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NASA NTRS2010

Improved Phased Array Imaging of a Model Jet

An advanced phased array system, OptiNav Array 48, and a new deconvolution algorithm, TIDY, have been used to make octave band images of supersonic and subsonic jet noise produced by the NASA Glenn Small Hot Jet Acoustic Rig (SHJAR). The results are much more detailed than previous jet noise images. Shock cell structures and the production of screech in an underexpanded supersonic jet are observed directly. Some trends are similar to observations using spherical and elliptic mirrors that partially informed the two-source model of jet noise, but the radial distribution of high frequency noise near the nozzle appears to differ from expectations of this model. The beamforming approach has been validated by agreement between the integrated image results and the conventional microphone data.

Dougherty, Robert P.↗

NASA NTRS2007

X-ray Properties of an Unbiased Hard X-ray Detected Sample of AGN

The SWIFT gamma ray observatory's Burst Alert Telescope (BAT) has detected a sample of active galactic nuclei (AGN) based solely on their hard X-ray flux (14-195keV). In this paper, we present for the first time XMM-Newton X-ray spectra for 22 BAT AGXs with no previously analyzed X-ray spectra. If our sources are a representative sample of the BAT AGN, as we claim, our results present for the first time global X-ray properties of an unbiased towards absorption (n(sub H) < 3 x 10(exp 25)/sq cm), local (< z >= 0.03), AGN sample. We find 9/22 low absorption (n(sub H) < 10(exp 23)/sq cm), simple power law model sources, where 4 of these sources have a statistically significant soft component. Among these sources, we find the presence of a warm absorber statistically significant for only one Seyfert 1 source, contrasting with the ASCA results of Reynolds (1997) and George et al. (1998), who find signatures of warm absorption in half or more of their Seyfert 1 samples at similar redshifts. Additionally, the remaining sources (13122) have more complex spectra, well-fit by an absorbed power law at E > 2.0 keV. Five of the complex sources (NGC 612, ESO 362-G018, MRK 417, ESO 506-G027, and NGC 6860) are classified as Compton-thick candidates. Further, we find four more sources (SWIFT J0641.3+3257, SWIFT J0911.2+4533, SWIFT J1200.8+0650, and NGC 4992) with properties consistent with the hidden/buried AGN reported by Ueda et al. (2007). Finally, we include a comparison of the XMM EPIC spectra with available SWIFT X-ray Telescope (XRT) observations. From these comparisons, we find 6/16 sources with varying column densities, 6/16 sources with varying power law indices, and 13/16 sources with varying fluxes, over periods of hours to months. Flux and power law index are correlated for objects where both parameters vary.

Winter, Lisa M.↗

NASA NTRS1982

Numerical modelling of turbulent flow in a combustion tunnel

A numerical technique is presented for the analysis of turbulent flow associated with combustion. The technique uses Chorin's random vortex method (rvm), an algorithm capable of tracing the action of elementary turbulent eddies and their cumulative effects without imposing any restriction upon their motion. In the past, the rvm has been used with success to treat nonreacting turbulent flows, revealing in particular the mechanics of large-scale flow patterns, the so-called coherent structures. Introduced here is a flame propagation algorithm, also developed by Chorin, in conjunction with volume sources modelling the mechanical effects of the exothermic process of combustion. As an illustration of its use, the technique is applied to flow in a combustion tunnel where the flame is stabilized by a back-facing step. Solutions for both nonreacting and reacting flow fields are obtained which satisfactorily describe the essential features of turbulent combustion in a lean propane-air mixture that were observed in the laboratory by means of high speed Schlieren photography.

Ghoniem, A. F.↗

NASA NTRS1994

Do gamma-ray burst sources repeat?

The demonstration of repeated gamma-ray bursts from an individual source would severely constrain burst source models. Recent reports of evidence for repetition in the first BATSE burst catalog have generated renewed interest in this issue. Here, we analyze the angular distribution of 585 bursts of the second BATSE catalog (Meegan et al. 1994). We search for evidence of burst recurrence using the nearest and farthest neighbor statistic ad the two-point angular correlation function. We find the data to be consistent with the hypothesis that burst sources do not repeat; however, a repeater fraction of up to about 20% of the bursts cannot be excluded.

Meegan, C. A.↗

NASA NTRS2013

X-Ray Detection and Processing Models for Spacecraft Navigation and Timing

The current primary method of deepspace navigation is the NASA Deep Space Network (DSN). High-performance navigation is achieved using Delta Differential One-Way Range techniques that utilize simultaneous observations from multiple DSN sites, and incorporate observations of quasars near the line-of-sight to a spacecraft in order to improve the range and angle measurement accuracies. Over the past four decades, x-ray astronomers have identified a number of xray pulsars with pulsed emissions having stabilities comparable to atomic clocks. The x-ray pulsar-based navigation and time determination (XNAV) system uses phase measurements from these sources to establish autonomously the position of the detector, and thus the spacecraft, relative to a known reference frame, much as the Global Positioning System (GPS) uses phase measurements from radio signals from several satellites to establish the position of the user relative to an Earth-centered fixed frame of reference. While a GPS receiver uses an antenna to detect the radio signals, XNAV uses a detector array to capture the individual xray photons from the x-ray pulsars. The navigation solution relies on detailed xray source models, signal processing, navigation and timing algorithms, and analytical tools that form the basis of an autonomous XNAV system. Through previous XNAV development efforts, some techniques have been established to utilize a pulsar pulse time-of-arrival (TOA) measurement to correct a position estimate. One well-studied approach, based upon Kalman filter methods, optimally adjusts a dynamic orbit propagation solution based upon the offset in measured and predicted pulse TOA. In this delta position estimator scheme, previously estimated values of spacecraft position and velocity are utilized from an onboard orbit propagator. Using these estimated values, the detected arrival times at the spacecraft of pulses from a pulsar are compared to the predicted arrival times defined by the pulsar s pulse timing model. A discrepancy provides an estimate of the spacecraft position offset, since an error in position will relate to the measured time offset of a pulse along the line of sight to the pulsar. XNAV researchers have been developing additional enhanced approaches to process the photon TOAs to arrive at an estimate of spacecraft position, including those using maximum-likelihood estimation, digital phase locked loops, and "single photon processing" schemes that utilize all available time data associated with each photon. Using pulsars from separate, non-coplanar locations provides range and range-rate measurements in each pulsar s direction. Combining these different pulsar measurements solves for offsets in position and velocity in three dimensions, and provides accurate overall navigation for deep space vehicles.

Sheikh, Suneel↗

NASA NTRS1995

Do gamma-ray burst sources repeat?

The demonstration of repeated gamma-ray bursts from an individual source would severely constrain burst source models. Recent reports (Quashnock and Lamb, 1993; Wang and Lingenfelter, 1993) of evidence for repetition in the first BATSE burst catalog have generated renewed interest in this issue. Here, we analyze the angular distribution of 585 bursts of the second BATSE catalog (Meegan et al., 1994). We search for evidence of burst recurrence using the nearest and farthest neighbor statistic and the two-point angular correlation function. We find the data to be consistent with the hypothesis that burst sources do not repeat; however, a repeater fraction of up to about 20% of the observed bursts cannot be excluded.

Meegan, Charles A.↗

NASA NTRSDate not supplied

Using remotely sensed information to support landslide hazard and exposure assessment throughout the disaster lifecycle

The global coverage and temporal frequency that satellites provide offers a unique opportunity to estimate landslide hazard and exposure throughout the disaster lifecycle, from pre-event planning and forecasting to post-event mapping and impact assessment, and finally to recovery and mitigation. The relevance of satellite-derived data and model products is largely contingent on the spatiotemporal sampling, the hazard characteristics, and the needs from the research or applications community. This work presents an advanced Landslide Hazard Assessment for Situational Awareness (LHASA) framework that brings together satellite and model products with new machine learning techniques and global inventory data to better model landslide hazard and exposure. We present several new ways to map, model, and assess landslide hazard using a range of satellite data. Two new thrusts of this work are to better account for the exacerbating impacts of fires and to provide a multi-day forecast of potential hazard. Together, these additional components blend information from a suite of satellite and model sources to improve early warning of potentially hazardous areas, identify landslide occurrence and impacts in near real-time, and better characterize the spatiotemporal patterns of landslide hazard and exposure more broadly for future awareness and planning. This suite of tools and products is open to the public and provides information to better assess the potential occurrence and impacts of landslides within different regions of the world. This presentation explores both the architecture behind this framework and examples of how the model components and products have been used by different stakeholders around the world.

Thomas Stanley↗

DOE OSTI2023

Acoustic Codes in 2D Spherical Coordinate

Finite-difference methods are widely used to simulate infrasound propagation in the atmosphere. Flexibility of finite-difference scheme allows implementation of highly heterogeneous media for sound propagation as well as complex source models for sound generation. While full 3-D finite-difference methods have been utilized for local infrasound propagation with pronounced topography, 2-D modeling approach has been preferred for regional and global propagation as full 3-D methods generally require enormous computational resources. Infrasound propagation is often simulated with a second-order finite difference scheme. This lowest-order finite-difference scheme is robust and straightforward to implement complex boundary conditions, but the solution includes large error with numerical dispersion and dissipation. This large numerical error may make the second-order finite-difference not optimal for long range infrasound propagation modeling as the numerical dispersion degrades the accuracy of the solution unacceptably. Here, we developed a high-order finite-difference solver for long-range infrasound simulation. The high-order scheme is particularly popular for linear wave modeling in aeroacoustics owing to its low-dispersive and low-dissipative behavior. We develop and evaluate a high-order finite difference scheme in 2-D axisymmetric coordinates. The axisymmetry allows to approximate 3-D spherical sound propagation and amplitude attenuation by a 2-D method. AC2Dr is developed to simulate infrasound propagation in realistic atmosphere, but can be used for linear acoustic waves in general materials with background flow. AC2Dr in an axisymmetric coordinates allows for spherical radiation of acoustic waves from compact sources.

Sjogreen, Bjorn↗

DOE OSTI2023

Dispatch optimization of electric thermal energy storage within System Advisor Model

A stand-alone electric thermal energy storage (ETES) system converts low-value electricity into heat using resistance heating elements. During periods of high-value electricity, an ETES system uses a thermodynamic power cycle to convert stored thermal energy back to electricity. These dispatchable systems derive value from their ability to store energy when prices are low and generate electricity when prices are favorable, i.e., energy arbitrage. Consequently, dispatch optimization of system operations, through maximizing revenue subject to system constraints, is essential to evaluate the economic value of a particular system design. While stand-alone ETES systems offer potential advantages as dispatchable grid storage technologies, there is a lack of a neutral third-party, publicly available, open-source model to evaluate the performance, dispatch, and financial viability of these systems. To address this problem, we have developed a techno-economic model for stand-alone ETES systems, within National Renewable Energy Laboratory's (NREL's) System Advisor Model (SAM). We implement a mixed-integer linear program to determine an ETES optimal operating schedule that maximizes electricity sales less maintenance costs caused by operation and cycling given temporal-varying grid electricity prices. Our contributions include a mixed-integer linear program for energy arbitrage of an ETES system, an ETES performance model through a publicly-available software (i.e., SAM), and an exercise of our model through case studies that compare ETES operational strategies and annual financial metrics. With our dispatch optimization model, we were able to improve revenue by 20% compared to a myopic heuristic while reducing the operational cost of the ETES system through decreases in cycle starts, cycles per day, and heater starts.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Hydrogen Risk Assessment Models (HyRAM) (V.3.1) (Technical Reference Manual)

The HyRAM software toolkit provides a basis for conducting quantitative risk assessment and consequence modeling for hydrogen infrastructure and transportation systems. HyRAM is designed to facilitate the use of state-of-the-art science and engineering models to conduct robust, repeatable assessments of hydrogen safety, hazards, and risk. HyRAM includes generic probabilities for hydrogen equipment failures, probabilistic models for the impact of heat flux on humans and structures, and experimentally validated first-order models of hydrogen release and flame physics. HyRAM integrates deterministic and probabilistic models for quantifying accident scenarios, predicting physical effects, and characterizing hydrogen hazards (thermal effects from jet res, overpressure effects from deflagrations), and assessing impact on people and structures. HyRAM is developed at Sandia National Laboratories for the U.S. Department of Energy to increase access to technical data about hydrogen safety and to enable the use of that data to support development and revision of national and international codes and standards. HyRAM is a research software in active development and thus the models and data may change. This report will be updated at appropriate developmental intervals. This document provides a description of the methodology and models contained in HyRAM version 3.1. There have been several impactful updates since version 3.0. HyRAM 3.1 contains a correction to use the volume fraction for two-phase speed of sound calculations; this only affects cryogenic releases in which two-phase ow (vapor and liquid) is predicted in the orifice. Other changes include clarifications that inputs for tank pressure should be given in absolute pressure, not gauge pressure. Additionally, the interface now rejects invalid inputs to probability distributions, and the less accurate single-point radiative source model selection was removed from the interface.

08 HYDROGEN↗

DOE OSTI2020

Refreshing Waste Tracking Software for the 2020's - 20059

The Waste Compliance and Tracking System (WCATS) is a software application designed to manage waste operations from cradle to grave. The system provides the support needed for characterization, generation, processing, and shipment of all waste generated or stored at Los Alamos National Laboratory (LANL). Waste is tracked with task records for containers in real-time via both desktop and mobile device applications. The software allows application administrators to configure sites with appropriate limits for Pu-239 equivalent Curies (PE-Ci), fissile gram equivalents (FGEs), volume limits, as well as custom limits for site-specific technical safety requirements (TSRs). WCATS was developed based on a need at Los Alamos National Laboratory for a modern, customizable waste tracking system that could support many different waste types. The application is built using Oracle{sup R} database and Oracle{sup R} ADF Swing application framework. The technologies and software frameworks selected for the system were finalized in 2006. Although Oracle{sup R} Database has proven to be robust, the user interface and application framework interface with the database have been deprecated and need to be replaced. The technologies selected for modernization follow the open-source model, which allows the WCATS team to more easily recruit developers, improve the speed of development, and ease the deployment and adoption of the system. The tools that support the re-engineering effort are Vue.js{sup R}, a JavaScript{sup R} framework, and Spring Boot{sup R}, an API (application programming interface) development tool written in Java{sup R}. An API allows for simplified communication from the database to the user interface of the application. In addition, legacy Crystal Reports{sup R} will be replaced with reports developed with the open source Pentaho{sup TM}f Report Designer community edition. Because the application uses HTML and JavaScript{sup R}, it builds upon tools that have been used for many years in web development. These tools have large community support and their proven track record will ensure that enhancements to WCATS can continue for years to come. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2014

Search for Gravitational Waves Associated with Gamma-Ray Bursts Detected by the Interplanetary Network

We present the results of a search for gravitational waves associated with 223 gamma ray bursts (GRBs) detected by the InterPlanetary Network (IPN) in 2005-2010 during LIGO's fifth and sixth science runs and Virgo's first, second, and third science runs. The IPN satellites provide accurate times of the bursts and sky localizations that vary significantly from degree scale to hundreds of square degrees. We search for both a well-modeled binary coalescence signal, the favored progenitor model for short GRBs, and for generic, unmodeled gravitational wave bursts. Both searches use the event time and sky localization to improve the gravitational wave search sensitivity as compared to corresponding all-time, all-sky searches. We find no evidence of a gravitational wave signal associated with any of the IPN GRBs in the sample, nor do we find evidence for a population of weak gravitational wave signals associated with the GRBs. For all IPN-detected GRBs, for which a sufficient duration of quality gravitational wave data are available, we place lower bounds on the distance to the source in accordance with an optimistic assumption of gravitational wave emission energy of 10(exp−2) solar mass c(exp 2) at 150 Hz, and find a median of 13 Mpc. For the 27 short-hard GRBs we place 90% confidence exclusion distances to two source models: a binary neutron star coalescence, with a median distance of 12 Mpc, or the coalescence of a neutron star and black hole, with a median distance of 22 Mpc. Finally, we combine this search with previously published results to provide a population statement for GRB searches in first-generation LIGO and Virgo gravitational wave detectors and a resulting examination of prospects for the advanced gravitational wave detectors.

gravitational wave detectors↗

NASA NTRSDate not supplied

Urban Air Mobility Noise: Current Practice, Gaps, and Recommendations

AN Air Mobility (UAM) is an opportunity for aviation to improve transportation systems across the world. Representative UAM vehicle attributes include electrical vertical takeoff and landing (eVTOL) vehicles that can accommodate up to 6 passengers (or equivalent cargo), are possibly autonomous, perform missions of up to 100 nautical miles at altitudes up to 3000 ft. above ground level, have flight speeds up to 200 knots, and weigh between 800 and 8000 pounds. Along with the many anticipated benefits, there will be noise issues that need to be addressed. In 2018, NASA formed an Urban Air Mobility Noise Working Group (UNWG) to assemble noise experts from industry, universities and government agencies to identify, discuss, and address UAM noise issues. This oral presentation summarizes technology gaps and goals associated with four areas of interest: Tools & Technologies, Ground & Flight Testing, Human Response & Metrics, and Regulation & Policy, and is drawn from a draft white paper [1] by the same title. Tools & Technologies include noise prediction tools and noise reduction technologies that have been developed for conventional rotorcraft and fixed-wing vehicles that may be applicable or need to be modified for UAM. Prediction tools need to be able to account for variable speed rotors and other temporal variation effects that impact community noise. A reprioritization of noise sources needs to be done since UAM vehicles include multiple rotors/propellers, often in proximity to one another and/or the airframe, with dynamic transition, and new noise sources such as electric motors or hybrid-electric propulsion. Scattering and propagation methods need to be developed that include the vehicle components and surfaces near a receiver such as buildings and vertiports. Validation databases are needed to quantify prediction uncertainties. Prediction tools used to evaluate community noise will need source models appropriate for a wide range of UAM vehicles. Existing noise reduction technologies need to be evaluated and new noise reduction technologies should be developed in anticipation of future noise requirements. Although the prediction and treatment of interior cabin noise is a secondary goal, it is recognized that new tools and methods may be needed due to the uniqueness of the vehicle design and the presence of both acoustic and structure-borne loads. Ground & Flight Testing has been a critical part of validating noise reduction technologies and verifying that an air vehicle is ready for certification. UAM vehicles introduce new challenges for test procedures such as different source noise directivity, unsteady sources due to maneuvers, and a variety of takeoff and approach trajectories. The operating environment will be more complex than current aircraft with the introduction of vertiports in populated areas with “urban canyons” making reflections an important part of noise prediction and annoyance. It is expected that new test procedures and measurement methods will be necessary. Consideration will need to be given for both piloted and autonomous operations. Human Response & Metrics may be very different for UAM noise compared to current experience with airport noise. Current metrics used to certify rotorcraft and fixed-wing aircraft may not be as useful for evaluating UAM noise. Operations at lower altitudes may influence annoyance. Psychoacoustic and community testing will be needed to quantify annoyance and assess appropriate metrics. In addition to conventional noise level metrics, considerations such as audibility and temporal variation of the sound may be required. Differences between indoor or outdoor exposure will have an impact with dependence on urban and residential flight paths. Aircraft noise is currently regulated at a national level and typically involves partnerships with the industry to establish regulations. Regulators realize that current policies and procedures may not be appropriate for some of the emerging air vehicles and new procedures may be needed to address UAM noise. Development of new policies and procedures are needed so that local communities do not hastily attempt to establish their own restrictions that will both limit growth of the market and create an inconsistent and confusing regulatory environment. To expedite this development, it is crucial early measurement data are shared through partnership arrangements to support both noise certification and noise modeling/noise assessment. At the same time, an effective engagement strategy should be developed to address local community noise issues associated with UAM vehicles and flight operations as they arise.

urban air mobility↗

DOE OSTI2021

Data from: Learning coagulation processes with combinatorially-invariant neural networks

This dataset contains all the necessary information to recreate the study presented in the paper entitled "Learning coagulation processes with combinatorially-invariant neural networks". This consists of (1) the aggregated output files used for machine learning, (2) the machine learning codes used to learn the presented models, (3) the PartMC model source code that was used to generate the simulation data and (4) the Python scripts used construct the scenario library for training and testing simulations. This data was used to investigate a method (combinatorally-invariant neural network) for learning the aerosol process of coagulation. This data may be useful for application of other methods.

Atmospheric chemistry↗

DOE OSTI2023

On the influence of over-parameterization in manifold based surrogates and deep neural operators

Constructing accurate and generalizable approximators (surrogate models) for complex physico-chemical processes exhibiting highly non-smooth dynamics is challenging. The main question is what type of surrogate models we should construct and should these models be under-parameterized or over-parameterized. In this work, we propose new developments and perform comparisons for two promising approaches: manifold-based polynomial chaos expansion (m-PCE) and the deep neural operator (DeepONet), and we examine the effect of over-parameterization on generalization. While m-PCE enables the construction of a mapping by first identifying low-dimensional embeddings of the input functions, parameters, and quantities of interest (QoIs), a neural operator learns the nonlinear mapping via the use of deep neural networks. Here, we demonstrate the performance of these methods in terms of generalization accuracy by solving the 2D time-dependent Brusselator reaction-diffusion system with uncertainty sources, modeling an autocatalytic chemical reaction between two species. We first propose an extension of the m-PCE by constructing a mapping between latent spaces formed by two separate embeddings of the input functions and the output QoIs. To further enhance the accuracy of the DeepONet, we introduce weight self-adaptivity in the loss function. We demonstrate that the performance of m-PCE and DeepONet is comparable for cases of relatively smooth input-output mappings. However, when highly non-smooth dynamics is considered, DeepONet shows higher approximation accuracy. We also find that for m-PCE, modest over-parameterization leads to better generalization, both within and outside of distribution, whereas aggressive over-parameterization leads to over-fitting. In contrast, an even highly over-parameterized DeepONet leads to better generalization for both smooth and non-smooth dynamics. Furthermore, we compare the performance of the above models with another recently proposed operator learning model, the Fourier Neural Operator, and show that its over-parameterization also leads to better generalization. Taken together, our studies show that m-PCE can provide very good accuracy at very low training cost, whereas a highly over-parameterized DeepONet can provide better accuracy and robustness to noise but at higher training cost. In both methods, the inference cost is negligible.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Verification and Validation of Flow Confinement Modeling using OpenFAST Source Panel Method

Blockage or flow confinement effects in scale wind and water turbine testing in experimental facilities, including wind and water tunnels and tow tanks, can significantly alter incident aerodynamic and hydrodynamic loads, turbine performance (thrust, torque, power output), and wake dynamics compared to unconfined conditions. As scale model tests are key sources of OpenFAST model validation measurements, directly modeling flow confinement and its effects in OpenFAST enables improved model validat

Kim, Dongyoung [Sandia National Laboratories (SNL-↗

DOE OSTI2022

Classification of animal sounds in a hyperdiverse rainforest using convolutional neural networks with data augmentation

To protect tropical forest biodiversity, we need to be able to detect it reliably, cheaply, and at scale. Automated detection of sound producing animals from passively recorded soundscapes via machine-learning approaches is a promising technique towards this goal, but it is constrained by the necessity of large training data sets. Using soundscapes from a tropical forest in Borneo and a Convolutional Neural Network model (CNN), we investigate i) the minimum viable training data set size for accurate prediction of call types (‘sonotypes’), and ii) the extent to which data augmentation and transfer learning can overcome the issue of small and imbalanced training data sets. We found that even relatively high sample sizes (>80 per sonotype) lead to mediocre accuracy, which however improved significantly with data augmentation and transfer learning, including at extremely small sample sizes (3 per sonotype), regardless of taxonomic group or call characteristics. Neither transfer learning nor data augmentation alone achieved high accuracy. Our results suggest that transfer learning and data augmentation could make the use of CNNs to classify species’ vocalizations feasible even for small soundscape-based projects with many rare species. Retraining our open-source model requires only basic programming skills which makes it possible for individual conservation initiatives to match their local context, in order to enable more evidence-informed management of biodiversity.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A method for assessing economic, environmental, and reliability tradeoffs of interregional transmission connecting ERCOT (the Texas grid) to the eastern and western grids

Reliable development of the power grid is an evolving concern for humanity due to extreme weather that frequently threatens power sector infrastructure. The state of Texas is a uniquely structured testbed for grid planners to study when looking for solutions to development, innovation, and overcoming such challenges. Because of its size and islanded structure, Texas is small enough to model, but big enough to matter. Texas is a global leader in energy production, energy consumption, and maintains an unusually diverse fuel mix. In addition, the state has experienced winter freezes, heat waves, wind storms, droughts and floods that have threatened power sector infrastructure or caused recent blackouts and calls for demand side conservation. One of the most devastating of these events was the North American winter storm, dubbed “Winter Storm Uri” by the Weather Channel, that froze the region in February 2021 and led to an extended power outage event that put the majority of Texan residents in darkness for days. While preparing to avoid such outage events in the future, various tools have been proposed to improve grid reliability, including energy efficiency, demand response, and distributed energy resources. An additional option would be to develop interregional transmission that connects the Texas grid to other national grids. To assess the merits of this idea, we developed a novel, universally-applicable and internationally-relevant framework to study how the Texas grid would evolve alongside access to various interregional ties. This method allows us to stress the synthetic grid structure and analyze how it would respond to the shock of a simulated winter storm event. Our method leverages open-source modeling tools, such as PowerGenome, pyGRETA, and GenX to synthesize unique zonal grid data, construct a consolidated network of model regions, and simulate different developmental pathways of capacity expansion and operational dispatch. We demonstrate our method with an analysis connecting the Electric Reliability Council of Texas (ERCOT), the grid that serves most of Texas, the Western Electricity Coordinating Council (WECC), the grid that serves the western half of the contiguous U.S., and the Eastern Interconnect, the grid that serves the eastern half of the contiguous U.S. Our results indicate that the cost-optimal capacity of interregional transmission connecting the ERCOT grid to other grids lies between 9–13 GW assuming baseline conditions. Building this amount of connecting capacity in one or multiple directions lowers the costs and emissions of development and operation by up to $16 billion and 257 million metric tonnes (MMT) respectively. Additionally, our results show that the interregional connections between ERCOT and other national grids reduce the amount of total load shed required through mild winter storm events. However, our results also show that there is a threshold of very extreme winter storm conditions, spanning multiple service areas, above which the connections exacerbate resource adequacy problems. Therefore, the results indicate that the connections need to be carefully planned alongside the rest of the grid infrastructure to avoid over-reliance on specific resources or technology options.

24 POWER TRANSMISSION AND DISTRIBUTION↗