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DOE OSTI2025

Advanced Transmission Technologies – GETs and HPCs Session 2: Advanced Power Flow Control and Transmission Topology Optimization

The INL TADA GETs Cohort Session 2, held on November 7, 2025, conducted in collaboration with ScottMadden, focused on two core Advanced Transmission Technologies (ATTs): Advanced Power Flow Control (APFC) and Transmission Topology Optimization (TTO). These technologies are pivotal in enhancing grid flexibility, reliability, and cybersecurity resilience. APFC, particularly through modular FACTS devices like Modular Static Synchronous Series Compensators (M-SSSCs), enables dynamic voltage injection to reroute power flows. The session highlighted the deployment benefits of APFC, such as rapid installation, minimal civil works, and re-deployability. Regulatory drivers like FERC Order 2023 mandate the inclusion of Grid-Enhancing Technologies (GETs) in interconnection studies. Case studies from Central Hudson, CAISO, and National Grid (UK) demonstrated APFC’s effectiveness in congestion relief and cost savings. The session also addressed cybersecurity concerns, including firmware vulnerabilities, SCADA integration risks, and supply chain dependencies. Participants engaged in interactive exercises to rank cybersecurity and supply chain risks, emphasizing the need for robust digital assurance strategies. TTO involves software-based reconfiguration of transmission networks to optimize power flow without new infrastructure. The session showcased its operational value, with examples from SPP, PJM, and MISO showing significant congestion cost reductions. Cybersecurity vulnerabilities were discussed, particularly in API security and software supply chains, referencing incidents like SolarWinds and attacks on Danish utilities. Digital assurance exercises explored worst-case scenarios, attack paths, and mitigation responsibilities between vendors and utilities. Reliability challenges such as algorithm stability, vendor dependency, and operator trust were also examined. Cross-cutting themes emphasized the importance of digital assurance tools, including Software Bills of Materials (SBOMs) and hardware-in-loop testing. Human performance, training, and operational confidence were identified as critical enablers of technology adoption. The session concluded with a preview of Session 3, which will focus on High Performance Conductors (HPCs) and risk-based cybersecurity tools. Session 2 of 3.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Risk Informed Timelines (RIT) v.1.0

Risk Informed Timelines (RIT) is a more modern and holistic method for understanding how access delay timelines are developed and utilized in physical security system evaluations. The software utilizes Bayesian methods to combine subject matter expert judgement and small performance test datasets in a consistent and defensible way. It allows a more holistic view of delay performance that provides distributions of task times and task success probabilities to account for tasks that, if failed, would result in failure of the attack. The RIT methodology is meant to enable security risk managers to begin the transition into more comprehensive delay timeline analysis. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-8294 O

Noel, ToddG.↗
DOE OSTI2024

Toward digital design at the exascale: An overview of project ICECap

High performance computing has entered the Exascale Age. Capable of performing over 1018 floating point operations per second, exascale computers, such as El Capitan, the National Nuclear Security Administration's first, have the potential to revolutionize the detailed in-depth study of highly complex science and engineering systems. However, in addition to these kind of whole machine “hero” simulations, exascale systems could also enable new paradigms in digital design by making petascale hero runs routine. Currently, untenable problems in complex system design, optimization, model exploration, and scientific discovery could all become possible. Motivated by the challenge of uncovering the next generation of robust high-yield inertial confinement fusion (ICF) designs, project ICECap (Inertial Confinement on El Capitan) attempts to integrate multiple advances in machine learning (ML), scientific workflows, high performance computing, GPU-acceleration, and numerical optimization to prototype such a future. Built on a general framework, ICECap is exploring how these technologies could broadly accelerate scientific discovery on El Capitan. In addition to our requirements, system-level design, and challenges, we describe some of the key technologies in ICECap, including ML replacements for multiphysics packages, tools for human-machine teaming, and algorithms for multifidelity design optimization under uncertainty. As a test of our prototype pre-El Capitan system, we advance the state-of-the art for ICF hohlraum design by demonstrating the optimization of a 17-parameter National Ignition Facility experiment and show that our ML-assisted workflow makes design choices that are consistent with physics intuition, but in an automated, efficient, and mathematically rigorous fashion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Development of a Cloud-based Application to Enable a Scalable Risk-informed Predictive Maintenance Strategy at Nuclear Power Plants

Light-water reactor operations and maintenance (O&M) costs are prohibitively high, thus contributing to the premature decommissioning of nuclear power plants (NPPs). This is partly due to how the equipment is monitored. In recent years, cloud computing has emerged as a dominant technology by virtue of its low costs, computing and storage adaptability, and ability to host applications over numerous types of virtual infrastructures. Cloud computing can be a cost-effective alternative to onsite storage and diagnostics. This paper conducts a techno-economic assessment of a provisional cloud deployment architecture for a NPP predictive monitoring (PdM) system. The cloud-based monitoring system would enable maintenance and diagnostics (M&D) analysts and other authorized plant users to remotely monitor equipment functionality so as to enable PdM practices and early detection of faults. The Microsoft Azure cloud platform is included in the proposed cloud architecture to provide data processing and storage, sensor device networking, and database management; however, this analysis could be extended to other cloud computing service providers as well. For the techno-economic assessment, technical feasibility is measured in terms of network performance metrics such as response time, latency, and throughput, whereas economic feasibility is measured in terms of operational costs and capital expenditures. Finally, this report covers certain regulatory and security aspects that may concern licensees looking to implement cloud computing. The report focuses on the integration of sensor database storage, the application of cloud resources to PdM, and the identification of technological and economic hurdles associated with moving to a cloud-computing-based architecture.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

CAMAS 2025: Continuing to Advance Arctic Marine Science

The Consortium for the Advancement of Marine Arctic Science (CAMAS) held its second annual Workshop and Early-Career School in Seattle, WA, on April 15-18, 2025. The workshop attracted 74 participants, including a dozen scientists from Europe (8) and Asia (4). The goal of CAMAS is to facilitate and enhance international collaboration on marine Arctic science, in order to advance the understanding and model representation of key marine Arctic processes that contribute to the rapid changes in the Arctic Earth system. These rapid changes have profound impacts on operations in the Arctic, including those associated with the national and energy security of the United States. The Early-Career School started the event on Tuesday April 15. Thirty-three early-career scientists (postdocs and students) gathered for lectures and discussions on topics like high-resolution Arctic Ocean and sea ice modeling; biogeochemistry of the Arctic; Machine Learning for Arctic Earth system modeling; and an Arctic perspective on geo-engineering.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Computational Algorithms for Unit Commitment with AC Power Flows (Final Report)

Security-constrained unit commitment (SCUC) is a key component in power system operations. When AC power flow constraints are considered in the SCUC model (AC-SCUC), the problem becomes extremely difficult due to its discrete and non-convex nature, as described in “Grid Optimization Competition Challenge 3 Problem Formulation (GOCC)”. There are four main challenges: (i) Discrete decisions regarding unit online/offline status and start-up/shut-down procedures for every single unit. The number of discrete decision variables increases considerably when a system integrates multiple generators; (ii) Configuration-based combined-cycle formulations, and multi-commodity models that include ramping products, spin/non-spin products, and regulation up/down products. The combined-cycle units introduce additional discrete decision variables and auxiliary service products further complicate the model by connecting multi-commodity products’ continuous and discrete variables; (iii) SCUC models with AC power flow constraints are far more complex due to massive bilinear terms in the large-scale nonlinear power balance equations. The nonlinear power balance equations are further complicated by the discrete step control variables of shunts; (iv) N − 1 contingency analysis. The size of the model increases linearly with the number of contingencies considered, greatly increasing the size of the optimization model. Accordingly, there is an emergent need to develop a robust algorithm capable of deriving a high-quality solution in a short time and passing through contingency tests simultaneously. In this project, we explore innovative techniques to address this challenging problem by integrating advanced polyhedral theory, approximation methods, relaxation strategies, decomposition techniques, and parallel computing. Each technique approaches the problem from a different perspective, leveraging its specific strengths to tackle distinct challenges. Each individual method has demonstrated its effectiveness in the PI’s previous research. Their integration is expected to significantly reduce the computational time required to solve the proposed complex problem. Successful completion of this project has the potential to transform the industry by enhancing optimization solvers capable of handling large-scale day-ahead energy market clearing models within strict time constraints, while incorporating AC power flow constraints. This advancement will lead to reduced overall generation costs and, consequently, increased social welfare.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

An Approach to Shared Research Systems

World-leading research requires state-of-the-art systems for research and innovation. Operational excellence is the key component of scientific excellence by implementing ubiquitous security, repeatable processes, streamlined policies, and reliable and robust infrastructure. Building a research environment to meet these needs is a complex and expensive task. In many cases, it can be financially advantageous for one or more programs, projects, and/or stakeholder(s) to pool resources to build a shared research system to meet common goals. However, such a system brings some unique challenges that need to be addressed. This document describes the process to mitigate these challenges by documenting how the interested parties contribute to the overall success of the system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Final Design for Thermal/Epithermal eXperiments (TEX) with Lithium Absorbers to Provide Validation Benchmarks for Y-12 Electrorefining Facility

One of the main goals of the Thermal/Epithermal eXperiments (TEX) project is to use existing Nuclear Criticality Safety Program (NCSP) assets to create critical experiment plutonium and uranium test beds for materials important to criticality safety that have insufficient benchmark evaluations. The plutonium test bed experiments were completed in 2018 and are published in the 2020 edition of the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook. The uranium test bed assemblies were completed in 2023 and accepted in the 2024 edition of the ICSBEP Handbook. The Nuclear Criticality Safety (NCS) group at Y-12 National Security Complex has identified programmatic need for validation cases for uranium electrorefining operations at Y-12. The electrorefining operation credits lithium enriched in 6 Li in addition to 35 Cl as absorbers in the design criticality safety evaluation for precluding criticality under upset conditions in the large and geometrically unfavorable electro-refiner. There is, however, inadequate experimental validation for the 6 Li absorbers. As an extension of the TEX uranium test bed, TEX-Cl critical experiments were performed with sodium chloride salt to address the 35 Cl thermal absorption as well as other validation needs at Los Alamos National Laboratory (LANL). These experiments were completed in 2024 and accepted into the 2025 ICSBEP Handbook. To continue the methodology used in TEX-Cl, TEX-Li aims to accomplish the same. The overall design of both experiments was to use commercially available, high purity, salts with polyethylene moderator and HEU plates to configure a critical assembly. Three experiments are planned for TEX-Li using encapsulated lithium carbonate (Li 2 CO 3 ), with natural 6 Li abundance. For all experiments, the highly enriched uranium (HEU) Jemima plates will be used as fissile material. Multiple layers will be stacked together with encapsulated Li 2 CO 3 alternated with polyethylene in standard configurations. Standard stacking was found to be optimal in matching the different sensitivity profiles provided by the Y-12 models. Three configurations are proposed with varying polyethylene moderation and a constant 1/4” absorber thickness. The first uses 11 layers of 5/4” polyethylene, the second uses 9 layers of 3/4” polyethylene, and the third uses 10 layers of 1/2” polyethylene. Calculations showed that some alternative forms of lithium-based materials provided slightly less-optimal sensitivity profiles when compared to lithium carbonate but come with other drawbacks. These alternatives included lithium aluminate (LiAlO 2 ), Aluminum-2050 alloy, Aluminum-8090, Aluminum-2095, lithium hydride (LiH), and lithium fluoride (LiF). Lithium aluminate and aluminum-2050 provided comparable sensitivity profiles when compared to lithium carbonate and can be used instead if lithium carbonate cannot be readily procured. After a broad material study, lithium carbonate outperformed any alternative material with a balance in affordability and workability. The assessment of experimental uncertainties of the non-absorber and absorber components was predicted to be 0.00089 and 0.00093 Δk eff , respectively. The largest uncertainties may be reduced with precision dimensional inspection of the components. Many of the parts and equipment for IER 575 have already been fabricated or procured for previous projects and therefore do not contribute significantly to the overall cost of this experiment. This includes the Jemima plates and Comet critical assembly machine, which are existing NCSP assets, as well as the aluminum platen and polyethylene reflector rings, which were fabricated and authorized for the TEX experiment involving HEU with polyethylene. Lithium carbonate containers will be procured by LANL and will be filled by LLNL. The total material costs for TEX-Li experiments are estimated to be on the order of $\$$47,400. Precision inspection, including dimensional, mass, density, and impurity, is recommended for all components for an estimated cost of $\$$12,000.

35Cl↗
DOE OSTI2025

Energy Improvements of Fire Station 71

Since 2018, the City of Shawnee, Kansas has completed two phases of the State of Kansas Facility Conservation Improvement Program (FCIP), an initiative that guarantees operational cost and energy savings through targeted construction improvements on City facilities and infrastructure. The City is currently in the third phase of this FCIP, where one of the projects included an investment in energy improvements for Fire Station 71 (FS 71). The City partnered with Navitas, an Energy Service Company (ESCO), to implement a Photovoltaic Solar Array on FS71. The purpose of this project was to invest in sustainable building improvements with Energy Conservation Measures (ECM) to bring cost savings to the City and to provide sustainable benefits to the residents of Shawnee. In the first task of the project, Navitas collaborated with the City of Shawnee and the Community Development Department to determine the optimal layout and schedule for the installation of the solar array on FS 71. In the second task of the project, Navitas installed the 99.8 kW DC Photovoltaic solar array system. This system installation comprised of racking, inverters, optimizers, load center, and disconnect, which were all installed at a total ECM price of $\$$247,948. The third task focused on start-up and commissioning of the array. Navitas installed a real-time data analytics information management system integrated with utility meters, which evaluates the operations of the utility system and verifies operation of equipment and ensures optimum operation for energy efficiency. In the final task of this project, this analytics system was used for monitoring and verification, which will continue to be used to evaluate the success of the project for the coming years. The primary goal of the project was to install the 99.8 kW DC PV solar array at FS 71 to demonstrate the viability of solar energy systems in essential municipal facilities. Fire stations are energy demanding structures, as they require a constant intake of power and have a high baseline energy usage. The success of solar arrays on a fire station exemplifies their energy efficiency and effectiveness and displays their potential for application on other city facilities. By installing a solar array at such a facility, the City sought not only to offset electricity usage but also to serve as a model for ECMs in other municipal facilities and infrastructure projects. From an economic standpoint, this project demonstrates the feasibility of renewable energy at the municipal level. The total project cost of $\$$247,948 was split evenly between city funds and award funding, minimizing financial risk while ensuring guaranteed long-term savings. Any excess savings that are beyond the guaranteed minimums remain with the city, which enables future investment in sustainable energy initiatives. This project provides many benefits to the public. In addition to reducing the environmental footprint of city operations, it lowers taxpayer-funded utility spending and improves the energy security of a critical facility. The knowledge gained from this implementation motivates the City to focus on similar efforts across other public facilities in future FCIP phases and other City projects.

14 SOLAR ENERGY↗
DOE OSTI2023

Handheld Raman Spectrometers: Assessment Report

In April 2022 the National Urban Security Technology Laboratory’s System Assessment and Validation for Emergency Responders program conducted an operational assessment of ten commercially available handheld Raman spectrometers. Additionally, prior to the operational assessment, the Pacific Northwest National Laboratory conducted laboratory testing of the devices, including scanning a number of samples in various containers. This report summarizes the results of the assessment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Existing Methods for Grid Strength Assessment and Role of Hydropower in Future Grids

The power system is undergoing rapid evolution with increasing penetration of inverter-based resources (IBRs), large loads, microgrids, and power electronic devices. Ensuring reliable and stable operation of the modern power grid is a multi-faceted challenge that requires detailed understanding of this complex system. Dynamic stability is a major concern in maintaining the security of power grids as the generation mixes and large loads transitions to include high shares of power electronic devices. The operation of IBRs in regions with low system strength and higher grid impedances has been found to be the main reason behind many of the power system instabilities that manifest themselves in various types of oscillations and interactions that, if not properly addressed and damped, can jeopardize the reliable operation of the power system. Weak grid conditions compound such stability problems, particularly when many IBRs operate in proximity to and connect to weak power grids. Therefore, it is important to assess grid strength for planning, integration and operation of IBRs for a given power system. In this report, we aim to understand and classify existing methodologies for grid strength assessment, along with their limitations and future needs. Further, we aim to utilize the huge untapped potential in utilizing hydro energy resources in addressing some of the pertinent challenges of grid strength and reliable operation of modern power systems. Hydropower, historically valued for its flexibility and dispatchability, now faces new constraints due to reduced share of synchronous machines in the generation mix and seasonal variability of available water resources. Yet, these same plants present untapped potential beyond energy generation - notably, as providers of critical grid services. This report explores how hydropower plants, particularly through operation as synchronous condensers, can play a pivotal role in strengthening the grid amid evolving system dynamics.

13 HYDRO ENERGY↗
DOE OSTI2026

Smart Planning for Radioactive Source Transport Advanced Tools for Increased Safety and Efficiency

End-of-life (EOL) management of high-activity radioactive sources is made uniquely challenging by the inherent risks associated with storage and transportation of these sources, the complex logistics involved, and the strict requirements for regulatory compliance. Traditional methods lack comprehensive tools for accurate site assessments and precision planning for the transportation of radioactive sources. They also frequently fail to provide the adaptability required to consider diverse operational environments, resulting in inefficiencies and potential safety concerns. This paper introduces a novel software solution developed to address these issues by integrating advanced technologies such as light detection and ranging (LiDAR)-based 3D environment modeling, smart dynamic route planning, and customizable measurement functionalities. This software enables detailed terrain visualizations, facilitating thorough environmental assessments and enabling users to virtually navigate, analyze, and plan site-specific operations. Among the key features are a user-centric interface for virtual navigation, precise site measurement tools for site evaluations, interactive visualizations that highlight potential operational hazards, dynamic route planning capabilities, and real-time collision detection to promote safe workflows. By demonstrating the effectiveness of this tool through real-world application, the present work underscores the tool’s potential to revolutionize radioactive source EOL management by improving operational efficiencies, minimizing risk, and advancing the state of practice to achieve suitable and secure radioactive material handling.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Application of Hybrid K-Edge Densitometry in Reprocessing Facilities

During reprocessing operations, accurately determining the elemental concentrations of uranium and plutonium is critical for nuclear security. Reprocessing facilities processes hundreds of tons of nuclear material annually, requiring accurate measurements to ensure effective nuclear material control and accountability. Hybrid K-edge densitometry (HKED) system combines K-edge absorption densitometry with x-ray fluorescence to measure actinide elemental concentrations with a low uncertainty. This system uses high-purity germanium gamma-ray detectors and advanced signal processing equipment to detect x-ray accurately. This document provides guidance on how to achieve effective performance from an HKED system for measuring uranium and plutonium concentrations in reprocessing facilities. It includes best practices for the setup and operation of an HKED system, while highlighting factors influencing uncertainties.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Emerging Technologies for Privacy Preservation in Energy Systems

This study explores the intersection of digitalization and privacy within the energy sector, focusing on the emerging challenges and opportunities presented by integrating Distributed Energy Resources (DERs) and advanced metering infrastructure. The need for robust digital privacy measures has become crucial as the energy industry evolves towards a more decentralized, digitalized, and decarbonized future. This study delves into four cutting-edge privacy-preserving technologies—Homomorphic Encryption (HE), Secure Multiparty Computation (SMPC), Differential Privacy (DP), and Federated Learning (FL)—each offering unique solutions to safeguard consumer data by increasing digital connectivity and data exchange. Through a detailed examination of these methods, the study explains how each technology operates, its applications within the energy sector, and the specific privacy challenges it addresses. Homomorphic Encryption allows for secure computations on encrypted data, enabling data analysis without compromising privacy. Secure Multiparty Computation enables collaborative data analysis across different entities while protecting the confidentiality of the inputs. Differential Privacy introduces randomness into the assembled data set, preventing the identification of individual records in statistical databases. Lastly, Federated Learning offers a paradigm shift in data analysis, where machine learning models are trained at the edge, minimizing the centralization of sensitive data. The research underscores the significance of implementing these privacy-enhancing technologies to comply with strict data protection regulations, foster consumer trust, and enhance the security of the energy infrastructure. By providing a comprehensive overview of these methodologies and their practical implications for the energy sector, this study aims to contribute to the ongoing discourse on digital privacy, offering insights into how the energy industry can navigate the complexities of data privacy in the digital age.

Cali, Umit↗
DOE OSTI2024

Securing Grid-interactive Efficient Buildings (GEB) through Cyber Defense and Resilient System (CYDRES)

The DOE CYDRES project is driven by the urgent need to address critical research gaps in the domain of cyber-physical security of smart buildings, including Grid-interactive Efficient Buildings (GEBs). CYDRES, a real-time advanced building resilient platform, aims to enhance the cyber-attack-immune capabilities of buildings through multi-layered prevention, detection, and adaptation mechanisms. CYDRES consists of five key modules: a multi-layer network analyzer, an Automatic Fault Detection, Diagnosis, and Prognosis (AFDDP) framework, an intelligent mode selector, a cyber-resilient control framework, and a situation awareness platform. The Network Analyzer employs a data-driven framework that includes a protocol state learning tool and a CRF (Conditional Random Field) command validator. In Hardware-In-the-Loop (HIL) testbeds, it achieved 100% detection accuracy with a false alarm rate of 3%, validating its efficacy in identifying selected cyber-attacks. The AFDDP framework leverages pattern matching, PCA (Principal Component Analysis)-based strategies, and a DBN (Dynamic Bayesian Network)-based fault diagnosis approach to pinpoint the causes of physical system abnormalities using Building Automation System (BAS) data. In HIL experiments, the AFDDP module attained a detection accuracy of over 95% with a false alarm rate below 7%. Additionally, the fault detector utilized machine learning (Random Forest) and deep learning (Multi-Layer Perceptron) methods with acoustic sensor data to achieve a 100% fault detection accuracy in Heating, Ventilation, and Air-Conditioning (HVAC) equipment. The Mode Selector offered real-time impact analysis, allowing immediate actions to protect BASs in the face of emerging threats. The cyber-resilient control framework included an adaptive Model Predictive Control (MPC) and a measurement compensator, reducing temperature violations by up to 94% and improving the total demand flexibility by up to 70% in HIL experiments. Such HIL experiments covered a cyber-attack case and a physical fault case, showcasing CYDRES’ efficiency in maintaining operational continuity during threats. The situation awareness platform in Grafana enhanced real-time threat detection and response visualization, augmenting the operational awareness for building operators. CYDRES demonstrated high technical effectiveness in various test scenarios, particularly in HIL environments. The project's phased development approach ensured efficient use of resources, highlighting its practical feasibility and readiness for commercialization. By enhancing the security and resilience of building operations, CYDRES represents a significant advance in mitigating risks associated with cyber-physical systems, thereby enhancing public confidence in the safety of modern building infrastructure. Future directions for the project include expanding testing protocols, refining AFDDP methodologies, exploring more comprehensive resilient control strategies, and testing in real commercial buildings.

42 ENGINEERING↗
DOE OSTI2020

Resilient Autonomous Wind Farms

With the advent of an increasing number of control strategies that seek to optimize wind turbine performance on a farm level, taking into account individual wind turbine information to achieve wind-farm-level objectives has become an increasingly important goal. Methods for controlling wind turbines on an individual and farm level have experienced significant development, and an abundance of new implementations for gathering and using data from turbines have created potential for novel control mechanisms that can further optimize the performance and delivery characteristics of a wind farm. A key element of making these wind farms more efficient is to develop reliable algorithms that use local sensor information that is already being collected, such as from local meteorological stations, nearby radars, sodars, and lidars, and supervisory control and data acquisition (SCADA) data. Making use of information from all wind turbines in a wind farm can enable such approaches as determining the atmospheric conditions across the farm, improving fault-finding, and ensuring more efficient overall control of farmwide optimizations through mechanisms such as wake steering. However, these approaches typically involve a centralized communications and control center. In order to ensure the resilient operation of the farm, it is necessary to develop an approach that distributes the calculation and communication amongst multiple nodes throughout the farm. In this fashion, a redundant, robust, and secure network can be created, which can tolerate faults in calculation, communication, and even external attacks that seek to disrupt the operation of the wind farm. This paper introduces the use of the Raft-Byzantine-Fault-Tolerant algorithm in the implementation of autonomous control of a wind farm. This implementation will allow for fault tolerance for malfunctioning nodes, sensors, transmitters, and connectors. This approach is equally extensible to account for malicious actors. It will...

fault tolerance↗
DOE OSTI2020

Resilient Autonomous Wind Farms: Preprint

With the advent of an increasing number of control strategies that seek to optimize wind turbine performance on a farm-level, taking account of individual wind turbine information to achieve wind farm-level objectives has become an increasingly important goal. Methods for controlling wind turbines on an individual and farm level have seen significant development, and an abundance of new implementations for gathering and using data from turbines have created potential for novel control mechanisms which can further optimize the performance and delivery characteristics of a wind farm. A key element of making these wind farms more efficient is to develop reliable algorithms that use local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. Making use of information from all wind turbines in a wind farm can enable such approaches as determining the atmospheric conditions across the farm, improving fault-finding, and enabling more efficient overall control of farm-wide optimizations through mechanisms such as wake-steering. However, these approaches typically involve a centralized communications and control center. In order to ensure the resilient operation of the farm, it is necessary to develop an approach which distributes the calculation and communication amongst multiple nodes throughout the farm. In this fashion, a redundant, robust, and secure network can be created, which can tolerate faults in calculation, communication, and even external attacks which seek to disrupt the operation of the wind farm. This paper introduces the use of the Raft Byzantine Fault Tolerance algorithm in the implementation of autonomous control of a wind farm. This implementation will allow for fault tolerance for malfunctioning nodes, sensors, transmitters, and connectors. This approach is equally extensible to account for malicious actors. It will be shown to achieve overall consensus, provided the number of faults/malicious nodes is less than 3$n$+1, where $n$ is the number of turbine cluster faults which may occur, and to be robust in the face of multiple arbitrary faults.

autonomous↗