NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Biomass Scenario Model 

Documentation: Data and 

References 

Yolanda Lin, Emily Newes, and Brian Bush 

National Renewable Energy Laboratory

Steve Peterson and Dana Stright 

Lexidyne, LLC

Technical Report

NREL/TP-6A20-57831

May 2013
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Biomass Scenario Model 

Documentation: Data and 

References 

Yolanda Lin, Emily Newes, and Brian Bush 

National Renewable Energy Laboratory

Steve Peterson and Dana Stright 

Lexidyne, LLC

Prepared under Task No. BZ11.3003

Technical Report

NREL/TP-6A20-57831

May 2013
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iii

Acknowledgments 

The authors would like to acknowledge the Biomass Scenario Model developers and analysts. 

Robert Wallace (Booz Allen Hamilton) and John Sheehan (The University of Minnesota) were 

instrumental in initial model development. Zia Haq, Kristen Johnson, and Alicia Lindauer of the 

U.S. Department of Energy have supported analyses to shed light on the biofuels system. In 

addition, other BSM2 team members have provided helpful insights, discussions, and reviews: 

Mark Paich and Corey Peck of Lexidyne, LLC and Laura Vimmerstedt, Daniel Inman, David 

Hsu, Ryan Elmore, Andrew Argo, and Amy Schwab of the National Renewable Energy 

Laboratory. 
iv

List of Acronyms

A Appalachian

AEO Annual Energy Outlook 

BC biochemical

BCAP Biomass Crop Assistance Program

BSM Biomass Scenario Model

BSM2 Biomass Scenario Model, Version 2 

CB corn belt

CHST collection, harvest, storage, transportation

CM Feedstock Conversion Module 

CPT cost per ton 

CRP Conservation Reserve Program

D&C design and construction 

DCS design, construction, startup

DDG distillers dry grains

DLM Distribution Logistics Module

Dom domestic

DS delta states

DSM Dispensing Stations Module

EISA Energy Independence and Security Act 

EtOH ethanol 

FCI fixed capital investment 

FFV fuel-flex vehicle

FLM Feedstock Logistics Module 

FS feedstock

FSM Feedstock Supply Module 

FUM Fuel Use Module 

GP grower payment 

GPPA grower payment per acre

HC herbaceous cellulose

HCCPA harvest collection cost per acre

HCCPT harvest collection cost per ton 

IBSAL Integrated Biomass Supply Analysis and Logistics 

Model 

IM Imports Module 

K logit coefficient

L natural logarithm 

LS lake states

LT long term

M mountain 

NE New England 

Nom nominal

NP northern plains

NPV net present value 

ORNL Oak Ridge National Laboratory 

P pacific
v 

PCEC perennial cellulosic energy crops 

PCPA production cost per acre

PIM Pricing and Inventory Module

POD point of distribution

POP point of production 

POU point of use

PY process yield

RAT ratio of attractiveness

RP repurposing 

SE southeast 

SP southern plains

TC thermochemical

TPA tons per acre

USDA United States Department of Agriculture

VBL variable

VM Vehicles Module 

W woody 

WC woody cellulose

YPA yield per acre
vi 

Executive Summary 

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. We find that data based on 

POLYSYS datasets and U.S. Department of Agriculture baseline projections are the most 

utilized sources in the feedstock sector, whereas the conversion module relies heavily on data 

found in National Renewable Energy Laboratory technical reports dealing with the technoeconomic characteristics of different technologies. The distribution, dispensing, and fuel use 

modules utilize data on gasoline stations from the National Association of Convenience Stores. 
vii 

Table of Contents

1 Introduction ........................................................................................................................................... 1

2 BSM2 Modules ...................................................................................................................................... 4

2.1 Feedstock Supply Module............................................................................................................. 5

2.2 Feedstock Logistics Module.......................................................................................................... 7

2.3 Feedstock Conversion Module...................................................................................................... 8

2.4 Dispensing Station Module Inputs.............................................................................................. 10

2.5 Pricing and Inventory Module..................................................................................................... 12

2.6 Distribution Logistics Module .................................................................................................... 12

2.7 Dispensing Stations Module........................................................................................................ 13

2.8 Fuel Use Module ......................................................................................................................... 14

2.9 Imports Module ........................................................................................................................... 15

2.10 Vehicles Module ......................................................................................................................... 16

2.11 Outputs........................................................................................................................................ 17

3 Conclusion ..........................................................................................................................................19

References.................................................................................................................................................20

Appendix A ................................................................................................................................................23

Appendix B ................................................................................................................................................25
viii

List of Figures

Figure 1. Architecture of the BSM2.............................................................................................................. 2

Figure 2. Number of distinct input variables and total input data points by reference type in the BSM2.... 3

Figure 3. Distinct input variables versus total input data points among reference types and modules......... 5

Figure 4. Number of distinct input variables and total input data points by reference type in the FSM (left) 

and breakdown of referenced data by source, number of distinct variables and total data 

points (right)............................................................................................................................. 6

Figure 5. FSM: Interaction with other modules............................................................................................ 7

Figure 6. FLM: Interaction with other modules............................................................................................ 7

Figure 7. Number of distinct input variables and total input data points by reference type in the FLM (left) 

and breakdown of referenced data by source, number of distinct variables and total data 

points (right)............................................................................................................................. 8

Figure 8. Number of distinct input variables and total input data points by reference type in the CM (left) 

and breakdown of referenced data by source, number of distinct variables and total data 

points (right)............................................................................................................................. 9

Figure 9. CM: Interaction with other modules.............................................................................................. 9

Figure 10. DSM Inputs Module: Interaction with other modules............................................................... 10

Figure 11. Number of distinct input variables and total input data points by reference type in the DSM 

Inputs Module (left) and breakdown of referenced data by source, number of distinct 

variables and total data points (right)..................................................................................... 11

Figure 12. PIM: Interaction with other modules......................................................................................... 12

Figure 13. Number of distinct input variables and total input data points by reference type in the PIM ... 12

Figure 14. Number of distinct input variables and total input data points by reference type in the DLM . 13

Figure 15. DLM: Interaction with other modules....................................................................................... 13

Figure 16. Number of distinct input variables and total input data points by reference type in the DSM.. 14

Figure 17. DSM: Interaction with other modules ....................................................................................... 14

Figure 18. Number of distinct input variables and total input data points by reference type in the FUM.. 15

Figure 19. FUM: Interaction with other modules ....................................................................................... 15

Figure 20. Number of distinct input variables and total input data points by reference type in the IM (left) 

and breakdown of referenced data by source, number of distinct variables and total data 

points (right)........................................................................................................................... 16

Figure 21. IM: Interaction with other modules........................................................................................... 16

Figure 22. Number of distinct input variables and total input data points by reference type in the VM (left) 

and breakdown of referenced data by source, number of distinct variables and total data 

points (right)........................................................................................................................... 17

Figure 23. VM: Interaction with other modules ......................................................................................... 17

Figure 24. Number of distinct input variables and total input data points by reference type in the Outputs 

Module ................................................................................................................................... 18

List of Tables

Table 1. Variable Types................................................................................................................................ 1

Table 2. Distinct Input Variables Versus Total Input Data Points, by Module ............................................ 4
1 

1 Introduction

This report is a summary of all the data inputs to the Biomass Scenario Model, Version 2

(BSM2). The BSM2 is a state-of-the-art system dynamics model of the domestic biofuels supply 

chain that explicitly focuses on policy issues, feasibility, and potential side effects. It is built in 

STELLA (isee systems 2010) and accounts for resource availability, physical constraints,

technological constraints, economic constraints, behavior, and policy—while tracking the 

deployment of biofuels through a dynamic simulation (not optimization). BSM2 focuses on 

cellulosic ethanol, while the most recent version (Version 3) additionally treats the major 

infrastructure-compatible fuels such as biomass-based gasoline, diesel, and jet fuel. For more 

information about the modeling methodology and logic contained in the BSM2, see Newes et 

al. (2011). 

The Biomass Scenario Model (BSM) 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. There are over 12,000 total input values in the BSM2, contained in 506 distinct 

variables. Of the 506, any variable may be arrayed by one or two dimensions (e.g., contain 

subscripts) and/or be represented as a graph, with the ability to describe how values on the y-axis 

will respond depending on how the x-axis is defined. The six variable types are identified in 

Table 1. 

Table 1. Variable Types

Variable Type Subscript 1 Subscript 2 X-axis Description

Class 1: Single 

data point

The variable has a single scalar 

value.

Class 2: Graphical 

function X 

The variable is a single graphical 

function demonstrating how the 

y-axis changes depending on the 

x-axis values. 

Class 3: Array 

data series X The variable is one-dimensional array. 

Class 4: Matrix 

data series X X The variable is a two-dimensional array.

Class 5: Arrayed 

graphical function X X 

The variable is a one-dimensional 

array where each element has its 

own graphical function. 

Class 6: Double

subscripted 

graphical function X X X 

The variable is a two-dimensional 

array where each combination of 

elements has its own graphical 

function. 

The BSM2 is divided into 11 modules, each with a different focus. They are all interconnected in 

a system of systems, where the major modules (feedstock supply, feedstock conversion, and 

downstream) can be run separately or in concert with one another. Figure 1 shows a schematic of 

the different modules in the model and how they interact. The Pricing and Inventory Module 
2 

(PIM) receives the most inputs from outside modules whereas the Dispensing Station Module 

(DSM) Inputs Module and the Vehicle Module (VM) only provide inputs to other modules. 

Figure 1. Architecture of the BSM2

The BSM2 contains various types of input data, from modeler assumptions to referenced data 

values.1

 Figure 2 shows the breakdown of data sources in the model by number of distinct 

variables (not counting separately each input value of an array or graphical function) and number 

of total input data points, which includes all values contained in arrays and graphical functions. 

One distinct variable may have hundreds of data points if it is an arrayed graphical function; 

therefore, the share of data sources by total input data points can be very different from share by 

distinct variables. Whereas the calibrated values make up the majority of the total number of data 

points, distinct variables obtain the majority of their data from outside sources. In addition, a 

variable can be comprised of multiple data sources. For example, techno-economic data come 

from different sources for the various ethanol conversion processes—thermochemical, 

biochemical, and starch. After referenced data, the next-largest shares of variable sources are 

user-input scenario values (24%) and calibration based on historical data (22%).2

 

1 See Appendix A for definitions of all data types.

2 Additional information pertaining to the steps used in model calibration can be found in Appendix A.

Biofuels Supply Chain “Downstream”

Feedstock 

Supply

(FSM)

Feedstock 

Logistics

(FLM)

Feedstock 

Conversion

(CM)

Pricing & 

Inventory

(PIM)

Distribution 

Logistics 

(DLM)

Dispensing 

Stations 

(DSM)

Vehicle 

Module 

(VM)

Fuel Use 

(FUM)

DSM 

Inputs

Imports 

(IM)

Material flow (e.g., feedstock, fuel) Information flow (e.g., relative fuel price)
3 

Figure 2. Number of distinct input variables and total input data points by reference type in 

the BSM2
4 

2 BSM2 Modules

Of the 506 distinct variables, the majority reside in the Feedstock Supply Module (FSM), 

Feedstock Conversion Module (CM), and the DSM Inputs Module (see Table 2). In the 

following sections, the data sources for each of the 11 BSM2 modules will be discussed, 

including the major outside sources upon which each section of the model relies and how the 

modules are interconnected. A detailed list of every data input variable contained in the BSM2, 

with corresponding values, references, and graphical functions, can be found in Appendix B. 

Table 2. Distinct Input Variables Versus Total Input Data Points, by Module 

For the modules with the greatest number of distinct variables, the majority of the data come 

from outside sources, displayed in Figure 3 as “referenced data.” For modules that have very few 

data inputs, the data types of “scenario values” and “BSM calibration” play a large role. Modeler 

assumptions and expert opinion comprise the least number of distinct variables for the majority 

of the modules. 
5 

Figure 3. Distinct input variables versus total input data points among reference types 

and modules 

2.1 Feedstock Supply Module

The FSM accounts for 6 feedstock types (commodity crops, herbaceous energy crops, woody 

energy crops, agricultural residues, forest residues, and urban residues), 10 geographic regions, 

and different land uses including active crop land (planted with commodity crops or perennial

energy crops), pasture land, and Conservation Reserve Program (CRP) land. It models farmer 

decision logic and land allocation dynamics and also covers new agriculture practices, markets, 

and prices. For a more detailed explanation of the feedbacks and mechanisms in this module, see 

Newes et al. (2011). 

The FSM is the most data-heavy of all the modules. Almost half of the FSM inputs are based on 

outside sources, as illustrated in Figure 4. The FSM relies heavily upon datasets provided by 

Robert Perlack of Oak Ridge National Laboratory (ORNL),

3

 most of which are based on the 

POLYSYS model (The University of Tennessee). These data include feedstock yields, grower 

payments, costs, acreage by land use, crop prices, and other feedstock-related metrics. USDA 

projections (Interagency Agricultural Projections Committee 2011), the Biomass Logistics 

Model (Idaho National Laboratory), and Biomass Crop Assistance Program (Commodity Credit 

Corporation 2010) regulations are also major sources for the module. Because many of the 

projections do not provide numbers for the full timespan for which the BSM2 is generally run 

and/or do not provide points for every year, linear interpolation and/or extrapolation are

performed to obtain the missing data. 

 

3 Robert Perlack retired from Oak Ridge National Laboratory in 2012. See Appendix A for more details on 

this dataset. 
6 

Figure 4. Number of distinct input variables and total input data points by reference type in the 

FSM (left) and breakdown of referenced data by source, number of distinct variables, and total 

data points (right) 

Note: The sum of the distinct variables column on the right may not equal the number of distinct variables 

for referenced data because in an arrayed variable, elements may have different sources.

In addition to the input data variables that are included within the FSM, the module also receives 

inputs from the Feedstock Logistics Module (FLM), the CM, and the DSM Inputs Module. The 

FSM also contributes some of its outputs to the FLM, CM, and Outputs Module (shown in 

Figure 5). 
7 

Figure 5. FSM: Interaction with other modules

2.2 Feedstock Logistics Module

The FLM covers transferring feedstock from the field to the conversion facility. It offers the 

logistics in multiple stages (including conventional and advanced uniform systems), provides 

cost breakdowns, accounts for transportation distance, and factors in land eligibility constraints. 

The main type of data source contributing to the FLM is external referenced data, followed by 

scenarios. As is shown in Figure 7, the FLM relies upon values from the Biomass Logistics 

Model (Idaho National Laboratory), originally developed by Oak Ridge National Laboratory, for 

the majority of its inputs dealing with crop transportation (costs, truck capacity, efficiency, loss). 

Additional sources of information include the Forest Residues Transportation Costing Model 

(Rummer 2005) for informing the cost of transporting forest residues, communication with 

Robert Perlack for harvesting and collection costs, and an NREL design report (Dutta et al. 

2011) for conversion facility processing capacity characteristics. 

The FLM receives values from variables contained in the FSM and CM and provides outputs 

into the FSM, as is shown in Figure 6. 

Figure 6. FLM: Interaction with other modules

Feedstock 

Supply 

Module

FLM CM DSM inputs FLM CM Outputs

Feedstock 

Logistics

Module

FSM CM FSM
8 

Figure 7. Number of distinct input variables and total input data points by reference type in the 

FLM (left) and breakdown of referenced data by source, number of distinct variables, and total 

data points (right) 

Note: The sum of the distinct variables column on the right may not equal the number of distinct variables 

for referenced data because in an arrayed variable, elements may have different sources.

2.3 Feedstock Conversion Module

The CM currently has five conversion platforms, including starch, thermochemical, biochemical, 

starch plus, and hybrid. It covers four development stages (pilot, demo, pioneer, commercial) of 

pre-defined scales; six attributes (process yield, probability of success, input capacity, capital 

cost, risk, and debt fraction); cascading learning curves; and project economics. It also models 

industry growth and investment dynamics. The CM is the second-most input-intensive module 

after the FSM.

Outside data play a major role for the inputs to the CM, as is shown in Figure 8. Important 

references for this module include the NREL technical reports for biochemical, thermochemical, 

and starch pathways for ethanol conversion (Dutta et al. 2011; Humbird et al. 2011; McAloon et 

al. 2000). Data from these reports are utilized to inform capital and variable costs, feedstock 

throughput capacity, facility capacity, product yield, and other related items. Additionally, an 

internal list of potential biofuels conversion facilities4 summarizes all the known plants in the 

pipeline and is used to determine the number of exogenous plant start-ups in the model. These 

 

4 See Appendix A for more information on the internal list of conversion facilities.
9 

data are also utilized for an optimistic scenario, where all plants that have been announced but 

have not made much progress are assumed to still begin operations on the most recently 

publicized date. 

Figure 8. Number of distinct input variables and total input data points by reference type in the CM

(left) and breakdown of referenced data by source, number of distinct variables, and total data 

points (right) 

Note: The sum of the distinct variables column on the right may not equal the number of distinct variables 

for referenced data because in an arrayed variable, elements may have different sources.

The CM is the link between feedstock production and the “downstream” (distribution, 

dispensing, and fuel use). Therefore, it is interconnected with many modules (see Figure 9). It 

takes values from the FSM and PIM and feeds values into the FLM, FSM, Outputs, and PIM. 

Figure 9. CM: Interaction with other modules 

Conversion

Module

FSM PIM FSM FLM PIM Outputs
10 

2.4 Dispensing Station Module Inputs

The DSM Inputs Module holds most of the outside references, calibration values, and scenario 

values for the downstream modules—distribution, dispensing, and fuel use, as is shown in 

Figure 10. It feeds predominantly into Dispensing Stations Module (DSM) and Distribution 

Logistics Module (DLM) and does not have any actual logic within its own module. Housing all 

outside sources for the downstream modules in a separate module is a reasonable method for 

keeping track of all of the relevant inputs. 

Figure 10. DSM Inputs Module: Interaction with other modules

DSM Inputs has a few key references, as shown in Figure 11.5 The first is an internal NREL 

ethanol transportation memo, in which transportation costs for ethanol with and without 

infrastructure are examined.

6

 Another important data source is the National Association of 

Convenience Stores, which provides information on gasoline station sales and volumes (National 

Association of Convenience Stores 2011). In addition, an NREL report details ownership of gas 

stations in the United States (Johnson and Melendez 2007). 

 

5 In general, available data for the distribution, dispensing, and end use of ethanol are extremely limited. We have 

collected data from a variety of sources that are not peer-reviewed published papers. 6 See Appendix A for more information.

DSM

Inputs

FSM PIM DLM DSM FUM Outputs
11 

Figure 11. Number of distinct input variables and total input data points by reference type in the 

DSM Inputs Module (left) and breakdown of referenced data by source, number of distinct 

variables, and total data points (right) 

Note: The sum of the distinct variables column on the right may not equal the number of distinct variables 

for referenced data because in an arrayed variable, elements may have different sources.
12 

2.5 Pricing and Inventory Module

Ethanol price is set and inventory is calculated in the PIM. More information on how E85 price 

is formulated can be found in Newes et al. (forthcoming). Inventory is determined by supply of 

ethanol and consumption of E107 and E85 along with ethanol imports. Any outside references 

utilized by the PIM are contained in the DSM Inputs Module. The PIM is the most 

interconnected module in the BSM2. It receives information from 6 of the 10 other BSM2

modules. It sends relevant outputs to five of the modules (see Figure 12). 

Figure 12. PIM: Interaction with other modules

The PIM contains mainly calibration-related inputs (see Figure 13). The majority of these 

variables are tuning parameters that temper the model’s reaction to environmental changes. For 

example, one deals with how quickly ethanol production ramps up in response to a change in 

ethanol price while another determines how quickly a difference between supply and demand 

will affect ethanol price.

Figure 13. Number of distinct input variables and total input data points by reference type in 

the PIM

2.6 Distribution Logistics Module

The DLM contains logic regarding implicit ethanol distribution modes, regional depot and 

storage, transportation costs, and inter-regional transport. For more information on the DLM, see

Vimmerstedt et al. (2012). Because most of the downstream (distribution, dispensing, and fuel 

use) modules’ input values are contained in the DSM Inputs Module, there are no references 

associated directly with this module (see Figure 14). The BSM2 calibration variable deals with 

 

7 Although recent regulations for E15 have been passed, there are not many available E15 pumps at the writing of 

this report. Nonetheless, the BSM2 can be run taking E15 into account.

Pricing &

Inventory

Module

CM

FUM IM VM

DLM DSM inputs DSM

Outputs

CM

FUM IM

DLM
13 

the relationship between the terminals without ethanol storage capacity and the rate at which 

terminals are acquiring ethanol storage capability. The lone scenario variable allows the user to 

enable 100% of the storage facilities to accept ethanol.

Figure 14. Number of distinct input variables and total input data points by reference type in 

the DLM

As is shown in Figure 15, the DLM receives inputs from DSM Inputs, the PIM, and the Vehicles 

Module (VM). Some of its key outputs go to the PIM, the DSM, and Outputs. 

Figure 15. DLM: Interaction with other modules

2.7 Dispensing Stations Module 

The DSM provides fueling-station economics and houses fuel-choice dynamics. It also looks at 

the distribution-coverage effects across the different regions. For more information on the DSM, 

see Vimmerstedt et al. (2012). As was previously stated in Section 2.4, most of the inputs for the 

DSM are contained in the DSM Inputs Module; therefore, the DSM consists entirely of 

calibration, constants, and scenario values. There are no outside sources associated directly with 

this module (see Figure 16). 

Distribution

Logistics

Module

PIM DSM inputs VM PIM DSM Outputs
14 

Figure 16. Number of distinct input variables and total input data points by reference type in 

the DSM

As shown in Figure 17, the DSM receives information from the FUM, DSM inputs, DLM, PIM, 

and VM. Selected outputs from the DSM go to the FUM and Outputs Module. 

Figure 17. DSM: Interaction with other modules

2.8 Fuel Use Module

The FUM contains fuel choice logic. Consumers are categorized as three types of users: regular 

high-blend users, occasion high-blend users, and non-high-blend users. The major constraints to 

this module are the number of stations offering high-blend fuel, the price differential between 

high-blend fuel and gasoline, and the number of flex-fuel vehicles in the vehicle fleet. As a basecase scenario, the model uses projections from the 2011 Annual Energy Outlook (Energy

Information Administration 2011a) for gasoline prices (listed as a scenario variable in the DSM 

Inputs Module). As shown in Figure 18, the FUM contains only calibration variables: the percent 

of flex-fuel vehicle owners who will choose E85 on a regular basis, the rate at which E85 

consumption increases with the number of stations offering E85, and the rate at which occasional 

E85 users become regular users.

Dispensing

Station

Module

PIM DLM FUM DSM inputs VM FUM Outputs
15 

Figure 18. Number of distinct input variables and total input data points by reference type in 

the FUM

The FUM receives information from DSM Inputs, the VM, DSM, and PIM. It sends outputs to 

the DSM, PIM, and the Outputs Module (see Figure 19). 

Figure 19. FUM: Interaction with other modules

2.9 Imports Module

The Imports Module (IM) looks at costs, taxes, and regulations associated with importing 

ethanol. Most of the variables are scenario values that give different options for import scenarios 

(see Figure 20). The one external source used is the Energy Information Administration’s 

compilation of ethanol imports (Energy Information Administration 2011b). 

Fuel

Use

Module

PIM DSM VM DSM inputs PIM DSM Outputs
16 

Figure 20. Number of distinct input variables and total input data points by reference type in the 

IM (left) and breakdown of referenced data by source, number of distinct variables, and total data 

points (right) 

Note: The sum of the distinct variables column on the right may not equal the number of distinct variables 

for referenced data because in an arrayed variable, elements may have different sources.

The IM receives information from the PIM, while providing data to the PIM after a series of 

internal calculations (see Figure 21).

Figure 21. IM: Interaction with other modules

2.10Vehicles Module

The VM is a separate model, not embedded within the BSM, that generates different portfolios 

of vehicle shares by type within the fleet, with the attention focused on the number of available 

flex-fuel vehicles and overall demand for liquid fuel. Four output variables that address the 

potential consumption of gasoline versus E85 are then fed into the BSM2 (see Figure 22). The 

Imports

Module

PIM PIM
17 

baseline scenario for the BSM2 uses the Annual Energy Outlook’s projections for vehicle fleet 

composition (Energy Information Administration 2011a). 

Figure 22. Number of distinct input variables and total input data points by reference type in the 

VM (left) and breakdown of referenced data by source, number of distinct variables, and total data 

points (right) 

Note: The sum of the distinct variables column on the right may not equal the number of distinct variables 

for referenced data because in an arrayed variable, elements may have different sources.

Because the VM is simply outputs from a separate model, it only feeds data into modules of the 

BSM2, more specifically the DLM, DSM, PIM, FUM, and Outputs Module (see Figure 23). 

Figure 23. VM: Interaction with other modules

2.11Outputs

The outputs module is generally a place where many of the important outputs from all of the 

other modules are gathered in order to have one location where all of them are easily accessible.

Many of the variables are aggregated and cumulated here in order to create different ways to 

Vehicles

Module PIM DLM DSM FUM Outputs
18 

look at the output metrics. For this reason, almost all of the input variables are simply stock 

initializations, as illustrated in Figure 24.

Figure 24. Number of distinct input variables and total input data points by reference type in the 

Outputs Module 
19 

3 Conclusion

The BSM2 is a complex model that contains over 1,500 distinct variables, including over 500 

variables with data inputs, that aid in representing the biomass-to-biofuels supply chain and 

exploring system responses to a variety of scenarios. This report describes the structured 

modules of the BSM2, the nature of their data sources, and how they interact. The model is 

calibrated with established industry data and validated against expert opinions, when available. 

Most of the exogenous data sources that constitute the main inputs to the model are recognized 

as baseline values by the industry. These values are updated in the model when new data are 

released. By detailing the input data in this report, the foundation for initialization and calibration 

of the BSM2 can be easily located by analysts and others interested in the data fundamentals of 

the model. With this organized input information, we plan to build a more structured data 

provenance of the BSM2—using the established database that houses all current and historical 

variables, formulas, and references for the model—that will facilitate users’ locations of all 

important data streams in the model. 
20 

References

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Hydrology (6:5); pp. 22–23. 

American Petroleum Institute. (2011). “Gasoline Taxes.” http://www.api.org/oil-and-natural-gasoverview/industry-economics/~/media/Files/Statistics/Gasoline-Tax-Map.ashx. Accessed August 

23, 2011. 

Anderson, S. T. (2006). The Demand for E85 Ethanol. Ann Arbor, MI: University of Michigan. 

ASTM International. (2010). ASTM D5798 - 11 Standard Specification for Ethanol Fuel Blends 

for Flexible-Fuel Automotive Spark-Ignition Engines. 

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CFR Part 1450. Washington, DC: U.S. Department of Agriculture. 

Doctor Douglas. (2003). “Average Radial Distance of Points within a Circle.” The Math Forum 

@ Drexel. http://mathforum.org/library/drmath/view/62529.html. Accessed July 1, 2011. 

Dutta, A.; Talmadge, M.; Hensley, J.; Worley, M.; Dudgeon, D.; Barton, D.; Groenendijk, P. et 

al. (2011). Process Design and Economics for Conversion of Lignocellulosic Biomass to 

Ethanol. TP-5100-51400. Golden, CO: National Renewable Energy Laboratory. 

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Projections to 2035. DOE/EIA-0383(2011). Office of Integrated Analysis and Forecasting U.S. 

Department of Energy Washington, DC 20585: United States Department of Energy. 

http://www.eia.gov/forecasts/aeo/pdf/0383%282011%29.pdf. Accessed May 23, 2013. 

EIA. (2011b). “Fuel Ethanol Imports by Area of Entry.” 

http://www.eia.gov/dnav/pet/pet_move_imp_a_epooxe_im0_mbbl_m.htm. Accessed September 

14, 2011. 

Fehrs, J. (1999). Secondary Mill Residues and Urban Wood Waste Quantities in the United 

States. Washington, DC: Northeast Regional Biomass Program.

Global Biofuels Center. (2012). Global: Were Expectations Too High for Next-Generation 

Biofuels Development? Houston, TX: Hart Energy. 

Humbird, D.; Davis, R.; Tao, L.; Kinchin, C.; Hsu, D.; Aden, A.; Schoen, P. et al. (2011). 

Process Design and Economics for Biochemical Conversion of Lignocellulosic Biomass to 

Ethanol: Dilute-Acid Pretreatment and Enzymatic Hydrolysis of Corn Stover. TP-5100-47764. 

Golden, CO: National Renewable Energy Laboratory. 
21 

Idaho National Laboratory. Biomass Logistics Model v.6.32. 

Interagency Agricultural Projections Committee. (2009). Agricultural Projections to 2018.

Washington, DC: U.S. Department of Agriculture. 

Interagency Agricultural Projections Committee. (2011). USDA Agricultural Projections to 

2020. OCE-2011-1. Washington, DC: U.S. Department of Agriculture. 

isee systems. (2010). “STELLA: systems thinking for education and research software.” 

http://www.iseesystems.com/softwares/Education/StellaSoftware.aspx. Accessed October 26, 

2010. 

Johnson, C.; Melendez, M. (2007). E85 Retail Business Case: When and Why to Sell E85. 

NREL/TP-540-41590. Golden, CO: National Renewable Energy Laboratory. 

McAloon, A.; Taylor, F.; Yee, W.; Ibsen, K.; Wooley, R. (2000). Determining the Cost of 

Producing Ethanol from Corn Starch and Lignocellulosic Feedstocks. NREL/TP-580-

28893.National Renewable Energy Laboratory. 

McNulty, M.; Ahrens, A. (2009). Energy Price-Production Cost Coupling Analysis. Work 

performed by Pacey Economics Group, Boulder, CO. Golden, CO: National Renewable Energy 

Laboratory. 

Melendez, Margo; Moriarty, Kristi; Dafoe, Wendy; Noblet, Stacy. (2009). Status and Issues for 

Ethanol (E85) in the United States. U.S. Department of Energy: Energy Efficiency & Renewable 

Energy. 

Mengel, David; Tucker, Drew; Weber, Holly. (2005). Enhancing Nitrogen Use Efficiency in 

Sorghum in Kansas. Kansas State University.

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Accessed August 9, 2011. 

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Report: Convenience & Petroleum Retailing Totals, Trends and Analysis of 2007 Industry Data. 

NACS: The Association for Convenience and Petroleum Retailing. 

NACS. (2011). “2011 NACS Gas Price Kit.” NACS Online. 

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etSellGas.aspx. Accessed August 17, 2011. 

Newes, E; Bush, B.; Peck, C; Peterson, S. (forthcoming). Exploration of policy, shocks, and 

dynamic interactions within the cellulosic ethanol supply chain. Golden, CO: National 

Renewable Energy Laboratory.
22 

Newes, E.; Inman, D.; Bush, B. (2011). “Understanding the Developing Cellulosic Biofuels 

Industry Through Dynamic Modeling.” Economic Effects of Biofuel Production, InTech Open 

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23 

Appendix A 

Definitions of input data types: 

Scenario Value

A scenario variable is used to simulate a hypothetical situation that is not based on any 

referenced data. It is generally a user input that can be utilized to run the model under different 

situations. Scenario variables can be multipliers that are applied to existing values or distinct

variables specific to a certain situation. 

BSM Calibration

A number of functions, such as logit functions, require appropriate coefficients that cannot be 

determined through pure assumption or data. In these cases, a calibration method is used to 

determine the allowable value. Typical steps for calibrating the model include the following: 

• Outside data, upon which the model will be calibrated, are entered into the model as a 

graphical function [i.e., USDA feedstock projections (Interagency Agricultural 

Projections Committee 2011)]. 

• The BSM2 is run to see how closely the two trajectories match in the short term.

• Relevant parameters within the BSM2 are then adjusted to obtain a more closely aligned 

data path. 

Constant Value

Constant variables are comprised of scalar values. Many times these variables are simply 

conversion factors, such as how many days are in a year.

BSM Modeler Assumption

A variable labeled with modeler assumption means that, given the available information, the 

modeler made a best estimate. This data type differs from calibration in that, in theory, a 

referenced value for this variable could exist if appropriate research were performed. 

Expert Opinion

While published data on these values do not exist, the modelers spoke with experts in the field 

for their best estimates to arrive at the assigned values. 

Stock Initialization

In STELLA (isee systems 2010), stocks must be initialized. Most of these values are set to zero 

to indicate a lack of accumulation before the model run begins. 

Referenced Data

Data from specific sources that are not assumptions made by modelers or outside experts are 

considered to be referenced data. Most of these publications and models can be found on the 

internet. The following list details sources that are not available to the public.
24 

• Internal Conversion Facility List

A member of the BSM team compiled a spreadsheet of all the biorefineries 

currently in the pipeline for various technologies and scales. The spreadsheet is 

based on data from the U.S. Department of Energy (U.S. Department of Energy 

2012), Biofuels Digest (Biofuels Digest 2011), and Hart Energy (Global Biofuels 

Center 2012), which were carefully crosschecked against available information 

from additional sources such as press releases and news articles. From that 

information, plants were divided into a “likely” and “not likely” categories, 

indicating which plants are not likely to start up on schedule, either due to delays 

or cancellation. These data were aggregated to determine the number of 

exogenous plants starting up in the CM. 

• Communication with Robert Perlack of Oak Ridge National Laboratory 

The datasets obtained from Robert Perlack contain critical information about 

costs, prices, acreage, and yields for energy crops (woody and cellulosic), 

conventional crops, and agricultural residues. They also provide a breakdown of 

acreage by land use. The data were aggregated to the USDA region level, using 

averages when necessary. The information provided for the BSM2 was based on 

runs of POLYSYS (The University of Tennessee), USDA projections 

(Interagency Agricultural Projections Committee 2011), and data provided by the 

Economic Research Service of the USDA. Although the data were obtained 

through ORNL, they are not always synonymous with data from the Billion Ton 

Update (Perlack and Stokes 2011) because they originated from different runs 

of POLYSYS.

• Aggregation of Tankage Data from State Agencies 

The data were researched by state, mostly from the state Oil and Gas Commission

websites.

• Ethanol Transportation Memo

The internal transportation memo was compiled in 2011 by Olga Antonia, 

formerly of NREL, and includes information regarding biofuel delivery costs. 

Data are provided for terminal costs in addition to transportation by truck, rail, 

and barge. A simple calculator was also created to derive ethanol.

• Internal Risk Analysis for GPRA and SEDS 

This internal document written in 2008 by Robert Wallace, formerly of NREL, 

details the process and results for the Government Performance and Results Act 

(GPRA) and the Stochastic Energy Deployment System (SEDS – seds.nrel.gov) 

risk/uncertainty analysis performed for the biochemical conversion process for 

lignocellulosic biomass to ethanol. 
25 

Appendix B

This section contains a comprehensive list of all input variables in the BSM2 along with a description of the variable, the data source, the actual value (or group 

of values), and the units of measurement. The listed values are taken from the BSM2 reference policy case, which includes moderate incentives for ethanol 

production and a $0.50 per gallon gasoline tax—see Newes et al. (forthcoming). The variables are organized in the following manner: single data points are 

shown in the first list, followed by tables of arrayed functions and graphs of the graphical functions. 
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