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DOE OSTI2022

Verification and Validation of START: A Spent Nuclear Fuel Routing and Decision Support Tool

The Stakeholder Tool for Assessing Radioactive Transportation (START) is a web-based geospatial decision-support tool being developed by the US Department of Energy’s Office of Integrated Waste Management (IWM) to support federal interim storage for spent nuclear fuel (SNF) and associated transportation. START provides many functions for the IWM program including: serving as a communications tool for conveying geospatial data and information, an options analysis tool for exploring potential transport modes and routes for transporting SNF from nuclear power plants to future federal interim storage facilities, an emergency response planning tool for Tribes and States to identify training needs along potential SNF transport corridors, an environmental analysis tool for estimating potential radiation dose exposure from incident-free and incident-case SNF transport conditions, and a systems analysis support tool providing route-related inputs for system throughput analysis. As part of the START development process, a verification and validation (V&V) effort is being undertaken. In the initial V&V phase, several outputs of the START tool were checked such as the total distance, population and population densities within the buffer zone, and incident free dose. The V&V process is fluid as it will be utilized after each version change to ensure that the core functionalities of the tool are maintained and the results are consistent with the previous versions. Efforts have also been put into developing scripts to aid in the process of automating certain sections of the V&V work. As some of the V&V efforts use Environmental Systems Research Institute’s (ESRI) tools upon which the START framework is built, the START data were compared against outputs from tools like Quantum Geographic Information System (QGIS) for buffer zone populations and route lengths to ensure independence of the V&V process. Good agreement was observed between the START results and the independent V&V studies with the majority of the differences falling between 1% and 5% for populations within the buffer zone and route distance. This presentation describes the development and design of the START tool, V&V methods employed for various metrics of interest and their respective results, and future plans.

START, transportation, GIS, V&V↗

DOE OSTI2024

CO2 Transport Planning Database

The CO2 Transport Planning Database v3.0 is a geospatial resource, containing over 70 gigabytes of data representing critical considerations for the spatial routing of pipelines and transport of CO2, from source to sink. Considerations include state-specific legislation, land use requirements, existing infrastructure, and hazard prevention areas. Built to support strategic domestic energy transport planning and development, more than 60 layers of this database have been weighted (Weight fields) according to current legislation and pipeline construction recommendations. Weighted values range from zero to one, where zero represents potentially more acceptable areas for transport based on the various considerations, and a value of one represents areas that should be avoided. This geospatial database provides a baseline for the Smart CO2 Transport Planning Tool.

Boundaries↗

DOE OSTI2021

ADDS-EVS: An agent-based deployment decision-support system for electric vehicle services

Rapid and sustainable development of the electric vehicle (EV) industry places the requirement for the plan of EV deployment. For public EV, existing models mainly focus on the charging facility design and fail to capture the multi-modal scenarios. In this work, we develop an agent-based decision-support system for multi-modal electric transits to locate the optimal combinations of key parameters, including the fleet size, the transit schedule, the charging facility design, and the routing strategy. We demonstrate the utilities of our system by simulating public EV services deployed to serve travel needs related to a transportation hub in New York City. To support the decision of the fleet size, we summarize system-level performances including the total satisfied demand, passengers' waiting time, vehicle idling time. The spatial and temporal patterns are extracted to serve a deeper understanding of system dynamics and service quality. Finally, we investigate the interaction between the fleet size design and the routing strategy. The results suggest a necessity of integrating the operation strategies into the planning phase.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Integrated Resource Supply-Demand-Routing Model for the COVID-19 Crisis

As part of the Department of Energy response to the novel coronavirus disease (COVID-19) pandemic of 2020, a modeling effort was sponsored by the DOE Office of Science. Through this effort, an integrated planning framework was developed whose capabilities were demonstrated with the combination of a treatment resource demand model and an optimization model for routing supplies. This report documents this framework and models, and an application involving ventilator demands and supplies in the continental United States. The goal of this application is to test the feasibility of implementing nationwide ventilator sharing in response to the COVID-19 crisis. Multiple scenarios were run using different combinations of forecasted and observed patient streams, and it is demonstrated that using a "worst-case forecast for planning may be preferable to best mitigate supply-demand risks in an uncertain future. There is also a brief discussion of model uncertainty and its implications for the results.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Atlantic Offshore Wind Transmission Study

The Atlantic Offshore Wind Transmission Study (AOSWTS) is part of the U.S. Department of Energy's (DOE) efforts to understand and facilitate the transmission of electricity from wind in the Atlantic Ocean. It was informed by the Atlantic Offshore Wind Transmission Literature Review and Gaps Analysis (Bothwell et al. 2021) and the convening workshops hosted in 2022-2023 by DOE and the U.S. Department of the Interior's Bureau of Ocean Energy Management. The study results help to inform An Action Plan for Offshore Wind Transmission Development in the U.S. Atlantic Region (Baker et al. 2023). DOE's Wind Energy Technologies Office funded AOSWTS. The AOSWTS identifies and evaluates pathways to enable offshore wind energy deployment in the Atlantic Ocean through coordinated offshore transmission solutions in the near term (by 2030) and long term (by 2050). The study fills gaps in prior analyses by providing a multiregional planning perspective that evaluates offshore wind generation development with transmission planning. It incorporates environmental, ocean co-use, and other siting considerations into defining potential offshore transmission routes. The study also compares different multiregional offshore transmission topologies and their associated costs (using potential cable routes) and benefits (in terms of production cost savings and enhanced resource adequacy). In addition, the AOSWTS analyzes reliability impacts from a multiregional perspective. The study provides guidance for policymakers and transmission stakeholders on possible outcomes resulting from a proactive, coordinated, and interregional approach to transmission planning for offshore wind energy development in the Atlantic. While this study presents possibilities, additional work following system operator methods and procedures can help build on this analysis.

17 WIND ENERGY↗

DOE OSTI2024

Atlantic Offshore Wind Transmission Study: Executive Summary

17 WIND ENERGY↗

DOE OSTI2023

Investigating a Renewable-Resource-Targeting Mobile Aquaculture System Using Route Optimization Based on Optimal Foraging Theory

Aquaculture systems require careful consideration of location, which determines water conditions, pollution impacts, and hazardous conditions. Mobility may be able to address these factors while also supporting the targeting of renewable energy sources such as wind, wave, and solar power throughout the year. In this paper, a purpose-built mobile aquaculture ship is identified and modeled with a combination of renewable energy harvesting capabilities as a case study with the objective of assessing the potential benefits of targeting high renewable energy potentials to power aquaculture operations. A route optimization algorithm is created and tuned to simulate the mobility of the aquaculture platform and cost-basis comparisons are made to a stationary system. The small spatial variability in renewable energy potential when combining multiple resources significantly limits the benefits of a mobile, renewable-targeting aquaculture system. On the other hand, the consistent energy harvest from a blend of renewable energy types (13 kW installed wind capacity, 661 m 2 installed solar, and 1 m characteristic width wave-energy converter) suggests that the potential benefits of a mobile platform for offshore aquaculture (mitigation of environmental and social concerns, any potential positive impact on yields, hazard avoidance, etc.) can likely be pursued without significant increases in energy harvester costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

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↗

DOE OSTI2020

Cooperative Routing for an Air–Ground Vehicle Team—Exact Algorithm, Transformation Method, and Heuristics

This paper considers a cooperative vehicle routing problem for an intelligence, surveillance, and reconnaissance mission in the presence of communication constraints between the vehicles. The proposed framework uses a ground vehicle and an unmanned aerial vehicle (UAV) that travel cooperatively and visit a set of targets while satisfying the communication constraints. The problem is formulated as a mixed-integer linear program, and a branch-and-cut algorithm is developed to solve the problem to optimality. Furthermore, a transformation method and a heuristic are also developed for the problem. The effectiveness of all the algorithms is corroborated through extensive computational experiments on several randomly generated instances. Furthermore, this paper is motivated by an intelligence, surveillance, and reconnaissance mission involving a single unmanned aerial vehicle (UAV) and a ground vehicle, where the vehicles must coordinate their activity in the presence of communication constraints. The combination of a small UAV and a ground vehicle is an ideal platform for such missions, since small UAVs can fly at low altitudes and can avoid obstacles or threats that would be problematic for the ground vehicle alone. This paper addresses the coordinated routing problem involving these two vehicles and presents an algorithm to obtain an optimal solution for this problem, fast heuristics to obtain good feasible solutions, and also a transformation method to transform any instance of this cooperative vehicle routing problem to an instance of the one-in-a-set traveling salesman problem.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Autonomous Navigation and Control of UGVs' in Nuclear Power Plants - 20381

The purpose of the husky A200 ground robot is to autonomously navigate through the places where it is very hazardous for human beings to reach and operate, like nuclear power plants, chemical industries. The aim is to navigate the ground robot autonomously with an Arm mounted on the robot along with the different sensors as camera, and Lidar. The autonomous motion of the robot is controlled by the controller which uses path planner for trajectory generation of the robot. The mission planner uses the current position of the husky A200, given the way points of the initial and the destination it would extract a best possible route based on the current events provided using GMapping. The global reference frame is used for planning the way points. Creating the appropriate path and the actions required to follow the path are given by the motion planner. The motion planner depends on the active sensor data such as obstacles, lanes, based on the sensor data feasible path is generated. Feasibility of the path is determined by the dynamics of the husky and a series of points generated with certain velocity and acceleration profile. The controller adjusts the lateral, longitudinal and yaw motion of the husky to command the behaviors. The kinematic model is developed for kinematic motion of the husky and the dynamic model is developed for transient and steady state characteristics. The images and other type of data captured by the camera are processed through the computational framework used to build machine learning models. TensorFlow will be used for deep learning and to identify and classify different objects around the husky. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2026

System-of-systems optimization of hydrogen infrastructure for heavy-duty freight corridors: The interstate 10 case study

Medium and heavy-duty freight transportation requires hydrogen energy infrastructure that is cost-effective, operationally reliable, spatially coherent, and resilient to demand variability along major corridors. This paper presents an integrated hydrogen corridor planning framework using Oak Ridge National Laboratory's OR-AGENT that couples freight-driven, route-resolved hydrogen demand modeling with optimized station siting, sizing, and station-level techno-economic analysis. The framework is demonstrated for the Interstate 10 freight corridor and the Houston-to-Los-Angeles region. Hydrogen demand is derived from high-resolution origin–destination freight data, duty-cycle characterization, and physics-based energy consumption modeling. Candidate refueling sites are selected from existing heavy-duty diesel fueling locations and optimized subject to onboard storage and station capacity constraints. Resulting station throughputs are evaluated using established techno-economic models for electrolytic hydrogen production and dispensing infrastructure. Results show that a regional, portfolio-level aggregation, average dispensed electrolytic hydrogen cost of $6.87–$7.26/kg is currently feasible, and is strongly influenced by demand density and utilization.

Sujan, Vivek [ORNL] (ORCID:0000000269882342)↗

DOE OSTI2020

An Optimization-Based Planning Tool for On-Demand Mobility Service Operations

Regions worldwide are adopting and exploring low-speed automated electric shuttle (AES) service as an on-demand shared mobility service in dense geofenced urban areas. Building on this concept, the National Renewable Energy Laboratory (NREL) recently developed the Automated Mobility District (AMD) toolkit. The AMD toolkit—comprising of a travel micro-simulation model and an energy estimation model—estimates the mobility and energy impacts of a given shuttle configuration within an AMD. Early-stage AMD deployments need to find optimal operational configurations that include: (a) passenger capacity of an AES, (b) time-dependent routes, and (c) fleet size (AES units) to satisfy the demand for the region. This research extends the AMD toolkit functionality by developing an optimization-based planning module that will assist in the operations of AES units. We developed a constrained mixed-integer program accounting for passenger waiting time, battery range, and passenger capacity of AES units. For scalability, we demonstrated the Tabu search-based solution technique for a real-world network—a proposed AMD deployment in Greenville, South Carolina, USA. Compared to rule-based operations, our developed solution yields higher travel time and energy savings for the network at different demand levels. The sensitivity analyses for waiting time thresholds indicate nonlinearity in the system performance, underscoring the need to meet shared-use mobility user-level expectations. The developed optimization framework can be adapted and extended to accommodate different categories of shared-use on-demand mobility services.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

DOE OSTI2022

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (4th Annual Report)

Nuclear plant sites collect and store large volumes of data collected from various equipment and systems. These datasets typically include plant process parameters, maintenance records, technical logs, online monitoring data, and equipment failure data. The collection of such data affords an opportunity to leverage data-driven machine learning and artificial intelligence technologies to provide diagnostic and prognostic capabilities within the nuclear power industry to reduce operating and maintenance costs. In this way, nuclear energy can become more economically competitive with other energy sources, and premature closures can be avoided. From a maintenance standpoint, savings can be achieved by leveraging machine learning and artificial intelligence technologies to develop data-driven algorithms to better diagnose and predict potential faults within the system. Improved model accuracy can lead to reductions in unnecessary maintenance and more efficient planning of future maintenance, thus lowering the costs associated with parts, labor, and unnecessary planned, forced, or extended outages. From an operations perspective, cost savings can be generated by shifting from route-based monitoring to wireless technologies for online monitoring, and by transitioning from onsite- to cloud-based computing and storage services. Wireless monitoring would reduce the operator manhours required for taking routine measurements, while cloud computing services would generate cost savings by reducing the amount of hardware needing to be purchased and maintained—all while scaling to both computational and storage demands. This report summarizes this project’s effort to shift from costly, labor-intensive preventative maintenance to cheaper predictive maintenance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Multi-Sensor Optimal Motion Planning for Radiological Contamination Surveys by Using Prediction-Difference Maps

Distributed and networked mobile sensor platforms using unmanned aerial and/or ground vehicles to survey areas of interest offer a safer and more efficient method for radiological contamination mapping; however, most applications rely on uniformly sweeping of the area in a raster-type motion without utilizing the information available in a dynamic sense. We have developed a fully autonomous optimal motion planning procedure for networks with two or more mobile sensors. The procedure utilizes well-established concepts of Gaussian processes in combination with control laws based on centroidal Voronoi tessellations to achieve optimal next-iteration sensor movements. A new method of informing optimal motion planning is proposed, whereby the absolute difference between the prior and current full-map prediction, referred to as the prediction-difference map, is used as the spatial density function within each Voronoi cell, providing immediate and iterative feedback for dynamic use of available information. The Gaussian process regression model used to estimate the contamination in unvisited locations also provides prediction uncertainties, and can be used as a quantitative metric to assess the confidence in the calculated contamination map; these estimates and prediction uncertainties are unavailable for standard uniform survey routines as they can only produce maps in the vicinity of observed locations. We present through simulation the achievable performance gains from using this new method by directly comparing to a uniform survey method. Results show that using the prediction-difference maps to inform motion planning procedures offers a faster rate of producing an accurate and convergent map relative to a uniform survey route.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

The Importance of Inventory for Informing Waste Strategy - 20280

A waste inventory aims to present the best available information about the nature, quantity and location of radioactive wastes and materials at a facility, site or in a country. Quality inventory data is necessary for developing appropriate strategies and plans for managing radioactive wastes and materials, with high standards of protection for people and the environment. The inventory of wastes will change over time due to a wide range of technical, commercial or policy reasons, for example, if reactor lifetimes are extended, if decommissioning strategies change or if new technologies are introduced for managing waste. Inventory information can also improve as better waste characterisation data becomes available. At a facility or site level, good quality inventory data are needed to underpin decision making about the waste management options and to demonstrate compliance with the chosen treatment or disposal route. At a site and country level, knowledge of the inventory can drive the improvement to waste strategy; waste management asset and service planning; and implementation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Nanostructured Alumina Forming Austenitic Alloy (NAFA) Production Using Mechanical Alloying and High-Temperature Consolidation

Alumina-forming austenitic (AFA) alloys are a promising class of nuclear materials because of their high-temperature oxidation/corrosion resistance and mechanical properties. Unfortunately, these alloys are limited in use for core material applications, and they are specifically limited for use as nuclear fuel cladding because of their high Ni-transmutation and helium generation rate in-service. Improving these alloys through a fine dispersion of oxide precipitates and thus increasing the effective irradiation sink strength of the alloy system may mitigate many of the degradation phenomena expected during alloy deployment. These phenomena include high-temperature helium embrittlement and cavity swelling for lead-cooled fast reactor applications. This work effort uses combination of conventional and advanced manufacturing approaches are being used to fabricate nanostructured AFA (NAFA) materials. As the first objective of this initiative, a conventional AFA was modified using mechanical alloying and extrusion to alter the precipitation characteristics to include a fine dispersion of nanoscale oxides intended to serve as traps for irradiation-induced point defects and transmuted He within the lattice. This analysis compared the efficacy of the conventional mechanical alloying and extrusion approach with the unalloyed AFA consolidated approach using hot isostatic pressing (HIP). It was found that, although the mechanical alloying approach is successful in producing a fine distribution of oxides within the first nanostructured AFA (NAFA-1), the distribution is heterogeneous because of the mild milling parameters used to prevent cold welding of powder to the spherical milling media. The additional dispersion of oxides, coupled with a higher volume fraction of other secondary phases in the NAFA-1, produces higher alloy strengths that range up to 600°C in comparison to the unalloyed HIP AFA, thus exceeding the operating temperature of lead-cooled fast reactors. However, the strength of the NAFA-1 is lower than that of the HIP AFA at 800°C, which is presumed to be a function of increased secondary phases from the nonoptimized AFA chemistry. Future work is planned on a newly procured NAFA-2 chemistry that is more suitable for advanced reactor applications exploring new manufacturing routes.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Time-Dependent Electric Bus and Charging Station Deployment Problem

Battery electric buses (BEBs) have gained popularity due to their emission-free and energy-efficient features. Many transit authorities worldwide have set goals to gradually replace their bus fleets with BEBs. Considering the potential decline in BEB battery and charger prices, this study proposes a time-dependent bus fleet transition model to determine the optimal bus fleet transition plan, which includes selecting the bus lines to be electrified, determining the timing and type of BEBs to be purchased, and deploying on-route fast chargers and depot chargers. The model is a bi-objective integer linear program that considers the trade-off between electrified transit mileages and bus electrification costs. A normalized normal constraint method is applied to solve the bi-objective optimization model. The effectiveness of the proposed model is tested using a real-world bus network. Additionally, sensitivity analyses are conducted to better understand the impact of different parameter values on the optimal solutions. Our proposed model can provide transit authorities with a powerful tool to make informed decisions about their BEB fleet replacement plans.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Integrated Mid-Continent Stacked Carbon Storage Hub Project Phase II (Final Summary Report)

The Phase II Integrated Midcontinent Stacked Carbon Storage Hub (IMSCS-HUB) is part of the Carbon Storage Assurance Facility Enterprise (CarbonSAFE) established by the United States Department of Energy (DOE) National Energy Technology Laboratory (NETL). CarbonSAFE is phased to support the development of commercial-scale (50 million metric tonnes [Mt] over a 30-year period) carbon capture, utilization, and storage (CCUS) in the United States. The IMSCS-HUB study area comprises carbon dioxide (CO 2 ) sources in Iowa, Kansas, and Nebraska (the source corridor), and CO 2 sinks in Kansas and Nebraska (the storage corridor), representing the first large-scale project for the Midcontinent region. The stacked storage corridor is characterized by alternating sequences of deep saline formations, oil-bearing reservoirs, shale, and evaporite units that are conducive to vertically stacked CO 2 injection for geologic storage and enhanced oil recovery (EOR). Three sites within the IMSCS-HUB stacked storage corridor were evaluated in Phase II for commercial CCUS feasibility: one in southwest-central Nebraska, Sleepy Hollow Field (SHF), a second in southwestern Nebraska near Madrid (Madrid), and a third in southwestern Kansas, the Patterson Site (composed of the Patterson, Heinitz, Hartland, and Oslo fields). In Phase II, the team assessed the feasibility of storage complexes at the potential storage sites in Nebraska and Kansas to support a commercial-scale storage hub that integrates proven CO 2 capture technology and transport from nearby ethanol sources. Building on lessons learned from the DOE-NETL Regional Carbon Sequestration Partnerships (RCSPs), the Project Team has identified a clear strategy to meet DOE’s 2025 objective of commercial carbon capture and storage (CCS) implementation by developing a CO 2 market and infrastructure that relies on multiple ethanol-based CO 2 sources in the short term and the incorporation of multiple coal-fired power plant CO 2 sources when commercial capture is economically viable. The team also leveraged the updated 45Q tax credit to develop capture and transport infrastructure. Commercial-scale CCUS is feasible at two candidate storage sites studied, the Madrid, Nebraska Site and the Patterson Site in Kearny County, Kansas. The Sleepy Hollow Field in Nebraska was found to be an attractive candidate for stacked storage with CO 2 -EOR (Battelle 2020e). Outreach efforts facilitated engagement from industry, government, and research sectors (Battelle and GPI, 2020) and an outreach plan for future phases of the project was developed to address issues that are of concern in the IMSCS-HUB project area (Battelle, 2020f). All components of a CCUS project were determined to be feasible in the IMSCS-HUB region and Risk Mitigation Plan was developed and includes strategies to mitigate risks associated with each project component (Battelle, 2020j). A roadmap was developed to obtain the required UIC permits for an integrated CCUS project (Battelle, 2020k). The regional storage resource characterization demonstrated significant opportunity for commercial-scale projects in the IMSCS-HUB storage corridor with 577.4 Mt of stacked CO 2 storage capacity and the potential to produce 181.9 MMbbls of oil via EOR across 17 individual storage areas (Battelle and ARI, 2020). The pipeline assessment study found viable pipeline routes that connected 45Q-eligible ethanol plants, coal fired power plants, and other sources in the IMSCS-HUB corridor. The comprehensive results of subsurface characterization, modeling efforts, outreach assessment, and regulatory analysis from were integrated to develop a Detailed Commercial Development Plan for the IMSCS-HUB (Battelle, 2020n). Commercialization efforts will involve obtaining Class VI UIC permits, establishing and finalizing the pipeline route, and evaluating capture projects at participating CO 2 sources. Phases I and II of the IMSCS-HUB CarbonSAFE provide a strong foundation for safely, efficiently, and cost-effectively characterizing and permitting commercial-scale project sites in the region. The plan for implementation of commercial-scale CCUS projects in the IMSCS-HUB is aligned with the objectives of CarbonSAFE Phase III: Site Characterization and CO 2 Capture Assessment.

20 FOSSIL-FUELED POWER PLANTS↗