National Renewable Energy Laboratory 

Innovation for Our Energy Future 

A national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 

Use of Federated Object Modeling 

to Develop a Macro-System Model 

for the U.S. Department of 

Energy’s Hydrogen Program 

Preprint 

Mark F. Ruth 

National Renewable Energy Laboratory 

Keith B. Vanderveen and Timothy J. Sa 

Sandia National Laboratories

To be presented at the 2006 Winter Simulation Conference 

Monterey, California 

December 3–6, 2006 

Conference Paper 

NREL/CP-150-40222 

July 2006 
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promises to greatly improve national energy security. For 

these reasons, during his 2003 State of the Union Address, 

President Bush launched the Hydrogen Fuel Initiative with 

the objective of replacing petroleum-based transportation 

fuels with hydrogen (U.S. Department of Energy 2005). 

ABSTRACT 

USE OF FEDERATED OBJECT MODELING TO DEVELOP A MACRO-SYSTEM MODEL 

FOR THE U.S. DEPARTMENT OF ENERGY’S HYDROGEN PROGRAM∗

Mark F. Ruth 

National Renewable Energy Laboratory 

1617 Cole Blvd 

Golden, CO 80401, U.S.A. 

Keith B. Vanderveen 

Timothy J. Sa 

Sandia National Laboratories 

7011 East Avenue 

Livermore, CA 94550, U.S.A. 

The U.S. Department of Energy (DOE) is working on 

changing our transportation fuel from gasoline to hydrogen. To assist in that effort, we are developing a macrosystem model (MSM) that will link existing or developmental component models together to analyze crosscutting 

hydrogen issues. The MSM uses a federated simulation 

framework that extends the High Level Architecture 

(HLA). In this initial phase, three existing models have 

been linked to analyze two primary issues. The first issue 

we will examine will be the combined price of hydrogen 

production and delivery and the second will be a comparison of energy requirements and air emissions for multiple 

hydrogen production / delivery pathways (i.e., hydrogen 

produced from different feedstocks and transported via different means). Future work will involve linking other models to allow us to better analyze transition issues and making the MSM available to the hydrogen analysis 

community. 

As a result of the State of the Union Address, the National Research Council (NRC) convened a committee to 

study the opportunities, costs, barriers, and research and 

development needs for the hydrogen economy. Their recommendations have helped direct work within the Hydrogen Fuel Initiative. In the NRC’s 2004 report, they recommended that a systems analysis function be formed 

within the Hydrogen Fuel Initiative to analyze the systems 

and subsystems under development, the character of competitive approaches for providing energy services, potential 

future energy scenarios, and how proposed technologies 

might fit into a national system (NRC 2004). 

The Systems Analysis function recognized that the 

Hydrogen Initiative had already developed or has begun 

developing many models covering different aspects of a 

possible hydrogen economy. Those models fall into the 

1 INTRODUCTION following categories: 

• Models that estimate the cost and resources necessary to produce hydrogen through various pathways, 

1

 

Concerns about the availability of fossil fuels and the impact of carbon dioxide (CO2) emissions on the environment 

and human health have caused an increasing interest in the 

use of hydrogen as an energy carrier. Using hydrogen instead of hydrocarbons for transportation has the potential 

to reduce or virtually eliminate vehicular emissions of most 

pollutants including CO2 although it could just displace 

those emissions to the hydrogen production facilities. Additionally, the distributed nature of hydrogen production 

• Models that simulate the methods, costs, and resources necessary to deliver and distribute hydrogen to vehicles, 

• Models that simulate vehicle performance; 

• Both spatial and non-spatial models that estimate 

development and costs to deploy the necessary 

vehicle-fueling infrastructure, and ∗

 This work has been authored by an employee of the Midwest Research Institute under Contract No. DE-AC36-99GO10337 with the 

U.S. Department of Energy. The United States Government retains 

and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, 

paid-up, irrevocable, worldwide license to publish or reproduce the 

published form of this work, or allow others to do so, for United 

States Government purposes. 

• Models that simulate market transition from today’s petroleum economy to a future hydrogen 

economy. 

The Systems Analysis function determined that a 

macro-system model (MSM) would be necessary for analyzing cross-cutting issues because no existing model en-
2

compasses the entire system sufficiently. For example, no 

single model adequately represented all of the phenomena 

involved in the early stages of deployment of a hydrogen 

fuel infrastructure and hydrogen fueled vehicles. In addition, developing the MSM was expected to expose inconsistencies in methodologies and assumptions between different component models that arose because the individual 

models were developed under different philosophies and 

without thought of eventually integrating them. 

We could have followed either of two approaches to 

develop the MSM: (1) develop a new model on a single 

platform that included techniques and information from all 

other models, or (2) develop a tool to link or federate existing models together across multiple platforms. We selected the second approach because the task of building a 

single monolithic model incorporating all of the relevant 

information in the existing models would have been overwhelming, as the expertise necessary to do so was spread 

among half a dozen DOE laboratories and a dozen or more 

universities and private contractors. 

Linking models should allow us to generate consistent, 

valid results by publishing consistent values to integrated 

models. Currently, developers of integrated models get 

data from different sources. Some of those sources are 

easy to trace, others are more obscure. The data coming 

from most of the sources changes frequently, causing difficulties in tracking the timing of the data used in particular 

model runs. 

For example, transition models require projected future hydrogen production costs. Without the MSM, those 

costs are entered by the modeler and may be based on the 

modeler’s insights, other models, or other calculations. 

Historically, different modelers used different future hydrogen production costs as inputs and, therefore, obtained 

inconsistent results. Using the MSM, hydrogen production 

costs can be calculated by production models and transferred directly to the transition models. This technique 

provides the same data for all transition models and updates that data with minimal effort from the modelers, resulting in greater consistency between analysis projects. 

2 MACRO-SYSTEM MODEL REQUIREMENTS 

We are designing the MSM to be used only by analysts 

within the hydrogen community who understand the available models and how they work. Those analysts also 

helped identify the issues that the MSM needs to address. 

The issues the MSM needs to address fall into the following four categories: 

• Research and Development – hypothetical fuel 

cycle costs (i.e., what is the full cost per mile 

driven and how it might evolve over time) and the 

suitability of technical targets for the Hydrogen 

Initiative and their relationships to each other. 

• Transition – potential hydrogen infrastructures 

and how they might compete with the current petroleum infrastructure. Market issues and regional 

differences, different pathways, and legacy costs 

of retired infrastructure are included. 

• Financial – corporate and government investment 

options. 

• Environmental – resource requirements and emissions profiles. 

Other requirements for the MSM include extensibility 

to multiple platforms, dynamic time-steps, and modeling 

spatial issues associated with the build-out of hydrogen infrastructure. Multiple platforms are used for models within 

the hydrogen analysis community; therefore, the MSM will 

need to link models together across those different platforms including Microsoft Excel, the Generalized Algebraic Modeling System, MatLab, and programs written in 

C, Java, and FORTRAN. 

Some of the models are static in time, while others are 

dynamic. Among the dynamic models, some run in a timestepped fashion while others take a time period (e.g. fifty 

years) and attempt to compute an optimum transition strategy over the entire period. One of the challenges the MSM 

needs to address is to provide a coordinated view of time 

for all of the different models. Likewise, some of the 

models take into account spatial information (e.g. existing 

centers of population or location of resources), while others are non-spatial, so the MSM must somehow integrate 

these two types of models in a harmonious fashion. 

In addition, because the population of possible component models is growing as new models are developed to 

address different aspects of a hydrogen economy or transition to such an economy, the MSM framework must accommodate new models with a minimum of difficulty (i.e., 

be extensible). Furthermore, because model developers 

and analysts within the hydrogen community are widely 

dispersed across the United States and throughout the 

world, it is essential that the MSM framework support distributed operation, preferably over the Internet. Finally, 

because the number of models eventually incorporated into 

the MSM (or instantiations of the MSM) could be quite 

large for some simulations, it is essential that the MSM 

framework be scalable to large numbers of participating 

simulations. 

3 APPROACH 

Because of the need for extensibility, distributability, and 

scalability, we chose to use a federated object model 

(FOM) framework on which to base the MSM, as exemplified by the DOD High Level Architecture (HLA) which 

was described by Dahmann et al. (1997). The FOM approach uses a common interlingua and is extensible, allowing new models to be integrated easily provided they can 

interface with the objects being exchanged by other models 
3

in the federated simulation. It also solves the problem of 

proliferating interfaces as the number of integrated component models grows which helps to keep the model framework scalable. The FOM approach has been successfully 

applied to the problem of distributed discrete event simulation in the defense community, so the System Analysis 

function expected this approach to work for linking models 

pertaining to the evolution of a hydrogen economy as well. 

The framework used to develop the MSM is the Enterprise Modeling Framework (EMF), which was developed by researchers at Sandia National Laboratories. The 

EMF uses the HLA standard to exchange data between 

participating federated models. In addition to HLA, EMF 

provides support for modular, composable graphical user 

interfaces (GUIs) for each federate (i.e. each federate carries its own GUI code, which provides a run-time interface 

to the federate’s outputs, inputs, and configurable parameters). The EMF also provides support for distributed rolebased access control, which means that each federate can 

subscribe to only that information to which it is authorized 

through run-time set up. Web servers and browsers use http 

to transport data, and hence most Internet firewalls allow 

http traffic to pass through unhindered whereas firewalls 

would typically block HLA traffic. Having the MSM use 

http to communicate with component models would allow 

these models to sit behind different firewalls without having to ask network security administrators to reconfigure 

firewalls to let MSM traffic through. 

One framework we are examining is IDSim, which 

was developed by researchers at Georgia Tech and Sandia 

National Laboratories (Fitzgibbons and Fujimoto 2004). 

IDSim uses http and XML to exchange data between participating federated models, but is otherwise similar to 

HLA, which has been used successfully by the defense 

modeling and simulation community for many years. 

Despite the common need for extensibility, distributability, and scalability in a modeling framework, the Hydrogen Initiative faces challenges not encountered by the defense community in its efforts to link together disparate 

models. For example, different models have different notions of time. Some models, such as the H2A Production 

models (Mann 2005), do not change internal state as a 

function of time, but act as calculators which return answers given some set of inputs. The H2A Production models will return the cost of producing hydrogen for a wide 

variety of production methods, given parameters such as 

the cost of feedstock, the size of the plant and technology 

used, and the discount rate to use for capital investments. 

Other models, such as HyDS (Short 2005), use relatively 

small timesteps (2 years) to compute the infrastructure required to produce and distribute hydrogen to meet demand 

which is input as a parameter at each timestep. Still other 

models, such as HyTrans (Greene 2005), analyze infrastructure over a much longer period (30 or more years) assuming perfect foresight. They require a full set of inputs 

before beginning a simulation but cannot use new inputs or 

provide outputs to other models during the simulation. 

There are similar challenges when integrating models 

with different levels of awareness of spatial issues, e.g. 

how to best lay out a pipeline system or a truck route for 

distributing hydrogen. 

4 INITIAL DEVELOPMENT STEPS 

Having a list of high priority issues, we selected the first 

issue that the MSM would address: “Compare the economics, primary energy source requirements, and emissions of different hydrogen production / delivery pathways 

to help choose which are most likely to be developed and 

determine some of the environmental tradeoffs between 

them.” This comparison will provide insights into two of 

the categories: research and development (e.g., $ / kg H2 

for the fuel cycle) and environmental (e.g., resource requirement and CO2 emissions). 

To analyze that issue, we linked H2A production 

models to the Hydrogen Delivery Scenario Analysis Model 

(HDSAM) and the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model. 

We selected these models because all three are built in Microsoft Excel® and they all have static timeframes. Because they are on the same platform and dynamics did not 

need to be considered, the combination was considered an 

achievable first step and a useful proof-of-concept for the 

MSM’s development approach. 

This MSM analysis will allow decision-makers to 

make comparisons between hydrogen production/delivery 

pathways and current and future petroleum pathways with 

confidence that different results are not caused by different 

parameters. Future additions to the MSM will be necessary to analyze additional research and development, transition, financial, and environmental issues. 

4.1 H2A PRODUCTION 

The H2A production models are the result of an effort 

to standardize production economic calculations. They are 

a set of spreadsheets with vetted resource requirements and 

capital cost inputs that use the discounted cash flow rate of 

return (DCFROR) method to solve for a profited cost of 

hydrogen at the plant gate, including operating costs, capital payback, and return on capital investment (Mann 2005). 

Many different hydrogen production technologies have 

been input into the H2A production models. The technologies include both large, central production plants from 

which the hydrogen needs to be transported to distribution 

facilities that are similar in size to today’s gas stations, and 

small, distributed forecourt stations which also are similar 

in size to today’s gas stations and produce the hydrogen on 

site. Modeled technologies include the following: 
4

• Production from natural gas in central plants via 

steam methane reforming, 

• Production from coal in central plants via gasification and steam methane reforming (both with 

and without sequestration), 

• Production from biomass in central plants via 

gasification and steam methane reforming, 

• Production in nuclear plants via thermochemical 

cycles, 

• Production in central plants via electrolysis, 

• Production at forecourt stations via steam methane reforming, and 

• Production at forecourt stations via electrolysis 

(U.S. Department of Energy 2006a). 

4.2 HDSAM 

HDSAM estimates the hydrogen requirements of a 

given city and cost and resource requirements for delivering and distributing that hydrogen. The user selects the 

city, market penetration of hydrogen in the transportation 

fuels market, and the method used for delivering hydrogen 

to the distribution stations. Three methods of delivery are 

available: compressed gas trucks, liquid hydrogen trucks, 

and pipelines. The model estimates the amount of hydrogen required annually, the number of distribution stations 

in the city, and the profited cost of delivering and distributing the hydrogen (Mintz 2005). 

4.3 GREET 

The GREET model analyzes energy use and emissions 

from complete vehicle/fuel cycles – commonly termed 

well-to-wheels (WTW) analysis. It includes energy use 

and emissions associated with both fuel production and vehicle operation activities. For the fuel production activity, 

GREET reports total amounts of energy resources (petroleum, natural gas, coal, and biomass) consumed. It also 

reports the emissions profile including greenhouse gases 

(GHG) and criteria pollutants (volatile organic compounds, 

carbon monoxide, nitrogen oxides, particulate matter, and 

sulfur oxides) emitted during fuel production, delivery, distribution, and utilization (Wang 2005). 

4.4 Building the MSM 

Three steps were necessary to link these three models 

within the FOM framework: (1) identify the order in which 

the models will be run; (2) identify the data that need to be 

transferred between the models and how they may be different in each model; and (3) publish data to models, run 

the models, and subscribe to each model’s results. 

We selected the following order for hydrogen production / delivery pathway comparisons: HDSAM followed 

by H2A production and, finally, GREET. HDSAM calculates the profitized cost of hydrogen delivery and distribution and the quantity of hydrogen necessary to supply a selected community. HDSAM estimates losses during 

delivery when a central facility produces the hydrogen as 

well as energy requirements and form (e.g., diesel for 

trucks, electricity for liquefaction and/or compression) for 

delivery and distribution. The H2A production models then 

determine the profited cost of hydrogen at the plant gate 

and the energy requirements and form for hydrogen production. The energy requirements, yields, distances, and 

losses calculated by HDSAM and H2A production are published to GREET, which then calculates primary energy 

source requirements, WTW energy requirements, and 

emission profiles. 

We developed an intimate understanding of the three 

models to determine what information needed to be transferred between the models and how the information is used 

by different models. Some data transfers were almost trivial and only needed consistent units. Examples of those 

transfers include mass of hydrogen on trucks, fraction of 

CO2 that is captured during hydrogen production, and vehicle fuel efficiency. 

Other transfers required data to be calculated from 

previous models’ results. One example of that type of data 

is the efficiency of a hydrogen liquefier. It is calculated 

using data published by HDSAM (hydrogen throughput, 

hydrogen losses, and electricity requirements) and then is 

published to GREET. 

Some data need additional thought before determining 

what values should be transferred because different models 

are based on different philosophies. One example of this 

issue is pipe length. GREET requires a pipe length to calculate energy used for hydrogen transport (pipe length 

multiplied by energy required per unit length). HDSAM 

calculates lengths of multiple types of pipe: transmission, 

trunk, and service lines and uses each length for capital 

cost estimation. No molecule of hydrogen travels the total 

length of each type because the trunks are circular and each 

service line transports only a fraction of the total hydrogen 

entering a city. In this case, an algorithm to calculate a 

single length was agreed upon by the developers of 

HDSAM, GREET, and the MSM. 

The first set of models integrated into the MSM were 

implemented using Microsoft Excel. Having chosen Java 

as our development platform, our initial task was to find a 

means to manipulate and link the Excel models from 

within a running Java application. An open source solution, 

Jakarta POI, allowed us to read and write Excel files using 

Java. However, POI did not allow us to interact with a running Excel process, which we needed to do in order to call 

macros contained in the files. Because of this need, our 

current prototype employs a commercial third-party product (Intrinsyc J-Integra for COM) that provides a complete 

Java-COM bridge (future prototypes may use a Java-.NET 

bridge). J-Integra has proven to be a reasonably robust 
The basis for the analysis shown in Figure 1 is 

116,000 Btu of hydrogen gas (lower heating value) which 

is essentially equivalent to 1-kg of hydrogen. That basis 

was chosen because it is the average amount of energy 

available in 1 gal of unleaded gasoline; it is commonly referred to as 1 gal of gasoline equivalent (GGE). The figure 

shows that 11,000 Btu of hydrogen are lost during liquefaction, transport, storage, and compression. Those losses 

are due to boil off while the hydrogen is in liquid form and 

leaks while the hydrogen is in gaseous form. Because of 

those losses, the production facility needs to produce 

127,000 Btu of hydrogen gas for every GGE distributed. 

In addition to the losses, 42,000 Btu (12 kW hr) of electricity and 1,000 Btu (0.01 gal) of diesel fuel are required for 

liquefaction, transportation, storage, and compression for 

dispensing. That energy is lost to kinetic energy, heat, and 

unusable energy in pressure. 

product and allows us to focus our development efforts on 

other areas. 

In order to permit external manipulation of the Excel 

models via macros and still provide the model owners with 

the security of protecting their Visual Basic for Applications (VBA) projects, we requested that the owners create 

public functions and macros for our use. Where the model 

developer has tied these functions and macros to VBA GUI 

elements, we can programmatically interact with a model’s 

GUI and expose its functionality to the larger system. This, 

coupled with our ability to directly read and write cells and 

formulae, gives us the necessary access and behavior for 

integration with non-Excel models. 

Remote (i.e. web browser) access to the MSM application will likely be enabled using Java Server Pages (JSP) 

and a more mature version or our existing code base. Our 

initial implementation will place all models and integrating 

framework on the web server. Future versions will allow 

model developers to host their models at the location of 

their choosing. Typical access control and user authentication mechanisms will be phased in prior to release for general use. 

For each 127,000 Btu of hydrogen the production facility will produce, it will require 172,000-Btu natural gas 

(180 Normal ft3

) and 7,000-Btu (2.0 KW hr) of electricity. 

That energy is required to produce the hydrogen and sequester 90% of the CO2 generated during the production 

process. 

Table 1 shows energy efficiencies for the process 

shown in Figure 1. Energy efficiencies are defined as energy available in the hydrogen divided by all energy 

sources. The production process energy efficiency covers 

only the central plant that produces hydrogen from natural 

gas. The pathway energy efficiency includes production, 

liquefaction, transportation, storage, and compression. 

Well-to-pump (WTP) efficiency includes every step in the 

pathway as well as the energy required for supplying natural gas, electricity, and diesel (e.g., drilling for and refining 

natural gas, mining coal and using it generate electricity, 

and drilling, refining, and transport for diesel). 

5 PRELIMINARY RESULTS 

The initial phase of the MSM focused on making economic 

and environmental comparisons between different hydrogen production / delivery pathways; therefore, the H2A 

production models were linked to HDSAM and the 

GREET model. With these models linked the user can 

generate information on each pathway. 

Figure 1 shows results from one production / delivery 

pathway. The analysis is for hydrogen production from 

natural gas using current steam-methane reforming technology and capturing 90% of the generated CO2. In this 

analysis the hydrogen is produced in a large (approximately 125-million-kg annually) facility which is located 

at the edge of the city where the hydrogen would be used. 

Two options are available for delivery: pipeline and trucks 

carrying liquid hydrogen. In this analysis the liquidhydrogen truck option is chosen so the hydrogen will be 

liquefied at the production plant, transported to distribution 

stations via semi, and gasified and pressurized at the distribution station for fueling automobiles. 

Table 1. Energy Efficiencies for Hydrogen Production 

from Natural Gas with Liquefaction and Truck Delivery 

Production Process 71% 

Pathway 52% 

Well-to-Pump 41% 

WTW energy requirements and emissions include energy use and emissions associated with both fuel production and vehicle operation activities. In this case, the vehicle is assumed to be 2.5 times more efficient than today’s 

gasoline-powered automobile because it uses a fuel cell instead of today’s internal combustion engine (i.e., the hydrogen-powered fuel cell vehicle is expected to get 57.5 

mile/GGE and today’s gasoline-powered internal combustion engine gets 23.1 mile/gal gasoline, on average). The 

total energy required to power the vehicle for one mile is 

estimated at 4900 Btu; that energy includes not only the 

coal, electricity, and diesel to produce and distribute the 

hydrogen, but also the energy included in the WTP calcula41,000 Btu Electricity 1,000 Btu

1,000 Btu Diesel Electricity for

Forecourt

7,000 Btu

Electricity 127,000 Btu 116,000 Btu

172,000 Btu Hydrogen Hydrogen

Natural Gas

52,000 Btu Energy Lost 11,000 Btu 42,000 Btu Energy Lost

Hydrogen Lost

Central Production Liquefaction & 

Transport

Storage and 

Compression for 

Dispensing

Figure 1. Preliminary Pathway Results for Central Hydrogen Production from Natural Gas with Carbon Sequestration and Using Liquefaction and Truck Delivery 

5
6

tion. The total energy requirement is almost 2.5 times 

greater than the 2000 Btu of hydrogen necessary for vehicle operation due to losses in the process and energy necessary to mine and refine the primary energy sources. 

In this case, the petroleum energy use for a hydrogen 

powered vehicle is much lower than that for the standard 

vehicle driven today (35 Btu/mile compared to over 5,500 

Btu/mile for the baseline vehicle). This supports the Hydrogen Initiative’s claim of reducing the need for petroleum. Likewise, the GHG emissions of 205 g CO2 equivalent/mile are lower than those of today’s vehicles used for 

a baseline (485 g/mil). The profited cost at the pump is estimated at $5.36/GGE. That is the production cost including a 10% after-tax discounted cash flow rate of return 

over a 20 year equipment life. It includes both federal and 

state income taxes but does not include the gasoline tax. It 

also includes the cost to capture CO2 for sequestration but 

not the cost of sequestering it. 

To date, we have analyzed four central hydrogen production technologies in addition to production from natural 

gas with carbon capture as reported above. Those technologies include production from natural gas without sequestration, production from coal with and without sequestration, and production from biomass. Table 2 shows 

resulting profited costs for the full pathway (production, 

liquefaction, delivery, and distribution) and can be used to 

compare the technologies. The analysis methodology is 

recognized as having possible error bars of 30% on capital 

investment because of potential issues like unexpected 

catalyst problems; therefore, within the capability of this 

analysis all five technologies have the same profited cost. 

Table 2. Profited Costs of Hydrogen Production/Delivery 

Pathways for Five Production Technologies 

Natural Gas with Sequestration $5.36 / GGE 

Natural Gas without Sequestration $5.15 / GGE 

Coal with Sequestration $5.30 / GGE 

Coal without Sequestration $4.98 / GGE 

Woody Biomass without Sequestration $5.23 / GGE 

Profited cost is not the only parameter that we are 

comparing between technologies. The DOE is also interested in reducing petroleum imports; a primary driver of 

that reduction could be reduced use of petroleum. Table 3 

shows the WTW petroleum energy use for the five technologies. For comparison, over 5400 Btu/mil are necessary to operate today’s average gasoline-powered vehicle. 

WTW petroleum consumption includes not only petroleum 

required for production and delivery but also drilling, mining, and/or growing. Biomass requires more petroleum 

than the other technologies because modern tractors run on 

diesel. The differences between coal and natural gas are 

primarily due to the assumption that if hydrogen is made 

from coal the necessary electricity will be as well; likewise, if hydrogen is made from natural gas the necessary 

electricity will be as well. Coal mining requires much 

more petroleum than natural gas drilling and refining. 

Table 3. Well-to-Wheels Petroleum Energy Consumption 

for Five Hydrogen Production Technologies 

Natural Gas with Sequestration 35 Btu/mil 

Natural Gas without Sequestration 34 Btu/mil 

Coal with Sequestration 67 Btu/mil 

Coal without Sequestration 24 Btu/mil 

Woody Biomass without Sequestration 216 Btu/mil 

GHG emissions are also an issue that many people are 

interested in. Table 4 shows a comparison of GHG emissions for the five hydrogen production technologies. For 

comparison, GREET estimates that today’s gasoline vehicles emit approximately 485 g CO2 equivalent / mile traveled on a WTW basis. Biomass has extremely low emissions because plants remove more CO2 from the air (and 

sequester some of it in the ground in the form of roots) 

than is generated producing hydrogen. Note that coal 

without sequestration has particularly high GHG emissions 

because the ratio of CO2 to energy available in coal is low. 

Table 4. Well-to-Wheels Greenhouse Gas Emissions for 

Five Hydrogen Production Technologies (all on g CO2

equivalent basis) 

Natural Gas with Sequestration 205 g / mil 

Natural Gas without Sequestration 312 g / mil 

Coal with Sequestration 255 g / mil 

Coal without Sequestration 613 g / mil 

Woody Biomass without Sequestration 8 g / mil 

Other parameters also need to be compared to make 

qualitative decisions on technology selection. Those parameters include feedstock sources and availability in the 

region in question; since the MSM does not yet have a spatial aspect. Another set of parameters that should be considered is criteria pollutants. Those are calculated in the 

pathway analyses and will be considered by decision makers. 

All of the results reported here are preliminary and require additional validation; however, they are good examples of the analysis results we expect to get from the MSM. 

6 FUTURE WORK 

The next steps for the MSM involve completing pathway 

analysis for additional technologies, adding more models 

to the framework, making the MSM available to the hydrogen analysis community, and adding stochastic calculations. We are working to complete additional pathway 

analyses, like the ones reported above. To do that work, 

additional H2A production models will be added to the 

MSM and changes to other models (HDSAM and GREET) 

will be incorporated into the MSM structure. H2A produc-
7

tion models that might be added to the MSM include electrolysis from the grid, electrolysis coinciding with wind 

turbines, and high-temperature thermo-chemical processes 

for use at nuclear plants. Models using research and development targets will also be added to compare potential 

improvements. 

We will also address the cross-cutting issue of hydrogen quality. Hydrogen quality is the purity of the hydrogen and the concentration of specific impurities. It is an 

important cross-cutting issue because low quality hydrogen 

will affect the performance and lifetime of fuel cells; however, increasing quality increases production expense and 

energy use and impurities may be introduced during delivery and distribution of the hydrogen. To analyze that issue, 

models that calculate the material and energy balances for 

hydrogen production as well as their capital costs will be 

linked within the MSM. Vehicle performance will be included in the MSM by linking fuel cell performance models that are being modified to include performance loss due 

to low quality hydrogen. 

The three models that have been linked thus far have 

static timeframes and are essentially location independent. 

Their results are useful for comparing technologies and 

making initial estimates regarding profited cost and emissions. They are not useful, however, for locationdependent results or for developing strategies that might 

overcome issues in the transition from today’s petroleum 

economy to a future, hydrogen economy (one of the major 

issues identified as requiring analysis within the Hydrogen 

Initiative). Instead, other models exist that use different 

techniques to understand those spatial and temporal issues. 

Those models will be linked using the MSM framework so 

that modeling changes in hydrogen production, delivery, 

and emissions can be propagated to the spatial/temporal 

models. That propagation is necessary to verify that the 

current technical targets are sufficient to result in a system 

that can compete in the transportation sector of the economy. 

To further improve consistency between models, the 

Hydrogen Analysis Resource Center (HyARC) will be 

linked to the MSM. HyARC is being developed to standardize parameters for all modeling for the Hydrogen Initiative because different models have been using different 

parameters (U.S. Department of Energy 2006b). Linking 

the MSM to HyARC will allow changes to HyARC to be 

easily transferred to other models. 

The MSM will be made available to hydrogen analysts 

via a password-protected website. We are developing a 

GUI for that site with the intent that analysts will be able to 

access the website, design the desired analysis using the 

GUI, and run the MSM to get results. The website will 

also be designed to allow model-developers to update the 

MSM to link to their updated models as they become 

available. 

MSM will need to be able to make stochastic calculations because very few of the parameters in any of the 

component models are well known. Once that capability is 

added, users will be able to select probability distribution 

parameters and the MSM will calculate probability distributions for the results. The Monte Carlo method is the 

most likely technique for calculating the resulting probability distributions. 

7 CONCLUSIONS 

We are developing a macro-system model for DOE’s Hydrogen, Fuel Cell, and Infrastructure Program to analyze 

cross-cutting issues. We have found the FOM framework 

to link other models effective for this purpose. To date, we 

have linked three existing models and are comparing economic and environmental parameters between different 

production and delivery pathways. In the future, we will 

involve dynamic models to analyze infrastructure development and spatial tools to study regional issues. We also 

intend to make the MSM available to the hydrogen analysis community via a password protected website. 

ACKNOWLEDGEMENTS 

The authors wish to thank Dale Gardner, Johanna Levene, 

Margaret Mann, Margo Melendez, and Matthew Ringer of 

the National Renewable Energy Laboratory; Jerry Gillette, 

Marianne Mintz, and Michael Wang of Argonne National 

Laboratory; Amgad Elgowainy of Tennessee State University; and Jay Keller and Jason Arnold of Sandia National 

Laboratory for technical assistance. This work has been 

supported by the Hydrogen, Fuel Cell and Infrastructure 

Technology Program of the US Department of Energy. 

REFERENCES 

Bush, G.W. 2003. State of the Union Address, Washington, DC. 

Committee on Alternatives and Strategies for Future Hydrogen Production and Use, National Research Council, National Academy of Engineering. 2004. The hydrogen economy: Opportunities, costs, barriers, and 

R&D needs. Washington, DC: The National Academies Press: 9-4 – 9-5. 

Dahmann, J. S., R. Fujimoto, and R. M. Weatherly. 1997. 

The department of defense high level architecture. In 

Proceedings of the 1997 Winter Simulation Conference: 142–149. 

Fitzgibbons, J. and R. Fujimoto. 2004. IDSim: An extensible framework for interoperable distributed simulation. 

In Proceedings of the International Conference on 

Web Services. San Diego, CA. 
8

Greene, D.L. 2005. HyTrans model development. 2005

Annual Progress Report – DOE Hydrogen Program. 

Available via http://www.hydrogen.energy.gov/ 

pdfs/progress05/iii_3_greene.pdf. [accessed July 5, 

2006] 

Mann, M.; J. Levene, B. James, S. Lasher, D. Mears, M. 

Placet, M. Ringer, M. Rutkowski, M. Wang. 2005. 

Moving toward consistent analysis in the HFC&IT 

Program: H2A. 2005 Annual Progress Report – DOE 

Hydrogen Program. Available via 

http://www.hydrogen.energy.gov/pdfs/progress05/iii_

2_mann.pdf. [accessed July 5, 2006] 

Mintz, M. 2005. Hydrogen delivery analysis chapter V.F.1. 

2005 Annual Progress Report – DOE Hydrogen Program. Available via 

http://www.hydrogen.energy.gov/pdfs/progress05/v_f

_1_Mintz.pdf. [accessed July 5, 2006] 

Short, W. 2005. Energy systems analysis: Hydrogen deployment system modeling. 2005 Annual Progress 

Report–DOE Hydrogen Program. Available via 

http://www.hydrogen.energy.gov/pdfs/progress05/iii_

4_short.pdf. [accessed July 5, 2006] 

U. S. Department of Energy. 2005. Hydrogen, Fuel Cells & 

Infrastructure Technologies Program multi-year research, development and demonstration plan – 

Planned program activities for 2003-2010. DOE/GO102005-1741. Golden, CO: The US Department of 

Energy Golden Field Office. 

U.S. Department of Energy. 2006 (a). DOE H2A Production Analysis. Available via 

http://www.hydrogen.energy.gov/h2a_production.html

[accessed July 3, 2006]. 

U.S. Department of Energy. 2006 (b). Hydrogen Analysis 

Resource Center. Available via 

http://hydrogen.energy.gov/resource_center.html

[accessed July 3, 2006]. 

Wang, M.Q. 2001. Development and use of GREET 1.6 

fuel-cycle model for transportation fuels and vehicle 

technologies. Technical report ANL/ESD/TM-163, 

Argonne National Laboratory, Argonne, IL. Available 

via 

http://www.transportation.anl.gov/pdfs/TA/153.pdf. 

[accessed July 5, 2006] 

AUTHOR BIOGRAPHIES 

MARK F. RUTH is the Macro-System Model Engineer in 

the Systems Integration Office at the National Renewable 

Energy Laboratory. He moved into that position in 2005 

after over 10 years as an engineer researching biomass 

conversion to ethanol, other fuels, and other chemicals. 

Mark’s primary research interests are in using models to 

understand progress in research and development and in 

analysis to prioritize research. His e-mail address is 

mark_ruth@nrel.gov. 

KEITH B. VANDERVEEN is a Principal Member of 

Technical Staff at Sandia National Laboratories in Livermore, California. He has eight years of experience in networking, network management, and distributed systems 

assessment and development. As a member of Sandia’s 

Homeland Security Systems and Development Center, he 

has participated in numerous modeling and simulation projects, technical assessments, and development of software 

and hardware systems for homeland security and other national security domains. His e-mail address is 

kbvande@sandia.gov. 

TIMOTHY J. SA is a Distinguished Technologist at Sandia National Laboratories in Livermore, California. He has 

over ten years of experience in Java software development 

and specializes in GUI design and graphics. As a member 

of the Exploratory Computer and Software Engineering 

Department he has been involved in various proof-ofconcept projects as well as creating applications and simulations to support systems analysis and decision making. 

His e-mail address is tjsa@sandia.gov. 
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The U.S. Department of Energy is working on changing transportation fuel from gasoline to hydrogen. To assist in 

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