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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Dust-Tolerant Intelligent Electrical Connection System

Faults in wiring systems are a serious concern for the aerospace and aeronautic (commercial, military, and civilian) industries. Circuit failures and vehicle accidents have occurred and have been attributed to faulty wiring created by open and/or short circuits. Often, such circuit failures occur due to vibration during vehicle launch or operation. Therefore, developing non-intrusive fault-tolerant techniques is necessary to detect circuit faults and automatically route signals through alternate recovery paths while the vehicle or lunar surface systems equipment is in operation. Electrical connector concepts combining dust mitigation strategies and cable diagnostic technologies have significant application for lunar and Martian surface systems, as well as for dusty terrestrial applications. The dust-tolerant intelligent electrical connection system has several novel concepts and unique features. It combines intelligent cable diagnostics (health monitoring) and automatic circuit routing capabilities into a dust-tolerant electrical umbilical. It retrofits a clamshell protective dust cover to an existing connector for reduced gravity operation, and features a universal connector housing with three styles of dust protection: inverted cap, rotating cap, and clamshell. It uses a self-healing membrane as a dust barrier for electrical connectors where required, while also combining lotus leaf technology for applications where a dust-resistant coating providing low surface tension is needed to mitigate Van der Waals forces, thereby disallowing dust particle adhesion to connector surfaces. It also permits using a ruggedized iris mechanism with an embedded electrodynamic dust shield as a dust barrier for electrical connectors where required.

Lewis, Mark↗
DOE OSTI2025

A Review of Quantum Computing Technologies in Power System Optimization

As modern power grids increasingly integrate variable renewable generation, distributed energy resources, and energy storage systems, classical optimization techniques are facing unprecedented challenges. This review examines the emerging application of quantum computing to overcome these challenges in power system optimization, including optimal power flow (OPF), unit commitment (UC), economic dispatch (ED), and intelligent switching and topology optimization (IS-TO). Recent research has introduced various quantum methodologies—such as gate-based, annealing-based, variational algorithms, and quantum-inspired algorithms—to address the combinatorial complexity inherent in grid reconfiguration and energy management. The review summaries the quantum algorithms, quantum devices and the power system test cases, highlighting hybrid quantum–classical strategies that leverage the complementary strengths of both paradigms. Some quantum advantages have been observed, including theoretical speedup, accurate simulation results, scalable qubit usage, efficient QUBO mapping. In particular, the review emphasizes the importance of integrating quantum optimization techniques with classical control frameworks, these hybrid approaches demonstrate the potential to improve real-time grid management and operational reliability. A significant portion of the analysis is devoted to the practical limitations of current quantum devices. Present-day quantum hardware, operating in the noisy intermediate-scale quantum (NISQ) era, remains highly sensitive to noise and limited in qubit connectivity, which constrains the scale and accuracy of implemented algorithms. The review delves into specific challenges such as the need for qubit-efficient encoding techniques and error mitigation strategies that are critical for handling real-world grid optimization problems. In addition, the work draws attention to the performance discrepancies between theoretical quantum speedups and experimental validations, underscoring the importance of rigorous benchmark studies using representative power grid test cases. In summary, this review highlights both the promise and limitations of quantum computing for power system optimization. It provides a comprehensive overview of the state-of-the-art technologies, categorizes recent advancements in algorithm design, and discusses practical considerations for implementation, and serves as an informative resource on current research. Future research directions include developing robust hybrid frameworks, advancing qubit-efficient formulations, and scaling up experimental demonstrations to confirm the theoretical advantages of quantum methods in large-scale power system operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Metrics for Interzonal Dispersion Assessment of Airborne SARS-CoV-2 Within Office Buildings

High ventilation rate is increasingly considered as a mitigation strategy in the rapid spread of COVID-19 in existing buildings in addition to other options like UVGI and added filtration. However, this and other similar measures may also impact energy use and the efficiency of the buildings in which they are installed. Without concrete metrics, the impact of all these potential changes to the system cannot be properly assessed relative to the other impacts the measures might have, including additional energy use, added operation and maintenance costs, and reduced system life. Multizone airflow and contaminant transport simulations involving parameters such as infiltration rate, system configuration, and level of occupancy can provide critical information on relative risk distribution within a building. In this study, prototype airflow models are used to assess the degree to which ventilation-related measures can mitigate the spread of a virus like SARS-CoV-2 in a similar way that prototype energy models are used to study energy efficiency measures. A new detailed medium-sized office building prototype model is developed using the CONTAM software to represent contaminant transport in buildings like those modeled by the U.S. Department of Energy’s detailed medium office prototype model. The goal of this study is to generate a set of suitable metrics that will give a whole-building picture of airborne SARS-CoV-2 virus distributions across the different zones of the building under different parameters and scenarios.

DeGraw, Jason↗
NASA NTRS2023

Compact Lightweight Aerial Sensor System (CLASSy)

In the wake of increasingly intense wildfires, innovative solutions are imperative to enhance wildfire mitigation strategies. Current technological integrations have hit a communicative limit. Between limited flight time, computational expenses as well as financial expenses, there is a hole in the market for an effective, low-tech, and disposable solution. The Compact Lightweight Aerial Sensor System (CLASSy) is designed to revolutionize active disaster operations through comprehensive decision support. CLASSy consists of a lightweight launch mechanism and a flight body equipped with a sensor package and parachute. The assembly integrates sensor networks with data analytics to provide real-time, high-resolution information to incident commanders, directly facilitating decision-making and resource allocation. Infrared imagery and temperature differentials are processed and analyzed throughout flight, offering valuable insights into fire behavior, hotspot detection, and fire spread trajectories. CLASSy is intended to meet a variety of natural disaster mitigation needs through its variable launch height and disposability. CLASSy’s goal is to assist wildfire fighting without taking up any human or material resources. As a result, CLASSy is as lightweight as possible, easily expendable, inexpensive to manufacture, and only requires one operator for effective use. CLASSy’s integrated sensor suite, real-time analytics, and closed loop active communications empower firefighting teams to proactively address wildfire challenges. As the frequency of wildfires continues to rise, technological innovations like CLASSy are crucial to effective wildfire management systems.

Kyleigh Anderson↗
DOE OSTI2021

Janthinobacter additions reduce rotifer grazing of microalga Microchloropsis salina in biotically complex communities

Grazing and parasitism in algal production systems are significant causes of crop loss. Biological control of algal ponds, such as application of protective bacteria, represents a promising alternative to current mitigation strategies. The major barriers to probiotic methods are the lack of identified protective bacteria and uncertainty of scale-up effects, including potential negative interactions with the resident algal microbiome. Here, we report that application of Janthinobacter lividum affords robust protection against rotifer (Brachionus plicatilis) grazing on Microchloropsis salina. Protection was observed at laboratory scale from live bacteria and solvent extract additions as well as in biologically complex communities from bacterial additions to outdoor mesocosms. J. lividum persisted in outdoor communities for at least 3 days following addition, and did not lead to selective exclusion of specific taxa in the resident microbiome. These results demonstrate that J. lividum can provide protection against grazers in these diverse communities, without severe effects on the resident microbiome. Our results suggest that J. lividum is a promising treatment to mitigate algal crop losses due to pests. Additionally, the study highlights the importance of an ecologically-informed approach to algal crop management that will aid future applications in the biological control of algal ponds.

09 BIOMASS FUELS↗
DOE OSTI2021

Modeling potential air temperature reductions yielded by cool roofs and urban irrigation in the Kansas City Metropolitan Area

In this work, we evaluate two mitigation strategies for urban heat island (UHI) in the Kansas City Metropolitan Area (KCMA). Using the Weather Research and Forecasting (WRF) model, we assess the potential benefits of reflective “cool” roofs and urban irrigation on air temperature in typical summer conditions between 2011 and 2015, and during six of the strongest historical heat waves from 2005 to 2016. Under the typical summer conditions, we simulate 2-m air temperature for 10 summer weeks, finding average daytime (07:00–19:00 local time) temperature reductions of 0.08 and 0.28 °C for cool roofs and urban irrigation, respectively. During the six heat-wave episodes, we find daytime temperature reductions of 0.02 and 0.26 °C for the two scenarios, similar to those under typical summer conditions. Our results suggest that urban irrigation can be more efficient than cool roofs in mitigating UHI in metropolitan regions where the majority of the land cover is comprised of areas with low urban (i.e., non-vegetated) fractions. Finally, we find the alteration of surface conditions due to enhanced roof albedos influences precipitation within the WRF simulation, in particular during the heat waves. Further research would be necessary to determine the robustness of this last finding.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Duration of super-emitting oil and gas methane sources

The duration of super-emitting events (>100 kg h -1 ) in oil and gas basins remains insufficiently understood but is key for reporting programs and mitigation strategies. Carbon Mapper conducted aerial surveys from April 30 to May 17, 2024, over the New Mexico Permian Basin, covering 276,000 wells, 1100 compressor stations, 175 gas processing plants, and 27,000 km of pipeline. We find over 500 super-emitting sources with 300 of these sources observed repeatedly across multiple days. We quantify total super emissions by integrating individual events with observationally constrained event durations (5.98 −14.7 Gg CH 4 ) and compare to total emissions derived from basin average snapshots (12.7 ± 0.92 Gg CH 4 ). This gap between emission estimates is reconciled through assumptions on missed detections, characteristic event duration, detection frequency, and diurnal variability. Emission events generally lasted for at least 2 hours, and a small subset of sources (18 total), persistently emitted throughout the entire campaign, representing a near-term opportunity for mitigation. When compared to regional flux estimates derived from independent observations, we estimate super-emitters to contribute approximately 50% (37-73%) towards total emissions. Frequent wide-area monitoring is crucial for capturing rare super-emitter events that, together with other emission sources, drive basin-level variability and emission intensity.

Cusworth, Daniel H. [Carbon Mapper, Pasadena, CA (↗
NASA NTRS2007

Plume Impingement to the Lunar Surface: A Challenging Problem for DSMC

The President's Vision for Space Exploration calls for the return of human exploration of the Moon. The plans are ambitious and call for the creation of a lunar outpost. Lunar Landers will therefore be required to land near predeployed hardware, and the dust storm created by the Lunar Lander's plume impingement to the lunar surface presents a hazard. Knowledge of the number density, size distribution, and velocity of the grains in the dust cloud entrained into the flow is needing to develop mitigation strategies. An initial step to acquire such knowledge is simulating the associated plume impingement flow field. The following paper presents results from a loosely coupled continuum flow solver/Direct Simulation Monte Carlo (DSMC) technique for simulating the plume impingement of the Apollo Lunar module on the lunar surface. These cases were chosen for initial study to allow for comparison with available Apollo video. The relatively high engine thrust and the desire to simulate interesting cases near touchdown result in flow that is nearly entirely continuum. The DSMC region of the flow field was simulated using NASA's DSMC Analysis Code (DAC) and must begin upstream of the impingement shock for the loosely coupled technique to succeed. It was therefore impossible to achieve mean free path resolution with a reasonable number of molecules (say 100 million) as is shown. In order to mitigate accuracy and performance issues when using such large cells, advanced techniques such as collision limiting and nearest neighbor collisions were employed. The final paper will assess the benefits and shortcomings of such techniques. In addition, the effects of plume orientation, plume altitude, and lunar topography, such as craters, on the flow field, the surface pressure distribution, and the surface shear stress distribution are presented.

Lumpkin, Forrest↗
NASA NTRS2009

Ares I-X Roll Control System Development

Project Managers often face challenging technical, schedule and budget issues. This presentation will explore how the Ares I-X Roll Control System Integrated Product Team (IPT) mitigated challenges such as concurrent engineering requirements and environments and evolving program processes, while successfully managing an aggressive project schedule and tight budget. IPT challenges also included communications and negotiations among inter- and intra-government agencies, including the US Air Force, NASA/MSFC Propulsion Engineering, LaRC, GRC, KSC, WSTF, and the Constellation Program. In order to successfully meet these challenges it was essential that the IPT define those items that most affected the schedule critical path, define early mitigation strategies to reduce technical, schedule, and budget risks, and maintain the end-product focus of an "unmanned test flight" context for the flight hardware. The makeup of the IPT and how it would function were also important considerations. The IPT consisted of NASA/MSFC (project management, engineering, and safety/quality) and contractors (Teledyne Brown Engineering and Pratt and Whitney Rocketdyne, who supplied heritage hardware experience). The early decision to have a small focused IPT working "badgelessly" across functional lines to eliminate functional stove-piping allowed for many more tasks to be done by fewer people. It also enhanced a sense of ownership of the products, while still being able to revert back to traditional roles in order to provide the required technical independence in design reviews and verification closures. This presentation will highlight several prominent issues and discuss how they were mitigated and the resulting Lessons Learned that might benefit other projects.

Unger, Ronald J.↗
NASA NTRS2010

The New Face of Data Accessibility

Management of medical and research data at NASA's Johnson Space Center has been addressed with two separate, independent systems: the Lifetime Surveillance of Astronaut Health (formerly, The Longitudinal Study of Astronaut Health) (LSAH) and the Life Sciences Data Archive (LSDA). Project management for these has been autonomous with little or no cross-over of goals, objectives or strategy. The result has been limited debate and discussion regarding how contents from one repository might impact or guide the direction of the other. It is decidedly more efficient to use existing data and information than to re-generate them. Ensuring that both clinical and research data / information are accessible for review is a central concept to the decision to unify these repositories. In the past, research data from flight and ground analogs has been held in the LSDA and medical data held in the Electronic Medical Record or in console flight surgeon logs and records. There was little cross-pollination between medical and research findings and, as a result, applicable research was not being fully incorporated into clinical, in-flight practice. Conversely, findings by the console surgeon were not being picked up by the research community. The desired life cycle for risk mitigation was not being fully realized. The goal of unifying these repositories and processes is to provide a closely knit approach to handling medical and research data, which will not only engender discussion and debate but will also ensure that both categories of data and information are used to enhance the use of medical and research data to reduce risk and promote the understanding of space physiology, countermeasures and other mitigation strategies

Fitts, Mary A.↗
NASA NTRSDate not supplied

Predicting the Likelihood of Human-Elephant Conflict and Assessing Patterns in Elephant Movements Over Varying Habitat Conditions in the Kavango-Zambezi Area

In the Kavango-Zambezi area of southern Africa, three million people live within areas frequently traveled by free-ranging elephants. As the region continues to develop rapidly, urban and agricultural settlements further encroach upon the land that these elephants use. As elephants come into more frequent contact with urban and agricultural areas, human populations face financial loss through crop damage and the potential for injury from direct conflict with elephants. Elephant populations are also at risk of injuries from conflict as well as illness related to the consumption of waste. In order to implement human-elephant conflict mitigation strategies, local conservation groups need to be informed on best practices for coexistence. This project aided The Ecoexist Project and Connected Conservation in understanding the ecological factors that drive elephant movement into human settlements and provided Earth observation data to support conflict management in the future. The team used Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI) data to create land use land cover maps and calculate vegetation indices, and used TerraClimate data to analyze drought conditions. These classified maps allowed us to display a time series of human settlement from 1990 to the present and were made explorable alongside other environmental variables in an updated Google Earth Engine (GEE) tool. This project also provided heat maps that show the risk of human-elephant conflict based on historical data of HEC locations. This analysis will provide support for conservation experts in determining best practices for future mitigation and prevention of human-elephant conflict.

Ariel Calle↗
DOE OSTI2024

Problem-tailored Simulation of Energy Transport on Noisy Quantum Computers

The transport of conserved quantities like spin and charge is fundamental to characterizing the behavior of quantum many-body systems. Numerically simulating such dynamics is generically challenging, which motivates the consideration of quantum computing strategies. However, the relatively high gate errors and limited coherence times of today's quantum computers pose their own challenge, highlighting the need to be frugal with quantum resources. In this work we report simulations on quantum hardware of infinite-temperature energy transport in the mixed-field Ising chain, a paradigmatic many-body system that can exhibit a range of transport behaviors at intermediate times. We consider a chain with L = 12 sites and find results broadly consistent with those from ideal circuit simulators over 90 Trotter steps, containing up to 990 entangling gates. To obtain these results, we use two key problem-tailored insights. First, we identify a convenient basis – the Pauli Y basis – in which to sample the infinite-temperature trace and provide theoretical and numerical justifications for its efficiency relative to, e.g., the computational basis. Second, in addition to a variety of problem-agnostic error mitigation strategies, we employ a renormalization strategy that compensates for global nonconservation of energy due to device noise. We discuss the applicability of the proposed sampling approach beyond the mixed-field Ising chain and formulate a variational method to search for a sampling basis with small sample-to-sample fluctuations for an arbitrary Hamiltonian. This opens the door to applying these techniques in more general models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

An Inventory of AI-ready Benchmark Data for US Fires, Heatwaves, and Droughts

Extreme weather events, including fires, heatwaves, and droughts, have significant impacts on earth, environmental, and energy systems. Mechanistic and predictive understanding, as well as probabilistic risk assessment of these extreme weather events, are crucial for detecting, planning for, and responding to these extremes. Records of extreme weather events provide an important data source for understanding present and future extremes, but the existing data needs preprocessing before it can be used for analysis. Moreover, there are many nonstandard metrics defining the levels of severity or impacts of extremes. In this study, we compile a comprehensive benchmark data inventory of extreme weather events, including fires, heatwaves, and droughts. The dataset covers the period from 2001 to 2020 with a daily temporal resolution and a spatial resolution of 0.5°×0.5° (~55km×55km) over the continental United States (CONUS), and a spatial resolution of 1km × 1km over the Pacific Northwest (PNW) region, together with the co-located and relevant meteorological variables. By exploring and summarizing the spatial and temporal patterns of these extremes in various forms of marginal, conditional, and joint probability distributions, we gain a better understanding of the characteristics of climate extremes. The resulting AI/ML-ready data products can be readily applied to ML-based research, fostering and encouraging AI/ML research in the field of extreme weather. This study can contribute significantly to the advancement of extreme weather research, aiding researchers, policymakers, and practitioners in developing improved preparedness and response strategies to protect communities and ecosystems from the adverse impacts of extreme weather events. Usage Notes We presented a long term (2001-2020) and comprehensive data inventory of historical extreme events with daily temporal resolution covering the separate spatial extents of CONUS (0.5°×0.5°) and PNW(1km×1km) for various applications and studies. The dataset with 0.5°×0.5° resolution for CONUS can be used to help build more accurate climate models for the entire CONUS, which can help in understanding long-term climate trends, including changes in the frequency and intensity of extreme events, predicting future extreme events as well as understanding the implications of extreme events on society and the environment. The data can also be applied for risk accessment of the extremes. For example, ML/AI models can be developed to predict wildfire risk or forecast HWs by analyzing historical weather data, and past fires or heateave , allowing for early warnings and risk mitigation strategies. Using this dataset, AI-driven risk assessment models can also be built to identify vulnerable energy and utilities infrastructure, imrpove grid resilience and suggest adaptations to withstand extreme weather events. The high-resolution 1km×1km dataset ove PNW are advantageous for real-time, localized and detailed applications. It can enhance the accuracy of early warning systems for extreme weather events, helping authorities and communities prepare for and respond to disasters more effectively. For example, ML models can be developed to provide localized HW predictions for specific neighborhoods or cities, enabling residents and local emergency services to take targeted actions; the assessment of drought severity in specific communities or watersheds within the PNW can help local authorities manage water resources more effectively.

Lin, Xinming↗
DOE OSTI2024

Arctic Critical Infrastructure: Assessing and Predicting the Risk to Critical Permafrost Infrastructure from Climate Change: A New Thermomechanical Approach

This study presents the development of a computational framework designed to predict the interaction between permafrost and infrastructure, addressing potential failure modes and mitigation strategies in the context of climate change. The framework, rooted in advanced modeling and simulation (mod/sim) techniques, integrates thermomechanical coupling to account for the complex interplay between heat flow, ice content, and mechanical behavior in permafrost. Existing models fail to fully capture these dynamics, particularly as they relate to the effects of ice saturation on structural integrity. Our innovative Arctic Coastal Erosion (ACE) framework fills this gap by coupling thermal and mechanical models to accurately simulate subsidence and deformation in permafrost environments. We applied the ACE framework to a representative runway, demonstrating its capability to predict settlement due to rising temperatures and subsequent permafrost thaw. This proof-of-concept showcases the potential of the framework to evaluate risks to Arctic infrastructure, which supports over four million people and 70% of existing permafrost-based structures. By simulating various infrastructure types and environmental conditions, our research offers insights into failure mechanisms and evaluates structural solutions to mitigate risk. The anticipated deliverables, including a prototype runway exemplar, position this project as a critical advancement in permafrost infrastructure modeling, with applications in national security and resilience planning.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Prediction of Calcium Oxalate Stone Risk with Interventions

Introduction Mitigations intended to reduce bone mineral loss in-flight also influence the level of renal stone risk to astronauts. Extending a recent study completed by our group [1], we investigate the likely influence of combined fluid intake and Ca excretion mitigation strategies. Methods A computational biochemistry model representing CaOx crystal precipitation, growth, and agglomeration [2] is combined with a probabilistic analysis to predict the in-flight CaOx renal stone incidence risk ratio (IRR) relative to pre-flight values using 1517 astronaut 24-hour urine chemistries. The output of this simulation is used to assess the combined impact of controlling fluid intake and Ca excretion on maintaining IRR < 1.2 for > 95% of the population, a threshold determined from initial analysis of the model output compared to referent populations [1]. Results and Conclusions Figure 1 illustrates the simulated in-flight astronaut population (n=40000) with CaOx IRR > 1.2. The red lines indicate the known clinical risk boundary of each factor, and the white box is considered the area of high combined clinical risk. The simulation and identified risk threshold conform well to the expected clinical risk boundaries. The results, assuming insensible water losses equal or exceed 0.7 L/day, imply mitigations achieve risks similar to pre-flight levels when either Ca < 150 mg/day or fluid volume intake > 3.2 L/day individually, or combined Ca < 200 and fluid volume intake > 2.5 L/day. We continue to investigate similar compounded effects and look to apprise engineering trades by informing relative risk changes during mission design. References [1] D. A. Goodenow-Messman, S. A. Gokoglu, M. Kassemi, and J. Myers, Jerry G., “Characterization of Astronaut CaOx Renal Stone Incidence Rates to Quantify In-flight and Post-flight Relative Risk,” npj Microgravity, in review, 2021. [2] M. Kassemi and D. Thompson, “Prediction of renal crystalline size distributions in space using a PBE analytic model. 1. Effect of microgravity-induced biochemical alterations,” Am. J. Physiol. Physiol., vol. 311, no. 3, pp. F520–F530, Sep. 2016, doi: 10.1152/ajprenal.00401.2015.

Debra A Goodenow↗
DOE OSTI2026

A cryogenic muon tagging system based on kinetic inductance detectors for superconducting quantum processors

Ionizing radiation has emerged as a potential limiting factor for superconducting quantum processors, inducing quasiparticle bursts and correlated errors that challenge fault-tolerant operation. Atmospheric muons are particularly problematic due to their high energy and penetration power, making passive shielding ineffective. Therefore, monitoring the real-time muon flux is crucial to guide the development of alternative error-correction or mitigation strategies. We present the design, simulation, and first operation of a cryogenic muon-tagging system based on kinetic inductance detectors (KIDs), developed as a stand-alone cryogenic particle-tagging module for superconducting quantum processors. The system consists of two KIDs arranged in a vertical stack and operated at ∼20 mK. Monte Carlo simulations based on Geant4 guided the prototype design and provided reference expectations for muon-tagging efficiency and accidental coincidences due to ambient γ-rays. We observed a muon-induced coincidence rate among the top and bottom detectors of (192 ± 9) $\times\,10^{-3}$ events s$^{−1}$, in excellent agreement with the Monte Carlo prediction. The prototype achieves a muon-tagging efficiency of about 90% with negligible dead time. These results demonstrate the feasibility of operating a muon-tagging system at millikelvin temperatures and represent a key step toward the integration of cryogenic veto systems with multi-qubit chips to mitigate muon-induced errors.

Mariani, Ambra [INFN, Rome] (ORCID:000000028184857↗
DOE OSTI2024

Cybersecurity Assessment in DER-rich Distribution Operations: Criticality Levels and Impact Analysis

The integration of distributed energy resources (DERs) in distribution networks has become a pivotal strategy for achieving decarbonization, enhancing grid resilience, and optimizing grid efficiency. Remote monitoring and control op- erations of such resources rely on a network of sensors and communication infrastructure, exposing the system to potential cyber threats. Therefore, as the deployment of DERs increases, ensuring secure monitoring and control becomes an imperative challenge. This paper utilizes real-time feeder models, which are instrumental in developing cybersecurity testbeds tailored for hardware-in-loop (HIL) systems. These models enable users to simulate cyber attacks in a real-world environment and analyze the power distribution operations during vulnerabilities. Furthermore, we discuss several practical sets of grid parameters to identify critical levels of DERs and evaluate various scenarios that simulate cyber threats on sensitive DERs. The modified IEEE 123-bus model is used as the test case for demonstrating the proposed scenarios. The findings from this study provide valuable insights into the vulnerabilities and potential consequences of cyber attacks on DERs, allowing for better mitigation strategies and improved cyber resilience in future distribution networks.

Maharjan, Manisha↗
DOE OSTI2026

Risks to the DOW Mission From Global Advanced Energy Adoption

Advanced energy technologies have the potential to enhance energy security and resilience; however, they can introduce new vulnerabilities even as they mitigate existing ones. This dichotomy highlights that both action and inaction carry strategic risks in a contested and logistically complex operating environment. Regardless of U.S. civilian or military adoption of such technologies, key allies and adversaries are on adoption paths that will impact the U.S. military at the tactical, operational, and strategic level. The global adoption of advanced energy technologies presents the potential for both positive and negative consequences for U.S. Department of Defense (DOD) missions in the next 10-20 years. Three categories of risk drivers are presented in this analysis: infrastructure-related, adoption-related, and response-related. Each risk type can generate consequences for DOD, including power disruptions, suboptimal DOD mission performance and operational effectiveness, higher costs and shortages for both legacy and advanced energy technologies, and loss of U.S. influence and strategic deterrent value. Specific impacts could include tactical impacts, operational impacts, and strategic impacts. Mitigation strategies could include energy resource and technology planning, cybersecurity, supply chain resilience, workforce development, exercises and simulations, investments in commercialized technology, operational testing and pilot programs, research into emerging technologies, partnerships and working groups, common standards, budget planning, and repurposing infrastructure.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗