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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Service-Based, Segmented, 5G Network-Based Architecture for Securing Distributed Energy Resources: Preprint

As the number of connected devices in the energy grid increase exponentially, so too are the cybersecurity risks. With the development of modern communications standards such as 5G and beyond the extent to which devices will continue to connect will continue to increase exponentially along with the inherent risks. However, 5G also includes features to help address cybersecurity concerns and therefore helping to mitigate many of these risks. This paper proposes a new service-based network architecture implementing network-slicing capabilities for connected systems and devices to improve performance, availability, security, and reliability of the grid devices and services. This paper considers the quality of service requirements and criticality of services needed for securely monitoring, operating, and securing Distributed Energy Resource (DER) devices. From developed use cases, network slicing is implemented based on these requirements and resource allocations. This work then highlights examples of how slicing can help prevent standard existing attack methods such as a denial-of-service or similar attack which limits resource availability and network bandwidth to the service and thus limiting its ability to affect other services by misbehaving. The designed network architecture use case will be further tested on a local virtualized testbed to verify secure operation and availability of services. Using hardware-in-the-loop devices and systems on this local testbed, this fully segmented, secure network may be realized and evaluated. Finally, this paper presents the results of this testing.

5G↗
DOE OSTI2022

Variable Resource Resilience: How Systems Experience Increased Resilience from Variable and Hybrid Resources

Variable resources like wind and solar are often seen as detriments to system resilience rather than benefits because they may not be available with the capacities or services required during a high-impact low-frequency (HILF) event, whether that is a physical threat, natural disaster, or cyber attack. However, resilience goals and metrics are inadequate for electric energy delivery systems with inverter-based resources. Examination of this topic reveals that renewable resources are well suited to combat many resilience hazards due to local resource availability. Metrics that demonstrate the resilience value of variable resources are presented and categorized for resource (wind, solar, storage, hybrid) and installation type (bulk utility scale, behind-the-meter, front-of-the-meter, isolated). Distributed and hybrid systems can further enhance resilience benefits my maximizing resource potential for a locality. A case study demonstrating quantitative resilience benefits from wind alone is provided for St. Mary's, AK, which concludes that hundreds of thousands of dollars are saved by the addition of a wind turbine in the face of realistic fuel shortage and extreme winter weather scenarios.

17 WIND ENERGY↗
DOE OSTI2025

Cognitive IoT and Edge Computing for Intrusion Detection with Federated TinyML

Internet of Things (IoT) and Edge Computing (EC) are rapidly becoming an integral part of the modern society. By 2030, there is estimated to be over 40 billion active and connected IoT devices [1]. This rapid progress also comes with a significant implication on cybersecurity. Back-end infrastructure and systems have a much broader attack than they did previously due to vulnerable IoT/EC devices being connected to wireless networks. This expanding attack surface is a growing concern because IoT/EC are increasingly being used in critical systems such as power grids, health care, and smart homes. To effectively address a problem of this scale, cognitive cyber methods—which can autonomously detect and react to cyber attacks as they develop—are needed. To address this, we bring Artificial Intelligence (AI) and Machine Learning (ML) to IoT/EC devices, using tinyML to monitor voluminous IoT data against cyber threats, and using Federated Learning (FL) to share local detection knowledge across the system while preserving privacy. We propose a novel three-layer architecture: (1) an IoT layer for tinyML-based inference, (2) an edge layer for ML model training, and (3) a cloud layer for FL operations. Using the publicly available 11-class N-BaIoT dataset [2], we demonstrate that this architecture mitigates resource constraints at the IoT layer while improving detection accuracy over standard two-layer designs. An outlier-resistant scaler, feature reduction, and quantization enable the tinyML model to maintain detection accuracy with a reduced model size. Additionally, federated learning that only utilizes the intersection (across heterogenous devices) of the reduced feature set achieves superior detection accuracy compared to locally trained models.

Li, Mingyan [ORNL] (ORCID:0009000569532640)↗
DOE OSTI2023

Precursor Analysis Report: SQL Slammer Worm Infection of Davis-Besse Nuclear Power Plant 2003

The SQL Slammer Worm Infection of Davis-Besse Nuclear Power Plant 2003 Precursor Analysis Report leverages publicly available information about Davis-Besse’s 2003 cyber attack and catalogs anomalous observables for each technique employed in the attack. This analysis is based upon the methodology of the Cybersecurity for the Operational Technology Environment (CyOTE) program. On 25 January 2003, the SQL Slammer worm infected more than 90% of vulnerable hosts and crashed the internet in 10 to 15 minutes, making it one of the fastest spreading worms in history. SQL Slammer is a fileless, memory-resident worm that remotely exploits a stack-based buffer overflow vulnerability on local hosts to intensively scan and rapidly self-propagate across the internet. The worm infected approximately 300,000 unpatched hosts running Microsoft Structured Query Language (SQL) Server 2000 or Microsoft Desktop Engine (MSDE) 2000 with SQL Server Resolution Service. The SQL Slammer worm indirectly infected FirstEnergy’s Davis-Besse nuclear power plant by first infecting a consultant’s company network server and then propagating through an external misconfigured connection into Davis-Besse’s site network. The infection caused major network congestion, slow performance, data overloads, and the inability of local hosts to communicate with each other, which eventually caused a loss of availability and a loss of view when the Safety Parameter Display System (SPDS) and Plant Process Computer (PPC) crashed. At the time of the infection, the plant was already offline, the digital monitoring systems had redundant analog backups, and the plant control and safety functions were not affected, so there were no concerns of a safety breach. However, this incident resulted in many lessons learned and spawned important discussions about cybersecurity’s role in nuclear safety and electric power reliability regulation, policy, and guidance. Researchers and analysts identified 10 unique techniques utilized during the attack with a total of 640 observables using MITRE ATT&CK® for Industrial Control Systems. The CyOTE program assesses observables accompanying techniques used prior to the triggering event to identify opportunities to detect malicious activity. If observables accompanying the attack techniques are perceived and investigated prior to the triggering event, earlier comprehension of malicious activity can take place. Eight of the identified techniques used during Davis-Besse cyber attack were precursors to the triggering event. Analysis identified 596 observables associated with these precursor techniques, 428 of which were assessed to have an increased likelihood of being perceived in the 331 days preceding the triggering event. The response and comprehension time could have been reduced if the observables had been identified earlier. The information gathered in this report contributes to a library of observables tied to a repository of artifacts, data sources, and technique detection references for practitioners and developers to support the comprehension of indicators of attack. Asset owners and operators can use these products if they experience similar observables or to prepare for comparable scenarios.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Reinforcement Learning Approach to Cybersecurity in Space (RELACSS)

Securing satellite groundstations against cyber-attacks is vital to national security missions. However, these cyber threats are constantly evolving. As vulnerabilities are discovered and patched, new vulnerabilities are discovered and exploited. In order to automate the process of discovering existing vulnerabilities and the means to exploit them, a reinforcement learning framework is presented in this report. We demonstrate that this framework can learn to successfully navigate an unknown network and detect nodes of interest despite the presence of a moving target defense. The agent then exfiltrates a file of interest from the node as quickly as possible. This framework also incorporates a defensive software agent that learns to impede the attacking agents progress. This setup allows for the agents to work against each other and improve their abilities. We anticipate that this capability will help uncover unforeseen vulnerabilities and the means to mitigate them. The modular nature of the framework enables users to swap out learning algorithms and modify the reward functions in order to adapt the learning tasks to various use cases and environments. Several algorithms, viz., tabular Q learning, deep Q networks, proximal policy optimization, advantage actor-critic, generative adversarial imitation learning, are explored for the agents and the results highlighted. The agent learns to solve the tasks in a light-weight abstract environment. Once the agent learns to perform sufficiently well, it can be deployed in a minimega virtual machine environment (or a real network) with wrappers that map abstract actions to software commands. The agent also uses a local representation of the actions called a ‘slot-mechanism’. This allows the agent to learn in a certain network and generalize it to different networks. The defensive agent learns to predict the actions taken by an offensive agent and uses that information to anticipate the threat. This information can then either be used to raise an alarm or to take actions to thwart the attack. We believe that with the appropriate reward design, a representative environment, and action set, this framework can be generalized to tackle other cybersecurity tasks. By sufficiently training these agents, we can anticipate vulnerabilities leading to robust future designs. We can also deploy automated defensive agents that can help secure satellite groundstation and their vital national security missions.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Phase Transfer‐Mediated Degradation of Ether‐Based Localized High‐Concentration Electrolytes in Alkali Metal Batteries

Abstract Localized high‐concentration electrolytes (LHCEs) have attracted interest in alkali metal batteries due to the advantages of forming stable solid‐electrolyte interphases (SEIs) on anodes and good chemical/electrochemical stability. Herein, a new degradation mechanism is revealed for ether‐based LHCEs that questions their compatibility with alkali metal anodes (Li, Na, and K). Specifically, the ether solvent reacts with alkali metals to generate solvated electrons (e s − ) that attack hydrofluoroether co‐solvents to form a series of byproducts. The ether solvent essentially acts as a phase‐transfer reagent that continuously transfers electrons from solid‐phase metals into the solution phase, thus inhibiting the formation of stable SEI and leading to continuous alkali metal corrosion. Switching to an ester‐based solvating solvent or intercalation anodes such as graphite or molybdenum disulfide has been shown to avoid such a degradation mechanism due to the absence of e s − .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Structure of Iridium Oxides and Their Oxygen Evolution Electrocatalysis in Acidic Media

Proton exchange membrane water electrolyzers (PEMWEs) have emerged as one of the most promising technologies for the large-scale production of clean hydrogen. Gigawatt scale deployment of PEMWEs requires substantial reduction in the loading of iridium (Ir), which is one of the most expensive and rarest elements. Substantial reduction in Ir loading calls for the development of innovative Ir-based anodes, which requires a clear understanding of how iridium oxides accelerate the sluggish oxygen evolution reaction (OER) in acidic media. Herein, we studied the structure and OER electrocatalysis of three representative iridium oxides ─ hydrous, amorphous, and rutile ─ by employing a combination of physicochemical and electrochemical characterization. Additionally, we found that the hydrous iridium oxide had a different local structure of IrO 6 octahedra and a superior OER intrinsic activity compared with the other two, and that the OER activities of all three types decreased with decreasing pH of acidic solution. We proposed that the OER process of these iridium oxides is limited by water nucleophilic attack on the OER intermediate oxygenated adsorbates. Based on this mechanism, we attributed the superior OER activity of hydrous iridium oxides to their longer Ir–O bonds and the pH-dependent OER activity of iridium oxides to the pH-dependent oxidation of Ir.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A Privacy-Aware Federated Learning Framework for Distributed Energy Resource Analytics in Constrained Environments

To be resilient against extreme weather events, the rural communities in Puerto Rico are leveraging distributed energy resources (DER). However, computing frameworks sup-porting the grid in critical decision-making are still largely centralized. Sensitive consumer data are transmitted over the Internet or cellular networks to a secondary or tertiary node. It guarantees better situational awareness at the cost of a wider attack surface, jeopardizing user privacy, as more DER come online. Cloud, Edge, and Fog computing all require data aggregation at some level. This paper introduces a privacy-aware federated learning framework that leverages the Fog model by pushing analytics all the way to the DER and load assets. These local models train on individual asset data and transmit only learned parameters (such as weights) over secure communications to a global decision-maker. By abstracting personally identifiable consumer data without impacting decision optimality, this framework better aligns with distributed power generation paradigm.

Sundararajan, Aditya↗
DOE OSTI2023

Maximum-impact Adversary Design for Network-based Control System: A Case Study on Grid-interactive Efficient Buildings

The Internet of Things (IoT) technology has dramatically improved the efficiency of today's building operation and management. By connecting controllable devices into a communication network, control signals can be easily passed to the devices, and operating status can be acquired from measurable ends with minimal effort. However, this all-connected configuration could also expose the network-based control system (NBCS) to malicious actions, such as cyberattacks. One of the common NBCSs is the building automation system. With the promotion of grid-interactive efficient buildings (GEBs), there has been increasing attention on securing the buildings from the network perspective. This research proposes a maximum-impact adversary design framework so that the adversary can provide the most adversarial impact on the controlled system while remaining stealthy. The proposed framework is numerically demonstrated on a network-based building energy and control system. The building energy system is built in a Modelica-based simulation environment and controlled by the state-of-the-art ASHRAE Guideline 36 control sequences. The control commands at the supervisory level, generated from the Guideline 36 controller, are assumed to be sent to local devices through communication networks using the BACnet protocol. Simulation results show that the proposed maximum-impact adversary on such a system can stealthily affect the building system's performance to its maximum extent. It is anticipated that results can be used by researchers and practitioners in the building automation industry to design efficient and robust cyber-attack detection algorithms, especially for stealthy attacks.

Chu, Mengyuan↗
DOE OSTI2023

Robust Decentralized Secondary Control Scheme for Inverter-based Power Networks

Inverter-dominated microgrids are quickly becoming a key building block of future power systems. They rely on centralized controllers that can provide reliability and resiliency in extreme events. Nonetheless, communication failures due to cyber-physical attacks or natural disasters can make autonomous operation of islanded microgrids challenging. This paper examines a unified decentralized secondary control scheme that is robust to inverter clock synchronization errors and can be seamlessly applied to grid-following or grid-forming control architectures. The proposed scheme overcomes the well-known stability problem that arises from parallel operation of local integral controllers. Theoretical guarantees for stability are provided along with criteria to appropriately tune the secondary control gains to achieve good frequency regulation performance while ensuring fair power sharing. The efficacy of our approach is demonstrated through simulations on a 5-bus microgrid with four grid-forming inverters.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Peer-to-Peer Energy Management System for Distributed Microgrid Coordination [SWR-21-92]

Resiliency is one of the key challenges in today's power system. Natural disasters and cyber-attacks both can limit communications between microgrids and the central management system. Thus, having a Distributed Microgrid Coordination (DMC) algorithm can improve the system resiliency, which enables the microgrids to operate without communication with the central management system. Peer-to-Peer Energy Management System for Distributed Microgrid Coordination adopts a primal-dual approach, where each microgrid controller keeps a local estimate of the dual variables. The estimate is updated with local measurements and peer-to-peer communication, leading to a fully distributed algorithm. While the DMC is developed for microgrid coordination, it can be used for general distributed control purpose.

Li, Yashen↗
DOE OSTI2023

Spatio-Temporal Deep Graph Network for Event Detection, Localization, and Classification in Cyber-Physical Electric Distribution System

This work proposes a deep graph learning framework to identify, locate, and classify power, cyber, and cyber power events at the distribution system level. The proposed algorithm jointly exploits spatial, temporal, and node-level cyber and physical data features. The developed graph neural network, together with a deep autoencoder, utilizes physical measurements from distribution level phasor measurement units and cyber data from communication network logs. The spatial structure of the synchrophasor measurements and network is incorporated through a weighted adjacency matrix. The temporal structure is incorporated by defining a spatial operation in the gated recurrent unit. This spatio-temporal learning element resides inside a power event detection, localization, and classification module that provides the degree of confidence for an event label. To accurately pinpoint the location of an event to the nearest bus equipped with a measurement unit, a combination of squared error and proximity score is utilized. Also included is a cyber event detection module that employs heteroskedasticity to analyze the significance of various cyber features during different types of attacks. Finally, a dual-bit cyber-power decision table determines the nature of the event. The proposed method is validated on two distribution systems modeled in OPAL-RT/Hypersim with limited phasor measurement units for different possible physical and cyber events. Further analyses include comparison with other state-of-the-art methods and validation in the presence of measurement noise. As a result, our method outperforms existing approaches and achieves an average detection accuracy of 97.97%, F1-score of 96.88%, precision of 96.53%, and recall of 98.57%.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Self-Security for Grid-Interactive Smart Inverters Using Steady-State Reference Model

Smart inverters exchange information with other devices through a shared communication link, making the inverters more prone to receive harmful commands from external parties. This erroneous data can be received due to an anomaly in the system, such as a device fault, unintentional utility operator action, or a cyber-attack. In this paper, a device-level self-security strategy is implemented using reference models for a grid-interactive inverter to examine the incoming power setpoints, detect the anomalies, and protect the system accordingly. The PQ setpoints received from the utility supervisory controller are autonomously examined using the inverter’s normal and stable operating regions before engaging the setpoints to the inverter’s local controller. Grid parameters are estimated in real-time during the examination process. The efficacy of the self-security algorithm is tested using a three-phase 3-kVA SiC-MOSFET inverter and a 12-kW NHR 9410 regenerative grid emulator. The results verify that the proposed method can detect harmful PQ setpoints that can cause abnormal or unstable inverter operation.

Gursoy, Mehmetcan↗
DOE OSTI2022

Cyber-Physical Resiliency for Wind Power Generation

During the next three years, wind power generation is expected to add more generation capacity to the national electrical grid than any other energy sector. With new wind turbine designs rated power over 10 MW and wind farms reaching over 1 GW capacities, the consequences of cyber-attacks on wind power generation are becoming increasingly more critical. Moreover, the known vulnerabilities of wind turbine control systems and the potential damaging effects of intrusions, motivate an urgent protection improvement for the wind power generation sector. This program has developed a variety of new adaptive defense technologies that enable wind power generation systems to survive sophisticated cyberattacks by enhancing the control systems capabilities of detection, localization, and accommodation. The introduction of these technologies in the on-shore and the emerging off-shore market will result in a significantly more reliable and secure wind power infrastructure.

17 WIND ENERGY↗
DOE OSTI2022

Phase Transfer‐Mediated Degradation of Ether‐Based Localized High‐Concentration Electrolytes in Alkali Metal Batteries

Abstract Localized high‐concentration electrolytes (LHCEs) have attracted interest in alkali metal batteries due to the advantages of forming stable solid‐electrolyte interphases (SEIs) on anodes and good chemical/electrochemical stability. Herein, a new degradation mechanism is revealed for ether‐based LHCEs that questions their compatibility with alkali metal anodes (Li, Na, and K). Specifically, the ether solvent reacts with alkali metals to generate solvated electrons (e s − ) that attack hydrofluoroether co‐solvents to form a series of byproducts. The ether solvent essentially acts as a phase‐transfer reagent that continuously transfers electrons from solid‐phase metals into the solution phase, thus inhibiting the formation of stable SEI and leading to continuous alkali metal corrosion. Switching to an ester‐based solvating solvent or intercalation anodes such as graphite or molybdenum disulfide has been shown to avoid such a degradation mechanism due to the absence of e s − .

Chen, Xiaojuan↗
DOE OSTI2023

Influence of native oxide film on corrosion behavior of additively manufactured stainless steel 316L

The influence of the native oxide film on passive film properties and localized corrosion of additively manufactured SS 316L was studied in 1 wt% HCl by XPS characterization, electrochemical polarization curves, and post-test morphology analysis by SEM. Increased Cr oxidation kinetics was observed in the as-polished sample with the native oxide film resulting in formation of an overall more protective and compact film compared to the cathodically-activated sample. Electrochemical analysis showed that corrosive attack varied between dislocation cell boundaries to cell interiors depending on the initial surface state and polarization conditions. In conclusion, a corrosion mechanism is proposed to explain this variation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Securing Solar for the Grid: Extreme Control Whitepaper

Emerging standards outlining desired behaviors for Distributed Energy Resources (DER), such as IEEE 1547-2018, define several device-level control functions to regulate DER power injections/consumptions in response to locally sensed grid conditions. The ability to adjust settings of aggregations of DER with standardized embedded control functionality constitutes a mechanism which can, potentially, create undesirable or deleterious effects on the power grid. The purpose of this white paper is to highlight unintended effects stemming from improperly tuned embedded control functions in Distributed Energy Resources (DER), which could be exploited by a malicious entity as a means to attack the power grid.

14 SOLAR ENERGY↗
DOE OSTI2026

In-Situ TEM Molten Salt Corrosion

Molten salt reactors (MSRs) offer a compelling pathway for next-generation nuclear energy, with advantages in thermal efficiency, inherent safety, and flexible fuel management. Yet, halide-based molten salts introduce significant materials challenges, particularly alloy corrosion. Alloy performance in these environments ultimately depends on understanding how corrosion initiates and progresses at the nanoscale, however most existing models rely on post-exposure characterization, leaving degradation mechanisms largely inferred rather than directly observed. NiCr alloys have garnered interest in MSRs applications as the Ni-based matrix provides strength and creep resistance, while Cr content offers oxidation resistance in air. However, NiCr corrosion resistance in chloride salts has proven poor due to preferential chromium dissolution, the formation of Cr-depleted pathways, and grain-boundary attack. This work aims to directly visualize corrosion of Ni-20Cr exposed to LiCl-KCl using in-situ Transmission Electron Microscopy (TEM) to capture real-time microstructural evolution during corrosion. Experiments will be performed at ~800 °C under controlled pressure conditions while utilizing Energy-Dispersive X-ray Spectroscopy (EDS) to analyze elemental redistribution. Observation of chromium depletion fronts, associated surface restructuring, and localized chloride enrichment are expected. Ultimately, this study is expected to provide a link between microscale processes and the macroscopic degradation behaviors relevant to MSR operation in advanced reactor environments. Simultaneously, this approach enables future in-situ investigations regarding alloy composition, salt chemistry, and their influence on corrosion pathways and long-term stability.?

36 - MATERIALS SCIENCE↗