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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Cool rooms for indoor heat resilience: Evaluating affordable cooling strategies in heat-stressed California homes

Extreme heat conditions pose significant indoor survivability challenges for resource-constrained communities, which often lack access to cooling, have poorly insulated homes, and face compounding socioeconomic vulnerabilities. Moreover, concurrent power outages worsen health risks and heat-related illnesses. It is therefore crucial to develop innovative and affordable cooling approaches to protect vulnerable populations. This study assesses the efficacy of “cool rooms”– a designated space within a home equipped with passive and low-power active cooling measures to maintain safe indoor temperatures during extreme heat events and power disruptions. Using a physics-based building energy modeling approach, we evaluate the efficacy of various retrofit packages in maintaining thermal safety within the cool room under recent extreme heat conditions. The results indicate that passive measures can reduce 64% of hours with unmet standard effective temperatures, while the combination of passive and low-power active measures with built-in batteries further cuts this to 86%. Nevertheless, these strategies remain insufficient to maintain indoor thermal safety during extended outages. In contrast, integrating a solar-powered mini-split heat pump, whose technical potential was evaluated in this study, reduces indoor air temperatures below the 28 °C overheating threshold and significantly improves indoor habitability. The localized cool room strategy also offers potential for grid resilience by reducing peak electricity demand by up to 70% compared to whole house cooling during heat waves. The findings can inform the development of actionable heat mitigation plans and retrofit policies for residential communities with relatively low adoption of air conditioning such as warm marine climates.

Cool room↗
DOE OSTI2026

A bi-level advanced control framework for large-scale control of buildings with system-level impact

Increased electricity consumption combined with new forms of generation is testing the reliability of our grid infrastructure. This work describes a method to improve the reliability of the grid through large-scale advanced building control. This paper develops a bi-level distributed control framework to shift the load of 153 buildings to achieve a system-level objective of tracking a power reference signal. This bi-level control is based on the previously-developed ANPV-MPC, a predictive controller that uses a Bayesian neural network to generate an accurate control model and adapt to changing conditions over time. By shifting the building electricity demand to better match the available power, the grid system supplying the buildings is more reliable as evidenced by the analysis of node voltages across an IEEE 13-bus distribution system. The proposed bi-level control framework tracks the system-level power reference with enough accuracy to regulate node voltages across the IEEE 13-bus distribution system within ANSI limits of ±5%. Additionally, the adaptive nature of ANPV-MPC allows each building across the system to adapt to changing conditions, further amplifying the system-level reliability.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Hierarchical semi-Markov models with duration-aware dynamics for activity sequences

Residential electricity demand at granular scales is driven by what people do and for how long. Accurately forecasting this demand for applications like microgrid management and demand response therefore requires generative models for activities that can produce realistic daily activity sequences, capturing both the timing and duration of human behavior. This paper develops a generative model of human activity sequences using nationally representative time-use diaries at a 10-min resolution. We use this model to quantify which demographic factors are most critical for improving predictive performance. We propose a hierarchical semi-Markov framework that addresses two key modeling challenges. First, a time-inhomogeneous Markov router learns the patterns of “which activity comes next.” Second, a semi-Markov hazard component explicitly models activity durations, capturing “how long” activities realistically last. To ensure statistical stability when data are sparse, the model pools information across related demographic groups and time blocks. The entire framework is trained and evaluated using survey design weights to ensure our findings are representative of the U.S. population. On a held-out test set, we demonstrate that explicitly modeling durations with the hazard component provides a substantial and statistically significant improvement over purely Markovian models. Furthermore, our analysis reveals a clear hierarchy of demographic factors: Sex, Day-Type, and Household Size provide the largest predictive gains, while Region and Season, though important for energy calculations, contribute little to predicting the activity sequence itself. The result is an interpretable and robust generator of synthetic activity traces, providing a high-fidelity foundation for downstream energy systems modeling.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

A data-driven model for thermodynamic properties of a steam generator under cycling operation

The varying electricity demand from coal power plants due to the intermittent nature of renewable sources leads to load-follow and on/off operations referred to as cycling. Cycling causes transients of properties such as pressure and temperature within various components of the steam generation system.These transients cause increased damage because of fatigue and creep-fatigue interactions shortening the life of components. An algorithm is developed to identify cycling operations from the gross power data. The data-driven model based on artificial neural networks (ANN) is developed using 10 years data from Coal Creek Station power plant located in North Dakota, USA to estimate properties of the steam generator components during cycling operations. Furthermore, the uniqueness of this model is the ability to predict component properties for the cycling as well as base-load operations and is reported for the first time. The ANN model estimates the component properties, for a given gross power profile and initial conditions, as they vary during cycling operations. As a representative example, the ANN estimates are presented for the superheater outlet pressure, reheater inlet temperature, and flue gas temperature at the air heater inlet. The changes in these variables as a function of the gross power over the time duration are compared with measurements to assess the predictive capability of the model. Mean square errors of 4.49E-04 for superheater outlet pressure, 1.62E-03 for reheater inlet temperature, and 4.14E-04 for flue gas temperature at the air heater inlet were observed.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

An interregional optimization approach for time series aggregation in continent-scale electricity system models

Modeling electric power systems with high shares of weather-dependent resources requires tradeoffs between temporal, spatial, and operational resolution. Many studies perform time series aggregation using clustering algorithms to reduce the temporal dimension, but when modeling continent-scale electricity systems that are large enough to contain multiple independent weather systems, this approach requires large numbers of representative periods to minimize errors in regional wind and solar capacity factors. Here, a new optimization-based approach for representative period selection and weighting is introduced that minimizes regional errors in average renewable capacity factors and electricity demand. The method delivers higher regional fidelity with fewer representative periods than alternative clustering methods when applied to wind, solar, and demand profiles for the contiguous United States. When representative periods are selected from multiple weather years, the optimized method reproduces regional averages with lower error than a complete 365-day time series from any single weather year. The method identifies only representative (as opposed to outlying) periods but can be combined with an iterative "stress period" identification approach to guide efficient decision-making considering both average and high-risk weather conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Go slow to go fast? A review of the impacts of permitting on large-scale solar project development

State and local permitting challenges could impede the ability of large-scale solar (LSS) to meet growing electricity demand in the United States. Here, we review research that explores LSS permitting and its impacts on the pace and scale of LSS project development. Research on LSS permitting is relatively scarce, such that we support our review with research in the context of wind permitting, where appropriate. Further, few studies attempt to rigorously quantify the effects of permitting on the pace and scale of LSS project development. The available evidence allows us to identify various hypotheses and identify gaps for further research. Our review suggests that differences in permitting policies, regulations, and ordinances explain relatively little variation in LSS permitting and development outcomes across jurisdictions, except where jurisdictions implement rules designed to impede LSS. The evidence suggests LSS permitting challenges largely accrue during the implementation of permitting processes. Recent research suggests that community opposition to project development is a key driver of LSS permitting challenges, given that project opponents often use permitting processes to translate opposition into legal action. We call on future researchers to more concretely describe the LSS permitting challenge and to identify the specific actors responsible for implementing solutions.

Community opposition↗
DOE OSTI2020

Experimental apparatus and methodology to test and quantify thermal performance of micro and macro-encapsulated phase change materials in building envelope applications

Thermal energy storage (TES) is used as a viable technology to shift peak electricity demand caused by the space cooling requirements in buildings. Passive TES is implemented in building envelope via micro and macroencapsulation methods. This study describes the use of a state-of-the-art laboratory to test different PCM inclusions in simplified building walls. A microencapsulated PCM and two macroencapsulated PCMs are tested in a controlled environment to gather data for validation purposes of PCM modelling algorithms in building energy modelling programs. Data indicates that the chamber environment and the heating and cooling system can conduct full-cycle tests of wall panels with PCM inclusions. This study also generates data from parallel tests on 4 wall panels which can be used in building energy modelling programs to validate the PCM modelling algorithms. The cyclic tests also capture the thermal effects of PCMs and complex PCM behaviors like sub-cooling in PCM hydrate-salts.

25 ENERGY STORAGE↗
DOE OSTI2026

An approach for fast and accurate simulation of phase change material based thermal energy storage in buildings

Latent heat thermal energy storage (LHTES) has significant potential for mitigating peak electricity demand and enabling load shifting in buildings. Phase Change Material embedded heat exchangers (PCM-HX) can significantly improve energy demand management due to high storage capacity. However, PCM-HX evaluation typically depends on computationally expensive fully transient simulations, posing significant challenges for scalable system- and building-level energy assessments across different climates and system architectures. This paper presents a generalized, accurate, and computationally efficient methodology for simulating building energy systems integrated with LHTES. The PCM-HX transient performance is represented by performance maps generated using a Generalized Resistance-Capacitance Model (GRCM) that enables accurate predictions of arbitrary PCM-HXs at low computational cost. The feasibility of the proposed approach was verified using a case study considering a dual-mode heat pump-thermal energy storage (HP-TES) system simulated in Modelica with Spawn of EnergyPlus™ for a DOE prototype small office building in two locations: Tampa, FL, and International Falls, MN. The PCM-HX performance maps provided accurate predictions of PCM-HX transient behavior, with mean absolute percentage deviations within 2–4% compared to GRCM while also achieving at least 1800× reduction in computational time. Moreover, the HP-TES system achieved energy savings of up to 17.4% in Tampa, FL, and 62.2% in International Falls, MN, demonstrating the broader applicability of the proposed methodology across different climate zones. This work highlights the importance of robust PCM-HX models in enabling accurate and computationally efficient building-level simulations and enabling future research opportunities for investigating optimized HP-TES designs and advanced control strategies for grid-interactive buildings.

Modelica↗
DOE OSTI2020

Empirical characterization of vertical-tube inlets in hot-water storage tanks

Hot-water storage in residential and commercial tanks is of growing interest for peak-shifting the electrical demand associated with hot-water loads. How the water enters the tank can significantly affect the extraction efficiency of the storage. Vertical-tube inlets (VTIs) direct cold water into the tank. The VTI design influences whether large-scale mixing occurs, which can reduce the delivery capacity of the hot-water storage. In this work, the mixing at the VTI discharge was characterized by the eddy diffusivity factor (EDF) in an allometric curve fit to the ratio Re:Ri using inlet superficial velocities. The full experimental campaign tested VTI's with Re’s between 170 and 22,300 and Ri's between 0 and 376. Animated visualization of the experimentally measured data is provided, showing tank temperature evolution during inlet flows. The most predictive correlations for EDF are for VTI Re in the transition zone (i.e. ~2300 < Re < ~10,000) and at a Ri corresponding to a stably stratified tank (i.e. Ri > 5). For vertical-tubes that distribute cold water below the thermocline, increasing the EDF will decrease the stratification in a hot-water tank as EDF quantifies inlet mixing. Overall, the new correlations for VTIs can be used to design and model thermally-stratified storage tanks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Oxygen enrichment combustion to reduce fossil energy consumption and emissions in hot rolling steel production

The reheating furnace operation in the hot mill is natural gas- and electricity-intensive. Oxygen enrichment combustion for reheating furnaces has been proposed to curb and replace natural gas use. In this study, heat transfer in steel slabs in the combustion environment of a push-type reheating furnace was simulated using a computational fluid dynamics (CFD) model. Two oxygen enrichment methods that optimized for performance were selected— a medium oxygen enrichment (MOE) case and an oxy-fuel (OF) case. A life cycle analysis (LCA) characterized the energy and emission profiles of an integrated iron and steel manufacturing process using the two oxygen enrichment cases for the hot mill. These conditions were evaluated for energy use and carbon intensity and compared with a baseline case. Results show that with oxygen enrichment, natural gas consumption can decrease by 19.6%–26.8%, total energy consumption (natural gas and electricity) can decrease by 15.1%–20.7% in the hot mill. Emissions of greenhouse gases can decrease by 11.1%–15.2% in the two optimized cases with 14%–27% reductions in regulated criteria pollutants (nitrogen oxides, carbon monoxide, particulate matter, volatile organic compounds, black carbon, organic carbon, and volatile organic carbons). There is a tradeoff between reducing natural gas consumption and increasing electricity demand from a life cycle perspective. Although the OF case resulted in higher energy- and emissions-related benefits, the MOE case showed the more desirable heat flux uniformity, which is key to maintaining product quality. The analysis suggests that oxygen enrichment in the reheating furnace process can have a significant impact on hot mill environmental performance and become a contributing factor in transitioning to low-carbon steel manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Are CO2 batteries ready for the grid? A cross-cutting deployment checklist

Metal-CO2 batteries have attracted considerable research interest in the past decade due to the utilization of the greenhouse CO2 gas. The low cost and abundance of CO2 feedstocks, together with high specific capacity metal anodes, make this technology a promising candidate for electricity-demanding data centers and grids. However, nonstandard data reporting, ambiguous hardware designs, and questionable cycling parameters have emerged as the field has grown. A clear checklist and standardized reporting format for research and development are urgently needed. Here, after carefully re-analyzing 15 Na-CO2 and 9 Li-CO2 studies, we propose a general checklist and a flowchart for CO2 batteries and related gas conversion systems. Specific performance values from a deployment-oriented perspective, based on commercial lithium iron phosphate batteries, are integrated into the list. We frame research and development efforts around grid-relevant outputs, including areal rate and capacity, cyclability, gas utilization, and safety. While illustrated for Na- and Li-CO2 cells, this framework is directly transferable to other CO2 batteries and air/oxygen batteries, helping align benchtop demonstrations with systematic metrics that ultimately govern future deployment.

Wu, Wenda [ORNL] (ORCID:000900033307687X)↗
DOE OSTI2024

Optimizing and Exploring Untapped Micro-Hydro Hybrid Systems: a Multi-Objective Approach for Crystal Lake as a Large-Scale Energy Storage Solution

Increasing electricity demand and concerns about climate change and fossil fuel consumption have highlighted the importance of renewable energy resources and storage systems. This paper proposes a method for exploring untapped pumped hydro storage potentials to accommodate intermittent renewable energy generation profiles. Hourly measured data from 2022 in Benzie County, Michigan, United States, were gathered for system sizing and a thorough, realistic analysis. By employing the multi-objective grey wolf optimization algorithm, we formulated optimal sizing and energy-management strategies for three different scenarios. Unlike similar studies, the 3rd with triple objective functions (OFs) scenario aims to maximize both reliability and ecological OFs while minimizing the cost OF. It has shown promising results with multiple solutions, considering economic, environmental, and reliability factors. A case study conducted in Crystal Lake, Michigan, revealed that although Crystal Lake would function only as a micro-hydro power facility, it is a promising and huge storage unit with a substantial storage capacity of around 14.9734GWh. The system investigated is significant in the USA due to its rapid deployment capabilities, minimal construction requirements, and ease of integration with the distribution grid. The fuzzy logic method was employed to identify the best non-dominant solution among the other solutions. Furthermore, these outcomes include a notably low levelized cost of energy at 0.046147$/kWh, a robust index of reliability of 99.705%, and a significant reduction in CO₂ emissions amounting to 7.9142×10 3 tons/year, when considering the triple OFs. The paper’s methodology provides valuable insights for regions aiming to utilize renewable energy from untapped storage sources.

13 HYDRO ENERGY↗
DOE OSTI2022

System benefits of industrial battery storage: A comparison of grid and facility control and dispatch

Customer-owned, distributed battery installations are being incentivized by utilities to increase installed battery capacity. In many of these incentive agreements, the battery owner relinquishes battery control to the utility in exchange for incentive money. The industrial sector has lagged in storage installation when compared to the residential and commercial sectors. This study compares the economic advantages to utilities and industrial facilities in different dispatch control situations. The study presents a novel framework for the optimization of multiple systems using load profiles from the industrial, residential, and commercial sectors. Case studies are presented to illustrate different dispatch scenarios. Further, the simulations showed more fiscal benefit for the industrial facilities to dispatch the battery for electrical demand reduction than utility dispatch. In the case studies, facility dispatch control resulted in an increase of facility savings by a factor of about 8.7 when compared to utility dispatch. Battery size plays a significant factor on the impact of the grid’s generating costs, showing that larger batteries can provide significant benefit even if dispatched by the facility. Future policies concerning industrial battery installations should consider overall economic benefits to utility and facility in the form of rate structures and incentive participation based on battery size.

25 ENERGY STORAGE↗
DOE OSTI2020

Too Much of a Good Thing? Global Trends in the Curtailment of Solar PV

Solar photovoltaic (PV) systems generate electricity with no marginal costs or emissions. As a result, PV output is almost always prioritized over other fuel sources and delivered to the electric grid. However, PV curtailment is increasing as PV composes greater shares of grid capacity. In this paper, we present a novel synthesis of curtailment in four key countries: Chile, China, Germany, and the United States. We find that about 6.5 million MWh of PV output was curtailed in these countries in 2018. We find that: Policy and grid planning practices influence where, when, and how much PV is curtailed; Some PV curtailment is attributable to limited transmission capacity connecting remote solar resources to load centers; PV curtailment peaks in the spring and fall, when PV output is relatively high but electricity demand is relatively low. We discuss available measures to reduce PV curtailment as well as increasing PV curtailment in the contexts of evolving grids and energy technologies.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2026

Integrated Carbon Capture and Storage in Hydrogen Production: A Combined Techno-Economic and Life Cycle Assessment

This paper presents a coupled techno-economic and life cycle assessment of “blue” hydrogen to be produced at a hydrogen facility through steam methane reforming (SMR) equipped with carbon capture and storage (CCS). Blue hydrogen was modeled in ChemCAD, while an integrated asset model represented the carbon capture and storage chain. An unabated carbon dioxide (CO 2 ) configuration release 11.99 kgCO 2 -eq/ kgH 2 . Capturing ≥95% of the CO 2 stream lowers the carbon footprint to 6.59 kgCO 2 -eq/kgH2 but raises the levelized cost of hydrogen (LCOH) from $\$$1.82/kgH 2 (no CO 2 capture) to $\$$3.22/kgH 2 ; the U.S. 45Q tax credit reduces it to $\$$2.59/kgH 2 . Incorporating CCS reduces the levelized net present value from $\$$0.87/kgH 2 to $\$$0.74/kgH 2 , owing to additional capture, transport, and storage costs. Supplying SMR with low-carbon electricity, especially nuclear, wind, or hydro, delivers the lowest carbon footprint relative to geothermal or grid mixes. Sensitivity analysis identifies that hydrogen sales price, internal rate of return, and CCS cost as the strongest economic levers, while electricity demand dominates residual lifecycle emissions. The results underscore a clear trade-off; substantial CO 2 reductions are achievable, but only with higher production costs, making supportive policy instruments, access to clean power, and robust hydrogen markets essential for large-scale deployment of blue hydrogen.

carbon capture↗
DOE OSTI2025

Influence of Electrolyte Additives on Interfacial Stability of Manganese-Rich Lithium-Ion Battery Cathodes

Affordable, long-lasting energy storage has become critical to support increased electricity demand in recent years. Cobalt-free, lithium- and manganese-rich lithium nickel manganese oxide (LMR-NM) cathodes stand to reduce cost and supply-chain concerns associated with traditional cobalt-containing cathodes for lithium-ion batteries by leveraging more earth-abundant materials; however, they have shown issues with long-term cycling stability. Here, we investigate lithium difluoro(oxalate)borate (LiDFOB), tris(trimethylsilyl) phosphite (TMSPi), and vinylene carbonate (VC) electrolyte additives for their ability to improve cycling performance of LMR-NM (0.3 Li 2 MnO 3 + 0.7 LiMn 0.5 Ni 0.5 0 2 ) cells. Cryogenic scanning transmission electron microscopy (cryo-STEM) with electron energy loss spectroscopy enables the construction of a structure–function relationship between cathode electrolyte interphase (CEI) characteristics and the electrochemical performance of cells aged with these additives. We find the combination of 2 wt % TMSPi + 1 wt % LiDFOB performs better than any single additive, achieving a 28% improvement in specific capacity over the baseline electrolyte after long-term cycling. We attribute this to LiDFOB mitigating Mn ion dissolution, with cryo-STEM showing Mn stabilized up to the CEI surface, coupled with improved CEI structure and chemistry enabled by TMSPi, evidenced by a moderately thick (∼7–15 nm) CEI that appears to protect against further electrolyte reactions with the particle. These results, achieved through site-specific nanoscale characterization, directly reveal mechanisms through which electrolyte engineering can improve the performance of earth-abundant cathodes, enabling informed development of more affordable and reliable batteries to meet future energy storage needs.

25 ENERGY STORAGE↗
DOE OSTI2026

Optimal Pathways from Alternative Carbon Feedstocks to Organic Commodity Chemicals

The use of biogenic and waste feedstocks is a promising strategy to improve the chemical sector's supply chain resiliency and carbon intensity. To help inform research efforts that transform these feedstocks into industrial chemicals, we used a systematic analysis framework to consistently evaluate the economics and environmental impacts of >200 alternative production pathways for 51 organic commodity chemicals in the United States under an optimistic future scenario that reflects the potential upper bounds of process scalability, energy availability, and carbon uptake. Lower-impact and lower-cost alternative pathways were identified for all but three chemicals, with 75% using thermochemical routes and half leveraging existing manufacturing infrastructure. Scenario analysis shows that the ranking of these pathways for half of the assessed chemicals is particularly sensitive to carbon uptake assumptions and criteria prioritization (i.e., cost only, environmental impact only, or both), with changes in electricity grid mix, hydrogen source, and underlying mass and energy flow data proving less influential. Implementing alternative pathways for just 11 chemicals could support a transition to net-zero greenhouse gas emissions from chemical production by 2050, with 11% lower cost than business as usual, similar water requirements, quadrupled electricity demand, and the use of most available woody biomass. These findings provide an exploratory guide toward a future chemical industry that harnesses alternative feedstocks.

09 BIOMASS FUELS↗