INL’s Human Reliability Analysis Data Collection Based on the SHEEP Framework
This presentation introduces INL's human reliability analysis data collection studies.
99 GENERAL AND MISCELLANEOUS↗
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Combined effects of horizontal and vertical resolution on reliable turbulence prediction at tidal energy sites: A systematic study in the Salish Sea, WA
Predicting turbulence characteristics with coastal ocean models is essential for tidal energy converter deployment. While large eddy simulation provides a detailed turbulence representation, computational limitations restrict its use to smaller domains. We systematically evaluate whether well-configured coastal models can provide reliable turbulence prediction through progressive refinement of 3D model representation. We implemented four model configurations (Levels 1–4) using terrain-following coordinates, isolating the impacts of horizontal resolution, vertical resolution, and layer distribution. We validated all configurations against field measurements from the Salish Sea, WA. Results show that tidal current velocity predictions remain unchanged regarding model configuration, but turbulence properties are sensitive to resolution refinement. Increasing vertical resolution alone proved insufficient; even with vertical sigma-levels rising from 11 to 41, significant underprediction persisted until finer horizontal resolution better captured bathymetric variations. The Level 4 configuration, incorporating geometric sigma-levels distribution, achieved turbulence prediction skill scores exceeding 0.90. Turbulence closure comparison revealed Mellor- Yamada 2.5 outperformed k-epsilon in TKE prediction (skill scores 0.84–0.94 versus 0.72–0.81) due to better boundary layer parameterization. This study shows that well-configured coastal models effectively bridge the gap between simplified tools and costly high-fidelity modeling, offering the tidal energy industry practical and cost-effective turbulence data at commercially relevant scales.
Marine Energy↗
Reliability-Informed Life-Cycle Warranty Cost Analysis: A Case Study on a Transmission in Agricultural Equipment
In agricultural and industrial equipment, both new and remanufactured systems are often available for warranty coverage. In such cases, it may be challenging for equipment manufacturers to properly trade-off between the system reliability and the cost associated with a replacement option (e.g., replace with a new or remanufactured system). To address this problem, we present a reliability-informed life-cycle warranty cost (LCWC) analysis framework that enables equipment manufacturers to evaluate different warranty policies. These warranty policies differ in whether a new or remanufactured system is used for replacement in the case of product failure. The novelty of this LCWC analysis framework lies in its ability to incorporate real-world field reliability data into warranty policy assessment using probabilistic warranty cost models that consider multiple life cycles. First, the reliability functions for the new and remanufactured systems are built as the time-to-failure distributions that provide the best-fit to the field reliability data. Then, these reliability functions and their corresponding warranty policies are used to build the LCWC models according to the specific warranty terms. Finally, Monte Carlo simulation is used to propagate the time-to-failure uncertainty of each system, modeled by its reliability function, through each LCWC model to produce a probability distribution of the LCWC. The effectiveness of the proposed reliability-informed LCWC analysis framework is demonstrated with a real-world case study on a transmission used in some agricultural equipment.
agricultural equipment↗
Study of Storage Requirements and Costs for Shaping Renewables and Nuclear Energy (FY23 Summary Report)
To decarbonize electricity generation primarily by using wind and solar resources, it is likely that additional low (or zero) carbon dioxide (CO 2 ) alternatives will be required for ensuring a reliable electricity supply. This study extends the work and modeling framework that we consolidated in FY 2022, by assessing the zero-CO 2 pathways of a Texas power system in which the availability of variable renewable energy (VRE), nuclear energy, and energy storage types (i.e., battery and thermal energy storage [TES]) are considered the sole resources available for expansion. Using the Risk Analysis Virtual Environment (RAVEN) and Holistic Energy Resource Optimization Network (HERON) frameworks, this study investigates the market of two nuclear energy technologies (i.e., large light-water reactors [LWRs] and LWR-type small modular reactors [SMRs]) under two plausible storage coupling scenarios (i.e., electric coupling and direct thermal coupling). We also explore optimal environments for nuclear energy deployment, and identify key performance characteristics that make nuclear energy economically viable in areas with significant intermittent energy sources. Our analysis encompasses 24 cases. We model the least-cost grid systems, highlight the potential for a coupled LWR-TES approach, and utilize SMRs to achieve deep decarbonization in an affordable, technically feasible manner. We also examine seasonal variations in balancing electricity supply and demand, and account for varying performance, costs, and grid constraints. Overall, the findings of our modeling afford valuable insights for supporting future technology investment decisions in the energy sector.
25 ENERGY STORAGE↗
Achieving an 80% carbon-free electricity system in China by 2035
Dramatic reductions in solar, wind, and battery storage costs create new opportunities to reduce emissions and costs in China’s electricity sector, beyond current policy goals. This study examines the cost, reliability, emissions, public health, and employment implications of increasing the share of non-fossil fuel (“carbon free”) electricity generation in China to 80% by 2035. The analysis uses state-of-the-art modeling with high resolution load, wind, and solar inputs. The study finds that achieving an 80% carbon free electricity system in China by 2035 could reduce wholesale electricity costs, relative to a current policy baseline, while maintaining high levels of reliability, reducing deaths from air pollution, and increasing employment. In our 80% scenario, wind and solar generation capacity reach 3 TW and battery storage capacity reaches 0.4 TW by 2035, implying a rapid scale up in these resources that will require changes in policy targets, markets and regulation, and land use policies.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Independent test results: comparing and quantifying bifacial reliability and performance
This conference presentation analyzes early outdoor performance results from PVEL's DOE-funded bifacial study as well as indoor reliability test results for bifacial modules, including manufacturer-level comparisons across different albedos and times of day.
Meydbray, Jenya↗
Reduced-Order Models of Static Power Grids based on Spectral Clustering
For large-scale interconnected power systems that cover large geographical areas, certain electrical studies are required so that appropriate decisions ensure system reliability and low cost. For such studies, it is often neither practical nor necessary to model in detail the entire power system, which is increasingly complex due to a more diverse range of grid assets to choose from in both short and long-term planning. The goal of this paper is to present a methodology to reduce the order of large-scale power networks based on spectral graph theory given that current methods for static network reduction are not scalable. A brief analysis of some spectral clustering properties to determine which graph Laplacian matrix should be used and why is included. The analysis shows that the utilization of the normalized graph Laplacian is more advantageous for clustering purposes. Techniques are proposed to approximate cost functions for the aggregated generators. This is done via linear regression. The reduced-order model obtained with the proposed methodology has an accuracy above 94% and solves the scalability issue commonly present in other reduction methods. If the utilization of the reduced-order model is either constrained to load levels above mid-peak demand, or cost functions of aggregated units are approximated via a piecewise quadratic approach, then the error distribution is in the order of 10^-3. .
Baquedano-Aguilar, Mario D.↗
India Power Sector Reliability Analysis [Slides]
The objective of the study is to assess pathways for India to achieve its 2070 power sector goals reliably and cost-effectively by considering various supply- and demand-side factors to support planning by national stakeholders including the Central Electricity Authority (CEA).
08 HYDROGEN↗
COVID-19 Lead Time: Evaluating the timeliness and reliability of reported COVID-19 cases and hospitalizations as leading indicators of hospitalizations and death in the US
The COVID-19 pandemic prompted a reliance on real-time data sources to understand the global spread and impact of the SARS-CoV-2 virus. Reported cases were presumed leading indicators for hospitalizations, while hospitalizations were considered predictive of deaths. However, studies have questioned the consistency of reported cases as leading indicators. This study systematically assesses the reliability of United States (US) reported cases and hospitalizations as leading indicators for hospitalizations and deaths respectively, examining the first 2.5 years of the pandemic (January 2020 - June 2022) across different phases of the pandemic and states. Using correlation analysis, population data, and forecasting accuracy measures, we investigate the temporal relationships and identify possible determinants of lead time variability. Notably, we found that the average lead time between reported cases and hospitalizations across US states is relatively short at 1.76 days, implying that reported cases might not be as effective a leading indicator for hospitalizations as previously believed. Populations with higher comorbidity burdens, such as proportion of smokers, are expected on average to have shorter lead times, possibly due to shorter time to hospitalization among these vulnerable populations.
59 BASIC BIOLOGICAL SCIENCES↗
Evaluating Probabilistic Deep Learning Methods for Uncertainty Quantification of Precipitation Bias Correction
Climate models often exhibit biases in their precipitation predictions, particularly underestimating high-intensity events and overestimating low precipitation. Deep learning approaches offer promising solutions, but their epistemic uncertainty associated with a deep learning–based bias correction method has not previously been quantified for reliable downstream climate impact studies. While methods for capturing the epistemic uncertainty in deep learning frameworks exist, there is currently no consensus on the best method. In this work, we compare three uncertainty quantification (UQ) methods—Deep Ensembles (DEns), Monte Carlo Dropout (MCD), and Flipout—by assessing the reliability of their uncertainty estimates using standard measures such as sharpness and calibration. These UQ methods are applied to an existing deep learning precipitation bias correction model known as UFNet: a coupled U-Net and fully connected neural network. The methods utilized to assess the models’ uncertainties are 1) calibration, which ensures that the expected probabilities of the model align with reality and 2) sharpness, which is a measure of the precision of the model’s probabilistic predictions. Of the three UQ methods evaluated, the DEns and MCD methods demonstrated the best-calibrated performance (expected calibration error of 0.36 and 0.35, respectively), compared to Flipout (0.58). In contrast, Flipout had the sharpest predictions and the highest metric performance in bias correcting precipitation—especially for higher-order moments such as kurtosis with a spatial correlation of 72% compared to 32% and 55% spatial correlation for DEns and MCD, respectively. Of the three UQ methods, MCD was found to be the most suitable method for UQ purposes based on its calibration, sharpness, and computational requirements.
Bayesian methods↗
Nonconvex Robust Optimization for the Design and Operation of Advanced Energy Systems Using PyROS
This work discusses recent advances of the two-stage robust optimization (RO) solver PyROS and applications to advanced energy systems optimization. To demonstrate the computational performance and reliability of PyROS, a study on a monoethanolamine (MEA)-based CO2 absorption flowsheet is presented. (Near-)robust feasible designs for CO2 absorption flowsheet at high carbon capture are obtained with the PyROS solver. The results demonstrate that the PyROS solver, including recent extensions to multi-stage RO settings, provides a reliable avenue to optimize the design and operation of advanced energy systems subject to various sources of parametric uncertainty.
Sherman, Jason↗
Quantum-Inspired Power System Reliability Assessment
To enable an in-depth study of power system operation and planning, the assessment of standard reliability indices is inevitable. The Monte Carlo Simulation (MCS) approach is a broadly used method in replacing the analytical methods in reliability indices assessment. The accuracy of MCS, however, highly depends on the sampling size, and hence, a complicated system with large number of components requires a large sampling size and daunting computational effort. To address this shortcoming, we, in this paper attempt to take advantage of potentials of the quantum computing (QC) for power system reliability assessment by realizing the following contributions: 1) an innovative quantum model designed for reliability assessment; 2) a quantum circuit that achieves the quadratic speed up compared to the classical MCS method; 3) an efficient quantum amplitude estimation (QAE) algorithm to accurately evaluate the reliability indices. The accuracy and efficacy of the quantum reliability method are extensively verified and demonstrated on both radial and mesh distribution systems.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Photonic Doppler Velocimetry for Valve Analysis and Surveillance
This study is an experimental evaluation to measure and surveil the velocity of stainless steel (Carpenter 455) pistons within a forged 21-6-9 stainless steel valve body. The velocity of the piston “stroke” will be identified using Photonic Doppler Velocimetry (PDV). The piston velocity will be the measured variable of interest. The outcome of this testing series provides further understanding to whether the extracted piston velocity can provide enough information to determine valve integrity. PDV has been identified as a diagnostic of interest as it can detect surface motion at high speeds with reliable accuracy. Through preliminary studies conducted with velocity interferometer system for any reflector (VISAR), the velocity of the piston is expected to stroke with a speed of 135 m/s. The PDV technique is much more reliable and repeatable than the VISAR technique because of its high-speed precision and accuracy, so it will not only validate the preliminary data, but also provide a testing technique that is capable of being used in future valve qualification efforts. The initial testing was completed using valves that were assembled per qualified procedures in a pristine condition. These initial tests were conducted to provide baseline data. This baseline data was then compared to data collected from valves where man-made defects were introduced. These defects are discussed in later sections. Lastly, actuators used to operate the valves were loaded with varied amounts of high explosives to identify if velocity is a function of the amount of explosive in an actuator. These parameters were adjusted to conclusively determine and identify if PDV can be used as a method to assess valve integrity. It was concluded through the testing discussed in this paper that in fact, PDV is capable of measuring the differences in piston velocities based on varied parameters. As far as understanding the PDV accuracy of the measured differences, this is ongoing and will have to be assessed in continued testing series.
42 ENGINEERING↗
Advanced HVAC Humidity Control for Hot-Humid Climates
During this project we develop and validate a cost-effective, integrated control solution to improve humidity control and comfort for energy-efficient homes in hot-humid climates. This study focuses on developing a strategy that is effective, field tested, and practical for builders to install with minimal disruption to standard practices. A successful solution would simplify the transition to high-performance humidity control and be the basis for design and installation guidance. By relying on the central system as a starting point, the strategy employed minimizes system complexity and cost for builders, while improving comfort and operating cost for homeowners. The solution strategy was to coordinate the cooling, dehumidification, and ventilation functions of central, ducted HVAC systems to better control indoor humidity, improve occupant thermal comfort, and capture energy savings. The primary strategic goals were to: (1) optimize dehumidification by the central air-conditioning system, particularly during part-load conditions, using conventional equipment with modified control settings and lower system airflows; (2) maximize ventilation during heating/cooling on-cycles, to “bank” and condition outdoor ventilation air, and minimize ventilation during off-cycles; (3) quantify the effectiveness and energy impact of the dehumidification and ventilation strategies, while identifying a metric that would be useful to evaluate latent effectiveness. For the test houses in our study, located in Richmond Hill, Georgia; Houston, Texas; and Monroe, Louisiana we observed: (1) the indoor humidity did not exceed 60% RH during the monitored cooling season for 99% of the time in Richmond Hill, 96% of the time in Houston, and 90% of the time in Monroe; (2) the dehumidification strategy improved the steady-state latent capacity of the HVAC system at design conditions by 16% to 49% at the Houston test house and by 28% to 71% at the Monroe test house, depending on which mode the system was operating in; and (3) the good results at the test houses were primarily due to the amount of time the air-conditioning system operated in ramping or dehumidification modes, or both, particularly during the early cooling season. This study demonstrates that air conditioners or heat pumps with a single-stage compressor can provide good humidity control without the need for a two-stage or variable-stage compressor system. The airflow and control settings for ramping and dehumidification modes are critical to control indoor humidity in hot-humid climates, particularly during part-load and shoulder season conditions. The dehumidification strategy used in this study did not jeopardize the mechanical reliability of the cooling equipment. The strategies used in this study are applicable across various equipment brands, models, and efficiency levels, and also applicable to a broad range of homes in hot-humid climates. Results will vary by specific equipment, location, and house configuration and construction.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Reliability-Informed Economic and Energy Evaluation for Design for Remanufacturing: A Case Study on a Hydraulic Manifold
Design for Remanufacturing (DfRem) is an attractive approach for sustainable product development. Evaluation of DfRem strategies, from both economic and environmental perspectives, at an early design stage can allow the designers to make informed decisions when choosing the best design option. Studying the long-term implications of a particular design scenario requires quantifying the benefits of remanufacturing for multiple life cycles while considering the reliability of the product. In addition to comparing designs on a one-to-one basis, we find that including reliability provides a different insight into comparing design strategies. We present a reliability-informed cost and energy analysis framework that accounts for product reliability for multiple remanufacturing cycles within a certain warranty policy. The variation of reuse rate over successive remanufacturing cycles is formulated using a branched power-law model which provides probabilistic scenarios of reusing or replacing with new units. To demonstrate the utility of this framework, we use the case study of a hydraulic manifold, which is a component of a transmission used in some agricultural equipment, and use real-world field reliability data to quantify the transmission’s reliability. Three design improvement changes are proposed for the manifold and we quantify the costs and energy consumption associated with each of the design changes for multiple remanufacturing cycles.
design for remanufacturing↗
Common Information Model for Electromagnetic Transients (CIM for EMT): CRADA 533 [Abstract only}
The control and protection functions of inverter-based resources (IBR) have raised concerns with bulk system reliability. Most of the current interest lies with solar photovoltaic generation but increasing amounts of storage would pose the same risks. Newer North American Electric Reliability Corporation (NERC) guidelines call for electromagnetic transient (EMT) studies of IBR and recommend that transmission operators collect distributed energy resource (DER) data to support such modeling. IEEE Standard P2800.1 is defining tests for model parameterization, so good model data should become available from inverter vendors. (EMT studies also apply to large power transformer reliability, and transformer vendors can provide EMT models.) Utilities don’t currently have the rest of the bulk system represented for EMT studies at large scale. An International Electrotechnical Commission (IEC) standard Common Information Model (CIM) provides a way of supporting these detailed models from physical asset data, e.g., conductors, towers, transformer data sheets, control block diagrams, while avoiding software vendor lock-in. CIM-for-EMT, with proposed schema extensions and open-source converters, provides a way to exchange EMT data between organizations and tools. This project leverages Office of Electricity (OE) funding of CIM-for-EMT code base through the GridAPPS-DTM project, and of GridPACKTM (parallelized transmission solver), for interoperability testing in CIM-for-EMT. The project also leverages partner PGSTech investments in EMTP® interoperability with CIM.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Nanomechanical and Fluorescence Characterizations of Weathered PV Module Encapsulation
Nanoindentation and fluorescence spectroscopy provide spatially resolved mechanical and chemical characterization for degradation of poly (ethylene-co-vinyl acetate) (EVA) encapsulants aged under accelerated weathering and in field deployed photovoltaic (PV) modules. For the accelerated weathering tests, two different EVA formulations in the glass/EVA/glass-laminated coupons were exposed to ultraviolet (UV) light for 180 days at three different temperatures (40, 60, and 80 °C) and UV intensities (0.4, 2.2, and 2.9 W/m2/nm at 340 nm). The fluorescence spectra and moduli were uniform through the cross section of both EVA formulations at the lowest temperature and UV intensity; however, under the highest temperature and UV intensity, the UV irradiated sides showed lower moduli and higher fluorescence compared to the opposite sides. For EVA obtained from PV modules deployed 27 years in Sacramento, CA, USA, changes in fluorescence intensity across the thickness were relatively greater than changes in modulus. On the other hand, for EVA obtained from the same type of module stored in the shed for 27 years, no fluorescence was observed, and a uniform modulus was seen through the EVA layer, but with substantially higher values than those obtained from the field-deployed modules. The different extent of changes between laboratory and field exposures suggests the importance of considering effects of temperature and UV intensity for designing a reliable accelerated testing. This study uniquely demonstrates the capability of microscale characterization using nanoindentation and fluorescence microscopy to monitor mechanical and chemical degradation of EVA encapsulants in PV modules.
14 SOLAR ENERGY↗
Studying Wind Loading on CSP Collectors to Improve Performance and Reliability
Electricity generation through Concentrating Solar-thermal Power (CSP) adds the advantage of thermal energy storage and heat production. CSP collector costs constitute nearly one-third of total plant costs. Managing wind loading, especially dynamic wind loading caused by the turbulent wind flow, is a significant design challenge. Traditional collector designs rely on wind tunnel experiments and numerical simulations, which do not fully capture dynamic effects. Here, we provide an overview of NREL's activities on wind loading on CSP collectors, aiming to improve their reliability and cost-efficiency. We present insights from field measurements at operational parabolic trough and power-tower CSP plants. Over two years, we measured turbulent wind fields and resulting structural loads in a parabolic trough plant, revealing wind, turbulence, and collector field interactions. Similar measurements are ongoing at the Crescent Dunes heliostat field. These campaigns show how atmospheric turbulence, wind direction, and collector orientation affect dynamic wind loading. Upwind collector structures can generate turbulent structures, causing fluctuating loads at downstream collectors, impacting fatigue lifetime and optical efficiency. Preliminary results indicate that interior collectors experience higher turning moments than those at the edges, suggesting higher drive wear. At Crescent Dunes, we use instrumented heliostats at the field's edge and interior to study the translation of turbulent wind to dynamic structural loads. The campaign is still ongoing, and we will present first findings. Another focus of our work is tying the experimental wind loading findings to optical performance of the collectors, using simulation tools. For example, we derive torsional errors of the parabolic troughs - the angular offset from the sun position along a collector row. This error is impacted by wind-induced mirror deformations. Future work will focus on continuing the measurement efforts and combining them with computational fluid dynamic simulations. By providing detailed field measurements and validated models, our efforts aim to enhance understanding of wind loading on CSP collectors, improving their structural integrity and optical performance.
CSP↗