Towards the Design of Grid Cyber-Physical Integrated Security Operations Center Visualizations
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Reyna, Alexander Arthur↗
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2018 Renewable Energy Grid Integration Data Book
The 2018 Renewable Energy Grid Integration Data Book identifies the status, key trends, challenges, and solutions of renewable energy grid integration in a highly visual format. It provides an overview of selected key grid integration metrics that represent complex interactions among generation characteristics, market rules, and environmental and safety factors, which may vary by geography, season, and time of day; and the data book presents metrics that either indicate how much variable renewable energy (VRE) is currently being integrated onto the grid or measure factors that may increase or decrease the challenges associated with integrating VRE generation onto the grid.
24 POWER TRANSMISSION AND DISTRIBUTION↗
A Dynamic Contingency Analysis Visualization Tool
We are developing a web-based visualization that shows the results of running a contingency analysis on a power grid system. Power grid analysists will be able to use this tool to visualize power event simulations and be better prepared for contingencies that may arise. The tool consists of a map, showing the power grid and its current state, and tables and charts showing the current status of various elements on the grid. The user can iterate over a number of cascading contingencies to visualize how the power grid will change under various scenarios.
Contingency analysis, power grid, smart grid, visu↗
CEEP (Cyber-Energy Emulation Platform) [SWR-20-102]
NREL's Cyber-Energy Emulation Platform (CEEP) provides the capability to realize cyber-energy security and resilience through automation and orchestration of virtualized systems and software defined networks for the electric grid. CEEP enables testing and validation of grid-security and -control methodologies as the grid evolves to include smart technologies/systems, such as virtualization and containerization of grid components, software defined networking, simulation and co-simulation frameworks, and hardware in the loop. CEEP is a modular system that can be distributed and deployed across different hardware infrastructure sizes and network architectures. For example, CEEP can visualize, emulate, and/or coordinate the Smart-Grid Network Visualization, Intrusion Detection, and Network Healing system. Using CEEP, intrusion-detection and network-self-healing solutions can be deployed at grid control centers, within secure private clouds, and in cyber-energy appliances.
Vaughan, Evan↗
Cyber Energy Emulation Platform (CEEP) [SWR-20-102]
NREL's Cyber-Energy Emulation Platform (CEEP) provides the capability to realize cyber-energy security and resilience through automation and orchestration of virtualized systems and software defined networks for the electric grid. CEEP enables testing and validation of grid-security and -control methodologies as the grid evolves to include smart technologies/systems, such as virtualization and containerization of grid components, software defined networking, simulation and co-simulation frameworks, and hardware in the loop. CEEP is a modular system that can be distributed and deployed across different hardware infrastructure sizes and network architectures. For example, CEEP can visualize, emulate, and/or coordinate the Smart-Grid Network Visualization, Intrusion Detection, and Network Healing system. Using CEEP, intrusion-detection and network-self-healing solutions can be deployed at grid control centers, within secure private clouds, and in cyber-energy appliances.
Rivera, Joshua↗
Alternatives to Contour Visualizations for Power Systems Data
Electrical grids are geographical and topological structures whose voltage states are challenging to represent accurately and efficiently for visual analysis. The current common practice is to use colored contour maps, yet these can misrepresent the data. We examine the suitability of four alternative visualization methods for depicting voltage data in a geographically dense distribution system - Voronoi polygons, H3 tessellations, S2 tessellations, and a network-weighted contour map. We find that Voronoi tessellations and network-weighted contour maps more accurately represent the statistical distribution of the data than regular contour maps.
MATHEMATICS AND COMPUTING,POWER TRANSMISSION AND D↗
Alternatives to Contour Visualizations for Power Systems Data
Electrical grids are geographical and topological structures whose voltage states are challenging to represent accurately and efficiently for visual analysis. The current common practice is to use colored contour maps, yet these can misrepresent the data. We examine the suitability of four alternative visualization methods for depicting voltage data in a geographically dense distribution system-Voronoi polygons, H3 tessellations, S2 tessellations, and a network-weighted contour map. We find that Voronoi tessellations and network-weighted contour maps more accurately represent the statistical distribution of the data than regular contour maps.
MATHEMATICS AND COMPUTING↗
2016 Renewable Energy Grid Integration Data Book
The 2016 Renewable Energy Grid Integration Data Book identifies the status, key trends, challenges, and solutions of renewable energy grid integration in a highly visual format. This data book provides an overview of selected key grid integration metrics that represent complex interactions among generation characteristics, market rules, and environmental and safety factors, which may vary by geography, season, and time of day; and presents metrics that either indicate how much variable renewable energy (VRE) is currently being integrated onto the grid, or measure factors that may increase or decrease the challenges associated with integrating VRE generation onto the grid.
variable, renewable, grid, integration, wind, sola↗
Automating Natural Disaster Impact Analysis: An Open Resource to Visually Estimate a Hurricane s Impact on the Electric Grid
An ORNL team working on the Energy Awareness and Resiliency Standardized Services (EARSS) project developed a fully automated procedure to take wind speed and location estimates provided by hurricane forecasters and provide a geospatial estimate on the impact to the electric grid in terms of outage areas and projected duration of outages. Hurricane Sandy was one of the worst US storms ever, with reported injuries and deaths, millions of people without power for several days, and billions of dollars in economic impact. Hurricane advisories were released for Sandy from October 22 through 31, 2012. The fact that the geoprocessing was automated was significant there were 64 advisories for Sandy. Manual analysis typically takes about one hour for each advisory. During a storm event, advisories are released every two to three hours around the clock, and an analyst capable of performing the manual analysis has other tasks they would like to focus on. Initial predictions of a big impact and landfall usually occur three days in advance, so time is of the essence to prepare for utility repair. Automated processing developed at ORNL allowed this analysis to be completed and made publicly available within minutes of each new advisory being released.
EARSS↗
Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis
Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.
Explainable AI↗
Demonstration of a Novel Synchrophasor-based Situational Awareness System: Wide Area Power System Visualization, On-line Event Replay and Early Warning of Grid Problems
Since the large North Eastern power system blackout on August 14, 2003, U.S. electric utilities have spent lot of effort on preventing power system cascading outages. Two of the main causes of the August 14, 2003 blackout were inadequate situational awareness and inadequate operator training In addition to the enhancements of the infrastructure of the interconnected power systems, more research and development of advanced power system applications are required for improving the wide-area security monitoring, operation and planning in order to prevent large- scale cascading outages of interconnected power systems. It is critically important for improving the wide-area situation awareness of the operators or operational engineers and regional reliability coordinators of large interconnected systems. With the installation of large number of phasor measurement units (PMU) and the related communication infrastructure, it will be possible to improve the operators’ situation awareness and to quickly identify the sequence of events during a large system disturbance for the post-event analysis using the real-time or historical synchrophasor data. The purpose of this project was to develop and demonstrate a novel synchrophasor-based comprehensive situational awareness system for control centers of power transmission systems. The developed system named WASA (Wide Area Situation Awareness) is intended to improve situational awareness at control centers of the power system operators and regional reliability coordinators. It consists of following main software modules: • Wide-area visualizations of real-time frequency, voltage, and phase angle measurements and their contour displays for security monitoring. • Online detection and location of a major event (location, time, size, and type, such as generator or line outage). • Near-real-time event replay (in seconds) after a major event occurs. • Early warning of potential wide-area stability problems. The system has been deployed and demonstrated at the Tennessee Valley Authority (TVA) and ISO New England system using real-time synchrophasor data from openPDC. Apart from the software product, the outcome of this project consists of a set of technical reports and papers describing the mathematical foundations and computational approaches of different tools and modules, implementation issues and considerations, lessons learned, and the results of lidation processes.
24 POWER TRANSMISSION AND DISTRIBUTION↗
TCF-20-20213: Advanced Power Distribution Sensing and Communications through the Cable TV Broadband Network (Final Report. Period of Performance: June 2020 to June 2022)
To enable the rapid, widespread commercial availability of secondary distribution grid voltage and phase angle data, the DOE OE Technology Commercialization Fund (TCF) project: 1) created a new American National Standard for next-generation grid power quality sensing and communications, 2) created a new prototype broadband-based standard-compliant grid power sensing system for use by utilities and others, and 3) improved by several orders of magnitude the spatiotemporal scale of the in-progress DOE CESER project: Situational Awareness of Grid Anomalies (SAGA) for Visual Analytics.
24 POWER TRANSMISSION AND DISTRIBUTION↗
The Impact of Behind-the-Meter Heterogeneous Distributed Energy Resources on Distribution Grids
The increasing integration of distributed energy resources (DERs) on the electric grid brings new challenges and opportunities for utility grid operations. With the rapid deployment of DERs, there is emerging interest in integrating these controllable devices with utility operations at all levels for monitoring and management. To understand the challenges with increasing behind-the-meter (BTM) DERs and to identify the needs in deploying advanced controls, a comprehensive grid impact study is indispensable. This paper presents the analysis which help visualize the DER impact on the grid, identify the challenges and provides an insight into the new distribution management and control needs to enable reliable and resilient grid operations.
27 ARPA - Advanced Research Projects Agency-Energy↗
FunMC^2: A Filter for Uncertainty Visualization of Marching Cubes on Multi-Core Devices
Visualization is an important tool for scientists to extract understanding from complex scientific data. Scientists need to understand the uncertainty inherent in all scientific data in order to interpret the data correctly. Uncertainty visualization has been an active and growing area of research to address this challenge. Algorithms for uncertainty visualization can be expensive, and research efforts have been focused mainly on structured grid types. Further, support for uncertainty visualization in production tools is limited. In this paper, we adapt an algorithm for computing key metrics for visualizing uncertainty in Marching Cubes (MC) to multi-core devices and present the design, implementation, and evaluation for a Filter for uncertainty visualization of Marching Cubes on Multi-Core devices (FunMC2). FunMC2 accelerates the uncertainty visualization of MC significantly, and it is portable across multi-core CPUs and GPUs. Evaluation results show that FunMC2 based on OpenMP runs around 11× to 41× faster on multi-core CPUs than the corresponding serial version using one CPU core. FunMC2 based on a single GPU is around 5× to 9× faster than FunMC2 running by OpenMP. Moreover, FunMC2 is flexible enough to process ensemble data with both structured and unstructured mesh types. Furthermore, we demonstrate that FunMC2 can be seamlessly integrated as a plugin into ParaView, a production visualization tool for post-processing.
Wang, Jay↗
2016 Renewable Energy Grid Integration Data Book
The 2016 Renewable Energy Grid Integration Data Book identifies the status, key trends, challenges, and solutions of renewable energy grid integration in a highly visual format.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Advanced Grid Operational Technology Edge-Level Threat Detection
This report presents a deployable solution to improve the cybersecurity situational awareness of the legacy SCADA system infrastructure in power grids. The main goal of this project is to provide system owners and operators a highly trusted, intelligent alarm system and comprehensive situational awareness of ongoing or potential cybersecurity threats on the grid network. The key contributions of this project include: (1) the development of software, the Intrusion Detection Visualizer for the Operational Technology Network (IViz-OT), to visualize and locate intrusions on the grid network; (2) testing the signature-based Hybrid Intrusion Detection for Energy Systems (HIDES) for different types of intrusions; (3) the integration of HIDES and IViz-OT into the visualization dashboard; and (4) real-time testing using a hardware-in-the-loop test bed.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Immersive Visualization for Scientific Data Analysis
We will present the use of immersive visualization at the National Renewable Energy Laboratory (NREL), showcasing how immersive visualization is advancing scientific research and engineering practices and transforming our day-to-day operations. We are leveraging immersive visualization to support scientific discovery and engineering in various domains, including material design, computational fluid dynamics, immersive analytics, grid modernization, digital twins, and situated visualization. We have observed several benefits across four key areas: enhanced spatial judgments, improved understanding through interaction, increased capacity to embed high-dimensional data, and improved collaboration.
immersive analytics↗
Online Heatmap Generation with Both High and Low Weights
Heatmap is a common geovisualization method that interpolates and visualizes a set of point observations on a map surface. Most of online web mapping libraries implement a one-pass heatmap algorithm using HTML5 canvas or WebGL for efficient heatmap generation. However, such implementation applies additive operations that accumulate the rendering of point weights on the map surface grid, making it inappropriate for visualizations that require the highlighting of both low and high weights. We introduce \textit{hilomap}, an online heatmap algorithm that highlights surface areas where points with both low and high trends are located. An HTML5 Canvas-based reference implementation on OpenLayers is presented and evaluated.
Liu, Yan↗