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Highlights: [•] Inter-row shading can be approximated by a simple analytical model. [•] Good agreement was found with typical c-Si modules (<6% error). [•] This inter-row shading model has been implemented in NREL’s System Advisor Model (SAM). [•] Comparison with experiment and prior models confirm the new model for a range of PV configurations. [Copyright &y& Elsevier]

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 and 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°, for an albedo of 0.5. The relative energy output is reversed in latitudes above 30°. A detailed and systematic comparison with data from Asia, Africa, Europe, and North America validates the model presented in this paper. [ABSTRACT FROM AUTHOR]

The article discusses the target of achieving sustained global growth in photovoltaics (PV) of 25 percent per year over the decade of 2023-40. PVs exceeded a global installed capacity, but despite considerable growth and cost reduction over time, PV is yet a small part of global electricity generation, and the opportunity is getting reduced to take action at scale to cut greenhouse gas emissions while meeting global energy needs for the future.

In the presence of partial shading and other mismatch factors, bypass diodes may not offer complete elimination of excessive power dissipation due to cell reverse biasing, commonly referred to as hot-spotting in photovoltaic (PV) systems. As a result, PV systems may experience higher failure rates and accelerated ageing. In this paper, a cell-level simulation model is used to assess occurrence of hot-spotting events in a representative residential rooftop system scenario featuring a moderate shading environment. The approach is further used to examine how well distributed power electronics converters mitigate the effects of partial shading and other sources of mismatch by preventing activation of bypass diodes and thereby reducing the chances of heavy power dissipation and hot-spotting in mismatched cells. The simulation results confirm that the occurrence of heavy power dissipation is reduced in all distributed power electronics architectures, and that submodule-level converters offer nearly 100% mitigation of hot-spotting. In addition, the paper further elaborates on the possibility of hot-spot-induced permanent damage, predicting a lifetime energy loss above 15%. This energy loss is fully recoverable with submodule-level power converters that mitigate hot-spotting and prevent the damage. [ABSTRACT FROM AUTHOR]

Large commercial photovoltaic (PV) systems can experience regular and predictable energy loss due to both inter-row shading and reduced diffuse irradiance in tightly spaced arrays. This paper investigates the advantages of replacing bypass diodes with submodule-integrated DC–DC converters (subMICs) to mitigate these losses. Yearly simulations of commercial-scale PV systems were conducted considering a range of row-to-row pitches. In the limit case of array spacing (unity ground coverage), subMICs can confer a 7% increase in annual energy output and peak energy density (kW h/m 2 ). Simulation results are based on efficiency assumptions experimentally confirmed by prototype submodule differential power-processing converters. [ABSTRACT FROM AUTHOR]

This paper explains the use of a convolutional neural network (CNN) to segment solar panels in a satellite image containing solar arrays, and extract associated metadata from the arrays. A novel unsupervised technique is introduced to estimate the azimuth of each individual solar panel from the predicted mask of the convolutional neural network. This pipeline was developed with the aim of extracting necessary metadata for a solar installation, using only a set of latitude–longitude coordinates. Azimuth prediction results for 669 individual solar installations associated with 387 sites located across the United States are provided. A mean average error and median average error of 21.65 degrees and 1.0 degrees were obtained, respectively, when predicting the azimuth of the solar fleet data set, with about 80% of the results within an error of zero degrees of the ground truth azimuth value and about 85% within an error of 25 degrees. The predicted azimuth was then used to estimate the energy conversion of the solar arrays. Results show a 90.9 and 90.6 R-squared value for estimating alternating current (AC) and direct current (DC) energy, respectively, and a mean absolute percentage error (MAPE) of 1.70% in estimating the alternating current (AC) energy using the fully automated algorithm. [Display omitted] • A novel unsupervised technique is used to estimate the azimuth of solar arrays. • Segmentation of solar panels in the satellite image, using transfer learning and a CNN. • The proposed approach is can be used with high resolution satellite or aerial imagery. [ABSTRACT FROM AUTHOR]