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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The Design of a Multiphase CO2 Turbine Test Facility

As the energy market continues to evolve with increased renewables, such as solar and wind energy, the need to balance energy demand with its availability has become increasingly more challenging. The country’s aggressive decarbonization goals in the upcoming decades will increase renewable energy market penetration, thus exacerbating the disparity between energy demand and renewable resource availability. Long-duration energy storage technologies, such as pumped thermal energy storage (PTES), are potential solutions to this challenge with high potential round-trip efficiency (RTE), no geological or geographical constraints, and low safety risks. The charge mode of a PTES system operates a heat pump cycle, converting input power to thermal energy in hot and cold reservoirs. Fundamentally, heat pumps are commonly used in daily life, but the operating conditions required by high RTE PTES systems are outside the design experience of these systems. The low-pressure side of the cycle for a supercritical CO2 (sCO2) PTES operates just above the vapor-dome and potentially drives the turbine to expand into the dome, resulting in multiphase operation in the rear stages of the turbine. This paper presents the design of the test facility for an advanced multiphase-tolerant sCO2 turbine for the heat pump of a PTES system. Heat rejection and pressure loss estimations for the multiphase sections of the test loop presented unforeseen challenges.

25 ENERGY STORAGE↗
DOE OSTI2021

RAVIS: Resource Forecast and Ramp Visualization for Situational Awareness of Variable Renewable Generation: Preprint

The Resource Forecast and Ramp Visualization for Situational Awareness (RAVIS) is an innovative, open-source tool for visualizing variable renewable resource forecasts and alerts for significant up- and down-ramps in renewable generation and the consequent system net load. The modular dashboard of RAVIS contains configurable panes for viewing probabilistic time-series forecasts, ramp event alerts on the look-ahead timeline, and spatially resolved resource sites, and forecasts. For comprehensive situational awareness, the tool can add additional data layers from simulation and independent system operator (ISO) market clearing data—including electric line utilization, nodal price, and available generation flexibility—in response to continuously updated renewable forecasts. This paper introduces the RAVIS technology suite employed to provide optimum visualization and flexible design characteristics. The paper also illustrates some use cases of the tool using site-specific solar power forecast data obtained from the IBM Watt-Sun forecasting platform for the California ISO and Midcontinent ISO footprints. The source code for RAVIS is public (https://github.com/ravis-nrel/ravis), and the intended users are forecasters, utility planners, ISO operators, and researchers.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2023

Experimental and numerical investigation of proppant embedment and conductivity reduction within a fracture in the Caney Shale, Southern Oklahoma, USA

The current worldwide energy supply is insufficient to meet the rising demand. As a result, the energy prices are expected to keep soaring despite the recent increases in a variety of renewable energy resources. Although not renewable, shale oil and gas — “unconventional” hydrocarbon resources are relatively clean forms of energy resources, which still hold a vast share of the energy market. For many oil and gas companies, meeting profitable production goals from shale reservoirs is sometimes challenging, due to the loss of fracture conductivity and premature declines in the production. In this paper we investigate the stress-dependent changes in the hydraulic conductivity of proppant-filled fractures and mechanical fracture–proppant interactions in Caney Shale, a calcareous, organic-rich mudrock, through laboratory experiments and numerical modeling. American Petroleum Institute (API) fracture conductivity tests were conducted using 2% KCl on five locations within the Caney Shale that consisted of selecting three brittle (reservoir) zones and two ductile zones. Confining pressures ranged from 1,000 psi to 12,000 psi at 210 °F. Conductivity, permeability as well as embedment were measured during the test. Also, an additional, laboratory in-situ visualization test was conducted to examine the detailed proppant-shale matrix interaction under elevated stress (3,920 psi effective stress) and temperature (252 °F), with a synthetic reservoir fluid. Our experimental results have confirmed that improved fracture conductivity is attributed to proppant size, and that the increase in porosity of the proppant pack, closure pressure changes and the reduction in fracture conductivity are a function of many factors such as fracture closure stress.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2022

Large Scale Pumped Storage to Support Renewable Deployment; PSH Resource Assessment and Complementary Analysis for Alaska

The State of Alaska has a rather unique electric power system, as it has two larger transmission grids (Railbelt and Southeast Alaska) and over 150 islanded stand-alone power systems that are serving remote rural communities. In 2010, the Alaska legislature enacted a non-binding goal for 50% of renewable electricity generation by 2025. With the expected increase in wind and solar generation in the future, the role of energy storage becomes increasingly important. Considering the specific power system characteristics in Alaska, energy storage technologies that can supply electricity over an extended period of time, such as pumped storage hydropower (PSH), may play a key role in enabling the reliability and resiliency of both integrated and rural power systems. The overarching objective of the subject study of this presentation is to investigate the prospects and opportunities for PSH in Alaska.

Alaska↗
DOE OSTI2023

Protecting Customer Privacy Through Distributed Energy Resource Anonymization

Due to their stochastic nature, the increase of Renewable Energy Resources (RERs) as a primary source of energy for power grids creates challenges regarding the reliability and resilience of the system. In order to combat these obstacles, expansion of Distributed Energy Resources (DERs) and their participation in Demand Response (DR) programs is necessary. Widespread participation requires prioritizing customer privacy and addressing concerns that may arise regarding communication between DERs and the Grid Service Provider (GSP). This paper discusses the use of flow reservation resources to split the operating cycles of DER load profiles into unique phases. The splitting of phases increases anonymization of the DERs by making it more difficult to determine the individual characteristics of the device. We discuss an example of this using simulated DER load profile data and examine the resulting effectiveness by using a machine learning algorithm for classification, called Support Vector Machine (SVM).

Distributed Energy Resource, Anonymization, Renewa↗
DOE OSTI2022

Evaluating Grid Strength under Uncertain Renewable Generation

The increasing displacement of synchronous generators with renewable resources such as wind and solar via power electronic interfaces causes a reduction in short-circuit strength and weak grid issues. The variation and uncertainty of renewable energy increase challenges for identifying weak grid conditions. This paper proposes an efficient method to analyze the impact of uncertain renewable energy on grid strength. The proposed method uses the probabilistic collocation method (PCM) to approximate the results of grid strength assessment under uncertain renewable generation, in order to reduce computational burden without compromising result accuracy when compared with traditional Monte Carlo simulation (MCS). To improve the accuracy of the approximation results, the proposed method integrates the K-means clustering technique with PCM to select the approximation samples of input variables. The efficacy of the proposed method is demonstrated by comparison with MCS on the modified IEEE 9-bus system and modified IEEE 39-bus system with multiple renewable generators.

grid strength↗
DOE OSTI2025

Tunable and Degradable Dynamic Thermosets from Compatibilized Polyhydroxyalkanoate Blends

Polyhydroxyalkanoates (PHAs) are versatile, biobased polyesters that are often targeted for use as degradable thermoplastic replacements for polyolefins. Given the substantial chemical diversity of PHA, their potential as cross-linked polymers could also enable similar platforms for reversible, degradable thermosets. In this work, we genetically engineered Pseudomonas putida KT2440 to synthesize poly(3-hydroxybutyrate-co-3-hydroxyundecenoate) (PHBU), which contains both 3-hydroxybutyrate and unsaturated 3-hydroxyundecenoate components. To reduce the brittleness of this polymer, we physically blended PHBU with the soft copolymer poly(3-hydroxydecanonate-co-3-hydroxyundecenoate) in mass ratios of 1:3, 1:1, and 3:1. Upon observing varying degrees of immiscibility by scanning electron microscopy, we installed dynamic boronic ester cross-links via thiol–ene click chemistry, which resulted in compatibilized dynamic thermoset blends ranging in hard, medium, and soft rubber or elastomer thermomechanical profiles. These dynamic thermoset blends were subjected to controlled biological degradation experiments in freshwater conditions, achieving timely mass loss despite the cross-linked architectures. Overall, this work highlights a two-component platform for the production of degradable and reprocessable dynamic thermoset blends suitable for several classes of cross-linked polymer technologies from tailored, biological PHA copolymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Catalytic Autoxidation for Depolymerization of Multilayer Plastic Films

Recycling multilayer plastic films is challenged by a diversity of polymers, prompting development of new recycling methods. For the depolymerization of mixed polymers like those in multilayer films, metal-catalyzed autoxidation offers a versatile chemical recycling method to deconstruct multiple polymers to useful oxygenates. Here, we demonstrate that catalytic autoxidation is effective for depolymerizing multilayer films across diverse chemistries. We investigated conditions for a model polyethylene substrate using a Co, Mn, and Br cocatalyst system, achieving full carbon closure with oxygenated small molecules contributing up to 48 mol% carbon. Subsequently, we characterized product distributions for several common polymers used in multilayer films using high-resolution mass spectrometry (HRMS) and developed analytical methods to quantify the resulting complex product streams. Optimized conditions for polyethylene were applied to 11 multilayer plastic films containing 10 different polymers, including films with nonpolymeric potential disrupters like aluminum foil and titanium dioxide, showing that catalytic autoxidation is effective across a broad range of polymer types and is resistant to disrupters and additives. The generation of CO 2 in these reactions overall suggests that both reaction engineering and modifications to the reaction conditions will be required to achieve higher yields of soluble oxygenated products.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Recommended Functionalities for Improving Cybersecurity of Distributed Energy Resources

The current electric grid is transitioning through increasing penetration of distributed energy resources (DERs), which include intermittent renewable generation resources on the distribution side. Both the monitoring and control of DERs require extensive data-exchange and communication networks. These networks lead to cyber vulnerabilities and risks of new kinds of cyberattacks that may be extremely destructive for power system operations. Although current standards, such as IEEE Std. 1547-2018, do not discuss cybersecurity measures for DERs, cybersecurity controls should be developed for securing DER systems at the device level, communications level, and applications level. This paper discusses the current industry's best practices related to DER cybersecurity and proposes recommended functionalities for improving the cybersecurity posture of DERs, specifically at the device/distribution level. These practical recommendations have been discussed and verified with the industry through a DER cybersecurity working group.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Bias Correction and Statistical Downscaling of Future Solar Irradiance Projections Using the NSRDB

Assessing renewable energy resources under future climate scenarios has been highlighted to understand potential impacts of future climate change in renewable generation on the power sector. Climate model projection has been recognized by the renewable energy community as a useful data set to analyze the impacts of future climate change on renewable resources. However, future climate projections generated from general circulation models (GCMs) contain inherent biases that need to be corrected for accurate analysis of future projections of climate variables. In addition, the coarse spatiotemporal resolution of GCMs needs to be improved for regional climate studies. In this work, we develop statistical methods to downscale future projections of global horizontal irradiance (GHI) in a computationally efficient way. Our approach builds statistical downscaling models that correct bias of climate projection of GHI and downscale the future GHI projection from daily-scale to hourly-scale. The National Solar Radiation Database (NSRDB) is used to calibrate the statistical models and validate the downscaled GHI projections across the contiguous United State (CONUS). Preliminary results show that the statistical approach efficiently downscales climate projections of GHI with a nBIAS of 3%, nMAE of 34 % and nRMSE of 46% calculated against NSRDB for CONUS. This study describes the implemented methodology and initial results as well as future research to create high-resolution climate data sets for solar energy applications.

analytical models↗
DOE OSTI2025

Marine energy supported multi-energy system planning and operation optimization for sustainable coastal community

The growing need for sustainable energy solutions in coastal areas necessitates the development of integrated systems that leverage abundant marine resources. In this study, a standalone Marine Energy Supported Multi-Energy System (MRE-MES) is designed for sustainable coastal community development, utilizing renewable marine resources, including offshore wind, wave, and solar energy, to address the energy needs of electricity, heat, freshwater, and hydrogen. The proposed MRE-MES incorporates a co-optimization model that simultaneously balances capacity planning and operational efficiency to minimize costs and environmental impacts. The system is tested under different renewable energy penetration levels and demand uncertainties, using a two-stage stochastic programming to account for variability in renewable resources and consumption needs. The experimental results indicate that in the optimal system capacity configuration, the percentage of total renewable energy generation is around 80 %, with or without capacity limitation constraints on PV, water tank, and hydrogen storage. Compared to the worst-case scenario in Monte Carlo experiments, two-stage stochastic optimization results in a more robust decision that effectively mitigates the risks posed by future uncertain demand conditions. In conclusion, the findings highlight the viability of marine energy for providing a resilient, comprehensive energy solution to coastal communities.

Capacity planning↗
DOE OSTI2024

The National Climate Data Base (NCDB): A Bias-Corrected High-Resolution Climate Dataset

Assessing renewable energy resources under future climate scenarios has been highlighted in recent years to analyze and understand potential impacts of future change in renewable generation on the power sector. Solar energy is well-known as the most plentiful among various renewable resources and usually converted to electricity using photovoltaics (PV) technologies, and the global deployment of PV technology has increased rapidly in recent decades. In this study, we develop a statistical technique to downscale the future projection of solar irradiance for PV energy-related applications. A set of Regional Climate Model (RCM)-based projections obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) are used as inputs to statistical methods to generate high-resolution global horizontal irradiance (GHI) over the contiguous United States (CONUS). The main steps of the statistical downscaling method include (1) regridding RCM output (0.22 degree and daily resolutions) to handle the modeled-observed data sets on a common grid, (2) correcting bias of RCM GHI using satellite-derived observation, and (3) implementing temporal and spatial downscaling to generate GHI at 8-km and hourly resolution. Basically, complex physical processes and interactions between solar radiation and various atmospheric constituents lead solar irradiance to be highly variable and uncertain. Underrepresentation of clouds from the RCM parameterizations is the main source of error and uncertainty in modeling solar irradiance. Thus, we adapt and use the high-quality satellite-derived data from the National Solar Radiation Database (NSRDB) to analyze the bias and error of RCM GHI as well as estimate the statistical parameters for spatial and temporal downscaling. This presentation will summarize the comprehensive analysis conducted to produce and assess the results under two climate scenarios (RCP4.5 and RCP8.5). We will also present a detailed validation demonstrating the strengths of the proposed downscaling method and future extension of this research.

climate data↗
DOE OSTI2025

Storage-Induced Collapse of Lignin Macromolecular Structure and Its Impacts on the Biorefinery

Lignin plays a vital role in the economics of biorefineries, serving as a source of process energy and a feedstock for sustainable fuels and chemical production. While understanding lignin’s chemical composition is crucial, emerging evidence suggests that a more comprehensive understanding of its macromolecular structure is critical to explaining its complex behavior in the biorefinery. This study investigated the partial collapse of the lignin network in corn stover feedstock after harvest and storage as a result of the microbial digestion of hemicellulose. Fluorescence microscopy was used to detect the collapse of lignin in terms of lignin’s inter-molecular interaction and the re-orientation of lignin’s chromophores, by the changes in lignin’s fluorescence lifetime, anisotropy, and the number of effective emitters. With minimal sample perturbation, our in-situ microscopic results revealed lignin's coil-globule transition phenomena, which was only previously predicted by molecular dynamics modeling extracted lignin in solvent. This collapse of lignin macromolecular structure was confirmed by results from NMR, IR, Raman, and powder X-ray diffraction. We also investigated the impact of this storage-induced collapse on the downstream biorefinery processes. Our study revealed that the two major approaches for lignin valorization in the lignin-first biorefinery model, namely monomer extraction and milled wood lignin extraction, were negatively impacted by the lignin collapse. As changes during storage are a source of feedstock variability, our study highlights the importance of understanding the effect of feedstock handling on biorefinery operations and economics.

09 BIOMASS FUELS↗
DOE OSTI2020

A Machine Learning-based Approach to Predict the Aggregate Flexibility of HVAC Systems

Use of renewable energy resources can play a significant role in mitigation of climate change since they reduce the dependency on fossil fuels. However, high penetration of renewable energy resources into the power grid may cause some reliability problems. Ancillary services are key to realize the potential of renewable resources without disturbing the grid reliability. In recent years, many control approaches focused on exploiting the aggregate flexible heating, ventilation, and air-conditioning (HVAC) loads of buildings. However, there are still research gaps in assessing in advance the resulting indoor temperature responses for given total power and weather profiles before implementing the controls in the buildings. Such an assessment is critical to guarantee satisfying both the grid related objective (reliability) and the building related objective (occupants' comfort). Towards addressing this research gap, this paper presents a machine-learning approach for predicting the ratings of indoor temperature responses. In this approach, a prediction model was developed using a dataset generated by a set of model-free control simulations. The developed model achieved high prediction accuracy and showed the potential of the proposed approach.

Amasyali, Kadir↗
DOE OSTI2023

Accelerating Sustainable Aviation Fuel Technology From Laboratory to Deployment: An Overview of NREL's Sustainable Aviation Fuel Pathways

The National Renewable Energy Laboratory (NREL) develops and de-risks a portfolio of technologies for converting renewable carbon resources into sustainable fuels, including sustainable aviation fuel (SAF). NREL works with industry, academia, and other national laboratories to develop, scale, and de-risk technology pathways for producing SAF from renewable resources such as biomass and other forms of waste carbon, including carbon dioxide.

BIOMASS FUELS↗
DOE OSTI2024

When energy doesn’t add up: use of an energyshed framework in assessing progress towards renewable energy transitions

Global progress in energy transitions to support climate mitigation goals has been slower than anticipated; this has prompted shifts away from traditional paradigms of regulated energy ownership towards a model of energy democratization by local communities and individuals. For example, in the United States, local communities in over 250 cities, counties, and states have made pledges to reach 100% renewable electrification by target dates ranging from 2020 to 2050. However, the availability of infrastructure and the competition for renewable energy resources, as well as lack of awareness of these limitations, present significant barriers to overcome. In this study, we explored a subset of 31 of these cities to assess their current electricity generation and how much further they have to go to meet their goals. Through an energyshed framework, we estimated powerplant electricity allocation to each city assuming competition for power from various renewable and non-renewable resource types, as well as look at the ‘best case scenario’ assuming 100% allocation of renewable-sourced electricity for a handful of cities in order to understand the existing and planned energy mixes for 2021 and the following 20 years. It is likely most cities will meet 10% of their energy demand with renewable energy, with best cases scenarios reaching between 35% and 65% renewable penetration, within the next 20–30 years. This highlights the need for infrastructural development in the energy sector, as well as intentional planning efforts in order to make these energy goals a reality.

54 ENVIRONMENTAL SCIENCES↗