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DOE OSTI2023

Local and utility-wide cost allocations for a more equitable wildfire-resilient distribution grid

Climate-induced extreme weather conditions make electricity infrastructure more vulnerable. They increase the risk of power-line-ignited wildfires which can, in turn, jeopardize electric power delivery. Here, leveraging machine learning, we show that lower-income communities in California not only have lower fractions of power distribution lines undergrounded, but overhead lines and poles in their neighbourhoods are also more vulnerable to wildfires. Should they bear the cost of undergrounding fire-prone lines themselves, they would have to pay a disproportionately higher cost per household. We propose a cost allocation scheme with an income threshold below which the cost is borne by utility-wide ratepayers and above which the cost is borne locally. This scheme can not only minimize the average of undergrounding costs per household as a share of income, but also homogenize such cost–income ratios across communities. Furthermore, our research demonstrates the opportunity to appropriately integrate existing policies to make electricity infrastructure affordable, equitable and reliable amidst climate change.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Cybersecurity Certification Recommendations for Interconnected Grid Edge Devices and Inverter Based Resources

Escalating deployment of PV and grid-edge devices on the distribution grid has increased the sustainability and efficiency of the electric grid. However, the increasing number of distributed energy resources (DERs) deployed creates a heightened cyber-physical interdependency on the distribution grid and thus creates more vectors for cyber-attacks to exploit through information and communication technology (ICT) systems and networks. For example, control signal packets can be modified, intercepted, or corrupted due to vulnerabilities in communication protocols used by microgrid controllers and grid edge devices for power control. Therefore, to mitigate and prevent cyber-attacks on grid edge devices and the inverter-based resources connected to the distribution grid, the U.S. Department of Solar Energy Technologies Office (SETO) awarded funding to the National Renewable Energy Laboratory and Sandia National Laboratory (SNL) to research, develop, and harmonize cybersecurity standards for Photovoltaic (PV) systems and for other kinds of DERs. To help develop a standard for DER cybersecurity, NREL established certification recommendations and test cases, in consensus with the solar industry and UL, for ensuring intrinsic design security for DERs. These recommendations were developed to bolster the cybersecure functionalities such as TLS, MAC, CRL, session resumption/renegotiation, and password, system, and service security management within the DER devices. The proposed test cases verify authentication, authorization, confidentiality, and data integrity for data and communications of DERs that use Transmission Control Protocol/Internet Protocol (TCP/IP). They were also developed to protect DER communications from eavesdropping, replay, man-in-the-middle, denial of service (DoS), spoofing through security certificates, least-privilege violation, and brute-force credentials. This report, which has been validated and reviewed by UL, expands upon those test cases to provide DER cybersecurity certification recommendations which increase DER resiliency and help to mitigate cyber-attacks. UL's collaboration with NREL and approval of this document will accelerate the adoption of a UL standard for DER cybersecurity.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Projecting end-of-century climate extremes and their impacts on the hydrology of a representative California watershed

Abstract. In California, it is essential to understand the evolution of water resources in response to a changing climate to sustain its economy and agriculture and to build resilient communities. Although extreme conditions have characterized the historical hydroclimate of California, climate change will likely intensify hydroclimatic extremes by the end of the century (EoC). However, few studies have investigated the impacts of EoC extremes on watershed hydrology. We use cutting-edge global climate and integrated hydrologic models to simulate EoC extremes and their effects on the water-energy balance. We assess the impacts of projected driest, median, and wettest water years under Representative Concentration Pathway (RCP) 8.5 on the hydrodynamics of the Cosumnes River basin. Substantial changes to annual average temperature (>+2.5 ∘C) and precipitation (>+38 %) will characterize the EoC extreme water years compared to their historical counterparts. A shift in the dominant form of precipitation, mostly in the form of rain, is projected to fall earlier. These changes reduce snowpack by more than 90 %, increase peak surface water and groundwater storages up to 75 % and 23 %, respectively, and drive the timing of peak storage to occur earlier in the year. Because EoC temperatures and soil moisture are high, both potential and actual evapotranspiration (ET) increase. The latter, along with the lack of snowmelt in the warm EoC, causes surface water and groundwater storages to significantly decrease in summer, with groundwater showing the highest rates of decrease. These changes result in more ephemeral EoC streams with more focused flow and increased storage in the mainstem of the river network during the summer.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Community-Engaged Modeling of Urban Flood Adaptation Pathways

Climate change is intensifying the hydrologic cycle, leading to more frequent and severe rainfall-driven (pluvial) flooding in urban areas. In the mid-Atlantic US cities, aging and under-designed stormwater infrastructure is increasingly strained by these events, resulting in recurring damage to property and disruptions to transportation networks. In this study, we combine community engagement with hydrologic modeling to develop and evaluate potential urban flood adaptation strategies. Over a three-year period, local technical experts and community representatives met regularly to discuss flooding concerns, identify priorities, and co-develop adaptation strategies. These discussions informed the development of an urban flooding model (EPA Storm Water Management Model) for the Baltimore Harbor watershed, the focus location of this study. The flooding model integrates complex surface and subsurface stormwater infrastructure data, local expert knowledge, and community insights. We simulate stakeholder-prioritized adaptations, such as green and gray infrastructure strategies. Model results demonstrate that enhanced infrastructure maintenance is the most effective adaptation for reducing flood depths, but has varied effects across the watershed, and can increase flooding in some locations. Spatially concentrated greening provides limited benefit to the watershed as a whole, but moderate benefit in community priority areas. Together, these adaptations have the potential to reduce flood depths by as much as 58% in some locations, greatly reducing property damage and mobility impacts, primary concerns of stakeholders. Future work will implement robust optimization tools to search for adaptations which meet stakeholder objectives and perform highly under varied future climate conditions. This work contributes to the expanding literature on collaborative modeling and demonstrates that community-engaged approaches can enhance model credibility and generate more actionable insights for communities seeking to strengthen climate resilience.

Spangler, Ava [Pennsylvania State University] (ORC↗

DOE OSTI2026

Community-Informed Urban Flood Modeling for Impact Mitigation

Baltimore MD↗

DOE OSTI2024

A Cast of Thousands: How the IDEAS Productivity Project Has Advanced Software Productivity and Sustainability

Computational and data-enabled science and engineering are revolutionizing advances throughout science and society, at all scales of computing. For example, teams in the U.S. Department of Energy’s Exascale Computing Project have been tackling new frontiers in modeling, simulation, and analysis by exploiting unprecedented exascale computing capabilities—building an advanced software ecosystem that supports next-generation applications and addresses disruptive changes in computer architectures. However, concerns are growing about the productivity of the developers of scientific software. Members of the Interoperable Design of Extreme-scale Application Software project serve as catalysts to address these challenges through fostering software communities, incubating and curating methodologies and resources, and disseminating knowledge to advance developer productivity and software sustainability. This article discusses how these synergistic activities are advancing scientific discovery—mitigating technical risks by building a firmer foundation for reproducible, sustainable science at all scales of computing, from laptops to clusters to exascale and beyond.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Risk-Aware Measurement Synchronization and Recovery for DSSE With Heterogeneous Data Sources

Power distribution systems are increasingly integrating heterogeneous sensors with varying data reporting rates and types, which pose challenges to achieving observability at the desired temporal resolution of distribution system state estimation (DSSE). Multisensor failures caused by extreme events exacerbate these issues, introducing substantial uncertainties into DSSE. This article proposes a novel solution to these challenges by ensuring high-resolution system observability despite heterogeneous data sources and multisensor failures. First, a deep learning architecture combining long short-term memory (LSTM) and graph convolutional network (GCN) is employed to synchronize meters with different reporting rates, aiming to achieve system observability. A random-walk-model-based approach is introduced to generate pseudo-measurements while properly characterizing their uncertainties under multisensor failures. Finally, a disaster-risk-informed observability metric (RiOM) is defined to quantify the uncertainty associated with state estimation results. The proposed framework offers deeper insights into the system observability on the fly compared with conventional analysis. The effectiveness of the framework is demonstrated on an IEEE standard test case and a large-scale real-world distribution feeder in mid-Minnesota in the U.S.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Motivation and Design of the OCPP Security Service

Pacific Northwest National Laboratory is conducting in-depth research aimed at exploring how zero trust security principles can be effectively applied to electric vehicle charging infrastructure. This investigation seeks to enhance the resilience and reliability of these systems against cyber threats, ensuring secure and uninterrupted access to charging services for electric vehicle users and electric supply. Zero trust is a security concept centered on the belief that system operators should not automatically trust users or systems based on their location, whether inside or outside the organization, but instead must verify everything trying to connect to their systems before granting access. A key aspect of the project is to demonstrate and validate zero trust approaches targeted to electric vehicle (EV) charging infrastructure. It has been observed that both open-source and commercial solutions often overlook the specific protocols employed in managing EV charging stations and proceeded with a general, protocol-agnostic approach. While these strategies effectively block non-authorized routes to the charging infrastructure, they do not tackle the situations where attackers may exploit legitimate access channels, such as the inattentive operator model posited by the Idaho National Laboratory. To address this gap, this paper proposes and discusses a new security service targeted to the Open Charge Point Protocol (OCPP), which is the de facto protocol for the management of charging stations and serves a critical role in the broader adoption of electric vehicles. The design and architecture of the proposed OCPP security service are discussed in detail, outlining how it aims to safeguard charging station management system (CSMS) functions. The service is particularly important in scenarios where the charging station operator (CSO), responsible for the maintenance and operation of charging stations, and the charging network provider (CNP), which manages the charging network's accessibility and billing, are separate entities. This distinction is crucial because CSOs and CNPs often have different priorities, objectives, and operational responsibilities, which may not always align perfectly. For instance, a CSO might prioritize uptime and customer satisfaction, while a CNP might focus on maximizing revenue and network utilization. Such misalignment can create security vulnerabilities, as each entity might implement different policies and standards, potentially leaving gaps in the overall security posture.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Optimized V1G and V2G Electric Vehicle Fleet Management and Grid Transaction at Marine Corps Air Station Miramar in San Diego, CA

The overall technical goal of the project was to demonstrate an all-electric bi-directional non-tactical fleet at Marine Corps Air Station (MCAS) Miramar that was integrated and controlled with other distributed energy resources (DERs) (i.e., PV, stationary battery, and building loads) to provide resilience to critical electric loads in the event of grid outages, to minimize charging costs, and to provide economic energy resources to electricity markets. In this project, the specific, technical objectives were: 1. Demonstrate that bi-directional electric vehicles can provide critical complementary services to fixed storage batteries in microgrid applications while performing function as non-tactical vehicles. 2. Demonstrate participation of bi-directional (V2G) and unidirectional (V1G) PEVs for demand management and minimization of charging costs. 3. Demonstrate integration of multiple DERs for grid service participation. US Marine Corps Air Station (MCAS) Miramar in San Diego was the site of this electric vehicle-to-microgrid-utility grid test and demonstration project. Existing microgrid assets in this study included (1) a public works building; (2) a 30-kW rooftop photovoltaic (PV) system and (3) a separate 250 kW carport PV system. In this project, six bi-directional V2G vans were located at the MCAS Miramar’s showcase building-scale microgrid to develop and test technical capabilities that V2G can provide in microgrid applications (e.g., cost reduction and resiliency). These resources provided aggregated demand management and simulated participation in current retail DR programs. The vehicles used in this demonstration were selected because they provided functionality that MCAS Miramar needed, 15 passenger transport and facilities work cargo carrying capacity, and bi-directional charging capability that the research project required. All vehicles in this study were manufactured and distributed by VIA Motors, Inc. There were six vehicles total and each was VIA’s VTRUX eREV V2G model, a modified General Motors Chevrolet 2500 2WD van. Three of the vans were configured as passenger vans and the other three were configured as cargo vans. Each van had an on-board bi-direcrtional inverter/charger, Bel Power Solutions model 350INVCHGT150-120-240-8G nominally rated at +/-15 kW. The VIA van’s charging connector follows the J1772 charging protocol. The bi-directional EVSEs demonstrated in this study were manufactured by Coritech, Inc. Each VGI-80-AC charging station enabled enhanced V2G charging capability to a Clipper Creek CS-100 charging module. The enhanced capabilities included ethernet communication following the SEP2.0 protocol with a distributed energy resource function set and an operator screen displaying real-time SOC, voltage, and current. The VGI-80-AC charging stations are classified as level 2 with a maximum current output of 80 A or effectively 19 kW. The VIA van’s onboard charger limited the charging and discharging power to 15 kW in each direction. A control computer was installed in the EWOC and connected to an existing monitor. The V2G control communication network was a completely stand-alone closed system that did not have any connection to any other networks on the base. A cybersecure remote communication connection was created with a cellular modem, firewall hardware, and a virtual private network configuration.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Advanced Energy Topics for Hill District Community Leaders [Slides]

Through Communities LEAP, a community coalition focused on the Hill District neighborhood of Pittsburgh is working with a technical assistance provider network led by the National Renewable Energy Laboratory (NREL). The coalition includes community organizations, nonprofits, the city government, and the utility. Technical assistance provides analysis and information to support Hill District stakeholders in their goals to create informed residential energy efficiency and renewable energy transition strategies that improve housing conditions and lower energy bills, incorporate energy efficiency and renewable energy strategies into existing, community-driven development efforts, and generate quality local jobs. The Advanced Energy Topics resource is intended to support Hill District community leaders with strategic planning on energy-related topics. The topics included are intended to serve as a technical reference for community leaders to help inform decision-making around energy. The Advanced Energy Topics resource was informed by engagement through the Communities LEAP pilot program with three Hill District Registered Community Organizations: the Hill District Collaborative, the Hill District Consensus Group, and the Hill Community Development Corporation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

A Physics-Based Data-Driven Approach for Modeling of Environmental Degradation in Elastomers

Abstract Elastomers are now commonly used in a number of industries, including aerospace, structure, transportation, shipbuilding, and automotive, due to their excellent workability, formability, and flexibility. During their activity, elastomers are subjected to harsh environmental conditions, which decreases their resilience. False predictions made early in their lives can have major financial and environmental implications. Elastomers’ performance and properties, such as strength, durability, and density, are influenced by chemical changes in these materials, known as degradation, which occurs over time. This process can alter the morphology of a polymer matrix as well as cause chain scission and cross-linking, resulting in different behaviors than that of the unaged material. To demonstrate the effect of thermaloxidative aging on the mechanical behavior of elastomers, several experimental and theoretical models have been proposed. In view of the large volume of experimental data available on micro-structural evolution in the course of aging, we propose a physics-based data-driven approach to overcome the shortcomings of both phenomenological and micro-mechanical models. This work presents a novel thermodynamically consistent, multiagent machine-learned model for predicting the constitutive behavior of cross-linked elastomers during environmental aging, such as thermo-oxidative and hydrolytic aging for various states of deformation. Single mechanism degradation changes the polymer matrix over time where it is causing chain scission, reduction of cross-links, and morphology change. To capture the idealized Mullins effect and permanent set due to the effect of single aging mechanisms on nonlinear mechanical responses of elastomers, we propose a data-driven model for simulating inelastic elements in a polymer matrix. By using a sequential order reduction, we were able to reduce the 3D stress-strain tensor mapping problem to a small number of super-constrained 1D mapping problems. To systematically classify such mapping problems into a few categories, an assembly of multiple replicated conditional neural network learning agents (L-agents) is used based on our recent work. Each category is represented by a different type of agent. The effect of deformation history, aging time, and aging temperature is captured by this model. The model is validated using a broad collection of data, ranging from our experimental results to data from the literature. In addition, thermodynamic consistency and frame independence are investigated. The most significant achievements of this model are its precision, simplicity, and prediction of inelasticity under various states of deformation. The model’s accuracy and simplicity make it a good option for commercial and industrial applications. Conveniently, due to the model modular nature, it can be expanded in the future to include viscoelasticity and non-isotropic formation for better precision.

Ghaderi, Aref↗

DOE OSTI2025

Generative Vulnerability Assessment for Cyber-Physical Systems

Cyber-physical systems (CPS) are highly susceptible to malicious attacks due to their complex dynamics and interconnectivity. A comprehensive understanding of their vulnerabilities is essential for designing effective resilience measures. This paper presents a data-driven attack generative system for evaluating the vulnerability of CPS. The proposed approach formulates the vulnerability assessment problem as determining the feasibility of a specific attack set based on two boundary functions that represent the effectiveness and stealthiness of attacks. The attack generative model is trained using a custom loss function, with two universal approximators designed to learn the effectiveness and stealthiness functions simultaneously. Theoretical results for successful generation and asymptotic convergence of the resulting training algorithm are given. As a result, the proposed approach is evaluated via numerical simulation of an IEEE 14-bus system and gas pipeline systems, demonstrating its viability in learning how to attack nonlinear CPS and identify potential vulnerabilities.

Computer systems organization↗

DOE OSTI2022

Anticipating water distribution service outages from increasing temperatures

With projected temperature increases and extreme events due to climate change for many regions of the world, characterizing the impacts of these emerging hazards on water distribution systems is necessary to identify and prioritize adaptation strategies for ensuring reliability. To aid decision-making, new insights are needed into how water distribution system reliability to climate-driven heat will change, and the proactive maintenance strategies available to combat failures. To this end, we present the model Perses, a framework that joins a water distribution network hydraulic solver with reliability models of physical assets or components to estimate temperature increase-driven failures and resulting service outages in the long term. A theoretical case study is developed using Phoenix, Arizona temperature profiles, a city with extreme temperatures and a rapidly expanding infrastructure. By end-of-century under hotter futures there are projected to be 1%–5% more pump failures, 2%–5% more PVC pipe failures, and 3%–7% more iron pipe failures (RCP 4.5–8.5) than a baseline historical temperature profile. Service outages, which constitute inadequate pressure for domestic and commercial use are projected to increase by 16%–26% above the baseline under maximum temperature conditions. The exceedance of baseline failures, when compounded across a large metro region, reveals potential challenges for budgeting, management, and maintenance. An exploration of the mitigation potential of adaptation strategies shows that expedited repair times are capable of offsetting the additional outages from climate change, but will come with a cost.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Non-destructive structural characterization of graphite components using mechanical resonance and deep learning

As compared to conventional nuclear reactors, microreactors have the potential to significantly reduce construction timelines and capital costs, decreasing the barriers for advanced nuclear reactor technologies. However, the lower power output of these microreactors (typically < 20 MWe) creates challenging economics if operation and maintenance costs cannot be sufficiently reduced. The compact size of these designs presents an opportunity for comprehensive in-situ structural health monitoring to provide real-time feedback in order to reduce operational costs associated with maintenance and downtime. Many microreactor concepts use graphite for both in-core neutron moderation and as a structural material, which has typically required some form of periodic and laborious inspection. This report provides a description and assessment of recent work with graphite to couple acoustic-based experimental measurements and characterization with machine learning models to mature structural health monitoring capabilities and generate benefits for the nuclear microreactor industry. With resilient embedded sensors in development in other programs funded by the US Department of Energy’s Office of Nuclear Energy and elsewhere, the work described herein builds upon previously funded efforts to mature non-destructive testing technology that relates measured vibrational signatures to structural changes, using a combination of new experimental measurements and machine learning processing. Building on past successful demonstrations of predictive workflows to identify structural changes in a hexagonal stainless steel test article with excellent acoustic propagation, we first performed baseline characterization on graphite samples with canonical geometries to ensure compatibility and confidence in the applied techniques for a material with distinctly different mechanical properties. In contrast to efforts in previous years, we worked exclusively with unidirectional vibration data that is more comparable to those expected from the existing embedded sensor technologies which are suitable for deployment in a reactor setting. Established acoustic and modern machine-learning-based characterization approaches were applied to the resulting datasets from these simple geometries. Both approaches were found to be highly capable of detecting even small geometric irregularities amongst nominally identical samples. As such, we then moved to testing these approaches for detection of artificial local stress perturbations introduced into a more complex geometry: a hexagonal block with drilled holes. A main outcome of this work is that a generalizable ML workflow can be used to detect and predict the characteristics of small artificial anomalies in a graphite component with a relevant geometry. While this work was performed using surficial vibration data, we expect the approach to be flexible and viable for other monitoring scenarios, such as those with different arrangements or types of sensor arrays. As compared to previously funded efforts, an existing ML workflow based on neural networks was enhanced through the addition of recently developed Fourier neural operators. As applied to previously collected and new vibration datasets, prediction accuracies of anomaly characterizations were greatly improved with minimal added computational cost. As trained on small durations of vibration data (tens of seconds) collected over a realistic number of locations, the model was able to reliably determine the presence of a subtle stress anomaly and begin to provide location estimates. Such an approach is likely to be viable for more relevant reactor damage scenarios for graphite components, such as progressive crack growth or creep.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Securing Distributed Energy Resource Integration

The penetration of distributed energy resources (DER) is growing at much higher rates than predicted 20 years ago. Far from being used only in residential settings, DER are now installed on distribution and transmission circuits. In this position, they do not have the same properties as traditional generators and are more flexible in many cases. The growing penetration and range of uses for DER motivate the need to reliably and safely integrate them into the grid. Operators must be able to rely on them not only for normal operation, but also during abnormal conditions like black starts or adverse cyber scenarios. To that end, we study the communications, device interfaces, and potential consequences of DER operation under abnormal and adversarial conditions. The weaknesses of communications networks are studied based on the industrial protocols used, and the benefits of security features are examined. The device interfaces are found to be vulnerable to attack based on the requirements in the IEEE-1547 standard for DER interconnection and interoperability, which is expected to be adopted in the next ten years. In addition to exploring the requirements of the standard, we show that these vulnerabilities and others do exist and can be used maliciously in a modern storage system DER. Consequences of these vulnerabilities range from exacerbated grid instability, to simultaneous loss of large portions of DER penetration, to physical damage to inverters or DER themselves and other sensitive equipment. We tie these outcomes to specific attacker actions in an effort to give operators a better threat intelligence view that allows them to prioritize mitigations. Finally, we discuss mitigations that could prevent many of the adversarial scenarios described. Some solutions can be added to existing infrastructure, while others may require longer term planning for grid modernization with consideration for security.

25 ENERGY STORAGE↗

DOE OSTI2024

Power System Waveform Datasets for Machine Learning

The desire for increased visibility across the electricity grid will necessarily increase the deployment of sensing and measurement devices and associated data management needs to unprecedented levels. For the existing sensing and measurement infrastructure, there remains a great amount of “value” yet to be extracted through advanced data management and analytics. Availability of more data will not, by itself, lead to changes in grid visibility, security, and resiliency. To create the predictive and prescriptive environment required to enable new markets and transactions for customer revenue and a reliable grid, the data must be collected, organized, evaluated, and analyzed using sophisticated algorithms to provide actionable information allowing operators and customers to reliably manage an increasingly complex grid. Progress in artificial intelligence (AI) has been largely driven by large, publicly available datasets that can be used to train AI algorithms such as MNIST, a database of handwritten images of digits, and ImageNet, an image database of everyday objects. These types of publicly available databases of real-world training datasets have been largely credited for advancement of image processing, computer vision, and deep learning algorithms that these use cases deploy. However, in the power systems industry to date, there are few databases with proper event labeling, and data access to a publicly available collection of power system event waveforms that will allow users to interact with grid signature data. Publicly available datasets of power system event waveforms, such as the DOE/EPRI dataset, often lack critical metadata or contain limited examples of each event type, and data formats vary widely across these datasets.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRSDate not supplied

NSCOR for Evaluating Risk Factors and Biomarkers for Adaptation and Resilience to Spaceflight: Emotional Valence and Social Processes in ICC/ICE Environments

Space exploration class missions, such as a mission to Mars, will require optimization of human performance, adaptability, and resilience. This NASA Specialized Center of Research (NSCOR) utilizes the NIMH Research Domain Criteria (RDoC) framework to identify biological and behavioral markers of individual social adaptation and emotional resilience (as well as vulnerability) to spaceflight-relevant stressors such as living in extended isolation. The overarching goal of this NSCOR is to obtain novel information to help identify biomarkers of individuals who are resilient and/or adaptable to the stressors of isolated, confined, and controlled (ICC) and isolated, confined, and extreme (ICE) environments.A total of N=90 healthy adult astronaut surrogates are being studied in three spaceflight-analog environments: (1) n=40 healthy adults in the Isolation and Confinement Analog Research Unit (ICARUS), an ICC at the University of Pennsylvania, during 7-day missions, for a target total of 280 subject days; (2) n=32 healthy adult astronaut surrogates studied in NASA’s Human Exploration Research Analog (HERA), an ICC at Johnson Space Center during 45-day missions, for a target total of 2,112 subject days; and (3) n=18 healthy adults in the Alfred-Wegener-Institute’s Neumayer Station III, an ICE in Antarctica, during 14-month missions, for a target total of 7,560 subject days. Dr. Nindl’s Laboratory at the University of Pittsburgh is analyzing a priori selected protein biomarkers in blood, saliva, and urine. Complementary rodent models of exposure to early life stressors, confinement, and isolation are being evaluated at Dr. Hensch’s Laboratory to further validate the neurobehavioral and biological findings from the human studies.Given the inconsistency and varied definition of resilience/adaptation in the scientific literature, the NSCOR team developed a composite resilience/adaptation measure that reflects the most relevant outcomes to resilience/adaptation across psychosocial and neurobehavioral functions, as well as neurocognitive and spaceflight-relevant operational performance. To achieve this, group consensus was attained from subject matter experts to produce a rank-order of importance for each input variable. The final resilience/adaptation score included 36 variables that were collected across spaceflight analogs. As of 10/1/2021, the NSCOR project acquired data on n=27 subjects at ICARUS, n=16 at HERA, and n=18 at Neumayer. The COVID-19 pandemic delayed data acquisition at ICARUS and HERA.Among subjects studied to date, 99% of neural and neurobehavioral data (e.g., neuroimaging for structure and function, behavioral measures) as well as blood, saliva, and urine for biochemical assays have been acquired. For rodent models, Dr. Hensch’s laboratory has established biochemical and behavioral parameters reflecting confinement stress in social networks of mice for comparison to stress responses in the human spaceflight analog environments.Group social behaviors were measured with a Social Network Analysis (SNA) approach to define objective parameters associated with sociability and its plasticity by sex. This analytic approach may help identify a network of individuals who are more effective teammates or more likely to generate new social relationships. Data acquisition, biomarker assessment, and data quality control will continue through September 2022.

D F Dinges↗

DOE OSTI2024

Robust Restoration From Cyber-Physical Attacks in Active Distribution Grids With Grid-Edge IBRs

The inverter-based resources (IBRs) have enabled the integration of renewable energy at the grid edge with enhanced control capabilities to support the reliable operation of power grids. Different control frameworks, such as hierarchical or distributed architecture, have been proposed with the expansion of cyber networks for real-time monitoring and control. This evolution of critical infrastructure into cyber-physical systems also brings more vulnerabilities for the broadened attack surfaces, and significantly increases the possibility of physical system failures or outages caused by cyberattacks. Among tremendous efforts in the defense-in-depth approach, it remains challenging to provide prompt detection and accurate location of attack entry points or paths. Therefore, the prevailing restoration framework may struggle to fully consider the cyber-physical interdependence, successfully isolate the compromised cyber and physical components, and safely recover the systems without the potential risks leading to secondary outages. This paper is motivated to develop a cyber-physical restoration framework for distribution grids to recover from cyber attacks by harnessing grid-edge IBRs. The framework is first built on the operational guidelines of IBRs considering the compromised cyber layer. Then, an ambiguity set is established to represent the uncertainty of attack scenarios and their possibility levels. Next, a distributionally robust optimization model is developed to provide the optimal load restoration strategy across all scenarios. The effectiveness of the proposed model is demonstrated through various use cases on the modified IEEE 13-node and 123-node test systems. Finally, simulation results demonstrate the effectiveness and advancement of developed post-attack restoration strategies.

Cybersecurity↗