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.
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.
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.
Holmgren, William F.; Andrews, Robert W.; Lorenzo, Antonio T.; Stein, Joshua
We describe improvements to the open source PVLIB-Python modeling package. PVLIB-Python provides most of the functionality of its parent PVLIB-MATLAB package and now follows standard Python design patterns and conventions, has improved unit test coverage, and is installable. PVLIBPython is hosted on GitHub.com and co-developed by GitHub contributors. We also describe a roadmap for the future of the PVLIB-Python package.
Sandia National Laboratories (Sandia) manages four of the five PV Regional Test Centers (RTCs). This report reviews accomplishments made by the four Sandia-managed RTCs during FY2015 (October 1, 2014 to September 30, 2015) as well as some programmatic improvements that apply to all five sites. The report is structured by Site first then by Partner within each site followed by the Current and Potential Partner summary table, the New Business Process, and finally the Plan for FY16 and beyond. Since no official SOPO was ever agreed to for FY15, this report does not include reporting on specific milestones and go/no-go decisions.
PV performance models are used to quantify the value of PV plants in a given location. They combine the performance characteristics of the system, the measured or predicted irradiance and weather at a site, and the system configuration and design into a prediction of the amount of energy that will be produced by a PV system. These predictions must be as accurate as possible in order for finance charges to be minimized. Higher accuracy equals lower project risk. The Increasing Prediction Accuracy project at Sandia focuses on quantifying and reducing uncertainties in PV system performance models.
The Characterizing Emerging Technologies project focuses on developing, improving and validating characterization methods for PV modules, inverters and embedded power electronics. Characterization methods and associated analysis techniques are at the heart of technology assessments and accurate component and system modeling. Outputs of the project include measurement and analysis procedures that industry can use to accurately model performance of PV system components, in order to better distinguish and understand the performance differences between competing products (module and inverters) and new component designs and technologies (e.g., new PV cell designs, inverter topologies, etc.).
