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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Integration of cryogenic energy storage with renewables and power plants: Optimal strategies and cost analysis

Energy storage is critical for overcoming challenges associated with the intermittency and the variable availability of renewable sources for decarbonizing the energy sector. Cryogenic energy storage (CES) is of interest due to its high technology readiness level, no geographical limitations, and moderate round-trip efficiency. The time-varying nature of demands and renewable availability needs to be considered at the design and integration stages of energy storage. We develop a mixed-integer nonlinear program (MINLP) model to obtain the energy storage costs on a daily basis for different scenarios that typically arise over an entire year. Using this optimization-based framework, we address key decision-making questions towards energy transition: What is the energy cost when CES is integrated with renewables and power plants? How does each scenario affect the overall energy cost? How much storage is needed for complete transition to renewables? What is the optimal integration towards 100% renewable energy? What are the optimal storage designs for both renewables and fossil-based power generation with current and future energy demands? Here, we discuss different scenarios and solutions to these questions.

25 ENERGY STORAGE↗
DOE OSTI2022

Bottom-up assessment of industrial heat pump applications in U.S. Food manufacturing

Decarbonization of the industrial process heat supply through electrification could contribute significantly to climate change mitigation efforts. In the U.S. industry, thermal processes accounted for more than two-thirds of the total final energy demand in 2021. Cross-cutting electrification technologies like industrial heat pumps are suitable for the process heat supply to several industrial unit operations in a sustainable way while also improving overall energy efficiency. This study employs a bottom-up approach to investigate the techno-enviro-economic potentials of deploying high-temperature and steam-generating heat pumps in the major U.S. food manufacturing sectors in different timeframes. The results show that the annual technical potential energy and CO 2 savings by electrifying process heat supply are 325 PJ (or approximately 20% of the total final energy demand in U.S. food manufacturing) and 31 MtCO 2 (equivalent to the annual CO 2 emissions from over 6 million cars in the U.S.) in 2050, respectively; however, these incur additional costs in each sector. Although there may be individual cost-effective opportunities for electrifying heat supply in specific industrial sites, the overall costs are estimated to be high in the food sectors due to the large disparity between electricity and natural gas prices and low heat source temperatures. To overcome the identified techno-economic barriers, comprehensive action plans for different stakeholders are needed. In conclusion, this study provides novel insights that should inform policymakers’ and executives’ decisions about the electrification of the current and future U.S. industrial heat supply in relevant industrial sectors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Opportunities for green hydrogen production with land-based wind in the United States

Hydrogen (H 2 ) is an efficient energy carrier and storage mechanism that can supply both stationary and transport energy demand. Rapidly declining renewable energy generation costs; technology innovations in wind, solar, battery storage, and electrolysis; and a global push for more sustainable and secure energy have driven increased interest in green H 2 production. In this study, we develop an H 2 scenario analysis tool to assist in rapid, high-resolution insights into future, green H 2 pathways to achieve policy goals and market competitiveness. Using this tool, we estimate H 2 production and costs for U.S., off-grid scenarios given varying policy and cost scenarios from 2025–2035. Results indicate that achieving economically competitive green H 2 production (below $\$$2/kg) is possible in 2030 with no policy incentives (one site achieves this target), while increasing policy support to include wind and green H 2 production tax credits enables widespread economic viability sooner, with sub-$\$$2/kg LCOH targets achieved by 2025 and 51.7% of sites achieving this target by 2035. Maximizing policy support through prevailing wage and apprenticeship credit multipliers enable widespread economic viability, including sub-$\$$2/kg of green H 2 by 2025 and even negative pricing by 2035. Regions with lowest LCOH values correspond to high wind resource areas and capacity factors. Achieving decarbonization goals with green H 2 depends on technology cost reductions and policy support, with a maximum average LCOH reduction of $\$$23.10 between no and maximum policy support scenarios, and a maximum average LCOH reduction of $\$$25.86 between current, conservative technology costs and 2035 projected technology cost assumptions.

08 HYDROGEN↗
DOE OSTI2020

Spatio-temporal impacts of a utility’s efficiency portfolio on the distribution grid

Energy Efficiency has historically focused on delivering savings to offset growth in energy supply. Today’s growing emphasis on decarbonization of the energy supply is driving renewables adoption and increased interest in electrification. As a result, energy efficiency is being assessed not just in its ability to offset load growth, but also for its ability to alleviate location-specific constraints on transmission and distribution infrastructure. This work demonstrates that advanced measurement and verification modeling techniques can be used to estimate the spatio-temporal grid impact of a portfolio of energy efficiency programs. It extends measurement-based methods to an entire Demand Side Management portfolio and uses a single model to predict annual as well as seasonal building energy use with near-zero bias. In addition, new metrics are introduced to assess grid level impacts of energy efficiency. The results show that the efficiency program portfolio delivers savings of over 12% at the territory-wide proxy level, with substation and feeder level savings ranging from 0.4% to 26%, and ~5%-42% respectively. Overall, these savings impacted 1.0%–1.4% of the energy used at these locations in the grid. This work provides a methodology with potential to connect efficiency with distribution planning, carrying implications for non-wires alternatives and targeted delivery of efficiency programs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Evaluating the current perceived cost of ownership for buses and trucks in China

Decarbonizing commercial vehicles, including buses, trucks, and special purpose vehicles, is essential for China to achieve its carbon neutrality target by 2060. However, understanding the adoption barriers of alternative fuel technologies for commercial vehicles has largely been limited to the total cost of ownership approach that mainly considers tangible costs. Additionally, this study proposes a perceived cost of ownership (PCO) model, which monetizes both tangible and implicit costs, including charging annoyance for buses and trucks by size and classification under different use scenarios. In the case study for China's current bus and truck market, the PCO of alternative fuel technologies, such as battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs) are 27%–129% and 31%–86% higher than the most economically competitive powertrains – the compressed natural/liquefied petroleum gas technology and hybrid electric technology. BEVs could be cost-effective in the mini/midsize bus segment or in the daily short-distance driving scenario, and FCEVs could firstly be deployed in cities with a low hydrogen price. Though the implicit costs caused by range anxiety and charging annoyance account for at least 27% of BEV's PCO, making BEV uncompetitive, the battery swapping technique could reduce the implicit cost and overall cash outflow for BEV owners.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Design and optimization of a modular hydrogen-based integrated energy system to maximize revenue via nuclear-renewable sources

Here, this paper demonstrates a novel modular distributed framework that uses optimal energy-dispatching strategies to enable greater flexibility and profitability in nuclear-renewable integrated energy systems (NR-IES). Hydrogen is used as a commodity in this framework since its production can improve grid stability and system operational flexibility, decarbonize heavy industry, and create an additional revenue stream for electricity generators, particularly nuclear power plants with high operational expenses. The proposed solution addresses the challenges associated with merging multiple software and services from various domains by using functional mock-up units (FMU) to co-simulate diverse subsystems designed in various platforms. The tightly coupled integrated energy system (IES) is optimized to maximize revenue by utilizing the deep reinforcement learning (DRL) technique to make smart dispatching decisions based on variable electricity prices and the availability of renewable energy. Proximal policy optimization (PPO) algorithm is used in training and testing the DRL agent. Over a period of 120 days, the proposed hydrogen-based IES framework showed about 10% revenue boost compared to a non-hydrogen generating baseline IES while also providing an easily-adoptable framework which can help to improve the flexibility of future generation nuclear power plants.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Nuclear—thermal energy storage configurations for industrial combined heat and power supply—conceptual and thermodynamic study with high temperature gas-cooled reactor

Nuclear systems are promising candidates for delivering resilient heat and power for future energy security and independence. Traditionally, nuclear plants have been used for baseload electricity production and cogeneration of heat has seen relatively limited application utilizing typically only small portion of a reactor's thermal output. This paradigm may shift due to the increasing penetration of intermittent renewables and need for resource flexibility, various decarbonization efforts aimed at both electricity and heat demands, along with the perspective of small modular nuclear reactor applications, which can be sized based on local industrial needs. Here, this study provides a comprehensive guide for the nuclear and industrial sectors, emphasizing controllability in the combined heat and power configuration options for high temperature gas-cooled reactor and process steam supply. It investigates the integration of thermal energy storage to improve nuclear energy's responsiveness to varying industrial demands. The study emphasizes placing thermal energy storage between the nuclear primary loop and steam cycle to achieve greater efficiency and flexibility in power and heat output, surpassing traditional combined heat and power systems and avoiding efficiency losses seen in other thermal energy storage integration approaches.

combined heat and power (CHP)↗
DOE OSTI2023

Building thermal dynamics modeling with deep transfer learning using a large residential smart thermostat dataset

Understanding thermal dynamics and obtaining the computational model of residential buildings enable its scaled application in energy retrofits, control optimization and decarbonization. In this paper, we present a deep learning approach to model building thermal dynamics with smart thermostat data collected from residential buildings, with the goal to investigate model generalizability. In the first stage, we developed and compared different Deep Learning architectures including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models and CNN-LSTM to predict indoor air temperature in a multi-step time horizon. In the second stage, we implemented a Transfer Learning (TL) process, which aims to improve the prediction performance on a new set of buildings (targets), exploiting the knowledge of related or similar buildings (sources). Different TL strategies and source model identification methods were investigated. The study showed that the CNN-LSTM performed the best among the architectures compared, with an average Mean Absolute Error (MAE) of 0.26 °C for one-hour-ahead (twelve 5-min future steps) predictions. Furthermore, the results showed that freezing the LSTM layer and fine-tuning the other layers of the CNN-LSTM achieved the best performance among four TL strategies, which further improved the performance with respect to a machine learning approach by 10%, and proving the effectiveness and generalizability of the proposed approach. A comparison of three different source model identification methods showed that randomly selecting source models constrained by similar building characteristics can provide good TL performance while retaining simplicity comparing with other quantitative source identification methods.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Short-term electricity load forecasting: Application-driven evaluation of machine learning models across spatial and temporal scales

As we transition towards a decarbonized economy, the integration of variable renewable energy resources and new demands (e.g., electric vehicles, heat pumps) into the electricity grid places unprecedented pressure on grid operators to effectively anticipate and manage peak load. In this context, machine learning algorithms are proving to be indispensable for accurate short-term load forecasting, a crucial task to address these challenges. This study benchmarks 6 machine learning algorithms, including three neural networks and three tree-based algorithms, across various levels of spatial aggregation and time horizons (1, 4, 8, 24, and 48 h). The central contribution of this work is the comparison and analysis of load forecasting models not only based on statistical metrics, but also based on a novel error metric, which evaluates the cost implications of forecast errors for power system stakeholders. Results show that tree-based models outperform neural networks, based on statistical metrics, and yield less skewed error distributions for most spatial scales. However, through the lens of the novel error metric, neural networks are the more competitive choice, especially for forecast horizons that exceed 8 h. The study concludes with actionable recommendations to grid operators and highlights the need for the development of error metrics that link forecasting accuracy to operational costs. To promote transparency and open science, the datasets and Python code are open-sourced via a supplementary repository.

Houben, Nikolaus↗
DOE OSTI2023

Prospects for engineering Ralstonia eutropha and Zymomonas mobilis for the autotrophic production of 2,3-butanediol from CO 2 and H 2

The decarbonization of the chemical industry and a shift toward circular economies because of high global CO 2 emissions make CO 2 an attractive feedstock for manufacturing chemicals. Moreover, H 2 is a low-cost and carbon-free reductant because technologies such as solar-driven electrolysis and supercritical water (scH 2 O) gasification enable sustainable production of molecular hydrogen (H 2 ). We review the recent advances in engineering Ralstonia eutropha, the representative species of "Knallgas" bacteria, for utilizing CO 2 and H 2 to autotrophically produce 2,3-butanediol (2,3-BDO). This assessment is focused on state-of-the-art approaches for splitting H 2 to supply energy in the form of ATP and NADH to power cellular reactions and employing the Calvin-Benson-Bassham cycle for CO 2 fixation. Major challenges and opportunities for application and future perspectives are discussed in the context of developing other promising CO 2 and H 2 -utilizing microorganisms, exemplified by Zymomonas mobilis.

42 ENGINEERING↗
DOE OSTI2020

Biofuels Policies That Have Encouraged Their Production and Use: An International Perspective

This study evaluated the effectiveness of technology-push and market-pull policies in encouraging the production and use of biofuels in 15 countries including major biofuels producers. Biofuels production and consumption data for the 12-year period of 2006-2017 showed that in most of the surveyed countries, biofuels policies played an important role in developing and growing regional and national biofuels markets. Blending mandates continue to be the main market-pull policy used to create and expand biofuels markets. Countries with a mixture of market-pull and technology-push policy instruments have been most successful at increasing biofuels production and use and also developing and deploying less mature advanced biofuels. Most policies have primarily promoted the production and use of biofuels for road transport with key long-distance transport sectors (aviation and shipping) drawing less policy attention despite being significant fuel consumers and carbon emitters. While many older policies were originally developed to promote energy security, more recent policies, such as low carbon fuel standard, have reduction in the carbon intensity of transportation fuels as a primary goal. While policies have been essential in promoting the on-going growth of biofuels, they have not been sufficient to drive the level of development needed to decarbonize the transport sector.

biofuels for transport↗
DOE OSTI2021

Modeling Variable Renewable Energy and Storage in the Power Sector

The emergence of variable renewable energy and battery storage technologies have fundamentally transformed the electric power sector and generated demand for analysis to understand their roles in future energy systems. Although unique characteristics of these resources are well-recognized and require more sophisticated methodologies to capture effectively, guidance is limited on best practices and research gaps. This paper selectively reviews recent literature and draws upon our collective modeling experience to offer recommendations to analysts and consumers of model outputs on approaches for modeling variable renewable energy and storage in long-term electric sector models. We focus on regional- and national-scale models with technological, temporal, and spatial detail given their prevalence in planning and policy analysis, though insights are applicable in other settings. The review highlights how the research frontier has advanced in representing renewables and energy storage over the past decade; however, given the many considerations involved with appropriately capturing salient economic and operational characteristics of renewables, there is a gap between commonly used models and state-of-the-art methods. Model simplifications can materially impact policy analysis associated with power sector decarbonization and high renewables deployment, and improving model representations of variable renewables can enhance insights for policymakers and other stakeholders. This review can point the way for improved methods for established models and designs for emerging ones.

energy and environmental policy↗
DOE OSTI2025

Designing robust energy policy packages under deep uncertainty: A multi-metric decision support framework

The complexity of transitioning to sustainable energy systems requires policy frameworks capable of balancing multiple objectives while addressing deep uncertainty. However, existing approaches often lack systematic methods to identify combinations of policy levers that remain effective across a wide range of uncertain futures. This paper presents a novel decision support framework that guides the selection of robust policy packages based on their performance across multiple objectives under uncertainty. Our method leverages a large ensemble of scenarios and applies scenario discovery techniques to identify influential policy levers. Here, we introduce new indicators to assess the robustness of policies by evaluating their ability to mitigate adverse outcomes across metrics. These indicators support an iterative process to build a robust policy package. Finally, we map the technological and energy pathways associated with the robust policy package by leveraging an energy system optimization model. We illustrate the application of this framework to the Spanish energy system, providing insights into how specific combinations of policy levers shape decarbonization pathways under uncertainty.

Decision-support method↗
DOE OSTI2026

The global policy landscape of ISO 50001 energy management systems

While many options exist to improve industrial demand-side energy efficiency, energy management systems (EnMSs)—particularly those aligned with ISO 50001—are proven to drive continuous and meaningful energy performance improvements. Governments leverage these EnMSs in their policies to advance national objectives including enhancing industrial competitiveness and achieving environmental goals. Existing research has focused on the impact of EnMSs at the company level, while comprehensive work on EnMSs in a global policy context is lacking. We seek to close this gap by investigating the extent to which current national policies incorporate the utilization of EnMSs, particularly the ISO 50001 standard. Our paper employs a hybrid approach, combining a literature review and expert interviews across 28 governments representing > 86% of global primary energy consumption. We dissect policy mechanisms, governance levels, underlying motivations, and trends in present EnMS policies. We find that > 96% of the investigated countries include EnMSs within their policy scope; 90% of policies including EnMSs utilize the ISO 50001 standard in some capacity. Primary policy motivations include decarbonization, energy savings for industrial competitiveness, and energy system resilience. We highlight that in the EnMS context, policy mixes—combining economic incentives, regulatory instruments, and information-based approaches—are more effective than standalone measures. Our work provides a novel global overview of governmental EnMS policies, moving beyond whether EnMS should be adopted to focus on how they can be implemented most effectively.

Moreno, Francisco Luis↗
DOE OSTI2023

Exploring sustainable electricity system development pathways in South America’s MERCOSUR sub-region

South America has abundant natural water and energy resources, and exploiting these resources to achieve a clean energy future is central to the continent’s economic and sustainable development objectives for the next several decades. Designing pathways to achieving this clean energy future requires better understanding the structural, techno-economic, and policy forces that may influence the future development of the electricity sector in the region. Here, we focus on an interconnected electricity system of five South American countries – Argentina, Brazil, Chile, Paraguay, and Uruguay – which represent major electricity generation, consumption, and trade dynamics in the region. We explore the implications of various forces that could shape the future composition of the power sector in the sub-region, including: evolving renewable energy cost and performance, natural gas prices, cross-border interconnection facilities, early retirement of installed hydropower, and different decarbonization goals. We use a model framework based on a power system planning platform (GridPath) to co-optimize investment and operations of generation, storage, and transmission facilities out to 2050. Our results in a Reference scenario indicate that the electricity system can maintain a relatively clean energy portfolio by leveraging existing hydropower capacity and integrating increasingly cost-competitive wind and solar power. However, dependence on natural gas in the region is likely to remain high. A low-carbon electricity system can cost-effectively be achieved through policy interventions (e.g., renewable portfolio standards) and by diversifying investments in wind, solar, battery storage, and some new hydropower capacity. We also find that existing hydropower is critical for maintaining reliable future grid operations. Enhanced regional electricity trade, mostly based on existing interconnection capacities with nominal investment in new transmission, can significantly benefit the clean energy transition in the region.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

The path to 2060: Saudi Arabia's long-term pathway for GHG emission reduction

Saudi Arabia, as part of its Saudi Green Initiative, has announced its goal to achieve net zero green-house gas emissions by 2060. This ambitious target underscores the nation's dedication to address-ing climate change. However, there is a significant gap in comprehensive analysis regarding the long-term effects of Saudi Arabia's climate policies and their collective contribution towards the net-zero objective. This study endeavors to bridge this gap through a detailed examination using the GCAM-KSA, a specialized version of the Global Change Analysis Model tailored for Saudi Arabia, employing a multi-sectoral methodology that integrates economic, energy, and land use systems within a coherent framework to assess the impact of climate policies on GHG emissions. Our anal-ysis reveals that reaching net-zero GHG emissions by 2060 is a complex challenge requiring con-certed efforts across all sectors of the economy. While transitioning to low-carbon electricity and improving energy efficiency offer considerable emission reductions, fully decarbonizing the indus-trial and transportation sectors poses a significant hurdle. Our findings suggest that Saudi Arabia must triple its emission reduction commitments in its next Nationally Determined Contributions (NDCs) update to align with its 2060 net-zero goal. Early action and increased ambition could avoid the chances of getting locked into the high emission assets and give enough time to transform the energy system. Furthermore, the adoption and integration of Carbon Dioxide Removal (CDR) tech-nologies are identified as crucial for offsetting residual emissions, especially in sectors that might continue to rely on fossil fuels.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

The Peaking Potential of Long-Duration Energy Storage in the United States Power System

In this work, we investigated the peaking potential for storage with durations of 4 h up to durations of 168 h (1 week). The peaking potential for a given storage duration is the amount of storage that can be added to a power system before that storage can no longer serve the peak net demand period at full rated capacity. We found that for the United States, 168 h of storage would be sufficient to serve about 27 % of peak demand, or about 215 GW in the current system. However, more than one-half of this amount could be served by storage with 12 h or less of capacity. As deployment of wind and solar grows, the peaking potential increases significantly, and under decarbonization scenarios, approximately one-half of the peak demand could be served by storage of up to 168 h; but again, the majority of this storage could be with durations of up to 12 h. The potential is also driven by the mix of wind and solar, and by storage efficiency, with the deployment of solar having the largest impact for both storage peaking potential and the mix of durations.

capacity expansion↗