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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Review on Artificial Intelligence for Grid Stability Assessment

Artificial intelligence provides a convenient route for power grid stability assessment. Compared with simulation-based approaches, artificial intelligence can potentially save time on model development and numerical computation in stability assessment. This paper first reviewed existing literature on using artificial intelligence for power grid stability assessment. Then a machine-leaning-based tool is presented and developed to assess power grid transient stability, frequency stability, and small signals stability. Test results verified the accuracy and effectiveness of the AI tool for power grid stability assessment.

You, Shutang↗
DOE OSTI2024

Hydropower Flexibility and Environmental Tradeoffs Analysis

The importance of hydropower increases as the power grid evolves with the higher variable renewable contribution. As conventional thermal power plants are retired, the importance of hydropower contribution increases to balance the variability of solar and wind generation. However, reservoir water resources are constrained by multiple constraints, and variability of water inflow to the reservoirs creates limitations to dam water releases for power grid needs. Coordinating multiple tools, including water resources, ecological, and technical and economic power grid modeling, informs dam water releases. The case study, the Columbia River Basin multipurpose reservoir project, is operated for hydropower production and many other purposes considering the aquatic habitat of the river basin. Specifically, the river basin fish population is a vital element for the tribal community of the river basin. We integrated a production cost model, a water resource model, and decades of tribal knowledge to analyze the fish-friendly way of operating Columbia hydropower scheduling and grid impacts. We measure power grid impacts for various water resources planning scenarios in terms of total system operating cost, system reliability indicators, changes in wind and solar generation and curtailments, local marginal prices, and revenue for hydropower producers. The study results inform reservoir operating rules decisions from hydropower power producers, system operators, other water users, tribes, environmentalists, and other stakeholders.

Columbia River↗
DOE OSTI2023

Efficient Network Partitioning: Application for Decentralized State Estimation in Power Distribution Grids: Preprint

Increase in the proliferation of DERs requires real-time situational awareness for efficient grid operations. State estimation plays an important role for real time control and management of the power grid. As the sensing infrastructure grows, aggregating and handling high volumes of data at a centralized location is extremely difficult. To address this challenge, this paper first proposes a novel and efficient hierarchical spectral clustering-based network partition algorithm followed by a decentralized compressive sensing (DCS) based state estimation. The applicability of the proposed network partitioning algorithm is tested on IEEE-123 bus, IEEE-8500 node, and a 6204-node distribution network. The results shows that the proposed approach efficiently divides the network into multiple sub-networks with the minimum edge connections among the neighbors. Then, we perform DCS-based state estimation on the 6204-node distribution network after dividing the network into 18 optimal partitions. Simulation results show that DCS-based state estimation recovers the system states with high accuracy and low complexity.

ADMM↗
DOE OSTI2023

Efficient Network Partitioning: Application for Decentralized State Estimation in Power Distribution Grids

Increase in the proliferation of distributed energy resources require real-time situational awareness for efficient grid operations. State estimation plays an important role for the real-time control and management of the power grid. As the sensing infrastructure grows, aggregating and handling high volumes of data at a centralized location is extremely difficult. To address this challenge, this paper first proposes a novel and efficient hier-archical spectral clustering-based network partitioning algorithm followed by a decentralized compressive sensing (DCS)-based state estimation. The applicability of the proposed network partitioning algorithm is tested on an IEEE 123-bus network, an IEEE 8,500-node system, and a 6,000+ node distribution network. The results shows that the proposed approach efficiently divides the network into multiple sub-networks with the minimum number of edge connections among the neighbors. Then, we perform DCS-based state estimation on the 6,000+ node distribution network after dividing the network into 18 optimal partitions. Simulation results show that the DCS-based state estimation recovers the system states with high accuracy and low complexity.

alternating direction method of multipliers↗
DOE OSTI2023

AI-Based Faster-Than-Real-Time Stability Assessment of Large Power Systems with Applications on WECC System

Achieving clean energy goals will require significant advances in regard to addressing the computational needs for next-generation renewable-dominated power grids. One critical obstacle that lies in the way of transitioning today’s power grid to a renewable-dominated power grid is the lack of a faster-than-real-time stability assessment technology for operating a fast-changing power grid. This paper proposes an artificial intelligence (AI) -based method that predicts the system’s stability margin information (e.g., the frequency nadir in the frequency stability assessment and the critical clearing time (CCT) value in the transient stability assessment) directly from the system operating conditions without performing the conventional time-consuming time-domain simulations over detailed dynamic models. Since the AI method shifts the majority of the computational burden to offline training, the online evaluation is extremely fast. This paper has tested the AI-based stability assessment method using multiple dispatch cases that are converted and tuned from actual dispatch cases of the Western Electricity Coordinating Council (WECC) system model with more than 20,000 buses. The results show that the AI-based method could accurately predict the stability margin of such a large power system in less than 0.2 milliseconds using the offline-trained AI agent. Therefore, the proposed method has great potential to achieve faster-than-real-time stability assessment for practical large power systems while preserving sufficient accuracy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

2024.03.13 Bushing Subcommittee presentation on Transformer Bushing Seismic Resilience: Design, Hazard, & Prevention Analysis [Slides]

Presents results from power grid simulations on synthetic grids that estimate the impact from power transformer failures due to earthquakes. In the best-case scenario, there is an 82.9% likelihood of load loss greater or equal to 51 MW. Methodologies are described as well as path forward to improving transformer resilience and, consequently, the resilience of the bulk power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

High Penetration Power Electronics Grid: Modeling and Simulation Gap Analysis

Increased penetration of power electronics in the grid is happening through development of high-power drives (like in Type 3 or 4 wind turbines, industrial drives, etc.), high-voltage direct current (HVdc) systems, flexible alternating current transmission systems (FACTS), energy storage systems (ESSs), inverter-based renewables like solar and wind, electric vehicle chargers, and other technologies. Ongoing research and development in new power electronic technologies including, but not limited to, solid-state power substations (SSPS), extreme fast charging (XFC), solid-state transformers, and multi-port power electronics that integrate multiple sources/loads will further increase penetration levels. To ensure stakeholders can integrate high penetration of power electronic technologies safely and reliably requires tools and methods to assess and evaluate their impact on the grid. Objectives: This report surveys, assesses, and analyzes commercially available and open-source tools that can support the assessment and evaluation of power electronics in future grids with high penetration levels. The study includes aspects that range from power flow analysis to dynamics evaluation (including hardware-in-the-loop – HIL testing) for such systems. The challenges and gaps associated with the current generation of toolsets available to assess the technical impact of introducing high penetration of power electronics are reported. The method is summarized in Figure ES-1.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Control and Management of Multiple Converters in a Residential Smart Grid

Power electronic systems are becoming a staple building block for electric grid networks. However, these systems have been largely designed to grid integrate in ad hoc configurations with little or no coordination of control. This work proposes a central controller and power electronic system hardware and software design for a residential system (photovoltaic, energy storage and residential building). This system supports auto-integration, plug and play capabilities, and optimal energy management to meet different use cases. Modelling and multi-day testing of the system have been conducted in a controller hardware-in-the-loop (C-HIL) testbed. Multiple use cases and pricing options have been considered as part of the simulation and testing. Results are presented of these systems as a proof of principle.

Starke, Michael↗
DOE OSTI2021

Passive cooling designs to improve heat resilience of homes in underserved and vulnerable communities

Disadvantaged communities face a growing threat to staying safe during heat waves, especially during coincident power outages. This study develops a methodology to evaluate the effectiveness of passive cooling measures (those that operate without power) to improve residential building heat resilience. Building performance is simulated for representative homes and on district scales in two disadvantaged communities in Fresno, California. Eleven passive measures are evaluated using four heat resilience metrics with and without grid power. Results show performance of the mitigation measures varies by building characteristics, surrounding environment, and power scenario. The two most effective measures were installing solar-control window films and adding roof insulation. For pre-1978 single-family homes, these two measures can reduce unmet degree-hours (UDH) indoors by 12% and 11% respectively without grid power, or 28% and 37% with grid power. Their respective UDH reductions at district scale typically range 8% — 20% and 4% — 12% without grid power, or 14% — 44% and 8% — 51% with grid power. Top floors have higher overheating risk than lower floors during extreme heat events with coincident power outages. Natural ventilation can help, reducing UDH by 21% — 26%. The methodology and findings from this study can help cities, communities, and utilities develop effective and targeted strategies to promote greater residential heat resilience.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

A Plug-and-Play Design Suite of Converters for the Electric Grid

Power electronic (PE) systems are increasingly being integrated into the electric grid in a distributed fashion. As the number of installations increases, the challenge of integration of these systems into the grid is also growing. Advanced control features of these systems are often overlooked or neglected due to the complex nature and novelty of these systems. This is also complicated from the vendor point of view as different converter topology integration is usually composed of single system designs with tight coupling and configurations. The presented work discusses a novel architecture for the integration of PE systems with other sources and loads to create a coordinated controllable resource for the grid. The presented work also describes a system level controller to coordinate these systems. 1 Testing results on a hardware in the loop (HIL) platform are provided to show the validity of the proposed architecture and framework.

Starke, Michael↗
DOE OSTI2021

Deadband Voltage Control and Power Buffering for Extreme Fast Charging Station

Voltage fluctuation is one of the most common challenges that electric vehicle charging station (EVCS) may introduce to the power grid. Local reactive power compensation (Q-compensation) capability of bi-directional electric vehicle (EV) chargers can mitigate the steady-state voltage violations caused by the EV charging itself or changes in the neighboring loads. Power buffering, using energy storage system (ESS), can be utilized to address the voltage transients (sags and swells) as a result of EV charging at the EVCS. To address PI controller’s ‘hunting’ issue, this paper proposes a Q-sign triggered deadband voltage control (V-control) method at the point of common coupling (PCC). In addition, to ensure the ramp rate of EV charging is within the allowable limits set forth by the grid code, a ramp rate control is proposed that uses the ESS as a ‘power buffer’. Lastly, different from most reported work in the literature where no explicit limit of the power electronic converters (PECs) is considered, this work considers a reasonable apparent power capacity limit of the PECs when achieving the V-control. This limit also affects the amount of active power that can be obtained from the grid, and subsequently may require ESS to function as ‘load sharing’ device to provide supplemental active power to satisfy EV load. A case study simulated in MATLAB (interfaced with PLECS) is presented to demonstrate the effectiveness of the proposed approaches for EVCS operation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

FNET/GridEye: A Tool for Situational Awareness of Large Power Interconnetion Grids

This paper gives an overview of a wide-area measurement system deployed at the distribution level: FNET/GridEye, which consists of hundreds of sensors, communication and a data center. The sensors are utilized to take frequency, voltage phase angle and magnitude measurements from the ordinary 110 or 220V outlets in offices or residential houses. These measurements are continuously transmitted to the data center via Internet. Many applications have been implemented to monitor large-scale interconnected power grids, and interpret grid operation status to improve system operators' situational awareness capability. Some representative online and offline applications are presented in this paper, as well as several recently developed new applications.

Zhu, Lin↗
DOE OSTI2026

Differential Privacy in Grid Kitchen: Implementation & Software Documentation

Sharing of power grid feeder models faces significant challenges due to the potential risk of exposing sensitive operational information. Traditional anonymization techniques have shown notable limitations in other sensitive domains, as evidenced by documented re-identification attacks that combine supposedly anonymized datasets with auxiliary information, raising concerns that similar vulnerabilities could affect power grid data. Consequently, there is a pressing need for a more rigorous privacy protection strategy that not only delivers formal mathematical guarantees but also preserves the analytical value of the shared models. To address this challenge, we have enhanced the Grid Kitchen framework by implementing differential privacy mechanisms within the distribution model dehydration pipeline. This implementation carefully calibrates and applies noise to sensitive attributes in feeder models according to configurable privacy levels—low, moderate, and high—each offering different balances between data utility and privacy protection. Our approach uses established noise functions (Gaussian for continuous data and Discrete Laplace for integer values) with parameters carefully calibrated so that the impact of individual data points is effectively masked in the final output. The integration leverages our Noise Catalog, which we developed to categorize feeder model properties by component type, data type, and sensitivity. This catalog guides the application of appropriate noise functions and privacy parameters ($\varepsilon$ and $\delta$) to each attribute, ensuring consistent privacy protection across the model while maintaining its structural integrity and analytical usefulness. This implementation also includes evaluation tools that allow model owners to assess the impact of privacy-preserving transformations before sharing data with external parties. This report provides documentation for the differential privacy capabilities added to the Grid Kitchen project. It includes a primer on differential privacy concepts and their importance in modern data sharing, details the architecture of our implementation, explains the privacy modes and parameter configurations, and offers practical guidance on using the code for applying differential privacy to grid feeder models. Through examples and code snippets, we demonstrate the effective application of these privacy-enhancing technologies, enabling utility operators and researchers to confidently share grid data while protecting sensitive information.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

A Review on Artificial Intelligence (AI) for Stability Assessment: Preprint

Artificial intelligence provides a convenient route for power grid stability assessment. Compared with simulation-based approaches, artificial intelligence can potentially save time on model development and numerical computation in stability assessment. This paper first reviewed existing literature on using artificial intelligence for power grid stability assessment. Then a machine-leaning-based tool is presented and developed to assess power grid transient stability, frequency stability, and small-signal stability. Test results verified the accuracy and effectiveness of the artificial intelligence tool for power grid stability assessment.

artificial intelligence↗
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 OSTI2023

Integrated Resource and System Plannning Based on Energy Costs and Emission Analysis for a Sustainable Future

Efficient integration of renewable energy sources (RES) - such as wind, solar, geothermal, tidal, and wave - into the electric power grids can help improve energy security and contribute toward a sustainable energy future. To date, however, electric power grids are critically dependent on conventional sources, such as fossil fuels. Given the increasing penetration of RES and dynamic loads, there is a need to thoroughly reanalyze the performance of power grids in terms of cost, efficiency, and energy security. This paper presents a comprehensive analysis in terms of energy costs and emissions estimates of dynamic power grids incorporating various power-generating sources.

distributed energy resources↗