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 

Geographically Based Hydrogen 

Consumer Demand and 

Infrastructure Analysis 

Final Report 

M. Melendez and A. Milbrandt 

Technical Report 

NREL/TP-540-40373 

October 2006 

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

Consumer Demand and 

Infrastructure Analysis 

Final Report 

M. Melendez and A. Milbrandt 

Prepared under Task No. HF65.8310 

Technical Report 

NREL/TP-540-40373 

October 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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Table of Contents 

Executive Summary........................................................................................................................ 1 

Background..................................................................................................................................... 1 

Research Methodology ................................................................................................................... 2 

Demographics............................................................................................................................. 2 

Attribute Scores and Weights ..................................................................................................... 3 

Hydrogen Vehicle Demand—Consumer Baseline Strategy....................................................... 8 

Stakeholder Review and Sensitivity Analyses.......................................................................... 10 

Hydrogen Vehicle and Fuel Demand at Various Penetration Rates......................................... 13 

Regional Analysis......................................................................................................................... 15 

Conclusion .................................................................................................................................... 16 

Future Work.............................................................................................................................. 16 

Appendix A: Attributes in the U.S. 20-mile by 20-mile Grid ...................................................... 18 

Appendix B: Monte Carlo Simulation Probability Functions....................................................... 23 

Appendix C: Sample Regional Hydrogen Vehicle Demand—Consumer Strategy Baseline 

Scenario................................................................................................................................... 24 

Appendix D: Bibliography............................................................................................................ 29 

iii
Executive Summary 

Infrastructure development analysis plays a critical role in understanding the best methods and techniques 

to use in the nascent hydrogen economy to foster hydrogen use in transportation. Most alternative fuel 

experts agree that infrastructure issues have been among the top barriers to transitioning to alternative 

transportation fuels. Matching emerging hydrogen vehicle demand with emerging infrastructure is critical 

to a successful transition. Because demand varies spatially, using a geographic information system (GIS) 

method to differentiate demand across the country is advantageous in assisting hydrogen stakeholders in 

targeting key markets. 

Using literature relating to alternative fuel deployment, along with the transportation sector's experience 

and expertise in this area, key attributes of consumers and policies were identified as critical for market 

acceptance of hydrogen vehicles. Consumer attributes are those that describe the consumer themselves, 

such as income, education level, and the number of vehicles they own. Policy attributes are those that are 

the result of external factors that influence the market for hydrogen vehicles, such as government 

incentives and local air quality. These attributes lay the foundation for a national look at how these 

efforts combine, resulting in the most preferred or likely locations for hydrogen demand to grow. 

The attributes were spatially quantified using GIS. Each attribute was quantified and combined with other 

attributes to achieve a relative demand for hydrogen in areas measuring 20 miles by 20 miles covering the 

entire United States. The analytical results indicate that the most suitable areas for early hydrogen market 

acceptance are in metropolitan areas, with rural areas being least desirable for initial deployment of 

hydrogen vehicles. 

The metropolitan areas with the greatest relative likelihood of adopting hydrogen vehicles include: 

1) New York—Northern NJ—Long Island 

2) Los Angeles—Riverside—Orange County 

3) San Francisco—Oakland—San Jose 

4) Boston—Worcester—Lawrence 

5) Philadelphia—Wilmington—Atlantic City 

6) Chicago—Gary—Kenosha 

7) Washington—Baltimore 

8) Sacramento—Yolo 

9) San Diego 

10) Dallas—Fort Worth 

11) Houston—Galveston—Brazoria 

12) Hartford 

13) Minneapolis—St. Paul 

14) Atlanta 

15) Detroit—Ann Arbor—Flint 

16) Phoenix—Mesa 

17) Denver—Boulder—Greeley 

18) Cleveland—Akron 

19) Providence—Fall River—Warwick 

20) Rochester 

The results of this national-level analysis led to an initial analysis of some of these metropolitan areas to 

identify infrastructure that is most suitable within the metropolitan area. Similar methods were used to 

the national analysis. However, in metropolitan areas the attributes were examined on a census tract 

basis. These results demonstrate how demand varies within a given metropolitan area. The shape or 

breakdown of metro area demand varies widely from one city to another. Future work will examine 

emerging demand by region, including the incorporation of additional attributes such as traffic and 

existing infrastructure. 

Background 

Infrastructure development analysis explores the benefits and drawbacks of various options for installing 

hardware to serve a developing hydrogen demand. Most alternative fuel experts agree that infrastructure 

issues have been among the top barriers to transitioning to alternative transportation fuels. Therefore, 

1
infrastructure analysis is a key component in the development of a hydrogen transportation system. 

Understanding consumer demand on a geographic basis is an important part of this analysis. Matching 

emerging hydrogen demand with emerging infrastructure is critical to a successful transition. 

In fiscal years (FY) 2004 and 2005, the National Renewable Energy Laboratory (NREL) developed a 

proposed minimal infrastructure to support nationwide deployment of hydrogen vehicles by offering 

infrastructure scenarios that facilitated interstate travel. The current (FY 2006) project aims to identify 

key metropolitan areas and regions on which to focus infrastructure efforts during the early hydrogen 

transition. 

The objectives of this analysis are the following: 

1. Quantify projected hydrogen vehicle demand across the country and in targeted metropolitan 

areas. 

2. Quantify the projected hydrogen fuel demands corresponding with different levels of hydrogen 

vehicle demand to inform infrastructure analyses such as citing hydrogen fueling stations and 

selecting between centralized and distributed hydrogen production. 

Research Methodology 

There are two general types of vehicle purchasers: consumers and fleets. Both groups have unique 

characteristics that affect how they choose vehicles to purchase and drive. This study examines how 

hydrogen vehicles could be deployed nationwide based on the consumer market. 

Various factors influence a consumer’s vehicle purchase decision, including purchaser characteristics 

(e.g., income and age) and external factors (e.g., vehicle rebates, interest rates, and tax incentives). To 

match emerging consumer demand for hydrogen vehicles with transitional hydrogen infrastructure, the 

spatial/geographic component of consumer demand must be understood. This analysis projects consumer 

demand for hydrogen vehicles based on geographic distribution and suggests how this demand would 

affect infrastructure requirements. 

The analysis consists of the following steps: 

• Define Demographics: Key attributes affecting consumer acceptance of hydrogen vehicles are 

identified and spatially analyzed using geographic information systems (GIS). 

• Score and Weight Each Attribute: Values are assigned to each attribute, and a total “score” is 

identified representing the likelihood of a consumer purchasing/operating a hydrogen vehicle. 

• Conduct Stakeholder Review and Sensitivity Analyses: Stakeholder review and sensitivity 

analyses ensure that scores assigned for each demographic/characteristic are acceptable. 

• Suggest Vehicle and Fuel Demand Scenarios at Various Penetration Rates: Hydrogen vehicle 

and fuel demands are evaluated for various rates of hydrogen vehicle penetration (e.g., 1%, 5%, 

and 10%). 

Demographics

Key attributes affecting hydrogen vehicle penetration into the consumer market were identified through a 

literature search (from FY 2004 and 2005 work) and interviews with vehicle technology transition 

experts, then reviewed and ranked by a focus group consisting of NREL personnel with expertise in 

advanced technology vehicle deployment (Table 1). These assumptions were also confirmed by various 

market studies and analyses related to hybrid and hydrogen vehicles conducted by researchers at UC 

Davis and Synovate. 

2
Table 1. Attributes Affecting Hydrogen Vehicle Adoption by Consumers 

Attribute Impact Rationale 

Households with Two or 

More Vehicles High Households with multiple vehicles more likely to adopt hydrogen vehicles 

Education Medium Higher education leads to earlier adoption 

Household Income High Higher incomes lead to earlier adoption 

Commute Distance Medium More time spent in a vehicle commuting interests consumers in 

newer and more efficient vehicles 

State Incentives Medium Alternative fuel vehicle incentives could indicate future or 

existing hydrogen incentives 

Clean Cities Coalitions Medium Coalitions pull funding opportunities together and create 

alternative fuel awareness 

Air Quality Medium Low air quality leads to educated consumers and incentives 

Hybrid Vehicle 

Registrations Medium Early adopters of new gasoline vehicle technologies could be early adopters of new hydrogen vehicle technologies 

Zero-Emission Vehicle 

(ZEV) Sales Mandate Medium Hydrogen vehicles qualify for these mandates 

These attributes apply to a strategy in which the general public consumer is the primary focus of early 

hydrogen vehicle deployment, and they apply to the contiguous United States. Alternative or additional 

attributes might be considered for a strategy focusing on an early transition to fleets or for a targeted 

metropolitan area analysis for which more detailed data can be considered. Hydrogen vehicle demand can 

be described as a function of these attributes: 

Equation 1. 

Consumer hydrogen vehicle demand = F (attributes) 

The data to support analysis of these attributes were collected from various sources, including the U.S. 

Census Bureau, the U.S. Department of Energy’s (DOE) Clean Cities Alternative Fuels Data Center 

(AFDC), and the Environmental Protection Agency. 

Attribute Scores and Weights

The original datasets of each attribute have varying spatial presentations—some are at census tract level, 

whereas others are at county or state levels or actual locations. All these datasets were aggregated to a 

nationwide grid with cell size 20 miles by 20 miles using two standard GIS techniques: area-weighting 

and dominant component. Area-weighting is a common form of quantitative aggregation. Data values are 

multiplied by the percentage of the area a component covers and then divided by the total of the area 

percentages. Dominant component is applied to qualitative datasets such as air quality and state 

incentives. It takes the data value for the component covering the largest area of the cell and assigns that 

value to the entire cell. Appendix A shows the attributes in the U.S. 20-mile × 20-mile grid. 

As outlined in Table 1, not all the attributes have an equal impact on hydrogen vehicle demand. To 

incorporate these inequalities into the analysis, preferences were incorporated into the corresponding 

model developed in ArcGIS 9.1 Model Builder. The Model Builder Spatial Analyst extension evaluates 

multiple attributes through classification, ranking, and weighted-overlay techniques to produce the results 

for each demand scenario. The Model Builder is very flexible. Models can be saved and rerun with 

different parameters. In addition, data can be added or replaced, and other modules can be attached. 

To perform the analysis effectively, attribute datasets were first ranked internally to value the data within 

the dataset. The attributes were then weighted in relation to other attributes. 

3
Internal Dataset Ranking—There is no single best data classification method; each has advantages and 

disadvantages depending on the nature of the data and the type of information and analysis desired. In 

general, a classification method should maximize the between-class differences and minimize the withinclass differences. 

The natural breaks classification was chosen for this study. This method identifies break points by 

looking for groupings and patterns inherent in the data. ArcGIS uses a complex statistical formula (Jenk’s 

Optimization) to identify break points by choosing the class breaks that best group similar value and 

maximize the differences between classes. The features are divided into classes with boundaries set where 

there are relatively big jumps in the data values. The major disadvantage is that the concept behind the 

classification may not be easily understood by all map users, and the legend values for the class breaks 

may not be intuitive. The advantage, however, is that it is one of the best ways to classify data that model 

natural human behaviors and patterns. The natural break method best applies to hydrogen vehicle demand 

because hydrogen vehicle demand patterns are not uniform by nature. 

Using the natural break classification method, we created seven classes within each data layer. The 

selection of seven groups was chosen because of the depth of analysis and the refinement of results it 

would provide. Then, we employed a ranking system of 1 to 7 to rate the values within each class used in 

the hydrogen demand model. A class was ranked 1 if its values had a “very low” influence on the chosen 

strategy (e.g., people with the lowest income would generate the lowest hydrogen vehicle demand). A 

class was ranked 7 if its values had a “very high” influence (e.g., people with the highest income would 

generate the highest hydrogen vehicle demand). 

Attribute Descriptions, Rankings, and Weightings—Based on the transportation experts’ valuing of 

the attributes (Table 1), attributes were weighted in relation to each other in ArcGIS in terms of low, 

medium, or high impact on hydrogen vehicle adoption. These are normalized so the weightings of all the 

attributes are equal to 100%. The following section describes each attribute as well as the attribute 

classification, ranking, and weighting for the consumer strategy baseline scenario. The results of this 

scenario are described below. 

• Households with Two or More Vehicles 

o Data origin: 2000 U.S. Census 

o Data representation: number of households that have two or more vehicles 

o Rationale: Initial customers for hydrogen vehicles will be those in households that have at 

least two vehicles because of limited hydrogen range and refueling opportunities. The NREL 

focus group considered this to be the most important factor in predicting hydrogen vehicle 

demand 

4
Table 2. Households with Two or More Vehicles 

Attribute 

Values and 

Classification 

(number of 

households) 

Scoring of 

Classification Weighting 

0 – 8,064 2 

8,065 – 30,239 3 

30,240 – 68,542 4 

68,543 – 118,940 5 

118,941 – 179,418 6 

179,419 – 312,470 7 

Two or More 

Vehicles per 

Household 

312,471 – 516,079 7 

15% (High) 

• Education 

o Data origin: 2000 U.S. Census 

o Data representation: number of people with Bachelor degree or higher 

o Rationale: Initial customers for hydrogen vehicles will be those with higher education levels 

Table 3. Education 

Attribute 

Values and 

Classification 

(number of people) 

Scoring of 

Classification Weighting 

0 – 14,106 2 

14,107 – 51,562 3 

51,563 – 123,778 4 

123,779 – 228,464 5 

228,465 – 415,520 6 

415,521 – 943,876 7 

Education 

943,877 – 1,770,650 7 

10% (Medium) 

• Household Income 

o Data origin: 2000 U.S. Census 

o Data representation: Median household income 

o Rationale: Initial customers for hydrogen vehicles will be those with higher income levels 

Table 4. Household Income 

Attribute 

Values and 

Classification 

(median income, 

$U.S.) 

Scoring of 

Classification Weighting Score 

0 – 15,404 1 

15,405 – 24,747 2 

24,748 – 30,672 3 

30,673 – 36,151 4 

36,152 – 43,108 5 

43,109 – 54,954 6 

Household Income 

54,955 – 86,901 7 

15% (High) 

5
• Commute Distance 

o Data origin: 2000 U.S. Census 

o Data representation: Workers age 16+ who commute 20 or more minutes each way 

o Rationale: More time spent in a vehicle commuting might make these consumers more 

interested in newer and more efficient vehicles 

Table 5. Commuter Distance 

Attribute 

Values and 

Classification 

(number of people) 

Scoring of 

Classification Weighting Score 

0 – 12,528 2 

12,529 – 47,248 3 

47,249 – 109,576 4 

109,577 – 219,919 5 

219,920 – 418,739 6 

418,740 – 908,658 7 

Commute Distance 

908,659 – 1,572,668 7 

10% (Medium) 

• State Incentives

o Data Origin: NREL/DOE Alternative Fuels Data Center (AFDC) Incentives and Laws Web 

site, January 2006 

o Data Representation: number of incentives per state 

o Rationale: States with current incentives promoting advanced transportation goals are likely 

to have such programs in place for hydrogen vehicles 

Table 6. Commuter Distance 

Attribute 

Values and 

Classification 

(number of 

incentives) 

Scoring of 

Classification Weighting Score 

None 1 

1 – 4 5 

5 – 11 6 State Incentives 

12 – 18 7 

10% (Medium) 

• Clean Cities Coalition 

o Data origin: NREL/DOE Clean Cities Web site, January 2006 

o Data representation: existence of Clean Cities Coalition in area 

o Rationale: Having a local Clean Cities champion to assist in identifying funding, 

partnerships, and other positive factors in the area is critical to early adoption of hydrogen 

vehicles 

Table 7. Clean Cities Coalitions 

Attribute 

Values and 

Classification 

(existence of 

coalition) 

Scoring of 

Classification Weighting Score 

Clean Cities No 1 

Coalitions Yes 7 10% (Medium) 

6
• Air Quality 

o Data origin: U.S. Environmental Protection Agency, 2004 

o Data representation: non-attainment status of area (for one or more pollutants) 

o Rationale: Issues with local air pollution make consumers more aware of the impacts of 

vehicles on air pollution and can lead to additional funding or programs for consumers to 

purchase cleaner vehicles 

Table 8. Air Quality 

Attribute 

Values and 

Classification (level of 

non-attainment) 

Scoring of 

Classification Weighting Score 

None 1 

Marginal 5 

Moderate 6 Air Quality 

Severe 7 

10% (Medium) 

• Hybrid Vehicle Registrations 

o Data origin: R.L. Polk, 2005 

o Data representation: number of hybrid vehicles registered 

o Rationale: Early adopters of new gasoline vehicle technologies could also be the early 

adopters of new hydrogen vehicle technologies 

Table 9. Registered Hybrid Vehicles 

Attribute 

Values and 

Classification 

(number of vehicles) 

Scoring of 

Classification Weighting Score 

0 – 11 1 

12 – 67 2 

68 – 168 3 

169 – 371 4 

372 – 685 5 

686 – 1,550 6 

Registered Hybrid 

Vehicles 

1,551 – 2,875 7 

10% (Medium) 

• ZEV Sales Mandate 

o Data origin: NREL/DOE AFDC Incentives and Laws Web site, January 2006 

o Data representation: existence of state ZEV mandate 

o Rationale: This regulation will increase the likelihood that hydrogen vehicles are offered by 

manufacturers in these states 

Table 10. Registered Hybrid Vehicles 

Attribute 

Values and 

Classification 

(existence of 

mandate) 

Scoring of 

Classification Weighting Score 

No 1 

ZEV Sales Mandate Yes 7 10% (Medium) 

7
Hydrogen Vehicle Demand—Consumer Baseline Strategy

The consumer vehicle demand results are determined using the weighted overlay technique in ArcGIS 

Model Builder. It integrates all attributes mentioned above, their classification’s ranking, and weights 

them in relation to each other. These results are illustrated in Figure 1, and indicate that urban areas are 

the primary hydrogen demand centers. They were translated into demands from “low” to “very high” 

with no “very low” demand areas determined for the contiguous United States. The method for 

quantifying hydrogen vehicle and fuel demand is discussed in subsequent sections of this report. 

Figure 1. Hydrogen Vehicle Demand—Consumer Strategy Baseline Scenario 

How demand grows from an urban center varies. For example, in Denver the growth shows a more northsouth spread. In Atlanta and Dallas growth radiates in a roughly uniform pattern outward from the urban 

center. These are important distinctions when identifying geographic citing of infrastructure to meet the 

needs in these areas. Table 11 shows the breakdown of area and population encompassed by each demand 

category. 

8
Table 11: Baseline Hydrogen Vehicle Demand by Area and Population 

Hydrogen Vehicle 

Demand Area (sq. mile) Area (%) Population (age 30–64) Population (%) 

Very high 1,955 0.07% 4,612,816 3.96% 

High 13,356 0.45% 18,040,285 15.47% 

Very good 57,417 1.93% 27,047,587 23.19% 

Good 239,576 8.06% 24,756,867 21.23% 

Fair 893,775 30.08% 24,108,028 20.67% 

Low 1,765,407 59.41% 18,061,607 15.49% 

In the baseline scenario, 10% of U.S. land area and 64% of the targeted population are in areas with a 

good to very high likelihood of hydrogen vehicle demand. This analysis represents only the age groups 

most likely to be hydrogen vehicle drivers (ages 30–64); future analyses will examine the overall 

population. 

Monte Carlo Analysis and Baseline Case Adjustment—Monte Carlo analysis was performed on the 

baseline attributes to determine the likely weightings of attributes based on the probabilities of each 

attribute having a low, medium, or high influence on a consumer to purchase a hydrogen vehicle. Using 

Crystal Ball as a tool for Monte Carlo simulation, triangular distributions were created that varied from 1 

(low) to 3 (high) with the most likely point matching with the baseline scenario weighting assumption. 

The probability distributions used for the Monte Carlo simulation are shown in Appendix B.

Monte Carlo simulation works by generating random numbers that fit within the probability distributions 

for each assumption. These random values generate results that show how each assumption changes, 

relative to each other, for a large sampling of random numbers. Running 10,000 iterations, the simulation 

generated slight modifications to the weighting percentages from the baseline scenario, as shown in Table 

12. The adjusted results are shown in Figure 2. 

Table 12: Adjusted Base Case Attribute Weighting 

Attribute Adjusted Weighting Score 

Households with 2+ vehicles 12% 

Education 10% 

Household income 12% 

Commute distance 11% 

State incentives 11% 

Clean Cities Coalitions 11% 

Air quality 11% 

Hybrid vehicle registrations 11% 

ZEV sales mandate 11% 

9
Figure 2. Hydrogen Vehicle Demand—Consumer Strategy Adjusted Baseline Scenario 

Stakeholder Review and Sensitivity Analyses 

Stakeholder review was performed to validate attributes and their characteristics. Review meetings with 

hydrogen stakeholders—including representatives from automotive original equipment manufacturers and 

fuel companies—have resulted in attribute refinements. For example, the representation of the attribute 

households with two or more vehicles was changed from a percentage of households with two or more 

vehicles to an absolute number of households with two or more vehicles; this change shifts predicted 

demand more toward urban areas. Also, the attribute ZEV sales mandate was created by breaking it out of 

the state incentives attribute; stakeholder feedback indicated that ZEV mandates have a particularly strong 

effect on vehicle manufacturers’ ZEV compliance and sales plans. These and other stakeholder 

suggestions are incorporated into the attribute descriptions above. 

Sensitivity analyses were conducted to explore the sensitivity of hydrogen vehicle demand projections to 

changes in attribute weighting scores. Two scenarios—the demographic emphasis scenario and the policy 

emphasis scenario—were created and are described below. 

Demographic Emphasis Scenario—Under this scenario, attributes related to the consumers themselves 

were given the highest weighting. In this case, “who the consumer is” was very important, whereas their 

environment (e.g., air quality or government policies) was assigned less importance. The weightings are 

shown in Table 13, and the resulting demand is shown in Figure 3. 

10
Table 13. Attribute Weightings—Demographic Emphasis Scenario 

Attribute Weighting 

Households with 2+ vehicles High 

Education High 

Household income High 

Commute distance High 

State incentives Low 

Clean Cities Coalitions Low 

Air quality Low 

Hybrid vehicle registrations Medium 

ZEV sales mandate Low 

Figure 3. Hydrogen Vehicle Demand—Consumer Strategy Demographic Emphasis Scenario 

11
Under this scenario, demand is more concentrated in areas with high population, i.e., in urban centers. 

The spread of demand from these urban centers is less than it is when policies and other external factors 

(air quality and Clean Cities Coalitions) have a higher influence on consumer hydrogen vehicle demand 

Table 14 shows the breakdown of area and population encompassed by each demand category. 

Table 14. Demographic Emphasis Scenario Hydrogen Vehicle Demand by Area and Population 

Hydrogen Vehicle Demand Area (sq. miles) Area (%) 

Population 

(age 30–64) Population (%) 

Very high 356 0.01% 898,742 0.77% 

High 8,895 0.30% 17,330,758 14.86% 

Very good 34,015 1.14% 24,914,511 21.36% 

Good 108,184 3.64% 24,434,580 20.95% 

Fair 719,476 24.21% 28,746,209 24.65% 

Low 2,100,560 70.69% 20,302,390 17.41% 

In the demographic emphasis scenario, 5% of U.S. land area and 58% of the targeted population are 

covered in areas with a good to very high likelihood of hydrogen vehicle demand. 

Policy Emphasis Scenario—Under this scenario, attributes related to or leading to government policy 

were considered most important, whereas the importance of consumer demographic information was 

minimized. The exception was households with two or more vehicles, which was considered to be the 

most important attribute in all scenarios and a virtual prerequisite for consumer hydrogen vehicle demand. 

Attribute weightings are shown in Table 15, and the resulting demand is shown in Figure 4. The results 

show the effect that government policies and incentives, if effective in influencing consumers, can have 

on hydrogen vehicle demand. Table 16 shows the breakdown of area and population encompassed by 

each demand category.

Table 15. Attribute Weightings—Policy Emphasis Scenario 

Attribute Weighting 

Households with 2+ vehicles High 

Education Low 

Household income Low 

Commute distance Low 

State incentives High 

Clean Cities Coalitions High 

Air quality High 

Hybrid vehicle registrations Medium 

ZEV sales mandate High 

12
Figure 4. Hydrogen Vehicle Demand—Consumer Strategy Policy Emphasis Scenario 

Table 16. Policy Emphasis Scenario Hydrogen Vehicle Demand by Area and Population 

Hydrogen Vehicle 

Demand Area (sq. miles) Area (%) 

Population 

(age 30–64) Population (%)

Very high 2,885 0.10% 8,016,523 6.87% 

High 20,439 0.69% 15,846,827 13.59% 

Very good 111,105 3.74% 30,420,350 26.08% 

Good 317,461 10.68% 20,469,898 17.55% 

Fair 751,636 25.29% 20,885,021 17.91% 

Low 1,767,960 59.50% 20,988,571 18.00% 

In the policy emphasis scenario, 15% of U.S. land area and 64% of the targeted population is covered in 

areas with a good to very high likelihood of hydrogen vehicle demand. 

Hydrogen Vehicle and Fuel Demand at Various Penetration Rates

This analysis identifies the top 20 urban areas for likely penetration of hydrogen vehicles in the near-term, 

i.e., the urban areas most likely to have high numbers of early technology adopters for hydrogen vehicles. 

Following are the top areas, ranked in order of projected hydrogen vehicle penetration: 

13
1) New York—Northern NJ—Long Island 11) Houston—Galveston—Brazoria 

2) Los Angeles—Riverside—Orange County 12) Hartford 

3) San Francisco—Oakland—San Jose 13) Minneapolis—St. Paul 

4) Boston—Worcester—Lawrence 14) Atlanta 

5) Philadelphia—Wilmington—Atlantic City 15) Detroit—Ann Arbor—Flint 

6) Chicago—Gary—Kenosha 16) Phoenix—Mesa 

7) Washington—Baltimore 17) Denver—Boulder—Greeley 

8) Sacramento—Yolo 18) Cleveland—Akron 

9) San Diego 19) Providence—Fall River—Warwick 

10) Dallas—Fort Worth 20) Rochester 

To translate the relative demand weightings into actual quantities of hydrogen fuel demand in kilograms, 

the calculated values (very high, high, very good, good, fair, and low) were applied to the penetration of 

vehicles across the country. Using the U.S. Energy Information Administration estimate for passenger 

vehicles in the United States in 2030—281 million vehicles—hydrogen vehicles were distributed 

nationwide based on the relative demand weightings in each area of the country and penetration rates of 

1%, 5%, and 10% using the following methodology. 

 

Equation 2. 

VehiclePenetration USVehicleFleet

Cells CellWeighting

VehiclesPe lowrCell

VeryHigh

Low

×

×

=

∑#

)(

This resulted in the total number of vehicles in a cell categorized as “low”. To get the values for the 

number of vehicles per cell for cells categorized as “fair” through “very high”, the low values were 

multiplied by 2 through 7 respectively. 

An annual consumption of 250 kg per vehicle was used to calculate the annual demand for hydrogen. 

The hydrogen fuel demand for each 20-mile × 20-mile cell was calculated using the above equations and 

is shown in Table 17. These values represent the maximum likely hydrogen fuel demand in each cell 

based on the relative demand from cell to cell. 

Table 17. Annual Hydrogen Fuel Demand 

Based on Relative Consumer Demand for Hydrogen Vehicles 

Individual Cell (20 × 20 mi2) Hydrogen Fuel 

Relative Hydrogen Vehicle Demand (1,000 kg/day) 

Demand Weightings 1% 

Penetration

5% 

Penetration 

10% 

Penetration 

Very high 302 1,508 3,016 

High 251 1,257 2,514 

Very good 201 1,005 2,011 

Good 151 754 1,508 

Fair 101 503 1,005 

Low 50 251 503 

At a 1% penetration, the demand for a cell with a “low” likelihood of hydrogen vehicle demand would 

range from 0 to 135 kg/day. At the same vehicle penetration rate, an area of “very high” demand would 

be up to 827 kg/day. Using these assumptions, all areas, even with high demand, could be served by 

small, forecourt stations in the early transition, where there is only a 1% penetration into the US fleet. 

14
At a 5% penetration, “very high” areas would require up to 4,130 kg/day. At this level, it may still be 

feasible to meet demand with forecourt stations. However, by the time penetration is 10% of the US fleet, 

“very high” demand areas would require over 8250 kg/day. This may best be served through the use of 

central production. To fully understand the tradeoffs between centralized and distributed hydrogen 

production, analysis on a regional or local level would be necessary to select systems that meet the 

specific demand patterns of a state or metropolitan area. This regional approach is discussed in the 

following section. 

Regional Analysis 

The national analysis presents a broad look at the United States, suggesting where hydrogen vehicles will 

first be accepted by consumers and where policies and initiatives may make the most difference in the 

early transition. By identifying these primary areas, transitional hydrogen demand can be examined more 

closely in the most promising regions. 

The national results indicate areas that should be considered primary targets for hydrogen vehicle 

introduction because the attributes of these areas show a high likelihood of hydrogen demand. These areas 

include the following (in rank order): 

21) New York—Northern NJ—Long Island 

22) Los Angeles—Riverside—Orange County 

23) San Francisco—Oakland—San Jose 

24) Boston—Worcester—Lawrence 

25) Philadelphia—Wilmington—Atlantic City 

26) Chicago—Gary—Kenosha 

27) Washington—Baltimore 

28) Sacramento—Yolo 

29) San Diego 

30) Dallas—Fort Worth 

31) Houston—Galveston—Brazoria 

32) Hartford 

33) Minneapolis—St. Paul 

34) Atlanta 

35) Detroit—Ann Arbor—Flint 

36) Phoenix—Mesa 

37) Denver—Boulder—Greeley 

38) Cleveland—Akron 

39) Providence—Fall River—Warwick 

40) Rochester 

To better understand the market for hydrogen in these urban areas, the demand attributes can be examined 

in more detail. The national analysis was performed using a 20-mile × 20-mile grid across the lower 48 

states. More detailed urban analysis can be performed at the U.S. Census Bureau tract level for key 

regions. 

Internal dataset rankings were assigned using natural breaks into five categories. Attributes were weighted 

against each other using the baseline scenario. The results for the Los Angeles Basin Urbanized Area are 

shown in Figure 5. Results from several other areas are shown in Appendix C. The Los Angeles Basin 

results indicate the majority of hydrogen vehicle demand will surround the heart of the city; thus that is 

where infrastructure development efforts should be concentrated during the transition. 

15
Figure 5. Hydrogen Vehicle Demand—Consumer Strategy Baseline Scenario, Los Angeles Basin 

This detailed urban area analysis is also beneficial because additional attributes for which datasets are 

difficult to obtain on a national basis can be incorporated and used specifically to site hydrogen 

infrastructure. Examples of additional attributes include traffic volumes, retail centers, and existing 

conventional refueling stations. These attributes have been considered and infrastructure proposed in a 

related NREL project. The details of that project are being documented and published in parallel with this 

project. 

Conclusion 

This analysis shows that projected consumer demand for hydrogen vehicles and fuel is not uniform. It 

also shows how projected demand can be affected geographically by various demographic and policyrelated attributes. The resulting spatial demand calculations are an asset to other models and analyses that 

depend on demand to evaluate feasibility and costs. Using these analyses, specific infrastructure can be 

proposed and evaluated based on expected demand on a regional or local basis. 

Future Work

Further development of the regional approach to infrastructure selection to meet consumer demand will 

be conducted in FY 2007. This work will examine key metropolitan statistical areas and the regions 

surrounding and connecting them to determine the best placement of infrastructure to meet consumer 

16
needs. Pending FY 2007 funding, consumer refueling habits and market research regarding consumer 

refueling also will be considered and incorporated into this analysis. 

This project addresses emerging near-term hydrogen demand for the general public consumer. However, 

another key strategy in the near-term is the use of fleets as a seed for further hydrogen market 

development. For an infrastructure to serve fleet customers and public consumers, it is important to 

understand where hydrogen demand will develop and where the needs of consumers and fleets overlap. 

Additional funding will be required in FY 2007 for collection of fleet data and analysis of fleet demand 

using the methods described in this FY 2006 analysis. 

17
Appendix A: Attributes in the U.S. 20-mile by 20-mile Grid 

18
19
20
21
22
Appendix B: Monte Carlo Simulation Probability Functions 

Assumption: Air Quality 

Assumption: Clean Cities Coalitions 

Assumption: Commute Distance 

Assumption: Education 

Assumption: Registered Hybrid Vehicles 

Assumption: Household Income 

Assumption: State Incentives 

Assumption: Two or More Vehicles/Household 

Assumption: ZEV Sales Mandate 

23
Appendix C: Sample Regional Hydrogen Vehicle Demand—Consumer 

Strategy Baseline Scenario 

24
25
26
27
28
Appendix D: Bibliography

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Topic Team. Sacramento, CA: California Hydrogen Highway Network, November 2004. 

Baxter-Clemmons, S. “California Hydrogen Highway Network Rollout Strategy” [Presentation]. 

Sacramento, CA: California Environmental Protection Agency, 2005. 

Brown, M.H.; Breckenridge, L. State Alternative Fuel Vehicle Incentives: A Decade and More of 

Lessons Learned. Item #4151. Denver, CO: National Conference of State Legislatures, February 

2001. 

California Environmental Protection Agency. California Hydrogen Blueprint Plan, Volume 1. 

Sacramento, CA: California Environmental Protection Agency, May 2005. 

Code of Federal Regulations, 10 CFR Part 490. Alternative Fuel Transportation Program: Private 

and Local Government Fleet Determination. Washington, DC: U.S. Government Printing Office, 

January 2004. 

CONEG Policy Research Center. Refueling Alternative Fuel Vehicles: Lessons Learned from the 

Marketplace. Washington, DC: Coalition of Northeastern Governors, 1995. 

DeCicco, J.; Bernow, S.; Gordon, D.; Goldstein, D.; Holtzclaw, J.; Ledbetter, M.; Miller, P.; Sachs, 

H. “Transportation on a Greenhouse Planet: A Least-Cost Transition Scenario for the United States.” 

T931. Washington, DC: American Council for an Energy-Efficient Economy, 1993. 

DeCicco, J.M. “Fuel Cell Vehicles: Technology, Market, and Policy Issues.” Future Car Congress; 

June 2002, Hyatt Crystal City, Virginia. 2002-01-1973. Warrendale, PA: Society of Automotive 

Engineers, June 2002. 

DeCicco, J.M. “The ‘Chicken or Egg’ Problem Writ Large: Why a Hydrogen Fuel Cell Focus is 

Premature,” Chapter 15. Sperling, D., Cannon, J., eds. The Hydrogen Energy Transition. St. Louis, 

MO: Elsevier Academic Press, 2004. 

Jackson, M.D.; Kaahaaina, N.; Fable, S. “Lessons Learned from Past Strategies to Reduce Petroleum 

Dependence.” Presentation to the California Air Resources Board—California Energy Commission 

Staff Workshop on California Strategy on Petroleum Dependence, Sacramento, CA, September 2001. 

Leiby, P.; Rubin, J. “Transition Modeling of AFVs and Hybrids: Lessons Learned.” Presentation at 

the Asilomar Conference Center, Pacific Grove, CA, July 2005. 

Leiby, P.; Rubin, J. “Understanding the Transition to New Fuels and Vehicles: Lessons Learned 

from Analysis and Experience of Alternative Fuel and Hybrid Vehicles,” Chapter 14. Sperling, D., 

Cannon, J., eds. The Hydrogen Energy Transition. St. Louis, MO: Elsevier Academic Press, 2004. 

McCormick, G.L.; Russell, R.B. Alternative Bus Fuels: What Have We Learned. Camp Hill, PA: 

Gannett Fleming. 

McNutt, B.; Rodgers, D. “Lessons Learned from 15 Years of Alternative Fuels Experience, 1988 to 

2003,” Chapter 12. Sperling, D., Cannon, J., eds. The Hydrogen Energy Transition. St. Louis, MO: 

Elsevier Academic Press, 2004. 

29
Miller, Scott; “Consumer Requirements for a Hydrogen Fueling Infrastructure: Findings from a Six 

Month In-Market Simulation”, Presentation to EVS 20, December 2003. 

Nesbitt, K.; Sperling, D. Myths Regarding Alternative Fuel Vehicle Demand by Light-Duty Vehicle 

Fleets. UCD-ITS-REP-98-09. Davis, CA: University of California, Davis Institute of Transportation 

Studies, July 1998. 

Parish, R. “Implementing Alternative Fuels in Transportation Vehicles” [Abstract]. Golden, CO: 

National Renewable Energy Laboratory, 2005. 

Patterson, P.; Alson, J.; Lance, L.; Brown, K.; Hawkins, D.; Ditlow, C.; Dana, G. “Lessons from 30 

Years of Automotive Energy and Air Quality Policy: An Interactive Round Table.” Davis, CA: 

University of California, Davis Institute of Transportation Studies, 1999. 

Research Reports International. The Market for Alternative Fuel Vehicles. Evergreen, CO: Research 

Reports International, 2004. 

Robertson, B.I.; Beard, L.K. “Lessons Learned in the Deployment of Alternative Fueled Vehicles,” 

Chapter 13. Sperling, D., Cannon, J., eds. The Hydrogen Energy Transition. St. Louis, MO: Elsevier 

Academic Press, 2004. 

Santini, D.J.; Vyas, A.D. “How is Technology Adopted? A Discussion of Hybrid-Electric and Diesel 

Technology Consumer Preferences.” Argonne, IL: Argonne National Laboratory, 2004. 

Schulte, I.; Hart, D.; van der Vorst, R. “Issues Affecting the Acceptance of Hydrogen Fuel.”

International Journal of Hydrogen Energy; Vol. 29, 2003; p. 677. 

Kurani, Ken; Heffner, Reid; Turrentine, Tom, “Consumers and Hybrids”, Presentation to SAE 

Hybrid Vehicle Technology Forum, February 2006. 

U.S. Department of Energy. A National Vision of America's Transition to a Hydrogen Economy - to 

2030 and Beyond. Washington, DC: U.S. Department of Energy, February 2002. 

U.S. Department of Energy. Clean Cities 2004 Roadmap. Washington, DC: U.S. Department of 

Energy, 2004. 

Wells, J. “Alternative Motor Fuels Impact on the Transportation Sector.” Testimony before 

Committee on Finance, U.S. Senate, July 10, 2001. 

Wells, J. “Research and Development, Lessons Learned from Previous Research Could Benefit 

FreedomCar Initiative.” Testimony before the U.S. House of Representatives, June 6, 2002. 

Wells, J. Energy Policy Act of 1992: Limited Progress in Acquiring Alternative Fuel Vehicles and 

Reaching Fuel Goals. GAO/RCED-00-59. Washington, DC: U.S. General Accounting Office, 

February 2000. 

Zhao, J.; Melaina, M.W. “Transition to Hydrogen-Based Transportation in China: Lessons Learned 

from Alternative Fuel Vehicle Programs in the United States and China.” Energy Policy; Vol. 34, 

2006; p. 1299. 

30
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Geographically Based Hydrogen Consumer Demand and 

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In FY 2004 and 2005, NREL developed a proposed minimal infrastructure to support nationwide deployment of 

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