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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Enhancing Performance Contracts with Monitoring-Based Commissioning (MBCx)

To support federal and commercial building owners in understanding, procuring, and implementing monitoring-based commissioning (MBCx) software tools, this report provides an overview of their capabilities, the MBCx process, and how MBCx can be integrated into—and benefit—each phase of a performance contract. This report is intended to spur increased use of MBCx in performance contracts, whether an agency has previously installed or is considering adding MBCx at the time of their project. Additionally, case studies illustrate successful experiences using MBCx within performance contracts at a university campus and across a large number of buildings managed by the General Services Administration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Facility Cybersecurity Framework Best Practices Version 2.0

Federal facilities are increasingly adopting automation and connecting to the Internet creating an energy-internet-of-things environment that converges operational technology (OT) and information technology (IT). Today's buildings increasingly weave together networked sensors and cyber and physical systems that enable data to be collected, aggregated, exchanged, stored and monetized in new ways. Building technological advances have created new energy technology, services, markets and value creation opportunities (e.g. transactive energy, two-way grid communications, machine learning, and increased use of renewable and distributed energy resources). But as larger data sets are being exchanged at faster speeds between an increasing number of OT systems, it becomes more difficult to protect the security of the data lifecycle and the physical equipment it interacts with. These challenges are especially difficult to overcome because the economic and environmental gain (interoperability, big data, social networks and ubiquitous information sharing) are driving these prominent trends in the digital age. Often cybersecurity is an afterthought.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Facility Cybersecurity Framework Best Practices Version 2.0

Federal facilities are increasingly adopting automation and connecting to the Internet creating an energy-internet-of-things environment that converges operational technology (OT) and information technology (IT). Today's buildings increasingly weave together networked sensors and cyber and physical systems that enable data to be collected, aggregated, exchanged, stored and monetized in new ways. Building technological advances have created new energy technology, services, markets and value creation opportunities (e.g. transactive energy, two-way grid communications, machine learning, and increased use of renewable and distributed energy resources). But as larger data sets are being exchanged at faster speeds between an increasing number of OT systems, it becomes more difficult to protect the security of the data lifecycle and the physical equipment it interacts with. These challenges are especially difficult to overcome because the economic and environmental gain (interoperability, big data, social networks and ubiquitous information sharing) are driving these prominent trends in the digital age. Often cybersecurity is an afterthought.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Deployable Wind-Hybrid Power Systems for Defense and Disaster Response Applications

This report presents an analysis of the performance of deployable energy systems comprised of wind energy systems integrated with diesel generators, photovoltaic systems, and battery storage to meet the load requirements of a representative U.S. Army forward operating base. The analysis is conducted using HOMER, a microgrid analysis software that can search through a wide range of parameters to design and optimize microgrid power systems. The search parameters include the system architecture, the wind and solar resources, and the availability of diesel fuel. The results of the analysis measure the relative performance of the different systems and environments in terms of the overall transportation cost to deploy the system and the ability to provide resilience in terms of meeting mission critical loads.

02 PETROLEUM↗
DOE OSTI2022

Hierarchical Conceptual Model Event and Activity Domains for Forecasting State-Sponsored Civil Nuclear Power Activities

In FY20, the Savannah River National Laboratory and the Sanghani Center for Artificial Intelligence and Data Analytics (Virginia Tech) entered a collaboration funded by the Department of Energy’s Office of Defense Nuclear Nonproliferation Research and Development to develop a machine learning based modeling pipeline to extract proliferation events of interest from open data sources. The prototype modeling pipeline that was developed relies on the use of time dependent word embedding models to identify contextual shifts in key words and phrases that act as indicators of events of interest. The FY20- 21 efforts were focused on a narrow topical domain of forecasting “fissile core fabrication” at the Savannah River Site prior to its official announcement in 2018. In FY22, the research team was funded to continue development of the modeling pipeline by applying it to the problem of forecasting new, and/or significant changes to existing, civil nuclear power reactors around the world. In this effort, the development will focus on proving applicability to a broader topical domain and in data environments that may contain more sparse information, relative to the United States. In this report, the worldwide landscape of civil nuclear reactors is outlined, the timeline of interest is defined, and a hierarchical conceptual model is established to identify the activity domains of interest and the event domains of interest. This preliminary effort will guide the curation of a glossary of key terms for data acquisition, as well as the downstream modeling efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

FOA 1861 Data Curation Overview

This document describes the process executed to collect, examine, and consolidate Phasor Measurement Unit (PMU) data from multiple transmission operators into a common dataset. The consolidated PMU data set was further anonymized and distributed to the Department of Energy Funding Opportunity Announcement (FOA) 1861 Big Data Analysis of Synchrophasor Data awardees.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Machine Learning for Automated Metadata Assignment in Buildings: Cooperative Research and Development (Final Report, CRADA Number CRD-18-00767)

RealTerm Energy and NREL have identified a shared vision to evaluate opportunities to facilitate the organization and assignment of metadata to building control system (BCS) data via industry-informed machine learning (ML). Manual metadata assignment is labor intensive and costly, slowing down any Energy Management and Information System (EMIS) deployment in the building space. This project aims to develop methodologies to accurately assign this metadata and significantly decrease the level of effort associated with deploying EMIS. The objective of this project is to identify/design methodologies to assign metadata to HVAC control points automatically. The identified methodologies will be programmed in analytics algorithms so they can ingest a list of points and produce a detailed tagging following the Haystack classification nomenclature. To validate the efficacy of each methodology, tagging results will be compared utilizing a list of points extracted from RealTerm's building database-as well as data extracted from the NREL campus via the Intelligent Campus program-enabling testing against large datasets with real world challenges. The developed methodologies may leverage building manager/operator input on a limited basis to add context to the classifying algorithms. The partnership aims to advance global efforts in areas related to the DOE missions through improving operational performance of commercial buildings. It is well documented that buildings fall out of commission after they are occupied, wasting significant energy and incurring associated costs simply due to poor operational performance. Emerging EMIS technologies that perform continuous commissioning help to address this issue, yet integration of these systems can be labor intensive both for the technology vendor and the building owner/operator. This project will enable more efficient and cost-effective analytics for buildings, enabling improvement in building operations at lower cost points.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Federated Learning and Differential Privacy: What might AI-Enhanced co-design of microelectronics learn?

Data is a valuable commodity, and it is often dispersed over multiple entities. Sharing data or models created from the data is not simple due to concerns regarding security, privacy, ownership, and model inversion. This limitation in sharing can hinder model training and development. Federated learning can enable data or model sharing across multiple entities that control local data without having to share or exchange the data themselves. Differential privacy is a conceptual framework that brings strong mathematical guarantee for privacy protection and helps provide a quantifiable privacy guarantee to any data or models shared. The concepts of federated learning and differential privacy are introduced along with possible connections. Lastly, some open discussion topics on how federated learning and differential privacy can tied to AI-Enhanced co-design of microelectronics are highlighted.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Cybersecurity and Digital Components: Supply Chain Deep Dive Assessment

The report “America’s Strategy to Secure the Supply Chain for a Robust Clean Energy Transition” lays out the challenges and opportunities faced by the United States in the energy supply chain as well as the federal government plans to address these challenges and opportunities. It is accompanied by several issue-specific deep dive assessments, including this one, in response to Executive Order 14017 “America’s Supply Chains,” which directs the Secretary of Energy to submit a report on supply chains for the energy sector industrial base. The Executive Order is helping the federal government to build more secure and diverse U.S. supply chains, including energy supply chains. As the energy sector has become more globalized and increasingly complex, digitized, and even virtualized, its supply chain risk for digital components – the software, virtual platforms and services, and data – in energy systems has evolved and expanded. All digital components in U.S. energy sector systems are vulnerable and may be subject to cyber supply cha in risks stemming from a variety of threats, vulnerabilities, and impacts. This includes digital components in all systems within the ESIB, namely those systems operated by asset owners across different energy subsectors (e.g., electricity, oil and natural gas, and renewables) and the systems operated by a worldwide industrial complex with capabilities to perform research and development and design, produce, operate, and maintain energy sector systems, subsystems, components, or parts to meet U.S. energy requirements. Supply chain risks for digital components including software, virtual platforms and services, and data have grown in recent years as increasingly sophisticated cyber adversaries have targeted exploiting vulnerabilities in these digital assets. Supply chain risks for digital components in energy sector systems will continue to evolve and likely increase as these systems are increasingly interconnected, digitized, and remotely operated.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Coordinated Ramping Product and Regulation Reserve Procurements in CAISO and MISO using Multi-Scale Probabilistic Solar Power Forecasts (Pro2R)

How can probabilistic solar forecasts lower costs and improve reliability for independent system operator (ISO) markets? We tackle this question in three steps. First, we enhance an existing solar forecasting system to provide well-calibrated hours-ahead probabilistic forecasts. We then relate the degree of uncertainty in those forecasts to error distributions for net load ramps for the California ISO (CAISO) using statistical and machine learning methods. Projected net load errors conditioned on solar uncertainty are translated into flexible ramp requirements that therefore reflect real-time meteorological and solar conditions, improving on typical ISO procedures. Finally, a multi-period look-ahead production cost model quantifies how conditional ramp requirements can a) decrease operating costs by lowering requirements compared to often conservative unconditional methods, and b) reduce generation scarcity events and consequently improve reliability by increasing flexibility requirements at times when unconditional forecast-based requirements understate actual ramp uncertainty. In addition to the products just described (quantification of solar uncertainty, its translation into requirements for ramp capability product, and quantification of the benefits of more accurate ramp requirements), this project also developed a visualization system that alerts system operators of ramp and uncertainty conditions within the network based on solar forecasts. The system is called Resource Forecast and Ramp Visualization for Situational Awareness (RaVIS). These four products represent significant advances in the state-of-the-art of probabilistic solar forecasting, development of weather-informed reserve requirements, production costing methods for estimating the benefits of more accurate reserve requirements, and visualization of system status, respectively. Yet the products are also practical and can be immediately implemented, potentially enabling system operators to save millions of dollars in ramp product procurement costs per year.

14 SOLAR ENERGY↗
DOE OSTI2021

Harnessing Condorcet Methods to Improve Decision-making Based on Ranked Data

Decision-makers must often choose between multiple alternatives based on their relative merits across a variety of criteria. These multiple-criteria decision-making (MCDM) problems must be approached in an objective, measurable, and transparent fashion to obtain well-supported outcomes. The challenge of using sets of ranked data to identify the optimal choice is not unlike the challenge of using ranked ballots to identify the winner of an election in a preference voting system. As such, the methods of rank aggregation used in elections can be applied to help resolve MCDM problems. Previous research into the design of elections and ballots has yielded many algorithms with well-understood properties. A subset of these, called Condorcet methods, reliably identify the “Condorcet winner,” if it exists, giving the result that would defeat any other in a pairwise comparison. That, in addition to several other desirable qualities, makes Condorcet methods like the Schulze method, Ranked Pairs, and Copeland’s method, effective and scalable means for tackling MCDM problems. These rank aggregation methods have been implemented in JavaScript functions for incorporation into a web-based MCDM decision tool. These algorithms can be applied in a wide variety of contexts (for example, determining the “best” environmental remediation method or selecting a subcontractor) to facilitate effective decision-making. The performance of the JavaScript Condorcet prototype tool was compared across the implemented algorithms and with both a non-Condorcet rank aggregation method as well as an implementation of the Simple Multi-Attribute Rating Technique (SMART) algorithm.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Design-Space Data: Informing Common Design Decisions with Pre-Simulated Data

Design Space Exploration (DSE) analysis techniques represent a data-centric approach to integrating performance analysis in early design phases when there is the greatest potential to cheaply improve the energy efficiency of a building. We focus on a novel extension of DSE called Universal Design Space Exploration (UDSE), which leverages massive databases of pre-simulated analysis that represent all possible outcomes of common analysis workflows. These databases, called Design Spaces, become “universal” when a single pre-simulated design space can be re-applied to future unknown projects. Unlike current simulation methods, which require a design to exist before it can be analyzed and often take minutes or hours to simulate, UDSE leverages pre-simulation to deliver rapid and relevant insight as new designs are conceptualized. The data underpinning UDSE enables advanced statistical and Artificial Intelligence methods, allowing UDSE to deliver a greater understanding of the larger problem being explored, rather than simply delivering analysis of several pre-conceived design options. We believe that UDSE can provide instantaneous, relevant analysis for all building design projects at negligible cost. This paper has two main goals, to develop a relevant Universal Design Space that showcases the potential of UDSE and to release this data freely to industry and academia; thereby lowering the barrier to entry to digital literacy in statistics, ML and AI within the architecture, engineering, construction (AEC) industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Application and Interpretation of Acumen Risk ® Schedule and Cost Risk Analysis

Schedule risk and cost risk analyses use statistical techniques to predict a level of confidence in meeting a program’s completion date and cost. This paper describes how to interpret key Schedule Risk Analysis (SRA) and Cost Risk Analysis (CRA) results to determine the likelihood that the program completion dates are met, how much management reserve (MR) is needed to provide the acceptable certainty of completion by a specific date, and the risks and activities that are most likely to delay or add cost to a project. The combined process is often called a Schedule/Cost Uncertainty and Risk Analysis or “SCURA.”

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Operational Energy Life Cycle Data Development for the National Institute of Standards And Technology (NIST) Building Industry Reporting and Design for Sustainability (BIRDS) Neutral Environmental Software Tool (NEST)

For this analysis, regionalized life cycle assessment (LCA) results for environmental impacts (using the Tool for Reduction and Assessment of Chemicals and Other Environmental Impacts [TRACI] 2.1) and cumulative energy demand (using the Federal Life Cycle Analysis Commons Elementary Flow List [FEDEFL] Inventory Methods v1.0.0) were evaluated for the production and utilization of electricity, natural gas, fuel oil, and propane as commodities within residential and commercial buildings. These results can used as a framework for future research into net zero, high-performance buildings, such as done here for the Building Industry Reporting and Design for Sustainability (BIRDS) database by the National Institute of Standards and Technology (NIST) Engineering Laboratory. The geographical results were assigned to each United States (U.S.) Zone Improvement Plan (ZIP) code based on the ZIP code location and corresponding Balancing Authority Area, natural gas basin, and Petroleum Administration for Defense Districts (PADDs). Additionally, previously developed models were utilized to develop future life cycle profiles. Projections were based on data available from the U.S. Energy Information Administration Annual Energy Outlook 2022 through 2050 (AEO 2022). Electricity LCA models were updated based on AEO 2022 projected annual generation mixes, while the natural gas baseline model was updated based on projected shares of natural gas types (conventional, shale, tight, and coalbed methane). Projections of crude oil production rates and export rates were applied to the petroleum baseline model in five-year increments to investigate their effects on the life cycle profile of fuel oil and propane. While only 100-year Global Warming Potential (GWP-100) with climate carbon feedback (CC-FB) and Cumulative Energy Demand are shown in Section 4: Results, the complete results, including Acidification Potential, Eutrophication Potential, Freshwater Ecotoxicity Potential, GWP-100 without inclusion of CC-FB, Human Health Impacts Potentials (Cancer, Non-Cancer), Ozone Depletion Potential, Particulate Matter Formation Potential, and Photochemical Smog Formation Potential, are tabulated for each ZIP code in the Excel worksheets that accompany this analysis. For the Excel spreadsheet tools associated with this report, please go to https://www.netl.doe.gov/energy-analysis/details?id=f8890fac-be55-44ac-aaa9-e2888bfabe93

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Natural Language Processing for Text Based Event Extraction: Identifying Events of Interest Related to Worldwide State-Sponsored Civil Nuclear Power

Beginning in FY20, SRNL was funded by the National Nuclear Security Administration’s Office of Defense Nuclear Non-Proliferation Research and Development to develop a prototype natural language processing/natural language understating machine learning-based modeling and analysis pipeline to extract and forecast events of interest from massive open data sources. The working hypothesis within the approach is that contextual shifts in key words and phrases act as indicators of events of interest over time. Therefore, by identifying points in time where contextual shifts occur, events of interest can be extracted along with explicit and implicit connections of entities and activities. The development of the preliminary prototype pipeline proved successful, meriting further testing of the pipeline on more broad topical domains and in a worldwide data environment. Therefore, SRNL, in collaboration with the Sanghani Center for Artificial Intelligence and Data Analytics at Virginia Tech, have continued development with a test case of identifying events of interest related to worldwide state-sponsored civil nuclear power in open data sources. In the first year of this follow-on effort, the team has curated domain-specific data corpuses using an automated scheme and applied the modeling and analysis pipeline. This robust, focused, and efficient approach consists of an ensemble of analyses applied to time dependent word embedding models that are trained on the data corpuses. In this report, the team has demonstrated the capability of the existing pipeline (as development has continued in parallel) by exploring several specific case-studies centered around Rosatom’s international activities regarding the planning, construction, operation, and/or shutdown of nuclear reactors. A basic timeline events has been generated by manually cataloging known “milestone” events that have occurred at reactors in Turkey, Finland, Hungary, and Egypt and compared with the output of the modeling pipeline. In this approach, the team has characterized the lead time using the prototype pipeline, as well as the ability to capture relevant information, which proved 100% successful. A deep dive example of the Akkuyu reactor (Turkey) is presented that shows the breadth of information that can be captured using the approach. In this case study, events were extracted pertaining to the planning/construction of Akkuyu including protests from the population, information campaigns in response to the protests, forged regulatory documents and lawsuits, budgetary/shareholder information, geopolitical tensions, and the various construction milestones. This has demonstrated the pipeline’s utility as a research aid or real-time event extraction tool, where summary-level information and detailed text extractions from millions of articles or Tweets across long time periods can be generated with significantly less effort than current techniques.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Nonlinear Optimization and the Modeling of Energy Systems [Slides]

Energy delivery systems are critical for the function of modern society. (Up to) continental-scale engineered systems move energy from source points to consumers. These systems are increasingly complex and interconnected.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗