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At least 19 records

Optimizing Transmission of Acoustic Signals to Monitor Internal Conditions of Canisters for Dry Storage of Commercial Spent Nuclear Fuel

Safe storage of spent nuclear fuel (SNF) is critical to the nuclear fuel cycle and the future of nuclear energy. In the United States, SNF is stored primarily via two methods regulated by the U.S. Nuclear Regulatory Commission: wet storage in SNF pools and dry storage in dry cask storage systems (DCSSs). After about five years of cooling in spent fuel pools, the fuel assemblies are transferred into DCSSs, and the systems are filled with helium and sealed by welding. Deterioration of conditions inside of a DCSS is reflected in changes in the internal gas properties; this motivates the development of acoustic techniques to monitor internal gas properties, over extended storage periods, using sensors mounted on the exterior of the storage packages. However, a major challenge in collecting acoustic signals is the impedance mismatch between the steel canister shell and the gas. Only a small fraction of the ultrasonic signal can be transmitted through the gas medium. This paper documents experimental studies conducted on a full-scale canister mock-up to capture the gas-borne signals. Damping materials were pasted on the outside, and blocking and unblocking tests were conducted to identify the gas-borne signal. The results show that the excitation frequency plays an important role in maximizing the gas-borne signals. The gas-borne signal was successfully detected at around the theoretical time-of-flight. A high signal-to-noise ratio was achieved in the measurements. Next, the acoustic impedance matching layers were introduced, and the gas signal was drastically improved compared with that using no AIM layers.

Spent nuclear fuel (SNF), Canisters, Internal cond↗

Transmission Optimization with Grid Enhancing Technologies Fact Sheet

The Transmission Optimization with Grid Enhancing Technologies (TOGETs) project is funded by the Department of Energy (DOE) Office of Electricity (OE) and Wind Energy Technologies Office (WETO). Idaho National Laboratory’s TOGET Task force consists of regulatory, utility, system operator, vendor, academic, and other industry participants, who will guide a two-pronged approach to fill in gaps on publicly available knowledge of GETs and conduct a full scale, multi-faceted field exercise. Ultimately the project aims to test, verify, and validate deployed GETs in a transmission system.

24 POWER TRANSMISSION AND DISTRIBUTION↗

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↗

ACOPF Transmission Switching Using Open-Source MINLP Solvers

The optimal transmission switching (OTS) problem with AC physics represents a mixed integer non-linear non-convex optimization problem which can provide benefits to transmission level power system operations. In this paper we benchmark a set of open-source mixed integer non-linear programming (MINLP) solvers on the OTS problem with AC physics using the pglib set of power system test cases. Results characterizing the performance of the different solvers are reported and discussed.

ACOPF↗

Cooperation in Transmission Expansion Planning: Enhancing Grid Reliability and Efficiency Under a Changing Climate

Electricity grids are challenged to maintain reliability during more intense and frequent extreme weather events due to climate change. This challenge is exacerbated by multi-sector electrification and power sector decarbonization through increased reliance on variable renewable energy, which necessitates the expansion of transmission infrastructure. However, transmission expansion planning is often complicated by intertwined planning authorities and jurisdictions, and allocation of large capital investment needs. These factors cause authorities to manage transmission investments individually (i.e., only/mostly intraregional planning), which can lead to suboptimal transmission networks. This study investigates the potential benefits of cooperative transmission expansion planning (i.e., both intraregional and interregional planning that optimizes transmission investments across the entire physical system). Using sectoral and economic optimization, and machine learning models, it analyzes the impact of different levels of cooperation among transmission planning regions within U.S. Western Interconnection in 2019 and 2059 via an iterative investment process. Furthermore, it examines the effects of future climate change on transmission cooperation by simulating historical heat waves from 2019 under conditions of 2059. The results indicate that cooperative transmission planning leads to lower wholesale electricity prices, decreased energy outages, and reduced greenhouse gas emissions. However, the advantages of collaboration diminish during widespread heat waves, despite remaining beneficial especially for regions like California Independent System Operator with substantial solar installations. The study underscores the importance of transmission cooperation in reducing costs and enhancing reliability, emphasizing the need for strategic investments in storage to address challenges posed by future extreme weather events with varying spatial scales.

Capacity Expansion Model↗

Cooperative Transmission Expansion Planning Experiment Data and Results

GO WEST is an open-source power grid modeling framework for U.S. Western Interconnection, which allows users to tailor the model depending on their research study and science questions. It covers 28 balancing authorities (BA) and 12 states in U.S. Western Interconnection. GO WEST allows users to select different number of nodes and come up with a simplified network by utilizing 10,000 nodal topology of U.S. Western Interconnection created by Texas A&M University. Users can try and select different number of nodes, mathematical formulations (linear programming vs. mixed-integer linear programming), transmission line limit scaling factors, and hurdle rate scaling factors. GO WEST offers a unit commitment and economic dispatch (UC/ED) module to simulate grid operations on an hourly scale. In this sense, users can calibrate and validate their model versions by comparing model outputs to historical datasets. TEP is an open-source transmission capacity expansion model, built on GO WEST framework. It utilizes linear programming to optimize transmission capacity addition investment on existing lines within GO WEST framework. In this sense, TEP model only increases the thermal capacity of existing transmission lines and does not add new lines to the system, which leaves the topology preserved. TEP minimizes the total cost of the system which comprises the operational cost of satisfying electricity demand (i.e., generation cost), cost of loss of load (i.e., unserved energy), cost of power flow, and cost of new transmission capacity additions (i.e., investment cost). In order to use TEP model, users need to create scenarios with GO WEST framework. In this analysis, outputs from several models are used to create future inputs to GO WEST and TEP models, including GCAM-USA, TELL, CERF and reV. This dataset includes experiment inputs and outputs from three different transmission expansion scenarios (cooperative, intermediate, and individual) for 2019 and 2059. For 2019, a base scenario to illustrate the default (i.e., historical) power grid operations is also included. This study utilizes rcp45hotter_ssp3 scenario from a previous version of GCAM-USA simulations. Sources of the shapefiles in supplementary data are HIFLD Open and U.S. Energy Atlas. Please see the README file for a detailed description of the main and supplementary data.

Capacity Expansion Model↗

Investigation of potential driver modules and transmission lines for a high frequency power system on the space station

The objective was to assess the feasibility of using the Series Resonant inverter as the driver module for the high frequency power system on the Space Station. This study evaluates the performance of the Series Resonant driver when it was operated with a dc input voltage and run through a series of tests to determine its start-up performance, response to load changes, load regulation, and efficiency. Also, this study compares the Series Resonant driver to another kind of driver that uses a Power Transistor snubber. An investigation of the various types of transmission lines is initiated. In particular, a simplified approach is utilized to describe the optimal transmission line.

Brush, H. T.↗

IM3 Projected U.S. Western Interconnection Grid Stress Dataset

This dataset provides projected grid stress and reliability results (including all model inputs and outputs from GO WEST and TEP) for Integrated Multisector, Multiscale Modeling (IM3) Phase 2 simulations across eight different scenarios for the U.S. Western Interconnection through 2055. The scenarios include combinations of two Shared Socioeconomic Pathways (SSP3 and SSP5) with four high-resolution climate projections specific to the United States from a set of Thermodynamic Global Warming (TGW) simulations. These climate projections include "hotter" and "cooler" variants for two Representative Concentration Pathways (RCP4.5 and RCP8.5). The resulting eight simulations are: rcp45cooler_ssp3 rcp45cooler_ssp5 rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85cooler_ssp3 rcp85cooler_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 GO WEST is an open-source power grid modeling framework for the U.S. Western Interconnection, which allows users to tailor the model depending on their research study and science questions. It covers 28 balancing authorities (BAs) and 12 states in U.S. Western Interconnection. GO WEST allows users to select different number of nodes and come up with a simplified network by utilizing 10,000 nodal topology of the U.S. Western Interconnection (ACTIVSg10k). Users can select different number of nodes, mathematical formulations (linear programming vs. mixed-integer linear programming), transmission line limit scaling factors, and hurdle rate scaling factors. GO WEST offers a unit commitment and economic dispatch (UC/ED) module to simulate grid operations on an hourly scale. In this sense, users can calibrate and validate their model versions by comparing model outputs to historical datasets. TEP is an open-source transmission capacity expansion model, built on the GO WEST framework. It utilizes linear programming to optimize transmission capacity addition investment on existing lines within the GO WEST framework. The TEP model only increases the thermal capacity of existing transmission lines and does not add new lines to the system, which leaves the topology preserved. In order to use TEP model, users need to create scenarios with the GO WEST framework. Please refer to README file for a detailed description of the dataset including individual files and references.

Capacity Expansion Model↗

Investigation of potential driver modules and transmission lines for a high frequency power system on the space station

The feasibility of using Series Resonant Inverter as the driver module for high frequency power system on the Space Station was assessed. The performance of the Series Resonant Inverter that was used in the testing of the single-phase, 2.0-kw resonant AC power system breadboard is summarized. The architecture is descirbed and the driver modules of the 5.0 kw AC power system breadboard are analyzed. An investigation of the various types of transmission lines is continued. Measurements of equivalent series resistor and inductor and equivalent parallel capacitors are presented. In particular, a simplified approach is utilized to describe the optimal transmission line.

Brush, Harold T.↗

Design of An Improved Miniature Ion Neutral Mass Spectrometer for NASA Applications

The ion optics of NASA's Ion Neutral Mass Spectrometer (INMS) sensor was simulated with three dimensional models of the open source, the quadrupole deflector, the exit lens system and the quadrupole mass analyzer to design more compact models with lower weight. Comparison of calculated transmission with experimental results shows good agreement. Transmission analyses with varying geometrical parameters and voltages throw light on possible ways of reducing the size of the sensor. Trajectories of ions of mass 1-99 amu were simulated to analyze and optimize transmission. Analysis of open source transmission with varying angle of attack shows that the angular acceptance can be considerably increased by programming the voltages on the ion trap/ collimator. Analysis of transmission sensitivity to voltages and misalignments of the quadrupole deflector rods indicate that increased transmission is possible with a geometrically asymmetrical deflector and a deflector can be designed with much lower sensitivities of transmission. Bringing the disks closer together can decrease the size of the quadrupole deflector and also increase transmission. The exit lens system can be redesigned to be smaller by eliminating at least one electrode entirely without loss of transmission. Ceramic materials were investigated to find suitable candidates for use in the construction of lighter weight mass spectrometer. A high-sensitivity, high-resolution portable gas chromatograph mass spectrometer with a mass range of 2-700 amu has been built and will be commercialized in Phase 3.

Swaminathan, Viji K.↗

TOGETs Overview

The Transmission Optimization with Grid Enhancing Technologies (TOGETs) project will guide future research to fill in gaps on publicly available knowledge of Grid Enhancing Technologies (GETs) and conduct a full scale, multi-faceted field exercise. Ultimately the project aims to test, verify and validate deployed GETs in a transmission system.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Reinforcement Learning for Intentional Islanding in Resilient Power Transmission Systems

Intentional islanding is the process of identifying and deliberately decomposing the transmission network to form self-sustained islands from an endangered network during disruptions to improve resilience and security. Most existing intentional islanding models are offline resilience decision tools and hence do not provide outage responses in a timely manner. In this paper, a reinforcement learning (RL) based model for intentional islanding is developed, which offers real-time switching control, online deployability, and adaptability to varying system conditions. The intentional islanding process is formulated as a Markov decision process, where the optimal transmission switching policy is learned using the RL approach. The control policy is learned over an environment that encompasses a Power System Simulator for Engineering (PSS/E) model of the transmission network, facilitated by an interface to the standard openAI Gym framework. The proposed RL-based methodology aims to form stable and self-sustainable islands by ensuring voltage stability while reducing the power mismatch in the formed islands. A proximal policy optimization algorithm is designed, which is suitable for controlling the on/off status of the switches with multi-layer perceptron as value and actor networks. The effectiveness of the proposed framework in the self-recovery of the grid by island formation is applied on the modified IEEE 39-bus test network and validated by dynamic simulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Real Time and Forecasted DLR: Use Cases

This document contains two use cases for the utilization of dynamic line ratings in operational practices. These use cases were developed in support of the Transmission Optimization and Grid Enhancing Technology (TOGETS) task force aimed at facilitating the deployment of dynamic line rating and power flow technologies across the United States. The project included a demonstration of the technology on the Idaho National Laboratory system. The procurement documents, tests scripts, and field-validated results from multiple vendors will be published to improve industry’s familiarity with the products. Because of the urgency in performing this work and INL’s unique test setup, the Task Force identified a need for additional public information on the information exchanges required for operationalizing DLR (i.e. integrating DLR’s within actual system operations). As such, this use case document and an accompanying interoperability profile have been developed with input from the Task Force and various vendors whose systems would ultimately support the functionality described. The use case and interoperability profile support two distinct functionalities with different purposes.

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Evaluating grid stress and reliability in future electricity grids across a range of demand, generation mix, and weather trends

The reliability of power grids in the future will depend on how system planners account for the integration of new technologies, extreme weather events, and uncertainties in demand growth from increased electrification and data centers. This study introduces an open-source, multisectoral, multiscale modeling framework that projects grid stress and reliability trends between 2020 and 2055 in the Western Interconnection of the United States. The framework integrates global to national energy-water-land dynamics with power plant siting and hourly grid operations modeling. We analyze future wholesale electricity price shocks and unserved energy events across eight scenarios spanning a range of population growth and economic change, generation mixes, and weather conditions. Our results show future grids with high percentage of non-renewable generation and strong economic growth are characterized by higher reliability and lower wholesale electricity prices than lower growth scenarios because of larger reliance on dispatchable generators and lower fossil fuel extraction costs. Scenarios with high percentage of renewable resources have lower median but more volatile wholesale electricity prices as well as more frequent and severe unserved energy events compared to scenarios relying more on dispatchable generators. These events occur because higher proportion of solar and wind energy causes net demand curves to deepen during midday (duck curves get progressively severe), exacerbating the challenge of meeting demand during summer evening peaks. This study suggests that robust and co-optimized transmission and energy storage planning could help maintain low wholesale electricity prices and high reliability levels in future electricity grids across uncertainties in generation mixes.

Electric grid reliability↗

DLR Calculation Engine Interoperability Profile: A Project for the TOGETs Task Force

This document contains a detailed interoperability profile outlining how the components of the CIM can be used to implement DLR in support of the two use cases identified in the Use Case Document. An interoperability profile is defined by the Smart Electric Power Alliance (SEPA) as a document that “specifies standards-based requirements for interfaces or applications that are accepted by the user-community, testing authorities, and other stakeholders. It specifies configuration options within a standard’s requirements sufficient to deliver the desired level of interoperability and functionality.” To that end, this document is not in itself a standard, nor has it been approved by any formal body. Rather, it is an example of how to implement the CIM standard and has been reviewed by industry, including transmission operators and DLR vendors.

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reVRt (reV Routing) [SWR-25-112]

The reV Routing (reVRt) tool is a computational framework for modeling and optimizing transmission infrastructure requirements for electrical grid connections. By employing a spatially-aware least-cost-path methodology, it allows users to incorporate a wide range of factors including siting constraints, regional component costs, land composition costs, point-of-interconnection costs, and network upgrade costs. Additionally, the tool enables advanced follow-on analyses, such as land characterization for potential transmission line routes, to support informed decision-making. Although it's designed to integrate seamlessly with the reV model, the reV Routing tool is versatile and can also be utilized independently for standalone analyses in transmission planning and resource assessment scenarios.

Pinchuk, Pavlo (Paul) [National Renewable Energy L↗

Optimizing Power Line Undergrounding Decisions under Varying Wildfire Risk and Weather Scenarios

Abstract—The threat of wildfire ignitions from electric power equipment has led utilities to increasingly turn to preemptive power shutoffs, which, while effective in reducing grid-induced wildfire risk, can cause significant load loss. Undergrounding power lines is an alternative strategy for preventing grid-induced wildfires. However, undergrounding lines is costly, so an efficient undergrounding plan must balance reductions in wildfire risk and load loss with the cost of undergrounding lines. We propose a robust optimization model to identify which power lines to underground to maximize load served while limiting wildfire risk across a range of wildfire risk and weather scenarios. Since solving this problem may be computationally heavy for large power grids and many operating scenarios, we present a delayed constraint generation algorithm to iteratively add scenarios until an optimal solution is found. We evaluate the performance of this framework on the RTS-GMLC with scenarios representing a year of operating conditions and compare it with a stochastic programming formulation. Our results indicate that our undergrounding model is successful in reducing load shed and risk compared to baseline cases in which no mitigation action is taken and only power shutoffs are implemented (no undergrounding). The robust formulation also reduces more load shed than the stochastic formulation in the most extreme scenarios. Index Terms—grid resilience, optimization, transmission systems, underground power lines, wildfire risk.

Taylor, S. [Department of Electrical and Computer ↗