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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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141 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Outage Forecast-Based Preventative Scheduling Model for Distribution System Resilience Enhancement

Distribution system resilience enhancement is an important topic to ensure customers have access to power supply during extreme events. In fact, certain weather-related extreme events can be predicted ahead of time. Therefore, it is important to investigate how to predict grid outages using extreme weather forecasts, and how outage predictions can be incorporated into distribution system resilience enhancement. In this paper, a preventative scheduling model for distribution systems is proposed. The model targets at allocating resources, especially mobile responsive resources such as mobile backup generators and mobile energy storage systems, to prepare for an extreme event in the day-ahead context. To achieve efficient resource allocation and scheduling, a machine learning-based outage prediction module is developed to predict vulnerable or risky segments of the distribution system based on historical operating records and extreme weather event forecast. By integrating the outage prediction results into the scheduling model, optimal resource allocation can be derived to help distribution systems prepare for an upcoming event and improve resilience performance. A real distribution feeder in North Carolina, U.S. is used in the case study to validate the proposed approach.

distributed energy resources↗
DOE OSTI2026

V2XConnect: Harmonizing the Landscape of Bidirectional Charging Codes, Standards, and Communication Protocols

The utility grid is constantly evolving with new generation sources and diverse loads from backup generators, backup batteries, rooftop photovoltaics (PV), and flexible charging loads. Each of these technologies presents consumers with an opportunity to work with their local utility to promote grid stability, reliability, and affordability. Through a wide range of programs, utilities are incentivizing the use of these resources to benefit the grid. However, especially for devices with inverters that supply power back to the grid, it is important that these assets comply with local grid codes to ensure human safety, power quality, and grid reliability. One of the most promising of these new technologies is the bidirectional electric vehicle (EV) coupled with bidirectional electric vehicle supply equipment (EVSE). This report introduces the technologies that enable bidirectional charging and how these systems operate. It then outlines the necessary interconnection codes and industry standards that govern the operations of grid-tied inverters, including those in bidirectional charging. A key challenge is then outlined from the complex landscape of communication protocols and proprietary systems designed to coordinate these grid assets. Finally, a single gateway solution is proposed to harmonize communications between all assets across an entire site for a cohesive response to grid codes and utility programs that is flexible enough to support a range of market options.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

High Performance Computing Peak Shaving for Microreactor Operation

There are multiple nuclear microreactors currently under development that are designed to provide autonomous power for as many as ten or more years without refueling and are designed to power high performance computing (HPC) datacenters. But the load-follow speeds for a nuclear microreactor will be much slower than grid power and slower than the power variance typical of a HPC system. HPC datacenters experience peak power load variance driven by several factors ranging from the operation of cooling systems to remove heat from the servers to supporting a wide range of user application workflows and architectures each with different power signatures. One mechanism to support the limited load-follow of a microreactor is peak shaving where an energy storage mechanism is used to shed peak load and reduce significant power variance. This work explores peak electrical load shaving using uninterruptible power supply (UPS) systems designed for HPC support in the context of peak shaving when operating using a nuclear microreactor with a load-follow limited to 10% of load per minute. Using a self contained HPC datacenter complete with stand-alone cooling system and provisioned with an x86 cluster, an ARM cluster, and a graphics processing unit (GPU) cluster, peak shaving for microreactor operation using the UPS battery backup is explored while running two classes of typical HPC user applications. HPC architecture suitability for microreactor operation under this type of peak shaving is examined.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Real Time Applications Using Linear State Estimation Technology (RTA/LSE)

Electric Power Group, LLC (EPG) was awarded DOE OE0000849 to design, develop, and demonstrate three real-time applications for monitoring power system stability. The applications use phasor measurements and processing by a linear state estimator (LSE) to improve system visibility and accuracy. These applications leverage the large and expanding phasor measurement systems being deployed by utilities throughout the world, and specifically in the United States. Phasor measurements provide a much more detailed view of the power systems than traditional SCADA systems, so can provide better visibility of events and an early warning of developing problems. These three applications are real-time contingency analysis (RTCA), area angle monitoring (AAM), and a voltage stability index (VSI). The project was fully completed as proposed. All three applications were successfully implemented and demonstrated. There were challenges the caused project delays, however. While the RTCA is based on well-known concepts and readily available tools, its deployment using phasor measurements meant it needed to be adapted to sometimes sparse measurements and limited coverage areas. EPG met these challenges with extensive modeling and testing. The AAM is based on unproven research, so EPG had to deal with a lot of unknowns and develop additional methods for practical implementation. These activities took more time than anticipated, so required extra time for development. In addition, there was difficulty getting adequate measurement locations for successful deployment and utility installation delays. Consequently, the project required a time extension to complete. These three real-time applications, RTCA, AAM, and VSI, use only phasor measurement input. Since they do not require the EMS or other measurement systems, they can serve as a backup and security check for the EMS. Phasor measurements are validated and extended by an LSE. The LSE will always produce an output even when some inputs are impaired, so the RT applications will operate even when the EMS state estimator does not converge and the applications it supports cannot function. Consequently, these RT applications can provide valuable additional reliability for control center operation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Techno-economic implications and cost of forecasting errors in solar PV power production using optimized deep learning models

Accurate solar Photovoltaic (PV) power forecasting is important for enhancing both the performance and economic feasibility of PV systems. This study evaluates several deep learning models, including Dense Neural Networks (DNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and a hybrid LSTMCNN model, for predicting PV power production one day in advance. Prior to optimization, the models exhibited relatively high errors, with the best model (DNN) achieving a Root Mean Square Error (RMSE) of 31.13 kW and a coefficient of determination (R 2 ) of 62.15 %. After employing Bayesian optimization, the LSTM-CNN model demonstrated the best performance, with the RMSE reduced to 9.79 kW and R 2 improved to 97.62 %, showcasing significant enhancement in predictive accuracy. Here, the economic evaluation considered three cases: rewards for underestimation (0.08 USD/kWh), no rewards, and penalties for both over-and underestimation (120 % of the utility tariff). In the rewards scenario, the LSTM-CNN model reduced the Levelized Cost of Electricity (LCOE) by 4 %, while in the penalty scenario, a backup diesel generator would have increased the LCOE by 49 %. Additionally, the LSTM-CNN model minimized financial losses, achieving the lowest penalties and maximizing net cash flow compared to other models, demonstrating its overall technical and economic superiority.

Deep learning↗
DOE OSTI2023

Impact of system parameters and geospatial variables on the reliability of residential systems with PV and energy storage

A reliable power supply is the foundation of modern society, enabling technologies used to function within a society. Residential systems are places where the end users directly consume power, enabling technologies to sustain life. With the emergence of behind-the-meter resources, the end-users have some control over power supply reliability. The intermittency and variability of these resources impact residential system reliability. In this work, we study the reliability performance of a grid-supplemented residential system with behind-the-meter Distributed Energy Resources(DER) subject to various system parameters and geospatial variables. We propose a multistate reliability model for the behind-the-meter microinverter-based Photo Voltaic (PV) system and integrated inverter-based energy storage (ES) system. A sequential Monte Carlo method is then presented to evaluate the reliability indices for the residential system with behind-the-meter DERs as the main supply and the grid as the backup. The sequential Monte Carlo method is used to analyze the reliability performance of the residential model at the top 100 populous counties of the United States, where actual load and solar irradiance data at the counties is used. In the analysis, the sensitivity of indices to system parameters such as DER size and the sensitivity of the indices to climate zone and gross horizontal irradiation (GHI), which affects the load conditions and the PV output of the residential system, is performed. The analysis results show that the system parameters and the geospatial variables significantly impact the residential system’s reliability. The insights from this analysis will be of immense value to the distribution system planners to provide zone-specific guidelines for DER system sizing and toward the evolution of utility business models. Finally, the methodology developed can be used to extend the analysis to other locations.

14 SOLAR ENERGY↗
DOE OSTI2021

Resilience and fault tolerance in high-performance computing for numerical weather and climate prediction

Progress in numerical weather and climate prediction accuracy greatly depends on the growth of the available computing power. As the number of cores in top computing facilities pushes into the millions, increased average frequency of hardware and software failures forces users to review their algorithms and systems in order to protect simulations from breakdown. This report surveys hardware, application-level and algorithm-level resilience approaches of particular relevance to time-critical numerical weather and climate prediction systems. A selection of applicable existing strategies is analysed, featuring interpolation-restart and compressed checkpointing for the numerical schemes, in-memory checkpointing, user-level failure mitigation and backup-based methods for the systems. Numerical examples showcase the performance of the techniques in addressing faults, with particular emphasis on iterative solvers for linear systems, a staple of atmospheric fluid flow solvers. The potential impact of these strategies is discussed in relation to current development of numerical weather prediction algorithms and systems towards the exascale. Trade-offs between performance, efficiency and effectiveness of resiliency strategies are analysed and some recommendations outlined for future developments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Report on local data recovery approaches suitable for weather and climate prediction (Deliverable 1.3) (V.1.0)

Numerical weather and climate prediction rates as one of the scientific applications whose accuracy improvements greatly depend on the growth of the available computing power. As the number of cores in top computing facilities pushes into the millions, increasing average frequency of hardware and software failures forces users to review their algorithms and systems in order to protect simulations from breakdown. This report surveys approaches for fault-tolerance in numerical algorithms and system resilience in parallel simulations from the perspective of numerical weather and climate prediction systems. A selection of existing strategies is analyzed, featuring interpolation-restart and compressed checkpointing for the numerics, in-memory checkpointing, user-level failure mitigation-based and backup-based methods for the systems. Numerical examples showcase the performance of the techniques in addressing faults, with particular emphasis on iterative solvers for linear systems, a staple of atmospheric fluid flow solvers. The potential impact of these strategies is discussed in relation to current development of numerical weather prediction algorithms and systems towards the exascale. Trade-offs between performance, efficiency and effectiveness of resiliency strategies are analyzed and some recommendations outlined for future developments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The Best of Both Worlds: Combined Thermal and Battery Storage for Widespread Building Decarbonization

To meet 2050 decarbonization targets, widespread building electrification is a critical complement to clean power generation. Behind-the-meter storage (BTMS) (e.g., battery electric energy storage [EES] and thermal energy storage [TES]) integrated with buildings or building end uses to store and supply energy at optimal times can minimize burdens associated with operation, planning, and upgrades to the electrical grid sometimes triggered by building electrification. Such BTMS systems can serve the dual purpose of providing enhanced resilience at the building and grid level, and support the deployment of renewable generation needed for wide-scale decarbonization. While TES can cost-effectively shed and shift thermal loads, it cannot generally backup or shift non-thermal building end uses. EES, by contrast, is more expensive, but applicable to all end uses (i.e., thermal and electrical loads). Combined together, these storage systems can be traded off against one another to perform optimally in meeting demand flexibility, decarbonization goals, and energy resilience of the buildings at a lower total system cost. This paper proposes a framework to define BTMS benefits, provides four illustrative electrification scenarios using TES and EES, and discusses the combined TES/EES benefits with building energy modeling results. The paper also highlights potential barriers to adoption of BTMS and a path forward.

buildings↗
DOE OSTI2026

Downwelling Shortwave and Longwave Irradiance from CC-RIDER on Mount Soledad

The Clouds and Climate - Remote Integrated Deployment of Radiometers (CC-RIDER) is a suite of five Eppley Laboratory (Inc.) instruments that was deployed during EPCAPE at the secondary Mount Soledad site in La Jolla, CA. A primary and backup Precision Spectral Pyranometer (PSP Primary, PSP Backup) measured broadband downwelling shortwave irradiance in the spectral interval 280-2800 nm. A third Precision Spectral Pyranometer (PSP NIR) was fitted with a near-infrared long pass filter and measured downwelling shortwave irradiance in the spectral interval 780-2800 nm. A Total Ultraviolet Radiometer (TUVR) measured broadband downwelling broadband ultraviolet irradiance in the spectral interval 295-385 nm. A Precision Infrared Radiometer (PIR) Pyrgeometer measured downwelling broadband longwave irradiance in the spectral interval 3.5 - 50 microns. Data collection began on 18 April 2023 at 21:31 UTC and ended on 20 February 2024 at 22:34 UTC. Data were recorded by a Campbell Scientific (Inc.) CR1000X datalogger in one-minute intervals, for a total of 443584 data records. The datalogger was solar powered enabling data collection to proceed without interruption from start to finish.

Clouds and Climate – Remote Integrated DEployement↗
DOE OSTI2023

Sensitivity Analysis of Effectiveness of FLEX Strategies Using a CDF-Based Importance Measure Under Accident Conditions

One of the lessons learned from the Fukushima Daiichi nuclear power plant (NPP) accident is strengthening the station blackout (SBO) mitigation capabilities by enhancing defense in depth for all existing and new NPPs. One of the possible remedies is diverse and flexible coping strategies (FLEX). The objective of this study is to address the benefits of FLEX in various accident scenarios in terms of both risk and cost. FLEX was originally devised against SBO accidents. In this research, we investigate the fundamental plant responses against accidents considering two fundamentally different cases: accidents that lead to high pressure on the primary side and accidents that lead to low pressure on the primary side. Several uncertainties are associated with the characteristics of the FLEX portable equipment. Specifically, the time for FLEX deployment may depend on several factors such as type of accident, point of injection, availability of safety systems, battery backup timings, and human actions. This study utilizes a dynamic risk assessment framework to analyze accident scenarios and suggests a novel importance measure, which is a cumulative distribution function– based importance metric that characterizes the influence of input distribution on complete output distribution. The importance of the existing and newly developed FLEX strategy based on risk significance is illustrated with examples. The suggested measure provides clear insight into how FLEX affects risk of the whole system and additional risk margins thanks to new safety systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

A Reverse Logistics Tool For Ev Battery Recycling And Repurposing,

The demand for electric vehicles (EVs) in the United States is projected to rise significantly, with sales expected to reach approximately 4.1 million units by 2030. However, the U.S. remains heavily reliant on imports for the batteries and critical raw materials—such as lithium, cobalt, and nickel—that power these vehicles. As of 2024, around 70% of these imports originate from China. This dependency has become even more precarious following China’s imposition of export restrictions in April 2025, a retaliatory move against U.S. tariffs. These developments highlight the strategic vulnerabilities posed by China’s dominant position in the critical materials market. Compounding the issue, decades of intensive extraction have severely depleted global reserves of critical materials, widening the gap between supply and growing demand. This situation underscores the urgent need for the U.S. and other nations to diversify their sources of critical materials and enhance domestic capabilities to secure these resources—an essential step toward ensuring long-term energy security. At the end of their lifecycle—whether due to the battery’s degradation or the retirement of the vehicle—EV batteries are often improperly disposed of or sent to landfills. However, many of these batteries still retain usable capacity and can follow one of three alternative pathways: (a) Re-used: deployed in another vehicle with a shorter driving range, (b) Re-purposed: utilized act as a backup storage/power for data centers, solar panels, and e-scotters or (c) Recycled: broken down to recover the critical materials. To that end, the proposed tool (REBORN) is designed to optimize the reverse logistics network for battery repurposing and recycling. Its goal is to minimize associated costs while identifying optimal locations for battery collection and processing. Ultimately, REBORN ensures that each battery is used to its fullest potential.

Srinivas, SrikarV. [Idaho National Laboratory (INL↗
DOE OSTI2020

Advanced High-Performance Computational Modeling of the Seismic Response of High-Hazard and/or Nuclear Facilities and Critical Infrastructure at the NNSS

New methods for predicting the amplitude and variability of ground shaking from earthquakes (and explosions) are needed for seismic hazard analysis for buildings, nuclear power plants, and critical infrastructure at the NNSS. We are comparing existing 1-D and new 3-D geophysical methods for estimating the shear-wave velocity structure in the upper 30 meters of the ground surface (Vs30), which plays a major role in ground motion amplification and seismic response of buildings. We evaluate the performance of these methodologies at the U1a Complex at the NNSS and develop simple 1-D and high-resolution 3-D Vs30 models. We then emplace these high-resolution models into a background seismic velocity model. We will collaborate with Lawrence Livermore National Laboratory (LLNL) to conduct numerical modeling of the ground shaking at the NNSS using their high-performance computing technology and state-of-the-art ground motion simulation methodology. The primary work that was completed in FY 2019 was to acquire the seismic systems and familiarize staff at the NNSS with their use. We also worked on developing a collection plan with the Device Assembly Facility (DAF) at the NNSS, but due to time constraints and other ongoing projects at the DAF, we had to use U1a as a backup. We were able to coordinate the seismic survey, and we will complete the collection of seismic data in FY 2020. Additionally, during FY 2019, we completed the geologic framework model (GFM) for the U1a Complex and modeled the Yucca fault. LLNL worked with us through FY 2019 to prepare the data files for their modeling software and tested the software for reliability. In FY 2020 we will develop the end-to-end capability so that any facility could easily be modeled and the expected shaking from a local earthquake understood. The work in FY 2020 will include building fault models from the GFM and finalizing the velocity model analysis. The final simulations will be run for multiple rupture models, and final assessments will demonstrate the seismic hazard at the U1a Complex.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Applications of Measuring and Valuing Resilience in Energy Systems

The electricity sector is vulnerable to numerous hazards that are being exacerbated by climate change, which can cause an increase in the hazards' frequency and intensity. Consumers, system regulators, system operators, and communities are now preparing to mitigate the increased risks posed by climate change. New York State's energy infrastructure resilience can be increased with targeted investments including but not limited to installing emergency backup systems, integrating microgrid solutions, weatherizing buildings, increasing energy efficiency, adding redundancy, investing in restoration and recovery, and hardening critical components. Such investments can reduce the likelihood, impact, and consequences of disruptive events but can also increase capital and operating costs. A barrier to prioritizing investments in resilience is that there is no widely established method for quantifying and assigning the benefits of resilience investments across various stakeholders. Decision-makers need better information detailing the value of resilience improvements. Developing methods to quantify, value, and price resilience helps meet resilience needs in an effective manner that also supports broader societal welfare. This report lays out considerations for quantifying and valuing resilience, discusses the current state of resilience valuation tools, and provides case studies of resilience projects that demonstrate how resilience attributes could be measured while highlighting broader, project-specific challenges to increasing resilience. Further, we present insights into methods and challenges to measuring, valuing, and enacting resilience investments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Digital Grid Twin–Direct Communication Scheme Test Bed for Assessing Relay-to-Relay Radio Antenna and Optical Fiber Performance and Misoperations

This study introduces a novel “Digital Grid Twin–Direct Communication Scheme” test bed. This advanced platform evaluates point-to-point communication between transmitter and receiver relays with optical fiber and radio omnidirectional antenna systems, implemented at the Advanced Protection lab in the Grid Research Innovation and Development Center at Oak Ridge National Laboratory. The increased diversity of energy sources has led to more protective relay misoperations. In North America, microgrid protection schemes now use point-to-point communication along distribution lines between relays to implement advanced logic in nonradial grids that include both high- and low-inertia generators. This trend challenges utilities to minimize misoperations while ensuring rapid fault clearance and accurate selectivity coordination between primary and backup relays. This study assesses relay-to-relay communication schemes by introducing an advanced testing platform based on a digital grid twin protection test bed using a synchronized time source system. The platform evaluates the communication system using radio antennas or optical fiber links by integrating protective relays that operate breakers within the digital twin and record relay events and communication signals. In the experiments, transmitter and receiver relays were configured with inverse time overcurrent and breaker trip detection logic to assess the total time of the communication protection schemes based on the sum of the relay protection element operating time, radio latency, propagation delay, baud rate delay, and relay processing time. These delays were derived from recorded relay events and communication signals from the interface of a real-time simulator set as a digital grid twin. The test bed successfully simulated various electrical faults along a distribution line while ensuring effective and reliable point-to-point communication between transmitter and receiver relays. The radio antenna communication system exhibited latency because of the radio. This latency depends on the baud rate setting and type of radio application; in general, the higher the baud rate, the lower the radio latency. The measured radio latency (for Mirrored Bits with an encryption card at 9,600 bps) was about 9–10 ms. Additionally, calculated propagation delay per mile for radio antennas and optical fiber was 5.36 µs/mi and 8.04 µs/mi, respectively. Optical fiber communication did not demonstrate radio latency. Instead, the protection element operating time depends mainly on the protection logic function set in the relay, and the relay processing time depends on the processing rate of the relay in samples per power system cycle.

24 POWER TRANSMISSION AND DISTRIBUTION↗