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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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DOE OSTI2012

Ethanol Distribution, Dispensing, and Use: Analysis of a Portion of the Biomass-to-Biofuels Supply Chain Using System Dynamics

The Energy Independence and Security Act of 2007 targets use of 36 billion gallons of biofuels per year by 2022. Achieving this may require substantial changes to current transportation fuel systems for distribution, dispensing, and use in vehicles. The U.S. Department of Energy and the National Renewable Energy Laboratory designed a system dynamics approach to help focus government action by determining what supply chain changes would have the greatest potential to accelerate biofuels deployment. The National Renewable Energy Laboratory developed the Biomass Scenario Model, a system dynamics model which represents the primary system effects and dependencies in the biomass-to-biofuels supply chain. The model provides a framework for developing scenarios and conducting biofuels policy analysis. This paper focuses on the downstream portion of the supply chain-represented in the distribution logistics, dispensing station, and fuel utilization, and vehicle modules of the Biomass Scenario Model. This model initially focused on ethanol, but has since been expanded to include other biofuels. Some portions of this system are represented dynamically with major interactions and feedbacks, especially those related to a dispensing station owner's decision whether to offer ethanol fuel and a consumer's choice whether to purchase that fuel. Other portions of the system are modeled with little or no dynamics; the vehicle choices of consumers are represented as discrete scenarios. This paper explores conditions needed to sustain an ethanol fuel market and identifies implications of these findings for program and policy goals. A large, economically sustainable ethanol fuel market (or other biofuel market) requires low end-user fuel price relative to gasoline and sufficient producer payment, which are difficult to achieve simultaneously. Other requirements (different for ethanol vs. other biofuel markets) include the need for infrastructure for distribution and dispensing and widespread use of high ethanol blends in flexible-fuel vehicles.

02 PETROLEUM↗
DOE OSTI2021

BETO 2021 Peer Review - WBS 4.1.2.32: Bioeconomy Scenario Analysis

The Bioeconomy Scenario Analysis project uses systems thinking and analysis to assess current and/or prospective techno-economics, research and development, deployment strategies, policy, and market conditions and their impact on the potential development trajectories of the bioenergy industry over time. Results from this project include identification of opportunities and constraints to industrial development, quantification of multiple metrics (energy, economic, environmental) and informing researchers, decision makers, and industry of the steps needed for a sustainable, nationwide biofuels industry. Analyses from this project enable the creation of a bioenergy industry by (1) inciting policy-makers to explore scenarios for nationwide biofuels production, identifying policy actions consistent with pathways for growth; (2) improving industry’s understanding of industry growth potential under different technology and investment conditions, better targeting their development efforts; and (3) providing universities and other interested stakeholders with tools and analyses that can be adapted to meet research and teaching objectives, connecting students with careers that build the industry. One of the many modeling tools used in this project, the Biomass Scenario Model (BSM) is a publicly-available, unique, validated, state-of-the-art, award-winning, fourth-generation model of the domestic biofuels supply chain which explicitly focuses on how and under what conditions biofuel technologies might be deployed to contribute to the U.S. transportation energy sector. We use models like the BSM to examine the implications of policies and incentives as well as their potential side-effects. The BSM uses a system-dynamics simulation to model dynamic interactions and transitions across the supply chain; it tracks the deployment of biofuels given industrial learning and the reaction of the investment community in the context of land availability, projected oil markets, consumer demand for biofuels, and government policies over time. Under expected market conditions, analyses using the BSM suggest that the biofuels industry may require significant external actions in the early years to thrive. Interventions that accelerate the industrial learning process (e.g. operation of pre-commercial and commercial facilities) have been identified as having strong influence in starting the growth of a commercial biofuel industry. Policies which are coordinated across the whole supply chain in BSM foster the growth of the biofuels industry and production of tens of billions of gallons of biofuels may occur under sufficiently favorable conditions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

Dynamic Modeling of Learning in Emerging Energy Industries: The Example of Advanced Biofuels in the United States; NREL (National Renewable Energy Laboratory)

This paper (and its supplemental model) presents novel approaches to modeling interactions and related policies among investment, production, and learning in an emerging competitive industry. New biomass-to-biofuels pathways are being developed and commercialized to support goals for U.S. advanced biofuel use, such as those in the Energy Independence and Security Act of 2007. We explore the impact of learning rates and techno-economics in a learning model excerpted from the Biomass Scenario Model (BSM), developed by the U.S. Department of Energy and the National Renewable Energy Laboratory to explore the impact of biofuel policy on the evolution of the biofuels industry. The BSM integrates investment, production, and learning among competing biofuel conversion options that are at different stages of industrial development. We explain the novel methods used to simulate the impact of differing assumptions about mature industry techno-economics and about learning rates while accounting for the different maturity levels of various conversion pathways. A sensitivity study shows that the parameters studied (fixed capital investment, process yield, progress ratios, and pre-commercial investment) exhibit highly interactive effects, and the system, as modeled, tends toward market dominance of a single pathway due to competition and learning dynamics.

09 BIOMASS FUELS↗
DOE OSTI2016

Dynamic Modeling of Learning in Emerging Energy Industries: The Example of Advanced Biofuels in the United States

This paper (and its supplemental model) presents novel approaches to modeling interactions and related policies among investment, production, and learning in an emerging competitive industry. New biomass-to-biofuels pathways are being developed and commercialized to support goals for U.S. advanced biofuel use, such as those in the Energy Independence and Security Act of 2007. We explore the impact of learning rates and techno-economics in a learning model excerpted from the Biomass Scenario Model (BSM), developed by the U.S. Department of Energy and the National Renewable Energy Laboratory to explore the impact of biofuel policy on the evolution of the biofuels industry. The BSM integrates investment, production, and learning among competing biofuel conversion options that are at different stages of industrial development. We explain the novel methods used to simulate the impact of differing assumptions about mature industry techno-economics and about learning rates while accounting for the different maturity levels of various conversion pathways. A sensitivity study shows that the parameters studied (fixed capital investment, process yield, progress ratios, and pre-commercial investment) exhibit highly interactive effects, and the system, as modeled, tends toward market dominance of a single pathway due to competition and learning dynamics.

09 BIOMASS FUELS↗
DOE OSTI2015

Dynamic Modeling of Learning in Emerging Energy Industries: The Example of Advanced Biofuels in the United States: Preprint

This paper (and its supplemental model) presents novel approaches to modeling interactions and related policies among investment, production, and learning in an emerging competitive industry. New biomass-to-biofuels pathways are being developed and commercialized to support goals for U.S. advanced biofuel use, such as those in the Energy Independence and Security Act of 2007. We explore the impact of learning rates and techno-economics in a learning model excerpted from the Biomass Scenario Model (BSM), developed by the U.S. Department of Energy and the National Renewable Energy Laboratory to explore the impact of biofuel policy on the evolution of the biofuels industry. The BSM integrates investment, production, and learning among competing biofuel conversion options that are at different stages of industrial development. We explain the novel methods used to simulate the impact of differing assumptions about mature industry techno-economics and about learning rates while accounting for the different maturity levels of various conversion pathways. A sensitivity study shows that the parameters studied (fixed capital investment, process yield, progress ratios, and pre-commercial investment) exhibit highly interactive effects, and the system, as modeled, tends toward market dominance of a single pathway due to competition and learning dynamics.

09 BIOMASS FUELS↗
DOE OSTI2016

Chapter 15: Using System Dynamics to Model Industry's Developmental Response to Energy Policy

In this chapter we explore the potential development of the biofuels industry using the Biomass Scenario Model (BSM), a system dynamics model developed at the National Renewable Energy Laboratory through the support of the U.S. Department of Energy. The BSM is designed to analyze the implications of policy on the development of the supply chain for biofuels in the United States. It explicitly represents the behavior of decision makers such as farmers, investors, fueling station owners, and consumers. We analyze several illustrative case studies that explore a range of policies and discuss how incentives interact with individual parts of the supply chain as well as the industry as a whole. The BSM represents specific incentives that are intended to approximate policy in the form of selected laws and regulations. Through characterizing the decision making behaviors of economic actors within the supply chain that critically influence the adoption rate of new biofuels production technologies and demonstrating synergies among policies, we find that incentives with coordinated impacts on each major element of the supply chain catalyze net effects of decision maker behavior such that the combined incentives are greater than the summed effects of individual incentives in isolation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2013

Effects of Deployment Investment on the Growth of the Biofuels Industry

In support of the national goals for biofuel use in the United States, numerous technologies have been developed that convert biomass to biofuels. Some of these biomass to biofuel conversion technology pathways are operating at commercial scales, while others are in earlier stages of development. The advancement of a new pathway toward commercialization involves various types of progress, including yield improvements, process engineering, and financial performance. Actions of private investors and public programs can accelerate the demonstration and deployment of new conversion technology pathways. These investors (both private and public) will pursue a range of pilot, demonstration, and pioneer scale biorefinery investments; the most cost-effective set of investments for advancing the maturity of any given biomass to biofuel conversion technology pathway is unknown. In some cases, whether or not the pathway itself will ultimately be technically and financially successful is also unknown. This report presents results from the Biomass Scenario Model -- a system dynamics model of the biomass to biofuels system -- that estimate effects of investments in biorefineries at different maturity levels and operational scales. The report discusses challenges in estimating effects of such investments and explores the interaction between this deployment investment and a volumetric production incentive. Model results show that investments in demonstration and deployment have a substantial positive effect on the development of the biofuels industry. Results also show that other conditions, such as supportive policies, have major impacts on the effectiveness of such investments.

09 BIOMASS FUELS↗
DOE OSTI2016

Effects of Deployment Investment on the Growth of the Biofuels Industry. 2016 Update

This report updates the 2013 report of the same title. Some text originally published in that report is retained and indicated in gray. In support of the national goals for biofuel use in the United States, numerous technologies have been developed that convert biomass to biofuels. Some of these biomass to biofuel conversion technology pathways are operating at commercial scales, while others are in earlier stages of development. The advancement of a new pathway toward commercialization involves various types of progress, including yield improvements, process engineering, and financial performance. Actions of private investors and public programs can accelerate the demonstration and deployment of new conversion technology pathways. These investors (both private and public) will pursue a range of pilot, demonstration, and pioneer scale biorefinery investments; the most cost-effective set of investments for advancing the maturity of any given biomass to biofuel conversion technology pathway is unknown. In some cases, whether or not the pathway itself will ultimately be technically and financially successful is also unknown. This report presents results from the Biomass Scenario Model--a system dynamics model of the biomass to biofuels system--that estimate effects of investments in biorefineries at different maturity levels and operational scales. The report discusses challenges in estimating effects of such investments and explores the interaction between this deployment investment and a volumetric production incentive. Model results show that investments in demonstration and deployment have a substantial growth impact on the development of the biofuels industry. Results also show that other conditions, such as accompanying incentives, have major impacts on the effectiveness of such investments. Results from the 2013 report are compared to new results. This report does not advocate for or against investments, incentives, or policies, but analyzes simulations of their hypothetical effects.

09 BIOMASS FUELS↗
DOE OSTI2012

Biomass Resource Allocation among Competing End Uses

The Biomass Scenario Model (BSM) is a system dynamics model developed by the U.S. Department of Energy as a tool to better understand the interaction of complex policies and their potential effects on the biofuels industry in the United States. However, it does not currently have the capability to account for allocation of biomass resources among the various end uses, which limits its utilizationin analysis of policies that target biomass uses outside the biofuels industry. This report provides a more holistic understanding of the dynamics surrounding the allocation of biomass among uses that include traditional use, wood pellet exports, bio-based products and bioproducts, biopower, and biofuels by (1) highlighting the methods used in existing models' treatments of competition forbiomass resources; (2) identifying coverage and gaps in industry data regarding the competing end uses; and (3) exploring options for developing models of biomass allocation that could be integrated with the BSM to actively exchange and incorporate relevant information.

09 BIOMASS FUELS↗
DOE OSTI2018

Application of Variance-Based Sensitivity Analysis to a Large System Dynamics Model

Variance-based sensitivity methods can provide insights into large computational models. We present a novel application of sensitivity analysis to the Biomass Scenario Model (BSM) a large and complex system dynamics model of the developing biofuels industry in the United States. We apply a two-stage sensitivity approach consisting of an initial sensitivity screening, followed by a variance decomposition approach. Identifying key system levers and quantifying their strength is not straightforward in complex system dynamics models that have numerous feedbacks and nonlinear results. Variance-based sensitivity analysis (VBSA) offers a systematic, global approach to assessing system dynamics models because it addresses nonlinear responses and interactive effects. Especially when a large model's size makes manual exploration of the input space difficult and time-consuming, the approach can help to provide a comprehensive understanding of interactions that drive model behaviors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Chapter 1: Insights From Over 10 Years of Cellulosic Biofuel Modeling

TBDWe present insights gained from over 10 years of system dynamic modeling of the cellulose to biofuel industry in the United States. We use a publicly-available Biomass Scenario Model to explore the impact of logistics system, economies of scale, and shared industrial learning on the developing cellulose-to-biofuels industry in the United States. One theme from this study as well as from the work performed over the last decade is the importance of the movement of the system toward maturation, both in terms of the supply system and the conversion processes. Mature processes imply lower investment risk, better yields, and better process economics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

Maturation of biomass‐to‐biofuels conversion technology pathways for rapid expansion of biofuels production: a system dynamics perspective

Abstract The Biomass Scenario Model ( BSM ) is a system‐dynamics simulation model intended to explore the potential for rapid expansion of the biofuels industry. The model is not predictive — it uses scenario assumptions based on various types of data to simulate industry development, emphasizing how incentives and technological learning‐by‐doing might accelerate industry growth. The BSM simulates major sectors of the biofuels industry, including feedstock production and logistics, conversion, distribution, and end uses, as well as interactions among sectors. The model represents conversion of biomass to biofuels as a set of technology pathways, each of which has allowable feedstocks, capital and operating costs, allowable products, and other defined characteristics. This study and the BSM address bioenergy modeling analytic needs that were identified in recent literature reviews. Simulations indicate that investments are most effective at expanding biofuels production through learning‐by‐doing when they are coordinated with respect to timing, pathway, and target sector within the biofuels industry. Effectiveness metrics include timing and magnitude of increased production, incentive cost and cost effectiveness, and avoidance of windfall profits. Investment costs and optimal investment targets have inherent risks and uncertainties, such as the relative value of investment in more‐mature versus less mature pathways. These can be explored through scenarios, but cannot be precisely predicted. Dynamic competition, including competition for cellulosic feedstocks and ethanol market shares, intensifies during times of rapid growth. Ethanol production increases rapidly, even up to Renewable Fuel Standards‐targeted volumes of biofuel, in simulations that allow higher blending proportions of ethanol in gasoline‐fueled vehicles. Published 2014. This article is a U.S. Government work and is in the public domain in the USA. Biofuels, Bioproducts, Biorefining published by John Wiley & Sons, Ltd on behalf of Society of Chemical Industry.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

US alternative jet fuel deployment scenario analyses identifying key drivers and geospatial patterns for the first billion gallons ,

The aviation sector's commitments to carbon-neutral growth in international aviation starting in 2020, and the desire to improve supply surety, price stability, and the environmental performance of aviation fuels, have led to broad interest in sustainable alternative jet fuels. Here, we use the system-dynamics-based biomass scenario model (BSM), focused on alternative jet fuel production capacity evolution, and the geospatially explicit Freight and Fuel Transportation Optimization Tool (FTOT), focused on optimal feedstock and fuel flows over the transportation system, to explore the incentive effects on alternative jet fuel production capacity trajectories and potential geospatial patterns of production and delivery in the USA. Scenarios presented here focus on readily available waste feedstocks (waste fats, oils and greases, municipal solid waste, and crop and forestry residues) and conversion technologies included in the ASTM D7566 synthesized aviation turbine fuels specification. The BSM modeling of possible deployment trajectories from 2015 to 2045 suggests that up to 8 billion gallons may be available by 2045 depending on the policies and incentives implemented. Both approaches suggest that 200 million to 1 billion gallons per year of alternative jet fuel production are possible in 2030 given multiple incentives and a favorable investment climate, and that capital costs and technology maturation rates will affect deployment of different fuel production technologies, and therefore the feedstocks needed. Further collaboration on these modeling approaches would reduce methodological blind spots while providing insights into future industry trajectories.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Annual Technology Baseline (ATB): The 2024 Transportation Update

The Transportation Annual Technology Baseline (ATB) provides detailed cost and performance data, estimates, and assumptions for vehicle and fuel technologies in the United States. It includes current and projected estimates for vehicle technologies as well as fuels, and it details the assumptions used to calculate those costs, such as gas and electricity prices, discount rates, and vehicle miles traveled. The 2024 update added more biofuels pathways to align with pathways used in the Biomass Scenario Model.

ADVANCED PROPULSION SYSTEMS,DIRECT ENERGY CONVERSI↗
DOE OSTI2024

Algae to HEFA : Economics and potential deployment in the United States

Abstract To reach the goals set by the US Department of Energy's Sustainable Aviation Fuel (SAF) Grand Challenge, currently available feedstocks may be insufficient. Giving priority to developing, prototyping and reducing the cost of algal feedstock before investing and lining up locations is important. As the production of algal feedstocks advances, a simplified conversion approach using more mature technologies can help reduce the investment risk for algae‐based fuels. Reducing process complexity to the steps described here [namely, conversion of lipids to HEFA (hydroprocessed esters and fatty acids) fuels and relegating the remainder of the biomass to anaerobic digestion or food/feed production] enables the near‐term production of algal SAF but presents challenging economics depending on achievable cultivation costs and compositional quality. However, these economics can be improved by present‐day policy incentives. With these incentives, the modeled algae‐to‐HEFA pathway could reach a minimum fuel selling price as low as $4.7 per gasoline gallon equivalent depending on the carbon intensity reduction that can be achieved compared with petroleum. Uncertainty about algal feedstock production maturity in the current state of technology and the future will play a large role in determining the economic feasibility of building algae‐to‐HEFA facilities. For example, if immaturity increases the feedstock price by even 10%, SAF production in 2050 is about 58% of the production which could have been achieved with mature feedstock. Additionally, growth in this conversion pathway can be notably boosted through the inclusion of subsidies, and also through higher‐value coproducts or higher lipid yields beyond the scope of the process considered here.

09 BIOMASS FUELS↗