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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A systematic evaluation of wind's capacity credit in the Western United States

Abstract The degree to which wind energy can contribute to the capacity needed to meet resource adequacy requirements, also known as capacity credit (CC), can be impacted by many factors. The CC varies regionally with wind resource and correlation to net load and may also vary with wind technology (land‐based versus offshore) and turbine specifications. We use a probabilistic resource adequacy tool to systematically assess the CC of multiple technologies for near‐term wind deployment across the Western US power system to help inform planning decisions. We find that wind CC varies by weather year and location but averages 16% for land‐based turbines and 41% for offshore turbines, with large regional variations. The average wind CC increases to 20% for land‐based and 53% for offshore when considering only the sites in the top 25th percentile of capacity factor. The CC of land‐based wind is generally lower than its capacity factor, which indicates a low correlation of generation to system needs. However, offshore wind shows a substantially higher CC, which is driven by not only a higher capacity factor but also a greater correlation with system needs.

17 WIND ENERGY↗
DOE OSTI2021

Streamlined Approach for Environmental Restoration (SAFER) Plan for Corrective Action Unit 114: Area 25 EMAD Facility Nevada National Security Site, Nevada, (Rev.0, with ROTC 1)

This Streamlined Approach for Environmental Restoration (SAFER) Plan addresses the actions needed to achieve closure for Corrective Action Unit (CAU) 114, Area 25 EMAD Facility, identified in the Federal Facility Agreement and Consent Order (FFACO). CAU 114 comprises the following corrective action sites (CASs) located in Area 25 of the Nevada National Security Site (formerly, the Nevada Test Site): 25-41-03, EMAD Facility; 25-99-23, Manned Control Car (MCC) and Engine Installation Vehicle (EIV); and 25-33-05, Building 3901, Engine Transport System Maintenance Building. This plan provides the methodology for field activities needed to gather the necessary information for closing CAU 114. There is sufficient information and process knowledge from historical documentation and investigations of similar sites regarding the expected nature and extent of potential contaminants to recommend closure of CAU 114 using the SAFER process. Additional information will be obtained by conducting a field investigation before selecting the appropriate corrective actions for CAU 114. It is anticipated that the results of the field investigation and implementation of corrective actions will support a defensible recommendation that no further corrective action is necessary. The purpose of the corrective action investigation will be to document and verify the adequacy of existing information; to affirm the decision for either clean closure, closure in place, or no further action; and to provide sufficient data to implement the corrective action. The actual corrective action selected will be based on characterization activities implemented under this SAFER Plan. If specific conditions or findings fall outside the bounds of the SAFER Plan, the Nevada Division of Environmental Protection (NDEP) will be consulted to determine the path forward before proceeding. Upon completion of SAFER activities, a closure report will be prepared and submitted to NDEP for review and approval. The schedule for completion of the closure report will be established at the FFACO annual meeting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Impact of suprathermal ions on neutron yield in the pre-DT phase of ITER operation

An assessment of neutron production during the pre-fusion power-operation (PFPO) phase has been carried out for a representative set of plasma scenarios predicted by the ITER Research Plan. A range of heating systems, namely neutral beam injection (NBI) (hydrogen), electron cyclotron resonance heating (ECRH), and ion cyclotron resonance heating (ICRH) are planned to be used for PFPO studies in helium, hydrogen, and mixed hydrogen–helium plasmas. Fast ions (protons and 3He) originating from NBI and ICRH systems can increase neutron production in PFPO plasmas by directly interacting with intrinsic Be impurities or through secondary processes, as also evidenced at JET. The generation of fast ions in ITER PFPO scenarios has been modelled using the ASTRA-NBI and TORIC-SSFPQL codes. A significant impact of the synergy between hydrogen NBI and hydrogen-minority ICRH on neutron production in helium plasmas is reported. In addition, the stability of the toroidicity-induced Alfvén eigenmodes (TAE) is analyzed for PFPO plasmas with a high pressure of suprathermal ions and a weak reversed shear. The possible impact of sawtooth oscillations and TAEs on neutron production is discussed, based on a linear stability analysis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Automated Adversary-in-the-Loop Cyber-Physical Defense Planning

Security of cyber-physical systems (CPS) continues to pose new challenges due to the tight integration and operational complexity of the cyber and physical components. To address these challenges, this article presents a domain-aware, optimization-based approach to determine an effective defense strategy for CPS in an automated fashion—by emulating a strategic adversary in the loop that exploits system vulnerabilities, interconnection of the CPS, and the dynamics of the physical components. Our approach builds on an adversarial decision-making model based on a Markov Decision Process (MDP) that determines the optimal cyber (discrete) and physical (continuous) attack actions over a CPS attack graph. The defense planning problem is modeled as a non-zero-sum game between the adversary and defender. We use a model-free reinforcement learning method to solve the adversary’s problem as a function of the defense strategy. We then employ Bayesian optimization (BO) to find an approximate best-response for the defender to harden the network against the resulting adversary policy. This process is iterated multiple times to improve the strategy for both players. We demonstrate the effectiveness of our approach on a ransomware-inspired graph with a smart building system as the physical process. Numerical studies show that our method converges to a Nash equilibrium for various defender-specific costs of network hardening.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

High-Fidelity Large-Signal Order Reduction Approach for Composite Load Model

With the increasing penetration of electronic loads and distributed energy resources, conventional load models cannot capture their dynamics. Therefore, a new comprehensive composite load model is developed by Western Electricity Coordinating Council (WECC). However, this model is a complex high-order non-linear system with multi-time-scale property, which poses challenges on power system studies with heavy computational burden. In order to reduce the model complexity, the authors firstly develop a large-signal order reduction (LSOR) method using singular perturbation theory. In this method, the fast dynamics are integrated into the slow ones to preserve transient characteristics of the former. Then, accuracy assessment conditions are proposed and embedded into the LSOR to improve and guarantee the accuracy of reduced-order model. Finally, the reduced-order WECC composite load model is derived by using the proposed algorithm. Overall, simulation results show that the reduced-order large-signal model significantly alleviates the computational burden while maintaining similar dynamic responses as the original composite load model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
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 OSTI2023

Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC)

The Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC) data is a collection of 4km hourly wind, solar, temperature, humidity, and pressure fields for the contiguous United States under various climate change scenarios. Sup3rCC is downscaled Global Climate Model (GCM) data. The downscaling process was performed using a generative machine learning approach called sup3r: Super-Resolution for Renewable Energy Resource Data (linked below as "Sup3r GitHub Repo"). The data includes both historical and future weather years, although the historical years represent the historical climate, not the actual historical weather that we experienced. You cannot use Sup3rCC data to study historical weather events, although other sup3r datasets may be intended for this. The Sup3rCC data is intended to help researchers study the impact of climate change on energy systems with high levels of wind and solar capacity. Please note that all climate change data is only a representation of the possible future climate and contains significant uncertainty. Analysis of multiple climate change scenarios and multiple climate models can help quantify this uncertainty.

Array↗
DOE OSTI2023

I Know I'm Right, But Does My Phone?

Transportation is the largest source of green-house gas emissions in the United States. Reducing transportation emissions depends on human travel behavior, which relies on local land use and planning. Travel diaries, consisting of sequences of trips between places for a particular individual, are typically used to instrument human travel behavior. However, these diaries are only as accurate as the underlying methods used to construct them. Travel diary algorithms have been a popular research topic since the advent of GPS tracking surveys. Mode inference algorithms in particular have been well represented in literature. However, these algorithms have typically been validated using prompted recall of pre-segmented trips, which doesn't account for segmentation error, thus disregarding the continuity of mode inference. Furthermore, phone operating systems and applications have adopted battery-conserving techniques, but we are not aware of prior work that has characterized the resulting data collection errors or evaluated procedures to mitigate them. We introduce a framework to evaluate accuracy of trip length computations and mode inference. We develop a temporal alignment procedure in analyzing continuous mode-segmented trajectories for groups of trips. We then apply our framework to evaluate an example set of travel diary algorithms from the open-source OpenPATH travel diary platform against MobilityNet, a public dataset containing information from three artificial timelines that cover 15 different travel modes. Our results show that inference based on an integration with map features results in weighted F_1 scores of 0.60 (iOS) and 0.74 (android). We also show that OpenPATH tends to under count trip length, with mean of signed relative error of -0.0438 on android and -0.0704 on iOS. We hope that other travel diary algorithms will be evaluated using this standardized process, and that the results used to understand and improve the state-of-the-art in this field.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2025

Quantifying Distribution System Resilience From Utility Data: Large Event Risk and Benefits of Investments

We focus on blackouts in electric distribution systems that have a large cost to customers. To quantify resilience to these events, we show how to calculate risk metrics from the historical outage data routinely collected by utilities' outage management systems. Risk is defined using a customer cost exceedance curve. The exceedance curve has a heavy tail that implies large fluctuations in large blackout costs, and this makes estimating the mean large cost in the usual way impractical. To avoid this problem, we use new resilience metrics describing the large event risk; these metrics are the probability of a large cost event, the annual log cost resilience index, and the average of the logarithm of the cost of large-cost events or the slope magnitude of the tail on a log–log exceedance curve. Resilience can be improved by planned investments to upgrade system components or speed up restoration. The benefits that these investments would have had if they had been made in the past can be quantified by “rerunning history” with the effects of the investment included, and then recalculating the large event risk to find the improvement in resilience. An example using utility data shows a 2% reduction in the probability of a large cost event due to 10% wind hardening and 6%–7% reduction due to 10% faster restoration in two different areas of a distribution utility. This new data-driven approach to quantify resilience and resilience investments is realistic and much easier to apply than complicated approaches based on modeling all the phases of resilience. Moreover, an appeal to improvements to past lived experience may well be persuasive to customers and regulators in making the case for resilience investments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

The upgrade and re-validation of the Compact Muon Solenoid Electromagnetic Calorimeter Control and Safety Systems during the Second Long Shutdown of the Large Hadron Collider at CERN

The Electromagnetic Calorimeter (ECAL) is one of the subdetectors of the Compact Muon Solenoid (CMS), a general-purpose particle detector at the CERN Large Hadron Collider (LHC). The CMS ECAL Detector Control System (DCS) and the CMS ECAL Safety System (ESS) have supported the detector operations and ensured the detector’s integrity since the CMS commissioning phase, more than 10 years ago. Over this long period, several changes to both systems were necessary to correct issues, extend functionality and keep them in-line with current hardware technologies and the evolution of software platforms. Due to the constraints imposed on significant changes to a running system, major hardware and software upgrades were therefore deferred to the second LHC Long Shutdown (LS2). This paper presents the architectures of the CMS ECAL control and safety systems, discusses the ongoing and planned upgrades, details implementation processes and validation methods and highlights the expectations for the post-LS2 systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Enhancing the EVI-X National Framework to Address Emerging Questions on Charging Infrastructure Deployment

As large national investments in EV charging continue to be made, this project seeks to improve the state-of-the-art in EV infrastructure analysis. New and improved quantitative analysis capabilities will position VTO to support EERE Leadership, assist the DOT/DOE Joint Office through timely analysis, and provide a strategy for the deployment of the national charging network. This project supports on-going development of NREL's EVI-X Suite of charging infrastructure analysis tools, including tools supporting large-scale network planning, local site design decisions, and comprehensive financial analysis. EVI-X development complements and leverages other VTO-funded efforts to ensure inputs to EVI-X and output produced by EVI-X are consistent with and well-integrated into the VTO portfolio (e.g., EV adoption scenarios from TEMPO, charging infrastructure design provided to EVs@Scale, etc.).

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2023

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

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

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Least-Cost Storage Mix for Low-Carbon Operations

As communities plan for high- or ultrahigh-renewable power systems, they face diverse options for energy storage. Each energy storage technology has a unique power capacity and discharge duration, and each community has a unique load, generation mix, and weather pattern. Determining the optimal storage portfolio across these parameters could save planners significant costs when building out high-renewable power systems.

analysis↗
DOE OSTI2025

EV Champion Training Webinar 2: ZEV and EV Charging Planning [Slides]

The Electric Vehicle (EV) Champion Training Series, hosted by the National Renewable Energy Laboratory (NREL), is tailored for fleet managers, facility managers, and other stakeholders involved in the deployment of EVs and charging stations. This series equips participants with the skills and knowledge necessary to become subject matter experts in EV implementation. This is the second training in a four-part series and serves as an intermediate training. This training covers the first four steps in the ZEV Ready Center process, including how to identify and train your zero-emission vehicle (ZEV) team, align headquarters strategy with site-level planning, identify ZEV opportunities, and identify charging needs for your project sites. Participants will gain a solid foundation to support the effective deployment and management of EVs and their infrastructure.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

McGrath Community Energy Planning

The City of McGrath, a remote Alaskan community reliant on diesel fuel, developed a Community Energy Plan through the Energy Technologies Innovation Partnership Project (ETIPP) to address high energy costs, infrastructure vulnerabilities, and long-term sustainability. Guided by community-led priorities and technical assistance from partners including NREL, REAP, and ANTHC, the plan identifies eight focus areas: distribution system upgrades, solar and battery integration, river energy potential, emergency backup power, housing efficiency, water system losses, independent power producer models, and targeted funding strategies. The plan combines local knowledge with technical analysis to chart a path toward a more resilient, affordable, and sustainable energy future for McGrath.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Flexible Siting Criteria and Staff Minimization for Micro-Reactors

The economic potential of micro-reactors is vast and underestimated. Commonly-emphasized applications include niche markets such as remote communities, mines and military bases. However, micro-reactors could be used as flexible energy generators also for larger markets, such as mobile and containerized agriculture and manufacturing facilities, district heating, micro-grids for data centers, sea ports, airports and hospitals. The implication is that micro-reactors may have to be deployed also in non-remote locations. Successful implementation of micro-reactors needs a navigable and predictable licensing process, technology-appropriate siting restrictions, risk-informed emergency and safety requirements, and practical operating and maintenance requirements. The primary goal of this project was to develop siting criteria that are tailored to micro-reactors deployable in densely-populated areas, e.g., urban environments. To achieve that goal, we compared the characteristics of the MIT research reactor (MITR) with those of leading micro-reactor concepts (e.g., eVinci, USNC, Aurora), and evaluated whether and how the MITR design basis (e.g., inherent safety features, engineered safety systems, source term, emergency planning and emergency operating procedures) and associated regulations may be applicable to these new micro-reactors as well. What makes MITR a unique analogue in this context is its small power rating (6 MWt) and physical size, mode of operations (24/7 with a somewhat more commercial flavor than typical university reactors), and especially its urban location. Of course significant differences exist, such as mission (power production vs. research) and the reactor design itself. Leveraging the MITR experience, this project was able to generate criteria that will allow micro-reactors to realize their full economic potential as flexible heat and electricity generators for a diverse portfolio of applications in non-remote locations. As such, the outcome of this project might encourage investment in and use of micro-reactors. A second goal of the project was to conceptualize a model of operations for micro-reactors that would minimize the staffing requirements, and thus reduce the cost of electricity and heat generated by these systems. Here too our approach was to systematically review the MITR experience and requirements, as well as survey the innovations in autonomous control technologies and monitoring (e.g., advanced sensors, drones, robotics, AI) that would permit a dramatic reduction in staffing at future micro-reactor installations. The scope of work was expanded after the start date to include also an evaluation of micro-reactor security, using the so-called consequence-based analysis, and the development of a methodology to perform dynamic risk assessment for micro-reactors, using system theory and modeling and simulation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Navigating Urban Mobility: Evaluating Infrastructure Strategies for Enhanced Energy-Efficient Access for Micromobility

Cities and communities continue to expand pedestrian and bicycle infrastructure as part of their sustainable mobility and post-pandemic recovery plans. However, the emergence of micro-mobility (e.g., electric bicycles) has created new challenges for urban transport planning. As the popularity of micro-mobility modes has grown, so have safety concerns due to rising injuries, thus challenging many cities to come up with regulatory measures that enable efficient access while minimizing negative impacts from micro-mobility. Leveraging the Open-Source Tool (Mobility Energy Productivity metric), powered by an open-source dataset (OpenStreetMap), and enhanced through the incorporation of perceived discomfort (level-of-traffic stress), this research study focuses on evaluating the accessibility implications of infrastructure planning and regulatory measures for micro-mobility. Five scenarios pertaining to level of traffic stress, sidewalk access, traffic calming, and bike lane coverage were tested in the Denver-Aurora region in Colorado. Maximum improvements in energy-efficient access are realized when allowing sidewalks for micro-mobility use. Cities and planning agencies could leverage this information to assess sidewalk use policies for micro-mobility, while ensuring pedestrian safety and ADA access. Results indicate that expansion of bicycle lane coverage yields 11% accessibility benefits for micro-mobility compared to implementing traffic calming measures yielding 3% accessibility improvements. Further, it was observed most of the population in the Denver-Aurora region is experiencing lower accessibility in-part due to presence of a high-stress connections in the network. Although not generalizable, the use of open-source data and access calculation methodology, make this analysis reproducible and transferable to other locations.

ADVANCED PROPULSION SYSTEMS↗