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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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DOE OSTI2024

Leveraging neural control variates for enhanced precision in lattice field theory

Results obtained with stochastic methods have an inherent uncertainty due to the finite number of samples that can be achieved in practice. In lattice QCD this problem is particularly salient in some observables like, for instance, observables involving one or more baryons and it is the main problem preventing the calculation of nuclear forces from first principles. The method of control variables has been used extensively in statistics and it amounts to computing the expectation value of the difference between the observable of interest and another observable whose average is known to be zero but is correlated with the observable of interest. Recently, control variates methods emerged as a promising solution in the context of lattice field theories. In our current study, instead of relying on an educated guess to determine the control variate, we utilize a neural network to parametrize this function. Using 1 + 1 dimensional scalar field theory as a testbed, we demonstrate that this neural network approach yields substantial improvements. Notably, our findings indicate that the neural network ansatz is particularly effective in the strong coupling regime. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

DOE OSTI2025

Cybersecurity Enhancement in Digital Substations: Hidden Markov Model-Based Smart Cyber Switching and Threat Response

The rising incidence of cyber-attacks on critical infrastructure and power grids poses significant threats to the stability and reliability of electrical substations, with potentially devastating consequences such as extended blackouts. This paper introduces an advanced cybersecurity framework aimed at safeguarding IEC 61850-based substations through the integration of software-defined networking (SDN) and digital twin (DT) technologies. The proposed DT-based framework employs smart cyber switching (SCS) for proactive threat mitigation and concurrent intelligent electronic device (CIED) for swift system restoration, thereby maintaining continuous operational integrity and robust cybersecurity defenses. Central to this framework is the adaptive port controller (APC), which enables dynamic port management to adapt to evolving threats, and an intrusion detection system (IDS) designed to detect and neutralize malicious attacks on IEC 61850-based sampled value (SV) and generic object-oriented substation event (GOOSE) messages within the substation’s communication network. Further, novel predictive intrusion detection and response (PIDR) algorithm is implemented on a digital substation (DS) to predict the best route to be taken by the attacker. The efficacy of these comprehensive cybersecurity frameworks is validated through rigorous simulations and a hardware-in-the-loop (HIL) testbed, showcasing the system’s ability to sustain substation operations amidst cyber-attacks.

Digital substation↗

DOE OSTI2021

Control and Management of Multiple Converters in a Residential Smart Grid

Power electronic systems are becoming a staple building block for electric grid networks. However, these systems have been largely designed to grid integrate in ad hoc configurations with little or no coordination of control. This work proposes a central controller and power electronic system hardware and software design for a residential system (photovoltaic, energy storage and residential building). This system supports auto-integration, plug and play capabilities, and optimal energy management to meet different use cases. Modelling and multi-day testing of the system have been conducted in a controller hardware-in-the-loop (C-HIL) testbed. Multiple use cases and pricing options have been considered as part of the simulation and testing. Results are presented of these systems as a proof of principle.

Starke, Michael↗

DOE OSTI2023

ProvLight: Efficient Workflow Provenance Capture on the Edge-to-Cloud Continuum

Modern scientific workflows require hybrid infrastructures combining numerous decentralized resources on the IoT/Edge interconnected to Cloud/HPC systems (aka the Computing Continuum) to enable their optimized execution. Understanding and optimizing the performance of such complex Edge-to-Cloud workflows is challenging. Capturing the provenance of key performance indicators, with their related data and processes, may assist in understanding and optimizing workflow executions. However, the capture overhead can be prohibitive, particularly in resource-constrained devices, such as the ones on the IoT/Edge.To address this challenge, based on a performance analysis of existing systems, we propose ProvLight, a tool to enable efficient provenance capture on the IoT/Edge. We leverage simplified data models, data compression and grouping, and lightweight transmission protocols to reduce overheads. We further integrate ProvLight into the E2Clab framework to enable workflow provenance capture across the Edge-to-Cloud Continuum. This integration makes E2Clab a promising platform for the performance optimization of applications through reproducible experiments.We validate ProvLight at a large scale with synthetic workloads on 64 real-life IoT/Edge devices in the FIT IoT LAB testbed. Evaluations show that ProvLight outperforms state-of-the-art systems like ProvLake and DfAnalyzer in resource-constrained devices. ProvLight is 26—37x faster to capture and transmit provenance data; uses 5—7x less CPU; 2x less memory; transmits 2x less data; and consumes 2—2.5x less energy. ProvLight [1] and E2Clab [2] are available as open-source tools.

Rosendo, Daniel↗

DOE OSTI2022

Machine Learning-based False Data Injection Attack Detection and Localization in Power Grids

Cyberattacks on critical infrastructures can be catastrophic and bring nations to their knees. Therefore, detecting these attacks is crucial and challenging. This paper presents a novel approach for detecting and locating cyberattacks affecting an electrical power system. The adversary employs a man-in-the-middle technique to inject false data into the communication between distributed energy resources (DER) and Microgrid Controller (MGC) with the goal of disrupting power delivery. The approach for detection and localization is based on integrating multiple machine learning-based anomaly detection models that combine network traffic data and grid measurements. Experiments are performed to assess the method's performance using a hardware-in-the-loop real-time simulation testbed which includes Modbus TCP/IP communication. Power system topology and operating conditions are based on actual topology and real-world data provided by the Holy Cross Energy utility network. Results confirm that the method can be successfully employed for detecting and localizing cyberattacks.

Leao, Bruno P.↗

DOE OSTI2021

Power-Sharing between Grid-Forming and Grid-Following Inverters

Power-sharing between the grid-forming and the grid-following inverter in a small microgrid is a challenging task. Specifically, the decentralized droop-based power-sharing is difficult to achieve when the grid-forming inverter regulates the voltage and the frequency. This article proposes controllers for the grid-forming and the grid-following inverters, which ensure proportional power-sharing as well as regulate the voltage and the frequency. Both controllers use active power-frequency and reactive power-voltage droops. Additional secondary loops and secondary forward paths are added to the controller of the grid-forming inverter and the grid-following inverter, respectively, to ensure simultaneous voltage and frequency regulation and power-sharing. The efficacy of the proposed controllers for power-sharing is verified through experiment results in a multi-inverter testbed.

Sadeque, Fahmid↗

DOE OSTI2025

A Comparison of Battery Charge Controller Technologies for Wave Energy Converters

Wave energy is a uniquely challenging field for electrical system designers. High peak and low average power potential with a constantly varying energy input is difficult to harness and control through conventional means. To power the blue economy, low-powered wave energy converters (WECs) need batteries for energy storage. Safely and effectively charging batteries from waves requires a charge controller to properly monitor and control voltage and current going to the battery. Currently, off-the-shelf charge controllers exist for other renewable generation such as wind, hydro, and solar. Two topologies were validated: a buck converter and a pulse width modulation (PWM) charge controller. Using an in-lab dry testbed, wave energy power inputs were simulated to properly validate the effectiveness of existing charge controller technologies, identifying the shortcomings and improvements needed to effectively harness wave energy.

battery storage↗

DOE OSTI2024

Microgrid to Microgrid Synchronization with Grid-Forming Inverters: Challenges and Solution

Research on network microgrids has primarily focused on control and optimization. However, application aspects such as microgrid-to-microgrid synchronization have not been fully addressed in existing literature. This paper first addresses the challenges of networking microgrids with grid-forming inverter in droop control. Then, it proposes a pre-synchronization algorithm to improves the synchronization speed and transient stability. To improve the speed, the phase difference between adjacent microgrids is minimized. Transients such as inrush current and oscillations are effectively mitigated by increasing the virtual impedance during interconnection. The proposed strategy allows smooth transitions when reconfiguration of the microgrids is needed. The applicability of this strategy to today’s microgrid systems is demonstrated through a validation in a 480V three-phase network microgrid testbed with commercial-grade inverters, protective devices, and switchgear.

Ferrari Maglia, Max↗

DOE OSTI2022

An Intelligent Power Electronic System for Secondary Use Batteries

This paper presents a plug-and-play power electronic system for integration of multiple energy storage system technologies. The system uses a communication and control architecture with individual developed modules to support scaling to larger systems for the electric grid. An optimization framework is also presented to demonstrate the functionality of the system. This system is shown to work in hardware with a constructed testbed to demonstrate different use cases and systems. Results are presented for a full efficiency cycle test.

Starke, Michael↗

DOE OSTI2022

Ransomware Security Threat Modeling for Photovoltaic Systems

Ransomware attacks are one of the most dangerous cyber-attacks which can disrupt the operation of photovoltaic (PV) systems and incur an enormous economic loss. This paper introduces a ransomware security threat modeling method that identifies potential vulnerabilities, threats, and impacts of ransomware attacks targeting a PV system. Here, the security threat modeling consists of three steps: 1) system identification, 2) threat modeling that finds existing vulnerabilities, 3) attack modeling that designs attack profiles to succeed ransomware attacks, and 4) penetration testing that performs authorized cyber-attacks and analyzes impacts of the ransomware attack profiles using a real-time hardware-in-the-loop (HIL) PV system security testbed.

attack modeling↗

DOE OSTI2022

Throughput Analytics of Cloud Networks

A network of virtual machines at cloud server sites connected over virtual IO connections is a flexible, easily deployable, and cost-effective alternative to a physical network infrastructure with dedicated servers connected over leased fiber lines. We study the throughput performance of such a cloud network by collecting measurements over Google Cloud infrastructure spanning multiple continents. To study its ideal performance and impact of packet losses, we utilize its emulation using dedicated servers and connection hardware emulation devices. We compare the measurements over the cloud network to those over its emulation on a testbed. We examine the throughput profiles of both networks as a function of the round trip time and their utilization-concavity coefficients, estimated using measurements for common TCP versions. The throughput profile's concave-convex shape and its coefficient are critical indicators of the network performance, qualitatively and quantitatively, respectively. The results indicate their overall agreement between the production cloud network and its emulation using dedicated connections, and a near optimal throughput performance of the former except for a few under-performing connections. Also, the number of parallel flows is found to be a dominant factor in optimizing the throughput across various conditions and TCP versions.

Phanekham, Derek↗

DOE OSTI2024

High-Penetration Microgrids Providing Grid Stability Using Frequency Watt Control

The U.S. grid is rapidly transitioning towards utilizing inverter-based renewable energy resources such as solar, wind, and batteries, reducing the carbon emission footprint. Inverter-based microgrid control architectures remain a critical focus to address power system stability issues in future high penetration markets lacking spinning generation assets. Idaho National Laboratory (INL) is researching an active layered inverter based frequency-Watt control scheme that provides distribution level stability in high-penetration markets where grid inertia is lacking. Hardware in the loop case study was implemented using INL’s Microgrid Testbed to combat scalable frequency deviations ranging from 60 Hz down to 50 Hz initialized by a hydropower model implementing step loads using a 540-kW grid emulator. Our research findings demonstrate the importance of distribution level, inverter-based active frequency-Watt controls utilizing a battery energy storage system (BESS) to provide adequate frequency support at the point of common coupling without major power infrastructure upgrades.

14 SOLAR ENERGY↗

DOE OSTI2023

HCPerf: Driving Performance-Directed Hierarchical Coordination for Autonomous Vehicles

The rapid development of autonomous driving poses new research challenges to the on-vehicle computing system. In particular, the execution time of autonomous driving tasks highly depends on the specific driving environment. For instance, the execution time of configurable sensor fusion increases significantly as the scene becomes complex, which leads to end-to-end deadline misses from sensing to control and may cause accidents. Thus, a framework that can effectively utilize the system resources to guarantee the end-to-end deadlines of autonomous driving tasks as well as effectively prioritize the responsiveness and throughput of the control commands is crucial for autonomous driving. In this paper, we propose HCPerf, a performance-directed hierarchical coordination framework that intelligently coordinates the autonomous driving tasks with high execution time variation and complex dependencies according to the driving performance in real-time. Specifically, HCPerf mainly consists of two coordinators. The internal coordinator intelligently schedules the tasks according to the driving performance of the vehicle in order to help them meet the end-to-end deadlines while well prioritizing the responsiveness and throughput of the control commands. At the same time, the external coordinator dynamically tunes the rates of tasks according to the schedulability in order to efficiently utilize the system resource. We conduct extensive experiments on both simulation and hardware testbeds with the representative autonomous driving application. The results show that HCPerf can effectively improve the driving performance by 7.69%-45.94% in different driving scenarios.

Ma, Jialiang↗

DOE OSTI2023

Attention-Augmented Parametric Kernel Graph Neural Network (APKGNN) for Node Classification

We present a new graph neural network, the Attention-based Parametric-Kernel augmented Graph Neural Network (APKGNN), developed for node classification tasks. Despite extensive work on modeling multi-faceted relationships between connected nodes of a graph, the effect of attention on edge features mapped to relationships has not yet been analyzed through learning representation. This study derives such an attention vector by first calculating node features corresponding to endpoints of an edge and then aggregating these with extracted local intrinsic patches of a given graph to generate augmented local patch vectors. This process uses a parametric kernel based on Gaussian mixture models (GMMs) to embed local neighborhoods of the graph in local patches. The patch vectors then convolve with the above node features to produce an updated node representation. We show that this new learning representation (APKGNN) achieves higher node classification accuracy on tasks - both standard benchmarks (Cora, PubMed, Citeseer) and new experimental short text corpora where nodes correspond to text documents and words. This implementation of the GNN convolution layer outperforms state-of-the-art (SOTA) algorithms, achieving higher training, validation, and test accuracy by a significant margin on three standard benchmark data sets under both SOTA experimental settings and those for new testbeds.

Bose, Avishek↗

DOE OSTI2023

Throughput Measurements and Capacity Estimates for Quantum Connections

The throughput of conventional and quantum network connections is an important performance metric, which is typically specified by bits per second (bps) and entangled quntum bits per second (ebps), respectively. It is measured over practical quantum network connections using specialized methods, and estimated using analytical bounds for which extensive theory has been developed. For practical connections, however, these two quantities have often been hard to correlate due to the lack of measurements and estimates derived under well-characterized common conditions. They both differ significantly from the conventional network throughput of TCP which employs buffers and loss recovery mechanisms. We describe a conventional-quantum testbed that enables the comparison of these two quantities both qualitatively and quantitatively. The bps and ebps throughput is measured over fiber connections of lengths over 75 kilometers, which show that the former decreases significantly slower with distance than the latter and in a qualitatively different way. The analytic capacity estimates of ebps are derived using approximations based on light intensity measurements, and they decrease more rapidly with distance than the measured ebps throughput. These results provide qualitative insights into the conventional transport mechanisms based on buffers, and the conditions used in deriving the analytical ebps capacity estimates.

Rao, Nageswara↗

DOE OSTI2021

Coding the Computing Continuum: Fluid Function Execution in Heterogeneous Computing Environments

Advances in network technologies have greatly decreased barriers to accessing physically distributed computers. This newfound accessibility coincides with increasing hardware specialization, creating exciting new opportunities to dispatch workloads to the best resource for a specific purpose, rather than those that are closest or most easily accessible. We present Delta, a service designed to intelligently schedule function-based workloads across a distributed set of heterogeneous computing resources. Delta implements an extensible architecture in which different predictors and scheduling algorithms can be integrated to provide dynamically evolving estimates of function execution times on different resources-estimates that can be used to determine the most appropriate location for execution. We describe predictors for function runtime, data transfer time, and cold-start resource provisioning and configuration delay; dynamic learning methods that update predictor models over time; and scheduling strategies that take into account both function and endpoint information. We show that these methods can halve workload makespan when compared with a strategy that selects the fastest resource, and decrease makespan by a factor of five when compared to a round robin strategy, when deployed on a heterogeneous testbed with resources ranging from a Raspberry Pi to a GPU node in an academic cloud.

Computing continuum↗

DOE OSTI2021

Impact Analysis of Data Integrity Attacks on FACTS-based Wide-Area Voltage Control System

Energy management system (EMS) consists of several wide-area control applications that serve as a backbone for security, stability, and reliability of the power system. Wide-area voltage control system (WAVCS), one of the critical wide-area applications, operates in coordination with local Flexible AC Transmission System (FACTS) devices to provide voltage security and optimal management of active and reactive power resources. Since the WAVCS relies on wide-area communication and data sharing devices, possible cybersecurity vulnerabilities have to be addressed to ensure the closed-loop operation of WAVCS. In this paper, we present a methodology for performing an impact analysis of cyber-attacks in WAVCS cybersecurity. In particular, different types of data integrity attacks, such as malicious tripping, fault replay, and signal altering attacks, are considered, and detailed impact analysis is conducted in a testbed environment using the Kundur's four machine two-area system. For performing an impact analysis, the transient voltage stability of the sensitive bus voltage is studied, followed by the quantitative assessment and severity ranking using the voltage profile index. Our experimental evaluation reveals that the data integrity attacks on control signals exhibit a higher attack severity than on the measurement signals. Further, the severity of these attacks varies with nature (static or dynamic), location, and types of attacks.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

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

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

Maharjan, Manisha↗