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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

HPC-Enabled Optimization of High Temperature Heat Exchangers (CRADA Final Report)

This project was a collaborative effort between Lawrence Livermore National Security, LLC (LLNS) as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Materials Sciences, LLC, to develop a technology for design and optimization of heat exchangers using powerful desktop and laptop computers. The project was originally designated as a 12-month project, and consisted of 3# major tasks and the following 8# major deliverables: 1) CFD models of 3D heat exchangers based on existing and new geometry. 2) Validation against experimental data provided by MSC and published in the literature. 3) CFD models of 3D unit cells based on TPMS. 4) Surrogate models capable of delivering the gradients of the homogenized properties with respect to the parametrization. 5) 3D design methodology using TO algorithms. 6) Conventional reference and topology optimized designs. 7) 3D optimized designs stored in a 3D printer build format. 8) Verification of the improved performance. All of the deliverables for this project were successfully completed with two no-cost time extensions.

36 MATERIALS SCIENCE↗
DOE OSTI2026

HPC-Enabled Optimization of High Temperature Heat Exchangers (CRADA Final Report)

This project was a collaborative effort between Lawrence Livermore National Security, LLC (LLNS) as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Materials Sciences, LLC, to develop a technology for design and optimization of heat exchangers using powerful desktop and laptop computers. The project was originally designated as a 12-month project, and consisted of 3# major tasks and the following 8# major deliverables: 1) CFD models of 3D heat exchangers based on existing and new geometry. 2) Validation against experimental data provided by MSC and published in the literature. 3) CFD models of 3D unit cells based on TPMS. 4) Surrogate models capable of delivering the gradients of the homogenized properties with respect to the parametrization. 5) 3D design methodology using TO algorithms. 6) Conventional reference and topology optimized designs. 7) 3D optimized designs stored in a 3D printer build format. 8) Verification of the improved performance. All of the deliverables for this project were successfully completed with two no-cost time extensions.

13 HYDRO ENERGY↗
DOE OSTI2013

An Optical Streak Diagnostic for Observing Anode-Cathode Plasmas for Radiographic Source Development

National Security Technologies, LLC, and Sandia National Laboratories are collaborating in the development of pulsed power–driven flash x-ray radiographic sources that utilize high-intensity electron beam diodes. The RITS 6 (Radiographic Integrated Test Stand) accelerator at Sandia is used to drive a self magnetic pinch diode to produce a Bremsstrahlung x-ray source. The high electric fields and current densities associated with these short A-K gap pinch beam diodes present many challenges in diode development. Plasmas generated at both the anode and cathode affect the diode performance, which is manifested in varying spot (source) sizes, total dose output, and impedance profiles. Understanding the nature of these plasmas including closure rates and densities is important in modeling their behavior and providing insight into their mitigation. In this paper we describe a streak camera–based optical diagnostic that is capable of observing and measuring plasma evolution within the A-K gap. By imaging a region of interest onto the input slit of a streak camera, we are able to produce a time-resolved one-dimensional image of the evolving plasma. Typical data are presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2012

Spiral Development for Safeguards Instrumentation

Safeguards instrumentation is highly specialized, so a common approach in the US has been to develop initial prototypes for performance, operability and security within the US National Laboratories for the IAEA and then seek one or more commercial partners. Transfer of technology from US National Laboratories is a legal requirement for products that have the potential for mass production. Other important objectives include minimizing time to deployment and lifecycle cost, and optimizing product maintainability, sustainability and manufacturability. Unfortunately, the deployment of systems developed via this model has sometimes been seriously delayed or never adopted because of the difficulty of optimizing the significant parameters of the process between the public and private sectors. The authors suggest that forming an R&D partnership between a research laboratory and a commercial company much earlier in the process would provide significant advantages. The present US practice leads to unnecessary expenditures during the early R&D phase since many decisions are made based on research needs that are counterproductive for commercialization and manufacturability. If the ultimate goal of the project is to produce a reliable and cost effective commercial product, the commercial input is needed early and often. The new “model” of developing systems in a closer collaboration with the private sector, in a spiral “Commercialization by Design” approach, should also limit the long term financial mortgages that Member States frequently experience with respect to safeguards instrumentation development using the present process. As a concrete example, the potential for incorporating Wire Integrity Verification Technology into the iCobra Reader System is discussed.

Koskelo, M.↗
DOE OSTI2017

A lightweight network anomaly detection technique

While the network anomaly detection is essential in network operations and management, it becomes further challenging to perform the first line of detection against the exponentially increasing volume of network traffic. In this paper, we develop a technique for the first line of online anomaly detection with two important considerations: (i) availability of traffic attributes during the monitoring time, and (ii) computational scalability for streaming data. The presented learning technique is lightweight and highly scalable with the beauty of approximation based on the grid partitioning of the given dimensional space. With the public traffic traces of KDD Cup 1999 and NSL-KDD, we show that our technique yields 98.5% and 83% of detection accuracy, respectively, only with a couple of readily available traffic attributes that can be obtained without the help of post-processing. Finally, the results are at least comparable with the classical learning methods including decision tree and random forest, with approximately two orders of magnitude faster learning performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

The Impact of Time-Aware Design Choices in ICS Anomaly Detection

Industrial control systems (ICS) remain vulnerable to increasingly sophisticated cyberattacks, yet evaluating anomaly detection models in these environments is challenging due to temporal dependencies, missing-not-at-random patterns, and extremely imbalanced datasets. These factors make common practices—especially random data splits and na¨ıve imputation— prone to severe temporal leakage, which can inflate reported performance and obscure real-world limitations. In this work, we systematically examine classical machine learning models, temporal deep learning architecture, and tensordecomposition– based methods on a gas-pipeline dataset using a fully temporally separated evaluation pipeline designed to mimic realistic deployment conditions. Our findings show that proper temporal handling and MNAR-aware preprocessing significantly alter the relative performance of popular anomaly-detection methods, providing practical guidance for designing reliable, leakage-resistant ICS intrusion-detection systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Magnetostrictive materials for enhanced sensors and electronic components

Magnetostriction is a property of magnetic materials that causes them to change their shape or dimensions when their magnetization changes. Low-cost, mechanically robust magnetostrictive sensors would be valuable for a wide range of applications addressing DOE's energy, environmental, and national security missions. Several examples include monitoring internal conditions of pipelines, enhancing implantable systems for the human body, and improving the electrical grid by providing real time sensors for detecting high impedance faults that may act as ignition sources for forest fires. Historically, magnetostrictive materials tended to be expensive and mechanically brittle, but recent developments in iron-based alloys, especially those that include dilute solutions of rare earth elements, have demonstrated useful magnetostriction values, mechanical robustness, and low costs. This project investigated addition of dilute cerium doping on the magnetostrictive properties of Fe-Ga and Fe-Al alloys. Advanced manufacturing techniques were applied to these materials to take advantage of rapid cooling of the alloy to stabilize desired phases and to make near net shape coupons. These coupons develop significant texturing, offering a route to target microstructures with exceptional performance.

36 MATERIALS SCIENCE↗
DOE OSTI2014

simuwatt - A Tablet Based Electronic Auditing Tool

'simuwatt Energy Auditor' (TM) is a new tablet-based electronic auditing tool that is designed to dramatically reduce the time and cost to perform investment-grade audits and improve quality and consistency. The tool uses the U.S. Department of Energy's OpenStudio modeling platform and integrated Building Component Library to automate modeling and analysis. simuwatt's software-guided workflow helps users gather required data, and provides the data in a standard electronic format that is automatically converted to a baseline OpenStudio model for energy analysis. The baseline energy model is calibrated against actual monthly energy use to ASHRAE Standard 14 guidelines. Energy conservation measures from the Building Component Library are then evaluated using OpenStudio's parametric analysis capability. Automated reporting creates audit documents that describe recommended packages of energy conservation measures. The development of this tool was partially funded by the U.S. Department of Defense's Environmental Security Technology Certification Program. As part of this program, the tool is being tested at 13 buildings on 5 Department of Defense sites across the United States. Results of the first simuwatt audit tool demonstration are presented in this paper.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

A source-attractor approach to network detection of radiation sources

Radiation source detection using a network of detectors is an active field of research for homeland security and defense applications. We propose Source-attractor Radiation Detection (SRD) method to aggregate measurements from a network of detectors for radiation source detection. SRD method models a potential radiation source as a magnet -like attractor that pulls in pre-computed virtual points from the detector locations. A detection decision is made if a sufficient level of attraction, quantified by the increase in the clustering of the shifted virtual points, is observed. Compared with traditional methods, SRD has the following advantages: i) it does not require an accurate estimate of the source location from limited and noise-corrupted sensor readings, unlike the localizationbased methods, and ii) its virtual point shifting and clustering calculation involve simple arithmetic operations based on the number of detectors, avoiding the high computational complexity of grid-based likelihood estimation methods. We evaluate its detection performance using canonical datasets from Domestic Nuclear Detection Office s (DNDO) Intelligence Radiation Sensors Systems (IRSS) tests. SRD achieves both lower false alarm rate and false negative rate compared to three existing algorithms for network source detection.

Wu, Qishi↗
DOE OSTI2013

Large-Scale Data Challenges in Future Power Grids

This paper describes technical challenges in supporting large-scale real-time data analysis for future power grid systems and discusses various design options to address these challenges. Even though the existing U.S. power grid has served the nation remarkably well over the last 120 years, big changes are in the horizon. The widespread deployment of renewable generation, smart grid controls, energy storage, plug-in hybrids, and new conducting materials will require fundamental changes in the operational concepts and principal components. The whole system becomes highly dynamic and needs constant adjustments based on real time data. Even though millions of sensors such as phase measurement units (PMUs) and smart meters are being widely deployed, a data layer that can support this amount of data in real time is needed. Unlike the data fabric in cloud services, the data layer for smart grids must address some unique challenges. This layer must be scalable to support millions of sensors and a large number of diverse applications and still provide real time guarantees. Moreover, the system needs to be highly reliable and highly secure because the power grid is a critical piece of infrastructure. No existing systems can satisfy all the requirements at the same time. We examine various design options. In particular, we explore the special characteristics of power grid data to meet both scalability and quality of service requirements. Our initial prototype can improve performance by orders of magnitude over existing general-purpose systems. The prototype was demonstrated with several use cases from PNNL’s FPGI and was shown to be able to integrate huge amount of data from a large number of sensors and a diverse set of applications.

Smart Grid, Real Time, Commercial Products, Big Da↗
DOE OSTI2020

Economic Model for Estimation of GDP Losses in the MACCS Offsite Consequence Analysis Code

The MACCS (MELCOR Accident Consequence Code System) code is the U.S. Nuclear Regulatory Commission (NRC) tool used to perform probabilistic health and economic consequence assessments for atmospheric releases of radionuclides. It is also used by international organizations, both reactor owners and regulators. It is intended and most commonly used for hypothetical accidents that could potentially occur in the future rather than to evaluate past accidents or to provide emergency response during an ongoing accident. It is designed to support probabilistic risk and consequence analyses and is used by the NRC, U.S. nuclear licensees, the Department of Energy, and international vendors, licensees, and regulators. This report describes the modeling framework, implementation, verification, and benchmarking of a GDP-based model for economic losses that has recently been developed as an alternative to the original cost-based economic loss model in MACCS. The GDP-based model has its roots in a code developed by Sandia National Laboratories for the Department of Homeland Security to estimate short-term losses from natural and manmade accidents, called the Regional Economic Accounting analysis tool (REAcct). This model was adapted and modified for MACCS and is now called the Regional Disruption Economic Impact Model (RDEIM). It is based on input-output theory, which is widely used in economic modeling. It accounts for direct losses to a disrupted region affected by an accident, indirect losses to the national economy due to disruption of the supply chain, and induced losses from reduced spending by displaced workers. RDEIM differs from REAcct in its treatment and estimation of indirect loss multipliers, elimination of double counting associated with inter-industry trade in the affected area, and that it is designed to be used to estimate impacts for extended periods that can occur from a major nuclear reactor accident, such as the one that occurred at the Fukushima Daiichi site in Japan. Most input-output models do not account for economic adaptation and recovery, and in this regard RDEIM differs from its parent, REAcct, because it allows for a user-definable national recovery period. Implementation of a recovery period was one of several recommendations made by an independent peer review panel to ensure that RDEIM is state-of-practice. For this and several other reasons, RDEIM differs from REAcct. Both the original and the RDEIM economic loss models account for costs from evacuation and relocation, decontamination, depreciation, and condemnation. Where the original model accounts for an expected rate of return, based on the value of property, that is lost during interdiction, the RDEIM model instead accounts for losses of GDP based on the industrial sectors located within a county. The original model includes costs for disposal of crops and milk that the RDEIM model currently does not, but these costs tend to contribute insignificantly to the overall losses. This document discusses three verification exercises to demonstrate that the RDEIM model is implemented correctly in MACCS. It also describes a benchmark study at five nuclear power plants chosen to represent the spectrum of U.S. commercial sites. The benchmarks provide perspective on the expected differences between the RDEIM and the original cost-based economic loss models. The RDEIM model is shown to consistently predict larger losses than the original model, probably in part because it accounts for national losses by including indirect and induced losses; whereas, the original model only accounts for regional losses. Nonetheless, the RDEIM model predicts losses that are remarkably consistent with the original cost-based model, differing by 16% at most for the five sites combined with three source terms considered in this benchmark.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Mitigate: An Adaptive Network Data Anonymization Tool Using Condensation-Based Differential Privacy

Modern network devices collect a large amount of data that can be analyzed to identify bottlenecks, anomalies, cyber-attacks, etc. Therefore, there is often a need to analyze such collections of network data quite often by an external expert or by the research community. However, these collections of data contain sensitive, proprietary information. In order for the network data to be shared, it must first be anonymized. The overall objective of this project is to develop an innovative privacy management tool to anonymize network data and achieve sufficient privacy, acceptable data utility, and efficient data analysis at the same time. No existing anonymization methods can achieve all of these at the same time. The core of this technology is a differential private clustering algorithm that provides strong privacy protection, preserves data properties important for subsequent analysis, and allows the party receiving the anonymized data to conduct analysis directly on anonymized data without the need of decryption or any extra processing. The research carried out was to design, implement and verify a solution to this problem by completing the following tasks: 1) developing the core technology; 2) developing a context based method that automatically recommends fields that must be anonymized; 3) conducted experiments showing superior results using our approach compared to existing tools, and 4) developed an intuitive but basic user interface. The research that was conducted generated novel algorithmic techniques that utilize state-of-the-art methods such as condensation, differential privacy preservation, clustering, automated tuning based on contextual awareness, and recommendation techniques to specify columns to users for anonymization leading to optimal privacy that allows research analysis on the dataset. Experiments were conducted to evaluate the efficacy of these novel algorithmic techniques by performing analysis on original non-anonymized datasets, then conducting analysis on the same yet anonymized datasets and comparing the results of the analyses. Overall, the anonymized analysis results were within 1% of the original results, verifying that the generated technology not only guarantees a high level of privacy but also enables research analysis as if it were conducted on the original dataset. Potential applications of this technology include anonymization of any type of structured network datasets that contain sensitive identifiers, such as IP addresses, that can be used in multiple applications. For example, to create an AI or machine learning model for cyber security, e.g., to detect attacks, or for performance analysis, e.g., identify bottlenecks or predict performance. In addition, a market analysis that was conducted for potential applications of this technology identified a broader range of applications of our anonymization technology beyond the network sector that includes healthcare, banking, insurance, securities, finance (FISB), data brokering, cloud services, ad sales, and government.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Environmental cleanup of the East Tennessee Technology Park - Year three - A status report - 15522

On August 1, 2011, URS - CH2M Oak Ridge LLC (UCOR) began its five-year, $1.4 billion cleanup of the East Tennessee Technology Park (ETTP), the former Oak Ridge Gaseous Diffusion Plant, located on the U.S. Department of Energy's (DOE) Oak Ridge Reservation (Reservation) in Tennessee. Under the contract, the base period scope of work to be completed by 2016 includes demolition of both the K-25 and K-31 gaseous diffusion buildings and all demolition waste dispositioned, initiation of demolition on the remaining K-27 gaseous diffusion building, safe storage of transuranic (TRU) waste for eventual transfer to the Transuranic Waste Processing Center, previously designated -No-Path-To-Disposition Waste- dispositioned to the extent possible, and completion of design and engineering of a mercury treatment system to be installed at the Y-12 National Security Complex (Y-12). UCOR will have also managed DOE Oak Ridge Office of Environmental Management (OREM)-owned facilities at ETTP, Oak Ridge National Laboratory (ORNL), and Y-12 in a safe and cost-effective manner. Since assuming responsibilities as the ETTP and Oak Ridge Reservation cleanup contractor, UCOR has completed its life-cycle Performance Measurement Baseline; received its Earned Value Management System (EVMS) certification; completed the deactivation and demolition (D and D) of the 44-acre K-25 gaseous diffusion building; removed and disposed of a contaminated underground storage tank (Tank W-1A) at ORNL; dispositioned mercury-contaminated storage tanks at Y-12; completed closure of the Toxic Substances Control Act Incinerator; disposed of more than 158,000 cubic yards of cleanup waste while managing the on-site Environmental Management Waste Management Facility (EMWMF); and conducted operations, surveillance, and maintenance activities at 180 DOE EM facilities at ETTP, ORNL, and Y-12 in a safe and cost-effective manner. In 2014, UCOR completed the removal and disposal of all of the K-25 demolition debris; completed the removal of asbestos-laden transite siding on the K-31 gaseous diffusion building and began demolition of that 17-acre building; advanced the characterization and deactivation of the K-27 gaseous diffusion building, readying the building for the start of demolition in 2015; advanced the design and engineering of a Mercury Treatment Facility to be located at Y-12; continued the cost-effective and safe disposition of all cleanup wastes; and readied ETTP for Voluntary Protection Program Star Status Assessment in 2015. Project performance as of December 31, 2014, continued to be excellent: - Cost Performance Index - 1.12 - Schedule Performance Index - 1.02 At the same time, since safety is the foundation of all cleanup work, UCOR's safety record goes hand-in-hand with its excellent project performance. Through calendar year 2014, UCOR's recordable injury rate was 1.04 occurrences per 200,000 work hours, and the lost workday case rate was 0.52. This performance continues to be due, in large part, to the people and processes URS and CH2M HILL, the parent companies of UCOR, brought to the project. Key enablers included: - Selected and deployed experienced staff in key leadership positions throughout the organization, continually re-energizing the organization with new leaders - Implemented previously successful safety programs and initiatives from other DOE sites, such as DOE's Voluntary Protection Program - Brought fresh thinking and innovation to tackling tough project issues, such as the deactivation of the Technetium-99-contaminated areas of K-27, applying lessons learned from the D and D of K-25; and implementing a -waste factory- approach to cleanup - Maintained continuing alignment between UCOR's contract with DOE and its Performance Measurement Baseline - rapidly addressing contract changes when they arose - Maintained a readiness both from a process as well as experienced personnel perspective to support DOE OREM in performing other significant Oak Ridge Reservation cleanup activities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses

This paper surveys the deep learning (DL) approaches for intrusion-detection systems (IDSs) in Internet of Things (IoT) and the associated datasets toward identifying gaps, weaknesses, and a neutral reference architecture. A comparative study of IDSs is provided, with a review of anomaly-based IDSs on DL approaches, which include supervised, unsupervised, and hybrid methods. All techniques in these three categories have essentially been used in IoT environments. To date, only a few have been used in the anomaly-based IDS for IoT. For each of these anomaly-based IDSs, the implementation of the four categories of feature(s) extraction, classification, prediction, and regression were evaluated. We studied important performance metrics and benchmark detection rates, including the requisite efficiency of the various methods. Four machine learning algorithms were evaluated for classification purposes: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and an Artificial Neural Network (ANN). Therefore, we compared each via the Receiver Operating Characteristic (ROC) curve. The study model exhibits promising outcomes for all classes of attacks. The scope of our analysis examines attacks targeting the IoT ecosystem using empirically based, simulation-generated datasets (namely the Bot-IoT and the IoTID20 datasets).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Boron-10 ABUNCL Prototype Initial Testing

The Department of Energy Office of Nuclear Safeguards and Security (NA-241) is supporting the project Coincidence Counting With Boron-Based Alternative Neutron Detection Technology at Pacific Northwest National Laboratory (PNNL) for the development of a 3 He proportional counter alternative neutron coincidence counter. The goal of this project is to design, build and demonstrate a system based upon 10 B-lined proportional tubes in a configuration typical for 3 He-based coincidence counter applications. This report provides results of initial testing of an Alternative Boron-Based Uranium Neutron Coincidence Collar (ABUNCL) design built by General Electric Reuter-Stokes. Several configurations of the ABUNCL models, which use 10 B-lined proportional counters in place of 3 He proportional counters for the neutron detection elements, were previously reported. The ABUNCL tested is of a different design than previously modeled. Initial experimental testing of the as-delivered passive ABUNCL was performed, and modeling will be conducted. Testing of the system reconfigured for active testing will be performed in the near future, followed by testing with nuclear fuel.

Kouzes, Richard T.↗
DOE OSTI2025

Optimization of Scrap Melting Using an Electric Arc in Steel Manufacturing

Steel industry is crucial to the national economy and security. Around 67% of crude steel in the U.S is produced in electric arc furnaces (EAF), which is energy intensive. Around 140 EAFs operate in the U.S., consuming about 8.6x10 7 MMBtu/year of electricity. One of major challenges for EAFs includes maximizing the efficiency of the electrical energy provided in the form of electric arcs to melt various scrap mixes. To address this issue, a computational fluid dynamics (CFD) methodology is chosen to analyze scrap melting using the electric arc. Due to complex furnace phenomena and the wide variety of potential scenarios, high performance computing (HPC) is essential to yield comprehensive and detailed CFD analyses and systematic parametric studies for optimized EAF operation. The objectives are to 1) simulate scrap melting using electric arc, 2) evaluate electrode/arc position for optimum scrap melting and 3) establish reduced order model for CFD data-base for fast model calculation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Multi-level impacts of climate change and supply disruption events on a potato supply chain: An agent-based modeling approach

Context: The world is experiencing frequent extreme weather events like droughts, snowstorms, and shifting of seasons due to climate change. Increased frequency and severity of these extreme weather events threaten food security because agriculture depends on climate conditions. Impacts of climate change on the agricultural system not only occur at the grower’s level, but also cascade to other levels along the supply chain. Objective: In this study, we aim to quantify a wide range of economic impacts of different extreme climate events on different stages of a food supply chain. Methods: We chose the potato supply chain in Idaho as a case study. We developed a multi-echelon supply chain simulation model using an agent-based modeling (ABM) approach with five types of agents—farmers, shippers, processors, retailers, and logistics companies. In addition to the business-as-usual (BAU) scenario, we designed two climate-related disruption events—drought and snowstorm. We quantified the heterogeneous impacts at different stages of the supply chain for both fresh and processed potatoes using key performance indicators (KPIs) including revenues, prices, lead times, traded quantities, and food waste quantity. Results and Conclusion: The impacts of the disruption events are different on different agents in the supply chain for different product categories. The price hike of fresh potatoes is far higher than processed potatoes during disruption events. This price hike makes consumers switch to processed potatoes, which require more fresh potatoes as an input that further reinforces the price hike. However, because processed potatoes have an elastic demand, once their prices go up due to higher input cost, their demand drops. Non-contracted farmers gain additional revenues from the disruption events, whereas contracted farmers incur a loss due to lock-in price and lower than usual harvest. Significance: The methodology developed in this study could be applied to other food and agricultural supply chains for understanding the vulnerabilities at agent levels due to climate change disruption events. Furthermore, the findings would help develop mitigation strategies or policies to improve the well-being of the overall supply chain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Cyber‐Resilient Distributed Energy Resource Control Algorithms for Smart Distribution Grids

ABSTRACT This paper focuses on the development of cyber‐resilient gradient‐based optimisation algorithms and theoretical proof for grid‐interactive distributed energy resource (DER) control to enable two grid services of virtual power plants (VPPs) dispatch and grid voltage regulation, considering the communication and security impacts. Firstly, the combined DER dispatch and voltage regulation as a real‐time gradient‐based optimisation problem is recapped. Thereafter, we consider a probabilistic traffic model to characterise packet delays and loss in a communication network, and study how the delays enter the process of information exchange among the grid measurement units, local DER controllers and the grid control centre that execute this control algorithm in a coordinated manner. Then, a strategy combining delay thresholds and message update rules is proposed to immunity the asynchrony resulting from the communications traffic and it avoids possible numerical instabilities and sensitivities of the power tracking and voltage regulation capabilities, resulting as cyber‐resilient DER control algorithms. Additionally, their convergence is theoretically proved. Effectiveness of proposed cyber‐resilient algorithms has been validated on the IEEE 37‐bus system in terms of convergence, VPP tracking and voltage regulation performance for smart distribution systems with high penetration of DERs.

24 POWER TRANSMISSION AND DISTRIBUTION↗