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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Learning Distributed Geometric Koopman Operator for Sparse Networked Dynamical Systems

Koopman operator theory provides an alternative to study nonlinear networked dynamical systems by mapping the state space to an abstract higher dimensional space where the system evolution is linear. Recent works show the application of graph neural networks (GNNs) to learn state to object-centric embeddings and achieve centralized block-wise computation of Koopman operator (KO) under additional assumptions on the underlying agents properties and constraints on the KO structure. However, the computational complexity of learning the Koopman increases exponentially for networked systems where the number of possible system states grows in a combinatorial fashion with the number of nodes. The learning challenge is further amplified for sparse networks by two factors: 1) sample sparsity for learning the Koopman operator in the non-linear space, and 2) the divergence in the dynamics of individual nodes or from one subgraph to another. Our work aims to address these challenge by formulating the representation learning of networked dynamical systems into a multi-agent paradigm and learning the Koopman operator in a distributive manner. The computational as well as performance advantages of distributed Koopman is predominant for sparse networks whereas for fully connected networks, it is shown to coincide with the centralized one. The empirical study on rope system, network of oscillators and a synthetic power system show comparable and superior performance along with computational benefits with the state-of-the-art methods.

Mukherjee, Sayak↗
DOE OSTI2023

High Performance Adaptive Deep-Reinforcement-Learning-based Real-time Emergency Control (HADREC) to Enhance Power Grid Resilience in Stochastic Environment (Final Report)

The HADREC technology developed in this project features real-time, validated, and predictive emergency control capabilities needed to effectively safeguard the grid against costly disturbances from extreme weather and other disruptive events. It has the potential to revolutionize the emergency control by providing faster and more effective solution for power grid emergency control. The technology can reduce the grid emergency control action time from minutes to seconds and mitigate the impact of outages and reduce system recovery time. The evaluation results using the models and data from PacifiCorp system have shown ~20% improvement in responding time and 70% improved in tripped generator output, comparing against today’s industrial application. This performance can help reduce preventive measurements and improve network utilization and market efficiency, and help lead to a more reliable and resilient power grid with renewable integration. The project has generated significant interest from industry, academic and commercial companies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Wind and Structural Loads on Parabolic Trough Solar Collectors at Nevada Solar One

Wind loading is a main contributor to structural design costs of Concentrating Solar Power (CSP) collectors, such as heliostats and parabolic troughs. These structures must resist the mechanical forces generated by turbulent wind. At the same time, the reflector surfaces must exhibit the necessary rigidity to maintain their optimal optical performance in windy conditions. Over two years, NREL conducted comprehensive field measurements of the atmospheric turbulent wind conditions and the resulting structural wind loads on parabolic troughs at the Nevada Solar One (NSO) plant. The measurement set-up included meteorological masts and structural load sensors on four trough rows. Additionally, we commissioned a lidar scanning the horizontal plane over the trough field. This data set catalogs the high-resolution data set characterizing the complex flow field and resulting structural loads on parabolic trough collectors. By providing this first-of-its-kind data set to the CSP community, we aim to enhance the community's understanding of wind-loading experienced by CSP collector structures. This data set will also help design next-generation solar collectors and photovoltaic trackers.

Array↗
DOE OSTI2023

The Baseline Performance Reference for Irradiance in PV System Applications

This report proposes the definition of a new baseline performance reference (BPR). The definition goes beyond existing standards pertaining to photovoltaic (PV) reference cells and devices to define the response under all possible operating conditions in the field. Field evaluations using BPR devices will be more sensitive to performance anomalies than pyranometers because they track PV system power output more closely. At the same time, they will be able to detect a broader range of performance anomalies than traditional matched reference devices, which might have matching defects. The BPR definition also opens the door to new practices in resource assessment and yield prediction. Solar resource data can be collected or modeled and validated directly as BPR irradiance, and PV system simulations based on BPR irradiance need fewer assumptions and less processing to obtain the effective irradiance on modules. As a result, lower uncertainty in yield assessments can be expected.

14 SOLAR ENERGY↗
DOE OSTI2022

A Universal Converter for DC, Single-phase AC, and Multi-phase AC Systems (Final Report)

The goal of this project was to develop a new class of universal power converters that utilize the unique properties of WBG switches, including high reverse voltage blocking and fast switching, to achieve superior performance in universal converters. Some of the features of the proposed converter are as follows: 1) Significantly longer lifetime, higher power density, lower costs associated with bill of materials, and lower shipping, installation, and repair costs compared to the existing WBG-based solutions, 2) Very flexible operation for transferring power between any type of source (dc, single-phase ac, or multi-phase ac) and any type of load (dc, single-phase ac, or multi-phase ac) without using large passive components or additional stages/components, even when the instantaneous values of input and output power are not the same, and 3) High performance operation at all power levels. Considering its advantages and wide range of applications, the proposed converter has the potential to create a new paradigm in power electronics. Among numerous applications of this converter are electric vehicles, wind energy systems, PV systems, industrial motor drives, residential variable frequency drive systems, and nanogrid applications. This project focused on the application of this converter for residential PV, residential motor drive, and industrial motor drive applications, and evaluated the performance of this converter for these applications.

42 ENGINEERING↗
DOE OSTI2022

Preliminary Technoeconomic Analysis for Power Generation from Coal Water Slurry Using Modular Staged-OMB Gasifier

Trimeric performed a technoeconomic analysis for a proposed 5 MWe gross modular gasification power generation facility. The cost estimate was used to determine the economic advantages of the proposed modular, staged-OMB gasifier in comparison to other modular gasifier designs. The project, Staged OMB for Modular Gasifier/Burner, is led by The University of Kentucky Center for Applied Energy Research (UK CAER) and supported by East China University of Science and Technology (ECUST) and Trimeric Corporation. A high-level summary of the findings from this project are presented below.

10 SYNTHETIC FUELS↗
DOE OSTI2023

Assessment of Nuclear Energy to Support Negative Emission Technologies

The feasibility and performance of nuclear energy coupled with Negative Emission Technology (NET) processes were investigated in this report. Three overarching questions from nuclear NET systems guided this research: which NET would be able to use heat and/or electricity from nuclear power plants (NPPs); what is the performance and cost of a nuclear NET system; and what would be the market outlook for this system? Among the various NETs that are actively being developed, several were found to potentially benefit from coupling with an NPP via (1) large amounts of decarbonized and constant-output electricity; (2) free waste heat or cheap low-temperature heat; or (3) high-temperature heat. NPPs were found to be compatible with Direct Air Capture (DAC) systems, and a detailed techno-economic analysis of coupled NPP&DAC systems was performed. Preliminary analysis also indicated that biomass and water-based NETs are potentially compatible with NPPs, but further work is needed to quantify the performance of these nuclear NET systems. Design and performance analyses were completed for both liquid solvent DAC (L-DAC) and solid sorbent DAC (S-DAC) technologies. A 1.0-GWth NPP coupled with L-DAC and S-DAC was found to be able to capture 12–15 Mt CO 2 /yr and 1.0–1.5 Mt CO 2 /yr, respectively. While the L-DAC process enables much greater CO 2 capture than the S-DAC process when both are sized with a 1 GWth NPP, the NPP&L-DAC system considered also requires >2 GWth natural gas oxy-combustion to reach adequate temperature in the calciner. CO 2 generated from natural gas combustion is also captured as part of the calcination process, in addition to the CO 2 captured from air, resulting in overall CO 2 sequestration of close to 30% more than what is captured from air. The cost of carbon capture calculated with the levelized cost of DAC (LCOD) had a range of $\$170–260$/tCO 2 for NPP&L-DAC systems and a range of $\$650–680$/tCO 2 for NPP&S-DAC systems. For both DAC systems, the NPP provides economic benefit when compared to previous National Energy Technology Laboratory (NETL) studies of non-nuclear DAC systems, leading to reduction of LCOD by 5–7% for L-DAC, and 8–13% for S-DAC. For the NPP&DAC systems, a preliminary market analysis reviewed potential CO 2 market prices and eligibility for incentives. The estimated potential revenues for CO 2 capture (coming from federal incentive, CO 2 commodity markets, or offset market) is in the range of $\$170–979$ tCO 2 , and the results show that because of lower LCOD, the NPP&L-DAC process would be more attractive to a market than the NPP&S-DAC process. The large investment needed for NPP&DAC processes would require long-term certainty of sufficient market size, CO 2 prices, and incentives. Enabling NPPs to ramp DAC operation up or down based on electricity market price is not expected to significantly increase revenues of the NPP&DAC system. This is because the revenues from CO 2 sequestration are required to be very high to justify the deployment and continuous operation of the very expensive DAC technologies. In this analysis, several new research questions were uncovered, and follow-up analyses are recommended for further investigation, including a detailed feasibility study of NPP coupled with other NET systems such as biomass pyrolysis and gasification with carbon capture and storage, and seawater carbon capture.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Advanced control techniques for modern inertia based inverters

In this research three artificial intelligent (AI)-based techniques are proposed to regulate the voltage and frequency of a grid-connected inverter. The increase in the penetration of renewable energy sources (RESs) into the power grid has led to the increase in the penetration of fast-responding inertia-less power converters. The increase in the penetration of these power electronics converters changes the nature of the conventional grid, in which the existing kinetic inertia in the rotating parts of the enormous generators plays a vital role. The concept of virtual inertia control scheme is proposed to make the behavior of grid connected inverters more similar to the synchronous generators, by mimicking the mechanical behavior of a synchronous generator. Conventional control techniques lack to perform optimally in nonlinear, uncertain, inaccurate power grids. Besides, the decoupled control assumption in conventional VSGs makes them nonoptimal in resistive grids. The neural network predictive controller, the heuristic dynamic programming, and the dual heuristic dynamic programming techniques are presented in this research to overcome the draw backs of conventional VSGs. The nonlinear characteristics of neural networks, and the online training enable the proposed methods to perform as robust and optimal controllers. The simulation and the experimental laboratory prototype results are provided to demonstrate the effectiveness of the proposed techniques.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Optimization-Based Model Reduction Scheme for Renewable Energy Power Plants Using Standardized Testing Scenarios

This paper presents an optimization-based model reduction scheme for renewable energy (RE) power plants consisting of inverter-based resources (IBRs) operating in grid-following (GFL) or grid-forming (GFM) modes. More importantly, the datasets feeding the optimization-based model reduction scheme are generated and re-used through the standardized grid-interactive testing scenarios. Particularly, the proposed scheme makes use of the power plant point of common coupling (PCC) measurements of various quantities specified by standardized tests (e.g., voltage and frequency ride through) as per IEEE 2800, to estimate the parameters of the reduced-order model such that its dynamic performance aligns with the original detailed power plant model. The proposed model reduction approach does not require the parameters of individual IBRs and using standardized test data as input to the formulated optimization problem simplifies the reduced-order modelling scheme. Extensive case studies following standardized test scenarios verified the remarkable accuracy of the proposed approach.

Yallamilli, Ram S. [Purdue University]↗
DOE OSTI2021

Preprocessing for Unintended Conducted Emissions Classification with ResNet

Characterization of Unintended Conducted Emissions (UCE) from electronic devices is important when diagnosing electromagnetic interference, performing nonintrusive load monitoring (NILM) of power systems, and monitoring electronic device health, among other applications. Prior work has demonstrated that UCE analysis can serve as a diagnostic tool for energy efficiency investigations and detailed load analysis. While explaining the feature selection of deep networks with certainty is often not fully comprehensive, or in other applications, quite lacking, additional tools/methods for further corroboration and confirmation can help further the understanding of the researcher. This is true especially in the subject application of the study in this paper. Often the focus of such efforts is the selected features themselves, and there is not as much understanding gained about the noise in the collected data. If selected feature and noise characteristics are known, it can be used to further shape the design of the deep network or associated preprocessing. This is additionally difficult when the available data are limited, as in the case which the authors investigated in this study. Here, the authors present a novel work (which is a proposed complementary portion of the overall solution to the deep network classification explainability problem for this application) by applying a systematic progression of preprocessing and a deep neural network (ResNet architecture) to classify UCE data obtained via current transformers. By using a methodical application of preprocessing techniques prior to a deep classifier, hypotheses can be produced concerning what features the deep network deems important relative to what it perceives as noise. For instance, it is hypothesized in this particular study as a result of execution of the proposed method and periodic inspection of the classifier output that the UCE spectral features are relatively close to each other or to the interferers, as systematically reducing the beta parameter of the Kaiser window produced progressively better classification performance, but only to a point, as going below the Beta of eight produced decreased classifier performance, as well as the hypothesis that further spectral feature resolution was not as important to the classifier as rejection of the leakage from a spectrally distant interference. This can be very important in unpredictable low-FNR applications, where knowing the difference between features and noise is difficult. As a side-benefit, much was learned regarding the best preprocessing to use with the selected deep network for the UCE collected from these low power consumer devices obtained via current transformers. Baseline rectangular windowed FFT preprocessing provided a 62% classification increase versus using raw samples. After performing a more optimal preprocessing, more than 90% classification accuracy was achieved across 18 low-power consumer devices for scenarios in which the in-band features-to-noise ratio (FNR) was very poor.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

ExaCA: A performance portable exascale cellular automata application for alloy solidification modeling

Modeling the as-solidified grain structures that form during alloy processing is a critical component in understanding process-property relationships, particularly for additive manufacturing (AM) where grain structure is very sensitive to processing conditions. While cellular automata (CA)-based models have proven able to predict aspects of microstructure for several alloys and AM process conditions, long run times and large resource sets required limit the utility and the problem size to which existing CA models can be applied. As part of the ExaAM project, an initiative within the Exascale Computing Project (ECP) to develop, test, and optimize an exascale-capable coupled and self-consistent model of AM parts, we developed ExaCA (https://github.com/LLNL/ExaCA) for the liquid–solid phase transformation in the wake of AM melt pools. The CA-based code is parallelized using MPI and the Kokkos programming model, the latter enabling simulation on both CPUs and GPUs within a single-source implementation. Here, we detail the steps taken to transform a baseline, MPI-based CA code into one that is performant on CPUs and GPUs. Performance testing of ExaCA on Summit (a pre-exascale machine at Oak Ridge National Laboratory) was used to quantify CPU–GPU speedup comparing with equal numbers of nodes. Testing showed comparable CPU performance to the MPI-only CA code and a 5-20x speedup when running AM-based test problems using GPUs. The improved performance of CA through GPU utilization and the performance portable nature of ExaCA will enable accurate part-scale modeling by harnessing the power of current and future generations of high performance computing resources. Future work will include improving the strong scaling of ExaCA on GPUs by reducing load imbalance associated with the locality of the problem, and continuing performance optimization across exascale hardware.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Advanced Power Electronics and Electric Machines

NLR's advanced power electronics and electric machines (APEEM) research group develops world-class experimental and modeling capabilities to design and evaluate efficient and reliable power electronics and electric machines, as well as innovative thermal management systems to maximize their performance. They also design, fabricate, and characterize advanced power electronics packaging and are developing state-of-health monitoring techniques.

25 ENERGY STORAGE↗
DOE OSTI2022

Improving Coal-Fired Plant Performance Through Integrated Predictive and Condition-Based Monitoring Tools

The project demonstrated the ability to improve boiler performance and reliability through the integrated use of condition-based monitoring (CBM) and predictions of the impacts of coal quality on boiler operations at a full-scale coal-fired power plant. The advanced tool developed actively monitors and manages coal quality and overall boiler conditions that maximizes availability and maintains generating capacity while reducing cost. The tool is used to forecast and alert plant operators and engineers about poor boiler conditions which may occur as a result of incoming coal and/or current power plant operating conditions. The Combustion System Performance Indices (CSPI) and CoalTracker (CT) programs predicts fireside performance of the plant including slagging, fouling, erosion, slag flow, strength development (sintering/densification), and slag layer thickness based on the composition of delivered coal. CT program is tailored for each plant and is used to track the coal from the point of delivery to the burner. The most successful application of the CSPI-CT has been at plants where the coal composition is determined through the use of a full stream elemental analyzers based on prompt gamma neutron analysis (PGNAA). The CSPI-CT program information is used by coal procurement, coal mining and plant operations personnel to select and blend coal properties for the best plant performance.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Lightning radiometry in visible and infrared bands

Calibrated measurements of lightning optical emissions are critical for both quantifying the impacts of lightning in our atmosphere and devising detection instruments with sufficient dynamic range capable of yielding close to 100% detection efficiency. However, to date, there is only a limited number of investigations that have attempted to take such calibrated measurements. Here, in this work, we report the power radiated by lightning in both visible and infrared bands, assuming isotropic emission, and accounting for atmospheric absorption. More precisely, we report peak radiated power and total radiated energy in the combined visible plus near-infrared range (VNIR, 0.34–1.1 μm), around the H α line (652–667 nm), and for the 2–2.5 μm infrared band. The estimated peak power and total energy radiated by negative cloud-to-ground return strokes in the VNIR range is 130 MW and 20 kJ, respectively. Additionally, we detected peak radiated powers of 12 and 0.19 MW in the H α and infrared bands, respectively. We cross-reference the optical data set with peak current reported by a lightning detection network. The resulting trend is that optical power emitted around the H α line scales with peak return stroke current according to a power law with exponent equal to 1.25. This trend, which should be approximately true across the entire visible spectrum, can be attributed to the plasma negative differential resistance of the lightning return stroke channel. We conclude by discussing the challenges in performing calibrated measurements of lightning optical power in different bands and comparing the results with previously-collected data with different experimental setups, observation conditions, and calibration methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Comparison of Long-Term Bioenergy with Carbon Capture and Storage to Reference Power Generation Technologies Using CO2 Avoidance Cost in the U.S.

Bioenergy with carbon capture and storage (BECCS) can sequester atmospheric CO2, while producing electricity. The CO2 avoidance cost (CAC) is used to calculate the marginal cost of avoided CO2 emissions for BECCS as compared to other established energy technologies. A comparative analysis using four different reference-case power plants for CAC calculations is performed here to evaluate the CO2 avoidance cost of BECCS implementation. Results from this work demonstrate that BECCS can generate electricity at costs competitive with other neutral emissions technologies, while simultaneously removing CO2 from the atmosphere. Approximately 73% of current coal power plants are approaching retirement by the year 2035 in the U.S. After considering CO2 sequestered from the atmosphere and coal power plant CO2 emissions displaced by BECCS, CO2 emissions can be reduced by 1.4 billion tonnes per year in the U.S. alone at a cost of $88 to $116 per tonne of CO2 removed from the atmosphere, for 10% to 90% of available biomass used, respectively. CAC calculations in this paper indicate that BECCS can help the U.S. and other countries transition to a decarbonized electricity grid, as simulations presented in this paper predict that BECCS power plants operate at lower CACs than coal plants with CCS.

25 ENERGY STORAGE↗
DOE OSTI2025

Analysis of a Disturbance Event with Inverter-Based Resources Using EMT Simulations

Increasing penetration of inverter-based resources (IBRs) necessitates newer methods of planning and analysis of disturbances. The existing phasor-domain transient stability (TS) analysis may not capture the dynamics of IBRs during fault events. Here, in this paper, electromagnetic transient (EMT) simulations using high-fidelity detailed model of power grid and one of the affected photovoltaic (PV) plants during the Angeles Forest disturbance in 2018 are performed. In these simulations, the processes to develop EMT models of power grid from traditional phasor-domain TS data and PV plant from collected data are described. Thereafter, using these simulations, the response of the PV plant during the fault event in 2018 is replicated and a sensitivity analysis is performed. The sensitivity analysis consists of making changes to the components within the PV plant and in the power grid to evaluate the impact they have on the response observed by the PV plant during the fault event. This analysis provides an understanding of the components that impact the operation of a PV plant during fault events and provide guidance to system planners on the studies that need to be performed to maintain a reliable power grid as new IBR plants are integrated.

42 ENGINEERING↗