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DOE OSTI2026

ENDF/B-VIII.1: Updated Nuclear Reaction Data Library for Science and Applications

The ENDF/B-VIII.1 library is the newest recommended evaluated nuclear data file by the Cross Section Evaluation Working Group (CSEWG) for use in nuclear science and technology applications, and incorporates advances made in the six years since the release of ENDF/B-VIII.0. Among key advances made are that the 239 Pu file was reevaluated by a joint international effort and that updated 16,18 O, 19 F, 28–30 Si, 50–54 Cr, 55 Mn, 54,56,57 Fe, 63,65 Cu, 139 La, 233,235,238 U, and 240,241 Pu neutron nuclear data from the IAEA coordinated INDEN collaboration were adopted. Over 60 neutron dosimetry cross sections were adopted from the IAEA's IRDFF-II library. In addition, the new library includes significant changes for 3 He, 6 Li, 9 Be, 51 V, 88 Sr, 103 Rh, 140,142 Ce, Dy, 181 Ta, Pt, 206–208 Pb, and 234,236 U neutron data, and new nuclear data for the photonuclear, charged-particle and atomic sublibraries. Numerous thermal neutron scattering kernels were reevaluated or provided for the very first time. On the covariance side, work was undertaken to introduce better uncertainty quantification standards and testing for nuclear data covariances. The significant effort to reevaluate important nuclides has reduced bias in the simulations of many integral experiments with particular progress noted for fluorine, copper, and stainless steel containing benchmarks. Data issues hindered the successful deployment of the previous ENDF/B-VIII.0 for commercial nuclear power applications in high burnup situations. These issues were addressed by improving the 238 U and 239,240,241 Pu evaluated data in the resonance region. The new library performance as a function of burnup is similar to the reference ENDF/B-VII.1 library.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Assessment of coated cladding impact on large-break LOCA with TRACE-DAKOTA

Since 2011, the U.S. Department of Energy has launched new research and development (R&D) programs for developing new accident tolerant fuels (ATFs). These new fuel concepts are expected to bring several advancements in terms of plant safety. However, development, licensing, as well as deployment of ATF require a substantial investment among fuel vendors, operating utilities, and regulatory authorities. For ATF to be economically feasible, credited safety benefits may be necessary to provide an economic incentive for transition of nuclear power plants from Zr/UO2-based fuel technologies to ATFs. As a result, it is essential to evaluate the economic viability of ATF by conducting a comprehensive safety benefits study informed by R&D and demonstration studies. In the present work, based on the best estimate plus uncertainty methodology, sensitivity analysis and uncertainty quantification for key safety quantities under LB-LOCA are performed for the Peach Bottom boiling water reactor (BWR) with GE14 nuclear fuel design adopting the ATF concepts. A coupled TRACE-DAKOTA analysis framework is adopted to perform the uncertainty quantification. Based on the computational results and obtained statistics, the coping time increase, fuel performance improvement as well as the most dominating phenomena during the accident for Cr-coated cladding are investigated in detail. Overall, the results obtained are useful for subsequent plant PRA study and support a move to more modernized performance-based regulatory requirements to realize plant economic benefits.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Heterogeneous Multi-Domain Dataset Synthesis to Facilitate Privacy and Risk Assessments in Smart City IoT

The emergence of the Smart Cities paradigm and the rapid expansion and integration of Internet of Things (IoT) technologies within this context have created unprecedented opportunities for high-resolution behavioral analytics, urban optimization, and context-aware services. However, this same proliferation intensifies privacy risks, particularly those arising from cross-modal data linkage across heterogeneous sensing platforms. To address these challenges, this paper introduces a comprehensive, statistically grounded framework for generating synthetic, multimodal IoT datasets tailored to Smart City research. The framework produces behaviorally plausible synthetic data suitable for preliminary privacy risk assessment and as a benchmark for future re-identification studies, as well as for evaluating algorithms in mobility modeling, urban informatics, and privacy-enhancing technologies. As part of our approach, we formalize probabilistic methods for synthesizing three heterogeneous and operationally relevant data streams—cellular mobility traces, payment terminal transaction logs, and Smart Retail nutrition records—capturing the behaviors of a large number of synthetically generated urban residents over a 12-week period. The framework integrates spatially explicit merchant selection using K-Dimensional (KD)-tree nearest-neighbor algorithms, temporally correlated anchor-based mobility simulation reflective of daily urban rhythms, and dietary-constraint filtering to preserve ecological validity in consumption patterns. In total, the system generates approximately 116 million mobility pings, 5.4 million transactions, and 1.9 million itemized purchases, yielding a reproducible benchmark for evaluating multimodal analytics, privacy-preserving computation, and secure IoT data-sharing protocols. To show the validity of this dataset, the underlying distributions of these residents were successfully validated against reported distributions in published research. We present preliminary uniqueness and cross-modal linkage indicators; comprehensive re-identification benchmarking against specific attack algorithms is planned as future work. This framework can be easily adapted to various scenarios of interest in Smart Cities and other IoT applications. By aligning methodological rigor with the operational needs of Smart City ecosystems, this work fills critical gaps in synthetic data generation for privacy-sensitive domains, including intelligent transportation systems, urban health informatics, and next-generation digital commerce infrastructures.

IoT↗

DOE OSTI2022

Inferring plant–plant interactions using remote sensing

Rapid technological advancements and increasing data availability have improved the capacity to monitor and evaluate Earth's ecology via remote sensing. However, remote sensing is notoriously ‘blind’ to fine-scale ecological processes such as interactions among plants, which encompass a central topic in ecology. Here, we discuss how remote sensing technologies can help infer plant–plant interactions and their roles in shaping plant-based systems at individual, community and landscape levels. At each of these levels, we outline the key attributes of ecosystems that emerge as a product of plant–plant interactions and could possibly be detected by remote sensing data. We review the theoretical bases, approaches and prospects of how inference of plant–plant interactions can be assessed remotely. At the individual level, we illustrate how close-range remote sensing tools can help to infer plant–plant interactions, especially in experimental settings. At the community level, we use forests to illustrate how remotely sensed community structure can be used to infer dominant interactions as a fundamental force in shaping plant communities. At the landscape level, we highlight how remotely sensed attributes of vegetation states and spatial vegetation patterns can be used to assess the role of local plant–plant interactions in shaping landscape ecological systems. Synthesis . Remote sensing extends the domain of plant ecology to broader and finer spatial scales, assisting to scale ecological patterns and search for generic rules. Robust remote sensing approaches are likely to extend our understanding of how plant–plant interactions shape ecological processes across scales—from individuals to landscapes. Combining these approaches with theories, models, experiments, data-driven approaches and data analysis algorithms will firmly embed remote sensing techniques into ecological context and open new pathways to better understand biotic interactions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Incorporating Wind Turbine Choice in High-Resolution Geospatial Supply Curve and Capacity Expansion Models

To achieve national decarbonization goals, U.S. annual deployment of wind energy will need to increase by at least fivefold compared to the recent past. Modeling and analysis frameworks can help inform where and how wind energy deployment might occur and thereby help enable the achievement of decarbonization goals. However, most prior wind energy modeling and analysis studies rely on generalized representations of wind energy technologies. Since wind energy technology advancements are expected to increase the competitiveness of wind energy, it is important to incorporate more detailed representations of turbine technology into wind energy modeling. Here we present a new method that incorporates wind turbine choice into the technology representation of land-based wind energy in long-term planning models. Our method integrates three previously published modeling and analysis capabilities: 1) bottom-up cost modeling to estimate future technology costs, 2) geospatial modeling to represent siting decisions, and 3) power sector modeling to evaluate potential deployment. We refer to this approach as a "customized turbine choice" methodology because it creates a composite turbine scenario by choosing from multiple wind turbine technologies—using site-specific optimized turbine layout and selecting the least-cost technology at each location. We demonstrate the capabilities of this new modeling pipeline by examining how the selection of four different wind turbine configurations might evolve from 2021 through 2040. Our results show that using our customized turbine choice methodology could lead to higher estimates for wind future deployment, which indicates that more simplified modeling might underestimate the role that wind energy could play in meeting decarbonization goals. Future research is needed to further explore the implications of turbine choice and to better inform technology researchers, original equipment manufacturers, and other wind industry stakeholders about the market potential of different wind turbine technologies.

17 WIND ENERGY↗

DOE OSTI2022

Evaluation of crystalline quality of traveling heater method (THM) grown Cd0.9Zn0.1Te0.98Se0.02 crystals

Because of its excellent opto-electronic properties, CdZnTe (CZT) has been the material of choice for x- and gamma-ray detectors operable at room temperature. CZT is the leading commercially available room-temperature radiation detector material today. Although much progress has been made over the past three decades, today's CZT crystals still face certain challenges, especially the presence of the performance-limiting materials defects and the associated relatively high production cost. In this regard, CdxZn1−xTeySe1−y (CZTS) is emerging as a next-generation compound semiconductor, which overcomes some of the limitations of CZT technology for the stated applications. Here, we conducted a study to evaluate the crystalline quality of the traveling heater method grown CZTS with an optimized alloy composition, i.e., Cd0.9Zn0.1Te0.98Se0.02. The as-grown samples were evaluated by low-temperature photoluminescence (PL) spectroscopy and high-resolution x-ray diffraction using the synchrotron light source at Brookhaven National Laboratory. The full width at half maximum of both the PL and x-ray rocking curves was observed to be broadened due to the lattice disorder of the quaternary compound, eventually degrading the crystalline quality. This was consistent with density functional theory calculations.

Roy, U. N. (ORCID:0000000282089680)↗

DOE OSTI2020

Remote Hardware-in-the-Loop Approach for Microgrid Controller Evaluation: Preprint

Utilities have been installing microgrids due to the advancements in the distributed energy resources technologies. Microgrids also improve the resiliency of the system due to their capability to isolate themselves from the grid and run in islanded mode of operation. Microgrid controllers are management systems that can run the microgrids under varied operating condition and enable the smooth operation of microgrids under various system conditions. It is of high value to de-risk the installation of such controllers. Hardware-in-the-loop evaluation is used by controller developers and utilities to evaluate the controllers under stressful conditions to understand the performance and to understand the necessary improvements to the microgrid controller. In this research work, the team has leveraged the existing infrastructure of real-time simulation at the Energy Systems Integration Facility (ESIF) and the capability of the microgrid controller to control assets over the Internet Protocol (IP) to evaluate a university developed microgrid controller in an efficient and cost-effective manner. The full paper will provide the complete setup, the experimental use cases and the results. In this abstract, one use case will be expalined, and the results from the use case is provided.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Physics Prospects for a near-term Proton-Proton Collider

Hadron colliders at the energy frontier offer significant discovery potential through precise measurements of Standard Model processes and direct searches for new particles and interactions. A future hadron collider would enhance the exploration of particle physics at the electroweak scale and beyond, potentially uniting the community around a common project. The LHC has already demonstrated precision measurement and new physics search capabilities well beyond its original design goals and the HL-LHC will continue to usher in new advancements. This document highlights the physics potential of an FCC-hh machine to directly follow the HL-LHC. In order to reduce the timeline and costs, the physics impact of lower collider energies, down to $\sim 50$~TeV, is evaluated. Lower centre-of-mass energy could leverage advanced magnet technology to reduce both the cost and time to the next hadron collider. Such a machine offers a breadth of physics potential and would make key advancements in Higgs measurements, direct particle production searches, and high-energy tests of Standard Model processes. Most projected results from such a hadron-hadron collider are superior to or competitive with other proposed accelerator projects and this option offers unparalleled physics breadth. The FCC program should lay out a decision-making process that evaluates in detail options for proceeding directly to a hadron collider, including the possibility of reducing energy targets and staging the magnet installation to spread out the cost profile.

FOS: Physical sciences↗

DOE OSTI2023

Environmental and Social Justice Implications of a Circular Plastics Economy

A consideration of environmental justice (EJ) and social justice (SJ) is critical to minimize the impacts of technology deployment on local communities. SJ and EJ impacts occur in specific geographic locations but can cover a wide range of effects (e.g., air pollution, access to clean water, jobs, wages, and education), making it challenging to determine which metrics are appropriate to evaluate and which data are required. Thus, there is currently a gap in the analysis community's ability to provide useful and universal EJ and SJ metrics for emerging technologies. Here, we present a draft framework for evaluating the human health, local environment, and job implications of processes that are at an early or middle technology readiness level (TRL). Using a case study on enzymatic polyethylene terephthalate (PET) recycling (middle TRL), we demonstrate how to qualitatively and quantitatively assess these EJ and SJ metrics for a circular economy context and how to communicate the results in a manner beneficial to both researchers and local communities.

circular economy↗

DOE OSTI2020

An Authentication Vulnerability Assessment of Connected Lighting Systems

Emerging connected lighting systems (CLS) that incorporate distributed intelligence, network interfaces, and sensors can become data-collection platforms that enable a wide range of valuable new capabilities as well as greater energy savings in buildings and cities. However, CLS technology is currently at an early stage of development, and its increased connectivity introduces cybersecurity risks that are new to the lighting industry and that must be addressed for successful integration with other systems. While a number of existing frameworks, guidelines, and tests for evaluating cybersecurity vulnerability may apply to CLS in whole or in part, there is currently no mandatory requirement for cybersecurity testing or certification. The lighting industry, including technology developers and specification organizations, is currently evaluating the suitability of existing frameworks and guidelines for CLS. To support these efforts, Pacific Northwest National Laboratory (PNNL) is conducting a series of studies intended to educate lighting industry stakeholders on specific cybersecurity practices and characterize their implementation in commercially available CLS with varying system architectures, network-communication technologies, and degrees of maturity. This study demonstrates that tests for authentication vulnerabilities can be developed with objective pass/fail criteria, therby facilitating comparisons between CLS. Based on the limited results of this study, it appears that the CLS that are being brought to market have varying levels of authentication vulnerability. It is hoped that these evaluations will support and perhaps accelerate industry discussions on the risks of specific security vulnerabilities, what vulnerabilities should be addressed by in-development of future lighting-specific best practices, and whether any such practices should be included in voluntary lighting standards.

42 ENGINEERING↗

DOE OSTI2020

Markov Process to Evaluate the Value Proposition of a Risk-Informed Predictive Maintenance Strategy

To achieve high-capacity factors, the nuclear fleet has relied on labor-intensive and time-consuming operation and preventive maintenance programs for plant systems. Manually performed inspection, calibration, testing, and maintenance of plant assets at periodic frequencies, along with time-based replacement of assets irrespective of condition, have resulted in a costly, labor-centric business model. Fortunately, there are technologies that can eliminate unnecessary preventive maintenance activities by deploying risk-informed predictive maintenance, enabling the transition to a technology-centric business model. The technology-centric business model will enable plants to optimize and automate maintenance activities, leading to cost reductions since labor is a rising cost and technology is a declining cost. The implementation of scalable technologies and methodologies across plant systems and across the nuclear fleet is critical for successful deployment of a risk-informed predictive maintenance strategy at commercial nuclear power plants. The work presented in the report is being developed as part of a collaborative research effort between Idaho National Laboratory and Public Service Enterprise Group Nuclear, LLC. This report describes the technical basis using the Markov Process to evaluate the value proposition for the risk-informed predictive maintenance strategy for the circulating water system. The plant process data from the Salem nuclear plant’s circulating water system is utilized to develop a Markov chain risk models and formulation to estimate the loss and gain in revenue based on plant availability. The outcomes presented in this report provides the technical basis for extensive quantitative evaluation of a scalable risk-informed predictive maintenance strategy as part of future research.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Coastal Acoustic Buoy for Offshore Wind: Project Synthesis

The population of North Atlantic right whales is critically endangered and their habitat overlaps with offshore windfarm leases. It is therefore imperative that effective mitigation strategies be used to avoid impacts on right whales during the construction of offshore windfarms. The Department of Energy issued FOA Number DE-FOA-0001924 to encourage the development of technology that could monitor large exclusion zones for right whales in order to mitigate potential impact of construction noise on right whales. This report summarizes the past two years of the development and evaluation of the Coastal Acoustic Buoy for Offshore Wind (CABOW) project which aimed to develop technology to monitor large exclusion zones for North Atlantic right whales. Over the course of the project SMRU Consulting have implemented a rigorous design process including comparison of different approaches (e.g., single sensor vs multiple sensors), as well as consideration of placement and timing of acoustic monitoring. We have evaluated critical components of the CABOW system including, reliability, detection range, and bearing accuracy in areas adjacent to offshore windfarm leases in Maryland by conducting 3,536 playbacks of simulated right whale upcalls. The maximum call detection range was 7.5 km when noise was 99 dB re 1µPa rms (50-225 Hz), but this reduced to < 1 km when ambient noise levels were high. Our detection probability in the field was measured as a function of range as well as the source-to-noise level ratio allowing us to build a model to predict the probability of detection under various scenarios (sample size: 3,536 calls x 5 buoys = 17,680). The median bearing error was -0.25° but this is likely an underestimate of error due to experimental design. Using the published recall and precision of the two detectors we implemented in the CABOW system, we estimate that at a recall of 80%, our precision was > 80%, within the range of what we were aiming for in this project. To estimate our exclusion zone false negative and false positive rates, we built a simulation model using the empirical data from our field trial. We modelled three to nine CABOW units placed on the 10 km exclusion zone and estimated our false negative rate to be 1% or less (which was our project goal) and our false positive rate to be between seven and nine percent, slightly above our goal of 5%. However, we also modelled an equivalent PAM system that does not have bearing capabilities and found the false positive rates for that system to be six to eight times higher than the CABOW rate. This higher false positive rate of PAM systems with low spatial information could have significant cost repercussions for offshore wind developers by adding work shutdowns or delays without providing additional protection for right whales. The model we built allows us to explore the placement of PAM systems under various scenarios and will thus help facilitate planning of PAM mitigation systems to meet NOAA Incidental Harassment Authorizations for specific windfarms. We achieved an average system uptime of 98.3%, just below our goal of 99%. The issues that caused these short losses of data have been identified and fixed. Right whale detections and audio clips were typically transferred via radio from the buoys to the base station in two to four seconds. We therefore believe we have developed a highly robust real-time PAM system. Based on the above, we feel we have achieved the stated funding goal of developing a cost-effective and robust real-time PAM system that enables the monitoring of large exclusion zones for right whales during the constructions of offshore windfarms. This should lead to decreased costs and risks for the offshore wind sector while providing robust mitigation for right whales. It is important to state that PAM mitigation will need to be implemented with other mitigation strategies (e.g., visual observers) to provide a complete mitigation strategy to ensure that any effects on right whales from offshore windfarm construction is minimized.

17 WIND ENERGY↗

DOE OSTI2022

Evaluation of steam supply performance: Steamer vs. bubbler

Water/steam electrolysis is a key enabling technology for clean, low-carbon and sustainable production of hydrogen and will play a crucial role in future hydrogen economy. For high temperature solid oxide electrolytic cells, steam is the chemical feedstock. A stable and accurate supply of steam to solid oxide electrolytic cells is of vital importance to smooth production of hydrogen. In this study, we compare steam supply performance of two commonly used steam generators: steamer and bubbler. Our results show that bubbler with proper volume and fritted inlet gas tubing can provide more stable and accurate steam supply than steamer for laboratory use. We also provide the explanation for the unstable steam supply observed in steamer. Overall, we conclude that bubbler is generally a better choice for small-scale laboratory use (e.g., ≤50%H 2 O, ≤100 sccm carrier gas flow) to produce stable and accurate steam and steamer might be a better choice for higher steam contents and flow rates (e.g., >60% H 2 O and >200) encountered in large-scale testing and/or aggressive high steam conditions.

08 HYDROGEN↗

DOE OSTI2021

Spatial and Temporal Analysis of the Total Cost of Ownership for Class 8 Tractors and Class 4 Parcel Delivery Trucks

The medium- and heavy-duty transportation sector is experiencing rapid changes in powertrain technology innovation, with recent announcements of battery electric and fuel cell electric trucks being offered. The economics of these alternative powertrain vehicles are uncertain and difficult to compare directly. This analysis seeks to provide a rigorous, techno-economic analysis of all of these alternative powertrain vehicles within the same analytic framework. Specifically, this report evaluates the total cost of ownership (TCO) of six different truck powertrain technologies (diesel, diesel hybrid electric, plug-in hybrid electric, compressed natural gas, battery electric, and fuel cell electric) for three different truck vocations (Class 8 long haul [750-mile range and 500-mile range], Class 8 short haul [300-mile range], and Class 4 parcel delivery [120-mile range]), for three different time frames (2018, 2025, and Ultimate).

08 HYDROGEN↗

DOE OSTI2023

Dispatch optimization of electric thermal energy storage within System Advisor Model

A stand-alone electric thermal energy storage (ETES) system converts low-value electricity into heat using resistance heating elements. During periods of high-value electricity, an ETES system uses a thermodynamic power cycle to convert stored thermal energy back to electricity. These dispatchable systems derive value from their ability to store energy when prices are low and generate electricity when prices are favorable, i.e., energy arbitrage. Consequently, dispatch optimization of system operations, through maximizing revenue subject to system constraints, is essential to evaluate the economic value of a particular system design. While stand-alone ETES systems offer potential advantages as dispatchable grid storage technologies, there is a lack of a neutral third-party, publicly available, open-source model to evaluate the performance, dispatch, and financial viability of these systems. To address this problem, we have developed a techno-economic model for stand-alone ETES systems, within National Renewable Energy Laboratory's (NREL's) System Advisor Model (SAM). We implement a mixed-integer linear program to determine an ETES optimal operating schedule that maximizes electricity sales less maintenance costs caused by operation and cycling given temporal-varying grid electricity prices. Our contributions include a mixed-integer linear program for energy arbitrage of an ETES system, an ETES performance model through a publicly-available software (i.e., SAM), and an exercise of our model through case studies that compare ETES operational strategies and annual financial metrics. With our dispatch optimization model, we were able to improve revenue by 20% compared to a myopic heuristic while reducing the operational cost of the ETES system through decreases in cycle starts, cycles per day, and heater starts.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Innovation in complementary energy technologies from renewable energy policies

Complementary renewable technologies support renewable energy use as they can help balance the intermittency of solar and wind generation. Previous research has shown that environmental policies directly targeting renewable energy can drive innovation in solar PV and wind technology. This study explores the impact of renewable policies on innovation in complementary renewable technology that assists with integration, including combustion with mitigation potential, efficient power transmission or distribution, and enabling technologies. We use negative binomial models and a panel of 25 OECD countries from 1997 to 2015 to evaluate these relationships. We are not able to find evidence that renewable policies incentivize innovation in complementary combustion or transmission and distribution technologies, and only limited evidence that demand-pull instruments are associated with increased innovation in smart grid technology. Technology-push policies, such as public research and development funds for renewable energy, have had limited impact on grid-supporting energy storage innovation. Our findings suggest that policymakers wishing to accelerate these complementary technologies need to go beyond policies targeting renewable energy in order to drive innovation in complementary fields.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Recommendations for Comprehensive and Independent Evaluation of Machine Learning‐Based Earth System Models

Abstract Machine learning (ML) is a revolutionary technology with demonstrable applications across multiple disciplines. Within the Earth science community, ML has been most visible for weather forecasting, producing forecasts that rival modern physics‐based models. Given the importance of deepening our understanding and improving predictions of the Earth system on all time scales, efforts are now underway to develop Earth‐system models (ESMs) capable of representing all components of the coupled Earth system (or their aggregated behavior) and their response to external changes over long timescales. Building trust in ESMs is a much more difficult problem than for weather forecast models, not least because the model must represent the alternate (e.g., future or paleoclimatic) coupled states of the system for which there are no direct observations. Given that the physical principles that enable predictions about the response of the Earth system are often not explicitly coded in these ML‐based models, demonstrating the credibility of ML‐based ESMs thus requires us to build evidence of their consistency with the physical system. To this end, this paper puts forward five recommendations to enhance comprehensive, standardized, and independent evaluation of ML‐based ESMs to strengthen their credibility and promote their wider use.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Large Scale Field Demonstration Evaluation Plan

This Evaluation Plan defines practical, commissioning-oriented test protocols for in-field assessment of grid-forming (GFM) inverter-based resources (IBRs) deployed under the UNIFI Consortium Field Demonstration. The demonstration’s overarching objective is to validate multi vendor GFM technologies at impactful unit/plant scale, assess interoperability and grid-service performance, and evaluate the applicability of the UNIFI Specifications for Grid-forming Inverter-based Resources in real utility environments, including integration with operational systems and protection schemes.

24 POWER TRANSMISSION AND DISTRIBUTION↗