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At least 145 records · Page 8

GridDS: Data Science Toolkit for Energy Grid Data

According to the U.S. Energy Information Administration (EIA), the demand for energy is expected to increase 50% by the year 20501. While energy standards, such as the Institute of Electrical and Electronics Engineers (IEEE) Standard 1547, (Basso 2015) and monitoring with wide area management systems (WAMS) (Liu 2017, Zhou 2016) have enabled large scale data collection and storage, the application of this data in mitigating costs associated with increased consumer demand is an ongoing focus for energy research. This ubiquitous data collection presents a promising opportunity for machine learning and data science to improve efficiency of distributed energy resources (DERs). The GridDS software toolkit is designed to leverage advanced metering infrastructure (AMI), outage management systems data (OMS), Supervisory control Data Acquisition (SCADA), and geographic information systems (GIS) to forecast future energy demands and detect incipient grid failures. GridDS is a python software library designed to be modular and generalizable to data recorded by DERs. In adapting to disparate datasets recorded by various WAMS, GridDS provides a range of unique functionality not presently implemented in current WAMS which have highly specific software infrastructure by design. GridDS functionality ranges from data specification and preparation, to training and validation for state of the art machine learning, to interactive data visualization. For data intake, GridDS combines: Pandera: a library for creating data specifications. TimeScaleDB: a postgresSQL database infrastructure for efficient storage of timeseries data. Dataset class: A custom dataset class / interface that ensures modularity between a range of synthetic and live recorded datasets. Is

Ladd, Alexander↗

Datacenter Explorer

An Unreal Engine plugin for visualizing and interacting with hierarchical datacenter infrastructure through JSON configurations.

Greenwood, Scott↗

datasight [SWR-26-045]

This software is an AI-powered data exploration with natural language. datasight connects an AI agent to your database and provides a web UI where you can ask questions in natural language. The agent writes SQL, runs queries, and generates interactive Plotly visualizations. Supports DuckDB, PostgreSQL, SQLite, and Flight SQL databases. Also queries local CSV and Parquet files directly — no database setup required. Supports Anthropic Claude (default), GitHub Models (open source), and Ollama (local) as LLM backends.

Thom, Daniel [National Laboratory of the Rockies (↗

Income Trends among U.S. Residential Rooftop Solar Adopters [Slides]

Berkeley Lab tracks and analyzes solar-adopter demographic characteristics. A central element of this work is an annual report describing income trends of residential solar adopters over time and across geographies. The report is based on household-level income estimates for single-family residential solar adopters across the United States, and is intended to serve as a foundational reference document for policy-makers, industry stakeholders, and other researchers interested in demographic trends among residential solar adopters. The report is published with an accompanying interactive data visualization tool that allows users to further explore the underlying data. In addition to the annual report, Berkeley Lab also conducts targeted topical analyses on issues related to solar-adopter demographics and provides direct analytical support to organizations working to expand access to solar energy among low-to-moderate income households.

14 SOLAR ENERGY↗

Utility-Scale Solar, 2021 Edition: Empirical Trends in Deployment, Technology, Cost, Performance, PPA Pricing, and Value in the United States [Slides]

Berkeley Lab’s “Utility-Scale Solar, 2021 Edition” provides an overview of key trends in the U.S. market, with a focus on 2020. Highlights of this year’s update include: A record of nearly 9.6 GWAC of new utility-scale PV capacity came online in 2020, bringing cumulative installed capacity to more than 38.7 GWAC across 43 states. 89% of all new utility-scale PV capacity added in 2020 uses single-axis tracking. Median installed project costs declined to $\$$1.4/WAC (or $\$$1.1/WDC) in 2020. Project-level capacity factors vary widely, from 9% to 36% (on an AC basis), with a sample median of 24%. The report explores drivers of this variation. Utility-scale PV’s LCOE fell to $\$$34/MWh in 2020 ($\$$28/MWh if factoring in the federal investment tax credit, or ITC). PPA prices have largely followed the decline in solar’s LCOE over time, but have stagnated more recently. Prices from a sample of recent contracts average just above $\$$20/MWh (levelized). In 2020, solar’s average market value (defined in the report to include only energy and capacity value) exceeded average wholesale prices in 12 of the 17 balancing authorities analyzed (including 4 of the 7 independent system operators across the United States). Adding battery storage is one way to increase the value of solar. Our public data file tracks metadata for more than 150 PV+battery hybrid projects that are already online or that have secured offtake arrangements. At the end of 2020, there were at least 460 GW of utility-scale solar power capacity within the interconnection queues across the nation, 160 GW of which include batteries. For more information, and to explore related interactive data visualizations, go to utilityscalesolar.lbl.gov.

14 SOLAR ENERGY↗

Utility-Scale Solar, 2022 Edition: Empirical Trends in Deployment, Technology, Cost, Performance, PPA Pricing, and Value in the United States [Slides]

Berkeley Lab’s “Utility-Scale Solar, 2022 Edition” provides an overview of key trends in the U.S. market, with a focus on 2021. Highlights of this year’s update include: -A record of nearly 12.5 GWAC of new utility-scale PV capacity came online in 2021, bringing cumulative installed capacity to more than 51.3 GWAC across 44 states. -90% of all new utility-scale PV capacity added in 2021 uses single-axis tracking. -Median installed project costs declined to $\$1.35$/WAC (or $\$1.02$/WDC) in 2021. -Project-level capacity factors vary widely, from 9% to 35% (on an AC basis), with a sample median of 24%. The report explores drivers of this variation. -Utility-scale PV’s LCOE fell to $\$33$/MWh in 2021 ($\$27$/MWh if factoring in the federal investment tax credit, or ITC). -PPA prices have largely followed the decline in solar’s LCOE over time, but have recently stagnated and even moved slightly higher. Prices from a sample of recent contracts average around $\$20$/MWh (levelized) in the West and $\$30-40$/MWh elsewhere in the continental US. In 2021, solar’s average market value (defined in the report to include only energy and capacity value) rose by 55% to $\$47$/MWh and exceeded average wholesale prices in 13 of the 17 balancing authorities analyzed. -Adding battery storage is one way to increase the value of solar. Our public data file tracks metadata and PPA prices from 67 PV+battery hybrid projects that are already online or that have secured offtake arrangements. -At the end of 2021, there were at least 674 GW of utility-scale solar power capacity within the interconnection queues across the nation, 284 GW of which include batteries. For more information, and to explore related interactive data visualizations, go to utilityscalesolar.lbl.gov.

14 SOLAR ENERGY↗

Utility-Scale Solar, 2023 Edition: Empirical Trends in Deployment, Technology, Cost, Performance, PPA Pricing, and Value in the United States [Slides]

Berkeley Lab’s “Utility-Scale Solar, 2023 Edition” presents analysis of empirical plant-level data from the U.S. fleet of ground-mounted photovoltaic (PV), PV+battery, and concentrating solar-thermal power (CSP) plants with capacities exceeding 5 MWAC (PV plants of 5 MWAC or less, including residential rooftop systems, are covered separately in Berkeley Lab’s companion annual report, Tracking the Sun). Highlights of this year’s update include: -10.4 GWAC of new utility-scale PV capacity came online in 2022, bringing cumulative installed capacity to more than 61.7 GWAC across 46 states. -94% of all new utility-scale PV capacity added in 2022 uses single-axis tracking. -Median installed project costs declined to $\$1.32$/WAC (or $\$1.07$/WDC) in 2022. -Plant-level capacity factors vary widely, from 9% to 35% (on an AC basis), with a sample median of 24%. The report explores drivers of this variation. -Utility-scale PV’s LCOE fell to $\$39$/MWh in 2022 ($\$29$/MWh if factoring in the federal investment tax credit, or ITC). -PPA prices have largely followed the decline in solar’s LCOE over time, but have recently stagnated and even moved slightly higher. Prices from a sample of recent contracts average around $\$20-30$/MWh (levelized) in the West and $\$30-40$/MWh elsewhere in the continental US. -In 2022, solar’s average market value (defined in the report to include only energy and capacity value) rose by 40% to $\$71$/MWh and exceeded average wholesale prices in 4 of the 7 ISOs/RTOs and 11 of 18 other balancing authorities analyzed. -Adding battery storage is one way to increase the value of solar. Our public data file tracks metadata and PPA prices from ~100 PV+battery hybrid projects that are already online or that have secured offtake arrangements. -the end of 2022, there were at least 947 GW of utility-scale solar power capacity within the interconnection queues across the nation, 456 GW of which include batteries. For more information, and to explore related interactive data visualizations, go to utilityscalesolar.lbl.gov.

14 SOLAR ENERGY↗

Utility-Scale Solar, 2024 Edition: Empirical Trends in Deployment, Technology, Cost, Performance, PPA Pricing, and Value in the United States [Slides]

Berkeley Lab’s “Utility-Scale Solar, 2024 Edition” presents analysis of empirical plant-level data from the U.S. fleet of ground-mounted photovoltaic (PV), PV+battery, and concentrating solar-thermal power (CSP) plants with capacities exceeding 5 MWAC (PV plants of 5 MWAC or less, including residential rooftop systems, are covered separately in Berkeley Lab’s companion annual report, Tracking the Sun). Key findings from this year’s report include: -18.5 GWAC of new utility-scale PV capacity came online in 2023, bringing cumulative installed capacity to more than 80.2 GWAC across 47 states. Installed costs continued to fall in 2023. Relative to 2022, capacity-weighted averages decreased by 8% to -$\$1.43$/WAC (or $\$1.08$/WDC). Costs, based on a 7.1 GWAC sample of 76 plants completed in 2023, have fallen by 75% (averaging 10% annually) since 2010. Plant-level capacity factors vary widely, from 6% to 36% (on an AC basis), with a sample median of 24%. -Levelized cost of energy (LCOE) of new 2023 projects increased slightly to $\$46$/MWh prior to the application of tax credits but continued to fall to $\$31$/MWh when accounting for federal incentives. PPA prices have largely followed the decline in solar’s LCOE over time, but newly signed longer-term PPA prices have increased since 2021, to an average of $\$35$/MWh (levelized, in 2023 dollars). -Solar’s average energy and capacity value (i.e., ability to offset costs of other power generation sources) across the U.S. was $\$45$/MWh in 2023. Solar’s average market value was lowest in CAISO ($\$27$/MWh), the market with the greatest solar generation share, and highest in ERCOT ($\$67$/MWh). -Newer solar projects had greater market value in 2023 than their generation costs, yielding $\$1.1$ billion in benefits. Projects built in 2022 delivered on average $\$15$/MWh more market value than their costs in 2023. -Solar’s combined value from wholesale electricity markets, public health and climate damage reduction were greater than generation costs and incentives, yielding $\$13.7$ billion in net benefits in 2023. We estimate U.S. health benefits of $\$24$/MWh and reduced global climate damages of $\$101$/MWh. -Adding battery storage is one way to increase the value of solar. Deployment of 52 new PV+battery hybrid plants set a record with 5.3 GW installed in 2023. Our public data file tracks metadata and PPA prices from more than 100 PV+battery hybrid projects that are already online or that have secured offtake arrangements. -Looking ahead, a massive pipeline of at least 1,085 GW of solar capacity dominates the nation’s interconnection queues at the end of 2023. Nearly 571 GW, or 53%, of that total was paired with a battery – in CAISO it was a staggering 98%. Historically only 10% of the requested solar capacity is built. -For more information, and to explore related interactive data visualizations, go to utilityscalesolar.lbl.gov.

14 SOLAR ENERGY↗

The Dark Energy Survey Data Release 2

In this work, we present the second public data release of the Dark Energy Survey, DES DR2, based on optical/near-infrared imaging by the Dark Energy Camera mounted on the 4 m Blanco telescope at Cerro Tololo Inter-American Observatory in Chile. DES DR2 consists of reduced single-epoch and coadded images, a source catalog derived from coadded images, and associated data products assembled from 6 yr of DES science operations. This release includes data from the DES wide-area survey covering ~5000 deg 2 of the southern Galactic cap in five broad photometric bands, grizY. DES DR2 has a median delivered point-spread function FWHM of g = 1.11'', r = 0.95'', i = 0.88'', z = 0.83'', and Y = 0farcs90, photometric uniformity with a standard deviation of < 3 mmag with respect to Gaia DR2 G band, a photometric accuracy of ~11 mmag, and a median internal astrometric precision of ~27 mas. The median coadded catalog depth for a 1farcs95 diameter aperture at signal-to-noise ratio = 10 is g = 24.7, r = 24.4, i = 23.8, z = 23.1, and Y = 21.7 mag. DES DR2 includes ~691 million distinct astronomical objects detected in 10,169 coadded image tiles of size 0.534 deg2 produced from 76,217 single-epoch images. After a basic quality selection, benchmark galaxy and stellar samples contain 543 million and 145 million objects, respectively. These data are accessible through several interfaces, including interactive image visualization tools, web-based query clients, image cutout servers, and Jupyter notebooks. DES DR2 constitutes the largest photometric data set to date at the achieved depth and photometric precision.

79 ASTRONOMY AND ASTROPHYSICS↗

GRIP: Constraint-based Explanation of Missing Answers for Graph Queries

Abstract: A useful feature in graph query engines is to clarify “Why certain entities (nodes, attribute values or edges) are missing” in query answers. This task is even more challenging when the relevant data is already missing in the underlying data source. Missing data, on the other hand, can be inferred by enforcing data constraints for graphs. We demonstrate GRIP, a system that exploits data constraints to clarify missing answers for graph queries. (1) Constraint-based ex- planation. Given a desired yet missing entity in the query answer, GRIP ensures to generate finite and minimal sequences of data con- strains (an “explanation”) that should be consecutively enforced to ?? to ensure its occurrence for the same query. (2) Answering “why” and“how” questions. Users can query GRIP with both“Why”(“Why” the element is missing) and “How” questions (“How” to refine the graph to include the missing answer). GRIP engine supports run- time generation of explanations by incrementally maintaining a set of bi-directional search trees. (3) Interactive exploration. GRIP provides a user-friendly GUI to support interactive ad visual exploration of explanations, including both automated generation and step-by-step inspection of graph manipulations.

graphs↗

Human-system Interface Style Guide for ACORN Control System

The purpose of this style guide is to provide a clear, consistent framework for the design and development of human system interfaces (HSIs) used throughout the Fermilab accelerator complex. It establishes a shared visual and interaction foundation to ensure that interfaces remain intuitive, effective, and cohesive, regardless of when or by whom they are developed. By adhering to these guidelines, developers can avoid introducing unnecessary deviations that compromise usability or increase system training burden. This consistency is especially critical in long-term, multi-contributor projects where interface continuity and maintainability are paramount. This document serves as a practical reference for all HSI development activities related to the accelerator control environment. While the guidance provided is comprehensive, it is not exhaustive of every potential design scenario. As such, the style guide is intended to function as a living document, subject to regular review and revision. Updates will be made at least annually to incorporate emerging best practices, operational feedback, and evolving system needs. Areas where detailed guidance is still under development are clearly indicated in gray throughout the document and will be addressed in future revisions according to project priorities.

Hill, Rachael [Idaho Natl. Lab.] (ORCID:0000000263↗

In-Situ Visualization of Long-Range Defect Interactions at the Edge of Melting

Connecting a bulk material’s microscopic defects to its macroscopic properties is an age-old problem in materials science. Long-range interactions between dislocations (line defects) are known to play a key role in how materials deform or melt, but we lack the tools to connect these dynamics to the macroscopic properties. We introduce time-resolved dark-field X-ray microscopy to directly visualize how dislocations move and interact over hundreds of micrometers, deep inside bulk aluminum. With real-time movies, we reveal the thermally activated motion and interactions of dislocations that comprise a boundary, and show how weakened binding forces inhomogeneously destabilize the structure at 99% of the melting temperature. Connecting dynamics of the microstructure to its stability, we provide important opportunities to guide and validate multiscale models that are yet untested.

36 MATERIALS SCIENCE↗

Immersive Digital Twins and Decision Making

This work focuses on developing and implementing immersive digital twins to support decision-making processes in construction automation and energy simulation analysis. The use of digital twins enables users to explore and interact with virtual replicas of physical systems, providing a unique and valuable perspective for problem-solving. In the context of construction automation, we present two case studies that integrate immersive visualization and worker-machine interaction. In the context of energy simulation analysis using digital twins, we describe our digital twin framework that combines interactive visualization with machine learning techniques to approximate computationally expensive simulations, allowing for faster and more efficient analysis of complex energy systems. Through these research endeavors, we demonstrate the potential of immersive digital twins in decision-making across various domains.

digital twins↗

Interactive Web Application for Traffic Simulation Data Management and Visualization

As traffic simulation software becomes more effective for realistically simulating and analyzing traffic dynamics and vehicle interactions on the mesoscopic and microscopic level, the management, dissemination, and collaborative visualization of traffic simulation results produced by individual transportation planners presents a significant challenge. Existing online content management systems have a very limited capability in allowing users to query specific traffic simulation scenarios and geospatially visualize simulation results through shareable and interactive web interfaces. This paper presents a web-based application for promoting the archiving, sharing, and visualization of large-scale traffic simulation outputs. The application is developed to enhance cyber-physical controls, communications, and public education for collaborative transportation planning. Unique features of the web application include: (a) allowing users to upload their new traffic simulation scenarios (parameters and outputs), as well as search existing scenarios using easily accessible interfaces; (b) optimizing simulation output files with heterogeneous data formats and projected coordinate systems for web-based storage and management using a scalable and searchable data/metadata standard; (c) standardizing user-uploaded simulation outputs using web interfaces and data processing libraries with parallel computing capacity; and (d) providing shareable web visual interfaces for visualizing the traffic flow and signal information stored in simulation outputs (e.g., regional traffic patterns and individual vehicle interactions) and visually comparing multiple simulation outputs both spatially and temporally. Furthermore, the paper presents the conceptual design and implementation of this application, and demonstrates the application’s performance for sharing, comparing, and visualizing simulation outputs from VISSIM and SUMO, two commonly used traffic simulation software programs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

I ntera C hem : Exploring Excited States in Virtual Reality with Ab Initio Interactive Molecular Dynamics

InteraChem is an ab initio interactive molecular dynamics (AI-IMD) visualizer that leverages recent advances in virtual reality hardware and software, as well as the graphical processing unit (GPU)-accelerated TeraChem electronic structure package, in order to render quantum chemistry in real time. We introduce the exploration of electronically excited states via AI-IMD using the floating occupation molecular orbital-complete active space configuration interaction method. The optimization tools in InteraChem enable identification of excited state minima as well as minimum energy conical intersections for further characterization of excited state chemistry in small- to medium-sized systems. We demonstrate that finite-temperature Hartree–Fock theory is an efficient method to perform ground state AI-IMD. InteraChem allows users to track electronic properties such as molecular orbitals and bond order in real time, resulting in an interactive visualization tool that aids in the interpretation of excited state chemistry data and makes quantum chemistry more accessible for both research and educational purposes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Vis-SAGA: Visual Analytics for Situational Awareness of Grid Anomalies: Preprint

We describe supporting near real-time situational awareness of the electric distribution system by visualizing novel data from voltage sensors deployed on existing broadband cable television network equipment. Our scalable web-based visual analytics platform supports interactive geospatial exploration, time-series analysis, and summarization of grid behavior during potentially anomalous events. The broadband cable television sensor network provides observability of the electrical distribution system at a higher local spatial resolution than is typically available to most utilities, revealing the operational state of the network and aiding in the detection of abnormal behaviors or deviations from expected patterns, particularly across electric utility service areas. We outline the design and development of interactive geospatial and time-series visualization components and the scalable data services that supply metadata, historical, and real-time streams of sensor data across the network. We evaluate our platform during periods of extreme weather, demonstrating its ability to assist in detecting patterns of operation that affect power availability, quality, resiliency, and service restoration.

cable television↗