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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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13 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Secure Policy-Based Configuration Framework (PBCONF)

In 2013, EPRI and a project team comprised of the University of Illinois at Urbana-Champaign, Ameren, and Schweitzer Engineering Laboratories began a multi-year effort under an award from the U.S. Department of Energy Office of Electricity Delivery and Energy Reliability Cyber Security or Energy Delivery Systems (CEDS) to develop a framework for secure remote configuration of modern and legacy devices. A goal of this Secure Policy-Based Configuration Framework (PBCONF) is to develop an extensible, open source, policy-based configuration framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Policy-based secure communication with automatic key management for industrial control and automation systems

A method includes generating at least one access vector associated with a specified device in an industrial process control and automation system. The specified device has one of multiple device roles. The at least one access vector is generated based on one or more communication policies defining communications between one or more pairs of devices roles in the industrial process control and automation system, where each pair of device roles includes the device role of the specified device. The method also includes providing the at least one access vector to at least one of the specified device and one or more other devices in the industrial process control and automation system in order to control communications to or from the specified device.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Intelligent network slicing and policy-based routing engine

One or more aspects of the present disclosure are directed to network optimization solutions provided as software agents (applications) executed on network nodes in a heterogenous multi-vendor environment to provide cross-layer network optimization and ensure availability of network resources to meet associated Quality of Experience (QoE) and Quality of Service (QoS). In one aspect, a network slicing engine is configured to receive at least one request from at least one network endpoint for access to the heterogeneous multi-vendor network for data transmission; receive information on state of operation of a plurality of communication links between the plurality of nodes; determine a set of data transmission routes for the request; assign a network slice for serving the request; determine, from the set of data transmission routes, an end-to-end route for the network slice; and send network traffic associated with the request using the network slice and over the end-to-end route.

Mody, Apurva N.↗
DOE OSTI2025

Improving the prediction of daily reservoir releases over the CONUS using conditioned LSTM

Reservoirs play a vital role in regulating streamflow timing and variability for hydroelectricity, flood control, water supply, irrigation, and recreation. Despite their importance, many reservoirs lack comprehensive operational guidelines, making their management complex due to conflicting operational objectives. Hence traditional policy-based reservoir models often fail to capture real-world conditions accurately and they depend on perfect streamflow predictions, which are not always available. In contrast, data-driven models like Long Short-Term Memory (LSTM) networks offer a robust alternative. This study introduces an approach that integrates reservoir characteristics—such as main use, climate, and maximum capacity—into the LSTM model to enhance reservoir release predictions. Using data from nearly 200 reservoirs in the contiguous United States (CONUS), our conditioned LSTM model (LSTM_cond) was compared with both the vanila LSTM and a traditional policy-based approach. Furthermore, our results show that while both LSTM_cond and LSTM perfoms better than the policy-based approach, LSTM_cond consistently outperforms LSTM for hydroelectric, water supply, irrigation, and recreation reservoirs. The KGE median values for LSTM_cond for out-sample reservoirs are 0.764, 0.565, 0.821, and 0.779, respectively, for the aforementioned reservoir types, which are consistently higher that the corresponding KGE values of 0.737, 0.413, 0.775, and 0.713 of LSTM, demonstrating its advantages in improving generalizability.

CONUS↗
DOE OSTI2016

Midmarket Solar Policies in the United States: A Guide for Midsized Solar Customers

The midscale market for solar photovoltaics (PV) has not experienced the same high growth rate as residential- or utility-scale market segments in the past five years when solar PV deployment increased rapidly. Midscale solar can be defined as behind-the-meter solar PV between 50 kilowatts and 2 megawatts adopted by multi-housing residential, commercial, industrial, non-profit, and other entities. A number of challenges face the midscale segment, including difficulties in contracting, mismatch between tenant lease and PV financing terms, high transaction costs relative to project sizes, and inefficiencies in matching prospective projects with capital. The changing policy landscape across U.S. states provides both opportunities and challenges to midmarket solar. Some states, such as California, are expanding system capacity limits for policies such as net metering, thus enabling a wider range of customers to benefit from excess generation. A number of states and utilities are making changes to rate design to introduce new or higher user fees for solar customers or reduced tariffs for net metering, which decrease the value of solar generation. An understanding of these policies relative to project feasibility and economics is important for prospective customers to make informed decisions to adopt solar PV. This guide complements existing solar policy resources to help potential customers navigate through the policy landscape in order to make informed decisions for their solar investment. The first part of this guide introduces the key solar policies necessary for policy-based decision-making, which involves using knowledge of a solar policy to improve project economics and efficiency. Policies that could result in policy-based decisions include interconnection standards, net metering, user fees, incentives, and third-party ownership policies. The goal of this section is to equip prospective customers and project developers with the tools necessary to understand and use solar policies in a dynamic policy environment. The second part of this guide provides a complete, state-by-state inventory of midmarket solar policies for potential customers and developers to use as reference when making policy-based decisions. Although solar policies are dynamic, the profiles provide a framework for assessing policies to build the parameters that could be used to determine feasibility and structure of a solar PV system for midmarket customers and developers.

14 SOLAR ENERGY↗
DOE OSTI2024

Implications of stop-and-go traffic on training learning-based car-following control

Learning-based car-following control (LCC) of connected and autonomous vehicles (CAVs) is gaining significant attention with the advancement of computing power and data accessibility. While the flexibility and large model capacity of model-free architecture enable LCC to potentially outperform the model-based car-following (CF) model in improving traffic efficiency and mitigating congestion, the generalizability of LCC for traffic conditions different from the training environment/dataset is not well-understood. Herein, this study seeks to explore the impact of stop-and-go traffic in the training dataset on the generalizability of LCC. It uses the characteristics of lead vehicle trajectories to describe stop-and-go traffic, and links the theory of identifiability (i.e., obtaining a unique parameter estimation result using sensor measurements) to the generalizability of behavior cloning (BC) and policy-based deep reinforcement learning (DRL). Correspondingly, the study shows theoretically that: (i) stop-and-go traffic can enable the property of identifiability and enhance the control performance of BC-based LCC in different traffic conditions; (ii) stop-and-go traffic is not necessary for DRL-based LCC to generalize to different traffic conditions; (iii) DRL-based LCC trained with only constant-speed lead vehicle trajectories (not sufficient to ensure identifiability) can be generalized to different traffic conditions; and (iv) stop-and-go traffic increases variance in the training dataset, which improves the convergence of parameter estimation while negatively impacting the convergence of DRL to the optimal control policy. Numerical experiments validate the above findings, illustrating that BC-based LCC entails comprehensive training datasets for generalizing to different traffic conditions, while DRL-based LCC can achieve generalization with simple free-flow traffic training environments. This further suggests DRL as a more promising and cost-effective LCC approach to reduce operational costs, mitigate traffic congestion, and enhance safety and mobility, which can accelerate the deployment and acceptance of CAVs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Comparison of Current and Future PM2.5 Air Quality in China Under CMIP6 and DPEC Emission Scenarios

The latest scenarios for the sixth Coupled Model Intercomparison Project (CMIP6) provide opportunities to explore future climate and air pollution mitigation pathways with socioeconomic features. However, to what extent inadequate consideration of region-specific policies in the CMIP6 scenarios would affect regional air quality projections is still not clear. Here, we simulated China’s future PM2.5 concentrations and compositions with local policy-based scenarios and the original CMIP6 datasets, for the first time estimating future air quality differences induced by the lack of local pollution control policies in the CMIP6 scenarios. We found that the CMIP6-driven projections overestimate major chemical aerosols by 10% – 70% in 2015 and fail to capture the rapid PM2.5 concentration decline during 2015–2019. Near-term differences further lead to 43% – 49% (5 – 9 µg/m3) PM2.5 overpredictions in 2050 in CMIP6 scenarios compared with policy-based mitigation scenarios. Our study demonstrates the indispensable influences of local policies on regional analyses and suggests that the research community should incorporate more region-specific information in future scenario designs.

Cheng, Jing↗
DOE OSTI2017

Simplified Interface to Complex Memory Hierarchies 1.x

Memory systems are expected to get evermore complicated in the coming years, and it isn't clear exactly what form that complexity will take. On the software side, a simple, flexible way of identifying and working with memory pools is needed. Additionally, most developers seek code portability and do not want to learn the intricacies of complex memory. Hence, we believe that a library for interacting with complex memory systems should expose two kinds of abstraction: First, a low-level, mechanism-based interface designed for the runtime or advanced user that wants complete control, with its focus on simplified representation but with all decisions left to the caller. Second, a high-level, policy-based interface designed for ease of use for the application developer, in which we aim for best-practice decisions based on application intent. We have developed such a library, called SICM: Simplified Interface to Complex Memory.

Lang, Michael↗
DOE OSTI2022

Cabana: A Performance Portable Library for Particle-Based Simulations

Particle-based simulations are ubiquitous throughout many fields of computational science and engineering, spanning the atomistic level with molecular dynamics (MD), to mesoscale particle-in-cell (PIC) simulations for solid mechanics, device-scale modeling with PIC methods for plasma physics, and massive N-body cosmology simulations of galaxy structures, with many other methods in between (Hockney & Eastwood, 1989). While these methods use particles to represent significantly different entities with completely different physical models, many low-level details are shared including performant algorithms for short- and/or long-range particle interactions, multi-node particle communication patterns, and other data management tasks such as particle sorting and neighbor list construction. Cabana is a performance portable library for particle-based simulations, developed as part of the Co-Design Center for Particle Applications (CoPA) within the Exascale Computing Project (ECP) (Alexander et al., 2020). The CoPA project and its full development scope, including ECP partner applications, algorithm development, and similar software libraries for quantum MD, is described in (Mniszewski et al., 2021). Cabana uses the Kokkos library for on-node parallelism (Edwards et al., 2014; Trott et al., 2022), enabling simulation on multi-core CPU and GPU architectures, and MPI for GPU-aware, multi-node communication. Cabana provides particle simulation capabilities on almost all current Kokkos backends, including serial execution, OpenMP (including OpenMP-Target for GPUs), CUDA (NVIDIA GPUs), HIP (AMD GPUs), and SYCL (Intel GPUs), providing a clear path for the coming generation of accelerator-based exascale hardware. Cabana builds on Kokkos by providing new particle data structures and particle algorithms resulting in a similar execution policy-based, node-level programming model that is intended to be used in addition to the core Kokkos library within an application. Cabana is designed as an application and physics agnostic, but particle-specific toolkit which can either be used to generate a new application, or to be used as needed in existing applications at various levels of invasiveness including through interfaces that wrap user memory in existing data structures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Revenue Sufficiency and Reliability in a Zero Marginal Cost Future: Preprint

Features of existing wholesale electricity markets, such as administrative pricing rules and policy-based reliability standards, can distort market incentives from allowing generators sufficient opportunities to recover both fixed and variable costs. Moreover, these challenges can be amplified by other factors, including (1) inelastic demand resulting from a lack of price signal clarity, (2) low- or near-zero marginal cost generation, particularly arising from low natural gas fuel prices and variable generation (VG), such as wind and solar, and (3) the variability and uncertainty of this VG. As power systems begin to incorporate higher shares of VG, many questions arise about the suitability of the existing marginal-cost-based price formation, primarily within an energy-only market structure, to ensure the economic viability of resources that might be needed to provide system reliability. This article discusses these questions and provides a summary of completed and ongoing modelling-based work at the National Renewable Energy Laboratory to better understand the impacts of evolving power systems on reliability and revenue sufficiency.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Simplified Interface to Complex Memory Hierarchies 1.x

Memory systems are expected to get evermore complicated in the coming years, and it isn't clear exactly what form that complexity will take. On the software side, a simple, flexible way of identifying and working with memory pools is needed. Additionally, most developers seek code portability and do not want to learn the intricacies of complex memory. Hence, we believe that a library for interacting with complex memory systems should expose two kinds of abstraction: First, a low-level, mechanism-based interface designed for the runtime or advanced user that wants complete control, with its focus on simplified representation but with all decisions left to the caller. Second, a high-level, policy-based interface designed for ease of use for the application developer, in which we aim for best-practice decisions based on application intent. We have developed such a library, called SICM: Simplified Interface to Complex Memory.

Lang, Michael↗
DOE OSTI2017

Revenue Sufficiency and Reliability in a Zero Marginal Cost Future

Features of existing wholesale electricity markets, such as administrative pricing rules and policy-based reliability standards, can distort market incentives from allowing generators sufficient opportunities to recover both fixed and variable costs. Moreover, these challenges can be amplified by other factors, including (1) inelastic demand resulting from a lack of price signal clarity, (2) low- or near-zero marginal cost generation, particularly arising from low natural gas fuel prices and variable generation (VG), such as wind and solar, and (3) the variability and uncertainty of this VG. As power systems begin to incorporate higher shares of VG, many questions arise about the suitability of the existing marginal-cost-based price formation, primarily within an energy-only market structure, to ensure the economic viability of resources that might be needed to provide system reliability. This article discusses these questions and provides a summary of completed and ongoing modelling-based work at the National Renewable Energy Laboratory to better understand the impacts of evolving power systems on reliability and revenue sufficiency.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗