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The curious case of the conflicting roles of hydrogen in global energy scenarios

Christopher J. Quarton a, Olfa Tlili b, Lara Welder cd, Christine Mansilla b, Herib Blanco e, Heidi Heinrichs c, Jonathan Leaver f, Nouri J. Samsatli g, Paul Lucchese h, Martin Robinius c and Sheila Samsatli *a
aDepartment of Chemical Engineering, University of Bath, Claverton Down, Bath BA2 7AY, UK. E-mail:
bCEA, I-tésé, Université Paris-Saclay, Gif-sur-Yvette, France
cForschungszentrum Jülich, Institute of Energy and Climate Research – Electrochemical Process Engineering (IEK-3), Wilhelm-Johnen-Straße, 52428 Jülich, Germany
dChair for Fuel Cells, RWTH Aachen University, c/o Institute of Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich GmbH, D-52425 Jülich, Germany
eCenter for Energy and Environmental Sciences, IVEM, University of Groningen, Nijenborgh 6, 9747 AG Groningen, The Netherlands
fSchool of Engineering, Unitec Institute of Technology, Auckland, New Zealand
gProcess Systems Enterprise Ltd., London SW7 2AZ, UK
hCEA, Université Paris-Saclay, Gif-sur-Yvette, France

Received 21st September 2019 , Accepted 8th October 2019

First published on 9th October 2019


As energy systems transition from fossil-based to low-carbon, they face many challenges, particularly concerning energy security and flexibility. Hydrogen may help to overcome these challenges, with potential as a transport fuel, for heating, energy storage, conversion to electricity, and in industry. Despite these opportunities, hydrogen has historically had a limited role in influential global energy scenarios. Whilst more recent studies are beginning to include hydrogen, the role it plays in different scenarios is extremely inconsistent. In this perspective paper, reasons for this inconsistency are explored, considering the modelling approach behind the scenario, scenario design, and data assumptions. We argue that energy systems are becoming increasingly complex, and it is within these complexities that new technologies such as hydrogen emerge. Developing a global energy scenario that represents these complexities is challenging, and in this paper we provide recommendations to help ensure that emerging technologies such as hydrogen are appropriately represented. These recommendations include: using the right modelling tools, whilst knowing the limits of the model; including the right sectors and technologies; having an appropriate level of ambition; and making realistic data assumptions. Above all, transparency is essential, and global scenarios must do more to make available the modelling methods and data assumptions used.

1. Introduction

In order to combat climate change there is increasing interest in achieving net-zero greenhouse gas (GHG) emissions before the end of the century.1 Energy systems decarbonisation is an essential part of this, as energy sectors contribute around three-quarters of global GHG emissions.2

Renewable energy technologies have progressed tremendously in recent decades, now offering economically credible alternatives to fossil fuels in many sectors.3 However, these technologies are fundamentally different to fossil fuels, so a like-for-like replacement is not possible. Renewable resources such as wind and solar are diffuse and intermittent, creating new challenges for matching energy supplies to demands, in both time and space.4,5 Furthermore, fossil fuels have unrivalled storage capabilities. It is essential to find low-carbon energy storage options, for temporal balancing of supply and demand, and use in transport.6 We need to develop technologies that will enable increased energy systems flexibility and interconnectivity, while maintaining reliability and stability.7,8

In this context, hydrogen has potential. Apart from small reserves of “natural” hydrogen,9 hydrogen is not a resource that can be extracted at scale in the same way as fossil fuels. However, it can be produced with minimal GHG emissions, for example through electrolysis powered by renewable electricity,10 or from bioenergy or fossil fuels with carbon capture and storage (CCS).11 Hydrogen has many possible energy applications, including for heating, transport, industry, and electricity generation.12,13

Energy scenarios can provide valuable insights into possible future trajectories of energy systems. Many different national, regional and global energy scenarios exist. Some scenarios, such as those produced by global institutions (e.g.ref. 14–16), can be very influential to political discourse.

However, energy scenarios are generated using various methods and, given the complexity of the systems being represented, it is unsurprising that the scenarios produce differing results. In particular, the prominence of hydrogen in different scenarios varies noticeably. Hanley et al.17 reviewed the role of hydrogen across different energy scenarios, finding a range of results regarding the uptake of hydrogen. Whilst many scenarios included some hydrogen in the transport sector, uptake of hydrogen in other sectors varied significantly depending on the emphasis in the scenario design. Furthermore, the review found a correlation between the level of ambition (e.g. decarbonisation or renewables integration targets) and the contribution of hydrogen in the scenario results.

Given hydrogen's potential to transform energy systems, the variation in its contribution in global energy scenarios is surprising. Whilst Hanley et al.17 identified some of the trends in hydrogen prevalence, they did not explore the reasons for differing results in detail.

In this perspective, we assess hydrogen's potential as a contributor to energy systems, and examine the methods used in global energy scenarios in order to understand the reasons for differing results regarding hydrogen. We focus on global energy scenarios produced by prominent institutions, as these are typically the most influential. The entire scenario development process is considered, including conceptualisation, model construction, and input data. Based on this analysis, we suggest some best practices for energy scenarios so that they can provide the best insight, and correctly quantify the potential of energy technologies such as hydrogen.

Section 2 provides an overview of hydrogen as an energy carrier. Section 3 provides details of hydrogen prevalence in scenarios from 12 global studies. In Section 4, the reasons for varying results between scenarios are discussed. Finally, some conclusions and suggestions for best practice in scenario development are provided in Section 5.

2. Opportunities for hydrogen in energy systems

There are many possible pathways for hydrogen in energy systems and in some cases they are already being realised in real projects. In this section, the main pathways are summarised; an overview is provided in Fig. 1, whilst Pivovar et al.18 describe them in more detail.
image file: c9se00833k-f1.tif
Fig. 1 Overview of key hydrogen production and usage pathways. With multiple production options and applications, hydrogen could be valuable in providing flexibility and sector-coupling to energy systems.

Currently, most hydrogen is produced from fossil fuels, such as reforming of natural gas or gasification of coal. Similar processes can be used to convert biomass feedstocks to hydrogen.19 Water electrolysis has been used to produce hydrogen in certain industrial applications for over a century, but in recent decades it has seen growing interest due to newly emerging technologies and availability of low-cost electricity.10 Many future projections for hydrogen are based on large contributions from electrolysis but there are other new technologies emerging, such as thermolysis and photolysis, that may offer a more efficient use of thermal or solar energy for hydrogen production.20

Applications of hydrogen include conversion to electricity using a fuel cell,19 contributing to industrial processes,21,22 and combustion for heat and/or power generation.23 Hydrogen can be stored in quantities from MW h to TW h, for example in pressurised cylinders or underground in salt caverns, depleted oil and gas reservoirs and saline aquifers.19,24 Pressurised hydrogen storage has a volumetric energy density greater than 500 kW h m−3, far exceeding low-carbon energy storage alternatives (up to 1.5 kW h m−3 for pumped hydro storage (PHS) and 12 kW h m−3 for compressed air energy storage (CAES)).25

Hydrogen's high energy density makes it particularly interesting for system-wide energy balancing. Hydrogen could be manufactured from electricity at times of excess supply, stored, and later converted back to electricity or used for other purposes at times of high demand.10 However, hydrogen storage round-trip efficiencies are around 20–36%, which is low compared to alternatives (PHS: 70–85%; CAES: 65–80%; battery: 86–95%).6 Therefore, the value of hydrogen energy storage depends on the trade-off between the benefits of time-shifting bulk energy, and the costs of the efficiency losses.

Whilst hydrogen for electricity storage has not yet been deployed at large scale, already several projects have deployed electrolysers to absorb electricity from wind farms, to be stored and used at a later date in various applications (for example Energiepark Mainz26 and Lam Takhong27). For the 2020 Olympics, Tokyo plans to power the Olympic village with hydrogen from solar-powered electrolysis.28

Hydrogen's suitability for storage also makes it appealing as a transport fuel. A hydrogen fuel tank and fuel cell can provide the electricity supply for an electric vehicle, or hydrogen can be burned in an internal combustion engine. Hydrogen is seen as a possible low-carbon fuel in transport sectors that require long ranges, such as road freight, rail and shipping.13,29 Hydrogen in passenger vehicles could also offer greater driving ranges, faster refuelling times and in some cases lower cost of ownership compared to battery electric vehicles.30,31

The transport sector has seen the greatest interest in hydrogen so far and there is considerable interest globally in expanding the use of hydrogen as a transport fuel. There are over 350 hydrogen fuelling stations worldwide, across the Americas, Europe, Asia and Oceania.32 Hydrogen buses are in use in many cities around the world including in USA, Japan, China and several countries in Europe.33,34 Alstom have developed a hydrogen train, the first of which went into operation in Lower Saxony, Germany in 2018.35

Hydrogen is already a key chemical component in many industrial markets: the main applications include ammonia synthesis (55% of hydrogen demand); hydrocracking and hydrodesulphurisation in refineries (25%); and methanol production (10%).36

Nonetheless, the “hydrogen economy” is still in the early stages of development. In most applications, there has been limited deployment of hydrogen beyond demonstration projects.37 Most of the hydrogen used today is produced on-site for specific applications. Consequently, there has been limited infrastructure development other than for transportation between chemical manufacturing sites. Today, there are around 16[thin space (1/6-em)]000 km of hydrogen pipelines globally12 compared to 2.91 million km for natural gas.38 For expansion beyond the chemical sector, it will be necessary either to build new hydrogen infrastructure, or to utilise existing infrastructure (e.g. partial injection or conversion of existing gas networks).37

Low-cost, low-carbon hydrogen production at scale is also still a challenge. Conventional production such as steam methane reforming (SMR) would require carbon capture and storage (CCS) to minimise GHG emissions, but this adds around 45% to the cost,11 and CCS deployment remains limited. Low-carbon production of hydrogen using electrolysis requires both significant electrolysis capacity and sufficient low-carbon electricity production. Although costs of renewable electricity are falling rapidly with increasing installed capacity,3 electrolysis installed capacity is low and reductions in capital costs through economies of scale are still required.39,40 Lastly, fuel cell costs are relatively high (around $280 kW−1 (ref. 41)), and manufacturing scale-up is required to make hydrogen competitive with other energy carriers.

Hydrogen can also be combined with captured CO2 in carbon capture and utilisation (CCU) processes. CCU can produce useful energy carriers that are already in use and have existing infrastructures, such as methane, methanol and liquid hydrocarbons.42,43 The CO2 used in CCU could be captured from fossil sources, but increased environmental benefit would be achieved if the CO2 were captured from biomass or directly from the air.44 The challenges for CCU are energy losses associated with the additional conversion step (20–35% (ref. 45)), and high costs compared to the fossil alternatives they would replace (e.g. CCU transport fuel may cost € per 30 GJ, compared to € per 15 GJ for petroleum-based fuels46). Hydrogen can also be combined with nitrogen to produce ammonia, which has advantages for storage and transport, and can be used for heat and power generation.47

3. Global energy scenarios and the representation of hydrogen

3.1 Energy scenarios

Energy scenarios can address the uncertainties surrounding the socio-technical evolution of energy sectors. Scenarios can be qualitative, relying on inputs from experts and stakeholders, or quantitative, usually based on energy systems models.48 Scenario development aims to construct possible futures and the paths leading to them, and can guide strategic decision-making processes, for example for maintaining long-term energy supply-demand balances and optimising investment decisions. Consequently, these scenarios can be highly influential to the future of the technological “ecosystem” in different sectors. Due to the size and complexity of the energy systems being represented by energy scenarios, simplifying assumptions must be made, and these can have significant implications for the scenario results.

Several reviews of model-based scenarios and the modelling tools they use have been carried out, highlighting a variety of methods and results. Pfenninger et al.58 reviewed energy systems models in the context of present-day energy systems, and identified several challenges that these models face, stemming from the increased complexity of modern energy systems. The review also provided recommendations for modelling practice, encouraging innovation with modelling methods, appropriate handling of uncertainty and modelling transparency. Meanwhile, Gambhir et al. reviewed energy scenario results, finding that the level of climate change ambition has a significant effect on the scenario results.59 Lopion et al.60 investigated trends in energy system models developed for national greenhouse gas reduction strategies, in the context of underlying research questions and their shift over time, and found that there is an increasing need for high temporal and spatial resolutions.

As Hanley et al.17 found, the prominence of hydrogen varies significantly between energy scenarios. Whilst many of the scenarios Hanley et al. studied included some hydrogen in the transport sector, hydrogen prevalence in other sectors was low, except where hydrogen was a specific focus of the study. The scenarios that focus on hydrogen, such as the IEA Energy Technology Perspectives (ETP) 2 °C “high hydrogen” scenario,61 have begun a trend of greater hydrogen representation, and hydrogen prominence is growing in the most recent scenarios.

In this perspective, we discuss why there has been an historical absence of hydrogen in global energy scenarios, and why that is beginning to change. Many energy scenarios exist at regional and national levels, such as the EU Reference scenario,62 ASEAN Energy Outlook (SE Asia),63 IDB Lights On scenario (Latin America),64 EIA Annual Energy Outlook (USA),65 China Renewable Energy Outlook,66 the Japan Strategic Energy Plan,67 and the Deep Decarbonization Pathways Project (various countries).68 However, in this perspective we focus on global scenarios with the greatest international impact.

The 12 studies that were considered are shown in Table 1. We focus on the scenarios from 10 model-based studies and also consider two hydrogen-focussed qualitative scenarios: the IEA Hydrogen and Fuel Cells Technology Roadmap30 and the Hydrogen Council “Scaling Up” scenario,57 as they provide a counterpoint for the potential for hydrogen, as perceived by experts and stakeholders.

Table 1 Details of the studies and scenarios that were reviewed. Global studies from influential institutions were chosen, focussing on quantitative (model-based) scenarios. Two qualitative scenarios were also included
Study Abbreviation Model used Scenario end year Scenarios
World Energy Outlook (IEA) 2016 (ref. 49) WEO 2016 World Energy Model + MoMo 2040 Current policies
New policies
450 scenario
World Energy Outlook (IEA) 2017 (ref. 50) WEO 2017 World Energy Model + MoMo 2040 Current policies
New policies
Sustainable development
World Energy Outlook (IEA) 2018 (ref. 14) WEO 2018 World Energy Model + MoMo 2040 Current policies
New policies
Sustainable development
The future is electric
The Grand Transition (WEC) 2016 (ref. 15) WEC GMM 2060 Hard Rock
Unfinished Symphony
Modern Jazz
REmap (IRENA)51 REmap E3ME 2050 Reference
Energy Technology Perspectives (IEA) 2016 (ref. 52) ETP 2016 ETP TIMES + MoMo 2050 6DS
Energy Technology Perspectives (IEA) 2017 (ref. 53) ETP 2017 ETP TIMES + MoMo 2060 RTS
Energy Revolution (Greenpeace)54 ER REMix 2050 Reference
Shell scenarios16,55 Shell Shell World Energy Model 2100 Mountains
Global Energy Assessment (IIASA)56 GEA MESSAGE + IMAGE 2050 Supply (Conv. Trans)
Mix (Conv. Trans)
Efficiency (Conv. Trans)
Supply (Adv. Trans)
Mix (Adv. Trans)
Efficiency (Adv. Trans)
Hydrogen Council (2017)57 H2 Council Qualitative 2050 Hydrogen – scaling up
Technology Roadmap: Hydrogen and Fuel Cells (IEA)30 H2FC Roadmap Qualitative 2050 2DS high H2

3.2 Hydrogen representation in global energy scenarios

Between the 35 scenarios considered there is significant variation regarding which hydrogen technologies and end-use applications are considered, and the level of detail with which they are included. In Fig. 2, the level of representation of these hydrogen technologies is presented, including whether the technology is modelled, whether data assumptions are provided, and whether hydrogen contributes to the final results. Whilst there are conflicts in the prominence of hydrogen between scenarios, what is common is that limited specific techno-economic information is provided. Often, concepts are discussed but with little detail, so it is difficult to understand how these concepts are represented and what assumptions have been made.
image file: c9se00833k-f2.tif
Fig. 2 Differing representation of hydrogen in scenarios from 12 global studies. Hydrogen representation is separated into seven sectors, covering the supply-side (production, storage, transportation), and applications of hydrogen (conversion to electricity, mobility, industry, gas grid). Colours refer to the level of representation in the scenario design; “R” denotes technologies that are included in the results of the scenario. See the legend for more details.

Regarding technologies, hydrogen production is covered in the most detail, and in this case techno-economic assumptions are often provided. Electrolysis is commonly considered, although the technology type is rarely specified (WEO 2018,14 Shell,16,55 GEA,56 ER,54 REmap69). ETP 2017 specifically considers the more commercially developed alkaline electrolysis, whereas the H2 Council focus on PEM electrolysis, which many expect to overtake alkaline as the favoured technology.40 The qualitative H2FC road map30 is the only study to consider solid-oxide electrolysis.

Several studies discuss other production options, such as SMR, coal gasification and biomass-based production. These production options are typically mentioned when comparing hydrogen production costs (WEO 2018,14 H2FC Roadmap30) or as a transitional step to fully decarbonised hydrogen (Shell16,55). The techno-economic assumptions related to these technologies (mainly SMR, with or without CCS) are often presented, and it is observed that the costs of electrolysis and SMR + CCS are converging.30

Other hydrogen infrastructures, such as transportation and storage, receive little coverage in most studies. A few studies discuss storage, but provide no data, suggesting it is not modelled (GEA,56 ER,54 H2 Council57). Hydrogen transportation receives slightly more coverage, most commonly shipping for global transportation (WEO 2018,14 H2 Council,57 GEA56). In general, limited data is provided for transportation, so it is unclear what assumptions are made (e.g. how transportation is costed), or whether it is considered at all.

End-use applications are described in more detail in the scenarios. The most prominent end-use is mobility, which is considered in some form in all but WEO 2016 (ref. 49) and WEO 2017.50 Fuel Cell Electric Vehicles (FCEVs) for light-duty passenger vehicles (LDVs) are predominant but heavier duty vehicles (HDVs, e.g. trucks and buses) are also discussed in more-recent studies (though rarely quantified). Instead, discussion is more focussed on societal issues, such as government policies. The qualitative studies30,57 provide more techno-economic data for HDVs. Finally, there is some interest in hydrogen for alternative fuels but limited details on techno-economic assumptions are provided (E[R],54 ETP 2017,53 H2 Council57).

Beyond mobility, other applications for hydrogen are discussed in less detail. Several studies consider industrial applications, with refining applications such as steel and iron, and chemical applications such as ammonia production being the most popular. Electrification of processes via electrolysis is mentioned (WEO 2018 (ref. 14)), but again with little detail. Interactions with the gas grid (either direct hydrogen injection or methanation) are often mentioned in discussion, but rarely quantified in the results (GEA;56 WEO 2017,14 H2FC Roadmap,30 H2 Council57). Finally, conversion of hydrogen to electricity and heat is rarely mentioned. Where it is considered, the most common technologies are fuel cells, gas turbines and combined heat and power applications. The E[R] scenarios54 are the only ones to include these applications in the scenario results.

3.3 Conflicting roles of hydrogen in global scenario results

The variability in representation of hydrogen in scenarios leads to conflicts in the level of contribution of hydrogen in the scenario results. Fig. 3 shows the contribution of hydrogen to final energy demand in 2050 in different sectors, for each of the scenarios that includes hydrogen in its results.
image file: c9se00833k-f3.tif
Fig. 3 Contribution of hydrogen to final energy demand in 2050 in power, mobility, industrial and heat sectors for a range of scenarios. Where studies state the inclusion of hydrogen in the results without precisely quantifying it, values have either been estimated by the author (IEA ETP 2016, Shell Sky and H2 Council scenarios), or the result has been denoted by a hashed box.

Overall, the scenarios indicate that hydrogen has the most potential in the mobility sector. Most scenarios have some level of hydrogen in this sector but they offer conflicting levels of contribution: in many cases this is less than 2% of transport energy demand in 2050 (e.g. WEC15 and ETP 2017 (ref. 53) scenarios); whereas the Greenpeace E[R] and Adv E[R] scenarios give contributions as high as 19% and 25%, respectively.54

Similarly, the contribution of hydrogen in the industrial sector ranges between 0.7% of 2050 industrial demands (Shell Sky16) and 12% (H2 Council57) but many scenarios do not include it at all.

The focus between these two sectors can also shift between scenarios: the Grand Transition scenarios suggest hydrogen should contribute to the mobility sector and not to industry whereas several of the Global Energy Assessment scenarios advocate the opposite.

The Greenpeace scenarios54 are the only quantitative scenarios to include hydrogen in the results for the power and heating sectors and both qualitative scenarios also include it (H2FC Roadmap30 and H2 Council57).

4. Discussion: what must scenarios do to represent hydrogen fairly?

From the results in Section 3, and from previous reviews, there is clearly significant variation between scenarios concerning the prominence of hydrogen in energy systems. Although most of these scenarios rely on energy system models, the representation in these models is not sufficient to capture all of the advantages of hydrogen. In this section, we examine the key steps in quantitative scenario development, to understand why differing results may arise, and consider what scenario developers should be doing to make sure hydrogen, and other flexibility options (such as alternative storage technologies, demand-side response, electricity grid expansion and interconnectivity70), are appropriately represented.

4.1 Scenarios must use appropriate modelling tools

Energy systems models form the basis of most quantitative energy scenarios. A vast number of energy system modelling tools exist and can be categorised in different ways, including simulation vs. optimisation, top-down vs. bottom-up, etc. In a review of computing tools for energy systems, Connolly et al.71 identified 68 different energy system modelling tools. Lopion et al.60 reviewed 24 energy system models in detail, also categorising them as above, and found a clear trend towards techno-economic bottom-up optimisation models in order to answer current research questions.

Each energy systems model is designed for its own unique purpose and has its own strengths and weaknesses. Some of the oldest models were developed in the second half of the 20th century to help understand energy systems in the context of the oil crisis and concerns over security of energy supply.58 These models are the predecessors of many models in use today, where due to climate change, we face significantly different energy challenges. It is important that energy systems models in use today are appropriately designed to represent the challenges we face in the twenty-first century.

The most difficult task for modern day energy systems models is to capture the full degree of variability and complexity that exists in energy systems. Traditionally, energy systems were centralised and underpinned by fossil fuels. In the electricity sector for example, supply would be made up of either base-load or dispatchable generation. However, as more and more renewable sources such as solar and wind are introduced to aid decarbonisation, systems are becoming more spatially distributed, technologically diverse and temporally variable. Meanwhile, new technologies and increased interconnectivity are enabling more interaction between different energy sectors, known as “sector-coupling”.72 To ensure that energy system models not only provide an accurate representation of energy systems but also do not miss the potential of new technologies such as hydrogen-based technologies, they must capture the required level of temporal, spatial, technological, and inter-sectoral detail.

4.1.1 Models must capture sufficient temporal detail. Many large-scale energy models are unable to represent the time scales at which flexibility technologies such as electrolysers, hydrogen storage and fuel cells are most useful. For example, traditional energy system models typically use representative time slices, such as day, night, and peak for a series of day types throughout the year. In some cases, within-day chronology is retained, meaning that it may be possible to model some level of intraday storage. However longer-term chronology is rarely retained, thus losing the ability to represent long-term storage,73,74 which is an area where hydrogen is seen to have strong potential.6,75 Novel methods for modelling seasonal storage are beginning to emerge76,77 but they have not been applied to any of the global energy scenarios. Meanwhile, short-term dynamics, such as electricity dispatch on a sub-hour basis, are also not modelled by large-scale energy models. This means that another opportunity for hydrogen, as a short-term load balancer through electrolysis,78,79 is also missed. The effects of under-representing temporal detail in energy scenarios have been explored and it has been found that investment optimisations will underestimate the contribution of dispatchable power generation and instead favour baseload and intermittent renewables.80 It is therefore likely that flexibility options such as those based on hydrogen are also being under-valued.

The challenge for large-scale energy systems models is to capture the full range of time scales necessary. The models are designed for long-term investment planning, and therefore require multi-decadal time horizons. However, the dynamics of the energy system at all time scales (including seasonal, weekly, daily, and sub-hourly) are important to how the system should be designed and operated.81 Approaches to improve the accuracy of the time-slicing method include using a higher resolution of time intervals; probabilistic representation of the loads and renewable energy supplies; and using real historical data for the time intervals.73 However, each of these approaches suffers the same issue of failing to maintain chronology across the whole time horizon, hence some representation of flexibility is lost. Alternatively, energy systems models can be soft-coupled to power sector models, taking advantage of the latter's improved temporal representation.73 However, this approach can increase overall complexity, as there are two separate models to maintain and run. Furthermore, due to the required iteration between the two models, there is no guarantee that an optimal solution will be obtained.

4.1.2 Models must capture sufficient spatial detail. As well as temporal flexibility, hydrogen can provide spatial flexibility to energy systems. Hydrogen transportation by road, pipeline and shipping provide opportunities for the transportation of energy that cannot be provided by other energy carriers (e.g. electricity). Large-scale (e.g. global) energy models usually have limited spatial detail, using average resource demands and supplies over large spatial regions.58 Consequently, they do not capture the value of energy transportation at a smaller scale, such as across country. Furthermore, spatial variabilities in solar and wind generation will affect supply profiles across a region: this “spatial smoothing” cannot be fully represented with too coarse a spatial resolution.73

One option for improving this modelling would be to include a higher spatial resolution but this would significantly increase the complexity of the model. Alternatively, models should seek to use representative data and relationships to value within-region energy transportation and distribution.

4.1.3 Models must appropriately represent technologies and inter-sectoral connectivity. Technological representation in large-scale energy models is often restricted to blanket details for each technology type, rather than representing individual technologies or plants.80 Consequently, realistic operation of plants, taking their flexibility constraints into account, is not modelled. This is not helped by the lack of temporal resolution and chronology.

To improve technological representation, approaches include further modelling of ancillary markets (e.g. flexibility markets), and broader constraints that attempt to represent the overall behaviour of many individual technologies of a given type.73

Finally, hydrogen is central to several sector-coupling options, including power-to-gas (for the gas grid),37 power-to-heat,82 power-to-liquids,83 and power-to-ammonia.84 Energy systems models need to include the opportunity for transfers of energy between sectors, as this can unlock potential for cost and resource efficiency savings.

4.1.4 Models must represent the complexity of consumer behaviour. Uptake of new technologies is not only driven by cost or efficiency-based metrics for the entire energy system, but also by consumer choice, dependent on social factors and personal preference. For example, market adoption of FCEVs is sensitive to consumer perception of factors such as driving range, battery life, depreciation and capital cost. Furthermore, vehicle uptake is affected by consumer perception in the used vehicle market.

There are significant variations between models regarding how consumer choices are represented, for example the inclusion and relative importance of different utility factors representing consumer choice. Improvements in modelling can be achieved with more readily available data on elasticities and utility factors. Furthermore, a more detailed representation of different technology types (e.g. different weight and range categories for vehicles) will allow for a more accurate representation of consumer choice.

4.1.5 Models must remain manageable and user-friendly. Increasing computational power means that larger, more complex and more realistic models can be developed. However, this greater detail can introduce difficulty for the model users, in terms of managing the much larger datasets that are required as inputs and generated as outputs, analysing the results and communicating them to a general audience, such as policy makers and the general public. The challenge for energy systems models is therefore to use appropriate techniques such as those described above whilst preventing the model from becoming too difficult to use and to communicate. Although the detailed outputs of a complex model can be summarised using averages and high-level metrics, some of the important insights can only be understood from the details and presenting these in a manner that is easy to understand remains a key goal and challenge.
4.1.6 Model methodologies must be transparent. Due to the complexities in representing the details of energy systems, it is important that when scenarios are presented, the methodologies behind them are shared. The fact that these models are being used to predict what future energy systems may be, often many decades into the future, means that there is no real-life system against which the models can be validated. As most energy system models use optimisation and today's energy systems are far from optimal, it is difficult even to validate these models against current data. For this reason, it is important that the mathematical formulations behind the models be published so that they can be appropriately peer reviewed. However, this practice is very rare among the global energy scenarios: none of the scenarios reviewed in Section 3 have published the mathematical formulations of their models. Indeed, most give no or very little information regarding the modelling approaches used and only the IEA ETP studies52,53 describe qualitatively the modelling framework that is used to generate the results (four soft-linked models are used, including ETP TIMES models for energy conversion and industry, the MoMo model for transport, and the Global buildings sector model for buildings). One might argue that if the results over a wide range of scenarios appear sensible, behave as expected and can be explained, then that is a sufficient test. However, since many modelling assumptions must be made even in complex models, different formulations of the same physical phenomena are possible and these can result in different but still sensible results.

One barrier to the publication of a model's mathematical formulation is the intellectual property rights of the organisation that developed the model. This is understandable, but the IP is more than just the mathematical constraints employed by the model. It is not practical to publish all of the know-how in the implementation and solution of the model (the minute details required to obtain robust and reliable solutions) and there are many other elements to the IP: data management, user interface, results management and analysis.

The main advantage of model transparency is that this allows other modellers to review the model, highlight any deficiencies and suggest improvements. This will provide researchers and policy makers with the confidence that the results of the scenarios are truly meaningful and that they can be taken forward with real enthusiasm. This can only really be possible by publishing the mathematical formulation of the model, as has been done in other similar areas (see e.g.ref. 85–90).

Finally, given that models each have their own strengths and weaknesses, transparency enables scenario developers to choose the model that is best suited to the application. Where energy scenarios are used to inform policy decisions, decision making cannot be considered fully transparent if the methodologies behind the modelling are not themselves transparent.

4.1.7 Challenges and pitfalls. We have argued that models must be much more detailed, and therefore complex, than are currently being used in global energy scenarios. Including features such as high spatial and temporal resolutions, uncertainty analysis, consumer behaviour and including a large range of technologies and energy carriers in a model is extremely challenging. Of course, the models should be made only as complex as is necessary to represent all of the features and details of hydrogen (and other) technologies that may play a role in the future energy system (such as rapid-response load balancing technologies). Modellers and scenario planners should follow a structured approach to developing new models similar to the one below:

1. Describe the purpose of the study carefully.

2. Define the scope so that the purpose can be achieved satisfactorily and with sufficient accuracy.

3. Build the simplest model that can accurately represent all of the features and interactions of the system defined in the scope.

4. Provide assumptions and limitations.

5. Discuss results in light of assumptions and limitations, acknowledging that the model is imperfect.

Deciding the necessary level of detail and accuracy is itself a difficult decision but this can be helped by performing smaller studies involving particular technologies to determine what level of spatial and temporal detail are required. The greatest difficulty for a modeller is when the required level of detail is so high that the model becomes computationally very demanding but further simplifications make the model no longer fit for purpose.

It is understandable that time pressure or intractability may tempt researchers into oversimplifying models in order to obtain results. This is a pitfall that needs to be avoided or at least taken with extreme caution. The results and conclusions obtained from an oversimplified model can be misleading and possibly erroneous. In the context of hydrogen, if a technology does not appear in the results then it is not possible to determine whether this is because of an inherent disadvantage of the technology or whether it is due to the inadequacy of the model to represent the technology's benefits.

Despite the challenges of including an unprecedented level of detail in energy system models, these are not insurmountable goals. As has been mentioned, techniques have already been developed that allow national energy systems to be optimised with high levels of spatial and temporal disaggregation. With increasing computing power and further research into advanced techniques and algorithms, more complex and detailed models will be possible in the near future. Scenario developers should be aiming to take advantage of these developments in order to obtain more reliable, and perhaps surprising, results.

4.2 Scenarios must be designed appropriately

Scenario design, including which sectors and technologies are included, what the level of ambition is, and what performance metrics are used, has a significant influence on scenario results. Scenario design will partly be determined by the capabilities of the model used. However, many decisions will also be made by the developer.
4.2.1 Scenarios must include all relevant sectors. As the results in Section 3 show, there is significant variation in the sectors that are included in different scenarios. Some sectors, such as mobility, are represented in almost all scenarios, but others have significant variability. For example, hydrogen is widely discussed as a key decarbonisation option for industry, as shown by its strong representation in the qualitative scenarios. Furthermore, in almost all quantitative scenarios where hydrogen in industry is included as an option, it contributes to the final results (e.g. ReMap, Shell and the Global Energy Assessment). However, several studies omit hydrogen in industry altogether, such as the early WEO and ETP scenarios, the WEC Grand Transition, and even the ambitious Energy Revolution scenarios. Given that hydrogen does appear in the results of many of the scenarios that included it, it is reasonable to wonder if it would have also played a role in the other scenarios had they included it.

The other applications of hydrogen (re-conversion, gas grid) show similar variability between different scenarios and there is no consistent trend regarding which scenarios include which sectors. For studies that have re-produced scenarios in consecutive years (WEO, ETP), it is noticeable that the newer scenarios have a more comprehensive inclusion of sectors than the older scenarios. For example, WEO 2018 had at least some discussion of re-conversion, mobility, industry and the gas grid, whereas the previous iterations of the study (2016 and 2017) did not consider any of these sectors. Assuming that the modelling methods for these scenarios are not changed significantly from one year to the next, this again suggests that had these sectors been included earlier, they would have been seen in the scenario results. This shows the importance of including the sectors that have the most potential and suggests that awareness of the potential solutions of applications such as hydrogen is important for their prevalence in scenario results.

4.2.2 Scenarios must be technology rich: a technology not included will not appear in the results. As well as the importance of which sectors are included in a given scenario, it is important to consider which specific technologies are included. Again, Fig. 2 shows the variability in the hydrogen technologies that are included in each scenario. Fig. 2 would suggest that electrolysis is a key technology for hydrogen, as it is included in almost all scenarios. However, some scenarios even omit this technology. Despite referring to hydrogen as a transport fuel and the use of fuel cells, the WEC Grand Transition15 makes no reference to electrolysis or any other hydrogen production technology. The scenarios with a richer representation of hydrogen production technologies (e.g. fossil or biomass-based options as well as electrolysis) typically also include a greater representation of hydrogen in the scenario results.

A challenge for energy scenarios is to keep pace with and to estimate future technology developments so that they can be appropriately represented in scenarios for energy systems several decades in the future. For example, solid oxide electrolysis is a technology with significant interest due to its potential for higher efficiencies, reversible operation and co-electrolysis with carbon dioxide.39 This is reflected in the technology's inclusion in the H2FC Roadmap.30 However, the technology currently has a low level of commercial development, so is not included in any other scenarios.

Some of the most widely discussed advantages of hydrogen are its usefulness as an alternative energy vector, particularly for large-scale storage and transportation. However, these technologies are omitted from many scenarios. Hydrogen has a high volumetric energy compared to alternative energy storage options, so it is seen to have potential for large scale energy storage applications, for example for balancing electricity supplies and demands in systems with large penetrations of intermittent renewable energy. This potential is reflected in the qualitative scenarios, as well as the Shell and GEA scenarios, however no other scenarios include hydrogen storage.

Similarly, another advantage of hydrogen is that it can be transported easily at a range of scales. Unlike electricity, hydrogen can be shipped across long distances internationally, creating the potential for global supply chains.91 Pipelines also provide the opportunity for hydrogen transportation, and there is interest in both purpose-built hydrogen pipelines and re-purposing existing natural gas grids.37 At a smaller scale, hydrogen can also be transported on road by truck. Like storage, hydrogen transportation is hardly included in any of the scenarios.

The omission of these key hydrogen infrastructures is significant, as they are central to what makes hydrogen a potentially valuable energy carrier in future systems. Whilst the technologies for hydrogen production and consumption may not be the most efficient or the lowest cost, benefits arise from the efficiency with which hydrogen can be stored and transported, and hence these infrastructures should be included in energy scenarios.

4.2.3 Scenarios must have an appropriate level of ambition. In addition to the technologies and sectors included in the scenario, the level of scenario ambition also influences the prevalence of hydrogen in the results. Most scenarios investigate how an energy system may evolve over time, under existing or expected policies, and can be described as “explorative”; whereas other scenarios impose strict targets on the final energy system and can be referred to as “normative”. Reduction of greenhouse gas emissions is a typical target in normative scenarios. While some explorative global energy scenarios can even show an increase in global greenhouse gas (GHG) emissions, normative scenarios often target drastic cuts in GHG emissions, including nearly net-zero emission scenarios.

Scenarios with higher levels of GHG reduction ambition show a tendency towards a greater prevalence of hydrogen in their results. Drawing quantitative correlations between GHG reductions and hydrogen prevalence is challenging, due to the tendency for scenarios to discuss hydrogen usage without providing specific data. However, Fig. 4 shows estimated hydrogen usage as percentage of total final energy demand in several scenarios, compared with the GHG emissions reduction in the scenario. A negative GHG emissions reduction represents an increase in emissions over the scenario time horizon.

image file: c9se00833k-f4.tif
Fig. 4 Effect of greenhouse gas (GHG) emissions reduction on hydrogen prevalence in energy scenarios. A negative GHG emissions reduction represents an increase in emissions over the scenario time horizon. Explorative scenarios are displayed in purple, while normative are displayed in green.

Ambitious GHG reduction targets are achieved to some extent with increased uptake of intermittent renewables such as wind and solar. Consequently, energy system flexibility is required to balance electricity supplies and demands. With intermediate decarbonisation objectives, such as an 80% reduction in emissions, this “backup” can be provided by fossil fuels. However, in close to “net-zero” scenarios, nearly any usage of fossil fuels must be balanced by carbon sequestration. Where carbon sequestration is unattractive (due to technical, economic or social factors), alternatives such as hydrogen for energy storage become much more attractive.

Furthermore, with more variable renewable electricity generators on the grid in ambitious GHG scenarios, there is increased complexity in energy markets, for example with increased occurrence of near-zero power prices arising from excess electricity generation. In these situations, there is greater potential for alternative technologies such as power-to-gas to find viable business cases.92,93

Finally, scenarios with less ambitious decarbonisation objectives do not always consider the decarbonisation of the more challenging sectors, such as industry or long-haul transport. Certain hydrogen pathways, such as power-to-fuels, are particularly attractive in these sectors.94

4.2.4 Scenarios must consider other objectives. Besides the level of decarbonisation and renewables integration ambition, many other objectives and constraints, such as political interest, social acceptance and national strategies, may be included in a scenario that will affect its outcomes. For example, nuclear power is a politically controversial technology that many countries are choosing to phase out.95 Other potentially controversial technologies include CCS, and even onshore wind power. Meanwhile there are also resource-based constraints: e.g. some regions have limited biomass potential, limiting this option for future energy systems aiming for energy independence. These choices shape the scenario design and the evolution of the energy system. As these become more constrained, it is possible that hydrogen pathways will emerge as one of the remaining degrees of freedom to achieve ambitious climate targets.

4.3 Scenarios must use consistent and substantiated data assumptions

As well as broad scenario design, the thousands of data parameters that are input into each scenario will influence the scenario results.

Typical input data for technologies in energy systems models will include cost data (e.g. capital and operating costs) and performance data (e.g. operating rates, efficiencies, environmental impacts, etc.). For any technology there will be an uncertainty range in these data, depending on how, when and where the technology is installed and operated. As an example, some cost estimates for key hydrogen technologies are shown in Table 2, showing the wide uncertainty range in the literature. Energy scenarios are not able to capture this range in every detail, due to the large number of variables already being considered, and consequently must carry out some “averaging”.

Table 2 Cost estimates for key hydrogen technologies for present day and 2050
Technology Units Capex Ref.
Today 2050
Electrolyser (alkaline) € per kWel 800–1700 400–700 39, 97 and 98
Electrolyser (PEM) € per kWel 1300–3200 300–700 39, 97 and 98
SMR (with CC) € per kWH2 (HHV) 600–1300 400–600 11, 30, 98 and 99
H2 storage (vehicle on-board) € per kW hH2 (HHV) 13–20 8 (target) 100
Fuel cell (vehicle on-board) € per kWel 38–152 34 (target) 100
H2 storage (UG compressed) € per kW hH2 (HHV) 0.1–2.0 0.1–2.0 98, 99 and 101
Fuel cell (stationary) € per kWel 640–2900 330–1500 30 and 102

Energy scenarios also need to capture the changes in cost and performance data that will occur over time. Rapid progress in energy technologies has been seen before, for example in solar PV3 and lithium-ion batteries.96 This sort of progress is dependent on the scale of production. Learning curves can be used to estimate improvements in cost and technical performance with increased production rates but estimating the rates of uptake of technologies is challenging, particularly as these can be influenced by government policy.

Large-scale energy scenarios are typically based on policies that are already in place and free-market decisions. For the future, usually broad policies (e.g. system wide GHG targets) are used rather than sector specific. Technology agnostic measures are usually preferred, to promote the development of the most competitive options, and ensure that governments do not choose technologies with higher costs for society. However, due to the learning curve effect, some technologies that are not economically attractive in the early stages of deployment may deliver a lower long-term cost. This requires additional incentives to go beyond this “valley of death” region to be able to reach that long-term target.103

For example, although electrolysis is a relatively well established technology, studies that find hydrogen from electrolysis to be competitive with conventional hydrogen production or even fossil fuel alternatives usually rely on reductions in cost resulting from significant scale-up of production (e.g.ref. 97), which most likely would only occur with strong government support. Similarly, for technologies at the R&D level, incentives need to be technology specific since this will determine the research strategy and priorities. In turn, this R&D can lead to cost and efficiency improvements, which will influence the prominence of the technology in energy scenarios. Experience from the power sector has shown that a mix of technology specific and technology neutral policies achieve the best results in promoting low carbon options.104

Model-based scenario studies should model a full range of technology and policy assumptions. Ideally, sensitivity analysis would be used to understand the significance of different data uncertainties on scenario results. This analysis may also provide insights into the relative value of R&D for different technologies and sectors. Of course, sensitivity analyses can be expensive when applied to large, complex models, hence there is an argument for simpler models, with a more thorough treatment of data uncertainty.105 Despite this, the models should not be simplified to the point where they no longer represent the energy system with sufficient accuracy, as this will result in unrealistic sensitivities, especially when non-linear effects are involved. The simplified model should only be used for sensitivity analysis and the more-detailed model used to explore interesting “corner” points identified in the analysis – to check that the analysis is correct.

As a minimum, studies should share the data assumptions that were made in their analysis but unfortunately even this is rare. The IEA H2FC Roadmap30 and IIASA Global Energy Assessment56,106 contain detailed descriptions of the technical and economic performance of most hydrogen technologies throughout the supply chain. However, as Fig. 2 shows, several studies include hydrogen in their scenario results but little or no information at all is given on the data assumptions made (e.g. WEC,15 Shell16).

5. Conclusions

Energy systems are becoming more technologically diverse, spatially distributed and temporally variable. Consequently, there is an opportunity for new “flexibility” options, such as hydrogen, to play a role. In the authors' view, the greatest opportunities for hydrogen lie in the industrial and heavy-duty transport sectors, where hydrogen's high energy density and low greenhouse gas emissions could make it the preferred energy carrier. With the establishment of large-scale hydrogen production, transportation and storage infrastructure for these sectors, there will be many opportunities to use hydrogen for additional flexibility in other sectors, such as the power sector.

However, the exact role that new technologies such as hydrogen will have is unclear, and it is the purpose of energy scenarios to help to indicate what the role might be. In the authors' view, global energy scenarios, especially those based on energy system models, have been pessimistic with respect to hydrogen. This is beginning to change but coverage of hydrogen is still often restricted to a few main applications, such as mobility.

The main challenge for energy systems models is that many of the opportunities for new technologies such as hydrogen are in spaces that previously have not existed in energy systems, for example in energy storage (both at short and long time scales) and for sector-coupling. Energy systems models have traditionally not been good at representing the fine details, such as temporal variability. Capturing these details, whilst also encompassing the big picture of a long-term global energy transition is computationally and practically complex, and therefore a big challenge for the modelling community. Nonetheless, techniques are emerging to handle these complexities, and computational power is improving all the time, enabling more ambitious projects. We believe that overcoming these challenges will be necessary to determine with confidence the role that hydrogen should play in the future energy mix.

Meanwhile, if global energy scenarios are currently unable to represent all of the fine details and nuances of future energy systems, it is essential that they acknowledge this and do not present their scenario results with overconfidence. Much greater sharing of the methodologies and input assumptions behind energy scenarios is needed, so that the implications of the results can be correctly interpreted. Scenario developers should also constantly improve their practice, informed by findings from elsewhere. Numerous alternative approaches have been developed for exploring the role of new technologies in future energy systems, including qualitative scenarios and more detailed energy systems modelling at smaller scales. All of this research is valuable and should be taken into account with as much esteem as global energy scenarios.

Authors' contribution

All authors conceptualised the study at an initial workshop. CJQ & SS coordinated and drafted the paper. OT & LW performed the review of global energy scenarios. HH provided the analysis of scenario ambition and hydrogen prevalence. CM, NJS & HB helped structure the paper, contributed to the draft and provided feedback. JL and MR provided feedback and additional arguments.

Conflicts of interest

There are no conflicts of interest to declare.


The present work was carried out within the framework of Task 38 of the Hydrogen Technology Collaboration Programme of the International Energy Agency. The task is coordinated by the Institute for techno-economics of energy systems (I-tésé) of the CEA, supported by the ADEME. CJQ and SS would like to acknowledge the Department of Business, Energy and Industrial Strategy (BEIS) and the Engineering and Physical Sciences Research Council (EPSRC) for funding his studentship. Thanks also to Dr Ian Llewelyn and Dr Jose M. Bermudez from BEIS for their very valuable inputs and feedback on this work. OT acknowledges the funding provided by Air Liquide to support her PhD thesis (framework of her contribution to this article). MR acknowledges the Helmholtz Association under the Joint Initiative “Energy System 2050 – A Contribution of the Research Field Energy” and Detlef Stolten for very important contributions and insights. SS would like to thank the EPSRC for partial funding of her research through the BEFEW project (Grant No. EP/P018165/1).


  1. Committee on Climate Change, Net Zero, The UK's contribution to stopping global warming,, 2019 Search PubMed.
  2. D. G. Victor, D. Zhou, E. Ahmed, P. K. Dadhich, J. G. J. Olivier, H.-H. Rogner, K. Sheikho and M. Yamaguchi, Introductory Chapter, in Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2014 Search PubMed.
  3. International Renewable Energy Agency, Renewable Power Generation Costs in 2017,, Abu Dhabi, 2018 Search PubMed.
  4. H. Ibrahim, A. Ilinca and J. Perron, Energy storage systems – Characteristics and comparisons, Renewable Sustainable Energy Rev., 2008, 12(5), 1221–1250 CrossRef CAS.
  5. A. Gallo, J. Simões-Moreira, H. Costa, M. Santos and E. Moutinho dos Santos, Energy storage in the energy transition context: A technology review, Renewable Sustainable Energy Rev., 2016, 65, 800–822 CrossRef CAS.
  6. A. Abdon, X. Zhang, D. Parra, M. K. Patel, C. Bauer and J. Worlitschek, Techno-economic and environmental assessment of stationary electricity storage technologies for different time scales, Energy, 2017, 139, 1173–1187 CrossRef.
  7. R. Schlögl, The Revolution Continues: Energiewende 2.0, Angew. Chem., Int. Ed., 2015, 54(15), 4436–4439 CrossRef PubMed.
  8. H. Lund, P. Alberg Østergaard, D. Connolly and B. Vad Mathiesen, Smart energy and smart energy systems, Energy, 2017, 137, 556–565 CrossRef.
  9. A. Prinzhofer, I. Moretti, J. Francolin, C. Pacheco, A. D'Agostino, J. Werly and F. Rupin, Natural hydrogen continuous emission from sedimentary basins: The example of a Brazilian H2-emitting structure, Int. J. Hydrogen Energy, 2019, 44(12), 5676–5685 CrossRef CAS.
  10. S. Schiebahn, T. Grube, M. Robinius, V. Tietze, B. Kumar and D. Stolten, Power to gas: Technological overview, systems analysis and economic assessment for a case study in Germany, Int. J. Hydrogen Energy, 2015, 40, 4285–4294 CrossRef CAS.
  11. IEA Greenhouse Gas R&D Programme, Techno-Economic Evaluation of SMR Base Standalone (Merchant) Hydrogen Plant with CCS,, 2017 Search PubMed.
  12. M. Ball and M. Weeda, The hydrogen economy – Vision or reality?, Int. J. Hydrogen Energy, 2015, 40, 7903–7919 CrossRef CAS.
  13. N. Brandon and Z. Kurban, Clean energy and the hydrogen economy, Philos. Trans. R. Soc., A, 2017, 375, 20160400 CrossRef PubMed.
  14. International Energy Agency, World Energy Outlook 2018,, 2018 Search PubMed.
  15. World Energy Council, World Energy Scenarios 2016 – The Grand Transition,, 2016 Search PubMed.
  16. Shell, Shell Scenarios, Sky, Meeting the goals of the Paris agreement,, 2018 Search PubMed.
  17. E. S. Hanley, J. Deane and B. Ó. Gallachóir, The role of hydrogen in low carbon energy futures – A review of existing perspectives, Renewable Sustainable Energy Rev., 2018, 82, 3027–3045 CrossRef.
  18. B. Pivovar, N. Rustagi and S. Satyapal, Hydrogen at Scale (H2@Scale) key to a clean, economic, and sustainable energy system, Electrochem. Soc. Interface, 2018, 27, 47–52 CrossRef CAS.
  19. F. Zhang, P. Zhao, M. Niu and J. Maddy, The survey of key technologies in hydrogen energy storage, Int. J. Hydrogen Energy, 2016, 41, 14535–14552 CrossRef CAS.
  20. P. Nikolaidis and A. Poullikkas, A comparative overview of hydrogen production processes, Renewable Sustainable Energy Rev., 2017, 67, 597–611 CrossRef CAS.
  21. C. Philibert, Renewable Energy for Industry: From green energy to green materials and fuels, International Energy Agency, 2017 Search PubMed.
  22. R. Ramachandran and R. K. Menon, An overview of industrial uses of hydrogen, Int. J. Hydrogen Energy, 1998, 23(7), 593–598 CrossRef CAS.
  23. P. E. Dodds and S. Demoullin, Conversion of the UK gas system to transport hydrogen, Int. J. Hydrogen Energy, 2013, 38, 7189–7200 CrossRef CAS.
  24. R. Tarkowski, Underground hydrogen storage: Characteristics and prospects, Renewable Sustainable Energy Rev., 2019, 105, 86–94 CrossRef CAS.
  25. A. B. Gallo, J. R. Simones-Moreira, H. K. M. Costa, M. M. Santos and E. Moutinho dos Santos, Energy storage in the energy transition context: A technology review, Renewable Sustainable Energy Rev., 2016, 65, 800–822 CrossRef CAS.
  26. Energiepark Mainz, Energiepark Mainz,, accessed 04 September 2019.
  27. Electricity Generating Authority of Thailand, EGAT to develop the first wind hydrogen hybrid in Asia to support the future of renewable energy, 11 April 2018,, accessed 04 September 2019 Search PubMed.
  28. Ministerial Council on Renewable Energy, Hydrogen and Related Issues, Basic Hydrogen Strategy,<?pdb_no 1226?>1226<?pdb END?>_003b.pdf, 2017 Search PubMed.
  29. G. Anandarajah, W. McDowall and P. Ekins, Decarbonising road transport with hydrogen and electricity: Long term global technology learning scenarios, Int. J. Hydrogen Energy, 2013, 38, 3419–3432 CrossRef CAS.
  30. International Energy Agency, Technology Roadmap: Hydrogen and Fuel Cells,, 2015 Search PubMed.
  31. E. Ruffini and M. Wei, Future costs of fuel cell electric vehicles in California using a learning rate approach, Energy, 2018, 150, 329–341 CrossRef.
  32., Hydrogen Refuelling Stations Worldwide,, accessed 11 September 2019.
  33. T. Hua, R. Ahluwahlia, L. Eudy, G. Singer, B. Jermer, N. Asselin-Miller, S. Wessel, T. Patterson and J. Marcinkoski, Status of hydrogen fuel cell electric buses worldwide, J. Power Sources, 2014, 269, 975–993 CrossRef CAS.
  34. B. Verheul, Overview of hydrogen and fuel cell developments in China, Holland Innovation Network China,, 2019 Search PubMed.
  35. Alstom, Alstom Coradia iLint,, accessed 04 September 2019 Search PubMed.
  36. Hydrogen Europe, Hydrogen in Industry, 2019,, accessed 14 June 2019 Search PubMed.
  37. C. Quarton and S. Samsatli, Power-to-gas for injection into the gas grid: What can we learn from real-life projects, economic assessments and systems modelling?, Renewable Sustainable Energy Rev., 2018, 98, 302–316 CrossRef.
  38. Central Intelligence Agency, CIA World Factbook, 2019,, accessed 14 June 2019 Search PubMed.
  39. A. Buttler and H. Spliethoff, Current status of water electrolysis for energy storage, grid balancing and sector coupling via power-to-gas and power-to-liquids: a review, Renewable Sustainable Energy Rev., 2018, 82, 2440–2454 CrossRef CAS.
  40. O. Schmidt, A. Gambhir, I. Staffell, A. Hawkes, J. Nelson and S. Few, Future cost and performance of water electrolysis: An expert elicitation study, Int. J. Hydrogen Energy, 2017, 42, 30470–30492 CrossRef CAS.
  41. G. Morrison, J. Stevens and F. Joseck, Relative economic competitiveness of light-duty battery electric and fuel cell electric vehicles, Transportation Research Part C: Emerging Technologies, 2018, 87, 183–196 CrossRef.
  42. S. M. Jarvis and S. Samsatli, Technologies and infrastructures underpinning future CO2 value chains: A comprehensive review and comparative analysis, Renewable Sustainable Energy Rev., 2018, 85, 46–68 CrossRef CAS.
  43. C. J. Quarton and S. Samsatli, The value of hydrogen and carbon capture, storage and utilisation in decarbonising energy: Insights from integrated value chain optimisation, Appl. Energy, 2019 DOI:10.1016/j.apenergy.2019.113936.
  44. H. Daggash, C. Heuberger and N. Mac Dowell, The role and value of negative emissions technologies in decarbonising the UK energy system, Int. J. Greenhouse Gas Control, 2019, 81, 181–198 CrossRef CAS.
  45. S. Brynolf, M. Taljegard, M. Grahn and J. Hansson, Electrofuels for the transport sector: A review of production costs, Renewable Sustainable Energy Rev., 2018, 81, 1887–1905 CrossRef.
  46. D. Connolly, B. Mathiesen and I. Ridjan, A comparison between renewable transport fuels that can supplement or replace biofuels in a 100% renewable energy system, Energy, 2014, 73, 110–125 CrossRef.
  47. Institute for Sustainable Process Technology, Power to Ammonia,, 2017 Search PubMed.
  48. A. Ernst, K. H. Biss, H. Shamon, D. Schumann and H. U. Heinrichs, Benefits and challenges of participatory methods in qualitative energy scenario development, Technol. Forecast. Soc. Change, 2018, 127, 245–257 CrossRef.
  49. International Energy Agency, World Energy Outlook 2016,, 2016 Search PubMed.
  50. International Energy Agency, World Energy Outlook 2017,, 2017 Search PubMed.
  51. International Renewable Energy Agency, Accelerating the Energy Transition through Innovation, a working paper based on global REmap analysis,, Abu Dhabi, 2017 Search PubMed.
  52. International Energy Agency, Energy Technology Perspectives 2016,, 2016 Search PubMed.
  53. International Energy Agency, Energy Technology Perspectives 2017,, 2017 Search PubMed.
  54. Greenpeace, Energy [R]evolution, A sustainable World Energy Outlook 2015, 100% Renewable Energy for all,, 2015 Search PubMed.
  55. Shell, New Lens Scenarios, A shift in perspective for a world in transition,, 2018 Search PubMed.
  56. International Institute for Applied Systems Analysis, Global Energy Assessment: Toward a Sustainable Future,, 2012 Search PubMed.
  57. Hydrogen Council, Hydrogen scaling up, A sustainable pathway for the global energy transition,, 2017 Search PubMed.
  58. S. Pfenninger, A. Hawkes and J. Keirstead, Energy systems modeling for twenty-first century energy challenges, Renewable Sustainable Energy Rev., 2014, 33, 74–86 CrossRef.
  59. A. Gambhir, J. Rogelj, G. Luderer, S. Few and T. Napp, Energy system changes in 1.5 °C, well below 2 °C and 2 °C scenarios, Energy Strategy Reviews, 2019, 23, 69–80 CrossRef.
  60. P. Lopion, P. Markewitz, M. Robinius and D. Stolten, A review of current challenges and trends in energy systems modeling, Renewable Sustainable Energy Rev., 2018, 96, 156–166 CrossRef.
  61. International Energy Agency, Energy Technology Perspectives 2012: Pathways to a Clean Energy System,, 2012 Search PubMed.
  62. European Commission, EU Reference Scenario,, 2016 Search PubMed.
  63. ASEAN Centre for Energy, The 5th ASEAN Energy Outlook 2015–2040,, 2017 Search PubMed.
  64. L. H. Balza, R. Espinasa and T. Serebrisky, Lights on? Energy needs in Latin America and the Caribbean to 2040, Inter-American Development Bank, 2016 Search PubMed.
  65. U.S. Energy Information Administration, Annual Energy Outlook 2019,, 2019 Search PubMed.
  66. China National Renewable Energy Centre, China Renewable Energy Outlook 2018,, 2018 Search PubMed.
  67. Government of Japan, Strategic Energy Plan,<?pdb_no 0703?>0703<?pdb END?>_002c.pdf, 2018 Search PubMed.
  68. Deep Decarbonization Pathways Project, Pathways to deep decarbonization 2015 report, SDSN – IDDRI,, 2015 Search PubMed.
  69. International Renewable Energy Agency, Global Energy Transformation,, 2018 Search PubMed.
  70. P. D. Lund, J. Lindgren, J. Mikkola and J. Salpakari, Review of energy system flexibility measures to enable high levels of variable renewable electricity, Renewable Sustainable Energy Rev., 2015, 45, 785–807 CrossRef.
  71. D. Connolly, H. Lund, B. Mathiesen and M. Leahy, A review of computer tools for analysing the integration of renewable energy into various energy systems, Appl. Energy, 2010, 87, 1059–1082 CrossRef.
  72. M. Robinius, A. Otto, P. Heuser, L. Welder, K. Syranidis, D. S. Ryberg, T. Grube, P. Markewtiz, R. Peters and D. Stolten, Linking the Power and Transport Sectors – Part 1: The Principle of Sector Coupling, Energies, 2017, 10, 956 CrossRef.
  73. S. Collins, J. P. Deane, K. Poncelet, E. Panos, R. C. Pietzcker, E. Delarue and B. P. Ó. Gallachóir, Integrating short term variations of the power system into integrated energy system models: A methodological review, Renewable Sustainable Energy Rev., 2017, 76, 839–856 CrossRef.
  74. L. Kotzur, P. Markewitz, M. Robinius and D. Stolten, Impact of different time series aggregation methods on optimal energy system design, Renewable Energy, 2018, 117, 474–487 CrossRef.
  75. S. Samsatli and N. J. Samsatli, The role of renewable hydrogen and inter-seasonal storage in decarbonising heat – Comprehensive optimisation of future renewable energy value chains, Appl. Energy, 2019, 233–234, 854–893 CrossRef.
  76. S. Samsatli and N. J. Samsatli, A general spatio-temporal model of energy systems with a detailed account of transport and storage, Comput. Chem. Eng., 2015, 80, 155–176 CrossRef CAS.
  77. L. Kotzur, P. Markewitz, M. Robinius and D. Stolten, Time series aggregation for energy system design: Modeling seasonal storage, Appl. Energy, 2018, 213, 123–135 CrossRef.
  78. Fuel Cells Bulletin, ITM achieves rapid response electrolysis in P2G energy storage, Fuel Cell. Bull., 2016, 2016(1), 9 Search PubMed.
  79. Fuel Cells Bulletin, Hydrogenics wraps up Ontario utility-scale grid stabilization trial, Fuel Cell. Bull., 2011, 2011(7), 9 Search PubMed.
  80. K. Poncelet, E. Delarue, D. Six, J. Duerinck and W. D'haeseleer, Impact of the level of temporal and operational detail in energy-system planning models, Appl. Energy, 2016, 162, 631–643 CrossRef.
  81. S. Samsatli, I. Staffell and N. J. Samsatli, Optimal design and operation of integrated wind-hydrogen-electricity networks for decarbonising the domestic transport sector in Great Britain, Int. J. Hydrogen Energy, 2016, 41, 447–475 CrossRef CAS.
  82. L. G. Ehrlich, J. Klamka and A. Wolf, The potential of decentralized power-to-heat as a flexibility option for the german electricity system: A microeconomic perspective, Energy Policy, 2015, 87, 417–428 CrossRef.
  83. H. Blanco, W. Nijs, J. Ruf and A. Faaij, Potential for hydrogen and Power-to-Liquid in a low-carbon EU energy system using cost optimization, Appl. Energy, 2018, 232, 617–639 CrossRef.
  84. J. Ikäheimo, J. Kiviluoma, R. Weiss and H. Holttinen, Power-to-ammonia in future North European 100% renewable power and heat system, Int. J. Hydrogen Energy, 2018, 43, 17295–17308 CrossRef.
  85. D. Henning, S. Amiri and K. Holmgren, Modelling and optimisation of electricity, steam and district heating production for a local Swedish utility, Eur. J. Oper. Res., 2006, 175(2), 1224–1247 CrossRef.
  86. B. H. Bakken, H. I. Skjelbred and O. Wolfgang, eTransport: Investment planning in energy supply systems with multiple energy carriers, Energy, 2007, 32(9), 1676–1689 CrossRef.
  87. A. Bischi, L. Taccari, E. Martelli, E. Amaldi, G. Manzolini, P. Silva, S. Campanari and E. Macchi, A detailed MILP optimization model for combined cooling, heat and power system operation planning, Energy, 2014, 74, 12–26 CrossRef.
  88. S. Samsatli, N. J. Samsatli and N. Shah, BVCM: a comprehensive and flexible toolkit for whole-system biomass value chain analysis and optimisation – mathematical formulation, Appl. Energy, 2015, 147, 131–160 CrossRef.
  89. S. Samsatli and N. J. Samsatli, A multi-objective MILP model for the design and operation of future integrated multi-vector energy networks capturing detailed spatio-temporal dependencies, Appl. Energy, 2018, 220, 893–920 CrossRef.
  90. S. Samsatli and N. J. Samsatli, A general mixed integer linear programming model for the design and operation of integrated urban energy systems, J. Cleaner Prod., 2018, 191, 458–479 CrossRef.
  91. A. Chapman, T. Fraser and K. Itaoka, Hydrogen import pathway comparison framework incorporating cost and social preference: Case studies from Australia to Japan, Int. J. Energy Res., 2017, 41, 2374–2391 CrossRef.
  92. G. Guandalini, S. Campanari and M. C. Romano, Power-to-gas plants and gas turbines for improved wind energy dispatchability: Energy and economic assessment, Appl. Energy, 2015, 147, 117–130 CrossRef.
  93. D. Parra, X. Zhang, C. Bauer and M. K. Patel, An integrated techno-economic and life cycle environmental assessment of power-to-gas systems, Appl. Energy, 2017, 193, 440–454 CrossRef.
  94. S. J. Davis, N. S. Lewis, M. Shaner, S. Aggarwal, D. Arent, I. L. Azevedo, S. M. Benson, T. Bradley, J. Brouwer, Y.-M. Chiang, C. T. M. Clack, A. Cohen, S. Doig and J. Edmonds, et al., Net-zero emissions energy systems, Science, 2018, 360, 6396 CrossRef PubMed.
  95. World Nuclear Association, Nuclear Power in the World Today, February 2019,, accessed 17 June 2019 Search PubMed.
  96. B. Nykvist and M. Nilsson, Rapidly falling costs of battery packs for electric vehicles, Nat. Clim. Change, 2015, 5, 329–332 CrossRef.
  97. G. Glenk and S. Reichelstein, Economics of converting renewable power to hydrogen, Nat. Energy, 2019, 4, 216–222 CrossRef CAS.
  98. I. Walker, B. Madden and F. Tahir, Hydrogen Supply Chain Evidence Base, Element Energy Ltd,, 2018 Search PubMed.
  99. Northern Gas Networks, Equinor and Cadent, H21 North of England,, 2018.
  100. S. Satyapal, U.S. Department of Energy Hydrogen and Fuel Cell Technology Overview, FC EXPO, 2018,, 2018 Search PubMed.
  101. HyUnder, Assessment of the Potential, the Actors and Relevant Business Cases for Large Scale and Long Term Storage of Renewable Electricity by Hydrogen Underground Storage in Europe,, 2014 Search PubMed.
  102. L. Welder, P. Stenzel, N. Ebersbach, P. Markewitz, M. Robinius, B. Emonts and D. Stolten, Design and evaluation of hydrogen electricity reconversion pathways in national energy systems using spatially and temporally resolved energy system optimization, Int. J. Hydrogen Energy, 2019, 44, 9594–9607 CrossRef CAS.
  103. C. Azar and B. A. Sandén, The elusive quest for technology-neutral policies, Environmental Innovation and Societal Transitions, 2011, 1, 135–139 CrossRef.
  104. S. de Mello and H. Paulo, Cost-effectiveness as energy policy mechanisms: The paradox of technology-neutral and technology-specific policies in the short and long term, Renewable Sustainable Energy Rev., 2016, 58, 1216–1222 CrossRef.
  105. S. Pfenninger, L. Hirth, I. Schlecht, E. Schmid, F. Wiese, T. Brown, C. Davis, M. Gidden, H. Heinrichs, C. Heuberger, S. Hilpert, U. Krien, C. Matke, A. Nebel, R. Morrison and B. Müller, et al., Opening the black box of energy modelling: Strategies and lessons learned, Energy Strategy Reviews, 2018, 19, 63–71 CrossRef.
  106. International Institute for Applied Systems Analysis, Global Energy Assessment Scenario Database, 11 November 2013,, accessed 6 September 2019 Search PubMed.

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