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At least 109 records · Page 6

Grid Communications: Cybersecurity and Supply Chain Challenges and Emerging Regulation Session Three

The TADA Grid Communications Workshops are designed to strengthen cybersecurity and digital assurance across the energy sector by focusing on secure deployment and management of grid communications technologies. These workshops bring together state energy offices, utilities, and technology suppliers to explore the intersection of communications infrastructure, supply chain risks, and emerging regulatory requirements. Participants will apply Cyber-Informed Engineering (CIE) principles to reduce risks in communications systems, engage with INL’s procurement guidance, and explore future tools. Through scenario-based exercises and peer exchange, attendees will develop risk-based security strategies and actionable compliance roadmaps tailored to their grid communications projects. The workshops also help participants navigate evolving regulatory frameworks such as FEOC rules in the OBBB, NERC CIP-013, and NDAA 2024, while identifying compliance gaps in mixed-technology environments. A key outcome is the formation of a practitioner network with ongoing access to INL expertise and resources, fostering long-term resilience in the digital energy ecosystem. This is Session 3 of 3 (Full Version).

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

Grid Communications Supply Chain & Emerging Regulation Challenges Session 1

The TADA Grid Communications Workshops are designed to strengthen cybersecurity and digital assurance across the energy sector by focusing on secure deployment and management of grid communications technologies. These workshops bring together state energy offices, utilities, and technology suppliers to explore the intersection of communications infrastructure, supply chain risks, and emerging regulatory requirements. Participants will apply Cyber-Informed Engineering (CIE) principles to reduce risks in communications systems, engage with INL’s procurement guidance, and explore future tools. Through scenario-based exercises and peer exchange, attendees will develop risk-based security strategies and actionable compliance roadmaps tailored to their grid communications projects. The workshops also help participants navigate evolving regulatory frameworks such as FEOC rules in the OBBB, NERC CIP-013, and NDAA 2024, while identifying compliance gaps in mixed-technology environments. A key outcome is the formation of a practitioner network with ongoing access to INL expertise and resources, fostering long-term resilience in the digital energy ecosystem.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

CRCNS22 Learning Rules in the Hippocampus and their Mapping to Neuromorphic Systems (Final Technical Report)

Large scale biologically-realistic computational models are key to investigating the interplay between structure and function in nervous systems, thus paving the way to new clinical methods and neuro-inspired computing solutions. This project focuses on the hippocampus, in particular the CA3-CA1 regions, due to their role in associative learning and memory, pattern separation and completion, and spatial navigation. Investigations into the neuronal organization and learning rule(s) of this circuit can shed light into how declarative memories are formed, stored, recalled and forgotten and inform computational, experimental and clinical neuroscience work. Our project aims at developing a novel data-driven methodology supported by a broad heterogeneous base of neuroscience experimental knowledge and inspired from advances in computer science and engineering. Specifically, this work will benchmark existing and new learning rules within a full-scale spiking neural network simulation of the CA3-CA1 region. The model will be based on an open-source repository, called the Hippocampome, which contains neuronal morphologies, firing patterns, synapse probabilities, and most other required parameters for all known neuron types in the rodent hippocampal formation. The model will be first trained in a supervised fashion for associative memory tasks using backpropagation through time traditionally used in computer science, enhanced with a new technique called the surrogate gradient method. This optimization method will be used to obtain a global loss minimization, but it is not biologically inspired as it assumes the use of data not locally available to the synapses. However, we propose its use as a benchmarking tool, to compare the training performance of local biologically plausible and hardware-mappable learning rules at scale. New rules or combinations will be proposed and tested as needed, based on the obtained results. Progress in this area will also drive the development of novel hardware-mappable algorithms for continual lifelong learning and categorization of new events from few presented examples. This project goes beyond the existing state-of-the-art by looking at large scale realistic neuronal circuits as networks trainable via global optimization methods such as surrogate gradient descent. The objective function of the brain that supports learning is largely unknown, but it is likely that it operates through local learning rules. Studying network trajectories around local minima as proposed in this work represents a useful strategy for understanding whether a network is training by using a specific (set of) learning rule(s). Starting from a completely untrained network is a challenging test since it is difficult to determine how the learning rule affects the trajectory of the network. This interdisciplinary project will help understand what rule governs learning in these regions or if multiple learning rules are involved. The work will develop a robust methodology to measure if the network is converging to the target solution, oscillating around it, or diverging away.

59 BASIC BIOLOGICAL SCIENCES↗

Revolutionizing Materials Design: The Intersection of Quantum Mechanics and Data Modeling

The field of materials design is currently experiencing a notable evolution, driven by the convergence of sophisticated computational methodologies based on first principles and data-driven modeling approaches. I will review our recent endeavors employing AI/ML to expedite first-principles simulations and mitigate traditional methods' temporal and spatial limitations. Central to our efforts is developing and utilizing ML interatomic potentials (MLPs) across a diverse spectrum of materials. We show that MLPs serve as invaluable tools for navigating the complexities of the simulations, such as understanding the behavior of MgO at extreme environments of ~1 terapascal and temperatures >10,000 Kelvin. Moreover, we show that MLPs can provide precise details of the intricate dynamics governing the oxidation processes of binary alloy systems due to the competition between surface segregation and reconstruction tendencies. In summation, advancements in MLPs open the door to fresh possibilities in material modeling and, ultimately, discovery.

Saidi, Wissam↗

Data and scripts associated with a manuscript modeling microbial regulation of priming effects

This data package is associated with the publication “Modeling Microbial Regulatory Feedback in Organic Matter Decomposition Identifies Copiotrophic Traits as Key Drivers of Positive Priming” published as a preprint on BioRXiv by Ahamed et al. (2026); https://doi.org/10.1101/2024.08.11.607483. The package contains MATLAB scripts and saved simulation outputs used to implement a cybernetic model of microbial regulation during complex organic matter (OM) decomposition governing priming effects. It includes models of (i) single microbial functional groups (copiotrophic or oligotrophic degraders) and (ii) binary consortia composed of degraders and non-degraders with contrasting or common growth traits. Simulation results were generated using Monte Carlo analyses, with randomized key model parameters across a range of environmental mixing fractions of complex and labile OM. The dataset was created to provide a transparent and reusable computational framework for systematically exploring how microbial growth traits, metabolic regulation, and community composition influence OM decomposition dynamics and priming effects. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes the variable definitions. This package includes: (1) annotated MATLAB code implementing the system of ordinary differential equations and cybernetic control laws; (2) saved output files containing data (e.g., biomass, substrates, enzyme levels, priming metrics); and (3) scripts for processing saved outputs and regenerating figures. Specifically, the data package contains three main MATLAB scripts: runPrimingModel.m, runPlotData.m, and runPlotSuppFigS1.m, along with this readme and supporting documentation. Users should begin with runPrimingModel.m, which contains the annotated code implementing the system of ordinary differential equations and cybernetic control laws. This script runs the Monte Carlo simulations of microbial OM decomposition and allows users to modify microbial trait definitions, adjust parameter distributions, or define new community configurations. Simulation outputs are automatically saved as .mat files in the folder named SavedData, which stores all pre-generated results included in this package. The second script, runPlotData.m, reads files from the SavedData folder and processes them to regenerate the figures presented in the manuscript. The third script, runPlotSuppFigS1.m, specifically generates Figure S1 in the Supplementary Material of the manuscript. The package also includes the aforementioned files in non-proprietary .txt format. If users intend to use them, they should first save the files in their respective .m or .mat formats prior to execution in MATLAB.

Biomass concentration↗

Equity-Centered Engagement Through Climate Resilience Policy in Massachusetts

Communities who experience disproportionate climate change impacts tend to be excluded from resilience planning (Vale, 2014). Those efforts typically follow top-down processes within established governance practices that are inaccessible to marginalized folks and reinforce inequalities (Malloy & Ashcraft, 2020; Adger, 2003). Such participatory planning processes may offer the public little opportunity to influence the process itself or the outcomes (Smith & McDonough, 2001). They might ignore important public values or alternative ways of knowing which can be critical assets in resilience (Few et al., 2007). Designing communities for climate action and resilience means creating opportunities for everyone to meaningfully shape those decisions and experience related benefits. Having the opportunity to shape one’s community is necessary for human flourishing (Allen, 2016). Resilience planning that shifts power into communities and focuses on social vulnerability can affect how people survive and thrive in a climate changed world. The Massachusetts Municipal Vulnerability Preparedness (MVP) 2.0 program is an attempt to change the status quo in resilience planning by bringing new voices into decision-making power, recognizing their labor, addressing root causes of vulnerability, and investing in social infrastructure. It aspires to build capacity for equity-focused community engagement within teams of municipal staff and community liaisons, and ultimately build social capital and community cohesion. My mixed methods research investigates implementation of this state grant program in several western Massachusetts towns. I am using document review, participant observation, and interviews to understand the MVP 2.0 process as written, how different towns navigate it, and how individuals make sense of their experiences in it. I seek to understand how those experiences explain relationships between engagement approaches, mediating factors, and process outcomes. I am interested in the conditions that allow for community empowerment and how a model like MVP 2.0 can shift conditions that hold systems in place. In a practical sense, our findings will help municipalities reflect on their work during MVP 2.0 and plan for future community engagement. They may be informative for designing future iterations of the MVP program and for other municipalities, offices of community engagement, and practitioners. The findings will also contribute to the participation, resilience, and climate justice literatures, by adding perspectives on equity-centered resilience and community engagement approaches in smaller towns and rural settings. References: Adger, W. N. (2003). Social capital, collective action, and adaptation to climate change. Economic Geography, 79, 387-404. Allen, D. (2016). Toward a connected society. Our compelling interests: The value of diversity for democracy and a prosperous society, 71-105. Few, R., Brown, K., & Tompkins, E. L. (2007). Public participation and climate change adaptation: avoiding the illusion of inclusion. Climate Policy, 7(1), 46–59. Malloy, J. T., & Ashcraft, C. M. (2020). A framework for implementing socially just climate adaptation. Climatic Change, 160(1), 1–14. Smith, P. D., & McDonough, M. H. (2001). Beyond public participation: Fairness in natural resource decision making. Society & natural resources, 14(3), 239-249. Vale, L. J. (2014). The politics of resilient cities: whose resilience and whose city? Building Research & Information, 42(2), 191–201.

Callaham, Shannon↗

Equity-Centered Engagement Through Climate Resilience Policy in Massachusetts

Resilience efforts tend to reinforce injustices. Communities who experience disproportionate climate impacts are often excluded from resilience planning, and those plans do not typically address root causes of social vulnerability. More transformational approaches to resilience would shift decision-making power into communities and affect how people survive and thrive in a climate changing world. The Massachusetts Municipal Vulnerability Preparedness (MVP) 2.0 program is an attempt to change the status quo in resilience planning by bringing new voices into decision-making power, recognizing their labor, addressing root causes of vulnerability, and investing in social infrastructure. It aims to build capacity for equity-focused community engagement within teams of municipal staff and community liaisons, and ultimately build community cohesion. My mixed methods research investigates implementation of this state grant program in several Massachusetts towns. I am using document review, participant observation, and interviews to understand the MVP 2.0 process as written, how different municipalities navigate it, and how individuals make sense of their experiences in it. I seek to understand how those experiences explain relationships between engagement approaches, mediating factors, and process outcomes. I am interested in the conditions that allow for community empowerment and how a model like MVP 2.0 can shift conditions that hold systems in place. In a practical sense, my empirical results will help municipalities reflect on their work during MVP 2.0 and plan for future community engagement. They may be informative for designing future iterations of the MVP program and for other municipalities, offices of community engagement, and practitioners. The findings will also contribute to the participation, resilience, and climate justice literatures, by adding perspectives on equity-centered resilience and community engagement approaches in smaller towns and rural settings.

Callaham, Shannon↗

Equity-Centered Engagement Through Climate Resilience Policy in Massachusetts

Communities who experience disproportionate climate change impacts tend to be excluded from resilience planning. Those efforts typically follow top-down processes within established governance practices that are inaccessible to marginalized folks and reinforce inequalities. Regenerating the commons includes creating opportunities for everyone to meaningfully shape community resilience decisions and experience related benefits. Resilience planning that shifts power into communities and focuses on social vulnerability can affect how people survive and thrive in a climate changed world. The Massachusetts Municipal Vulnerability Preparedness (MVP) 2.0 program is an attempt to change the status quo in resilience planning by bringing new voices into decision-making power, recognizing their labor, addressing root causes of vulnerability, and investing in social infrastructure. It aspires to build capacity for equity-focused community engagement within teams of municipal staff and community liaisons, and ultimately build social capital and community cohesion. My research investigates implementation of this state grant program in several western Massachusetts towns. I am using document review, participant observation, and interviews to understand the MVP 2.0 process as written, how different towns navigate it, and how individuals make sense of their experiences in it. I seek to understand how those experiences explain relationships between engagement approaches, mediating factors, and process outcomes. I am interested in the conditions that allow for community empowerment and how a model like MVP 2.0 can shift conditions that hold systems in place. In a practical sense, our findings will help municipalities reflect on their work during MVP 2.0 and plan for future community engagement. They may be informative for designing future iterations of the MVP program and for other municipalities or offices of community engagement. The findings will also contribute to the participation, resilience, and climate justice literatures, by adding perspectives on equity-centered resilience and community engagement approaches in smaller towns and rural settings.

Callaham, Shannon↗

Embracing Uncertainty and Perseverance. A Brief Perspective on Conducting On-Site NDT Research

Dr. Judi E. See, a Systems Analyst and Human Factors Engineer at Sandia National Laboratories, reflects on her experience conducting NDT research in a male-dominated environment. She emphasizes the importance of persistence, flexibility, and persuasive skills in overcoming challenges, ranging from gaining access to test sites and equipment to building trust with inspectors. She shares her personal experience of navigating professional situations where gender disparities were evident, highlighting the need for women to adapt and overcome obstacles in traditionally male-dominated settings. See's journey demonstrates that perseverance and ingenuity can lead to significant contributions, process improvements, and recognition in the NDT field.

42 ENGINEERING↗

NARUC Resilience Framework

The NARUC Resilience Framework provides state regulators and other key stakeholders with a structured approach to considering policies and programs that will enhance grid resilience amid evolving technological, environmental and economic challenges. This Framework consolidates insights from nationwide workshops and peer discussions into six actionable components: (1) setting goals and objectives, (2) leveraging use cases, (3) establishing shared definitions, (4) ensuring inclusive process leadership, (5) addressing critical design questions, and (6) guiding implementation. This Framework is intended as a strategic tool for regulators to navigate resilience investments, prioritize affordability, integrate stakeholder needs, and foster collaboration across agencies, all while balancing cost-effectiveness with societal and economic resilience outcomes. By presenting a structured approach to decision-making rather than prescriptive solutions, the Framework supports nuanced, jurisdiction-specific resilience planning and is adaptable to the dynamic demands of modern energy systems

24 POWER TRANSMISSION AND DISTRIBUTION↗

Data Structure Alchemy

In an increasingly more data-driven world, the project set out to uncover the first principles of data-structure design, chart the immense design space they form, and build automation that can synthesize an optimal structure, or even a whole storage engine, for any given workload, hardware platform, and cost target. Data structures are at the center of every computational system and are directly responsible for its performance. Two core technical thrusts were defined: 1) Mapping design spaces for key data-centric abstractions (filters, hash functions, storage-engine layouts, neural-network topologies, blockchain protocols, image layouts, etc.). 2) Developing search & synthesis algorithms, initially analytical cost models, later neural-guided bi-level optimisers that navigate sextillions of candidate designs in seconds and materialise the best one as ready‐to-run code. This report distills the key insights, accomplishments, and impact.

97 MATHEMATICS AND COMPUTING↗

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on the Banshee Distribution Network: Preprint

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on Banshee Distribution Network

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Active learning of ternary alloy structures and energies

Abstract Machine learning models with uncertainty quantification have recently emerged as attractive tools to accelerate the navigation of catalyst design spaces in a data-efficient manner. Here, we combine active learning with a dropout graph convolutional network (dGCN) as a surrogate model to explore the complex materials space of high-entropy alloys (HEAs). We train the dGCN on the formation energies of disordered binary alloy structures in the Pd-Pt-Sn ternary alloy system and improve predictions on ternary structures by performing reduced optimization of the formation free energy, the target property that determines HEA stability, over ensembles of ternary structures constructed based on two coordinate systems: (a) a physics-informed ternary composition space, and (b) data-driven coordinates discovered by the Diffusion Maps manifold learning scheme. Both reduced optimization techniques improve predictions of the formation free energy in the ternary alloy space with a significantly reduced number of DFT calculations compared to a high-fidelity model. The physics-based scheme converges to the target property in a manner akin to a depth-first strategy, whereas the data-driven scheme appears more akin to a breadth-first approach. Both sampling schemes, coupled with our acquisition function, successfully exploit a database of DFT-calculated binary alloy structures and energies, augmented with a relatively small number of ternary alloy calculations, to identify stable ternary HEA compositions and structures. This generalized framework can be extended to incorporate more complex bulk and surface structural motifs, and the results demonstrate that significant dimensionality reduction is possible in thermodynamic sampling problems when suitable active learning schemes are employed.

Chemistry↗

SANE: strategic autonomous non-smooth exploration for multiple optima discovery in multi-modal and non-differentiable black-box functions

Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and multimodal parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, material structure image spaces, and molecular embedding spaces. Often these systems are black-boxes and time-consuming to evaluate, which resulted in strong interest towards active learning methods such as Bayesian optimization (BO). However, these systems are often noisy which make the black box function severely multi-modal and non-differentiable, where a vanilla BO can get overly focused near a single or faux optimum, deviating from the broader goal of scientific discovery. To address these limitations, here we developed Strategic Autonomous Non-Smooth Exploration (SANE) to facilitate an intelligent Bayesian optimized navigation with a proposed cost-driven probabilistic acquisition function to find multiple global and local optimal regions, avoiding the tendency to becoming trapped in a single optimum. To distinguish between a true and false optimal region due to noisy experimental measurements, a human (domain) knowledge driven dynamic surrogate gate is integrated with SANE. We implemented the gate-SANE into pre-acquired piezoresponse spectroscopy data of a ferroelectric combinatorial library with high noise levels in specific regions, and piezoresponse force microscopy (PFM) hyperspectral data. SANE demonstrated better performance than classical BO to facilitate the exploration of multiple optimal regions and thereby prioritized learning with higher coverage of scientific values in autonomous experiments. Our work showcases the potential application of this method to real-world experiments, where such combined strategic and human intervening approaches can be critical to unlocking new discoveries in autonomous research.

Biswas, Arpan [University of Tennessee, Knoxville,↗

Business Models for Scaling Demand Flexibility Volume II – Customer relationship management strategies, challenges, and lessons learned from U.S. programs

Load growth at the grid edge is driving increased attention to the distribution system and its ability to enable customer technology adoption in an affordable and timely manner. Key industry stakeholders, including electric utilities and regulators, can benefit from strategies to manage and balance customer needs with infrastructure investments, such as demand flexibility. This report focuses on demand flexibility—the ability to reduce, shift, shed, generate, or modulate loads in response to building and grid needs—to reduce the need for costly grid upgrades by deferring investment needs and increase system reliability by shifting electricity usage during periods of high risk. Specifically, we focus on the emerging characteristics of business models for demand flexibility as a framework to understand how demand flexibility programs generate value. In this report, we focus on demand flexibility program customer relationship management strategies, which provide information on value creation and focus on ensuring customers can navigate programs smoothly. This report discusses the role of customer relationship management strategies in demand flexibility programs, characterizes customer relationship management strategies that can be considered during program design and implementation, identifies existing challenges to customer relationship management strategies, and describes lessons learned. This report is part of a series that includes reports on value propositions, stakeholder ecosystem management, and program life cycle.

24 POWER TRANSMISSION AND DISTRIBUTION↗

BESS Digital Assurance, Supply Chain Risk Management, and Emerging Regulation Session One - Abridged

The TADA BESS Supply Chain Workshops are designed to equip participants with the knowledge and tools necessary to address the evolving challenges at the intersection of battery energy storage systems (BESS), cybersecurity, and supply chain vulnerabilities. The workshops emphasize the application of Cyber-Informed Engineering (CIE) principles using INL’s procurement guide and the CIE-BAT tool. Attendees will develop risk-based security strategies and actionable compliance roadmaps tailored to their BESS projects. Additionally, the program fosters a collaborative network of practitioners and provides guidance on navigating emerging regulatory requirements, including FEOC rules under the OBBB framework, to assess and enhance organizational readiness. This is Session 1 of 3.

25 - ENERGY STORAGE↗