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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
NASA NTRS2022

SatNet: A Benchmark for Satellite Scheduling Optimization

Satellites provide essential services such as networking and weather tracking, and the number of near-earth and deep space satellites are expected to grow rapidly in the coming years. Communications with terrestrial ground stations is one of the critical functionalities of any space mission. Satellite scheduling is a problem that has been scientifically investigated since the 1970s. A central aspect of this problem is the need to consider resource contention and satellite visibility constraints as they require line of sight. Due to the combinatorial nature of the problem, prior solutions such as linear programs and evolutionary algorithms require extensive compute capabilities to output a feasible schedule for each scenario. Machine learning based scheduling can provide an alternative solution by training a model with historical data and generating a schedule quickly with model inference. We present SatNet, a benchmark for satellite scheduling optimization based on historical data from the NASA Deep Space Network. We propose formulation of the satellite scheduling problem as a Markov Decision Process and use reinforcement learning (RL) policies to generate schedules. The nature of constraints imposed by SatNet differ from other combinatorial optimization problems such as vehicle routing studied in prior literature. Our initial results indicate that RL is an alternative optimization approach that can generate candidate solutions of comparable quality to existing state-of-the-practice results. However, we also find that RL policies overfit to the training dataset and do not generalize well to new data, thereby necessitating continued research on reusable and generalizable agents.

Wilson, Brian↗
DOE OSTI2024

Effective Communication of Energy Science and Technology

Although research, development, and deployment of advanced energy technologies are essential for the clean energy transition, communication about these technologies is equally important to their success. Energy is part of everyday life; therefore, changes in energy systems should be accepted by communities and industries. Yet details about energy generation, transmission, and environmental impacts are complex. The combination of commonality and complexity requires communications to use visualization, localization, narrative, and understandable terminology to reach a range of stakeholders. Collaboration between technology experts and communications professionals builds integrity and accessibility of energy information that enables community-based solutions for energy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Extreme-scale EV charging infrastructure planning for last-mile delivery using high-performance parallel computing

Here, this paper addresses stochastic charger location and allocation problems under queue congestion for last-mile delivery using electric vehicles (EVs). The objective is to decide where to open charging stations and how many chargers of each type to install, subject to budgetary and waiting-time constraints. We formulate the problem as a mixed-integer non-linear program, where each station-charger pair is modeled as a multiserver queue with stochastic arrivals and service times to capture the notion of waiting in fleet operations. The model is extremely large, with billions of variables and constraints for a typical metropolitan area; even loading the model in solver memory is difficult, let alone solving it. To address this challenge, we develop a Lagrangian-based dual decomposition framework that decomposes the problem by station and leverages parallelization on high-performance computing systems, where the subproblems are solved by using a cutting plane method and their solutions are collected at the master level. We also develop a three-step rounding heuristic to transform the fractional subproblem solutions into feasible integral solutions. Computational experiments on data from the Chicago metropolitan area with hundreds of thousands of households and thousands of candidate stations show that our approach produces high-quality solutions in cases where existing exact methods cannot even load the model in memory. We also analyze various policy scenarios, demonstrating that combining existing depots with newly built stations under multiagency collaboration substantially reduces costs and congestion. These findings offer a scalable and efficient framework for developing sustainable large-scale EV charging networks.

Capacity allocation↗
DOE OSTI2026

Strengthening Resilience: Florida Resident Voices on Resource Needs During Power Outages

Extreme weather events related to climate change, and an aging electricity infrastructure are disrupting reliable electricity services to a greater degree. Further, previous research has found that more socially vulnerable populations are more likely to live in areas with a higher probability of power outages. Here, this study examines the issues that people face during power outages and the resources that help individuals maintain resilience during power outages caused by extreme weather events in socially vulnerable communities. Using qualitative data from focus groups with 56 individuals in Central and North Florida, the research highlights lived experiences during outages and difficulties using and accessing resources during these conditions. Based on a qualitative review of the focus group discussions, this paper explores the solutions and support systems residents believe would improve their ability to cope. The findings offer insights to guide policy and strategic planning, with the goal of strengthening personal preparedness and response by focusing on the resources people consider most helpful for enduring frequent and severe outages.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Evaluating the efficacy and equity of environmental stopgap measures

Contemporary environmental policy is rife with measures that do not fully resolve a problem, but instead are proposed to “buy time” for the development and future implementation of more durable solutions. In this perspective, we define such measures as “stopgap measures,” and examine examples from wildfire risk management, hydrochlorofluorocarbon regulation, and Colorado River water management. We introduce an analytical framework to assess stopgaps, and apply this framework to solar geoengineering, a controversial stopgap for climate action. Studying stopgaps as a novel category of policy and management measures can help us understand the why stopgaps emerge, and weigh the equity and efficacy of stopgaps against other policy proposals

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2019

Orbital Debris Ontology, Terminology, and Knowledge Modeling

The looming threat orbital debris poses to assets in orbit demands solutions. As the orbital population grows, so does this hazard, but so does the sea of data. The problem is also an opportunity for interdisciplinary innovation and cooperation. This paper focuses on the data and information management aspect of developing solutions for a sustainable and safe orbital space environment. The corresponding author’s in-progress work to develop an orbital debris domain ontology is summarized in order to discuss knowledge modeling for this domain. Methodological approaches of this effort can also contribute to standards efforts and address terminological and policy questions. Leveraging the growing volumes of orbital debris and space situational awareness (SSA) data will create a more complete picture of the orbital space environment. Part of the solution will be: consistent and correct data interpretation, sharing orbital debris and SSA data in one form or another, terminology development & harmonization, and knowledge or domain modeling. To facilitate this, [Rovetto, 2015/16] discussed ontology development for the orbital debris domain. This paper lists concepts from that paper, and subsequently developed concepts [2-9]. Ontology engineering is an interdisciplinary field related to knowledge representation and reasoning in artificial intelligence, semantic technologies and the so-called semantic web. An ontology is effectively a computable and semantically rich terminology that presents a knowledge or domain model for a topic area. Expressions of knowledge or assertions are stored using formally defined term. This knowledge base is reasoned over to yield answers to queries, among other things. Ontologies have been developed in knowledge-based projects across various disciplines, and used for such things as search engines, chatbots, enterprise knowledge graphs, etc. Ontologies support: interoperability, automated reasoning, data sharing and integration, data search and retrieval, and communicating the meaning of data. The Orbital Debris Ontology (ODO), and related ontologies [Rovetto & Kelso 2016] [Rovetto 2016, 2017], were proposed to help achieve this. ODO, for instance, is intended as a domain ontology that can be used across federated databases, offering an explicitly specified set of concepts describing the orbital debris domain. Its meaning-rich taxonomy will provide a sharable semantics for orbital debris data to, in part, consistently communicate the meaning of data to both humans and machines, and tag data elements in space object catalogs to help afford inference tasks, decision support, knowledge discovery, and information integration. ODO and the SSA ontology (SSAO) is part of the overall Orbital Space Domain Ontology concept, which is conceived as a broader domain reference ontology. It aims to provide a knowledge representation structure of the orbital space environment, a common semantic model, and develop a sharable terminology. Collectively this will provide common meaning for datasets, a high-level taxonomy or classification for orbital space objects, and thus means to characterize space objects. Ongoing efforts have included using visualizations, R, JSON-LD, and contemporary semantic technologies. Potential applications and interdisciplinary partnerships include web-based platforms, web apps, visualizations, and academia projects. Community input and participation may yield a more widely understood domain model as well as facilitate terminological standards. For example, the proposed conceptual, terminological and ontological analysis may contribute to such efforts as the Space Debris Mitigation Requirements in the International Standards Organization by developing more precise, consistent and coherent terms and definitions. Projects that seek to develop in-house ontologies can use ODO and related ontologies as domain reference ontologies. This paper was developed independent of author affiliations. Readers are encouraged to contact corresponding author(1) with general interest and potential opportunities to support or realize the described project.

Robert J. Rovetto↗
DOE OSTI2024

Intelligent, grid-friendly, modular extreme fast charging system with solid-state DC protection

The development of electric vehicle (EV) charging infrastructure is crucial for the widespread adoption of electric transportation. However, implementing such infrastructure is a complex task that requires consideration of factors such as space limitations, adherence to industry standards, grid capacity, and other technical and policy issues. This project seeks to create a framework for the efficient design of compact medium voltage (MV) extreme fast charging (XFC) stations for EVs. The station design involves the use of a solid-state transformer (SST) that connects to the MV distribution network, delivering power to a shared DC bus. This innovative approach eliminates the need for a step-down transformer to provide low-voltage service by connecting directly to the MV distribution network. Eliminating the low-frequency transformer not only reduces the system footprint and losses but also eliminates inrush currents during grid black-start. Additionally, placing power electronics directly on the distribution system allows for high-bandwidth filtering and power factor correction. The inclusion of a shared DC bus enables multiple charging dispensers and DC storage/generation units to connect, forming a DC microgrid. This setup facilitates power sharing with minimal conversion stages. The project showcases a DC distribution network protected by intelligent solid-state (SS) DC circuit breakers (DCCB) capable of isolating the smallest section of the faulted circuit much faster than existing mechanical solutions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

PWR loading pattern optimization with reinforcement learning

The core loading pattern optimization problem belongs to the class of combinatorial optimization problem and has been studied since the dawn of commercial nuclear energy industry. It is characterized by multiple objectives and constraints, with a very high number of candidate patterns, which makes it impossible to solve explicitly. Stochastic optimization methodologies including Genetic Algorithms and Simulated Annealing are used by different nuclear utilities and vendors to perform fuel cycle reload design. Nevertheless, hand-designed solutions continue to be the prevalent method in the industry. To improve the state-of-the-art core reload patterns, we aim to create a method as scalable as possible, that agrees with the designer's goal of performance and safety. To help in this task Deep Reinforcement Learning (DRL), in particular Proximal Policy Optimization is leveraged. DRL has recently experienced a strong impetus from its successes applied to games, sometimes even reaching 'super-human' performances. This paper lays out the foundation of this method and proposes to study the behavior of several hyper-parameters that influence the DRL algorithm. The algorithm is highly dependent on multiple factors such as an exploration/exploitation trade-off that manifests through different parameters such as the number of loading patterns seen and the number of samples collected before a policy update, but also the shape of the objective function derived for the core design. Experimental results also demonstrate the effectiveness of the method in finding high-quality solutions from scratch within a reasonable amount of time. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Scientists’ call to action: Microbes, planetary health, and the Sustainable Development Goals

Microorganisms, including bacteria, archaea, viruses, fungi, and protists, are essential to life on Earth and the functioning of the biosphere. Here, we discuss the key roles of microorganisms in achieving the United Nations Sustainable Development Goals (SDGs), highlighting recent and emerging advances in microbial research and technology that can facilitate our transition toward a sustainable future. Given the central role of microorganisms in the biochemical processing of elements, synthesizing new materials, supporting human health, and facilitating life in managed and natural landscapes, microbial research and technologies are directly or indirectly relevant for achieving each of the SDGs. More importantly, the ubiquitous and global role of microbes means that they present new opportunities for synergistically accelerating progress toward multiple sustainability goals. By effectively managing microbial health, we can achieve solutions that address multiple sustainability targets ranging from climate and human health to food and energy production. Emerging international policy frameworks should reflect the vital importance of microorganisms in achieving a sustainable future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Electric light-duty vehicles have decarbonization potential but may not reduce other environmental problems

Electric vehicles are promoted as ‘clean’ technologies and offer promising reductions in transportation emissions. Nevertheless, their environmental benefits critically depend on the local electricity grid mix and the type of emission being considered. Here, we conduct a comparative life cycle assessment of the four dominant light-duty vehicle categories at both the global scale and in three representative countries: Norway, the US, and China. By analyzing different environmental indicators, particularly global warming potential and respiratory effects, and quantifying related parametric uncertainties, we reveal that the advantages of electric vehicles vary across these regions and across environmental impact types. While electric vehicles offer considerable decarbonization potential as the grid mix becomes cleaner, they might not mitigate other environmental impacts, such as increased respiratory effects on rural, low-income communities. Our results support stakeholders in identifying environmentally friendly vehicle and policy options while considering multiple factors, and emphasize the importance of tailored approaches over one-size-fits-all solutions in sustainable transportation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Assessing the Reliability Benefits of Energy Storage as a Transmission Asset

Utilizing energy storage solutions to reduce the need for traditional transmission investments has been recognized by system planners and supported by federal policies in recent years. This work demonstrates the need for detailed reliability assessment for quantitative comparison of the reliability benefits of energy storage and traditional transmission investments. First, a mixed-integer linear programming expansion planning model considering candidate transmission lines and storage technologies is solved to find the least-cost investment decisions. Next, operations under the resulting system configuration are simulated in a probabilistic reliability assessment which accounts for weather-dependent forced outages. The outcome of this work, when applied to TPPs, is to further equalize the consideration of energy storage compared to traditional transmission assets by capturing the value of storage for system reliability.

co-optimization↗
DOE OSTI2024

Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models

Purpose To calibrate Cancer Intervention and Surveillance Modeling Network (CISNET)'s SimCRC, MISCAN-Colon, and CRC-SPIN simulation models of the natural history colorectal cancer (CRC) with an emulator-based Bayesian algorithm and internally validate the model-predicted outcomes to calibration targets.Methods We used Latin hypercube sampling to sample up to 50,000 parameter sets for each CISNET-CRC model and generated the corresponding outputs. We trained multilayer perceptron artificial neural networks (ANNs) as emulators using the input and output samples for each CISNET-CRC model. We selected ANN structures with corresponding hyperparameters (i.e., number of hidden layers, nodes, activation functions, epochs, and optimizer) that minimize the predicted mean square error on the validation sample. We implemented the ANN emulators in a probabilistic programming language and calibrated the input parameters with Hamiltonian Monte Carlo-based algorithms to obtain the joint posterior distributions of the CISNET-CRC models' parameters. We internally validated each calibrated emulator by comparing the model-predicted posterior outputs against the calibration targets.Results The optimal ANN for SimCRC had 4 hidden layers and 360 hidden nodes, MISCAN-Colon had 4 hidden layers and 114 hidden nodes, and CRC-SPIN had 1 hidden layer and 140 hidden nodes. The total time for training and calibrating the emulators was 7.3, 4.0, and 0.66 h for SimCRC, MISCAN-Colon, and CRC-SPIN, respectively. The mean of the model-predicted outputs fell within the 95% confidence intervals of the calibration targets in 98 of 110 for SimCRC, 65 of 93 for MISCAN, and 31 of 41 targets for CRC-SPIN.Conclusions Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis, such as the CISNET CRC models. In this work, we present a step-by-step guide to constructing emulators for calibrating 3 realistic CRC individual-level models using a Bayesian approach.

artificial neural networks↗
DOE OSTI2021

Geothermal District Heating in the United States: 2021 Update

As of 2021, there are 23 geothermal district heating (GDH) systems in the United States. Most are over 30 years old. This paper presents an overview of GDH development in the United States and the performance of GDH systems over time. Calculations of the estimated levelized cost of heat (LCOH) for existing U.S. GDH systems were made using NREL's GEOPHIRES tool. Estimated LCOH for existing U.S. GDH systems ranges from $15 to $105/MWhth. This paper explores other factors in GDH development such as resource and system size, capacity factor, and the role of policy. U.S. GDH utilization and deployment are compared to worldwide trends. Results show that the market for GDH in the United States has been weak over the past 40 years due to the combination of inexpensive fossil fuel alternatives (mostly natural gas), lack of incentives focused on heating/cooling, and other factors. Future opportunities for increased GDH deployment in the United States related to increased demand for low-carbon heating and cooling solutions (driven by decarbonization goals in the residential, commercial and industrial heating/cooling sectors, and particularly aggressive ones on college campuses) are outlined. Lastly, this paper identifies policy mechanisms that have been implemented in other countries to incentivize GDH (such as financial incentives targeting GDH, geothermal risk reduction mechanisms, carbon prices benefiting low-carbon heat production, and others).

geothermal district heating↗
DOE OSTI2022

Regulatory and Policy Considerations for the Reuse and End-of-Life Management of Solar and Batteries in the U.S.

The demand for solar photovoltaics (PV) and lithium-ion batteries (LIB) is expected to continue in the U.S. as the call for solar is projected to quadruple by 2030 to meet renewable energy and decarbonization goals. The expected demand for PV and LIB brings supply chain concerns and a growing need for a circular economy for PV and LIB materials. Domestic reuse and recycling is one potential circular economy solution for PV and LIB. This presentation explores the current U.S. law and regulatory landscape for the reuse and recycling of PV and LIB materials, and how certain policy frameworks impact reuse and end-of-life management decisions for PV and LIB materials.

batteries↗
DOE OSTI2024

Convex Relaxations of Maximal Load Delivery for Multi-Contingency Analysis of Joint Electric Power and Natural Gas Transmission Networks

Recent increases in gas-fired power generation have engendered increased interdependencies between natural gas and power transmission systems. These interdependencies have amplified existing vulnerabilities in gas and power grids, where disruptions can require the curtailment of load in one or both systems. Although typically operated independently, coordination of these systems during severe disruptions can allow for targeted delivery to lifeline services, including gas delivery for residential heating and power delivery for critical facilities. To address the challenge of estimating maximum joint network capacities under such disruptions, we consider the task of determining feasible steady-state operating points for severely damaged systems while ensuring the maximal delivery of gas and power loads simultaneously, represented mathematically as the nonconvex joint Maximal Load Delivery (MLD) problem. To increase its tractability, we present a mixed-integer convex relaxation of the MLD problem. Then, to demonstrate the relaxation’s effectiveness in determining bounds on network capacities, exact and relaxed MLD formulations are compared across various multi-contingency scenarios on nine joint networks ranging in size from 25 to 1191 nodes. The relaxation-based methodology is observed to accurately and efficiently estimate the impacts of severe joint network disruptions, often converging to the relaxed MLD problem’s globally optimal solution within ten seconds.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

MOPED: An Alternative Workflow for HERON

MOPED is an alternate workflow to HERON. Preliminary tests display computational advantages of MOPED in terms of run time. While maintaining these advantages, solution convergence remained similar.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Supporting U.S. National Security Through Cybersecurity Partnerships

At NLR, we're studying energy evolutions and threats to understand the challenges they pose and uncover ways to leverage grid advancements to achieve more secure, defensible, and reliable systems. Our integrated research approach bridges the gap between cyber threats and real-world consequences to deliver actionable solutions that reduce vulnerabilities and help strengthen U.S. national security.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Transmission Interconnection Roadmap: Transforming Bulk Transmission Interconnection by 2035

The U.S. electricity system is amid a rapidly occurring and widespread energy transition. Regional, Tribal, state, and customer demand for new energy resources, combined with favorable policies, is driving a rapid rise of interconnection requests. Interconnection processes will need to evolve to handle this larger number of requests today and into the future, as policy and economic drivers continue to motivate significant resource development. This roadmap identifies and organizes nearer- and longer-term solutions to enable transmission interconnection processes to meet this expected demand, and it is intended for a diverse audience of stakeholders participating within transmission interconnection processes. The roadmap is a result of the Interconnection Innovation e-Xchange (i2X) program launched by the U.S. Department of Energy (DOE) in June 2022 to convene stakeholders and address interconnection challenges. The roadmap is organized into four primary goal areas, each important to the overall i2X mission to enable a simpler, faster, and fairer interconnection of clean energy resources while enhancing the reliability, resiliency, and security of our electric grid. The first goal aims to improve interconnection data transparency, to aid interconnection customers’ ability to screen and site potential projects, better enable third-party modeling, facilitate more process automation, enhance competition while ensuring equitable outcomes, and enable benchmarking, tracking, and auditing of interconnection processes and reforms. The second goal covers solutions to improve queue management practices, affected system studies, fair processes, and workforce development. The third goal incorporates solutions that aim to improve cost allocation, reduce costs to electricity consumers, enhance the coordination between transmission planning and the interconnection process, and optimize the rightsizing of transmission investment through improvements in interconnection studies. The fourth and final goal aims to reduce the performance issues not identified during interconnection studies by updating technical requirements within interconnection studies, models, and tools while also improving industry interconnection standards.

24 POWER TRANSMISSION AND DISTRIBUTION↗