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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.

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DOE OSTI2013

Respiratory Amplitude Guided 4-Dimensional Magnetic Resonance Imaging

Purpose: To evaluate the feasibility of prospectively guiding 4-dimensional (4D) magnetic resonance imaging (MRI) image acquisition using triggers at preselected respiratory amplitudes to achieve T{sub 2} weighting for abdominal motion tracking. Methods and Materials: A respiratory amplitude-based triggering system was developed and integrated into a commercial turbo spin echo MRI sequence. Initial feasibility tests were performed on healthy human study participants. Four respiratory states, the middle and the end of inhalation and exhalation, were used to trigger 4D MRI image acquisition of the liver. To achieve T{sub 2} weighting, the echo time and repetition time were set to 75 milliseconds and 4108 milliseconds, respectively. Single-shot acquisition, together with parallel imaging and partial k-space imaging techniques, was used to improve image acquisition efficiency. 4D MRI image sets composed of axial or sagittal slices were acquired. Results: Respiratory data measured and logged by the MRI scanner showed that the triggers occurred at the appropriate respiratory levels. Liver motion could be easily observed on both 4D MRI image datasets by sensing either the change of liver in size and shape (axial) or diaphragm motion (sagittal). Both 4D MRI image datasets were T{sub 2}-weighted as expected. Conclusions: This study demonstrated the feasibility of achieving T{sub 2}-weighted 4D MRI images using amplitude-based respiratory triggers. With the aid of the respiratory amplitude-based triggering system, the proposed method is compatible with most MRI sequences and therefore has the potential to improve tumor-tissue contrast in abdominal tumor motion imaging.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

Removal of Historic Low-Level Radioactive Sediment from the Port Hope Harbour - 13314

At the Port Hope Harbour, located on the north shore of Lake Ontario, the presence of low-level radioactive sediment, resulting from a former radium and uranium refinery that operated alongside the Harbour, currently limits redevelopment and revitalization opportunities. These waste materials contain radium-226, uranium, arsenic and other contaminants. Several other on-land locations within the community of Port Hope are also affected by the low-level radioactive waste management practices of the past. The Port Hope Project is a community initiated undertaking that will result in the consolidation of an estimated 1.2 million cubic metres of the low-level radioactive waste from the various sites in Port Hope into a new engineered above ground long-term waste management facility. The remediation of the estimated 120,000 m{sup 3} of contaminated sediments from the Port Hope Harbour is one of the more challenging components of the Port Hope Project. Following a thorough review of various options, the proposed method of contaminated sediment removal is by dredging. The sediment from the dredge will then be pumped as a sediment-water slurry mixture into geo-synthetic containment tubes for dewatering. Due to the hard substrate below the contaminated sediment, the challenge has been to set performance standards in terms of low residual surface concentrations that are attainable in an operationally efficient manner. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2012

International Spine Radiosurgery Consortium Consensus Guidelines for Target Volume Definition in Spinal Stereotactic Radiosurgery

Purpose: Spinal stereotactic radiosurgery (SRS) is increasingly used to manage spinal metastases. However, target volume definition varies considerably and no consensus target volume guidelines exist. This study proposes consensus target volume definitions using common scenarios in metastatic spine radiosurgery. Methods and Materials: Seven radiation oncologists and 3 neurological surgeons with spinal radiosurgery expertise independently contoured target and critical normal structures for 10 cases representing common scenarios in metastatic spine radiosurgery. Each set of volumes was imported into the Computational Environment for Radiotherapy Research. Quantitative analysis was performed using an expectation maximization algorithm for Simultaneous Truth and Performance Level Estimation (STAPLE) with kappa statistics calculating agreement between physicians. Optimized confidence level consensus contours were identified using histogram agreement analysis and characterized to create target volume definition guidelines. Results: Mean STAPLE agreement sensitivity and specificity was 0.76 (range, 0.67-0.84) and 0.97 (range, 0.94-0.99), respectively, for gross tumor volume (GTV) and 0.79 (range, 0.66-0.91) and 0.96 (range, 0.92-0.98), respectively, for clinical target volume (CTV). Mean kappa agreement was 0.65 (range, 0.54-0.79) for GTV and 0.64 (range, 0.54-0.82) for CTV (P<.01 for GTV and CTV in all cases). STAPLE histogram agreement analysis identified optimal consensus contours (80% confidence limit). Consensus recommendations include that the CTV should include abnormal marrow signal suspicious for microscopic invasion and an adjacent normal bony expansion to account for subclinical tumor spread in the marrow space. No epidural CTV expansion is recommended without epidural disease, and circumferential CTVs encircling the cord should be used only when the vertebral body, bilateral pedicles/lamina, and spinous process are all involved or there is extensive metastatic disease along the circumference of the epidural space. Conclusions: This report provides consensus guidelines for target volume definition for spinal metastases receiving upfront SRS in common clinical situations.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

High-Grade Glioma Radiation Therapy Target Volumes and Patterns of Failure Obtained From Magnetic Resonance Imaging and {sup 18}F-FDOPA Positron Emission Tomography Delineations From Multiple Observers

Purpose: The objective of this study was to compare recurrent tumor locations after radiation therapy with pretreatment delineations of high-grade gliomas from magnetic resonance imaging (MRI) and 3,4-dihydroxy-6-[{sup 18}F]fluoro-L-phenylalanine ({sup 18}F-FDOPA) positron emission tomography (PET) using contours delineated by multiple observers. Methods and Materials: Nineteen patients with newly diagnosed high-grade gliomas underwent computed tomography (CT), gadolinium contrast-enhanced MRI, and {sup 18}F-FDOPA PET/CT. The image sets (CT, MRI, and PET/CT) were registered, and 5 observers contoured gross tumor volumes (GTVs) using MRI and PET. Consensus contours were obtained by simultaneous truth and performance level estimation (STAPLE). Interobserver variability was quantified by the percentage of volume overlap. Recurrent tumor locations after radiation therapy were contoured by each observer using CT or MRI. Consensus recurrence contours were obtained with STAPLE. Results: The mean interobserver volume overlap for PET GTVs (42% ± 22%) and MRI GTVs (41% ± 22%) was not significantly different (P=.67). The mean consensus volume was significantly larger for PET GTVs (58.6 ± 52.4 cm{sup 3}) than for MRI GTVs (30.8 ± 26.0 cm{sup 3}, P=.003). More than 95% of the consensus recurrence volume was within the 95% isodose surface for 11 of 12 (92%) cases with recurrent tumor imaging. Ten (91%) of these cases extended beyond the PET GTV, and 9 (82%) were contained within a 2-cm margin on the MRI GTV. One recurrence (8%) was located outside the 95% isodose surface. Conclusions: High-grade glioma contours obtained with {sup 18}F-FDOPA PET had similar interobserver agreement to volumes obtained with MRI. Although PET-based consensus target volumes were larger than MRI-based volumes, treatment planning using PET-based volumes may not have yielded better treatment outcomes, given that all but 1 recurrence extended beyond the PET GTV and most were contained by a 2-cm margin on the MRI GTV.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

WE-FG-207B-07: Feasibility of Low Dose Lung Cancer Screening with a Whole-Body Photon Counting CT: First Human Results

Purpose: To evaluate the feasibility of using a whole-body photon counting detector (PCD) CT scanner for low dose lung cancer screening compared to a conventional energy integrating detector (EID) system. Methods: Radiation dose-matched EID and PCD scans of the COPDGene 2 phantom and 2 human volunteers were acquired. Phantom images were acquired at different radiation dose levels (CTDIvol: 3.0, 1.5, and 0.75 mGy) and different tube voltages (120, 100, and 80 kVp), while human images were acquired at vendor recommended low-dose lung cancer screening settings. EID and PCD images were compared for quantitative Hounsfield unit accuracy, noise levels, and contrast-to-noise ratios (CNR) for detection of ground-glass nodules (GGNs) and emphysema. Results: The PCD Hounsfield unit accuracy was better for water at all scan parameters, and for lung, GGN and emphysema equivalent regions of interest (ROIs) at 1.5 and 0.75 mGy. PCD attenuation accuracy was more consistent for all scan parameters (all P<0.01), while Hounsfield units for lung, GGN and emphysema ROIs changed significantly for EID with decreasing dose (all P<0.001). PCD showed lower noise levels at the lowest dose setting at 120, 100 and 80 kVp (15.2±0.3 vs 15.8±0.2, P=0.03; 16.1±0.3 vs 18.0±0.4, P=0.003; and 16.1±0.3 vs 17.9±0.3, P=0.001, respectively), resulting in superior CNR for the detection of GGNs and emphysema at 100 and 80 kVp. Significantly lower PCD noise levels were confirmed in volunteer images. Conclusion: PCD provided better Hounsfield unit accuracy for lung, ground-glass, and emphysema-equivalent foams at 1.5 and 0.75 mGy with less variability than EID. Additionally, PCD showed less noise, and higher CNR at 0.75 mGy for both 100 and 80 kVp. PCD technology may help reduce radiation exposure in lung cancer screening while maintaining diagnostic quality.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Optimizing a Multiple Right-Hand Side Dslash Kernel for Intel Knights Corner

Lattice Quantum Chromo-dynamics (LQCD) stands unique as the only computationally tractable, non-perturbative, and model-independent quantum field theory of the strong nuclear force. LQCD achieves tractability by discretizing our familiar 4-dimensional space-time onto a hypercubic lattice. The core component of the linear solver used in LQCD is the Wilson-Dirac operator whose finite difference component is called the Dslash operator. Dslash is a nearest neighbor stencil operator summing matrix-vector multiplications over lattice points. The arithmetic intensity of a regular Dslash operator acting on a single vector in single precision is between 0.88-2.29 (depending on the tricks employed) making it memory bandwidth bound on most current architectures, including Xeon Phi. Maximizing the performance of Dslash on Xeon Phi requires deliberate exploitation of its architectural features. One key feature of Xeon Phi is its powerful vector processing unit with lengthy vector registers. There is a class of LQCD computations which aims to solve the Dirac equation for the same lattice gauge fields multiple times. In this case one can gain additional reuse of the gauge field, and potentially simplified vectorization, through applying Dslash to several vectors at the same time; often referred to as Multiple Right Hand Side (MRHS) Dslash. Previous work in this area focused on applying this approach to the Highly Improved Staggered operator, and in cases to the multi-Grid algorithm for Wilson clover fermions. Here, we present two approaches to MRHS Dslash: a vector register blocking approach and one using the software package QPhiX with a custom Code Generator for low-level intrinsics. We explore the Knights Corner instruction set, memory hierarchy, and threading capabilities. For standard lattice sizes used in testing, we observed significant speedups, with sustained performance of over 700 GFLOPS (single precision) in one instance. We achieved up to 27% of theoretical peak performance compared to a maximum of 12% obtained by the previous single RHS method in QPhiX.

Joó, Bálint↗
DOE OSTI2025

Robustness of Deep Learning Classification to Adversarial Input on GPUs: Asynchronous Parallel Accumulation Is a Source of Vulnerability

The ability of machine learning (ML) classification models to resist small, targeted input perturbations—known as adversarial attacks—is a key measure of their safety and reliability. We show that floating-point non associativity (FPNA) coupled with asynchronous parallel programming on GPUs is sufficient to result in misclassification, without any perturbation to the input. Additionally, we show that this misclassification is particularly significant for inputs close to the decision boundary and that standard adversarial robustness results may be overestimated up to 4.6 when not considering machine-level details. We first study a linear classifier, before focusing on standard Graph Neural Network (GNN) architectures and datasets used in robustness assessments. We develop a novel black-box attack using Bayesian optimization to discover external workloads that can change the instruction scheduling which bias the output of reductions on GPUs and reliably lead to misclassification. Motivated by these results, we present a new learnable permutation (LP) gradient-based approach to learning floating-point operation orderings that lead to misclassifications. The LP approach provides a worst-case estimate in a computationally efficient manner, avoiding the need to run identical experiments tens of thousands of times over a potentially large set of possible GPU states or architectures. Finally, using instrumentation-based testing, we investigate parallel reduction ordering across different GPU architectures under external background workloads, when utilizing multi-GPU virtualization, and when applying power capping. Our results demonstrate that parallel reduction ordering varies significantly across architectures under the first two conditions, substantially increasing the search space required to fully test the effects of this parallel scheduler-based vulnerability. These results and the methods developed here can help to include machine-level considerations into adversarial robustness assessments, which can make a difference in safety and mission critical applications.

Shanmugavelu, Sanjif [Maxeler Technologies, a Groq↗
DOE OSTI2021

Unsupervised Power System Event Detection and Classification Using Unlabeled PMU Data

This paper proposes a novel data-driven power system event detection and classification method based on 5TB of actual PMU measurements collected from the US western interconnect. Firstly, a set of comprehensive power quality rules are proposed to pre-filter the raw data and extract the regions of interest (ROI). Six distinct event categories are defined and corresponding patterns are chosen as references. Meanwhile, detailed characteristics of patterns are summarized to enhance our understanding of the actual events. Then, the time-independent feature vectors are generated by extracting the statistical, temporal, and spectral features from the raw time-series data. Furthermore, an ensemble model is proposed to cluster the events by combining multiple K-means clustering models using a voting strategy. Besides, both system-level and PMU-level clustering models are developed. The accuracy and robustness of the event detection method are further improved through interactive evaluation of the two-level clustering results. This paper summarizes the actual characteristics of each event category and provides a reliable basis for accurate label generation. The experiments demonstrate the effectiveness of the proposed event detection and classification method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Unsupervised Power System Event Detection and Classification Using Unlabeled PMU Data

This paper proposes a novel data-driven power system event detection and classification method based on 5TB of actual PMU measurements collected from the US western interconnect. Firstly, a set of comprehensive power quality rules are proposed to pre-filter the raw data and extract the regions of interest (ROI). Six distinct event categories are defined and corresponding patterns are chosen as references. Meanwhile, detailed characteristics of patterns are summarized to enhance our understanding of the actual events. Then, the time-independent feature vectors are generated by extracting the statistical, temporal, and spectral features from the raw time-series data. Furthermore, an ensemble model is proposed to cluster the events by combining multiple K-means clustering models using a voting strategy. Besides, both system-level and PMU-level clustering models are developed. The accuracy and robustness of the event detection method are further improved through interactive evaluation of the two-level clustering results. This paper summarizes the actual characteristics of each event category and provides a reliable basis for accurate label generation. The experiments demonstrate the effectiveness of the proposed event detection and classification method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

TH-A-9A-01: Active Optical Flow Model: Predicting Voxel-Level Dose Prediction in Spine SBRT

Purpose: To predict voxel-level dose distribution and enable effective evaluation of cord dose sparing in spine SBRT. Methods: We present an active optical flow model (AOFM) to statistically describe cord dose variations and train a predictive model to represent correlations between AOFM and PTV contours. Thirty clinically accepted spine SBRT plans are evenly divided into training and testing datasets. The development of predictive model consists of 1) collecting a sequence of dose maps including PTV and OAR (spinal cord) as well as a set of associated PTV contours adjacent to OAR from the training dataset, 2) classifying data into five groups based on PTV's locations relative to OAR, two “Top”s, “Left”, “Right”, and “Bottom”, 3) randomly selecting a dose map as the reference in each group and applying rigid registration and optical flow deformation to match all other maps to the reference, 4) building AOFM by importing optical flow vectors and dose values into the principal component analysis (PCA), 5) applying another PCA to features of PTV and OAR contours to generate an active shape model (ASM), and 6) computing a linear regression model of correlations between AOFM and ASM.When predicting dose distribution of a new case in the testing dataset, the PTV is first assigned to a group based on its contour characteristics. Contour features are then transformed into ASM's principal coordinates of the selected group. Finally, voxel-level dose distribution is determined by mapping from the ASM space to the AOFM space using the predictive model. Results: The DVHs predicted by the AOFM-based model and those in clinical plans are comparable in training and testing datasets. At 2% volume the dose difference between predicted and clinical plans is 4.2±4.4% and 3.3±3.5% in the training and testing datasets, respectively. Conclusion: The AOFM is effective in predicting voxel-level dose distribution for spine SBRT. Partially supported by NIH/NCI under grant #R21CA161389 and a master research grant by Varian Medical System.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Exploratory Visual Analysis of Anomalous Runtime Behavior in Streaming High Performance Computing Applications

Online analysis of runtime behavior is essential for performance tuning in streaming scientific workflows. Integration of anomaly detection and visualization is necessary to support human-centered analysis, such as verification of candidate anomalies utilizing domain knowledge. In this work, we propose an efficient and scalable visual analytics system for online performance analysis of scientific workflows toward the exascale scenario. Here, our approach uses a call stack tree representation to encode the structural and temporal information of the function executions. Based on the call stack tree features (e.g., execution time of the root function or vector representation of the tree structure), we employ online anomaly detection approaches to identify candidate anomalous function executions. We also present a set of visualization tools for verification and exploration in a level-of-detailed manner. General information, such as distribution of execution times, are provided in an overview visualization. The detailed structure (e.g., function invocation relations) and the temporal information (e.g., message communication) of the execution call stack of interest are also visualized. The usability and efficiency of our methods are verified in a real-world HPC application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

An optimization approach to design monitoring schemes for CO 2 leakage detection

This paper demonstrates an approach to identify optimal monitoring designs that minimize the time to first detection of CO 2 leakage from a subsurface storage formation. This research is part of the National Risk Assessment Partnership (NRAP), a DOE project tasked with conducting risk and uncertainty analysis in the areas of reservoir performance, natural leakage pathways, wellbore integrity, groundwater protection, monitoring, and systems level modeling. Our approach applies a simulated annealing algorithm that searches the solution space by iteratively mutating potential monitoring designs. An example application is provided to demonstrate the effectiveness and efficiency of this method compared to an exhaustive search of the entire solution space. We took into account uncertainty by evaluating the performance of potential monitoring designs across a set of simulated leakage realization. A flexible two-tiered signature was implemented to infer that CO 2 leakage had occurred. This approach was ~50 times faster than an exhaustive search. A user-friendly tool, MDLD, is being developed for use on personal computers to make this method available to stakeholders, regulators, and researchers.

03 NATURAL GAS↗
DOE OSTI2025

Characterization and prediction of the electromechanical wear of contact tips during wire arc additive manufacturing of 316L stainless steel

Here, this study seeks to better understand the degradation of the contact tip with respect to WAAM for a 316L wire electrode as well as explore methods of monitoring the contact tip state from process data. The contact tip, a consumable component, positions the wire and serves as the electrical contact surface between the wire electrode and the welding power supply. The wear of the contact tip was characterized in terms of material loss and material contamination for a set of tips worn to discrete levels as measured by the amount of wire fed or arc time. Geometrical characterization found a 49% increase in the bore exit area at 180 meters of wire fed. Machine learning models were developed to predict the relative bore exit area of the contact tip from arc-based process data and a random forest classifier exhibited favorable performance with a cross-validated f1-score of 0.84. The regression architecture implemented a multi-layer perceptron with the ability to predict the relative exit area with an $R^2$ score of 0.75. Key features used in the prediction include the standard deviation of the voltage and the time between shorts.

Contact tip wear↗
DOE OSTI2011

ON STAR FORMATION RATES AND STAR FORMATION HISTORIES OF GALAXIES OUT TO z {approx} 3

We compare multi-wavelength star formation rate (SFR) indicators out to z {approx} 3 in the GOODS-South field. Our analysis uniquely combines U to 8 {mu}m photometry from FIREWORKS, MIPS 24 {mu}m and PACS 70, 100, and 160 {mu}m photometry from the PEP, and H{alpha} spectroscopy from the SINS survey. We describe a set of conversions that lead to a continuity across SFR indicators. A luminosity-independent conversion from 24 {mu}m to total infrared luminosity yields estimates of L{sub IR} that are in the median consistent with the L{sub IR} derived from PACS photometry, albeit with significant scatter. Dust correction methods perform well at low-to-intermediate levels of star formation. They fail to recover the total amount of star formation in systems with large SFR{sub IR}/SFR{sub UV} ratios, typically occuring at the highest SFRs (SFR{sub UV+IR} {approx}> 100 M{sub sun} yr{sup -1}) and redshifts (z {approx}> 2.5) probed. Finally, we confirm that H{alpha}-based SFRs at 1.5 < z < 2.6 are consistent with SFR{sub SED} and SFR{sub UV+IR} provided extra attenuation toward H II regions is taken into account (A{sub V,neb} = A{sub V,continuum}/0.44). With the cross-calibrated SFR indicators in hand, we perform a consistency check on the star formation histories inferred from spectral energy distribution (SED) modeling. We compare the observed SFR-M relations and mass functions at a range of redshifts to equivalents that are computed by evolving lower redshift galaxies backward in time. We find evidence for underestimated stellar ages when no stringent constraints on formation epoch are applied in SED modeling. We demonstrate how resolved SED modeling, or alternatively deep UV data, may help to overcome this bias. The age bias is most severe for galaxies with young stellar populations and reduces toward older systems. Finally, our analysis suggests that SFHs typically vary on timescales that are long (at least several 100 Myr) compared to the galaxies' dynamical time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Fast and accurate CMB computations in non-flat FLRW universes

We present a new method for calculating CMB anisotropies in a non-flat Friedmann universe, relying on a very stable algorithm for the calculation of hyperspherical Bessel functions, that can be pushed to arbitrary precision levels. We also introduce a new approximation scheme which gradually takes over in the flat space limit and leads to significant reductions of the computation time. Our method is implemented in the Boltzmann code class. It can be used to benchmark the accuracy of the camb code in curved space, which is found to match expectations. For default precision settings, corresponding to 0.1% for scalar temperature spectra and 0.2% for scalar polarisation spectra, our code is two to three times faster, depending on curvature. We also simplify the temperature and polarisation source terms significantly, so the different contributions to the C{sub ℓ} 's are easy to identify inside the code.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Spatiotemporal predictions of toxic urban plumes using deep learning

Industrial accidents, chemical spills, and structural fires can release large amounts of harmful materials that disperse into urban atmospheres and impact populated areas. Computer models are typically used to predict the transport of toxic plumes by solving fluid dynamical equations. However, these models can be computationally expensive due to the need for many grid cells to simulate turbulent flow and resolve individual buildings and streets. In emergency response situations, alternative methods are needed that can run quickly and adequately capture important spatiotemporal features. Here, we present a novel deep learning model called ST-GasNet inspired by the mathematical equations that govern the behavior of plumes as they disperse through the atmosphere. ST-GasNet learns the spatiotemporal dependencies from a limited set of temporal sequences of ground-level toxic urban plumes generated by a high-resolution large eddy simulation model. On independent sequences, ST-GasNet accurately predicts the late-time spatiotemporal evolution, given the early-time behavior as an input, even when a building splits a large plume into smaller plumes. By incorporating large-scale wind boundary condition information, ST-GasNet achieves a prediction accuracy of at least 90% on test data for the entire prediction period.

Civil and Environmental Engineering↗
DOE OSTI2016

Active Power Control of Wind Turbines for Ancillary Services: A Comparison of Pitch and Torque Control Methodologies

As wind energy generation becomes more prevalent in some regions, there is increased demand for wind power plants to provide ancillary services, which are essential for grid reliability. This paper compares two different wind turbine control methodologies to provide active power control (APC) ancillary services, which include derating or curtailing power generation, providing automatic generation control (AGC), and providing primary frequency control (PFC). The torque APC controller provides all power control through the power electronics whereas the pitch APC controller uses the blade pitch actuators as the primary means of power control. These controllers are simulated under various wind conditions with different derating set points and AGC participation levels. The metrics used to compare their performance are the damage equivalent loads (DELs) induced on the structural components and AGC performance metrics, which are used to determine the payments for AGC services by system operators in the United States. The simulation results show that derating the turbine reduces structural loads for both control methods, with the APC pitch control providing larger reductions in DELs, lower AGC performance scores, and higher root-mean-square pitch rates. Providing AGC increases the structural loads when compared to only derating the turbine, but even the AGC DELs are generally lower than those of the baseline control system. The torque APC control methodology also allows for more sustained PFC responses under certain derating conditions.

17 WIND ENERGY↗
DOE OSTI2022

Behavioral and Population Data-Driven Distribution System Load Modeling

Distribution system residential load modeling and analysis for different geographic areas within a utility or an independent system operator territory are critical for enabling small-scale, aggregated distributed energy resources to participate in grid services under Federal Energy Regulatory Commission Order No. 2222 [1]. In this study, we develop a methodology of modeling residential load profiles in different geographic areas with a focus on human behavior impact. First, we construct a behavior-based load profile model leveraging state-of-the-art appliance models. We simulate human activity and occupancy using Markov chain Monte Carlo methods calibrated with the American Time Use Survey data set. Second, we link our model with cleaned Current Population Survey data from the U.S. Census Bureau. Finally, we populate two sets of 500 households using California and Texas census data, respectively, to perform an initial analysis of the load in different geographic areas with various group features (e.g., different income levels). To distinguish the effect of population behavior differences on aggregated load, we simulate load profiles for both sets assuming fixed physical household parameters and weather data. Analysis shows that average daily load profiles vary significantly by income and income dependency varies by locality.

American Time Use Survey↗