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Conference Paper 

NREL/CP-620-38138 

May 2005 

Modeling the Market Potential 

of Hydrogen from Wind and 

Competing Sources 

Preprint 

W. Short, N. Blair, and D. Heimiller 

To be presented at WINDPOWER 2005 

Denver, Colorado 

May 15–18, 2005 
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1

Modeling the Market Potential of Hydrogen from Wind and 

Competing Sources 

WINDPOWER 2005 CONFERENCE 

POSTER SESSION 

Walter Short, Nate Blair, Donna Heimiller 

National Renewable Energy Laboratory 

1617 Cole Boulevard 

Golden, Colorado 80401-3393 

walter_short@nrel.gov

1. ABSTRACT 

The Wind Deployment Systems (WinDS) model is a multiregional, multitime-period, 

Geographic Information System (GIS), and linear programming model, designed to address the 

principal market issues related to the penetration of wind energy into the electric sector over the 

next 50 years. These issues include transmission cost/access and the intermittency of wind 

power. WinDS examines these issues via a highly discrete regional structure, with explicit 

accounting for the transient variability in wind output, and consideration of ancillary services 

requirements and costs. 

The modified WinDS model, HyDS (Hydrogen Deployment Systems model), is expanded to 

include the production of hydrogen from three competing technologies—wind, steam methane 

reforming (SMR), and distributed electrolysis powered by electricity from the grid—along with 

storage and transportation of hydrogen. Electrolyzers at the wind site can be powered either by 

wind power or by power from the electric grid. Hydrogen from wind or distributed electrolyzers 

can be used as either transportation fuel or as input energy to a fuel cell. The fuel cell is assumed 

to generate power during peak electric-load periods. In HyDS, a market for hydrogen as a 

transportation fuel exists in each city. The market clearing price is set exogenously. The three 

competing sources supply hydrogen up to the total transportation demand for fuel, but only if 

they can do so at the market clearing price or less. This paper presents initial results from this 

new model. 

2. BACKGROUND and MODEL OVERVIEW 

Developed from the WinDS model, HyDS is a computer model of U.S. market expansion of 

hydrogen production from wind and other sources over the next 50 years. The WinDS model was 

developed in 20031

 to model the expansion of generation and transmission capacity in the U.S. 

 

1

 Short, Walter; et al., May 2003, “Modeling the Long-Term Market Penetration of Wind in the United States,” 

WindPower 2003 Proceedings, Austin, Texas 
2

electric sector spanning the next 50 years. It minimizes system-wide costs of meeting loads, 

reserve requirements, and emission constraints by building and operating new generators and 

transmission in 26 two-year periods from 2000 to 2050. While it includes all major types of 

conventional generators, the WinDS model focuses on addressing the market issues of greatest 

significance to wind—specifically issues of electricity transmission and intermittency. WinDS 

includes class 3-7 wind for both onshore and offshore wind resources. More details on the 

WinDS model can be found in the companion paper prepared for the AWEA 2005 Conference, 

“Reduced Form of Detailed Modeling of Wind Transmission and Intermittency for Use in Other 

Models.” 

HyDS incorporates all the wind details of the WinDS model, but expands the focus to include 

hydrogen production and transportation. As shown in Figure 1, it includes not only the 

production of hydrogen from wind electrolysis, but also the transport of that hydrogen to load 

centers—and competition at those load centers with hydrogen from distributed electrolysis and 

steam methane reforming. The model also provides for fuel cells at the wind sites and at 

distributed electrolyzers to firm-up the wind power and to provide general energy storage for the 

electric grid. 

Figure 1. Hydrogen and Electricity Production and Transport from Wind 

HyDS is designed to address the issues of greatest importance to the production of hydrogen 

from wind. These include the seasonal and diurnal timing of hydrogen production from wind, the 

cost of that production, use of the hydrogen as a form of storage within the electric grid, and the 

transport of the hydrogen to load centers for use as a transportation fuel. 

Similar to WinDS, HyDS addresses the critical issues of hydrogen transport by using a much 

higher level of geographic disaggregation than other models. As Figure 2 represents, HyDS 

includes 358 different regions in the United States. Much of the data inputs to HyDS are tied to 

these regions and derived from a detailed GIS model/database of fuel demands, the wind 

H2

Electricity 
3

resource, transmission grid, and existing plant data. This relatively fine regional disaggregation 

in HyDS allows the model to calculate transportation distances for hydrogen, as well as 

transmission distances for electric power. 

Figure 2. Regions within HyDS 

Linear programs, such as HyDS, work by minimizing an objective function. The HyDS objective 

function is a minimization of all the costs associated with: 

• Producing hydrogen to meet transportation fuel demands 

• Transporting that hydrogen to load centers as needed 

• Electric generation and transmission capacity installed in each period 

• Operating costs for the electric system—fixed and variable operation and maintenance 

(O&M), fuel costs, and ancillary services. 

By minimizing these costs while meeting the system constraints (discussed below), the linear 

program determines which types of new hydrogen and electric capacity are the most economical 

to add in each period and for each region in the country. It also determines when to operate that 

capacity. 

The cost minimization that occurs within HyDS is subject to 73 types of constraints, which result 

in thousands of equations in the model (due primarily to the large number of regions). These 

constraints fall into several main categories, including: 
4

• Hydrogen demand limits: Preclude the hydrogen production from exceeding the demand 

for transportation fuels in any individual region. HyDS will produce hydrogen for each 

region only if the cost of production is less than the exogenously specified market price. 

Transportation fuel demand not met by hydrogen is assumed to be met by other fuels. 

• Hydrogen production limits: The hydrogen produced in a region in a time period must be 

less than the production capacity available. In addition, for wind, it must be less than the 

wind generation available from the wind capacity. 

• Fuel cell generation transmission limits: Prevent fuel cell and direct wind turbine output 

at a wind site from exceeding the transmission capacity available to the site. 

• Wind resources: The total amount of wind installed in each region must be less than the 

wind resource potentially available. 

• Wind access to existing transmission lines: The amount of new wind-power capacity that 

can be transmitted on existing lines is limited by the transmission capacity available on 

nearby lines. The GIS portion of HyDS determines which wind resources would be most 

cost-effective in using existing transmission capacity. 

• Load constraints: The primary load constraint is that the electric load in each power 

control area (there are 136 power control areas in HyDS) must be met in each time-slice 

(of which there are 16) throughout a year. The load and its rate of growth in each North 

American Electric Reliability Council (NERC) region are derived from the Reference 

Case of U.S. Energy Information Agency’s (EIA) Annual Energy Outlook. 

• Reserve margin constraint: There are two types of reserves constraints—planning 

reserve margin and operating reserve. For the reserve margin constraint, HyDS updates 

its estimate (for each period) of the marginal capacity value of the next wind farm built in 

each region, using a detailed statistical approach. The capacity value is set equal to the 

amount of load that could be added—along with the wind—without changing the risk of 

a shortage in generation capacity at peak load times. The approach accounts for the 

dispersion of the wind sites contributing to the load and the correlation in the output of 

those sites. 

• Operating reserve constraint: The operating reserve requirement induced by each new 

wind farm is also modified each period for each region. The additional operating reserve 

requirements due to wind are not proportional to the amount of wind, but rather to the 

variance in the sum of the normal operating reserve and the wind generation. This means 

that the operating reserves induced by wind are generally low per unit of wind capacity 

initially, but can grow quickly if the wind capacity becomes a significant part of system 

capacity—especially if the output of the new wind capacity is highly correlated with that 

of existing capacity. 

• Wind Surplus: HyDS also accounts for surplus wind-generated electricity that is lost at 

the interconnection region level. When demand is low and the wind is blowing, there can 

be instances where the wind generation cannot all be used. HyDS uses the variance of the 

sum of all wind generation—together with a load duration curve and the forced outage 

rates of conventional technologies—to stochastically compute the expected amount of 

wind that cannot be used. 

• Emissions constraints: At the national level, HyDS caps the air emissions from fossilfueled generators for sulfur dioxide, nitrogen oxides, mercury, and carbon. The annual 

national emission caps and the emissions per MWH are input by the user. 
5

3. BASE CASE COST ASSUMPTIONS 

In this analysis, the Base Case is a business-as-usual case that relies heavily on the Reference 

Case scenario of the U.S. Energy Information Agency Annual Energy Outlook for 20042

 to 

determine inputs that fall outside the scope of HyDS. These include electricity demand, fossil 

fuel prices, existing federal energy policies, and the cost and performance of non-wind electricgenerating technologies. 

Table 1 shows the hydrogen technology cost inputs over time, which are derived from the DOE 

Program goals for hydrogen as published in the spring of 2004. A key assumption in this base 

case analysis is that hydrogen will be produced by these technologies for use as a transportation 

fuel, only if it is competitive with other transportation fuels. Based on the DOE Program goals, 

the breakeven cost of hydrogen with these alternative fuels is assumed to be $2/kg in 2003 

dollars in the base case. 

Table 1. Base Case Hydrogen Technology Cost Inputs

Onshore wind-power cost and performance data are derived from projections made in 2002 by 

Princeton Energy Resources International3

 (PERI) for the DOE Wind Program. In addition to the 

R&D driven improvements over time shown in Table 2, HyDS also allows for “learning” 

improvements in both the hydrogen and wind technologies’ costs and efficiencies. 

 

2

 United States Department of Energy (DOE), Energy Information Administration (EIA), “Annual Energy Outlook 

2004,” January 2004, DOE/EIA-0383(2004) 3

 Personal communication with Joe Cohen of Princeton Energy Resources International (PERI), April 29, 2003 

Technology Capital Cost Efficiency %

Electrolyzer $150/kWe 80 

Fuel Cell $400/kWe 50 

Steam Methane Reformer $4/kg per year 70 
6

Table 2. Base Case R&D-Driven Onshore Wind Costs and Performance 

Capital Cost 

$/kW Class 2005 2010 > 2020 

3 929 923 916 

4 916 914 899 

5 898 898 881 

6 880 880 864 

7 880 880 864 

Capacity 

Factor 

3 0.25 0.275 0.30 

4 0.29 0.35 0.36 

5 0.35 0.40 0.41 

6 0.42 0.45 0.47 

7 0.44 0.47 0.49 

There are two possible cost synergisms between the wind turbines and the co-located 

electrolyzers and hydrogen storage. First, the electronic controls for the wind turbines and 

electrolyzers can be combined into one. This was assumed to reduce the wind-sited electrolyzer 

capital cost by 12.5%. Second, the wind turbine towers could provide a storage vessel for the onsite storage of hydrogen. This was assumed to reduce the capital cost of on-site storage by 40% 

Fuel prices are probably as important as the above technology costs in determining the potential 

for hydrogen from wind. Figure 3 shows the average national base case fuel prices (prices vary 

by NERC region within HyDS). Through 2025, the price of natural gas and coal are derived from 

the DOE’s Annual Energy Outlook 2004, with post-2025 prices extrapolated at the same rate as 

between 2005 and 2025. These prices are obviously subject to considerable uncertainty, 

especially given that through 2025 the natural gas prices are lower than today’s prices. 

0

1

2

3

4

5

6

7

8

9

2000 2010 2020 2030 2040 2050

Price ($/MBtu)

natural gas

coal

Figure 3. Base Case Fuel Prices 
7

4. BASE CASE RESULTS 

Wind: With these Base Case inputs, HyDS projects that wind will provide about 210 GW of 

electricity capacity to the grid in 2050, far larger than today’s 6 GW (see top slice of graph in 

Figure 4). This growth is largely attributable to improvements in the cost and performance of 

wind turbines—however, there are other drivers. By about 2014, the increase in natural gas 

prices (as forecast by the AEO2004) stalls the recent growth in new installations of combinedcycle, natural gas-fired power plants (third slice from the bottom in Figure 4). At about the same 

time, installations of advanced pulverized coal plants with stringent emission controls begin to 

penetrate the new-build market (second slice from the top in Figure 4). To accommodate these 

new coal plants under the Phase 2 provisions of Title IV XXX of the Clean Air Act, existing coal 

plants switch to low-sulfur coal and install scrubbers (third and fourth slice from the top in 

Figure 4). 

0

500

1000

1500

2000

2500

2000 2010 2020 2030 2040 2050

National Capacity (GW)

Total Wind

Coal-new

Coal-no

scrub

Coalw/scrub

Gas-CT

nuclear

Gas-CC

Hydro

oil-gassteam

Figure 4. National Capacity Projections for the HyDS Base Case 

Realistically, the role of future coal plants in the base case may be somewhat mitigated by the 

fact that the increase in demand for coal will put upward pressure on coal prices—HyDS 

currently does not capture such feedback. Because it is not a general equilibrium model, but only 

a model of capacity expansion in the electric sector, HyDS does not include price elasticity for 

coal or other fossil fuels. In addition, our base case does not include any penalty for carbon 

emissions. 

Hydrogen: Additional wind capacity is constructed in this base case to generate a limited 

amount of hydrogen at the wind sites. As the map in Figure 5 shows, hydrogen production in 

2050 from this base case is greatest where there is a reasonable wind resource near a significant 

population center. For example, southeastern California near the Los Angeles region, western 
8

Iowa near Chicago, and southwestern New York state near New York City all have areas with 

annual hydrogen production greater than 195 kilotons in 2050. Additionally, wind hydrogen 

production is seen in areas with significant wind resources such as the north-central United 

States and the Midwest. 

Figure 5. Annual H2 Production from Wind in 2050 (kilo tons) 

From the base case, we can also examine the viability of various hydrogen production pathways. 

For example, Figure 6 shows that, by 2050, approximately 28 GWe of distributed electrolyzers 

are deployed to produce hydrogen. To help meet the additional electricity demand of these 

distributed electrolyzers, approximately 25 GWe of additional wind energy are deployed by 

2050, as shown in Figure 6. Much of the electricity from these new wind turbines is transported 

to the distributed electrolyzers, as the transport of electricity across regions is generally more 

economical than the transport of hydrogen. 

While the distributed electrolyzers provide more hydrogen, hydrogen is also produced at wind 

sites for use as a transportation fuel and to shore up wind power in 2030 and beyond. Also shown 

in Figure 6, co-located wind turbine/electrolyzer systems are deployed with approximately 

9 GWe of capacity in 2050. In addition, fuel cells are deployed at wind sites with approximately 

8 GWe of capacity by 2050. 
9

0

5

10

15

20

25

30

2030 2040 2050

GWe

Wind electrolyzers

Wind fuel cells

Dist. Electrolyzers

Dist. fuel cells

Additional Wind 

Figure 6. Base Case Hydrogen Technology Capacity 

Figure 7 shows the amount of hydrogen produced by each technology, and reveals that a 

majority of the electricity fed to the wind-sited electrolyzers is drawn from the grid when the 

wind is not blowing. This grid power allows the amortization of the wind-sited electrolyzer costs 

over a substantially larger output. Figure 7 also shows that, in 2050, the bulk (1.3 Bkg) of the 

hydrogen produced by the wind-sited electrolyzers (1.4 Bkg) is used as a transportation fuel, 

with the remainder (0.1 Bkg) stored on-site for generation of power through a fuel cell at times 

of peak electric load. Because the base case produces hydrogen from wind only when it can be 

produced for less than or equal to $2/kg, a higher market price for hydrogen would significantly 

increase the amount of fuel produced. Most of this wind-sited production of hydrogen 

transportation fuels occurs in regions where the wind resource is close to towns and cities, and 

therefore requires little or no transport by pipeline. Less than 2% of the hydrogen transportation 

fuel from wind is transported between regions within the model. 

In the base case, the distributed electrolyzers are not accompanied by fuel cells. Although 

distributed fuel cells would have capacity value equal to their nameplate capacity, they do not 

allow the reduction of transmission-line size, as do their counterparts located at wind sites. 
10

Figure 7. Base Case Hydrogen Fuel Production 

Figure 8 shows that the 8 GW (see Figure 6) of fuel cells installed at the wind sites by 2050 are 

located in remote regions with substantial wind resources. These fuel cells provide two 

advantages. First, they allow a reduction in the size of any new transmission built, because they 

allow the wind generation at times of peak wind speeds to be stored as hydrogen and used to 

produce power at times of peak electric load. Second, they are sited in regions where significant 

wind generation capacity has already been installed. Without the storage provided by 

electrolysis/fuel cells, any additional wind capacity installed at these sites would have negligible 

capacity value, because the output from the new turbines would be completely correlated with 

the output of existing turbines in the region. Thus, fuel cells at these sites provide capacity value 

essentially equal to their nameplate capacity. Figure 7 shows that these fuel cells require very 

little of the hydrogen produced by the wind-sited electrolyzers, because they operate only during 

peak load periods. 
11

Figure 8. 2050 Wind-Sited Fuel Cells (MW) in the Base Case 

Although Figure 6 shows considerable potential exists for electrolysis capacity, based on the fuel 

prices shown in Figure 3, the most economical method for producing hydrogen in the early years 

is steam methane reforming (SMR) of natural gas. In Figure 9, SMR production of hydrogen 

dominates electrolysis by several orders of magnitude in the first quarter century. In those early 

years, the HyDS results are constrained only by price increases due to rapid growth in SMR 

plants and the size of the transportation fuel market itself. In the real world, the production of 

such large amounts of hydrogen from natural gas would probably strain natural gas supplies and 

increase natural gas prices; in HyDS, such feedbacks on fuel prices are not modeled. In HyDS, 

SMR hydrogen production starts to decline by 2040, when the price of natural gas is too high to 

warrant the construction of new SMR plants to replace those built in the first quarter of the 

century. 
12

Figure 9. Hydrogen Production from Steam Methane Reforming and Electrolysis 

CONCLUSIONS 

• If the cost and performance of wind turbines continue to improve, wind energy will 

contribute a substantial share of future U.S. electric generation. 

• Wind energy will also contribute to the production of hydrogen through electrolyzers 

located primarily at load centers, but also at wind sites. 

• Hydrogen production at wind sites will probably be largely limited to wind sites that are 

coincident with hydrogen loads, minimizing hydrogen transport costs. 

• Remote, well-developed wind sites with some remaining high-quality undeveloped wind 

resources may use electrolyzers and fuel cells to shift wind electricity production to peak 

periods and minimize the costs of transmission lines. 

• The most economical source of hydrogen for the near term is steam methane reforming of 

natural gas. 
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4. TITLE AND SUBTITLE

Modeling the Market Potential of Hydrogen from Wind and 

Competing Sources: Preprint 

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W. Short, N. Blair, and D. Heimiller 

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14. ABSTRACT (Maximum 200 Words)

Developed from the Wind Deployment Systems (WinDS) model, the Hydrogen Deployment Systems (HyDS) model is 

a computer model of U.S. market expansion of hydrogen production from wind and other sources over the next 50 

years. The WinDS model was developed in 2003 to model the expansion of generation and transmission capacity in 

the U.S. electric sector spanning the next 50 years. It minimizes system-wide costs of meeting loads, reserve 

requirements, and emission constraints by building and operating new generators and transmission in 26 two-year 

periods from 2000 to 2050. While it includes all major types of conventional generators, the WinDS model focuses on 

addressing the market issues of greatest significance to wind—specifically issues of electricity transmission and 

intermittency. 

15. SUBJECT TERMS

Wind Deployment Systems Model; WinDS; Hydrogen Deployment Systems Model; HyDS; GIS; wind; analysis 

models; wind power; production tax credit; PTC; renewable portfolio standard; RPS; wind resources; wind classes; 

wind markets; load constraints; offshore wind; PERI; Walter Short; Nate Blair; Donna Heimiller 

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