Supporting Data

Price Scenarios at $54 and $119 were simulated for Switchgrass, Miscanthus and Willow production from 2017 to 2040. These analyses were used in Woodbury, Peter B., et al. 2018. "Improving water quality in the Chesapeake Bay using payments for ecosystem services for perennial biomass for bioenergy and biofuel production." Biomass and Bioenergy 114:132-142. doi: https://doi.org/10.1016/j.biombioe.2017.01.024.

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.

This spreadsheet serves as an Input file to the National Renewable Energy Laboratory's Waste-to-Energy System Simulation (WESyS) model developed in Stella Pro (isee systems, Lebanon, NH). WESyS is a national-level system dynamics model that simulates energy production from three sectors of the U.S. waste-to-energy industry: landfills, confined animal feeding operations (CAFOs), and publically owned treatment works (POTWs).

For our purposes, a scenario is a set of model conditions (i.e. parameter settings) that approximate a specified condition or potential reality. The RIN/LCFS scenario represents biofuel production incentives from both the Renewable Fuel Standard (RFS) and the Low-Carbon Fuel Standard (LCFS). To implement this scenario, we modified the model structure to 1) accept time series data that represent the production incentives from the Renewable Identification Number (RIN) market and 2) mimic the low carbon fuel standard credit calculations. For both of these programs, the incentive is accrued at the point of production.

This spreadsheet serves as an Input file to the National Renewable Energy Laboratory's Waste-to-Energy System Simulation (WESyS) model developed in Stella Pro (isee systems, Lebanon, NH). WESyS is a national-level system dynamics model that simulates energy production from three sectors of the U.S. waste-to-energy industry: landfills, confined animal feeding operations (CAFOs), and publically owned treatment works (POTWs).

For our purposes, a scenario is a set of model conditions (i.e. parameter settings) that approximate a specified condition or potential reality. The rate-based development scenario outlined here represents the ability of publically owned treatment works (POTWs) to take advantage of rate of return regulation to recover the cost of capital associated with the development of waste-to-energy facilities (i.e., POTWs can recover costs by increasing the rates that they charge their customers for water treatment). Under rate-based financing, there is a tendency to invest in the most expensive technology. This is a well-recognized drawback of using a rate-based mechanism to cover capital investment. In California, recommendations have been made to limit rate-base increases to finance projects that directly tie into a state goal and increase demand for electricity. As a result, we limited the rate-based option to be available only to California wastewater treatement plants investing in electricity generation.

This spreadsheet serves as an Input file to the National Renewable Energy Laboratory's Waste-to-Energy System Simulation (WESyS) model developed in Stella Pro (isee systems, Lebanon, NH). WESyS is a national-level system dynamics model that simulates energy production from three sectors of the U.S. waste-to-energy industry: landfills, confined animal feeding operations (CAFOs), and publically owned treatment works (POTWs).

For our purposes, a scenario is a set of model conditions (i.e. parameter settings) that approximate a specified condition or potential reality. The non-negative feedstock cost scenario outlined here represents a potential future reality where feedstock costs for waste become positive (i.e., there is a market for wastes). For the three types of facilities represented in WESyS (i.e., wastewater treatment plants, landfills, and concentrated animal feeding operations (CAFOs)), only CAFOs are expected to have positive feedstock costs for manure in the near future. If this were to happen, CAFOs might chose to sell their waste rather than build on-site waste-to-energy (WTE) facilities. For this scenario, we adopted a farmer-owned cooperative model in which farmers may sell their manure to a cooperatively owned and operated WTE facility. For these farmer-owned cooperatives, we have allow for technology development under three pathways (Hydrothermal Liquifaction (HTL), Fischer-Tropsch (FT), and Renewable Natural Gas (RNG)) that are assumed to operate at full commercial scale. For a given technology pathway to become feasible, there must be enough animal units available to supply the commercial throughput requirement of the technology.

This spreadsheet serves as an Input file to the National Renewable Energy Laboratory's Waste-to-Energy System Simulation (WESyS) model developed in Stella Pro (isee systems, Lebanon, NH). WESyS is a national-level system dynamics model that simulates energy production from three sectors of the U.S. waste-to-energy industry: landfills, confined animal feeding operations (CAFOs), and publically owned treatment works (POTWs).

For our purposes, a scenario is a set of model conditions (i.e. parameter settings) that approximate a specified condition or potential reality. For the landfill diversion scenario outlined here, we examined California Senate Bill 1383, which sets forth a timeline for diversion of organic wastes from landfills. This scenario is only applied to the California landfill (CA LF) and California wastewater treatement plant (CA WWTP) modules. For this scenario, we modified the model to perform a set of calculations that reduce the amount of organic waste (in tons per year) based on the SB 1383 timeframe – 50% by 2020, 75% by 2025 and the total amount of waste and organics that went to California landfills in 2014. We also assumed that all waste diverted from landfills would go to wastewater treatment plants.

Growing interest in renewable and domestically produced energy motivates the evaluation of woody bioenergy feedstock production. In the southeastern U.S., woody feedstock plantations, primarily of loblolly pine (Pinus taeda), would be intensively managed over short rotations (10-12 years) to achieve high yields. The primary differences in managing woody feedstocks for bioenergy production vs for pulp/sawtimber production include a higher frequency of pesticide and fertilizer applications, whole-tree removal, and greater ground disturbance (i.e., more bare ground during stand establishment and more frequent disturbance). While the effects of pulp/sawtimber production on water quality are well-studied, the effects of growing short-rotation loblolly pine on water quality and the efficacy of current forestry Best Management Practices (BMPs) have not been evaluated for this emerging management system. We used a watershed-scale experiment in a before-after, control-impact design to evaluate the effects of growing loblolly pine for bioenergy on water quality in the Upper Coastal Plain of the southeastern U.S. Intensive management for bioenergy production and implementation of current forestry BMPs occurred on ~50% of two treatment watersheds, with one reference watershed in a minimally managed pine forest. Water quality metrics (nutrient and pesticide concentrations) were measured in stream water, groundwater, and interflow (i.e., shallow subsurface flow) for a two-year pre-treatment period, and for 3.5 years post-treatment. After 3.5 years, there was little change to stream water quality. We observed a few occurrences of saturated overland flow, but sediments and water dissipated within the streamside management zones in over 75% of these instances. Stream nutrient concentrations were low and temporal changes mainly reflected seasonal patterns in nitrogen cycling. Nitrate concentrations increased in groundwater post-treatment to <2 mg N L-1, and these concentrations were below the U.S. drinking water standard (10 mg N L-1). Applied pesticides were almost always below detection in streams and groundwater. Overall, these findings highlight that current forestry BMPs can protect stream water quality from intensive pine management for bioenergy in the first 3.5 years. However, groundwater quality and transit times need to be considered in these low-gradient watersheds of the southeastern U.S. that are likely to become an important location for woody bioenergy feedstock production.

The U.S. Department of Energy’s (DOE’s) Co-Optimization (Co-Optima) initiative is accelerating the introduction of affordable, scalable, and sustainable fuels and high-efficiency, low-emission engines with a first-of-its-kind effort to simultaneously tackle fuel and engine research and development (R&D).

Co-Optima is conducting research to identify the fuel properties and engine design characteristics needed to maximize vehicle performance and affordability, while deeply cutting harmful emissions. Rather than endorsing a single solution, this initiative is designed to arm industry, policymakers, and other key stakeholders with the scientific foundation and market intelligence required to make investment decisions, break down barriers to commercialization, and bring new high-performance fuels and advanced engine systems to market sooner.

DOE’s Office of Energy Efficiency & Renewable Energy has brought together nine national laboratories—the National Renewable Energy Laboratory and Argonne, Idaho, Lawrence Berkeley, Lawrence Livermore, Los Alamos, Oak Ridge, Pacific Northwest, and Sandia National Laboratories—to collaborate on this groundbreaking research. The outcome of this effort will be new tools, data, and knowledge to pave the way for future generations of fuel and vehicle innovations.

In its first year, the Co-Optima initiative moved from robust concept to concrete results. The two DOE offices, nine national laboratories, and industry stakeholders that compose Co-Optima successfully worked to integrate fuels and engine R&D, breakdown barriers, and tackle challenges. This report highlights the progress made by Co-Optima in fiscal year 2016.

In this inaugural year, our parallel Co-Optima research tracks have focused on fuels and engine technologies related to spark-ignition and advanced compression ignition systems.

This study provides a spatially comprehensive assessment of sustainable agricultural residue removal potential across the United States for bioenergy production. Earlier assessments determining the quantity of agricultural residue that could be sustainably removed for bioenergy production at the regional and national scale faced a number of computational limitations. These limitations included the number of environmental factors, the number of land management scenarios, and the spatial fidelity and spatial extent of the assessment. This study utilizes integrated multi-factor environmental process modeling and high fidelity land use datasets to perform the sustainable agricultural residue removal assessment. Soil type represents the base spatial unit for this study and is modeled using a national soil survey database at the 10–100 m scale. Current crop rotation practices are identified by processing land cover data available from the USDA National Agricultural Statistics Service Cropland Data Layer database. Land management and residue removal scenarios are identified for each unique crop rotation and crop management zone. Estimates of county averages and state totals of sustainably available agricultural residues are provided. The results of the assessment show that in 2011 over 150 million metric tons of agricultural residues could have been sustainably removed across the United States. Projecting crop yields and land management practices to 2030, the assessment determines that over 207 million metric tons of agricultural residues will be able to be sustainably removed for bioenergy production at that time. This biomass resource has the potential for producing over 68 billion liters of cellulosic biofuels.

A presentation to share author's perspective on the importance of the 2011 BT2 data; to acknowledge the ARS REAP (Renewable Energy Assessment Project) team for their contributions to the BT2 report; and to predict ARS REAP team uses for BT2 data with regard to research needs and implementation of actual biofuel projects.