Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “grid application”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Computer Science Research Needs for Parallel Discrete Event Simulation (PDES)

Historically, scientific computing efforts have demonstrated the clear need for, and effective use of, supercomputing with traditional time-stepped simulations. Nevertheless, there are several areas in the mission spaces of the U.S. Department of Energy and other agencies waiting to tap advanced computing research using a different, discrete event style of modeling, simulation, and analysis. These span a wide spectrum of applications including energy grid resilience, urban planning and policy, transportation science, building technologies, emergency response and planning, environmental impact analysis, computational epidemiology, Internet communications, cyber security, and cyber-physical systems, to name only a few. Even within traditional scientific applications, the role of discrete event modes of execution is increasing in the form of new event-based mathematical solvers such as quantized state integration methods and discrete-continuous hybrid system solvers. Co-design of advanced supercomputing hardware systems is another area that exploits discrete event simulation at its core for effective analyses. Complex systems, entity behaviors and interconnections play a significant role in all these applications, which are mapped to large-scale models with discrete event formulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Multilevel Techniques for Compression and Reduction of Scientific Data---The Unstructured Case

Previous work on multilevel techniques for compression and reduction of scientific data is extended to the case of data given on unstructured meshes in two and three dimensions. The centerpiece of the work is a decomposition algorithm which is shown to be optimal, in terms of both storage and operational complexity, applicable to unstructured grids in both two and three dimensions, and which implicitly gives a Riesz basis that can be exploited to reduce the data while maintaining rigorous bounds on the loss incurred. The flexibility of the approach is illustrated by applications to potential flow around an airfoil and the effect of compression on quantities of interest relevant to airfoil design; compression of computational simulation of a nonlinear reaction-diffusion system with special attention given to the problem of time series reduction; and, data from a simulation of magnetically confined plasma in a fusion reactor reduced so as to preserve the electric field computed from the data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Real-Time Implementation and Validation of Federated Learning for Grid Services

Grid-edge devices are becoming increasingly important in the energy transition. Preserving privacy was not previously considered an important aspect for power grid operations, but with the increased proliferation of customer-owned assets, it is now an essential consideration. Several mechanisms have been proposed to provide privacy for non-utility owned assets in the power grid. Federated learning (FL) is one method gaining prominence in this area. Although FL has been used for other applications, such as auto-complete in phones, there has not been much investigation into whether these approaches are feasible for grid applications. In this work, we use a research platform with real-time simulators and hardware-in-the-loop capabilities to investigate how FL can be applied to grid-edge devices, and we present the potential grid services that can be derived for these devices. We discuss the computational challenges with deploying complex FL approaches, and we explore several grid services, including participation in retail electricity markets, voltage control, and resilience-driven reconfiguration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Generative AI for Power Grid Operations

Generative artificial intelligence (AI) has captured into the mainstream, demonstrating capabilities that once belonged solely to the realm of human cognition. From defeating world champions in complex games to generating human-quality text and images, Generative AI has proven its potential to revolutionize countless industries. The electric power grid is no exception. Generative AI's ability to process vast amounts of data rapidly, assist decision support and identify patterns could significantly enhance power grid operations. For example, Generative AI could improve state estimation where measurements are not available or integrate renewable energy sources more efficiently with probabilistic forecasting. The key contributions of this whitepaper are outlined below: (1) Comprehensive overview of Generative AI's applications in power grid operations: It highlights the opportunities in areas such as forecasting, state estimation, and demonstrating the potential for enhancing efficiency, reliability, and resilience. (2) Expanding Generative AI's impact through synergies with emerging technologies: The paper introduce NREL developed eGridGPT and explores how AI orchestration, multi-agent systems, and Digital Twins can collaborate to optimize grid operations, addressing the complexities of a decarbonized and electrified future. (3) In-depth analysis of challenges in implementing Generative AI: This includes considerations like data availability and quality, model validation, certification, and ethical concerns, ensuring responsible AI deployment. (4) Emphasizing human-AI collaboration: The whitepaper underscores the importance of trustworthy, transparency, and explainability in AI systems to promote seamless interaction between human operators and AI, ultimately improving decision-making. (5) Exploring future research and development: It identifies critical areas for further advancement to fully realize Generative AI's potential in power grid operations. This whitepaper serves as a valuable resource for researchers, practitioners, and policymakers looking to harness Generative AI for a more reliable, stable, and cost-effective power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Challenges and Prospects of Sodium‐Ion and Potassium‐Ion Batteries for Mass Production

The exponential growth of the lithium‐ion (LIB) market is causing a significant disparity between the supply chain and demand for its resources. In this regard, sodium‐ion and potassium‐ion batteries are promising alternatives to LIBs due to their low cost. However, the larger sizes of Na + and K + ions create challenges that prevent them from achieving energy densities comparable to LIBs while maintaining an acceptable cycle life. Here, in this perspective, the aim is to evaluate the status of Na‐ion and K‐ion batteries and the challenges associated with them on both fundamental and commercial levels. The focus is on the structural instability arising from phase transitions during cycling, intricate chemical degradation processes, and potential avenues for enhancing their performance with a specific goal of improving their viability for grid‐scale energy storage. Materials production and abundance limitations for the chemistries of the state‐of‐the‐art materials and account for critical parameters from both the perspective of researchers and investors are analyzed. This analysis aims to provide insights into the strategic trade‐offs required to effectively implement the technology in real‐world applications, such as grid‐scale storage and other areas. Furthermore, the utilization of metals with low or no supply‐chain problems as an important aspect of these trade‐offs is considered.

25 ENERGY STORAGE↗
DOE OSTI2025

Gaussian integral method for void fraction

Here, a novel method, the Gaussian Integral Method (GIM), is presented for calculating void fractions in Computational Fluid Dynamics–Discrete Element Method (CFD-DEM) simulations. GIM is versatile and applicable to various grid types, including structured and unstructured polyhedral meshes, without requiring special boundary treatments. An optimization technique is introduced to make GIM independent of grid resolution and type. The method is validated against experimental data from a fluidized bed, demonstrating that GIM produces realistic simulations closely resembling experimental observations. Additionally, unstructured polyhedral grids using GIM outperform structured grids of equivalent resolution, yielding results more aligned with experimental data. The gradient of the void fraction is computed in the CFD solver and utilized in the DEM solver for precise estimation at particle locations. Overall, GIM provides an effective solution for void fraction calculations in particulate media simulations with complex geometries, enhancing the accuracy and applicability of CFD-DEM simulations for industrial processes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Frequency Resilience Enhancement for Power Systems with High Penetration of Grid-Forming Inverters

Abstract—Grid following inverter-based renewable generation has replaced conventional generation in recent years, resulting in lower system inertia. The frequency resilience in such a lower inertia system is critical for emergency mitigation. In this paper, we propose a resilience metric based on frequency recovery to quantitatively represent system resilience in terms of the rate of change of frequency. Application of grid-forming converters provides a means to improve system resilience by providing virtual inertia. An under-frequency load shedding strategy is applied to further support frequency recovery in cases with high penetration of grid-forming inverters. Case studies are designed and performed in a modified IEEE 9-bus test system using the PSCAD/EMTDC platform. Simulation results demonstrate the validity of the proposed resilience metric and the effectiveness of the strategy for reducing frequency excursion in inverter-based power systems. citation: J. Gui, H. Lei, T. R. McJunkin and B. K. Johnson, "Frequency Resilience Enhancement for Power Systems with High Penetration of Grid-Forming Inverters," 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 2023, pp. 1-5, doi: 10.1109/ISGT51731.2023.10066357.

14 SOLAR ENERGY↗
DOE OSTI2021

Test methodology for validation of multi-frequency models of renewable energy generators using small-signal perturbations

Providing trustworthy and accurate multi-frequency (or harmonic) models for renewable energy generators (REG) is an ongoing challenge for harmonic studies. There have been effective attempts to propose and design a test device to validate the harmonic models, mainly based on shunt current perturbations. However, using additional devices for perturbations is costly for converter-based test sites. This paper provides the test specifications to extend the application of the grid emulators for voltage perturbations and appropriate harmonic model validation. Besides, the effects of the sequence couplings, initial emissions, and power set-points on the test results have been overlooked in the literature. Considering these effects, this paper proposes a generic test methodology to obtain more accurate models in the sequence domain. The experimental verification of the proposed methodology is demonstrated using a 7 MVA grid emulator for testing of a 2 MVA photo-voltaic converter and a 2 MVA Type 3 wind turbine. This way, the test challenges, specifications, and recommendations are presented using the MW-scale experiments on different REGs. Furthermore, the effects of sequence couplings and initial emissions on the calculation results are investigated and compared. The proposed methodology is applicable for harmonic model validation as well as empirical modelling.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Multi-Fidelity Stochastic Economic Dispatch for Operating Low Carbon Power Grids

Grid operators can address the inherently stochastic nature of renewables by solving a two-stage stochastic programming model that minimizes the cost of dispatch decisions while accounting for the complex grid dynamics. It is common to use a sample average approximation to estimate the expectation of the second stage costs in this model. However, the large sample count needed for numerical accuracy makes effective modeling large-scale electric grids computationally intractable. We introduce a control variate multi-fidelity estimator for the second-stage recourse that enables high quality dispatch decisions in real-time with a reduced computational burden. We obtain a hierarchy of model fidelities by linearizing the AC power flow system representation to DC power flow, and by relaxing transmission and voltage network constraints. We evaluate the performance of our proposed method on a synthetic grid with 73 buses against a deterministic baseline with persistence forecast and a high-fidelity reference. Our analysis shows a computational speed-up of 7.62x with a minimal loss in accuracy. The multi-fidelity method is well suited to fidelity combinations that use a simplified network topology in their lower fidelity model and is an attractive option for applications where accurate grid modeling needed on a limited computational budget.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Modified Andronov-Hopf Oscillator-Based Grid-Forming Converter with Emulated Virtual Cable for Enhanced Power Sharing Performance

Nonlinear oscillator-based grid-forming converters offer superior dynamic and steady-state performance, making them an attractive solution for interconnecting renewable resources. This paper proposes a novel modified Andronov-Hopf oscillator to enhance the operating spectrum and facilitate the integration of renewable energy sources. An inner loop controller based on the Lyapunov energy function is implemented to achieve robust stability and performance, while a virtual cable emulation strategy enables seamless parallel operation. Comprehensive modeling and simulation studies validate the effectiveness of the proposed system, demonstrating its capabilities in addressing diverse operating scenarios, including grid faults, renewable energy fluctuations, and parallel operation. The proposed solution exhibits fast transient response, robust stability, and flexible operation, making it a valuable contribution to the field of renewable energy integration. The results of this study can be used to inform the design and implementation of next-generation grid-forming converters, enabling a more sustainable and reliable energy future. Additionally, the proposed system's ability to operate in both grid-connected and islanded modes makes it an ideal candidate for remote and off-grid renewable energy applications. The proposed solution's scalability and modularity also make it suitable for large-scale renewable energy integration. The proposed system is verified through MATLAB/Simulink and PLECS simulations, demonstrating its effectiveness in ensuring robust and efficient operation.

Andronov-Hopf Oscillator (AHO)↗
DOE OSTI2022

A review of active probing-based system identification techniques with applications in power systems

System identification is becoming a relevant research area for numerous applications in power grids due to the increasing complexity of the system. A paradigm shift in power system infrastructure driven by renewable energy resources, controllable loads, and new power electronics technologies have given rise to new challenges in power systems operation and control. This increased complexity of power systems and unavailability of physics-based models of most inverter-based resources requires that traditional modeling of power systems approaches be complemented by system identification-based black or grey box modeling techniques. In light of the importance of system identification and little attention paid to the applications of these techniques in transforming power systems, this paper provides a comprehensive review of active probing-based system identification methods in the context of power system applications. It reviews applications of both linear and nonlinear system identification with discussion on their potential and key takeaways. To motivate a further practical use in power systems, the paper provides an example of system identification, to develop a state space model of an unknown plant with step-by-step details. The paper highlights the advantages of using modern power electronics-based sources in the identification process and discusses the emerging research directions for future.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Techno-Economic Analysis of a Cryogenic Flux Capacitor for Grid Storage

Abstract The Cryogenic Flux Capacitor (CFC) is a cold, dense fluid storage core with integrated design features that afford the designer flexibility and provide new possibilities for the storage and discharge of energy. The stored energy, in this case, is represented by hydrogen physically bonded within the nanoscale pores within the aerogel composite blanket material, and the process of bonding or debonding is governed by principles of physical adsorption (physisorption) and thermodynamics. The large surface area afforded by the nanoporous aerogel (∼1,000 m2/g) allows for storage densities close to, or in some cases exceeding, that of normal boiling point liquids. Its performance easily exceeds what can be achieved via ambient temperature, high-pressure gas storage for an equivalent volume. CFC storage is predicted to be easily scalable, is constructed from readily available commercial materials, lends itself to a range of pressure applications, and is geometry insensitive. The high energy density of CFC-stored hydrogen allows long durations of storage, such as daily to monthly cycling, which corresponds to approximately 10 to 100 hours of duration, respectively. The techno-economic analysis examines different types of hydrogen storage for commercial viability. The assessment includes the use of hydrogen blended with natural gas in a combined cycle gas turbine. The study provides results for a range of blends from 100% natural gas to 100% hydrogen. The required fuel usages are estimated for the intended application in a grid scenario that features large amounts of renewable penetration, 10 hours, and as a baseload, 100 hours. The study presents preliminary estimates of the capital cost of storage and operating cost of storage. In addition, the cost of firing natural gas and capturing the carbon dioxide (CO2) in a carbon capture and sequestration (CCS) system is assessed. Based on the study, it is shown that CFC can competitively meet performance and cost needs for grid-based energy storage.

08 HYDROGEN↗
DOE OSTI2022

Intensity Modulated Fiber Optic Sensor: A Novel Grid Measurement Unit

This paper presents a novel approach to physical displacement-based power grid measuring via an Intensity Modulated Fiber Optic Sensor (IMFOS). An IMFOS utilizes one fiber to transmit the intensity modulation light from its Electro-Optic controller to a fiber optic probe. The power grid voltage and current can induce physical displacements in transducers via the piezoelectric effect and the Lorentz law, respectively, which then result in a distance change between the optical probe and the reflective surface of the transducers. In parallel, multiple fibers are used to collect the reflective light for electro-optic conversion. Furthermore, a National Instruments-based characterization platform is set up for performance evaluation. Here, the testing result demonstrates that the IMFOS is immune to the inherent DC and low-frequency saturation issues prevalent in conventional potential and current transformers. Finally, the IMFOS is implemented in a Universal Grid Analyzer to illustrate its applicability for phasor estimation in actual power grids.

42 ENGINEERING↗
DOE OSTI2025

Investigation of electrode passivation during oxidation of a nitroxide radical relevant for flow battery applications

Nitroxide-radicals such as 2,2,6,6-tetramethylpiperidin-1-oxyl (TEMPO) and their derivatives have gained interest as redox-active organic molecules for applications in grid-scale energy storage. In particular, the higher solubility of 4-hydroxy-TEMPO in aqueous media greatly improves its energy density, but unusual kinetics associated with its surface-mediated electrooxidation have limited further development. Here, the apparent passivation behavior of species formed during 4-hydroxy-TEMPO electrooxidation in concentrated electrolytes is investigated. A combination of surface microscopy, X-ray photoelectron spectroscopy, and quartz-crystal gravimetry confirms the formation of a polymeric-type layer over the electrode surface composed of 4-hydroxy-TEMPO-like subunits, which is otherwise not observed with TEMPO. This study indicates that the design of high energy density and stable TEMPO-based redox molecules must also consider the reactivity that may occur due to the molecular characteristics of solubility-enhancing moieties. It is found that the extent of passivation is dependent on the voltage scan rate and 4-hydroxy-TEMPO concentration, underscoring the importance of studying materials at conditions relevant for their proposed applications. Evidence of incomplete passivation and an electrode self-cleaning process is presented, suggesting a materials design strategy to mitigate surface passivation from side reactions that may occur in redox active materials for energy storage applications.

Buchanan, Cailin [Argonne National Laboratory (ANL↗
DOE OSTI2025

SEM Grid Testing at NLCTA in BeamNetUS Program

We report on the performance of a secondary electron monitor (SEM) grid used to measure the transverse profile of an MeV range electron beam at SLAC National Accelerator Laboratory’s Next Linear Collider Test Accelerator (NLCTA) facility. When inserted into the path of the electron beam, secondary electron emission results in a measurable current on the wires that make up the grid. We present measurements using this technique to reconstruct the beam profile. The SEM grid was designed and manufactured by a team of undergraduate students at Harvey Mudd College (HMC) and was tested at SLAC’s NLCTA facility in collaboration with NCLTA staff as part of the BeamNetUS program. Developed for real-time diagnostics of MeV-range electron beams, this SEM grid has potential applications in both industrial and medical contexts.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Generating realistic building electrical load profiles through the Generative Adversarial Network (GAN)

Building electrical load profiles can improve understanding of building energy efficiency, demand flexibility, and building-grid interactions. Current approaches to generating load profiles are time-consuming and not capable of reflecting the dynamic and stochastic behaviors of real buildings; some approaches also trigger data privacy concerns. In this study, we proposed a novel approach for generating realistic electrical load profiles of buildings through the Generative Adversarial Network (GAN), a machine learning technique that is capable of revealing an unknown probability distribution purely from data. The proposed approach has three main steps: (1) normalizing the daily 24-hour load profiles, (2) clustering the daily load profiles with the k-means algorithm, and (3) using GAN to generate daily load profiles for each cluster. The approach was tested with an open-source database – the Building Data Genome Project. We validated the proposed method by comparing the mean, standard deviation, and distribution of key parameters of the generated load profiles with those of the real ones. The KL divergence of the generated and real load profiles are within 0.3 for majority of parameters and clusters. Additionally, results showed the load profiles generated by GAN can capture not only the general trend but also the random variations of the actual electrical loads in buildings. We report the proposed GAN approach can be used to generate building electrical load profiles, verify other load profile generation models, detect changes to load profiles, and more importantly, anonymize smart meter data for sharing, to support research and applications of grid-interactive efficient buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗