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

Office of Energy Efficiency & Renewable Energy

National Renewable Energy Laboratory 

Innovation for Our Energy Future 

Hydrogen Deployment System 

Modeling Environment (HyDS ME) 

Documentation 

Milestone Report FY 2006 

Keith Parks

National Renewable Energy Laboratory

Milestone Report 

NREL/MP-560-40763 

November 2006 

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

Modeling Environment (HyDS 

ME) Documentation 

Milestone Report FY 2006 

Keith Parks

National Renewable Energy Laboratory

Prepared under Task No. HY65.2100 

Milestone Report 

NREL/MP-560-40763 

November 2006 

National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, Colorado 80401-3393 

303-275-3000 • www.nrel.gov 

Operated for the U.S. Department of Energy 

Office of Energy Efficiency and Renewable Energy 

by Midwest Research Institute • Battelle 

Contract No. DE-AC36-99-GO10337
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Introduction 

The Hydrogen Deployment System Modeling Environment (HyDS ME) considers 

hydrogen infrastructure at the regional level. The user interface is shown in Figure 1. The 

unique elements of this model include: 

• Regional perspective with granularity down to the urbanized area. 

• Geographic Information Systems (GIS) platform to consider spatial relationships 

between markets. 

• Competes different production and delivery technologies for least-cost solution. 

This report introduces the model, assumptions, and basic operation. Initial results are 

presented at the end of the document. 

Figure 1 – HyDS ME User Interface 

Overview 

The HyDS Modeling Environment, or HyDS ME, is a GIS-based infrastructure 

optimization model. The model combines existing cash flow models, GIS capability, and 

an optimization routine to determine the layout of a least-cost infrastructure. The user 

chooses the region, a forecasted year, a desired hydrogen vehicle penetration, a 

forecasted natural gas price, and other options. HYDS ME then determines the optimal 

least-cost infrastructure (Figure 2). 

1
Figure 2 – HyDS ME Operations Schematic 

A regional supply curve is output along with a corresponding map of the infrastructure 

(Figures 3 and 4). The map illustrates the spatial relationship of the hydrogen 

infrastructure while the supply curve identifies the cost impact. These outputs are 

described at length later in this document. 

2
Figure 3 – Infrastructure Map 

Region Supply Curve for Chicago-Detroit Area 

$0.00 

$0.50 

$1.00 

$1.50 

$2.00 

$2.50 

$3.00 

$3.50 

$4.00 

$4.50 

$5.00 

0 500 1000 1500 2000 2500 

cumulative kg/day (000s) 

$/kg 

Distributed SMR (1500 kg/day) 

Central Biomass 

Central Coal Gasification 

Central SMR 

2026 with Energy Information Administration (EIA) High A Gas Forecast, 25% Penetration 

Existing H2 Production 

Figure 4 – Regional Supply Curve 

3
Methodology 

HyDS ME uses a modified minimum spanning tree algorithm to compete different 

production and delivery technologies to determine a least-cost solution. This algorithm 

falls in the optimization realm of graph theory. A graph V is a set of nodes N and edges E. 

Graphically, edges connect nodes (Figure 5). By assigning a cost to each edge, a defined 

problem emerges with regard to connecting the nodes at least cost. A minimum spanning 

tree is a special subset of edges that connect all the nodes within the graph at a least cost. 

That is, a minimum spanning tree answers the question: “how can one connect all the 

dots (nodes) with least effort, reflected as least cost?” 

N3 

N1 

N2 

N4 

N5 

E2 

E3 

E1 

E5 

E6 

E7 

E4 

Figure 5 – A graph (V) Consists of Nodes (N) and Edges (E) 

In the hydrogen infrastructure deployment context, urban areas are nodes while the 

various modes of delivering hydrogen are edges. In essence, there are three primary 

pathways or choices associated with each urban area. It can serve its load through (a) its 

own distributed hydrogen generation, (b) its own centralized hydrogen generation, or (c) 

piggybacking onto a neighboring community’s centralized hydrogen production. There 

are many options for production, transport, and delivery associated each of these three 

choices. HyDS ME assesses these options to determine the least cost city and its choice 

for production, transport, and delivery. All other cities’ costs are updated to reflect this 

choice. The model works iteratively through all the cities within a specified region, 

allowing for regional clusters to develop, leveraging the demands of nearby larger cities, 

and creating an interdependent delivery network. 

Table 1 lists the production, transport, and delivery components considered for hydrogen 

pathway optimization within the HyDS ME. Component costs are derived from the H2A 

Production spreadsheets and the H2A Scenario Model. These costs are further explained 

in the following section. 

4
Table 1 – Production, Transport, and Delivery Infrastructure Available within the

HyDS ME

Production, Transport, and Delivery Technologies 

Central Production 

SMR w/out CCS 

SMR w/ CCS 

Coal Gasification w/out CCS 

Coal Gasification w/ CCS 

Biomass 

Distributed Production 

SMR (100 kg/day) 

SMR (1500 kg/day) 

Electrolysis (100 kg/day) 

Electrolysis (1500 kg/day) 

Transport & Delivery* 

Compressed Gas Truck 

Liquid Truck 

Pipeline 

Wind 

Wind w/ Co-Product 

Nuclear Hi Temp Etrol 

Nuclear Sulfur-Iodine 

* Transport and Delivery are further divided into component parts 

Transport and Delivery Components 

Compressed Gas Truck Liquid Truck Pipelines 

Truck & Trailer Truck & Trailer Transport Pipeline 

Compressor Liquefier Distribution Pipelines 

Terminal Terminal Compressor 

Station Station Station 

Note: “SMR” refers to Steam Methane Reforming; “CCS” refers to Carbon Capture and Sequestration. 

Pathway Costs 

The complete pathway cost is the sum of the production, transport, and delivery costs. 

Figure 6 illustrates the various HyDS ME pathways. The model considers each step of 

the pathway, taking into account different technologies and their respective scale 

economies. Production refers to producing hydrogen and enabling the hydrogen to be 

transported. Transport refers to the transport of hydrogen from the production facility to 

the city-gate. Finally, delivery, or distribution, considers the transport of hydrogen from 

the city-gate to the station. 

Figure 6 – Pathway Costs 

5
Production 

Production cost is the sum of the cost of producing hydrogen and the cost of enabling the 

hydrogen to be transported. For central production, enabling technology is sized relative 

to the production facility and is assumed to be proximal to the production facility. Several 

assumptions regarding the cost of producing and enabling hydrogen are made. 

Central Production: Fixed and Variable Costs 

The cost of producing hydrogen is the sum of the fixed and variable production costs 

(Figure 7), both derived from the H2A Production spreadsheets. These spreadsheets 

forecast the unit cost (expressed in units of output, $/kg) necessary to achieve a specified 

return on investment. As the return is assumed to incorporate profit, this unit cost is 

referred to as the profited cost. For use in HyDS ME, the profited cost is broken into 

fixed and variable components. Fixed costs are the sum of the capital and fixed 

operations and maintenance costs. Variables costs are the sum of feedstock and variable 

operating costs. 

Economies of scale affect fixed cost as a function of plant capacity but are bounded by 

minimum and maximum capacities. Since H2A evaluates many technologies at a single, 

discrete capacity, its framework does not inherently consider economies of scale. HyDS 

ME assumes the fixed cost for most technologies varies by an exponential function of 

capacity, where the exponent is fixed at 0.6. The exception to this approach is steam 

methane reforming (SMR). SMR has three data points (15 ton, 30 ton, 340 ton) within the 

H2A framework. From these three points, the model derives a least-squares exponential 

function1. 

Minimum and maximum capacities of plants are enforced. The limitations are intended to 

preserve real limits on the expected deployable size of the technologies. In Figure 7, 

minimum capacity is enforced by pricing the technology at an extraordinary cost (1). The 

maximum capacity is enforced by eliminating economies of scale (2). Without this 

limitation, scale economies would extend to unrealistically large facilities. The model 

assumes variable costs specific to each technology will remain the same regardless of 

scale2. 

1

 These assumptions are consistent with conversations and correspondence with researchers in hydrogen

technology validation and other transition infrastructure modelers. 2

 Again, these assumptions are consistent with conversations and correspondence with researchers in

hydrogen technology validation and other transition infrastructure modelers. 

6
Central SMR w/out CCS 

0 

0.5 

1 

1.5 

2 

2.5 

3 

3.5 

4 

4.5 

5 

0 50000 100000 150000 200000 250000 300000 350000 400000 

kg/day (000s) 

$/kg 

Profited Cost 

Variable Component 

Fixed Component 

2 

1 

Figure 7 – Total Cost is the Sum of Fixed and Variable Costs 

Operationally, the H2A Production spreadsheets are directly linked to HyDS ME, 

facilitating sensitivity runs. For example, the assumed feedstock prices are a major input 

to the H2A Production spreadsheets. To simplify sensitivity runs, natural gas prices are 

an input to HyDS ME3. Since the model links to H2A Production, the new price is 

reflected in HyDS ME in the form of higher variable costs for SMR. 

Enabling Transport and Delivery 

The model considers some components (liquefiers, compressors, and terminals) as central 

technologies. These technologies enable the transport of hydrogen from production into 

the delivery system. The model considers the sharing of these potentially significant 

costs. A cluster of cities may share a central enabling technology; thereby gaining scale 

economies, much the same way cities gain scale from implementing central production 

technology. Therefore, as more and more cities link to a central production facility, they 

experience gains from scale economies in production as well as in the liquefier, terminal, 

and/or compressor. 

Enabling technology costs are derived from the H2A Scenario Model. The methodology 

is discussed later in the delivery section of this report. 

Distributed Production versus Central Production 

Production technology comes in two basic types: central and distributed. These types 

balance production scale economies with transport and delivery costs. Because 

distributed technologies produce hydrogen on site, they incur no transport and delivery 

costs. However, due to their small capacities, they also cannot take advantage of 

3

 There are placeholders in the HyDS ME for prices of other feedstocks such as coal and electricity. These 

can be added but currently are not active due to time constraints. 

7
significant scale economies that could reduce their production fixed unit costs. 

Conversely, central technologies can reduce the fixed component of production costs 

through increasing scale, but incur sometimes substantial costs to transport hydrogen to 

the dispensing site. 

Distributed production is assumed to be at either 1500 kg/day or 100 kg/day. The two 

technologies available are SMR and electrolysis. The costs for these technologies are 

taken directly from the H2A Production spreadsheets. 

HyDS ME competes central versus distributed technologies. As demand increases 

(associated with larger penetration), central production gains scale economies thereby 

reducing production costs. Eventually, these costs drop enough so that the combined cost 

of production, transport, and delivery becomes less than the production cost of distributed 

technologies. Until such scale is feasible and economic, the model selects distributed 

technology, opting for the smaller, less efficient production technology but avoiding the 

additional costs of transport and delivery. 

Leveraging Central Production 

One way a smaller city can lower its delivered cost is by leveraging the centralized 

production capability of a neighboring community. By itself, the smaller community may 

opt for a distributed technology. With a neighboring larger market, however, the 

community has the option to link to the neighbor’s production center and incur the 

additional cost of transport and delivery. Other communities may also join, creating a 

transport network extending out from the central plant. Eventually, as the distribution 

system increases in area, small communities or communities distant from the central 

facility will have transport and delivery costs that overwhelm the production scale 

economies. These communities will forgo the network in favor of distributed technology. 

Geographically, this leveraging opportunity creates, with sufficient demand, a hybrid 

hydrogen infrastructure incorporating both central and distributed production. Figure 8 

depicts a representation of the Chicago-Detroit region assuming a uniform penetration of 

fuel cell vehicles to 15% of total light duty vehicle stock. The dark blue lines connecting 

Chicago and Detroit with the outlying urban areas represent hydrogen pipelines. The light 

blue shaded areas emphasize the extent of the pipeline coverage. Areas outside the 

pipeline network chose to install distributed technologies to avoid the increasing cost of 

transport and delivery. Thus, a geographic break is anticipated with regard to central 

versus distributed technology. 

8
Figure 8 – Central Versus Distributed Interface 

Transport 

Transport refers to the transport of hydrogen from the production facility to the city-gate. 

Transport costs are derived from the H2A Scenario Model. 

Gas and Liquid Truck 

For truck delivery, transport costs largely depend on distance. In order to model this 

relationship, the H2A Scenario model was run for several distances to the city gate (zero 

miles, thirty miles, and 100 miles) while also varying the level of hydrogen demand. The 

additional distance, reflected in additional transit time, not only drives nominal additional 

labor and diesel costs, but also requires more truck/trailers due to lower utilization of the 

delivery infrastructure. The model assumes that additional hydrogen demand does not 

affect truck transport costs. A linear equation, graphed separately for gas and liquid 

transport in Figure 9, was derived to explain the truck/trailer transport costs. 

9
Gas and Liquid Truck Transportation Costs 

$0.00 

$0.20 

$0.40 

$0.60 

$0.80 

$1.00 

$1.20 

10 50 100 150 200 

Miles 

$/kg 

Gas Truck Liquid Truck 

Per Mile Costs: 

Gas Truck: $0.0055/Mile 

Liquid Truck: $0.0011/Mile 

Figure 9 – Gas and Liquid Truck/Trailer Transport Costs 

Pipelines 

The pipeline component costs were extracted from the H2A Scenario Model to create a 

stand-alone transport pipeline cost model. Since the relationship between demand, 

distance, and cost is not a simple linear one, more sample points were necessary to reduce 

the pipeline model to a more simple form. Crystal Ball4 was used to create 10,000 sample 

points. A least-squares regression was performed in this sample set (Figure 10). 

Consistent with H2A Scenario Model assumptions, pipeline transport costs are most 

sensitive to hydrogen demand and distance. Distance is an obvious cost component (more 

distance requires more pipe). Increased demand drives larger pipeline diameters. If larger 

pipelines can be justified, then costs are amortized over substantially more units of 

hydrogen, thereby driving down the unit cost of transport. Regardless, transport costs are 

relatively small when compared to production and delivery. 

4

 An Excel add-in normally used for Monte Carlo probability simulation. 

10
25000

75000

125000

175000

225000

275000

325000

375000 

20 

120 

-$0.10 

$0.10 

$0.30 

$0.50 

$0.70 

$0.90 

$1.10 

$1.30 

$1.50 

$/dg 

Demand (kg/day) 

Distance 

(mi) 

Transportation Pipeline Costs 

Pipeline Equation: 

$/kg = 10.6*Demand0.75*Length1.13 

Figure 10 – Pipeline Transport Costs 

Delivery 

Delivery considers the transport of hydrogen from the city gate to the station. For gas and 

liquid truck delivery, these costs include the truck and trailer, station, and dispensing 

costs. For pipeline delivery, the costs include main and feeder pipelines along with the 

station and dispensing costs. Every urban area must bear the cost of delivery on its own. 

Delivery costs depend on the city size and the demand. Small communities may have 

large costs due to the small market while large cities may suffer from their large urban 

expanse. Since delivery costs are derived from the H2A Scenario Model, the HyDS ME 

treatment of delivery inherits all the assumptions embedded in that model. 

Delivery Costs 

The H2A Scenario Model was used to estimate delivery costs. Component costs were 

first recorded for several hundred model runs. An equation was then fit to each sample 

set via least-squares regression using demand and city area as the determinants of cost. 

The functional form of the resulting equation varies by component (Table 2). 

11
Table 2 – H2A Scenario Model Reduced Form Delivery Cost Equations by Component 

Gas Truck (100 kg/day) Liquid Truck (1500 kg/day) Pipelines (1500 kg/day) 

Station A 

A 2.38 

Terminal A*LN(X)^B 

A 4015.40 

B -3.36 

Delivery A+B(1/X)+C(Y 1/2 ) 

A 1.50 

B 299.11 

C 0.02 

X = Demand 

Y = City Area 

Station A 

A 0.57 

Terminal A+B*(1/X) 

A 0.24 

B 2927.23 

Liquifier A*LN(X)^B 

A 98502.51 

B -4.46 

Delivery A+B(1/X)+C(Y 1/2 ) 

A 0.09 

B 231.42 

C 0.00 

Station A 

A 0.86 

Compressor A*LN(X)^B 

A 278.31 

B -3.03 

Pipelines 

A 8.55 

B 0.06 

C -1.72 

D 1.03 

GeoStorage A*LN(X)^B 

A 6501.56 

B -4.39 

Values of R2, a measure of the goodness of the regression fit to the original data, are 

routinely 0.98 or higher. The regression fit declines somewhat at low demands for very 

small city areas and high demands for very large city areas. Given the high R2, NREL is 

confident that these reduced form equations are an accurate and appropriate reduction of 

the H2A Scenario Model for the most likely scenarios where central technologies would 

be applied. 

As an example, the H2A Scenario Model sample set and the resulting reduced form 

equation fit are displayed in Figure 11 for pipeline delivery costs in several large urban 

areas in relation to hydrogen demand. The simplified equation (in blue) maps closely to 

the values reported in the H2A Scenario Model (in purple). Delivery costs, in both H2A 

and reduced form, exhibit similar behavior across metropolitan areas. For example, for 

the NYC Metro Area, intra-city pipeline costs drop initially with the number of vehicles 

served, but then flatten out and rise slightly as demand further increases. The pipeline 

costs associated with NYC are substantially higher than those for Seattle due mostly to 

NYC’s larger city aerial extent and higher concentration of vehicles. 

12
Reduction of H2A Scenario Model Intra-City Pipeline Costs 

$0.00 

$0.20 

$0.40 

$0.60 

$0.80 

$1.00 

$1.20 

$1.40 

$1.60 

$1.80 

$2.00 

0 1M 2M 3M 4M 5M 6M 7M 8M 

Vehicles 

$/kg 

Reduced Form Equation 

H2A Scenario Mode 

3322 mi2 ~ NYC Metro Area 

953 mi2 ~ Seattle Metro Area 

Figure 11 – Intra-city Pipeline Assumptions 

Hydrogen Demands 

To calculate hydrogen demands, HyDS ME uses GIS information to spatially represent 

the distribution of vehicles across the U.S. The Census Bureau’s Urban Area definition is 

used to define city boundaries. An Urban Area (UA) is defined as a core census block 

group having a population density of 1,000 people per square mile plus all contiguous 

areas with more than 500 people per square mile. Further, Urban Areas have populations 

of 50,000 or greater. Urban Clusters (UC) have the same density requirements but are 

limited to populations under 50,000.5 HyDS ME combines the Census UA and UC 

definitions and enforces a lower bound of 10,000 (i.e., a UA/UC in HyDS ME has a 

population greater that 10,000). 

The Census reports, by census block, average vehicles per household and the number of 

households. Consider each UAk. The number of household vehicles within UAk can be 

calculated as follows: 

i∈UAk 

Vehiclesk = ∑ AverageVehiclesPerHouseholdi * NumberOfHouseholdsi 

i 

for all census blocks i with UAk. 

Using this approach, the total number of household vehicles in the urban areas definition, 

corresponding to the red areas in Figure 12, is 110 million vehicles. The total number of 

household vehicles for the entire United States, represented by the shades of blue in the 

figure, is 182 million vehicles. Therefore, there are 72 million vehicles, or 40% of the 

5

 US Census Bureau Website; 

Census 2000 Urban and Rural Classification; http://www.census.gov/geo/www/ua/ua_2k.html

13
total vehicles population, that do not reside at households included in the UA/UC 

definition. 

Figure 12 – Vehicle Population Overlaid with the UA/UC City Definition 

Note: Urban Areas with populations above 10,000 are shown in red. Vehicle population is shown as shades 

of blue. 

Household vehicles account for most, but not all, vehicles. Commercial and government 

vehicles are excluded from these Census-based figures. Alternative sources of data verify 

that the Census calculation undercounts the total number of vehicles (Table 3). The 

Energy Information Administration (EIA) data is based on the Department of 

Transportation’s 2001 National Household Travel Survey. The EIA data, reflecting a 

survey performed by household, are relatively close (within 6%) to the Census 

information. 

14
Table 3 – Reported National Vehicle Data by Source 

Data Source 2000 % Difference 

Census Bureau 178,390,043 

2 Energy Information Agency 187,944,000 5.4 

1 Bureau of Transportation Statistics 212,706,399 19.2 1

Passenger Cars plus other 2-axel 4-wheeled vehicles for 2000 2

2001 reported data reduced by 1.6% (prevailing 10-yr average) 

Sources: 

Census - 2000 Census (2000) 

BTS - National Transportation Statistics 2005 (December 2005) 

EIA - Household Vehicles Energy Use: Latest Data & Trends (2001) 

The Bureau of Transportation Statistics (BTS) collects data on all registered vehicles by 

state. For our purposes, registered vehicles comprise all road-worthy vehicles including 

private, commercial, and government vehicles. The difference between the BTS and 

Census totals, 19.2% on a national basis, reflects the Census’ lack of inclusion of nonhousehold vehicles. Assuming non-household vehicles are distributed proportionally by 

the household vehicle population, the Census UA/UC vehicle estimates are increased by 

this factor for use in HyDS ME to reflect the inclusion of non-household vehicles (Table 

4). 

Table 4 – Most Populous Urban Areas and Vehicle Counts 

Urban Area State Population Census - Orig Census - Mod 

New York--Newark NY 17,340,042 6,983,737 8,327,178 

Los Angeles--Long Beach--Santa Ana CA 11,784,473 6,265,225 7,470,448 

Chicago IL 8,299,353 4,278,249 5,101,243 

Philadelphia PA-DE 5,142,385 2,675,069 3,189,664 

Miami FL 4,901,994 2,666,928 3,179,957 

Detroit MI 3,900,539 2,410,285 2,873,944 

Dallas--Fort Worth--Arlington TX 4,140,851 2,384,910 2,843,688 

Washington DC 3,936,201 2,268,652 2,705,066 

Boston MA 4,014,865 2,147,915 2,561,104 

Houston TX 3,819,632 2,039,996 2,432,423 

Note: “Census – Orig” refers to original Census data (household vehicles only). “Census – Mod” values result from 

inflating the original by a constant factor to reflect the addition of non-household vehicles. 

15
User Guide for Operating the HyDS ME 

HyDS ME combines Microsoft Excel and ESRI MapObjects through Visual Basic for 

Applications (VBA) programming. Six worksheets are included in the Excel front-end: 

START, H2A Files, Commodity Prices, Cost Curves, Components, and OUTPUT. As this 

section is an introduction to the capability and operation of HyDS ME, some capability is 

mentioned, but is not covered in depth. 

Spreadsheet Inputs 

There are many inputs within the HyDS ME spreadsheets. A subset of the most crucial 

functionality is listed below. 

Commodity Prices Spreadsheet 

The feedstock price assumptions are input into the Commodity Prices spreadsheet. Figure 

13 shows the natural gas price assumptions column. The values highlighted in orange are 

those used in the computations and can be changed by the user. Various Annual Energy 

Outlook (AEO) forecast price series are placed in the gray-highlighted columns as 

references and should not be changed. 

Natural Gas Prices UPDATE ? TRUE 

Year 

Base AEO 2005 

Industrial 

$(2003)/mmbtu 

High A AEO 

2005 Industrial 

$(2003)/mmbtu 

Base AEO 2006 

Industrial 

$(2003)/mmbtu 

High A AEO 

2006 Industrial 

$(2003)/mmbtu 

Base AEO 2006 

Total Consumption 

(TCF) 

High A AEO 

2006Total 

Consumption (TCF) 

Modeled Price 

$(2003)/mmbtu 

2003 

2004 

2005 

2006 

$5.72 

$6.22 

$6.25 

$5.59 

$5.72 

$6.22 

$6.25 

$5.59 

$5.62 

$6.13 

$8.45 

$7.60 

$5.62 

$6.13 

$8.45 

$7.66 

22.34 

22.41 

22.23 

22.18 

22.34 

22.41 

22.21 

22.15 

$5.62 

$6.13 

$8.45 

$7.66 

Figure 13 – Commodity Prices spreadsheet functionality 

Cost Curves Spreadsheet 

HyDS ME can compete up to three different central production technologies and two 

different distributed production technologies at once. These technologies are selected 

through a drop down menu available on the Cost Curves spreadsheet (Figure 14). 

Figure 14 – Production Technology Selection 

Additionally, the cost curve assumptions are available in graph format in the Cost Curves 

spreadsheet. These graphs are dynamically updated when new technologies are selected 

in the above dropdown. 

16
The remaining spreadsheets, Components, Demand, and H2A Files are for advanced use 

only and are not addressed in this report. The OUTPUTS spreadsheet holds all the final 

results and merits its own section below. 

User Interface: Inputs and Operation 

The user interface (Figure 15) is initiated by clicking the “PipesPlus” button on the 

START page. The interface consists of a map and a tool bar on the right margin. The map 

includes the entire United States with state boundaries (black lines), the Interstate 

Highway System (purple lines), urban areas (UA/UC as black blobs), and existing 

hydrogen production facilities (yellow triangles). 

Figure 15 – Opening View of HyDS ME 

The user interface toolbar is divided into three parts: Navigation, Assumptions, and 

Execution (Figure 16). 

17
NAVIGATION

ASSUMPTIONS

EXECUTION

Pan

Zoom In

Zoom Out

Show Names/Size

Choose Cities 

Build (Penetration %) 

Excess Capacity 

Distribution Technology 

Build Year 

Choose Cities

Build (Penetration %)

Excess Capacity

Distribution Technology

Build Year

Copy Map

Save Outputs

CC Update

Optimize!

Figure 16 – The HyDS Toolbar 

Navigation 

The Navigation bar is used to control the display of a study region. This part of the 

toolbar is used to zoom in on a study region. Press the appropriate button and then either 

grab or drag over the map to pan, zoom in, or zoom out. These navigation controls are 

similar to those used in popular mapping software found on the Internet. 

The Show Names and Show Size check boxes will toggle on and off the relative names 

and populations of the urban areas, respectively. These display features are only useful 

when zoomed in close to a particular region. 

Assumptions 

There are several inputs, or assumptions, the user must select or specify to run HyDS 

ME. These inputs are addressed individually below: 

Choose Cities – This button enables the user to select a study region. Drag the left mouse 

button to draw a rectangle over the map. All cities inclusive of the rectangle (within and 

touched by) are selected for the study. The right mouse button can be used to select a 

state by clicking within the state boundary. Multiple states may be selected by rightclicking within additional states. 

Initial Build (% of LDV) – This value is the assumed penetration of fuel cell vehicles in 

the study region expressed as a percentage of total light duty vehicles (household and 

non-household). The penetration is assumed to be uniform across all urban areas selected. 

18
It is possible to specify different penetrations for different cities, but this functionality is 

beyond the scope of this introductory description. 

Current Excess Capacity – This value is the percentage of existing hydrogen capacity 

available for the transportation sector. This percentage is applied to every existing facility 

uniformly. The default value is 10%. 

Distribution Technology – The user selects the distribution, or transport and delivery, 

technology of choice for central facilities. The use of this variable reflects a design choice 

by the developers to enable a user to isolate one distribution technology impact and cost 

from another. To compare different distribution technologies, two runs are made, each 

with the respective distribution technology selected. The least-cost solution between the 

two distribution options can be inferred through inspection. 

Build Year – This value refers to the assumed year in which the infrastructure is built. 

This value determines the specific H2A Production spreadsheet that is used to price 

production technologies and the feedstock price paths. The build year is a drop down list 

of even numbered years. Later years incorporate the assumed cost reducing impact of 

new technologies. 

Execution 

The execution portion of the interface toolbar manages the operation, outputs, and 

assumptions of HyDS ME. 

Study Name – Refers to the name of the study spreadsheet into which the run outputs will 

be saved. A name is required only when the Save Output checkbox is activated. 

Copy Map – Copies the existing view of the map to the OUTPUT spreadsheet. 

Save Output – Enables outputs to be copied into an external Excel file as defined by the 

Study Name input box. 

CC Update – Enables the dynamic update of the cost curve links to the H2A Production 

spreadsheets. If any changes are made to feedstock prices, IRR, or any other H2A 

assumptions within the H2A Production spreadsheets, invoking this update will ensure 

that these changes are carried into the appropriate HyDS ME worksheets and cells. . This 

update can take several minutes to complete. If assumptions regarding H2A are not 

changed, then the cost curves do not need to be updated. 

Optimize! – Once all assumptions are input (e.g., selected region, % of LDV), the 

Optimize! Button is clicked. This action invokes the cost curve update (CC Update, if 

selected), followed by the minimum spanning tree algorithm and finally reporting 

algorithm. 

19
Distributed Production

Central Production

Existing H2 Production

Pipeline Delivery

Interstate System

Truck Delivery

Interpreting Results- The OUTPUTS spreadsheet 

The result of a single run of the HyDS ME can be broken into two parts: The Map and 

the Supply Curve. 

The Map is a spatial representation of the least-cost solution for the region selected. The 

Map outputs include symbols for distributed and central production, pipeline, and truck 

transport, as well as the existing state boundaries, hydrogen facilities, and the Interstate 

Highway System (Figure 17). This output is intended to give the user an immediate 

The ‘Map’demonstrates 

the extent ofcentral 

versus distributed

production te tion technology,

the delivery mode,as

well as the state 

boundaries and 

Interstate system...

geographical view of the hydrogen infrastructure across the region. 

Distributed Production 

Central Production 

Existing H2 Production 

Pipeline Delivery 

Interstate System 

Truck Delivery 

Figure 17 – The Map Output 

The cost of hydrogen varies regionally. This variation has less to do with the commodity 

price of feedstock as with the spatial relationship between large and small urban areas. 

Given the assumptions regarding production, transport, and delivery, a single urban area 

is allocated a cost of delivered hydrogen. A neighboring city will, potentially have a 

different cost due to factors such as demand, distance from production, and urban aerial 

extent. The costs are sorted from least to most expensive and then plotted against the 

cumulative hydrogen demand to yield a regional supply curve (Figure 18). 

20
Region SupplyCurve

The ‘Supply Curve’ $4.00

demonstrates the $/kg $3.80

incremental cost of $3.60

hydrogen fdrogen for the enti the entire $3.40

region. The hydrogen $3.20 $/kg

cost for each urban area ch urban area 

is sorted from leafrom least to $2.80

$3.00

Biomass Gasification

Coal Gasification w/o CCS

SMR w/o CCS

most expensive for the $2.60

Existing Producc tion

entire region. Each city $2.40 Distributed SMR

is color-coded $2.20

depending on its epending on its $2.00

0 2000 4000 6000 8000 10000 12000

production technology. cumulative kg/day (000s)

Figure 18 – Regional Supply Curve 

The supply curve reflects the spatial diversity of the region, the assumed demand, 

feedstock prices, build year, and competed technologies. 

The map and supply curve are integrally related. They portray the same information in 

different ways. The following example was created to better explain this relationship. A 

run was made focused on the Washington/Baltimore region. A natural gas price of 

$9/MMBtu was assumed as well as a 15% vehicle penetration. Figure 19 displays the two 

outputs side by side. 

Figure 19 – The Map and Supply Curves Side by Side 

First note that Washington and Baltimore are the largest urban markets in the region 

(Figure 20). They contribute the majority of the hydrogen demand. Since these two large 

demand centers are near one another, they have nearly the same delivered cost of 

hydrogen. Central production is shared by both urban centers via a pipeline. 

21
Figure 20 – Washington/Baltimore Share a Central Production Facility 

With a nearby central production facility, medium sized markets choose to build to the 

Washington/Baltimore pipeline (Figure 21). While the incremental cost of transport and 

delivery drives their total costs up, it is still most cost effective to leverage the 

Washington/Baltimore urban cluster central production. The blue lines on the map 

represent the resulting pipeline transport. 

Figure 21 – Medium sized markets link to the Washington/Baltimore pipeline 

Finally, outlying small communities opt for distributed production priced at $3.81/kg 

(Figure 22). Long distance and small demands result in high transport and delivery costs. 

associated with Washington/Baltimore centralized production. Without some kind of 

cost sharing, small communities will tend to opt for the cheaper distributed option. 

22
Figure 22 – Small Markets Choose Distributed SMR 

Example Runs, Discussion, and Conclusions 

For purposes of discussion, two scenarios were run for the United States. Lower 48 

region: a Base Case of $6.26/MMBtu6 and a Sensitivity Case of $12/MMBtu natural gas, 

both at 15% penetration. 

Distributed SMR

Central Coal

Existing H2 Production

Hydrogen Pipeline

Interstate System

Figure 23 - $6.26/MMbtu Natural Gas Price at 15% Penetration 

6

 AEO 2006 Forecasted 2015 High A Natural Gas Price in 2003 dollars. Feedstock forecasts, excluding

natural gas, are based on the AEO 2005 High A forecast. 

23
In the Base case (Figure 23), distributed SMR (natural gas) dominates. The cost of 1500 

kg/day distributed SMR is $2.78/kg. None of the central technologies can compete with 

hydrogen production at that cost. 

The Sensitivity case (Figure 24) demonstrates a large transition to coal gasification. 

Twenty seven production centers proximal to the largest demand areas emerge. 

Communities near these centers leverage the new production via transport pipelines. 

Communities too small and distant from these centers employ distributed SMR. 

Distributed SMR 

Central Coal

Existing H2 Production 

Hydrogen Pipeline 

Interstate System

Figure 24 - $12.00/MMBtu Natural Gas Price at 15% Penetration 

The corresponding supply curve for the Lower 48 is displayed in Figure 25. The x-axis 

represents the cumulative hydrogen demand. The y-axis represents the total delivered 

cost. In constructing the supply curve, the delivered cost for individual urban areas is 

sorted from least to highest cost. From left to right, the costs rise depending on the 

central plant size, relative transport costs, and intra-city delivery costs. Eventually, the

cost to build and transport/deliver from central production reaches the cost of distributed 

SMR using $12/mmbtu natural gas ($3.80/kg). Communities then opt for the less 

expensive SMR rather than assuming ever larger transport and delivery costs associated 

with central production. 

24
Region Supply Curve 

$2.00 

$2.20 

$2.40 

$2.60 

$2.80 

$3.00 

$3.20 

$3.40 

$3.60 

$3.80 

$4.00 

0 2000 4000 6000 8000 10000 12000 

cumulative kg/day (000s) 

$/kg 

Distributed SMR 

Existing Production 

Biomass Gasification 

Coal Gasification w/o CCS 

SMR w/o CCS 

Figure 25 – Hydrogen Supply Curve for the Lower 48 United States at 15% Penetration 

In this example, the 82 urban areas that chose central technology represent 18% of the 

urban areas and 7.8 million kg/day, or 69% of the total demand of 11.3 million kg/day. 

The remaining 366 urban areas represent 3.5 million kg/day, or 31% of the total demand. 

That is, a small number of large urban areas comprise the majority of the potential 

hydrogen demand due to the sheer size of their vehicle populations. The remaining small 

and more remote communities suffer from their own small market size as well as their 

relative distance from demand centers. 

Conclusions 

HyDS ME provides a platform for analyzing hydrogen infrastructure impacts. 

Geographic characteristics such as vehicle population, aerial extent, size, and relative 

distances to larger markets are considered. Production, transport, and delivery costs and 

economies of scale are considered together on a regional basis. 

The natural gas price is a major driver in determining least-cost hydrogen infrastructure 

as it impacts the cost of SMR, a primary distributed technology. For recent EIA gas 

forecasts ($6.26/MMBtu in 2015), distributed SMR is a least-cost infrastructure choice at 

15% and lower penetration of vehicles. When natural gas price is nearly doubled 

($12/MMBtu), central technologies may serve up to 69% of the total demand of hydrogen 

nationwide at 15% penetration. Smaller and more rural urban areas, representing the 

remaining 31% of demand, suffer from small market size and distance from major 

demand centers and opt for distributed SMR at this level of penetration. 

25
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Hydrogen Deployment System Modeling Environment (HyDSME) 

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Keith Parks 

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This report introduces the Hydrogen Deployment System Modeling Environment model, assumptions, and basic 

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