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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

TSDC: Transportation Secure Data Center: Real-World Data for Planning, Modeling, and Analysis

The Transportation Secure Data Center is a centralized repository for high-resolution transportation data from hundreds of travel and transit surveys and studies. It makes vital transportation data broadly available to users while preserving the privacy of survey participants. It houses surveys and studies conducted by state departments of transportation, metropolitan planning organizations, transit agencies, cities, and other public agencies. Meanwhile, the Livewire Data Platform empowers research, industry, and academic partners to easily and securely preserve, maintain, share, discover, and gain access to transportation and mobility data. Livewire accommodates a range of datasets, including behavioral, experimental, model, analytical, and raw data at the vehicle, traveler, and system levels. Datasets support mobility research and planning spanning urban science, connected and automated vehicles, fueling and charging infrastructure, mobility decision science, multimodal transportation, vehicle efficiency, and more.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Energy to Communities Program Short Introduction: Evaluation of Enhanced Geothermal System for Cornell District Heating [Slides]

Through the U.S. Department of Energy's Energy to Communities (E2C) program, NLR, other national laboratory experts, and select organizations provide Expert Match - free, short-term technical assistance to address near-term energy challenges and questions. Expert Match is for community stakeholders who have decision-making power or influence in their community but need access to additional energy expertise to inform key upcoming decisions. This Expert Match request supported Cornell University (Itaca, NY) community efforts in developing Enhanced Geothermal System (EGS) reservoir performance, by simulating fracture flow to estimate pressure drop, flow distribution, and heat extraction efficiency in fractured rock. Thermal drawdown modeling was used to assess long-term resource sustainability, while system optimization studies refined key parameters such as well configuration and flow rate to improve overall reservoir efficiency.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Distribution Capacity Expansion Planning: Current Practice, Opportunities, and Decision Support

The distribution utility industry and its engineers are experiencing monumental shifts in consumer needs and expectations. Characterizing future native loads as compared to net load demand for long-term capacity planning is especially difficult, as consumers are increasingly adopting prosumer technologies. This paper is the culmination of 5 months of utility interviews coordinated by the National Renewable Energy Lab (NREL) and Kevala, Inc. (Kevala) to better understand distribution capacity planning challenges. The interviews covered all aspects of capacity planning including load and DER forecasting, criteria for assessing system constraints, solution types, and organizational and decision-making structures. Our intent is to provide insight into distribution capacity planning decision support needs for utilities and the increasing number of stakeholders involved, from state and regulatory agencies to community and solution providers with interest in increasing their understanding in the distribution capacity planning process.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Steady-State Analysis of Microgrid Distributed Control Under Denial of Service Attacks

Distributed control is an effective method to coordinate multiple components in a microgrid. It usually has mandatory requirements for communication graph topology; changing the topology unexpectedly could disrupt the operation of a distributed controller. A denial of service (DoS) attack blocks communication channels to cause variations in communication graph. Unforeseen topology changes render a controller to receive incomplete system information. Control decisions made based on incorrect information can lead to significantly reduced grid supporting capability. As a result, the vital power balance between generation and load may be challenged. Determining power balance requires steady-state power flow analysis; however, the cyber-induced steady-state analysis is not well studied in the literature. Here, in this article, we show that a cyber–physical microgrid with unknown DoS attacks has augmented uncertain power flow equations, whose special structure creates new computational issue to determining power balance. To deal with the issue, we develop a novel cyber-induced microgrid model: the cyber–physical system is transformed into a pure physical model, where the disruption effects of DoS in the cyber layer are shown to be equivalent to the variations of the physical model parameters. With the transformed system, we develop a sufficient condition to ascertain the system power balance under unknown DoS attacks. In addition, practical insights for resilient distributed control design are shown as well.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Applying Particle Swarm Optimization and Extended Kalman Filtering to Model Kaplan Generation Dynamics for Hydropower Systems

Variable renewable generation is increasing the need for hydropower plants to provide fast and flexible grid support, which places new demands on plant-level dynamic models used for monitoring, control, and operational decision-making. This need is especially important for hydroelectric systems, where turbine and generator dynamics are strongly coupled, nonlinear, and time-varying, making accurate real-time representation difficult. To address this problem, this paper develops a digital twin (DT) framework for a synchronous generator–Kaplan turbine system using an explicit separation of slow turbine dynamics and fast generator dynamics. The turbine subsystem is represented by a six-coefficient model, whose parameters are identified offline using particle swarm optimization, while the generator subsystem is updated online through an extended Kalman filter for real-time state and parameter estimation. These models are integrated within a closed-loop simulation that includes a proportional–integral–derivative–double-derivative governor and excitation system, allowing the DT to track plant behavior under realistic operating conditions. Unlike prior studies that treat turbine and generator modeling separately or rely mainly on simulated inputs, the proposed framework is validated using real operational data from a hydropower plant. Results show that the DT reproduces terminal voltage, active power, and reactive power with a normalized root mean square error of approximately 5%. This hybrid offline–online formulation constitutes the main contribution of the work, providing an adaptive and practically deployable DT for hydropower systems with direct relevance to control improvement, performance monitoring, and grid-support applications under high renewable penetration.

13 HYDRO ENERGY↗
DOE OSTI2023

Estimate long-term impact on battery degradation by considering electric vehicle real-world end-use factors

Many estimates of battery capacity degradation are based on accelerated lab tests that involve charge-discharge cycles or rely on data or electrochemical modeling. These methods are reasonable for technology benchmarking but rarely consider real-world end-use factors. To address this issue, this study develops the Battery Run-down under Electric Vehicle Operation (BREVO) model. It links the driver's travel pattern to physics-based battery degradation and powertrain energy consumption models. The model simulates the impacts of charging behavior, charging rate, driving patterns, and multiple energy management modules on battery capacity degradation. It finds that, over a 10-year timespan, firstly, for a random driver situated in the New England area, daily direct-current fast charging (60 kW) could lead to up to 22% less battery capacity when compared to daily Level-1 charging (1.8 kW). Second, the battery thermal management system can delay battery degradation by approximately 0.5% in the New England area. Third, warmer ambient temperatures enhance BEV battery usage. The model indicates that the battery capacity in the Los Angeles area is 6% higher than that in the New England area. The BREVO model provides crucial information for consumers and BEV manufacturers on range anxiety, BEV battery design, and decision support of battery warranty.

25 ENERGY STORAGE↗
DOE OSTI2023

Inventory of Public Key Cryptography in US Electric Vehicle Charging

Electric vehicles (EVs) and charging infrastructure are networked systems, which employ high-level communications in support of charging and grid service decisions. Public key cryptography (PKC) underlies much of the security and privacy protections of the information exchange. We are entering a new epoch where quantum computing threats must be seriously considered. A sufficiently large quantum computer, so named Cryptographically Relevant Quantum Computer (QRQC), will be able to perform the mathematical operations to efficiently attack the underpinnings of traditional PKC, thus jeopardizing the digital foundations for trust, communications security, and data security. Estimates suggest a QRQC can break public key encryption and digital signatures in the manner of tens to hundreds of hours, compared to traditional computing that would demand more than 10 18 years in a brute force-style attack. A consensus belief of quantum theorists, quantum experimenters, and cryptographers suggest that the quantum threat will be likely realized in the next twenty years. To address the threat, post-quantum cryptography, which is cryptosystems that are designed to be secure against both traditional and quantum computing threats, must be adopted. Migration from traditional PKC to quantum-resilient cryptography is a global undertaking and likely represents the largest transition in computing history. The nascent state of EV public key infrastructure, combined with limited adoption of the vehicle secure charging features, presents an opportunity to establish a preference for quantum-resistant cryptography as a step on the migration path. Delays will stunt the efforts as rapidly accelerating EVs sales and huge infrastructure investments will create large growing bases of long-lived vehicles and infrastructure. Migration preparations can commence while NIST continues the process to standardize post-quantum cryptography (PQC), which are quantum-resilient algorithms designed to be secure against traditional and quantum computing threats. The first step in preparing EV charging is to identify the presence of traditional public key cryptography algorithms and applications. With this objective in mind, this report is intended to advise the vehicle manufacturers, charging station manufacturers, charging station operators, charge network providers and other EV charging stakeholders with information on traditional PKC application and the potential risks when PKC becomes insecure. This report, the first in a series of reports discussing the topics existing at the confluence of post-quantum cryptography adoption and EV charging, identifies traditional public key applications employed and identifies potential consequences of leaving EV charging infrastructure vulnerable to quantum computing. The focus remains squarely on the of EV charging and infrastructure with respect to PKC and is believed by the authors to complement the NIST SP 1800-38 Migration to Post-Quantum Cryptography. While the report is centered on infrastructure, there are implications to vehicles.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

NREL CITIES Support (CRADA CRD-14-00558 Final Report)

The Technical University of Denmark has received a Grant from the Danish Council for Strategic Research for the project "CITIES - Centre for IT-Intelligent Energy Systems in Cities", a strategic research centre, which involves industrial partners and universities. The Alliance for Sustainable Energy, LLC will participate with DTU as an industrial partner in "CITIES" under this Cooperative Research and Development Agreement (CRADA). The Centre for IT based Intelligent Energy Systems in Cities (CITIES) Project is a Danish national research center focused on work in building short-term operational integrated energy system models that feed longer term plam1ing models, considering the spatio-temporal variations, interactions, dynamics and stochastics in the energy system, from production to consumption. CITIES will harness the extensive expertise and experience of its industrial and academic partners to conduct meaningful research with tangible outputs, including a roadmap to a fossil free future. Decision support tools will be developed to inform stakeholders of the impact of investment, control and policy measures and to identify further opportunities for energy system efficiency. Dissemination and transferring knowledge is a key value to this CRADA, and the use of results by Danish policymakers, industry and society is vital. All participants in the Centre for IT based Intelligent Energy Systems in Cities (CITIES) Project are therefore encouraged to use all means to communicate their findings to society. Wherever possible, and compatible with applicable intellectual property regulations, publications should be produced and disseminated under Open Science, Data, Source and Access principles.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A Hybrid Energy System Workflow for Energy Portfolio Optimization

This manuscript develops a workflow, driven by data analytics algorithms, to support the optimization of the economic performance of an Integrated Energy System. The goal is to determine the optimum mix of capacities from a set of different energy producers (e.g., nuclear, gas, wind and solar). A stochastic-based optimizer is employed, based on Gaussian Process Modeling, which requires numerous samples for its training. Each sample represents a time series describing the demand, load, or other operational and economic profiles for various types of energy producers. These samples are synthetically generated using a reduced order modeling algorithm that reads a limited set of historical data, such as demand and load data from past years. Numerous data analysis methods are employed to construct the reduced order models, including, for example, the Auto Regressive Moving Average, Fourier series decomposition, and the peak detection algorithm. All these algorithms are designed to detrend the data and extract features that can be employed to generate synthetic time histories that preserve the statistical properties of the original limited historical data. The optimization cost function is based on an economic model that assesses the effective cost of energy based on two figures of merit: the specific cash flow stream for each energy producer and the total Net Present Value. An initial guess for the optimal capacities is obtained using the screening curve method. The results of the Gaussian Process model-based optimization are assessed using an exhaustive Monte Carlo search, with the results indicating reasonable optimization results. The workflow has been implemented inside the Idaho National Laboratory’s Risk Analysis and Virtual Environment (RAVEN) framework. The main contribution of this study addresses several challenges in the current optimization methods of the energy portfolios in IES: First, the feasibility of generating the synthetic time series of the periodic peak data; Second, the computational burden of the conventional stochastic optimization of the energy portfolio, associated with the need for repeated executions of system models; Third, the inadequacies of previous studies in terms of the comparisons of the impact of the economic parameters. The proposed workflow can provide a scientifically defendable strategy to support decision-making in the electricity market and to help energy distributors develop a better understanding of the performance of integrated energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Visual Systems Mapping to Define and Compare Woody Biomass LCAs for Sustainable Systems

The challenge addressed in this research centres on the need to choose between several biomass sources and energy production processes, while supporting rural economies and resilience of forest systems. A key barrier to effective decision-making for strategies using biomass is the lack of standardized and transparent life cycle assessment (LCA) baselines. These baselines are critical for assessing the impacts of biomass strategies but often vary due to regional factors and chosen simplifying assumptions of the LCAs. However, omitting key variables can mean the LCA omits key feedback and balancing loops relevant to fully assessing impacts of the change or test scenario. To address these complexities, this project employs a systems engineering approach: visual systems mapping. This technique is used to define the boundaries and dynamic behaviours of LCA baselines, enhancing transparency. By examining five literature sources and their documented baseline scenarios, the systems mapping case-studies demonstrates an approach to documenting and archiving these baselines. Recommendations are that visual systems mapping should be used to document key assumptions, such as baselines, of LCAs. Further, where possible open data repositories should hold key information about LCA baselines and reproducible workflows (e.g., using open-source tools) should be used to improve transparency and comparability in LCAs. Given the consensus within the broader scientific community on the importance of replicable data practices, this research reinforces the need for standardized frameworks and systems engineering tools in LCAs. This research demonstrates a pathway to more transparent, standardized, and comparable LCAs, that may bolster decisions for biomass systems.

Davis, Maggie [ORNL] (ORCID:0000000181319328)↗
DOE OSTI2024

Weather effects on the lifecycle of U.S. Department of Defense equipment replacement (WELDER)

Extreme weather has a direct and significant impact on buildings and infrastructure, resulting in billions of dollars of damage each year. This problem continues to grow as climate patterns change and buildings are exposed to new and different hazards than what they were designed to withstand. In order to better plan for the long-range sustainment, restoration, modernization, and eventual recapitalization of these buildings, organizations with large building portfolios, such as the U.S. Department of Defense (DoD), must have an awareness of the risks that these extreme weather events present. This research aimed to develop an approach to estimate condition loss and reduction in service life for the components of a building due to extreme weather hazards, to understand the risks that may be present in certain buildings and building systems. To achieve this objective, a damage association matrix was developed that categorizes climate hazards, the damage modes that they produce, and the individual component types impacted. This damage matrix formally links state-of-the-art climate model output, which provides projections of the probability of various climate hazards with a damage effects model that quantifies the consequence on component-level condition and service life. This method is applied to an actual portfolio of buildings in a particular geographic location and with a pre-defined component inventory that comprises the building. This approach can be aggregated to the system-, facility-, and site-level thus helping support billions of dollars in recapitalization decisions related to restoration/modernization of facilities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Process Optimization and Modeling for Minerals Sustainability (PrOMMiS) v1.0.0

The U.S. Department of Energy's ("DOE") Process Optimization and Modeling for Minerals Sustainability project ("PrOMMiS Project") was initiated in 2023 to transform the national Critical Minerals and Rare Earth Elements (CM & REE) landscape, thereby supporting DOE's three enduring strategic objectives: security, economic competitiveness, and environmental responsibility. To address this initiative, the PrOMMiS Project will develop, demonstrate, and deploy an open-source, advanced system modeling, optimization, and analysis application ("PrOMMiS Software Application") that will support industry decision-makers by accelerating the scale-up of novel CM & REE technologies by de-risking the development and deployment of commercial scale processes and maximizing learning throughout the development cycle.

Beattie, KeithS [Lawrence Berkeley National Labora↗
DOE OSTI2021

Pumped Storage Hydropower Valuation Guidebook: A Cost-Benefit and Decision Analysis Valuation Framework

As an energy storage technology, pumped storage hydropower (PSH) supports various aspects of power system operations. However, determining the value of PSH plants and their many services and contributions to the system has been a challenge. While there is a general understanding that PSH resources provide many services and benefits for the operation of power systems, estimating the value of these services—and especially the monetary value of some of those services—has been a challenge. The objective of this project, funded by the U.S. Department of Energy’s (DOE’s) Water Power Technologies Office (WPTO), is to advance the state of the art in assessing the value of PSH plants and their contributions to the power system. The specific goal is to develop detailed, step-by-step valuation guidance that PSH developers, plant owners or operators, and other stakeholders can use to assess the value of existing or potential new PSH plants and their services. The specific goals of this project are: (1) to develop comprehensive and transparent valuation guidance that will support consistent valuation assessments and comparisons of PSH projects or project design alternatives, (2) to test the PSH valuation guidance and its underlying methodology by applying it to two selected PSH projects, and (3) to transfer and disseminate the PSH valuation guidance to the hydropower industry, PSH developers, and other stakeholders. The authors believe that the application of a consistent, transparent, and repeatable valuation process will advance valuation assessments and allow stakeholders to compare valuation analyses performed for different PSH projects or design alternatives. It will also increase the acceptance of valuation results and enable better understanding of the true value that PSH technology brings to the grid.

13 HYDRO ENERGY↗
DOE OSTI2020

Risk-Informed ATF and FLEX Analysis for an Enhanced Resilient BWR Under Design-Basis and Beyond-Design-Basis Accidents

This report documents the activities performed by the Idaho National Laboratory (INL) during fiscal year (FY) 2020 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk-Informed System Analysis (RISA) Pathway, Enhanced Resilient Plant (ERP) Systems research. The purpose of the RISA Pathway research and development is to support plant owner-operator decisions with the aim to improve the economics, reliability, and maintain the high levels of safety of current nuclear power plants over periods of extended plant operations. The concept of ERP refers to the combinations of accident-tolerant fuel (ATF), optimal use of diverse and flexible coping strategy (FLEX), enhancements to plant components and systems, and the incorporation of augmented or new passive cooling systems, as well as improved fuel cycle efficiency. The objective of the ERP research effort is to use the RISA methods and toolkit in industry applications, including methods development and early demonstration of technologies, in order to enhance existing reactors’ safety features (both active and passive) and to substantially reduce operating costs through risk-informed approaches to plant design modifications to the plant and their characterization. One main focus of the FY 2020 efforts documented in this report was to extend the analyses conducted in FYs 2018 and 2019 for a pressurized water reactor (PWR) to a boiling water reactor (BWR). The same analysis process, risk analysis approaches, and analysis tools as in the previous work for PWR were used for a generic BWR with near-term ATF cladding (i.e., Iron-Chromium-Aluminum [FeCrAl] cladding and Chromium [Cr]-coated cladding) designs under the postulated station blackout (SBO) and medium loss-of-coolant (MLOCA) accident scenarios. In addition, a FLEX model was developed and incorporated into a generic BWR probabilistic risk assessment (PRA) model using the INL-developed software tool, Systems Analysis Programs for Hands-on Integrated Reliability Evaluations (SAPHIRE), to assess the risk impact from FLEX. The other main focus of the FY 2020 efforts was to advance analysis methods, including developing dynamic approach for FLEX human reliability analysis (HRA) using the INL-developed software tool, Event Modeling Risk Assessment using Linked Diagrams (EMRALD), as well as developing a multicriterion benefit evaluation (MCBE) method for evaluating costs and benefits of safety enhancements in nuclear power plants (NPPs). As a case study, the MCBE method was applied to evaluate the costs and benefits brought by FLEX implementation.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Risk-Informed Analysis for Enhanced Resilient Nuclear Power Plant with Initiatives including ATF, FLEX, and Advanced Battery Technology

This report documents the activities performed by the Idaho National Laboratory (INL) during fiscal year (FY) 2021 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk-Informed Systems Analysis (RISA) Pathway, Enhanced Resilient Plant (ERP) Systems research. The purpose of the RISA Pathway research and development is to support plant owner-operator decisions with the aim to improve the economics, reliability, and maintain the high levels of safety of current nuclear power plants over periods of extended plant operations. The concept of ERP refers to the combinations of accident-tolerant fuel (ATF), optimal use of diverse and flexible coping strategy (FLEX), enhancements to plant components and systems, the incorporation of augmented or new passive cooling systems, the advanced battery technology with extended capacity, as well as improved fuel cycle efficiency. The objective of the ERP research effort is to use the RISA methods and toolkit in industry applications, including methods development and early demonstration of technologies, in order to enhance existing reactors? safety features and to substantially reduce operating costs through risk-informed approaches to plant design modifications to the plant and their characterization. The ERP R&D efforts in FY 2021 are focused on three industry initiatives, including accident-tolerant fuel (ATF), optimal use of diverse and flexible coping strategy (FLEX), and advanced battery technology with extended capacity.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Promoting Domestic and International Consensus - Program Area 1: Carbon Capture & Storage & Clean Energy Systems

This is the Final Report for the U.S. Department of Energy Cooperative Agreement DE-FE-0024159, Promoting Domestic and International Consensus on Fossil Energy Technologies: Carbon Capture and Storage and Clean Energy Systems carried out by the United States Energy Association. It is a compendium of each quarterly report submitted over the 7-year period of the agreement accounting for all accomplishments, including major activities, significant results, major findings or conclusions, key outcomes or other achievements. Changes in approach or aims, and reasons for those changes, are included as well as any problems or delays and actions taken and planned to resolve them. It also summarizes budget statuses and any changes in key personnel throughout the life of the agreement. Originally planned for 5 years, the agreement was extended twice through back-to-back no-cost extensions, one to enable full completion of tasks critical to the program’s success and the other due to the Covid-19 Pandemic. The goals of the program were to provide increased knowledge regarding Carbon, Capture, and Storage (CCS) and Clean Energy Systems (CES) to Industry and Government decision makers, technology developers, educators, policymakers, environmental and other stakeholders, and the public by sharing research, technologies, and best practices with domestic and international partners. Increasing such knowledge was key to the other goals of the program of building of a broad consensus among domestic and international decision makers, stakeholders, and the public to take action to support and implement CCS and CES technologies and systems to ensure clean, secure, and affordable energy while enhancing environmental protection. The program accomplished these goals through a series of activities including hosting of conferences and workshops, in-person briefings, virtual webinars, reports and white papers, and keeping stakeholders informed of industry happenings through email distribution lists. It is the belief of the authors of this report, that these activities can be considered to have been successful in their goals.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Curriculum-based Reinforcement Learning for Distribution System Critical Load Restoration

This paper focuses on the critical load restoration problem in distribution systems following major outages. To provide fast online response and optimal sequential decision-making support, a reinforcement learning (RL) based approach is proposed to optimize the restoration. Due to the complexities stemming from the large policy search space, renewable uncertainty, and nonlinearity in a complex grid control problem, directly applying RL algorithms to train a satisfactory policy requires extensive tuning to be successful. To address this challenge, this paper leverages the curriculum learning (CL) technique to design a training curriculum involving a simpler steppingstone problem that guides the RL agent to learn to solve the original hard problem in a progressive and more effective manner. We demonstrate that compared with direct learning, CL facilitates controller training to achieve better performance. To study realistic scenarios where renewable forecasts used for decision-making are in general imperfect, the experiments compare the trained RL controllers against two model predictive controllers (MPCs) using renewable forecasts with different error levels and observe how these controllers can hedge against the uncertainty. Results show that RL controllers are less susceptible to forecast errors than the baseline MPCs and can provide a more reliable restoration process.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Decision Support Tool for Planning Neighborhood-Scale Deployment of Low-Speed Shared Automated Shuttles

Increasing interest and investment in connected, automated, and electric vehicles as well as mobility-as-a-service (MaaS) concepts are paving the way for the next major transformation in transportation through automated and shared mobility. The initial excitement toward rapid deployment and adoption of automated vehicles (AVs) has subsided, and low-speed automated shuttles are emerging as a more pragmatic pathway for introducing automated mobility in geofenced districts. Such shuttles hold the promise to provide a viable alternative for serving short trips in urban districts with high travel densities. As interest in low-speed automated shuttle systems (to improve urban mobility) increases, the need for tools that can inform communities in relation to benefits or disadvantages of automated shuttle deployments is imminent. However, most of the existing transportation planning and simulation tools are not capable of handling emerging shared automated mobility options. This paper presents a microscopic simulation toolkit that can be used by cities and communities to plan for the deployment of low-speed automated shuttles systems, as well as other shared mobility options. Labeled as the Automated Mobility District modeling and simulation toolkit, the proposed decision support tool intends to help cities evaluate the mobility and sustainability impacts of deploying shared automated vehicles (SAVs) in geofenced regions. This paper describes the toolkit, as well as a sample scenario analysis for the deployment of low-speed automated shuttles in Greenville, South Carolina, U.S. Results from the scenario study demonstrate the effectiveness of the proposed simulation toolkit in planning for advanced mobility systems.

47 OTHER INSTRUMENTATION↗