Single-Axis Tracked Bifacial System Results
Results of a study on single-axis tracked bifacial PV in Klamath Falls, Oregon. Includes the adaptive tracking angle, edge brightening, and torque tube shading.
14 SOLAR ENERGY↗
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bifacial_radiance: a python package for modeling bifacial solar photovoltaic systems
bifacial_radiance is a national-laboratory-developed, community-supported, open-source toolkit that provides a set of functions and classes for simulating the performance of bifacial photovoltaic (PV) systems. (Bifacial PV modules collect light on the front as well as the rear side.) bifacial_radiance automates calculations of PV system layout and performance to use along with the popular ray-tracing software tool RADIANCE (Ward, 1994). Specific algorithms include design and layout of PV modules, reflective ground surfaces, shading obstructions, and irradiance calculations throughout the system, among others. bifacial_radiance is an important component of a growing ecosystem of open-source tools for solar energy (William F Holmgren et al., 2018).
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UV Degradation in Backsheets: A Ray-Tracing Irradiance Simulation Approach
Around 90% of current photovoltaic (PV) modules are less than ten years old. New PV technologies and materials are deployed without documented durability and performance histories. Accelerated testing attempts to capture degradation modes but can produce false results with such rapid deployment of new materials. Current testing assumes UV dosage on the rear of a module to be 10% of that incident on the front. We present a method to quantify degradation on PV backsheets in the field. We aim to evaluate if current acceleration factors for UV damage in chambers are properly estimating degradation for different PV site installations. The method leverages bifacial_radiance to ray-trace and evaluate irradiance on the front and the rear of the modules. Then an equation to estimate the relative degradation is proposed.
accelerated testing↗
The Subtle Art of Bifacial Performance Modeling
The PV industry is set for rapid uptake of bifacial PV if key barriers are eliminated, such as accurate performance models, standards around the rating of bifacial modules, and accurate assessment of site albedo. In this presentation we will talk about the changes to the ray-trace program bifacial_radiance, it's new capabilities that allow it to model the performance for any module at any size array. A new graphical user interface has been released on time for this conference. Preliminary results for torque-tube shading are also presented.
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Bifacial PV System Performance: Separating Fact from Fiction
The performance of bifacial PV systems depends greatly on the installed conditions. Previous simulations and results have shown very high bifacial gain improvement, but this may not be the case for all conditions, particularly for large-scale systems with self-shading, lower-cost PV modules (PERC) which might have lower bifaciality coefficient, and field deployments over natural ground cover. But not to worry! Financial models indicate that even with these lower performance conditions, and with bifacial gain of 4%-9%, bifacial modules can still provide improved LCOE.
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Comparative Analysis of Rear Irradiance Modeling Methods for Bifacial PV Systems on Single-Axis Trackers Under Varying Albedo Conditions
This study compares three rear-side irradiance modeling methods for bifacial PV systems on single-axis trackers: (i) the 2D View Factor (VF) model in PVsyst(R), (ii) the open-source PVFactors VF model, and (iii) the Ray Tracing (RT) technique using bifacial_radiance. Simulations were benchmarked against field measurements from a pilot PV plant with rear-side sensors at the torque tube height. Results show that all models underestimated the non-uniformity of rear irradiance along the module length and overestimated the total rear irradiance incident on the module. However, since bifacial gain represents only a fraction of the system's total energy, the resulting energy yield differences among methods remained within +- 2 % of measured values, which is typical for such simulations. While the overall energy impact is limited, this study characterizes the specific limitations of each modeling approach, supporting further improvements in bifacial PV performance assessment methods.
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Understanding Bifacial PV Modeling: Raytracing and View Factor Models
As part of the PV Magazine Webinar 'View-factor vs. ray tracing - which bifacial modelling techniques should you use?,' the second part lead by NREL will explore the different rear-irradiance calculation software, which fall into two categories: view-factor and ray-tracing models. View factor models assume isotropic scattering of reflected rays, allowing for calculation of irradiance by integration. Due-diligence software such as PVSyst or SAM use the view-factor model. Ray-tracing models simulate multipath reflection and absorption of individual rays entering a scene. Raytracing software such as bifacial_radiance, which is the only available open-source toolkit, offers the possibility of reproducing complex scenes, including shading or finite-system edge effects. This can be useful to evaluate specific system geometries, shading and obstructions, areas of different albedo and electrical mismatch, all topics of interest to the prediction of bifacial performance. Model agreement for view factors and bifacial_radiance software is improving and has been shown to be between 2% (absolute) when compared with measured results.
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Measuring the Stress Factors for Photovoltaic (PV) Backsheet Degradation
Back sheet failure has resulted in power loss and large-scale recall of photovoltaic modules, resulting in billions of dollars in lost revenue. The light exposure on the backside of a photovoltaic module comes primarily from reflected light which alters the distribution of natural sunlight. Because of this, modelling the backside exposure and duplicating the exposure is much more difficult than modeling the frontside exposure. This project aims to study how various back sheets and junction box materials degrade under different conditions and to develop Python code to help model and predict degradation. The stress factors for back-sheet degradation must be quantified to extrapolate accelerated stress tests to the field. Test samples were placed in the A3, A4, and A5 conditions, as defined in IEC 62788-7-2, to assess the temperature and humidity dependence of ultraviolet (UV) induced degradation. We are utilizing a custom chamber with exposure from 0.5 UV-suns to 5 UV-suns to understand the dependence of degradation on light intensity. A group of samples put in the A3 condition had glass filters with 50% UV cut-offs of 320 nm, 335 nm, and 360 nm to assess the wavelength dependence of UV degradation. All this data is necessary to assess the impact of non-standard UV light exposure. The material evaluation tests include gloss measurements, attenuated total internal reflectance Fourier transform infrared spectroscopy (ATR-FTIR), UV-visible reflectance/transmittance utilizing a Cary Ci7000 spectrophotometer, and a nano-indenter for surface hardness and modulus measurements. Alongside the experimental work, there is a computational effort using raytracing and Python open-source tools in PVDeg , PVLib, and Bifacial_Radiance. This code will create specific exposure scenarios and enable the evaluation of chamber degradation relative to field degradation. Equation 1 is a strawman equation used to model degradation on the backside of a PV module. We will create simplified code, based on the results of ray-tracing calculations, which uses a view factor approach to provide fast calculations for the most common exposure scenarios.
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