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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Understanding the Behavioral Aspects of Rate Design

Consumer demand for electricity has often been thought of as homogenous and inelastic. However, consumers may have preferences for their electricity consumption that they have been unable to realize due to the structures of electricity rates, and historic methods of regulation. As ratemaking becomes more dynamic, consumers will have a newfound ability to realize these preferences. This paper conducts a review of this issue through the lenses of utility regulation, neoclassical economics, and behavioral economics. It surveys what aspects of consumer preferences and behavior have been well established for the electricity sector and identifies gaps that could be filled by future research. This analysis has implications for stakeholders looking to understand how households will function in transactive and real time pricing environments.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Distributed deep reinforcement learning for simulation control

Abstract Several applications in the scientific simulation of physical systems can be formulated as control/optimization problems. The computational models for such systems generally contain hyperparameters, which control solution fidelity and computational expense. The tuning of these parameters is non-trivial and the general approach is to manually ‘spot-check’ for good combinations. This is because optimal hyperparameter configuration search becomes intractable when the parameter space is large and when they may vary dynamically. To address this issue, we present a framework based on deep reinforcement learning (RL) to train a deep neural network agent that controls a model solve by varying parameters dynamically. First, we validate our RL framework for the problem of controlling chaos in chaotic systems by dynamically changing the parameters of the system. Subsequently, we illustrate the capabilities of our framework for accelerating the convergence of a steady-state computational fluid dynamics solver by automatically adjusting the relaxation factors of the discretized Navier–Stokes equations during run-time. The results indicate that the run-time control of the relaxation factors by the learned policy leads to a significant reduction in the number of iterations for convergence compared to the random selection of the relaxation factors. Our results point to potential benefits for learning adaptive hyperparameter learning strategies across different geometries and boundary conditions with implications for reduced computational campaign expenses 4 4 Data and codes available at https://github.com/Romit-Maulik/PAR-RL . .

42 ENGINEERING↗
DOE OSTI2024

A Strategic Condition Framework for Energy Innovation

Building upon previous work in innovation, technology, and economic fields, we propose a conceptual framework to address the interplay dynamics of public policy and the value chains of clean energy technology innovation systems, by focusing on market structural and strategic conditions in relation to the rate and direction of knowledge diffusion within activity-based value chains. Our framework extends beyond socio-technical transition models by considering such nuances as: 1) the implications of geography, 2) the locus of knowledge types, 3) the dynamics of financial and knowledge flow, and 4) emphasizing the importance of both market structure and institutional conditions. At the firm level, we consider dynamics of interactions between suppliers and customers, innovation activities, the competitive environment, and the firm’s broader strategic relationship to governance activities and structures; we allow for the fact that each of these aspects of a firm’s identity may have a strategically relevant geographic dimension (e.g., service territories, locational concentration of input resources, etc.). We provide a proof-of-concept application of the U.S. utility-scale solar photovoltaic (PV) and onshore wind sectors, including their interactions with the U.S. power market, which we in present in a web-based information platform (Energy I-SPARK, ei-spark.lbl.gov).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Sentiment analysis of the United States public support of nuclear power on social media using large language models

This study utilized large language models (LLMs) to analyze public sentiment in the United States (US) regarding nuclear power on social media, focusing on X/Twitter, considering climate change challenges and advancements in nuclear power technology. Approximately, 1.26 million nuclear tweets from 2008–2023 were examined to fine-tune LLMs for sentiment classification. We found the crucial role of accurate data labeling for model performance, with potential implications for a 15% improvement, achieved through high-confidence labels. LLMs demonstrated better performance compared to traditional machine learning classifiers, with reduced susceptibility to overfitting and up to 96% classification accuracy. LLMs are used to segment the US public tweets into policy and energy-related categories, revealing that 68% are politically themed. Policy tweets tended to convey negative sentiment, often reflecting opposing political perspectives and focusing on nuclear deals and international relations. Energy-related tweets covered diverse topics with predominantly neutral to positive sentiment, indicating broad support for nuclear power in 48 out of 50 US states. The US public positive sentiments toward nuclear power stemmed from its high power density, reliability regardless of weather conditions, environmental benefits, application versatility, and recent innovations and advancements in both fission and fusion technologies. Negative sentiments primarily focused on waste management, high capital costs, and safety concerns. The neutral campaign highlighted global nuclear facts and advancements, with varying tones leaning towards positivity or negativity. An interesting neutral theme was the advocacy for the combined use of renewable and nuclear energy to attain net-zero goals.

Energy & Fuels↗
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 OSTI2022

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

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

electrolysis↗
DOE OSTI2023

The Convergence of Vehicle Electrification and Automation - Enabling Sustainable Automated Mobility Districts

The transportation industry is entering a new frontier with three major simultaneous transformations: electrification, automation, and on-demand services. All three directly affect the same spaces of vehicle technology, infrastructure, and system operational performance. Further, each area of change adds its own driving forces of complexity to the others' design challenges. This paper explores these transformations, framing the implications for vehicle technology and infrastructure and their impact on operating concepts and strategies, which in turn impact the sustainability of the transportation infrastructure in the built environment. Specifically, this paper examines battery-electric vehicles operating in a fully automated fleet within an "automated mobility district" (AMD) setting with multiple on-demand services. From this examination, the paper assesses the implications for transportation electrification, comparing and contrasting traditional fixed-route services with those of on-demand transit and automated transit networks, including the trade-offs between vehicle range (battery capacity), charging parameters, power availability, and operations predictability with respect to policy and infrastructure configurations. The paper also addresses the configuration and management of boarding and alighting zones (also called pickup and drop-off zones), and the need for complementary infrastructure intelligence at intersections and curbs. Finally, conclusions are drawn with respect to the system integration and analysis needs for AMDs regarding resilience and sustainability of mobility infrastructure.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Decarbonizing the US Energy System

Recent rapid and unexpected cost reductions in decarbonization technologies have accelerated the cost-effective decarbonization of the US economy, with greenhouse gas (GHG) emissions falling by 20% from 2005 to 2020. The literature on US economy-wide decarbonization focuses on maximizing long-term GHG emissions reduction strategies that rely mostly on renewable energy expansion, electrification, and efficiency improvements to achieve net-zero GHG emissions by 2050. While these studies provide a valuable foundation, further research is needed to properly support decarbonization policy development and implementation. In this review, we identify key decarbonization analysis gaps and opportunities, including issues related to cross-sectoral linkages, spatial and temporal granularity, consumer behavior, emerging technologies, equity and environmental justice, and political economy. We conclude by discussing the implications of these analysis gaps for US decarbonization pathways and how they relate to challenges facing major global emitters.

08 HYDROGEN↗
DOE OSTI2020

Implications of climate change mitigation strategies on international bioenergy trade

Most climate change mitigation scenarios rely on increased use of bioenergy to decarbonize the energy system. Here we use results from the 33rd Energy Modeling Forum study (EMF-33) to investigate projected international bioenergy trade for different integrated assessment models across several climate change mitigation scenarios. Results show that in scenarios with no climate policy, international bioenergy trade is likely to increase over time, and becomes even more important when climate targets are set. More stringent climate targets, however, do not necessarily imply greater bioenergy trade compared to weaker targets, as final energy demand may be reduced. However, the scaling up of bioenergy trade happens sooner and at a faster rate with increasing climate target stringency. Across models, for a scenario likely to achieve a 2 °C target, 10–45 EJ/year out of a total global bioenergy consumption of 72–214 EJ/year are expected to be traded across nine world regions by 2050. While this projection is greater than the present trade volumes of coal or natural gas, it remains below the present trade of crude oil. This growth in bioenergy trade largely replaces the trade in fossil fuels (especially oil) which is projected to decrease significantly over the twenty-first century. As climate change mitigation scenarios often show diversified energy systems, in which numerous world regions can act as bioenergy suppliers, the projections do not necessarily lead to energy security concerns. Nonetheless, rapid growth in the trade of bioenergy is projected in strict climate mitigation scenarios, raising questions about infrastructure, logistics, financing options, and global standards for bioenergy production and trade.

09 BIOMASS FUELS↗
DOE OSTI2022

Sensitivity of the U.S. economy to oil prices controlling for domestic production and imports

This paper investigates the sensitivity of U.S. economic performance to oil price changes, accounting for changes in the domestic petroleum supply-demand balance over the last decade. Herein, a non-linear (asymmetric) autoregressive distributed lag (NARDL) model is used to estimate the U.S. GDP elasticity with respect to the oil price, controlling for oil production, consumption and imports, and macroeconomic variables. The positive and negative components of the oil price both have statistically significant long-run impacts on the real U.S. GDP. The parameter estimates imply that a 1% positive and permanent oil price shock, all else the same, would have a long-run impact of –0.045% on the U.S. economy (an elasticity of –0.045), but short-run parameters on the positive oil price terms are not significant at the 10% level. The long-run parameter on the negative oil price component term implies an elasticity of –0.034. Thus, controlling for domestic U.S. oil production and oil trade, the long-run oil price elasticity of the U.S. GDP remains within the range of estimates from previous studies. The results also show that domestic oil production and consumption have short-run impacts on the U.S. GDP. The potential extent of interactions among these variables, and implications for the net economic impacts, under an oil price shock are subjects of future research.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Financing Storage as a Transmission Asset: Initial Considerations for an Emerging Use Case

Deploying energy storage as an electric transmission system asset is a unique use case that, despite a body of policy and regulatory support, has received little attention or investment in the United States. The benefits of using storage on the transmission system—and the remaining barriers to that use—have been explored elsewhere. This paper complements that body of research by exploring the finance implications of using energy storage as a transmission asset (SATA). Because transmission infrastructure in the U.S. is generally subject to rate-of-return regulation, in which asset owners receive both a return of their invested capital and a return on that capital, storage assets deployed for that use are not subject to market volatility and have a much lower risk profile overall. That lower risk profile would, in theory, correspond to lower interest rates and other more favorable financing terms relative to a storage project deployed in a market setting. This paper draws from corollaries in other markets to estimate the expected finance impacts of SATA projects.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Implications of an emission trading scheme for India’s net-zero strategy: a modelling-based assessment

To help meet its near-term NDC goals and long-term net-zero 2070 target, the Government of India has planned to establish a Carbon Credit Trading Scheme (CCTS), i.e. a domestic emission trading scheme (ETS). An ETS is an inherently cost-effective policy instrument for emission reduction, providing the greatest flexibility to reduce emissions from within and across sectors. An effective ETS requires design features that consider country-specific challenges and reflect its role within the larger policy package to achieve long-term emission reduction. Within the Indian context and in this study we therefore investigate—(i) what might be the role of the ETS in achieving India’s long-term mitigation targets? (ii) How might the various sectors interact under an emissions cap? (iii) How might the ETS interact with existing energy and climate policies? We do this analysis by running four main scenarios using the integrated assessment model GCAM (v6.0), adapted to India-specific assumptions and expectations. These scenarios are—(i) NZ (net-zero), (ii) NZ + ETS, (iii) NZ + CC (command and control), and (iv) NZ + RPO (renewables purchase obligations) + ETS. The NZ scenario assumes India’s near-term and long-term climate commitments of net zero by 2070. Scenarios with ETS (ii) and (iv) apply an emissions cap on four sectors—electricity, iron and steel, cement, and fertilizer. The scenario with CC applies a homogenous emission cap on each of the chosen sectors but does not allow cross-sectoral trading. The last scenario includes renewables purchase obligations (RPOs along with an ETS. We show that under a specific ETS emissions cap: (i) the electricity sector emerges as the largest source of cost-effective greenhouse gas (GHG) reduction options; (ii) ETS with trading across sectors is around 24% more cost-effective than ETS with trading only within sectors, (iii) RPOs can be complementary to an ETS although the impact of RPOs on GHG reductions in the electricity sector would need to be considered when setting the level of the ETS cap (or emissions intensity targets) or the RPO targets to avoid low carbon prices, and (iv) the direction and volume of financial transfers across sectors depends on allocation targets set by the government. Based on these results we provide design recommendations for India’s ETS.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Market Implications of Alternative Operating Reserve Modeling in Wholesale Electricity Markets

Pricing and settlement mechanisms are crucial for efficient resource allocation, investment incentives, market competition, and regulatory oversight. In the United States, Regional Transmission Operators (RTOs) adopts a uniform pricing scheme that hinges on the marginal costs of supplying additional electricity. This study investigates the pricing and settlement impacts of alternative reserve constraint modeling, highlighting how even slight variations in the modeling of constraints can drastically alter market clearing prices, reserve quantities, and revenue outcomes. Focusing on the diverse market designs and assumptions in ancillary services by U.S. RTOs, particularly in relation to capacity sharing and reserve substitutions, the research examines four distinct models that combine these elements based on a large-scale synthetic power system test data. Our study provides a critical insight into the economic implications and the underlying factors of these alternative reserve constraints through market simulations and data analysis.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

R-SEEDS Final Report

DER aggregation and grid digitalization imply a future grid that would benefit from more fluid interaction between the bulk power system and the distribution system. This scenario presents a challenge for grid modeling, which treats the two systems separately. Grid modeling also focuses on the engineering aspects of grids without incorporating modeling of the human and social dimensions of the grid as a cyber-physical-social system. The R-SEEDS project examines the integration of human and social models into grid modeling and its implications on decarbonization, energy justice, and distributional impacts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Aggregation in bottom-up vulnerability assessments and equity implications: The case of Jordanian households’ water supply

Bottom-up methods for water resources modeling rely on acceptability thresholds to find, through a response surface, which deeply uncertain futures lead to system failure. They commonly treat water users as aggregate actors, which may preclude analysis of the equity impacts of interventions. This paper explores how aggregation choices for large groups of water users lead to different policy recommendations in response surface assessments. Herein, two aggregation methods with varying parameters are considered: percentile satisfaction targets and generalized mean. A 2-dimensional stress-test assessment across groundwater availability and population is applied to household water supply in Jordan. The study compares six different policies covering supply enhancement and rebalancing, using a country-wide multi-agent model that characterizes households across socioeconomic strata. For different aggregation levels, policies are ordered by their associated robustness index. Results show that aggregation choices may modify response surfaces as much as policy changes and strongly determine policy preference. Modifying allocation rules can substantially reduce the disparity in household vulnerability. Preferences defined by aggregation intervals provide a finer understanding of trade-offs among water users and may improve deliberation over equity under deep uncertainty.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Bioenergy for climate change mitigation: Scale and sustainability

Many global climate change mitigation pathways presented in IPCC assessment reports rely heavily on the deployment of biomass for bioenergy, often used in conjunction with carbon capture and storage (BECCS). We review the literature on bioenergy, including the modelling of bioenergy in integrated assessment models (IAMs) and bottom-up modelling and non-modelling studies on the implications of bioenergy use. We summarise the limitations of existing modelling studies and what is known about the potential co-benefits and adverse side-effects of bioenergy systems. We find that the implications of bioenergy supply on mitigation and other sustainability criteria are context dependent and influenced by feedstock, management regime, climatic region, scale of deployment and the counterfactual land use and energy system. However, due to limitations of the existing models, and uncertainty over the future context with respect to the many variables that influence availability of biomass and land resources, it is not possible to precisely quantify the sustainability implications for different scales of bioenergy implementation. Given these uncertainties, the dependence on large-scale deployment of bioenergy in mitigation scenarios carries risks. The deployment of bioenergy technologies and the evolution of biomass supply chains at a scale that achieves significant mitigation and carbon sequestration requires integrative policies, coordinated institutions and improved governance mechanisms. As a result, bioenergy, and the use of land to produce biomass, is an essential part of many climate mitigation strategies but there are limits to its use due to trade-offs with sustainability.

09 BIOMASS FUELS↗
DOE OSTI2025

Investigating Building Energy Consumption and CO2 Emission in Phoenix Using AutoBEM and Future Typical Meteorological Year (fTMY) Weather Data

This research investigates the energy performance and CO2 emissions of each building stock across the Phoenix metropolitan area using the Automatic Building Energy Modeling (AutoBEM) framework and Model America v2 (MAv2) dataset from Oak Ridge National Laboratory (ORNL). Typical Meteorological Year (TMY) and Future Typical Meteorological Year (fTMY) files were used for AutoBEM simulation. The simulation results from TMY and fTMY were compared. It was found that a projected 10.28% increase in total CO2 emissions and a 9.30% rise in total energy consumption by 2080–2099 relative to current typical conditions. The results highlight the disparities in emissions among different building stocks and the influence of climate change on future energy demand. The findings underscore the necessity of targeted policy interventions and retrofitting strategies (eg. advanced HVAC systems, improved insulation, reflective roofing) to mitigate emissions in high-energy-use and emission-intensed buildings, particularly as climate conditions evolve. This study contributes to the growing understanding of building-sector emissions and their long-term implications under future climate scenarios.

Li, Hang [ORNL] (ORCID:0000000306001920)↗
DOE OSTI2025

ILLICIT TRANSIT INTERDICTION GLOBAL ANALYSIS

This study aims to enhance the security of radioactive materials during transport by analyzing commonalities in cargo thefts conducted by non-state groups such as thieves and terrorists. This research focuses on identifying patterns and trends in the methods used to steal high-value cargo, with the goal of applying these insights to improve transport security of radioactive materials. Key questions addressed include the frequency of specific tools, techniques, and insider involvement in thefts, as well as the use of weapons, electronic jamming equipment, and specialized tools. Findings will inform security design improvements and industry practices to mitigate vulnerabilities. The study involves a comprehensive review of literature and case studies, utilizing data sources from 2018 to 2023. Articles will be selected based on their relevance to thefts of valuable cargo in transit, with a focus on incidents involving non-state actors. The methodology will include statistical and inferential analysis to identify trends, with results visualized through pie charts, frequency analyses, and terrain maps. The discussion will highlight the implications of findings and provide actionable recommendations for strengthening security measures. Limitations such as data availability and reporting inconsistencies will be acknowledged. Suggestions for future improvements will be constructed using existing case studies and expert feedback. The study’s outcomes aim to raise awareness within the industry, inform policy decisions, and enhance security protocols for radioactive material transport. Metrics for impact include the potential publication of findings, presentations at conferences to raise awareness, and the subsequent actions taken by stakeholders based on the research. By identifying trends and vulnerabilities and suggesting improvements, this research contributes to preventing the illicit use of nuclear and radiological materials.

Zineddin, Dr. Z. [ORNL] (ORCID:0009000848740725)↗