Results for “power distribution network” — Page 44

DOE OSTI2025

Leveraging FPGA Advantages for Quicker Data Processing for LBNF

The Long Baseline Neutrino Facility (LBNF) will deliver a 2.4 MW muon neutrino beam from Fermilab to the Deep Underground Neutrino Experiment (DUNE), requiring unprecedented precision in beamline alignment to achieve DUNE's neutrino oscillation measurement goals. Vertical misalignments of beamline components as small as 0.5 mm can contribute 6-7\% uncertainty in predicted neutrino flux, necessitating sub-0.1 mm alignment monitoring capabilities. The Horn Location Sensor (HLS) system employs frequency sweep interferometry (FSI) in a distributed hydrostatic leveling network to achieve the required precision under harsh radiation conditions up to 5000 kRad/year. Traditional FSI implementations suffer from laser sweep nonlinearities that degrade resolution and require computationally intensive post-processing corrections using gas reference cells. This work presents a real-time FPGA-based implementation of the HLS data acquisition and processing system using a sweep tracker interferometer for dynamic sweep linearization. The system utilizes a PYNQ-Z2 FPGA with programmable logic implementing parallel 16k-point FFT processing across four channels, synchronized by the sweep tracker signal to eliminate post-processing requirements. Spectral performance testing demonstrates significant improvements in peak sharpness compared to traditional fixed-frequency digitization. The FPGA implementation enables real-time displacement monitoring with processing speeds orders of magnitude faster than software-based approaches, essential for the operational requirements of LBNF's eventual distributed sensor network. This advancement in real-time FSI processing directly supports DUNE's precision neutrino physics program by providing the rapid feedback necessary for maintaining stringent beamline alignment tolerances during high-power beam operations.

Rossel, Jacob↗

DOE OSTI2025

Real-Time FPGA Implementation For Frequency Sweep Interferometry In The LBNF Complex

The Long Baseline Neutrino Facility (LBNF) will deliver a 2.4 MW muon neutrino beam from Fermilab to the Deep Underground Neutrino Experiment (DUNE), requiring unprecedented precision in beamline alignment to achieve DUNE's neutrino oscillation measurement goals. Vertical misalignments of beamline components as small as 0.5 mm can contribute 6-7\% uncertainty in predicted neutrino flux, necessitating sub-0.1 mm alignment monitoring capabilities. The Horn Location Sensor (HLS) system employs frequency sweep interferometry (FSI) in a distributed hydrostatic leveling network to achieve the required precision under harsh radiation conditions up to 5000 kRad/year. Traditional FSI implementations suffer from laser sweep nonlinearities that degrade resolution and require computationally intensive post-processing corrections using gas reference cells. This work presents a real-time FPGA-based implementation of the HLS data acquisition and processing system using a sweep tracker interferometer for dynamic sweep linearization. The system utilizes a PYNQ-Z2 FPGA with programmable logic implementing parallel 16k-point FFT processing across four channels, synchronized by the sweep tracker signal to eliminate post-processing requirements. Spectral performance testing demonstrates significant improvements in peak sharpness compared to traditional fixed-frequency digitization. The FPGA implementation enables real-time displacement monitoring with processing speeds orders of magnitude faster than software-based approaches, essential for the operational requirements of LBNF's eventual distributed sensor network. This advancement in real-time FSI processing directly supports DUNE's precision neutrino physics program by providing the rapid feedback necessary for maintaining stringent beamline alignment tolerances during high-power beam operations.

Rossel, A. Jacob [Fermilab; Unlisted]↗

DOE OSTI2024

A Survey of Open-Source Tools for Transmission and Distribution Systems Research

This work presents a review of open-source electric power transmission and distribution systems analysis tools suitable for use by industry professionals and academic researchers. Due to the high complexity of the electric grid, there exist numerous tools and extensive research pertaining to nearly every aspect of the design, operation, and control of transmission and distribution networks. In addition to the commercial tools, a wide range of free, open-source tools, models, and data usable by the scientific community for related research have been developed by different organizations, including both international and US universities and national laboratories. However, due to the absence of a catalog of available tools, models and data, researchers often lack a knowledge of existing capabilities and may develop duplicative software and tools. Increasing awareness of these available resources seeks to accelerate their broader use, leading to more efficient and standardized grid analysis. This review paper (which is part of a larger survey effort that studied over 400 tools in the transmission, distribution, buildings, and electric vehicles space) outlines selected open-source resources that have been developed in power transmission and distribution systems research. It is anticipated that this work can serve as a guide for industry and academic researchers alike, ensuring that research efforts are well-channeled.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Evaluation of Distributed Power Apportioning with Net Load Management Engine in Microgrids Using Power Hardware-in-the-Loop Simulation

This article presents the performance evaluation of ratio consensus-based distributed power apportioning engine along with centralized net load management (NLM) engine that ensures viable and stable operation of an islanded microgrid. Managing net load variability in a microgrid with high penetrations of uncertain renewable generation and ever-changing load demands is a crucial need in order to ensure viable and stable operation of the microgrid. Centralized “dispatch-rule”-based and/or multi-agent-based distributed control of distributed energy resources (DERs) in microgrid are well accepted for microgrid by adapting ANSI/ISA-95-based hierarchical control architecture. In the application where microgrid network has large geographical span with multiple DERs dispersed in the network, high penetration of uncertain renewable energy resources, and ever-changing load demands, a judicious selection of techniques/solutions for managing net-load resources for maintaining viability and stability is required. With this motivation, this article proposes a novel solution to mitigate the challenges by incorporating a mixed centralized NLM engine and distributed power apportioning control of DERs and loads. A power-hardware-in-the-loop (PHIL) -based experiment is conducted with the centralized NLM engine and the distributed power apportioning engine along with two commercial inverters. The experimental results validates the efficacy of the proposed method in ensuring viability and stability of a microgrid.

hardware-in-the-loop simulation↗

DOE OSTI2023

Distributed PV Hosting Capacity Evaluation Considering Equitable PV Accommodation

Distributed photovoltaic (DPV) hosting capacity is widely used by distribution system operators to evaluate how much DPV the distribution networks can accommodate without violating operational constraints. If the proliferation of DPV deployment is appropriately managed, it offers an excellent opportunity to mitigate the energy inequity issue in electric grids. Hosting capacity improvement plans that neglect energy equity can exacerbate unequal DPV access. To consider energy equity in distribution network planning to enhance DPV accommodation, hosting capacity evaluations should be improved to consider equity constraints endogenously. This paper proposes a DPV hosting capacity evaluation method that considers equitable DPV access among sub-regions of a distribution feeder partitioned by socioeconomic indicators, such as income level. The results demonstrate that considering equity in DPV hosting capacity evaluations can provide a valuable reference in the equitable energy transition. This work provides guidance for future distribution network upgrade planning for equitable DPV deployment.

distributed PV↗

DOE OSTI2020

Resilient Operations of Networked Microgrids (RONM)

Objective: Improve the resiliency of power systems with optimization-based methods that leverage advanced microgrid technologies to reduce system recovery times after extreme event induced outages. Outcome: First-of-kind, high-fidelity physics-based optimization method for modeling networked microgrids which includes key engineering constraints associated with system recovery after extreme events.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

A dataset of cyber-induced mechanical faults on buildings with network and buildings data

We have collected data of cyber-induced mechanical faults on buildings using a simulation platform. A DOE reference building model was used for running the simulation under a Rogue device attack and collected the network data as well as the physical buildings data to better understand the impacts of cyber attacks on the building and help identify the source of the mechanical fault with the network data. Alfalfa is the tool used for simulating the DOE reference buildings and acts as an interface to the model for querying the status and providing input externally. The Building Automation System (BAS) is the centralized controller providing control commands to other BACnet devices on the network based on the building status received from Alfalfa. The BACnet devices like damper will listen for the control commands from BAS on the BACnet network and implement it. The attacker is the malicious actor on the network creating disruptions by placing cyber-attacks.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Resilience of the Electric Grid Through Trustable IoT-Coordinated Assets

The electricity grid has evolved from a physical system to a cyberphysical system with digital devices that perform measurement, control, communication, computation, and actuation. The increased penetration of distributed energy resources (DERs) including renewable generation, flexible loads, and storage provides extraordinary opportunities for improvements in efficiency and sustainability. However, they can introduce new vulnerabilities in the form of cyberattacks, which can cause significant challenges in ensuring grid resilience. We propose a framework in this paper for achieving grid resilience through suitably coordinated assets including a network of Internet of Things devices. A local electricity market is proposed to identify trustable assets and carry out this coordination. Situational Awareness (SA) of locally available DERs with the ability to inject power or reduce consumption is enabled by the market, together with a monitoring procedure for their trustability and commitment. With this SA, we show that a variety of cyberattacks can be mitigated using local trustable resources without stressing the bulk grid. Multiple demonstrations are carried out using a high-fidelity cosimulation platform, real-time hardware-in-the-loop validation, and a utility-friendly simulator.

distributed energy resources↗

DOE OSTI2020

Network-Cognizant Time-Coupled Aggregate Flexibility of Distribution Systems Under Uncertainties

Increasing integration of distributed energy resources (DERs) within distribution feeders provides unprecedented flexibility at the distribution-transmission interconnection. To exploit this flexibility and to use the capacity potential of aggregate DERs, feasible substation power injection trajectories need to be efficiently characterized. This paper provides an ellipsoidal inner approximation of the set of feasible power injection trajectories at the substation such that for any point in the set, there exists a feasible disaggregation strategy of DERs for any load uncertainty realization. The problem is formulated as one of finding the robust maximum volume ellipsoid inside the flexibility region under uncertainty. Though the problem is NP-hard even in the deterministic case, this paper derives novel approximations of the resulting adaptive robust optimization problem based on optimal second-stage policies. The proposed approach yields less conservative flexibility characterization than existing flexibility region approximation formulations. The efficacy of the proposed method is demonstrated on a realistic distribution feeder.

adaptive robust optimization↗

DOE OSTI2021

Network-Cognizant Time-Coupled Aggregate Flexibility of Distribution Systems Under Uncertainties

Increasing integration of distributed energy resources (DERs) within distribution feeders provides unprecedented flexibility at the distribution-transmission interconnection. To exploit this flexibility and to use the capacity potential of aggregate DERs, feasible substation power injection trajectories need to be efficiently characterized. This paper provides an ellipsoidal inner approximation of the set of feasible power injection trajectories at the substation such that for any point in the set, there exists a feasible disaggregation strategy of DERs for any load uncertainty realization. The problem is formulated as one of finding the robust maximum volume ellipsoid inside the flexibility region under uncertainty. Though the problem is NP-hard even in the deterministic case, this paper derives novel approximations of the resulting adaptive robust optimization problem based on optimal second-stage policies. The proposed approach yields less conservative flexibility characterization than existing flexibility region approximation formulations. The efficacy of the proposed method is demonstrated on a realistic distribution feeder.

aggregates↗

DOE OSTI2024

A Distributed Model Identification Algorithm for Multi-Agent Systems: Preprint

In this study, we investigate agent-based approach for system model identification with emphasis on power distribution system applications. Departing from conventional practices of relying on historical data for offline model identification, we adopt online update approach utilizing real-time data by employing the latest data points for gradient computation. This methodology offers advantages including a large reduction in the communication network's bandwidth requirements by minimizing the data exchanged at each iteration and enabling the model to adapt in real-time to disturbances. Furthermore, we extend our model identification process from linear frameworks to more complex non-linear convex models. This extension is validated through numerical studies demonstrating improved control performance for a synthetic IEEE test case.

data-driven control↗

DOE OSTI2022

Precursor Analysis Report: Industroyer Targeting Ukraine Electric Power Transport Utility (Ukrenergo) 2016

The Industroyer Targeting Ukraine Electric Power Transport Utility (Ukrenergo) 2016 Precursor Analysis Report leverages publicly available information about the December 2016 cyber attack against the Ukrainian Ukrenergo electric transmission utility 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. Industroyer is a modular malware framework designed to deploy several Industrial Control System (ICS) protocol-specific attack payloads to disrupt electricity distribution. Adversaries deployed Industroyer within the target network on a Microsoft Windows endpoint capable of directly manipulating or communicating with ICS. Industroyer abuses the functionality of a targeted ICS’s legitimate control system to achieve its intended impact. Adversaries likely first gained access to Ukrenergo enterprise networks in early 2016 after a successful spearphishing campaign against organizations in the electric power sector. Adversaries then began capturing credentials beginning on 1 December 2016. This allowed access to the ICS environment at the Pivnichna electric transmission substation outside Kyiv through a device dual-homed on the Information Technology (IT) and ICS networks. Adversaries conducted discovery, targeting, and access to this device using information and previously captured credentials from compromised enterprise IT machines. Finally, the adversaries deployed and launched the Industroyer malware just before midnight on 17 December. By midnight, Ukrenergo had lost control of a targeted substation, resulting in electric power outages for over an hour in the city of Kyiv and the Kyiv region. Researchers and analysts identified 31 unique techniques (used in a sequence of 33 steps) utilized during the attack with a total of 846 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. Twenty-nine of the identified techniques used during the Industroyer cyber attack were precursors to the triggering event. Analysis identified 548 observables associated with these precursor techniques, 353 of which were assessed to have an increased likelihood of being perceived in the 300 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 OSTI2025

Computational Power of Random Quantum Circuits in Arbitrary Geometries

Empirical evidence for a gap between the computational powers of classical and quantum computers has been provided by experiments that sample the output distributions of two-dimensional quantum circuits. Many attempts to close this gap have utilized classical simulations based on tensor network techniques, and their limitations shed light on the improvements to quantum hardware required to frustrate classical simulability. In particular, quantum computers having in excess of approximately 50 qubits are primarily vulnerable to classical simulation due to restrictions on their gate fidelity and their connectivity, the latter determining how many gates are required (and, therefore, how much infidelity is suffered) in generating highly entangled states. Here, we describe recent hardware upgrades to Quantinuum’s H2 quantum computer, enabling it to operate on up to 56 qubits with arbitrary connectivity and 99.843(5)% two-qubit gate fidelity. We define a class of circuits with random geometries that become hard to classically simulate in very low depth and implement them utilizing the flexible connectivity of H2. A careful analysis demonstrating the fast saturation of classical simulation complexity with depth indicates that H2 can yield data well beyond the reach of state-of-the art classical simulation methods at unprecedented fidelities. We find that the considerable difficulty of classically simulating H2 is likely limited only by qubit number, demonstrating the promise and scalability of the quantum charge-coupled device architecture as continued progress is made toward building larger machines. Published by the American Physical Society 2025

DeCross, M.↗

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 OSTI2026

Dynamic Validation of CNN-Based Surrogate Models for Inverter-Based Resources in Open-Source Solvers

Traditionally, distribution system planning has focused on steady-state analyses, with limited consideration of dynamic behavior. However, as large or medium-scale inverter-based resources (IBRs), particularly grid-following (GFL) inverters in commercial or industry buildings, become more prevalent, understanding their dynamic impact is essential for grid planning and operation. This article presents an innovative deep-learning (DL)-approach using convolutional neural networks technique to model the GFL inverters. Developed from real grid-tied commercial IBR transient data, these dynamic DL models overcome proprietary constraints by requiring minimal knowledge of internal converter physics while maintaining high accuracy and flexibility. To demonstrate their applicability, the models were incorporated into GridLAB-D, an open-source, three-phase distribution analysis tool. This integration enables dynamic simulations of large-scale distribution networks with high IBR penetration stability analysis. Rigorous testing and validation, aligned with industry standards, confirmed the reliability and efficiency of this approach, paving the way for enhanced planning and operational assessments of modern power systems.

Deep-learning↗

DOE OSTI2025

American-Made Solar Prize: Edgeli Enables DER Integration (CRADA 615) (Final Report)

The purpose of this project was to demonstrate how granular time series data and automated data transformation, and impact assessment tools could speed interconnection approvals for distributed energy resource projects of various types and sizes. Types included community solar, rooftop solar, and EV charging projects. Using software routines to automate the transformation of data (e.g. GIS) to a network database and power flow model then applying scenarios to create hourly (8760) hosting capacity values and voltage and thermal impacts for specific projects, we were able to demonstrate the feasibility of quickly assembling and analyzing key utility data sets for interconnection purposes. The outcomes of this effort will become the foundation for future work that will enhance and encapsulate the software components developed as part of this project, into web services (e.g. APIs) that can be integrated into queue management systems and automate interconnection screening processes.

14 SOLAR ENERGY↗

DOE OSTI2020

Modeling Single and Two-Phase Transport in Thin Porous Layers Using a Composite Continuum-Pore Network Formulation

In this work, a composite continuum-pore network formulation is presented to model single and two-phase transport thin porous layers, such as gas diffusion layers in polymer electrolyte fuel cells (PEFCs) and active electrodes in redox flow batteries (RFBs). The formulation can be integrated into CFD codes, thus combining the ease of implementation of continuum-based modeling and the computational power of pore-network modeling. The composite model includes a control volume (CV) mesh at the layer scale, which embeds a cubic pore network [1,2]. The pore-network model is used to determine analytically local anisotropic effective transport properties (local effective diffusivity and permeability), which are mapped onto the CV mesh to simulate transport in the porous transport layer [3,4]. Good agreement is found between the predicted global effective transport properties (global effective diffusivity and permeability) under dry and wet conditions and previous experimental data reported in the literature for Toray TGP-H series carbon paper. Water saturation distributions are also compared with results obtained using X-ray computed tomography [4]. [1] P.A. García-Salaberri, I.V. Zenyuk, J.T. Gostick, A.Z. Weber, Modeling Gas Diffusion Layer in Polymer Electrolyte Fuel Cells Using a Continuum-Based Pore-Network Formulation, ECS Trans. 97 (2020) 615. [2] P.A. García-Salaberri, Modeling diffusion and convection in thin porous transport layers using a composite continuum-network model: Application to gas diffusion layers in polymer electrolyte fuel cells, Int J. Heat Mass Transf. (2020), submitted. [3] P.A. García-Salaberri, J.T. Gostick, G. Hwang, A.Z. Weber, M. Vera, Effective diffusivity in partially-saturated carbon-fiber gas diffusion layers: Effect of local saturation and application to macroscopic continuum models, J. Power Sources 296 (2015) 440–453. [4] P.A. García-Salaberri, G. Hwang, M. Vera, A.Z. Weber, J.T. Gostick, Effective diffusivity in partially-saturated carbon-fiber gas diffusion layers: Effect of through-plane saturation distribution, Int. J. Heat Mass Transf. 86 (2015) 319–333.

Garcia-Salaberri, Pablo↗

DOE OSTI2019

Recommended Functionalities for Improving Cybersecurity of Distributed Energy Resources: Preprint

The legacy electric grid is transitioning into a 'present' modern grid due to increasing penetration of intermittent renewable generation at the distribution side, also known as distributed energy resources (DERs). Monitoring and control of such DERs requires extensive data-exchange and bidirectional communication networks. This bidirectional communication paves the way to cyber vulnerabilities and risks for new kinds of cyber-attacks that may have destructive impacts on power system operations. Since IEEE Std. 1547-2018 does not discuss cybersecurity measures for DERs, cybersecurity controls should be developed for securing DER systems at all three levels: device level, communications level, and applications level. This paper discusses the current industry's best practices related to DER cybersecurity and proposes recommended functionalities for improving the cybersecurity posture of DERs. These practical recommended functionalities have been discussed and verified with the industry through a DER cybersecurity working group and are meant to improve the device level security of DERs.

24 POWER TRANSMISSION AND DISTRIBUTION↗