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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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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Solar and battery can reduce energy costs and provide affordable outage backup for US households

Distributed energy resources are promising solutions for household energy affordability and resilience as weather extremes and aging infrastructure intensify grid reliability risks. This study presents a comprehensive nationwide assessment of over 500,000 U.S. households, evaluating economic and backup viability of solar-battery systems. We find that 60% of households could reduce electricity costs with average savings of 15%, while 63% of households could achieve affordable backup power during power outages covering an average of 51% of their essential energy needs. However, these benefits show limited alignment with areas of greatest need, particularly in regions facing high outage risks. We also identify significant disparities in access to solar and battery, with less-populated and disadvantaged communities showing consistently lower viability. Furthermore, these findings demonstrate the need for targeted policy interventions to ensure equitable access to solar-battery benefits, especially as states transition from net energy metering to other electricity tariff policies.

14 SOLAR ENERGY↗
DOE OSTI2024

Transitioning GlideinWMS, a multi domain distributed workload manager, from GSI proxies to tokens and other granular credentials

GlideinWMS is a distributed workload manager that has been used in production for many years to provision resources for experiments like CERN’s CMS, many Neutrino experiments, and the OSG. Its security model was based mainly on GSI (Grid Security Infrastructure), using X.509 certificate proxies and VOMS (Virtual Organization Membership Service) extensions. Even when other credentials, like SSH keys, were used to authenticate with resources, proxies were also added all the time, to establish the identity of the requestor and the associated memberships or privileges. This single credential was used for everything and was, often implicitly, forwarded wherever needed. The addition of identity and access tokens and the phase-out of GSI forced us to reconsider the security model of GlideinWMS, to handle multiple credentials which can differ in type, technology, and functionality. Both identity tokens and access tokens are supported. GSI proxies even if no more mandatory, are still used, together with various JWT (JSON Web Token) based tokens and other certificates. The functionality of the credentials, defined by issuer, audience, and scope, also differ: a credential can allow access to a computing resource, or can protect the GlideinWMS framework from tampering, or can grant read or write access to storage, can provide an identity for accounting or auditing, or can provide a combination of any the formers. Furthermore, the tools in use do not include automatic forwarding and renewal of the new credentials so credential lifetime and renewal requirements became part of the discussion as well. In this paper, we will present how GlideinWMS was able to change its design and code to respond to all these changes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

The ePIC Simulation Campaign Workflow on the Open Science Grid

The ePIC collaboration is realizing the first experiment of the future Electron-Ion Collider (EIC) at the Brookhaven National Laboratory that will allow for a precision study of the nucleons and the nucleus at the scale of sea quarks and gluons through the study of electron-proton/ion collisions. This paper will discuss the current workflow for running centralized simulation campaigns for ePIC on the Open Science Grid (OSG) infrastructure. This involves monthly releases of ePIC software and container deployments to CVMFS, generation of input datasets in HepMC format according to collaboration-defined policy, using Snakemake in CI/CD for validation and benchmarking, and submitting jobs to the OSG condor scheduler for opportunistic running on available resources. File transfers utilize XrootD, and Rucio is used for data management. The workflow is continuously refined to improve daily throughput (currently 50-100k core hours per day) and minimize job failures. Since May 2023, monthly simulation campaigns employing the workflow have cumulatively used over 20 million core hours on the OSG and produced over 350 TB of simulation data. The campaigns incorporate simulations for the broad science program of the EIC and are actively used for the detector and physics studies in preparation of the Technical Design Report (TDR).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Using Artificial Intelligence to Improve Reliability and Operational Efficiency of Small-Scale Hydroelectric Distributed Generation

Reliability and resilience are critical concerns for distributed generation (DG) at the rural electric level. The integration of renewable energy sources, such as small-scale hydroelectric distributed generators (hydro DGs), introduces operational challenges, particularly regarding aging infrastructure and grid stability. Artificial Intelligence (AI)-driven Machine Learning (ML) models and applications of Large Language Models (LLMs) offer promising solutions for optimizing DG operations and enhancing resilience. This paper explores AI-based models for improving efficiency, fault resolution, and outage mitigation in small-scale hydro DGs. Furthermore, it highlights the development of a centralized, AI-powered information portal for rural electric cooperatives and municipalities. The research evaluates hydro DG plant models and discusses the applicability of AI-powered question-answering tools for real-time operations, focusing on statistical data, load flow, voltage regulation, and generation power. The findings demonstrate AI’s potential to transform DG management to ensure greater stability and resilience in rural electric grids.

Bhattacharyya, Arjun [ORNL] (ORCID:000900060976046↗
DOE OSTI2020

Offshore Wind Technology Data Update (2019) [Slides]

The 2019 Offshore Wind Technology Data Update compiles information from peer-reviewed literature, market reports, press releases, industry news reports, manufacturer specification sheets, and offshore wind project announcements to provide a comprehensive snapshot of the state of the global wind industry in 2019. These data, including capacity projections, project characteristics, technology trends, and cost estimates, are categorized, tabulated, and plotted to provide easily recognizable and understandable summaries. A particular focus is given to the United States offshore wind project pipeline, including project announcements, state procurements, deployment timelines, infrastructure investments, grid interconnections, and other related developments. The update is intended to be used as a reference document by researchers, analysts, industry practitioners, and government officials.

17 WIND ENERGY↗
DOE OSTI2021

FTS3: Data Movement Service in containers deployed in OKD [Slides]

The File Transfer Service (FTS3) is a data movement service developed at CERN which is used to distribute the majority of the Large Hadron Collider's data across the Worldwide LHC Computing Grid (WLCG) infrastructure. At Fermilab, we have deployed FTS3 instances for Intensity Frontier experiments (e.g. DUNE) to transfer data in America and Europe, using a container-based strategy. In this article we summarize our experience building docker images based on work from the SLATE project (slateci.io) and deployed in OKD, the community distribution of Red Hat OpenShift. Additionally, we discuss our method of certificate management and maintenance utilizing Kubernetes CronJobs. Finally, we also report on the two different configurations currently running at Fermilab, comparing and contrasting a Docker-based OKD deployment against a traditional RPM-based deployment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Guide to the Distributed Energy Resource Risk Management Framework

The emergence of distributed energy resources (DERs) has transformed the electric power sector and will likely have even more profound impacts on the future evolution of the United States energy sector as it modernizes and becomes more reliant upon complex informatics programming and systems to ensure that our power grid remains safe from malicious interference. To mitigate risks associated with the increased and diversified use of DERs, the Distributed Energy Resource Cybersecurity Framework (DER-CF) was developed in 2019. The National Renewable Energy Laboratory extended the scope of the DER-CF to include the RMF. To address the challenges faced by federal energy managers and energy system stakeholders in applying the RMF to DER systems, the Distributed Energy Resource Risk Manager (DER-RM) is a six-step process to proactively manage cybersecurity risk in a methodical manner. The DER-RM is independent of the DER-CF's existing assessment, allowing users to focus specifically on the RMF steps. The tools are targeted to different processes - DER-CF enables organizations to perform self-assessments to improve their cybersecurity posture, while DER-RM assists organizations in achieving compliance with specific requirements. This document provides an overview of the DER-RM. The RMF process outlined in this report serves as a guide to diagnose information and operational system threats, gather required materials to comply with industry standards, and document plans for achieving Authority to Operate. Using the DER-RM, federal agencies and other organizations can easily and intuitively follow the RMF process, manage the risks to their grid-edge infrastructure through the integration of their on-site DERs, and comply with appropriate requirements.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

An Inventory of AI-ready Benchmark Data for US Fires, Heatwaves, and Droughts

Extreme weather events, including fires, heatwaves, and droughts, have significant impacts on earth, environmental, and energy systems. Mechanistic and predictive understanding, as well as probabilistic risk assessment of these extreme weather events, are crucial for detecting, planning for, and responding to these extremes. Records of extreme weather events provide an important data source for understanding present and future extremes, but the existing data needs preprocessing before it can be used for analysis. Moreover, there are many nonstandard metrics defining the levels of severity or impacts of extremes. In this study, we compile a comprehensive benchmark data inventory of extreme weather events, including fires, heatwaves, and droughts. The dataset covers the period from 2001 to 2020 with a daily temporal resolution and a spatial resolution of 0.5°×0.5° (~55km×55km) over the continental United States (CONUS), and a spatial resolution of 1km × 1km over the Pacific Northwest (PNW) region, together with the co-located and relevant meteorological variables. By exploring and summarizing the spatial and temporal patterns of these extremes in various forms of marginal, conditional, and joint probability distributions, we gain a better understanding of the characteristics of climate extremes. The resulting AI/ML-ready data products can be readily applied to ML-based research, fostering and encouraging AI/ML research in the field of extreme weather. This study can contribute significantly to the advancement of extreme weather research, aiding researchers, policymakers, and practitioners in developing improved preparedness and response strategies to protect communities and ecosystems from the adverse impacts of extreme weather events. Usage Notes We presented a long term (2001-2020) and comprehensive data inventory of historical extreme events with daily temporal resolution covering the separate spatial extents of CONUS (0.5°×0.5°) and PNW(1km×1km) for various applications and studies. The dataset with 0.5°×0.5° resolution for CONUS can be used to help build more accurate climate models for the entire CONUS, which can help in understanding long-term climate trends, including changes in the frequency and intensity of extreme events, predicting future extreme events as well as understanding the implications of extreme events on society and the environment. The data can also be applied for risk accessment of the extremes. For example, ML/AI models can be developed to predict wildfire risk or forecast HWs by analyzing historical weather data, and past fires or heateave , allowing for early warnings and risk mitigation strategies. Using this dataset, AI-driven risk assessment models can also be built to identify vulnerable energy and utilities infrastructure, imrpove grid resilience and suggest adaptations to withstand extreme weather events. The high-resolution 1km×1km dataset ove PNW are advantageous for real-time, localized and detailed applications. It can enhance the accuracy of early warning systems for extreme weather events, helping authorities and communities prepare for and respond to disasters more effectively. For example, ML models can be developed to provide localized HW predictions for specific neighborhoods or cities, enabling residents and local emergency services to take targeted actions; the assessment of drought severity in specific communities or watersheds within the PNW can help local authorities manage water resources more effectively.

Lin, Xinming↗
DOE OSTI2021

FTS3: Data Movement Service in containers deployed in OKD

The File Transfer Service (FTS3) is a data movement service developed at CERN which is used to distribute the majority of the Large Hadron Collider's data across the Worldwide LHC Computing Grid (WLCG) infrastructure. At Fermilab, we have deployed FTS3 instances for Intensity Frontier experiments (e.g. DUNE) to transfer data in America and Europe, using a container-based strategy. In this article we summarize our experience building docker images based on work from the SLATE project (slateci.io) and deployed in OKD, the community distribution of Red Hat OpenShift. Additionally, we discuss our method of certificate management and maintenance utilizing Kubernetes CronJobs. Finally, we also report on the two different configurations currently running at Fermilab, comparing and contrasting a Docker-based OKD deployment against a traditional RPM-based deployment.

Lobato Pardavila, Lorena↗
DOE OSTI2021

A hub and spoke approach to optimizing energy wheeling of renewable resources

The deployment of zero carbon renewable energy sources needs to increase significantly to support the goal of net zero greenhouse gas emissions by 2050. At the same time energy end use needs to decarbonize. This will change both energy supply and energy demand patterns, requiring the energy delivery infrastructure (grid-based transmission circuits) to become increasingly flexible to maintain security of supply everywhere and always. The integration of zero carbon renewable energy requires cross-border and cross energy system coupling and a fit-for-purpose design. Nowadays, energy systems are planned, designed and operated in silos with a strong national focus. However, large-scale offshore wind production needs to be transported to deep inland locations, across country borders. The increased peak generation capacity of renewable energy sources will, at times, significantly exceed demand (Matthew Langholtz, 2020). The traditional solution of continuously reinforcing and extending the electricity grid is not sustainable from a cost and societal perspective. This paper will, however, propose a deterministic approach on how networked (interconnected grid) Points of receipt (POR) to Points of Delivery (POD) can be optimized for wheeling renewable energy resources while minimizing energy cost with a hub and spoke approach. The statistical approach will be done via using existing daily energy market clearing prices, available transmission capacity and firm daily transmission prices in open access energy markets. Renewable energy targets, including specific offshore wind targets, need to be in line with the ramp-up as implied by the Paris Agreement. These targets are required to provide industry with a secure market outlook that allows them to build up supply chains accordingly. Optimizing wheeled energy paths from carbon neutral resources such as renewables make them not only cost competitive on the unit commitment stack, but also more accessible on the dispatch stack to other carbon heavy forms of generation such as coal and natural gas turbines (Matthew Langholtz, 2020). This correlates to maximizing renewable resource inertia (wind, solar, biomass) within an interconnected grid without having to consider additional expansion of resources via land purchases and de-forestation.

Mukherjee, Srijib↗
DOE OSTI2023

NREL Project CloudZero

The National Renewable Energy Laboratory's (NREL's) CloudZero project is evaluating the ability to reliably and securely manage complex energy systems from the cloud, identifying major technical and regulatory barriers to cloud adoption, suggesting new security controls and best practices for cloud applications, and empowering industry to embrace the cloud, where appropriate.

cloud↗
DOE OSTI2024

Geothermal Uncertainty Representation in reV: the Renewable Energy Potential Model

We present a preliminary methodology for including geothermal resource uncertainty into the Renewable Energy Potential model, which estimates potential capacity and costs on a gridded surface at the national scale. The uncertainty outputs characterize the 10th, 50th and 90th percentile for geothermal resources using two energy capacity estimation equations. We then present a method and results that demonstrate how other geologic data layers, which may be indicative of permeability, can be used to inform the mean and standard deviation of the geothermal capacity. We demonstrate how the mean and standard deviation can be defined or partially informed by using collocated regression estimates and estimate errors, respectively . These regression results are from 36 observed geothermal power plants in the Great Basin region and are also used to benchmark the P10-P90 calculations.

exclusions↗
DOE OSTI2022

Smart Microgrids

The Nation’s electrical power depends on one bulk power grid to support security and economic prosperity. According to the Department of Homeland Security’s Homeland Threat Assessment of 2020, the largest cyber threat to homeland security is potential disruption to critical infrastructure, including power grids. Critical infrastructure includes the physical and cyber systems which generate, transmit, and distribute electricity with an impact on economic security, public health, or safety. The surety of the Nation’s power grid is vital for providing essential services and would put the population at risk if disrupted. Power outages can have catastrophic consequences for critical organizations such as hospitals and military installations. Additionally, the current fossil-fuel dependent power grid is extremely fragile and vulnerable to overloads, storms that destroy power lines, and cyber-attacks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Grid-Enhanced, Mobility-Integrated Network Infrastructures for Extreme Fast Charging (GEMINI-XFC)

GEMINI-XFC will use first-of-a-kind integrated high-fidelity grid and transport modeling to identify effective pathways for widespread electrification, to design and evaluate integrated vehicle-grid control schemes, and to optimize electric vehicle integration at a full regional scale with individual customer resolution. Control variables will include: Charging station design and planning (where and what kind of charging stations); EV route scheduling considering grid "status"; and Dispatch of behind-the-meter energy storage and legacy voltage control actuators (on-load tap changes, voltage regulators, capacitors).

DIRECT ENERGY CONVERSION,POWER TRANSMISSION AND DI↗
DOE OSTI2023

Electrification 2022 Annual Progress Report

The Electric Drive Technologies (EDT) program's mission is to conduct early-stage research and development on transportation electrification technologies that accelerate the development of cost-effective and compact electric traction drive systems that meet or exceed performance and reliability requirements of internal combustion engine (ICE)-based vehicles, thereby enabling electrification across all light-duty vehicle types. The Grid and Charging Infrastructure (G&I) program's mission is to conduct early-stage research and development on transportation electrification technologies that enable reduced petroleum consumption by light, medium, and heavy-duty vehicles. The program identifies and enables the role of vehicles in the future electrical grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Standard Modular Architecture for Consumer End Plug and Play Interfaces

The growth in load and generation sources at the edge of the grid is driving the innovation in power electronic (PE) grid interfaces at the consumer end to improve the grid resiliency, reliability, security, and cost of the future infrastructure. Novel PE technologies are key enablers for the future grid infrastructure to handle the issues that arise because of the projected growth. This paper introduces a novel fundamental building block (FBB) architecture with plug-and play features. An example framework that enables co-ordination of multiple FBBs hierarchically to provide various grid functions is also presented. The FBBs are designed to be equipped with advanced features like online health monitoring, embedded intelligence, and decision-making capability for enhancing the metrics of consumer end plug and play interfaces. The paper elaborates on the proposed architecture and the developed framework i.e., the controls, communication, protection, and the corresponding timing requirements for the building blocks. The architecture and the framework have been validated through simulations and the communication framework has been validated with a control hardware in the loop platform.

Chinthavali, Madhu Sudhan↗