error in the measurement of PV modules performance of about 2% even in the case of c‐Si against c‐Si and class AAA solar simulators.

AbstractThis work presents the results of a high‐efficiency (HE) photovoltaic (PV) module round‐robin intercomparison between five Asian and European ISO/IEC 17025 accredited laboratories and one industrial laboratory based in Europe. The scope of the round‐robin was to examine the measurements comparability for this PV technology with respect to ISO/IEC 17025 laboratory conformity assessment and also to examine the accuracy of step‐like methods towards transient errors against already validated methods. The devices under test were four types of HE c‐Si PV modules with efficiencies varying between 16.5% and 19.0%. The results indicate that a satisfactory agreement was achieved with maximum deviations of 1.59% in Pmax, 1.13% in Isc, and 0.64% in Voc for all devices under test. The weighted standard deviations in Pmax per device type, which can be seen as a conservative estimate of interlaboratory agreement for HE c‐Si PV, ranged within 0.82% to 2.23% (k = 2). The accuracy of step‐like methods towards transient errors was evaluated by comparing a second series of results at fixed Isc for each module under test, eliminating the influence of the effective irradiance measurement. This work suggests that the contribution of capacitive errors was in the range (0.47 ± 0.19) % (k = 2). A spectral mismatch sensitivity analysis showed that an accurate measurement of the spectral irradiance and of the involved spectral responsivities together with the punctual correction for the spectral mismatch can reduce the error in the measurement of PV modules performance of about 2% even in the case of c‐Si against c‐Si and class AAA solar simulators.

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. 37th European Photovoltaic Solar Energy Conference and Exhibition; 811-815

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. 37th European Photovoltaic Solar Energy Conference and Exhibition; 811-815