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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Methods for R&D Portfolio Analysis and Evaluation (Workshop Report)

The Workshop on Methods for R&D Portfolio Analysis and Evaluation convened on 17–18 July 2019 at the National Renewable Energy Laboratory in Golden, Colorado, and examined strengths and weaknesses of the various methodologies applicable to R&D portfolio modeling, analysis, and decision support, given pragmatic constraints such as data availability, uncertainties in estimating the impact of R&D spending, and practical operational overheads. Participants employed their deep expertise in approaches such as stochastic optimization, real options, Monte-Carlo analysis, Bayesian networks, decision theory, complex systems analysis, deep uncertainty, and technology-evolution modeling to critique the initial example models developed by the project’s core team and to conduct thought experiments grounded in real-life technology models, progress data, expert elicitation, and portfolio information. This engagement of participants’ methodological expertise with the practical requirements of real-life portfolio decision support yielded ideas for improved approaches, alternative methodological hypotheses, and hybridization of methodologies that are well-grounded theoretically, computationally sound, and realistically executable given data availability and other practical constraints.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Methods for R&D Portfolio Analysis and Evaluation (Workshop Report)

The Workshop on Methods for R&D Portfolio Analysis and Evaluation convened on 17-18 July 2019 at the National Renewable Energy Laboratory in Golden, Colorado, and examined strengths and weaknesses of the various methodologies applicable to R&D portfolio modeling, analysis, and decision support, given pragmatic constraints such as data availability, uncertainties in estimating the impact of R&D spending, and practical operational overheads. Participants employed their deep expertise in approaches such as stochastic optimization, real options, Monte-Carlo analysis, Bayesian networks, decision theory, complex systems analysis, deep uncertainty, and technology-evolution modeling to critique the initial example models developed by the project’s core team and to conduct thought experiments grounded in real-life technology models, progress data, expert elicitation, and portfolio information. This engagement of participants’ methodological expertise with the practical requirements of real-life portfolio decision support yielded ideas for improved approaches, alternative methodological hypotheses, and hybridization of methodologies that are well-grounded theoretically, computationally sound, and realistically executable given data availability and other practical constraints.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Greenhouse gas emissions reduction strategies that maximize portfolio-wide life cycle cost reduction, resilience, and environmental justice benefits

While strategies to achieve net-zero emissions at an individual site are well understood, new analysis methods are required for organizations seeking to achieve net-zero across multiple facilities, each with concurrent priority goals. At a portfolio level, distinct locations present varied challenges that cannot be addressed through singular solutions, and competing goals can take precedence with the assumption that net-zero emissions strategies deter from energy resilience and cost savings, therefore negatively impacting nearby communities. This study tests these assumptions by analyzing 16 diverse sites (varying in size, climate, and energy use) to identify strategies that reduce emissions and assess the impact these strategies have on life cycle costs, resilience, and communities with environmental justice concerns. Methods were developed to approximate missing information essential to net-zero evaluation. Established methods were augmented to evaluate life cycle costs, resilience, and environmental justice impacts across a set of strategies and accommodate the multi-criteria analyses. Potential benefits from identified strategies were quantified using site characteristics and a set of corresponding metrics. The net-zero analysis found that 11 sites could use on-site strategies to eliminate all but 2% of emissions generated. The remaining emissions can be offset, for instance through sequestration, executed at the portfolio scale. On-site carbon-free energy was found to reduce 51% of emissions across all sites; efficiency reduced 19% of emissions; sequestration 16%; procured carbon-free energy 15%; fuel switching 1.6%; and fleet electrification 1.3%. Building electrification, however, increased emissions by 4.4%. Different strategies also provide cost, resilience, and/or environmental justice benefits—the degree to which varies with individual site conditions. The findings indicate an advantage to considering the strategies as a comprehensive set, which leads to co-benefits, both in the ability to achieve net-zero goals and in advancing other goals. The results present the case for comprehensive advanced planning at the portfolio level to prioritize investments that will balance the minimization of emissions and life cycle cost with the maximization of resilience and environmental justice benefits. The novel methods for evaluation and integration, valuation of benefits, and consideration at the portfolio scale allow organizations to select investments that simultaneously address multiple key priorities.

Net-Zero Emissions↗
DOE OSTI2022

National Community Solar Partnership Multifamily Affordable Housing Portfolio Screening Approach

Community solar offers multiple benefits to providers and residents of multifamily affordable housing (MFAH), including access to affordable renewable energy for low- and moderate-income households, tax credits and other financial benefits for property owners, and increased community resilience when paired with energy storage. Even so, MFAH providers interested in realizing these benefits may need support to identify where to start or how to expand and accelerate solar deployment across their property portfolios once they have completed their first few installations. This fact sheet describes a portfolio screening approach for MFAH providers to evaluate their multi-state building portfolios for community solar project viability. The approach was developed by the National Renewable Energy Laboratory as part of its support for the National Community Solar Partnership (NCSP) MFAH Collaborative. By utilizing NREL's companion Policy Screening Workbook in conjunction with the web-based tools PVWatts® Calculator and REopt™ (Renewable Energy Integration and Optimization), MFAH providers can take a data-informed approach to identifying and prioritizing buildings in their portfolios that are most amenable to community solar development.

community solar↗
DOE OSTI2024

NREL Marine Energy Desalination R&D Portfolio

The National Renewable Energy Laboratory (NREL), in collaboration with the U.S. Department of Energy's (DOE's) Water Power Technologies Office (WPTO), has developed a unique R&D approach to advance marine energy desalination. Desalination is a foundational investment within WPTO's Powering the Blue EconomyTM portfolio [1], and was the first investment within this portfolio. NREL's marine energy desalination spans techno-economic feasibility studies, numerical modeling, and laboratory testing at the component and subsystem level, as well as development of the Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC). This multilayered approach enables an innovative feedback loop where the data and lessons learned from laboratory and field experiments are used to refine modeling tools and analysis techniques, prioritize out-year activities, and refine strategic directions within NREL and across the WPTO portfolio. The primary objective of the NREL-led research is to identify key barriers associated with the commercialization of wave-powered desalination and develop solutions that can be adopted by the marine energy industry. It should be noted, while WPTO's marine energy portfolio includes wave, tidal, ocean current, thermal gradients, and pressure gradients the bulk of the marine energy desalination work has been focused on wave powered desalination. In parallel, these R&D activities can help inform technical assistance and support of industry and academic technologies. These two tracks help build a common solver community approach, while also identifying key stakeholders, government agencies, and other organizations outside of the marine sector that will be necessary for developing a robust industry.

desalination↗
DOE OSTI2020

The good, the bad, and the ugly: Data-driven load profile discord identification in a large building portfolio

Reducing the overall energy consumption and associated greenhouse gas emissions in the building sector is essential for meeting our future sustainability goals. Recently, smart energy metering facilities have been deployed to enable monitoring of energy consumption data with hourly or subhourly temporal resolution. This unprecedented data collection has created various opportunities for advanced data analytics involving load profiles (e.g., building energy benchmarking programs, building-to-grid integration, and calibration of urban-scale energy models). These applications often need preprocessing steps to detect daily load profile discords, such as: 1) outliers due to system malfunctions (the bad) and 2) irregular energy consumption patterns, such as those resulting from holidays (the ugly) compared to normal consumption patterns (the good). However, current preprocessing methods predominantly focus on filtering using statistical threshold values, which fail to capture the contextual discords of daily profiles. In addition, discord detection algorithms in building research are often aimed at finding individual building-level discords, which are not suitable at a large scale. Thus, here, we develop a method for automated load profile discord identification (ALDI) in a large portfolio of buildings (more than 100 buildings). Specifically, ALDI 1) uses the matrix profile (MP) method to quantify the similarities of daily subsequences in time series meter data, 2) compares daily MP values with typical-day MP distributions using the Kolmogorov-Smirnov test, and 3) identifies daily load profile discords in a large building portfolio. We evaluate ALDI using the metering data of both an academic campus and a residential neighborhood. Our results demonstrate that ALDI efficiently discovers measurement errors by system malfunctions and low energy consumption days in the academic campus portfolio, and it detects unique load shape patterns likely driven by occupant behavior and extreme weather conditions in the residential neighborhood.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

An exploration of online-simulation-driven portfolio scheduling in Workflow Management Systems

Workflow Management Systems used to automate the execution of scientific workflow applications on parallel and distributed computing platforms must make scheduling decisions at runtime. A large number of workflow scheduling algorithms have been proposed in the literature, but often these algorithms are evaluated based on simplifying assumptions that may not hold in practice. Furthermore, published algorithm evaluation and/or comparison results are necessarily only for a subset of all possible scenarios, and thus may not include scenarios relevant to particular use-cases. Consequently, it is difficult for Workflow Management Systems (WMSs) developers to decide which scheduling algorithm should be implemented. To obviate this difficulty, one possible approach is to implement a portfolio of scheduling algorithms and select the most effective algorithm at runtime. One method for performing this selection is to run an online simulation for each algorithm in the portfolio. The algorithm that leads to the best performance, in simulation, is selected for future use. The above simulation-driven portfolio scheduling (SDPS) approach has been proposed in a few parallel and distributed computing contexts. The main objective of this work is to evaluate the feasibility and potential merit of SDPS if implemented in WMSs. Here we perform this evaluation using simulated WMS executions, where the simulations are instantiated from real-world platform and workflow configurations. Our main finding is that SDPS is on par with or outperforms an approach in which a single algorithm is used, where this algorithm is the one that performs best on average across all our experimental scenarios. Furthermore, we find that SDPS remains an attractive proposition even in the presence of high levels of simulation error and for simulators with relatively low levels of sophistication. In many of our experimental scenarios we find that mitigating simulation error at runtime can further improve performance. Finally, we show that simulation overhead can be made sufficiently low for SDPS to be feasible in practice.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Machine Learning Based Metamodel for Faster Life Cycle Assessment of Large Portfolio of Buildings

Managing a large portfolio of buildings involves decisions on reuse, retrofit, renovation, rehabilitation, and new construction, influenced by trade-offs between performance metrics such as cost, time, and operational flexibility over the building's life cycle. Traditional life cycle assessment tools for evaluating these metrics can be labor- and compute-intensive, requiring extensive data and modeling for each building. Metamodels (or surrogate models) using machine learning have been explored as faster alternatives, but training these models has been hindered by the limited availability of comprehensive data on key life cycle metrics. Recent advancements in machine learning, particularly deep learning techniques like zero-shot and few-shot learning, allow models to learn from sparse or limited data. We propose a machine learning-based metamodel that leverages these techniques for rapid estimation of key building life cycle metrics. This presentation will cover the model architecture, data collection, training, and validation processes, along with an ongoing case study applied to a large portfolio of buildings. We will discuss the model's performance in terms of accuracy, compute time, limitations, and its potential for expanding to additional life cycle metrics. This data-driven approach offers a promising direction for the rapid evaluation of large building portfolios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

On the Feasibility of Simulation-Driven Portfolio Scheduling for Cyberinfrastructure Runtime Systems

Runtime systems that automate the execution of applications on distributed cyberinfrastructures need to make scheduling decisions. Researchers have proposed many scheduling algorithms, but most of them are designed based on analytical models and assumptions that may not hold in practice. The literature is thus rife with algorithms that have been evaluated only within the scope of their underlying assumptions but whose practical effectiveness is unclear. It is thus difficult for developers to decide which algorithm to implement in their runtime systems.To obviate the above difficulty, we propose an approach by which the runtime system executes, throughout application execution, simulations of this very execution. Each simulation is for a different algorithm in a scheduling algorithm portfolio, and the best algorithm is selected based on simulation results. The main objective of this work is to evaluate the feasibility and potential merit of this portfolio scheduling approach, even in the presence of simulation inaccuracy, when compared to the traditional one-algorithm approach. We perform this evaluation via a case study in the context of scientific workflows. Our main finding is that portfolio scheduling can outperform the best one-algorithm approach even in the presence of relatively large simulation inaccuracies.

Casanova, Henri↗
DOE OSTI2020

Spatio-temporal impacts of a utility’s efficiency portfolio on the distribution grid

Energy Efficiency has historically focused on delivering savings to offset growth in energy supply. Today’s growing emphasis on decarbonization of the energy supply is driving renewables adoption and increased interest in electrification. As a result, energy efficiency is being assessed not just in its ability to offset load growth, but also for its ability to alleviate location-specific constraints on transmission and distribution infrastructure. This work demonstrates that advanced measurement and verification modeling techniques can be used to estimate the spatio-temporal grid impact of a portfolio of energy efficiency programs. It extends measurement-based methods to an entire Demand Side Management portfolio and uses a single model to predict annual as well as seasonal building energy use with near-zero bias. In addition, new metrics are introduced to assess grid level impacts of energy efficiency. The results show that the efficiency program portfolio delivers savings of over 12% at the territory-wide proxy level, with substation and feeder level savings ranging from 0.4% to 26%, and ~5%-42% respectively. Overall, these savings impacted 1.0%–1.4% of the energy used at these locations in the grid. This work provides a methodology with potential to connect efficiency with distribution planning, carrying implications for non-wires alternatives and targeted delivery of efficiency programs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

The Effect of Clean Energy Generation Targets on the Portfolio of Electric Grid Generation Technologies

A least-cost generation and transmission expansion planning model is used to optimize the U.S. generation portfolio in 2030 under a range of Clean Energy Generation Target (CEGT) policy goals. As reference cases for comparison, the model is used to optimize the generation portfolio for the future electric grid with and without the Investment and Production Tax Credits (ITC and PTC). Next, the model is used to optimize the generation portfolio with a CEGT ranging from 10% to 70%. The results show that due to the ITC and PTC extensions by the 2022 Inflation Reduction Act, there is little additional investment in renewable energy generation assets at low CEGT levels. Significant additional installations of both renewable energy generation and energy storage systems – both batteries and pumped storage hydro – become important at CEGT levels above approximately 30%.

Aldeman, Matthew↗
DOE OSTI2021

Energy Storage Financing: Project and Portfolio Valuation

This study investigates the issues and challenges surrounding energy storage project and portfolio valuation and provide insights in to improving visibility into the process for developers, capital providers, and customers so they can make more informed choices. Energy storage project valuation methodology is typical of power sector projects through evaluating various revenue and cost assumptions in a project economic model. The difference is that energy storage projects have many more design and operational variables to incorporate, and the governing market rules that control these variables are still evolving. This makes project valuation for energy storage more difficult. As the number of operating projects grow, portfolios of these projects are being developed, garnering the interest of larger investors. Valuation challenges of these portfolios can be even more challenging as market role and geographical diversity can actually exacerbate the variability, not mitigate it. By proposing additional visibility of key factors and drivers for industry participants, the US DOE can reduce investment risk, expanding both the number and types of investors, plus helping emerging energy storage technology into sustained commercialization.

25 ENERGY STORAGE↗
DOE OSTI2024

NREL Marine Energy Desalination Research and Development Portfolio: Preprint

The National Renewable Energy Laboratory (NREL), in collaboration with the U.S. Department of Energy's (DOE's) Water Power Technologies Office (WPTO), has developed a unique R&D approach to advance marine energy desalination. Desalination is a foundational investment within WPTO's Powering the Blue Economy portfolio, and was the first investment within this portfolio. NREL's marine energy desalination spans techno-economic feasibility studies, numerical modeling, and laboratory testing at the component and subsystem level, as well as development of the Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC). This multilayered approach enables an innovative feedback loop where the data and lessons learned from laboratory and field experiments are used to refine modeling tools and analysis techniques, prioritize out-year activities, and refine strategic directions within NREL and across the WPTO portfolio. The primary objective of the NREL-led research is to identify key barriers associated with the commercialization of wave-powered desalination and develop solutions that can be adopted by the marine energy industry. In parallel, these R&D activities can help inform technical assistance and support of industry and academic technologies. These two tracks help build a common solver community approach, while also identifying key stakeholders, government agencies, and other organizations outside of the marine sector that will be necessary for developing a robust industry. For the Spanish version of this report, see NREL/CP-5700-88483 (https://www.nrel.gov/docs/fy24osti/88483.pdf).

analysis↗
DOE OSTI2021

Energy sector portfolio analysis with uncertainty

Governments are dealing with the challenge of how to efficiently invest in research and development portfolios related to energy technologies. Research and development investment decisions in the energy space are especially difficult due to numerous risks and uncertainties, and due to the complexity of energy's interactions with the broad economy. Historically, much of the U.S. Department of Energy's in-depth research and development analyses focused on assessing the impact of a research and development activity in isolation from other available opportunities and did not substantially consider risk and uncertainty. Endeavoring to combine integrated energy-economy modeling with uncertainty analysis and technology-specific research and development activities, the U.S. Department of Energy commissioned the development of the Stochastic Energy Deployment System to support and improve public energy research and development decision-making. The Stochastic Energy Deployment System draws from expert-elicited probability distributions for research and development-driven improvements in technology cost and performance, and it uses Monte Carlo simulations to evaluate the likelihood of outcomes within a system dynamics energy-economy model. The framework estimates the uncertain benefits and costs of various research and development portfolios and provides insight into the probability of meeting national technology goals, while accounting for interactions with the larger economy and for interactions among research and development investments spanning many energy sectors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Framework for Greenhouse Gas Emissions Reduction Planning: Industrial Portfolios

The Framework for Greenhouse Gas Emissions Reduction Planning: Industrial Portfolios articulates a process to help industrial organizations develop a specific, actionable plan to achieve Scope 1 and Scope 2 greenhouse gas (GHG) emissions reduction – an Emissions Reduction Plan (ERP). An ERP covers an entire portfolio of facilities, yet contains enough detail to be practically useful at the facility level.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Developing a Coal to Products Technology Portfolio

At Ramaco Carbon we are working to bring into existence a new carbon economy with the mottos “coal is too valuable to burn” and “no molecule left behind.” Our research focus is using coal (carbon ore) as a feedstock for a variety of high-value carbon products. We have developed a process and manufacturing technology portfolio including carbon fiber, porous carbons, nano-structured carbons (bulk graphene), building products, monolayer graphene, and rare earth elements (REEs). A review of these product lines, the associated processes, and economics will provide insight into the value of carbon ore as a feedstock for high-value and specialty products. We will present our current progress in manufacturing and tailoring the properties of porous carbons and nano-structured carbon to enhance existing products and produce novel products. Included is a discussion of the properties of carbon fiber derived from direct liquefaction of sub-bituminous coal, and from pitches produced by pyrolysis and solvation by supercritical CO2. New processes for converting carbon ore into structural building materials and monolayer graphene and the evaluation of REE deposits in a Powder River Basin thermal coal reserve will also be described. The presentation will provide a review of our carbon ore to products technology portfolio and the economic advantages of using carbon ore as a feedstock for advanced carbon products and several associated end use applications.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Developing a Coal to Products Technology Portfolio

At Ramaco Carbon we are working to bring into existence a new carbon economy with the mottos “coal is too valuable to burn” and “no molecule left behind.” Our research focus is using coal (carbon ore) as a feedstock for a variety of high-value carbon products. We have developed a process and manufacturing technology portfolio including carbon fiber, porous carbons, nano-structured carbons (bulk graphene), building products, monolayer graphene, and rare earth elements (REEs). A review of these product lines, the associated processes, and economics will provide insight into the value of carbon ore as a feedstock for high-value and specialty products. We will present our current progress in manufacturing and tailoring the properties of porous carbons and nano-structured carbon to enhance existing products and produce novel products. Included is a discussion of the properties of carbon fiber derived from direct liquefaction of sub-bituminous coal, and from pitches produced by pyrolysis and solvation by supercritical CO2. New processes for converting carbon ore into structural building materials and monolayer graphene and the evaluation of REE deposits in a Powder River Basin thermal coal reserve will also be described. The presentation will provide a review of our carbon ore to products technology portfolio and the economic advantages of using carbon ore as a feedstock for advanced carbon products and several associated end use applications.

01 COAL, LIGNITE, AND PEAT↗