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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Dual-season thermal energy storage-integrated heat pump system for load shifting: experiments, modeling and nationwide analysis

Here, this paper proposes and validates a dual-season thermal energy storage-integrated heat pump (TES-HP) system that shifts building electric load in both cooling and heating modes using a single phase-change TES. The design uses the TES as a heat sink for the condenser in summer and a heat source for the evaporator in winter, thereby reducing on-peak electric demand while ensuring compatible with existing air-distribution systems. A 14-kW TES-HP prototype with six operating modes and novel refrigerant charge management strategies was experimentally tested under varied conditions. Subsequently, data-driven polynomial performance curves were developed and validated against experiments, then coupled with a phase change material (PCM) model and a calibrated building model within a rule-based controller. Single-day case studies demonstrated effective on-peak demand reduction while maintaining thermal comfort. During cooling, hourly power was lowered by up to 1.5 kW; in heating, average hourly power decreased from over 3.5 kW to 1.8 kW, compared to an HP-only system, achieving 64.5 % load shifting, and the need for resistance heating was eliminated. Seasonal simulations showed typical on-peak electricity savings of 25–35 % in cooling and 40–65 % in heating, with the largest benefits on the hottest and coldest days. Extended response-surface analysis and nationwide mapping quantified load shifting as functions of ambient and TES temperatures, revealing a seasonal trade-off in TES phase-change temperature. These results demonstrate the TES-HP system as a practical and scalable solution for grid-interactive buildings that reduces on-peak demand and electricity use while maintaining thermal comfort and enhancing grid flexibility and reliability.

Grid-interactive buildings↗
DOE OSTI2023

Integrated Resource and System Plannning Based on Energy Costs and Emission Analysis for a Sustainable Future

Efficient integration of renewable energy sources (RES) - such as wind, solar, geothermal, tidal, and wave - into the electric power grids can help improve energy security and contribute toward a sustainable energy future. To date, however, electric power grids are critically dependent on conventional sources, such as fossil fuels. Given the increasing penetration of RES and dynamic loads, there is a need to thoroughly reanalyze the performance of power grids in terms of cost, efficiency, and energy security. This paper presents a comprehensive analysis in terms of energy costs and emissions estimates of dynamic power grids incorporating various power-generating sources.

distributed energy resources↗
DOE OSTI2021

Integrated Thermal Energy Storage System for Air-Conditioners with Phase-Change Composites

Thermal energy storage (TES) is a promising solution to store and dispatch energy and shave peak electric load, reducing the operational cost of HVAC systems. We present results of a TES system using phase-change materials (PCM) integrated with an air conditioner. The proposed system uses an organic PCM (tetradecane) encapsulated within compressed expanded natural graphite foams referred to as phase-change composite. The graphite foam encapsulates the PCM eliminating the need for expensive storage vessels, reduces installation costs, and provides higher thermal conductivity that can lead to faster charge/discharge rates. Two serpentines, multi-pass circuits, operating as a heat source and a sink, exchange heat to and from the phase-change composite. These two circuits are embedded in multiple slabs of this material. The "charge" circuit contains the refrigerant that is directly coupled to a vapor compression system, and the "discharge" circuit removes heat from an airstream and releases it into the PCM composite through a water-glycol liquid coupling. This configuration allows for multiple modes of operation depending on the state of charge of the thermal energy storage module, the building air-conditioning load, and the current electricity and demand charges. This flexible operation allows variable air volume capacity control without the need to have a variable capacity refrigeration system. We developed a 21 kW-hr (6 RT-hr) prototype TES system coupled with a commercial air-conditioner to characterize the component-and system-level performance.

air-conditioning↗
DOE OSTI2023

Dynamic Model Agnostic Reliability Evaluation of Machine-Learning Models Integrated in Instrumentation & Control Systems

In recent years, the field of data-driven neural network-based machine learning (ML) algorithms has grown significantly and spurred research in its applicability to instrumentation and control systems. While they are promising in operational contexts, the trustworthiness of such algorithms is not adequately assessed. Failures of ML-integrated systems are poorly understood; the lack of comprehensive risk modeling can degrade the trustworthiness of these systems. In recent reports by the National Institute for Standards and Technology, trustworthiness in ML is a critical barrier to adoption and will play a vital role in intelligent systems' safe and accountable operation. Thus, in this work, we demonstrate a real-time model-agnostic method to evaluate the relative reliability of ML predictions by incorporating out-of-distribution detection on the training dataset. It is well documented that ML algorithms excel at interpolation (or near-interpolation) tasks but significantly degrade at extrapolation. This occurs when new samples are "far" from training samples. The method, referred to as the Laplacian distributed decay for reliability (LADDR), determines the difference between the operational and training datasets, which is used to calculate a prediction's relative reliability. LADDR is demonstrated on a feedforward neural network-based model used to predict safety significant factors during different loss-of-flow transients. LADDR is intended as a "data supervisor" and determines the appropriateness of well-trained ML models in the context of operational conditions. Ultimately, LADDR illustrates how training data can be used as evidence to support the trustworthiness of ML predictions when utilized for conventional interpolation tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Integration of Electrolysis Systems Into Isolated Microgrid Systems at Extreme Cold Climates

Isolated microgrid systems are generally used to power communities in remote locations where transmission line installations are cost prohibitive. These systems are usually powered by diesel generators, delivering expensive energy with high carbon emissions. However, most of these communities have special geographic access to renewable energy. These special conditions make it interesting to investigate the deployment of renewable energies and storage solutions. Within this scope, this paper analyses the integration of water electrolysis systems into off-grid communities, simulating the electrolyzer as well as the whole electrical grid in real time.

digital real-time simulation↗
DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗
DOE OSTI2023

Dynamic Model Agnostic Reliability Evaluation of Machine-Learning Models Integrated in Instrumentation & Control Systems

In recent years, the field of machine learning (ML), specifically neural networks, has grown significantly and has spurred research in its applicability to digital instrumentation and control systems (DI&C). While ML models have shown promise in operational contexts, the trustworthiness of using such algorithms has not been adequately assessed. Failures of ML integrated systems are not well understood, and the lack of comprehensive risk modeling can degrade the trustworthiness in these systems. In recent reports by the National Institute for Standards and Technology (NIST) [1] and the Nuclear Regulatory Commission (NRC) [2], they indicate that trustworthiness in ML is a critical barrier and will play a vital role in the safe, accountable, and secure operation of intelligent systems. Thus, in this work, we demonstrate a dynamic model-agnostic method to quantify the relative reliability of AI/ML predictions by incorporating out-of-distribution (OOD) detection on the training dataset. It is well documented that most ML algorithms excel at interpolation (or near-interpolation) tasks but experience significant performance degradation at extrapolation. The method, referenced as the Laplacian distributed decay for reliability (LADDR), determines the difference between the operational and training datasets which can used to the relative reliability of AI/ML predictions. LADDR is then demonstrated on a feedforward neural network based digital twin used for the prediction of safety significant factors during a loss-of-flow transient. LADDR is used to demonstrate how training data can be used as evidence to support the relative reliability of ML/AI predictions enhancing the overall trustworthiness of the system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

PIPES (Pipeline for Integrated Projects in Energy Systems) [SWR-24-89]

The Pipeline for Integrated Projects in Energy Systems (PIPES) is a comprehensive project, data, and workflow management tool designed for integrated modeling teams. PIPES facilitates the management of data requirements, tasks, and progress tracking, serving as a higher-level integration layer that works across various data and modeling software. This tool integrates models, data, and tools to perform large-scale, integrated analysis work at scale. PIPES is designed to streamline integrated modeling projects, enhance collaboration, and ensure the quality and efficiency of data management and workflow processes. https://github.com/nrel-pipes/pipes-api https://github.com/nrel-pipes/pipes-web https://github.com/nrel-pipes/nrel-pipes

Gu, Jianli↗
DOE OSTI2024

Influence of Thermal Energy Storage Integration Strategy on System Performance and Refrigerant Charge for Small-Scale R290 Heat Pumps

As small-scale (2.5 – 5 kW) heat pumps across the US increase straining the grid during peak hours, integrated heat pump thermal energy storage (HP-TES) systems can assist by load shifting. However, challenges arise when assembling these systems, as direct refrigerant-phase change material (PCM) heat exchangers (HXs) increase refrigerant charge, and indirect secondary loops degrade HP-TES performance. In this work, direct and indirect HP-TES configurations were sized using commercially available HXs to compare the available load shift time and performance at similar refrigerant charges. For the same indirect 5 kW HP-TES refrigerant charge, directly HP-TES can operate up to 72 and 145 minutes in cooling and heating modes, respectively, using small diameter tubes. Using Modelica, COP increases for direct and indirect HP-TES were 22% and 9%, respectively, given the parasitic losses in the indirect HP-TES. Overall, small-scale direct HP-TES can have comparable refrigerant charges with indirect HP-TES maximizing COP improvements.

25 ENERGY STORAGE↗
DOE OSTI2022

Development of Integrated Thermal Fluids Modeling Capability for MSRs

The DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program supports a full range of computational thermal fluids analysis capabilities and code developments for a broad class of light-water and advanced reactor concepts. The research and development approach under thermal fluids technical area synergistically combines three length and time scales in a hierarchal multi-scale approach. To demonstrate the feasibility and capabilities of a multi-scale thermal fluids capability using these codes, a key joint effort has been pursued to develop an integrated system-and engineering-scale thermal fluids analysis capability with the MOOSE-based codes, through integration of SAM and Pronghorn, both based on the MOOSE framework. This report summarizes the progress in developing an integrated system- and engineering-scale thermal fluids analysis capability based on SAM and Pronghorn for molten salt reactors (MSRs), which gained significant interest in recently years. Two coupling approaches were studied, i.e. separate domain or domain-segregated coupling approach and the domain-overlapping approach. A series of coupled multi-physics models have been developed for a common reference molten salt fast reactor (MSFR) concept, ranging from standalone SAM system model to integrated SAM-Pronghorn-Griffin models. Both the steady state and the transient simulations are performed to the state-of-the-art simulation capabilities of NEAMS software suite in MSFR system applications. This report also covers further development and testing of the gas transport model in SAM for MSR modeling support. The presence of noncondensable gas in MSR systems would have strong impacts on fission gas removal and transport of noble metals throughout the system. Fission products removed through the off-gas system can also impact reactivity and can act as an additional point of heat removal. To ensure that the gas transport model is adequately tested, the supported modeling capabilities and features of SAM were identified, and a suite of tests were developed to test the model for each identified feature. Validation and UQ testing were also performed for the model and demonstrated that the buoyancy term, which was originally developed using non-salt/helium gas experimental data, can capture the experimental gas bubble velocity and diameter to within experimental and code uncertainty. An existing MSRE model was also modified in this work to include the gas transport model and to demonstrate the gas model behavior for realistic conditions of interest.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

HIDES: Hybrid Intrusion Detector for Energy Systems

The establishment of a resilient electric grid accompanied by a secure communications network is an ongoing battle as advanced persistent threats continue to exploit existing vulnerabilities in legacy supervisory control and data acquisition system (SCADA) infrastructure. Traditional intrusion detection systems (IDSs) lack consistent performance because of the continuously evolving attack surface of SCADA systems. These shortcomings can be overcome by integrating logical system behavior, protocol-specific knowledge, and data-based learning to develop a comprehensive IDS solution. In this paper, we present a Hybrid Intrusion Detector for Energy Systems by integrating a network-based IDS, state-of-the-art machine learning-based IDS, and model-based IDS to detect unknown and stealthy cyberattacks targeting the SCADA networks. The proposed IDS uses synchrophasor measurements and cyber logs to learn patterns of different scenarios based on spatiotemporal behaviors of power systems. As a proof of concept, we implement and validate the proposed IDS by leveraging resources available at the National Renewable Energy Laboratory's Energy Systems Integration Facility test bed. Experimental results show promising performance in detecting cyberattacks while providing realtime visualization of power system measurements and cyber logs.

machine-learning intrusion detection system↗
DOE OSTI2024

Developing an Energy-Conscious Traffic Signal Control System for Optimized Fuel Consumption in Connected Vehicle Environments

The project titled “Developing an Energy-Conscious Traffic Signal Control System for Optimized Fuel Consumption in Connected Vehicle Environments” addresses energy-related challenges associated with adaptive traffic control systems by integrating connected vehicles (CV) and connected infrastructure (CI). The system developed in this project, a CV-based adaptive traffic control system, aims to improve fuel consumption in mixed traffic environments by capitalizing on emerging CV and CI communication technologies, as well as leveraging recent advances in Artificial Intelligence (AI), optimization, and edge computing. The system was tested at the MLK Smart Corridor, an urban testbed managed by the University of Tennessee at Chattanooga (UTC) and the City of Chattanooga. The system was validated through extensive simulations, both Software-in-the-Loop (SILS) and Hardware-in-the-Loop (HILS), and was further implemented and tested in real-world conditions at several intersections along the corridor. The Fuel Consumption Performance Index (FC-PI) and the Ecological Performance Index (Eco-PI) were developed as the key components for evaluating the system’s impact on fuel consumption and emissions. These metrics provided a comprehensive means of understanding the impact of traffic signal control optimization in mixed traffic environments. The report presents an in-depth analysis of the Eco-PI, FC-PI, adaptive traffic control system integration, and the testing and field implementation of the system. The results demonstrate significant reductions in fuel consumption and emissions, showcasing the system’s capability to contribute to more sustainable urban traffic management. The report also documents the challenges encountered and recommendations for scaling and further improving the system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

MULTI-FIDELITY MODELING AND UNCERTAINTY QUANTIFICATION OF INVERTER BASED RESOURCES IN INTEGRATED T&D SYSTEMS

Uncertainty quantification plays a pivotal role in improving the accuracy and reliability of inverter operation within modern power systems that are increasingly dominated by inverter-based resources (IBRs). IBRs, especially those operating under grid forming (GFM) control, rely heavily on a complex set of control parameters and system measurements to maintain voltage, frequency, and power balance. Traditional deterministic modeling approaches often fail to capture these parameter deviations, potentially resulting in suboptimal control actions, reduced system stability, or even instability under high penetration of IBRs. In this paper, we demonstrate the application of model calibration and uncertainty quantification (UQ) principles to an integrated transmission and distribution (T&D) model involving a GFM converter and provide a framework for prioritizing control improvements, guiding robust design, and informing adaptive strategies that can accommodate real-time variability in system conditions. The proposed approach could be valuable in enhancing the robustness of current and future power systems under increased IBR penetrations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Integrating Cybersecurity with System Operations and Restoration

This presentation covers the interaction of the discipline of system operations with the discipline of cybersecurity. First, a common mental model for risk - both cybersecurity and all-hazards - is presented, followed by a discussion of high-level management strategies for different kinds of cyber harm facing system operators, based on the consequences and frequencies of the harm. The next section covers the importance of cybersecurity for a system operator organization and explains some general concepts to understand the relationships. Finally the role of system operators in the security of the grid as a larger system of systems is discussed over the framework of a resilience event.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Priorities, opportunities, and challenges for integrating microorganisms into Earth system models for climate change prediction

ABSTRACT Climate change jeopardizes human health, global biodiversity, and sustainability of the biosphere. To make reliable predictions about climate change, scientists use Earth system models (ESMs) that integrate physical, chemical, and biological processes occurring on land, the oceans, and the atmosphere. Although critical for catalyzing coupled biogeochemical processes, microorganisms have traditionally been left out of ESMs. Here, we generate a “top 10” list of priorities, opportunities, and challenges for the explicit integration of microorganisms into ESMs. We discuss the need for coarse-graining microbial information into functionally relevant categories, as well as the capacity for microorganisms to rapidly evolve in response to climate-change drivers. Microbiologists are uniquely positioned to collect novel and valuable information necessary for next-generation ESMs, but this requires data harmonization and transdisciplinary collaboration to effectively guide adaptation strategies and mitigation policy.

Microbiology↗
DOE OSTI2022

Market-Integrated Optimization of Wind-Battery-Hydrogen Hybrids for Peaking Capacity via Storage

As Integrated Energy Systems (IES) combine multiple energy and storage technologies to provide potentially more value and less risk via resource diversification, complementary overbuild, increased flexibility, and revenue-stacking, IES value is dependent on electricity market dispatch and grid interactions should play an important role in IES design and operation. This study hybridizes and retrofits wind and combustion turbine plants to study the impacts of replacing gas generation capacity with wind, battery, PEM electrolysis, hydrogen tanks and hydrogen turbines. The optimized design is co-simulated in a production cost model with different bidding strategies in order to compare performance and highlight the importance of grid-interactions. We analyze the revenue and dispatch changes as well as the price and cost implications of wind-battery-hydrogen IESs.

electrolysis↗