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A novel unsupervised machine learning approach for analyzing time-series data is applied to the topic of photovoltaic (PV) system degradation rate estimation, sometimes referred to as energy-yield degradation analysis. This approach only requires a measured power signal as an input--no irradiance data, temperature data, or system configuration information. We present results on a data set that was previously analyzed and presented by NREL using RdTools, validating the accuracy of the new approach and showing increased robustness to data anomalies while reducing the data requirements to carry out the analysis., Comment: Presented at 46th IEEE Photovoltaic Specialists Conference (2019), accepted with revisions to IEEE Journal of Photovoltaics, final proof submitted November 2019

With the rapidly growing interest in bifacial photovoltaics (PV), a worldwide map of their potential performance can help assess and accelerate the global deployment of this emerging technology. However, the existing literature only highlights optimized bifacial PV for a few geographic locations or develops worldwide performance maps for very specific configurations, such as the vertical installation. It is still difficult to translate these location- and configuration-specific conclusions to a general optimized performance of this technology. In this paper, we present a global study and optimization of bifacial solar modules using a rigorous and comprehensive modeling framework. Our results demonstrate that with a low albedo of 0.25, the bifacial gain of ground-mounted bifacial modules is less than 10% worldwide. However, increasing the albedo to 0.5 and elevating modules 1 m above the ground can boost the bifacial gain to 30%. Moreover, we derive a set of empirical design rules, which optimize bifacial solar modules across the world, that provide the groundwork for rapid assessment of the location-specific performance. We find that ground-mounted, vertical, east-west-facing bifacial modules will outperform their south-north-facing, optimally tilted counterparts by up to 15% below the latitude of 30 degrees, for an albedo of 0.5. The relative energy output is the reverse of this in latitudes above 30 degrees. A detailed and systematic comparison with experimental data from Asia, Europe, and North America validates the model presented in this paper. An online simulation tool (https://nanohub.org/tools/pub) based on the model developed in this paper is also available for a user to predict and optimize bifacial modules in any arbitrary location across the globe.

In this paper, we present a physics-based analytical model for CIGS solar cells that describes the illumination- and temperature-dependent current-voltage (I-V) characteristics and accounts for the statistical shunt variation of each cell. The model is derived by solving the drift-diffusion transport equation so that its parameters are physical, and, therefore, can be obtained from independent characterization experiments. The model is validated against CIGS I-V characteristics as a function of temperature and illumination intensity. This physics-based model can be integrated into a large-scale simulation framework to optimize the performance of solar modules as well as predict the long-term output yields of photovoltaic farms under different environmental conditions.

We analyze the problem of partial shading in monolithically integrated thin film photovoltaic (TFPV) modules, and explore how the shape and size of the shadows dictate their performance and reliability. We focus on the aspects of shading problem unique to monolithic TFPV, arising from thin long rectangular series-connected cells, with partial shadows covering only a fraction of cell area. We find that due to the cell shape, the unshaded portion of a partially shaded cell experiences higher heat dissipation due to a redistribution of voltages and currents in this two dimensional geometry. We also analyze the effect of shadow size and orientation by considering several possible shading scenarios. We find that thin edge shadows can cause potentially catastrophic reverse bias damage, depending on their orientation. Finally, we show that external bypass diodes cannot protect the individual cells from shadow-induced reverse stress, but can improve the string output for larger shadows., Comment: 31 pages, 10 figures