Publications

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The key objectives of this project were to increase meaningful stakeholder engagement in photovoltaic performance modeling and reliability areas. We did this by hosting six workshop over the past three years, giving conference and workshop presentations and contributing to technical standards committees. Our efforts have made positive contributions by increasing the sharing of information and best practices and by creating and sustaining a technical community in PV Performance Modeling. This community has worked together over the past three years and has improved its practice and decreased performance modeling uncertainties.

This project has three main objectives: (1) to field and collect performance data from bifacial PV systems and share this information with the stakeholder community; (2) to develop and validate bifacial performance models and deployment guides that will allow users to accurately predict and assess the use of bifacial PV as compared with monofacial technologies and (3) to help develop international power rating standards for bifacial PV modules.

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Started in 2016, the PV Lifetime Project is measuring PV module and system degradation profiles over time with the aim of distinguishing different module types and technology. Outdoor energy monitoring in different climates is supplemented with regular testing under repeatable test conditions indoors. The focus is on the PV module, as well as other hardware components (junction boxes, bypass diodes, and module-level electronics) attached to it. Hardware is installed at Sandia National Laboratories in New Mexico, at the National Renewable Energy Laboratory in Colorado, and at the University of Central Florida. The systems are continuously monitored for DC current and voltage, as well as periodic I-V curves at the string level. In the future, once degradation trends have been identified with more certainty, results will be made available to the public online. This data is expected to enable an increase in the accuracy and precision of degradation profiles used in yield assessments that support investments made in new PV plants. Current practice is to assume that degradation is constant over the life of the system. Forthcoming results in the next few years will help to determine whether this assumption is still appropriate.

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In this paper, we present the effect of installation parameters (tilt angle, height above ground, and albedo) on the bifacial gain and energy yield of three south-facing photovoltaic (PV) system configurations: a single module, a row of five modules, and five rows of five modules utilizing RADIANCE-based ray tracing model. We show that height and albedo have a direct impact on the performance of bifacial systems. However, the impact of the tilt angle is more complicated. Seasonal optimum tilt angles are dependent on parameters such as height, albedo, size of the system, weather conditions, and time of the year. For a single bifacial module installed in Albuquerque, NM, USA (35 °N) with a reasonable clearance (∼1 m) from the ground, the seasonal optimum tilt angle is lowest (∼5°) for the summer solstice and highest (∼65°) for the winter solstice. For larger systems, seasonal optimum tilt angles are usually higher and can be up to 20° greater than that for a single module system. Annual simulations also indicate that for larger fixed-tilt systems installed on a highly reflective ground (such as snow or a white roofing material with an albedo of ∼81%), the optimum tilt angle is higher than the optimum angle of the smaller size systems. We also show that modules in larger scale systems generate lower energy due to horizon blocking and large shadowing area cast by the modules on the ground. For albedo of 21%, the center module in a large array generates up to 7% less energy than a single bifacial module. To validate our model, we utilize measured data from Sandia National Laboratories' fixed-tilt bifacial PV testbed and compare it with our simulations.

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The U.S. DOE Regional Test Center for Solar Technologies program was established to validate photovoltaic (PV) technologies installed in a range of different climates. The program is funded by the Energy Department's SunShot Initiative. The initiative seeks to make solar energy cost competitive with other forms of electricity by the end of the decade. Sandia National Laboratory currently manages four different sites across the country. The National Renewable Energy Laboratory manages a fifth site in Colorado. The entire PV portfolio currently includes 20 industry partners and almost 500 kW of installed systems. The program follows a defined process that outlines tasks, milestones, agreements, and deliverables. The process is broken out into four main parts: 1) planning and design, 2) installation, 3) operations, and 4) decommissioning. This operations manual defines the various elements of each part.

This report provides a preliminary (three month) analysis for the SolarWorld system installed at the New Mexico Regional Test Center (RTC.) The 8.7kW, four-string system consists of four module types): bifacial, mono-crystalline, mono-crystalline glass-glass and polycrystalline. Overall, the SolarWorld system has performed well to date: most strings closely match their specification-sheet module temperature coefficients and Sandia 's f lash tests show that Pmax values are well within expectations. Although the polycrystalline modules underperformed, the results may be a function of light exposure, as well as mismatch within the string, and not a production flaw. The instantaneous bifacial gains for SolarWorld 's Bisun modules were modest but it should be noted that the RTC racking is not optimized for bifacial modules, nor is albedo optimized at the site. Additional analysis, not only of the SolarWorld installation in New Mexico but of the SolarWorld installations at the Vermont and Florida RTCs will be provide much more information regarding the comparative performance of the four module types.

A 9.6 kW test array of Prism bifacial modules and reference monofacial modules installed in February 2016 at the New Mexico Regional Test Center has produced one year of performance data. The data reveal that the Prism modules are out-performing the monofacial modules, with bifacial gains in energy over the twelve-month period ranging from 17% to 132%, depending on the orientation and ground albedo. These measured bifacial gains were found to be in good agreement with modeled bifacial gains using equations previously published by Prism Solar. The most dramatic increase in performance was seen among the vertically mounted, west-facing modules, where the bifacial modules produced more than double the energy of monofacial modules in the same orientation. Because peak energy generation (mid- morning and mid-afternoon) for these bifacial modules may best match load on the electric grid, the west-facing orientation may be more economically desirable than traditional south-facing module orientations (which peak at solar noon).

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The US Department of Energy’s Regional Test Center (RTC) program provides outdoor validation and bankability data for innovative solar technologies at five sites across the US representing a range of climate conditions. Data helps get new technologies to market faster and improves US industry competitiveness. Managed by Sandia National Laboratories and the National Renewable Energy Laboratory (NREL), the RTC program partners with US manufacturers of photovoltaic (PV) technologies, including modules, inverters, and balance-of-system equipment. The study is collaborative, with manufacturers (also known as RTC industry partners) and the national labs working together on a system design and validation strategy that meets a clearly defined set of performance and reliability objectives.

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In 2014, the IEA PVPS Task 13 added the PVPMC as a formal activity to its technical work plan for 2014-2017. The goal of this activity is to expand the reach of the PVPMC to a broader international audience and help to reduce PV performance modeling uncertainties worldwide. One of the main deliverables of this activity is to host one or more PVPMC workshops outside the US to foster more international participation within this collaborative group. This report reviews the results of the first in a series of these joint IEA PVPS Task 13/PVPMC workshops. The 4th PV Performance Modeling Collaborative Workshop was held in Cologne, Germany at the headquarters of TÜV Rheinland on October 22-23, 2015.

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We describe and compare two methods for modeling irradiance on the back surface of rack-mounted bifacial PV modules: view factor models and ray-tracing simulations. For each method we formulate one or more models and compare each model with irradiance measurements and short circuit current for a bifacial module mounted a fixed tilt rack with three other similarly sized modules. Our analysis illustrates the computational requirements of the different methods and provides insight into their practical applications. We find a level of consistency among the models which indicates that consistent models may be obtained by parameter calibrations.

PVLIB is a set of open source modeling functions that allow users to simulate most aspects of PV system performance. The functions, in Matlab and Python, are freely available under a BSD 3 clause open source license. The Matlab version is maintained by Sandia and is available on the PV Performance Modeling Collaborative (PVPMC) website (pvpmc.sandia.gov). The Python version is available on GitHub with packages easily installable through conda and pip. New functions were released on the Matlab version 1.3 in January 2016 and are actively being ported to Python.

Photovoltaic system monitoring can generate vast amounts of data. Analytical methods are required to post-process this data into useful information. Pecos is open source software designed to address this need. The software was developed at Sandia National Laboratories and is compatible with performance models in PVLIB-Python. The software can be used to automatically run a series of quality control tests and generate reports which include performance metrics, test results, and graphics.

Current-voltage (I-V) curve traces of photovoltaic (PV) systems can provide detailed information for diagnosing fault conditions. The present work implemented an in situ, automatic I-V curve tracer system coupled with Support Vector Machine and a Gaussian Process algorithms to classify and estimate abnormal and normal PV performance. The approach successfully identified normal and fault conditions. In addition, the Gaussian Process regression algorithm was used to estimate ideal I-V curves based on a given irradiance and temperature condition. The estimation results were then used to calculate the lost power due to the fault condition.

Monitoring of photovoltaic (PV) systems can maintain efficient operations. However, extensive monitoring of large quantities of data can be a cumbersome process. The present work introduces a simple, inexpensive, yet effective data monitoring strategy for detecting faults and determining lost revenues automatically. This was achieved through the deployment of Raspberry Pi (RPI) device at a PV system's combiner box. The RPI was programmed to collect PV data through Modbus communications, and store the data locally in a MySQL database. Then, using a Gaussian Process Regression algorithm the RPI device was able to accurately estimate string level current, voltage, and power values. The device could also detect system faults using a Support Vector Novelty Detection algorithm. Finally, the RPI was programmed to output the potential lost revenue caused by the abnormal condition. The system analytics information was then displayed on a user interface. The interface could be accessed by operations personal to direct maintenance activity so that critical issues can be solved quickly.

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This report provides performance data and analysis for two Stion copper indium gallium selenide (CIGS) module types, one framed, the other frameless, and installed at the New Mexico, Florida and Vermont RTCs. Sandia looked at data from both module types and compared the latter with data from an adjacent monocrystalline baseline array at each RTC. The results indicate that the Stion modules are slightly outperforming their rated power, with efficiency values above 100% of rated power, at 25degC cell temperatures. In addition, Sandia sees no significant performance differences between module types, which is expected because the modules differ only in their framing. In contrast to the baseline systems, the Stion strings showed increasing efficiency with increasing irradiance, with the greatest increase between zero and 400 Wm -2 but still noticeable increases at 1000 Wm -2 . Although baseline data availability in Vermont was spotty and therefore comparative trends are difficult to discern, the Stion modules there may offer snow- shedding advantages over monocrystalline-silicon modules but these findings are preliminary.

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To address the lack of knowledge of local solar variability, we have developed and deployed a low-cost solar variability datalogger (SVD). While most currently used solar irradiance sensors are expensive pyranometers with high accuracy (relevant for annual energy estimates), low-cost sensors display similar precision (relevant for solar variability) as high-cost pyranometers, even if they are not as accurate. In this work, we present evaluation of various low-cost irradiance sensor types, describe the SVD, and present validation and comparison of the SVD collected data. The low cost and ease of use of the SVD will enable a greater understanding of local solar variability, which will reduce developer and utility uncertainty about the impact of solar photovoltaic (PV) installations and thus will encourage greater penetrations of solar energy.

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A 9.6 kW test array of Prism bifacial modules and reference monofacial modules installed in February 2016 at the New Mexico Regional Test Center has produced six months of performance data. The data reveal that the Prism modules are out-performing the monofacial modules, with bifacial gains in energy over the six-month period ranging from 18% to 136%, depending on the orientation and ground albedo. These measured bifacial gains were found to be in good agreement with modeled bifacial gains using equations previously published by Prism. The most dramatic increase in performance was seen among the vertically tilted, west-facing modules, where the bifacial modules produced more than double the energy of monofacial modules and more energy than monofacial modules at any orientation. Because peak energy generation (mid-morning and mid-afternoon) for these bifacial modules may best match load on the electric grid, the west-facing orientation may be more economically desirable than traditional south-facing module orientations (which peak at solar noon).

The Regional Test Centers are a group of several sites around the US for testing photovoltaic systems and components related to photovoltaic systems. The RTCs are managed by Sandia National Laboratories. The data collected by the RTCs must be transmitted to Sandia for storage, analysis, and reporting. This document describes the methods that transfer the data between remote sites and Sandia as well as data movement within Sandia’s network. The methods described are in force as of September, 2016.

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Advances in sensor technology have rapidly increased our ability to monitor natural and human-made physical systems. In many cases, it is critical to process the resulting large volumes of data on a regular schedule and alert system operators when the system has changed. Automated quality control and performance monitoring can allow system operators to quickly detect performance issues. Pecos is an open source python package designed to address this need. Pecos includes built-in functionality to monitor performance of time series data. The software can be used to automatically run a series of quality control tests and generate customized reports which include performance metrics, test results, and graphics. The software was developed specifically for solar photovoltaic system monitoring, and is intended to be used by industry and the research community. The software can easily be customized for other applications. The following Pecos documentation includes installation instructions and examples, description of software features, and software license. It is assumed that the reader is familiar with the Python Programming Language. References are included for additional background on software components.

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To address the lack of knowledge of local solar variability, we have developed, deployed, and demonstrated the value of data collected from a low-cost solar variability sensor. While most currently used solar irradiance sensors are expensive pyranometers with high accuracy (relevant for annual energy estimates), low-cost sensors display similar precision (relevant for solar variability) as high-cost pyranometers, even if they are not as accurate. In this work, we list variability sensor requirements, describe testing of various low-cost sensor components, present a validation of an alpha prototype, and show how the variability sensor collected data can be used for grid integration studies. The variability sensor will enable a greater understanding of local solar variability, which will reduce developer and utility uncertainty about the impact of solar photovoltaic installations and thus will encourage greater penetrations of solar energy.

To address the lack of knowledge of local solar variability, we have developed, deployed, and demonstrated the value of data collected from a low-cost solar variability sensor. While most currently used solar irradiance sensors are expensive pyranometers with high accuracy (relevant for annual energy estimates), low-cost sensors display similar precision (relevant for solar variability) as high-cost pyranometers, even if they are not as accurate. In this work, we list variability sensor requirements, describe testing of various low-cost sensor components, present a validation of an alpha prototype, and show how the variability sensor collected data can be used for grid integration studies. The variability sensor will enable a greater understanding of local solar variability, which will reduce developer and utility uncertainty about the impact of solar photovoltaic installations and thus will encourage greater penetrations of solar energy.

The current state of PV module monitoring is in need of improvements to better detect, diagnose, and locate abnormal module conditions. Detection of common abnormalities is difficult with current methods. The value of optimal system operation is a quantifiable benefit, and cost-effective monitoring systems will continue to evolve for this reason. Sandia National Laboratories performed a practicality and monitoring investigation on a testbed of 15 in-situ module-level I-V curve tracers. Shading and series resistance tests were performed and examples of using I-V curve interpretation and the Loss Factors Model parameters for detection of each is presented.

PV project investments need comprehensive plant monitoring data in order to validate performance and to fulfil expectations. Algorithms from PV-LIB and Loss Factors Model are being combined to quantify their prediction improvements at Gantner Instruments' Outdoor Test facility at Tempe AZ on multiple Tier 1 technologies. The validation of measured vs. predicted long term performance will be demonstrated to quantify the potential of IV scan monitoring. This will give recommendations on what parameters and methods should be used by investors, test labs, and module producers.