Björn Müller

A key aspect for achieving a high-accuracy Photovoltaic (PV) power simulation, and reliable digital twins, is a detailed description of the PV system itself. However, such information is not always accurate, complete, or even available. This work presents a novel approach to learn features of unknown PV systems or subsystems using genetic algorithm optimization. Based on measured PV power, this approach learns and optimizes seven PV system parameters: nominal power, tilt and azimuth angles, albedo, irradiance and temperature dependency, and the ratio of nominal module to nominal inverter power (DC/AC ratio). By optimizing these parameters, we create a digital twin that accurately reflects the actual properties and behaviors of the unknown PV systems or subsystems. To develop this approach, on-site measured power, ambient temperature, and satellite-derived irradiance of a PV system located in south-west Germany are used. The approach proposed here achieves a mean bias error of about 10% for nominal power, 3° for azimuth and tilt angles, between 0.01%/C and 0.09%/C for temperature coefficient, and now-casts with an accuracy of around 6%. In summary, we present a new solution to parametrize and simulate PV systems accurately with limited or no previous knowledge of their properties and features.

This article sheds new light on photovoltaic (PV) module rating according to predicted yield rather than power measured at standard testing conditions (STC). We calculate module performance ratios (MPR) for measured characteristics of eight different module types and compare them with a referenceMPRcalculated with typical crystalline silicon characteristics. In place of the not yet existing standardized weather data, we use commercially available weather data for three different locations. The referenceMPRfor the three locations were 95.5%, 94.6%, and 91.0%, respectively, with differences to the other module types of ±8% at maximum.MPRwas calculated with reference to nominal power, and—following IEC 61853—without consideration of potential degradation. The strongest contribution to the initial differences between the module types was due to differences in irradiance dependency. Standard uncertainties for all initialMPRvalues were calculated and range from 1.8% to 3.0%, including STC power uncertainty. We propose a module rating method that indicates whether a module type's performance is significantly above, below, or essentially equal to the reference. The method evaluates theMPRdifference between module type and reference, taking uncertainty into account. Significant differences were only found between modules with obviously different characteristics, but not between the crystalline silicon module types under scrutiny. As the uncertainty analysis did not cover degradation and influences due to the use of not standardized weather data, a sensitivity analysis was performed. Long‐term degradation can change the comparative energy rating significantly, whereas the selection of tilt angle and assumptions regarding module operating temperature did not have a strong effect. Copyright © 2015 John Wiley & Sons, Ltd.

Yield predictions are performed to predict the solar resource, the performance and the energy production over the expected lifetime of a photovoltaic (PV) system. In this study, we compare yield predictions and monitored data for 26 PV power plants located in southern Germany and Spain. The monitoring data include in‐plane irradiance for comparison with the estimated solar resource and energy yield for comparison with predicted performance. The results show that because of increased irradiance in recent years (‘global brightening’) the yield predictions systematically underestimate the energy yield of PV systems by about 5%. Because common irradiance databases and averaging times were used for the yield predictions analysed in this paper, it is concluded that yield predictions for areas where the global brightening effect occurred in general underestimated the energy yield by the same magnitude. Using recent satellite‐derived irradiance time series avoids this underestimation. The observed performance ratio of the analysed systems decreases by 0.5%/year in average with a relatively high spread between individual systems. This decrease is a main factor for the combined uncertainty of yield predictions. It is attributed to non‐reversible degradation of PV cells or modules and to reversible effects, like soiling. Based on the results of the validation, the combined uncertainty of state of the art yield predictions using recent solar irradiance data is estimated to about 8%. Copyright © 2015 John Wiley & Sons, Ltd.