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

Macro-System Model: A 

Federated Object Model for 

Cross-Cutting Analysis of 

Hydrogen Production, Delivery, 

Consumption and Associated 

Emissions

Preprint

M. Ruth and V. Diakov

National Renewable Energy Laboratory

M.E. Goldsby and T.J. Sa 

Sandia National Laboratories 

Presented at 4th Transatlantic Infraday Conference

Washington, D.C. 

November 5, 2010

Conference Paper

NREL/CP-6A10-49544

December 2010
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1

MACRO-SYSTEM MODEL: A FEDERATED OBJECT MODEL FOR CROSS-CUTTING ANALYSIS OF 

HYDROGEN PRODUCTION, DELIVERY, CONSUMPTION AND ASSOCIATED EMISSIONS

Mark Ruth Victor Diakov Michael E. Goldsby Timothy J. Sa

National Renewable 

Energy Laboratory

National Renewable 

Energy Laboratory Sandia National Laboratories Sandia National Laboratories

1617 Cole Blvd. 1617 Cole Blvd. 7011 East Ave. 7011 East Ave. 

Golden, CO, 80401, USA Golden, CO, 80401, USA Livermore, CA, 94550, USA Livermore, CA, 94550, USA

ABSTRACT

It is commonly accepted that the introduction of hydrogen as an energy carrier for light-duty vehicles involves concomitant 

technological development of infrastructure elements, such as production, delivery, and consumption, all associated with

certain emission levels. To analyze these at a system level, the suite of corresponding models developed by the United States 

Department of Energy and involving several national laboratories is combined in one macro-system model (MSM). The 

macro-system model is being developed as a cross-cutting analysis tool that combines a set of hydrogen technology analysis 

models. Within the MSM, a federated simulation framework is used for consistent data transfer between the component 

models. The framework is built to suit cross-model as well as cross-platform data exchange and involves features of “overthe-net” computation.

1 INTRODUCTION

The Hydrogen Macro-System Model (MSM) is a simulation tool that links existing and emerging hydrogen-related models to 

perform rapid, cross-cutting analysis. It combines analysis of the economics, primary energy-source requirements, emissions,

and infrastructure evolution of hydrogen production and delivery pathways. The MSM is the first model to simulate cost, 

energy use, and emissions of the entire hydrogen system (including feedstock, conversion, infrastructure, and vehicles) with 

the necessary level of technical detail in an integrated fashion, and its analyses and sensitivity runs can provide a basis for 

decisions regarding focus of research needs.

The MSM tool helps users understand the effects of varying parameters on a pathway’s results without requiring 

expertise in all of its models. The MSM promotes consistency between the methodologies and assumptions of each model by 

transferring information between models as well as identifying contradictions so they can be corrected.

MSM was jointly developed by the Systems Engineering and Process Integration office (SEPI) at the National 

Renewable Energy Laboratory (NREL) and the Sandia National Laboratories (SNL). SEPI provides domain expertise and 

leads the project; SNL provides computer science expertise.

The MSM was designed to act as an overarching system that provides a cross-cutting analysis and simulation capability 

to the United States Department of Energy’s (DOE) Hydrogen Program. In addition, MSM may be used to guide the 

development of other, similar simulation tools.

MSM was developed to accomplish the following specific objectives:

• To perform rapid, cross-cutting analysis in a single location by linking existing applicable models

• To improve consistency of technology representation (i.e., consistency between models)

• To allow for consistent use of hydrogen models without requiring all users to be experts in all models

• To support decisions regarding programmatic investments, focus of funding, and research milestones through 

analyses and sensitivity runs.

2 SCOPE

The MSM <http://h2-msm.ca.sandia.gov/> (Ruth et al. 2009) can currently perform pathway, also known as well-to-wheels 

(WTW), analysis of hydrogen production and delivery pathways. Geospatial and temporal features are included in the MSM 

to answer more complex questions regarding the market dynamics and infrastructure needs related to developing a hydrogen 

economy. 

Pathway, or WTW, analysis responds to the need to understand costs, the breakdown of these costs, energy use, and 

emissions related to different hydrogen production/delivery pathways. This approach follows pathways from extraction of the

feedstock for hydrogen through the production, storage, and delivery processes all the way to the use of hydrogen in vehicles. 
Ruth, Diakov, Goldsby, and Sa 

2

Through its links to component models such as the H2A Production model (Steward et al. 2008), the Hydrogen Delivery 

Scenario Analysis Model (HDSAM) (Mintz et al. 2008), and the Greenhouse Gases, Regulated Emissions, and Energy Use in 

Transportation (GREET) model (Wang et al. 2009), the MSM is capable of performing a comprehensive WTW analysis that 

provides users with details such as the amount and type of feedstock used to produce hydrogen, efficiencies of different 

technologies, energy use and emissions of various pathways, hydrogen production capacity to meet demand, and cost of 

hydrogen at the pump achievable under different scenarios. 

Briefly, the constituent models incorporated in the MSM include the following:

• HDSAM is a delivery-scenario model that links various hydrogen delivery component costs to develop 

capacity/flow parameters for a hydrogen delivery infrastructure. This approach allows the model to calculate the 

full cost of hydrogen delivery and accounts for any tradeoffs between components. The structure provided by 

this model allows efficient examination of new technologies, alternative delivery pathways/packaging solutions, 

and the effect of demand density and scale. HDSAM uses financial calculation methodologies and parameters 

consistent with H2A Production to provide a “snapshot” of delivery cost results based upon input assumptions.

• The H2A Production model is used to assess the cost of producing hydrogen for central and forecourt (filling 

station) technologies. Users are permitted to define several characteristics of the production such as process 

design, capacity, capacity factor, efficiency, feedstock requirements, capital costs, and operating costs. For more 

customized analyses, users may also manipulate various financial parameters, including internal rate of return, 

plant life, feedstock costs, and tax rate. In the MSM, assumptions and data on several key technologies were 

also taken from the H2A Production case studies.

• The GREET model, which was created by the Argonne National Laboratory, allows for evaluation of various 

vehicle and fuel combinations on a full fuel-cycle/vehicle-cycle basis. The MSM uses only the fuel-cycle 

portion of GREET (version 1.8d), which allows researchers to evaluate a fuel cycle from WTW. More than 100 

fuel production pathways (e.g., corn to ethanol and soybean-based biodiesel) are included in GREET 1.8d to 

calculate the consumption of total energy, greenhouse gas (GHG) emissions (primarily CO2, CH4, and NOx), 

and six criteria pollutants. For use in the MSM, energy requirements in H2A Production and HDSAM are 

converted to standard GREET inputs (yields, shares, distances, etc.).

• To account for vehicle cycle-related emissions (in addition to the fuel cycle), the GREET2.7 model (developed 

at Argonne as well) is also linked to the MSM.

Because the MSM can integrate multiple variables, the user may modify these variables to observe the results while

highlighting different aspects of various technologies and pathways. The primary variables currently included in the WTW 

analysis structure of the MSM are technology year, city size and hydrogen fuel penetration, production and delivery 

technology, and vehicle fuel economy. In some cases, users may also choose whether to use a model’s default input values or 

their own.

The ability to compare critical factors such as levelized hydrogen costs at the pump using different hydrogen 

production/delivery technologies, raw material needs required to meet a city’s potential hydrogen demands, energy use, 

efficiencies, and emissions profiles (CO2, CH4, other GHG, VOCs, CO, NOx, PM10, SOx) of hydrogen use for varying 

populations and hydrogen penetration levels is a capability that delivers a comprehensive and cross-cutting view of factors 

related to the development of a hydrogen economy.

The following production and delivery technologies are included in the MSM:

• Central production technologies (involving hydrogen delivery to refueling stations)

− Biomass gasification

− Coal gasification with carbon dioxide sequestration 

− Coal gasification without carbon dioxide sequestration

− Natural gas reforming with carbon dioxide sequestration

− Natural gas reforming without carbon dioxide sequestration

− Electrolysis using electricity generated with wind turbines

• Distributed production technologies (hydrogen is produced at the refueling station site)

− Electrolysis

− Natural gas reforming

− Ethanol reforming

− Delivery technologies

− Piping of gaseous hydrogen

− Truck-transport of liquid hydrogen
Ruth, Diakov, Goldsby, and Sa 

3

As additional component models are integrated into the MSM (such as spatial and temporal models), the structure and 

components of the MSM will allow users to discover answers to more complex questions regarding the market dynamics and 

infrastructure needs related to transitioning to a hydrogen economy.

3 APPROACH

In linking disparate constituent models together, the MSM needs to feature extensibility, distributability, and scalability. We 

were inspired by the example of the federated object model (FOM), as exemplified in the Department of Defense High Level 

Architecture (Dahmann et al. 1997). The FOM approach requires explicit definition of the messages (objects and 

interactions) through which the models interact with their environment, providing a common language for the models that is 

extensible as new models are added. It solves the problem of proliferating interfaces as the number of integrated components 

grows, which helps keep the model framework scalable. The models in the MSM were in general not designed with 

federation in mind, so we have to write specialized code to extract data from them and provide data to them; these modules 

constitute an implicit statement of the interaction of the models with their environment. We achieve scalability because there 

is only one such interface module per model, rather than one for each pair of models. The FOM approach has been a success 

in the arena of distributed simulation in the defense community, so we expect this approach to work for linking models 

pertaining to the evolution of the hydrogen economy as well.

Web servers and browsers use the HTTP protocol (Fielding et al. 1999) to transport data, and most Internet firewalls 

allow HTTP traffic to pass through unhindered. Having the MSM use the HTTP option to communicate with component 

models allows these models to lie in other security domains without requiring their administrators to reconfigure the firewalls 

to let MSM traffic through.

Figure 1 illustrates how the MSM software application interconnects the component models.

Figure 1: The GUI and the MSM operation process
Ruth, Diakov, Goldsby, and Sa 

4

The interconnects are implemented by the following means:

• Unifying Framework: Implemented in the Ruby scripting language; consists of model application programming 

interfaces (APIs), model control scripts, unit conversion facility, global data storage (GDS), and execution 

control

• Graphical User Interface (GUI): Implemented in Java; delivered via Java Web Start and the user’s installed 

browser

• Database Management System (DBMS): Implemented in MySQL; contains archived jobs and user data

• Web Services: Web services are used to link the MSM with the geospatial tool HyDRA. 

The general framework is extensible (accommodates new models with minimal difficulty), distributable (can be used by 

multiple people in different areas of the country), and scalable to large numbers of participating models.

Through the GUI, the user sets variables such as timeframe, production technology, feedstock, delivery method, city 

size, and penetration of the technology. (Future capabilities of the MSM will involve significantly more parameter inputs 

available for the user through the Web interface.) User input data are initially transferred to the GDS, which holds all data in 

a consistent set of units. As each component model is run, data from it are transferred to the GDS, and calculations are done 

by the GDS script. Input data for subsequent models are taken from the GDS. As the capabilities of the MSM are expanded in 

the future, optimization routines and solution methodology schemes may also be added.

The current MSM version co-locates most of the back-end resources on a single server (application server) while the 

future planned version will allow models, their APIs, and their control scripts to be located at the model owner’s/developer’s 

site or other location (model server). Because the models are currently co-located with the unifying framework, SOAP (a 

simple XML-based protocol that lets applications exchange information over HTTP) is not necessary. This protocol will 

more likely be used in future versions of the MSM when some models are physically located in other places.

Delivered hydrogen costs, primary energy requirements, and emissions have been estimated for multiple pathways. 

Figure 2 shows the results for production of hydrogen from woody biomass via gasification in central plants followed by 

liquefaction and delivery of liquid hydrogen in trucks. To distribute 116,000 Btu of hydrogen (lower heating value – similar 

to the energy in one gallon of gasoline or 1.02 kg hydrogen), 127,000 Btu of hydrogen need to be produced. A total of 11,000 

Btu are lost due to unrecovered boil-off. In addition, 41,000 Btu of electricity are necessary to liquefy the hydrogen; 1,000 

Btu of diesel fuel to transport the hydrogen; and 1,000 Btu to compress the hydrogen that has been revaporized so it can be 

dispensed to vehicles. To produce the necessary hydrogen, energy sources (e.g., biomass, electricity, and natural gas) are 

required, as shown in Figure 2. The hydrogen cost at the pump for this pathway is estimated to be $5.43/kg. 

Figure 2: MSM results for hydrogen production from biomass with liquid hydrogen delivery to refueling stations

4 RECENT DEVELOPMENTS

Enhancing the MSM performance involves several paths. First, the component models are updated on a regular basis to 

include latest technology improvements; concurrently, the MSM is updated to account for these changes. Second, the first 

web-based user interface allowed user access only to the most critical parameters of a pathway to be analyzed, the vast 

majority of parameters being “invisible” to the user at their default values; this restriction is removed by the new expanded 

GUI ,which allows convenient access to virtually all input and output parameters. Third, new models are being added into 
Ruth, Diakov, Goldsby, and Sa 

5

the MSM to allow new types of analysis; the MSM is being linked with a geospatial analysis tool (HyDRA, 

<http://rpm.nrel.gov/>) and a technology transition (temporal) model (HyPro).

4.1 Expanding GUI capabilities

Because MSM is a collection of several models, it can potentially involve all variables present in each of the constituent 

models. Thus, the total number of MSM input parameters can significantly exceed the number for an individual model, hence 

the need for compact ways of accessing large numbers of variables via a user-friendly interface. Clearly, just giving the user a 

long list of variables that might be useful someday is not a very user-friendly solution.

To allow user access to the entire set of MSM input parameters, a “branch and leaf” structure has been adopted. The 

inputs are grouped into blocks, where each block represents a branch containing other blocks or input parameters. One of the 

challenges encountered while building the expanded GUI is that a compact user interface should exclude user access to 

parameters that are irrelevant for the hydrogen production/delivery pathway of interest. In other words, the “branches and 

leaves” structure is conditional upon some basic choices made by the user. For example, when analyzing hydrogen 

production at refueling stations, we should avoid offering the user the ability to specify hydrogen delivery parameters via 

pipeline (simply because there is no hydrogen delivery involved). 

All the conditional logic behind the GUI is specified by the domain experts at NREL, while the server-based GUI is 

developed by the computer experts at SNL. Further, with the upgrade of the MSM to include newer constituent model 

versions, the “branches and leaves” structure will be modified on an almost continual basis. To give the MSM developers 

more flexibility, the GUI server is built in such a way as to take a complete and extensive list of instructions (written in XML 

by the domain experts) and create (according to the conditional logic given in the instructions) a set of grouped input 

parameters that might be of interest to the user. The schematics of this interaction are shown on the left side of Figure 3, 

along with an example of “branches and leaves” structure on the left.

Figure 3: Example of “branches and leaves” structure denoting grouped input parameters (left) and the schematics of the user 

interface (right). 
Ruth, Diakov, Goldsby, and Sa 

6

NREL domain experts provide the XML list of conditionally grouped input parameters, and the GUI web server provides 

interaction with the user and supplies the MSM back-end with the set of user-specified inputs necessary to start the job. The 

expanded GUI both allows the user to have substantially wider capabilities in controlling MSM runs and gives domain 

experts virtually unlimited flexibility in defining and structuring user access options without altering the web server. 

A conceptually similar approach is employed in displaying the MSM outputs. Again, the output parameters are 

structured based on an XML template specified by the domain experts. Handling the MSM outputs differs from handling the 

inputs in several ways. First, the conditional grouping can be omitted altogether. Second, the output parameters are even 

more numerous than the MSM inputs, and it is preferable keep an intuitive and recognizable grouping pattern throughout 

large chunks of output data. We achieve this by structuring the outputs related to different infrastructure elements 

(production, delivery, dispensing, etc.) into the similar sets of subgroups (material, financial, emissions-related outputs, etc.). 

4.2 Adding geospatial and temporal capabilities

The economic characteristics of hydrogen production are one of the main types of data being analyzed in the MSM. Reality 

suggests that these characteristics strongly vary with position and with time. Hence, different production/delivery pathways 

are more advantageous depending on the geographical region and on the time-span of the project; thus, the analysis has to be 

geographically and time-specific. Therefore, it is important to have geospatial and temporal capabilities in the MSM.

To add geospatial capabilities, the MSM is linked with HyDRA. The approach is to use geographically specific data 

from HyDRA as MSM inputs to generate geographically specific outputs. An example of the MSM–HyDRA interaction 

involves hydrogen production via electrolysis at refueling stations. Geospatial MSM outputs (cost of H2 generated from 

electrolysis, $/kg, associated GHG emissions, g/mile vehicle distance) are generated using geo-specific HyDRA inputs 

(county-by-county electricity cost, $/MWh, and electricity mix, state-by-state). The results are visualized using HyDRA. Two 

areas with both low hydrogen production cost (less than $6/kg) and low associated emissions (not exceeding 550 g per mile 

vehicle travel) were identified (red ovals on Fig. 4). The two areas include a large region covering parts of Idaho, Oregon,

and Washington and a smaller shore region in Maine. 

Figure 4. HyDRA–MSM link helps to identify geographic areas with both favorable emissions and favorable cost 

characteristics of hydrogen production via electrolysis

It is not surprising that key parameters (such as potential demand, feedstock costs, and structure) for hydrogen 

production/delivery pathways analysis are both geographically and time-dependent. To account for the time variable, the 
Ruth, Diakov, Goldsby, and Sa 

7

MSM is linked to the HyPRO model by providing production and delivery technical data as inputs for HyPRO. HyPRO 

analyzes more than 100 production/delivery combinations and finds the optimal succession of pathways depending on the 

demand evolution curve. Using the MSM, key HyPro inputs are updated to correspond to the latest 

production/delivery/dispensing model versions. Conversely, we use the MSM to build hydrogen pathway evolution scenarios

depending on an extensive array of possible input parameters.

5 CONCLUSIONS

The Hydrogen Macro-System Model was developed for DOE’s Hydrogen, Fuel Cell, and Infrastructure Program to analyze 

cross-cutting issues. The federated object model structure proved to be efficient for this purpose. The large number of 

variables involved in the process of combining several models under one framework poses specific challenges, which call for 

specific solutions in building the user interface. 

The wide variety of input parameters is presented to the user in the form of “branches and leaves” structure, which 

proves to be useful in several ways. First, it provides easy access to the input parameters without overcrowding the screen. 

Second, the “branches and leaves” have built-in conditional logic dependent on previous user choice, which eliminates 

irrelevant branches from the interface screen. Third, for flexibility, the structure is defined separately from the server, which 

allows for easy modification and addition of input parameters. Similar principles (except for the conditional logic 

dependence) are applied to facilitate access to the output parameters as well.

Currently, MSM includes spatial and temporal analysis tools. When using geospatial information from HyDRA as inputs 

for MSM, the output becomes geographically specific and is visualized within HyDRA. “Live” interaction between the 

models via the internet has been developed. HyPRO is linked with MSM as a technology evolution analysis tool. 

ACKNOWLEDGEMENTS

The authors thank Johanna Levene, Chris Helms, and Darlene Steward of the National Renewable Energy Laboratory; Andy 

Lutz of Sandia National Laboratory; Brian James, Julie Perez and Andrew Spisak of DTI for technical assistance and useful 

discussions. This work was supported by the U.S. Department of Energy under Contract No. DE-AC36-08-GO28308 with 

the National Renewable Energy Laboratory.

REFERENCES

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, eds. S.G. Henderson, B. Biller, M. Hsieh, J. Shortle, J.D. Tew, 

R.R. Barton. pp. 142–149. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.

Fielding, R., J. Gettys, J. Mogul, H. Frystyk, L. Masinter, P. Leach, T. Berners-Lee. 1999. Hypertext Transfer Protocol –

HTTP/1.1. Available at <http://www.ietf.org/rfc/rfc2616.txt> [accessed April 10, 2009].

James, B. D., P. O. Schmidt, J. Perez. 2008. Using HyPro to evaluate competing hydrogen pathways. Report on DOE 

contract DE-FG36-05GO15019. Available at <http://www.hydrogen.energy.gov/pdfs/progress07/viii_1_james.pdf> 

[Accessed April 10, 2009].

Mintz, M., J. Gillette, A. Elgowainy. 2008. H2A Delivery Scenario Model Version 1.0 User’s Manual. Available at 

<http://www.hydrogen.energy.gov/pdfs/hdsam_users_guide.pdf> [Accessed April 10, 2009].

Ruth, M., V. Diakov, T. Sa, M. Goldsby, K. Genung, R. Hoseley, A. Smith, E. Yuzugullu. 2009. Hydrogen Macro-System 

Model User Guide. Technical Report NREL/TP-6A1-44799. Available at 

<http://www.nrel.gov/docs/fy09osti/44799.pdf> [Accessed April 10, 2009].

Steward, D., T. Ramsden, J. Zuboy. 2008. H2A Production Model, Version 2 User Guide. Technical Report NREL/TP-560-

43983. Available at <http://www.nrel.gov/docs/fy08osti/43983.pdf> [Accessed April 10, 2009].

Wang, M., A. Elgowainy et al. 2009. The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation 

(GREET) Model. Available at <http://www.transportation.anl.gov/modeling_simulation/GREET/index.html> [Accessed 

April 10, 2009].
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14. ABSTRACT (Maximum 200 Words)

It is commonly accepted that the introduction of hydrogen as an energy carrier for light-duty vehicles involves 

concomitant technological development of infrastructure elements, such as production, delivery, consumption, all 

associated with certain emission levels. To analyze these at a system level, the suite of corresponding models 

developed by the DOE and involving several national laboratories is combined in one macro-system model (MSM). 

The macro-system model is being developed as a cross-cutting analysis tool which combines a set of hydrogen 

technology analysis models. Within the MSM, federated simulation framework is used for consistent data transfer 

between the component models. The framework is built to suit cross-model as well as cross-platform data exchange 

and involves ‘over-the-net’ computation.

15. SUBJECT TERMS

Macro-System Model; MSM; hydrogen; technology analysis; computer model

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