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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Mitigating Stranded Asset Risks to Utility Customers: an Exploration of Securitization and Retiring Coal Generation

Coal generation currently accounts for approximately 19.5 percent of electricity generated in the United States, down from 51.7 percent of the generation mix in 2000. In 2023, the EIA expects 8.9 gigawatts (GW) of planned retirements of coal-fired capacity. Many of the coal-fired power plant retirements that have already occurred or are planned for the next decade will be plants that have not yet reached the end of their useful life, and are therefore not fully depreciated. Ensuring that utility customers do not face an undue burden in paying for these stranded assets will become an increasingly important issue over the next few years for PUCs overseeing the safety, reliability, and affordability of investor-owned utility service. This report reviews the role that securitization can play in reducing the costs associated with stranded assets due to early coal plant retirements.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Software Defined Architectures for Portability and Performance

The Software Defined Architectures for Portability and Performance (SODAPOP) project developed a co-design framework to partition and map converged applications on specialized heterogeneous architectures. We started from key domain applications that combine scientific simulation with data analytics and machine learning as drivers to integrate our framework. The framework includes high-level compilers that interfaces with high-level programming frameworks, domain-specific optimization passes, and hardware-oriented optimizations. The framework leverages hardware generators to enable specialization and facilitate exploration of custom system designs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Safely Exploring Solar: A Guide for Austin Energy Customers

It's easy to get overwhelmed as you start to explore your solar options. There is a wealth of information available out there, particularly online, and it can be hard to know who to trust. This informational guide has been adapted from Austin Energy's Solar Education Course and other national best practice resources, as of May 1, 2023. It will help you understand some of the key concepts and resources available to help you on your solar journey if you have a home in Austin Energy's service territory and are considering installing solar. This document includes: (1) Austin Energy Programs and Services Related to Solar; (2) Tips for Spotting a Solar Scam; (3) Things to Know When Designing Your System; (4) Key Considerations When Shopping for a Solar Contractor; and (5) Final Takeaways.

14 SOLAR ENERGY↗
DOE OSTI2026

Progressing Analysis of Variable Electric Rates (PAVER) Study

The Progressing Analysis of Variable Electric Rates (PAVER) study analyzed the impact of a range of time-varying electric rates on the performance of a regional electric grid and the resulting costs for participating and non-participating customers. This analysis leveraged and extended the work of PNNL’s Distribution System Operator with Transactive (DSO+T) study. Five different rate designs were included: a flat volumetric energy charge, a typical Time of Use (TOU) rate, a dynamic energy (DE) rate (based on wholesale locational marginal prices), a dynamic energy and capacity (DE+C) rate, and, finally, a Block and Swing (B&S) rate that billed customers based on their average load profile at constant pricing, but used the DE+C dynamic price for load deviations from their average profile. These rates were analyzed in a large-scale co-simulation of an entire regional grid with a customer population representative of the current state. A large fraction (80%) of residential and commercial customers were assumed to participate in these time-varying rates with automatically controlled HVAC, water heaters, electric vehicles, and batteries. This study assumed no industrial sector participation. The DE and DE+C rates saw system peak loads reduced by 6-7%, while the large participation in the TOU rate case saw a significant rebound effect and a resulting peak load increase of >5%. The impacts to the annual and peak system demand impacted system wholesale prices and the overall grid operating costs. This cost structure determined the revenue needed to be collected from customers by each rate design. Participating customers on the DE and DE+C rates (located in one of the modeled DSOs) saw reductions in average annual electricity bills of 11-17% with average increases in monthly bill variation of no more than 13%. At such high participation levels, TOU customers saw 10% higher average annual bills (due to system-wide rebound effects) and average increased monthly bill variation of 16%. Residential owners of large flexible loads (such as electric vehicles) saw larger bill savings (17-20%) when on a fully dynamic rate. The presence of on-site generation (such as rooftop solar) did not appear to appreciably change customer outcomes. Customers on the Block and Swing rate did see 6% lower monthly bill variation (as intended) than the flat rate case, but at the expense of appreciable bill savings, which were only 3%, comparable to the savings seen by non-participants. Given this finding we recommend that additional research be conducted into how best various bill protection mechanisms can balance minimizing customer bill variation with providing financial incentives commensurate with the flexibility customers provide. We also recommend that customer outcomes be explored across a range of regions using current actual customer and system cost data.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Approximate Dynamic Programming With Enhanced Off-Policy Learning for Coordinating Distributed Energy Resources

Herein this paper proposes an innovative approximate dynamic programming (ADP) method for distributed energy resource coordination with the loss of life of battery energy storage system (BESS) explicitly modeled. The dispatch policy is designed to account for both calendrical and cyclical aging effects on BESS, explicitly modeling the impacts of ambient temperature on BESS lifespan. The proposed ADP employs an adaptive critic method and enhanced off-policy deterministic policy gradient (DPG) strategy, addressing the limitations of the on-policy gradient-based ADP approaches, including inadequate exploration, low data usage, and computational complexity. In particular, a customized policy is proposed to guide the algorithm to explore some promising decisions and thereby improve exploration capability and learning efficiency compared to conventional DPG-based learning approaches, which may struggle to find a global optimum due to random noisy action-based exploration or require expert demonstration with extra effort. The proposed method is illustrated using the IEEE 123-node system and compared with the existing ADP methods to prove solution accuracy and demonstrate the effects of incorporating degradation models into control design. Case studies showed that the proposed ADP effectively coordinates DERs with a 10 times smaller optimization gap compared to existing methods, and the incorporation of the BESS life loss model ensures the expected lifespan and results in significant cost savings.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Applying deep learning methods to develop new models of molecular charge transfer, nonadiabatic dynamics, and nonlinear spectroscopy in the condensed phase

Photon- and field-induced charge transfer has central importance in the generation and storage of electricity, the novel properties of materials, photo-induced catalysis, and electro-optic activity (e.g., photovoltaic cells, fuel cells, and organic chromophores for use in optical fibers and light-emission diodes). These non-equilibrium electronic and chemical transformations are probed by ultrafast, nonlinear spectroscopies. Accurate simulations play a crucial role in our ability to understand, optimize, and control these transformations. This project applies modern deep learning and machine learning (ML) methods to dramatically improve models of electronic dynamics, electronic-nuclear dynamics, and spectroscopic measurements for improved simulations of chemistry in complex environments, far from equilibrium phenomena, and processes in extreme environments, such as materials exposed to strong or resonant fields. This project develops accurate neural net models that go beyond predictive capability to also provide new insight into the fundamental physics underlying electron and nuclear dynamics. To achieve its objectives, this project explores and develops customized versions of high-capacity deep learning algorithms/models. These techniques are developed with an emphasis on fundamental chemical insight, not just predictive accuracy, to assist the development of the next generation of quantum simulation methods.

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

Large Format Composite Additive Manufacturing for Low-Head Hydropower

Hydropower with a small elevation change from inlet to outlet, known as “low-head” hydropower, is a relatively untapped resource for reliable green power generation. One major barrier to entry is the cost of the components needed to generate the power. Each installation site is unique, with various head levels, flow rates, and other unique site characteristics that drive up the cost of development and installation. As a result, custom-made components are necessary because the sites are intrinsically inefficient. However, customized parts are generally more expensive to manufacture than ready-made parts. Often times, the cost of custom-made components is so high that the low-head hydropower installation becomes non-viable. Additive manufacturing offers the ability to make custom components, ideal for one-off applications, at low costs that are well suited for the needs of low-head hydropower. Indirect additive manufacturing, such as making tools or dies rather than end use components, can also be used to make low-cost composite tooling as needed for these custom applications. This paper explores the use of additive manufacturing, both directly and indirectly, to produce the components of a turbine system for a low-head hydropower site. The parts were designed to form a unique modular system, which saves time for future designs and iterations. The system has operated for more than three years without failure at a test site in Wisconsin, USA. This work serves as a basis for future application of AM to low-head systems, in which the modular components can be customized for each unique hydropower installation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multihead Attention U‐Net for Magnetic Particle Imaging–Computed Tomography Image Segmentation

Magnetic particle imaging (MPI) is an emerging noninvasive molecular imaging modality with high sensitivity and specificity, exceptional linear quantitative ability, and potential for successful applications in clinical settings. Computed tomography (CT) is typically combined with the MPI image to obtain more anatomical information. Herein, a deep learning‐based approach for MPI‐CT image segmentation is presented. The dataset utilized in training the proposed deep learning model is obtained from a transgenic mouse model of breast cancer following administration of indocyanine green (ICG)‐conjugated superparamagnetic iron oxide nanoworms (NWs‐ICG) as the tracer. The NWs‐ICG particles progressively accumulate in tumors due to the enhanced permeability and retention (EPR) effect. The proposed deep learning model exploits the advantages of the multihead attention mechanism and the U‐Net model to perform segmentation on the MPI‐CT images, showing superb results. In addition, the model is characterized with a different number of attention heads to explore the optimal number for our custom MPI‐CT dataset.

Juhong, Aniwat↗
DOE OSTI2025

Electricity Rate Designs for Large Loads: Evolving Practices and Opportunities

Electricity demand from large load customers such as data centers is projected to grow significantly in the near term. While data centers play an important role in advancing technology innovation and economic growth in the United States, data center energy needs present challenges and opportunities for electricity supply and infrastructure. This technical brief serves as a foundation for the discussion of issues and sharing of perspectives among utilities, regulators, large load customers, and other stakeholders. As utilities and regulators explore rate structures to address growing data center electricity demand, several issues have emerged: -Fair allocation of electricity system costs to large-load customers without unfair shifting of costs to other customers -Appropriate mitigation of the financial risks associated with stranded assets from underutilized utility system investments -Mitigation of operational and resource adequacy risks if electricity demand exceeds supply -Appropriate risk-sharing in commercializing newer electricity technologies such as advanced geothermal, small modular reactors, and long duration energy storage -Accommodating the diverse needs of large-load customers, such as having the option to match electricity consumption with output from carbon-free resources or using onsite generation to provide system capacity The technical brief also identifies key design elements that aim to address these issues and uses leading examples from pending and approved rate structures, agreements, and special contracts to ground the elements in practice.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

ASEAN Technical Exchange Workshop for System Operators, Regulators, and Policymakers

This presentation provides an in-depth exploration of power system planning, cross-border electricity trading, and battery energy storage systems (BESS), offering actionable insights for system operators, regulators, and policymakers. The first section delves into power system planning and analysis, focusing on capacity expansion models and resource adequacy studies, including their role in optimizing system efficiency, managing emissions, and addressing system reliability risks. Key considerations, such as integration of transmission into generation planning and the forecasting versus optimization of customer distributed energy resources (DER) technologies, are explored. The session highlights critical trade-offs in spatial granularity and model runtimes, as well as the feasibility of aligning distribution investments with capacity expansion efforts. The second section examines cross-border electricity trading, with an emphasis on resource adequacy concepts such as reliability targets, loss of load expectation (LOLE), and planning reserve margins (PRM). Case studies on reserve market design and coordination across US regions provide insights into improving reserve deliverability and managing interregional power balance and congestion. This section also addresses market-to-market congestion management, including advanced strategies for high-voltage direct current (HVDC) optimization and ancillary service delivery. Finally, the presentation covers the rapid evolution of Battery Energy Storage Systems (BESS), highlighting their operational growth, regulatory frameworks, and use cases in grid flexibility, energy storage, and reliability. The discussion focuses on the benefits of BESS for system stability, resilience, and integration of renewable energy, offering insights into its role as a vital component in the transition toward a more sustainable and flexible grid. Key performance parameters, such as throughput, round-trip efficiency, and state of charge, are also examined.

25 ENERGY STORAGE↗
DOE OSTI2026

Pre-training Vision Models for the Classification of Alerts from Wide-field Time-domain Surveys

Modern wide-field time-domain surveys facilitate the study of transient, variable and moving phenomena by conducting image differencing and relaying alerts to their communities. Machine learning tools have been used on data from these surveys and their precursors for more than a decade, and convolutional neural networks (CNNs), which make predictions directly from input images, saw particularly broad adoption through the 2010s. Since then, continually rapid advances in computer vision have transformed the standard practices around using such models. It is now commonplace to use standardized architectures pre-trained on large corpora of everyday images (e.g., ImageNet). In contrast, time-domain astronomy studies still typically design custom CNN architectures and train them from scratch. Here, we explore the effects of adopting various pre-training regimens and standardized model architectures on the performance of alert classification. We find that the resulting models match or outperform a custom, specialized CNN like what is typically used for filtering alerts. Moreover, our results show that pre-training on galaxy images from Galaxy Zoo tends to yield better performance than pre-training on ImageNet or training from scratch. We observe that the design of standardized architectures are much better optimized than the custom CNN baseline, requiring significantly less time and memory for inference despite having more trainable parameters. On the eve of the Legacy Survey of Space and Time and other image-differencing surveys, these findings advocate for a paradigm shift in the creation of vision models for alerts, demonstrating that greater performance and efficiency, in time and in data, can be achieved by adopting the latest practices from the computer vision field.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Bill Savings vs. Backup Power: Evaluating operational tradeoffs for home solar+storage systems [Slides]

Adoption of residential solar photovoltaic+energy storage systems (PVESS) is driven by both bill savings opportunities and customer demand for backup power. Prior work by this team (Gorman et al., 2022; Gorman et al., 2023) explored PVESS backup power capabilities during long-duration power interruptions (e.g., due to severe weather events), when customers are assumed to be able to anticipate the event and charge their batteries in advance. In many cases, however, power interruptions are unpredictable (and often relatively short); for those types of events, a customer will typically set its battery to maintain some minimum capacity in reserve in case of an interruption, which reduces the capacity available for managing utility bills. This study evaluates this operational tradeoff to help customers and installers configure backup reserve settings, and to inform decision-making more generally about the customer value of backup power services compared to utility bill savings. This study utilizes Berkeley Lab’s PRESTO tool to produce stochastic simulations of (predominantly short-duration) power interruption events, and builds on an earlier case-study demonstrating PVESS backup performance during short-duration interruptions (Baik et al., 2023).

14 SOLAR ENERGY↗
DOE OSTI2025

Design, fabrication, simulation, and testing of additively manufactured lattice-based copper heat sinks

Here, this study investigates the potential advantages of lattice structure-based bound metal material extrusion (MEX) 3D printing for fabricating high-performance copper heat sinks. Copper powder-filled polymer filaments, with a copper content of > 90 wt.%, were developed specifically for the bound metal MEX 3D printing process. Three types of structures—planar, strut, and surface lattices—were 3D printed to facilitate efficient heat transfer pathways within the heat sinks. Subsequent post-processing steps, including polymer removal and sintering, were performed to achieve dense copper parts. Hot isostatic pressing was further employed to enhance the sintered density from 93 to 98%. Finite element analysis (FEA) simulations were conducted to assess the heat transfer efficiency of the designs, and heat transfer experiments were performed using a custom setup to validate the simulation results. Additionally, this research explores the use of extended hold times during pre-sintering and a reduced atmosphere to enhance the %IACS values (electrical conductivity) and thermal performance of the bound metal MEX 3D printed copper heat sinks. The investigation combines experimental analysis, including simulations and heat transfer experiments, to gain insights into the structure-material property relationships and optimize the thermal performance of the printed heat sinks.

Ajjarapu, Kameswara Pavan Kumar [Oak Ridge Nationa↗
DOE OSTI2025

Orbital Engineering Band Degeneracy in a Dual-Square Carbon-Oxide Framework

Electron band degeneracies in momentum space give rise to exotic quantum phenomena that have sparked intense interest in condensed matter physics and materials science. Nodal-lines─isolines in k-space formed by the incidental touching of two bands that share the same energy but belong to discrete eigenstates─arise in the presence of symmetries that preclude effective hybridization. Despite recent advances in the design, bottom-up assembly, and engineering of exotic electronic states in graphene nanomaterials, the extension of this approach to access synthetic two-dimensional (2D) quantum materials derived from metal- or covalent-organic frameworks (COFs) has lagged behind. Here we present a molecular orbital engineering approach for designing and fabricating an edge-centered dual square lattice within a π-conjugated 2D-tetraoxa[8]circulene (2D-TOC) COF. First-principles calculations and scanning tunnelling spectroscopy reveal the emergence of Frontier states at the center of a 3 × 3 lattice that give rise to Dirac nodal-lines in 2D-TOC. Our findings not only provide a general guide for the design of conjugated COFs with custom tailored electronic properties from molecular fragments but enable the exploration of emergent topological phenomena in synthetic 2D materials with potential application for high-speed, low-power data processing, transmission, and storage.

Dirac nodal line semimetal↗
DOE OSTI2025

RWRtoolkit: multi-omic network analysis using random walks on multiplex networks in any species

Abstract We introduce RWRtoolkit, a multiplex generation, exploration, and statistical package built for R and command-line users. RWRtoolkit enables the efficient exploration of large and highly complex biological networks generated from custom experimental data and/or from publicly available datasets, and is species agnostic. A range of functions can be used to find topological distances between biological entities, determine relationships within sets of interest, search for topological context around sets of interest, and statistically evaluate the strength of relationships within and between sets. The command-line interface is designed for parallelization on high-performance cluster systems, which enables high-throughput analysis such as permutation testing. Several tools in the package have also been made available for use in reproducible workflows via the KBase web application.

Kainer, David (ORCID:0000000172714676)↗
DOE OSTI2024

Low-cost Manufacturing of Semitransparent CdTe PV for Building Integration

Solar has been demonstrated to be a robust renewable energy source, constituting a significant portion of the United States’ renewable energy portfolio. Despite its growth across residential, commercial, and utility sectors over the past decades, it remains a small fraction of the overall energy infrastructure. Challenges persist in fully harnessing solar power to meet the nation's escalating energy demands. Among these challenges lies the hurdle of efficiently distributing large quantities of solar-generated electricity to densely populated regions with the highest energy needs and costs. Traditional utility-scale arrays demand extensive land, a luxury often unavailable in metropolitan areas. Consequently, installations must be situated at a distance, necessitating additional infrastructure for electricity transmission to service areas. While metropolitan landscapes lack sprawling open spaces suitable for conventional utility-scale solar deployment, they offer a different resource: windows. Semitransparent photovoltaic window technology has the potential to not only bolster the grid's energy capacity, but to also provide HVAC and economic advantages to building owners. However, commercial availability of building-integrated photovoltaic windows remains limited. Silicon based photovoltaics currently dominate the solar market but adapting them for use in windows poses a variety of engineering and economic challenges such as relatively low power density, high costs associated with custom manufacturing, and aesthetic considerations. Addressing these challenges, this project explored the use of laser ablation patterning to manufacture cost-effective, high-efficiency, semitransparent Cadmium Telluride photovoltaic modules. Results showcased the potential of this methodology in developing photovoltaic windows and other innovative semitransparent PV applications. The ablation manufacturing technique demonstrated great versatility in achieving different patterns and levels of visible light transmission, and the power loss due to ablation was nearly directly proportional to the amount of material removed. Furthermore, the manufacturing process for Cadmium Telluride modules already has established advantages in material and energy efficiency, and the conversion of a standard submodule to semitransparent essentially requires a single additional process step, ensuring scalability. It is important to note that during this work, Toledo Solar experienced substantial organizational upheaval stemming from a lawsuit with First Solar. An external investigation led the board of directors to remove and replace the previous management team, and several other members of the staff elected to depart as well, including the then acting Principle Investigator on this project. The remaining Toledo Solar team attempted to recover from the disruption and deliver on the remaining tasks, but upon its own review, the Department of Energy ruled the project in default and terminated the contract in December 2023.

14 SOLAR ENERGY↗
DOE OSTI2024

Grid-connected heat pump water heater benefits for low-income households in the Southeastern United States

Energy efficient heat pump water heaters (HPWHs) reduce customer energy costs; grid-connected controls further increase HPWH value by enabling energy storage and shifting demand to cheaper, off-peak periods. However, a HPWH’s first cost premium can put it out of reach for low-income customers. This paper will present results of an ongoing study exploring the benefits of load shifting HPWHs for 24 low-income households, aiming to support increased product deployment in this demographic. The study uses EcoPort modules to shift HPWH load for a study sample consisting primarily of low-income adults and seniors in North Carolina. Building upon prior studies, the study’s HPWH operating schedules are designed to maximize shifted energy and minimize participant electricity costs based on local time-of-use rates. The study documents load profiles of grid-connected HPWHs in a less-studied demographic group and explores how certain groups may have particularly flexible loads due to their unique usage profiles. The results are relevant for HPWH product performance in the Southeast. The study will quantify HPWH load shifting performance during North Carolina’s hot summer, temperate shoulder, and occasionally sub-freezing winter seasons, and the study includes installations in conditioned, semi-conditioned, and unconditioned spaces. Finally, study results will also explore how controlled HPWHs can provide value to the regional grid by reducing seasonal peaks via demand response. Lessons learned include HPWH acceptance for low-income and senior users, best practices for maintaining HPWH connectivity among users without prior product experience, and strategies to leverage remote monitoring to ensure optimal operation and enhance HPWH reliability.

Urigwe, Daniela↗
DOE OSTI2023

A Component-Level Bottom-Up Cost Model for Pumped Storage Hydropower

Pumped Storage Hydropower (PSH) is currently the largest source of utility-scale electricity storage in the U.S. and worldwide. As the accelerating deployment of variable renewable technologies creates opportunity and value for energy storage, it has become increasingly important to characterize PSH costs to understand how it competes. Site-specific considerations and limited cost data in the public domain make it difficult to estimate capital costs for potential new PSH sites. This report documents a spreadsheet-based tool that addresses this challenge and creates a component-level bottom-up cost model for PSH that can be made publicly available for widespread use. It uses detailed site-level physical characteristics and design specifications to calculate key performance and cost parameters for individual components and the project as a whole. The model was developed in consultation with HDR, Inc. and Small Hydro Consulting to ensure it aligns with industry expectations. It enables PSH cost exploration across a wide range of system assumptions and could be customized or extended for the needs of a variety of users.

cost model↗