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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Mathematical Framework for Representing Cyber-Physical System Interdependencies and Resilience

U.S. critical infrastructure is increasingly composed of integrated cyber-physical systems (CPS) whose components are operationally interdependent. These interdependencies can create additional vulnerabilities beyond those that are typically evaluated through independent physical and cyber risk assessments. These additional vulnerabilities, previously undetected at the sub-system and/or component level, could cause cascading effects across both the physical and cyber domains that could lead to infrastructure disruption or failure. This paper will review an initial mathematical framework developed to help understand the dynamics of these interfacing physical and cyber components at a holistic system level. The resulting framework also provides a method for modeling and assessing the resilience of CPS in complex and adaptive systems. It frames the system in terms of mission performance and the potential effects to performance caused by attacks that are cyber, physical, or blended in nature. The framework utilizes a systems-based, state-space modeling approach to reason about risk, resilience, and interdependencies in CPS. Probabilities are applied to each CPS component regarding a threat (both man-made and natural) and the likelihood of that particular component being impacted, along with indirect impact to other interdependent components. These component-level impacts from threats then translate into mission-level impact and systemic state change.

Brigantic, Robert T.↗
DOE OSTI2025

Energy efficiency and thermal resilience analysis for row houses using representative building energy models

Row houses, a prevalent medium-density urban archetype, were popularized during industrial era to accommodate the working class in the U.S. cities. Today, many of these structures are deteriorating, exacerbating the energy cost for residents with limited economic resources. Moreover, many households that lack Air Conditioning (AC) are vulnerable to extreme summer heat, an issue expected to worsen in the future. Energy-efficient retrofits can help mitigate these challenges. However, current literature provides limited insight into how existing building energy codes impact the energy efficiency and thermal resilience of row houses in current and future weather scenarios. This study provides a detailed method to address this gap by employing calibrated representative energy models of row houses as a baseline, and evaluating the annual energy savings and summertime thermal safety improvements from code compliant retrofits under current and future typical weather conditions. Using Baltimore City as a case study, results show that under current weather, retrofitting row houses yields significant energy savings along with notable thermal resilience improvements. The end unit row house realizes higher energy savings, whereas the interior unit experiences greater thermal resilience improvement after retrofitting. In future, retrofitted row houses can still save energy, but residents will face numerous hours of fatal indoor temperatures without AC in summer. Additionally, findings reveal stark zone-wise variations in thermal safety, and elevated humidity risks in certain zones post-retrofit, emphasizing the need for zonal retrofit strategies and better natural ventilation strategies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Data-Informed Synthetic Networks of Water Distribution Systems for Resilience Analysis in Puerto Rico

The increasing potential of infrastructure disruptions calls for high-quality infrastructure models to be used in resilience analysis and decision making. Unfortunately, many utilities and communities do not have access to accurate and detailed models due to a lack of data and resources. Furthermore, security restrictions on sharing infrastructure models present roadblocks to research, analysis, and decision making. Recent advances in the development of synthetic water distribution models provide a potential solution to this problem. There is an opportunity to improve these methods by leveraging incomplete pipe datasets to aid synthetic network generation. To address this gap, we developed a methodology for synthetic network generation that incorporates partial pipe data using a modification of the minimum cost flow algorithm for network generation and pipe sizing. This methodology demonstrates how partial pipe data can be leveraged to improve site-specific synthetic network generation. For the study area of Mayagüez, Puerto Rico, a synthetic model generated using 50% of real pipe data matches the pressure of the validation system with an average error of 23.5 m of head, which improves upon the average error of 31.6 m of head produced by a synthetic model generated using no data of the real pipes. Additionally, synthetic networks are shown to replicate the pressure response under a disruption scenario of the validation network, suggesting potential use in resilience analysis.

resilience analysis↗
DOE OSTI2021

Planning for a Resilient Home Electricity Supply System

Resilience of power systems is already a key issue that is getting frequent attention all over the world. It is useful to analyze resilience issues not only for bulk supply, but at all levels including at a customer level. This is because distributed energy resources can play a prominent role in enhancing resilience. Although the literature on planning models, tools and data for bulk supply and distribution systems have expanded in recent years, customer-centric planning, e.g., for an individual household, is yet to receive adequate attention. Although solar PV and battery storage at a household level have been analyzed, how these resources can be optimally combined, together with grid supply, from a resilience perspective is the focus of this study. The study demonstrates how a conceptual framework can be developed to show the trade-off between system costs and resilience including its dimensions such as duration, depth and frequency of service outages. A planning model is developed that incorporates multiple facets of resilience and individual customer preferences. The model considers power system resilience explicitly as a constraint. The model is implemented for a household level case study in Miami, Florida. The results show there are complex trade-offs among different dimensions of resilience. The study demonstrates how combined resilience metrics can be formulated and evaluated using the proposed least-cost planning model at a household level to optimize grid supply together with solar, battery storage and diesel generators. The model allows a planner to directly embed a resilience standard to drive the optimal supply mix. These concepts and the modeling construct can also be applied at other levels of planning, including community level and bulk supply system planning.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Deep Reinforcement Learning based Model-free On-line Dynamic Multi-Microgrid Formation to Enhance Resilience

Multi-microgrid formation (MMGF) is a promising solution for enhancing power system resilience. This paper proposes a new deep reinforcement learning (RL) based model-free on-line dynamic MMGF scheme. Additionally, the dynamic MMGF problem is formulated as a Markov decision process, and a complete deep RL framework is specially designed for the topologytransformable micro-grids. In order to reduce the large action space caused by flexible switch operations, a topology transformation method is proposed and an action-decoupling Q-value is applied. Then, a convolutional neural network (CNN) based multi-buffer double deep Q-network (CM-DDQN) is developed to further improve the learning ability of the original DQN method. The proposed deep RL method provides real-time computing to support the on-line dynamic MMGF scheme, and the scheme handles a long-term resilience enhancement problem using an adaptive on-line MMGF to defend changeable conditions. The effectiveness of the proposed method is validated using a 7-bus system and the IEEE 123-bus system. The results show strong learning ability, timely response for varying system conditions and convincing resilience enhancement.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Multi-scale planning model for robust urban drought response

Increasingly severe droughts are straining municipal water resources and jeopardizing urban water security, but uncertainty in their duration, frequency, and intensity challenges drought planning and response. We develop the Drought Resilient Interscale Portfolio Planning model (DRIPP) to generate optimal planning responses to urban drought. DRIPP is a generalizable multi-scale framework for optimizing dynamic planning strategies of long-term infrastructure deployment and short-term drought response. It integrates climate and hydrological variability with high-fidelity representations of urban water distribution, available technology options, and demand reduction measures to yield robust and cost-effective water supply portfolios that are location-specific. We apply DRIPP in Santa Barbara, California to assess how least cost water supply portfolios vary under different drought scenarios and identify portfolios that are robust across drought scenarios. In Santa Barbara, we find that drought intensity, not duration or frequency, drives cost increases, reliability risk, and regret of overbuilding infrastructure. Under uncertain drought conditions, a diversified technology portfolio that includes both rapidly deployable, decentralized technologies alongside larger centralized technologies minimizes water supply cost while maintaining high robustness to climate uncertainty.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

PR100: Puerto Rico Grid Resilience and Transition to 100% Renewable Energy: Preventing and Responding to Extreme Events

This presentation discusses the aftermath of Hurricane Maria's landfall in Puerto Rico on September 20, 2017. Maria incapacitated Puerto Rico's power systems, leaving the entire island without electricity and access to fresh water. Some parts are still recovering years later. A resilient electric grid is vital to Puerto Rico's security, economy, and way of life.

demand impacts↗
DOE OSTI2024

Probabilistic Analysis of Long-Term Degradation of Microwave Cavity Flow Sensor

We are investigating a microwave resonant cavity transducer for flow sensing in the vessel of a high temperature fluid advanced reactor (AR), such as a molten salt cooled reactor (MSCR) or a sodium fast reactor (SFR). This transducer is a hollow metallic cylindrical cavity, with the flat wall of the cylinder flexible enough to undergo microscopic deflection due to dynamic fluid pressure. Membrane deflection leads to a shift in the resonant frequency, which can be detected with a spectrum analyzer. We have performed a proof-of-concept experiment of flow sensing with the transducer in liquid sodium at 340°C in impinging liquid jet geometry. The transducer remained in liquid sodium for 70 days. After removal, no structural damage was observed, and the expected transducer response was verified in a water test. Because long-term (multi-year) experimental tests of transducer resilience to harsh environment are not practical, we have developed a probabilistic model of creep to estimate transducer resilience to the harsh environment. The probabilistic model considers diffusion creep under the condition of high temperature and low stress, where the stress and temperature are allowed to be random variables with Gaussian distributions. Using the probabilistic model, we estimate inelastic membrane deflections due to creep for several temperature ranges. We conclude that for temperatures less than 650°C, creep has negligible long-term effect on the transducer performance. Since a yellowish residue was observed on the transducer surface after 70 days of immersion in liquid sodium, we have investigated possible evidence of corrosion. Chromium depletion is a typical indicator of the corrosion process in stainless steel. Scraping off a residue from the transducer and performing scanning electron microscopy (SEM) with energy dispersive analysis (EDS) did not find any chromium in the residue. Approximately 60% of the residue consisted of copper, which can be attributed to contamination of sodium due to powder residue from machining of copper and brass components of the transducer.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

City Decision Analysis Resources and Tools

The City Decision Analysis at Any Scale Workshop was held February 17-18, 2021. This brochure was created to provide participants with a collection of tools and resources that support planning and implementation of clean energy goals for communities and businesses.

ACES↗
DOE OSTI2020

Dynamic Restoration Strategy for Distribution System Resilience Enhancement

In electric power distribution systems, distributed energy resources (DERs) can act as controllable power sources and support utility operators to minimize power outages after extreme weather events (e.g., hurricane, earthquake, wildfire) and thus help enhance the grid's resilience. Meanwhile, the influences of extreme events and the capabilities of DERs are dynamic and difficult to predict. Hence, the desired distribution system restoration strategy should be able to evolve according to real-time fault/dis-turbance information and the availability of DERs. In this paper, we propose a new dynamic restoration strategy for distribution systems to enhance system resilience against potential hazards. An efficient reconfiguration algorithm is developed to eliminate the use of integer variables to relieve the computational burden. Model predictive control is implemented to adjust the system topology and DER operation set points based on the updated fault information and DER forecasts. The effectiveness of the proposed restoration model in enhancing distribution system resilience is validated through an IEEE 123-bus test system. Simulation results also validate that the proposed restoration model can mitigate the occurrence of unexpected events and the fluctuations of DERs.

distributed energy resources (DERs)↗
DOE OSTI2020

Dynamic Distribution System Restoration Strategy for Resilience Enhancement: Preprint

In electric power distribution systems, distributed energy re-sources (DERs) can act as controllable power sources and support utility operators to minimize power outages after ex-treme weather events (e.g., hurricane, earthquake, wildfire) and thus help enhance the grid's resilience. Meanwhile, the influ-ences of extreme events and the capabilities of DERs are dy-namic and difficult to predict. Hence, the desired distribution system restoration strategy should be able to evolve according to real-time fault/disturbance information and the availabil-ity of DERs. In this paper, we propose a new dynamic distribu-tion system restoration strategy to enhance system resilience against potential hazards. An efficient reconfiguration algo-rithm is developed to eliminate the use of integer variables to relieve the computational burden. Model predictive control is implemented to adjust the system topology and DER opera-tion setpoints based on the updated fault information and DER forecasts. The effectiveness of the proposed restoration model in enhancing distribution system resilience is validated through an IEEE 123-bus test system. Simulation results also validate that the proposed restoration model can mitigate the occurrence of unexpected events and the fluctuations of DERs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

VISILIENCE: An Interactive Visualization Framework for Resilience Analysis using Control-Flow Graph

Soft errors have become one of the major concerns for the error resilience of HPC applications, as those errors can cause HPC applications to generate serious outcomes such as Silent Data Corruptions (SDCs). A large body of approaches has been proposed to analyze the resilience of HPC applications. However, existing studies rarely address the challenges of the analysis result perception. Specifically, resilience analysis techniques often produce a massive volume of unstructured data, making it difficult for programmers to conduct the resilience analysis due to non-intuitive raw data. Furthermore, different analysis models produce diverse results with multiple levels of details, which may create hurdles to compare and explore the resilience of HPC program execution. To this end, we present VISILIENCE, an interactive VISual resILIENCE analysis framework to allow programmers to facilitate the resilience analysis of HPC applications. In particular, VISILIENCE leverages an effective visualization approach Control Flow Graph (CFG) to present a function execution. In addition, three widely-used models for resilience analysis (i.e., Y-Branch, IPAS, and TRIDENT) are seamlessly embedded into the framework for resilience analysis and result comparison. Multiple case studies have been conducted to demonstrate the effectiveness of our proposed framework VISILIENCE.

Jiang, Hailong↗
DOE OSTI2025

Quantifying Distribution System Resilience From Utility Data: Large Event Risk and Benefits of Investments

We focus on blackouts in electric distribution systems that have a large cost to customers. To quantify resilience to these events, we show how to calculate risk metrics from the historical outage data routinely collected by utilities' outage management systems. Risk is defined using a customer cost exceedance curve. The exceedance curve has a heavy tail that implies large fluctuations in large blackout costs, and this makes estimating the mean large cost in the usual way impractical. To avoid this problem, we use new resilience metrics describing the large event risk; these metrics are the probability of a large cost event, the annual log cost resilience index, and the average of the logarithm of the cost of large-cost events or the slope magnitude of the tail on a log–log exceedance curve. Resilience can be improved by planned investments to upgrade system components or speed up restoration. The benefits that these investments would have had if they had been made in the past can be quantified by “rerunning history” with the effects of the investment included, and then recalculating the large event risk to find the improvement in resilience. An example using utility data shows a 2% reduction in the probability of a large cost event due to 10% wind hardening and 6%–7% reduction due to 10% faster restoration in two different areas of a distribution utility. This new data-driven approach to quantify resilience and resilience investments is realistic and much easier to apply than complicated approaches based on modeling all the phases of resilience. Moreover, an appeal to improvements to past lived experience may well be persuasive to customers and regulators in making the case for resilience investments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Applying Utility's Advanced Grid Technologies to Improve Resiliency of a Critical Load

The US DOE Office of Electricity's Energy Storage Program's joint R&D work with the Cordova Electric Cooperative (CEC) has deployed several advanced grid technologies that are providing benefits today to Cordova Alaska's electricity users. Advanced grid technologies deployed through DoE co-funded R&D include a 1MW Battery Energy Storage System (BESS), and enhanced monitoring including Phasor Measurement Units (PMU's) to help better understand the operational impacts of the added BESS. This paper will highlight key accomplishments to-date in deploying and using advanced grid technologies, and then outline the next phase of work that will use these technologies to implement an operating scheme to reconfigure the utility's distribution system and utility resources including BESS to provide emergency back-up power to a critical load: the Cordova Community Medical Center (CCMC). This paper will include additional insights on the use of utility resources to support critical loads via case study examples by the National Rural Electric Cooperative Assoc. (NRECA).

BESS↗
DOE OSTI2021

Integrating the Value of Resilience into Energy Decisions

The value of resilience can be quantified and integrated into decision making to help inform economic, resilient, and sustainable energy deployment decisions using tools and analysis. Traditionally, distributed energy deployment decisions have often been based on how much energy, or how much money, a system can save. More and more, building owners and communities are recognizing that these systems can also provide a resilience value by sustaining critical loads during an outage. In order to consider the value of resilience in investment decisions, we must quantify how much resilience a system provides, assign a value to that resilience, and monetize that value through grid services, incentives, or other methods. This presentation discusses methods, tools, advantages, and challenges inherent to the process.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Dynamic Restoration Strategy for Distribution System Resilience Enhancement

In electric power distribution systems, distributed energy resources (DERs) can act as controllable power sources and support utility operators to minimize power outages after extreme weather events (e.g., hurricane, earthquake, wildfire) and thus help enhance the grid's resilience. Meanwhile, the influences of extreme events and the capabilities of DERs are dynamic and difficult to predict. Hence, the desired distribution system restoration strategy should be able to evolve according to real-time fault/disturbance information and the availability of DERs. In this paper, we propose a new dynamic restoration strategy for distribution systems to enhance system resilience against potential hazards. An efficient reconfiguration algorithm is developed to eliminate the use of integer variables to relieve the computational burden. Model predictive control is implemented to adjust the system topology and DER operation set points based on the updated fault information and DER forecasts. The effectiveness of the proposed restoration model in enhancing distribution system resilience is validated through an IEEE 123-bus test system. Simulation results also validate that the proposed restoration model can mitigate the occurrence of unexpected events and the fluctuations of DERs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A Markov framework for generalized post-event systems recovery modeling: From single to multihazards

State-dependent models can be used to represent the system recovery process as a series of stochastic transitions from lower to higher functional states. However, the applications of these models have been limited in scope and there is a lack of a generalized recovery modeling framework. A generalized framework would permit a robust forecasting of systems and system-of-systems recovery under multiple hazards, and more broadly, would contribute to community disaster preparedness. This paper develops a generalized post hazard-event recovery modeling framework based on state-dependent Markov-type processes. We then apply the proposed framework to solve a spectrum of problems that range from hind-casting single-system recovery following a single hazard event to forecasting post-event trajectories under multiple hazards and modeling the recovery of a system-of-systems. First, Markov chains are used to hind-cast the observed recovery for a portfolio of buildings affected by the 2014 South Napa, California, earthquake. Next, Markov processes are used to formulate a parametric post hazard-event recovery model, which can be updated using Bayesian statistics when relevant datasets become available. Semi-Markov processes are then used to develop a more general model of single hazard recovery, which accounts for the intensity of the loading and level of damage caused by the event. Semi-Markov processes with non-renewal features are then used to account for multihazard interactions in a post-event recovery model, and applied to a case study that involves a community in Charleston, South Carolina. Lastly, Markov-type processes are combined with Bayesian networks to model the recovery of residential, commercial, educational, and industrial buildings (system-of-systems) following a hazard event. Overall, these applications demonstrate the versatility of the Markov framework towards handling recovery problems with varying levels of complexity.

42 ENGINEERING↗