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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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Reimagining What's Possible: How NREL's Energy Analysis and Decision Support Capabilities are Guiding Energy Systems Transformation at Home and Around the World; NREL (National Renewable Energy Laboratory)

This engaging brochure shows examples of how NREL enables energy system transformation through robust capabilities in energy analysis and decision support. By reimagining what's possible for renewable energy, NREL contributes to the Department of Energy mission to create energy systems that are cleaner, more reliable, and more secure.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2013

Texas Clean Energy Project: Decision Point Application, Section 2: Topical Report - Phase 1, February 2010-October 2013

Summit Texas Clean Energy, LLC (STCE) is developing the Texas Clean Energy Project (TCEP or the Project) to be located near Penwell, Texas. The TCEP will include an Integrated Gasification Combined Cycle (IGCC) power plant with a nameplate capacity of 400 megawatts electric (MWe), combined with the production of urea fertilizer and the capture, utilization and storage of carbon dioxide (CO 2 ) sold commercially for regional use in enhanced oil recovery (EOR) in the Permian Basin of west Texas. The TCEP will utilize coal gasification technology to convert Powder River Basin subbituminous coal delivered by rail from Wyoming into a synthetic gas (syngas) that will be cleaned and further treated so that at least 90 percent of the overall carbon entering the IGCC facility will be captured. The clean syngas will then be divided into two highhydrogen (H 2 ) concentration streams, one of which will be combusted as a fuel in a combined cycle power block for power generation and the other converted into urea fertilizer for commercial sale. The captured CO 2 will be divided into two streams: one will be used in producing the urea fertilizer and the other will be compressed for transport by pipeline for offsite use in EOR and permanent underground sequestration. The TCEP was selected by the U.S. Department of Energy (DOE) Office of Fossil Energy (FE) for cost-shared co-funded financial assistance under Round 3 of its Clean Coal Power Initiative (CCPI). A portion of this financial assistance was budgeted and provided for initial development, permitting and design activities. STCE and the DOE executed a Cooperative Agreement dated January 29, 2010, which defined the objectives of the Project for all phases. During Phase 1, STCE conducted and completed all objectives defined in the initial development, permitting and design portions of the Cooperative Agreement. This topical report summarizes all work associated with the project objectives, and additional work required to complete the financing of the Project. In general, STCE completed project definition, a front-end, engineering and design study (FEED), applied for and received its Record of Decision (ROD) associated with the NEPA requirements summarized in a detailed Environmental Impact Statement. A topical report covering the results of the FEED is the subject of a separate report submitted to the DOE on January 26, 2012. References to the FEED report are contained herein. In August 2013, STCE executed fixed-price turnkey EPC contracts and previously, in December 2011 a long-term O&M agreement, with industry-leading contractors. Other work completed during Phase 1 includes execution of all commercial input and offtake agreements required for project financing. STCE negotiated long-term agreements for power, CO 2 and urea offtake. A contract for the purchase of coal feedstock from Cloud Peak Energy’s Cordero Rojo mine was executed, as well as necessary agreements (supplementing the tariff) with the Union Pacific Railroad (UPRR) for delivery of the coal to the TCEP site. STCE executed firm agreements for natural gas transportation with ONEOK for long-term water supply with a private landowner. In addition, STCE secured options for critical easements and rights-of-way, completed and updated a transmission study, executed an interconnection agreement and has agreed a target October 31, 2013 financial closing date with debt and conventional and tax equity.

Mattes, Karl↗
DOE OSTI2016

Transportation Big Data: Unbiased Analysis and Tools to Inform Sustainable Transportation Decisions

Today, transportation operation and energy systems data are generated at an unprecedented scale. The U.S. Department of Energy's National Renewable Energy Laboratory (NREL) is the go-to source for expertise in providing data and analysis to inform industry and government transportation decision making. The lab's teams of data experts and engineers are mining and analyzing large sets of complex data -- or 'big data' -- to develop solutions that support the research, development, and deployment of market-ready technologies that reduce fuel consumption and greenhouse gas emissions.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2014

Gila San Francisco Decision Support Tool - 2010

The Gila-San Francisco Decision Support Tool analyzes the water demand and supply for the Gila San Francisco region spanning four counties in southwestern New Mexico (Catron, Hidalgo, Luna and Grant). Catalyzed by the 2004 Arizona Water Settlement Act and prompted by a keen awareness for the unique ecology in the region, the model was developed by Sandia with a collaborative modeling team from federal, state, local, and public stakeholders

Sun, Amy Cha-Tien↗
DOE OSTI2017

Decision Support For Digester Algae Integration For Improved Environmental And Economic Sustainability

The Idaho National Laboratory (INL) has teamed with University of Idaho and Boise State University to make the use of ADs more attractive by implementing a two-stage AD and coupling additional processes to the system. The addition of a polyhydroxyalkanoate (PHA) reactor, algae cultivation system, and a biomass treatment system such as fast-pyrolysis or hydrothermal liquefaction (HTL) would further sequester carbon and nutrients, as well as add valuable products that can be sold or used on-site to mitigate costs. The Decision-support for Digester-Algae IntegRation for Improved Environmental and Economic Sustainability (DAIRIEES) technoeconomic model will play a key role in evaluating the effectiveness and viability of this system to achieve economic and environmental sustainability by the dairy industry.

Guillen, Donna P.↗
DOE OSTI2018

Decision support system for outage management and automated crew dispatch

A decision support system is provided for utility operations to assist with crew dispatch and restoration activities following the occurrence of a disturbance in a multiphase power distribution network, by providing a real-time visualization of possible location(s). The system covers faults that occur on fuse-protected laterals. The system uses real-time data from intelligent electronics devices coupled with other data sources such as static feeder maps to provide a complete picture of the disturbance event, guiding the utility crew to the most probable location(s). This information is provided in real-time, reducing restoration time and avoiding more costly and laborious fault location finding practices.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Integrated Decision Methodology for Final Disposal of Radioactive Sources

Radioactive sources are used throughout the world, and their beneficial uses are many. Sources are at the core of many applications in different branches of industry, medicine, agriculture and research. Sources are present in a very wide range of equipment that is used for, inter alia, cancer treatment, killing bacteria in food, sterilizing medical supplies, measuring instruments such as gauges used to measure soil moisture and soil density and many other components, irradiating seeds for enhancing food production, protecting buildings from lightning strikes, mapping underground sources of water, prospecting for oil and gas reserves, measuring density of soil for construction projects, or even detecting smoke. At their “cradle” and when in use, radioactive sources are usually properly managed and controlled. It is when a source has reached the end of its useful life when they are at a higher risk of being under inadequate control, poorly managed, or even becoming orphan. Sources that have reached the end of their useful life must be carefully and safely managed. The “grave” of disused sources needs a proper disposal process to prevent them from posing a potential threat to people or the environment. To protect the public and the environment from the potential hazards of ionizing radiation, and to prevent disused sources from becoming orphan, a “cradle-to-grave” control of radioactive sources is essential, as promoted by the Code of Conduct on the Safety and Security of Radioactive Sources. This approach requires a national policy and strategy, an adequate legal and regulatory framework, and adequate resources and infrastructure. Many IAEAMember States have expressed the need for effective solutions to this challenge. There is also a gap related to the life cycle costs of potential disposal practices. This paper proposes a multi-attribute decision making methodology to compare disposal options using both economic and non-economic criteria leading to a preferred option for disposal and a strategy for final disposal of disused radioactive sources that is specific and optimized for each IAEA Member State.

Beatty, Randy↗
DOE OSTI2018

City Energy: From Data to Decisions. Hutchinson, Minnesota: Evaluating Local Solar Energy Generation Potential

The City of Hutchinson, Minnesota, partnered with the Energy Department and the National Renewable Energy Laboratory (NREL) to demonstrate how data and analysis can inform more strategic energy decisions. NREL based its analysis in part on the City Energy Profiles on the State and Local Energy Data (SLED) website (eere.energy.gov/sled). The profiles contain data compiled by SLED and the Cities Leading through Energy Analysis and Planning (Cities-LEAP) program. Cities across the country can follow the same approach and use data-driven analysis in their own energy planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

City Energy: From Data to Decisions. Rochester, New York: Reducing Energy Costs for Low-Income Households

The City of Rochester partnered with the Energy Department and the National Renewable Energy Laboratory (NREL) to demonstrate how data and analysis can inform more strategic energy decisions. NREL based its analysis in part on the State and Local Energy Data (SLED) website's City Energy Profiles (eere.energy.gov/sled). The profiles contain data compiled by SLED and the Cities Leading through Energy Analysis and Planning (Cities-LEAP) program. Cities across the country can follow the same approach and use data-driven analysis in their own energy planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Intelligently-Automated Facilities Expansion with the HEPCloud Decision Engine

The next generation of High Energy Physics experiments are expected to generate exabytes of data---two orders of magnitude greater than the current generation. In order to reliably meet peak demands, facilities must either plan to provision enough resources to cover the forecasted need, or find ways to elastically expand their computational capabilities. Commercial cloud and allocation-based High Performance Computing (HPC) resources both have explicit and implicit costs that must be considered when deciding when to provision these resources, and to choose an appropriate scale. In order to support such provisioning in a manner consistent with organizational business rules and budget constraints, we have developed a modular intelligent decision support system (IDSS) to aid in the automatic provisioning of resources---spanning multiple cloud providers, multiple HPC centers, and grid computing federations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

HEPCloud, an Elastic Hybrid HEP Facility using an Intelligent Decision Support System

HEPCloud is rapidly becoming the primary system for provisioning compute resources for all Fermilab-affiliated experiments. In order to reliably meet the peak demands of the next generation of High Energy Physics experiments, Fermilab must plan to elastically expand its computational capabilities to cover the forecasted need. Commercial cloud and allocation-based High Performance Computing (HPC) resources both have explicit and implicit costs that must be considered when deciding when to provision these resources, and at which scale. In order to support such provisioning in a manner consistent with organizational business rules and budget constraints, we have developed a modular intelligent decision support system (IDSS) to aid in the automatic provisioning of resources spanning multiple cloud providers, multiple HPC centers, and grid computing federations. In this paper, we discuss the goals and architecture of the HEPCloud Facility, the architecture of the IDSS, and our early experience in using the IDSS for automated facility expansion both at Fermi and Brookhaven National Laboratory.

Mhashilkar, Parag↗
DOE OSTI2018

Reliability Indexing of Intra-Facility Dispatch Decisions

The Powertrain Reliability Index (PRI) quantifies the trade-offs made in system reliability for efficiency, water use, and environmental purposes for relevant stakeholders. Using historical data from five North American hydroelectric facilities on a shared river system, this metric characterizes the annual probabilistic reliability of intra-facility dispatch decisions. The metric characterizes the operational risk of actual dispatch based on the lowest risk-exposed dispatch and the highest risk-exposed dispatch. By normalizing facility reliability by the number of components described within the facility, constraining operation by the historic dispatch total of unit starts and generating hours, and bounding by required grid flexibility, each facility’s dispatch can be scored for reliability.

Signore, Stephen R.↗
DOE OSTI2019

LeptonJet Categorization with Decision Tree

With much evidence for dark matter arising from astronomical observations, particle physics has also begun to look for dark matter candidates. Here, a boosted decision tree machine learning algorithm (BDT) is used to distinguish signal from background in a search for self-interacting dark matter at CMS with two different final states of signal. The signal final states are two displaced collimated pairs of leptons (leptonJets). In this project, possible improvements to the BDT are explored. Various methods of signal separation are studied, as well as various accuracy metrics to study and compare the different models. Through the separated models and improvements in training, a slight increase in accuracy score was obtained (0.01% on electrons, 0.46% on muons). Additionally, the separated models were able to recognize a significantly increased percentage of signal, particularly against the electron signal. Separated models are being considered as part of the larger an alysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

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

Increasing interest and investment in connected, automated, and electric vehicles, and mobility-as-a-service concepts are paving the way for the next major shift in transportation through automated and shared mobility. The initial excitement towards rapid deployment and adoption of automated vehicles has subsided, and low-speed automated shuttles are emerging as a more pragmatic pathway for introducing automated mobility in geo-fenced 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 regarding benefits or dis-benefits 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 presentation 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 (AMD) modeling and simulation toolkit, the proposed decision support tool can help cities evaluate the mobility and sustainability impacts of deploying shared automated vehicles in geofenced regions. This paper presents a description of the toolkit, as well as a sample scenario analysis for the deployment of low-speed automated shuttles in Greenville, South Carolina. Results from the scenario study demonstrate the effectiveness of the proposed simulation toolkit in planning for advanced mobility systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Charging and Repositioning Decision Making for Fully Automated Ride-Hailing Fleet

This is the presentation profile for 2020 DOE Vechicle Technologies Office Annual Merit Review. It introduces the research results in the project "Charging and Repositioning Decision Making for Fully Automated Ride-Hailing Fleet". These results come from the work of developing a framework for integrated dispatching and charging management of an autonomous electric vehicle ride-hailing fleet, and a case study in New York City was conducted to investigate the benefits of systematic optimization approach comparing to a heuristic approach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

FEMP Facility Evaluation (Audit) Decision Tree

This document is a resource to be used in concert with FEMP Audit Definitions, FEMP Consolidated Facility Management Guidance, and agency best practices and expert judgment—for agencies to use to determine the best approaches to evaluate their facilities. It features a decision tree that provides a framework for agencies to interpret gathered facility data to understand the existing conditions; benchmark the facility against others in the portfolio to determine whether an on-site or remote audit is appropriate for a facility; determine the best remote or on-site audit level of detail to meet agency facility auditing goals, such as meeting Energy Independence and Security Act of 2007 requirements and developing projects; and improve their energy and water management programs.

auditing↗
DOE OSTI2021

Making Data-Driven Policy Decisions for the Nation’s First Building Energy Performance Standards

Nearly every major U.S. city has committed itself to ambitious climate action goals – for Washington, DC this means a 50 percent reduction in greenhouse gases by 2032 and carbon neutrality by 2050. In support of these goals, Washington, DC has passed one of the most aggressive and practical climate action bills in the nation—with the Clean Energy DC Omnibus Act, DC became the first city in the U.S. to adopt energy performance standards for existing buildings. DC’s Building Energy Performance Standards (BEPS) require energy efficiency improvements for all commercial and multifamily buildings that do not meet a sector-specific minimum ENERGY STAR score or equivalent metric, with iterative compliance cycles every five years that will accelerate the pace of whole building retrofits. This paper explores this revolutionary policy framework and uses two data analysis projects that DC conducted to evaluate the potential impact of the BEPS and move towards carbon neutrality. First, we analyze the potential energy savings and greenhouse gas reductions, as well as potential cost impacts, from the implementation of a BEPS policy in DC We then examine the role of BEPS in a carbon neutrality strategy, how BEPS savings iterate over time, and what additional existing building improvements will be driven by the gravitational pull of new building codes on median performance. The paper highlights the benefits and limitations of such data-driven approaches to support policy decisions. Finally, we will review ongoing BEPS implementation, including expected policy directions, critical supportive programs, and lessons learned to date.

Bergfeld, Katie↗