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DOE OSTI2022

A Hybrid Data-Driven and Model-Based Anomaly Detection Scheme for DER Operation: Preprint

This paper proposes a hybrid data and model-based anomaly detection for securing the operation of distributed energy resources (DERs) in distribution grids. Data-driven autoencoders (AE) are set up at the edge level by taking local DER data and detect anomalous operations by leveraging the reconstruction ability. In parallel, model-based state estimation (SE) is running at the system level by taking system models and measurements, the anomalies are identified by analyzing the measurements residual. The hybrid scheme preserves the benefits of both data-driven and model-based analysis and thus improves the robustness and accuracy of anomaly detection. It can be established by getting full use of the existing infrastructures in distribution grids. Numerical tests on a realistic distribution feeder in Southern California highlight the effectiveness as well as benefits of the proposed scheme.

anomaly detection↗

DOE OSTI2020

US Department of Energy, Office of Science High Performance Computing Facility Operational Assessment 2019 Oak Ridge Leadership Computing Facility

Oak Ridge National Laboratory's (ORNL's) Leadership Computing Facility (OLCF) continues to surpass its operational target goals: supporting users; delivering fast, reliable computational ecosystems; creating innovative solutions for high performance computing (HPC) needs; and managing risks, safety, and security associated with operating some of the most powerful computers in the world. The results can be seen in the cutting-edge science conducted by users and the praise from the research community. Calendar year (CY) 2019 was a big year as OLCF staff ran five world-class resources (the leadershipclass computers Titan and Summit, the large analysis cluster called Eos, and the massive parallel filesystems called Atlas and Alpine)) and also began power and cooling upgrades for a 2021 exascale system called Frontier. While continuing exceptional operation of Titan, Eos, and Rhea, the OLCF released the Summit supercomputer for production on January 1, 2019. Summit debuted as the most capable and efficient system in its class and has been recognized as the most powerful system in the world for its performance on both the high performance linpack (HPL) and conjugate gradient (HPCG) benchmark applications since June 2018 according to TOP500. Summit represents the culmination of a multiyear effort between the OLCF, IBM, NVIDIA, and Mellanox to deliver a system that is unmatched for modeling, simulation, data analysis, and learning. To hit the ground running with science-ready applications on day one, application teams worked closely with the OLCF through the Center for Accelerated Application Readiness (CAAR) program for years in advance of the Summit deployment. CY 2019 was filled with outstanding results and accomplishments: a very high rating from users on overall satisfaction for the sixth year in a row; a tremendous amount of core-hours delivered to researchers from two leadership-class systems; and success in delivering on the allocation split of roughly 60%, 30%, and 10% of core-hours offered for the Innovative and Novel Computational Impact on Theory and Experiment (INCITE), Advanced Scientific Computing Research Leadership Computing Challenge (ALCC), and Director's Discretionary (DD) programs, respectively (see Operational Performance section). These accomplishments, coupled with the high utilization rates (overall and capability usage), represent the fulfillment of the promise of both leadership-class machines: efficient facilitation of leadership-class computational applications. Table ES.1 presents a summary of the 2019 OLCF metric targets and the associated results. More information can be found in the Operational Performance section for each OLCF resource. The scientific accomplishments of OLCF users are a strong indication of long-term operational success, with publications this year in such notable journals and publications as Nature, Nature Physics, Nature Plants, Physical Review X, Journal of the American Physical Society, Cell, Nano Letters, and Trends in Biotechnology. Crucial domain-specific discoveries facilitated by resources at the OLCF are described in the High Performance Computing Facility Operational Assessment 2019 Oak Ridge Leadership Computing Facility (OAR) Strategic Results section. For example, researchers used Summit to pinpoint and understand the production of proteins from genetic information, including mutations and the functional expression of disease (Section 8.2).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

US Department of Energy, Office of Science High Performance Computing Facility Operational Assessment 2021: Oak Ridge Leadership Computing Facility

Oak Ridge National Laboratory’s (ORNL’s) Leadership Computing Facility (OLCF) continues to surpass its operational target goals of supporting users; delivering fast, reliable computational ecosystems; creating innovative solutions for high-performance computing (HPC) needs; contributing to the community to build the next generation HPC workforce, and managing risks, safety, and security associated with operating some of the most powerful computers in the world. The results can be seen in the cutting-edge science conducted by users and the praise from the research community. Calendar year (CY) 2021 saw continued excellence in research supported by the OLCF’s leadership-class computing resources, including Summit (the nation’s most powerful supercomputer), the global scratch file system Alpine, the Scalable Protected Infrastructure (SPI), the Exploratory Visualization Environment for Research in Science and Technology (EVEREST), and the archival mass-storage resource High-Performance Storage System (HPSS). While maintaining access and exceptional user support for Summit, the OLCF continued to make progress on the installation and deployment of Frontier, which will be the nation’s first exascale system when it comes online at the start of CY 2023. Users have already begun running and optimizing scientific codes on Crusher, the OLCF test and development system equipped with Frontier’s architecture. Throughout the year, the OLCF maintained a strong culture of operational excellence, including risk management, workplace safety, and cybersecurity. The OLCF’s rigorous risk management strategy anticipated and mitigated risks, and at this time there are no high-priority operational risks. Similarly, ORNL and the OLCF were committed to operating under the US Department of Energy’s (DOE’s) safety regulations that ensure a safe workplace. Technical staff tracked and monitored existing threats and vulnerabilities within the OLCF while continually developing tools and practices to enhance operations without increasing the facility’s risk. CY 2021 was filled with outstanding results and accomplishments, including a very high rating from users on overall satisfaction for the eighth consecutive year; a tremendous number of node hours delivered to 1,671 researchers on Summit; and the successful delivery of the allocation split of roughly 60%, 20%, and 20% of core-hours offered for the Innovative and Novel Computational Impact on Theory and Experiment (INCITE), Advanced Scientific Computing Research Leadership Computing Challenge (ALCC), and Director’s Discretionary (DD) programs, respectively (Section 2). COVID-19 research remained a focus in 2021, and the ALCC and DD programs allocated over 1 million Summit hours to the COVID-19 High Performance Computing Consortium. These accomplishments, coupled with the high utilization rates (i.e., overall and capability usage), represent the fulfillment of the promise of leadership class machines: efficient facilitation of leadership-class computational applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

TRIM: AI Guided Random Number Generation for Resource-Constrained IoT Systems

Random numbers often serve as the backbone for many security solutions in diverse domains such as cryptography, side channel leakage prevention, and moving target defense. However, generating true random numbers requires a physical source of entropy (e.g. hardware, quantum, environmental phenomenon) making it difficult to realize at a large scale and at a low cost. On the flip side, pseudorandom number generators (easy to implement) following a specific distribution (e.g. Gaussian) can be easily compromised given a sufficient amount of traces. In this work, we have developed a machine learning-guided generative approach that can be used to create portable, resource-efficient, and cost-effective random number generators with high throughput and true randomness characteristics. We implement the proposed approach as a highly parameterized framework and perform extensive evaluation for different settings. The framework was able to learn from true random sources such as irrational numbers and environmental audio noise and imitate those sources towards generating new good quality random numbers on demand. We have generated more than 1 billion bits and observed robust performance in terms of true randomness metrics obtained from NIST SP 800-22 and FIPS 140-1 randomness test suites achieving a throughput of up to 142.85 Mbps. Compared to the state-of-the-art (SOTA) technique, the iso-cost setup of our framework can achieve more than 500 Mbps in a distributed setting. We have evaluated the efficacy of running the true randomness imitation AI models on target edge devices such as Raspberry Pi 4 (Model B), Nvidia Jetson Nano, Nvidia Jetson Orin Nano and Nvidia Jetson Xavier. We have also looked at the security of the TRIM framework itself against different adversarial threat models.

Cybersecurity↗

DOE OSTI2021

Standard Modular Architecture for Consumer End Plug and Play Interfaces

The growth in load and generation sources at the edge of the grid is driving the innovation in power electronic (PE) grid interfaces at the consumer end to improve the grid resiliency, reliability, security, and cost of the future infrastructure. Novel PE technologies are key enablers for the future grid infrastructure to handle the issues that arise because of the projected growth. This paper introduces a novel fundamental building block (FBB) architecture with plug-and play features. An example framework that enables co-ordination of multiple FBBs hierarchically to provide various grid functions is also presented. The FBBs are designed to be equipped with advanced features like online health monitoring, embedded intelligence, and decision-making capability for enhancing the metrics of consumer end plug and play interfaces. The paper elaborates on the proposed architecture and the developed framework i.e., the controls, communication, protection, and the corresponding timing requirements for the building blocks. The architecture and the framework have been validated through simulations and the communication framework has been validated with a control hardware in the loop platform.

Chinthavali, Madhu Sudhan↗

DOE OSTI2023

Enabling Advanced Energy Systems for Clean, Secure, and Sustainable Economic Growth Across Asia

The U.S. Agency for International Development (USAID) has partnered with the U.S. Department of Energy's national laboratories to form the Advanced Energy Partnership for Asia. Led by USAID and the National Renewable Energy Laboratory (NREL), the Partnership supports energy sector transformation and the growth of clean, secure, and sustainable energy markets across Asia.

Advanced Energy Partnership for Asia↗

DOE OSTI2024

BE-SATED: Building Energy Storage At The Edges of Demand

Otherlab is leading research efforts to validate and deploy battery-integrated appliances in real-world settings. The primary goal is to demonstrate the ability of these appliances to shift residential energy loads and reduce peak demand. Additionally, the project aims to secure safety certifications for battery-integrated appliances and establish a key industrial partnership with an appliance manufacturer for broader market adoption.

25 ENERGY STORAGE↗

DOE OSTI2024

Lawrence Livermore National Laboratory (LLNL) Laboratory Directed Research and Development (LDRD) Annual Report (FY 2023)

As Lawrence Livermore National Laboratory’s most significant resource for supporting internally directed research and development, the LDRD Program provides investments in cutting-edge science and technology that allow the Laboratory to attract and retain the world’s most talented scientists and engineers and enables them to expand the frontiers of knowledge and anticipate emerging national security challenges. In this annual report, we summarize how Lawrence Livermore National Laboratory (LLNL) uses LDRD investments to advance our knowledge in strategic science and technology domains, develop our world-class workforce, and foster innovation in key programmatic areas.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Port scanner and Testing Suite

This project addresses the challenge of identifying and managing open network ports across physical and virtual hosts. The current form of verifying ports in use required manually searching individual ports - a process that was both time- consuming and a potential bottleneck for deployment timelines. To resolve this, an automated port scanning tool was developed in Python. The tool supports simultaneous multiple port scans. To ensure functionality and long-term maintainability, a comprehensive testing suite was implemented using Python’s unittest framework. Edge cases, including valid port numbers, reversed ranges, and closed ports, were explicitly tested to ensure robust handling of real-world scenarios. The resulting tool reduces the time required to verify port security across a network, supporting both targeted and host checks and broader Classless Inter-Domain Routing (CIDR) -based network scans. This work demonstrates the value of automation and test-driven development in strengthening network security practices, and provides a foundation for future enhancements.

Rivera, Linda [Fermilab]↗

DOE OSTI2024

Technical Bulletin 005: NTP Monitoring

The Center for Alternative Synchronization and Timing (CAST) views Precision Time Protocol (PTP) and Network Time Protocol (NTP) as critical elements of the grid, from generation to the grid edge. This tech bulletin discusses the operation of NTP. The document shows not only commercial but also open-source applications. In some cases, the commercial applications may already have existing feature sets to secure and monitor NTP streams. From a security standpoint, using subscription NTP is risky unless the open bidirectional ports on local firewalls and routers are properly monitored and controlled.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Architecting the Grid Edge: Ensuring Reliability and Resilience

Changes in technology, customer expectations, and business and regulatory environments are rapidly evolving causing fundamental changes in the nation’s electrical infrastructure. Nowhere is this more apparent that at the “grid edge”, where there is an increasing number of new devices and systems, as well as complex new interactions between them. This is leading to the traditional relationship between the end-use customers and their utilities being expanded by an increasing number of stakeholders, each with their own operational and financial objectives, governed by regulatory policy. While there are concerns about the rapidly increasing complexity negatively impacting reliable and resilience of the electrical infrastructure, these changes are also bringing new resources and opportunities that hold great potential if they can be properly coordinated. This white paper outlines the considerations for the coordination of multi-stakeholder objectives with electric utility requirements using the concept of grid services. Describing a framework that enables new stakeholders to achieve their local technical and economic objectives, while simultaneously delivering operational benefits to the electrical infrastructure. The concepts of grid architecture are presented as a tool to evaluate how stakeholders might participate in, and benefit from, services, and how utilities can make decision on the reliance on services to ensure reliability and resilience, translating abstract concepts into actionable information for utilities and grid edge stakeholders. The end result of proper coordination, informed by grid architecture, will be a range of new devices and systems, operated by new stakeholders, achieving their local objectives while also increasing the reliability, resilience, security, and affordability of the nation’s critical electrical infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Architecting the Grid Edge: Ensuring Reliability and Resilience

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

2022 Annual Report Laboratory Directed Research & Development

Idaho National Laboratory’s (INL’s) mission is “to discover, demonstrate and secure innovative nuclear energy solutions, other clean energy options and critical infrastructure.” INL executes this mission through research and development across the continuum from basic science to applied science to engineering demonstration and then deployment. The Department of Energy (DOE) Laboratory Directed Research and Development (LDRD) program enables INL to conduct high-risk, impactful research that enriches the laboratory capabilities in order to further its missions. INL’s LDRD portfolio specifically advances the core capabilities of the laboratory aligned with its five science and technology initiatives: 1) nuclear reactor sustainment and expanded deployment, 2) integrated fuel cycle solutions, 3) integrated energy systems, 4) advanced design and manufacturing for extreme environments, and 5) secure and resilient cyber-physical systems. The 45 projects that ended in fiscal year 2022 and highlighted in this report are just a small sample of the impressive breadth and depth of cutting-edge science, technology, and engineering ongoing at INL.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

The Effects of Compounded Model Size Reductions on Adversarial Robustness

Recent advances in Edge AI and Tiny Machine Learning (TinyML) have enabled the deployment of machine learning models on resource-constrained environments. However, deploying these models on edge devices, such as micro-controllers, requires significant model footprint reduction through a variety of techniques such as quantization, pruning, and clustering. While these optimization methods offer considerable advantages, they potentially introduce AI-related security vulnerabilities, particularly concerning model robustness with respect to adversarial AI attacks. Prior research has extensively examined the impact of quantization on adversarial robustness; however, the effects of alternative reduction techniques and their combinations remain understudied. This paper investigates the impact of model size reduction techniques on adversarial robustness, when applied individually and combined. We utilized Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks to generate adversarial perturbations for both training and testing data, and then evaluated the models' accuracy under adversarial training conditions. Our findings revealed that reduction techniques generally diminished robustness; although, combining techniques was not found to make robustness any worse than when applied individually. Moreover, specific techniques can potentially enhance resistance to small size perturbations. This research provides insights into the trade-offs between model size reduction and security, establishing a foundation for future investigations into improving adversarial training techniques and methodologies for maintaining robustness while preserving memory footprint benefits.

Austria, Phillipe [ORNL] (ORCID:0000000236223973)↗

DOE OSTI2020

IIA Incremental Interval Assignment

IIA is a solver for optimizing integer matrix problems Ax=b. It was developed to decide the number of mesh edges on model curves (intervals) for quad and hex meshing. Meshing schemes impose constraints ranging from the mild requirement (i.e. that any quad mesh must have an even number of edges on its boundary) to structured mapped patches where opposite sides of a rectangle must have exactly equal numbers of edges. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2020-13618 M

MItchell, Scott↗

DOE OSTI2025

LLM Generation of Online Courses from a Curated Set of Documents in the Nuclear Safeguards Domain

A multidisciplinary team at Argonne National Laboratory explores the application of advanced technologies to enhance knowledge transfer and retention within the nuclear safeguards domain. Specifically, it examines the feasibility of leveraging secure large language models (LLMs) to streamline the creation of e-learning modules for the U.S. National Nuclear Security Administration (NNSA) Office of International Nuclear Safeguards (NA-241). The initiative addresses the critical need for preserving institutional memory and accelerating skill development amidst the imminent retirement of senior professionals in the field in addition to supporting good knowledge management practices. The project integrates instructional design theory with cutting-edge AI technologies to transform curated document sets from the Safeguards Knowledge Repository (SKR) into modular online courses. By automating the generation of learning objectives and instructional content, the effort aims to reduce manual effort while maintaining high-quality educational outcomes. A limited measure of human supervision, however, ensures accuracy, relevance, and alignment with NNSA’s strategic priorities. Key findings highlight the potential of AI-assisted course generation to support safeguards professionals by creating structured, interactive learning experiences. The report underscores the importance of SME validation to address limitations in AI-generated content, such as terminology errors and gaps in coverage. Recommendations include adopting a structured workflow combining LLM acceleration with expert oversight to ensure accuracy, usability, and alignment with learner needs. This work demonstrates Argonne’s commitment to advancing national security and scientific excellence through innovative knowledge management solutions.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2022

Exploring Tradeoffs in Federated Learning on Serverless Computing Architectures

Federated learning is driving the development of new techniques to efficiently and securely use data across multiple sites while using diverse resources. One of these techniques is the use of the serverless computing paradigm to abstract away resource specific configurations, allowing federated learning across heterogeneous environments. However, deploying federated learning across edge resources, the cloud, and traditional HPC sites will require specialized approaches in order to best account for the weaknesses and strengths of each resource. In this work, we explore the new tradeoffs presented by managing a federated learning task across heterogeneous resources and demonstrate these tradeoffs with experiments using a serverless federated learning framework.

Baughman, Matt↗

DOE OSTI2024

Human-Centered and Explainable Artificial Intelligence in Nuclear Operations

Nuclear power plants in the United States are critical to the nation’s energy security, accounting for 20% of all electricity produced for the power grid. As energy needs grow, 100 gigawatts of additional nuclear power will be necessary by 2050, more than double the current capacity. Realizing this target requires cutting-edge technology like artificial intelligence (AI) and machine learning (ML) that can bring about significant increases in the level of automation. Human-centered AI (HCAI) is a combination of human-centered design (human factors, human-in-the-loop, etc.) with AI/ML to help produce an efficient and reliable system with full consideration for human engagement. This paper provides a comprehensive and novel discussion of HCAI considerations in nuclear power, introducing unique applications for the existing fleet as well as new advanced reactor designs. We include real-life use cases of AI applications to work management processes at nuclear power sites and highlight lessons learned for HCAI.

Hall, Anna↗