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Hanes, Rebecca

Publications and source records attributed to Hanes, Rebecca.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Cyber100 Compass [SWR 23-64]

Cyber100 Compass ("Compass") is a unique risk assessment framework that will enable grid system planners to understand and mitigate cybersecurity risk for grids transitioning to high levels of renewable generation, including 100%. The idea for Compass was developed by NREL based on past work on high-renewable grids and a series of discussions with DOE. Compass is part of Cyber100, a portfolio of proposed research activities that would greatly expand understanding of cybersecurity for high-renewable grids. Compass is a desktop application designed with a user-friendly interface. The tool gathers information from users, conducts probabilistic backend calculations, and outputs a series of visualizations to help users understand and analyze their cybersecurity risks based on the unique features of their future grid. Compass will take as inputs the values for different conditions and produce a risk score of the resulting grid. By trying different configurations, system planners can compare the resultant risks against their own risk tolerance and decide which system-of-system controls to implement as they transition toward a 100% renewable grid.

Martin, Maurice↗
DOE OSTI2024

Cyber100 Compass: Quantification of Cybersecurity Risks for Systems Transitioning to High Levels of Renewables (Final Report)

The shift to high levels of renewable deployment will entail a significant re-engineering of the grid. As investors, utilities, customers, and others prepare for clean energy transitions, there is need to understand how restructuring the grid to accommodate renewables will change the attack surface of the grid and accompanying cyber risk. However, today the cyber-physical risks associated with electric grids incorporating high levels of renewable deployment remain largely unknown. The Cyber100 Compass proof-of-concept application attempts to quantify future cyber-physical security risks by combining risk data gathered from subject matter experts (SMEs) with input from system planners about conditions they expect to be true about their electric systems in the future. Users provide data about their organization’s tolerance for risk; the value they place on avoiding the consequences of different cyber events; and conditions that they expect to be true on their systems at some point in the future. The SMEs provide baseline probabilities for different cyber events; the probability that an event will be low-, moderate-, or high-impact; and the amount by which user-identified conditions on their systems will change the likelihood of the cyber events. The application takes both the user and SME input and performs a series of Monte Carlo simulations to arrive at a quantification of risk.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

The inclusion of uncertainty in circularity transition modeling: A case study on wind turbine blade end-of-life management

The transition to a more circular economy (CE) is complex and hard to predict, including in sustainable energy technologies. Many sources of uncertainty make it challenging to model CE scenarios and their potential benefits. As an example, the high variability in costs and revenues of different recycling options makes future wind turbine blade recycling highly uncertain. To better understand this challenge, the circular economy, life cycle assessment and visualization (CELAVI) framework - a discrete event simulation and life cycle assessment framework - is modified to incorporate uncertainty analysis capabilities. Moreover, a 3-step procedure that covers different aspects of uncertainty in CE studies and includes a Monte-Carlo analysis is proposed. The procedure is tested in a case study on wind turbine blade recycling using CELAVI. Results highlight that grinding and landfilling costs are the most influential parameters for wind power circularity. The model's output coefficients of variation (when input parameter uncertainties are propagated) are between 92% and 384% depending on the indicator. The approach developed in this study may help researchers and decision-makers who study circularity prioritize their data collection effort. Finally, our method contributes to a mounting yet critical body of research: the measurement of uncertainty in circularity transitions.

17 WIND ENERGY↗
DOE OSTI2023

Yield, chemistry, and TEA/LCA data for 84 switchgrass (Panicum virgatum L.) variants

Switchgrass yield, chemistry, technoeconomic assessment (TEA), and life cycle assessment (LCA) data referenced in the manuscript "Economics and Sustainability impacts of yield and composition variation in bioenergy crops: Panicum virgatum L.", Happs, et. al. (manuscript in revision at ACS Sustainable Chemistry & Engineering). These results are based on analysis of yield data generated from a set of switchgrass common garden experiments established by the Center for Bioenergy Innovation (CBI; https://cbi.ornl.gov/). These data include estimates of commercial-scale yields and characterization of biomass chemistry (fermentable carbohydrate fraction) for 84 different switchgrass variants from a genome-wide association study population, as well as estimates of minimum fuel selling price and various life-cycle footprint metrics for cellulosic ethanol produced from that switchgrass biomass.

switchgrass, biofuels↗
DOE OSTI2022

CELAVI (Circular Economy Lifecycle Assessment and VIsualization) v.1.3.1 9/30/2022 [SWR-20-87]

A circular economy emphasizes the efficient use of all resources (e.g., materials, land, water). Despite anticipated overall benefits to society, the transition to a circular economy is likely to create regional differences in impacts. Current tools are unable to fully evaluate these potential externalities, which will be important for informing research prioritization and regional decision making. The Circular Economy Lifecycle Assessment and VIsualization (CELAVI) framework allows stakeholders to quantify and visualize potential regional and sectoral transfers of impacts that could result from transitioning to a circular economy, with particular focus on energy materials. The framework uses system dynamics to model material flows for multiple circular economy pathways and decisions are based on learning-by-doing and are implemented via cost and strategic value of different circular economy pathways. It uses network theory to track the spatial and sectoral flow of functional units across a graph and discrete event simulation to to step through time and evaluate lifecycle assessment data at each time step. The framework is designed to be flexible and scalable to accommodate multiple energy materials and multiple energy technologies. The primary goal of CELAVI is to help answer questions about how material flows and environmental and economic impacts of energy systems might change if the circularity of energy systems increases.

Eberle, Annika↗
DOE OSTI2021

The Circular Economy Lifecycle Assessment and Visualization Framework: A Case Study of Wind Blade Circularity in Texas

Moving the current linear economy toward circularity is expected to have environmental, economic, and social impacts. Various modeling methods, including economic input-output modeling, life cycle assessment, agent-based modeling, and system dynamics, have been used to examine circular supply chains and analyze their impacts. This work describes the newly developed Circular Economy Lifecycle Assessment and Visualization (CELAVI) framework, which is designed to model how the impacts of supply chains might change as circularity increases. We first establish the framework with a discussion of modeling capabilities that are needed to capture circularity transitions; these capabilities are based on the fact that supply chains moving toward circularity are dynamic and therefore not at steady state, may encompass multiple industrial sectors or other interdependent supply chains and occupy a large spatial area. To demonstrate the capabilities of CELAVI, we present a case study on end-of-life wind turbine blades in the U.S. state of Texas. Our findings show that depending on exact process costs and transportation distances, mechanical recycling could lead to 69% or more of end-of-life turbine blade mass being kept in circulation rather than being landfilled, with only a 7.1% increase in global warming potential over the linear supply chain. We discuss next steps for framework development.

17 WIND ENERGY↗
DOE OSTI2021

CELAVI (Circular Economy Lifecycle Assessment and VIsualization) [SWR-20-87]

A circular economy emphasizes the efficient use of all resources (e.g., materials, land, water). Despite anticipated overall benefits to society, the transition to a circular economy is likely to create regional differences in impacts. Current tools are unable to fully evaluate these potential externalities, which will be important for informing research prioritization and regional decision making. The Circular Economy Lifecycle Assessment and VIsualization (CELAVI) framework allows stakeholders to quantify and visualize potential regional and sectoral transfers of impacts that could result from transitioning to a circular economy, with particular focus on energy materials. The framework uses system dynamics to model material flows for multiple circular economy pathways and decisions are based on learning-by-doing and are implemented via cost and strategic value of different circular economy pathways. It uses network theory to track the spatial and sectoral flow of functional units across a graph and discrete event simulation to to step through time and evaluate lifecycle assessment data at each time step. The framework is designed to be flexible and scalable to accommodate multiple energy materials and multiple energy technologies. The primary goal of CELAVI is to help answer questions about how material flows and environmental and economic impacts of energy systems might change if the circularity of energy systems increases.

Eberle, Annika↗
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 OSTI2019

Techno-Economic Analysis and Life Cycle Assessment for Guiding Bioenergy Pathway Development

The Center for Bioenergy Innovation (CBI) is a bioenergy research center recently launched by the U.S. Department of Energy Office of Science's Biological and Environmental Research program. CBI's objective is to enable innovations across the bioenergy supply chain through development of poplar and switchgrass phenotypes, microorganisms for consolidated bio-processing (CBP), in which biomass fermentation and conversion is accomplished in a single unit operation, and lignin valorization processes. In addition to basic science, CBI's activities involve the application of techno-economic analysis (TEA) and life cycle assessment (LCA) to the bioenergy pathways under study. TEA and LCA is used to quantify the impacts that research and development breakthroughs have on the economic viability and sustainability of a bioenergy pathway, and to identify drivers for reducing costs and improving sustainability through targeted research efforts. Combining TEA and LCA allows for understanding the economic and sustainability benefits and trade-offs of these integrated bioenergy systems. The focus of this presentation is a parameterized, integrated TEA-LCA model, which will be discussed along with an application to bioenergy pathways currently under study. The model combines data and information developed by CBI researchers into a superstructure of potential bioenergy pathways and their life cycles. Model parameters include feedstock species and phenotype, farming, harvesting, storage and transportation practices, and the microbes used for biomass conversion and lignin valorization, among others. The parameterized TEA-LCA model allows for the rapid quantification of pathway economic and sustainability metrics such as minimum fuel selling price, payback period, fossil fuel consumption and net greenhouse gas emissions. To demonstrate the model, economic and sustainability metrics for a bioenergy pathway utilizing CBP will be quantified and compared to the same metrics for a conventional bioenergy pathway. Results of the comparison will be used to identify and prioritize high-impact research areas within the CBP pathway. fermentation and conversion is accomplished in a single unit operation, and lignin valorization processes. In addition to basic science, CBI's activities involve the application of techno-economic analysis (TEA) and life cycle assessment (LCA) to the bioenergy pathways under study. TEA and LCA is used to quantify the impacts that research and development breakthroughs have on the economic viability and sustainability of a bioenergy pathway, and to identify drivers for reducing costs and improving sustainability through targeted research efforts. Combining TEA and LCA allows for understanding the economic and sustainability benefits and trade-offs of these integrated bioenergy systems. The focus of this presentation is a parameterized, integrated TEA-LCA model, which will be discussed along with an application to bioenergy pathways currently under study. The model combines data and information developed by CBI researchers into a superstructure of potential bioenergy pathways and their life cycles. Model parameters include feedstock species and phenotype, farming, harvesting, storage and transportation practices, and the microbes used for biomass conversion and lignin valorization, among others. The parameterized TEA-LCA model allows for the rapid quantification of pathway economic and sustainability metrics such as minimum fuel selling price, payback period, fossil fuel consumption and net greenhouse gas emissions. To demonstrate the model, economic and sustainability metrics for a bioenergy pathway utilizing CBP will be quantified and compared to the same metrics for a conventional bioenergy pathway. Results of the comparison will be used to identify and prioritize high-impact research areas within the CBP pathway.

09 BIOMASS FUELS↗
DOE OSTI2019

FPEAM (Feedstock Production to Air Emissions Model)

FPEAM is a modeling framework that integrates agricultural and forestry production, supply data, and equipment budgets for those activities with methodologies for estimating air pollutant emissions.

Eberle, Annika↗
DOE OSTI2019

Quantifying adoption rates and energy savings over time for advanced energy-efficient manufacturing technologies

Energy-efficient manufacturing technologies can reduce energy consumption and lower operating costs for manufacturing facilities, but up-front costs and increased process complexity frequently lead to manufacturers being reluctant to adopt such technologies. To avoid overestimating the benefits of advanced energy-efficient manufacturing technologies, it is necessary to account for how quickly and how widely the technology will be adopted by manufacturers. This work develops a method for estimating manufacturing technology adoption rates using quantitative technology characteristics including energetic, economic, and technical criteria that capture both incentives (such as energy and cost savings) and disincentives (such as increased process complexity) for technology adoption. This method is unique in that it can be applied before or after a technology reaches the market; other adoption rate estimation methods require sales data and can only be applied post-market. Eleven technology characteristics are considered, with each characteristic weighted to reflect its impact on the overall technology adoption rate. Technology characteristic data is used to estimate model parameters for the Bass diffusion curve, which quantifies the change in the number of new technology adopters in a population over time. Finally, energy savings at the sector level are calculated over time by multiplying the number of new technology adopters at each time step with the technology’s facility-level energy savings. The proposed method is demonstrated with an application to glass industry manufacturing technologies using technology data obtained from the U.S. Department of Energy's 2017 bandwidth study. The potential energy savings for each technology and the rate at which each technology is adopted in the sector are quantified and used to identify the technologies which offer the greatest cumulative sector-level energy savings over a period of 20 years.

advanced manufacturing↗
DOE OSTI2019

Data-Driven Modeling of Spatially Explicit Air Pollutant Inventories for Agricultural and Forestry Feedstock Production

As biomass feedstock production expands, air pollutants generated from feedstock supply chain activities may impact air quality and human health. The National Renewable Energy Laboratory's Feedstock Production Emissions to Air Model (FPEAM) generates county-scale inventories of direct air pollutant emissions produced by feedstock supply chain activities. FPEAM can also be used to generate local, temporally resolved inventories for input into air quality models. This presentation provides an overview of FPEAM functionality and discusses several recent use cases prior to FPEAM's scheduled public release in September 2019. FPEAM is a modular, data-driven model. Each module contains calculations for an activity type, except for the feedstock transportation and on-farm equipment operation modules which connect to the U.S. Environmental Protection Agency's Motor Vehicle Emissions Simulator (MOVES) and Nonroad models, respectively. The data-driven aspect of FPEAM means that it is the input data provided for a scenario rather than the model itself that determines the pollutants and supply chain activities included in the scenario scope. Modules within FPEAM can also be turned on or off manually to adjust the scenario scope. Default input data packaged with the FPEAM code base can be used to calculate inventories of volatile organic compounds, particulate matter, ammonia, nitrogen oxides, sulfur oxides, and carbon monoxide generated by agricultural and forestry equipment operation, application of nitrogenous fertilizers, herbicide and insecticide, and feedstock transportation. Custom, user-developed data can be used in place of the default data to analyze scenarios with additional or alternate feedstocks, pollutants, and supply chain activities.

09 BIOMASS FUELS↗
DOE OSTI2019

Transition Dynamics in the Nascent Bioproducts Industry

Bioproducts are chemicals derived from a biomass feedstock that can be produced on their own or as biofuel co-products. Historically, bioproduct developers have faced significant barriers in scaling up bioproduct technologies and in bringing bioproducts to market, resulting in a high failure rate for bioproduct development projects. To date, there has been essentially no research done on the factors that lead to a bioproduct successfully capturing market share and why bioproduct projects fail. We address this knowledge gap with a model of the transition dynamics involved in taking a bioproduct technology from applied research to commercial production and the interactions between bioproduct developers, investors providing private funds, and federal government agencies which may provide cost-sharing and production incentives. This model incorporates several novel techniques including stochastic submodels and forecasts of market shares and company performance. Our research objectives are to identify relevant factors and mechanisms that impact bioproduct success and to develop a decision support tool, the Bioproduct Transition Dynamics (BTD) system dynamics model, that can be used by investors, bioproduct developers and government agencies to bring more bioproducts to market and to grow the nascent bioproducts industry.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Assessing Fossil Fuel and Feedstock Use in the U.S. Plastics and Rubber Sector: A Supply Chain Analysis

In plastics and rubber manufacturing, fossil fuels are consumed both as an energy source and as feedstocks that are converted to higher-value products. For example, in the manufacture of polyethylene, natural gas is consumed both as a fuel and as a feedstock during ethylene production. In this work, we expand on the U.S. Department of Energy's Bandwidth Study on Energy Use and Potential Energy Savings Opportunities in U.S. Plastics and Rubber Manufacturing, which quantified onsite fuel consumption and the potential for reducing fuel consumption by increasing energy efficiency in the plastics and rubber sector. The bandwidth study did not quantify fuel consumed upstream in the supply chain or as a feedstock; however, both contribute significantly to a plastic's total fossil fuel requirements. The U.S. Energy Information Administration's 2014 Manufacturing Energy Consumption Survey (MECS) estimates that fossil fuels consumed as feedstocks account for approximately 74% of total fossil fuel consumption in the U.S. plastic resin manufacturing sector (NAICS 325211). We employ the Materials Flows through Industry (MFI) supply chain modeling tool, developed at the National Renewable Energy Laboratory (NREL), to quantify process fuel and feedstock requirements within the supply chains of the same 10 categories of plastics and rubber commodities covered in the DOE bandwidth report. From these results, we identify supply chains that will have a high feedstock requirement even when energy efficiency is increased. We then use MFI to analyze several alternative plastics supply chains, including those utilizing biomass feedstocks and/or polyethylene terephthalate (PET) waste upcycling, to determine if these alternatives can potentially reduce both process fuel and feedstock requirements within the supply chains of interest. This presentation will include a case study comparison of supply chain energy consumption for petroleum-derived and bio-derived fiberglass reinforced plastic involving a novel waste PET upcycling technique recently discovered by NREL researchers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

A Parameterized Life Cycle Model for Rapid Quantification of Bioenergy Sustainability Metrics

Life cycle assessment (LCA) is a method for assessing all environmental impacts associated with provisioning a good or service. A life cycle encompasses the system which directly produces a good, as well as all systems and processes on which that system is dependent. The objective of this work is to develop a flexible, parameterized model representing a superstructure of life cycles for advanced biofuel production. Parameterizing the model allows users to define a variety of biofuel production scenarios and assess key sustainability metrics, without needing to acquire additional data or perform separate life cycle assessments. Within the model, parameters will include feedstock species and phenotype, a range of farming and harvest practices, feedstock pre-processing, and operating conditions within the biomass conversion process. The model will be used to identify major impact drivers within bioenergy life cycles and aid CBI researchers and leadership in targeting areas where additional R&D efforts will have the most beneficial impacts.

09 BIOMASS FUELS↗
DOE OSTI2018

Spatially Explicit Modeling of Criteria Air Pollutants from Agricultural and Forestry Feedstock Production

The Feedstock Production Emissions to Air Model (FPEAM) is a spatially explicit supply chain model that quantifies criteria air pollutants and precursors generated by biomass production, harvest and transportation to biorefineries. FPEAM was most recently used for the 2016 Billion Ton Study (BTS) published by the U.S. Department of Energy Bioenergy Technologies Office. This presentation describes several use cases for the model, and provides an overview of recent restructuring that increases FPEAM's usability and functionality. FPEAM currently estimates emissions of volatile organic compounds, particulate matter, nitrogen oxides, sulfur oxides, and carbon monoxide from on-farm equipment operation, on-farm fugitive dust, on-farm chemical (fertilizer, herbicide and insecticide) application, and biomass transportation. These calculations are implemented within FPEAM as modules, which independently perform calculations for each emissions category. This structure allows users to include or exclude emissions categories according to the goal and scope of their analysis. Each module's input data defines the activities taking place with information including fuel combusted, chemicals applied, and equipment used. Users can replace or augment the default input data to quantify additional pollutant types, emissions categories, farming practices and biomass types. Emissions from biomass transportation and on-farm equipment operation are by default calculated by the U.S. Environmental Protection Agency's Motor Vehicle Emission Simulator (MOVES) and NONROAD model, respectively. These models - which are computationally intensive but provide detailed results - can optionally be replaced with user-provided emissions factors for fuel combustion and vehicle operation. This approach reduces FPEAM's runtime and allows users to run sensitivity analyses around biomass production and transportation scenarios. Other enhancements currently under development are the use of farm-to-biorefinery route information to increase the spatial resolution of transportation emissions, and a module to connect FPEAM and the Intervention Model for Air Pollution (InMAP), which will enable the quantification of human health impacts.

09 BIOMASS FUELS↗