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 

Production of Hydrogen at the 

Forecourt Using Off-Peak 

Electricity 

June 2005 

J.I. Levene

Prepared for the U.S. Department of Energy Hydrogen, 

Fuel Cells & Infrastructure Technologies Program in 

fulfillment of the FY 2005 NREL Milestone 3.2.1.5 

Final Report on the Design and Cost of Electrolysis 

Scenario Options 

Milestone Report 

NREL/MP-560-38354 

February 2007 

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
Production of Hydrogen at the 

Forecourt Using Off-Peak 

Electricity 

June 2005 

J.I. Levene

Prepared under Task No. H278.2100 

Milestone Report 

NREL/MP-560-38354 

February 2007 

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

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

2. Overview .................................................................................................................... 3

3. Introduction............................................................................................................... 3

4. Assumptions............................................................................................................... 4

5. System design ............................................................................................................ 7

6. Modeling system........................................................................................................ 8

7. Analysis Stages........................................................................................................ 14

a. Stage 1 - Effect of Electricity Price Distribution on System Configuration...... 14

b. Stage 2 - Simple Electricity Pricing: When Does Off Peak Make Sense?......... 17

c. Stage 3 - Detailed 24 Hour Electricity Pricing Cases......................................... 18

d. Stage 4 - Real Time Cases.................................................................................... 23

8. Model Capabilities.................................................................................................. 29

a. Current capabilities.............................................................................................. 29

b. Future Enhancements......................................................................................... 35

9. Conclusions.............................................................................................................. 35

10. Appendix A - Bibliography .................................................................................... 38

Table of Figures

Figure 1: Assumed daily hydrogen demand distribution............................................. 4

Figure 2: Hope Model Flowchart ................................................................................. 12

Figure 3: Effect of Electricity Price Distribution on System Design, $0.01-

$0.24/kWh over 24-hour period..................................................................................... 15

Figure 4: A simple example showing when using off-peak electricity lowers the 

hydrogen price................................................................................................................. 18

Figure 5: Assumptions, Legend and Cost Contribution Charts for Electricity Price 

Range 1 Case ................................................................................................................... 21

Figure 6: Assumptions, Legend and Cost Contribution Charts for Electricity Price 

Range 2 Case ................................................................................................................... 22

iii
Figure 7: Hourly real time electricity prices for ISO-NE Node 4616........................ 24

Figure 8: Hourly real time electricity prices for ISO-NE Node 4616........................ 24

Figure 9: System variables and hydrogen pricing results for ISO-NE Node 4616 .. 26

Figure 10: Annual storage requirements for ISO-NE Node 4616 during Run 14 ... 27

Figure 11: System variables and hydrogen pricing results for ISO-NE Node 4010 28

Figure 12: Average electricity price at various capacity factors versus hydrogen 

price for Scenario 1......................................................................................................... 31

Figure 13: Average electricity price at various capacity factors versus hydrogen 

price for Scenario 2......................................................................................................... 32

Figure 14: Average electricity price at various capacity factors versus hydrogen 

price for Scenario 3......................................................................................................... 33

Figure 15: Average electricity price at various capacity factors versus hydrogen 

price for Scenario 4......................................................................................................... 34

Table of Tables

Table 1: Analysis Scenarios............................................................................................. 5

Table 2: H2A Assumptions ............................................................................................. 6

Table 3: Parameters passed from the hydrogen/electricity optimization side of 

HOPE to the H2A model. ............................................................................................... 10

Table 4: 3 Simple Electricity Pricing Structures ........................................................ 17

Table 5: 24 Hour Electricity Price Ranges.................................................................. 19

Table 6: System Assumptions for Initial Analysis...................................................... 30

iv
1. Executive Summary 

One of the driving costs of hydrogen production via electrolysis is the price of 

electricity. Often, when analyzing electrolysis, one of the ways researchers and 

analysts suggest lowering the price of the hydrogen produced is to use off-peak 

electricity. However, using off-peak electricity does not mean that the price of 

electricity can be lowered, and the system remains the same. When using offpeak electricity, the electrolysis system must change; for example, additional 

electrolyzer capacity is needed and additional storage is needed. The Hydrogen 

from Off-Peak Electricity (HOPE) model was developed to understand the 

system changes necessary, and resulting cost differences when using off-peak 

electricity. The HOPE model determines the most economic hydrogen 

production system configuration for a given electricity pricing structure and 

hydrogen demand. With this model, electricity-pricing data and hydrogen 

demand data can be quickly examined for the least cost configuration for 

hydrogen production via electrolysis. The model can use either 24-hour 

electricity pricing structures, which accounts for daily variations, or annual 8760-

hour annual data sets, which account for seasonal and daily variations. 

The system modeled in this analysis is a forecourt electrolysis system designed 

to produce 1500 kg/day of hydrogen production with varying hourly demand. The 

system components include an electrolyzer system, a compressor, storage 

tanks, and a dispensing unit. 

In order to determine the optimal system configuration for hydrogen production 

using off-peak electricity, the HOPE model combines a hydrogen/electric 

optimization and the Department of Energy’s (DOE) Hydrogen Analysis (H2A) 

model as the back end cash flow calculator. The hydrogen/electricity 

optimization uses hourly electricity pricing and demand data over the course of a 

single year, and optimizes the system by minimizing the number of electrolyzers 

and the number of storage tanks while ensuring the fuel demand is met every 

hour out of the day, either through hydrogen production or stored hydrogen. 

Four analysis stages were used to understand the effects of using off-peak 

electricity in a forecourt electrolysis system. In the first stage an analysis was run 

using electricity prices ranging from $0.01/kWh to $0.24/kWh varied hourly. 

Three cases were run: the electricity price was varied from high to low, low to 

high, and randomly. Some interesting conclusions can be drawn from this 

analysis stage. The number of hours electricity is available during the day 

determines the size of the electrolyzer and the size of the compressor, 

regardless of when the electricity is available. The differences in the three cases 

come about in the storage requirement. This is because storage requirements 

change either because hydrogen is produced, and there is no demand, or when 

there is a hydrogen demand, but no hydrogen is being produced. 

1
In the second analysis stage, three simple electricity pricing cases were run to 

determine if using off-peak electricity can result in lower hydrogen prices. The 

electricity price range for the cases was set to $0.04/kWh during 21 hours out of 

the day, and during the other three hours of the day, the price was $0.08/kWh, 

$0.09/kWh or $0.10/kWh. Off-peak electricity only resulted in lower hydrogen 

price when the peak was $0.10/kWh for the 21-hour/3-hour split. This analysis 

stage shows the importance of the size of the peak when using off-peak 

electricity. The heuristic used in this analysis, is that that high peak electricity 

prices are necessary to produce hydrogen at a lower price using off-peak 

electricity. 

In stage 3 of the analysis, two cases were run using two different electricity 

pricing structures where the electricity prices varied from $0.01/kWh - $0.20/kWh. 

Both electricity price ranges were run for four different sets of capital cost and 

efficiency scenarios to develop and understanding of how technology 

improvements can change the price of hydrogen and the system optimization. 

The results of this analysis stage show that as the system costs decrease and 

efficiencies improve, the resulting hydrogen price using off-peak electricity is 

lower. 

In stage 4 of the analysis, the model was used to test actual 8760-hour real time 

pricing data that were obtained from ISO New England. The goal of this stage 

was to determine if using off-peak electricity results in a lower hydrogen price 

when electricity prices vary daily and annually. This analysis stage shows that if 

utilities disrupt power to electrolysis units during peak electricity demands for a 

few hours out of the year the price of hydrogen is not adversely affected. 

However, it appears as though using off-peak electricity for more than a few 

hours of peak shaving leads to a higher hydrogen price. This is partially due to 

the storage assumptions made with this version of the HOPE model, which work 

well for 24-hour electricity price ranges, but need to be improved for 8760 price 

ranges. Modifications should be made to the model to better account for the 

storage needs and resulting hydrogen prices during seasonal peak electricity 

pricing periods. 

The HOPE model provides the capability to determine the best off-peak pricing 

structure for the production of hydrogen using both 24 hour and 8760 hour data; 

it determines how hydrogen production system configurations change with 

different electricity price distributions; it determines if off-peak electricity can lead 

to a lower hydrogen price; it evaluates how system improvements change the 

hydrogen production system and the optimum off peak pricing structure; and it 

can provide data to help calculate how best to run an electrolysis hydrogen 

production system for a given electricity pricing structure and demand curve. In 

short, this model can help answer the question for any site: does using off-peak 

electricity for electrolysis lead to lower hydrogen prices? Also, the HOPE model 

can help clarify under what conditions different locations can meet the DOE 

hydrogen cost targets of $2 - $3/kg of hydrogen using forecourt electrolysis. 

2
2. Overview 

The purpose of this analysis is to examine the production of hydrogen via 

electrolysis at the forecourt station using off-peak electricity. The goal of the 

analysis is to understand if hydrogen can be made more economically using offpeak electricity than by using electricity 24 hours a day. As a result, the 

Hydrogen from Off-Peak Electricity (HOPE) model was developed to find the 

system configuration that leads to the most economic production of hydrogen for 

a given electricity pricing structure and hydrogen demand. 

The model can use either 24-hour electricity pricing structures, which account for 

daily variations, or annual 8760 electricity pricing structures, which account for 

seasonal and daily variations. The 24-hour pricing structure allows for 

hypothetical pricing structures to be tested in order to better understand when, in 

general, using off-peak electricity results in a lower hydrogen price. The 8760 

pricing structure allows for actual hourly pricing data from utilities to be used to 

determine the best electricity usage for any location. 

The model was used to run several analysis cases to determine the optimum 

hydrogen system configuration for several 24-hour electricity pricing structures. 

These helped to develop an understanding of when, in general, off peak pricing 

results in a lower hydrogen price. 

The model was then used to test actual 8760 real time pricing data that were 

downloaded from ISO New England. These data provide pricing information 

from 950 different locations including hubs, hub nodes, load zones and network 

nodes. These data were then entered into a database where the data could be 

summarized into usable datasets. The 8760 data were extracted from the 

database for the two network nodes with the highest and lowest electricity price 

averages. These data were then entered into the model, and the optimum 

electricity usage and resulting hydrogen selling price was determined for each 

node. 

3. Introduction 

When studying hydrogen production via electrolysis, electricity is a driving cost in 

the price of hydrogen. One of the ways researchers and analysts often suggest 

lowering the cost of the hydrogen produced from electrolysis is by using off-peak 

electricity. However, when using off-peak electricity, the price of electricity 

cannot just be lowered, resulting in a lower cost of hydrogen. Using off-peak 

electricity means using electricity less hours out of the day. This means the 

electrolyzer would have to be upsized to produce the same amount of hydrogen 

over the course of a day or year, but its capacity factor would be lower. In 

addition, in a forecourt situation, more storage would be needed to meet the 

demands of the station if hydrogen was being produced when there was no 

demand, or if there was a demand, but no hydrogen was being produced. 

3
The HOPE model was built to determine if off-peak electricity could be used to 

produce lower cost hydrogen via electrolysis. It takes into account hydrogen 

demand curves, electricity pricing throughout an entire year, and hydrogen 

storage when using off-peak electricity and calculates a hydrogen price. That 

price can then be compared to a system using electricity 24 hours a day to see if 

using off-peak electricity is economically attractive. 

4. Assumptions 

A set of standard assumptions was used for all analyses run. The most critical 

assumption made in this analysis is that the electrolyzers at the forecourt, 

requiring around 3.3 MW per station, will be able to pay real time electricity rates, 

which vary hourly. As the hydrogen economy evolves, utilities will need to 

determine if such time-of-day pricing structures for such installations is 

appropriate. 

However, assuming such pricing is possible, several other assumptions need to 

be clarified. The first is that the system being modeled is a forecourt hydrogen 

production station with onsite electrolysis. This system is to meet the demands of 

1500 kg/day of hydrogen production, and the demand varies from hour to hour 

throughout the day. The demand profile, shown in Figure 1, is the same as the 

profile used in the H2A forecourt models. However, different demand profiles 

can be entered into the model and tested if desired. Note that for this demand 

profile, hydrogen is needed most during the hours from 7-8 a.m. and 5-6 p.m. 

Daily Hydrogen Demand

0

20

40

60

80

100

120

140

160

180

200

01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour End

Hydrogen demand (kg) .

Figure 1: Assumed daily hydrogen demand distribution 

4
The system is modeled using four Scenarios. These Scenarios represent 

different levels of cost and technological advancements in electrolysis, 

compression and storage technologies. Scenario 1 represents the technologies 

as they exist in 2005. Scenario 2 represents a less expensive electrolysis unit 

that has a lower energy requirement. In addition, the number of compressors 

required, and thus cost, is halved as the units become more reliable. It is also 

assumed that hydrogen can be stored at lower costs. Scenario 3 represents a 

further reduction in electrolyzer cost, and an additional reduction in the energy 

needs of the electrolyzer (i.e., higher efficiency). Hydrogen is also produced at a 

higher pressure from the electrolyzer, so compression needs are reduced. Also, 

costs of the compressors are reduced. Finally, there is a further reduction in 

storage costs. Scenario 4 represents the most optimistic set of assumptions with 

a further reduction in electrolyzer capital cost, an additional decrease in 

electrolyzer energy requirements, and the electrochemical compression of 

hydrogen to 6500 psi in the electrolysis unit without external compression. 

 

Table 1: Analysis Scenarios 

Scenario number 1 2 3 4

Electrolyzer Cost, 1046 kg/day ($/kW) 740 400 300 200

Electrolyzer Energy Requirement (kWh/kg H2) 53.4 47.9 44.7 43.0

Storage Tank Cost, 85 kg ($/tank) 100,000 40,000 26,000 26,000

Compressor Cost, 1500 kg ($/compression system) 600,000 300,000 100,000 0

Compressor Electricity requirement (kWh/kg H2) 2.09 2.09 2.09 0

Dispensing Cost ($ for a system with 3 dispensers) 67,000 67,000 67,000 67,000

Control and Safety Equipment ($) 19,000 19,000 19,000 19,000

The assumptions in Table 1 are derived from the following sources (Note that all 

costs are uninstalled costs): 

• Electrolyzer cost and efficiency assumptions 

o Scenario 1 is based on a quote from an electrolyzer vendor. 

o Scenarios 2 and 3 are based on the assumptions used in the H2A 

mid and long term electrolysis cases. 

o The Scenario 4 assumption for electrolyzer cost is the lowest cost 

presented in the Department of Energy’s (DOE) Hydrogen, Fuel 

Cells, and Infrastructure Technologies (HFC&IT) Program’s May 

2005 Multi Year Program Plan (MYPP), and the electrolyzer energy 

requirements are the best-case sensitivity value used in H2A 

electrolyzer sensitivity analysis for the long term. 

• Storage tank costs 

o Scenario 1 are based on quotes for 85 kg 6500 psi storage tanks in 

2005. 

o Scenarios 2, 3 and 4 are based on the storage costs in the H2A 

forecourt assumptions. 

• Compressor costs 

5
o All Scenario costs come from delivery analysts in the HFC&IT 

program 

 Scenario 1 assumptions represent two 1500 kg/day 

compressors which are needed due to their unreliability in 

2005 

 A single compressor in Scenario 2 

 An advanced compressor combined with some 

electrochemical compression in the electrolyzer in Scenario 

3 

 Complete electrochemical compression in the electrolyzer in 

Scenario 4. 

• Electricity requirement for compression and dispensing, control and safety 

equipment costs come from the H2A forecourt assumptions. 

In addition to the above capital cost assumptions, it is assumed that the 

electrolyzer cell stack needs to be replaced at 30% of the installed capital cost 

every 10 years, and the entire compression system needs to be replaced every 

10 years. 

The hydrogen price was determined using the H2A model. The assumptions 

used in the H2A model for all Scenarios are detailed in Table 2. 

Table 2: H2A Assumptions 

Parameter Assumption 

Process Parameters 

Primary Feedstock Electricity and Water 

Electricity Used Industrial Electricity 

Conversion Technology Electrolysis 

Financial Parameters 

Start-up Year 2005

After-Tax Real IRR (%) 10

Depreciation Type MACRS

Depreciation Schedule Length (No. of Years) 20

Analysis Period (years) 40

Plant Life (years) 40

Assumed Inflation Rate (%) 1.9

State Income Taxes (%) 6

Federal Income Taxes (%) 35

Effective Tax Rate (%) 38.9

% Equity Financing 100

Length of Construction Period (years) 1

% of Capital Spent in 1st Year of Construction 100

Start-up Time (years) 1

% of Revenues During Start-up (%) 75

% of Variable Operating Costs During Start-up (%) 75

% of Fixed Operating Costs During Start-up (%) 100

6
Parameter Assumption 

Salvage Value of Capital (% of Total Capital Investment) 10

Decommissioning Costs (% of Total Capital Investment) 10

Replacement Capital Parameters 

Electrolyzer cell stack lifetime (years) 10

Electrolyzer cell stack replacement cost (% of initial cost) 30%

Compressor lifetime (years until 100% replacement) 10

Indirect Depreciable Capital Parameters 

Buildings (% of fixed capital investment) 14

Yard Improvements (% of fixed capital investment) 3.5

Construction (% of fixed capital investment) 9

Engineering and design (% of fixed capital investment) 8

Contingency (% of fixed capital investment) 25

Non Depreciable Capital Parameters 

Land ($/acre) 5,000 

O&M Parameters 

Burdened Labor ($/hour) 50 

Overhead and G&A (% of labor cost) 20

Property Tax (% of depreciable capital costs) 1

Insurance Rate (% of depreciable capital costs) 1

Note that for this analysis, fractional pieces of equipment are used because the 

analysis is based on sizes of equipment that are available in 2005 and this 

analysis should not be constrained by the sizes of equipment currently available. 

5. System design 

The system modeled in this analysis is a forecourt electrolysis system. It 

consists of the following: 

• An electrolyzer system, or multiple electrolyzers, sized to meet the 

demand of the forecourt system. The electrolyzer system consists of the 

transformer, thyristor, electrolyzer unit, lye tank, feed water demineralizer, 

hydrogen scrubber, gas holder, deoxidizer, and a twin tower drying unit. 

• A compressor to compress the hydrogen to approximately 6500 psi sized 

to meet the maximum hourly flow rate of the system 

• Storage tanks for 6500 psi hydrogen sized to meet the demands of the 

forecourt system when the electrolyzer isn’t running 

• Dispensing unit for fueling, consisting of 3 dispensing pumps 

With regards to feedstock, raw materials, and utilities, the following are needed. 

• The system feedstock is considered to be electricity, which is needed for 

the electrolyzer system at a value equal to the electrolyzer energy 

requirement seen in Table 1. 

• Water for electrolysis, is used at a rate of 11.13 L/kg of hydrogen. 

• Cooling water is used at a rate of 1100 L/kg of hydrogen. 

• Compressed inert gas is needed for system purges. 

7
• A 25% solution of potassium hydroxide is needed for the electrolyte, and 

is replaced annually. 

• 2.09 kWh of electricity is needed per kilogram of hydrogen compressed, 

as seen in Table 1. 

For a more thorough overview of the electrolysis system, see the milestone 

report, “Summary of Electrolytic Hydrogen” by Johanna Ivy. 

6. Modeling system 

The assumption made going into this study was that whether or not using offpeak electricity is economical would be based largely on three variables: 

average electricity price, capacity factor for the electrolyzer, and capital cost. 

Those parameters are related in the following way: 

• The use of off-peak electricity first affects the average cost of electricity. 

For example: a location may have a peak price of $0.20/kWh for one hour 

out of the day, and $0.04/kWh pricing the rest of the day. For this 

location, the average electricity price would be $0.046/kWh for the entire 

day, but only $0.04/kWh if the peak hour were dropped. 

• This then affects the second parameter, capacity factor. If electricity is 

used all day, the electrolyzer runs 100% of the time it is available. 

Assuming each electrolysis unit is available 97% of the time, the capacity 

factor when the electrolyzer runs 24 hours a day is 97%. However, if the 

electrolyzer isn’t used during a single peak hour, the capacity factor drops 

to 93%. This means that 7% of the time the electrolyzer is not in use. In 

addition, it means that during that 7% of the time, hydrogen must be 

available in storage to meet the demands for that hour when hydrogen is 

not being produced. 

• This leads to the effect of the third parameter: capital cost. When the 

electrolyzer is only used 93% of the time, more electrolyzers are needed 

to meet the demands of the system and produce the same amount of 

hydrogen during the resulting run hours. In addition, more storage is 

needed so that hydrogen is available in storage to meet the demands 

when the electrolyzer is not running. Finally, additional compressors are 

needed to meet the larger production rates of the additional electrolyzers. 

Producing hydrogen using off peak power only results in a lower hydrogen 

price when the decrease in the average electricity price offsets, or more 

than offsets, the additional cost of the equipment due to the decrease in 

the capacity factor of the electrolysis unit. 

To model the effects of the above three parameters and find the optimal system 

configuration for hydrogen production using off-peak electricity, the HOPE model 

combines the H2A model with a hydrogen/electric optimization. 

The following terms are used throughout the report, and are presented for 

clarification: 

8
- Scenario: As seen in table 1, a Scenario is a set of assumptions with 

regards to capital cost and energy needs of the system. The H2A 

model is run using the Scenario assumptions. 

- Case: Cases represent an electricity pricing structure for which an 

optimized system and a resulting hydrogen price is solved. Each Case 

is run using a single Scenario, but a Case can be run with more then 

one Scenario to determine how capital cost reductions and system 

efficiencies affect the resulting optimized system and hydrogen price. 

The result of a Case is the optimized hydrogen system, and the 

hydrogen selling price resulting from that system 

- Run: A Run is a system optimization and resulting hydrogen price for a 

single Price Point in a Case. So if a Case had electricity prices that 

ranged from $0.01/kWh to $0.05/kWh, you could have Runs where the 

Price Point was $0.01/kWh, $0.02/kWh and $0.05/kWh. Many Runs 

make up a Case, and the Run with the lowest hydrogen price is the 

optimized result for any given Case. 

- Price Point: an electricity price that defines the range of electricity 

prices used in a Run. All electricity prices less then or equal to the 

Price Point are used. For example, if a three-hour electricity price 

range was $0.02, $0.04 and $0.06/kWh, and the Price Point was 

$0.04/kWh, two hours would be used for the Run, $0.02/kWh and 

$0.04/kWh. 

The hydrogen/electricity optimization side of the HOPE model uses hourly 

electricity pricing and demand data over the course of a single year, and 

optimizes the system by minimizing the number of electrolyzers and the number 

of storage tanks while ensuring the fuel demand is met every hour out of the day, 

either through hydrogen production or stored hydrogen. A Price Point is used to 

set the electricity price at or below which hydrogen will be produced. The 

number of electrolyzers, storage tanks and compressors, along with the average 

electricity price, capacity factor and plant design capacity are calculated for 

various Price Points in an electricity pricing structure. The equipment data, 

average electricity price, capacity factor and plant design capacity for each Price 

Point are fed into the H2A model and a price of hydrogen is calculated. The 

hydrogen prices for various Price Points can then be compared and the optimum 

system for a particular electricity pricing structure can be obtained. A flowchart of 

how the HOPE model works is presented in Figure 2. 

Table 3 details the parameters that are fed into the H2A model from the 

hydrogen/electricity optimization: 

9
Table 3: Parameters passed from the hydrogen/electricity optimization 

side of HOPE to the H2A model. 

Parameter Description 

Plant design capacity (kg/day) 

The optimization model changes the number of 

electrolyzers, so that the plant output meets the hourly 

demand of the forecourt. The number of electrolyzers 

changes the plant design capacity 

Operating capacity factor (%) 

The optimization model calculates the operating 

capacity factor of the system, given the number of 

electrolyzers and the hours when the electrolyzers are 

being used to produce hydrogen, when electricity 

prices are below the electricity Price Point. 

Plant Output (kg H2/year) 

The plant output is a calculation that is the product of 

the plant design capacity and the operating capacity 

factor. The plant output is approximately 550,000 

kg/year, which is the amount of hydrogen the 

forecourt station demands in a year. The only 

variation of this number occurs from the potential for 

an initial amount of hydrogen charged to the system 

to meet early hours of demand and does not need to 

be produced. 

Number of Electrolyzers 

The optimization model changes the number of 

electrolyzers so that the hydrogen available from the 

electrolyzers and storage tanks meets the hourly 

demand of the forecourt. The number of electrolyzers 

needed is based on the hours when the electrolyzers 

are being used to produce hydrogen, when electricity 

prices are below the electricity Price Point. 

Number of 85 kg storage tanks 

The optimization model changes the number of 

storage tanks so that the hydrogen available from the 

electrolyzers and storage tanks meets the hourly 

demand of the forecourt. 

Number of 1500 kg 

compressors 

The optimization model sizes the compressors to 

meet the maximum hourly flow rate of hydrogen being 

produced. 

Electricity feedstock cost 

The electricity feedstock costs are the average of all 

hourly electricity prices less than or equal to the 

electricity Price Point in the model. 

10
The required hydrogen selling price, along with the cost contributions, such as 

capital costs, fixed operation and maintenance (O&M), and feedstock costs, are 

fed from the H2A model back into the hydrogen/electricity optimization model for 

each electricity Price Point. These results can then be analyzed to determine if it 

is advantageous to use off-peak electricity for the electricity price range and 

demand curve entered. If the hydrogen price is lower using off-peak electricity 

than using electricity 24 hours a day, there is an advantage to using off-peak 

electricity. 

11
Figure 2: Hope Model Flowchart 

12
Figure 2: Hope Model Flowchart (continued) 

13
14

7. Analysis Stages 

In order to understand the effects of using off-peak electricity on a hydrogen 

production electrolysis system, four different stages of analysis were conducted. 

In the first stage, different electricity price distributions were used for the same 

electricity price range to determine if just changing when electricity peaks occur 

changes the hydrogen production system configuration. In the second stage, 

Cases were run to see if off-peak electricity could lead to a lower hydrogen price. 

In the third stage, Cases were run through all four Scenarios in order to 

determine the effect of system improvements when using 24-hour electricity price 

distributions. All three of the first stages use 24-hour electricity price ranges to 

understand how daily fluctuations in electricity price could affect hydrogen 

production costs. In the fourth stage, 8760 data was entered in the model to see 

how both daily and seasonal electricity price distribution affect the use of off-peak 

electricity to produce hydrogen. 

a. Stage 1 - Effect of Electricity Price Distribution on System 

Configuration 

Initially, unrealistic, but easy to understand Cases were run, in order to see if 

there were differences in hydrogen systems with the same electricity price 

ranges, but different distributions of prices during the day. Three Cases were 

run. Each Case varied electricity prices each hour from $0.01/kWh to $0.24/kWh 

by one cent. In the first Case, the prices started at $0.01/kWh at midnight, and 

increased to $0.24/Wh by 11:00 p.m. In the second Case, the electricity prices 

were varied inversely to Case 1, with electricity prices starting at $0.24/kWh at 

midnight, and decreasing through the day to $0.01/kWh. The final Case 

randomly varied the electricity price from $0.01/kWh - $0.24/kWh throughout the 

day. For each Case, 24 Runs were analyzed with Price Points for electricity 

price ranging from $0.24/kWh to $0.01/kWh in order to see how just varying 

when hydrogen is produced affected the systems. A graph of each system’s 

electricity price distribution, and the resulting system requirements can be seen 

in Figure 7. 

Some interesting conclusions can be drawn from this analysis. First, regardless 

of when hydrogen is produced during the day, the number of hours electricity is 

available at or below a certain price determines the size of the electrolyzer and 

the size of the compressor. In other words, regardless of when in the day the 

electrolyzer runs if the Price Point was $0.18/kWh, the electrolyzer only ran 18 

hours out of the day; in all three Cases two electrolyzers and 1.3 compressors 

were needed. Likewise, the design capacity of the plant, plant output, capacity 

factors, and average electricity price are the same for the same electricity Price 

Point across all three Cases. 
15

Price Point $0.24/kWh $0.18/kWh $0.12/KWh $0.0

Electricity Pricing Case Hours Operating 24 18 12 6 

# Electrolyzers 1.5 2.0 2.7 

Max # of tanks 13.82 15.28 18.80 

Kg H2 Design 

capacity 560,000 750,000 1,040,000 2,3

Kg H2 Plant output 550,000 550,000 550,000 5

Capacity factor 97.0% 72.8% 52.5% 

Avg Electricity Price 0.125 0.095 0.070 

# Compressors 1.0 1.3 1.8 

# Electrolyzers 1.5 2.0 2.7 

Max # of tanks 13.82 10.00 10.00 

Kg H2 Design 

capacity 560,000 750,000 1,040,000 2,3

Kg H2 Plant output 550,000 550,000 550,000 5

Capacity factor 97.0% 72.8% 52.5% 

Avg Electricity Price 0.125 0.095 0.070 

# Compressors 1.0 1.3 1.8 

# Electrolyzers 1.5 2.0 2.7 

Max # of tanks 13.82 14.31 15.02 

Kg H2 Design 

capacity 560,000 750,000 1,040,000 2,3

Kg H2 Plant output 550,000 550,000 550,000 5

Capacity factor 97.0% 72.8% 52.5% 

Avg Electricity Price 0.125 0.095 0.070 

# Compressors 1.0 1.3 1.8 

Figure 3: Effect of Electricity Price Distribution on System Design, $0.01-$0.24/kWh over 2
The storage requirement encompasses the differences between the systems. 

This is because storage is needed either to store hydrogen when there is no 

demand, or to have hydrogen in storage to meet a demand when there is no 

hydrogen being produced. Considering all three Cases where the Price Point for 

electricity is $0.18/kWh, and the system runs 18 hours out of the day, the 

maximum number of tanks needed for the system varies from 15 to 10 to 14 

depending on when the electricity price is high and the system does not run. 

Still considering the Price Point of $0.18/kWh, in the first Case, electricity prices 

are low during the first five hours of the day, and hydrogen is produced. 

However, there is no demand for the hydrogen during these hours, so the 

hydrogen must be stored (see Figure 1 for the hydrogen demand curves) In the 

second Case, when electricity price decreases during the day, the Price Point of 

$0.18/kWh eliminates five hours during the day when there is no demand for 

hydrogen, and one hour when there is low demand. As no hydrogen produced 

during these hours, and there is very little demand, and storage is only needed to 

meet the one-hour when there is a hydrogen demand. Finally, the third Case 

shows how a random distribution of electricity prices leads to a higher storage 

requirement than the second Case, but a lower storage requirement than the first 

Case. For one hour when hydrogen is not being produced, there is no demand, 

so no additional storage is needed. However, there is also some demand during 

two of the hours when hydrogen isn’t being produced, so hydrogen storage is 

needed to meet that demand. So there are more hours when no hydrogen is 

produced when there is no demand, as compared to Case 1, which leads to a 

reduction in storage from Case 1. However, there are more hours when no 

hydrogen is produced but there is a demand as compared to Case 3, thus 

increasing the storage needs as compared to Case 3. 

One item of note is that all of the Cases in Figure 7 were run with a standard 

assumption that the system started with ten 85 kg storage tanks of hydrogen 

charged with hydrogen at the beginning of the year. This was so that there was 

ample hydrogen in the system during early hours when hydrogen storage was 

not yet built up. However, in Case 2 for both the $0.18/kWh and $0.12/kWh 

Price Points, the maximum storage size was ten, and this maximum was 

determined by the size set by the initial tank charge assumption. Changing the 

initial tank charge assumption does not affect the overall conclusions of this 

analysis, but should be noted for future Cases when a hydrogen price is 

calculated, as an excess of storage tanks should not be included in costing 

analyses. 

Once a basic understanding of how hydrogen systems vary with electricity price 

ranges and electricity price constraints, additional analysis stages were used to 

see if a pattern could be established as to when using off-peak electricity results 

in a lower hydrogen production price. 

16
b. Stage 2 - Simple Electricity Pricing: When Does Off Peak 

Make Sense? 

Three simple electricity-pricing Cases were run, using Scenario 1 prices as seen 

in Table 1. The electricity price range for the Cases was set to $0.04/kWh during 

21 hours out of the day, and during the other three hours of the day, the price 

was varied from $0.08/kWh to $0.10/kWh. The Cases run were as follows: 

Table 4: 3 Simple Electricity Pricing Structures 

Case 1 Case 2 Case 3 

Hours 1- 12 $0.04/kWh $0.04/kWh $0.04/kWh 

Hours 13 –15 $0.08/kWh $0.09/kWh $0.10/kWh 

Hours 16 – 24 $0.04/kWh $0.04/kWh $0.04/kWh 

Average Electricity Price all day $0.045/kWh $0.046/kWh $0.048/kWh 

Average Electricity price at 

$0.04/kWh Price Point 

$0.04/kWh $0.04/kWh $0.04/kWh 

Each of the Cases was first run with a Price Point high enough so the 

electrolyzer ran the entire day and used the average electricity price of the entire 

24 hour day: 21 hours of $0.04/kWh electricity and 3 hours at the higher price. 

Then the Case was run where the Price Point was set to $0.04/kWh, so the 

electrolyzer only produced hydrogen 21 hours out of the day, and the average 

electricity price was $0.04/kWh. Note that this second Run should yield the 

same results for all three Cases, as it is the same system: an electrolyzer that 

runs 21 hours with $0.04/kWh electricity. 

The results of these Cases are seen in Figure 8. When the peak price of 

electricity is $0.08/kWh (the green bars) the price of hydrogen when producing 

24 hours a day is $4.04/kg, and is $4.16/kg when using off-peak electricity 21 

hours out of the day. This demonstrates that when the peak price of electricity is 

$0.08/kWh it is more expensive to use off-peak electricity to produce hydrogen 

when the electricity price is only $0.04/kWh. When the peak price of electricity 

rises to $0.09/kWh (the blue bars) the price of hydrogen continuously produced 

24 hours a day is $4.10/kg, and is $4.16/kg using off-peak electricity. In this 

Case, the two prices are closer, as the $0.09/kWh peak raises the average 

electricity price by an additional $0.001/kWh, but using off-peak electricity is still 

more expensive. Finally, when the peak price rises to $0.10/kWh, the price of 

hydrogen produced using off-peak electricity is still $4.16/kg, but is $4.21/kg 

when produced 24 hours a day. This means it is $0.05/kg less expensive to 

produce hydrogen using off-peak electricity when the peak is $0.10/kWh. 

17
$4.04 $4.16 $4.10 $4.16 $4.21 $4.16

3

3.5

4

4.5

5

Hydrogen Price ($/kg) .

$0.08/kWh

$0.04/kWh

$0.09/kWh

$0.04/kWh

$0.10/kWh

$0.04/kWh

Price Point

How Hydrogen Price Varies with Peak Electricity Prices

$0.08/kWh peak 

$0.09/kWh peak 

$0.10/kWh peak 

Figure 4: A simple example showing when using off-peak electricity lowers 

the hydrogen price 

This simple analysis shows the importance of the size of the peak when using 

off-peak electricity. As a general rule of thumb moving forward in this analysis, it 

was assumed that if the peak price of a system is large enough, off-peak 

electricity should be used. However, if the difference between the peak price and 

the off peak price is just a few cents, it is unlikely to reduce the cost of hydrogen 

enough to warrant using off-peak electricity. 

c. Stage 3 - Detailed 24 Hour Electricity Pricing Cases 

In stage 3, two Cases were run using varying electricity prices over 24 hours. 

The electricity prices varied from $0.01/kWh - $0.20/kWh, and the hour-by-hour 

price range breakdown can be seen in Table 6. Both electricity price ranges 

were run through Scenarios 1 – 4 to develop and understanding of how 

technology improvements change the price of hydrogen in each Case. 

The first Case uses an electricity price range with a 7-hour peak period where 

prices are $0.12/kWh, $0.16/kWh and $0.20/kWh. There is then a 10-hour 

shoulder period where electricity prices are $0.06/kWh and $0.04/kWh. Finally, 

there is a 7-hour off peak period when prices are $0.01/kWh. The prices and 

hours when the prices occur were chosen to give a good range of prices, a peak 

at the noontime, and a long off-peak period with low electricity prices. 

18
The second Case uses an electricity price range with a 9-hour peak period where 

prices are $0.12/kWh, $0.16/kWh and $0.20/kWh. There is then a 6-hour 

shoulder period where electricity prices are $0.06/kWh and $0.04/kWh. Finally, 

there is a 9-hour off peak period when prices are $0.02/kWh or $0.01/kWh. This 

second electricity price range also has a good range of electricity prices, but the 

peak is between 3:00 and 4:00 p.m., and the peak period and off peak period are 

each an hour longer than in the first electricity price ranges. 

Table 5: 24 Hour Electricity Price Ranges 

Time of Day 

Case 1 

Electricity Price Range 1 

($/kWh) 

Case 2 

Electricity Price Range 2 

($/kWh) 

12:00 AM 0.01 0.01 

1:00 AM 0.01 0.01 

2:00 AM 0.01 0.01 

3:00 AM 0.01 0.01 

4:00 AM 0.01 0.01 

5:00 AM 0.04 0.01 

6:00 AM 0.04 0.02 

7:00 AM 0.06 0.02 

8:00 AM 0.06 0.04 

9:00 AM 0.12 0.06 

10:00 AM 0.12 0.06 

11:00 AM 0.12 0.12 

12:00 PM 0.2 0.12 

1:00 PM 0.16 0.16 

2:00 PM 0.12 0.16 

3:00 PM 0.12 0.2 

4:00 PM 0.06 0.2 

5:00 PM 0.06 0.12 

6:00 PM 0.04 0.12 

7:00 PM 0.04 0.12 

8:00 PM 0.04 0.06 

9:00 PM 0.04 0.06 

10:00 PM 0.01 0.04 

11:00 PM 0.01 0.02 

Figure 9 below shows the results of the first Case. The figure is organized so 

that the assumptions common to all four Scenarios can be seen in the upper lefthand corner of the figure. The assumptions detail the Price Point, the average 

electricity price and the capacity factor for each of the six Runs completed for 

each Scenario. A Run is simply an optimization for a certain Price Point. So for 

example, Run 3 uses all electricity at $0.12/kWh and under, meaning that the 

system uses electricity 22 hours out of the day, from 12 a.m. to 11 a.m., and then 

from 2 p.m. until 11 p.m. This yields an average electricity price of $0.052 during 

the 22 hours the system runs, and a capacity factor of 89%. The capacity factor 

is calculated as the number of hours the electrolyzer is running (22 hours) 

divided by 24 hours (22/24) multiplied by the capacity factor of the electrolyzer, 

19
20

97%. On the upper right-hand corner is the legend for all four graphs. There is 

one graph for each Scenario that shows the resulting hydrogen price for each 

Run, and the numbers on the x-axis correlate to the Run in the system 

assumptions. The number on the graph correlates to the lowest price hydrogen 

that can be produced from this system in each Scenario. 

In each Scenario, with this hypothetical electricity price range, using off-peak 

electricity results in a lower hydrogen price. Scenario 1 has the lowest hydrogen 

price of $4.59/kg at Run 4, which corresponds to a $0.06/kWh electricity Price 

Point and a 69% capacity factor. If you consider the cost contribution bars, you 

can see that the capital cost contribution for Run 4 is higher than in Run 3, but 

the feedstock electricity price is lowered to the point that the overall cost of the 

hydrogen is lower. The capital cost contribution increases because the system is 

operating at a lower capacity factor, so more electrolyzer, storage and 

compression units are needed to meet the demand. In Run 5 the electricity 

feedstock cost contribution reduction no longer offsets the increased capital cost 

contribution. For a Run to result in lower hydrogen price then the previous Run, 

the growth of the capital cost bar needs to be more than offset by the reduction of 

the feedstock cost bar (electricity price) when compared to earlier Runs. 

In Scenario 2, the lowest price of hydrogen drops to $3.85/kg as the capital costs 

are reduced and electrolyzer efficiency is improved in Scenario 2. However, the 

lowest hydrogen price still occurs at Run 4 with a Price Point at $0.06/kWh of 

electricity. In Scenario 3, the capital cost reductions cause the lowest hydrogen 

price to drop to Run 5, an electricity price Price Point of $0.04/kWh and a 

capacity factor of 53%. This Run results in a hydrogen price of $2.70/kg, over a 

dollar cost reduction from Scenario 2. The reason the lowest price shifts to Run 

5 is because for all Scenarios, the electricity cost contribution stays roughly the 

same. The decrease in electrolyzer efficiency makes the contributions slightly 

smaller, but not significantly. However, as the capital costs get smaller in 

Scenario 3, the capital cost contribution bars do not grow as much with the 

decrease in capacity factor, and smaller electricity cost contributions can offset 

the smaller increase in capital costs, which leads to an overall lower cost. In 

Scenario 4 the hydrogen price drops to $1.90 a kilogram, and Run 5 has the 

lowest hydrogen price, the same as Scenario 3. In summary, as the capital costs 

of the system decrease in Scenarios 2-4, using lower cost electricity becomes 

more advantageous. 

The results of this analysis show that as the system costs reduce and efficiencies 

improve, the resulting hydrogen price using off-peak electricity is lower. In 

addition, these process improvements can lead to a shift in the Price Point where 

off-peak electricity becomes economically attractive. As the system improves, 

the Price Points, and thus capacity factors, can be lowered and hydrogen will be 

produced at a lower price. To validate the results seen in the first Case, a 

second Case was run with a second electricity price range. The results from this 

Case can be seen in Figure 10. Again, the analysis shows that in all four 

Scenarios using off-peak electricity results in a lower hydrogen price. 
Assumptions Legend – Cost Co

Run Units 1 2 3 4 5 6 Other Variable Price Point $/kWh $0.20 $0.16 $0.12 $0.06 $0.04 $0.01 Other Raw MaAvg. Electricity Price $/kWh $0.063 $0.057 $0.052 $0.032 $0.024 $0.010 Electricity FeeCapacity Factor % 97% 93% 89% 69% 53% 28% Fixed O&M 

Decommission

Capital Cost 

Note different y-axis scale on Scenario 1

Figure 5: Assumptions, Legend and Cost Contribution Charts for Electricity Price Ran

21
22

Assumptions Legend – Cost CoRun Units 1 2 3 4 5 6 7 Other Variable CPrice Point $/kWh $0.20 $0.1 $0.12 $0.06 $0.04 $0.02 $0.01 Other Raw MateAvg. Electricity Price $/kWh $0.073 $0.062 $0.052 $0.029 $0.018 $0.013 $0.010 Electricity FeedsCapacity Factor % 97% 89% 81% 61% 44% 36% 24% Fixed O&M 

Decommissionin

Capital Cost 

Note different y-axis scale on Scenario 1 Note different y-axis scale on Scenario 2

Figure 6: Assumptions, Legend and Cost Contribution Charts for Electricity Price Range 
d. Stage 4 - Real Time Cases 

The final analysis stage uses the HOPE model to determine if using off-peak electricity 

results in a lower hydrogen price using electricity prices that vary over the entire year, not 

just 24 hours. Until this point in the analysis, hypothetical 24-hour electricity price 

structures were used to determine if off-peak electricity could result in lower priced 

hydrogen. However, it should be determined if using off-peak electricity with actual real 

time electricity prices yields the same result: that using off-peak electricity results in lower 

hydrogen prices. Two sources of real time market electricity prices were considered from 

the New York Independent System Operator (NYISO) website and the Independent 

System Operator New England (ISO New England) website. Due to the ease in 

collecting the ISO New England data, it was chosen as the source for real time electricity 

pricing in this analysis, but the NYISO data could be considered in future Cases. From 

ISO New England’s website, the “Combined Day-Ahead and Preliminary Real Time 

Report” was downloaded for each day, and the Real Time Location Marginal Price (LMP) 

was used to calculate the price of electricity. The Real Time LMP is a sum of the Real 

Time Energy Component, the Real Time Congestion Component and the Real Time 

Marginal Cost Component. These data are available for all pricing nodes from ISO New 

England. It should be noted that the use of these data need to be vetted with industry for 

their applicability for this particular use, however, even if the prices are not exactly 

applicable to this analysis, their hourly and seasonal variations give a different view of 

electric pricing structures than the hypothetical 24 hour electricity price ranges. In 

addition, this model is meant for easy re-runs of data, so as additional sources of data 

are found and vetted, the results can be re-evaluated. 

Hourly real time pricing data for 950 nodes were downloaded from NE ISO from May 1, 

2004 to April 30, 2005 and entered into a Microsoft Access database. Queries were then 

run to determine the NE ISO nodes that had the highest average electricity price, and the 

lowest average electricity price for the entire time period. The node with the highest 

average electricity price was network node 4616, which had an average electricity price 

of $0.055/kWh, a max electricity price of $0.79/kWh and a minimum electricity price of 

$0.00002/kWh. The node with the lowest average electricity price was network node 

4010 with an average price of $ 0.048/kWh, a maximum price of $0.61/kWh and a 

minimum price of $0.00002/kWh. The electricity price distribution for these two nodes 

can be seen in Figures 11 and 12 

23
Figure 7: Hourly real time electricity prices for ISO-NE Node 4616 

Figure 8: Hourly real time electricity prices for ISO-NE Node 4616 

The hourly (8760) electricity prices for the two nodes were entered into the HOPE model. 

Appropriate Price Points for the data were entered into the model, and the system was 

optimized and the resulting hydrogen prices were found. For both systems, only 

Scenario 2 was run in order to get an understanding of whether or not using off-peak 

electricity results in lower hydrogen prices. Additional Runs for other Scenarios could be 

run in the future to develop an understanding of how technology improvements modify 

the results. 

24
25

Figure 13 shows the results of the HOPE model for ISO NE Node 4616. This model 

shows that reducing the Price Point from the high value of $0.80/kWh to $0.30/kWh 

makes the largest reduction in cost. This lowest hydrogen cost Run is shown as the 

yellow column of data on the chart in Figure 13. The reason this is the lowest cost Run is 

a result of the number of storage tanks needed for the system. As the Price Point 

decreases from $0.80/kWh to $0.30/kWh, only 17 hours in the day are eliminated from 

hydrogen production. After the electricity Price Point is $0.30/kWh (Run 6) the price of 

hydrogen gradually starts increasing as the storage needs of the system increase, even 

as the number of electrolyzers and compressors stays relatively constant. At Run 13, the 

hydrogen price jumps $0.25/kg while the number of electrolyzers and capacity factor stay 

relatively constant from Run 12. From Run 13 to 14, the price of hydrogen jumps an 

additional $0.64/kg, while again the number of electrolyzers stays basically constant and 

the capacity factor decreases 1%. The change in cost is due to the increased amount of 

storage needed from 35 tanks at Run 12, to 45 tanks at Run 13, and 75 tanks at Run 14. 

Upon detailed analysis of the electricity pricing data, it was found that there is a period in 

January when electricity prices are consistently high. During that time, hydrogen 

production is low, so hydrogen storage is being depleted during this time, and large 

hydrogen stores are needed before that time to be able to meet the demand of the 

system. Figure 14 shows the hydrogen storage depletion during Run 14 in the month of 

January. 
26

1 2 3 4 5 6 7 8 9 10 11 1

Hours operating hours 8760 8757 8754 8754 8753 8743 8736 8729 8714 8695 8672 86Electrolyzers # 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.49 1.49 1.49 1Electricity price point $/kWh 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.17 0.15 0.14 0.13 0Tanks needed # 13.82 13.82 13.82 13.82 13.82 14.62 15.62 16.20 16.88 19.46 26.96 34Tanks charged initially # 10 10 10 10 10 10 10 10 10 10 10 Electrolysis capacity factor % 97% 97% 97% 97% 97% 97% 97% 97% 96% 96% 96% 9Average Electricity Price $/kWh 0.059 0.059 0.059 0.059 0.059 0.058 0.058 0.058 0.058 0.058 0.057 0.0Compressors # 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.01 1.01 1H2 Cost $/kg $4.45 $4.45 $4.45 $4.45 $4.45 $4.41 $4.44 $4.45 $4.48 $4.54 $4.65 $4Capital Cost Contribution $/kg $1.20 $1.20 $1.20 $1.20 $1.20 $1.21 $1.23 $1.24 $1.27 $1.32 $1.46 $1Electricity Feedstock Cost Contribution $/kg $2.89 $2.89 $2.89 $2.89 $2.89 $2.85 $2.85 $2.85 $2.85 $2.85 $2.80 $2

Figure 9: System variables and hydrogen pricing results for ISO-NE Node 461
27

Figure 10: Annual storage requirements for ISO-NE Node 4616 during Run 

14 

The results for the second Case of real time data for ISO NE Node 4010 can be 

seen in Figure 15. In this Case the cost of hydrogen is $3.89/kg until the reaches 

$0.15/kWh (Run 7). Then the price of hydrogen starts rising. At the Price Point 

of $0.15/kWh only 17 hours out of the year are eliminated for hydrogen 

production. After this point the storage requirements begin rising, as in the Node 

4616 analysis. 

An interesting result of these two analyses is that if utilities could disrupt power to 

the electrolysis units during peak electricity demands, which are assumed to be 

correlated to when electricity prices are high, it would not adversely affect the 

price of hydrogen. However, it appears as though if more hours then just peak 

shaving were eliminated, the storage requirements would be detrimental to using 

off-peak electricity to produce hydrogen due to seasonal peaks in electricity 

prices. This is partially due to the assumptions made with this version of the 

HOPE model, which work well for 24-hour electricity price ranges, but need to be 

improved for 8760 price ranges. Additional work needs to be done to determine 

if modifications could be made to the model to better account for the storage 

needs during peak periods. For example, could a second electricity Price Point 

be used when storage levels decrease? What if the demand were allowed to not 

be met 5% of the time and it is assumed hydrogen is purchased during those 

times to meet the demand? What if a maximum storage size is set, and the 

model could calculate when the demand could not be met? All these questions 

need to be considered for inclusion by future versions of the HOPE model. 
28

1 2 3 4 5 6 7 8 9 10 11 

Hours operating Hours 8760 8759 8759 8759 8759 8757 8754 8741 8730 8683 8622 Electrolyzers # 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.48 1.49 1.50 Electricity price point $/kWh 0.61 0.60 0.50 0.40 0.30 0.20 0.15 0.13 0.12 0.10 0.09 Tanks needed # 13.82 13.82 13.82 13.82 13.82 13.82 13.82 17.36 22.59 41.48 62.90

1

Tanks charged initially # 10 10 10 10 10 10 10 10 10 10 10 Electrolysis capacity factor % 97% 97% 97% 97% 97% 97% 97% 97% 97% 96% 95% Average Electricity Price $/kWh 0.048 0.048 0.048 0.048 0.048 0.048 0.048 0.048 0.048 0.047 0.047 0Compressors # 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.01 1.02 H2 Cost $/kg $3.89 $3.89 $3.89 $3.89 $3.89 $3.89 $3.89 $3.96 $4.07 $4.45 $4.94 $Capital Cost Contribution $/kg $1.20 $1.20 $1.20 $1.20 $1.20 $1.20 $1.20 $1.27 $1.36 $1.74 $2.16 $Electricity Feedstock Cost Contribution $/kg $2.36 $2.36 $2.36 $2.36 $2.36 $2.36 $2.36 $2.36 $2.36 $2.31 $2.31 $

Figure 11: System variables and hydrogen pricing results for ISO-NE Node 40
8. Model Capabilities 

In addition to the capabilities already outlined in the four stages of the analysis, the 

HOPE model could be used for other analysis exercises. In addition, future 

enhancements could be envisioned that would make the model more user friendly, 

and give additional functionality. 

a. Current capabilities 

As outlined in the four hydrogen stages, the HOPE model has the current 

capabilities: 

• It can determine the best off-peak pricing structure for the production of 

hydrogen using both 24-hour and 8760-hour data. 

• It can determine how the hydrogen production system configuration needs 

to change with different electricity price distributions 

• It can determine if off-peak electricity can lead to a lower hydrogen price. 

• It can evaluate how system improvements change the hydrogen production 

system configuration and the optimum off peak pricing structure. 

However, there are other uses for the model. An additional set of Cases was run 

to help show some additional capabilities of the HOPE model. The purpose was 

to determine how hydrogen price varies with average electricity cost and capacity 

factor for a specific Case for each Scenario in Table 1. Such an analysis could be 

run with a known demand curve and a known electricity pricing structure for any 

utility node. The results of this type of analysis would provide graphs and 

equations to help calculate how best to operate an electrolysis hydrogen 

production system for a given location. In addition, they help analysts understand 

under what conditions locations can meet the DOE hydrogen cost targets of $2 - 

$3/kg of hydrogen with forecourt electrolysis. 

This analysis considered a system running 24 hours per day (97% capacity factor), 

18 hours per day from 3 p.m. until 8 a.m. (73% capacity factor), and 12 hours per 

day from 6 p.m. to 5 a.m. (49% capacity factor). Note that the time periods above 

are of importance, because of how the demand curve correlates to the hours when 

electricity is available. The results presented here are only applicable if the 

electricity is available in the timeframe listed above. If this timeframe were to shift, 

the results would also change. For example, the 73% capacity factor has the 

system using electricity to produce hydrogen during the two peak periods of fuel 

demand, at 8:00 a.m. and 6:00 p.m. If the hours were shifted so that electricity 

was not being used from 8:00 a.m. to 11:00 a.m. and 6:00 p.m. to 9:00 p.m., the 

resulting systems would change as hydrogen would not be produced during the 

peak periods, and storage demands would increase. 

The hydrogen/electricity optimization routine in the HOPE model was used to 

determine the equipment needed for each capacity factor and time of day listed 

29
above, and then the H2A model was used to determine the cost of the hydrogen 

produced for the different average electricity prices at different capacity factors. 

This was a manual process, as opposed to the process seen in Figure 2. 

The hydrogen/electricity optimization ensured that each system produced enough 

hydrogen to meet the forecourt demand, seen in Figure 1, by calculating the 

capacity factor, plant design capacity, electrolyzer size, number of compressors 

and number of storage tanks using the electricity availability timeframes listed 

above. The capacity factors result from the timeframes listed above: a 24 hour a 

day system has a 97% capacity factor because it is assumed that each 

electrolyzer only operates 97% of the time, but the system runs 24 hours a day. 

The 18 hour a day system has a 73% capacity factor because the system runs 18 

hours a day at a 97% capacity factor, or 18 hours a day/24 hours a day * 97% 

capacity factor. 

The equipment sizes and plant design capacities for each capacity factor are in 

Table 4; these values were entered into the H2A model for each Scenario. In 

addition, the capital cost and electricity requirement for the equipment was varied 

by Scenario as seen in Table 1. For each Scenario the average electricity price 

was varied from $0.01/kWh to $0.25/kWh in the H2A model. 

Table 6: System Assumptions for Initial Analysis 

Capacity Factor 97% 73% 49% 

Plant Design Capacity (kg/day) 1540 2060 3090 

Number of Electrolyzers 1.48 1.97 2.95 

Number of 85 kg high pressure tanks 13.82 15.29 18.23 

Number of 2000kg/day compressor systems 1.00 1.33 2.00 

Results for Scenario 1 can be seen in Figure 3. The chart and equations can be 

used to determine what average electricity price is needed to produce hydrogen at 

a given cost at different capacity factors. For example, for $3/kg hydrogen, a 97% 

capacity factor system would need electricity prices to be just above $0.001/kWh 

electricity, while a system with a 73% capacity factor, or a 49% capacity factor 

could not produce hydrogen for this low of a price at any electricity price. This can 

be seen both by the lines on the graph, and by using the equations of the lines. 

The x in the equation is the electricity price in $/kWh, and the y in the equation is 

the hydrogen selling price in $/kg. To find the minimum hydrogen cost if the 

electricity were free for the 73% and 49% capacity factors, set x to 0, and then y, 

the hydrogen price, equals the intercept of the equations. At an electricity price of 

0, where x equals the electricity price, the intercept (3.7088 at 73% and 5.2318 at 

49%) is higher than $3.00/kg of hydrogen. 

30
The equations for the three lines shown on the graph are as follows: 

Capacity Factor 97% 73% 49% 

Equation where x is 

electricity price in 

$/kWh and y is 

hydrogen price in $/kg 

y = 56.842x + 2.9412 y = 56.842x + 3.7088 y = 56.843x + 5.2318 

Figure 12: Average electricity price at various capacity factors versus 

hydrogen price for Scenario 1 

Results for Scenario 2 can be seen in Figure 4. For Scenario 2 to produce $3/kg 

hydrogen, the average electricity price would need to be below $0.031/kWh, 

$0.023/kWh, and $0.001/kWh for 97%, 73%, and 49% capacity factors, 

respectively. The equation and chart can also be used to determine what price 

hydrogen could be sold at if an average electricity rate is know for a certain 

capacity factor. For Scenario 2, if you could get $0.025/kWh average electricity 

prices from 3 p.m. until 8 a.m., the price of your hydrogen could be calculated 

using the Scenario 2 equation under 73% capacity factor. With x being your 

electricity price, you would calculate the hydrogen price (y) by multiplying 

51.208*0.025 and adding 1.8028, giving you $2.31/kg of hydrogen. 

Results for Scenario 2 can be seen in Figure 4. For Scenario 2 to produce $3/kg 

hydrogen, the average electricity price would need to be below $0.031/kWh, 

$0.023/kWh, and $0.001/kWh for 97%, 73%, and 49% capacity factors, 

respectively. The equation and chart can also be used to determine what price 

hydrogen could be sold at if an average electricity rate is know for a certain 

capacity factor. For Scenario 2, if you could get $0.025/kWh average electricity 

prices from 3 p.m. until 8 a.m., the price of your hydrogen could be calculated 

using the Scenario 2 equation under 73% capacity factor. With x being your 

electricity price, you would calculate the hydrogen price (y) by multiplying 

51.208*0.025 and adding 1.8028, giving you $2.31/kg of hydrogen. 

31
The equations for the three lines shown on the graph are as follows: 

Capacity Factor 97% 73% 49% 

Equation where x is 

electricity price in 

$/kWh and y is 

hydrogen price in $/kg 

y = 51.208x + 1.4276 y = 51.208x + 1.8028 y = 51.208x + 2.5475 

Figure 13: Average electricity price at various capacity factors versus 

hydrogen price for Scenario 2 

This analysis can also be used to see if using off-peak electricity can be 

advantageous economically. For example, in Scenario 2, if a system’s average 

electricity price were over $0.04/kWh at a 97% capacity factor the minimum 

hydrogen price would be $3.48/kg. If the average electricity price dropped to 

below $0.032/kWh at a 73% capacity factor the hydrogen produced would actually 

be less expensive at $3.44/kg. 

Results for Scenario 3 can be seen in Figure 5. For Scenario 3 to produce $3/kg 

hydrogen, a $0.043/kWh, $0.38/kWh, and $0.028/kWh electricity prices would be 

needed for 97%, 73% and 49% capacity factors, respectively. For this Scenario, 

even lower hydrogen prices are not out of the question. For $2/kg hydrogen 

$0.022/kWh, $0.017/kWh, and just under $0.01/kWh electricity would be needed 

at 97%, 73%, and 49% capacity factors, respectively. 

32
The equations for the three lines shown on the graph are as follows: 

Capacity Factor 97% 73% 49% 

Equation where x is 

electricity price in 

$/kWh and y is 

hydrogen price in $/kg 

y = 47.931x + 0.9391 y = 47.93x + 1.1776 y = 47.93x + 1.6504 

Figure 14: Average electricity price at various capacity factors versus 

hydrogen price for Scenario 3 

Results for Scenario 4 can be seen in Figure 6. For Scenario 4 to produce $3/kg 

hydrogen, $0.053, $0.50/kWh, and $0.43/kWh electricity prices would be needed 

for 97%, 73%, and 49% capacity factors, respectively. For $2/kg hydrogen 

Scenario 4 would require $0.030/kWh, $0.027/kWh, and $0.020/kWh electricity at 

97%, 73% and 49% capacity factors, respectively. This Scenario displays that 

even with the most optimistic capital costs and energy requirements if electrolysis 

is to produce hydrogen at values comparable to $2-3/gallon of gasoline, industrial 

electricity prices or lower will be required. However, this Scenario also shows that 

these prices could just be available during off peak hours, and the cost targets 

could be met. 

33
The equations for the three lines shown on the graph are as follows: 

Capacity Factor 97% 73% 49% 

Equation where x is 

electricity price in 

$/kWh and y is 

hydrogen price in $/kg 

y = 44.048x + 0.6668 y = 44.048x + 0.8161 y = 44.048x + 1.1115 

Figure 15: Average electricity price at various capacity factors versus 

hydrogen price for Scenario 4 

These results show that there is potential to produce hydrogen at the forecourt at 

the DOE cost target of $2-$3/kg using electricity 24 hours a day, or using off-peak 

electricity, if data for the site were consistent with the demand curve and electricity 

price ranges above. The potential increases if electricity prices are low enough 

and/or if some process improvements are realized. In the near term, Scenario 1, 

electricity prices would need to be available at essentially zero cost 24 hours a day 

to meet the cost target. However, Scenarios 2-4 show that if process 

improvements in the system are recognized, hydrogen may be produced below 

$3/kg if electricity is used 24 hours a day and is available in the $0.03-$0.05/kWh 

range. With off-peak electricity and electrolyzer capacity factors of 49% electricity 

prices ranging from $0.01/kWh - $0.04/kWh result in hydrogen at $3/kg in 

Scenarios 2-4. However, the low cost electricity must be available from 6 p.m. – 5 

a.m. Validation from industry should be obtained to determine where very low 

electricity prices are available off peak, and the times that those prices are 

34
available, in order to further understand the potential of hydrogen production from 

off-peak electricity. 

b. Future Enhancements

Several future enhancements could be included to make HOPE a more userfriendly model. The current version is useful to the developer, but would be 

difficult for others to use. A user interface front end could be added to aid in the 

entry of demand curves, electricity price ranges, and H2A parameters. In addition, 

currently graphs need to be manually generated. In future versions certain graphs 

could be automated. 

Additional work needs to be done to determine if modifications could be made to 

the model to better account for the storage needs during the seasonal peak 

periods that occur in 8760 data. For example, could a second electricity Price 

Point be used when storage levels decrease to a certain point? What if the 

demand were allowed to not be met 5% of the time and it is assumed hydrogen is 

purchased during those times to meet the demand? What if a maximum storage 

size is set, and the model could calculate when the demand could not be met? 

An analysis also needs to be completed to determine the effect of different 

demand profiles on the cost of hydrogen produced using off peak power. Does 

the scenario work better with a station in a residential area, or a business area, or 

on a highway? More realistic demand curves are needed for this type of analysis. 

9. Conclusions 

The goal of this report is to provide information on the HOPE model, and to use 

the model to understand if hydrogen can be produced economically at the 

forecourt using off-peak electricity. The HOPE model was developed to find the 

optimized hydrogen production system configuration for any electricity pricing 

structure. The optimized system was considered to be the system that resulted in 

the lowest price of hydrogen. 

The HOPE model was used to run four different analysis stages that help to 

understand the effects of using off-peak electricity on a forecourt electrolysis 

system. In the first stage an analysis was run using electricity prices from 

$0.01/kWh to $0.24/kWh where the prices were varied each hour by one cent; 

three Cases were run where the electricity price was varied from high to low, low 

to high, and randomly. This analysis showed that regardless of when hydrogen is 

produced during the day, the number of hours electricity is available determines 

the size of the electrolyzer and the size of the compressor. Likewise, the design 

capacity of the plant, plant output, capacity factors, and average electricity are the 

same for the same electricity Price Point across all three Cases. The differences 

35
in the systems come about in the storage requirement. Thus the storage 

requirement was deemed to be a critical parameter for this analysis. 

Three simple electricity pricing Cases were run in the second stage of the analysis 

where the electricity price range for the Cases was set to $0.04/kWh during 21 

hours out of the day, and during the other three hours of the day, the price was 

varied from $0.08/kWh to $0.09/kWh to $0.10/kWh. Two runs were made for each 

Case, one where the electricity Price Point was the high price, and one where it 

was the low price of $0.04/kWh. Using off-peak electricity was only advantageous 

when the peak price was $0.10/kWh, not at the lower peak prices. This analysis 

shows the importance of the size of the peak when using off-peak electricity. As a 

rule of thumb it was assumed that if the peak price of a system is large enough, 

off-peak electricity should be used. 

In the third stage, the HOPE model was used with hypothetical 24-hour pricing 

structures that varied electricity prices during the day from $0.01/kWh - 

$0.20/kWh. Two pricing structures were tested, and the results of both Cases 

show that for the Scenarios run, and the pricing structures and demand curve 

used, using off-peak electricity resulted in lower prices. In addition, as the system 

costs reduced and efficiencies improved from Scenario to Scenario, lower cost 

electricity can be used at a lower capacity factor to produce cheaper hydrogen. 

Finally, in the fourth stage, the results of the analysis using hypothetical data in 24 

hour increments was challenged by the addition of an analysis using real time data 

from ISO New England. The hourly (8760) electricity prices for two nodes were 

entered into the HOPE model. Appropriate Price Points for the data were entered, 

the system was optimized, and the resulting hydrogen prices were found. It was 

found that eliminating high peak prices lowered (or did not increase) the price of 

hydrogen, which demonstrates that if utilities could disrupt power to the 

electrolysis units during peak electricity demands, it would not adversely affect the 

price of hydrogen. However, as capacity factors even slightly decrease beyond 

peak shaving it appears as though increased storage requirements would be 

detrimental to using true off-peak electricity to produce hydrogen due to seasonal 

peaks in electricity prices. This finding should be verified with future versions of 

the HOPE model after storage functionality is enhanced. 

The HOPE model provides the following capabilities for any given pricing structure 

and demand curve: 

• Determines the best off-peak pricing structure for the production of 

hydrogen using both 24 hour and 8760 hour data 

• Determines how hydrogen production system configurations change with 

different electricity price distributions 

• Determines if off-peak electricity can lead to a lower hydrogen price 

• Evaluates how system improvements change the hydrogen production 

system and the optimum off peak pricing structure 

36
• Provides data to help calculate how best to run an electrolysis hydrogen 

production system 

In short, the HOPE model can help answer the question for any site: does using 

off-peak electricity for electrolysis lead to lower hydrogen prices? In addition, the 

HOPE model can help clarify under what conditions forecourt electrolysis at 

different locations can meet the DOE hydrogen cost targets of $2 - $3/kg.

37
10. Appendix A - Bibliography 

Acquiring Operational Data from ISO New England. ISO New England, 

<http://www.iso-ne.com/smd/operations_reports/acquiringdata.pdf>, March 

12, 2004 

Eliassen, Bjorn E. Norsk Hydro Electrolyzers Electrolyzer Hydrogen Plant 485 

Nm3/h Hydrogen. Norsk Hydro Electrolyzers, AS, Aug, 8 2002. 

H2A Model website, 

<http://www.eere.energy.gov/hydrogenandfuelcells/analysis/model.html>, 

1/31/2005. 

Hydrogen, Fuel Cells and Infrastructure Technologies Program: Multi-Year 

Research, Development, 

<http://www.eere.energy.gov/hydrogenandfuelcells/mypp/>, June 22, 2005 

ISO New England - Day-Ahead Locational Marginal Prices. ISO New England, 

<http://www.iso-ne.com/smd/operations_reports/hourly.php>, May 9, 2005 

Ivy, J. S. (2004). “Summary of Electrolytic Hydrogen Production”. Golden, CO: 

National Renewable Energy Laboratory; 27 pp.; NREL Report No. MP-560- 

36734 

James, Bryan. H2A Model - Forecourt Hydrogen Production (Electrolyzer, 

1500kgH2/day, Yr 2005), Directed Technologies Inc., 2004 

James, Bryan. H2A Model - Forecourt Hydrogen Production (Electrolyzer, 

1500kgH2/day, Yr 2015), Directed Technologies Inc., 2004 

James, Bryan. H2A Model - Forecourt Hydrogen Production (Electrolyzer, 

1500kgH2/day, Yr 2030), Directed Technologies Inc., 2004 

Levene, Johanna. H2A Model - Hydrogen Generation from Wind (2005), National 

Renewable Energy Laboratory, 2005 

Levene, Johanna. H2A Model - Hydrogen Generation from Wind (2015), National 

Renewable Energy Laboratory, 2005 

Levene, Johanna. H2A Model - Hydrogen Generation from Wind (2030), National 

Renewable Energy Laboratory, 2005 

NYISO (Market Data - Pricing Data). New York Independent System Operator, 

May 6, 2005 <http://www.nyiso.com/public/market_data/pricing_data.jsp> 

38
Paster, Mark. Personal interview. 9 Jun. 2005. 

Ringer, Matt. Personal interview. 20 Jun. 2005. 

39
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Production of Hydrogen at the Forecourt Using Off-Peak Electricity 

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