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Results for “bifacial field validation”

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Ultimate Bifacial Showdown: 75kW Field Results

This work presents one year results for a side-by-side comparison of 5 different bifacial technologies, deployed in a 75-kW single-axis tracked field at Golden, CO. Four PERC (multi and mono) and 1 Silicon Heterojunction manufacturers are represented. Reference strings of equivalent monofacial PV modules are also installed for a direct technology comparison and calculation of bifacial gain. Analysis indicates performance within expectation, with a cumulative model mean error within +/- 2% for both bifacial and monofacial models and cumulative bifacial energy gain between 6-9%. High accuracy string-level DC monitoring and module-level measurements are also presented, showing effects of snow and edge effects in the order of +/- 3% for non-uniform irradiance across the row. Data from a custom-module to measure shading loss from the torque-tube is installed, showing up to 6% irradiance non-uniformity for the month of January.

bifacial field validation↗

Analysis and Validation of Spectral Irradiance Simulations for 75 kW Bifacial Solar Test Field

This work investigates how the spectrum of irradiance incident on the rear of bifacial modules impacts their performance. Light incident on the rear of bifacial modules can be reflected from the ground, mounting structures, and other nearby objects, causing both the intensity and spectrum to be modified. As a result, PV cells and sensors with different spectral responses report different values for the rear irradiance, leading to increased uncertainty when modeling performance. Here, we calculate the spectral irradiance incident on the rear of modules through raytrace simulations and validate with measured field data collected from a 75kW single-axis-tracked bifacial test site. Spectral responses for different sensors and PV cells are applied and compared with field measurements for four days. Based on these results, a method for improving non-spectral simulations is proposed. In addition, a linear regression is applied to the modeled spectral results as a method to gain insight into the contributions of ground reflected light to total rear irradiance.

bifacial photovoltaics↗

Optimizing Bifacial PV Systems (Q3 FY2020 Project Report)

This project has four main technical objectives. Develop and improve bifacial performance models by adding the capability to evaluate electrical behavior and performance of bifacial modules and arrays under realistic field conditions including irradiance variability caused by racking, module frame, and position in the array. Instrument and monitor performance of fielded bifacial systems to validate performance models and to measure, analyze and publish on bifacial energy gain. These should include both research and commercial bifacial systems and cover a variety of deployment applications. Evaluate optimal bifacial system designs using simulations leveraging high performance computing, and also using full sized and miniaturized experimental field deployments. Establish and contribute to international test standards for bifacial system performance, testing, and safety, and work with the community to establish installation and siting best practices.

14 SOLAR ENERGY↗

2021 Q1 Project Report: Optimized Bifacial PV Systems (Q1 FY2021 Project Report)

This project has four main technical objectives: 1) Develop and improve bifacial performance models by adding the capability to evaluate electrical behavior and performance of bifacial modules and arrays under realistic field conditions including irradiance variability caused by racking, module frame, and position in the array. 2) Instrument and monitor performance of fielded bifacial systems to validate performance models and to measure, analyze and publish on bifacial energy gain. These should include both research and commercial bifacial systems and cover a variety of deployment applications. 3) Evaluate optimal bifacial system designs using simulations leveraging high-performance computing, and also using full sized and miniaturized experimental field deployments. 4) Establish and contribute to international test standards for bifacial system performance, testing, and safety, and work with the community to establish installation and siting best practices.

42 ENGINEERING↗

Bifacial Vertical Testbed and Ground Irradiance Data in Golden, Colorado

This data was collected for Tonita et al., “Vertical bifacial photovoltaic system model validation: study with field data, various orientations, and latitudes,” for validation of optical models for vertically-oriented photovoltaics under high albedo. Ground irradiance data for vertical PV arrays modeling in agrivoltaics is also provided. The dataset is provided for further use or study as open source. For any questions on the dataset, email silvana.ovaitt@nlr.gov.

14 SOLAR ENERGY↗

Solar-Tracker Diffuse-Response Algorithm for Balancing Energy Gain and Mechanical Wear in Photovoltaic Systems

Single-axis solar tracking maximizes photovoltaic energy production under clear-sky conditions; however, its effectiveness decreases under cloudy and overcast skies, where diffuse irradiance dominates and the optimal module orientation changes. Conventional tracking algorithms either neglect sky conditions or rely on simplified diffuse-response strategies that may trigger frequent tracker repositioning under variable cloud cover, leading to increased mechanical wear with marginal energy gains. This work proposes an enhanced diffuse-response tracking algorithm that explicitly accounts for both the intensity and temporal persistence of cloudiness. By requiring overcast conditions to persist for a minimum duration before reorienting the tracker to a diffuse-stow position, the proposed approach reduces unnecessary movements while preserving the benefits of diffuse-response operation. The algorithm is evaluated through numerical simulations based on historical meteorological data and validated using field measurements on monofacial and bifacial photovoltaic strings. The results show that the proposed strategy reduces excess tracker movement from 114% to 0.16% while maintaining nearly the same energy yield. Compared to a conventional diffuse-response algorithm, the associated energy reduction is minimal (≈0.17%) relative to the ≈0.37% yield gain observed at the studied location. These findings demonstrate that incorporating cloudiness duration enables a practical compromise between energy performance and tracker durability, particularly for monofacial photovoltaic systems.

14 SOLAR ENERGY↗

Spectral Rear Irradiance Testing and Modeling for Degradation and Performance of Solar Fields: Preprint

This work investigates how the spectrum of irradiance incident on the rear of solar modules impacts their degradation, and in case of bifacial PV their performance. We calculate the spectral irradiance incident on the rear of modules through raytrace simulations and validate with measured field data collected from a 75kW single axis-tracked bifacial test site. An equation to calculate relative degradation is shown, showing that current acceleration factors for UV damage in chambers can be off by +3% for the rear surfaces. Finally, a method for improving non-spectral simulations is proposed.

bifacial performance↗

Spectral Rear Irradiance Testing and Modeling for Degradation and Performance of Solar Fields

This work investigates how the spectrum of irradiance incident on the rear of solar modules impacts the degradation and performance of backsheets. We model the spectral irradiance incident on the rear of modules through raytrace simulations and validate with measured field data collected from a 75kW single axis-tracked bifacial test site. A generic equation to estimate relative degradation is proposed, and we show that current acceleration factors for UV damage in chambers can be sub-estimate up to 4.5% absolute from the usually assumed 10% dosage on the rear surfaces.

bifacial performance↗

Bifacial PV Module Energy Modeling Validation Study

The cost delta between monofacial and bifacial photovoltaic (PV) modules was decreasing in 2018 when this study was initially proposed, making bifacial modules an attractive offering. However, there were limited field studies available that could be considered utility-scale representative, and PV module manufacturers were stating large ranges for the expected energy yield improvements of bifacial technology. This generated uncertainties regarding bifacial performance gains and the actual LCOE (levelized cost of energy). There was also relatively low confidence in the industry’s energy modeling tools’ ability to accurately predict bifacial energy yield, meaning that early adopters of bifacial modules were not able to fully account for the increased energy yield in their energy and financial models. The intent of this project was to provide the solar industry with greater certainty on the energy yield gains of bifacial modules and provide higher confidence in the ability for different energy modeling software to model bifacial PV site performance. Achieving this would allow for bifacial technology to become bankable (i.e. accepted by Independent Engineers, site financiers and other PV site stakeholders), offering a step change in PV site performance that had not been seen since the widespread adoption of the single-axis tracker. Over the course of this study other economic factors including a significant tariff exemption for bifacial modules resulted in accelerated adoption of bifacial technology throughout the US utility scale solar segment. The number of early adopters increased rapidly, and with many investors accepting this module choice the question of bifacial bankability was seemingly answered faster than expected. However, PVEL’s study has still achieved significant accomplishments in demonstrating the accuracy of bifacial modeling across three different software platforms. The results of this study have shown that the mean biased error (MBE) between the field data and the predicted values from all three software platforms were aligned with a maximum MBE of 1.3% and a minimum MBE of -1.8%.

14 SOLAR ENERGY↗

Viewfactor and Raytracing for AgriPV Modeling

View factor models are used in due diligence software to calculate rear irradiance for bifacial modules. An intermediate step in this calculation is the irradiance at the ground level, which can be leveraged for evaluating the Photo Active Radiation available for crops in Agrivoltaic setups. This paper presents the metrics and modifications to the model for ground irradiance study with the view factor approach, compares it to the raytracing method, and validates it with field measurements of ground irradiance. It is found that for the clearances, row-to-row setups, and tilts studied, the view factor method matches with raytracing results within 2% MBD. The comparison is performed for the nine most common agriPV configurations using high-performance computing and the NSRDB database for the whole US, with results and data made available open-source on the InSPIRE AgriPV website.

AgriPV↗

Experimental Study of a Novel Semi-Transparent Bi-Facial Agri-Voltaic System: Initial Results

Solar (photovoltaic)-agriculture practices have experienced notable progress across several countries over the past few decades, creating a field widely known as agrivoltaics. This co-location practice has proved effective for certain crops that prefer partial shade like tomatoes, lettuce, and peppers, although it is less effective for crops needing more sun. Availability of natural sunlight with a specific spectral signature is necessary for agricultural practices as many plant species require an optimum-level and quality of sunlight for photosynthesis. The goal of this work is to model and validate ground-level irradiance for a semi-transparent bifacial solar collector equipped with a custom spectral reflector. By introducing intentional gaps between opaque solar cells, we hope to allow 50% of visible light to pass through the module. Additionally, this technology could reduce the soil temperature by 2° C, improve moisture retention, and enhance yield by providing partial solar radiation. In our model, we can calculate the total solar irradiance for the plants as a function of the cell distribution, material absorption, height of the panel and time of year with a spatial resolution of 0.0625 m2. When comparing theoretical estimates to experimental measurements of photosynthetically active radiation (PAR), we find a discrepancy of 32-48%, and a discrepancy of 19-38% after correction with TMY data.

McGraw, Duncan (ORCID:0000000256388118)↗

Reducing Uncertainty of Fielded Photovoltaic Performance (Final Technical Report)

Improved analysis and reporting of photovoltaic (PV) field performance increases the certainty of owners and financiers that systems will perform as expected. Advanced module technologies (e.g., PERC, HJT, and bifacial) introduce new degradation mechanisms and performance characteristics. The FY19-21 Reducing Uncertainty project leveraged data from the ever-increasing PV fleet to develop models and understanding of the field performance of existing and new technologies. Specifically, we accomplished: report on field performance and degradation rates for high-efficiency silicon (HJT, PERC, IBC) and more conventional technologies; developed automated analysis techniques to quantify system performance (performance ratio, energy yield) and production shortfalls (soiling, degradation, availability); refined the RdTools software toolkit to bring standard, validated analysis techniques to bear on third-party data; analyzed and reported on large datasets including Treasury data and Lawrence Berkeley National Laboratory's Utility-Scale dataset to expand the high-quality degradation-rate histogram published previously; worked with industry partners and the DuraMAT data hub to enable private parties to share and aggregate PV production data anonymously, leveraging cloud-based data analysis infrastructure and publishing on US fleet-scale performance comprising over 7GW of operating systems. (https://www.nrel.gov/pv/fleet-performance-data-initiative.html). Through our industry collaborations we have engaged in NDA-covered data transfer with twelve PV fleet owners as of January 2022, with more agreements in negotiation. Our scalable cloud-based time series database contains over 30 billion rows (20TB) of PV time series data, representing over 1700 commercial and utility-scale systems, and over 7.2 GW of DC capacity (Fig 1). Initial field performance results have been distributed in several public reports. Because our fleet composition and data quality methods are continually improving, annual updates to these results are published to our PV Fleet webpage [ https://www.nrel.gov/pv/fleet-performance-data-initiative.html ] and DuraMAT data hub [DOI: 10.21948/1842958]. Another existing dissemination channel used for observed soiling losses is a map we maintain for soiling losses. Additional products developed include a report detailing fleet-wide performance index, availability, startup loss and snow loss factors, a detailed report on the 1603 grant dataset comprising over 100,000 PV systems with failure and performance details and a utility-scale report coauthored with LBNL on 31 GW of system performance.

14 SOLAR ENERGY↗