Publications

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To benchmark the current U.S. wind turbine fleet reliability performance and identify the major contributors to component-level failures and other downtime events, the Department of Energy funded the development of the Continuous Reliability Enhancement for Wind (CREW) database by Sandia National Laboratories. This report is the third annual Wind Plant Reliability Benchmark, to publically report on CREW findings for the wind industry. The CREW database uses both high resolution Supervisory Control and Data Acquisition (SCADA) data from operating plants and Strategic Power Systems ORAPWindª (Operational Reliability Analysis Program for Wind) data, which consist of downtime and reserve event records and daily summaries of various time categories for each turbine. Together, these data are used as inputs into CREWs reliability modeling. The results presented here include: the primary CREW Benchmark statistics (operational availability, utilization, capacity factor, mean time between events, and mean downtime); time accounting from an availability perspective; time accounting in terms of the combination of wind speed and generation levels; power curve analysis; and top system and component contributors to unavailability.

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This report presents a system dynamics based model of the supply-demand interactions between the US light-duty vehicle (LDV) fleet, its fuels, and the corresponding primary energy sources through the year 2050. An important capability of our model is the ability to conduct parametric analyses. Others have relied upon scenario-based analysis, where one discrete set of values is assigned to the input variables and used to generate one possible realization of the future. While these scenarios can be illustrative of dominant trends and tradeoffs under certain circumstances, changes in input values or assumptions can have a significant impact on results, especially when output metrics are associated with projections far into the future. This type of uncertainty can be addressed by using a parametric study to examine a range of values for the input variables, offering a richer source of data to an analyst.The parametric analysis featured here focuses on a trade space exploration, with emphasis on factors that influence the adoption rates of electric vehicles (EVs), the reduction of GHG emissions, and the reduction of petroleum consumption within the US LDV fleet. The underlying model emphasizes competition between 13 different types of powertrains, including conventional internal combustion engine (ICE) vehicles, flex-fuel vehicles (FFVs), conventional hybrids(HEVs), plug-in hybrids (PHEVs), and battery electric vehicles(BEVs).We find that many factors contribute to the adoption rates of EVs. These include the pace of technological development for the electric powertrain, battery performance, as well as the efficiency improvements in conventional vehicles. Policy initiatives can also have a dramatic impact on the degree of EV adoption. The consumer effective payback period, in particular, can significantly increase the market penetration rates if extended towards the vehicle lifetime.Widespread EV adoption can have noticeable impact on petroleum consumption and greenhouse gas(GHG) emission by the LDV fleet. However, EVs alone cannot drive compliance with the most aggressive GHG emission reduction targets, even as the current electricity source mix shifts away from coal and towards natural gas. Since ICEs will comprise the majority of the LDV fleet for up to forty years, conventional vehicle efficiency improvements have the greatest potential for reductions in LDV GHG emissions over this time.These findings seem robust even if global oil prices rise to two to three times current projections. Thus,investment in improving the internal combustion engine might be the cheapest, lowest risk avenue towards meeting ambitious GHG emission and petroleum consumption reduction targets out to 2050.3 Acknowledgment The authors would like to thank Dr. Andrew Lutz, Dr. Benjamin Wu, Prof. Joan Ogden and Dr. Christopher Yang for their suggestions over the course of this project. This work was funded by the Laboratory Directed Research and Development program at Sandia National Laboratories.

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This report addresses the general data requirements for reliability analysis of fielded wind turbines and other wind plant equipment. The report provides a rationale for why this data should be collected, a list of the data needed to support reliability and availability analysis, and specific data recommendations for a Computerized Maintenance Management System (CMMS) to support automated analysis. This data collection recommendations report was written by Sandia National Laboratories to address the general data requirements for reliability analysis of operating wind turbines. This report is intended to help develop a basic understanding of the data needed for reliability analysis from a Computerized Maintenance Management System (CMMS) and other data systems. The report provides a rationale for why this data should be collected, a list of the data needed to support reliability and availability analysis, and specific recommendations for a CMMS to support automated analysis. Though written for reliability analysis of wind turbines, much of the information is applicable to a wider variety of equipment and analysis and reporting needs. The 'Motivation' section of this report provides a rationale for collecting and analyzing field data for reliability analysis. The benefits of this type of effort can include increased energy delivered, decreased operating costs, enhanced preventive maintenance schedules, solutions to issues with the largest payback, and identification of early failure indicators.

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Operators interested in improving reliability should begin with a focus on the performance of the wind plant as a whole. To then understand the factors which drive individual turbine performance, which together comprise the plant performance, it is necessary to track a number of key indicators. Analysis of these key indicators can reveal the type, frequency, and cause of failures and will also identify their contributions to overall plant performance. The ideal approach to using data to drive good decisions includes first determining which critical decisions can be based on data. When those required decisions are understood, then the analysis required to inform those decisions can be identified, and finally the data to be collected in support of those analyses can be determined. Once equipped with high-quality data and analysis capabilities, the key steps to data-based decision making for reliability improvements are to isolate possible improvements, select the improvements with largest return on investment (ROI), implement the selected improvements, and finally to track their impact.

This report addresses the general data requirements for reliability analysis of fielded wind turbines and other wind plant equipment. The report provides a list of the data needed to support reliability and availability analysis, and gives specific recommendations for a Computerized Maintenance Management System (CMMS) to support automated analysis. This data collection recommendations report was written by Sandia National Laboratories to address the general data requirements for reliability analysis of fielded wind turbines. This report is intended to help the reader develop a basic understanding of what data are needed from a Computerized Maintenance Management System (CMMS) and other data systems, for reliability analysis. The report provides: (1) a list of the data needed to support reliability and availability analysis; and (2) specific recommendations for a CMMS to support automated analysis. Though written for reliability analysis of wind turbines, much of the information is applicable to a wider variety of equipment and a wider variety of analysis and reporting needs.

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This report documents the status of the Sandia National Laboratories' Wind Plant Reliability Database. Included in this report are updates on the form and contents of the Database, which stems from a fivestep process of data partnerships, data definition and transfer, data formatting and normalization, analysis, and reporting. Selected observations are also reported.

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