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UKSHEC Working Paper No. 6 2012 Paul E. Dodds and Will McDowall UCL Energy Institute, University College London Central House, 14 Upper Woburn Place London WC1H 0NN, UK p.dodds@ucl.ac.uk

A review of hydrogen production technologies for energy system models

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Contents

1 Introduction ......................................................................................................................... 2

2 Methodology for producing energy systems data .................................................................. 3 2.1 Calculating plant investment and O&M costs ........................................................................ 3 2.2 Converting costs for use in UK MARKAL ................................................................................. 4 2.3 Heat of combustion ................................................................................................................ 4

3 Hydrogen Production technologies ....................................................................................... 5 3.1 Natural gas .............................................................................................................................. 6 3.2 Coal ......................................................................................................................................... 9

3.2.1 IGCC ................................................................................................................................. 9 3.3 Biomass ................................................................................................................................. 10 3.4 Water .................................................................................................................................... 11

3.4.1 Energy storage using hydrogen-electrolysis systems ................................................... 11 3.4.2 Thermochemical cracking of water ............................................................................... 12

4 Technology data for energy system models ......................................................................... 12 4.1 Summary of technologies in current MARKAL models ......................................................... 13 4.2 Recommendations for the future ......................................................................................... 13 4.3 Uncertainties in technology costing ..................................................................................... 18 4.4 Learning curves ..................................................................................................................... 19

5 Conclusion .......................................................................................................................... 19

6 References ......................................................................................................................... 20

1 Introduction

UK greenhouse gas (GHG) emissions predominantly result from the combustion of fossil fuels to provide energy services in all sectors of the economy. In the future, it will be necessary to either capture the emissions or to utilise zero-carbon sources of energy.

Hydrogen has been identified as a potential zero-emission energy carrier for the future, primarily for the transport sector but also for energy storage and CHP applications. Hydrogen gas does not exist naturally and must be produced from fossil fuels or water. It has been produced for industrial applications for many decades and several production technologies are now available that use a range of fuels.

The UKSHEC I project reviewed some of these technologies (Hawkins and Joffe 2005) and implemented a comprehensive hydrogen energy system in the UK MARKAL model (Joffe et al. 2007; Kannan et al. 2007). MARKAL is a partial-equilibrium E4 optimisation model (energy-environment-economics-engineering) that is used to represent the entire UK energy system. The technology characterisation is several years old and we have identified some shortcomings and inconsistencies in the data. As part of the UKSHEC II energy systems research programme, hydrogen production is being revisited to: (i) recommend consistent representations of hydrogen production technologies

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for the UK MARKAL and TIAM-UCL models (TIAM-UCL is a global E4 optimisation model that has been recently developed); (ii) ensure that the assumptions underlying the hydrogen technologies are also consistently applied to other competitor technologies; and, (iii) identify technologies for which the deployment costs are likely to substantially reduce with large-scale deployment. The third objective reflects the greater emphasis on innovation and technology learning in UKSHEC II.

Hughes (2008) provided an initial paper setting out some of the technological developments that have occurred since UKSHEC I was completed. This paper builds on that qualitative work using a comparison of the existing hydrogen production data in the UK and US9R MARKAL models and a literature review. We recommend revised technology data for the UKSHEC II project and consider the uncertainties in the data. The impacts of these uncertainties on model simulations will be considered in a separate paper.

2 Methodology for producing energy systems data

Hydrogen production technologies were identified from current MARKAL models and from the literature. For each technology, the E4 models require capital investment and operations and maintenance (O&M) costs, efficiency data, and fuel inputs and outputs. Few sources provided all of this data for each technology so we based our recommendations on a comparison of all available data rather than adopting a single source. This approach required a greater understanding of the factors underlying the costs and efficiencies of each technology which enabled us to consider data uncertainties. One potential drawback is that the recommended data might no longer represent a single plant design.

We encountered three difficulties in particular. Firstly, the investment and O&M costs must be calculated for each technology using a consistent approach with other energy system technologies so that an unbiased comparison can be performed. Secondly, it is necessary to convert the costs into consistent monetary units. Thirdly, the energy efficiency must be calculated consistently across the model for each fuel. The following sections expand on these difficulties.

2.1 Calculating plant investment and O&M costs

Direct plant investment costs refer to the actual cost of the installed equipment. All plant construction projects also have substantial indirect costs as well. System costs include design, site preparation, contingency costs and profits for the construction contractor (direct costs implicitly include profits for equipment manufacturers). The cost of land, licensing and permits vary between countries. Some plants have additional financial costs to cover financing for up-front fees.

Estimates of technology costs always include direct costs but some indirect costs are often omitted. Since indirect costs are often more than 50% of the direct costs, the inclusion or omission of indirect costs can be one of the principle drivers of cost differences between studies. It is important that comparable costs are included for all technologies in the energy system models, both for hydrogen production and elsewhere, so that unbiased comparisons of technologies can be performed. There are presently no clear guidelines about which indirect costs are included in the UK MARKAL or TIAM-UCL models. In this study, we chose to include all indirect costs except for additional financial costs, which are separately accounted for by the model investment discount rate equations.

O&M costs are also difficult to estimate because they include equipment outage costs, running costs, licensing costs and labour. Some are fixed each year while other variable O&M costs depend on the production rate. For hydrogen production technologies, the principle variable cost is the fuel

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and this is separately accounted for by the energy system models so we concentrated on fixed O&M costs. Our approach calculated the annual O&M costs as a fraction of the capital investment costs.

2.2 Converting costs for use in UK MARKAL

The UK MARKAL model uses British pounds in the year 2000 while the TIAM-UCL model uses US dollars in the year 2005. Investment costs in the literature are specified in both dollars and euros, for a range of years, so it is necessary to account for both currency conversion and inflation. The choices are (i) to adjust for inflation in the original currency and then apply the year 2000 currency conversion rate; or, (ii) to convert to £/$ in the technology year then adjust for the UK inflationary difference to the year 2000. Exchange rates are more volatile than inflation rates so the first option produces a more consistent conversion factor (Figure 1).

In this paper, in common with Lemus et al. (2010) and Bartels et al. (2010), the first approach is adopted. There is a 1.72 factor between the UK£(2000) and US$(2005). However, there is no ‘correct’ methodology as such and it is necessary to consider the conversion rates as a potential source of uncertainty when producing cost estimates of technologies.

Figure 1: $:£ conversion factors for two conversion methods

2.3 Heat of combustion

The energy that can be extracted from a fuel is often measured as the energy released as heat when the fuel undergoes complete combustion with oxygen. Table 1 shows this heat of combustion for several common fuels including hydrogen.

The heat of combustion can be specified in terms of the higher or lower heating value (HHV or LHV). The HHV represents the entire produced heat while the LHV excludes any energy that is used to vaporise water during combustion. The difference between the two is higher for lower-carbon fuels (Table 1). The extent to which energy is lost to water vaporisation depends on the technology; the LHV is most appropriate where large amounts of water vapour are produced at a temperature below 150°C or where condensation of the combustion products in impractical. Both HHV and LHV are used in different sources in the literature so it is necessary to use a consistent approach. Since UK MARKAL energy efficiencies are theoretically calculated on the basis of the HHV, HHV values are

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used throughout this report. However, the existing data in UK MARKAL has not always been consistently calculated using HHV data; for example, the vehicle technology data uses the LHV (which inflates the costs of hydrogen storage tanks, because it effectively reduces the assumed usable energy in hydrogen). The high hydrogen HHV:LHV ratio (Table 1) accentuates the discrepancies caused by using different approaches for different hydrogen technologies relative to other fuels.

Table 1. Heat of combustion of several fuels (MJ/kg)

Higher Heating Value Lower Heating Value HHV:LHV ratio

Hydrogen 142 121 1.17 Methane 56 50 1.12 Gasoline 47 44 1.07 Coal (Anthracite) 27 27 1.00 Wood 15 15 1.00

For solid fuels with higher carbon content (e.g. coal and biomass), the HHV and LHV can vary substantially depending on the exact fuel composition. For example, the efficiency of coal gasification will be substantially influenced by the composition of the coal being used. Great care must be taken to match the fuel type to the process efficiency; if necessary, the process technology should be defined several times for different fuel compositions with different process efficiencies.

3 Hydrogen Production technologies

Hydrogen is currently produced for industrial applications by cracking carbonaceous fossil fuels. Methane is the most common fuel but coal has also been used. Since the principle aim of a hydrogen economy is to reduce carbon emissions, it would be necessary to deploy carbon capture and storage (CCS) systems on these plants and this would only be economic for large-scale production.

Recently, a number of methods have been developed to produce hydrogen from biomass. Biomass gasification is the most advanced but has not been tested at the scale of a large plant. Most other biomass methods are still at the laboratory stage of development.

Hydrogen is also produced by cracking water, a technology that does not require a carbonaceous fuel. Electrolysis is the only method that has been demonstrated on an industrial scale. The principle drawback of this method is the high cost of electricity relative to other fuels; it has been proposed that high-temperature thermal cracking, using heat from nuclear reactors or photovoltaic cells, could improve the efficiency of the process and reduce the electricity use.

This section briefly reviews each of these technologies with the aim of identifying suitable data for energy systems models. Schoots et al. (2008) analysed the actual construction costs of numerous SMR, coal gasification and electrolysis plants and these data were used to estimate the actual built plant costs. Estimates of current and future costs and energy efficiencies for all of the principle technologies were extracted from the H2A (Steward et al. 2008) and TECHPOL (Krewitt and Schmid 2004) technology databases. Further data were extracted from studies in the literature (Gray and Tomlinson 2002; NRC 2004; Kreutz et al. 2005; Iaquaniello et al. 2008; NRC 2008; Cormos 2010; DEA 2010; Khamis and Malshe 2010). Where possible, we compared these data with existing data from the current UK and US9R MARKAL models.

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We found that only six hydrogen technologies have been sufficiently developed to produce realistic cost and efficiency data; these are compared in Figures 2 and 3, respectively. Each of these technologies is discussed in the following sections.

3.1 Natural gas

Natural gas reforming is currently the most efficient, economical and widely used process for production of hydrogen and has been utilised globally for many decades in the oil refinery and fertiliser industries. Steam reforming (SMR) is the standard method but membrane reforming has also been demonstrated in small-scale plants (Iaquaniello et al. 2008; Shirasaki et al. 2009).

Compact, small-scale reformers, suitable for refuelling stations, have been proposed as one option for a hydrogen economy (Ogden 2001). While this option would remove the requirement for expensive delivery infrastructure in the early stages of a transition to hydrogen, the systems would be too small for CCS to be used so substantial GHG emission savings would be not be achieved.

Both large-scale and small-scale SMR technologies are represented within UK MARKAL, each with two technology vintages (Figure 2). The current UK MARKAL data has one apparent data input mistake. Investment costs for future large-scale SMR are higher than those for current technologies, in contrast to the data in the NRC (NRC 2004) report on which the costs are based. Estimates of large SMR plant investment costs in the literature are substantially lower than for smaller plants (Damen et al. 2006), although small-scale plant costs are expected to reduce substantially in the future, perhaps through the adoption of membrane technology. SMR has the lowest capital costs of the hydrogen production technologies at around £4 GJ-1 y-1.

SMR efficiencies are currently in the range 60%–80%, with larger plants being more efficient (Figure 3). Efficiencies are expected to rise only slightly in the future but the scale gap should close. Membrane plants are likely to only slightly increase the operating efficiencies (Shirasaki et al. 2009).

Operating efficiencies will be reduced if plants employ CCS in future. The level of the reduction will depend on the energy required to capture, compress and transport the CO2 to the underground storage site. UK MARKAL currently assume a total process energy efficiency reduction of 5% to account for CCS.

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Figure 2. Capital investment costs from the literature for hydrogen production technologies

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Figure 3. Energy efficiencies from the literature for hydrogen production technologies. The SMR and coal data include a mix of CCS and non-CCS plants.

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3.2 Coal

Prior to the development of electricity networks, coal gasification was used to produce gas for lighting purposes. The technology is mature but is less-widely used that SMR, despite cheaper fuel costs, because the capital investment costs are higher and more variable and the energy efficiency is lower. Only large-scale plants are envisaged in the literature.

UK MARKAL investment costs for current vintages are comparable to constructed plants but future vintages are cheaper than forecasts in the literature (Figure 2). This is partly caused by the inclusion of coal membrane gasification technologies using cost data from Parson’s Group (Parsons Technology Group 2002) that is considered speculative and possibly unrealistic (Pers. comm. F. Starr 2011). It is unlikely that the capital investment costs for coal gasification will change substantially in the future.

The energy efficiency of coal gasification is lower than that of SMR (Figure 2), although the differential has been reduced historically by relatively lower coal prices. Efficiencies range from 50%–80%, which could represent both technological differences and the wide variations in the quality of different types of coal. Even if membrane technology becomes available, overall efficiencies are unlikely to change substantially in the future (e.g. see Li et al. 2010).

Any future energy efficiency improvements will be reduced by the incorporation of CCS. UK MARKAL uses the same approach as for SMR and currently assume a total process energy efficiency reduction of 5% to account for CCS. Since a greater quantity of CO2 is produced by coal gasification for each unit of produced hydrogen, one might expect a greater efficiency reduction for this technology. On the other hand, membrane technology is expected to reduce the efficiency loss due to CO2 capture (Amelio et al. 2007).

3.2.1 IGCC

The hydrogen gas stream from coal gasification can be combusted to produce electricity in an integrated gaseous combined cycle (IGCC) plant (Chiesa et al. 2005; García Cortés et al. 2009). The primary advantage of this technology is the flexibility it provides; in return for an additional capital investment in turbines and steam generators, it allows electricity to be produced at times of peak demand and hydrogen to be produced at other times.

The additional plant will be similar to a gas CCGT; the main rotating parts, condenser, cooling system and electricals account for around 75% of the gas CCGT cost (Mott MacDonald 2010:p25). For a new GTCC in 2030, technology ENGA-CC30B, there is an investment cost in UK MARKAL of £357/kW. We therefore estimate the cost of upgrading a coal gasification plant to an IGCC as £268/kW. The energy efficiency is more difficult to specify because it reduces as the proportion of electricity is increased, hence the large range from the literature in Figure 3. Figure 4 shows that the overall efficiency varies between 35% and 60% for one proposed plant (Cormos 2010), a conclusion supported by Li et al. (2010) using a different model.

The UK MARKAL IGCC representation has fixed outputs of hydrogen (10%) and electricity (90%). Since the principle advantage of this technology is increased flexibility over electricity generation, with only minor efficiency improvements over conventional power stations, the current UK MARKAL representation should be altered to reflect this increased flexibility.

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Figure 4. Overall IGCC efficiency as a function of the hydrogen production rate (data from Cormos 2010).

3.3 Biomass

Biomass accounts for 15% of global primary energy consumption and is particularly important in less-developed countries. Technologies to produce hydrogen from biomass are most strongly characterised by their diversity, in terms of both the types of technology and the range of different biomass fuels that are used. All technologies suffer from low yields because of the low hydrogen content of biomass (approximately 6%) and the 40% oxygen content which lowers the overall available energy; as a result, there are no completed industrial-scale demonstrations of any biomass technology for producing hydrogen (Kalinci et al. 2009) and cost and efficiency data must be considered speculative. Efficiencies are higher for biomass-derived biofuels (e.g. bioethanol) that are processed prior to the hydrogen production plant; the principle advantage of such fuels would be to reduce the fuel transport costs from the plantation to the hydrogen plant. Biofuel production has increased substantially in recent years, with the loss of land for food production causing controversy.

Sexena et al. (2008) identify three broad methods for producing hydrogen from biomass:

1. Thermochemical conversion: combustion, gasification or pyrolysis of biomass. The latter two are represented in the UK MARKAL model. Gasification is normally combined with steam reforming to maximise the hydrogen yield. The most suitable crops UK indigenous feedstocks would be wood or energy crops but imports would allow a greater range of technologies (for example, Haryanto et al. (2005) describe systems for steam reforming of ethanol which could be produced from sugar cane or maize).

2. Biochemical/biological conversion, which uses either algae to decompose complex biological molecules (e.g. anaerobic digestion) or photosynthesis with hydrogen as a by-product. These technologies are limited by high capital costs and low production rates and so are only ever likely to be niche solutions for problems such as biodegradable waste. Several small anaerobic digestion plants have been constructed in the UK for electricity generation (ENDS report 2011).

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3. Mechanical extraction, which produces very low yields and is never likely to be a viable technology.

Figure 2 shows investment costs for biomass gasification and pyrolysis. Costs must be considered speculative but are similar to coal gasification costs because the conversion processes and plant requirements are broadly similar. Pyrolysis uses biomass oil as a feedstock which can be produced from wood.

The range of energy efficiencies in Figure 3 reflects the range of potential fuels; the conversion efficiency of wood is unlikely to exceed 50% because of the low heat of combustion (Table 1) while the higher values represent steam reforming of biofuels. The latter efficiencies are artificially high because they do not account for the energy that is required to produce biofuel from the feedstock. None of these technologies include CCS, which could potentially lead to negative lifecycle emissions but would cause the process energy efficiency to reduce by perhaps 5%.

The biomass technology data in the UK and US9R MARKAL models are very different. The UK model data appears to be broadly consistent with literature while the US9R model has a very pessimistic representation.

3.4 Water

Alkaline electrolysis has been used to produce hydrogen since the eighteenth century and is the basis of most commercially-available electrolysers. Extremely pure hydrogen is produced but at a substantially higher cost than from SMR due to the substantially higher cost of electricity relative to

fossil fuel feedstocks. Low-temperature polymer electrolyte membrane (PEM) and high-temperature solid oxide electrolyser (SOE) electrolysers have been proposed as more efficient technologies for the future (DEA 2010). PEM electrolysers are suited to small-scale hydrogen production while SOE electrolysers can reduce electricity requirements by the use of high-temperature heat instead, a process called thermal cracking.

Figure 2 shows a wide range of capital investment costs for electrolysis systems from the literature. The most surprising aspect is the large number of estimates that are substantially higher than even the most expensive plants that have been built (the middle built estimate, at £18 GJ-1 y-1, represents the average cost of building an electrolysis plant at present). Costs for small systems are particularly high but all systems are expected to become substantially cheaper in the future through technological breakthroughs and learning.

Twentieth-century electrolysers achieved energy efficiencies in the range 58%–72% (Levene et al. 2007) and it is reasonable to assume that new electrolysers will be at the upper end of this range. The efficiencies of current electrolysers in both MARKAL models, at 60%–65%, are therefore representative of older technology vintages and are lower than recent estimates reported in the literature. Efficiencies in the range 85%–95% are expected to be achieved for both small and medium-sized plants in the future, particularly if PEM and SOE electrolysers can be successfully developed.

3.4.1 Energy storage using hydrogen-electrolysis systems

Increasing the share of wind in UK electricity generation is likely to introduce a problem of power generation intermittency. Electricity cannot be stored on a large scale but it has been proposed that the excess could be stored by producing hydrogen through electrolysis.

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The electrolysers would be operated intermittently, which could cause efficiency reductions and introduce heat management and safety issues (Sherif et al. 2005). If the electrolyser temperature is too low, due to a low production rate, then the efficiency of the electrolyser will reduce. A low production rate could also allow hydrogen and oxygen to permeate through the electrolyte and come into contact, reducing the efficiency and possible causing a fire. Alkaline electrolysers are particularly sensitive to these problems but PEM electrolysers are also affected to a lesser extent.

Hydrogen would be produced predominantly using off-peak electricity at times of low demand. While this would decrease the running costs, it would increase the capital investment costs of the electrolysers because production would be substantially below capacity. Any analysis of intermittent production would have to carefully consider the electrolyser utilisation factor.

3.4.2 Thermochemical cracking of water

It was mentioned above that SOE electrolysers operating at higher temperatures achieve greater hydrogen production efficiencies. Hundreds of other high-temperature chemical cycles have been identified for producing hydrogen (Rosen 2010). Studies have primarily concentrated on two technologies to produce the energy for these thermochemical cycles. Firstly, high-temperature nuclear reactors could supply both electricity and high-temperature waste heat to an adjacent production plant (Utgikar and Thiesen 2006; Lubis et al. 2010). Secondly, concentrated solar power (CSP) plants could produce both electricity and the required temperatures in a central tower for hydrogen production (Felder and Meier 2008; Coelho et al. 2010).

The USA has funded substantial research into fourth-generation high-temperature helium reactors for hydrogen production under the DOE Nuclear Hydrogen Initiative program (Bartels et al. 2010) but no plants have been demonstrated on an industrial scale. The high uncertainty in the capital costs of such a system are reflected by the wide range of estimates in Figure 2. Cost evaluation tools are now being developed (e.g. Khamis and Malshe 2010) so more authoritative estimates might become available in future. The different technology energy efficiencies (Figure 3) reflect the different efficiency measurements that are used in the literature. The higher estimate only considers the hydrogen production plant (Steward et al. 2008) while the lower estimate represents the entire nuclear reactor and hydrogen production system (Khamis and Malshe 2010). For the hydrogen production process alone, the energy efficiency should exceed 90% in the future.

The costs of CSP are even more speculative. Most authoritative studies are relatively old (e.g. Glatzmaier et al. 1998) and both the costs and efficiencies are uncertain at the moment due to the early stage of development of the technologies (Steinfeld 2005), so no data is presented here. Both costs and efficiencies would be strongly influenced by the location of the CSP plant due to the variations in solar radiation across the planet.

4 Technology data for energy system models

We presented energy systems data for a range of hydrogen production technologies in Section 3. In this section, we summarise the currently-modelled technologies in the UK and US9R MARKAL models, recommend revised data for the future and consider uncertainties in the data and the potential for technology learning to reduce investment costs and increase operating efficiencies.

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4.1 Summary of technologies in current MARKAL models

The hydrogen production technologies currently represented in the UK and US9R MARKAL models are listed in Table 2. The US9R model focuses on technologies that are already mature and does not incorporate improved technologies in the future. A wider range of technologies is represented in the UK model with only medium SMR, which is not considered viable in the UK due to the relatively smaller delivery distances compared to the USA, being excluded relative to the US9R model. The IGCC technology in the UK model has a fixed hydrogen output (10% of the total). Neither model includes nuclear or CSP thermochemical technologies.

Table 2. Summary of hydrogen production technologies represented in the UK and US9R MARKAL models. The UK model versions were used for the Department for Transport hydrogen study (Strachan et al. 2008) and the more recent Committee on Climate Change study (Usher and Strachan 2010).

Production UK DfT UK CCC US9R

Coal gasification (large) X X X Membrane coal gasification X X IGCC X SMR (large) X X X SMR (medium) X SMR (refuelling station) X X X Biomass gasification (medium) X X X Biomass pyrolysis (medium) X X Waste gasification (medium) X X Electrolysis (medium) X X X Electrolysis (refuelling station) X X X

4.2 Recommendations for the future

We recommend a number of changes to the hydrogen production technologies that are included in the UK MARKAL and TIAM-UCL models. Figure 5 shows the recommended energy system.

The IGCC is now represented as an additional add-on technology to coal gasification. Using this approach rather than a single technology, the models will calculate the overall energy efficiency as a function of the relative production of hydrogen and electricity rather than using fixed outputs as in the current model.

A small biomass gasification system could be added that would be situated at a refuelling station. To limit the labour requirements of such a system to acceptable levels, a high-energy imported liquid biofuel would be used rather than biomass. The UK MARKAL model does not currently represent biofuel imports for hydrogen production (although biofuels are used in E85 fuels) so this technology should only be implemented if such imports are likely. Since the principle objective of a hydrogen economy is to reduce GHG emissions, a new biomass gasification with CSS technology has been added. It is assumed that the biomass feedstock is solid ligno-cellusic in nature. The UK MARKAL model currently simulates biomass pyrolysis as a method of using domestic and imported biomass oil (from pyrolysis of wood). Waste gasification similarly allows the conversion of solid wastes to hydrogen although the current energy conversion efficiency of 65% seems very high. The latter is limited by the amount of available waste. These technologies should only be included if the models will be used to examine technological niches in the future or if substantial imports of biomass oil might become available.

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We have included data for a fourth-generation high-temperature nuclear technology but this should be considered extremely speculative and we recommend that this technology only be included if more reliable data becomes available. Conventional nuclear plants are represented by the electrolysis technology.

Figure 5. Recommended hydrogen production technologies for future versions of the UK MARKAL and UCL-TIAM models. Dotted technologies should only be included if the circumstances outlined in the main text are satisfied.

The recommended year of availability, capital investment costs and energy efficiencies for each technology are listed in Tables 3 and 4 for UK MARKAL and TIAM-UCL, respectively. Since capital costs tend to reduce and efficiencies tend to increase over time, data is provided for technology vintages in 2000, 2025 and 2050. All of the data represent averages of the study data plotted in Figures 2 and 3. In view of the range of data in the literature, we rounded the data to the nearest £1 GJ-1 y-1 for the investment costs and 5% for the energy efficiencies.

UK MARKAL v3.26 separately accounts for the carbon capture part of the hydrogen production plants (technology CCS-CPR). We instead assume, in the absence of better information, that retrofitting does not occur and that the capital cost is not affected by CCS because the plant is redesigned to separate the CO2 from other streams. It is, however, necessary to reduce the technology energy efficiency to account for CCS and we recommend a reduction of 5% in all cases.

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Table 3. Recommended UK MARKAL investment cost and energy efficiency data for hydrogen production technologies for the period 2000–2050, for use in energy systems models. Technologies with CCS should reduce the energy efficiencies by 0.05. Investment costs have units £(2000) GJ

-1 y

-1, except for those denoted by * that have units

£m(2000) GW-1

. See the text for a description of the data source.

Technology Size First year Investment costs Energy efficiency

2000 2025 2050 2000 2025 2050

Coal gasification Large 2000 18 16 14 65% 65% 65% IGCC generation (after coal gasification)

Large 2020 126* 126* 126* 60% 60% 60%

SMR Large 2000 5 4 3 80% 85% 85% SMR Medium 2000 12 10 8 75% 80% 80% SMR Small 2020 45 10 8 65% 80% 80% Biomass gasification Large 2020 15 15 15 50% 50% 50% Biomass gasification Medium 2020 30 20 20 50% 50% 50% Biomass gasification Small 2020 45 25 25 50% 50% 50% Biomass oil pyrolysis Medium 2020 30 20 20 50% 50% 50% Waste gasification Medium 2020 30 20 20 50% 50% 50% Nuclear Large 2030 40 40 75% 75% Electrolysis Medium 2000 18 10 10 75% 85% 90% Electrolysis Small 2000 65 16 10 75% 85% 90%

Table 4. Recommended TIAM-UCL investment cost and energy efficiency data for hydrogen production technologies for the period 2000–2050, for use in energy systems models. Technologies with CCS should reduce the energy efficiencies by 0.05. Investment costs have units $(2005) GJ

-1 y

-1, except for those denoted by * that have units $m(2005) GW

-1. See

the text for a description of the data source.

Technology Size First year Investment costs Energy efficiency

2000 2025 2050 2000 2025 2050

Coal gasification Large 2000 31 27 24 65% 65% 65% IGCC generation (after coal gasification)

Large 2020 217* 217* 217* 60% 60% 60%

SMR Large 2000 9 7 5 80% 85% 85% SMR Medium 2000 21 17 14 75% 80% 80% SMR Small 2020 77 17 14 65% 80% 80% Biomass gasification Large 2020 26 26 26 50% 50% 50% Biomass gasification Medium 2020 52 34 34 50% 50% 50% Biomass gasification Small 2020 77 43 43 50% 50% 50% Biomass oil pyrolysis Medium 2020 52 34 34 50% 50% 50% Waste gasification Medium 2020 52 34 34 50% 50% 50% Nuclear Large 2030 69 69 75% 75% Electrolysis Medium 2000 31 17 17 75% 85% 90% Electrolysis Small 2000 112 27 17 75% 85% 90%

Energy system models also require annual operations and maintenance (O&M) costs for each technology. The principle O&M cost for hydrogen production is the feedstock but this marginal cost is calculated separately within the models. In most literature studies, the remaining costs are predominantly fixed each year. The fixed O&M costs for each technology are plotted as a function of the investment costs in Figure 6. There are variations for each technology but the fixed O&M costs are generally around 5% of the investment costs.

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Figure 6. Fixed O&M costs from the literature for hydrogen production technologies.

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The UK MARKAL model currently represents all O&M costs as variable costs. We recommend that these be changed to fixed O&M costs in future. Tables 5 and 6 list fixed O&M costs for 2000, 2025 and 2050 vintages of each technology for the UK MARKAL and TIAM-UCL models, respectively. These costs were calculated by combining representative fixed O&M percentages from Figure 5 with investment cost data from Table 4.

Table 5. Recommended UK MARKAL annual fixed O&M cost data for hydrogen production technologies for the period 2000–2050, for use in energy systems models. O&M costs have units £(2000) GJ

-1 y

-1, except for those denoted by * that

have units £m(2000) GW-1

. See the text for a description of the data source.

Technology Size Fixed O&M (% of investment costs)

Fixed O&M costs

2000 2025 2050

Coal gasification Large 5% 0.9 0.8 0.7 IGCC generation (after coal gasification)

Large 5% 6.3* 6.3* 6.3*

SMR Large 4% 0.2 0.2 0.1 SMR Medium 4% 0.5 0.4 0.3 SMR Small 4% 1.8 0.4 0.3 Biomass gasification Large 7% 1.1 1.1 1.1 Biomass gasification Medium 7% 2.1 1.4 1.4 Biomass gasification Small 7% 3.2 1.8 1.8 Biomass oil pyrolysis Medium 7% 2.1 1.4 1.4 Waste gasification Medium 7% 2.1 1.4 1.4 Nuclear Large 6% 2.4 2.4 Electrolysis Medium 5% 0.9 0.5 0.5 Electrolysis Small 5% 3.3 0.8 0.5

Table 6. Recommended TIAM-UCL annual fixed O&M cost data for hydrogen production technologies for the period 2000–2050, for use in energy systems models. O&M costs have units $(2005) GJ

-1 y

-1, except for those denoted by * that

have units $m(2005) GW-1

. See the text for a description of the data source.

Technology Size Fixed O&M (% of investment costs)

Fixed O&M costs

2000 2025 2050

Coal gasification Large 5% 1.5 1.4 1.2 IGCC generation (after coal gasification)

Large 5% 10.8 10.8 10.8

SMR Large 4% 0.3 0.3 0.2 SMR Medium 4% 0.8 0.7 0.5 SMR Small 4% 3.1 0.7 0.5 Biomass gasification Large 7% 1.8 1.8 1.8 Biomass gasification Medium 7% 3.6 2.4 2.4 Biomass gasification Small 7% 5.4 3.0 3.0 Biomass oil pyrolysis Medium 7% 3.6 2.4 2.4 Waste gasification Medium 7% 3.6 2.4 2.4 Nuclear Large 6% 4.1 4.1 Electrolysis Medium 5% 1.5 0.9 0.9 Electrolysis Small 5% 5.6 1.4 0.9

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Energy system models require a breakdown of the energy inputs and outputs for each technology. Table 7 shows data extracted from Khamis & Malshe (2010) for high-temperature nuclear and from the H2A database (Steward et al. 2008) for the other technologies. Adding CCS to the plants would increase the electricity consumption.

Table 7. Recommended energy source input and output data for hydrogen production technologies for use in energy systems models. Technologies with CCS should increase the electricity input and reduce the feedstock by 4%. See the text for a description of the data source.

Technology Energy input Energy output

Feedstock Electricity GH2 Electricity

Coal gasification 100% 0% 100% 0% IGCC generation (after coal gasification)

100% 0% 0% 100%

SMR 100% 0% 100% 0% Biomass and waste 97% 3% 100% 0% Nuclear 100% 0% 70% 30% Electrolysis 0% 100% 100% 0%

4.3 Uncertainties in technology costing

The plant investment costs presented in Section 3 apply to plants built in the USA or mainland Europe. The indirect costs can vary in other countries because they are sensitive to land and labour costs and to national taxation and environmental legislation.

The impact of technology data uncertainties can be assessed using in an energy systems model using a formal sensitivity study, but this is a time-consuming process. The impacts will depend on both the size of the uncertainty, the importance of the technology data in the overall energy system and the proximity of the technology to a tipping point where an alternative technology would be chosen. The size of the uncertainty can be gauged by comparing data from different studies in Figures 2 and 3: mature gas and coal technologies have lower uncertainties than other immature technologies. The importance of the technology data depends on the relative size of the investment and feedstock costs (and hence the feedstock marginal price); Ahluwalia et al. (2011) show that the production plant investment costs are a relatively small part of the delivered hydrogen cost at current feedstock prices so it is likely that errors in the energy efficiency will have greater impacts than errors in the investment costs. The impact of tipping points can only be assessed using a model sensitivity study.

Energy system models tend to be conservative in nature because of a reliance on technologies that are already mature. Part of the reason is that finding accurate data for unproven, immature technologies is difficult; we encountered this problem in this project when looking for cost and efficiency data for CSP hydrogen production systems. Where estimates are available in the literature, it is necessary to critically appraise the rationality of the assumptions underlying the estimates. For example, Figure 7 shows estimates for a number of technologies converging to a single low cost in 2030 that is close to a coal + carbon tax cost. It seems highly unlikely that all of these technology costs will converge to a similar cost when the exergies vary so much between technologies.

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Figure 7: Hydrogen cost forecasts for a range of renewable systems (Lemus et al. 2010)

4.4 Learning curves

One of the aims of the UKSHEC II project is to examine how innovation and technology learning might reduce hydrogen technology costs and improve efficiencies in the future. Learning curves describe technology improvements, normally cost reductions, that are achieved as a result of large-scale deployment of a technology.

There are limited opportunities for technology learning to influence hydrogen production technologies. Natural gas and coal technologies are mature and are unlikely to change substantially over the coming decades. Biomass gasification is an evolution of coal gasification, while nuclear technologies are also evolutions of current technology. The technology with the most potential for learning is electrolysis, where currently high investment costs and low energy efficiencies have the potential to improve substantially as a result of fuel cell research and deployment. Electrolysis technologies could be included in a technology cluster driven by fuel cell deployment.

5 Conclusion

A range of mature and immature technologies produce hydrogen from a range of fuels. We have reviewed these technologies and recommended a number of changes to the UK MARKAL and TIAM-UCL energy system models. The revised technologies represent a range of production methods and feedstocks. Since the principle objective of a hydrogen economy is to reduce GHG emissions, CCS is included as an option where appropriate.

Previous UK MARKAL versions included several similar technologies for the same feedstock (e.g. coal gasification and membrane technologies). In the future, we recommend specifying a single technology and represented the cost/efficiency improvements as step changes to the technology

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vintages. Such improvements are generally small compared to the variations between different feedstocks.

We concentrated on mature technologies but included some immature technologies where appropriate. The two areas where our analyses are most likely to be superseded are biomass, where a great variety of technologies using different feedstocks are at the laboratory stage, and high-temperature hydrogen production.

6 References

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