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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 127 records · Page 7
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DOE OSTI2020

Machine Learning-Based Prediction of Distribution Network Voltage and Sensors Allocation: Preprint

Increasing penetration of fast-varying energy resources may negatively affect power systems' operation. At the same time, sensor deployment throughout distribution networks improves system awareness and enables the development of new and advanced voltage control solutions. Such control techniques rely on accurate prediction in anticipation to voltage violation scenarios. This paper analyzes various approaches for voltage prediction in a distribution system; it is shown that combining multiple techniques into a single regressor improves its predictive power. Moreover, a two-step regressor is proposed, where initial predictions based on a global regressor are refined by local regressors; in this case, prediction errors decrease significantly. Additionally, a clustering approach is employed for performing sensor allocation, so that only the most influential buses are selected for monitoring without diminishing prediction accuracy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Machine Learning-Based Prediction of Distribution Network Voltage and Sensor Allocation

Increasing penetration of fast-varying energy resources may negatively affect power systems' operation. At the same time, sensor deployment throughout distribution networks improves system awareness and enables the development of new and advanced voltage control solutions. Such control techniques rely on accurate prediction in anticipation to voltage violation scenarios. This paper analyzes various approaches for voltage prediction in a distribution system; it is shown that combining multiple techniques into a single regressor improves its predictive power. Moreover, a two-step regressor is proposed, where initial predictions based on a global regressor are refined by local regressors; in this case, prediction errors decrease significantly. Additionally, a clustering approach is employed for performing sensor allocation, so that only the most influential buses are selected for monitoring without diminishing prediction accuracy.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

Machine Learning-Based Prediction of Distribution Network Voltage and Sensor Allocation

Increasing penetration levels of fast-varying energy resources might negatively affect power system operation. At the same time, sensor deployment throughout distribution networks improves system awareness and enables the development of new and advanced voltage control solutions. Such control techniques rely on accurate prediction in anticipation of voltage violation scenarios. This paper analyzes various approaches to voltage prediction in a distribution system, and it is shown that combining multiple techniques into a single regressor improves its predictive power. Moreover, a two-step regressor is proposed in which initial predictions based on a global regressor are refined by local regressors; in this case, prediction errors decrease significantly. Additionally, a clustering approach is employed to perform sensor allocation so that only the most influential buses are selected for monitoring without diminishing prediction accuracy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Anomaly Detection and Mitigation in FACTS-based Wide-Area Voltage Control Systems using Machine Learning

With the increasing deployment of Flexible AC Transmission System (FACTS) devices in wide-area voltage control systems (WAVCS) for achieving improved voltage stability of bulk power systems, the possibility for cyber attacks on these systems is also increasing. Successful stealthy cyber attacks that are difficult to detect by traditional informational technology (IT)-based cybersecurity solutions or threshold-based bad data detectors can lead to a voltage collapse in power grid. This paper presents the testbed-based attacks implementation and real-time evaluation of machine learning (ML) algorithm for detecting and mitigating stealthy cyber attacks on FACTS-based WAVCS on a hardware-in-the-loop (HIL) testbed. Initially, we discuss the implementation of a fuzzy logic controller (FLC) that controls a Static VAR Compensator (SVC) device deployed in a two-area four-machine Kundur power system for improving transient voltage stability. Later, the ML-based Anomaly Detection and Mitigation (ADM) system is implemented on the cyber-physical HIL testbed to detect and mitigate various stealthy cyber attacks, which are injected in real-time over the wide-area network (WAN). The experimental results show accurate and effective performance of ADM system in detecting and mitigating anomalies while keeping the grid stable and within the system operating limits, as defined by the North America Electric Reliability Corporation (NERC).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Photovoltaic inverter-based quantification of snow conditions and power loss

Snow is a significant challenge for photovoltaic (PV) systems at northern latitudes, where the pace of deployment is rapid but snow-related power losses can exceed 30% of annual production. Accurate snow-related power loss estimation methods for utility-scale sites can support snow mitigation strategies, inform resource planning and validate predictive snow-loss models. This study builds on our previous work on inverter-based detection of snow, and its implications for utility-scale power production, by validating the accuracy of our snow-loss method across different PV sites and system designs and highlighting its value in bringing greater visibility to PV plant operations in winter. Our estimation method is both novel and scalable, requiring only standard monitoring data to correlate snow-related losses with meteorological data. As demonstrated here, our validation method involved three main steps: 1) estimation of performance losses for multiple systems by comparing measured inverter data to modeled data; 2) application of a detection framework to identify which performance losses are snow-related; and 3) comparison of snow-related losses among three utility-scale sites differing in tilt angle. Results show that utility-scale systems at higher tilt angles consistently shed snow more quickly/completely than their lower-tilt counterparts. Further, monthly and seasonal snow losses are inversely and non-linearly correlated with tilt angle when normalized for cumulative snowfall. These results are consistent with the findings of previous studies and support the broad applicability of this method to fixed-tilt utility-scale PV systems around the world that routinely experience snow-related performance losses.

Cooper, Emma C. (ORCID:0000000190554098)↗
DOE OSTI2025

A VSC-HVDC-Assisted Black-Start Strategy in Bulk Power Systems a Case Study in San Diego

With the worldwide growth in deploying high-voltage direct current (HVDC) transmission systems, their ability to facilitate black-start (BS) restoration has been a research topic of interest. In this context, voltage source converter (VSC)-HVDC is regarded as a BS resource, and this paper proposes a VSC-HVDC-assisted parallel BS restoration strategy in bulk power systems. The proposed strategy consists of two stages: 1) determination of the VSC and generator startup sequence and 2) load restoration simulation. In the first stage, the entire blackout system is sectionalized into multiple subsystems. Each subsystem includes a VSC-HVDC station or traditional BS unit, it independently determines its generator startup timeline and the energization timelines for buses and lines. The second stage involves load restoration, conceptualized as a modified unit commitment problem, with the timelines established in the first stage work as critical inputs. The proposed BS restoration strategy is tested on the San Diego power system to simulate the 2011 Southwest blackout. The simulation results validate the effectiveness of using VSC-HVDC links as a BS resource which not only speeds up the restoration process but also reduces both energy and economic losses.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Defending the Homeland: Growing Foreign Challenges to the U.S. Missile Defense Posture

The Office of the Secretary of Defense (OSD) for Policy requested that Lawrence Livermore National Laboratory conduct a Congressionally directed study on homeland missile defense, pursuant to Section 1692 of the fiscal year 2020 National Defense Authorization Act. In accordance with the statutory language, this study considers whether the security benefits obtained by deployment of homeland missile defenses of the United States are undermined or counterbalanced by adverse reactions of potential adversaries, and considers the effectiveness of homeland missile defense efforts of the United States to deter the development of ballistic missiles. Almost a half-century has elapsed since the United States and the Soviet Union signed the Anti-Ballistic Missile (ABM) Treaty, and almost two decades since the former withdrew. Since withdrawing, the United States has sought to develop and deploy a layered missile defense system to defend against regional threats to U.S. and allied interests abroad and to counter limited threats to the U.S. homeland. As such, missile defense supports key national defense policy objectives. Protecting the U.S. homeland, forces abroad, allies, and partners. Deterring attacks against the United States, its allies, and partners. Assuring allies an strengthening U.S. diplomatic activities in peacetime and crisis. We offer five key findings: 1. The primary benefit of the Ground-based Midcourse Defense (GMD) system is the protection it provides the U.S. homeland against a limited but evolving rogue-state missile threat. While this system has never been tested in combat, it appears thus far to have effectively paced North Korea’s development and deployment of intercontinental ballistic missiles (ICBMs). In the absence of such a defensive capability, the United States would likely have been more heavily exposed to North Korean actions, would have operated at a much higher-risk posture, and would have had less negotiating room with which to navigate coercive tactics and crises. Its allies would have been more concerned about U.S. willingness in time of crisis and war to run the risks of protecting them. The GMD system also serves as a hedge against Iranian breakout. 2. Potential adversaries continue to develop long-range ballistic missiles despite deployment of a U.S. homeland missile defense system. This includes both rogue states and major power rivals whose long-range missile programs predated the deployment of U.S. missile defenses. While North Korea has continued its long-range missile developments and achieved an intercontinental capability, Iran has not yet reached this threshold. The broader proliferation of long-range missiles anticipated in the late 1990s has not materialized. 3. While Russia has used the existence of a U.S. homeland missile defense system as a justification for its substantial and continuing weapon modernization program, neither Russian force modernization nor the limited U.S. homeland missile defense system has altered the strategic balance. Russia has long considered U.S. missile defenses—both theater and homeland—as directed against its strategic forces and as a capability that could rapidly advance, thereby eroding Russian confidence in its nuclear deterrent. Russia’s political and military actions appear excessive and negatively impact areas such as arms control, but they do not undermine the primary benefit of the U.S. system cited in Key Finding 1. 4. China’s expansion and diversification of nuclear and missile forces has been influenced by its concerns about U.S. homeland missile defense, but those concerns are only one of many factors in China’s force planning. Although China’s actions to preserve and expand its assured retaliation prospects have not fundamentally altered the strategic balance, its buildup and lack of transparency about its force modernization goals are troubling. Taken together, the aggregate pattern of China’s modernization activities over the past two decades strongly suggests a concerted effort to develop a modern military force commensurate with its intended geopolitical status. Whatever China’s legacy concerns over the survivability of its strategic nuclear forces, its ability to overwhelm the GMD system appears intact today and will be further strengthened in the years ahead as it continues its long-term modernization program. China’s political and military actions negatively affect U.S. security interests but do not undermine the primary benefit of the U.S. system cited in Key Finding 1. 5. The basic finding that benefits have not so far been undermined by adverse reactions is a function of circumstances that are increasingly in flux. The existing GMD-centered system is under increasing pressure from the pace and scope of North Korean missile deployments. The proposed “layered” system seeks to mitigate this impending capability gap in the near term and to complement future capabilities, such as the Next Generation Interceptor, when they come online. Given the pace of U.S. missile defense developments since 2000, it is possible that adversary advances in capability will outpace the U.S. system’s ability to adapt. Additionally, because this layered system is in principle more readily scaled, it will almost certainly be viewed skeptically by China and Russia. In a context of growing great-power security competition, the Department of Defense (DoD) should consider undertaking a broader net assessment of the U.S., Russian, and Chinese force balance.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Autonomous Tools for Attack Surface Reduction (Final Report)

The electric power grid is a complex critical infrastructure that forms the lifeline of modern society, and its secure and reliable operation is of paramount importance to national security and economic wellbeing. However, recent findings documented in authoritative sources indicate the threat of cyber-based attacks growing in numbers and sophistication. However, securing the grid against stealthy cyberattacks is a challenging task due to legacy nature of the infrastructure coupled with dynamic nature of threat landscape and ever-growing sophistication of the adversaries. Additionally, the grid’s attack surface continues to grow with the increased dependence on digital communications and control that now extends to each consumer through smart meters and distributed energy resources. Unfortunately, this expansive surface increases the grid’s vulnerability and further exposes critical control systems in both substations and control centers. To respond to this emerging need, we had successfully assembled an interdisciplinary team with academic- industry partnership to successfully conduct research, development, evaluation, demonstration, and commercialization of attack surface reduction tools, whose goal was to significantly reduce the cyber attack surface in the North American power grid. Our proposed project was a synergistic collaborative effort leveraging the synergistic expertise of the team members across power systems, cyber security and CPS security, testbeds, field deployments and demonstration, and successful commercialization. The following are the specific tasks that have been successfully completed two phases (2016-2020). Phase I: Task 1: Developed and implemented a robust Project Management and Data Management Plan, coupled with a well thought out Risk Mitigation Plan. Task 2.1: Developed a comprehensive framework that continually assesses and autonomously reduces the attack surface for the power grid control environment spanning across substations, control center and the SCADA network to significantly reduce the risks of cyber attacks. Task 2.2: Developed attack surface analysis techniques, metrics, and tools that assess the attack surface at multiple levels including the control center, substations, and the SCADA network. Task 2.3: Developed attack surface reduction techniques and tools that dynamically reduce attack surface and hence increase attacker’s cost without interfering in the critical functions of the system. Task 2.4: Prototyped, implemented, and quantitatively evaluated/validated the techniques and tools on a realistic industrial CPS security testbed environment by leveraging the unique resources of the team. Task 3: Developed Commercialization plan to transition the developed tools into power system industry stakeholders for a broader adoption by leveraging the expertise of our industrial members. Phase II: Task 4: Successfully completed field demonstration, verification, and evaluation of the effectiveness of the attack surface analysis and reduction techniques on a realistic utility testbed environment. This also involved the development of realistic scenarios, sound metrics, data sets, evaluation criteria, and documentation. Technology integration & Field demonstration: The project had significantly advanced the state-of-the-art research and practice in improving the cybersecurity of our nation’s power grid infrastructure against cyber threats. In particular, the proposed, designed, and deployed attack surface analysis and reduction algorithms and tools have contributed to significantly reducing the exposure and risk of the devices, substations, and the integrated SCADA/EMS/ DMS grid environment to cyber threat. Strong demonstration and evaluation techniques have verified the feasibility of the developed techniques on realistic cyber-physical testbeds and utility partner's real grid environment, and collaborative research and evaluation of attack surface reduction techniques (for wide-are monitoring and control) within a vendor (GE) EMS platform. The Attack Host Analyzer (AHA) tool that was developed through this project was made available through GitHub.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

2.2.2.405 - Verdant/NREL Research Measurement Campaign

The marine renewable energy industry primarily uses thermoset composite materials for blades and other hydrodynamic components, which can have up to 50% reduction in strength when exposed to seawater and are not recyclable. Thermoplastic composite materials have been shown at a coupon-scale to have improved seawater saturated properties but have not been validated at full scale. The primary goal of the project is to demonstrate the structural properties of thermoplastic-fiberglass composite blades compared to epoxy-fiberglass composite blades in seawater at a tidal energy site on an operational turbine. Through this work, NREL manufactured, tested and deployed thermoplastic blades and a data acquisition system on Verdant Power's turbine TriFrame in the East River, NYC. The thermoplastic blades and DAQ were retrieved after a 6-month deployment with no signs of degradation and all cable connections, structural supports and strain gages had a 100% survival rate. The blades are currently undergoing post-deployment structural validation to compare their performance to traditional epoxy blades that were deployed for the same amount of time. This will increase confidence in thermoplastic materials and move it closer to commercial adoption, as well as increase confidence in the design of a DAQ and instrumentation method for a tidal turbine. However, due to an error in the DAQ software that was introduced after the DAQ and software were validated, there was no loads data collected during the deployment.

blade testing↗
DOE OSTI2023

Operational resilience metrics for power systems with penetration of renewable resources

Abstract Modern power grid is evolving towards carbon neutrality by deploying increasing amount of renewable energy resources. However, the impact of renewable generation on power system planning and operation is not sufficiently investigated, especially the capability of renewable penetrated power systems to resist and recover from major disturbances, which is a critical concern for system operators. Novel metrics and evaluation methodologies are needed to depict systems’ ability in response to events caused by natural disasters, and quantitatively evaluate system performance in various time scales. In this paper, operational resilience metrics are proposed for power systems with penetration of renewable energy resources based on transient stability principles. A systematic methodology is proposed to quantitatively assess the evolution of system performance during various stages of the disaster process. Based on the proposed metrics, a resilience‐oriented disaster management strategy is designed and validated using the modified IEEE 39‐bus test system. The simulation results demonstrate the validity of the proposed metrics and strategy, and show that the system resilience is enhanced during the mitigation of fault conditions.

Gui, Jianzhong↗
DOE OSTI2023

The importance of capturing power system operational details in resource adequacy assessments

Traditional methods for assessing the resource adequacy (RA) of a power system are becoming obsolete due to emerging trends such as the increasing deployment of variable renewable energy and storage. Consequently, analysts are recommending that RA be assessed using a Monte Carlo simulation approach that models chronological power system operations over many instances of possible operating conditions. However, this approach is necessarily more complex and computationally demanding, which is an obstacle to real-world implementation. Here, in this study, we investigate which operational details of power systems are important to capture in order to accurately evaluate a system's RA, versus details that add complexity but do not meaningfully affect RA results. To do so, we develop a probabilistic RA assessment framework by adapting an existing production cost model and apply it to a case study based on the IEEE Reliability Test System. Our results indicate that multi-year data, storage dispatch, and transmission limits are key details to incorporate. Accurate RA results can be obtained using non-economic dispatch strategies as long as they are coordinated with detailed operational strategies. We also demonstrate how popular expectation-based RA metrics can mask important differences in the characteristics of loss of load events.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

2020 Hydrogen and Fuel Cell Technologies Market Report

This report features summaries of relevant hydrogen and fuel cell activity in 2020, consistent with prior annual reports across applications and sectors. However, 2020 was no ordinary year. Despite pandemic hardships, the hydrogen and fuel cell industry not only stepped up to assist with humanitarian and healthcare efforts but saw continued momentum in several growing market sectors with numerous partnerships, initiatives, and investments. Using hydrogen and fuel cell technologies to provide an environmental and economic pathway to clean, reliable power was a key feature of world economic recovery and energy infrastructure plans. Today, hydrogen energy and fuel cell systems are being developed and deployed in a range of stationary power, portable power, and transportation applications around the world. Throughout 2020, there were significant achievements in each sector, building on years of research and development (R&D), field testing, large-scale demonstration projects, and real-world customer experience. The heavy-duty truck market in particular continued to make substantial gains, with new players entering the space and a coalition of companies pledging to produce and deploy more than 100,000 fuel cell trucks in the next two decades. To provide hydrogen for trucks and other end users, larger and larger electrolyzer projects were announced throughout the year. Collectively, there were more than 430 megawatts (MW) of electrolyzer systems either deployed, ordered, or proposed around the world. This 2020 report also provides special “Spotlights” on the collective industry response to the COVID-19 pandemic as well as international commitments and hydrogen strategies, providing overviews of significant activity during and after the initial onset of COVID-19 around the world.

08 HYDROGEN↗
DOE OSTI2020

Reconfigurable Real-Time Power Grid Emulator for Systems With High Penetration of Renewables

Novel power system control and new utility devices need to be tested before their actual deployment to the power grid. To assist with such a testing need, real-time digital emulators such as RTDS and Opal-RT can be used to connect to the physical world and form a hardware in the loop (HIL) emulation. However, due to the limitations of today's computational resources, the accuracy and fidelity suffer from different levels of model reductions in purely digital simulations. CURENT has developed a reconfigurable electric grid hardware testbed (HTB) to overcome the limitations of digital emulators. The HTB has been used to develop measurement, control, modeling, and actuation techniques for a national grid with a high penetration of renewables. The power electronic-based system includes emulators for synchronous generators; photovoltaics with grid-interfacing inverter; wind turbines; induction motor loads, ZIP loads, power electronic loads; batteries; ac and dc transmission lines; short circuit faults and grid relay protection; and a multiterminal HVDC overlay including power electronics interfaces. The system contains real elements of power flow, measurement, communication, protection, and control that mimic what would be seen in an actual electric grid. This paper presents an overview of the HTB and several scenarios that have been run to determine control and actions needed for the future power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Design Assessment of Brayton Cycles for Combined Heat and Power from Nuclear Power Plants

Brayton cycles (BCs) are gaining renewed interest for use in high-temperature nuclear reactors for power production. This study explores the potential of nuclear Brayton systems in industrial combined heat and power (CHP) production. After a review of the historical deployment and technical development of BC systems for nuclear and cogeneration applications several BCs in CHP configurations are assessed when applied to representative high-temperature reactor types. The analysis emphasizes process heat delivery options, electrical efficiency, and component performance for the case of a direct cycle—Helium BC in a high temperature gas reactor, and an open-air Brayton cycle with a high temperature gas reactor. The results highlight thermodynamic trade-offs in cogeneration operation, particularly in recuperated configurations, and compare BC-based CHP with conventional Rankine cycle (RC) performance. Key findings suggest Helium BCs offer viable CHP performance primarily at lower process heat temperatures, with open air cycles being less efficient. The work also demonstrates trade-off between CHP performance and power production performance with a detailed comparison to where steam RC has better performance. The work is designed to be used as a reference work when cogeneration is proposed from high temperature reactors alongside the use of BCs.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Data-Driven Event Detection of Power Systems Based on Unequal-Interval Reduction of PMU Data and Local Outlier Factor

With the deployment of phasor measurement units (PMU) and wide area measurement system (WAMS), it is feasible to have an insight into the events occurred in power systems based on measured data. Thus, a novel data-driven algorithm based on local outlier factor (LOF) is proposed in this work to detect and locate events in power systems using reduced PMU data. First, the unequal-interval reduction method is presented to reduce the scale of PMU data in sub-stations and reconstruct it in master station of WAMS, which can relieve the burden of communication systems. Then, principle component analysis (PCA)-based similarity search method is proposed to measure the differences of operation state between any two buses. Next, LOF is presented to detect the abnormal events in power systems, and employed to determine the region of the event source. Finally, six cases from the Western electricity coordinating council (WECC) 179-bus power system, a case from the South China power system (SCPS), and a case from the Guangdong power system (GDPS) are utilized to demonstrate the effectiveness of the proposed algorithm. Overall, the results show that proposed algorithm is effective and can be applied to event detection, event location, and online monitoring, which can enhance the situation awareness ability of power system operators.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

AGGREGATE: dAta-driven modelinG preservinG contRollable dEr for outaGe mAnagemenT and rEsiliency (Final Report)

The AGGREGATE project team successfully developed and validated various modules for outage management. Brief summaries of each module are provided to showcase their strength for outage management and restoration for a distribution system with a high penetration of connected distribution energy resources (DERs). In recent years, inverter-based DERs have been widely deployed in distribution system. A most of behind-the-meter (BTM) solar power generation is not visible to the utility. The data-driven DER and load estimation modules are using machine learning (ML) and artificial intelligence (AI) to manage this issue, which provides an opportunity for distribution system operators (DSOs) to operate systems and make decisions in real-time for a distribution system with a high penetration of DERs deployed. Also, the estimated DER and true load can be further leveraged in network aggregation and cold-load pick up estimation for reducing the computing complexity and providing for fast restoration. After load demand and DER power generations have been estimated, the information will support topology and state estimation (SE). The topology estimation module demonstrated the viability of mixed integer linear programming (MILP) formulation to estimate the most likely operational radial topology and outage sections using power flow measurements, historical/estimated load and DERs data and smart meter ping measurements. Formulation includes continuous (power flow, load and DERs data) and binary measurements (smart meter ping measurements) in a single formulation. Errors in continuous data and binary data are modeled as normal distribution and Bernoulli distribution, respectively. In the future distribution grid, the power injection from controllable DERs will be essential for efficient and resilient grid operation. However, determining the optimal DER injections and restoration actions is dependent on knowledge of the system states. State estimation (SE), already the cornerstone of transmission energy management systems, will become commonplace in distribution management systems as more measurements become available from deployment of automated metering infrastructure (AMI). Observability analysis is the first step in SE, as it determines the sufficiency of the available measurements for accurately estimating the current system states. A new type of pseudo-measurement called a Correlational Measurement (CM) is introduced in this module, to enhance the observability of the system to enable more accurate SE. CMs encapsulate knowledge of correlation between demand patterns for similar classes of loads as well as injection patterns for same-technology renewable DERs. During grid contingency scenarios, DERs have been traditionally disconnected, without any fault ride-through capabilities. However, with new regulations and better technology, it is feasible for these resources to contribute to the grid’s restoration after an adverse event and hence enhance resilience. The controllability module proposes a two-step restoration scheme for the power system restoration process by leveraging additional degrees of freedom in power electronics interfaced DERs for mitigating voltage problems. In a resilience mode without the utility system, the distribution grid relies on DERs to serve critical load. In such a severe event with multiple faults on the distribution feeders, actuation of various protective devices (PDs) divides the distribution system into electrical islands. The undetected actuated PDs due to fault current contributions from DERs can delay the restoration process, thereby reducing the system resilience. The Advanced Outage Management (AOM) and the Advanced Feeder Restoration (AFR) modules developed in this project provide improved system resilience with multiple DERs. AOM identifies the faulted sections and actuated PDs in a distribution system with DERs by incorporating smart meter data. The most credible outage scenario including fault locations, PD actuations, and fault indicator (FI) failures is identified by a set of binary integer linear programming incorporating hypotheses. The AFR module serves to restore a distribution system with available energy resources taking into consideration the availability of utility sources and DERs. By partitioning the system into islands, critical load will be served with the available generation resources within islands based on the solution of a MILP. When the utility systems become available, the optimal path will be determined by a spanning tree search algorithm that reconnects these islands back to substations and restores the remaining load. The transmission and distribution (T&D) co-simulation module was used to validate the effect of a control action performed on the distribution side assets as it propagates to the transmission side. This ensures that the control action performed results in a feasible operating point on both the transmission and the distribution system. In addition to validation, the team used the T&D co-simulation module to demonstrate how distribution system assets can be used to mitigate issues on the transmission system. Specifically, the team demonstrated that appropriate switching operations on the distribution side can alleviate the line overload condition on the transmission side without causing new operational constraint violations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Electrical substation grid testbed for DLT applications of electrical fault detection, power quality monitoring, DERs use cases and cyber-events

Electrical utilities continue to deploy more intelligent electronic devices (IEDs) inside and outside electrical substation, and are associated with customer-owned distributed energy resources (DERs). The integrity and confidentiality of data from these IEDs, like power meters and protective relays, is crucial. Blockchain technology could improve the resilience of microgrids by improving the security of data sharing. The penetration of customer-owned DERs (renewable energy sources) and the increasing deployment of IEDs can lead to integrate power system applications with Distributed Ledger Technology (DLT). In this study, we implemented the electrical faulted phase detection and power quality monitoring algorithms with a Cyber Grid Guard (CGG) system using DLT. In addition, the DERs (wind turbine farms) use case and protective relay cyber-event tests were assessed, by using the CGG system with DLT. In the experimental model, the testbed was created by using a real-time simulator and CGG system with power meters/ protective relays in-the-loop. The data collected from the CGG system and IEDs were compared with the same time stamp source. These results had shown the successful assessment of protection, control and monitoring applications using a CGG system with DLT. In the future, the ESGT with DERs and the CGG system will be used in other power system applications, based on implementing smart contracts between electrical utilities with customer-owned DERs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Technological developments and accelerator improvements for the FRIB beam power ramp-up

The Facility for Rare Isotope Beams (FRIB) began operation with 1 kW beam power for scientific users in May 2022 upon completion of 8 years of project construction. The ramp-up to the ultimate beam power of 400 kW, planned over a 6-year period, will enable the facility to reach its full potential for scientific discovery in isotope science and applications. In December 2023, a record-high beam power of 10.4 kW uranium was delivered to the target. Technological developments and accelerator improvements are being made over the entire facility and are key to completion of the power ramp-up. Major technological developments entail the phased deployment of high-power beam-intercepting systems, including the charge strippers, the charge selection systems, the production target, and the beam dump, along with support systems, including non-conventional utilities (NCU) and remote handling facilities. Major accelerator improvements include renovations to aging legacy systems associated with experimental beam lines and system automation for improved operational efficiency and better machine availability. Experience must be gained to safely handle the increased radiological impacts associated with high beam power; extensive machine studies and advanced beam tuning procedures are needed to minimize uncontrolled beam losses for the desired operating conditions. This paper discusses the technological developments and accelerator improvements with emphasis on major R&D efforts.

43 PARTICLE ACCELERATORS↗