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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

State-Constrained Grid-Forming Inverter Control for Robust Operation of AC Microgrids

The control of grid-forming inverters has become an important problem to achieve high efficiency, reliability, and resilience of the future power grid. The distributed control that utilizes a sparse communication network has been proposed for AC microgrids, in which each DG only has access to the information of a small number of its neighboring DGs, reducing the computational complexity and improving the resiliency to faults and unknown parameters. However, these approaches have not provided a clear analysis of the coupling between active/reactive power injections and the voltage magnitude/frequency or a distributed updating law with a solid theoretical foundation. Additionally, in the existing approaches, tracking the references from the secondary control may violate operational constraints.

Xu, Ying↗
DOE OSTI2022

Designing the PR100 Study: Puerto Rico Grid Resilience and Transition to 100% Renewable Energy

Puerto Rico has committed to meeting its electricity needs with 100% renewable energy by 2050, along with realizing interim goals of 40% by 2025, 60% by 2040, the phaseout of coal-fired generation by 2028, and a 30% improvement in energy efficiency by 2040 as established in Act 17. [1] To meet these goals and support widespread end-use electrification, the territory must explore how renewable energy and other generation technologies can be developed with energy storage, distributed generation, distribution control, electric vehicles, and energy efficient and responsive loads in each of Puerto Rico's cities and communities. To support Puerto Rico in reaching its renewable energy goals; help ensure energy system resilience against future extreme weather events; improve energy justice; and provide inputs to LUMA Energy's walk, jog, and run approach in its coordinated planning roadmap, the U.S. Department of Energy, National Renewable Energy Laboratory, Argonne National Laboratory, Lawrence Berkeley National Laboratory, Oak Ridge National Laboratory, Pacific Northwest National Laboratory and Sandia National Laboratories propose to evaluate 100% renewable energy pathways through an integrated analysis process. This talk will present steps in designing a two-year study entitled Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy (PR100). [1] U.S. Energy Information Administration (EIA). 2020. Puerto Rico Territory Energy Profile.

100% renewable energy↗
DOE OSTI2022

Predictive modeling of a subcritical pulverized-coal power plant for optimization: Parameter estimation, validation, and application

As renewable power generation deployment increases, fossil fuel plants are increasingly required to operate more flexibly. Many coal-fired power plants were originally designed to operate at base load and do not operate optimally at partial load. Predictive first-principles plant-wide models can be employed to identify opportunities for flexibility improvements and diagnose low-load operating issues. This paper describes the application of the Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES) to model and optimize flexible power plant operations. The key benefits of using IDAES are that it provides an open-source, fully equation-oriented modeling framework for efficient modular model construction, reuse, and customization, together with a mathematical optimization framework leveraging powerful, state-of-the-art solvers. The process systems engineering workflow from predictive process simulation to parameter estimation, model validation, and plant optimization is applicable to a variety of existing and next-generation energy systems as well as other chemical and environmental processes. Here, to demonstrate this capability, a physics-based, steady-state model was developed to improve full- and part-load performance of the Escalante Generating Station, a 245 MWe (net) subcritical pulverized coal-fired power plant owned and operated by Tri-State Generation and Transmission Association. Specifically, sixty-nine model parameters were simultaneously estimated from several months of operating data enabling prediction of flow rates, temperatures, pressures, and steam quality throughout the plant. The validated model was leveraged by Escalante to reduce the minimum operating load from 90 MW to 50 MW by diagnosing a low-load water-hammer issue, enabling coal usage and emissions reductions during periods of low power demand. Additionally, opportunities for heat rate reduction (i.e., efficiency improvement) through a steeper sliding-pressure approach to load-following and optimization of other boiler operating variables were also identified and quantified. For example, a potential efficiency improvement of 0.7 percentage points was observed at half-load operation.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

Design of a SMART Valve Testbed for Nuclear Thermal Dispatch

By the year 2050, the United States aims to achieve net-zero carbon emissions. To achieve this target, the licensing of the Light Water Reactor (LWR) fleet has been extended for 20 more years. To stay economically competitive with other power sources such as renewable and fossil-fuel power plants, the U.S. Department of Energy has introduced a plan to modernize the existing LWR fleet and diversify the revenue stream. One of the plans is to dispatch thermal energy to endothermic industrial processes. SMART valves will play an important role in this initiative by efficiently balancing the load by regulating valves in a coordinated manner while monitoring the thermal-hydraulic systems to enhance safety and maintain the integrity of the power plant. This research aims to develop a facility to test the coordinated control algorithm and produce various test results for training the monitoring system. The constructed facility is capable of simulating various operational and accidental scenarios by coordinating all the valves (positions) and pump (flowrate). The facility is developed with an Internet of Things (IoT)-based custom system and a python-based valve position control and coordination mechanism. It has achieved stable sensor outputs, pump control, and coordinated valve regulation in all three valves with minimum obstruction in the system.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

A State-Space Model for Stability Boundary Analysis of Grid-Following Voltage Source Converters Considering Grid Conditions

With the growing significance of renewable energy resources and energy storage systems, the number of grid-connected inverters has been rising at an increasingly rapid pace. Generally, these inverters are directly integrated with the distribution network by synchronizing with the grid voltage at the point of common coupling. However, the low grid strength and varying R/X ratios, as the common characteristics of most distribution networks or weak grids, can lead to dynamic interactions that comprise stability and limit the power transfer capacity of grid-connected inverters. To ensure stable operation of the inverters, researchers must determine the stability boundary, described as the maximum power transfer capacity of grid-connected inverters under the premise of maintaining system small-signal stability. For this purpose, we propose to formulate a state-space model of the system in the synchronously rotating dq-frame of reference and perform eigenvalue analysis to determine the stability boundary. With a detailed model of the control structure and parameters of the grid-connected inverters, the stability boundary is identified as a surface with respect to different grid strengths and R/X ratios. Case study results of proposed eigenvalue analysis are compared with those of admittance model-based stability analysis as well as time-domain simulation using a switching model in Matlab/Simulink, validating the effectiveness and accuracy of the proposed eigenvalue analysis for stability boundary identification.

grid-connected inverters↗
DOE OSTI2024

The Best of Both Worlds: Combined Thermal and Battery Storage for Widespread Building Decarbonization

To meet 2050 decarbonization targets, widespread building electrification is a critical complement to clean power generation. Behind-the-meter storage (BTMS) (e.g., battery electric energy storage [EES] and thermal energy storage [TES]) integrated with buildings or building end uses to store and supply energy at optimal times can minimize burdens associated with operation, planning, and upgrades to the electrical grid sometimes triggered by building electrification. Such BTMS systems can serve the dual purpose of providing enhanced resilience at the building and grid level, and support the deployment of renewable generation needed for wide-scale decarbonization. While TES can cost-effectively shed and shift thermal loads, it cannot generally backup or shift non-thermal building end uses. EES, by contrast, is more expensive, but applicable to all end uses (i.e., thermal and electrical loads). Combined together, these storage systems can be traded off against one another to perform optimally in meeting demand flexibility, decarbonization goals, and energy resilience of the buildings at a lower total system cost. This paper proposes a framework to define BTMS benefits, provides four illustrative electrification scenarios using TES and EES, and discusses the combined TES/EES benefits with building energy modeling results. The paper also highlights potential barriers to adoption of BTMS and a path forward.

buildings↗
DOE OSTI2024

Industrial Energy Storage Review

As the United States moves to net-zero carbon emission by 2050, a transition to renewable energy generation is required. However, the variable nature of renewable energy generation at high penetrations can cause imbalances in generation and transmission of electricity. These imbalances can be circumvented by the deployment of energy storage. Global industrial energy storage is projected to grow 2.6 times in the coming decades, from just over 60 GWh to 167 GWh in 2030. The challenge is to balance energy storage capabilities with the power and energy needs for particular industrial applications. Energy storage technologies can be classified by the form of the stored energy. The most common forms include thermal, chemical, electrochemical, and mechanical storage technologies. The most appropriate storage technology will depend on the unique energy needs of the industrial application. The purpose of this report is to provide a review of energy storage technologies relevant to the U.S. industrial sector, highlighting the applications in industry that will benefit from increased integration of energy storage, as well as the respective challenges and opportunities unique to integrating different storage technologies.

25 ENERGY STORAGE↗
DOE OSTI2026

Hybrid Power Purchase Agreements for Flexible 24/7 Energy Delivery – A Comprehensive Review of Current Practices and Research Pathways

Power Purchase Agreements (PPAs) are becoming increasingly preferred among large energy consumers, such as data centers, to secure cost-effective energy and meet accelerating demand growth. Traditionally, variable renewable energy (VRE)-based PPAs operate on a pay-as-produced basis, balancing supply and demand for a relatively longer duration (e.g., annually). However, the focus is shifting toward matching supply and demand on an hourly basis to fully meet energy needs. This shift requires the integration of flexible energy resources, such as hydropower, thermal generation, and energy storage, to complement VRE sources like wind and solar, forming the foundation for 24/7 PPA. This work contributes by: (i) reviewing emerging market trends and current practices in PPA procurement, supported by data on PPA prices and technology portfolios; (ii) synthesizing the existing literature on modeling approaches for contract pricing, quantities, hybrid resource procurement, and risk management in 24/7 PPA design, while identifying key research gaps; and (iii) proposing an integrated 24/7 PPA design framework along with two contracting mechanisms from the perspectives of both PPA providers and consumers. The proposed framework highlights critical modeling challenges, risk-allocation issues, and future research opportunities for 24/7 PPA design.

24/7↗
DOE OSTI2022

Clean Energy Transition for Transportation Systems: Modeling Implications

Transportation is currently the least-diversified energy demand sector, with over 90% of energy use coming from petroleum. As a result, transportation recently became the largest source of GHG emissions in the U.S. and mobility needs for passenger and freight are growing rapidly. However, after over a century of petroleum dominance, new disruptive technologies and business models offer a pathway to decarbonize the sector. Transportation is at a turning point. On the horizon lies a future where affordable and abundant renewable electricity can be used to power cost-competitive battery electric vehicles (EVs) and produce energy-dense low-carbon fuels enabling to fully decarbonize transportation systems across all modes. Exploring the clean energy transition for the multitude of different transportation systems requires new analytical modeling and approaches. This talk reviewed current work at the National Renewable Energy Laboratory (NREL) to develop and use innovative tools and analytics approaches to inform the transformation to a sustainable mobility future and the integration of transportation systems with the broader energy sector.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Economic and environmental performance of biomass gasification for renewable natural gas production in the context of the U.S. natural gas supply

Bioenergy technologies offer potential for reducing greenhouse gas (GHG) emissions. One such promising technology is biomass gasification, which is the conversion of biomass into renewable natural gas (RNG) for use with a natural gas combined-cycle power generation system. However, the associated economic and emission effects need to be better understood to enable optimal decision-making and avoid missed opportunities for enhancing efficiency and increasing system circularity. This analysis explores opportunities to (1) decarbonize natural-gas-based systems and (2) leverage the extensive US natural gas infrastructure to mobilize biomass resources to achieve environmental and economic benefits. Here, in this analysis, the research team used a spatially explicit biomass logistics model (integrated with relevant biomass availability, technoeconomic analysis, and life cycle assessment information) to simulate economically optimal biomass allocation for RNG production and use for decarbonization in the United States. Results show that the United States has the potential to produce 9203 million GJ of RNG within the expected range of $\$$12–30/GJ. Further analyses tested the overall RNG production system's sensitivity to economic and emissions parameters of nine different processes. The sensitivity analysis results indicate that the median carbon abatement cost of RNG is most sensitive to changes in emissions associated with conversion processes and land use changes. These findings provide a deeper understanding of RNG's economic and emission potential for decision-making and guiding future research.

09 BIOMASS FUELS↗
DOE OSTI2025

TEAMER Technical Support for Optimal Control of an Oscillating Surge Wave Energy Converter (CRADA Final Report)

This project will focus on running experiments that evaluate the benefits of using model predictive control (MPC) to optimize power absorbed by a laboratory-scale oscillating surge wave energy converter (OSWEC). MPC is a promising technique to optimize wave energy converter (WEC) behavior while applying system constraints that can help promote structural integrity and device survivability, but there are few studies that experimentally test this control scheme on WECs. Therefore, the Participant is proposing a series of tests that will assess the benefits of MPC experimentally in response to a variety of sea states. For these tests, the Participant will provide the OSWEC device and Data Acquisition (DAQ) system, and request support from the Contractor to use and operate the wave tank for experiments.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Three different directions in which the European Union could replace Russian natural gas

Russia's invasion of Ukraine fuelled an energy crisis, which considerably impacted Europe given its heavy reliance on Russian natural gas imports. This study uses an ensemble of four global integrated assessment models, which are further soft-linked to two sectoral models, and explores the synergies and trade-offs among three approaches to living without Russian gas in Europe: (a) replacing with other gas imports, (b) boosting domestic energy production, and (c) reducing demand and accelerating energy efficiency. We find that substituting Russian gas from other trade partners would miss an opportunity to accelerate decarbonisation in end-use sectors while risking further fossil-fuel lock-ins, despite featuring the lowest gas price spikes and potentially reducing heating costs for end-users in the near term. Boosting domestic, primarily renewable, energy production on the other hand would instead require considerable investments, potentially burdening consumers. Energy demand reductions, however, could offer considerable space for further emissions cuts at the lowest power-sector investment costs; nonetheless, an energy efficiency-driven strategy would also risk relocation of energy-intensive industries, an aspect of increasing relevance to EU policymakers.

03 NATURAL GAS↗
DOE OSTI2024

Particle-based high-temperature thermochemical energy storage reactors

Solar and other renewable energy driven gas-solid thermochemical energy storage (TCES) technology is a promising solution for the next generation energy storage systems due to its high operating temperature, efficient energy conversion, ultra-long storage duration, and potential high energy density. Experimental and theoretical studies suggest that the respective gravimetric and volumetric TCES energy storage densities vary from 200 to 3000 kJ kg –1 and 1–3 GJ m –3 . Solar radiation or heat generated from electric furnaces powered by renewable electricity can be stored in the form of chemical energy through endothermic reactions, while the stored chemical energy can be converted to thermal energy via an exothermic reaction when needed. The design of highly effective reactors requires a deep understanding of materials, thermodynamics, chemical kinetics, and transport phenomena. At time of writing, TCES reactors are yet to be deployed at commercially relevant scales, leaving a substantial gap between development efforts and commercial feasibility. Therefore, this review aims to examine the state-of-the-art design and performance of particle-based TCES reactors with different reactive materials. Fundamentals related to TCES reactive materials, reaction conditions, thermodynamics and kinetics, and transport phenomena are reviewed in detail to provide a comprehensive understanding of the reactor design and operation. Five major types of TCES reactors have been comprehensively reviewed and compared, including fixed, moving, rotary, fluidized, and entrained bed reactors. Most reported prototype reactors in the literature operate at lab scale with thermal inputs below 40 kW, and scaled TCES reactors (e.g., at megawatt level) are yet to be demonstrated. The nominal reactor operating temperatures range from 300 to 1500 °C, depending on the selected chemistry, reactive material, and heat sources. To evaluate their designs, the reactors are assessed in aspects of performance, cost, and durability. Discrepancies in performance indicators of energy storage density, extent of reaction, and various energy efficiencies are highlighted. The scale-up of reactors and power block integration, which hold the key to the successful commercialization of TCES systems, are critically analyzed. Furthermore, advanced materials (both reactive materials and ceramic reactor housing materials), effective particle flow control, advanced modeling tools, and novel system design may bring significant improvement to the energy efficiency, storage density and cost competitiveness of particle-based TCES reactors.

25 ENERGY STORAGE↗
DOE OSTI2026

Hydrogen Dispersion Modeling for Development of Smart Distributed Monitoring

Studying hydrogen dispersion is crucial for ensuring the safe and effective deployment of hydrogen as an energy carrier. This study presents a comprehensive CFD modeling framework for simulating hydrogen dispersion at a real-world hydrogen production, storage, and utilization facility. Utilizing the Hydrogen Research Facility under the Advanced Research on Integrated Energy Systems (ARIES) at the National Renewable Energy Laboratory's (NREL) Flatirons campus, controlled hydrogen releases at 27 kg-H2/hr were simulated. The model incorporated site-specific atmospheric conditions, including hourly wind speeds and temperatures recorded between 8 AM and 8 PM from October to December 2023. To reduce computational demands, a statistical reduction technique was applied to condense the dataset to 100 representative scenarios, validated by statistical tests for wind speeds and power law coefficients. Simulations were conducted using the Reynolds-Averaged Navier-Stokes equations. Results demonstrated that wind speed substantially influences hydrogen dispersion, with low wind conditions forming concentrated clouds and higher wind speeds stretching the plume. Additionally, clustering analysis informed optimal sensor placement at various elevations with up to 10 sensor locations on each elevation. This framework offers a robust approach for understanding hydrogen behavior in ambient conditions and informing detection strategies.

08 HYDROGEN↗
DOE OSTI2026

Gearbox bearing crack growth prognostics and uncertainty quantification with physics-informed machine learning

This paper introduces the extreme theory of functional connections (X-TFC), a physics-informed machine learning algorithm, and tailors it to estimate the remaining useful life (RUL) of wind turbine gearbox bearings experiencing fatigue crack growth. Unlike purely data-driven methods, X-TFC embeds a physics model, based on Head's theory in this work, into its training objective. The core of X-TFC is a random-projection single-layer neural network trained via an extreme learning machine, which requires only limited damage progression data and solves for output weights with a least-squares optimization algorithm. A composite loss function balances the network's fit to observed degradation data against the residuals of the governing crack growth differential equation, ensuring the learned damage trajectory remains physically plausible. When applied to a vibration-based health-index (HI) dataset measured during the growth of a crack on the inner ring of a high-speed bearing in a wind turbine gearbox (Bechhoefer and Dubé, 2020), X-TFC achieves near-zero prediction bias. Even when trained on only the first 10 %–20 % of the damage progression data, with sufficient physics weighting its predictions remain monotonic and smooth, delivering high prognosability and trendability. To quantify the epistemic uncertainty, we employ a Monte Carlo ensemble of independently initialized X-TFC models trained on noise-perturbed data, which yields confidence intervals around each RUL estimate and captures both model-parameter and epistemic uncertainty. In addition to a vibration-based HI, we demonstrate that the proposed framework can be directly applied to a supervisory control and data acquisition (SCADA) data-based HI (Eftekhari Milani et al., 2026) measured during similar wind turbine gearbox bearing crack faults, preserving its accuracy and interpretability. This extension shows the versatility of our approach, which is applicable to bearings of multiple gearbox manufacturers, models, and ratings using only SCADA data. By integrating domain knowledge with machine learning, X-TFC offers a rapid, reliable tool for crack prognostics. Its adaptability to other bearing failure modes, such as pitch bearing ring cracks, positions X-TFC as a powerful enabler of data-driven, physics-informed asset management in the wind energy sector and beyond.

17 WIND ENERGY↗
NASA NTRS1994

Advances in polycrystalline thin-film photovoltaics for space applications

Polycrystalline, thin-film photovoltaics represent one of the few (if not the only) renewable power sources which has the potential to satisfy the demanding technical requirements for future space applications. The demand in space is for deployable, flexible arrays with high power-to-weight ratios and long-term stability (15-20 years). In addition, there is also the demand that these arrays be produced by scalable, low-cost, high yield, processes. An approach to significantly reduce costs and increase reliability is to interconnect individual cells series via monolithic integration. Both CIS and CdTe semiconductor films are optimum absorber materials for thin-film n-p heterojunction solar cells, having band gaps between 0.9-1.5 ev and demonstrated small area efficiencies, with cadmium sulfide window layers, above 16.5 percent. Both CIS and CdTe polycrystalline thin-film cells have been produced on a laboratory scale by a variety of physical and chemical deposition methods, including evaporation, sputtering, and electrodeposition. Translating laboratory processes which yield these high efficiency, small area cells into the design of a manufacturing process capable of producing 1-sq ft modules, however, requires a quantitative understanding of each individual step in the process and its (each step) effect on overall module performance. With a proper quantification and understanding of material transport and reactivity for each individual step, manufacturing process can be designed that is not 'reactor-specific' and can be controlled intelligently with the design parameters of the process. The objective of this paper is to present an overview of the current efforts at MMC to develop large-scale manufacturing processes for both CIS and CdTe thin-film polycrystalline modules. CIS cells/modules are fabricated in a 'substrate configuration' by physical vapor deposition techniques and CdTe cells/modules are fabricated in a 'superstrate configuration' by wet chemical methods. Both laser and mechanical scribing operations are used to monolithically integrate (series interconnect) the individual cells into modules. Results will be presented at the cell and module development levels with a brief description of the test methods used to qualify these devices for space applications. The approach and development efforts are directed towards large-scale manufacturability of established thin-film, polycrystalline processing methods for large area modules with less emphasis on maximizing small area efficiencies.

Lanning, Bruce R.↗
DOE OSTI2026

Evaluating the effect of meso/submesoscale current–wave interactions on wave energy resource characterization at northeast U.S. coast

Wave energy is a promising renewable resource, but accurate assessment is difficult in regions with strong currents due to wave–current interactions (WCI). Here, this study develops a two-way coupled WCI model within the Coupled Ocean Atmosphere Wave Sediment Transport (COAWST) framework at 2 km resolution to improve wave energy characterization along the northeastern U.S. coast, including the Mid-Atlantic Bight and Gulf of Maine. The model integrates WaveWatchIII (WWIII) and the Regional Ocean Modeling System (ROMS) to enhance wave hindcasting by accounting for Doppler-shift, refraction, and nonlinear energy exchanges. Validation against buoy and satellite observations confirms model accuracy. Analysis shows that Doppler-shifting can alter wave power density by over 20%, while strong current gradients and shear distort wave crests via focusing/defocusing and stretching/squeezing, modifying wave direction and frequency. These processes together can induce wave power fluctuations of up to 40% on synoptic scales. Applying a 2.5 MW Ocean Energy Converter power matrix shows that WCI may change harvested energy by up to 100% in shallow-waters and 60% in deep-waters. These results underscore the importance of incorporating nonlinear WCI for reliable wave climate predictions and resource assessments in energetic coastal regions.

doppler-shift↗