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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Fossil Power Plant Cyber Security Life-Cycle Risk Reduction: A Practical Framework for Implementation

Market conditions are forcing fossil electricity generation facility owners and operators to implement advanced digital technologies. These technologies enable efficiencies, operational flexibility, operations and maintenance efficiencies, and adapting to a transitioning workforce. These digital technologies, however, can increase the cybersecurity attack surface. The purpose of this research was to develop a holistic cybersecurity risk reduction framework for fossil generation facilities. The framework begins with assessing how cyber risk changes across facility life cycles, including plant, system, vendor, and business life cycles. The next phase performs consequence analysis to prioritize high consequence events. Focusing on high consequence events allows owners to use a graded, risk-informed approach to prioritize cybersecurity efforts. The final phase identifies the digital asset attack surface in sensors and instrumentation and control equipment. After the vulnerabilities are identified, the owner selects mitigating cybersecurity control measures (or countermeasures) based on the risk analysis from the previous phases. This report describes the current industry cybersecurity best practices in fossil generation that are based on the first principles for cybersecurity engineering. The report is divided into five sections that describe the implementation of the risk reduction framework and present identified research, methodological, and technology gaps that were identified through this course of research and development.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2020

Fossil Power Plant Cyber Security Life-Cycle Risk Reduction: A Practical Framework for Implementation

Market conditions are forcing fossil electricity generation facility owners and operators to implement advanced digital technologies. These technologies enable efficiencies, operational flexibility, operations and maintenance efficiencies, and adapting to a transitioning workforce. These digital technologies, however, can increase the cybersecurity attack surface. The purpose of this research was to develop a holistic cybersecurity risk reduction framework for fossil generation facilities. The framework begins with assessing how cyber risk changes across facility life cycles, including plant, system, vendor, and business life cycles. The next phase performs consequence analysis to prioritize high consequence events. Focusing on high consequence events allows owners to use a graded, risk-informed approach to prioritize cybersecurity efforts. The final phase identifies the digital asset attack surface in sensors and instrumentation and control equipment. After the vulnerabilities are identified, the owner selects mitigating cybersecurity control measures (or countermeasures) based on the risk analysis from the previous phases. This report describes the current industry cybersecurity best practices in fossil generation that are based on the first principles for cybersecurity engineering. The report is divided into five sections that describe the implementation of the risk reduction framework and present identified research, methodological, and technology gaps that were identified through this course of research and development.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2020

Energy Master Planning for Resilient Public Communities—Best Practices from U.S. Military Installations

Until recently, most planners at military installations addressed energy systems for new facilities on an individual facility basis without consideration of community-wide goals relevant to energy sources, renewables, storage, or future energy generation needs. Building retrofits of public buildings typically do not address energy needs beyond the minimum code requirements making it difficult, if not impossible, to achieve community-level targets on a building-by-building basis. Planning on the basis of cost and general reliability may also fail to deliver community-level resilience. For example, many building code requirements focus on hardening to specific threats, but in a multi-building community, only a few of these buildings may be mission-critical. Over the past two decades, the frequency and duration of regional power outages and water utility disruptions from weather, man-made events, and aging infrastructure have increased. Major disruptions of electric and thermal energy have degraded critical mission capabilities and caused significant economic impacts. In 2016, the U.S. Department of Defense issued guidance that each Service (Army, Navy, Air Force, Marines) complete comprehensive energy plans for the installations that consumed 75% of total building energy. Guidance was updated in 2017 to include metrics for energy resilience, and in some cases, water. This paper describes how community level quantitative and qualitative resilience analysis and metrics have been incorporated into community energy and water planning best practices for military installations in three geographically diverse locations. It is based on research performed under the International Energy Agency’s “Energy in Buildings and Communities Program Annex 73,” focusing on development of guidelines and tools that support the planning of Net Zero Energy Resilient Public Communities as well as research performed under the Department of Defense Environmental Security Technology Certification Program project EW18-D1- 5281, “Technologies Integration to Achieve Resilient, Low- Energy Military Installations.” The first case study reviews progress made on an energy and water planning study conducted at Fort Bliss, Texas. The second and third describes planning conducted at Fort Bragg, North Carolina and the Joint Region Marianas, Guam, respectively, under the updated guidance from 2017 regarding energy and water resilience. Analysis methods, key metrics, and key infrastructure and operational constraints are described, as well as technical, economic and business concepts used during the planning process.

Urban, Angela B.↗
DOE OSTI2020

Measure Utility, Gain Trust: Practical Advice for XAI Researchers

Research into explanation of machine learning models, i.e. explainable AI (XAI), has seen a sympathetic exponential growth alongside deep artificial neural networks throughout the past decade. For historical reasons explanation and trust have been intertwined. However this focus on trust is too narrow, and has led the research community astray from tried and true empirical methods that lead to more defensible scientific knowledge about people and explanations. To address this, we contribute a practical path forward for researchers in the XAI field. We recommend researchers focus on the utility and impact of their explanations instead of trust. We outline five broad use cases where explanations are useful and, for each, we describe pseudo-experiments that rely on objective empirical measurements and falsifiable hypotheses. We believe that this experimental rigor is necessary to contribute to scientific knowledge in the field of XAI.

Davis, Brittany F.↗
DOE OSTI2021

Absorbing the Sun: Operational Practices and Balancing Reserves in Florida's Municipal Utilities

The Florida Reliability Coordinating Council (FRCC) power system is comprised of multiple balancing authorities ranging in size from Gainesville Regional Utilities (GRU) with a 2019 summer net firm demand of 429 MW to Florida Power & Light with a 2019 summer net firm demand of 22,510 MW; and including cooperative, municipal, and investor-owned utilities. As all of these balancing authorities are and have plants to continue installing significant quantities of utility-scale solar photovoltaics, one relevant question is how much operating reserves they will need to hold as solar penetrations increase. While there are estimates of how regulating and flexibility reserve requirements change with solar penetration, the literature almost exclusively focuses on large balancing authorities with sub-hourly dispatch. In this work we analyze how reserve needs change not only with solar photovoltaic penetration, but also balancing authority size and operational practices. We find that, measured as a fraction of load, smaller balancing authorities with less frequent load and solar forecasts and less frequent dispatch need more reserves. Such utilities' reserve needs also increase more with increasing solar deployment as compared to larger or more frequently dispatched balancing authorities. These impacts are most acute for GRU. We find that moving from day-ahead to hour-ahead load and solar forecasting and system dispatch could enable GRU to incorporate 32% solar generation with median reserves at 20% instead of 60% of load; and that median reserve needs could drop further to about 10% of load if Florida's municipal utilities formed a reserve sharing group and moved to sub-hourly dispatch.

balancing authority↗
DOE OSTI2021

2021 ROMANIA TRANSPORT SECURITY TABLE-TOP EXERCISE (TTX) – LESSONS LEARNED AND GOOD PRACTICES

During the period of 15-18 March 2021, the IAEA conducted a national workshop addressing the security of radioactive material in transport. The workshop was jointly hosted by the Romanian regulatory authority and the General Inspectorate of Romanian Gendarmerie. The workshop assisted Romania in improving its capability to evaluate the ability of response forces to counter a malicious act against radioactive material in transport. The event consisted of briefings, exercises, a demonstration of transport security equipment, and a transport security table-top exercise (TTX). The workshop was conducted in a virtual-hybrid format in such a way as to meet the COVID-19 related requirements in compliance with Romania’s guidelines. This paper provides an overview of the workshop’s activities, lessons-learned, and good practices related to the transport security TTX exercises.

Maican, Catalin↗
DOE OSTI2021

IEEE P2988: Recommended Practice of Use and Functions of Virtual Synchronous Machines

Power systems are going through a paradigm shift from centralized generation to distributed generation. This brings unprecedented challenges to power systems stability, reliability, and security. Operating power electronic converters as Virtual Synchronous Machines (VSMs) can bring backward compatibility to power systems, unify and harmonize the integration and interaction of future power systems players, and improve power system stability, reliability, and security. This is vital for the large-scale adoption of distributed energy resources, the advancement of sustainability, and the development of a low-carbon economy. This IEEE 2988 Recommended Practice will define the fundamental principles, mandatory functions, and optional functions of a VSM, i.e., a power electronic converter that is operated to behave like conventional synchronous machines. It will not describe or specify power semiconductor devices, hardware topologies, or micro-controllers that are used to build a VSM. This talk will discuss the activities and development plan for IEEE 2988 with the aim to engaging broad stakeholders and widening participation.

Zhong, Qing-Chang↗
DOE OSTI2022

Best Practices for Enhancing Performance Contracts with Monitoring-Based Commissioning

Fact sheet highlights best practices for incorporating monitoring-based commissioning (MBCx), a type of energy management information system (EMIS), in performance contracts. It draws from the Federal Energy Management Program (FEMP) report, Enhancing Performance Contracts Using Monitoring-based Commissioning. MBCx is commercially available, yet is underutilized in performance contracts. MBCx can result in significant benefits for both a federal facility and the performance contractor.

EMIS↗
DOE OSTI2022

Toward designing effective exascale scientific computing workflows: experiences and best practices

Many fields within scientific computing have embraced advances in big-data analysis and machine learning, which often requires the deployment of large, distributed and complicated workflows that may combine training neural networks, performing simulations, running inference, and performing database queries and data analysis in asynchronous, parallel and pipelined execution frameworks. Such a shift has brought into focus the need for scalable, efficient workflow management solutions with reproducibility, error and provenance handling, traceability, and checkpoint-restart capabilities, among other needs. Here, we discuss challenges and best-practices for deploying exascale-generation computational science workflows on resources at the Oak Ridge Leadership Computing Facility (OLCF). We present our experiences with large-scale deployment of distributed workflows on the Summit supercomputer, including for bioinformatics and computational biophysics, materials science, and deep learning model optimization. We also present problems and solutions created by working within a Python-centric software base on traditional HPC systems, and discuss steps that will be required before the convergence of HPC, AI, and data science can be fully realized. Our results point to a wealth of exciting new possibilities for harnessing this convergence to tackle new scientific challenges.

Coletti, Mark↗
DOE OSTI2022

Methods of producing cobalt nanoparticles and hollow gold nanospheres and kits for practicing same

Provided are methods of producing cobalt nanoparticles (Co NPs). The methods include combining a cobalt salt, a capping agent, and a reducing agent, under Co NP synthesis conditions including a temperature selected to produce cobalt nanoparticles of a pre-selected diameter, where the temperature and pre-selected diameter are inversely related. In certain aspects, the methods further include producing hollow gold nano spheres (HGNs) using the cobalt nanoparticles as scaffolds. Also provided are cobalt nanoparticles and hollow gold nano spheres (HGNs) produced according to the present methods. Kits that find use in practicing the methods of the present disclosure are also provided.

Pu, Ying-Chih↗
DOE OSTI2021

Improved Nondestructive Isotopic Analysis with Practical Microcalorimeter Gamma Spectrometers

Advances in both instrumentation and data analysis software are now enabling the first ultra-high-resolution microcalorimeter gamma spectrometers designed for implementation in nuclear facilities and analytical laboratories. With approximately ten times better energy resolution than high-purity germanium detectors, these instruments can overcome important uncertainty limits. Microcalorimeter gamma spectroscopy is intended to provide nondestructive isotopic analysis capabilities with sufficient precision and accuracy to reduce the need for sampling, chemical separations, and mass spectrometry to meet safeguards and security goals. Key milestones were the development of the SOFIA instrument (Spectrometer Optimized for Facility Integrated Applications) and the SAPPY software (Spectral Analysis Program in PYthon). SOFIA is a compact instrument that combines advances in large multiplexed transition-edge sensor arrays with optimized cryogenic performance to overcome many practical limitations of previous systems. With a 256-pixel multiplexed detector array capable of 5,000 counts per second, measurement time can be comparable to high-purity germanium detectors. SAPPY was developed to determine isotopic ratios in data from SOFIA and other microcalorimeter instruments with an approach similar to the widely-used FRAM software. SAPPY provides a flexible framework with rigorous uncertainty analysis for both microcalorimeter and high purity germanium (HPGe) data, allowing direct comparison. Here, we present current results from the SOFIA instrument, preliminary isotopic analysis using SAPPY, and describe how the technology is being used to explore uncertainty limits of nondestructive isotopic characterization, inform safeguards models, and extract improved nuclear data including gamma-ray branching ratios.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Twenty Years and Counting—Where are they? Practical Recommendations for Commercializing AI/ML for Intrusion Detection in the Nuclear Industry

Research and development into applications for improving equipment condition monitoring programs at nuclear facilities has been around since the 1990s. However, while the field has moved from using data-driven machine learning (ML) algorithms for detection and prediction of equipment degradation and failure to prognostic capabilities, these applications are still not widely used in the U.S. nuclear industry. Additionally, there has been significant effort in designing both data-driven and physics-based artificial intelligence (AI) and ML models for many other potential applications in the nuclear industry, including cyber intrusion detection systems (IDS). However, as the last twenty years in condition-based maintenance research has shown us, there are significant hurdles that must be overcome for deployment of IDS on plant systems. This paper provides a discussion on the practical recommendations that researchers should consider for successful adoption of AI/ML IDS in the nuclear industry.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Panel Session 128: Best Practices in Project Communications

This panel focused on a sustainable, holistic and integrated approach to structure nuclear waste management, disposal and remediation decisions. This panel had participation from internal and external stakeholders and demonstrate use of tools and techniques to effectively communicate factors that inform decisions. The desired outcome was to effectively communicate how risk is evaluated at complex sites, highlight holistic site level approaches that are protective of human health and the environment, and incorporate policy and technical concerns related to achieving alternative end points. This discussion included existing decision-making tools in conjunction with case studies. Panelists with presentations: Removing the Cloak of Secrecy from Legacy Cleanup at Los Alamos National Laboratory (David Nickless); Best Practice In Project Communications (Alan Paulley); Stakeholder and Public Engagement (Mark Lesinski); Stakeholder Engaged Structured Decision Making (Kelly Black)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Challenges and Practical Solutions for Conducting Structural MARSSIM Surveys in Elevated Radon Environments - 20413

The Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM) [1] provides comprehensive guidance for the development and implementation of radiological surveys conducted to demonstrate compliance with a dose- or risk-based cleanup goal during final status survey. The MARSSIM methodology follows an integrated strategy combining direct measurements and sampling to assess uniform residual contamination, and scanning surveys to identify localized areas of elevated activity. Building surveys assessing residual surface contamination present unique challenges from reliance on field measurements that may be subject to less than ideal environmental conditions. Additionally, limited characterization and excessive conservatism incorporated into the conceptual site model may result in derived concentration guideline levels (DCGL) and, as a consequence of relatively inefficient field instrumentation, corresponding attributable count rates generally comparable to background. The presence of elevated ambient radon concentrations and associated progeny deposition both exacerbate these intrinsic survey challenges and introduce a series of technical, logistic, and administrative obstacles that can wreak havoc on the statistical legitimacy of each component of the MARSSIM process. Short-lived radon progeny including polonium-218, lead-214, bismuth-214, and polonium-214 produce excessive alpha and beta radiation that may decrease instrument sensitivity through an increase in background count rates, and, at worst, may result in incorrect survey unit failure through the simulation of residual surface activity. Aside from the obvious solution of exhaustive radon mitigation, the primary objectives are to distinguish between radon progeny deposition and actual surface activity, and collect sufficient defensible data. The following is a technical discussion of the consequences of performing building MARSSIM surveys in the presence of elevated ambient radon atmospheres, conceptual examples, and an overview of practical strategies to minimize the effects of radon interference while collecting sufficient defensible data to complete the survey. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Does practice make perfect? Lessons learned from full-scale power system incident response exercise

While threats to the energy sector occur daily, few utilities get the opportunity to fully test out their detection and response mechanisms to advanced threats in the real world. With the high demand for reliability, few grid operators would allow execution of simulated cyber-attacks on their live systems. The DOE-funded Liberty Eclipse project offers a unique opportunity for small and large utilities and coops to practice their combined IT/OT responses to a live red team executing attacks against an isolated power system on an island in New York. Both cyber teams and power operations teams must work together to detect and respond to attacks, even restoring the power system against extreme impacts. Lessons learned from these exercises reveal key takeaways for understanding what a real attack against the electric sector will look like, gaps in execution of the best-laid plans when the pressure of a real event is bearing down, and how organizations can better prepare for advanced attacks by optimizing participation in exercises. This presentation will discuss successes and opportunities for improvement both in how utilities can prepare for and respond to events, as well as how full-scale IT/OT exercises can be coordinated.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Streaming Matching and Edge Cover in Practice

Graph algorithms with polynomial space and time requirements often become infeasible for massive graphs with billions of edges or more. State-of-the-art approaches therefore employ approximate serial, parallel, and distributed algorithms to tackle these challenges. However, such approaches require storing the entire graph in memory and thus need access to costly computing resources such as clusters and supercomputers. In this paper, we present practical streaming approaches for solving massive graph problems using limited memory for two prototypical graph problems: maximum weighted matching and minimum weighted edge cover. For matching, we conduct a thorough computational study on two of the semi-streaming algorithms including a recent breakthrough result that achieves a $1/(2+\varepsilon)$-approximation of the weight while using $O( n \log W /\epsilon)$ memory (here $n$ is the number of vertices and $W$ is the maximum edge weight), designed by Paz and Schwartzman [SODA, 2017]. Empirically, we show that the semi-streaming algorithms produce matchings whose weight is close to the best $1/2$-approximate offline algorithm while requiring less time and an order-of-magnitude less memory. For minimum weighted edge cover, we develop three novel semi-streaming algorithms. Two of these algorithms require a single pass through the input graph, require $O(n \log n)$ memory, and provide a 2-approximation guarantee on the objective. We also leverage a relationship between approximate maximum weighted matching and approximate minimum weighted edge cover to develop a two-pass $3/2+\epsilon$-approximate algorithm with the memory requirement of Paz and Schwartzman's semi-streaming matching algorithm. These streaming approaches are compared against the state-of-the-art 3/2-approximate offline algorithm. The semi-streaming matching and the novel edge cover algorithms proposed in this paper can process graphs with several billions of edges in under 30 minutes using 6 GB of memory, which is at least an order of magnitude improvement from the offline (non-streaming) algorithms. For the largest graph, the best alternative offline parallel approximation algorithm (GPA+ROMA) could not finish in three hours even while employing hundreds of processors and 1 TB of memory. We also demonstrate an application of the semi-streaming algorithm by computing a matching using linearly bounded memory on item intersection graphs derived from three machine learning datasets, whereas the existing offline algorithms could not complete on one of these datasets since their memory requirements exceeded 1TB.

Ferdous, S M.↗