Hydrogen Infrastructure

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International Journal of Hydrogen Energy 30 (2005) 15231534www.elsevier.com/locate/ijhydene

Hydrogen infrastructure strategic planning usingmulti-objective optimization

Andr Hugoa, Paul Ruttera, Stratos Pistikopoulosa,, Angelo Amorellib, Giorgio Zoiac

a Centre for Process Systems Engineering, Imperial College London, London, SW7 2BY, UKbBP Gas, Power & Renewables, Sunbury, UK

cBP Gas, Power & Renewables, Naperville, IL, USA

Received 9 February 2005; received in revised form 15 February 2005; accepted 8 April 2005

Available online 29 June 2005

Abstract

Increasingly, hydrogen is being promoted as an alternative energy carrier for a sustainable future. Many argue that its use

as a transportation fuel in fuel cell vehicles offers a number of attractive advantages over existing energy sources, especially

in terms of well-to-wheel greenhouse gas emissions. Following this interest, several of the leading energy companies, like

BP, have started investigating strategies for its introduction. The challenge of developing a future commercial hydrogen

economy clearly still remains, though: what are the energy efficient, environmentally benign and cost effective pathways to

deliver hydrogen to the consumer? Establishing what these best pathways may be is not trivial, given that a large number

of technological options exist and are still in development for its manufacturing, storage, distribution and dispensing. Cost,

operability, reliability, environmental impacts, safety and social implications are all performance measures that should be

considered when assessing the different pathways as viable long-term alternatives. To aid this decision-making process, we

present a generic optimization-based model for the strategic long-range investment planning and design of future hydrogen

supply chains. By utilizing Mixed Integer Linear Programming (MILP) techniques, the model is capable of identifying optimal

investment strategies and integrated supply chain configurations from the many alternatives. Realizing also that multiple

performance criteria are of interest, the optimization is conducted in terms of both investment andenvironmental criteria, with

the ultimate outcome being a set of optimal trade-off solutions representing conflicting infrastructure pathways. Since many

agree that there is no one single template strategy for investing in a hydrogen infrastructure across the globe, emphasis is placed

on developing a generic model such that it can be readily applied to different scenarios, geographical regions and case studies.

As such, the model supports BPs strategic hydrogen infrastructure planning using high-level optimization programming, and

is coined bpIC-H2. The features and capabilities of the model are illustrated through the application to a case study.

2005 International Association for Hydrogen Energy. Published by Elsevier Ltd. All rights reserved.

Keywords: Hydrogen infrastructure; Strategic supply chain planning; Mixed integer linear programming; Multi-objective optimization;

Greenhouse gas emissions

Corresponding author. Tel.: +44 20 75946620;

fax: +44 20 75941129.

E-mail address: [email protected]

(S. Pistikopoulos).

0360-3199/$30.00 2005 International Association for Hydrogen Energy. Published by Elsevier Ltd. All rights reserved.

doi:10.1016/j.ijhydene.2005.04.017

1. Introduction

Driven by concerns over urban air quality, global warm-

ing caused by greenhouse gas (GHG) emissions and energy

security, a transition from the current global energy sys-

tem is receiving serious attention. Increasingly alternative
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1524 A. Hugo et al. / International Journal of Hydrogen Energy 30 (2005) 1523 1534

economies are being suggested, whereby the growing energy

demand of the future is met with greater efficiency and with

more renewable energy sources such as wind, solar and

biomass. This implies a gradual shift away from the reliance

on conventional hydrocarbon-driven technologies towards

more innovative carbon-neutral sustainable ones.

Using hydrogen in fuel cell applications offers a number

of advantages over existing fuels and other emerging com-

petitors, especially in the transportation sector. It is a high-

quality carbon-free energy carrier, which can achieve im-

proved efficiencies at the point of use with reduced or zero

GHG emissions over the entire well-to-wheel (WTW) life

cycle. These benefits are even further underpinned by the

fact that hydrogen can be manufactured from a number of

primary energy sources, such as natural gas, coal, biomass

and solar energy, contributing towards greater energy secu-

rity and flexibility. Based on these attributes, a number of

long-term strategic initiatives have been undertaken to pro-

mote the development of national and regional hydrogeneconomies [13].

Despite its benefits, the challenge of developing a future

hydrogen economy is clear: what are the most energy effi-

cient, least damaging and cost effective pathways to deliver

hydrogen to the consumer? For hydrogen to succeed as the

fuel of the future, we need technical and commercial break-

throughs not only in vehicle technology but also for the cre-

ation of an entirely new fuelling infrastructure. The intro-

duction of any new transportation fuel requires a significant

capital investment and long-term commitment while facing

high risks of poor short-term returns. It requires a simulta-

neous delivery of the new fuel at the refuelling stations andintroduction of new vehicles on the road, since neither is

of any use without the other. Vehicle manufacturers require

high density hydrogen refuelling stations before investing in

mass production of fuel cell vehicles (FCVs), while energy

companies are hesitant to install hydrogen production, dis-

tribution and refuelling infrastructures without having the

assurance of profitable demand levels.

The challenge is even further complicated when trying

to select the optimal delivery pathway, given that a large

number of technological options exist for hydrogen delivery.

Timing of the investment over the next 1030 years will

also be critical. The transition to a sustainable hydrogen

economy is therefore a complex strategic planning problemwith considerable economic consequences. It is essential

to model these interactions in advance so that the number

of options can be reduced to a manageable set for further

detailed analysis.

2. Hydrogen infrastructure pathway options

A hydrogen infrastructure is defined as the supply chain

required to manufacture, store and deliver hydrogen to the

consumer. Like any supply chain it consists of several dis-

tinct components. Production processes are required to con-

vert primary energy resources into hydrogen. Storage units

and terminals are needed to compensate for fluctuations in

demand. Distribution systems are essential for transporting

hydrogen from the production facilities to the point of sale.

Finally, dispensing/refuelling technologies allow transfer of

hydrogen to users at forecourt retail stations.

At each of these stages along the infrastructure a wide

variety of potential technological options exist, as is repre-

sented in Fig. 1. Not only can hydrogen be manufactured

from a variety of primary energy feedstocks, but it can also

be distributed in a variety of forms using different technolo-

gies. Gaseous hydrogen, for example, can be distributed in

dedicated pipelines over long-distance (as is in place in the

Rhein-Ruhr area in Germany over a distance of 200 km),

while liquefied hydrogen can be transported by rail, ship

or road in tankers. An additional dimension exists when

defining the location of production within the supply chain.

Unlike most other fuel infrastructures, hydrogen can be

produced either centrally or distributed. A centralized pro-duction option would be analogous to current gasoline sup-

ply chains, where the economies of scale are capitalized

upon within an industrial context and large quantities are

produced at a central site and then distributed. Alternatively,

through the use of small-scale reformers and electrolyzers,

hydrogen can be produced closer to the point of use, i.e. on-

site, in smaller quantities. Such a scenario would exploit the

existing natural gas and electricity grid to produce hydro-

gen at the forecourt refuelling stations, thereby alleviating

the significant cost of distribution.

For brevity, this article will not fully explain all the var-

ious technological options in detail, but instead emphasizethat there are many potential supply chain configurations

that can be invested in. Interested readers can consult the

extensive reviews by Padr and Putsche [4] and Ogden [5]

for detailed discussions of the individual hydrogen infras-

tructure technologies.

Each of the delivery pathway options has its own unique

advantages and disadvantages. Cost, operability, reliability,

environmental impacts, safety and social implications are

all performance measures that should be considered when

assessing and comparing the different pathways. It can also

be expected that trade-offs between these metrics will ex-

ist and each option will have its own attributes. Selection

of the best delivery pathway, therefore, involves compar-ison of the various technological options in terms of mul-

tiple performance criteria, with the ultimate goal being to

define a strategy whereby the infrastructure investment can

be planned with confidence.

One such strategy widely proposed for initiating and de-

veloping a hydrogen infrastructure is through incremental

additions and transitions [6,7]. According to this scenario

existing energy infrastructures for natural gas and electricity

are leveraged during the initial starting phases. Distributed

production would take place on-site at refuelling stations

using small-scale reformers supplied by the existing nat-

ural gas network or electrolyzers drawing electricity from
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A. Hugo et al. / International Journal of Hydrogen Energy 30 (2005) 1523 1534 1525

Fig. 1. Potential technology components within hydrogen infrastructure pathways (taken from BP hydrogen website: http://www.bp.com/

hydrogen/).

the grid. Initial applications will be in FCVs used in niche

markets involving fleet vehicles, such as busses, taxis and

courier services, returning to a central depot for nightly re-

fuelling. Purchasing of these first vehicles can be promoted

through government subsidies and publicprivate partner-

ships. Progressively, as the cost of FCV mass production

becomes cheaper, commercial sales can be launched to the

public and subsidies can be lifted. Increased demand will

then facilitate transferring the distributed production to cen-

tral facilities where the economies of scale associated with

large-scale hydrogen manufacturing can be exploited. Pro-

duction from natural gas using steam-methane-reforming

(SMR) can be complemented then with a number of otherenergy feedstocks, with a gradual introduction of renewable

energy sources. Delivery of the hydrogen from the central

production facilities to the refuelling stations can start out

by road tanker distribution, with the long-term objective be-

ing the installation of a dedicated hydrogen pipeline delivery

network similar to the natural gas grid that currently exists.

While such an infrastructure development strategy might

relieve the initial financial commitment and reduce the as-

sociated investment risk, it should be remembered that it is

merely one of many possible strategies that can be adopted.

Also of importance is the local market conditions and how

the regional primary energy feedstock availabilities can be

utilized. For example, Iceland is using electricity from their

large geothermal resources to generate hydrogen by elec-

trolysis to initiate their transition to a hydrogen economy.

In China, however, the use of polygeneration using coal as

a feedstock may create an economic source of hydrogen.

Most advocates agree that there is no single supply chain

solution template for investing in a hydrogen infrastructure.

Instead, it is necessary to have a generic framework that can

analyze and compare the performance of the various inte-

grated pathway options on a consistent basis.

A number of comparative studies have previously been

conducted to assess the performance of various pathway op-

tions [811]. In their work, a number of assumptions aremade concerning the level of expected hydrogen demand,

distribution distance, size of production units and relative

prices of the primary energy feedstocks. Individual prede-

fined pathways are simulated and compared using a key per-

formance indicator such as cost, GHG emissions or energy

efficiency. While such simulation-based analyses provide in-

valuable insight into the relative costs and benefits of the

various hydrogen infrastructure options, most of the studies

conducted so far are limited in their general applicability.

Few studies consider the dynamic changes to infrastructure

over time and how transitions from one pathway to another

should take place as market conditions change. Largely, the
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emphasis is on individual pathway steady state simula-

tions. Given the expected changes in hydrogen demand lev-

els, FCV geographical distribution patterns, energy prices,

GHG mitigation legislation and technology performances,

it is crucial to accommodate the timing of the investment

when analyzing the various pathway options.

The aim of this paper is to present a generic optimization-

based model to facilitate the design and planning of hy-

drogen infrastructures. As opposed to previous simulation-

based approaches this model utilizes formal optimization

techniques to allow advanced decisions such as the timing

of the investment to be captured and to provide comprehen-

sive integrated solutions for investment recommendations.

The model is also able to assess the performance of differ-

ent infrastructure scenarios involving different technologies

and raw material feedstocks. The model is generic and can

be applied on a case-specific basis across different regions,

e.g. Southern California, Greater London Area, Germany or

Japan, to take account of their unique characteristics. Sincemultiple performance indices are of interest, the model as-

sesses options both in terms of investment and environmental

impact criteria to identify optimal infrastructure pathways

and investment strategies. As such, the model supports BPs

hydrogen infrastructure strategic planning decisions and is

coined bpIC-H2. It is the result of a research collaboration

between BP Gas Power & Renewables and the Centre for

Process Systems Engineering, Imperial College London.

Fundamental to the model is the use of mixed integer

linear programming (MILP) techniques to capture the in-

teractions between the various components of the hydrogen

infrastructure. Most readers will be familiar with the linearprogramming (LP) model, which has a long established his-

tory of providing operational, management and investment

decision support in the processing and energy industries. The

standard LP problem can obtain an additional degree of func-

tionality when some of the decision variables are limited to

a discrete/integer domain, giving rise to the MILP problem.

While computationally more intensive, MILP allows vari-

ous propositional logical operations associated with strate-

gic decision-making to be modelled. For example, an inte-

ger variable can be defined such that it determines whether

a processing unit should be invested in or not. Because of

its capability to naturally capture logical conditions, appli-

cations of MILP have been widespread in areas of invest-ment planning, supply chain and logistics management, en-

ergy industry planning, engineering design and production

scheduling [12,13].

3. Putting theory into practice: Model overview

To apply a tool such as MILP to model the strategic

investment decisions associated with developing a future

hydrogen infrastructure, it is necessary to explicitly consider

some of the unique features of hydrogen supply chains. More

specifically, the model must be able to accommodate:

1. A long-term future planning horizon.

2. State of the existing infrastructureespecially, the natu-

ral gas distribution network, electricity grid and existing

mercantile hydrogen production facilities (e.g. any ex-

cess reforming capacity at refineries).

3. Multiple and diverse primary energy feedstocks and pro-

duction technologies.

4. Both large-scale centralized production and small-scale

distributed/on-site/forecourt production.

5. Both gaseous and liquid distribution.

6. Economies of scale of large-scale production and distri-

bution technologies.

7. Transitions from one supply chain structure to another

over time, involving the decommissioning of certain tech-

nologies and re-investment in others.

8. Geographical site allocation of technologies.

9. Multiple performance indicatorsboth financial andenvironmentalthat can drive the decision-making.

In Fig. 2 the superstructure representation that forms the

basis of the hydrogen supply model is shown [14]. The su-

perstructure acts as the overriding model, capturing all the

possible alternatives and interactions between the various

supply chain components. From this superstructure the opti-

mization algorithm then searches for the best combinations

by eliminating the existence of units and the links between

them. The superstructure starts with a set of primary energy

resources:

r R := {Natural Gas, Coal, Biomass,

Renewable Electricity, . . .}

which can be used as feedstocks for producing hydrogen

at a set of s S geographical industrial sitessuch as

refineriesusing any of the large-scale centralized manu-

facturing technologies:

j J := {Steam Methane Reforming, Gasification,

Electrolysis, . . .}.

Each of these production technologies are defined such thatthey can perform conversion of primary energy feedstocks

into an intermediate that is suitable for distribution:

l L := {Compressed Natural Gas, Liquid H2,

Compressed Gaseous H2 . . .}.

These intermediates are then delivered from the production

sites to the set of forecourt refuelling stations (markets),

m M, using a corresponding distribution technology:

p P := {Natural Gas Pipeline, Liquid H2 Truck,

Compressed Gaseous H2 Tube-Trailer . . .}.
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Fig. 2. Model superstructure.

At the refuelling stations, the intermediates are dispensed

as the final product, namely hydrogen for fuel cell vehicles,

using the appropriate forecourt technology, q Q. This

mathematical representation also allows distributed on-site

production to be explicitly considered as a pathway option.This is achieved by defining the set of forecourt technology

options to include both technologies for dispensing hydro-

gen received from the central production facilities as well

as technologies for small-scale production:

q Q := {Liquid H2 Dispensing, Small-Scale Reforming,

Small-Scale Electrolysis . . .}.

To understand how the representation can be used to model

specific supply chains, Fig. 3 shows an example where an

integrated supply chain involving natural gas as the primary

energy feedstock is derived from the superstructure. In thebottom pathway, production of hydrogen for FCVs takes

place on-site at the refuelling station using small-scale re-

forming. Compressed natural gas is supplied for this fore-

court production through a natural gas pipeline from a cen-

tralized compression facility. In the top pathway, centralized

production through large-scale reforming to liquid hydro-

gen is performed. Distribution from this production facility

takes place in a liquid hydrogen truck to the refuelling sta-

tion where liquid hydrogen dispensing is used to deliver the

final product to the customer.

The primary objective of the model is to support the op-

timal strategic investment planning and asset management

of hydrogen supply chain networks over a long-term future

horizon, t T. The model achieves this by making optimal

decisions in terms of four levels:

Level 1: Strategic supply chain design

Selection of primary feedstocks.

Allocation of conversion technologies to production

siteswhere to install which production techno-

logies.

Assignment of distribution technologies to link pro-

duction sites to forecourt marketswhich markets to

supply with the selected sites.

Level 2: Capacity and shut-down master planning

Capacity expansion planning of production, distri-

bution and refuelling technologieswhen to expand

which technologies.

Shut-down planning of production, distribution andrefuelling technologieswhen to switch production

technologies.

Level 3: Production planning

Estimation of how much of each primary energy feed-

stock the selected technologies require and what the

rates of H2 production, distribution and refuelling at

each stage along the supply chain are.

Level 4: Performance index assessment and trade-off

analysis

Computation of financial and ecological objectives.

Multi-objective optimization to establish set of optimal

compromise solutions.


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Fig. 3. How a natural gas based infrastructure can be represented using the superstructure.

Using MILP modelling techniques constraints can be for-

mulated representing these various decisions. For brevity,

the underlining mathematical model is not presented here,

but in Fig. 4 it is illustrated how such constraints and the use

of integer variables can capture some of the strategic deci-

sions. In addition to the constraints describing the physical

phenomena, objective functions also have to be formulated

for the financial and ecological performance criteria. These

objective functions drive the optimization in search of the

best investment strategy and supply chain design. Since a

long-term future investment horizon is of interest, the netpresent value (NPV) is chosen as the financial performance

measure, thereby capturing both the capital expenditure and

operating cost requirements of the supply chain as well as

the time value of the investment over the future horizon.

Assessment of the environmental performance of the com-

peting hydrogen infrastructures can be performed using the

cumulative WTW GHG emissions that result from deliver-

ing hydrogen to the FCV consumer.

To derive the GHG emissions objective function over the

entire supply chain (life cycle), from well to wheel, it is

firstly necessary to define the set of chemicals known to

contribute towards the greenhouse effect:

e E := {CO2, CH4, N2O, . . .}.

Next, using the Intergovernmental Panel for Climate Change

(IPCC) guidelines, a vector of corresponding global warm-

ing potential (GWP) factors, e, expressed as CO2 equiv-

alents need to be constructed [15]. These characterization

factors are expressed relative to the GWP of CO2 and de-

pend on the time horizon over which the global warming

effect is assessed. Short time periods (2050 years) consider

the more immediate effects of greenhouse gases on the cli-

mate, while longer periods (100500 years) are used to pre-

dict cumulative effects of gases on the global climate. For

example, when considering the effect over 100 years:

CO2 = 1; CH4 = 21; N2O = 310.

It is also necessary to determine the inventory of GHG emis-

sions associated with the unit reference flow of each supply

chain activity. For example, er is the amount of green-

house gas e emitted during the unit extraction, processing

and delivery of primary energy feedstockr, while ej is the

amount of greenhouse gas e emitted during the unit hydro-

gen production using technology j.

Then, assuming that emissions are linearly proportionalto the production, delivery and dispensing rates, the well-to-

wheel GHG emissions objective function is simply given as

the cumulative sum over the entire planning horizon, over

all the individual supply chain activities.In its entirety the model for the optimal planning and

design of hydrogen infrastructures is then formulated as amulti-objective MILP problem, summarized as follows:

minimizex,y

U

f1 = Net Present Value [$]

f2 = WTW GHG Emissions [kgCO2 eq]

subject to:

h(x,y) = 0

g(x,y)0

PRIMARY ENERGY SELECTION

SITE ALLOCATION

DISTRIBUTION NETWORK DESIGN

CAPACITY EXPANSION & SHUT-DOWN

PLANNING

TECHNOLOGY SELECTION

COST CORRELATIONS

MATERIAL & ENERGY BALANCES

DEMAND SATISFACTION

GHG EMISSIONS IMPACT ASSESSMENT

x Rn, y Y = {0, 1}m,

where the goal is to find values of the operational (x Rn)

and strategic (y Y={0, 1}m) decision variables, subject to

the set of equality (h(x, y) = 0) and inequality (g(x,y)0)

constraints, such that the utility function (U) is optimized


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Fig. 4. Example of mixed integer programming for modelling strategic decisions.

in terms of the two objective functions (f1, f2). In our for-

mulation, the continuous operational variables capture de-cisions related to, for example, production and distribution

rates, while the discrete strategic variables model the capac-

ity expansion/shut-down and investment decisions. The two

objective functions chosen are the net present value of the

investment evaluated over the long-term planning horizon

and the cumulative well-to-wheel GHG emissions.

It can be expected that there is a conflict between these

objectives, i.e. the most profitable infrastructure is not nec-

essarily also the least environmentally damaging. Because

of this trade-off there is not a single solution to this class

of problem. Instead, the solution is a set of multiple com-

promises known as the Set of Efficient or Pareto Optimal

Solutions (also referred to as non-inferior and non-dominant

solutions). Each solution within the set represents an alter-native supply chain configuration and corresponding invest-

ment strategy, each achieving a unique combination of envi-

ronmental and economic performance. A solution is said to

be efficient (pareto optimal) if it is not possible to find an-

other feasible solution so as to improve one objective with-

out worsening at least one of the others.

The value of formulating the decision-making process

within a multi-criteria optimization framework is that it does

not require the a priori articulation of preferences by the

decision-maker. Instead, the aim is to generate the full set

of trade-off solutions and not to present only one single

best alternative. From the setof alternatives, the decision-


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maker can then further investigate interesting trade-offs and

ultimately select a particular strategy that satisfies his/her

willingness to compromise.

4. Case study application

To illustrate the features of the model, the results of an

industrial case study conducted are presented here. The case

study problem specification is depicted in Fig. 5. It consists

of a geographical region where 6 production sites have been

identified for the potential installation of central production

technologies. Demand for hydrogen by FCV drivers is ex-

pected at 6 major cities, acting as the markets in the formu-

lation. Of the 6 central production sites, some are existing

refineries, chemical complexes and natural gas compression

stations. This limits the technologies that are allowed to be

installed there. In Fig. 6 the hydrogen demand forecast for

the geographical region over the planning horizon is rep-resented. It shows the expected number of FCVs that will

require hydrogen per year during each of the planning in-

tervals. The long-range planning horizon is defined as the

period from 2004 to 2038 divided into 5 intervals of 7 years

each. The shape and trajectory of the forecast is based upon

the common prediction, whereby three phases are expected.

During early initial introductory stages, hydrogen demand is

expected to be limited to niche markets involving fleet vehi-

cles in urban areas where refuelling can take place overnight

at a central depot. Later, as FCV manufacturing costs are

reduced and the problems associated with their range and

Fig. 5. Case study geographical problem specification.

Fig. 6. Hydrogen demand forecast for the case study.

refuelling are overcome, wider commercialization will lead

to the sharp growth in the demand for hydrogen. Eventually,

as market maturity is reached, FCVs sales will increase less

rapidly and the demand in the region will reach an equilib-

rium with other competing technologies and stabilize. For

the geographical region investigated in the case study, the

market forecast is based on the assumption that 25% of the

vehicle fleet will be FCVs by 2038.

Applying the multi-objective optimization approach to the

case study results in the set of trade-off solutions presented

in Fig. 7. At the one extreme of the curve lies the maxi-

mum NPV solution. Its corresponding infrastructure is en-tirely based upon natural gas utilizing centralized SMR at

optimally selected central production sites to manufacture

liquid hydrogen. Accordingly, the optimum distribution net-

work delivers the liquid hydrogen in trucks to the forecourt

markets where dispensing takes place. At the other extreme,

the minimum GHG emissions solution corresponds to an

infrastructure involving gaseous hydrogen being produced

from renewable electricity through electrolysis of water.

Also indicated are the WTW GHG emissions of the equiva-

lent gasoline infrastructure. All the hydrogen infrastructure

strategies captured within the optimal trade-off curve result

in less GHG emissions than the business-as-usual case.

Moving along the trade-off front from one extreme to theother involves a series of distinct infrastructures. Noting that

each solution within the set represents an alternative infras-

tructure design and investment strategy, the extent of the

compromise between the solutions achieving maximum re-

turn on the investment and minimum GHG emissions can be

explicitly quantified. The optimal trade-off front can be bro-

ken into critical enterprises based upon the different feed-

stock, production, distribution and refuelling components of

the supply chain that are consistent over a specific region

of the curve (as shown in Fig. 8). Table 1 contains the de-

tailed supply chain component descriptions corresponding

to these critical enterprises.


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Fig. 7. Optimal trade-off results for the case study.

Fig. 8. Critical enterprise breakdown of the trade-off front.

Starting at the maximum NPV strategy (Enterprise 1)

involving only natural gas, the optimal transition towards

reducing the GHG emissions requires the introduction of

biomass gasification (BM-GAS-LIQ) as a complimentary

production technology (Enterprise 2). Further reductions in

GHG emissions can be achieved while remain cost compet-

itive (Enterprise 3) by introducing on-site generation of hy-

drogen at the forecourts through reforming (OS-NG-SMR)

of natural gas delivered through the existing distribution grid

(NG-COMP-CNG, NG-PIPE). Progressively, natural gas re-

forming both centrally and on-site needs to be abandoned

to achieve the desired level of GHG emissions mitigation

(Enterprise 5). The most profitable option for such a supply

chain, based entirely upon renewable primary energy feed-

stocks, involves large-scale manufacturing of liquid hydro-

gen through biomass gasification (BM-GAS-LIQ) combined


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Table 1

Components of the critical enterprises in the trade-off front

Identifier Supply chain component

r j p q

Enterprise 1 NG NG-SMR-LIQ TRUCK LIQ-DIS

Enterprise 2 NG NG-SMR-LIQ TRUCK LIQ-DIS

BM BM-GAS-LIQ

Enterprise 3 NG NG-COMP-CNG

BM NG-SMR-LIQ NG-PIPE OS-NG-SMR

BM-GAS-LIQ TRUCK LIQ-DIS

Enterprise 4 NG NG-COMP-CNG NG-PIPE OS-NG-SMR

BM BM-GAS-LIQ TRUCK LIQ-DIS

RE RE-ELC-LIQ

Enterprise 5 BM BM-GAS-LIQ TRUCK LIQ-DIS

RE RE-ELC-LIQ


RE R E-ELC-LIQ TUBE GAS-DIS

RE-ELC-GAS


RE R E-ELC-LIQ TUBE GAS-DIS

RE-ELC-GAS OS-RE-ELC

Enterprise 8 RE RE-ELC-GAS TUBE GAS-DIS

H2-PIPE OS-RE-ELC

Enterprise 9 RE RE-ELC-GAS H2-PIPE GAS-DIS

OS-RE-ELC

with electrolysis using renewable electricity (RE-ELC-LIQ).

Any further optimal reduction in emissions beyond this point

requires replacing liquid hydrogen (-LIQ) with gaseous hy-

drogen (-GAS) and relying entirely on renewable electrol-

ysis (moving from Enterprise 6 to 9). The correspondingdistribution network design for this transition towards max-

imum emissions mitigation first involves gaseous hydrogen

distribution in tube-trailers (TUBE), followed by a hydro-

gen pipeline delivery network (H2-PIPE).

When analyzing the features of the optimal enterprises

in more detail, one realizes that certain technologies and

primary energy feedstocks are not present in the optimal

trade-off front. The multi-objective optimization framework,

therefore, not only facilitates the identification of the most

promising candidates, but also assists the elimination of

inferior ones. More specifically, under the specifications

of the case study, neither coal, petroleum coke, nuclear

electricity nor non-renewable electricity appear as primary

energy feedstocks in the set of efficient solutions. The reason

being that the production technologies relying upon these

feedstocks do not offer either competitive financial returns or

the environmental benefits relative to the others. Of course,

as technologies develop at different rates in the future the

structure of the optimal solutions may change radically. For

example, introduction of carbon sequestration, improvement

in biomass gasification technologies and reduced renewable

electricity costs can all drastically change the shape of the

optimal trade-off front. The solutions presented here for the

case study, though, are based upon best data presently avail-

able of technologies considered proven to date.

To highlight the characteristics of the solution obtained

from the model, one of the optimal compromise solutions

is isolated and presented in Fig. 9. It corresponds to a solu-

tion that has a GHG emissions objective of 4.6 1010 kg

CO2-eq (approximately half of the maximum emissions of

8.8 1010 kg CO2-eq). The optimal supply chain designand investment strategy for this particular level of GHG

emission mitigation starts with on-site generation through

small-scale reforming using natural gas from the grid. First

the forecourt stations within the cities with the earliest de-

mands utilize this strategy. Growing demand at the other

cities then later allows the economies of scale to be ex-

ploited by decommissioning the forecourt production and

switching to centralized manufacturing of liquid hydrogen

through biomass gasification. This substitutes the natural

gas reforming, thereby facilitating the GHG emissions mit-

igation. Further emissions reductions are achieved by intro-

ducing electrolysis using renewable electricity as a comple-mentary central manufacturing technology.

5. Conclusions

For hydrogen to succeed as the fuel of a sustainable future,

a commitment is required to create an entirely new fuelling

infrastructure, from production, through storage and distri-

bution, to dispensing. Any investment strategy for building-

up a hydrogen supply chain needs to be supported by rig-

orous quantitative analysis that takes into account all the

possible alternatives, interactions and trade-offs. To assist

this strategic decision-making process, the paper presentsa generic model for the optimal long-range planning and

design of future hydrogen supply chains for fuel cell ve-

hicles. Unlike previous studies, where discrete steady state

simulations of various pathways have been compared, the

model presented here utilizes mixed integer optimization

techniques to provide optimal integrated investment strate-

gies across a variety of supply chain decision-making stages.

Key high-level decisions addressed by the model are the

optimal selection of the primary energy feedstocks, allo-

cation of conversion technologies to either central or dis-

tributed production sites, design of the distribution tech-

nology network and selection of refuelling technologies.


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Fig. 9. Sample compromise investment strategy obtained from the model.

At the strategic planning level capacity expansions as well

as technology shut-downs are captured to explicitly address

the dynamics of the infrastructure and the timing of the in-

vestment. Low-level operational decisions addressed includethe estimation of primary energy feedstock requirements and

production, distribution and refuelling rates. Realizing that

both financial and ecological concerns are driving the inter-

est in hydrogen, formal multi-objective optimization tech-

niques are used to establish the optimal trade-off between

the NPV of the investment and the WTW GHG emissions.

To illustrate the capabilities of the model, the results of an

industrial case study have been presented. Through the study

it was shown how the model can identify optimal supply

chain designs, capacity expansion policies and investment

strategies for the given geographical region. In particular,

the set of trade-off solutions, allows the most promising

pathways to be isolated and the inferior ones to be eliminated

from further consideration.

Acknowledgments

Imperial College would like to gratefully acknowledge

the financial support of BP p.l.c. for conducting the research.

The authors would also like to thank the other members of

the BP hydrogen team for the engaging discussions.

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Hydrogen Infrastructure