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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Modeling Grid Data Flows for Transmission and Distribution Operations: Review, Design, Next Steps

Operational scenarios of the power grids grow multifold to accommodate the diverse needs of both the utilities and end consumers, and the various other stakeholders in-between. To comprehensively model and apply analytics to support objectives and business functions of grid sectors, a reliable approach to characterize and design data flows is crucial. The flows bridge business functions with communications protocols, stakeholders such as the grid actors, and data interfaces comprising different data objects. Additionally, constraints applied to the flow such as cybersecurity, trust, privacy, and ownership among others intersect these entities, requiring the delineation of their interactions under different scenarios. This paper aims to not only highlight relevant research in the space of grid data flows, but also proposes, for the transmission-distribution sector, a novel modeling approach that marries the aforementioned entities: objectives, business functions, data interfaces, communication protocols, data stakeholders, and flow constraints. It elaborates on the design philosophy and the significance of each entity within the model and applies it to an example function of fault location, isolation and service restoration (FLISR). Finally, the next steps to extend the application of this data flow model for other practical operational scenarios are discussed.

Sundararajan, Aditya [ORNL] (ORCID:000000033577854↗
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 OSTI2020

PMU and Communication Infrastructure Restoration for Post-Attack Observability Recovery of Power Grids

This paper is concerned about recovering the observability of cyber-physical power grids after massive cyber attacks, which helps to achieve the cyber-physical resilience of the grids. For recovering the observability of the grid, the measurability of the Phasor Measurement Units (PMUs) and the connectivity of the communication network are needed to be restored. The PMU and communication infrastructure restoration problem is jointly formulated as a Mixed Integer Linear Programming (MILP) problem to minimize the observability loss of the grid over time after a cyber attack while considering the constraint of limited resources. The efficacy of the proposed optimal restoration strategy is verified by comparing with heuristic methods on the IEEE 57-bus system.

cyber security↗
DOE OSTI2026

Understanding Regional Inertia Dynamics in CAISO from Real Grid Disturbances

The shift from synchronous generators to inverter-based resources has caused power system inertia to be unevenly distributed across power grids. As a result, certain grid regions are more vulnerable to high rate-of-change of frequency (RoCoF) during disturbances. This paper presents a measurement-based framework for estimating grid inertia in CAISO (California Independent System Operator) region using real disturbance-driven frequency data from the Frequency Monitoring Network (FNET/GridEye). By analyzing confirmed disturbances from 2013 to 2024, we identify trends in regional inertia and frequency dynamics, highlighting their relationship with renewable generation and the evolving duck curve. Regional RoCoF values were up to six times higher than interconnection-wide values, coinciding with declining inertia. Recent recovery in inertia is attributed to the increased deployment of battery energy storage systems with synthetic inertia capabilities. These findings underscore the importance of regional inertia monitoring, strategic resource planning, and adaptive operational practices to ensure grid reliability amid growing renewable integration.

Dulal, Saurav [University of Tennessee, Knoxville ↗
DOE OSTI2024

Multi-Agent Graph-Attention Deep Reinforcement Learning for Post-Contingency Grid Emergency Voltage Control

Grid emergency voltage control (GEVC) is paramount in electric power systems to improve voltage stability and prevent cascading outages and blackouts in case of contingencies. While most deep reinforcement learning (DRL)-based paradigms perform single agents in a static environment, real-world agents for GEVC are expected to cooperate in a dynamically shifting grid. Moreover, due to high uncertainties from combinatory natures of various contingencies and load consumption, along with the complexity of dynamic grid operation, the data efficiency and control performance of the existing DRL-based methods are challenged. To address these limitations, we propose a multi-agent graph-attention (GATT)-based DRL algorithm for GEVC in multi-area power systems. Here, we develop graph convolutional network (GCN)-based agents for feature representation of the graph-structured voltages to improve the decision accuracy in a data-efficient manner. Furthermore, a cutting-edge attention mechanism concentrates on effective information sharing among multiple agents, synergizing different-sized subnetworks in the grid for cooperative learning. We address several key challenges in the existing DRL-based GEVC approaches, including low scalability and poor stability against high uncertainties. Test results in the IEEE benchmark system verify the advantages of the proposed method over several recent multi-agent DRL-based algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

SiC-Based 5-kV Universal Modular Soft-Switching Solid-State Transformer (M-S4T) for Medium-Voltage DC Microgrids and Distribution Grids

Medium-voltage DC (MVDC) grids are attractive for electric aircraft and ship power systems, battery energy storage system (BESS), fast charging electric vehicle (EV), etc. Such EV or BESS applications need isolated bidirectional MVDC to LVDC or LVAC converters. However, the existing Si-based solutions cannot fulfill the requirements of a high-efficiency and robust converter for MVDC grids. This paper presents a 5 kV SiC-based universal modular solid-state transformer (SST). This universal current-source SST can interface either a LVAC or LVDC grid with a MVDC grid in single-stage power conversion, while the conventional dual active bridge (DAB) converter needs an additional inverter. The proposed SST module using 3.3 kV SiC MOSFETs and diodes is bidirectional and can serve as a building block in series or parallel for higher-voltage higher-power systems. The topology of each module is based on the soft-switching solid-state transformer (S4T) with reduced conduction loss, which features reduced EMI through controlled dv/dt, and high efficiency with full-range ZVS for main devices and ZCS for auxiliary devices. Operation principle of the modular S4T (M-S4T), capacitor voltage balancing control between the cascaded modules, design of components including a medium-voltage (MV) medium-frequency transformer (MFT) to realize a 50 kVA 5 kV DC to 600 V DC or 480 V AC M-S4T are presented. Importantly, the MV MFT prototype achieves very low leakage inductance (0.13%) and 15 kV insulation with coaxial cables and nanocrystalline cores. Here, the proposed universal modular SST is compared against the DAB solution and verified with DC-DC and DC-AC simulation and 4 kV experimental results. Significantly, the MV experimental results of a modular DC transformer with each module at MVDC are rarely covered in the literature and reported for the first time.

14 SOLAR ENERGY↗
DOE OSTI2020

Distributed Conditions for Small-signal Stability of Power Grids and Local Control Design

Operating modern power grids with stability guarantees is markedly important. Typical methods for analyzing and certifying power grid stability are largely centralized relying on the ability of the system operator to gather network-wide information and accurately compute the system's eigenvalues. These methods are oftentimes not privacy-preserving and computationally burdensome. They are therefore, not well-suited to modern power grids where small-signal stability has to be evaluated timely, efficiently and in a privacy-preserving fashion. Herein, we introduce a distributed methodology for certifying small-signal stability of power grids and designing the local controllers. First, we analytically derive distributed conditions for network-wide stability that bus agents can inspect using local information. By leveraging these conditions, we then introduce a distributed control design algorithm (DCDA) that can guide the local control design so that stability of the interconnected system is guaranteed. The agents that adopt the proposed distributed algorithm are responsible for tuning their local controllers, producing their local control commands and ensuring that their local stability condition is met. The system operator is only responsible for verifying network-wide stability upon receiving affirmative responses from all agents and, announcing, that the overall system is stable. The proposed DCDA algorithm is numerically validated via simulations using the IEEE 39-bus system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Modeling and Impact of Hyperloop Technology on the Electricity Grid

This paper provides an overview of hyperloop technologies, including a brief discussion of key components of a hyperloop system that determine the electric power requirements as a function of time. At-scale hyperloop systems do not yet exist, so a model was used to generate a set of load profiles for conceptual hyperloop realizations at four locations in the United States: two systems in California and one each in Colorado and Ohio. In all four cases, the modeled hyperloop load profiles included pulses in both active and reactive power. Grid modeling was performed to estimate the grid impacts and for dis-cussing grid integration challenges. The paper discusses how energy storage systems might be used to eliminate the pulsating load characteristics or significantly reduce it to accommodate current grid planning guidelines

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning

As power systems are undergoing a significant transformation with more uncertainties, less inertia and closer to operation limits, there is increasing risk of large outages. Thus, there is an imperative need to enhance grid emergency control to maintain system reliability and security. Towards this end, great progress has been made in developing deep reinforcement learning (DRL) based grid control solutions in recent years. However, existing DRL-based solutions have two main limitations: 1) they cannot handle well with a wide range of grid operation conditions, system parameters, and contingencies; 2) they generally lack the ability to fast adapt to new grid operation conditions, system parameters, and contingencies, limiting their applicability for real-world applications. Here, in this paper, we mitigate these limitations by developing a novel deep meta-reinforcement learning (DMRL) algorithm. The DMRL combines the meta strategy optimization together with DRL, and trains policies modulated by a latent space that can quickly adapt to new scenarios. We test the developed DMRL algorithm on the IEEE 300-bus system. We demonstrate fast adaptation of the meta-trained DRL polices with latent variables to new operating conditions and scenarios using the proposed method, which achieves superior performance compared to the state-of-the-art DRL and model predictive control (MPC) methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Control Design and EMT Simulation Toward the Restoration of Faulted Wind-Dominant Grids

Present power engineers have been challenged to employ electromagnetic transient (EMT) simulations to study the restoration of wind-dominant grids. This paper addresses this hurdle by developing strategies to: (i) reliably control the dc-link voltage of grid-forming wind turbines with dc-coupled batteries, (ii) tune controllers to stably interconnect wind power plants into a grid under recovery, and (iii) autonomously make restoration decisions if transmission faults occur during restoration. These contributions are assessed via EMT simulations of wind-dominant versions of the WSCC 9-bus grid and a 22-bus power system. Furthermore, this paper is significant to address recommendations by the North American Electric Reliability Corporation.

17 WIND ENERGY↗
DOE OSTI2020

A Numerical Study of Hypoxia in Chesapeake Bay Using an Unstructured Grid Model: Validation and Sensitivity to Bathymetry Representation

A three-dimensional unstructured-grid hydrodynamic and water quality model (Semi-implicit Cross-scale Hydroscience Intergrated System Model-Integrated Compartment Model) is applied successfully for Chesapeake Bay. The model is validated with observations of salinity, chlorophyll-a, dissolved oxygen, nutrients, and phytoplankton productions from the year 1991 to 1995 for the mainstem and some major tributaries, based on multiple model skill scores. Model experiments are conducted to test the importance of having (1) an accurate representation of bathymetry to correctly predict hypoxia and other processes and (2) a high-resolution model grid for tributaries to correctly simulate water quality variables. Further, comparison with the model experiment results with bathymetry smoothing indicates that bathymetry smoothing, as commonly used for many systems, changes the stratification and lateral circulation pattern, resulting in more salt intrusion into shallow water regions, and an increase in the freshwater age. Consequently, a model with bathymetry smoothing can lead to an unrealistic prediction of the distribution of hypoxia and phytoplankton production. Local grid refinement shows significant improvement of model simulations on local stratification and water quality variables. Overall, the use of high-resolution unstructured grid model leads to a faithful representation of the complex geometry, and thus a seamless cross-scale capability for simulating water quality processes in the Bay including tributaries and tidal creeks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Improving Trustworthiness of Data-Driven Power Grid Contingency Analysis With Bayesian Residual Graph Neural Networks

The evolving energy landscape requires novel tools to efficiently perform contingency analysis and reliability assessment of power grids, potentially in real-time. The high computational cost of traditional power flow solvers limits their applicability in practice. Machine learning (ML) surrogates such as deep neural networks (NNs) accelerate power flow solvers computations, enabling high-order contingency analysis and real-time decision-making by learning highly nonlinear functions and integrating grid topology via graph architectures. However, (graph) NNs lack predictive power away from training data and do not provide predictive confidence estimates. Here, we present a Bayesian residual graph NN that integrates knowledge from low-fidelity data via residual training and embeds granular quantification of uncertainties, improving trustworthiness critical for high-consequence decision-making. Applying Bayesian concepts to NNs is challenging due to the high-dimensionality of both the parameter space, complicating derivation of a meaningful prior, and the output space in large grid systems, requiring enhanced techniques to assess the predicted high-dimensional uncertainties. Our contributions include: (1) Deriving a prior for fully connected and graph NNs that leverages low-fidelity data to guide mean predictions and appropriately control prior predictive uncertainty. (2) Integrating this prior within an ensembling with anchoring scheme for efficient approximate posterior inference. (3) Deriving enhanced metrics to assess accuracy of both the mean and uncertainty predictions in high dimensions, appropriately accounting for correlations propagated through graph layers. The resulting Bayesian residual graph NN is tested on a contingency analysis task for 14-bus and 118-bus grids.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Reducing Operator Complexity of Galerkin Coarse-grid Operators with Machine Learning

Here, we propose a data-driven and machine-learning-based approach to compute non-Galerkin coarse-grid operators in multigrid (MG) methods, addressing the well-known issue of increasing operator complexity. Guided by the MG theory on spectrally equivalent coarse-grid operators, we have developed novel machine learning algorithms that utilize neural networks combined with smooth test vectors from multigrid eigenvalue problems. The proposed method demonstrates promise in reducing the complexity of coarse-grid operators while maintaining overall MG convergence for solving parametric partial differential equation problems. Numerical experiments on anisotropic rotated Laplacian and linear elasticity problems are provided to showcase the performance and comparison with existing methods for computing non-Galerkin coarse-grid operators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Power Grid Behavioral Patterns and Risks of Generalization in Applied Machine Learning

Recent years have seen a rich literature of data-driven approaches designed for power grid applications. However, insufficient consideration of domain knowledge can impose a high risk to the practicality of the methods. Specifically, ignoring the grid-specific spatiotemporal patterns (in load, generation, and topology, etc.) can lead to outputting infeasible, unrealizable, or completely meaningless predictions on new inputs. To address this concern, this paper investigates real-world operational data to provide insights into power grid behavioral patterns, including the time-varying topology, load, and generation, as well as the spatial differences (in peak hours, diverse styles) between individual loads and generations. Then based on these observations, we evaluate the generalization risks in some existing ML works caused by ignoring these grid-specific patterns in model design and training.

Li, Shimiao↗
DOE OSTI2022

Grid-tied Multilevel Inverter with Phase-locked Loop Algorithm

A multilevel inverter is an electronic device capable of changing direct current energy to alternant current energy with a voltage and frequency established by the user. They are ideal for connecting renewable energy sources to the AC grid, energy plants, and smart grids. The voltage must be balanced and synchronized with the electrical network for adequate performance. This paper shows the voltage synchronization between an inverter output voltage and the AC grid using a phase-locked loop based on an adaptive observer. The proposed algorithm can perform under grid uncertainties such as noise and generates a reference signal for the modulation used in the inverter. The algorithm is robust and computationally efficient and can be implemented through basic elements such as operational amplifiers, resistors, and capacitors, reducing its difficulty in executing it in a system.

42 ENGINEERING↗
DOE OSTI2022

Communication Requirements for Price-Based Grid Coordination

Meeting ambitious goals for carbon reduction and supporting an electric grid with high levels of renewable energy supply will require significant flexibility from the demand side. This paper outlines a system architecture and communication technology infrastructure to enable dynamic pricing to be used to finely tune coordination between the grid and its customers. Taking a cue from the success of Internet architecture, this system — Price-Based Grid Coordination — emphasizes simplicity and universality. It enables a wide variety of ways for prices and other signals to pass from the grid to individual flexible loads, including multiple possible locations for the intelligence that combines price signals with device functional needs. The paper includes a reference data model to describe how information from the utility level can be conveyed to customer devices, but independent of any particular protocol. The paper also summarizes technology standards development needs, and reviews research needs to address the full spectrum of coordination scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

ASGarD: Adaptive Sparse Grid Discretization

Many areas of science exhibit physical processes that are described by high dimensional partial differential equations (PDEs), e.g., the 4D, 5D and 6D models describing magnetized fusion plasmas, models describing quantum chemistry, or derivatives pricing. Such problems are affected by the so-called “curse of dimensionality” where the number of degrees of freedom (or unknowns) required to be solved for scales as N D where N is the number of grid points in any given dimension D. A simple, albeit naive, 6D example is demonstrated in the left panel of Figure 1. With N = 1000 grid points in each dimension, the memory required just to store the solution vector, not to mention forming the matrix required to advance such a system in time, would exceed an exabyte - and also the available memory on the largest of supercomputers available today. The right panel of Figure 1 demonstrates potential savings for a range of problem dimensionalities and grid resolution. While there are methods to simulate such high-dimensional systems, they are mostly based on Monte-Carlo methods, which rely on a statistical sampling such that the resulting solutions include noise. Since the noise in such methods can only be reduced at a rate proportional to $\sqrt{N_p}$ where N p is the number of Monte-Carlo samples, there is a need for continuum, or grid/mesh-based methods for high-dimensional problems, which both do not suffer from noise and bypass the curse of dimensionality. We present a simulation framework that provides such a method using adaptive sparse grids.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Determining Utility System Value of Demand Flexibility From Grid-interactive Efficient Buildings

This report focuses on ways current methods and practices that establish the value to electric utility systems of distributed energy resource (DER) investments can be enhanced to determine the value of demand flexibility in grid-interactive efficient buildings that can provide grid services. The report introduces key valuation concepts that are applicable to demand flexibility that these buildings can provide and links to other documents that describe these concepts and their implementation in more detail.The scope of this report is limited to the valuation of economic benefits to the utility system. These are the foundational values on which other benefits (and costs) can be built. Establishing the economic value to the grid of demand flexibility provides the information needed to design programs, market rules, and rates that align the economic interest of utility customers with building owners and occupants. By nature, DERs directly impact customers and provide societal benefits external to the utility system. Jurisdictions can use utility system benefits and costs as the foundation of their economic analysis but align their primary cost-effectiveness metric with all applicable policy objectives, which may include customer and societal (non-utility system) impacts.This report suggests enhancements to current methods and practices that state and local policymakers, public utility commissions, state energy offices, utilities, state utility consumer representatives, and other stakeholders might support. These enhancements can improve the consistency and robustness of economic valuation of demand flexibility for grid services. The report concludes with a discussion of considerations for prioritizing implementation of these improvements.

24 POWER TRANSMISSION AND DISTRIBUTION↗