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Peterson, Steve

Publications and source records attributed to Peterson, Steve.

7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Estimated attribution of the RFS program on soybean biodiesel in the U.S. using the bioenergy scenario model

Biofuels are supported by various governmental policies in the U.S. and globally as an alternative transportation fuel for environmental, geopolitical, and economic reasons. Much debate surrounds the effectiveness of these policies as well as the overall net environmental effect of increased biofuels use. In the U.S., recent studies have shown that the Renewable Fuels Standard (RFS) Program, overall, may not have been the leading driver of the ethanol industry from 2005 to 2020, contrary to common perception. Similar scrutiny has not been applied to biodiesel. Here, this study uses the Bioenergy Scenario Model, a well-vetted system dynamics model, to retrospectively apportion historical biodiesel production between the RFS Program and other potentially influential drivers, such as the economics of biodiesel vs. diesel, the Biodiesel Tax Credit (BTC), California's Low Carbon Fuel Standard, and other factors. From 2002 to 2020 about 36% of biodiesel production can be attributed to the RFS Program, 35% to the BTC, and the rest to other factors. Thus, the overall effect of the RFS Program appears much larger on biodiesel than on corn ethanol. The finding that the same policy may have very disparate effects on different biofuels helps inform the design of future policies on biofuels.

09 BIOMASS FUELS↗
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↗
DOE OSTI2023

Deep decarbonization and U.S. biofuels production: a coordinated analysis with a detailed structural model and an integrated multisectoral model

Scenarios for deep decarbonization involve biomass for biofuels, biopower, and bioproducts, and they often include negative emissions via carbon capture and storage or utilization. However, critical questions remain about the feasibility of rapid growth to high levels of biomass utilization, given biomass and land availability as well as historical growth rates of the biofuel industry. We address these questions through a unique coordinated analysis and comparison of carbon pricing effects on biomass utilization growth in the United States using a multisectoral integrated assessment model, the Global Change Analysis Model (GCAM), and a biomass-to-biofuels system dynamics model, the Bioenergy Scenario Model (BSM). We harmonized and varied key factors—such as carbon prices, vehicle electrification, and arable land availability—in the two models. We varied the rate of biorefinery construction, the fungibility of feedstock types across conversion processes, and policy incentives in BSM. The rate of growth in biomass deployment under a carbon price in both models is within the range of current literature. However, the reallocation of land to biomass feedstocks would need to overcome bottlenecks to achieve growth consistent with deep decarbonization scenarios. Investments as a result of near-term policy incentives can develop technology and expand capacity—reducing costs, enabling flexibility in feedstock use, and improving stability—but if biomass demand is high, these investments might not overcome land reallocation bottlenecks. Biomass utilization for deep decarbonization relies on extraordinary growth in biomass availability and industrial capacity. In this paper, we quantify and describe the potential challenges of this rapid change.

09 BIOMASS FUELS↗
DOE OSTI2023

BSM (Bioenergy Scenario Model) 2023 FKA: Biomass Scenario Model [SWR-09-09]

The U.S. Department of Energy's (DOE's) Bioenergy Technologies Office and the National Renewable Energy Laboratory (NREL) developed the BSM (Bioenergy Scenario model) to explore the development of a U.S. biofuels industry. The BSM is a system dynamics model built on the STELLA software platform. The model represents the dynamic interactions of the major sectors of the biofuels industry—feedstock production, feedstock logistics, biomass to biofuels conversion, and biofuels end use, including fuels inventory, dispensing, distribution, fuel use, and the vehicle fleet. The BSM represents contextual aspects of the developing biofuels industry, including investment in new biomass to biofuel conversion technologies, competition from petroleum fuels, vehicle demand for biofuels, and various government policies, using all of these to simulate the development of the industry. The purpose of the BSM is to generate and explore plausible scenarios for the evolution of a biofuels industry in the United States, and as a high-level system model it is not designed for precise, quantitative forecasting. Instead, it is best used to (1) analyze and evaluate alternate policies; (2) generate scenarios; (3) identify high-impact levers and bottlenecks to system evolution; and (4) seed focused discussion among policymakers, analysts, and stakeholders.

Bush, Brian↗
DOE OSTI2022

Battery Energy Storage Scenario Analyses Using the Lithium-Ion Battery Resource Assessment (LIBRA) Model

Meeting aggressive carbon emission goals will entail widespread deployment of renewable sources of electricity. Because these sources are variable, there is a need to develop scalable energy storage technologies. The U.S. Department of Energy is supporting efforts to increase U.S. manufacturing and recycling capabilities for LIBs and to decrease costs of stationary storage batteries. Many factors influence the domestic manufacturing and cost of stationary storage batteries, including availability of critical raw materials (lithium, cobalt, and nickel), competition from various demand sectors (consumer electronics, vehicles, and battery energy storage), resource recovery (recycling), government policies, and learning in the industry, among other factors. Understanding how these factors interact and identifying synergies and bottlenecks is important for developing effective strategies for the LIB stationary energy storage system. We developed the Lithium-Ion Battery Resource Analysis (LIBRA) model as a tool to help stakeholders better understand the following types of questions: What are the roles of R&D, industrial learning, and scaling of demand in lowering the cost of battery energy storage system production? How do the intersections between the EV and stationary storage sectors affect the battery supply chain? For various stationary storage and EV penetration scenarios, what volumes of critical materials might be required and what role can resource recovery play? What does expected demand for both EVs and stationary storage portend for mineral resources and overall mineral scarcity? The LIBRA model is developed using a System dynamics (SD) modeling approach to represent interactions across the segments of the battery materials supply chain. System dynamics models can capture the complex interactions and feedback between the various system components that influence supply and demand. The LIBRA model is comprised of several interacting modules that represent specific portions of the LIB supply chain. The model tracks the buildout of the domestic LIB industry over time (2020 - 2050) and in the context of competing demands for raw materials, recycling, and markets for LIBs. The LIBRA model represents major systemic feedback loops and delays across the supply chain. This report provides a complete documentation for the LIBRA model, including model assumptions, data, scenario analysis results, and sensitivity analysis of the model's input space.

25 ENERGY STORAGE↗
DOE OSTI2020

An Overview of the Waste-to-Energy System Simulation (WESyS) Model

The leveraging of waste streams for energy and chemical production could add revenue to waste disposal operations, and it presents opportunities for addressing a variety of economic and environmental objectives at the local, state, and national levels. The Waste-to-Energy System Simulation (WESyS) model is a system dynamics model that was created to simulate the development of the U.S. waste-to-energy industry over time. For each of the three primary waste resources modeled (landfills, concentrated animal feeding operations, and publicly owned treatment works), WESyS simulates technically feasible scenarios for use of the waste, including direct conversion to fuels, and anaerobic digestion followed by flaring, electricity generation, combined heat and power, cleanup and compression to compressed natural gas, and cleanup and injection into an existing pipeline. The model allows users to explore numerous plausible future scenarios for the development of the U.S. waste-to-energy industry. This report provides an overview of the WESyS model and documents the key assumptions, equations, and data sources used to create the model.

09 BIOMASS FUELS↗