biomass

This dataset was utilized in a report to highlight parameters that affect near-term sustainable supply of corn stover and forest resources at $56 and $74 per dry ton delivered. While the report focus is restricted to 2018, the modeling runs are available from 2016-2022. In the 2016 Billion-ton Report (BT16), two stover cases were presented. In this dataset, we vary technical levels of those assumptions to measure stover supply response and to evaluate the major determinants of stover supply. In each of these cases, the supply is modeled first at the farmgate at prices up to $80 per dry ton for five deterministic scenarios. Building on this dataset, a supplementary dataset of delivered supply was modeled for 800k dry ton per year capacity facilities in two facility siting approaches. Results were summarized across delivered supply curves for twelve scenarios. The resulting supply curves are highly elastic, resulting in a range of potential supplies across scenarios at specified prices. Interactive visualization of these data allows exploration into any specified nth plant supply sensitivity to key variables and spatial distribution of stover resources.

The analysis is economic supply risk and doesn’t account for disruptions from competing demands, namely livestock feed and bedding markets.

The Biomass Energy Data Book is a statistical compendium prepared and published by Oak Ridge National Laboratory (ORNL) under contract with the Biomass Program in the Energy Efficiency and Renewable Energy (EERE) program of the Department of Energy (DOE). Designed for use as a convenient reference, the book represents an assembly and display of statistics and information that characterize the biomass industry, from the production of biomass feedstocks to their end use, including discussions on sustainability.

This is the fourth edition of the Biomass Energy Data Book which is only available online in electronic format. There are five main sections to this book. The first section is an introduction which provides an overview of biomass resources and consumption. Following the introduction to biomass, is a section on biofuels which covers ethanol, biodiesel and bio-oil. The biopower section focuses on the use of biomass for electrical power generation and heating. The fourth section is on the developing area of biorefineries, and the fifth section covers feedstocks that are produced and used in the biomass industry. The sources used represent the latest available data. There are also four appendices which include frequently needed conversion factors, a table of selected biomass feedstock characteristics, and discussions on sustainability. A glossary of terms and a list of acronyms are also included for the reader's convenience.

Conventional feedstock supply systems exist and have been developed for traditional agriculture and forestry systems. These conventional feedstock supply systems can be effective in high biomass-yielding areas (such as for corn stover in Iowa and plantation-grown pine trees in the southern United States), but they have their limits, particularly with respect to addressing feedstock quality and reducing feedstock supply risk to biorefineries. They also are limited in their ability to efficiently deliver energy crops. New logistics technologies and systems are needed to address these challenges and support a growing bioenergy industry.

The proposed solution put forth by the DOE Bioenergy Technologies Office to address these challenges is Advanced Feedstock Supply Systems. The Advanced Feedstock Supply Systems incorporate densification, drying, and other preprocessing technologies to create a biomass commodity. A feature of these advanced systems is biomass preprocessing depots that format biomass in fairly close proximity to the location of production. However, validating assumptions used to develop these advanced systems is critical.

The Advanced Feedstock Supply System Validation Workshop gathered experts from industry, DOE offices, DOE-funded laboratories, and academia to discuss approaches to addressing challenges associated with an expanding bioenergy industry and assumptions used in the Advanced Feedstock Supply System. The workshop was sponsored by the DOE Bioenergy Technologies Office.

The Biomass Scenario Model (BSM) is a system dynamics model that represents the entire biomass-to-biofuels supply chain, from feedstock to fuel use. The BSM is a complex model that has been used for extensive analyses; the model and its results can be better understood if input data used for initialization and calibration are well-characterized. It has been carefully validated and calibrated against the available data, with data gaps filled in using expert opinion and internally consistent assumed values. Most of the main data sources that feed into the model are recognized as baseline values by the industry. This report documents data sources and references in Version 2 of the BSM (BSM2), which only contains the ethanol pathway, although subsequent versions of the BSM contain multiple conversion pathways. The BSM2 contains over 12,000 total input values, with 506 distinct variables. Many of the variables are opportunities for the user to define scenarios, while others are simply used to initialize a stock, such as the initial number of biorefineries. However, around 35% of the distinct variables are defined by external sources, such as models or reports. The focus of this report is to provide insight into which sources are most influential in each area of the supply chain.

This paper focuses ont he patterns of farmers' choices regarding dedicated perennial lignocellulosic energy crops.   We focus on choices abou perennial crops because two thirds of the mandated advanced biofuels are expected to be converted at biorefineries from perennials (USDA 2010). 

A framework for selecting and evaluating indicators of bioenergy sustainability is presented.
This framework is designed to facilitate decision-making about which indicators are useful for assessing
sustainability of bioenergy systems and supporting their deployment. Efforts to develop sustainability
indicators in the United States and Europe are reviewed. The fi rst steps of the framework for
indicator selection are defi ning the sustainability goals and other goals for a bioenergy project or program,
gaining an understanding of the context, and identifying the values of stakeholders. From the
goals, context, and stakeholders, the objectives for analysis and criteria for indicator selection can
be developed. The user of the framework identifi es and ranks indicators, applies them in an assessment,
and then evaluates their effectiveness, while identifying gaps that prevent goals from being met,
assessing lessons learned, and moving toward best practices. The framework approach emphasizes
that the selection of appropriate criteria and indicators is driven by the specifi c purpose of an analysis.
Realistic goals and measures of bioenergy sustainability can be developed systematically with the help
of the framework presented here. © 2015 Society of Chemical Industry and John Wiley & Sons, Ltd

A global energy crop productivity model that provides geospatially explicit quantitative details on biomass
potential and factors affecting sustainability would be useful, but does not exist now. This study describes a
modeling platform capable of meeting many challenges associated with global-scale agro-ecosystem modeling.
We designed an analytical framework for bioenergy crops consisting of six major components: (i) standardized
natural resources datasets, (ii) global field-trial data and crop management practices, (iii) simulation units and
management scenarios, (iv) model calibration and validation, (v) high-performance computing (HPC) simulation,
and (vi) simulation output processing and analysis. The HPC-Environmental Policy Integrated Climate
(HPC-EPIC) model simulated a perennial bioenergy crop, switchgrass (Panicum virgatum L.), estimating feedstock
production potentials and effects across the globe. This modeling platform can assess soil C sequestration,
net greenhouse gas (GHG) emissions, nonpoint source pollution (e.g., nutrient and pesticide loss), and energy
exchange with the atmosphere. It can be expanded to include additional bioenergy crops (e.g., miscanthus,
energy cane, and agave) and food crops under different management scenarios. The platform and switchgrass
field-trial dataset are available to support global analysis of biomass feedstock production potential and corresponding
metrics of sustainability.

Vimmerstedt, L. J., Bush, B. W., Hsu, D. D., Inman, D. and Peterson, S. O. (2014), Maturation of biomass-to-biofuels conversion technology pathways for rapid expansion of biofuels production: a system dynamics perspective. Biofuels, Bioprod. Bioref.. doi: 10.1002/bbb.1515
 
 
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Understanding the development of the biofuels industry in the United States is important to policymakers and industry. The Biomass Scenario Model (BSM) is a system dynamics model of the biomass-to-biofuels system that can be used to explore policy effects on biofuels development. Because of the complexity of the model, as well as the wide range of possible future conditions that affect biofuels industry development, we have not developed a single reference case but instead developed a set of specific scenarios that provide various contexts for our analyses. The purpose of this report is to describe the scenarios that comprise the BSM scenario library. At present, we have the following policy-focused scenarios in our library: minimal policies, ethanol-focused policies, equal access to policies, output-focused policies, technological diversity focused, and the point-of-production- focused. This report describes each scenario, its policy settings, and general insights gained through use of the scenarios in analytic studies.

Abstract: Farmgate prices (i.e. price delivered roadside ready for loading and transport) for biomass feedstocks directly infl uence biofuel prices. Using the latest available data, marginal (i.e. price for the last ton) farmgate prices of $51, $63, and $67 dry ton–1 ($2011) are projected as necessary to provide 21 billion gallons of biofuels from about 250 million dry tons of terrestrial feedstocks in 2022 under price-run deterministic, demand-run deterministic, and stochastic simulations, respectively. Sources of uncertainty in these feedstock supply and price projections include conversion effi ciency, global market impacts on crop price projections, crop yields, no-till adoption, and climate. Under a set of low, high, and reference assumptions, these variables introduce an average of +/– $11 dry ton-1 (~15%) uncertainty of feedstock prices needed to meet EISA targets of 21 billion gallons of biofuels produced with 250 million dry tons of biomass in 2022. Market uncertainty justifi es the need for fairly frequent (i.e. annual or biennial) re-assessment of feedstock price projections to inform strategies toward commercialization of biofuels. Published in 2014 by John Wiley & Sons, Ltd