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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Phase 1: Duke Energy Zero Emission Resource Integration Study (ZERIS); Phase 2: Carbon-Free Resource Integration Study for Duke Energy (Final Report)

Phase 1: This statement of work makes up Phase 1 of a larger effort. During this Phase 1 effort, NREL will work with Duke Energy to analyze the impacts of integrating significant amounts of new solar power into the Duke Energy power system under a variety of different penetrations scenarios, with a maximum of ten (10) full scenarios examined. The existing fleet, particularly the nuclear generation, will be considered in the quantitative assessments and discussions. Duke Energy is looking to quantify how much solar generation its system can handle. NREL will work with Duke Energy to quantify solar potential, identify likely integration challenges and possible opportunities for wind, storage, demand side resources and other technologies. Phase 2: This Statement of Work consists of a follow-up effort (Phase 2) to a recently completed Phase 1 modeling effort. During Phase 2, NREL will work closely with Duke Energy to analyze the impacts of integrating significant amounts of variable generation resources (wind and solar) and storage into Duke Energy's system in the Carolinas. The existing fleet, particularly nuclear generation, will be considered in the quantitative assessment and discussions. This Statement of Work also includes an extension to Phase II of the Carbon-Free Resource Integration Study for Duke Energy. In this extension, NREL will work closely with Duke Energy to extend the production cost analysis developed in Phase II to 2018 weather and load data for Duke Energy's territory. This extension leverages the modeling tools and datasets developed as part of Phase II. The analysis will compare results from Phase II (using 2012 weather and load) with 2018 results to assess system operations with increased penetration of renewables and storage. Simplifying assumptions will be made for modeling Duke Energy's neighbors in the production cost model.

14 SOLAR ENERGY↗
DOE OSTI2022

Uncertainty in Thermal Modeling of Spent Nuclear Fuel Casks

Uncertainty is a key metric in computational modeling that must be evaluated for results to have wide ranging applicability. A well characterized uncertainty range is ideal with clear error bars on results that can be presented to stakeholders. In the field of spent fuel cask modeling, this ideal has been historically difficult to achieve in practice because of the computationally intensive nature of the models used and the difficulty assigning reasonable uncertainties to quantities in as-built systems. The work in this report has been conducted to evaluate the overall state of uncertainty and sensitivity in spent fuel cask models and develop methodologies for evaluating these uncertainties. These methodologies must be practical for engineering applications. They should not require excessive computational resources or calendar time to achieve results. In engineering, the model must be on a scale such that it can be changed and adapted throughout a project as new information is discovered and project goals evolve. This report covers three major modeling task areas that provide an overview of the types of sensitivity and uncertainty present in a spent fuel storage and transportation system. Section 3 discusses sensitivity and uncertainty analysis in the effective thermal conductivity model for the fuel region and applies these results to a single assembly model. Section 4 shows sensitivity analysis of a full cask model in the TN-32B and Section 5 demonstrates the overall uncertainty workflow using Coolant Boiling in Rod Arrays – Spent Fuel Storage and STAR-CCM+ developed from the sensitivity work in the preceding sections.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

HYPSTAT (Hydrogen Production, Storage, and Transmission Analysis Tool) [SWR-23-04]

The Hydrogen Production, Storage, and Transmission Analysis Tool (HYPSTAT) is a modeling framework developed by the National Renewable Energy Laboratory (NREL) to support the analysis of hydrogen systems. HYPSTAT focuses on key components of hydrogen infrastructure, particularly electrolytic hydrogen production, hydrogen storage, and hydrogen transmission, as part of the transition to decarbonized energy systems HYPSTAT operates as a supply-to-demand model, taking a fixed exogenous demand as input and optimizing the design and operation of the hydrogen system to meet that demand. It determines cost-optimal configurations based on specified technology options and system constraints.

Brauch, Joseph [National Renewable Energy Laborato↗
DOE OSTI2022

Rheology and dynamics of a solvent segregation driven gel (SeedGel)

Bicontinuous structures promise applications in a broad range of research fields, such as energy storage, membrane science, and biomaterials. Kinetically arrested spinodal decomposition is found responsible for stabilizing such structures in different types of materials. A recently developed solvent segregation driven gel (SeedGel) is demonstrated to realize bicontinuous channels thermoreversibly with tunable domain sizes by trapping nanoparticles in a particle domain. As the mechanical properties of SeedGel are very important for its future applications, a model system is characterized by temperature-dependent rheology. The storage modulus shows excellent thermo-reproducibility and interesting temperature dependence with the maximum storage modulus observed at an intermediate temperature range (around 28 °C). SANS measurements are conducted at different temperatures to identify the macroscopic solvent phase separation during the gelation transition, and solvent exchange between solvent and particle domains that is responsible for this behavior. Further, the long-time dynamics of the gel is further studied by X-ray Photon Correlation Spectroscopy (XPCS). The results indicate that particles in the particle domain are in a glassy state and their long-time dynamics are strongly correlated with the temperature dependence of the storage modulus.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Modeling of a Networked Community Microgrid with High Solar Penetration Considering Control Dynamics of Inverter-Based Resources

Microgrids help facilitate the integration of renewable energy in distribution-level grids and increase the resilience of the electric grid to extreme weather, especially in rural areas. Compared to traditional microgrids, a networked microgrid leverages multiple grid-forming sources to form a potential meshed grid and is more flexible in operation. This paper demonstrates the simulation modeling of an actual networked microgrid located in Adjuntas, Puerto Rico. The model contains representations of power inverters that connect the battery energy storage systems and photovoltaic generation systems to the networked microgrid and is capable of simulating fast grid transients as well as long-term operation of the networked microgrid. The modeling technique for power inverters allows the time-efficient simulation of the microgrid with a minimal penalty on model accuracy.

Li, Dingrui↗
DOE OSTI2022

Nuclear Energy in Long-Term System Models: A Multi-Model Perspective

Long-term energy system models–including electric sector capacity expansion models–are widely used tools for informing planning, technology assessment, and policy analysis. Recent decarbonization goals and rapid technological change have increased the need to appropriately represent economic characteristics and technical details of energy system resources, including variable renewable energy, energy storage technologies, carbon-capture-equipped capacity, and nuclear energy. Nuclear power represents about 20% of electricity generation and 50% of carbon-free electricity in the United States as of 2021. However, there are many perspectives on the role of existing and new nuclear in the future U.S. energy system, which is reflected in the broad range of potential contributions reported in the literature. This project aims to understand how issues central to nuclear energy are represented in long-term energy models. Building on earlier collaborations that focused on variable renewable energy and energy storage, this project convenes four modeling teams that use national-scale long-term energy system models from the Electric Power Research Institute, the National Renewable Energy Laboratory, the U.S. Energy Information Administration, and the U.S. Environmental Protection Agency to share methods and data, update models, run coordinated scenarios, and identify research needs. Improving tools can provide more insightful analyses and ensure that methods are more transparent.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

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

Distribution system resilience enhancement is an important topic to ensure customers have access to the 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 forecasts. 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 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 OSTI2024

Hybrid Solar Thermal Energy System for District Heating Application

Solar district heating (SDH) systems can be good alternatives to conventional systems when they are optimized with hybrid configurations and thermal energy storage (TES). In this scope, a hybrid renewable thermal energy system (RTES) model has been built combining flat plate collector (FPC) solar system with parabolic trough collector (PTC) system via a heat exchanger and coupled with TES. To undertake the hybridization of the system, System Advisor Model (SAM) software was modified, which allowed control over configurations and more accurate modelling of heat transfer between the collectors. The model is first compared to an existing hybrid solar district heating systems (DHS) system in Taars, Denmark. The results showed a good correlation with an overestimation of only 6.4% compared to most recent heat output. Then the same system configuration was modeled in different geographic locations to investigate the impact of changes in direct normal irradiance (DNI) to the heat sink thermal output of the hybrid system. The results showed that the annual net thermal power output in California, USA can be three times more than the annual net thermal power output in Taars, Denmark. Finally, multiple hybrid configurations with varying solar field sizes were simulated based on the heat demand of two different university campuses DHS. The results showed that, retrofit applications of this hybrid DHS system coupled with TES could reduce the natural gas consumption of the existing systems between 25% and 41%. The use of hybrid RTES highlighted in this paper can be extended to many more opportunities.

district heating↗
DOE OSTI2020

Beyond Idealized Models of Nanoscale Metal Hydrides for Hydrogen Storage

Metal hydrides are attractive for compact, low-pressure hydrogen storage, yet a foundational understanding of factors governing their thermodynamics and kinetics is still lacking. Predictive modeling from the atomic to the microstructural scale plays a critical role in addressing these gaps, particularly for nanoscale materials, which promise improved performance but are difficult to probe. In this paper, we summarize strategies being developed within the Hydrogen Materials—Advanced Research Consortium (HyMARC) for going beyond conventional models to incorporate more complex physics, more realistic structures, and better approximation of operation conditions in simulations of nanoscale metal hydrides. We highlight four beyond-ideal factors that influence predicted performance: (1) surface anharmonic dynamics, (2) interface and surface energy penalties, (3) mechanical stress under confinement, and (4) the presence of native surface oxide. Approaches for addressing these factors are demonstrated on model materials representative of high-capacity hydrogen storage systems, and implications for understanding performance under operating conditions are discussed.

08 HYDROGEN↗
DOE OSTI2024

Revenue Analysis for Energy Storage Systems in the United States

In this work we evaluate the potential revenue from energy storage using historical electricity prices, forward-looking projections of hourly electricity prices, and actual reported revenue. This analysis examines the impact of storage characteristics, specifically duration and round-trip efficiency, as well as locational elements of storage revenue within the current and projected U.S. power system. Figure ES-1 illustrates the revenue for a 1 MW storage system in seven market regions with durations range from 1 hour to 12 hours using both historical and forward-looking price data. The historical analysis covers more than 500 price nodes for each market region, while the forward-looking analysis includes balancing areas under different 10 scenarios of the electricity generation mix. The results indicate that the revenues consistently increase with duration, though the marginal value declines as duration grows. Moreover, the range of revenue depends on the system's operational location, and the electricity generation mix changes for future years. This range also widens with increased durations. In addition, the sensitivity analysis of round-trip efficiency reveals that as efficiency improves, system revenue increases, though the value of better round-trip efficiency declines as at higher efficiency levels.

25 ENERGY STORAGE↗
DOE OSTI2025

Initial Mobility Analysis for ORNL VA-EDH Synthetic Populations

Travel burdens are a major barrier to healthcare access among US Veteran patient populations, particularly those residing in rural areas. Spatial accessibility to points of care for US Veteran populations is commonly assessed in two ways. The first approach uses open data from the US Census to represent collective travel burdens, for example the distance between population-weighted census tract centroids and VHA points of care. The second approach uses restricted-access VHA patient data to measure travel costs (e.g., distance, time) for accessing points of care with respect to geolocated patient addresses and real or approximated transportation networks. While the advantage of the open data approach lies in its reproducibility, it has notable limitations in its tendency to infer individual travel behavior from aggregate population characteristics, a problem known as ecological fallacy. Conversely, while the patient data approach is able to account for individual travel behavior, its ability to account for localized access disparities (e.g., a neighborhood with exceptionally high transportation costs) and patient demographics is limited as protecting individual patient data requires their storage in closed systems with limited capacity for adequately modeling real-world travel patterns or for supplementing patient attributes. Additionally, the patient data approach cannot account for veterans who are not enrolled in the VHA system but who may be eligible for care. These challenges limit the ability to perform “what if” analyses on the effects of place-specific interventions on veteran populations with high access barriers to healthcare. To address these challenges, we explore the application of realistic synthetic populations to examine travel burdens and spatial accessibility issues among veteran patient populations. Synthetic populations provide a virtual, individually-resolved and cross-sectional representation of the veteran patient population that enables investigation of spatial access to points of care in ways in which aggregate data and patient data do not. First, synthetic populations allow one to directly assess how individuals access points of care, from synthesized residential locations to outpatient facilities on real-world transportation networks. Modeling access to points of care at the individual scale addresses the ecological fallacy problem associated with using aggregated census data to represent veteran populations and patterns of movement. Second, synthetic populations provide a means of completely representing an area’s veteran population using only publicly available, anonymized census microdata from the American Community Survey (ACS) to ensure the privacy of real-world individuals. Generating synthetic populations from the ACS also expands descriptive characteristics beyond what patient data typically offers to include socio-demographic, economic, housing, and mobility attributes. More detailed profiles of both VHA patient populations and veterans not enrolled in the VA system will provide a comprehensive picture of groups that may benefit from interventions or outreach. As an initial exercise for using synthetic populations to measure veteran travel burdens to VA care, we apply Oak Ridge National Laboratory’s (ORNL) UrbanPop capability to generate a series of synthetic VHA patient populations for 9 Veterans Integrated Services Networks (VISN) market areas in 9 Census Divisions across the continental United States, which are listed in Table 1. We use UrbanPop to produce synthetic populations for the VISN markets selected for each US Census Division, then assign VA outpatient clinic destinations to synthetic VHA patients based on travel about each VISN market’s road network. To demonstrate using the synthetic populations to evaluate healthcare travel burdens, we compare the time-based impedance between simulated home locations and VA outpatient clinics in each VISN market. We then perform validation exercises on the synthetic populations with respect to neighborhood (block group) demographic composition as well as patient mobility, comparing aggregate origin-destination statistics for the synthetic population to outpatient visits available in restricted patient data from the VA’s Corporate Data Warehouse (CDW) database.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

System Advisor Model (SAM) Improvements for Emerging Solar Thermal Applications

The System Advisor Model (SAM) is an open-source tool developed by NREL to simulate the techno-economic performance of technologies like photovoltaics, solar thermal, wind, and geothermal. NREL has recently completed, active, and planned projects to improve the solar thermal models in SAM and its underlying code base to represent emerging component technologies, systems, and applications. This poster describes new capabilities in the SolarPILOT optical modeling tool, supercritical carbon dioxide (sCO2) power cycles, and solar industrial process heat.

14 SOLAR ENERGY↗
DOE OSTI2026

NREL Capabilities for TMCES

This is a panel presentation covering NREL's capabilities in the thermal-mechanical energy storage space, including ARIES, component R&D, and modeling tools and datasets.

25 ENERGY STORAGE↗
DOE OSTI2026

Deer Isle and Stonington Energy Reliability Opportunities

The towns of Deer Isle and Stonington, located on the island of Deer Isle in Maine, partnered with the U.S. Department of Energy's Energy Technology Innovation Partnership Project (ETIPP) to examine options to enhance energy resilience, reduce dependence on imported fuels, and mitigate high energy costs. With technical support from the National Laboratory of the Rockies (NLR), Lawrence Berkeley National Laboratory (LBNL), and regional partner the Island Institute, the project focused on evaluation of the local electricity distribution infrastructure, examination of ongoing activities to reduce the frequency of outages, review of the applicable policy and regulatory environment, and identification of potential on-site energy solutions. Key findings revealed the island's reliance on a single distribution line, and limited hosting capacity for additional distributed energy resources. Solar photovoltaics (PV) technology, with and without battery storage, was found to be the most viable technology given economic, technical, and social considerations. While also technically feasible, wind energy faces more difficult siting and less public acceptance compared to PV. Marine energy technologies were determined to be infeasible due to technology immaturity and lengthy permitting timelines. Modeling of potential building-scale PV plus storage systems resulted in successful grant applications for installations at two local facilities. Analysis of community scale energy generation and storage options resulted the local utility, Versant, submitting a grant application for a proposed community-scale battery storage system to increase grid resilience and hosting capacity. This initiative has strengthened the communities' energy planning capabilities and laid the groundwork for future clean energy development.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

System Analysis Support of Consent-Based Siting Efforts - 23400

To better enable informed decision-making regarding the back end of the nuclear fuel cycle, the Integrated Waste Management (IWM) program within the U.S. Department of Energy, Office of Nuclear Energy (DOE-NE) has been sponsoring the development and application of system analysis tools capable of analyzing various system options for the management of spent nuclear fuel (SNF) and high-level radioactive waste. With these tools, integrated waste management system (IWMS) architecture analyses are being conducted to support the future deployment of a comprehensive nuclear waste management system that considers all major back-end aspects of the nuclear fuel cycle (i.e., transportation, storage, and disposal). System analyses and assessments typically use these modeling and simulation tools to investigate things like the implications of changes in various assumptions and parameters such as acceptance rates, receipt logic, facility capacities and capabilities, use of standardized canisters, and different assumed system operation start dates. Some example system analysis results have been published previously. With the passage of the consolidated appropriations acts for fiscal years 2021 and 2022, Congress provided funding and direction for the DOE to move forward with interim storage to support near-term action in managing the nation’s SNF as an important component of an IWMS. Consent-based siting is a phased, adaptive, and collaborative approach to siting facilities that focuses on the needs and concerns of people and communities. Communities participate in the siting process by working carefully through a series of phases and steps with the DOE (as the implementing organization). Each step and phase helps a community determine whether and how hosting a facility to manage SNF is aligned to the community’s goals. By its nature, a consent-based siting process must be transparent, flexible, adaptive, and responsive to community concerns. Thus, the phases and steps are intended to serve as a guide, not a prescriptive set of instructions. System analysis research analysts interface with DOE’s consent-based siting for a federal interim storage program and can perform preliminary system analyses as requested to support various aspects of the consent-based siting team’s work. This paper provides an overview of IWM program’s system analysis activities that support the ongoing consent-based siting process. The questions that decision-makers, planners, communities, and stakeholders might have during the consent-based siting process are similar to questions that have been explored by system analysts previously. Specifically, this paper discusses work that will inform consent-based siting efforts in the areas of community impacts, cost estimates, various SNF acceptance rates by potential facilities, potential consolidated interim storage facility (CISF) capacities and configurations, and the implications of a CISF on the overall waste management system. It is expected that system analysis will continue to be performed to support the consent-based siting process in the future.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

MULTIMARKET CONTROL AND OPERATION OF AN ADVANCED NUCLEAR REACTOR WITHIN AN INTEGRATED ENERGY PARK

Integrated energy systems (IES) are increasing in popularity and relevance given the heightened penetration of variable renewable energy sources. This variability is causing traditional baseload generators to reconsider their business cases as exclusively electrical generation stations and to instead consider ancillary products (e.g., hydrogen) to remain competitive in the current energy market. This work investigates the coupling, control, and overall viability of IES consisting of an advanced nuclear reactor coupled with a high-temperature steam electrolysis (HTSE) plant and hydrogen storage. The goal of such IES is to produce hydrogen without impacting reactor operations during periods of off-peak electricity demand and then sell electricity to the grid during periods of high demand. To accomplish this, a novel heat exchanger, control scheme, and coupling strategy were needed to ensure that the advanced nuclear power plant could make these transitions. Idaho National Laboratory’s open-source Framework for Optimization of Resources and Economics (FORCE) framework was used to develop novel coupling and control schemes that demonstrate the viability of multi-market operation of advanced nuclear reactors to produce both electricity and hydrogen. The results demonstrated the coupled IES could operate without impacting reactor systems while monetizing the electricity market and meeting all contractual hydrogen consumer demands.

08 HYDROGEN↗
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↗