Towards Adaptive Decision Support: A Perspective from Intelligent and Annotated Visual Analytics for Exploring Big Urban Mobility Data
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Visualization–the use of visual elements to explore data, form hypotheses, or convey conclusions–is an integral part of the scientific process. Starting from an initial exploration of new data to illustrating outcomes to the general public, visualization is one of the most intuitive and powerful modes of communication. With the explosion of new data sources and types, unprecedented volumes of data, and new technologies, such as virtual reality and AI, visualization has become increasingly essential but also ever more challenging. Department of Energy’s (DOE) Office of Advanced Scientific Computing Research (ASCR) sponsored a Basic Research Needs workshop in January 2022 to understand the major opportunities and grand challenges in visualization tools and technologies for scientific computing, with a special focus on DOE-relevant applications and goals. The workshop identified five priority research directions (PRDs) for visualization to support scientific discovery, decision-making, and communication.
In support of the Office of Electricity Energy Storage program, Pacific Northwest National Laboratory (PNNL), hosted a roundtable to explore the relationship between energy equity and energy storage. Flexible and available at any scale, energy storage offers a useful framework and starting point in a larger conversation around energy equity. Through storage, we can imagine more broadly how improvements and investments in the grid can respond to social and health challenges and increase affordability, reliability, and community value in the future. The roundtable featured keynotes and conversations with people leading the charge in transitioning our energy system to a more equitable, affordable, accessible, and sustainable future. This event was presented in collaboration with Sandia National Laboratories.
Distributed wind (DW) hybrid systems, or combinations of distributed energy resources with wind technologies, have the potential to provide energy equity benefits for rural loads. However, project developers and end-users do not understand these benefits well. This report outlines energy equity opportunities that result directly from DW-hybrid projects and proposes a framework that demonstrates how to consider equity in the DW-hybrid system, which can enhance existing resilience and valuation tools by incorporating a way to include equity considerations. Energy equity opportunities achievable with DW-hybrid systems for rural loads are defined by the core principles of energy justice for use in a proposed equity framework. As well, a catalog of equity resources, metrics, technology-agnostic frameworks, and best practices are linked in an attached Excel file. Electric cooperatives, technical assistance providers (including national laboratory and DOE staff), DW project developers, and technology providers can use this work to understand, evaluate, and define equitable outcomes for potential DW-hybrid projects.
In the United States, individual coal plants have high variation in their construction and operation. Coal plants commonly have multiple units, with different commissioning ages, operational position, and maintenance conditions – even multiple fuels. These units may be in changing states of utilization, with variation in productivity depending on economic contexts. Coal plants themselves may be temporarily offline, mothballed, retired, or decommissioned due to owner/operator portfolios or market conditions, and the current posture of the plant may be difficult to ascertain without local news sources (Tarekegne et al. 2021). Anticipated dates of plant closures are prospectively represented to regulators and to the Energy Information Administration (EIA), but may also experience uncertainty due to the engineering, environmental, economic and social complexity of closing a very large power plant. As noted in this report, closure of the first unit to the last within a single plant may span more than 20 years. Yet tracking and predicting the position of our nation’s coal fleets on a national scale is deeply important. In developing a case for federal datasets and an approach to building and curating these data, this report identified that data must have high fidelity at the plant-level to be meaningful in aggregate. Plant-scale detail is also a match for its application: national priorities in federal investment range from economic revitalization for coal plant communities to maintaining reliable electric grid operations. This document is organized into a statistical review of coal plant retirements, past and prospective, in the United States; trends in the relationship between closures and existing federal investment programs for community revitalization; and potential methods for accelerating site redevelopment through advanced geospatial analysis based on federal data.
A global energy transition from fossil energy to clean energy is unfolding. Just transition is an iterative, continuous process of moving from a fossil-fuel economy to a green economy that provides decent, productive, and ecologically sustainable livelihoods; nurtures social dialogue; and equitably distributes costs and benefits of the energy system. It is aimed at supporting affected workers and communities and affirms that the transition is towards a more equitable energy system. Though the Asia-Pacific Economic Cooperation (APEC) region as a whole is largely representative of global energy transition trends, APEC’s member economies – and communities within them – will have their own transition nuances. Approaches to just transition must therefore recognize both the global and the local transition dynamics. The report offers eight just transition principles that can help inform work that assures the current energy transition, and its future iterations, are fair and equitable.
This work presents a detailed case study on using Generative AI (GenAI) to develop AI surrogates for simulation models in fusion energy research. The scope includes the methodology, implementation, and results of using GenAI to assist in model development and optimization, comparing these results with previous manually developed models.
Federal and state decarbonization goals have led to numerous financial incentives and policies designed to increase access and adoption of renewable energy systems. In combination with the declining cost of both solar photovoltaic and battery energy storage systems and rising electric utility rates, residential renewable adoption has become more favorable than ever. However, not all states provide the same opportunity for cost recovery, and the complicated and changing policy and utility landscape can make it difficult for households to make an informed decision on whether to install a renewable system. This paper is intended to provide a guide to households considering renewable adoption by introducing relevant factors that influence renewable system performance and payback, summarized in a state lookup table for quick reference. Five states are chosen as case studies to perform economic optimizations based on net metering policy, utility rate structure, and average electric utility price; these states are selected to be representative of the possible combinations of factors to aid in the decision-making process for customers in all states. The results of this analysis highlight the dual importance of both state support for renewables and price signals, as the benefits of residential renewable systems are best realized in states with net metering policies facing the challenge of above-average electric utility rates. This dataset is intended to allow readers to reproduce and customize the analysis performed in this work to their benefit. Suggested modifications include: location, household load profile, rate tariff structure, and renewable energy system design.
Energy insecurity affects most low-income households in the United States. Energy insecurity, which is characterized by a household’s inability to afford their energy needs, often leads to risky choices, causing other forms of insecurity including food and health. Although there are government programs designed to provide relief to low-income households that face energy insecurity, eligibility for these programs is usually determined by household income, and those with incomes close to the threshold face uncertainty or may be left out. In many cases, these households turn to energy-limiting behaviors as a strategy to lower their electric utility bills. This paper explores the relationship between energy insecurity and energy-limiting behaviors to investigate alternative solutions that target the households that may fall out of available energy assistance programs. We explore the role of battery energy storage systems and rooftop solar photovoltaics in improving energy affordability. The results show that residential rooftop solar and behind-the-meter energy storage can offset two-thirds of the bill savings that households attain through energy-limiting behavior. Household renewable energy systems could complement existing energy assistance programs to provide long-term bill relief, enabling occupants to live in their homes with comfort and dignity. This dataset is intended to allow readers to reproduce and customize the analysis performed in this work to their benefit.
Uninterrupted access to electricity is critical to the safety and security of American households. More frequent and extreme emergency events increase outages across the country, disproportionately impacting vulnerable communities that experience the most frequent and longest outages, are most sensitive to the loss of electric power, and have the least capacity to adapt to these conditions. This study devises a metric, the Electric Vulnerability Index (EVI), and validates this metric against the 2021 Winter Storm Uri in Texas. Though not ubiquitous, similar trends were observed between adjacent areas with higher EVI and those with higher outage rates from this storm. EVI is offered as a viable approach to quantify a population’s vulnerability to electric outages and maps that index across the continental United States to aid policymakers, advocates, and energy system stakeholders in the targeted deployment of resilience solutions, such as energy storage, to communities most in need. This dataset includes the geopackage file containing all relevant attributes used to generate the maps used in the accompanying paper.
Understanding mineral–fluid interactions in shale under supercritical CO 2 (scCO 2 ) conditions is relevant for assessing long-term geochemical containment. This study characterizes mineralogical transformations and elemental redistribution in five Caney Shale samples serving as proxies for reservoir (R1, R2, R3) and caprock (D1, D2) facies, subjected to 30-day static exposure to pure scCO 2 at 60 °C and 17.23 MPa (2500 psi), with no brine or impurities introduced. SEM-EDS analyses were conducted before and after exposure, with mineral phases classified into silicates, carbonates, sulfides, and organic matter. Initial compositions were dominated by quartz (38–47 wt.%), illite (16–23 wt.%), carbonates (12–18 wt.%), and organic matter (8–11 wt.%). Post-exposure, carbonate loss ranged from 15 to 40% in reservoir samples and up to 20% in caprock samples. Illite and K-feldspar showed depletion of Fe 2+ , Mg 2+ , and K + at grain edges and cleavages, while pyrite underwent oxidation with Fe redistribution. Organic matter exhibited scCO 2 -induced surface alteration and apparent sorption effects, most pronounced in R2 and R3. Elemental mapping revealed Ca 2+ , Mg 2+ , Fe 2+ , and Si 4+ mobilization near reactive interfaces, though no secondary mineral precipitates formed. Reservoir samples developed localized porosity, whereas caprock samples retained more structural clay integrity. The results advance understanding of mineral reactivity and elemental fluxes in shale-based CO 2 sequestration.
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Figure data for the paper 'Machine learning enhanced predictions of ICRF heating: overcoming numerical limitations via data curation' accepted in Physics of Plasmas, DOI TBD
This work presents a detailed case study on using Generative AI (GenAI) to develop AI surrogates for simulation models in fusion energy research. The scope includes the methodology, implementation, and results of using GenAI to assist in model development and optimization, comparing these results with previous manually developed models.
Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. A single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time to complete, which is acceptable for many purposes, but too slow for integrated modeling and real-time control applications. The machine learning models use a database of 16,000+ GENRAY/CQL3D simulations for training, validation, and testing. Latin hypercube sampling methods ensure that the database covers the range of 9 input parameters ($n_{e0}$, $T_{e0}$, $I_p$, $B_t$, $R_0$, $n_{||}$, $Z_{eff}$, $V_{loop}$, $P_{LHCD}$) with sufficient density in all regions of parameter space. The surrogate models reduce the inference time from minutes to ~ms with high accuracy across the input parameter space.
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Hinge makes both sides of electrical circuit boards readily accessible for component checkout and servicing. The hinge permits mounting of two circuit boards and incorporates connectors to maintain continuous electrical contact between the components on both boards.