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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Quantitative Small-Signal Stability Analysis of Grid-Forming Inverters in Large-Scale Power Systems

In this paper, the challenges induced by the integration of inverter-based resources (IBRs) into existing power systems are discussed. Although the grid-forming (GFM) control has been used to enhance system performance in multiple sections of electric power systems (e.g., transmission and distribution systems, microgrids, among others), the coupling mechanism between GFM inverter and various types of other components in modern power systems should be clearly explored. Existing analyses mainly focus on qualitative investigations that verify the effectiveness of deploying GFM inverters for stability enhancement. A holistic system-level quantitative analysis is still missing to verify the detailed benefits of GFM inverters and their interactions with other generation units (e.g., synchronous generators [SGs], conventional grid-following [GFL] inverters, synchronous condensers, among others). To fill in the technical gaps, a holistic small-signal model of the entire system with both rotating generation units and IBRs is established along with the customized power flow analysis for varying steady-state operating points. Further, a quantitative method is proposed to identify the impacts of GFM inverters on system performance with an emphasis on a quantifiable stability margin and a complete modal analysis at the system level. Finally, a realistic test system is used to verify the proposed approach.

grid-following↗
DOE OSTI2022

Power System Waveform Classification Using Time-Frequency and CNN

Many modern reclosers and circuit breakers have microprocessor relays that record waveforms of system events. In some cases, utilities may record a half-a-dozen event captures for every event. This is thousands of events per year. The industry needs faster, more automated, more conclusive, and easy-to-use systems that can process massive amounts of event recordings without extensive input/support from power system engineers. To address the need for a commercially viable solution that can classify waveform data, energies were directed to develop a universal neural network (NN) structure (deep learning algorithm) that works for a wide variety of system event types. The structure that showed the most promise was one that included the use of spectrograms. The technique has shown positive results in audio engineering, particularly with respect to speech recognition. A waveform signature could be treated as a spoken word like audio waveforms for specific things such as “YES” or “UP”. No two people produce the exact same waveform when speaking each of these words, but audio processing algorithms based on spectrograms and convolutional neural networks (CNN) can still distinguish the word regardless of the speaker. No two circuits produce the exact same waveform for a given event, but the NN can be trained to classify the event type regardless of the circuit or location on the circuit. A Power System Neural Network (PSNN) has been developed to use a CNN to classify events within waveform data for power systems. The waveform is converted to an array of values by way of spectrograms and interpreted as an image. This image is passed into the CNN. The test results on independent simulated test and validation datasets show greater than 99% accuracy. While the results thus far are based on simulated data, the performance of the PSNN is very promising and should work for a wide variety of power system conditions of interest. Ultimately, much of the custom code and tools used today and much of the manual effort expended today may be automated using this PSNN.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

A graph embedding‐based approach for automatic cyber‐physical power system risk assessment to prevent and mitigate threats at scale

Abstract Power systems are facing an increasing number of cyber incidents, potentially leading to damaging consequences to both physical and cyber aspects. However, the development of analytical methods for the study of large‐scale power infrastructures as cyber‐physical systems is still in its early stages. Drawing inspiration from machine‐learning techniques, the authors introduce a method inspired by the principles of graph embedding that is tailored for quantitative risk assessment and the exploration of possible mitigation strategies of large‐scale cyber‐physical power systems. The primary advantage of the graph embedding approach lies in its ability to generate numerous random walks on a graph, simulating potential access paths. Meanwhile, it enables capturing high‐dimensional structures in low‐dimensional spaces, facilitating advanced machine‐learning applications, and ensuring scalability and adaptability for comprehensive network analysis. By employing this graph embedding‐based approach, the authors present a structured and methodical framework for risk assessment in cyber‐physical systems. The proposed graph embedding‐based risk analysis framework aims to provide a more insightful perspective on cyber‐physical risk assessment and situation awareness for power systems. To validate and demonstrate its applicability, the method has been tested on two cyber‐physical power system models: the Western System Coordinating Council (WSCC) 9‐Bus System and the Illinois 200‐Bus System , thereby showing its advantages in enhancing the accuracy of risk analysis and comprehensiveness of situational awareness.

Sun, Shining↗
DOE OSTI2021

A Hybrid Approach to Estimating the Economic Value of Enhanced Power System Resilience

The costs that power interruptions impose on customers and society have emerged as essential considerations for decision making about power system reliability and resilience. There is well-established literature on the direct costs of localized and relatively short-duration power interruptions. However, far less is known about the costs of widespread and long-duration (WLD) power interruptions, especially the indirect costs and related economy-wide impacts of these events. As a result, utility planning activities generally incorporate these costs incompletely or not at all. This report proposes a new approach to estimating WLD power interruption costs to support utility resilience planning studies. Specifically, the paper describes a hybrid method that integrates: (1) empirical surveys of region- and sector-specific inherent and adaptive customer behaviors during and after power interruptions, and (2) computable general equilibrium (CGE) modeling, which estimates the direct and indirect impacts of WLD power interruptions. The report provides both a rationale for the proposed hybrid approach and a roadmap outlining how it could be implemented.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Valuing EV Managed Charging for Bulk Power Systems

When and where electric vehicle (EV) charging occurs has significant implications for power systems supporting widespread EV adoption, especially with high shares of wind and solar generation. This study extends previous works by leveraging detailed simulation models for EV adoption, EV use, EV charging, and bulk power system operations, and by linking them with methods for describing charging flexibility at both the individual vehicle and aggregate levels. This technical potential study focuses on how the value of EV managed charging (EVMC) changes depending on charging flexibility type (within-charging session or within-week scheduling), dispatch mechanism (direct load control or one of several price-based mechanisms), and managed charging participation rate. We show that naively aggregating EV charging flexibility from individual vehicles into megawatt-scale resources grossly overestimates the flexibility of the fleet, because such aggregate models can unrealistically pair, e.g., one already-fully-charged vehicle's ability to increase load with another already-charging vehicle's ability to accept more charge, effectively requesting a charging rate that is infeasible for the latter vehicle. We find per-vehicle bulk system value is highest at low participation rates for all dispatch mechanisms. Factoring in production cost savings, avoided firm capacity savings, and combustion-related power sector emissions savings, we estimate the value of EVMC at low participation rates (5%) to be $33/vehicle-year to $69/vehicle-yr for within-session charging flexibility and $40/vehicle-yr to $120/vehicle-yr for within-week charging flexibility in an envisioned 2038 New England power system and monetary value reported in 2016 U.S. dollars. At 100% participation, per-vehicle value declines to $25/vehicle-yr to $31/vehicle-yr for within-session charging flexibility and to $29/vehicle-yr to $36/vehicle-yr for within-week charging flexibility; however, 100% participation yields the highest total system savings.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Interactive Power to Frequency Dynamics Between Grid-Forming Inverters and Synchronous Generators in Power Electronics-Dominated Power Systems

With increased attention on grid-forming inverters as a power system stabilizing device during high shares of inverter-based resource operations, there is a present need for a transparent and methodical investigation of the inverted and direct power to frequency control capabilities and impacts of these devices on emerging power systems. Here, analysis of the frequency dynamics of the droop-controlled grid-forming inverter and the synchronous generator illuminates the inverted active power-frequency relationship and the frequency response order reduction, forming the basis for novel, nonlinear frequency control approaches. Device-level electromagnetic transient domain simulations corroborate the order-reduction findings, establish that a properly designed dc-side system has a negligible impact on power transfer and will not impede frequency regulation, and confirm the primary frequency response improvement with nonlinear control. Simulations of the 9- and 39-bus test systems validate the order reduction and associated decoupling of the nadir and rate of change of frequency in larger networks. Oscillatory mode analysis confirms the grid-forming benefit of increased damping; decreased damping is observed at shares above 80%, but not at 100%. Finally, simulations on a validated Maui power system model with a 96% of inverter-based resources model yield a trend toward a first-order response.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Data Mining and Machine Learning for Power System Monitoring, Understanding, and Impact Evaluation

This chapter presents results from the Big Data analysis framework to improve power system situational awareness and system reliability. For this purpose, a dataset with real-world phasor measurement unit data and historical transmission system outage data has been created and used to carry out the analysis. Several statistical analysis and machine learning methods have been developed and implemented for event and anomaly detection and modeling. Detection and analysis results for actual examples of power system events are presented. Finally, data-driven characterization and risk assessment methods for weather-related extremes in power systems are developed and demonstrated on the Bonneville Power Administration system. These applications demonstrate the capability of Machine Learning (ML) methods to monitor system abnormalities, to predict system events, and to characterize the impact of extreme events on power grid

data mining, power grid, machine learning, anomaly↗
DOE OSTI2024

Modeling the impact of extreme summer drought on conventional and renewable generation capacity: Methods and a case study on the Eastern U.S. power system

Across recent years, there has been a growing prevalence of extreme weather events throughout the United States, posing significant challenges to the reliable and resilient operation of power systems. Specifically, summer droughts threaten to severely reduce available generation capacity to meet regional electricity demand, potentially leading to power outages. This underscores the importance of accurate resource adequacy (RA) assessment to ensure the reliable operation of the nation’s energy infrastructure. Accurately evaluating the usable capacity of regional generation fleets is a challenging undertaking due to the intricate interactions between power systems and hydro-climatic systems. Here, this paper proposes a systematic and analytical framework to evaluate the impacts of extreme summer drought events on the available capacity of various generating technologies, incorporating both meteorological and hydrologic factors. The framework provides detailed plant-level capacity derating models for hydroelectric, thermoelectric, and renewable power plants, facilitating evaluations with high temporal and spatial resolution. The application of the proposed impact assessment framework to the 2025 generation fleet of the real-world power system within the PJM and SERC regions of the United States yields insightful results. By analyzing the daily usable capacity of 6,055 at-risk generators across the study region, it shows that the summer capacity deration is most significant for hydroelectric and once-through thermal power plants, followed by recirculating thermal power plants and combustion turbines. In the event of the recurrence of the 2007 southeastern summer drought event in the near future, the generation fleet could experience a substantial reduction in available capacity, estimated at approximately 8.5 GW, compared to typical summer conditions. The sensitivity analysis reveals that the usable capacity of the generation fleet would suffer an even more significant decrease under conditions of increasingly severe summer droughts. The proposed approach and the findings of this study provide valuable methodologies and insights, empowering stakeholders to bolster the resilience of power systems against the potentially devastating effects of future extreme drought events.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Turbine-based Power System Tool

Advanced research tools are essential to illustrate the effects of turbine-based power system operations on the electric grid network. This paper presents the development of an open-source tool to visualize the operation of turbine-based electricity generation. The tool exposes power system models, control parameters, and corresponding values of the turbine model and its electrical system, allowing them to customize the simulation according to their needs. The developed tool enables users to simulate power profiles for different turbinebased energy generation such as wind, tidal, and gas turbines. It allows users to investigate the details of the generated profiles of various types of turbine systems at various scales. It can provide valuable insights into model development and facilitate the analysis of integrated power systems. By enabling access to turbine-based operations visualization, the tool aims to bridge the gap between advanced research tools and users, facilitating broader adoption of renewable energy technologies and aiding in developing sustainable power systems.

Kini, Roshan L.↗
DOE OSTI2024

Robust PID Control Against Nonlinear Uncertainties in Modern Power Systems with Microgrids

This paper investigates the problem of robust PID control for nonlinear power systems with uncertainties. In this paper, we analyze the challenges posed by nonlinearities and uncertainties in power systems, as well as the limitations of traditional linearization methods. On this basis, a PID control design method is proposed, which utilizes only the Lipschitz constant information of the nonlinear uncertainties to achieve global stability control for the power systems. The paper provides a rigorous mathematical foundation to demonstrate the effectiveness and robustness of the controller. Finally, simulation studies are conducted to validate and evaluate the performance of the proposed model, algorithm, and controller design.

Yuan, Shuo↗
DOE OSTI2021

Impact of Increased Inverter Penetration on Power System Small-Signal Stability: Preprint

The transformation of the power system to include more distributed energy resources (DER) implies an increase in the number of inverter-based resources deployed on the grid. Envisioning future scenarios, this paper presents a small-signal stability analysis for a power grid comprising synchronous generators and inverter-based resources. Three types of inverter control are considered: grid following, droop-controlled grid forming, and virtual oscillator control grid forming. Although small-signal stability of power systems is a widely studied topic, systematic analysis of mixed machine-inverter systems with detailed control models at various inverter penetration levels are limited. This paper addresses the gap with numerical simulations tailored to the IEEE 39-bus system. Results show the system may become unstable at high inverter penetration level of grid-following inverters, and grid-forming inverter control can potentially improve system stability, thereby enabling very high level of DERs.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2024

A Review of Edge Computing Technology and Its Applications in Power Systems

Recent advancements in network-connected devices have led to a rapid increase in the deployment of smart devices and enhanced grid connectivity, resulting in a surge in data generation and expanded deployment to the edge of systems. Classic cloud computing infrastructures are increasingly challenged by the demands for large bandwidth, low latency, fast response speed, and strong security. Therefore, edge computing has emerged as a critical technology to address these challenges, gaining widespread adoption across various sectors. This paper introduces the advent and capabilities of edge computing, reviews its state-of-the-art architectural advancements, and explores its communication techniques. A comprehensive analysis of edge computing technologies is also presented. Furthermore, this paper highlights the transformative role of edge computing in various areas, particularly emphasizing its role in power systems. It summarizes edge computing applications in power systems that are oriented from the architectures, such as power system monitoring, smart meter management, data collection and analysis, resource management, etc. Additionally, the paper discusses the future opportunities of edge computing in enhancing power system applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Metal-Supported Ceria Electrolyte-based SOFC Stack for Scalable, Low-Cost, High-Efficiency and Robust Stationary Power Systems (Final Report)

This is the end of project report for the DOE Project DE-FE-0027844 “Metal-Supported Ceria Electrolyte-based SOFC Stack for Scalable, Low-Cost, High-Efficiency and Robust Stationary Power Systems”. The objectives of the project were to develop several technologies required for the mass adoption of Solid Oxide Fuel Cell systems for medium to large power generation applications. The key achievements of this project were: 1) Development of fuel cells with complete internal fuel reforming capability. 2) Fuel cell power scale up with production of cells with larger active areas. 3) Assembly of an integrated 5 kW modular stack platform capable of installation into medium to large power systems. 4) Demonstration of a 10 kW Fuel Cell Power System utilizing the integrated 5 kW modular stack platform. 5) Cost modelling to show system cost of $1,500/kW are achievable at production volumes. 6) Development of cathode air poison getters for reduced degradation and extended fuel cell life. 7) Assessment of fuel poison on anode degradation, particularly poisons frequently produced from the process of gasification of coal. The Cummins led Voice of Customer (VOC) work identified several significant markets for natural gas powered fuel cell systems, where the end customer is interested in reducing energy consumption and emissions, or increasing reliability of electrical supply. Data centers and truck Auxiliary Power Units (APU) were highlighted as 2 key future markets for fuel cell systems.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

Frequency-Nadir-Constrained Unit Commitment for Low-Inertia, High-IBR Island Power Systems [Slides]

The process of energy decarbonization in island power systems is accelerated due to the swift integration of inverter-based renewable energy resources (IBRs). The unique features of such systems, including rapid frequency changes resulting from potential generation outages or imbalances due to the unpredictability of renewable power, pose a significant challenge in maintaining the frequency nadir without external support. This paper presents a unit commitment (UC) model with data-driven frequency nadir constraints, including either frequency nadir or minimum inertia requirements, helping to limit frequency deviations after significant generator outages. The constraints are formulated using a linear regression model that takes advantage of real-world, year-long generation scheduling and dynamic simulation data. The efficacy of the proposed UC model is verified through a year-long simulation in an actual island power system using historical weather data. The alternative minimum inertia constraint, derived from actual system operation assumptions, is also evaluated. Findings demonstrate that the proposed frequency nadir constraint notably improves the system's frequency nadir under high photovoltaic (PV) penetration levels, albeit with a slight increase in generation costs, when compared to the alternative minimum inertia constraint.

14 SOLAR ENERGY↗
DOE OSTI2021

Topological Machine Learning Methods for Power System Responses to Contingencies

While deep learning tools, coupled with the emerging machinery of topological data analysis, are proven to deliver various performance gains in a broad range of applications, from image classification to biosurveillance to blockchain fraud detection, their utility in areas of high societal importance such as power system modeling and, particularly, resilience quantification in the energy sector yet remains untapped. To provide fast acting synthetic regulation and contingency reserve services to the grid while having minimal disruptions on customer quality of service, we propose a new topology-based system that depends on a neural network architecture for impact metric classification and prediction in power systems. This novel topology-based system allows one to evaluate the impact of three power system contingency types, in conjunction with transmission lines, transformers, and transmission lines combined with transformers. We show that the proposed new neural network architecture equipped with local topological measures facilitates more accurate classification of unserved load as well as the amount of unserved load. In addition, we are able to learn more about the complex relationships between electrical properties and local topological measurements on their simulated response to contingencies for the NREL-SIIP power system.

contingency analysis↗
DOE OSTI2021

Topological Machine Learning Methods for Power System Responses to Contingencies: Preprint

While deep learning tools, coupled with the emerging machinery of topological data analysis, are proven to deliver various performance gains in a broad range of applications, from image classification to biosurveillance to blockchain fraud detection, their utility in areas of high societal importance such as power system modeling and, particularly, resilience quantification in the energy sector yet remain untapped. To provide fast acting synthetic regulation and contingency reserve services to the grid while having minimal disruptions on customer quality of service, we propose a new topologybased system that depends on neural network architecture for impact metrics classification and prediction in power systems. This novel topology-based system allows one to evaluate the impact of three power system contingency types, namely, in conjunction with transmission lines, transformers, and transmission lines combined with transformers. We show that the proposed new neural network architecture equipped with local topological measures facilitates both more accurate classification of unserved load as well as the amount of unserved load. In addition, we are able to learn complex relationships between electrical properties and local topological measurements on the simulated response to contingencies for NREL-SIIP power system.

graph theory↗