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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Solid-State Transformer and Hybrid Transformer With Integrated Energy Storage in Active Distribution Grids: Technical and Economic Comparison, Dispatch, and Control

Solid-state transformer (SST) and hybrid transformer (HT) are promising alternatives to the line-frequency transformer (LFT) in smart grids. The SST features medium-frequency isolation, full controllability for voltage regulation, reactive power compensation, and the capability of battery energy storage system (BESS) integration with multiport configuration. The HT has a partially-rated converter for fractional controllability and can integrate a small BESS. Fast grid-edge voltage fluctuations from increased solar photovoltaic (PV) and electric vehicle (EV) penetration are difficult to manage for mechanical load tap changers. Hence, along with the trend towards more BESS in the grid, the controllability and the storage integration capability of the SST and HT are of strong interest. However, a review of literature shows existing SST and HT research is mostly at converter level, while system-level assessments are scarce. Assessing technical and economic impacts is critical to understanding the benefits and role of the SST and HT to guide future research, which is presented for the first time in this article. Experimental results from medium-voltage (MV) SST and MV HT prototypes are shown to confirm equipment-level feasibility, where the voltage controllability waveforms of a MV HT prototype are reported for the first time. Comparative simulations are performed on a modified IEEE 34-bus system. Here, a grid-model-less decentralized grid-edge voltage control method and a day-ahead BESS dispatch method are proposed for the SST and HT. The simulations show that the SST and HT with integrated storage can host more PV, achieve peak shaving, mitigate voltage fluctuation and reverse power flow, and support energy arbitrage for operational cost reduction, as compared to the LFT. Moreover, comprehensive analyses of net present value (NPV) and internal rate of return (IRR) are performed under different installed PV capacities, HT’s partial converter ratings, and BESS capacities. Sensitivities to future cost reductions of the PV and BESS are studied. Although the NPV and IRR are currently negative, 60% capital cost reduction or 150% revenue increase will make the SST and HT economically viable in the use case studied.

14 SOLAR ENERGY↗
DOE OSTI2025

Integrated Multiport Conductive and Wireless Architecture for Electric Vehicle Charging

This paper explores the opportunity of integrating conductive and wireless electric vehicle charging architectures and presents an integrated multiport charging architecture suitable for simultaneous conductive and wireless charging of electric vehicles. The architecture successfully integrates the primary side power electronic high frequency inverter and the high frequency transformer with the wireless transmitter and receiver to achieve independent power flow at each port. Design of the integrated magnetics is discussed, and results are presented that demonstrate the decoupled power flow at each port. The proposed integrated architecture increases the utilization of the same charging infrastructure without a proportionate increase in the cost.

Mukherjee, Subho [ORNL] (ORCID:0009000672297925)↗
NASA NTRS1988

Spectrometer technology recommendations

A typical heterodyne remote sensing system contains three major elements: the antenna, the radiometer, and the spectrometer. The radiometer consists of the local oscillator, the mixer, and the intermediate frequency amplifiers. This subsystem performs the function of down converting the high frequency incident thermal emission signal to a lower intermediate frequency. The spectrometer measures the power spectrum of the down-converted signal simultaneously in many contiguous frequency channels. Typical spectrum analysis requirements involve measurement of signal bandwidths of 100 to 1000 MHz with a channel resolution of 0.5 to 10 MHz. Three general approaches are used for spectrometers: (1) filter banks, (2) Acousto-Optic Spectrometers (AOS's), and (3) digital autocorrelators. In contrast to the two frequency domain techniques, an autocorrelator works in the time domain. The autocorrelation function (ACF) of the incoming signal is computed and averaged over the integration time. The averaged ACF is then Fourier transformed to obtain the signal power spectrum. Significant progress was made in the development of sub mm antennas and radiometers. It is now time to begin research in the development of low power spaceborne spectrometers and to reduce their size and weight. The near-term research goal will be to develop a prototype digital autocorrelation spectrometer, using VLSI gate array technology, which will have a small size, low power requirements, and can be used in spacecraft mm and sub mm radiometer systems. The long-range objective of this technology development is to make extremely low power, less than 10 mW/channel, small and stable wideband spectrometers which can be used in future mm and sub mm wavelength space missions such as the Large Deployable Reflector.

Wilson, William J.↗
NASA NTRS1992

Lunar PMAD technology assessment

This report documents an initial set of power conditioning models created to generate 'ballpark' power management and distribution (PMAD) component mass and size estimates. It contains converter, rectifier, inverter, transformer, remote bus isolator (RBI), and remote power controller (RPC) models. These models allow certain studies to be performed; however, additional models are required to assess a full range of PMAD alternatives. The intent is to eventually form a library of PMAD models that will allow system designers to evaluate various power system architectures and distribution techniques quickly and consistently. The models in this report are designed primarily for space exploration initiative (SEI) missions requiring continuous power and supporting manned operations. The mass estimates were developed by identifying the stages in a component and obtaining mass breakdowns for these stages from near term electronic hardware elements. Technology advances were then incorporated to generate hardware masses consistent with the 2000 to 2010 time period. The mass of a complete component is computed by algorithms that calculate the masses of the component stages, control and monitoring, enclosure, and thermal management subsystem.

Metcalf, Kenneth J.↗
DOE OSTI2022

Effects-Based Monitoring of Geomagnetically-Induced Current Using a Convolutional Neural Network

Geomagnetically-induced current (GIC) due to space weather can flow in the power grid causing undesirable effects such as transformer overheating, misoperation of protection devices, and potential blackouts. It is therefore important to monitor GIC in the power grid to improve online situational awareness and decision-making of system operators during a geomagnetic disturbance. To avoid the costly installation of GIC monitors at transformers’ neutrals, it is desirable to find correlations between GIC and already-monitored parameters. Hence, this work proposed the use of a convolutional neural network (CNN) to compute GIC amplitudes from learned patterns in the time-series data of transformer even harmonic currents. Using an electromagnetic transient program, GIC injection simulations were performed for a modeled Dominion Energy Virginia (DEV) substation with two 504 MVA, 500/230 kV transformers. Data collected from these offline simulations were used to train the CNN to provide online GIC monitoring. Testing the CNN performance involved using real GIC measurements from published literature and from a physical GIC monitor in the DEV area. Finally, the results showed that the proposed method was able to provide GIC readings with a root mean squared error of 1.56 A/phase (equivalent to an average accuracy of 94%) for these real GIC waveforms.

42 ENGINEERING↗
DOE OSTI2024

Towards intelligent emergency control for large-scale power systems: Convergence of learning, physics, computing and control

Here, this paper has delved into the pressing need for intelligent emergency control in large-scale power systems, which are experiencing significant transformations and are operating closer to their limits with more uncertainties. Learning-based control methods are promising and have shown effectiveness for intelligent power system control. However, when they are applied to large-scale power systems, there are multifaceted challenges such as scalability, adaptiveness, and security posed by the complex power system landscape, which demand comprehensive solutions. The paper first proposes and instantiates a convergence framework for integrating power systems physics, machine learning, advanced computing, and grid control to realize intelligent grid control at a large scale. Our developed methods and platform based on the convergence framework have been applied to a large (more than 3000 buses) Texas power system, and tested with 56 000 scenarios. Our work achieved a 26% reduction in load shedding on average and outperformed existing rule-based control in 99.7% of the test scenarios. The results demonstrated the potential of the proposed convergence framework and DRL-based intelligent control for the future grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A Transformational Natural Gas Fueled Dynamic SOFC for Data Center in-Rack Power (Final Technical Report)

Data centers consume 250TWh or 1% of global electricity use in 2019. This electricity demand is expected to grow exponentially in the future. The current data center power supply relies on electric grids with diesel engines and batteries as backup; the latter is critically important because a sudden loss of power could erase crucial data, causing catastrophic economic loss. However, such a double protection architecture is costly, inefficient and polluting. In search for better in-rack power system, fuel-cell based generators have recently emerged as a promising alternative to diesel engines and batteries because of its potential to be more cost-effective, more efficient and less polluting. Among many types of fuel cells, solid oxide fuel cells stand out to be a front runner because of their ability to directly operate on natural gas that has an existing infrastructure. However, there are several critical challenges facing SOFC generators to meet the rigorous power requirements of data centers: high cost, poor reliability and more importantly, slow response to the datacenters’ dynamic load change. The unexpected overload conditions in data centers can cause fuel starvation; the latter can initiate microcracks in the anode structure, thus jeopardizing the lifetime of the SOFC generator. The current efforts to increase transient power capabilities of traditional SOFCs and therefore protect SOFC stacks from internal damage during transient loading, rely primarily upon mathematical algorithms. One of the challenges to these control methodologies is the physical limitations of the fuel delivery system such as slow response of mass flow controllers to fast power demand.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Insulating Nanofluids and Solids to Upgrade our Large Aging Transformer Equipment (Advanced Research Projects Agency – Energy Research Final Report)

This project developed a highly promising, minimally invasive pathway for transformers with up to double the thermal life that is achieved today. Thermal life is important because it is occasionally necessary to overload transformers in service and this overload raises the internal temperature, accelerating aging2. A seminal initial finding in the research was that a likely achievable increase in the thermal conductivity of the transformer paper could double the thermal life. Oil-impregnated paper is used to wrap the electrical conductors in a transformer to provide a good balance of electrical insulation and thermal conductivity. The novel approach to increase the thermal conductivity was to add micrometer boron nitride particles to the paper during manufacturing. This is encouraging because it provides benefits with minimal impact on the manufacturing processes of the paper or of a transformer.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Powered By ERAD [Slides]

Energy Resilience Analysis for Distribution Power System (ERAD) is a free, open-source Python toolkit for estimating the energy and service impacts of hazards like earthquakes and flooding. It uses a graph-based approach to capture high resolution connectivity among the grid, critical services, and customers and rapidly compute household level metrics and aggregated statistics across large distribution systems. It uses asset fragility curves that relate hazard severity to survival probability for power system equipment including cables, transformers, substations, etc. The tool is designed to be modular and extensible, allowing it to interface with third-party hazard simulators and integrate into broader resilience analysis workflows. ERAD enables researchers, students, communities, distribution utilities, and other stakeholders to understand hazard impacts and evaluate the effectiveness of different programs to improve energy resilience. The webinar was hosted by NLR researcher Aadil Latif.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Securing the Modern Grid: Federal Investments, Digitization, and Supply Chain Strategy

Across the United States (U.S.) grid expansion and modernization is underway, paving the way for accelerated load growth and intelligent resource management. Digitization of the grid is supported by several state and federal programs, providing support for utilities installing advanced metering infrastructure (AMI), AI-powered analytics systems, battery energy storage systems (BESS), and distributed energy resource management systems (DERMS) to transform the grid from a one-way power delivery system into an intelligent, responsive network that will enable faster load growth and power expansion of data centers for advanced artificial intelligence (AI) applications. The digital transformation of America's grid presents opportunity for increased efficiency and resiliency but also introduces new digital risks that require careful management. Digital equipment often contains several vulnerabilities such as unencrypted communication protocols, and persistent remote access capabilities that could be exploited to manipulate device settings, coordinate service disruptions, or inject false data into grid operations. These digital risks become particularly important as the grid must rapidly scale to support AI-driven data centers, which the administration has identified as essential for maintaining U.S. technological leadership and economic competitiveness. These vulnerabilities are compounded by supply chain realities: Chinese manufacturers currently produce 70-90% of essential grid components including inverters, batteries, and control systems, with the U.S. lacking domestic manufacturing capacity for critical assets like extra-high voltage transformers. Recent federal legislation has established Foreign Entity of Concern (FEOC) restrictions to address these risks, requiring projects to achieve escalating thresholds of non-FEOC content to receive tax credits while utilities work to expand sourcing channels for their supply chains and strengthen security measures. These restrictions arrive precisely when utilities face unprecedented electricity demand growth driven by the rapid growth in data centers, creating a considerable challenge: rapidly expanding infrastructure while navigating complex compliance requirements while lacking viable alternatives for many critical components. Idaho National Laboratory (INL) and its partners have developed practical approaches to help utilities navigate these intersecting challenges as they leverage federal investment to strengthen and grow the grid. These solutions include Cyber-Informed Engineering (CIE) principles that build resilience directly into systems, the Cirrus tool for secure cloud migration, and enhanced procurement guidance that embeds security requirements throughout equipment lifecycles. Federal initiatives, such as the Technical Assistance for Digital Assurance (TADA) project, provide direct support to utilities implementing these approaches while facilitating knowledge sharing across the industry. While these tools and frameworks cannot eliminate all risks inherent in foreign supply chain dependencies, they offer pragmatic pathways for strengthening security posture without sacrificing the deployment momentum essential to meeting surging electricity demand. Ultimately, securing America's digital energy infrastructure demands dedicated coordination across multiple fronts: building domestic supply chains, implementing robust digital assurance practices, and maintaining the aggressive modernization timeline necessary for reliability, resilience, and energy independence.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Robust Medium-Voltage Distribution System State Estimation using Multi-Source Data

Due to the lack of sufficient online measurements for distribution system observability, pseudo-measurements from short-term load or distributed renewable energy resources (DERs) forecasting are used. However, the accuracy of them is low and thus significantly limits the performance of distribution system state estimation (DSSE). In this paper, a robust DSSE that integrates multi-source measurement data is proposed. Specifically, the historical low-voltage (LV) side smart meters are used to forecast load and DERs injections via the support vector machine (SVM) with optimally tuned parameters. By contrast, the online smart meters at LV side are utilized to derive equivalent power injections at the MV/LV transformers, yielding more accurate pseudo-measurements compared to the forecasted injections. Furthermore, to deal with bad data caused by communication loss, instrumental errors and cyber attacks, robust DSSE that relies on generalized maximum-likelihood (GM)-estimation criterion is developed. The projection statistics are developed to adjust the weights of each measurement, leading to better balance between pseudo- and real-time measurements. Numerical results conducted on modified IEEE 33-bus system with DG integration demonstrate the effectiveness and robustness of the proposed method.

distribution system state estimation↗
DOE OSTI2022

Non-Powered Dam Retrofit Exemplary Design for Hydropower Applications

Non-powered dams (NPDs) represent complex systems, situated at the intersection of natural stream environments and the built environment. The presence and operations of NPDs affect stream constituents (e.g., fish, recreational craft, sediment, water) and serve one or more engineered purposes (e.g., recreation, flood control, water supply, irrigation, tailings and debris control, navigation). By definition, NPDs do not provide hydropower generation. However, every NPD contains some untapped hydropower resource potential, represented by the pre-existing hydraulic head created by the dam and presence of flowing water. Adding power generation to an NPD requires retrofitting the NPD, which could involve the addition of any new component or function beyond that currently installed. Because this research is funded by the US Department of Energy Water Power Technologies Office, adding hydropower to NPDs is among the primary objectives for research investment. However, NPD retrofitting extends beyond the purview of hydropower additions. This report provides background information regarding NPD development in the United States, including an overview of the US NPD population, development potential, and recent development. It also summarizes challenges and opportunities facing NPD retrofit development, highlights the importance of maintaining or improving stream and dam functionality, notes key NPD characteristics, describes NPD retrofit methods, and identifies innovation areas for spurring future NPD retrofit development. Section 4 describes retrofit exemplary design principles and concepts that could apply to a wide range of NPD projects. These principles and concepts inform an NPD Retrofit Exemplary Design Specification (REDS), as included in Appendix A. The NPD REDS is heavily influenced by prior work under the standard modular hydropower technology acceleration research initiative, especially the work documented in the Exemplary Design Envelope Specification for Standard Modular Hydropower Technology (Witt et al., 2017). Whereas this prior work focuses on a particular technology class that applies most intentionally for new stream-reach development (i.e., greenfield sites), the NPD REDS contained herein applies more broadly to the NPD resource class and is technology-agnostic (i.e., the specific technologies intended to be described are not explicitly prescribed). This report serves as a part of early-phase research on NPD retrofit development, aimed toward spurring additional, follow-on efforts to specifically address development challenges, capitalize on development opportunities, and inspire innovation. As documented in the landmark “Hydropower Vision” report (DOE 2016), “transformative technical innovations able to meet the co-objectives of environmental sustainability and low-carbon energy will be critical to enabling additional hydropower growth.” This report serves as an important step in steering transformation of US non-powered dam infrastructure.

13 HYDRO ENERGY↗
DOE OSTI2022

Decoding the Mechanisms of Phase Transitions from In Situ Microscopy Observations

Abstract Analysis of the temperature‐ and stimulus‐dependent imaging data toward elucidation of the physical transformations is an ubiquitous problem in multiple fields. Here, temperature‐induced phase transition in BaTiO 3 is explored using the machine learning analysis of domain morphologies visualized via variable‐temperature scanning transmission electron microscopy (STEM) imaging data. This approach is based on the multivariate statistical analysis of the time or temperature dependence of the statistical descriptors of the system, derived in turn from the categorical classification of observed domain structures or projection on the continuous parameter space of the feature extraction‐dimensionality reduction transform. The proposed workflow offers a powerful tool for the exploration of the dynamic data based on the statistics of image representation as a function of the external control variable to visualize the transformation pathways during phase transitions and chemical reactions. This can include the mesoscopic STEM data as demonstrated here, but also optical, chemical imaging, etc., data. It can further be extended to the higher dimensional spaces, for example, analysis of the combinatorial libraries of materials compositions.

Valleti, Sai Mani Prudhvi↗
DOE OSTI2026

Review on Perovskite Solar Cells: From Single‐Junction Devices to Tandem Deployment in Space

Perovskite solar cells (PSCs) have emerged as a transformative photovoltaic technology, offering high power conversion efficiency (PCE) and the potential for cost-effective manufacturing. However, stability and large-scale manufacturing remain critical challenges that must be addressed for widespread adoption. This review provides a roadmap from single-junction perovskite solar cells to tandem deployment in space. First, material-level innovations are discussed, including mixed-cation and low-dimensional perovskites, transport materials, and additives that improve thermal and structural stability while enhancing efficiency. Then, we examine both established industrial standards and emerging scientific protocols aimed at stabilizing PSCs under operational conditions, including tandem cell integration strategies and encapsulation techniques to mitigate performance degradation. Manufacturing scalability is a focal point, where deposition methods and green solvents are explored to improve large-area film uniformity and reduce environmental impact. Additionally, the increasing viability of PSCs in extraterrestrial environments is assessed, with emphasis on their performance in space applications, radiation resistance, and flexible lamination methods for deployment in extreme conditions. Progress across materials innovation, device architectures, stability testing protocols, and both terrestrial and extraterrestrial applications collectively drives perovskite photovoltaics toward higher efficiency, stability, and cost-effectiveness.

flexible PSCs↗
DOE OSTI2026

Ten questions concerning Large Language Models (LLMs) for building applications

Large Language Models (LLMs) are emerging as powerful AI tools capable of transforming how building information is collected, processed, analyzed, and applied across diverse research areas. Their capabilities can help building operators, facility managers and other stakeholders such as designers, architects and engineers by providing actionable insights for decision-making across planning, construction, operations, and maintenance of buildings and facilities. This paper explores ten key questions concerning the role of LLMs in shaping sustainable, intelligent, and human-centric buildings. From fundamental definitions to advanced applications, we examine how LLMs facilitate decision-making across the life cycle of buildings and energy systems. LLMs can enhance life cycle assessments (LCA), building energy simulations, and real-time data integration, empowering more efficient and adaptive human-AI environments. They can also contribute to streamlining regulatory compliance, improving post-occupancy evaluations, and fostering more inclusive and participatory design processes. Additionally, this paper addresses the ethical challenges posed by LLMs, such as bias, data privacy, and environmental impacts, and explores their potentials in advancing intelligent digital twins (DT) for ongoing building operations and maintenance. Built upon our applied research using LLMs and the review of tools, datasets, and research gaps, we provide a forward-looking perspective on how LLMs can drive innovation, collaboration, and productivity in the built environment while supporting ethical and effective implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Infrared nanoimaging and nanospectroscopy of electrochemical energy storage materials and interfaces

Electrochemical interfaces are central to the function and performance of energy storage devices. Thus, the development of new methods to characterize these interfaces, in conjunction with electrochemical performance, is essential for bridging the existing knowledge gaps and accelerating the development of energy storage technologies. Of particular need is the ability to characterize surfaces or interfaces in a non-destructive way with adequate resolution to discern individual structural and chemical building blocks. To this end, sub-diffraction-limit low-energy infrared optical probes that exploit near-field interactions within atomic force microscopy platforms, such as pseudoheterodyne nanoimaging, photothermal nanoimaging and nanospectroscopy, and nanoscale Fourier transform infrared spectroscopy, are all powerful emerging techniques. These are capable of non-destructive surface probing and imaging at nanometer resolution. This review outlines recent efforts to characterize ex situ, in situ, and operando electrode materials and electrochemical interfaces in rechargeable batteries with these advanced infrared near-field probes.

25 ENERGY STORAGE↗