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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Wireless High-Temperature Sensor Network for smart boiler systems

This final project report describes the research data and findings. This project aims to develop a new wireless high-temperature sensor network for real-time continuous boiler condition monitoring in harsh environments. Such a wireless high-temperature sensor network enables network-based automatic temperature sensing and data collection, which combined with artificial intelligent (AI) algorithms allow the construction of smart boiler systems with boiling condition management and optimization for significant energy-saving and reliability improvement

42 ENGINEERING↗
DOE OSTI2025

Scalable Membrane Electrode Assembly Stack Production for Reliable Thermal Electrochemical Converters (SMART)

This Final Close Out Report documents all activities, progress, and results accomplished under the SMART project, funded by the Department of Energy (DOE), during Budget Period 1 (BP1), covering 10/01/2023 to 07/31/2025. Although official preaward approval was granted on 05/10/2024, contracting delays shifted the award and formal project start to 10/01/2024. In the absence of an official kick-off meeting, early interactions focused on planning for key technical challenges, particularly the design and logistics for the JTEC device test platform.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2021

Overvoltage Prevention and Curtailment Reduction Using Adaptive Droop-Based Supplementary Control in Smart Inverters

Recent developments in the renewable energy sector have seen an unprecedented growth in residential photovoltaic (PV) installations. However, high PV penetration levels often lead to overvoltage problems in low-voltage (LV) distribution feeders. Smart inverter control such as active power curtailment (APC)-based overvoltage control can be implemented to overcome these challenges. The APC technique utilizes a constant droop-based approach which curtails power rigidly, which can lead to significant energy curtailment in the LV distribution feeders. In this paper, different variations of the APC technique with linear, quadratic, and exponential droops have been analyzed from the point-of-view of energy curtailment for a LV distribution network in North America. Further, a combinatorial approach using various droop-based APC methods in conjunction with adaptive dynamic programming (ADP) as a supplementary control scheme has also been proposed. The proposed approach minimizes energy curtailment in the LV distribution network by adjusting the droop gains. Simulation results depict that ADP in conjunction with exponential droop reduces the energy curtailment to approximately 50% compared to using the standard linear droop.

42 ENGINEERING↗
DOE OSTI2022

Implementation Aspects of Smart Grids Cyber-Security Cross-Layered Framework for Critical Infrastructure Operation

Communication networks in power systems are a major part of the smart grid paradigm. It enables and facilitates the automation of power grid operation as well as self-healing in contingencies. Such dependencies on communication networks, though, create a roam for cyber-threats. An adversary can launch an attack on the communication network, which in turn reflects on power grid operation. Attacks could be in the form of false data injection into system measurements, flooding the communication channels with unnecessary data, or intercepting messages. Using machine learning-based processing on data gathered from communication networks and the power grid is a promising solution for detecting cyber threats. In this paper, a co-simulation of cyber-security for cross-layer strategy is presented. The advantage of such a framework is the augmentation of valuable data that enhances the detection as well as identification of anomalies in the operation of the power grid. The framework is implemented on the IEEE 118-bus system. The system is constructed in Mininet to simulate a communication network and obtain data for analysis. A distributed three controller software-defined networking (SDN) framework is proposed that utilizes the Open Network Operating System (ONOS) cluster. According to the findings of our suggested architecture, it outperforms a single SDN controller framework by a factor of more than ten times the throughput. This provides for a higher flow of data throughout the network while decreasing congestion caused by a single controller’s processing restrictions. Furthermore, our CECD-AS approach outperforms state-of-the-art physics and machine learning-based techniques in terms of attack classification. The performance of the framework is investigated under various types of communication attacks.

cross-layered↗
DOE OSTI2025

Artificial Intelligence for Smart Transportation

There are more than 7,000 public transit agencies in the U.S. (and many more private agencies), and together, they are responsible for serving 60 billion passenger miles each year. A well-functioning transit system fosters the growth and expansion of businesses, distributes social and economic benefits, and links the capabilities of community members, thereby enhancing what they can accomplish as a society. Since affordable public transit services are the backbones of many communities, this work investigates ways in which Artificial Intelligence (AI) can improve efficiency and increase utilization from the perspective of transit agencies. This book chapter discusses the primary requirements, objectives, and challenges related to the design of AI-driven smart transportation systems. We focus on three major topics. First, we discuss data sources and data. Second, we provide an overview of how AI can aid decision-making with a focus on transportation. Lastly, we discuss computational problems in the transportation domain and AI approaches to these problems.

Wilbur, Michael↗
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 OSTI2020

Smart Ventilation Controls Boost Energy Efficiency and Indoor Air Quality

The average American household spends more than $2,200 a year on energy, and heating, ventilating, and air conditioning (HVAC) costs comprise nearly half of the bill. This is one reason why home builders focus on tightening building envelopes to save energy. Yet, limiting the potential for air exchange can negatively impact indoor air quality (IAQ). When outdoor conditions are most extreme during occupied periods, there may be comfort implications from continuing high levels of ventilation during associated weather events. This is true even with heat recovery. To mitigate risks, the Florida Solar Energy Center developed and tested approaches for “smart” ventilation system controls that enable more reliable design, installation, and operation to achieve desired IAQ while also minimizing energy and comfort impacts.

Building America↗
DOE OSTI2020

Improved Access and Analysis of Data Provides New Opportunities for Smart Cities

Making smart, informed, data-driven energy decisions for cities requires large amounts of high-value data and analysis. Cities’ energy data has traditionally been difficult to access and costly to store and manage for city administrators. When data is available, many cities lack the expertise to properly analyze and model large data sets. NREL’s transparency and knowledge about data availability, integration, and interpretation is critical to decision-making for cities.

cities' data↗
DOE OSTI2020

Smart-DS: Synthetic Models for Advanced, Realistic Testing: Distribution Systems and Scenarios

The explosion of new ideas for distributed energy resources and advanced distribution grid algorithms faces a critical challenge: how can one effectively develop and evaluate these breakthrough technologies without an adequate set of power systems network, load, and resource data to test against? Today's open test systems, particularly for distribution systems, are tiny and incomplete; and though with enough effort, obtaining utility data under NDA can overcome scale challenges, the results can't be openly shared, preventing fair comparison. The Smart-DS project-led by NREL in partnership with MIT, Universidad Pontificia Comillas, CYME, and EDD-is working to overcome this gap by generating multiple large-scale, open, synthetic, distribution systems that are realistic but not real. These datasets go beyond a single medium voltage feeder (~1000 customers) to cover entire metropolitan areas and their surrounds with up to hundreds of feeders and millions of customers, complete with low voltage customer connections and high voltage sub-transmission. This scale allows adequate testing and analysis of not just local controls and advanced DERs, but also multiple substation interactions, optimized switching, full-scale distribution-OPF, and more. To accompany these datasets, the project is also building a rich set of scenario generation tools that can be used for distribution and transmission systems alike. This includes automated, spatially aware access to world-class, high-resolution solar, wind, and weather data: forward looking generation mixes: and highly configurable DER, load, climate, outage, control-scheme, and other scenarios. We will also highlight DiTTo, an exciting open-source, multi-way distribution dataset transformation tool for many-to-many format translation with scenario, merge/split, and many other manipulation capabilities.

DERs↗
DOE OSTI2020

Who Controls Energy in the Smart Home? A Multidisciplinary Taxonomy

Advances in technology have begun to open new opportunities for behavior-based and technical approaches to managing residential energy use and meet sustainability-related objectives. Visions of the future predict homes with smart technologies delivering enhanced comfort and cost savings to residents; utility-partners who can remotely optimize energy resources to meet grid needs; and occupants who play more active roles in the energy system supported by advanced information communication technologies. Each of these scenarios implies augmented control over home energy use, yet uncertainties remain regarding which ones will deliver the greatest grid benefits and services to customers in a given situation. These scenarios also raise broader questions regarding customer agency and the relationship between customers and third parties moving forward. While both the provision of information to spur behavior change and automated technologies theoretically enhance control over energy use in the built environment, these strategies are not often studied from an integrated perspective. Seeking to address this gap and develop a deeper understanding of the evolving paradigm of control over home energy use, this paper presents a taxonomy to evaluate perspectives from public policy (ex. demand-side management), technological innovation (automated controls), and user-agency (ex. the role of behavior change) on approaches to managing home energy use. We draw on theoretical and empirical evidence from across disciplines to detail the dimensions and implications of deploying programs that incorporate various levels of control and anticipate such a taxonomy will help holistically map out and evaluate tradeoffs between different approaches to demand-side management moving forward.

McIlvennie, Claire↗
DOE OSTI2020

SMART Model Comparison App

Presented at the DOE-NETL 2020 Virtual Meeting - SMART Initiative Annual Review Meeting November 2-3, 2020, 11/2

Hill, Brandon↗
DOE OSTI2021

Heat Based Power Augmentation for Modular Pumped Hydro Storage in Smart Buildings Operation

In the U.S., building sector is responsible for around 40% of total energy consumption and contributes about 40% of carbon emissions since 2012. Within the past several years, various optimization models and control strategies have been studied to improve buildings energy efficiency and reduce operational expenses under the constraints of satisfying occupants’ comfort requirements. However, the majority of these studies consider building electricity demand and thermal load being satisfied by unidirectional electricity flow from the power grid or on-site renewable energy generation to electrical and thermal home appliances. Opportunities for leveraging low grade heat for electricity have largely been overlooked due to impracticality at small scale. In 2016, a modular pumped hydro storage technology was invented in Oak Ridge National Laboratory, named Ground Level Integrated Diverse Energy Storage (GLIDES). In GLIDES, employing high efficiency hydraulic machinery instead of gas compressor/turbine, liquid is pumped to compress gas inside high-pressure vessel creating head on ground-level. This unique design eliminates the geographical limitation associated with existing state of the art energy storage technologies. It is easy to be scaled for building level, community level and grid level applications. Using this novel hydro-pneumatic storage technology, opportunities for leveraging low-grade heat in building can be economical. In this research, the potential of utilizing low-grade thermal energy to augment electricity generation of GLIDES is investigated. Since GLIDES relies on gas expansion in the discharge process and the gas temperature drops during this non-isothermal process, available thermal energy, e.g. from thermal storage, Combined Cooling, Heat and Power system (CCHP), can be utilized by GLIDES to counter the cooling effect of the expansion process and elevate the gas temperature and pressure and boost the roundtrip efficiency. Several groups of comparison experiments have been conducted and the experimental results show that a maximum 12.9% cost saving could be achieved with unlimited heat source for GLIDES, and a moderate 3.8% cost improvement can be expected when operated coordinately with CCHP and thermal energy storage in a smart building.

Chen, Yang↗
DOE OSTI2021

Utility-scale Building Type Assignment Using Smart Meter Data

United States building energy use accounted for 40% of total energy use, 74% of peak demand, and $412 billion in 2019. Building energy modeling allows researchers to simulate building physics, gain insights into possible energy/demand saving opportunities, and assess cost-effective resilience amidst climate change. Many building features needed to create building energy models are readily available such as 2D footprints and LiDAR (height). A critical feature that is not generally obtainable is the building type. In partnership with a utility, a years worth of real-world, 15-minute electrical use data has been examined. The smart meter data is compared to 97 different prototype building energy models to assign building type. Real-world considerations including data preparation, quality assurance, and handling of missing values for advanced metering infrastructure data are addressed. Euclidean distance for pattern-matching of energy use, dynamic time warping, and time-window statistics with machine learning are compared for determining building type from measured electricity use.

Bass, Brett↗
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy’s National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI’s Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: 1. Run in real-time on the edge (≥ 12 Hz) 2. Classification: Achieve less than 5% false positive detection 3. Classification: Achieve ≥ 95% methane plume detection rate 4. Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗
DOE OSTI2020

Smart-readout of the Skipper-CCD: Achieving Sub-electron Noise Levels in Regions of Interest

The skipper CCD is a special type of charge coupled device in which the readout noise can be reduced to sub-electron levels by averaging independent measurements of the same charge. Thus the charge in the pixels can be determined by counting the exact number of electrons. The total readout time is proportional to the number of measurements of the charge in each pixel. For some applications this time may be too long; however, researchers usually are interested only on certain region within the matrix of pixels. In this paper we present the development of a smart skipper readout technique that allows the user to specify regions of interest of the CCD matrix where an arbitrary (high) number of measurements of the same charge can taken to obtain the desired noise level, and far less measurements are performed in those regions that are less interesting to the researcher, therefore reducing the total readout time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Protecting Smart Buildings with STIG

This is a submission for the 2022 Intern Poster Session. The abstract of the poster is: Smart buildings are getting more common, and so are hackers that target them. The physical systems in our businesses and homes are now vulnerable to cyber attacks. Using STIG, an INL program that turns cybersecurity data into graphs, buildings can be better protected.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Smart dim fuse: electrical load flexibility controller using sub-circuit voltage modulation and load sensing

Improved control of electrical power consumption is provided with “Smart Dim Fuses” (SDF) which can alter their output voltage as provided to the load circuits they are connected to. SDF units can replace conventional circuit breakers in electrical panels. The voltage control capability provided by SDF units can lead to improved control of electrical power consumption, since many loads can smoothly operate at lower power consumption when the voltage they are driven with decreases. SDF units can comply with relevant safety requirements, such as uninterrupted neutral connections between electrical mains and load circuits. SDF units can also provide a current limiting function that can substitute for the protective action of conventional circuit breakers.

Goldin, Aaron↗