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DOE OSTI2018

Energy Efficiency and Infrastructure Upgrades on Nuclear Clean-up Sites: The Hanford Experience - 18641

The Hanford site, located in southeastern Washington, produced much of our nation's weapons-grade plutonium for the Defense nuclear weapons program. This production lasted the better part of four decades with production ending in 1987. Since 1987, the focus of the Hanford site has turned to clean-up activities. Mission Support Alliance (MSA) is the integrator of the multi-contractor effort to clean up the Hanford site. MSA collaborates with the U.S. Department of Energy (DOE) and all Hanford site contractors in finding and implementing new, cost-saving, safe and practical solutions to cleanup challenges with a continual focus on innovative processes and technologies that reduce costs, energy use and bring safety improvements to the site. Support Services include environmental integration and land management; fleet and road maintenance; water/electric and utilities; cyber security and information management as well as the preservation of the site's cultural artifacts. This paper describes the process, challenges, and focus on infrastructure upgrade and energy efficiency projects in the context of a multi-contractor nuclear clean-up site. Project development and new technology deployment at Hanford has long been plagued with economic challenges. For example, despite extensive auditing, analysis, and project development, most of the energy-related projects on the Hanford site have gone unfunded due to the low cost of electricity, the relatively high cost of labor, and costs associated with detailed safety and cultural reviews. - The Hanford site is a direct-service customer of the Bonneville Power Administration (BPA) and has a marginal electricity rate of $0.033/kWh. - Labor costs on the Hanford site track high because of the requirements associated with the safety and security necessary to operate a legacy nuclear weapons complex. - In partnership with three federally recognized and other affected Native American tribes (Yakama Nation, Confederated Tribes of the Umatilla Indian Reservation, the Nez Perce Tribe, and Wanapum People), most activities on the Hanford site are afforded in-depth cultural reviews. To work within these constraints, the MSA Energy Management team has embarked on a three-pronged approach to energy project development: Program Visibility: The MSA Energy Management team has promoted both project opportunities and requirements across the Hanford site. This information comes in the form of site-wide newsletters, group meetings, e-mails, success stories, and recognition of staff. Single-point Analysis: To by-pass the inevitable duplications of effort, learning curve issues, and to improve project consistency/delivery, the MSA Energy Management team serves as the single resource to the site for energy project analysis. This resource includes initial assessment, engineering analysis, efficiency calculation and project recommendations. Contractor/utility Interface: In addition to the infrastructure under its own purview, MSA provides all support services for the contractor/utility interface. These activities include formulation and submission of incentive-based efficiency projects, project tracking, and incentive awards. All incentive awards are forwarded to the relevant contractor and then highlighted in the visibility campaign. Successful energy efficiency projects at Hanford have included high efficiency heating, ventilation, air conditioning (HVAC) systems, interior and exterior lighting/lighting control systems, pump and motor systems, and the right-sizing of transformers and area-wide electrical distribution systems. Prior to the initiation of this program and structure (roughly 3 years ago) the average annual energy efficiency incentive applied for and received was under $10,000, with an annual energy savings of approximately 36,000 kilowatt hours (kWh). In fiscal year 2017, the annual incentive received was over $419,000, with identified energy savings of over 1.3 million kWh. Details and case studies of the major project accomplishments are included. The success of this program can be traced to an understanding of the constraints, the development of a program to address these constraints, and good communication/coordination across DOE and the multiple contractors serving the Hanford site. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2014

Feasibility of Tidal and Ocean Current Energy in False Pass, Aleutian Islands, Alaska final report

The Aleutian Pribilof Islands Association was awarded a U.S. Department of Energy Tribal Energy Program grant (DE-EE0005624) for the Feasibility of Tidal and Ocean Current Energy in False Pass, Aleutian Islands, Alaska (Project). The goal of the Project was to perform a feasibility study to determine if a tidal energy project would be a viable means to generate electricity and heat to meet long-term fossil fuel use reduction goals, specifically to produce at least 30% of the electrical and heating needs of the tribally-owned buildings in False Pass. The Project Team included the Aleut Region organizations comprised of the Aleutian Pribilof Island Association (APIA), and Aleutian Pribilof Island Community Development Association (APICDA); the University of Alaska Anchorage, ORPC Alaska a wholly-owned subsidiary of Ocean Renewable Power Company (ORPC), City of False Pass, Benthic GeoScience, and the National Renewable Energy Laboratory (NREL). The following Project objectives were completed: collected existing bathymetric, tidal, and ocean current data to develop a basic model of current circulation at False Pass, measured current velocities at two sites for a full lunar cycle to establish the viability of the current resource, collected data on transmission infrastructure, electrical loads, and electrical generation at False Pass, performed economic analysis based on current costs of energy and amount of energy anticipated from and costs associated with the tidal energy project conceptual design and scoped environmental issues. Utilizing circulation modeling, the Project Team identified two target sites with strong potential for robust tidal energy resources in Isanotski Strait and another nearer the City of False Pass. In addition, the Project Team completed a survey of the electrical infrastructure, which identified likely sites of interconnection and clarified required transmission distances from the tidal energy resources. Based on resource and electrical data, the Project Team developed a conceptual tidal energy project design utilizing ORPC’s TidGen® Power System. While the Project Team has not committed to ORPC technology for future development of a False Pass project, this conceptual design was critical to informing the Project’s economic analysis. The results showed that power from a tidal energy project could be provided to the City of False at a rate at or below the cost of diesel generated electricity and sold to commercial customers at rates competitive with current market rates, providing a stable, flat priced, environmentally sound alternative to the diesel generation currently utilized for energy in the community. The Project Team concluded that with additional grants and private investment a tidal energy project at False Pass is well-positioned to be the first tidal energy project to be developed in Alaska, and the first tidal energy project to be interconnected to an isolated micro grid in the world. A viable project will be a model for similar projects in coastal Alaska.

16 TIDAL AND WAVE POWER↗

DOE OSTI2019

Hurricane Harvey Highlights: Need to Assess the Adequacy of Probable Maximum Precipitation Estimation Methods

Probable maximum precipitation (PMP) is the primary criterion used to design flood protection measures for critical infrastructures such as dams and nuclear power plants. Based on our analysis using the Stage IV (ST4) quantitative precipitation estimates, precipitation associated with Hurricane Harvey near Houston, Texas, represents a PMP-scale storm and partially exceeds the Hydrometeorological Report No. 51 (HMR51) 72-h PMP estimates at 5,000 mi 2 (ST4=805 mm; HMR51=780 mm) and 10,000 mi 2 (ST4=686 mm; HMR51=673 mm). We also find statistically significant increasing trends since 1949 in the annual maximum total precipitable water and dew point temperature observations along the US Gulf Coast region, suggesting that, if the trend continues, the theoretical upper bound of PMP could be even larger. Our analysis of Hurricane Harvey rainfall data demonstrates that an extremely large PMP-scale storm is physically possible and that PMP estimates should not be considered overly conservative. Furthermore, this case study highlights the need for improved PMP estimation methodologies to account for long-term trends and to ensure the safety of our critical infrastructures.

42 ENGINEERING↗

DOE OSTI2024

Leveraging graph clustering techniques for cyber‐physical system analysis to enhance disturbance characterisation

Abstract Cyber‐physical systems have behaviour that crosses domain boundaries during events such as planned operational changes and malicious disturbances. Traditionally, the cyber and physical systems are monitored separately and use very different toolsets and analysis paradigms. The security and privacy of these cyber‐physical systems requires improved understanding of the combined cyber‐physical system behaviour and methods for holistic analysis. Therefore, the authors propose leveraging clustering techniques on cyber‐physical data from smart grid systems to analyse differences and similarities in behaviour during cyber‐, physical‐, and cyber‐physical disturbances. Since clustering methods are commonly used in data science to examine statistical similarities in order to sort large datasets, these algorithms can assist in identifying useful relationships in cyber‐physical systems. Through this analysis, deeper insights can be shared with decision‐makers on what cyber and physical components are strongly or weakly linked, what cyber‐physical pathways are most traversed, and the criticality of certain cyber‐physical nodes or edges. This paper presents several types of clustering methods for cyber‐physical graphs of smart grid systems and their application in assessing different types of disturbances for informing cyber‐physical situational awareness. The collection of these clustering techniques provide a foundational basis for cyber‐physical graph interdependency analysis.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Critical Infrastructure Decision-Making under Long-Term Climate Hazard Uncertainty: The Need for an Integrated, Multidisciplinary Approach

U.S. critical infrastructure assets are often designed to operate for decades, and yet long-term planning practices have historically ignored climate change. With the current pace of changing operational conditions and severe weather hazards, research is needed to improve our ability to translate complex, uncertain risk assessment data into actionable inputs to improve decision-making for infrastructure planning. Decisions made today need to explicitly account for climate change – the chronic stressors, the evolution of severe weather events, and the wide-ranging uncertainties. If done well, decision making with climate in mind will result in increased resilience and decreased impacts to our lives, economies, and national security. We present a three-tier approach to create the research products needed in this space: bringing together climate projection data, severe weather event modeling, asset-level impacts, and contextspecific decision constraints and requirements. At each step, it is crucial to capture uncertainties and to communicate those uncertainties to decision-makers. While many components of the necessary research are mature (i.e., climate projection data), there has been little effort to develop proven tools for long-term planning in this space. The combination of chronic and acute stressors, spatial and temporal uncertainties, and interdependencies among infrastructure sectors coalesce into a complex decision space. By applying known methods from decision science and data analysis, we can work to demonstrate the value of an interdisciplinary approach to climate-hazard decision making for longterm infrastructure planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Image-Driven Hybrid Structural Analysis Based on Continuum Point Cloud Method with Boundary Capturing Technique

Conventional approaches for the structural health monitoring of infrastructures often rely on physical sensors or targets attached to structural members, which require considerable preparation, maintenance, and operational effort, including continuous on-site adjustments. This paper presents an image-driven hybrid structural analysis technique that combines digital image processing (DIP) and regression analysis with a continuum point cloud method (CPCM) built on a particle-based strong formulation. Polynomial regressions capture the boundary shape change due to the structural loading and precisely identify the edge and corner coordinates of the deformed structure. The captured edge profiles are transformed into essential boundary conditions. This allows the construction of a strongly formulated boundary value problem (BVP), classified as the Dirichlet problem. Capturing boundary conditions from the digital image is novel, although a similar approach was applied to the point cloud data. It was shown that the CPCM is more efficient in this hybrid simulation framework than the weak-form-based numerical schemes. Unlike the finite element method (FEM), it can avoid aligning boundary nodes with regression points. A three-point bending test of a rubber beam was simulated to validate the developed technique. The simulation results were benchmarked against numerical results by ANSYS and various relevant numerical schemes. The technique can effectively solve the Dirichlet-type BVP, yielding accurate deformation, stress, and strain values across the entire problem domain when employing a linear strain model and increasing the number of CPCM nodes. In addition, comparative analysis with conventional displacement tracking techniques verifies the developed technique’s robustness. The proposed technique effectively circumvents the inherent limitations of traditional monitoring methods resulting from the reliance on physical gauges or target markers so that a robust and non-contact solution for remote structural health monitoring in real-scale infrastructures can be provided, even in unfavorable experimental environments.

Chemistry↗

DOE OSTI2015

Sustainable Hydropower in Lower Mekong Countries: Technical Assessment and Training Travel Report Thailand

The U.S. Agency for International Development (USAID), through their partnership with the U.S. Department of the Interior (DOI), requested the support of Oak Ridge National Laboratory (ORNL) to provide specialized technical assistance as part of the Smart Infrastructure for the Mekong (SIM) Program in Thailand. Introduced in July 2013 by U.S. Secretary of State John Kerry, SIM is a U.S. Government Inter-Agency program that provides Lower Mekong partner countries with targeted, demand-driven technical and scientific assistance to support environmentally sound, climate conscious and socially equitable infrastructure, clean energy development, and water resources optimization. The U.S. Government is committed to supporting sustainable economic development within the region by providing tools, best practices, technical assistance, and lessons learned for the benefit of partner countries. In response to a request from the Electricity Generating Authority of Thailand (EGAT), a SIM project was developed with two main activities: 1) to promote hydropower sustainability and efficiency through technical assessment training at two existing hydropower assets in Thailand, and 2) the design and implementation of one national and two or three regional science and policy workshops, to be co-hosted with EGAT, to build common understanding of and commitment to environmental and social safeguards for Mekong Basin hydropower projects. The U.S. Department of Energy (DOE) is leading the technical assessment (Activity 1), and has contracted ORNL to provide expert technical assistance focused on increasing efficiency at existing projects, with the goal of increasing renewable energy generation at little to no capital cost. ORNL is the leading national laboratory in hydropower analysis, with a nationally recognized and highly qualified team of scientists addressing small to large-scale systems (basin-, regional-, and national-scale) energy generation optimization analysis for DOE. The mission of the ORNL Water Power Program is to develop technologies, decision-support tools, and methods of analysis that enable holistic management of water-dependent energy infrastructure and natural resources in support of the DOE Energy Efficiency and Renewable Energy Office (DOE-EERE), Federal hydropower agencies, Federal Energy Regulatory Commission (FERC), Nuclear Regulatory Commission (NRC), energy producers, and other entities. In support of SIM, ORNL completed technical assessments of two hydropower plants owned and operated by the Electricity Generating Authority of Thailand (EGAT): Vajiralongkorn (VRK), with an installed capacity of 300 MW, and Rajjaprabha (RPB), with an installed capacity of 240MW. Technical assessment is defined as the assessment of hydropower operation and performance, and the identification of potential opportunities for performance improvement through plant optimization. At each plant, the assessment included an initial analysis of hydropower operating and performance metrics, provided by dam owners. After this analysis, ORNL engaged with the plant management team in a skills exchange, where best practices, operational methods, and technical challenges were discussed. The technical assessment process was outlined to plant management followed by a presentation of preliminary results and analysis based on 50 days of operational data. EGAT has agreed to provide a full year of operational data so a complete and detailed assessment that captures seasonal variability can be completed. The results of these assessments and discussions will be used to develop a set of best practices, training, and procedure recommendations to improve the efficiency of the two assessed plants.

13 HYDRO ENERGY↗

DOE OSTI2021

Advanced Computing, Data Science, and Artificial Intelligence Research Opportunities for Energy-Focused Transportation Science

The Energy Efficient Mobility Systems (EEMS) technology landscape is complex and rapidly evolving, which provides both tremendous opportunities and formidable challenges. Significant alterations to the mobility landscape are underway due to the advent of vehicle and infrastructure connectivity, autonomous driving, and rapid passenger- and freight-vehicle electrification. Advanced computing will play an increasingly important role in enabling the EEMS program to understand and identify the most important levers to improve the energy productivity of future integrated mobility systems. It is also driving new approaches to mobility and the research to unlock an affordable, efficient, safe, and accessible transportation future. Driving much of this change is the collection, analysis, and strategic use of massive amounts of diverse, complex data from infrastructure and vehicles with on-board sensors and data storage and transmission capabilities. Diverse and representative data are key to implementing approaches to maximize mobility energy productivity. While high-fidelity modeling of integrated transportation networks has strengthened our understanding of dynamic movement and behavior patterns, existing tools must be expanded beyond their current focus. This work necessitates data infrastructure investments (e.g., secure-streaming data platforms driven by ubiquitous sensors and video analytics) as well as investments in critical capabilities for large-scale automated analysis and organization using modern machine learning, statistics, and artificial intelligence. Other chief needs include agile, large-scale storage that can be quickly searched and queried for relevant data to support validation and model development, data-sharing agreements, and formatting standards for key data types. The future of public transit must be explored in greater detail, research must inform design, and opportunities must be identified for improving the mobility productivity of public transit in both urban and rural America.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Efficient Reliability Analysis using Generalized Multifidelity Modeling and Explainable Active Learning

To assess the reliability of critical technologies like nuclear plants and infrastructure systems and improve the robustness of design, engineers have to quantify the uncertainties surrounding the system behavior accurately. However, the complexity of the problem can make standard reliability analysis algorithms prohibitively expensive, primarily due to the high computational cost of estimating the system response at each iteration. This cost can be greatly reduced by using multi-fidelity modeling and machine learning to build a surrogate model to replace the expensive response function. We propose a general and robust method for building surrogates from multiple Low Fidelity (LF) models coupled with machine learning to retain accuracy. Our framework first constructs “Corrected Low Fidelity models” (CLFs) by coupling a High Fidelity (HF) model inferred Gaussian Process correction term with each of the LF models. It then uses the correction terms to assign model probabilities to each of these CLFs in an explainable way before using them to assemble the final surrogate. No assumptions are made about the type of the LF models or their correlation with the HF model. The proposed surrogate modeling framework is used within the subset simulation algorithm (a variance-reduced MCMC-based reliability analysis algorithm) for enhanced efficiency. Additionally, an active learning step is added to the algorithm to adaptively decide when the surrogate is not sufficiently accurate, at which point the HF model is called and used to refine the surrogate. Through a frame buckling example, our method is shown to be highly efficient at reducing the expensive HF model calls while accurately estimating the failure probability.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Towards a Scalable and Adaptive Application Support Platform for Large-Scale Distributed E-Sciences in High-Performance Network Environments

The advent of large-scale collaborative scientific applications has demonstrated the potential for broad scientific communities to pool globally distributed resources to produce unprecedented data acquisition, movement, and analysis. System resources including supercomputers, data repositories, computing facilities, network infrastructures, storage systems, and display devices have been increasingly deployed at national laboratories and academic institutes. These resources are typically shared by large communities of users over Internet or dedicated networks and hence exhibit an inherent dynamic nature in their availability, accessibility, capacity, and stability. Scientific applications using either experimental facilities or computation-based simulations with various physical, chemical, climatic, and biological models feature diverse scientific workflows as simple as linear pipelines or as complex as a directed acyclic graphs, which must be executed and supported over wide-area networks with massively distributed resources. Application users oftentimes need to manually configure their computing tasks over networks in an ad hoc manner, hence significantly limiting the productivity of scientists and constraining the utilization of resources. The success of these large-scale distributed applications requires a highly adaptive and massively scalable workflow platform that provides automated and optimized computing and networking services. This project is to design and develop a generic Scientific Workflow Automation and Management Platform (SWAMP), which contains a web-based user interface specially tailored for a target application, a set of user libraries, and several easy-to-use computing and networking toolkits for application scientists to conveniently assemble, execute, monitor, and control complex computing workflows in heterogeneous high-performance network environments. SWAMP will enable the automation and management of the entire process of scientific workflows with the convenience of a few mouse clicks while hiding the implementation and technical details from end users. Particularly, we will consider two types of applications with distinct performance requirements: data-centric and service-centric applications. For data-centric applications, the main workflow task involves large-volume data generation, catalog, storage, and movement typically from supercomputers or experimental facilities to a team of geographically distributed users; while for service-centric applications, the main focus of workflow is on data archiving, preprocessing, filtering, synthesis, visualization, and other application-specific analysis. We will conduct a comprehensive comparison of existing workflow systems and choose the best suited one with open-source code, a flexible system structure, and a large user base as the starting point for our development. Based on the chosen system, we will develop and integrate new components including a black box design of computing modules, performance monitoring and prediction, and workflow optimization and reconfiguration, which are missing from existing workflow systems. A modular design for separating specification, execution, and monitoring aspects will be adopted to establish a common generic infrastructure suited for a wide spectrum of science applications. We will further design and develop efficient workflow mapping and scheduling algorithms to optimize the workflow performance in terms of minimum end-to-end delay, maximum frame rate, and highest reliability. We will develop and demonstrate the SWAMP system in a local environment, the grid network, and the 100Gpbs Advanced Network Initiative (ANI) testbed. The demonstration will target scientific applications in climate modeling and high energy physics and the functions to be demonstrated include workflow deployment, execution, steering, and reconfiguration. Throughout the project period, we will work closely with the science communities in the fields of climate modeling and high energy physics including Spallation Neutron Source (SNS) and Large Hadron Collider (LHC) projects to mature the system for production use.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

A Petri Net model for distributed energy system

Electrical networks need to evolve to become more intelligent, more flexible and less costly. The smart grid is the next generation power energy, uses two-way flows of electricity and information to create a distributed automated energy delivery network. Building a comprehensive smart grid is a challenge for system protection, optimization and energy efficient. Proper modeling and analysis is needed to build an extensive distributed energy system and intelligent electricity infrastructure. In this paper, the whole model of smart grid have been proposed using Generalized Stochastic Petri Nets (GSPN). The simulation of created model is also explored. The simulation of the model has allowed the analysis of how close the behavior of the model is to the usage of the real smart grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2021

A Study of Layered Structural Configurations as Thermal and Impact Shielding of Lunar Habitats

The focus of this study is the analysis of a proposed multilayer material configuration as lunar habitat shielding so as to assess its ability to resist temperature extremes and impact. In order to resist this environmental hazard, the analysis will compare various configurations of potential shielding layers using both in-situ resource utilization (ISRU) and Earth-based materials. The technology for implementation of in-situ resources in construction is developing, though not yet able to produce a functional lunar habitat. As the lunar infrastructure is developed, ISRU techniques for manufacturing and construction may become more viable. Therefore, both types of materials are considered due to potential for application towards both near-term and long-term commencements of habitat construction. The thermal analysis in this paper utilizes the nonlinear heat equation to produce a depth-dependent temperature gradient from the habitat exterior to the interior that changes continuously with time during the lunar diurnal cycle. Static loading conditions meant to simulate the impact force of a 77.9 g meteorite were used to analyze the ability of a proposed multilayer shielding configuration to resist impact. A thermal stress analysis was performed to observe the effects of heat transfer through the proposed multilayer configuration. The results of this study should be useful in determining the potential for implementation of efficient shielding layers for the initial lunar base.

Layered systems↗

DOE OSTI2016

New York Solar Smart DG Hub-Resilient Solar Project: Economic and Resiliency Impact of PV and Storage on New York Critical Infrastructure

Resilient PV, which is solar paired with storage ('solar-plus-storage'), provides value both during normal grid operation and power outages as opposed to traditional solar PV, which functions only when the electric grid is operating. During normal grid operations, resilient PV systems help host sites generate revenue and/or reduce electricity bill charges. During grid outages, resilient PV provides critical emergency power that can help people in need and ease demand on emergency fuel supplies. The combination of grid interruptions during recent storms, the proliferation of solar PV, and the growing deployment of battery storage technologies has generated significant interest in using these assets for both economic and resiliency benefits. This report analyzes the technical and economic viability for resilient PV on three critical infrastructure sites in New York City (NYC): a school that is part of a coastal storm shelter system, a fire station, and a NYCHA senior center that serves as a cooling center during heat emergencies. This analysis differs from previous solar-plus-storage studies by placing a monetary value on resiliency and thus, in essence, modeling a new revenue stream for the avoided cost of a power outage. Analysis results show that resilient PV is economically viable for NYC's critical infrastructure and that it may be similarly beneficial to other commercial buildings across the city. This report will help city building owners, managers, and policymakers better understand the economic and resiliency benefits of resilient PV. As NYC fortifies its building stock against future storms of increasing severity, resilient PV can play an important role in disaster response and recovery while also supporting city greenhouse gas emission reduction targets and relieving stress to the electric grid from growing power demands.

14 SOLAR ENERGY↗

DOE OSTI2023

Hydropower Resilience Database for Assessing Microgrid Formation Capability and Enhancing Power Grid Resilience

In the face of increasing frequency of extreme events, enhancing resilience and reliability of energy infrastructure demands innovative solutions. Hydropower, with its inherent generation flexibility and grid-forming capability, offers significant potential for enhancing power grid resilience through the establishment of microgrids. To enable informed decision-making and strategic planning, we present the Hydropower Resilience Database (HRD) that integrates data from various sources such as Oakridge National Laboratory (ORNL) HydroSource, National Inventory of Dams, and US Western Grid database. By integrating relevant information on hydropower plant characteristics, including dams, reservoirs, and connected electrical grid, this resource enables hydropower plant owners, utilities, and community stakeholders to identify and evaluate the feasibility of using hydropower resources in microgrids to support nearby communities and critical infrastructure in various challenging scenarios. Using HRD, a set of metrics are evaluated to quantify the capability of hydropower plants to support essential microgrid functions. An interactive tool is developed using ArcGIS, enabling the visualization and analysis using HRD. Our research contributes to strategic planning efforts for fortifying energy infrastructure, ensuring reliable power supply during disruptions, and advancing the development of robust and resilient energy systems in regions susceptible to grid vulnerabilities.

13 HYDRO ENERGY↗

DOE OSTI2023

Hydropower Resilience Database for Assessing Microgrid Formation Capability and Enhancing Power Grid Resilience

13 HYDRO ENERGY↗

DOE OSTI2025

HexWeather: Hexagonal Spatial Data Aggregation for Weather-Driven Grid Resilience Analysis

Extreme weather accounts for over 8 0 % of major U.S. power outages since 2000, highlighting the need for spatial tools that align weather data with the irregular boundaries of electric infrastructure. This paper introduces HexWeather, a modular, resolution-aware framework for aggregating historical and forecasted weather data using Uber's H3 hexagonal spatial indexing system. Unlike traditional methods that rely on state or county-level grids, HexWeather enables weather analysis across custom geographies such as utility service areas where public datasets are often unavailable or misaligned. Using Open-Meteo data, we evaluate how H3 resolution affects anomaly detection, spatial variability, and forecast uncertainty across three scales: state, county, and utility. Results show that while coarse resolutions suffice for broad trend tracking, finer resolutions are essential for identifying localized variability and operational risks. By applying metrics like Z-score standard deviation and interquartile range, HexWeather quantifies the spatial spread of both historical anomalies and forecasted conditions, allowing users to assess resolution adequacy for each analysis. This framework supports rapid weather data reuse, reproducible anomaly detection, and predictive modeling for infrastructure resilience. By bridging spatial misalignment in traditional datasets and enabling retrospective and forward-looking analysis within the same pipeline, HexWeather lays the groundwork for better post event analysis, outage prediction, and resilience planning.

Morris, Jacob [ORNL]↗

NASA NTRSDate not supplied

A Multi-Year Effort to Forward the Validation of Solar Energetic Particle Prediction Models

To enable a consistent and quantitative understanding of solar energetic particle (SEP) model performance, a generalized, automated validation infrastructure, called SPHINX, is being developed by the NASA Space Radiation Analysis Group (SRAG) in close collaboration with the NASA Community Coordinated Modeling Center (CCMC), NASA Moon to Mars Space Weather Analysis Office (M2M), NOAA Space Weather Prediction Center (SWPC), and the Royal Belgian Institute for Space Aeronomy (BIRA-IASB). SPHINX is being developed to evaluate the SEP Scoreboards (https://ccmc.gsfc.nasa.gov/scoreboards/sep/), which are being used by SRAG to support space radiation operations for Mission Control. SPHINX is also being developed through a series of community challenges, which have defined a list of challenge events and a prescribed set of rules of participation. The participation of the research community has enabled the generalization of the infrastructure to validate all the types of outputs being produced by SEP models and fully exercised the framework. The most recent challenge includes 30 SEP events and 33 non-event (quiet) periods and was supported by two dedicated SEPVAL working meetings, one in the US and one in Europe (https://ccmc.gsfc.nasa.gov/community-workshops/ccmc-sepval-2023/). Here, we describe SPHINX and its interactive user interface, SPHINX-VIVID, which will eventually be made available for public use. We present selected results, outcomes, and lessons learned from the SEPVAL working meetings and validation results of the SEP Scoreboards from 2019 to present. These efforts aim to define a set of community standards for SEP model validation and describe the current state-of-the-art of SEP model performance.

Kathryn Whitman↗

DOE OSTI2021

Investigation of Cycling Coal-Fired Power Plants Using High-Fidelity Models

The project delivers a well-integrated and validated simulation platform for cycling operation analysis in coal-fired power plant. Two critical mechanical components of the boiler island were analyzed through mechanical integrity assessment and economic benefit analysis. The current phase of the project focuses on the development of the integrated simulation infrastructure and prove its feasibility and effectiveness using two typical use cases. This integrated platform can help save a lot of engineering efforts for model development and simulation analysis. Through the real simulation scenarios in this document, it was demonstrated that using this platform, an analysis can be completed in approximately 2 days, while it could cost several weeks before using this platform. Going forward, the platform built in this project can be used for more boiler service applications, and it can be further enhanced with more functions/features to maximize its usage and benefits. 1) Extend component-level analysis with more use cases to cover all the major critical components of boiler island under cycling operations. A library of critical components can be developed and validated for typical pulverized coal-fired subcritical boiler units. 2) Develop predictive maintenance features based on the integrated models (Digital Twins) and engineering analysis procedures. Predictive maintenance enables each asset to be serviced based on forecast on life consumption and cost profile for replacing/welding the critical mechanical parts of the boiler. This minimizes the chance of unscheduled shutdowns and emergency services at much higher costs and prevent the fatal accidents in unit operations. 3) Develop and maintain a standard library for critical component analysis under flexible plant operations, which will include libraries of: process models, MI models for typical pressure parts, and economic models with typical plant operating data and ISO power trade data.

01 COAL, LIGNITE, AND PEAT↗