SEARCH · Engineering Papers

Results for “network resilience”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 451 records · Page 25

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Programmable intrusion detection for distributed energy resources in cyber–physical networked microgrids

We present a programmable intrusion detection method is presented to identify the malicious attacks to distributed energy resources (DERs) in the cyber-physical networked microgrids. The proposed method injects small programmable signals into the system and uses the response to identify abnormal conditions. Because of the low or even zero inertia induced by integrations of DER power-electronic-interfaces, microgrids have very limited resilience capability; and thus, being sensitive to attacks. One microgrid's malfunction caused by attacks can easily propagate to its neighboring systems when several microgrids are connected, leading to catastrophic electricity supply failures. Through the presented method, malicious intrusions can be effectively detected, located, and defended for securing microgrids. Theoretical derivations are provided to define the programmable detection rules. The detection rule is easy and flexible to update, making it difficult for attack actors to gain the knowledge of the detection rules, in order to avoid being detected. Numerical results on a cyber-physical networked microgrids system show that the proposed method is effective and efficient in precisely locating intrusion attacks to the microgrids system.

42 ENGINEERING↗

DOE OSTI2023

Knots are not for naught: Design, properties, and topology of hierarchical intertwined microarchitected materials

Lightweight and tough engineered materials are often designed with three-dimensional hierarchy and interconnected structural members whose junctions are detrimental to their performance because they serve as stress concentrations for damage accumulation and lower mechanical resilience. We introduce a previously unexplored class of architected materials, whose components are interwoven and contain no junctions, and incorporate micro-knots as building blocks within these hierarchical networks. Tensile experiments, which show close quantitative agreements with an analytical model for overhand knots, reveal that knot topology allows a new regime of deformation capable of shape retention, leading to a ~92% increase in absorbed energy and an up to ~107% increase in failure strain compared to woven structures, along with an up to ~11% increase in specific energy density compared to topologically similar monolithic lattices. Our exploration unlocks knotting and frictional contact to create highly extensible low-density materials with tunable shape reconfiguration and energy absorption capabilities.

36 MATERIALS SCIENCE↗

DOE OSTI2021

A Risk-Averse Approach for Distribution Grid Expansion Planning

Recent episodes of natural disasters have challenged the resilience of power grids. Adequate distribution grid planning that properly captures the risk aversion of the utility system planner is a key factor to increase the flexibility of distribution networks to circumvent these events. In this paper, we propose a methodology to determine the optimal portfolio of investments in lines and storage devices in order to minimize a convex combination between expected value and CVaR of operational costs, including energy not served, while taking into account the multistage nature of the energy storage management within this context. While the expected value of energy not served has been traditionally employed to tackle routine failures, we also minimize the CVaR of energy not served to address high-impact, low-probability (HILP) events. We illustrate the performance of the proposed methodology with a 54-Bus system test case.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Digital Twin Applications in the Water Sector: A Review

As cities develop and resource demands rise, the water sector faces crucial challenges to deliver reliable, sustainable, and efficient services. Digital Twins (DTs), virtual replicas of physical systems, offer a promising tool to transform how we manage water infrastructure. Originally developed in the aerospace industry, DTs are now gaining traction in the water sector, enabling real-time monitoring, simulation, and predictive control of water and wastewater treatment, collection and distribution networks, and water reclamation and reuse systems. While still emerging in the water sector, DTs have shown potential to enhance operational efficiency, reduce environmental impacts, and support smarter, more resilient water management. This review study provides a comprehensive overview of current DT applications in the water sector, highlighting successful case studies, technical challenges, and knowledge gaps. It also explores how DTs can help bridge the water–energy nexus by optimizing resources utilized across interconnected systems. By synthesizing recent advances and identifying future research directions, this paper illustrates how DTs can play a central role in building sustainable, adaptive, and digitally-enabled water infrastructure.

digital twin↗

NASA NTRSDate not supplied

SERVIR - Connecting Space to Village in Africa, Asia and the Americas

SERVIR is a joint NASA and USAID program that partners with countries and organizations to support locally led efforts to strengthen climate resilience, food and water security, forest and carbon management, and air quality. This unique program integrates NASA’s world class scientists and data with USAID’s development expertise and network of partners and relationships around the world. SERVIR harnesses the power of satellite data and science collaboration to support healthy, sustainable communities, livelihoods and environments. SERVIR’s name is derived from Latin, meaning "to serve.” We serve and partner with leading local, national, and regional institutions in Africa, Asia, and Latin America and in partnership with scientists and subject matter experts to co-develop and implement activities called “services.” Each of SERVIR’s services provide a comprehensive suite of geospatial data, software, and training materials, all of which are tailored to the unique needs of SERVIR’s end users. Each service is collaboratively designed and implemented with the help of partners and users, such as local governments and NGOs, to better support decision-making. When SERVIR plans new services, it prioritizes long-term dialogue and engagement with communities to ensure that services are sustainable, socially inclusive, and suited to local needs. SERVIR strives to make the power of Earth science more accessible and inclusive. All web tools are made publicly available to help promote greater uptake and long-term use of SERVIR’s services.

SERVIR↗

DOE OSTI2022

Safety-assured, real-time neural active fault management for resilient microgrids integration

Federated-learning-based active fault management (AFM) is devised to achieve real-time safety assurance for microgrids and the main grid during faults. AFM was originally formulated as a distributed optimization problem. Here, federated learning is used to train each microgrid's network with training data achieved from distributed optimization. The main contribution of this work is to replace the optimization-based AFM control algorithm with a learning-based AFM control algorithm. The replacement transfers computation from online to offline. With this replacement, the control algorithm can meet real-time requirements for a system with dozens of microgrids. By contrast, distributed-optimization-based fault management can output reference values fast enough for a system with several microgrids. More microgrids, however, lead to more computation time with optimization-based method. Distributed-optimization-based fault management would fail real-time requirements for a system with dozens of microgrids. Controller hardware-in-the-loop real-time simulations demonstrate that learning-based AFM can output reference values within 10 ms irrespective of the number of microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Cross-sectoral impact of emerging technologies on U.S. manufacturing resilience and competitiveness

Introducing new technologies in one energy-intensive industry can affect how other industries operate and stay resilient, yet these cross-sector interactions are often underappreciated in conventional technology roadmaps. In practice, industrial systems do not evolve in isolation. They are linked through shared upstream and downstream dependencies, such as electricity and fuel supply, critical materials, transportation networks, and enabling infrastructure. As a result, large-scale technology deployment in one sector can reshape resource availability, infrastructure demand, and operational risk in others. These interdependencies mean that technology deployment decisions in one sector can create unintended bottlenecks or cascading benefits in others. Here, this article argues that a cross-sector, system-of-systems perspective is essential for evaluating and scaling emerging technologies in energy-intensive industries. By framing industrial transformation as an interconnected systems challenge rather than a set of isolated sectoral decisions, the study highlights how interdependence shapes technology feasibility, adoption pathways, and resilience outcomes. The article illustrates how cross-sector linkages can amplify both risks and benefits, and it emphasizes the importance of integrated planning approaches that account for shared dependencies, cascading impacts, and co-optimization opportunities. Adopting this broader perspective can support more robust technology roadmaps, improve strategic coordination across industries, and strengthen the long-term resilience of the industrial sector as a whole.

Nain, Preeti [Oak Ridge National Laboratory (ORNL)↗

NASA NTRS2022

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters using a Deep Neural Network

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) series, have been observing severe convection at 15–60-minute intervals for over 40 years. When properly assessed, such a data record can be valuable in efforts of estimating severe storm risk throughout the diurnal cycle based on automated detection of patterns consistently found atop severe storms. Furthermore, environmental conditions favorable for severe weather are well-known and are thought to be represented well by modern reanalysis products. Promoting resilience against such hazards on local and global scales is a chief goal the NASA Disasters program, which seeks to encourage use of satellite observations to mitigate risk. For instance, hail is the costliest severe weather hazard across the globe in terms of insured loss, but reporting inconsistencies for hail events globally make it difficult to develop models that can quantify the risk. Satellite observation and model reanalysis taken together have the potential to, with reasonable skill and specificity, characterize environmental conditions that are favorable for hazardous weather, and thereby enable creation of hazard climatologie. Such climatologies are particularly useful over regions without extensive radar networks or storm reporting. By mapping the multivariate combination of observed cloud features and reanalysis environmental parameters/indices to United States Next Generation Weather Radar (NEXRAD) radar-estimated Maximum Expected Size of Hail (MESH) by way of a deep neural network (DNN), estimates of likelihood for potentially severe hail can be produced. Such estimates are of greater complexity and efficiency than could be performed with previous multivariate or logistic regression analyses for observed points within convective systems. Statistical distributions of convective parameters from satellite and reanalysis are shown to highlight non-severe/severe class separation for well-known hailstorm predictors, e.g., overshooting cloud top characteristics, deep-layer wind shear, mid-level stability, helicity, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN, which can efficiently produce a quantitative hail risk metric with better than 70% detection rate and under 30% false alarms. These hail classifications can then be aggregated across the satellite record to yield a hazard climatology for hail frequency and severity – knowledge of which is of particular interest to those who manage risk (e.g., insurers) and are seeking opportunities to identify hail-prone regions, particularly in developing nations. This NASA study uses satellite observations and model parameters in a DNN to perform climatological hailstorm analysis in support of catastrophe model development, with the hope of promoting risk resilience particularly in regions without adequate weather radar coverage.

Passive Remote Sensing↗

DOE OSTI2026

Cascading economic losses from port disruptions under capacity constrained multimodal freight networks

This study quantifies how throughput disruptions at major seaports cascade through capacity-constrained multimodal freight networks and interregional production systems. We couple an agent-based model (ABM) multimodal freight simulation that resolves rerouting, terminal queueing, and inventory drawdown under binding modal and facility capacities with a multiregional output loss input-output (MRIIM) model that propagates realized delivery shortfalls across regions and sectors. The framework is demonstrated for the Port of Los Angeles using Freight Analysis Framework flows and Bureau of Economic Analysis input-output accounts and is evaluated over a 52-week horizon under deterministic sector targeted shocks and stochastic disruption realizations with uncertain severity and duration. Results indicate nonlinear amplification: realized national losses concentrate in manufacturing and transportation/warehousing even when exogenous port shocks are dispersed, suggesting that congestion spillback and limited short-run substitution can dominate the initial shock allocation. We further evaluate a tabular reinforcement-learning (Q-learning) intervention layer that selects among a small set of implementable system level levers (truck-to-rail and truck-to-barge shift settings) without overriding shipper routing, finding that such interventions reduce total losses for moderate disruptions but yield diminishing returns once substitute modes approach capacity. By linking operational freight behavior to system wide impacts under uncertainty, the proposed ABM-MRIIM pipeline provides a reusable workflow for port disruption stress testing, identification of structurally critical sectors/corridors, and evaluation of resilience interventions under realistic capacity limits.

42 ENGINEERING↗

DOE OSTI2022

Synchrophasors-based Master State Awareness Estimator for Cybersecurity in Power Grid: Testbed Implementation & Field Demonstration

The integration of distributed energy resources(DERs) and expansion of complex network in the distribution grid requires an advanced distributed state estimator to monitor the grid health at micro-level. The distribution state estimator will improve the situational awareness and resiliency of distributed power system. This paper proposes a synchrophasors-based master state awareness (MSA) estimator to enhance the cybersecurity in distribution grid by providing a real-time estimation of system operating states to control center operators. In this paper, the proposed MSA estimator utilizes only phasor measurements, bus magnitudes and angles, from phasor measurement units (PMUs),deployed in local substations, to estimate the system states and also detects data integrity attacks, such as load tripping attack that disconnects the load. To validate the proof of concept, we implement the proposed methodology in cyber-physical testbed environment at the Idaho National Laboratory (INL) Electric Grid Security Testbed. Further, to address the “valley of death” and support technology commercialization, field demonstration is also performed at the Critical Infrastructure Test Range Complex(CITRC) at the INL. Our experimental results reveal a promising performance in detecting load tripping attack and providing an accurate situational awareness through an alert visualization dashboard in real-time

42 ENGINEERING↗

DOE OSTI2023

The Global Climate Action Partnership

The Global Climate Action Partnership (GCAP; formerly the Low Emission Development Strategies Global Partnership) is a worldwide network that accelerates ambitious climate goals and advances implementation in Africa, Asia, and Latin America and the Caribbean (LAC). Our strategic approach emphasizes country-driven implementation actions to achieve resilient, just, and inclusive low-emission and net-zero economies. As an incubator for scalable knowledge and solutions, GCAP is leading the way to equitable, climate-resilient, low-emission development.

African Climate Action Partnership↗

DOE OSTI2024

Software-Defined Virtual Synchronous Condenser

Synchronous condensers (SCs) play important roles in integrating wind energy into relatively weak power grids. However, the design of SCs usually depends on specific application requirements and may not be adaptive enough to the frequently-changing grid conditions caused by the transition from conventional to renewable power generation. This paper devises a software-defined virtual synchronous condenser (SDViSC) method to address the challenges. Our contributions are fourfold: 1) design of a virtual synchronous condenser (ViSC) to enable full converter wind turbines to provide built-in SC functionalities; 2) engineering SDViSCs to transfer hardware-based ViSC controllers into software services, where a Tustin transformation-based software-defined control algorithm guarantees accurate tracking of fast dynamics under limited communication bandwidth; 3) a software-defined networking-enhanced SDViSC communication scheme to allow enhanced communication reliability and reduced communication bandwidth occupation; and 4) Prototype of SDViSC on our real-time, cyber-in-the-loop digital twin of large-wind-farm in an RTDS environment. Furthermore, extensive test results validate the excellent performance of SDViSC to support reliable and resilient operations of wind farms under various physical and cyber conditions.

17 WIND ENERGY↗

DOE OSTI2022

Assessing the energy equity benefits of energy storage solutions

Safety, reliability, efficiency, and affordability are no longer the sole tenets of electric grid planning. The evolving social and policy climate have placed new explicit requirements on the electric grid, including resilience, decarbonization, and energy equity and justice. Integrating energy equity strategies into modern grid design is intended to achieve a fair and just distribution of benefits within the energy system. This study aims to characterize the energy equity and community benefits of energy storage systems (ESS) under the following three use-case models: utility ESS that are operated within the distribution system; community-owned ESS; and behind-the-meter ESS that are customer-owned to serve the household. The goal of this energy equity analysis is to characterize the environmental, economic, and social benefits of ESS through applied metrics such as energy burden, energy poverty, energy vulnerability, job creation, and more. A 13.8 kV, 265 node representative feeder corresponding to the hot-humid climate of Louisiana, chosen for its high energy burden, frequent hurricanes and outage events, was used to perform a storage adequacy analysis for six different outage scenarios to determine energy storage access for each use-case. The results of this analysis are then used to characterize both the grid and equity benefits of storage solutions to inform a prioritization framework matching community needs with system preferences for utility planning processes, market regulations, and the wider network of energy system stakeholders.

Energy Equity, energy justice, equity, reliability↗

DOE OSTI2021

Side-channel Leakage Assessment Metrics: A Case Study of GIFT Block Ciphers

Determination of an adequate level of security and providing subsequent mechanisms to achieve it, is one of the most pressing problems regarding embedded computing devices. While there are some solutions available for resource-rich computer systems, direct application of these solutions to resource-constrained environments are often unfeasible. The fundamental problem for such resource-constrained systems is the fact that current cryptographic algorithms utilize significant energy consumption and storage overhead. Both the cryptographic algorithm and its physical implementation affect the resilience of a cryptosystem against side-channel attacks. A side-channel attack represents a process that exploits leakages in order to extract sensitive information such as the key. This paper focuses on Correlation Power Analysis (CPA) which is side-channel attack based on the power consumption leakage. In 2016 the U.S. Commerce Department’s National Institute of Standards and Technology (NIST) initiated the call for proposals of new cryptographic algorithms to strengthen the cryptographic defense of networked devices against cyberattacks and to protect the data created by those innumerable device. This work evaluates S-boxes used by NIST candidates PICCOLO, GIFT, and PRESENT, as well as several S-box variants that demonstrated sufficient weaknesses against classical cryptanalysis, for a quantitative comparison in terms of resiliency to CPA attack. Three well-known theoretical metrics are evaluated: transparency order (TO and RTO), nonlinearity, and signal-to-noise (SNR) ratio, aiming to characterize the resistance of these S-boxes against adversaries exploiting physical leakages. Experimental results from attacks on an 8- bit XMEGA were obtained via the ChipWhisperer platform and of all the S-boxes evaluated, GIFT64 with a PICCOLO S-box was found to be the most susceptible to CPA. Results showed that variations in TO and RTO were not sufficient to ensure practical CPA resistance and that among S-boxes with equal non-linearity there were no significant differences in the TO and SNR variants.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Side-channel Leakage Assessment Metrics: A Case Study of GIFT Block Ciphers

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Street-level temperature estimation using graph neural networks: Performance, feature embedding and interpretability

Estimating street-level air temperature is a challenging task due to the highly heterogeneous urban surfaces, canyon-like street morphology, and the diverse physical processes in the built environment. Though pioneering studies have embarked on investigations via data-driven approaches, many questions remain to be answered. Here, in this study, we leveraged an innovative framework and redefined the street-level temperature estimation problem using Graph Neural Networks (GNN) with spatial embedding techniques. The results showed that GNN models are more capable and consistent of estimating street-level temperature among tested locations, benefiting from its unique strength in handling extensive data over unstructured graph topology. In addition, we conducted in-depth analysis of feature importance to enhance the model interpretability. Among the urban features analyzed in this study, the time-variant canopy density and meter-level land use data emerge as crucial factors. Our findings highlight GNN 's high potential in capturing the complex dynamics between urban elements and their impacts on microclimate, thus offering valuable insights for comprehensive urban data collection and urban climate modeling in general. Collectively, this study also contributes to urban planning and policy by providing avenues to enhance city resilience against climate change, thereby advancing the agenda for environmental stewardship and urban sustainability.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Enabling Cybersecurity, Situational Awareness and Resilience in Distribution Grids with High Penetration of Photovoltaics (CARE-PV) (Final Report)

Since legacy distribution systems have very limited visibility beyond the substation, high penetration of PV at the grid edge presents some unique operational challenges. One approach to address these challenges is to use information from advanced metering infrastructure (AMI) and µPMUs. However, exploiting this information is impacted by a number of factors, including multi-timescale measurements, volume of data generated, communication network impairments (e.g., information loss and latency) and susceptibility to cyber-attacks. Therefore, one of the critical tasks involved in the management of a distribution grid is to develop complete situational awareness by integrating cyber-security mechanisms with state estimation strategies and leveraging this situational awareness to assure energy services at strategic locations while exploiting AMI/PV inverter/ µPMU data. This CARE-PV project addresses the fundamental challenges in situational awareness and resilience to cyber and physical vectors by exploiting the synergy between innovative modeling, estimation, data analytics, testing and validation using smart PV inverters designed at K-State and facilities at NREL. Specifically, the project involved the development, testing and validation of the following novel enabling technologies: (Thrust 1) Resilience to cyber vectors that impact data integrity was addressed via a two-level defense strategy that combines cyber intrusion detection using self-learning, cooperative smart PV inverters, and a novel moving target defense framework to combat data integrity attacks. (Thrust 2) Resilience to cyber-physical vectors that impact situational awareness by limiting data availability was addressed via novel centralized and decentralized, sparsity-based static and dynamic state estimation approaches that enhance observability even when the underlying system is unobservable. (Thrust 3) Leveraging a unique probabilistic sensitivity analysis approach accompanied by one-of-a-kind dominant influencer set computation, the vulnerability of critical infrastructure at strategic locations was evaluated so that proactive PV-based control strategies can be used to support operations under normal/outage scenarios. These CARE-PV project innovations were demonstrated on both small-scale IEEE and larger utility-scale testbeds (Thrust 4). Feedback from Industry Advisory Board members was used to formulate a commercialization pathway for a subset of CARE-PV technologies. These CARE-PV technologies will ultimately lead to reliable and secure, large-scale integration of renewable energy and mitigate the risk of energy disruption resulting from cyber incidents and other emerging threats within the energy environment.

14 SOLAR ENERGY↗

DOE OSTI2023

Recommendations for Minimum Required Error Codes for Electric Vehicle Charging Infrastructure

OCPP protocol manages the interaction between the EVSE and its respective back-end communication network. It plays a pivotal role in both error reporting and troubleshooting, carried out primarily through the CSMS. OCPP defines both standard error codes and a flexible framework for creating and communicating custom error codes. The OCPI protocol orchestrates the communication between different backhaul communication networks, incorporating the exchange of error codes. These error codes are instrumental in pinpointing and rectifying issues that can surface prior to, during, or after charging operations, fortifying the reliability and resilience of the EV charging infrastructure. The flexibility offered by the OCPP and OCPI frameworks through the introduction of custom error codes also creates its own set of challenges. While the integration of custom error codes allows for enhanced granularity, it also introduces inconsistencies and fragmentation within the overarching diagnostic reporting system. To address the challenges with custom error codes this report proposes a set of Minimum Required Error Codes (MRECs) for streamlined error reporting, interpretability, and diagnostics. Recommendations in this report are based on independent analysis of custom error codes from multiple stakeholders within the EV charging ecosystem. For better error resolution, this report also assigns one or more entities responsible for the resolution of every mentioned error code. Finally, a functional classification for each mentioned error code is also identified to describe the nature of the error. In summary, the purpose of this document is to simplify the troubleshooting process and increase charging reliability for all EV users. This report serves as a recommendation for industry stakeholders, encouraging a unified methodology to define, transmit, and interpret common error codes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗