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Hydrogen infrastructure design and optimization: A case study of China
international journal of hydrogen energy 33 (2008) 5275–5286
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Hydrogen infrastructure design and optimization: A case study of China Zheng Lia,*, Dan Gaoa, Le Changa, Pei Liub, Efstratios N. Pistikopoulosb a
Tsinghua BP Clean Energy Research and Education Center, Department of Thermal Engineering, Tsinghua University, Beijing 100084, People’s Republic of China b Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UK
article info
abstract
Article history:
Infrastructure issues pose more challenges and uncertainties for hydrogen than other
Received 30 January 2008
alternative ‘‘fuels’’ such as biofuels and electricity. A key challenge of developing a future
Received in revised form
commercial hydrogen economy is how the infrastructure will be best designed and operated as
18 June 2008
time progresses, given that numerous technological options exist and are still in develop-
Accepted 19 June 2008
ment for hydrogen production, storage, distribution and dispensing. This paper presents
Available online 20 September 2008
a generic optimization-based model for the strategic dynamic investment planning and design of future hydrogen supply chains. The features and capabilities of the model are
Keywords:
illustrated through a detailed case study of China. It is shown how the proposed meth-
Hydrogen infrastructure
odology can provide policy-makers with new tools for hydrogen development strategies.
Supply chain
ª 2008 International Association for Hydrogen Energy. Published by Elsevier Ltd. All rights
Mixed integer programming
reserved.
Superstructure
1.
Introduction
Driven by concerns over global warming caused by excessive greenhouse gas (GHG) emissions, energy security and urban air quality, a transition from the current energy system has become an urgent and essential task which receives significant attention. A possible solution is through large-scale implementation of alternative energy, where fast growing energy demand can be partly compensated by higher energy conversion efficiency and larger share of renewable energy sources, i.e. hydro, wind, solar and biomass, in the primary energy supply. This transition of the primary energy source implies corresponding substitutions of dominating fossil fuel based technologies by ones which are more carbon-neutral and sustainable. Shifting from conventional petroleum oriented fuel to hydrogen in the transportation sector is one potential direction as utilization of hydrogen with fuel cells may offer many advantages over the existing system. Hydrogen is a type
of high-quality carbon-free energy carrier which exhibits high efficiency at the point of use and low GHG emissions over the entire ‘‘well-to-wheel’’ (WTW) life cycle [12]. Moreover, utilization of hydrogen also enhances the energy security and flexibility of a country since it can be derived from many primary energy sources, such as natural gas, coal, biomass and solar energy. Because of these benefits, many countries have made long-term strategic plans of promoting development of national and regional hydrogen economies [9,20]. However, many uncertainties exist regarding the impact of hydrogen on the future energy system, especially since there are other competing options, such as electricity and biofuels [15]. For example, in the event of a major breakthrough in battery technologies, electrical vehicles might become the preferred alternative, especially as cleaner power generation technologies will become available. Biofuel from renewable sources is another strong competitor, especially when plug-in hybrid vehicles (PHV) are further developed.
* Corresponding author. Tel.: þ86 10 62795735; fax: þ86 10 62795736. E-mail address: [email protected] (Z. Li). 0360-3199/$ – see front matter ª 2008 International Association for Hydrogen Energy. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhydene.2008.06.076
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One key such uncertainty is understanding properly the impact to the hydrogen economy of the hydrogen infrastructure. A hydrogen infrastructure can be defined as a supply chain delivering hydrogen to customers, from production, via transportation, storage, to dispensation. Each phase of the hydrogen infrastructure may involve several technical choices. The study of appropriate hydrogen infrastructure evolution strategies, i.e. how the infrastructure design should evolve over a long-term horizon, is important – this is the subject of this work. Many models for planning and optimal design of hydrogen infrastructure have appeared in the literature [1,11,16,18,19,21], varying in quantitative and optimization methodologies. The main purpose of these models is to compare and evaluate different hydrogen pathways and to integrate them into existing energy systems. Tzimas [21] developed a hydrogen delivery infrastructure model in which three scenarios were estimated, representing different degrees of hydrogen penetration in the European energy system. Michael [14] assessed the geographic and temporal set-up of a hydrogen-based transport system in Germany until 2030 and its impacts on an existing national energy system. Brey [3] developed a multiobjective optimization model to deal with a scenario where around ten percent of energy demand for transport in Spain would be provided via hydrogen by 2010. Hugo [8] developed a multi-objective mixed integer linear programming (MILP) model, identifying optimal investment strategies and integrated supply chain configurations from many alternatives. The model presented in this work is an extension of previous work by Hugo [8]. It has been applied to several case studies set in China, representing different development scenarios of hydrogen energy. This application is of huge significance to the future development of modelling and optimization of hydrogen infrastructure, as China has the third largest [4] coal reserve and has implemented many direct and indirect coal-to-liquid projects, with an intending total coal conversion capacity of 50 million tons in 2020 [23], thus has great potential to become on of the places where a hydrogen economy is first established. Besides, an innovative hydrogen pathway specifically suitable for China is presented in the case studies. Through steam reforming of methanol and palladium membrane separation [17], it can provide a possibility to bypass the technical problems of storage and delivery of hydrogen by delivering methanol to forecourt hydrogen dispensing stations and onsite hydrogen production. The paper is structured as follows. First, a brief introduction of the technical options of a hydrogen infrastructure supply chain will be presented, followed by an analytical methodology for the dynamic planning of hydrogen infrastructures. The methodology is illustrated through a detailed application to a case study of China, where a pathway via methanol as a hydrogen carrier is suggested to be the most economically viable alternative.
hydrogen to consumers. A production process is required to convert primary energy resources to hydrogen. Storage units and terminals are needed to compensate demand fluctuations. A distribution system transports hydrogen from production plants to points for sale, where hydrogen is dispensed to customers at forecourt retail stations. For each of these stages along the infrastructure, there is a wide variety of potential technical options, represented in Fig. 1 (BP, 2008). Hydrogen can be produced from a variety of primary energy feedstocks and distributed in a variety of forms using different technologies. Gaseous hydrogen, for example, can be distributed in dedicated pipelines over a long distance (over 200 km in Rhein-Ruhr area, Germany), while liquefied hydrogen can be transported in tankers by rail, ship or road. An additional dimension exists when defining production locations within the supply chain. Unlike most other fuel infrastructures, hydrogen can be produced either centrally or distributed. A centralized production option would be similar to existing gasoline supply chains, where large quantities of fuel are produced at a central site and then distributed, taking the advantage of economies of large scale. Alternatively, through the use of small-scale reformers and electrolyzers, hydrogen can be produced close to the point of use, or onsite production in smaller quantities. Such a scenario could use existing natural gas and electricity grid to produce hydrogen at forecourt refueling stations, therefore significantly reduces the distribution cost. Each of the pathway options has its own unique advantages and disadvantages. Cost, operability, reliability, environmental impacts, safety and social impacts are performance indicators that should be considered when assessing and comparing different pathways. While each option has its own attributes, tradeoffs between them may exist. Selection of the ‘‘best’’ delivery pathway, therefore, involves comparisons of various technical options using multiple performance criteria, with the ultimate goal of defining a strategy such that the infrastructure investment can be planned with confidence. Local market conditions and availability of regional primary energy feedstocks have a large impact on selection of pathways. For example, Iceland produces hydrogen by means
2. Technical options for hydrogen infrastructure pathways A hydrogen infrastructure is defined as a supply chain for production, storage, transportation, and dispensation of
Fig. 1 – Potential technology components within hydrogen infrastructure pathways (BP, 2008).
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of electrolysis pathways using geothermal electricity to initiate a transition to a hydrogen economy. In China, however, coal oriented pathways could be much cheaper. Another issue involved with the hydrogen infrastructure planning is the dynamic changes with respect to external changes, for instance capacity expansions and shift from one technology to another under changing market conditions. External changes consist of market demand for hydrogen, geographical distribution patterns of Fuel Cell Vehicle (FCV), energy prices, GHG mitigation legislation and technical improvements. With all these possible technical options, various local specific conditions, and time-variant parameters, it is necessary to provide a generic framework in which the performance of various integrated pathway options can be analyzed and compared on a consistent basis and time-variant parameters are appropriately accommodated, then apply to case studies according to specific conditions. A methodology for building such a framework is presented in the next section.
3. Dynamic modelling of hydrogen infrastructure This section presents a dynamic model for hydrogen infrastructure design and optimization. It employs a superstructure representation, realized by mixed integer linear programming (MILP) techniques. The function of a superstructure representation is to integrate all possible alternatives and interactions between the various supply chain components within a single model, and select the optimum out of all combinations [2]. It can be realized by MILP optimization algorithms. Besides common applications of linear programming (LP), like providing operational, management and investment decision support in processing and energy industries, MILP allows various propositional logical operations associated with strategic decisionmaking to be modelled. For example, an integer variable can be defined in such a way that it determines whether
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a processing unit should be invested in or not. Because of its natural ability of capturing logical conditions, applications of MILP have been widely used in areas of investment planning, supply chain and logistics management, energy industry planning, engineering design and production scheduling [6,22]. A generic model based on this methodology can greatly facilitate the design and planning of hydrogen infrastructures. It could use these optimization techniques to produce designing decisions for hydrogen infrastructure investments. It should also be able to assess performances of different infrastructure scenarios involving different technologies and feedstocks. When multiple performance indices are of interest, for instance economic and environmental behavior, it should assess theses objectives simultaneously using multi-objective optimization techniques. Once established, it should be able to be applied in specific case studies for different regions according to their unique characteristics. The model described below is mainly based on previous work by Hugo [7,8]. Model structure, assumptions, and key definitions are presented here, while objective functions, constraints, and other further details are included in Appendix. Before building such a model, it is necessary to explicitly consider some 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 infrastructure-especially, the natural gas distribution network, electricity grid and existing mercantile hydrogen production facilities (e.g. any excess reforming capacity at refineries). (3) Multiple and diverse primary energy feedstocks and production technologies. (4) Both large-scale centralized production and small-scale distributed/onsite/forecourt production. (5) Both gaseous/liquid hydrogen and hydrogen-carrier agent distribution.
Fig. 2 – Superstructure representation of the model.
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(6) Economies of scale of large-scale production and distribution technologies. (7) Transitions from one supply chain structure to another over time, involving the decommissioning of certain technologies and reinvestment in others. (8) Geographical site allocation of technologies. (9) Multiple performance indicators – both financial and environmental – that can drive the decision-making. In Fig. 2 the superstructure representation that forms the model basis is illustrated [8]. It starts with defining a set of primary energy resources: r˛R :¼ fNatural Gas; Coal; Biomass; Renewable Electricity;.g which can be used as feedstocks for producing hydrogen at a set of s ˛ S geographical industrial sites – such as refineries – using any of the large-scale centralized manufacturing technologies: j˛J:¼fSteam Methane Reforming; Gasification; Electrolysis;.g Each of these production technologies are defined such that they can perform conversion of primary energy feedstocks into an intermediate energy carrier that is suitable for distribution: l˛L:¼ Compressed Natural Gas; Liquid H2 ; Compressed Gaseous H2 ;. These intermediate carriers are then delivered from the production sites to the set of forecourt refueling stations (markets), m ˛ M, using a corresponding distribution technology:
p˛P:¼ Natural Gas Pipeline;Liquid H2
Truck; Compressed GaseousH2 Tube-Trailer;.
At refueling stations, intermediate carriers are dispensed as final products, namely hydrogen for fuel cell vehicles, using appropriate forecourt technologies, q ˛ Q. This representation also allows distributed onsite production to be explicitly considered as a pathway option by allowing the set of forecourt technology options to include both technologies for dispensing hydrogen coming from central production facilities and smallscale ones: q˛Q :¼ Liquid H2 Dispensing; Small-Scale Reforming; Small-Scale Electrolysis;. The primary objective of the model is to support strategic investment planning and asset management of hydrogen supply chain networks over a long-term horizon, t ˛ T. The model achieves this by making optimal decisions at four levels: Level 1: strategic supply chain design Selection of primary feedstocks Allocation of conversion technologies to production sites, where to install which production technologies Assignment of distribution technologies to link production sites to forecourt markets, which markets to supply with the selected sites Level 2: capacity and shut-down master planning Capacity expansion planning of production, distribution and refueling technologies, when to expand which technologies
Fig. 3 – Geographical problem specification for the case study.
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Fig. 4 – Hydrogen demand forecast for the case study.
Shut-down planning of production, distribution and refueling technologies, when to switch production technologies Level 3: production planning Estimation of how much of each primary energy feedstock the selected technologies require and what the rates of H2 production, distribution and refueling at each stage along the supply chain are Level 4: performance index assessment and tradeoff analysis Computation of financial and ecological objectives Multi-objective optimization to establish set of optimal compromise solutions The model implements two objective functions to represent both economic and environmental performances.
Since planning over a long-term horizon is of interest, net present value (NPV) is chosen as the economic indicator, consisting of capital investment costs and operating costs. The other model indicator is the WTW GHG emissions which assess the environmental performance of hydrogen infrastructures. To derive the GHG emission objective function over the entire supply chain as a WTW life cycle, it is necessary to define a set of chemicals known to have contributions to the GHG emisstions: e˛E :¼ fCO2 ; CH4 ; N2 O; . Next, using guidelines from the Intergovernmental Panel for Climate Change (IPCC), a vector of corresponding global warming potential (GWP) factors ye , expressed as CO2
Fig. 5 – Hydrogen superstructure for the case study.
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equivalent, is constructed [10]. Values of these characteristic factors depend on the time horizon over which global warming effect is assessed. Short time periods (20–50 years) consider relatively immediate effects of greenhouse gases on the climate, while longer periods (100–500 years) are used to predict cumulative effects on the global climate. For example, considering the effect over 100 years, we get: yCO2 ¼ 1; yCH4 ¼ 21; yN2 O ¼ 310 It is also necessary to determine the inventory of GHG emissions associated with the unit reference flow of each supply chain activity. For example, her is the amount of greenhouse gas e emitted during the unit extraction, processing and delivery of primary energy feedstock r, while mej is the amount of greenhouse gas e emitted during the unit hydrogen production using technology j. Then, assuming that emissions are linearly proportional to production, delivery and dispensing rates, the WTW GHG emission objective function is given as the cumulative summation over the entire planning horizon, for all individual supply chain activities. The model for the optimal planning and design of hydrogen infrastructures is then formulated as a multiobjective MILP problem [8], summarized as follows:
where the goal is to find values of the operational ðx˛Rn Þ and strategic ðy˛Y ¼ ð0; 1Þm Þ decision variables, subject to equality ðhðx; yÞ ¼ 0Þ and inequality ðgðx; yÞ 0Þ constraints, such that the utility function (U ) is optimized in terms of the two objective functions ðf1 ; f2 Þ. Continuous variables represent decisions of production and distribution rates. Discrete variables represent capacity expansion and shut-down, and investment decisions. There is a conflict between the two objectives, i.e. the most profitable infrastructure is not necessarily the least environmentally damaging, and vice versa. Because of this tradeoff there is not a single optimal solution to the problem. Instead, the solution is a set of multiple compromises known as a set of Efficient or Pareto Optimal Solutions (also known as non-inferior and non-dominant solutions). Each solution within the set represents an alternative supply chain configuration and investment strategy, each
Fig. 6 – Optimal tradeoff results of the case study.
achieving a unique combination of economic and environmental performances. A solution is efficient (pareto optimal) if it is impossible to find a better feasible solution which improves one objective without worsening at least one of the others. The merit of formulating the decision-making process within a multi-criteria optimization framework is that it does not require a priori articulation of preferences by a decision maker. Instead, the aim is to generate the full set of tradeoff solutions and not to present only one single ‘‘best’’ alternative. From the set of alternatives, a decision maker can then further investigate interesting tradeoffs and ultimately select a particular strategy that satisfies specific willingness to compromise.
Fig. 7 – ‘‘Infrastructure Solutions’’ breakdown of the tradeoff frontier of Scenario A. Note: IS [ Infrastructure Solution.
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Table 1 – Hydrogen supply chain components for different ‘‘Infrastructure Solutions’’ in the frontier of Scenario A Supply chain components Feedstock
Distribution
Refueling
Production
Infrastructure Solution 1
Coal
Coal to MeOH
Truck
Onsite MeOH reforming Onsite non-renewable water electrolysis
Infrastructure Solution 2
Coal
Truck
Biomass
Coal to MeOH Coal to Liquid H2 Biomass to Liquid H2
Onsite MeOH reforming Liquid H2 refueling Onsite non-renewable water electrolysis
Infrastructure Solution 3
Coal Biomass
Coal to MeOH Biomass to Liquid H2
Truck
Onsite MeOH reforming Liquid H2 refueling Onsite non-renewable water electrolysis
Infrastructure Solution 4
Biomass
Biomass to Liquid H2
Truck
Liquid H2 refueling Gaseous H2 refueling Onsite non-renewable water electrolysis
Infrastructure Solution 5
Biomass
Biomass to Gaseous H2
Pipeline
Biomass to Gaseous H2
Pipeline
Infrastructure Solution 6
4.
Case study application
To illustrate the features of the model, results of a case study for China conducted are presented here.
4.1. Hydrogen energy system taking methanol as a carrier A key challenge for using hydrogen as fuel is the high cost of transportation and storage of hydrogen. Although R&D on innovative solutions is ongoing, there is still a long way to go before commercially satisfactory solutions are available [20]. One possible approach to the problem of high hydrogen transportation cost is to use methanol as an alternative energy carrier, rather than transporting hydrogen directly. In this context, the newly-introduced ‘‘methanol pathway’’ means using coal gasification or natural gas reforming technologies to produce methanol centrally, and transporting methanol by truck to the hydrogen demand areas, then applying onsite methanol reforming technology to produce hydrogen at forecourt station. The motivation for this approach arises from three factors. (1) The recent progress of small-scale methanol reforming process made in the Dalian Institute of Chemical Physics (DICP) of China Academy of Science [17]. The main part of the process is steam reforming of methanol integrated with H2 separation using Palladium membrane. This new process offers low-cost, high methanol conversion ratio and high hydrogen purity through the process integration of hydrogen production and purification, with the application of innovative catalyst and Palladium purification membrane. (2) The low-cost of methanol transportation compared with hydrogen transportation, especially in a long-distance transportation situation [5]. (3) The potential continuity of the current and future fuel supply chain. Suffering from high dependency on import oil, some countries, like China, are developing and promoting alternative fuels seriously including coal-derived methanol fuel. The vision this section sets reveals the
Gaseous H2 refueling Onsite non-renewable water electrolysis Onsite non-renewable water electrolysis
potential picture that, current methanol fuel activity is valuable not only for the near term and medium term energy security problem relieving, but also for a smooth transition to a possible hydrogen era, with all the infrastructures can be used continuously.
4.2. A case study of China hydrogen infrastructure strategic planning The case study problem specification is depicted in Fig. 3. It consists of a map of China where six production sites are identified for potential installation of central production technologies. Demand for hydrogen by FCVs is expected at 21 major cities on three different demand levels (Early demand, Mid demand and Late demand), acting as markets in the formulation. Of the six central production sites, C1, C2, C3 and C4 take coal as main primary energy, while C1, C2 and C3 also have some reserves of natural gas; N1 and N2 take natural gas
Fig. 8 – ‘‘Infrastructure Solutions’’ breakdown of the tradeoff frontier of Scenario B. Note: IS [ Infrastructure Solution.
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Table 2 – Hydrogen supply chain components for different ‘‘Infrastructure Solutions’’ in the frontier of Scenario B Feedstock
Supply chain components Production
Infrastructure Solution 1
Coal
Infrastructure Solution 2
Refueling
Distribution
Coal to Liquid H2
Truck
Liquid H2 refueling Onsite non-renewable water electrolysis
Coal Biomass
Coal to Liquid H2 Biomass to Liquid H2
Truck
Liquid H2 refueling Onsite non-renewable water electrolysis Liquid H2 refueling
Infrastructure Solution 3
Biomass
Biomass to Liquid H2 Biomass to Gaseous H2
Truck Pipeline
Gaseous H2 refueling Onsite renewable water electrolysis
Infrastructure Solution 4
Biomass
Biomass to Gaseous H2
Pipeline
Gaseous H2 refueling Onsite renewable water electrolysis
Infrastructure Solution 5
as main primary energy. Furthermore, the model assumes all the production sites have access to plenty of biomass resources and non-renewable electricity, while C3, C4, N1 and N2 have access to power resources derived from renewables, and C4 also has access to nuclear power sources. These primary energy availability conditions would limit technologies installed at the sites. In Fig. 4 the hydrogen demand forecast for all regions over the planning horizon is illustrated. It shows both the expected number of hydrogen FCVs and corresponding demands for hydrogen consumptions per year during each of the planning intervals. The long-term planning horizon is defined from 2010 to 2034 and divided into five time intervals of five years each. The superstructure for hydrogen infrastructure technologies is illustrated in Fig. 5. As stated above, we include the ‘‘methanol pathway’’ as an alternative. In order to express the impact of introducing the ‘‘methanol pathway’’, two different scenarios are set: Scenario A with the ‘‘methanol pathway’’ (also called ‘‘With MeOH’’) and Scenario B without the ‘‘methanol pathway’’ (also called ‘‘Without MeOH’’). Applying the multi-objective optimization approach to these two scenarios results in two sets of tradeoff solutions, presented in Fig. 6. The two sets of tradeoff solutions show that the largest NPV achieved in Scenario A is larger than that in Scenario B. The two sets of solutions merge into one when the GHG emissions are less than around 0.2 billion tonne, since there would not be any differences between Scenario A and B beyond this point where the emission constraint becomes stricter and the ‘‘methanol pathway’’ is eliminated from the supply chain components. When the GHG emissions are more than 0.2 billion tonne, Scenario A offers a larger NPV than Scenario B with the same GHG emission constraint. This means the introduction of the ‘‘methanol pathway’’ could improve the economic competitiveness of a hydrogen infrastructure with a relatively gentle emission constraint. However, this advantage disappears when the emission constraint becomes stricter. Each of the two tradeoff curves has two extremes, one for the maximum NPV solution and the other for the least GHG emission solution. In Scenario A, the infrastructure design according to the maximum NPV solution is based on coal oriented centralized gasification at optimally selected central
Onsite renewable water electrolysis
production sites to manufacture methanol. Accordingly, the optimal distribution network delivers methanol in trucks to forecourt markets where onsite methanol reforming process and hydrogen dispensing take place. Additionally, the small hydrogen demand during the market-introduction period is met by onsite non-renewable water electrolysis installed in forecourt markets. At the other extreme, the minimum GHG emission solution corresponds to an infrastructure entirely based on onsite hydrogen production from renewable electricity through electrolysis of water. In Scenario B, due to the absence of the ‘‘methanol pathway’’, the maximum NPV solution corresponds to an infrastructure based on coal oriented centralized gasification at optimally selected central production sites to produce liquid hydrogen. Accordingly, the optimal distribution network delivers liquid hydrogen in trucks to forecourt markets where liquid hydrogen dispensing takes place. Additionally, the small hydrogen demand during the market-introduction period is met by onsite non-renewable water electrolysis installed in the forecourt markets. At the other extreme, the minimum GHG emission solution corresponds to an infrastructure entirely based on renewable oriented water electrolysis for onsite hydrogen production. Noting that each solution within the set represents an alternative infrastructure design and investment strategy, the extent of the compromise between the solutions achieving maximum return on the investment and minimum GHG emissions can be explicitly quantified. Moving along the tradeoff frontier from one extreme to the other involves a series of distinct infrastructures. The optimal tradeoff frontier can be broken into ‘‘Infrastructure Solutions’’1 based on different feedstocks, production, distribution and refueling components of the supply chain that are consistent over a specific region of the curve (as in Scenario A, shown in Fig. 7). Table 1 contains detailed descriptions of the supply chain components corresponding to these ‘‘Infrastructure Solutions’’ of Scenario A. Starting from the maximum NPV strategy (Infrastructure Solution 1) which involves only coal to methanol investment, the transition towards lower emissions requires the 1 Others have used the term ‘‘Enterprise’’ instead of ‘‘Infrastructure Solution’’ to describe an optimized point of infrastructure character and operation. See for example Ref. [8].
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introduction of biomass gasification as a complimentary production technology and coal to liquid hydrogen production technologies (Infrastructure Solution 2). Further reductions in emissions can be achieved while remaining economically competitive (Infrastructure Solution 3) by eliminating coal to
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liquid hydrogen production technologies while increasing the share of biomass to liquid hydrogen technologies. Gradually, coal-based production is totally replaced by biomass-based production. The distribution procedure includes truck delivery of both liquid and gaseous hydrogen, in order to achieve the
Fig. 9 – Sample compromise investment strategy obtained from the model.
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desired level of emission mitigation (Infrastructure Solution 4). Any further emission reduction requires the distribution component in the supply chain transforming from liquid delivery to gaseous pipeline delivery (Infrastructure Solution 5). The least emission strategy involves only onsite water electrolysis by renewable power (Infrastructure Solution 6). Fig. 8 and Table 2 show the ‘‘Infrastructure Solution’’ division results for Scenario B in a similar evolution pattern of ‘‘Infrastructure Solutions’’ with emission constraint as Scenario A. When analyzing the features of the optimal ‘‘Infrastructure Solutions’’ in more details, one realizes that certain technologies and primary energy feedstocks are not present in the optimal tradeoff frontier. The multi-objective optimization framework, therefore, not only facilitates the identification of the most promising candidates, but also assists the elimination of suboptimal ones. More specifically, under the specifications of the case study, neither centralized water electrolysis nor nuclear electricity appears in the set of efficient solutions. The reason is that the absent candidates do not offer either competitive financial returns or environmental benefits compared with the optimal ones. Of course, as technologies develop at different rates in the future the structure of the optimal solutions may change radically. For example, changes in model parameters such as introduction of carbon capture and sequestration technologies, improvement in nuclear technologies and reduction in renewable electricity costs, can drastically change the shape of the optimal tradeoff frontier. The solutions presented here for the case study, though, are based on the best data presently available for proven technologies considered so far. To highlight the characteristics of the solution obtained from the model, one of the optimal compromise solutions is selected and presented in Fig. 9. It corresponds to the maximum NPV solution for Scenario A. The infrastructure evolution is described as following: (1) when t ¼ t1, while the hydrogen demand is low and only occurs at E1 and E2, super metropolis Beijing and Shanghai, it is not economic to implement centralized production. Instead, onsite water electrolysis is utilized in market places; (2) when t ¼ t2, no other markets become active and E1 and E2 remains the same hydrogen production pattern to meet the demand, namely onsite water electrolysis, with a capacity expansion; (3) when t ¼ t3, besides early markets E1 and E2, all mid demand markets become active. The growing demand allows the economies of scale to be exploited by decommissioning the forecourt onsite water electrolysis and switching to centralized manufacturing of methanol through coal gasification. Both C1 and C4 are optimally selected as the central production sites, and the distribution network is optimized based on the flow rates and distances between the sites and markets. All the markets provide hydrogen to customers by onsite methanol-to-hydrogen reforming technology; (4) when t ¼ t4,
all the late demand markets become active. Another site C2 is selected to produce methanol from coal centrally. The distribution network is re-organized through optimization because of the selection of C2 site. For instance, M3 and M9 are not supplied by C1 as they were in t3, now C2 supplies them instead; (5) when t ¼ t5, with increasing demand, all the existing production sites increase their capacity. At the same time, two other sites C3 and N1 start to manufacture methanol and hydrogen, respectively, and C3 becomes the main supplier of E1, while C1 shifts its supply to other markets. Because of the availability of natural gas and short distribution distance to L4, the newly-built N1 supplies natural gasderived gaseous hydrogen to L4 by pipeline. The above result of model running is based on the assumption of hydrogen price presented in Table 3. While setting 10% and 15% fluctuation to the basic price, Fig. 10 shows the sensitivity analysis results of Scenario A and B, respectively. A linear relationship between hydrogen price and NPV is shown. The atmosphere that all the NPV of optimal solutions falls below zero will appear if hydrogen price is low
Table 3 – Assumption of hydrogen prices Time Price ($/GJ)
t1
t2
t3
t4
t5
19.09
20.12
21.09
22.15
23.46
Fig. 10 – Sensitivity analysis result on hydrogen price in Scenario A and B.
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to a certain extend, such as in the case of Scenario B with 15% fluctuation. The hydrogen price, therefore, has a big impact on the economic performance of the infrastructure, and a comprehensive estimation of hydrogen price should be undertaken before the practical construction. Additionally, the figures show that the tradeoff curve presents a much sharper slope with the GHG emissions being lower than 0.2 billion tonne, compared with the rest part. This means the point with 0.2 billion tonnes of emissions can achieve great emission reduction while remains economic competitive. And this point corresponds to an infrastructure entirely based upon biomass as the primary energy, so biomass could play a special role during the hydrogen transition of China.
5.
Conclusions
Hydrogen is one of the most promising candidates for future transportation fuel. However, infrastructure issues pose more challenges and uncertainties for hydrogen than other alternative fuels such as biofuels and electricity. Focusing on hydrogen infrastructure design and optimization, this paper first described the options of hydrogen infrastructure pathways. The numerous combinations of potential technologies within a hydrogen supply chain make it necessary that any investment strategy for building up a hydrogen infrastructure is supported by rigorous quantitative analysis, taking into account all the possible alternatives, interactions and tradeoffs. To assist this strategic decision-making process, this paper presents a generic model for the optimal long-range planning and design of future hydrogen supply chains for fuel cell vehicles. The model presented here uses mixed integer optimization techniques to provide optimal integrated investment strategies 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, allocation of conversion technologies to either central or distributed production sites, design of the distribution network and selection of refueling technologies. At the strategic planning level, capacity expansions and process shut-downs are formulated to explicitly address the dynamic performance of the infrastructure and the timing of the investment. Low-level operational decisions addressed include the estimation of primary energy feedstock requirements and production, distribution and refueling rates. Realizing that both financial and ecological concerns are driving the interest in hydrogen, multi-objective optimization techniques are used to establish the optimal tradeoff between the NPV and the WTW GHG emissions. To illustrate the capabilities of the model, results of a case study of China 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 a given geographical region. In particular, the set of tradeoff solutions, allows the most promising pathways to be isolated and the inferior ones to be eliminated from further consideration.
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At last, the ‘‘methanol pathway’’, especially the one derived from coal, is shown to be an important option for hydrogen infrastructure development in China, as it could improve the economic competitiveness of a hydrogen infrastructure within a relatively gentle emission constraint. It is believed that the ‘‘methanol pathway’’ could play a key role in the future Chinese hydrogen economy.
Acknowledgements This work is part of the collaboration between Tsinghua-BP Clean Energy Research and Education Center in Tsinghua University and Centre for Process Systems Engineering in Imperial College London. The authors gratefully acknowledge the funding of ‘‘China Hydrogen Roadmap’’ project by BP and China State Key Fundamental Research Project (2005CB221207). Pei Liu and Efstratios N. Pistikopoulos also acknowledge the financial support from BP on the project of ‘‘Coal Gasification and Polygeneration’’.
Appendix Illustration of mixed integer programming for modelling strategic decisions Capacity expansions and process shut-downs are key strategic decisions made in the model. Mathematically this can be represented in a MILP framework using the following variables, parameters and constraints: Continuous variables:
Fjst FEjst
capacity of technology j at s during time internal t amount by which capacity of technology j at s is expanded during time interval t
Discrete variables:
YEjst YSjst
1 if capacity of technology j at site s is increased during time interval t, 0 otherwise 1 if capacity of technology j at site s is decommissioned/shut-down during time interval t, 0 otherwise
Parameters:
FELj FEU j lower and upper bound on the amount by which capacity of technology j can be expanded upper bound on the total capacity that may be FU j installed of technology j Constraints: Once a technology is decommissioned it remains ‘‘shut’’ for the remainder of the planning horizon: 1 YSjst YEjst0 ; cj; s; t; t0 t
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Available capacity is set to 0 for all time periods after the moment of shut-down: Fj;s;t0 FUj 1 YSjst ; cj; s; t; t0 t X
YSjst 1; cj; s
t
Expansions can only occur within physically allowed limits: YEjst FELj FEjst FEUj YEjst ; cj; s; t
references
[1] Agnolucci P. Hydrogen infrastructure for the transport sector. Int J Hydrogen Energy 2007;32:3526–44. [2] Biegler LT, Grossmann IE, Westerberg AW. Systematic methods of chemical process design. New Jersey: Prentice Hall; 1997. [3] Brey JJ, Brey R, Carazo AF, Contreras I, Herna´ndez-Dı´az AG, Castro A. Planning the transition to a hydrogen economy in Spain. Int J Hydrogen Energy 2007;32:1339–46. [4] BP, 2008, BP Statistical Review of World Energy, http://www. bp.com/statisticalreview. BP hydrogen website: http://www. bp.com/hydrogen. [5] Chang L, Li Z, Gao D, Huang H, Ni WD. Pathways for hydrogen infrastructure development in China: Integrated assessment for vehicle fuels and a case study of Beijing. Energy 2007;32:2023–37. [6] Floudas CA. Nonlinear and mixed integer optimization. New York: Oxford University Press; 1995. [7] Hugo, A. Environmentally conscious process selection, design and optimization. Ph.D. dissertation; 2005. Dept. Chem. Eng., Imperial College London, London, UK. [8] Hugo A, Rutter P, Pistikopoulos S, Amorelli A, Zoia G. Hydrogen infrastructure strategic planning using multiobjective optimization. Int J Hydrogen Energy 2005;30:1523–34.
[9] International Energy Agency (IEA). Hydrogen and Fuel Cells: Review of National R&D Programs (OECD/IEA); 2004. [10] IPCC. Impacts, adaptation and mitigation of climate change: scientific-technical analyses. Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press; 1996. [11] Karlsson K, Meibom P. Optimal investment paths for future renewable based energy systems - Using the optimisation model Balmorel. Int J Hydrogen Energy 2008;33:1777–87. [12] LBST. GM well-to-wheel analysis of energy use and greenhouse gas emissions of advanced fuel/vehicle systems – a European study, Technical report, L-B-Systemtechnik GmbH; 2002. [14] Michael B, Martin W, Otto R. Integration of a hydrogen economy into the German energy system: an optimising modelling approach. Int J Hydrogen Energy 2007;32:1355–68. [15] National Research Council and National Academy of Engineering. The hydrogen economy: opportunities, costs, barriers, and R&D needs. Washington, DC: The National Academies Press; 2004. [16] Ogden JM. Developing an infrastructure for hydrogen vehicles: a Southern California case study. Int J Hydrogen Energy 1999;24:709–30. [17] Pan LW, Wang SD. Methanol steam reforming in a compact plate-fin reformer for fuel-cell systems. Int J Hydrogen Energy 2005;30:973–9. [18] Rosyid OA, Jablonski D, Hauptmanns U. Risk analysis for the infrastructure of a hydrogen economy. Int J Hydrogen Energy 2007;32:3194–200. [19] Ruijven BV, Vuuren DPV, Vries BV. The potential role of hydrogen in energy systems with and without climate policy. Int J Hydrogen Energy 2007;32:1655–72. [20] Service RF. The hydrogen backlash. Science 2004;305:958–61. [21] Tzimas E, Castello P, Peteves S. The evolution of size and cost of a hydrogen delivery infrastructure in Europe in the medium and long term. Int J Hydrogen Energy 2007;32: 1368–80. [22] Williams H. Model building in mathematical programming. 3rd ed. New York: Wiley; 1993. [23] Zhang YZ. China’s development strategy for coal-to-liquids industry. Paris: IEA Coal Industry Advisory Board Workshop; 2006.
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/he
Hydrogen infrastructure design and optimization: A case study of China Zheng Lia,*, Dan Gaoa, Le Changa, Pei Liub, Efstratios N. Pistikopoulosb a
Tsinghua BP Clean Energy Research and Education Center, Department of Thermal Engineering, Tsinghua University, Beijing 100084, People’s Republic of China b Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UK
article info
abstract
Article history:
Infrastructure issues pose more challenges and uncertainties for hydrogen than other
Received 30 January 2008
alternative ‘‘fuels’’ such as biofuels and electricity. A key challenge of developing a future
Received in revised form
commercial hydrogen economy is how the infrastructure will be best designed and operated as
18 June 2008
time progresses, given that numerous technological options exist and are still in develop-
Accepted 19 June 2008
ment for hydrogen production, storage, distribution and dispensing. This paper presents
Available online 20 September 2008
a generic optimization-based model for the strategic dynamic investment planning and design of future hydrogen supply chains. The features and capabilities of the model are
Keywords:
illustrated through a detailed case study of China. It is shown how the proposed meth-
Hydrogen infrastructure
odology can provide policy-makers with new tools for hydrogen development strategies.
Supply chain
ª 2008 International Association for Hydrogen Energy. Published by Elsevier Ltd. All rights
Mixed integer programming
reserved.
Superstructure
1.
Introduction
Driven by concerns over global warming caused by excessive greenhouse gas (GHG) emissions, energy security and urban air quality, a transition from the current energy system has become an urgent and essential task which receives significant attention. A possible solution is through large-scale implementation of alternative energy, where fast growing energy demand can be partly compensated by higher energy conversion efficiency and larger share of renewable energy sources, i.e. hydro, wind, solar and biomass, in the primary energy supply. This transition of the primary energy source implies corresponding substitutions of dominating fossil fuel based technologies by ones which are more carbon-neutral and sustainable. Shifting from conventional petroleum oriented fuel to hydrogen in the transportation sector is one potential direction as utilization of hydrogen with fuel cells may offer many advantages over the existing system. Hydrogen is a type
of high-quality carbon-free energy carrier which exhibits high efficiency at the point of use and low GHG emissions over the entire ‘‘well-to-wheel’’ (WTW) life cycle [12]. Moreover, utilization of hydrogen also enhances the energy security and flexibility of a country since it can be derived from many primary energy sources, such as natural gas, coal, biomass and solar energy. Because of these benefits, many countries have made long-term strategic plans of promoting development of national and regional hydrogen economies [9,20]. However, many uncertainties exist regarding the impact of hydrogen on the future energy system, especially since there are other competing options, such as electricity and biofuels [15]. For example, in the event of a major breakthrough in battery technologies, electrical vehicles might become the preferred alternative, especially as cleaner power generation technologies will become available. Biofuel from renewable sources is another strong competitor, especially when plug-in hybrid vehicles (PHV) are further developed.
* Corresponding author. Tel.: þ86 10 62795735; fax: þ86 10 62795736. E-mail address: [email protected] (Z. Li). 0360-3199/$ – see front matter ª 2008 International Association for Hydrogen Energy. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhydene.2008.06.076
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One key such uncertainty is understanding properly the impact to the hydrogen economy of the hydrogen infrastructure. A hydrogen infrastructure can be defined as a supply chain delivering hydrogen to customers, from production, via transportation, storage, to dispensation. Each phase of the hydrogen infrastructure may involve several technical choices. The study of appropriate hydrogen infrastructure evolution strategies, i.e. how the infrastructure design should evolve over a long-term horizon, is important – this is the subject of this work. Many models for planning and optimal design of hydrogen infrastructure have appeared in the literature [1,11,16,18,19,21], varying in quantitative and optimization methodologies. The main purpose of these models is to compare and evaluate different hydrogen pathways and to integrate them into existing energy systems. Tzimas [21] developed a hydrogen delivery infrastructure model in which three scenarios were estimated, representing different degrees of hydrogen penetration in the European energy system. Michael [14] assessed the geographic and temporal set-up of a hydrogen-based transport system in Germany until 2030 and its impacts on an existing national energy system. Brey [3] developed a multiobjective optimization model to deal with a scenario where around ten percent of energy demand for transport in Spain would be provided via hydrogen by 2010. Hugo [8] developed a multi-objective mixed integer linear programming (MILP) model, identifying optimal investment strategies and integrated supply chain configurations from many alternatives. The model presented in this work is an extension of previous work by Hugo [8]. It has been applied to several case studies set in China, representing different development scenarios of hydrogen energy. This application is of huge significance to the future development of modelling and optimization of hydrogen infrastructure, as China has the third largest [4] coal reserve and has implemented many direct and indirect coal-to-liquid projects, with an intending total coal conversion capacity of 50 million tons in 2020 [23], thus has great potential to become on of the places where a hydrogen economy is first established. Besides, an innovative hydrogen pathway specifically suitable for China is presented in the case studies. Through steam reforming of methanol and palladium membrane separation [17], it can provide a possibility to bypass the technical problems of storage and delivery of hydrogen by delivering methanol to forecourt hydrogen dispensing stations and onsite hydrogen production. The paper is structured as follows. First, a brief introduction of the technical options of a hydrogen infrastructure supply chain will be presented, followed by an analytical methodology for the dynamic planning of hydrogen infrastructures. The methodology is illustrated through a detailed application to a case study of China, where a pathway via methanol as a hydrogen carrier is suggested to be the most economically viable alternative.
hydrogen to consumers. A production process is required to convert primary energy resources to hydrogen. Storage units and terminals are needed to compensate demand fluctuations. A distribution system transports hydrogen from production plants to points for sale, where hydrogen is dispensed to customers at forecourt retail stations. For each of these stages along the infrastructure, there is a wide variety of potential technical options, represented in Fig. 1 (BP, 2008). Hydrogen can be produced from a variety of primary energy feedstocks and distributed in a variety of forms using different technologies. Gaseous hydrogen, for example, can be distributed in dedicated pipelines over a long distance (over 200 km in Rhein-Ruhr area, Germany), while liquefied hydrogen can be transported in tankers by rail, ship or road. An additional dimension exists when defining production locations within the supply chain. Unlike most other fuel infrastructures, hydrogen can be produced either centrally or distributed. A centralized production option would be similar to existing gasoline supply chains, where large quantities of fuel are produced at a central site and then distributed, taking the advantage of economies of large scale. Alternatively, through the use of small-scale reformers and electrolyzers, hydrogen can be produced close to the point of use, or onsite production in smaller quantities. Such a scenario could use existing natural gas and electricity grid to produce hydrogen at forecourt refueling stations, therefore significantly reduces the distribution cost. Each of the pathway options has its own unique advantages and disadvantages. Cost, operability, reliability, environmental impacts, safety and social impacts are performance indicators that should be considered when assessing and comparing different pathways. While each option has its own attributes, tradeoffs between them may exist. Selection of the ‘‘best’’ delivery pathway, therefore, involves comparisons of various technical options using multiple performance criteria, with the ultimate goal of defining a strategy such that the infrastructure investment can be planned with confidence. Local market conditions and availability of regional primary energy feedstocks have a large impact on selection of pathways. For example, Iceland produces hydrogen by means
2. Technical options for hydrogen infrastructure pathways A hydrogen infrastructure is defined as a supply chain for production, storage, transportation, and dispensation of
Fig. 1 – Potential technology components within hydrogen infrastructure pathways (BP, 2008).
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of electrolysis pathways using geothermal electricity to initiate a transition to a hydrogen economy. In China, however, coal oriented pathways could be much cheaper. Another issue involved with the hydrogen infrastructure planning is the dynamic changes with respect to external changes, for instance capacity expansions and shift from one technology to another under changing market conditions. External changes consist of market demand for hydrogen, geographical distribution patterns of Fuel Cell Vehicle (FCV), energy prices, GHG mitigation legislation and technical improvements. With all these possible technical options, various local specific conditions, and time-variant parameters, it is necessary to provide a generic framework in which the performance of various integrated pathway options can be analyzed and compared on a consistent basis and time-variant parameters are appropriately accommodated, then apply to case studies according to specific conditions. A methodology for building such a framework is presented in the next section.
3. Dynamic modelling of hydrogen infrastructure This section presents a dynamic model for hydrogen infrastructure design and optimization. It employs a superstructure representation, realized by mixed integer linear programming (MILP) techniques. The function of a superstructure representation is to integrate all possible alternatives and interactions between the various supply chain components within a single model, and select the optimum out of all combinations [2]. It can be realized by MILP optimization algorithms. Besides common applications of linear programming (LP), like providing operational, management and investment decision support in processing and energy industries, MILP allows various propositional logical operations associated with strategic decisionmaking to be modelled. For example, an integer variable can be defined in such a way that it determines whether
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a processing unit should be invested in or not. Because of its natural ability of capturing logical conditions, applications of MILP have been widely used in areas of investment planning, supply chain and logistics management, energy industry planning, engineering design and production scheduling [6,22]. A generic model based on this methodology can greatly facilitate the design and planning of hydrogen infrastructures. It could use these optimization techniques to produce designing decisions for hydrogen infrastructure investments. It should also be able to assess performances of different infrastructure scenarios involving different technologies and feedstocks. When multiple performance indices are of interest, for instance economic and environmental behavior, it should assess theses objectives simultaneously using multi-objective optimization techniques. Once established, it should be able to be applied in specific case studies for different regions according to their unique characteristics. The model described below is mainly based on previous work by Hugo [7,8]. Model structure, assumptions, and key definitions are presented here, while objective functions, constraints, and other further details are included in Appendix. Before building such a model, it is necessary to explicitly consider some 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 infrastructure-especially, the natural gas distribution network, electricity grid and existing mercantile hydrogen production facilities (e.g. any excess reforming capacity at refineries). (3) Multiple and diverse primary energy feedstocks and production technologies. (4) Both large-scale centralized production and small-scale distributed/onsite/forecourt production. (5) Both gaseous/liquid hydrogen and hydrogen-carrier agent distribution.
Fig. 2 – Superstructure representation of the model.
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(6) Economies of scale of large-scale production and distribution technologies. (7) Transitions from one supply chain structure to another over time, involving the decommissioning of certain technologies and reinvestment in others. (8) Geographical site allocation of technologies. (9) Multiple performance indicators – both financial and environmental – that can drive the decision-making. In Fig. 2 the superstructure representation that forms the model basis is illustrated [8]. It starts with defining a set of primary energy resources: r˛R :¼ fNatural Gas; Coal; Biomass; Renewable Electricity;.g which can be used as feedstocks for producing hydrogen at a set of s ˛ S geographical industrial sites – such as refineries – using any of the large-scale centralized manufacturing technologies: j˛J:¼fSteam Methane Reforming; Gasification; Electrolysis;.g Each of these production technologies are defined such that they can perform conversion of primary energy feedstocks into an intermediate energy carrier that is suitable for distribution: l˛L:¼ Compressed Natural Gas; Liquid H2 ; Compressed Gaseous H2 ;. These intermediate carriers are then delivered from the production sites to the set of forecourt refueling stations (markets), m ˛ M, using a corresponding distribution technology:
p˛P:¼ Natural Gas Pipeline;Liquid H2
Truck; Compressed GaseousH2 Tube-Trailer;.
At refueling stations, intermediate carriers are dispensed as final products, namely hydrogen for fuel cell vehicles, using appropriate forecourt technologies, q ˛ Q. This representation also allows distributed onsite production to be explicitly considered as a pathway option by allowing the set of forecourt technology options to include both technologies for dispensing hydrogen coming from central production facilities and smallscale ones: q˛Q :¼ Liquid H2 Dispensing; Small-Scale Reforming; Small-Scale Electrolysis;. The primary objective of the model is to support strategic investment planning and asset management of hydrogen supply chain networks over a long-term horizon, t ˛ T. The model achieves this by making optimal decisions at four levels: Level 1: strategic supply chain design Selection of primary feedstocks Allocation of conversion technologies to production sites, where to install which production technologies Assignment of distribution technologies to link production sites to forecourt markets, which markets to supply with the selected sites Level 2: capacity and shut-down master planning Capacity expansion planning of production, distribution and refueling technologies, when to expand which technologies
Fig. 3 – Geographical problem specification for the case study.
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Fig. 4 – Hydrogen demand forecast for the case study.
Shut-down planning of production, distribution and refueling technologies, when to switch production technologies Level 3: production planning Estimation of how much of each primary energy feedstock the selected technologies require and what the rates of H2 production, distribution and refueling at each stage along the supply chain are Level 4: performance index assessment and tradeoff analysis Computation of financial and ecological objectives Multi-objective optimization to establish set of optimal compromise solutions The model implements two objective functions to represent both economic and environmental performances.
Since planning over a long-term horizon is of interest, net present value (NPV) is chosen as the economic indicator, consisting of capital investment costs and operating costs. The other model indicator is the WTW GHG emissions which assess the environmental performance of hydrogen infrastructures. To derive the GHG emission objective function over the entire supply chain as a WTW life cycle, it is necessary to define a set of chemicals known to have contributions to the GHG emisstions: e˛E :¼ fCO2 ; CH4 ; N2 O; . Next, using guidelines from the Intergovernmental Panel for Climate Change (IPCC), a vector of corresponding global warming potential (GWP) factors ye , expressed as CO2
Fig. 5 – Hydrogen superstructure for the case study.
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equivalent, is constructed [10]. Values of these characteristic factors depend on the time horizon over which global warming effect is assessed. Short time periods (20–50 years) consider relatively immediate effects of greenhouse gases on the climate, while longer periods (100–500 years) are used to predict cumulative effects on the global climate. For example, considering the effect over 100 years, we get: yCO2 ¼ 1; yCH4 ¼ 21; yN2 O ¼ 310 It is also necessary to determine the inventory of GHG emissions associated with the unit reference flow of each supply chain activity. For example, her is the amount of greenhouse gas e emitted during the unit extraction, processing and delivery of primary energy feedstock r, while mej is the amount of greenhouse gas e emitted during the unit hydrogen production using technology j. Then, assuming that emissions are linearly proportional to production, delivery and dispensing rates, the WTW GHG emission objective function is given as the cumulative summation over the entire planning horizon, for all individual supply chain activities. The model for the optimal planning and design of hydrogen infrastructures is then formulated as a multiobjective MILP problem [8], summarized as follows:
where the goal is to find values of the operational ðx˛Rn Þ and strategic ðy˛Y ¼ ð0; 1Þm Þ decision variables, subject to equality ðhðx; yÞ ¼ 0Þ and inequality ðgðx; yÞ 0Þ constraints, such that the utility function (U ) is optimized in terms of the two objective functions ðf1 ; f2 Þ. Continuous variables represent decisions of production and distribution rates. Discrete variables represent capacity expansion and shut-down, and investment decisions. There is a conflict between the two objectives, i.e. the most profitable infrastructure is not necessarily the least environmentally damaging, and vice versa. Because of this tradeoff there is not a single optimal solution to the problem. Instead, the solution is a set of multiple compromises known as a set of Efficient or Pareto Optimal Solutions (also known as non-inferior and non-dominant solutions). Each solution within the set represents an alternative supply chain configuration and investment strategy, each
Fig. 6 – Optimal tradeoff results of the case study.
achieving a unique combination of economic and environmental performances. A solution is efficient (pareto optimal) if it is impossible to find a better feasible solution which improves one objective without worsening at least one of the others. The merit of formulating the decision-making process within a multi-criteria optimization framework is that it does not require a priori articulation of preferences by a decision maker. Instead, the aim is to generate the full set of tradeoff solutions and not to present only one single ‘‘best’’ alternative. From the set of alternatives, a decision maker can then further investigate interesting tradeoffs and ultimately select a particular strategy that satisfies specific willingness to compromise.
Fig. 7 – ‘‘Infrastructure Solutions’’ breakdown of the tradeoff frontier of Scenario A. Note: IS [ Infrastructure Solution.
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Table 1 – Hydrogen supply chain components for different ‘‘Infrastructure Solutions’’ in the frontier of Scenario A Supply chain components Feedstock
Distribution
Refueling
Production
Infrastructure Solution 1
Coal
Coal to MeOH
Truck
Onsite MeOH reforming Onsite non-renewable water electrolysis
Infrastructure Solution 2
Coal
Truck
Biomass
Coal to MeOH Coal to Liquid H2 Biomass to Liquid H2
Onsite MeOH reforming Liquid H2 refueling Onsite non-renewable water electrolysis
Infrastructure Solution 3
Coal Biomass
Coal to MeOH Biomass to Liquid H2
Truck
Onsite MeOH reforming Liquid H2 refueling Onsite non-renewable water electrolysis
Infrastructure Solution 4
Biomass
Biomass to Liquid H2
Truck
Liquid H2 refueling Gaseous H2 refueling Onsite non-renewable water electrolysis
Infrastructure Solution 5
Biomass
Biomass to Gaseous H2
Pipeline
Biomass to Gaseous H2
Pipeline
Infrastructure Solution 6
4.
Case study application
To illustrate the features of the model, results of a case study for China conducted are presented here.
4.1. Hydrogen energy system taking methanol as a carrier A key challenge for using hydrogen as fuel is the high cost of transportation and storage of hydrogen. Although R&D on innovative solutions is ongoing, there is still a long way to go before commercially satisfactory solutions are available [20]. One possible approach to the problem of high hydrogen transportation cost is to use methanol as an alternative energy carrier, rather than transporting hydrogen directly. In this context, the newly-introduced ‘‘methanol pathway’’ means using coal gasification or natural gas reforming technologies to produce methanol centrally, and transporting methanol by truck to the hydrogen demand areas, then applying onsite methanol reforming technology to produce hydrogen at forecourt station. The motivation for this approach arises from three factors. (1) The recent progress of small-scale methanol reforming process made in the Dalian Institute of Chemical Physics (DICP) of China Academy of Science [17]. The main part of the process is steam reforming of methanol integrated with H2 separation using Palladium membrane. This new process offers low-cost, high methanol conversion ratio and high hydrogen purity through the process integration of hydrogen production and purification, with the application of innovative catalyst and Palladium purification membrane. (2) The low-cost of methanol transportation compared with hydrogen transportation, especially in a long-distance transportation situation [5]. (3) The potential continuity of the current and future fuel supply chain. Suffering from high dependency on import oil, some countries, like China, are developing and promoting alternative fuels seriously including coal-derived methanol fuel. The vision this section sets reveals the
Gaseous H2 refueling Onsite non-renewable water electrolysis Onsite non-renewable water electrolysis
potential picture that, current methanol fuel activity is valuable not only for the near term and medium term energy security problem relieving, but also for a smooth transition to a possible hydrogen era, with all the infrastructures can be used continuously.
4.2. A case study of China hydrogen infrastructure strategic planning The case study problem specification is depicted in Fig. 3. It consists of a map of China where six production sites are identified for potential installation of central production technologies. Demand for hydrogen by FCVs is expected at 21 major cities on three different demand levels (Early demand, Mid demand and Late demand), acting as markets in the formulation. Of the six central production sites, C1, C2, C3 and C4 take coal as main primary energy, while C1, C2 and C3 also have some reserves of natural gas; N1 and N2 take natural gas
Fig. 8 – ‘‘Infrastructure Solutions’’ breakdown of the tradeoff frontier of Scenario B. Note: IS [ Infrastructure Solution.
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Table 2 – Hydrogen supply chain components for different ‘‘Infrastructure Solutions’’ in the frontier of Scenario B Feedstock
Supply chain components Production
Infrastructure Solution 1
Coal
Infrastructure Solution 2
Refueling
Distribution
Coal to Liquid H2
Truck
Liquid H2 refueling Onsite non-renewable water electrolysis
Coal Biomass
Coal to Liquid H2 Biomass to Liquid H2
Truck
Liquid H2 refueling Onsite non-renewable water electrolysis Liquid H2 refueling
Infrastructure Solution 3
Biomass
Biomass to Liquid H2 Biomass to Gaseous H2
Truck Pipeline
Gaseous H2 refueling Onsite renewable water electrolysis
Infrastructure Solution 4
Biomass
Biomass to Gaseous H2
Pipeline
Gaseous H2 refueling Onsite renewable water electrolysis
Infrastructure Solution 5
as main primary energy. Furthermore, the model assumes all the production sites have access to plenty of biomass resources and non-renewable electricity, while C3, C4, N1 and N2 have access to power resources derived from renewables, and C4 also has access to nuclear power sources. These primary energy availability conditions would limit technologies installed at the sites. In Fig. 4 the hydrogen demand forecast for all regions over the planning horizon is illustrated. It shows both the expected number of hydrogen FCVs and corresponding demands for hydrogen consumptions per year during each of the planning intervals. The long-term planning horizon is defined from 2010 to 2034 and divided into five time intervals of five years each. The superstructure for hydrogen infrastructure technologies is illustrated in Fig. 5. As stated above, we include the ‘‘methanol pathway’’ as an alternative. In order to express the impact of introducing the ‘‘methanol pathway’’, two different scenarios are set: Scenario A with the ‘‘methanol pathway’’ (also called ‘‘With MeOH’’) and Scenario B without the ‘‘methanol pathway’’ (also called ‘‘Without MeOH’’). Applying the multi-objective optimization approach to these two scenarios results in two sets of tradeoff solutions, presented in Fig. 6. The two sets of tradeoff solutions show that the largest NPV achieved in Scenario A is larger than that in Scenario B. The two sets of solutions merge into one when the GHG emissions are less than around 0.2 billion tonne, since there would not be any differences between Scenario A and B beyond this point where the emission constraint becomes stricter and the ‘‘methanol pathway’’ is eliminated from the supply chain components. When the GHG emissions are more than 0.2 billion tonne, Scenario A offers a larger NPV than Scenario B with the same GHG emission constraint. This means the introduction of the ‘‘methanol pathway’’ could improve the economic competitiveness of a hydrogen infrastructure with a relatively gentle emission constraint. However, this advantage disappears when the emission constraint becomes stricter. Each of the two tradeoff curves has two extremes, one for the maximum NPV solution and the other for the least GHG emission solution. In Scenario A, the infrastructure design according to the maximum NPV solution is based on coal oriented centralized gasification at optimally selected central
Onsite renewable water electrolysis
production sites to manufacture methanol. Accordingly, the optimal distribution network delivers methanol in trucks to forecourt markets where onsite methanol reforming process and hydrogen dispensing take place. Additionally, the small hydrogen demand during the market-introduction period is met by onsite non-renewable water electrolysis installed in forecourt markets. At the other extreme, the minimum GHG emission solution corresponds to an infrastructure entirely based on onsite hydrogen production from renewable electricity through electrolysis of water. In Scenario B, due to the absence of the ‘‘methanol pathway’’, the maximum NPV solution corresponds to an infrastructure based on coal oriented centralized gasification at optimally selected central production sites to produce liquid hydrogen. Accordingly, the optimal distribution network delivers liquid hydrogen in trucks to forecourt markets where liquid hydrogen dispensing takes place. Additionally, the small hydrogen demand during the market-introduction period is met by onsite non-renewable water electrolysis installed in the forecourt markets. At the other extreme, the minimum GHG emission solution corresponds to an infrastructure entirely based on renewable oriented water electrolysis for onsite hydrogen production. Noting that each solution within the set represents an alternative infrastructure design and investment strategy, the extent of the compromise between the solutions achieving maximum return on the investment and minimum GHG emissions can be explicitly quantified. Moving along the tradeoff frontier from one extreme to the other involves a series of distinct infrastructures. The optimal tradeoff frontier can be broken into ‘‘Infrastructure Solutions’’1 based on different feedstocks, production, distribution and refueling components of the supply chain that are consistent over a specific region of the curve (as in Scenario A, shown in Fig. 7). Table 1 contains detailed descriptions of the supply chain components corresponding to these ‘‘Infrastructure Solutions’’ of Scenario A. Starting from the maximum NPV strategy (Infrastructure Solution 1) which involves only coal to methanol investment, the transition towards lower emissions requires the 1 Others have used the term ‘‘Enterprise’’ instead of ‘‘Infrastructure Solution’’ to describe an optimized point of infrastructure character and operation. See for example Ref. [8].
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introduction of biomass gasification as a complimentary production technology and coal to liquid hydrogen production technologies (Infrastructure Solution 2). Further reductions in emissions can be achieved while remaining economically competitive (Infrastructure Solution 3) by eliminating coal to
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liquid hydrogen production technologies while increasing the share of biomass to liquid hydrogen technologies. Gradually, coal-based production is totally replaced by biomass-based production. The distribution procedure includes truck delivery of both liquid and gaseous hydrogen, in order to achieve the
Fig. 9 – Sample compromise investment strategy obtained from the model.
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desired level of emission mitigation (Infrastructure Solution 4). Any further emission reduction requires the distribution component in the supply chain transforming from liquid delivery to gaseous pipeline delivery (Infrastructure Solution 5). The least emission strategy involves only onsite water electrolysis by renewable power (Infrastructure Solution 6). Fig. 8 and Table 2 show the ‘‘Infrastructure Solution’’ division results for Scenario B in a similar evolution pattern of ‘‘Infrastructure Solutions’’ with emission constraint as Scenario A. When analyzing the features of the optimal ‘‘Infrastructure Solutions’’ in more details, one realizes that certain technologies and primary energy feedstocks are not present in the optimal tradeoff frontier. The multi-objective optimization framework, therefore, not only facilitates the identification of the most promising candidates, but also assists the elimination of suboptimal ones. More specifically, under the specifications of the case study, neither centralized water electrolysis nor nuclear electricity appears in the set of efficient solutions. The reason is that the absent candidates do not offer either competitive financial returns or environmental benefits compared with the optimal ones. Of course, as technologies develop at different rates in the future the structure of the optimal solutions may change radically. For example, changes in model parameters such as introduction of carbon capture and sequestration technologies, improvement in nuclear technologies and reduction in renewable electricity costs, can drastically change the shape of the optimal tradeoff frontier. The solutions presented here for the case study, though, are based on the best data presently available for proven technologies considered so far. To highlight the characteristics of the solution obtained from the model, one of the optimal compromise solutions is selected and presented in Fig. 9. It corresponds to the maximum NPV solution for Scenario A. The infrastructure evolution is described as following: (1) when t ¼ t1, while the hydrogen demand is low and only occurs at E1 and E2, super metropolis Beijing and Shanghai, it is not economic to implement centralized production. Instead, onsite water electrolysis is utilized in market places; (2) when t ¼ t2, no other markets become active and E1 and E2 remains the same hydrogen production pattern to meet the demand, namely onsite water electrolysis, with a capacity expansion; (3) when t ¼ t3, besides early markets E1 and E2, all mid demand markets become active. The growing demand allows the economies of scale to be exploited by decommissioning the forecourt onsite water electrolysis and switching to centralized manufacturing of methanol through coal gasification. Both C1 and C4 are optimally selected as the central production sites, and the distribution network is optimized based on the flow rates and distances between the sites and markets. All the markets provide hydrogen to customers by onsite methanol-to-hydrogen reforming technology; (4) when t ¼ t4,
all the late demand markets become active. Another site C2 is selected to produce methanol from coal centrally. The distribution network is re-organized through optimization because of the selection of C2 site. For instance, M3 and M9 are not supplied by C1 as they were in t3, now C2 supplies them instead; (5) when t ¼ t5, with increasing demand, all the existing production sites increase their capacity. At the same time, two other sites C3 and N1 start to manufacture methanol and hydrogen, respectively, and C3 becomes the main supplier of E1, while C1 shifts its supply to other markets. Because of the availability of natural gas and short distribution distance to L4, the newly-built N1 supplies natural gasderived gaseous hydrogen to L4 by pipeline. The above result of model running is based on the assumption of hydrogen price presented in Table 3. While setting 10% and 15% fluctuation to the basic price, Fig. 10 shows the sensitivity analysis results of Scenario A and B, respectively. A linear relationship between hydrogen price and NPV is shown. The atmosphere that all the NPV of optimal solutions falls below zero will appear if hydrogen price is low
Table 3 – Assumption of hydrogen prices Time Price ($/GJ)
t1
t2
t3
t4
t5
19.09
20.12
21.09
22.15
23.46
Fig. 10 – Sensitivity analysis result on hydrogen price in Scenario A and B.
international journal of hydrogen energy 33 (2008) 5275–5286
to a certain extend, such as in the case of Scenario B with 15% fluctuation. The hydrogen price, therefore, has a big impact on the economic performance of the infrastructure, and a comprehensive estimation of hydrogen price should be undertaken before the practical construction. Additionally, the figures show that the tradeoff curve presents a much sharper slope with the GHG emissions being lower than 0.2 billion tonne, compared with the rest part. This means the point with 0.2 billion tonnes of emissions can achieve great emission reduction while remains economic competitive. And this point corresponds to an infrastructure entirely based upon biomass as the primary energy, so biomass could play a special role during the hydrogen transition of China.
5.
Conclusions
Hydrogen is one of the most promising candidates for future transportation fuel. However, infrastructure issues pose more challenges and uncertainties for hydrogen than other alternative fuels such as biofuels and electricity. Focusing on hydrogen infrastructure design and optimization, this paper first described the options of hydrogen infrastructure pathways. The numerous combinations of potential technologies within a hydrogen supply chain make it necessary that any investment strategy for building up a hydrogen infrastructure is supported by rigorous quantitative analysis, taking into account all the possible alternatives, interactions and tradeoffs. To assist this strategic decision-making process, this paper presents a generic model for the optimal long-range planning and design of future hydrogen supply chains for fuel cell vehicles. The model presented here uses mixed integer optimization techniques to provide optimal integrated investment strategies 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, allocation of conversion technologies to either central or distributed production sites, design of the distribution network and selection of refueling technologies. At the strategic planning level, capacity expansions and process shut-downs are formulated to explicitly address the dynamic performance of the infrastructure and the timing of the investment. Low-level operational decisions addressed include the estimation of primary energy feedstock requirements and production, distribution and refueling rates. Realizing that both financial and ecological concerns are driving the interest in hydrogen, multi-objective optimization techniques are used to establish the optimal tradeoff between the NPV and the WTW GHG emissions. To illustrate the capabilities of the model, results of a case study of China 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 a given geographical region. In particular, the set of tradeoff solutions, allows the most promising pathways to be isolated and the inferior ones to be eliminated from further consideration.
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At last, the ‘‘methanol pathway’’, especially the one derived from coal, is shown to be an important option for hydrogen infrastructure development in China, as it could improve the economic competitiveness of a hydrogen infrastructure within a relatively gentle emission constraint. It is believed that the ‘‘methanol pathway’’ could play a key role in the future Chinese hydrogen economy.
Acknowledgements This work is part of the collaboration between Tsinghua-BP Clean Energy Research and Education Center in Tsinghua University and Centre for Process Systems Engineering in Imperial College London. The authors gratefully acknowledge the funding of ‘‘China Hydrogen Roadmap’’ project by BP and China State Key Fundamental Research Project (2005CB221207). Pei Liu and Efstratios N. Pistikopoulos also acknowledge the financial support from BP on the project of ‘‘Coal Gasification and Polygeneration’’.
Appendix Illustration of mixed integer programming for modelling strategic decisions Capacity expansions and process shut-downs are key strategic decisions made in the model. Mathematically this can be represented in a MILP framework using the following variables, parameters and constraints: Continuous variables:
Fjst FEjst
capacity of technology j at s during time internal t amount by which capacity of technology j at s is expanded during time interval t
Discrete variables:
YEjst YSjst
1 if capacity of technology j at site s is increased during time interval t, 0 otherwise 1 if capacity of technology j at site s is decommissioned/shut-down during time interval t, 0 otherwise
Parameters:
FELj FEU j lower and upper bound on the amount by which capacity of technology j can be expanded upper bound on the total capacity that may be FU j installed of technology j Constraints: Once a technology is decommissioned it remains ‘‘shut’’ for the remainder of the planning horizon: 1 YSjst YEjst0 ; cj; s; t; t0 t
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Available capacity is set to 0 for all time periods after the moment of shut-down: Fj;s;t0 FUj 1 YSjst ; cj; s; t; t0 t X
YSjst 1; cj; s
t
Expansions can only occur within physically allowed limits: YEjst FELj FEjst FEUj YEjst ; cj; s; t
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