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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 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Multi-Time Scale Coordinated Control and Scheduling of Inverter-Based TCLs With Variable Wind Generation

To address microgrid tie flow errors caused by wind generation variability, here we propose and develop a multi-time scale coordinated control and scheduling strategy for inverter-based thermostatically controlled loads (TCLs). First, in hour-time scale, inverter-based TCLs with adjusting temperature set-point are modeled as virtual generators to compensate tie flow deviations in the day-ahead plan. Next, in minute-time scale, virtual batteries representing operating behaviors of inverter-based TCLs with frequency control are scheduled determined by the control of virtual generators in hour-time scale. The virtual batteries are scheduled to smooth out tie flow errors corresponding to day-ahead plan and hour-time scale schedules. The multi-time scale control methods are coordinated to employ the response potential of inverter-based TCLs and response curve-based methods are proposed to control inverter-based TCLs considering the customer privacy. The multi-time scale stochastic schedules which are based on response curves of inverter-based TCLs are coordinated to accommodate wind generation variability. Simulation results demonstrate that the microgrid tie flow errors are effectively mitigated by the proposed multi-time scale coordinated control and scheduling of inverter-based TCLs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

A Fast Flexibility-Driven Generation Portfolio Planning Method for Sustainable Power Systems

We are witnessing an acceleration in the uptake of renewable energy in power systems. Because of the associated variability and uncertainty of renewables, power systems need to have an adequate supply of flexibility to allow for suitable management of short-term operations. So far most of the work in this area has neglected how flexibility needs associated with renewables are fulfilled as part of dispatchable generation capital investments decisions. To address this challenge, we propose an approach to plan the dispatchable generation mix of a power system as needed to counteract variability and uncertainty associated with significant shares of variable renewable generation. The approach exploits the linear time-invariant feature of variable generation variability using historical phase planes of capacity (in MW) and ramp (in MW/min) to bridge the gap between long-term capacity planning and short-term intra-hour flexibility needs. This approach is much more computationally tractable than other proposals, while also being able to capture adequately short-term operational features like ramping and net load variability. Numerical tests are performed on realistic datasets to substantiate the effectiveness of the approach.

bulk power system planning↗
DOE OSTI2020

Joint Estimation of Behind-the-Meter Solar Generation in a Community

Distribution grid planning, control, and optimization require accurate estimation of solar photovoltaic (PV) generation and electric load in the system. Most of the small residential solar PV systems are installed behind-the-meter making only the net load readings available to the utilities. This paper presents an unsupervised framework for joint disaggregation of the net load readings of a group of customers into the solar PV generation and electric load. Our algorithm synergistically combines a physical PV system performance model for individual solar PV generation estimation with a statistical model for joint load estimation. The electric loads for a group of customers are estimated jointly by a mixed hidden Markov model (MHMM) which enables modeling the general load consumption behavior present in all customers while acknowledging the individual differences. At the same time, the model can capture the change in load patterns over a time period by the hidden Markov states. The proposed algorithm is also capable of estimating the key technical parameters of the solar PV systems. Our proposed method is evaluated using the net load, electric load, and solar PV generation data gathered from residential customers located in Austin, Texas. Testing results show that our proposed method reduces the mean squared error of state-of-the-art net-load disaggregation algorithms by 67%.

behind-the-meter solar generation↗
DOE OSTI2015

SU-E-T-21: A D-D Based Neutron Generator System for Boron Neutron Capture Therapy: A Feasibility Study

Purpose: To investigate the feasibility of a deuterium-deuterium (DD) neutron generator for application in boron neutron capture therapy (BNCT) of brain cancer Methods: MCNP simulations were performed using a head phantom and a monoenergetic neutron source, which resembles the point source in a DD generator that emits 2.45-MeV neutrons. Source energies ranging from 5eV to 2.45MeV were simulated to determine the optimal treatment energy. The phantom consisted of soft tissue, brain tissue, skull, skin layer, and a brain tumor of 5 cm in diameter. Tumor depth was varied from 5–10 cm. Boron-10 concentrations of 10 ppm, 15 ppm, and 30 ppm were used in the soft/brain tissues, skin, and tumor, respectively. The neutron flux required to deliver 60 Gy to the tumor as well as the normal tissue doses were determined. Results: Beam energies between 5eV and 10keV obtained doses with the highest dose ratios (3.3–25.9) between the tumor and the brain at various depths. The dose ratio with 2.45-MeV neutrons ranged from 0.8–6.6. To achieve the desired tumor dose in 40 minutes, the required neutron flux for a DD generator was between 8.8E10 and 5.2E11 n/s and the resulting brain dose was between 2.3 and 18 Gy, depending on the tumor depth. The skin and soft tissue doses were within acceptable tolerances. The boron-neutron interaction accounted for 54–58% of the total dose. Conclusion: This study shows that the DD neutron generator can be a feasible neutron source for BNCT. The required neutron flux for treatment is achievable with the current DD neutron technology. With a well-designed beam shaping assembly and treatment geometry, the neutron flux can be further improved and a 60-Gy prescription can be accurately delivered to the target while maintaining tolerable normal tissue doses. Further experimental studies will be developed and conducted to validate the simulation results.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TermGenie – a web-application for pattern-based ontology class generation

Biological ontologies are continually growing and improving from requests for new classes (terms) by biocurators. These ontology requests can frequently create bottlenecks in the biocuration process, as ontology developers struggle to keep up, while manually processing these requests and create classes. TermGenie allows biocurators to generate new classes based on formally specified design patterns or templates. The system is web-based and can be accessed by any authorized curator through a web browser. Automated rules and reasoning engines are used to ensure validity, uniqueness and relationship to pre-existing classes. In the last 4 years the Gene Ontology TermGenie generated 4715 new classes, about 51.4% of all new classes created. The immediate generation of permanent identifiers proved not to be an issue with only 70 (1.4%) obsoleted classes. Lastly, TermGenie is a web-based class-generation system that complements traditional ontology development tools. All classes added through pre-defined templates are guaranteed to have OWL equivalence axioms that are used for automatic classification and in some cases inter-ontology linkage. At the same time, the system is simple and intuitive and can be used by most biocurators without extensive training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Simulation for Wind Turbine Generators: With FAST and MATLAB-Simulink Modules

This report presents the work done to develop generator and gearbox models in the Matrix Laboratory (MATLAB) environment and couple them to the National Renewable Energy Laboratory's Fatigue, Aerodynamics, Structures, and Turbulence (FAST) program. The goal of this project was to interface the superior aerodynamic and mechanical models of FAST to the excellent electrical generator models found in various Simulink libraries and applications. The scope was limited to Type 1, Type 2, and Type 3 generators and fairly basic gear-train models. Future work will include models of Type 4 generators and more-advanced gear-train models with increased degrees of freedom. As described in this study, implementation of the developed drivetrain model enables the software tool to be used in many ways. Several case studies are presented as examples of the many types of studies that can be performed using this tool.

17 WIND ENERGY↗
DOE OSTI2016

On the Path to SunShot. Emerging Issues and Challenges in Integrating High Levels of Solar into the Electrical Generation and Transmission System

This report examines how the bulk power system may need to evolve to accommodate the increased photovoltaic (PV) penetration resulting from achievement of the U.S. Department of Energy's SunShot cost targets. The variable and uncertain nature of PV-generated electricity presents grid-integration challenges. For example, the changing net load associated with high midday PV generation and low electricity demand can create 'overgeneration' that requires curtailment of PV output and reduces PV's value and cost-competitiveness. Accommodating the changes in net load resulting from increased variable generation requires enhancements to a power system's 'flexibility,' or ability to balance supply and demand over multiple time scales through options including changes in system operation, flexible generation, reserves from solar, demand response, energy storage, and enhanced transmission and regional coordination. For utility-scale PV with a baseline SunShot levelized cost of electricity (LCOE) of 6 cents/kWh, increasing the annual energy demand met by solar energy from 10% to 20% would increase the marginal LCOE of PV from 6 cents/kWh to almost 11 cents/kWh in a California grid system with limited flexibility. However, increasing system flexibility could minimize solar curtailment and keep PV cost-competitive at penetrations at least as high as 25%. In the longer term, energy storage technologies--such as concentrating solar power with thermal energy storage--could facilitate the cost-effective integration of even higher PV penetration. Efficient deployment of the grid-flexibility options needed to maintain solar's value will require various innovations, from the development of communication, control, and energy storage technologies to the implementation of new market rules and operating procedures.

14 SOLAR ENERGY↗
DOE OSTI2016

Power Converter Control Algorithm Design and Simulation for the NREL Next-Generation Drivetrain: July 8, 2013 - January 7, 2016

The National Renewable Energy Laboratory (NREL) and NREL Next-Generation Drivetrain Partners are developing a next-generation drivetrain (NGD) design as part of a Funding Opportunity Announcement award from the U.S. Department of Energy. The proposed NGD includes comprehensive innovations to the gearbox, generator, and power converter that increase the gearbox reliability and drivetrain capacity, while lowering deployment and operation and maintenance costs. A key task within this development effort is the power converter fault control algorithm design and associated computer simulations using an integrated electromechanical model of the drivetrain. The results of this task will be used in generating the embedded control software to be utilized in the power converter during testing of the NGD in the National Wind Technology Center 2.5-MW dynamometer. A list of issues to be addressed with these algorithms was developed by review of the grid interconnection requirements of various North American transmission system operators, and those requirements that presented the greatest impact to the wind turbine drivetrain design were then selected for mitigation via power converter control algorithms.

17 WIND ENERGY↗
DOE OSTI2018

Illinois Distributed Generation Rebate Calculation Considerations

What is the value of distributed generation to the distribution system and how do we assign that value to a rebate? This white paper provides a preliminary look at the potential distributed generation valuation and compensation options for Illinois, taking into consideration data needs and availability, as well as stakeholder input. Pacific Northwest National Laboratory (PNNL) is supporting the Illinois Commerce Commission (ICC) with initial stakeholder engagement to advance the conversation around distributed generation valuation in Illinois. PNNL’s educational support will help set the stage for a productive formal process as the ICC will be called upon, in relatively short order, to start formal distributed generation valuation proceedings in response to Illinois Public Act 99-0906, also known as the Future Energy Jobs Act (FEJA), and codified in Illinois Public Utilities Act Section 16-107.6.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Endogenous Assessment of the Capacity Value of Solar PV in Generation Investment Planning Studies. Draft

There exist several different reliability- and approximation-based methods to determine the capacity contribution of solar resources towards resource adequacy. However, most of these approaches require knowing in advance the installed capacities of both conventional and solar generators. This is a complication since generator capacities are actually decision variables in capacity planning studies. In this article we study the effect of time resolution and solar PV penetration using a capacity planning model that accounts for the full distribution of generator outages and solar resource variability. We also describe a modification of a standard deterministic planning model that enforces a resource adequacy target through a reserve margin constraint. Our numerical experiments show that at least 50 days worth of data are necessary to approximate the results of the full-resolution model with a maximum error of 2.5% on costs and capacity. We also show that the amount of displaced capacity of conventional generation decreases rapidly as the penetration of solar PV increases. We find that using an exogenously defined and constant capacity factor based on time-series data can yield relatively accurate results for small penetration levels (less than 5%). For higher penetration levels (up to 20%), the modified deterministic planning model better captures avoided costs and the decreasing value of solar PV. Although our results are not general, they highlight the importance of accounting for the variation in both energy and capacity value of solar resources endogenously in capacity planning models. A11 numerical experiments are performed using the IEEE Reliability Test System and 7 years worth of demand and solar data from a utility in Arizona.

14 SOLAR ENERGY↗
DOE OSTI2021

Electrical Systems of Pumped Storage Hydropower Plants: Electrical Generation, Machines, Power Electronics, and Power Systems

Adjustable-speed (AS) pumped storage hydropower (PSH) technology has the potential to become a large, consistent contributor to grid stability enabling higher penetrations of wind and solar energy on the future U.S. power system. AS-PSH has high-value characteristics, such as fast response to provide ancillary services to the grid, because it is a power converter interface with the grid (like battery storage), but at the same time it has the energy content large enough to supply both short-term (seconds-to-minutes) and long-term (minutes-to-hours) of energy needs, like more conventional power plants. However, designs must be optimized to lower the capital expenditure (CapEx) and to provide a high-quality grid interface capability (e.g. power quality, ancillary service provider, fault-tolerant or fault ride-through capability), which is a primary factor in the acceptance of AS-PSH into a utility’s generation mix. This CapEx will be greatly affected by the cost savings associated with the civil structure, turbine design, power electronics, control systems, or unique generator designs. A holistic design must be considered to get a full picture of the benefits of the technology proposed. The AS-PSH can be controlled to reduce the impact of transient disturbance on a power system and at the same time can be controlled to minimized subsequent component fatigue and potential oscillation modes within the plant, with the overall impact in reducing the operational expenditures (OpEx).Generating clean power to meet standards such as Institute of Electrical and Electronics Engineers (IEEE) 519 and International Electrotechnical Commission (IEC) 1000-3-2 will be a continuing challenge. For many technology developers, however, improved AS-PSH technologies will become a key component of generator-storage systems of the future given the prospects of increased performance and decreasing costs, and the ever-increasing penetration of renewables (e.g wind power and solar power).

13 HYDRO ENERGY↗
DOE OSTI2022

Generation and Control of Self-Organized Nonlinear Kinetic Structures in High Energy Density Plasmas in the Presence of Intense Magnetic Fields and Ultrashort Laser Pulses

Goals were to study the interplay between electron plasma waves (EPW), KEEN waves and externally generated magnetic fields. In particular, the Weibel instability B field generation process and its interrelationship with the existence of nonlinear EPWs in high energy density plasmas. We focused on a number of models of how Kinetic, Nonlinear Electron Plasma Waves, KNL-EPW and KEEN waves create anisotropic electron velocity distribution functions, e- VDF, and how these anisotropic e- VDFs in turn drive the Weibel instability and generate B fields. Our goal is to control the SRS and SKEENS processes that generate the KNL-EPW, control the anisotropy, and thus also control the dynamics of the resulting B fields, their influence on the transport coefficients and heat transport that results, their modification of SRS itself and the reinforced anisotropy driven loop gain.

(Kinetic electrostatic electron nonlinear) KEEN wa↗
DOE OSTI2023

Grating-Based Imaging-Scattering with Portable Neutron Generator

Company: Adelphi Technology, Inc. Title: Grating-Based Imaging-Scattering with Portable Neutron Generator PI: Dr. Jay Theodore Cremer, Jr. Topic: 26a Statement of the problem or situation that is being addressed. Successful plant growth depends upon an efficient and robust root system. The plant root is part of a larger system of water and microbial flows in the soil system. While much effort has been exerted to develop an imaging system for water, microbes, and roots, the problem is challenging, and no widely accepted imaging method currently exists. The optical solutions use a highly modified soil system. X-ray imaging methods are insensitive to the soft tissues in the presence of sand. Thermal neutron imaging has been often tested, but found inadequate, due to limited access and low image resolution. This project will develop a new strategy for neutron imaging of plant/soil systems. The project will allow long duration experiments in greenhouse environments and increase the image information content to the micron scale. General statement of how this problem is being addressed. Portable, rugged thermal and fast neutron sources are being developed where portable means a two-soldier team can carry the source and power unit to survey rough terrain for explosives. In the past decade, microfabrication of X-ray and thermal/cold neutron optics has opened a new imaging strategy. The standard transmission image is now supplemented with simultaneous acquisition of a phase contrast image and an image revealing scattering features. In materials science, the interferometric neutron scattering image has been used to detect early crack formation in stressed additive manufacturing test samples. The detection requires sensitivity to scattering features at the 1-micron scale. By addition of our proposed grating-optic to Adelphi Technology’s radiographic/tomographic imaging system, which is based on portable thermal neutron source, the resulting thermal neutron scatter image of the plant/soil system, will reveal details at 1-micron. Commercial Applications and Other Benefits Neutron interferometry imaging has greater penetration through large metal components compared to industrial X-ray imaging. The low-cost, large area optics developed for greenhouse applications, combined with the robust, portable neutron generator, can then be marketed as a system for inspection of additive manufactured components. In the aerospace industry, all freshly printed components are validated with X-ray CT. Scheduled maintenance again requires X-ray CT as the ability to predict aerospace component lifetime does not yet exist. Large aerospace components are only partially observed with X-ray imaging. Key Words Neutron radiography/tomography, grating interferometry, thermal neutron generator imaging, plant root and soil imaging, rhizosphere imaging, deployable neutron imaging systems Summary for Members of Congress A rugged, portable source of thermal neutrons is adapted for neutron interferometry imaging with the addition of low-cost, 3D printed optics. The first application of our proposed deployable, compact thermal neutron generator imaging system, using a grating optic, is plant root/soil science in greenhouse settings and agricultural laboratories.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Once-Through Steam Generator Model Analysis Using Python and Advanced Optimization Tools (Summer Internship Report)

This study focuses on the parametric analysis of design parameters for a once-through steam generator (OTSG) model, using python and advanced optimization tools to facilitate applications such as the flowing autoclave steam generator (FASG) test cases. Building on previous research involving another OTSG with a different design, this project aims to enhance our understanding of how steam generators (SGs) behave and how their outputs are influenced by changes in design. The reason for this design change is to allow for more precise modeling and optimization of SG performance, to provide a comparative analysis between the two designs, and to set up the model for integration with the FASG test case. The OTSG python-model is a mathematical representation (including fluid flow and heat transfer equations/models/correlations) of a steam-generating unit in a pressurized water reactor-type small modular reactor system. Design studies involve changing the model’s input design parameters to observe the resulting effects on the output of the system. By using advanced optimization tools, such as the Risk Analysis Virtual Environment (RAVEN) developed at Idaho National Laboratory, detailed design parametric studies and model optimization were performed. Six input parameters—pressure, temperature, and mass flow rate for the inlet of the primary-side (hot fluid) and secondary-side (cold fluid), respectively, of the SG—were randomly perturbed via RAVEN’s Monte Carlo Sampler module, using uniform distributions (i.e., ±1%, ±5% and ±10% relative changes) for 600 samples. The analysis provides valuable insights into SG optimization and can be used for sensor placement optimization to effectively monitor and obtain experimental data in other tests.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2026

Generative AI for Grid Operations [Slides]

In the last few years, the development and use of generative artificial intelligence (AI) and large-language models (LLMs) have changed the landscape of how AI and machine learning (ML) are being used in power systems. LLMs are built on foundational models based on large data sets that can be trained to provide information rapidly and through simple natural language prompts. Generative AI can then perform human-like tasks using ML models to identify and mimic pattens in the data sets. This presentation explores how generative AI can enhance grid operations by improving forecasts, enabling rapid contingency analyses, and offering real-time operational suggestions. By providing grid operators with valuable insights, generative AI will empower them to manage power systems more effectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Reinforcement Learning Control for Enhancing Marine Hydrokinetic Turbine Energy Generation

This paper proposes a reinforcement learning-based method to maximize power generation for a direct-drive marine hydrokinetic turbine. A high levelized cost of energy (LCOE) is preventative in the widespread adoption of many marine energy conversion technologies. A straightforward way to reduce LCOE is to increase conversion efficiency and ensure maximum energy generation. The proposed method utilizes a damping control methodology, varying applied generator torque via a linear relationship between the applied damping coefficient and rotor speed. A state-action-reward-state-action (SARSA) algorithm has been used to learn the optimal control action for a given flow velocity. The proposed SARSA methodology uses Gaussian radial basis functions to create a three-dimensional surface to estimate the relationship between damping coefficient, incoming flow velocity, and coefficient of power (C p ). Here, the SARSA algorithm was compared against a baseline optimal tip speed ratio controller over a year-long flow velocity case profile while considering the effects of biofouling on the turbine system, where the proposed RL method generated 0.92% more energy than the baseline.

Damp↗
DOE OSTI2023

Novel Rotated Virtual Synchronous Generator Control for Power-Sharing in Microgrids with Complex Line Impedance

Virtual synchronous generator (VSG) control is based on the fact that the line impedance in a microgrid is highly inductive. This assumption was made due to the emulation of the stator winding of an electrical machine. This concept can affect the controllability of a microgrid with complex line impedance, generating deviations in the chosen operation point. To overcome this issue, additional techniques must be implemented. This paper describes a novel mathematical approach that uses the power line characteristics in a microgrid to rotate the power control reference frame and proposes a new control method called a “Rotated Virtual Synchronous Generator” (RVSG). This RVSG control approach integrates the effect of complex impedance on the microgrid’s operation and adjusts the reference frame accordingly to improve the system’s stability and performance. The use of this proposed mathematical approach in microgrids allows the further emulation of virtual inertia in microgrids that lack inertia. Finally, a comparison between RVSG control and the classical virtual synchronous generator method is carried out to show that it allows the improvement of the transient power response, power quality, stability, and performance, mainly in microgrids with complex line impedance.

42 ENGINEERING↗
DOE OSTI2022

A Comparative Study of Machine Learning Algorithms for Industry-Specific Freight Generation Model

According to Bureau of Transportation Statistics, the U.S. transportation system handled 14,329 million ton-miles of freight per day in 2020. Understanding the generation of these freight shipments is crucial for transportation researchers, planners, and policymakers to design and plan for a more efficient and connected freight transportation system. Traditionally, the freight generation modeling has been based on Ordinary Least Square (OLS) regression, although more advanced Machine Learning (ML) algorithms have been evaluated and proven to have excellent performance in various transportation applications in recent years. Furthermore, one modeling approach applied for one industry might not always be applicable for another as their freight generation logics can be quite different. The objective of this study is to apply and evaluate alternative ML algorithms in the estimation of freight generation for each of 45 industry types. Seven alternative ML algorithms, along with the base OLS regression, were evaluated and compared. In addition, the study considered different combinations of variables in both the original and logarithmic form as well as hyperparameters of those ML algorithms in the model selection for each industry type. The results showed statistically significant improvements in the root mean square error reduction by the alternative ML algorithms over the OLS for over 80% of cases. The study suggests utilizing the alternative ML algorithms can reduce the root mean square error by about 30%, depending on industry types.

97 MATHEMATICS AND COMPUTING↗