Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “power quality”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

KIPM Detector Characterization in QUIET

Kinetic Inductance Phonon‑Mediated (KIPM) detectors are superconducting microwave resonators on silicon that sense bursts of energy through tiny shifts in their resonance. In this work, we installed new devices in the QUIET facility's cryogenic test setup and used a network analyzer to drive them across a range of power levels. At each setting, we measured the transmitted signal and fit it to a simple notch‑filter model, allowing us to extract key performance metrics. And despite the increased noise at low power, our model consistently captured the resonance behavior. We observed that the resonant frequency stayed stable across most powers, the device s internal quality factor improved with stronger signals, and its coupling strength peaked mid‑range before leveling off. Overall, these findings confirm the detector s performance, laying groundwork for future work on energy calibration with pulsed optical sources.

Savitala, Akash [Washington U., Seattle; Fermilab]↗
DOE OSTI2020

Velocity Dispersions of Massive Quiescent Galaxies from Weak Lensing and Spectroscopy

We use MMT spectroscopy and deep Subaru Hyper Suprime-Cam (HSC) imaging to compare the spectroscopic central stellar velocity dispersion of quiescent galaxies with the effective dispersion of the dark matter halo derived from the stacked lensing signal. The spectroscopic survey (the Smithsonian Hectospec Lensing Survey) provides a sample of 4585 quiescent galaxy lenses with measured line-of-sight central stellar velocity dispersion ($\sigma_{\rm SHELS}$) that is more than 85\% complete for $R < 20.6$, $D_{n}4000> 1.5$ and $M_{\star} > 10^{9.5}{\rm M}_{\odot}$.The median redshift of the sample of lenses is 0.32. We measure the stacked lensing signal from the HSC deep imaging. The central stellar velocity dispersion is directly proportional to the velocity dispersion derived from the lensing $\sigma_{\rm Lens}$, $\sigma_{\rm Lens} = (1.05\pm0.15)\sigma_{\rm SHELS}+(-21.17\pm35.19)$. The independent spectroscopic and weak lensing velocity dispersions probe different scales, $\sim3$kpc and $\gtrsim$ 100 kpc, respectively, and strongly indicate that the observable central stellar velocity dispersion for quiescent galaxies is a good proxy for the velocity dispersion of the dark matter halo. We thus demonstrate the power of combining high-quality imaging and spectroscopy to shed light on the connection between galaxies and their dark matter halos.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Quality Guidelines for Energy Systems Studies: Cost Estimation Methodology for NETL Assessments of Power Plant Performance

This paper summarizes the methodology employed by the National Energy Technology Laboratory (NETL) in calculating power plant costs in its techno-economic studies, such as the Cost and Performance Baseline for Fossil Energy Systems series of reports. It also outlines the approach used to calculate the cost of electricity by which NETL evaluates electric power plants. These metrics and a clear understanding of the methodology used are essential in allowing different power plant technologies to be compared on a similar basis. These guidelines are tailored for power producing plants, although they can be applied to a variety of different revenue generating plants (e.g., coal to liquids, syngas generation, hydrogen).

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Tailoring Growth Interfaces of Virtual Substrates for Power Electronics

Power electronics materials are poised to play a critical role in fulfilling next generation energy needs, with up to 90% of future energy demand predicted to flow through power electronics at some point. AlxGa1-xN ranks high among candidate materials, having bipolar dopability, thermal and chemical stability and an ultra-wide bandgap. However, AlGaN growth is limited by a lack of lattice-matched substrates, ultimately stunting material quality at higher thicknesses needed for power electronics applications. Further, high power applications increasingly call for fully vertical device structures, necessitating a conductive substrate. Recently our group identified the (111) plane of TaC as a conductive surface lattice-matched to Al0.55Ga0.45N, taking inspiration from prior work of AlN and GaN binaries on carbide and boride substrates. In this talk we demonstrate the growth of (111)-oriented TaC by RF sputtering. We investigate the interface of TaC with sapphire and SiC substrates and identify means to suppress competing Ta2C nucleation in order to stabilize (111)-oriented TaC. Potential stacking sequences are identified with respect to crystal structure and observed twinning in the TaC films. We next assess structural changes and film recrystallization that results from face-to-face annealing of TaC thin films at high temperatures above 1500 degrees C. Changes to grain structure and domain size are assessed by x-ray diffraction and surface morphology is explored using atomic force microscopy. Figure 1 shows significant improvements to in- and out-of-plane strain following annealing along with the formation of terraced step edges at the film surface. Strain as a function of material composition and thickness is considered, as this may play a major role in future nucleation of AlGaN layers. (1) R. J. in a face-to-face configuration, as illustrated in the schematic at left. Kaplar et al 2017, ECS J. Solid State Sci. Technol. 6 Q3061; (2) D. M. Roberts et al 2022, https://arxiv.org/abs/2208.11769; (3) T. Aizawa et al 2008, J Crys Growth 310, 1 22; (4) R. Liu et al 2002, Appl. Phys. Lett. 81, 3182-3184.

ENGINEERING↗
DOE OSTI2020

Growth and characterization of homoepitaxial β-Ga 2 O 3 layers

ß-Ga 2 O 3 is a next-generation ultra-wide bandgap semiconductor (E g = 4.8 eV to 4.9 eV) that can be homoepitaxially grown on commercial substrates, enabling next-generation power electronic devices among other important applications. Analyzing the quality of deposited homoepitaxial layers used in such devices is challenging, in part due to the large probing depth in traditional x-ray diffraction (XRD) and also due to the surface-sensitive nature of atomic force microscopy (AFM). Here, a combination of evanescent grazing-incidence skew asymmetric XRD and AFM are investigated as an approach to effectively characterize the quality of homoepitaxial ß-Ga 2 O 3 layers grown by molecular beam epitaxy at a variety of Ga/O flux ratios. Accounting for both structure and morphology, optimal films are achieved at a Ga/O ratio of ~1.15, a conclusion that would not be possible to achieve by either XRD or AFM methods alone. Finally, fabricated Schottky barrier diodes with thicker homoepitaxial layers are characterized by J-V and C-V measurements, revealing an unintentional doping density of 4.3×10 16 cm -3 - 2×10 17 cm -3 in the epilayer. These results demonstrate the importance of complementary measurement methods for improving the quality of the ß-Ga 2 O 3 homoepitaxial layers used in power electronic and other devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Equipment List Comparing Balance of Plant Containing a Heat Pump against a Reference Electricity Generating Plant

Approximately two-thirds of U.S. energy consumption in the industrial and transportation sectors relies on fossil fuels. These sectors require high-quality heat, i.e., thermal energy at very high temperatures, for molecular transformation processes. The Integrated Energy Systems (IES) program aims to assess the economic potential of utilizing nuclear-grade heat from Advanced Reactors (ARs) to meet the high-quality heat demands. By having industrial processes (IPs) supplied with nuclear-generated heat, manufacturers could benefit from more stable and potentially lower energy costs, reducing reliance on volatile fossil fuel markets. The main outcome of the FY24 research was the thermodynamic assessment and gap analysis of steam generation for IP applications. The study completed in June 2024 demonstrated that the required steam temperatures could be achieved by integrating a heat pump into an AR power plant. After identifying the thermal demands of target IPs, multiple balance of plant configurations for the Xe-100 reactor by X-energy, incorporating a heat pump, were analyzed. Their technical feasibilities were assessed, including the design of suitable axial compressors for these applications. Comparative performance analysis showed that thermal efficiency alone is insufficient to evaluate system suitability. To address this, a new indicator (heat factor) was introduced in the report released in September 2024 to quantify the low-quality thermal power needed to produce one unit of high-quality heat for the IP. Results showed that integrating heat pumps into Rankine cycles enables higher steam temperatures, though at the expense of increased thermal energy input. This report builds upon and completes the foundational work previously undertaken. It focuses on the design of two Balance of Plant (BOP) configurations, both based on Rankine energy conversion cycles: “Case 1”, which involves electricity generation only, and “Case 2”, which combines electricity and high-temperature heat generation for industrial use. For each configuration, a comprehensive equipment list was developed, detailing all major components such as turbines, compressors, heat exchangers, pumps, and control systems. These lists will serve as the basis for future comparative cost analyses, with the goal of assessing the number and type of components required to integrate a heat pump into the Rankine cycle and to establish a heat transport system capable of delivering thermal energy from the nuclear plant to an industrial facility. Using the constitutive equations presented in the June 2024 milestone, the operating conditions of all BOP components for both “Case 1” and “Case 2” were evaluated. These parameters—such as temperature, pressure, mass flow rate, and steam quality—served as the basis for calculating the associated thermal and mechanical power flows. The net power required from the heat pump to raise the steam temperature to the target level was also determined. The thermodynamic performance of the configuration was then assessed using the heat factor metric. The key outcome of this analysis is a comparative table that presents the equipment that was used in the “Case 1” and “Case 2” configurations. This study offers a preliminary comparison of the two designs, providing insight into the impact of integrating a heat pump in terms of component requirements and thermal efficiency. This equipment list, together with the evaluated operating conditions, also serves as a foundation for the economic analyses scheduled for the current fiscal year.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Tailoring Growth Interfaces of Virtual Substrates for Power Electronics

Power electronics materials are poised to play a critical role in fulfilling next generation energy needs, with up to 90% of future energy demand predicted to flow through power electronics at some point.[1] Among a number of candidate materials, AlxGa1-xN is the strongest, having bipolar dopability, thermal and chemical stability, an ultra-wide bandgap, and demonstrated experimental feasibility. However, AlGaN growth is limited by a lack of lattice-matched substrates, ultimately stunting material quality at higher thicknesses needed for power electronics applications. Further, high power applications increasingly call for fully vertical device structures, necessitating a conductive substrate. [1] Recently our group identified the (111) plane of TaC as a conductive surface lattice-matched to Al0.55Ga0.45N, taking inspiration from prior work of AlN and GaN binaries on carbide and boride substrates. [2,3,4] In this talk we demonstrate the growth of (111)-oriented TaC by RF sputtering. We investigate the interface of TaC with sapphire and SiC substrates and identify means to suppress competing Ta2C nucleation in order to stabilize (111)-oriented TaC. Potential stacking sequences are identified with respect to crystal structure and observed twinning in the TaC films. We next assess structural changes and film recrystallization that results from face-to-face annealing of TaC thin films at high temperatures above 1500 degrees Celsius. Changes to grain structure and domain size are assessed by x-ray diffraction and surface morphology is explored using atomic force microscopy. Figure 1 shows significant improvements to in- and out-of-plane strain following annealing along with the formation of terraced step edges at the film surface. Strain as a function of material composition and thickness is considered, as this may play a major role in future nucleation of AlGaN layers.

CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SU↗
DOE OSTI2025

Investigation of Solid Particle Reactors for Nonoxidative Dehydrogenation of Ethane: Toward Solar Thermal Ethylene Production

Concentrating solar power plants can generate renewable heat at temperatures well above those of most industrial processes. Ceramic particles irradiated with concentrated sunlight can store high-quality sensible heat and transfer this to power generation systems. These concepts and materials hold great potential to also enable thermal processes in the chemical industry, but effective strategies for transferring heat from thermal energy storage media into chemical reactors are still under development. This present work evaluated the thermal and chemical compatibility of various solid particle media (including quartz, bauxite, and alumina particles) integrated directly into tube reactors and the subsequent effects on reactor performance for the nonoxidative dehydrogenation of ethane reaction. Empty tube reactors without loaded particles (representing conventional ethane cracking coils) showed significant heat transfer limitations as the tube diameter was scaled. The incorporation of media into the reactor significantly aided heat transfer to the gaseous ethane reactant and increased its conversion by as much as 10% at similar space velocities. Despite direct contact with hydrocarbon gases, alumina and quartz media showed negligible coke formation. Even during reaction in 100% ethane feed gas at 825 °C, the average selectivity of the coke product was only 0.57% when using the quartz media. These materials further demonstrated excellent thermal stability during subsequent reoxidation in air at 800 °C, which simulated the reheating of particles in a circulating particle solar receiver. Conversely, high rates of coke formation, with a product selectivity of 27.5%, were observed on sintered bauxite particles during the reaction, likely promoted by transition metal constituents. These particles fractured upon reoxidation due to exotherms generated from coke combustion. In conclusion, while the use of cofed steam could mitigate attrition of redox-active particles, the ability of inert metal oxide particles to efficiently transfer heat to concentrated ethane reactant gas while suppressing side reactions or degradation suggests that these media could effectively couple solar thermal plants to reactors for next-generation production of ethylene and other critical chemicals.

Hydrocarbons↗
DOE OSTI2021

Convolutional neural network based non-iterative reconstruction for accelerating neutron tomography *

Abstract Neutron computed tomography (NCT), a 3D non-destructive characterization technique, is carried out at nuclear reactor or spallation neutron source-based user facilities. Because neutrons are not severely attenuated by heavy elements and are sensitive to light elements like hydrogen, neutron radiography and computed tomography offer a complementary contrast to x-ray CT conducted at a synchrotron user facility. However, compared to synchrotron x-ray CT, the acquisition time for an NCT scan can be orders of magnitude higher due to lower source flux, low detector efficiency and the need to collect a large number of projection images for a high-quality reconstruction when using conventional algorithms. As a result of the long scan times for NCT, the number and type of experiments that can be conducted at a user facility is severely restricted. Recently, several deep convolutional neural network (DCNN) based algorithms have been introduced in the context of accelerating CT scans that can enable high quality reconstructions from sparse-view data. In this paper, we introduce DCNN algorithms to obtain high-quality reconstructions from sparse-view and low signal-to-noise ratio NCT data-sets thereby enabling accelerated scans. Our method is based on the supervised learning strategy of training a DCNN to map a low-quality reconstruction from sparse-view data to a higher quality reconstruction. Specifically, we evaluate the performance of two popular DCNN architectures—one based on using patches for training and the other on using the full images for training. We observe that both the DCNN architectures offer improvements in performance over classical multi-layer perceptron as well as conventional CT reconstruction algorithms. Our results illustrate that the DCNN can be a powerful tool to obtain high-quality NCT reconstructions from sparse-view data thereby enabling accelerated NCT scans for increasing user-facility throughput or enabling high-resolution time-resolved NCT scans.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Improving Coal-Fired Plant Performance Through Integrated Predictive and Condition-Based Monitoring Tools

The project demonstrated the ability to improve boiler performance and reliability through the integrated use of condition-based monitoring (CBM) and predictions of the impacts of coal quality on boiler operations at a full-scale coal-fired power plant. The advanced tool developed actively monitors and manages coal quality and overall boiler conditions that maximizes availability and maintains generating capacity while reducing cost. The tool is used to forecast and alert plant operators and engineers about poor boiler conditions which may occur as a result of incoming coal and/or current power plant operating conditions. The Combustion System Performance Indices (CSPI) and CoalTracker (CT) programs predicts fireside performance of the plant including slagging, fouling, erosion, slag flow, strength development (sintering/densification), and slag layer thickness based on the composition of delivered coal. CT program is tailored for each plant and is used to track the coal from the point of delivery to the burner. The most successful application of the CSPI-CT has been at plants where the coal composition is determined through the use of a full stream elemental analyzers based on prompt gamma neutron analysis (PGNAA). The CSPI-CT program information is used by coal procurement, coal mining and plant operations personnel to select and blend coal properties for the best plant performance.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

42 ENGINEERING↗
DOE OSTI2026

Investigation of coherence of niobium-based resonators enabled by a fast-sealing microwave cavity

Resonators and qubits with a niobium (Nb) base metal layer achieve some of the highest coherence times in superconducting quantum devices. The performance of such devices is often limited by loss associated with two-level systems, which are found primarily at material surfaces and interfaces. The metal-air (MA) interface is a major contributor to device loss. In this work, we develop a fast-sealing microwave cavity that enables devices to be placed under vacuum within five minutes of oxide removal, thereby significantly reducing the MA interface loss compared to common device processing and packaging approaches. Using coplanar stripline resonators, we demonstrate that devices sealed in such a cavity exhibit internal quality factors exceeding one million at single-photon power. After re-exposure to air, the devices show downward resonance frequency shifts and quality factor degradations, quantitatively consistent with a model of Nb oxide regrowth. The fast-sealing microwave cavity provides a practical and consistent method to mitigate MA interface loss and sustain high coherence in Nb devices, and establishes a controlled platform for studying metal oxide regrowth kinetics and dielectric properties, the understanding of which is critical to achieving high coherence in superconducting quantum devices.

Zhang, C. [Waterloo U., IQC; Waterloo U.]↗
DOE OSTI2020

Continuous Water Quality Sensing for FGD Wastewater

The coal fired plant power industry requires new solutions for on-line monitoring of flue gas desulfurization (FGD) wastewaters for regulated trace metals to reduce recurring operating and off-site laboratory analysis costs and provide a high level of confidence of compliance with state and federal discharge guidelines. The University of Alabama at Birmingham (UAB) developed an integrated water sensor package for continuous water quality monitoring of flue gas desulfurization (FGD) wastewaters for concentration measurements of Selenium (Se). UAB utilized a diverse team of University Professors/Staff Members/Student(s) (Engineering and Innovative Technology Development Group), a University Affiliated Research Institution from the local area (Southern Research), and a cost sharing Industry Partner (Metrohm). The team developed and validated a proprietary wet chemistry batch process for water sample preparation; and implemented this process as a fully automated on-line monitor prototype system. This prototype system’s performance was evaluated in both the laboratory and during in-field deployment to a coal fired power plant. In both environments it was found that the prototype system was unable to recreate the batch process’s success at preparing FGD water samples for total Se measurements with a commercial-off-the-shelf (COTS) cyclic anodic stripping voltammetry (CSV) system. Through this experience, UAB has provided insight into a number of specific areas where improvements can be made with respect to the COTS systems used in this project to yield a more complete and competitive industry solution for on-line monitoring of total Se in industrial wastewater.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Ultrasonic Characterization of Ethylene Vinyl Acetate (EVA) Crosslinking for Quality Assurance and Lamination Process Control (US-Xlink)

Module makers strive to cut lamination processing times to enhance production throughput and reduce costs. If overdone, this might lead to poor EVA quality due to incomplete EVA crosslinking and a large concentration of potentially harmful reactants. Those deficiencies are frequently missed during production quality testing as initially their impact on power output is small. Additionally, the crosslinking agent is frequently inhomogeneously dispersed across the EVA foils such that local destructive characterization procedures such as differential scanning calorimetry (DSC), Soxhlet extraction, swelling methods, or mechanical tests provide only a limited amount of information. However, these defects and inhomogeneities can become considerably more important during field operation, dramatically reducing long-term power yield, and increasing LCOE. Examples of typical long-term module degradation modes relating to poor lamination process conditions include cell breakage, corrosion of the metallization, delamination, and local quality deficiencies. To provide reliable material characterization in a manufacturing setting, we devised a non-destructive technology that uses ultrasound to evaluate the quality of interface adhesion and the degree of crosslinking. As a calibration reference, DSC measurements were employed. Although the potential of ultrasonic approaches for this purpose has already been noted, those previous methods were generally limited to local qualitative measurements. Furthermore, variations in EVA thickness and temperature had a substantial impact on them. The solution we propose solves these limitations by employing ultrasonic absorption rather than relying solely on sound velocity data.

14 SOLAR ENERGY↗
DOE OSTI2026

Biogas Utilization in Refuse Power Plants (BURP 2 )

The BURP 2 project investigated the technical and financial viability of co firing biogas with waste coal for power generation while using carbon capture and sequestration to achieve net negative emissions. A comparative assessment integrating geospatial mapping, technoeconomic analysis, and life cycle analysis was carried out to evaluate retrofitting an existing coal fired facility in West Virginia, versus developing a new greenfield power plant in Kentucky located near a low quality coal resource. The study found that CO 2 capture rates of 90% or higher, in combination with biogas feedstocks such as animal manure, could significantly reduce global warming potential compared to plants using neither biogas nor CO 2 capture systems. Economic feasibility depended heavily on federal tax credits and proximity to fuel sources. In both greenfield and retrofit scenarios, access to biogas played a key role. Because the retrofit site was located close to existing biogas resources, it represented a feasible option, whereas the greenfield site, being far from pipelines or biogas sources, would require prohibitively expensive biogas transport infrastructure. Overall, the research showed that repurposing waste or low-quality coal with renewable biogas and CO 2 capture systems could provide a viable approach for reducing carbon emissions in power production, if biogas resources are easily accessible and available in sufficient quantities.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Power System Resilience Metrics Augmentation for Critical Load Prioritization

One of the major goals of new grid operation regimes, such as transactive energy systems (TESs), is to make the power grid more resilient to withstand natural or man-made disasters and potential reliability events, and to continue to serve the maximum number of its customers. But it is a well-known fact to system operators that not all customers are the same. This implies that any discussion of TESs’ impacts on the resilience of the power system should consider the needs of its critical customers (such as the power system operation centers, fire and police stations, and hospitals) over those of other customers. When evaluating the resilience of the system, bonus points must be awarded to any system that could maintain its power supply to critical customers during a disturbance that may cause an outage. This report discusses critical infrastructure (CI) as found in the literature and then categorizes it based on the field to which the operations belong (such as human life/safety-related, operations management, necessary city operation, industrial customers, etc.). Each of these CI categories is further divided into types of critical customers (e.g., the human life/safety-related category has different types of customers like hospitals, fire and police stations, etc.). The entire demand of each of the critical customer types is not categorized as critical load (CL); instead, only a portion of the total load of these critical customers is characterized as critical load. This is done based on the categories of equipment, the function of which is crucial in the operation of the overall facility. CL categorization is performed to provide the ratio of the critical load portion to the overall load , so that it can serve as a parameter in the resilience evaluation of the grid through a metrics-based approach. Such categorization is important as it helps to augment the existing quantifiable resilience metrics with CL categorization. The metrics for a power system need to not only consider how well a system performed during a disturbance event, but also how it reduced strain and supplied power to its CLs. The first step in this process is characterize CLs in the system. After CL characterization, the next step is the inclusion of these loads in the resilience metrics. To that end, in this report weight-based augmentation of resilience metrics is proposed, where certain customers (the ones that are categorized as critical) are assigned higher weights than others. Though an overview of assigning weights to customers is discussed, there is no one-size-fits-all approach for every power system. The decisions made about assigning such weights to customers vary greatly from one operator to another, based on their unique systems and the current and predicted states of critical customers. This decision-making can include the type of disturbance event, which might only affect certain parts of the system. In general, analyzing critical customers before an event helps understand system vulnerabilities. It also helps in planning and conducting operations during the event, evaluating system performance after the event, and supporting better planning for future events. An alternative to the current practices of managing the grid for outages is an innovative TES, which has the potential to provide a platform for including distributed energy resources for managing CLs. This report also describes how TES qualities can help (1) to maintain power supply to critical customers for uninterrupted operations and (2) to restore lost power supply to the critical customers rapidly.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

PV Reference Cells for Outdoor Use: Stability Over Four Years of Deployment

Photovoltaic (PV) reference cells are frequently used to evaluate the performance of PV power plants. They provide a measure of irradiance that strongly correlates with the electrical output of PV modules, which makes them very useful for detecting short-term anomalies or long-term degradation in PV power plant output. One very important quality is long-term stability. This report presents observations about the stability of a set of 22 commercial reference cells that have been in continuous operation for a period of four years at the Solar Radiation Research Laboratory (SRRL) site at the National Renewable Energy Laboratory (NREL). The 22 reference cells at the SRRL represent 10 different models from 6 manufacturers.

14 SOLAR ENERGY↗