Publications

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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.

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.).

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IEEE Standard 1547-2003 conformance of several interconnected microinverters was performed by Sandia National Laboratories (SNL) to determine if there were emergent adverse behaviors of co-located aggregated distributed energy resources. Experiments demonstrated the certification tests could be expanded for multi-manufacturer microinverter interoperability. Evaluations determined the microinverters' response to abnormal conditions in voltage and frequency, interruption in grid service, and cumulative power quality. No issues were identified to be caused by the interconnection of multiple devices.

The proper modeling of Photovoltaic(PV) systems is critical for their financing, design, and operation. PV LIB provides a flexible toolbox to perform advanced data analysis and research into the performance modeling and operations of PV assets, and this paper presents the extension of the PV LIB toolbox into the python programming language. PV LIB provides a common repository for the release of published modeling algorithms, and thus can also help to improve the quality and frequency of model validation and inter comparison studies. Overall, the goal of PV LIB is to accelerate the pace of innovation in the PV sector.

The Sandia Inverter Performance Test Protocol defined two possible weighted-average efficiency values for use in comparing inverter performance, of which one definition was selected by the California Energy Commission for use in their Buydown incentive program leading to widespread use in the photovoltaic inverter market. This paper discusses the derivation of the efficiency weights originally proposed, and investigates the potential for defining new weights in light of increased array-to-inverter (DC-to-AC) system rating ratios in modern PV systems.

We present a method for measuring the series resistance of the PV module, string, or array that does not require measuring a full IV curve or meteorological data. Our method relies only on measurements of open circuit voltage and maximum power voltage and current, which can be readily obtained using standard PV monitoring equipment; measured short circuit current is not required. We validate the technique by adding fixed resistors to a PV circuit and demonstrating that the method can predict the added resistance. Relative prediction accuracy appears highest for smaller changes in resistance, with a systematic underestimation at larger resistances. Series resistance is shown to vary with irradiance levels with random errors below 1.5% standard deviation.

The performance of photovoltaic systems must be monitored accurately to ensure profitable long-term operation. The most important signals to be measured—irradiance and temperature, as well as power, current and voltage on both DC and AC sides of the system—contain rapid fluctuations that are not observable by typical monitoring systems. Nevertheless these fluctuations can affect the accuracy of the data that are stored. This report closely examines the main signals in one operating PV system, which were recorded at 2000 samples per second. It analyzes the characteristics and causes of the rapid fluctuations that are found, such as line-frequency harmonics, perturbations from anti-islanding detection, MPPT searching action and others. The operation of PV monitoring systems is then simulated using a wide range of sampling intervals, archive intervals and filtering options to assess how these factors influence data accuracy. Finally several potential sources of error are discussed with real-world examples.

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