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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A Model for Hybrid Systems for Production Cost Modeling Studies Considering Ancillary Services

This paper introduces a model for simulating hybrid plants participating in energy and ancillary services for bulk power system studies. The model considers a hybrid plant comprised of a renewable energy source, a thermal power unit, a storage unit, fixed power loads, or any combination of these technologies. The model focuses on Production Cost Modeling (PCM) studies under the assumption of centralized dispatch. We present an example case study to illustrate the use of the model in a single-stage production cost model similar to those conducted by planning agencies. We explore the allocation of behind-the-meter ancillary services products and total energy participation to hybrid plant sub-assets and the resulting impacts on the system's ancillary service allocation. The model is implemented and simulated in a unit commitment problem in the RTS test system, which was modified to include a hybrid plant asset.

ancillary services↗
DOE OSTI2022

Local Power: Comparing County-Level Renewable Energy Potential to Consumption Using the SLOPE Platform

Wide-scale deployment of renewable energy technologies has the potential to significantly reduce greenhouse gas emissions and mitigate the effects of climate change. Many communities have ambitious clean energy goals with targets for locally generated renewable energy. To inform state and local clean energy planning, analysts from the Joint Institute for Strategic Energy Analysis (JISEA) and National Renewable Energy Laboratory (NREL) used data from NREL's State and Local Planning for Energy (SLOPE) platform to compare annual technical generation potential of renewable energy technologies to modeled electricity consumption in every county of the contiguous United States. Annual costs were calculated to produce a 20% share of electricity consumed annually from each technology to examine localized cost effectiveness of a diversified mix of generation sources. For example, combining distributed and utility-scale wind and solar generation can offset the need for storage and nonintermittent fossil resources to achieve high deployment of renewables. This county-level analysis provides insight into where localized renewable energy generation could cost-effectively match annual electricity consumption.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

A Model for Hybrid Systems for Production Cost Modeling Studies Considering Ancillary Services: Preprint

This paper introduces a model for simulating hybrid plants participating in energy and ancillary services for bulk power system studies. The model considers a hybrid plant comprised of a renewable energy source, a thermal power unit, a storage unit, fixed power loads, or any combination of these technologies. The model focuses on Production Cost Modeling (PCM) studies under the assumption of centralized dispatch. We present an example case study to illustrate the use of the model in a single-stage production cost model similar to those conducted by planning agencies. We explore the allocation of behind-themeter ancillary services products and total energy participation to hybrid plant sub-assets and the resulting impacts on the system's ancillary service allocation. The model is implemented and simulated in a unit commitment problem in the RTS test system, which was modified to include a hybrid plant asset.

ancillary services↗
DOE OSTI2023

Opportunities for low-carbon generation and storage technologies to decarbonise the future power system

Alternatives to cope with the challenges of high shares of renewable electricity in power systems have been addressed from different approaches, such as energy storage and low-carbon technologies. However, no model has previously considered integrating these technologies under stability requirements and different climate conditions. In this study, we include this approach to analyse the role of new technologies to decarbonise the power system. The Spanish power system is modelled to provide insights for future applications in other regions. After including storage and low-carbon technologies (currently available and under development), batteries and hydrogen fuel cells have low penetration, and the derived emission reduction is negligible in all scenarios. Compressed air storage would have a limited role in the short term, but its performance improves in the long term. Flexible generation technologies based on hydrogen turbines and long-duration storage would allow the greatest decarbonisation, providing stability and covering up to 11-14 % of demand in the short and long term. The hydrogen storage requirement is equivalent to 18 days of average demand (well below the theoretical storage potential in the region). When these solutions are considered, decarbonising the electricity system (achieving Paris targets) is possible without a significant increase in system costs (< 114 euro/MWh).

08 HYDROGEN↗
DOE OSTI2021

Fuel Fabrication and Single Stage Aqueous Process Modeling

The Material Protection, Accounting, and Control Technologies program utilizes modeling and simulation to assess Material Control and Accountability (MC&A) concerns for a variety of nuclear facilities. Single analyst tools allow for rapid design and evaluation of advanced approaches for new and existing nuclear facilities. A low enriched uranium (LEU) fuel conversion and fabrication facility simulator is developed to assist with MC&A for existing facilities. Measurements are added to the model (consistent with current best practices). Material balance calculations and statistical tests are also added to the model. In addition, scoping work is performed for developing a single stage aqueous reprocessing model. Preliminary results are presented and discussed, and next steps outlined.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

PV DMFA [SWR-21-105]

The Photovoltaic Dynamic Material Flow Assessment (PV DMFA) model (also referred to here as “The model”) is a computational framework written in Python based on utility-scale PV electricity generation to quantify time-series stocks and flows of PV materials primarily in crystalline silicon PV technologies. The model evaluates cradle-to-cradle life cycle of utility-scale solar PV systems in the United States in the period 2000-2100. PV DMFA serves as a sustainability analysis tool to assess the impacts of different material circularity practices (i.e., reduce, reuse/refurbish, remanufacture, and recycle), PV module design shifts and sensitivity of material processing and technology related parameters to material installations, waste creation and raw material depletion in PV material supply chains. This tool enables advanced planning for future material needs and informs sustainable pathways for PV material management in the circular economy. This tool could be helpful to a wide range of stakeholders; Particularly, researchers and manufacturers looking for technoeconomic and/or environmental life cycle analysis (LCA) feedback for renewable energy (RE) systems.

Khalifa, SherifA.↗
DOE OSTI2021

Modeling and Impact of Hyperloop Technology on the Electricity Grid

This paper provides an overview of hyperloop technologies, including a brief discussion of key components of a hyperloop system that determine the electric power requirements as a function of time. At-scale hyperloop systems do not yet exist, so a model was used to generate a set of load profiles for conceptual hyperloop realizations at four locations in the United States: two systems in California and one each in Colorado and Ohio. In all four cases, the modeled hyperloop load profiles included pulses in both active and reactive power. Grid modeling was performed to estimate the grid impacts and for dis-cussing grid integration challenges. The paper discusses how energy storage systems might be used to eliminate the pulsating load characteristics or significantly reduce it to accommodate current grid planning guidelines

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Improving and Automating Building Model Data Exchange

There are many instances throughout a project’s lifecycle where there arises a need for quick and accurate risk assessment of building designs. For example, an unexpected design change during construction may necessitate structural engineers to perform a seismic risk assessment on analytical models of the updated building design using high fidelity structural analysis software, such as ANSYS or Abaqus. However, the efficiency of such workflows often depends upon the interoperability of architectural design software and structural analysis software. When the quality of this interoperability is lacking or even non-existent, the efficiency of virtual engineering workflows is hampered, which increases project costs. A McGraw Hill industry survey of professional users of Building Information Modeling (BIM) technologies found that there is high demand for BIM interoperability for structural analysis, but that the value/difficulty ratio is currently too low for practical use. There have been efforts by the academic community to facilitate model data exchange between the architectural design and structural analysis domains, but such solutions have not been widely adopted by industry, face technical challenges, and oftentimes are limited in applicability for users of various BIM software. Therefore, INL is developing capabilities to improve, automate, and generalize model data exchange between architectural BIM software (e.g., Revit) and structural analysis software (e.g., SAP2000, ANSYS). The goal is to help expedite and automate as much of the pre-processing step for creating analytical models in finite element analysis software as reasonably as possible. Such a "BIM-to-FEA" conversion tool should provide direct benefit to end-users through accuracy, automation, quick turn-around, and wide applicability. To generalize the application of this BIM-to-FEA conversion tool and increase its useability among the many different commercial BIM software currently used by industry, the program is being developed with the concept of openBIM. OpenBIM is the application of non-proprietary, open data standards that allow for BIM model data exchange in a format that is accessible, retainable, and useable for all users. The most widely used open, non-proprietary data exchange format for BIM is the Industry Foundation Classes (IFC) schema. IFC is developed by buildingSMART international and is ISO certified (ISO 16739-1:2018). The BIM-to-FEA conversion tool is being developed for compatibility with typical commercial building designs of steel framed structures. The tool is currently capable of importing architectural BIM data of framed building structures, recognizing and extracting the aspects of the model that are required for structural analysis, adjusting the connectivity of frame members, and finally exporting to an analytical model stored in the IFC format. The exported IFC analytical model can then be imported into various openBIM compliant software, such as SAP2000. Such capabilities have already been tested on commercial software, as shown above, and continue to be improved. Work is underway to test the conversion on various commercial BIM software, develop a user-friendly interface, incorporate the program into the broader DeepLynx data warehouse project being developed by INL, and to eventually open-source the tool for the benefit of the community. Future development of the tool envisions the ability for efficient iterative risk assessment of generative building designs, all within a workflow utilizing open-source tools. One such open-source tool will be MOOSE, an advanced finite element analysis tool developed at INL. The conversion tool will also branch out from typical commercial building designs and will aim to incorporate nuclear construction. The aim will be to convert both structural and non-structural components of nuclear facilities, such as curved concrete containment structures and piping systems, respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Understanding and Improving Energy Efficiency of Regional Mobility Systems Leveraging System-Level Data

Increased congestion required urban Americans to travel 6.8 billion hours more and purchase 3.1 billion gallons of fuel for a congestion cost of $\$$153 billion, according to the 2019 Urban Mobility Report. How to effectively manage the regional mobility system and improve the energy efficiency presents a big challenge to public agencies. Recent years have witnessed massive multi-jurisdictional multi-modal system-level data from various sources, which provides an unprecedented opportunity to improve the mobility system and its energy efficiency. However, implications of system-level data for mobility and energy efficiency are unclear. Those system-level data sets are siloed, spatially and temporally sparse, biased, not unified, and lacking of insights for system management. Consequently, there is a real need to acquire, fuse, mine and learn from multi-source system-level data to prepare public agencies to deal more effectively with large-scale energy efficiency modeling, management and planning. This project proposes to intensively review inexpensive, replicable and openly-accessible data from multi-modal systems, develop a data-driven system-level modeling framework enabled and validated by data, identify the energy inefficiencies of mobility systems from infrastructure, vehicles, passenger systems, and quantify the benefits of system-level strategies to improve mobility/energy efficiency. In addition, this research develops models to effectively estimate energy consumption and emissions from various types of vehicles on the roadway networks, with high granularity and high fidelity. Traditional models often heavily rely on aggregated infrastructure or vehicle/passenger data, for example, the census survey, land-use, and traffic counts of one or several classes, which may lead to research gaps considering the emerging vehicle technologies. Those models do not contain individual vehicular information. We propose an integrated data-driven method that combines multiple network modeling components, featuring the utilization of state-wide vehicle registration data. The additional vehicle registration data improve the model performance, and produce high-resolution vehicle-specific estimates of emissions and network performance metrics. Two case studies on the Pittsburgh and Philadelphia regional network show that the proposed method can efficiently and effectively estimate the emissions of a large-scale network, and provide valuable information for evaluating common management strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Powering Data Centers with Clean Energy: A Techno-Economic Case Study of Nuclear and Renewable Energy Dependability

Rising data demands from artificial intelligence (AI) and large language models (LLMs) generating images, videos, and text have prompted increased need for larger and more robust data centers in the United States. Major companies interested in these larger data centers face the choice of linking them to existing regional grids, building stand-alone power supplies onsite, or a combination of both. The request, review, and approval process for new transmission lines to grids in the United States, however, has grown in recent years to times spans rivaling those of new construction for nuclear power plants. Building an islanded power supply for each data center is therefore becoming a prominent option. In this case study, several technologies are modeled in techno-economic simulations for long-term system costs subject to fixed electricity demand from a singular data center. A 250 MWe data center is assumed with additional 50 MWe for resiliency. Techno-economic simulations are conducted using the Holistic Energy Resource Optimization Network (HERON) software, which is a part of the Framework for Optimization of Resources and Economics (FORCE) tool suite. Technologies considered include solar, wind, lithium-ion batteries, and several types of nuclear reactors: large-scale reactors, small modular reactors, and microreactors. A low- and high-cost estimate for each technology is assumed to develop a range of expected economic performance. Low-cost estimates included several clean energy production tax credits. Different combinations of renewable energy generators with nuclear reactors are considered, ranging from a fully renewable-powered data center to a fully nuclear-powered data center. Historic time series of wind and solar availability from the Texas grid are used to train a reduced order model; this model then generates unique time series with similar characteristics of the training dataset. Multiple scenarios of weather and subsequent operations are simulated for each renewable-nuclear combination to determine total costs throughout the project lifetime. Fully renewable-powered configurations required large amounts of installed capacity (GW scale) in the simulations to meet the fixed demand of the data center. This is due to some scenarios in the historical dataset which captured low-wind and low-solar days, requiring over-building of these technologies as well as batteries to compensate for the low amounts of electricity generation. Fully nuclear-powered configurations outperformed the fully renewable and mixed renewable-nuclear configurations in terms of cost, with ranges between $1B and $10B in 2023 USDs compared to $40B+ for fully renewable configurations. Of the nuclear technologies, small modular reactors performed better economically than large-scale nuclear models due to lower projected capital costs, and both performed better than the microreactor models. These results demonstrate the applicability of firm, dispatchable electricity resources from baseload generators like nuclear power plants for operating facilities that run at constant power without daily variability.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Assessing the Impact of Lubrication on Efficiency and Life Cycle Economics of the US Wind Turbine Fleet: Cooperative Research and Development (Final Report)

As with any mechanical system, lubrication plays a key role in the performance of wind turbines used to generate electricity. The lubricant design offers a way to optimize the competing requirements of efficiency, component reliability, and maintenance strategy. This project will estimate the impact of improved lubrication on the levelized cost of energy of the US wind turbine fleet as a means of identifying opportunities for disruptive innovation or system-level optimization. The models developed will provide a clear understanding of the benefits and potential for advanced lubrication of wind turbines. The project will enable identification of high value targets for wind turbine component suppliers and a roadmap that highlights where the greatest return on technology investment can be achieved. This will promote efficient resource allocation in areas of new technology development.

17 WIND ENERGY↗
DOE OSTI2022

Probabilistic Modeling of Commercial Building Occupancy Patterns Using Location-Based Map Data: Preprint

Considering occupancy patterns is crucial to simulate buildings' energy use. Current energy models use inputs that simplify the actual diversity in occupancy into static occupancy patterns and are not able to represent the numerous variations in occupancy patterns between buildings and across different locations. Recently, inferring occupancy schedules from metered electricity consumption data was used to model occupancy in commercial buildings. However, the translation from metered data to occupancy schedules requires many assumptions that might not capture the reality, and the process is hindered by the availability of data from advanced metering infrastructure. With the development of information technologies, occupancy modeling should not be limited to traditional approaches. The prevalence of social networks and location services with real-time user feedback provides publicly accessible data via Maps Application Programming Interfaces (APIs) such as Google Maps, SafeGraph, Mapbox, Foursquare, etc. This paper presents an automated framework for modeling parametric occupancy patterns using such APIs to calibrate commercial district buildings' energy models. This process includes three main steps: data extraction and processing, parametric schedules generation, and schedules integration. We demonstrated this framework in districts where we used maps API to generate more accurate behavioral patterns for operations and electric vehicle charging events. We used these patterns to determine differences in energy use across key sociodemographic and spatial parameters. The presented method has the potential for worldwide applications. Users can utilize this framework to extract data for selected locations of interest to create more realistic behavioral patterns for commercial facilities across different districts.

building energy modeling↗
DOE OSTI2022

GREET-Based Interactive Life-Cycle Assessment of Biofuel Pathways: User Manual

An interactive, web-based tool was developed to streamline the process design for biofuel pathways that the Department of Energy’s (DOE) Bioenergy Technologies Office (BETO) is developing. The tool utilizes the latest life-cycle analysis (LCA) data in the Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET) model developed by Argonne National Laboratory (Argonne National Laboratory, 2021). It provides techno-economic analysis (TEA) modelers with a interative interface that generates real-time LCA results based on life-cycle inventory (LCI) data and generates useful insights into key emissions drivers. Economic implications of the LCA results are also included in the tool. Overall, the tool aims to assist TEA researchers with the development of economically viable and environmentally beneficial biofuel technologies. This manual introduces the interface and analysis capabilities of the tool.

09 BIOMASS FUELS↗
DOE OSTI2024

Artificial Intelligence-Aided Wind Plant Optimization for Nationwide Evaluation of Land Use and Economic Benefits of Wake Steering

If clean energy pathways are to harness massive increases in wind power, innovations with broad geographic viability will be needed to support buildout in diverse locations. However, geodiversity in impact potential is seldom captured in technology assessment. Here we propose a scalable approach to plant-level optimization using artificial intelligence to evaluate land sparing and economic benefits of wake steering at more than 6,800 plausible onshore wind locations in the USA. This emerging controls strategy optimizes plant energy production by directing turbine wakes. On the basis of estimates from our artificial intelligence model trained on engineering wind flow simulations, co-optimizing plant layouts with wake steering can reduce land requirements by an average of 18% per plant (site-specific benefits range from 2% to 34%), subject to errors and uncertainties in the flow model, wind resource estimates, buildout scenario and geographic factors. According to model estimates, wake steering is predicted to increase power production during high-value (relatively low wind) periods, boosting the annual revenue of individual plants by up to US$3.7 million (equivalent to US$13,000 MW-1 yr-1) but producing negligible gains in some settings. Consideration of wake steering’s geographic potential reveals divergent nationwide prospects for improved economics and siting flexibility.

deployment↗
DOE OSTI2025

An unwanted guest in the electrochemical oxidation of high-voltage Li-ion battery electrolytes: the life of highly reactive protons

Lithium-ion batteries (LIBs) are central to the urgent societal need to decarbonize both transportation and energy storage on the grid. Unfortunately, despite their attractive energy/power density, as well as high coulombic and energy efficiencies, further improvement of this technology – especially their durability – is desperately needed. To support these efforts, our study focuses on fundamental understanding of the decomposition pathways for LIB electrolytes at the cathode–electrolyte interface (CEI), as the nature of these reactions directly controls the extent to which cell capacity and voltage decays in these systems. In this study, we employ electrochemical methods, coupled with product analysis using NMR spectroscopy and mass spectrometry, to determine the decomposition mechanisms in both model and technologically relevant electrolytes. Remarkably, we discovered the electrochemical formation of protons with high chemical activity, comparable to known superacids, at potentials relevant to practical Li-ion batteries. Their reactivity toward every individual component of the CEI provides a unified thermochemical origin for a myriad of side reactions that are commonly associated with the electrochemical reaction. In particular, electrochemically generated protons react with intact EC molecules to form CO 2 and other short and long chain ethers. They also undergo an acid–base reaction with LiPF 6 , to form the weaker acid HF, and with the cathode active material, leaching transition metals into the electrolyte. Collectively, the results of this study all point to the urgent need to either mitigate this proton formation or introduce benign harvesting additives via new electrolyte design strategies.

Ilic, Stefan [Argonne National Laboratory (ANL), A↗
DOE OSTI2026

Cradle-to-Grave Lifecycle Analysis of U.S. Medium- and Heavy-Duty Vehicle-Fuel Pathways: A Greenhouse Gas Emissions Assessment of Current (2021) and Future (2035) Technologies

This study presents a cradle-to-grave lifecycle analysis of energy use and greenhouse gas (GHG) emissions for U.S. medium- and heavy-duty vehicles across current (2021) and future (2035) technologies using the Greenhouse gas, Regulated Emissions, and Energy use in Technologies (GREET) model with industry-vetted assumptions. Results vary across vehicle classes but point to common trends: today, battery electric vehicles (BEVs) offer significant (10–60%) GHG emissions reduction compared to diesel internal combustion engine vehicles and are the lowest emissions option per ton-mile of cargo movement, followed by hydrogen fuel cell electric vehicles (FCEVs) (5–50% emissions reduction). Emissions savings depend largely on the duty cycle and fuel economy of the vehicle type. Future vehicle technology advancements result in comparable emission reductions associated with BEVs and hydrogen FCEVs. Weight-limited BEV trucks see less per-ton-mile emissions reduction due to the impact of battery weight on increased vehicle weight and reduced payload capacity. By 2035, improvements in vehicle efficiency can reduce emissions across all powertrains. However, very low levels of emissions require switching vehicles’ use-phase fuel/energy to low-carbon fuels and electricity. Renewable diesel, e-fuels, hydrogen produced from natural gas with carbon capture and storage or renewables, and use of low-carbon electricity can all achieve over 70% reduction in GHG emissions from the current day diesel-based internal combustion engine vehicle.

alternative fuels↗
DOE OSTI2026

A framework and tool for designing cost-effective, resilient, and circular net-zero supply chains under uncertainty with an application to multilayer plastic films

While 55% of Fortune 500 companies have committed to achieving net-zero emissions and/or zero-waste operations by 2035, only 2% are currently on track, revealing a critical gap between ambition and action. Designing supply chains that reduce both emissions and waste is a complex non-intuitive, multi-objective challenge, compounded by the high costs of new technologies and the need for resilient, profitable solutions. This paper aims to address this challenge by presenting a generic framework and multi-objective optimization formulation for designing cost-effective, circular, and resilient supply chains under uncertainty, implemented through a user-friendly decision-support tool with intuitive data visualization capabilities, enabling communication of results to both technical and non-technical stakeholders. We demonstrate the application of this framework in the context of multilayer plastic films (barrier films), which are widely used in food packaging and composite materials. The model quantifies trade-offs across three objectives: minimizing global warming potential, maximizing circularity, and minimizing cost. A key contribution of this work is the explicit modeling of technological resilience, the ability of supply chains to maintain function under disruption. In the cost-minimization case, the resilience constraint makes the design approximately three times more expensive in the short-term metric, but shifts the system from relying on a single recovery pathway to a portfolio of four recovery pathways, improving the robustness of the optimization solution under uncertainty. Lastly, we introduce TranZero, a decision-support tool that integrates material flow analysis, hotspot identification, and optimization-based scenario planning to support net-zero and circularity decisions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗