Determining the Production and Transport Cost for H2 on a Global Scale

Introduction

In order to combat climate change, many industrial sectors need to find ways to reduce greenhouse gas (GHG) emissions. (Rogelj, 2018) Growing industries such as steel and chemicals production, which make up 7% (International Energy Agency, 2020) and 2% (Leimkühler, 2010) of global GHG emissions, respectively, are under political pressure to find low-carbon alternatives to the fossil fuels currently used in such sectors. Hydrogen gas (H₂) has the potential to be used to reduce GHG emissions in some industries, including steel production and energy storage. (Jarraud and Steiner, 2014) It does not produce carbon dioxide (CO₂) or other carbon-based gases when burned, and when produced through electrolysis using renewable energy, has a relatively low GHG emissions footprint. (International Energy Agency, 2019) Currently, most H₂ is produced using fossil fuels, but there is a swift change underway to renewably produced H₂, and it is expected that both the total amount of H₂ produced and the share of renewable H₂ will grow dramatically in the next few decades. (International Energy Agency, 2019) It is crucial for industries planning to utilize renewable H₂ to know how much it will cost and the most cost-effective location to obtain it from.

To evaluate the most cost-effective location for H₂ production, both production and transport costs must be considered. A major component of production costs is electricity production, of which wind and solar are the two most flexible and developed renewable energy candidates. Places with strong wind or solar potential are therefore more likely to be candidates for producing low-cost renewable H₂. Transport costs of H₂ have been less investigated, resulting in a potentially significant neglection of a major cost component of H₂ cost. Estimations of the transport cost help to determine where the cheapest production location of H₂ might be for a particular desired use location. Possible transport methods include ship, truck, or pipeline. H₂ can also be converted into a variety of mediums for easier or cheaper transportation and storage, including ammonia (NH₃), liquid H₂ (LH₂), or organic compounds such as toluene known as liquid organic hydrogen carriers (LOHCs). (Preuster et al., 2016) While it is costly and energy-inefficient to convert and re-convert gaseous H₂ to these mediums and back again, they are generally cheaper, easier and safer to transport and store than gaseous H₂. Knowing which of these transport mediums and methods is the most cost-effective for the unique combination of H₂ demand, location, year of consumption, and other factors that each H₂ consumer requires would be necessary information to these consumers when developing the business case for their process.

Also needed for future development and planning is to investigate which electrolyser types and transport mediums have the highest potential for cost reduction due to efficiencies of conversion and further technological development. A stakeholder planning to purchase H₂ for a steel mill built in 2030 is not interested in using costs based on the year 2020, but rather an estimation for costs at the time they want to build the mill. As well as smaller individual H₂ consumers, it would likely be the case that groups of large consumers, for example in industrial parks, together require an extensive amount of renewable H₂. In these cases, large H₂ pipelines could potentially be built from regions with the potential to produce vast amounts of renewable H₂, similarly to the natural gas pipelines that exist today, for example between Russia and the EU. Economies of scale could work in favour of these scenarios and potentially reduce the H₂ cost for consumers by reducing the transport cost. Knowing if such pipelines are economically viable would help industrial and government stakeholders in planning for future H₂ transport networks. Government stakeholders want to ensure a reliable and cheap source of low carbon energy, and must therefore also take geopolitical factors into account as the energy landscape shifts away from a dependence on fossil fuels to renewables.

Some studies have performed localized techno-economic assessments on H₂ electrolysis, (Glenk and Reichelstein, 2019; Christensen, 2020) and also national level H₂ network optimization analyses, (Han et al., 2012; Baufumé, 2013; Hwangbo et al., 2017; Aditiya and Aziz, 2021) however no studies were found that aim to find the minimum delivered cost (and associated location) to produce and transport renewable H₂ to a given location on a global scale. While localized studies are useful in some cases, in reality, it may be more cost-effective to produce H₂ in another country or continent with more abundant renewable resources. Determining the most cost-effective location to purchase H₂ from, and the cost of the H₂, is currently a major knowledge gap and is essential information for stakeholders planning current or future processes that require renewable H₂, especially if a model could be tailored to their individual process needs. There is also a large discrepancy between the H₂ production costs estimated by various studies (values ranging from 1.5—14 €/kg) perhaps due to the variety of different assumptions and locations that are used in the studies, which can be confusing and intimidating to stakeholders interested in planning a process using H₂ for a specific location. Knowledge of the delivered H₂ cost for a particular location, demand, electrolyser type and year would help to clarify these discrepancies for stakeholders planning such a process.

Background

H₂ Production Methods

H₂ is conventionally produced by a variety of fossil-fuel-based methods such as steam reforming (Kaiwen et al., 2018) or auto-thermal reforming from natural gas, (Khojasteh Salkuyeh et al., 2017) with about 80% of the 120 Mt of H₂ produced worldwide annually coming from natural gas and 98% from fossil fuels in general. (Muradov, 2017) Steam methane reforming (SMR) is the most common production method, which reacts methane from natural gas with steam to form H₂, CO and CO₂ before being further reacted in a “water-gas shift” reaction to produce CO₂ and H₂. However, there is a relatively high GHG emissions impact associated with the SMR process (4.8 (Dufour et al., 2011)—11.9 (Spath et al., 2001) kg-CO₂-eq./kg H₂ for cradle-to-gate system boundaries). H₂ can also be produced from coal gasification, which has an even higher GHG emissions impact of about 19 kg-CO₂-eq./kg H₂. (International Energy Agency, 2019) As an economic fossil-fuel benchmark, SMR has a production cost of around 2.2 €/kg H₂. (Gielen et al., 2019)

Due to the need to reduce emissions of H₂ production, low-carbon production methods are being increasingly investigated. The most commonly studied low-emissions H₂ production method is water electrolysis, which splits H₂O into H₂ and O₂. (International Energy Agency, 2019) While H₂ electrolysis requires substantial amounts of electricity (39 kWh/kg H₂ if 100% efficient (Levene et al., 2007)), with quickly decreasing costs for renewable electricity from solar and wind sources, it is a promising option for renewable H₂ production, with solar-powered H₂ electrolysis having a GHG emissions impact of about 2.0 kg-CO₂-eq./kg H₂. (Bhandari et al., 2014) Electrolysis also produces the purest H₂ of the major production methods, up to 99.999% for standard production facilities. (Element Energy Ltd, 2018)

There are three major H₂ electrolysis technologies: alkaline (AE) electrolysis, proton exchange membrane (PEM) electrolysis, and solid oxide electrolysis (SOE). AE electrolysis is the most mature and developed production method, for which non-noble metals such as nickel are used for the electrodes, and the electrolyte is a concentrated lye. (Brauns and Turek, 2020) A diaphragm keeps the two electrodes and their product gases separate while allowing passage for water and hydroxide ions. It is relatively cheap because it does not require as many high-value materials, while also maintaining a relatively long lifetime of around 75,000 h (International Energy Agency, 2019) It is considered a low-temperature type of electrolysis, as it operates between 40 and 90°C. (Rashid et al., 2015) The weaknesses of AE electrolysis include a low partial load range (10—110%) and operating pressure (1–30 bar). It currently makes up the largest share of electrolysers in commercial use. (International Energy Agency, 2019)

PEM electrolysis was first developed by General Electric in the 1960s to overcome the weaknesses of AE electrolysis (Shiva Kumar and Himabindu, 2019) and uses more expensive noble metals such as platinum or iridium as electrode catalysts while utilizing a solid acidic polymer membrane to transmit protons from the anode to the cathode. (Rashid et al., 2015) They also operate at relatively low temperatures between 20 and 100°C. Advantages of PEM electrolysis include a compact design, high efficiency, small footprint (0.048 m²/kW compared to 0.095 m²/kW for AE electrolysers), easy scalability, high H₂ purity, and perhaps most importantly, the H₂ can already be produced at high pressures of 30—60 bar, reducing the need for energy-intensive compression after production. (International Energy Agency, 2019) Weaknesses include a high investment cost, especially as special construction materials are required due to the corrosive environment. (Carmo and Fritz, 2013) Recently, PEM electrolysis has been growing at a rapid rate since around 2015, making up the largest share of new electrolyser projects. (International Energy Agency, 2019)

The third main kind of electrolysis technology is solid oxide electrolyser (SOE) cells. These are run at very high temperatures of 700–1,000°C and are the least developed and used of the three major technology options, (Rashid et al., 2015) despite being theoretically able to reach stack efficiencies of almost 100%. (Brauns and Turek, 2020) Steam is used, requiring a high energy input, although the investment costs are relatively low as the electrodes are made of ceramic. (International Energy Agency, 2019) A ZrO₂ solid is commonly used as the electrolyte. (Laguna-Bercero, 2012) A negative aspect is that they have a relatively low lifetime (∼20,000 h) due to high material degradation, hindering their industrial adoption. Increasing the durability and lifetime of SOE cells is currently undergoing research and development to improve their viability. (Wang et al., 2020)

Other methods of producing low-carbon H₂ include combining carbon capture and storage (CCS) with H₂ produced from natural gas or coal, (Office of Energy Efficiency and Renewable Energy, 2020) from biomass using fermentation, or solar thermochemical production routes. (Christopher and Dimitrios, 2012) Coupling fossil-fuel-based H₂ methods with CCS uses up valuable CO₂ storage space, which could be used for other sectors that do not have a GHG emissions-free alternative technology. Other renewable-based methods of H₂ production such as photobiological H₂ production or biomass combustion are not yet developed enough to fill a significant market share or struggle with high costs in comparison to electrolysis and fossil-fuel-based H₂.

Uses of Renewable H₂

H₂ is a versatile energy carrier that can be used in a wide variety of applications and industries. Currently, about 120 Mt of H₂ is produced each year, used primarily for ammonia (NH₃) production, oil refining, and methanol production. (Gielen et al., 2019) As well as replacing the H₂ produced from fossil fuels that are used in these sectors, there is a range of other applications where renewable H₂ could replace fossil fuels. One major example is the steel sector, which is responsible for about 7% of global GHG emissions. (Collis et al., 2021) The most common route of steel production, the integrated steel mill, uses CO from coal gasification to reduce iron ore to pig iron. (Ho et al., 2013) Renewable H₂ could replace the coal used in this process, which would significantly reduce the GHG emissions of steel production, the majority of which are caused by coal combustion. (Germeshuizen and Blom, 2013) Demonstration steel mills using H₂ as a reduction agent are currently being built in several locations across Europe. (Bhaskar et al., 2020)

H₂ is also heavily used in the chemicals industry, where it is used to make base chemicals including NH₃ and methanol, but also more complex chemicals such as polymers and fuels. (Zang et al., 2021) Synthetically produced fuels or e-fuels are expected to grow in scale significantly as they are seen as promising options to decarbonise the shipping (Lindstad et al., 2021) and aviation (Scheelhaase et al., 2019) sectors, two industries that are difficult to decarbonise using batteries or CO₂ capture. (Ueckerdt et al., 2021) In some use cases, H₂ could also be directly combusted as a fuel source. (Cai et al., 2020)

H₂ could also be used for small to large scale energy storage. As renewably produced electricity takes up an increasingly larger share of electricity production, the intermittency of solar and wind power poses problems for the grid. Electricity supply could be much greater than demand during some hours, and much less at other times. This excess renewable electricity could be converted into H₂ during the times the demand outpaces supply and re-converted when demand is greater than supply. (Steilen and Jörissen, 2015) It could also be used in buildings as small scale energy storage in the form of fuel cells, (Andaloro et al., 2019) or blended with natural gas to reduce the GHG emissions intensity of stove burners. (Zhao et al., 2019) Overall, it is expected that H₂ could provide 24% of total energy demand by 2050 within the EU, mainly across the transportation, building and industry sectors. (Kakoulaki et al., 2021) A summary of the usages discussed here is shown in Figure 1.

FIGURE 1

FIGURE 1. Summary of the main possible usages of H₂ by sector.

Current Literature on H₂ Networks and Economic Assessment

A large number of studies have been performed assessing the economics of electrolytic H₂ production, with the most in-depth recent studies being Glenk et al. (Glenk and Reichelstein, 2019) and Christensen. (Christensen, 2020) Most assessments usually focus on a singular H₂ production location for the base scenario. While Christensen’s analysis is in-depth and detailed, the analysis is only based on data for the country level for Europe and the state level for the United States and does not account for the rest of the world. (Christensen, 2020) IEA (International Energy Agency, 2019) is the only study found to perform a global analysis on the H₂ production cost, which is based on a hybrid solar-wind system, but the assumptions used in the analysis are unclear. No date of analysis is shown, and the nature of the hybrid system and its capacity factor are not explained. Several important system costs are also not accounted for, resulting in very low H₂ production costs of 1.6—4 €/kg, as is the case for many reports by international agencies and businesses. (International Energy Agency, 2019; Hydrogen Council, 2020; International Renewable Energy Agency, 2020) For comparison, Christensen (Christensen, 2020) and Glenk et al. (Glenk and Reichelstein, 2019), who include a range of further costs required to build a functioning H₂ electrolysis system, such as compression and installation costs, find H₂ production costs in Europe to be between 6.5–14 €/kg in 2020 and 5.1—9.1 €/kg in 2050.

While calculating the H₂ production cost is naturally essential information, it is likely that for many locations worldwide importing renewable H₂ that is produced somewhere else could be cheaper due to better solar or wind conditions. Most studies do not consider transport costs, or if they do, only consider one or two predefined scenarios. IEA has a considerably detailed breakdown of the transport costs for H₂ and other mediums by several different transport modes, as well as analysing the production and transport costs together for a few specific scenarios, such as producing H₂ in Australia and shipping it to Japan. (International Energy Agency, 2019) Likewise, the Hydrogen Council has analysed the total costs for several supply chain scenarios, such as the Middle East to the EU and Chile to the US, including transport and conversion costs. (Hydrogen Council, 2021) However, it is not clear if the H₂ production locations chosen for these scenarios are the most economically viable for the given H₂ usage location, nor that the medium and mode of transportation selected are the most cost-efficient, as no different production locations are analysed and compared for each H₂ usage location. While the IEA also has an analysis on the production cost of renewable H₂ across the globe, no transport costs are included, making it impossible to determine the actual cheapest cost for obtaining renewable H₂ for a particular location. (International Energy Agency, 2019)

Many H₂ production, distribution and usage networks have been created, utilizing geo-analysis to create the most efficient network for a particular country or region. (De-León Almaraz et al., 2012; Han et al., 2012; Baufumé, 2013; Hörsch and Brown, 2017) Baufumé (2013) conduct a detailed simulation for a Germany-wide H₂ pipeline infrastructure in 2050; however, the study assumes H₂ fuel cell vehicles will be heavily used and focuses on H₂ supply to fuelling stations, which is far from certain given their relatively high costs, low durability and potential storage issues. (Wilberforce et al., 2017) Han et al. (2012) and De-León Almaraz et al. (2012) simulate a detailed nationwide network for South Korea and Great Britain, respectively, complete with production, storage and usage locations. Woo and Kim (2019) investigate the effectiveness of several different algorithms (standard genetic algorithm, hybrid genetic algorithm, genetic algorithm-based matheuristic) on a H₂ supply chain network problem, with a case study on Jeju island. In reality, as is the case with the current energy market, H₂ will likely often not be produced in the same country as it is used in. For example, Lahnaoui et al. (2021) optimize an in-depth network for Germany and France and find that Germany will likely have a greater H₂ demand than supply, while in France production may be able to match consumption. However, due to geographical variations of solar irradiation and wind power density, in some regions renewable electricity is much cheaper than in others. For example, it may be cheaper for France to obtain renewable H₂ from north Africa or the Middle East than producing it locally. Aditiya and Aziz (2021) uses technical, economic and social indicators to assess the potential for an inter-country H₂ network in the Asia-Pacific region, including likely H₂ production locations and potential H₂ flows. However, the paper does not provide a network capable of estimating production, transport or delivery costs for areas of high H₂ demand.

Instead of local models, an economic model with a global scope that could determine the cheapest point of H₂ supply for any desired end location would be useful for large future H₂ consumers. The closest such model found in the literature is that created by Hwangbo et al. (2017), who present a stochastic mixed integer linear model that optimizes for lowest cost while taking into account H₂ production, storage, and transport. While the model is thorough and complex, it only considers H₂ produced by SMR, which has a very high GHG emissions footprint (4.8 (Dufour et al., 2011)—11.9 (Spath et al., 2001) kg-CO₂-eq./kg H₂). A similar cost-optimized global model focusing on renewably produced H₂ would be more applicable for the next few decades, as most H₂ will have to be produced from low-emissions methods in order to meet the goals of the Paris agreement. (IEA. Global, 2017) Additionally, the case study presented by Hwangbo et al. (2017) focuses on production, storage and transportation in South Korea. While this would be interesting for public bodies or companies for infrastructure planning, large H₂ consumers also need information on the most cost-effective global H₂ production site, as well as transport method and route for their individual situation of H₂ demand, location, and build year.

Goal and Scope

The goal of the study is to create a model to evaluate the most cost-effective location to produce H₂ using a solar or wind-powered electrolyser for a variety of usage locations, as well as the identification of the most cost-efficient H₂ production sites and transport routes to meet global renewable H₂ demand. For example, if renewable H₂ is to be used in Cologne, Germany, the goal is to find the location globally with the lowest cost to produce and transport the desired amount of H₂ to Cologne, using solar or wind to power an electrolyser. As currently electrolytic H₂ makes up only 0.5% of global H₂ supply, (International Energy Agency, 2019) and production is expected to grow substantially in the medium term, it is expected that many new electrolyser sites will be built in the next few decades. Determining the most cost-effective location for these sites is beneficial for H₂ producers and consumers.

While the most cost-effective H₂ production location for individual processes is important information for that process, if there are multiple processes located close to each other requiring a large amount of H₂, for example in industrial parks, it could be feasible for larger pipelines to be built to take advantage of economies of scale. Several industrial parks within Europe are investigated and proposals for such pipelines are presented, along with the associated cost reductions. Large-scale pipelines are especially important for scenarios in later years (2035—2050), as by this time it is expected the volumes of renewable H₂ being consumed will be much greater than at present, and more processes will be adapted to use H₂ instead of fossil fuels (such as steel production).

The study has a geographically global scope, although it uses masks to remove areas of unfeasible terrain for H₂ production facilities, such as mountains, farmland, cities, and protected areas. From a process perspective, the scope includes the whole value chain until the time the H₂ reaches the purchaser, which can be broken down into production of H₂ and transport of H₂, as shown in Figure 2. Blending H₂ into natural gas pipelines is not considered in this study, as for many chemical processes relatively pure H₂ is required.

FIGURE 2

FIGURE 2. Scope of the study showing the two main parts, H₂ production and transportation. Everything within the dotted lines is the scope of the study. Transport is either directly to the destination (if possible) by truck or pipeline, or else it is shipped from the nearest port to the closest port to the destination. If both transportation routes are possible, the more cost-effective is chosen.

A grid-connected electrolyser is not within the scope of this study due to the difficulty of dealing with differing electricity prices, taxes, levies, curtailment data and emissions footprints for each country individually. Additionally, offshore wind is not considered in this study due to the high degree of complexity it would add to the spatial analysis—it is very unlikely that H₂ will also be produced offshore, which opens an excessive range of scenarios for where the H₂ would actually be produced. Additionally, data on promising offshore wind locations are scarce due to the logistical complexity of building offshore wind systems with regards to ocean depth, currents and tides, and ocean sovereignty. In most cases, it is more expensive than onshore wind (International Renewable Energy Agency, 2016) and it is likely there would be few scenarios in this study where it would provide cheaper electricity than onshore wind power. (Green and Vasilakos, 2011)

Hybrid wind-solar systems are also not considered in the scope of this study, due to the large degree of added complexity regarding operating times, size of each power source, and resulting capacity factor. While hydropower could be used in some locations, many countries do not have access to hydropower resources and as hydropower is considered to have a greater environmental impact, it is not accounted for in this study. (Botelho et al., 2017)

Scenario Definition

It is possible to investigate a large variety of scenarios where current knowledge gaps exist, such as different geographic regions, electrolyser types, years, and H₂ demand. The base scenario is defined as an alkaline electrolyser producing 100 kt/y H₂ in the year 2030 with the desired end location in Cologne, Germany. Other scenarios and parameters that are analysed are done so by altering variables from this base scenario. The full range of scenarios investigated in this study is listed below. Any of the below variables can be altered to best simulate the conditions applicable to individual stakeholders for planning purposes, creating thousands of possible scenarios, as shown in the Supplementary Figure S3.

End (H₂ usage) location (exact GPS oordinates):

• Cologne, Germany (50.9763, 6.9818)

• Abu Dhabi, UAE (24.2780, 54.4835)

• Tokyo-Yokohama, Japan (35.6205, 139.8965)

• Guangzhou, China (23.0550, 113.4242)

• Houston, USA (29.6084, -95.0527)

H₂ produced (electrolyser size assuming electrolyser efficiency of 0.7):

• 2 kt/y (20 MW—largest electrolyser available today)

• 10 kt/y (100 MW—largest electrolysers in construction)

• 100 kt/y (1000 MW—mass H2 production in future facilities)

• 1000 kt/y (10 GW—largest conceptual H2 production facilities for 2030)

Year

• 2020

• 2030

• 2040

• 2050

Electrolyser type:

• Alkaline (AE)

• Polymer electrolyte membrane (PEM)

• Solid oxide electrolyser (SOE)

Medium conversion and reconversion

• Centralised

• Decentralised

The locations were selected based on being areas of high industrial importance, in particular containing industries that are likely to require renewable H₂ in the future, such as chemicals and steel production. Additionally, the locations all have substantial civilian populations, which would require significant H₂ if building heating and cooking utilizes H₂ in the future. Cologne was chosen as the base scenario due to the large amounts of steel and chemical industry in the area, which includes not only the Ruhrgebiet area of Germany but also Belgium and the Netherlands. The exact locations were selected to be locations of already existing steel and chemical manufacturing areas.

The electrolyser sizes were chosen in order to have a large range of capacities, from the largest electrolysers available today to large-scale H₂ production that would be required in future facilities. However, it is conceivable that even larger H₂ production facilities will be required in the future. A singular steel mill producing 2 Mt of steel annually would require about 144 kt of H₂ to fully decarbonize using H₂. (Hoffmann et al., 2020) As steel production is only predicted to grow over the next decades, reaching 2200 Mt annual production by 2050, (Bellevrat and Menanteau, 2009) immense quantities of H₂ will be needed to decarbonise the steel industry, not to mention the myriad of other industries requiring renewable H₂, such as chemicals. However, as the largest currently conceptualised electrolysers have capacities of around 10 GW, renewable H₂ production plants larger than this are currently unimaginable, and therefore are not considered in this study.

The year 2030 is chosen as the base scenario as it is near enough in the future to have more certain predictions on cost data but far enough that renewable H₂ production will likely be considerably higher than it is today. 2030 is also seen as a short-term goal for GHG emissions reduction and many governments and industries are investigating what can be accomplished by this year, resulting in a range of predictions on renewable H₂ production and cost in 2030. The years 2040 and 2050 are also examined, with 2050 being a mid-term goal for many countries to reach net-zero GHG emissions. (Net Zero Tracker, 2021) Although renewable H₂ production will likely be higher still in 2040 or 2050, there is higher data uncertainty associated with looking further into the future. The present time of the data used in this study (2020) is also considered due to the relative certainty of data on costs; it could also be useful to stakeholders considering planning or constructing H₂ electrolysers in the short term.

All three major kinds of electrolysers are investigated in this study: AE, PEM and SOEs. As AE electrolysers are the most mature and commonly used technology, (International Energy Agency, 2019) they are taken as the base scenario. However, all three kinds of electrolysers are being further developed and studied and it is possible in the future that another kind could control the dominant market share, or even a more even spread between the three kinds. Therefore, it is still important to consider each of them in this study.

When gaseous H₂ is converted to a medium such as NH₃, LH₂ or LOHCs for cheaper and easier transportation, it must be re-converted to gaseous H₂ either at a distribution centre such as a port or the final usage location. When this is done at a large-scale distribution centre, known as centralised reconversion, economies of scale play a role in reducing reconversion costs. However, when reconversion is performed at each end-user (decentralised reconversion), it can be more expensive, especially for very small end-users such as a H₂ fueling station. Centralised reconversion is assumed to be the case for the baseline scenario as the H₂ consumption is relatively large.

Data Collection and Processing

Data Collection

Firstly, possible H₂ production sites had to be determined, as it is not feasible for manufacturing facilities to be built in mountainous areas, cities, farmland or protected areas. Geographic masks were used to determine spaces appropriate for production. The “practical PV potential” data set provided by Global Solar Atlas was used to mask unsuitable areas, of which the “Level 1” masks were chosen. (ESMAP. Global, 2020) These masks exclude ‘areas due to physical/technical constraints, such as rugged terrain, presence of urbanized/industrial areas, forests, and areas that are too distant from the centres of human activity’, as well as “areas that might be unsuitable due to regulations imposed by national or regional authorities (such as conservation of cropland or nature conservation)”. From the remaining area, one location at every full digit of latitude and longitude (e.g., 47, -52) was taken to create a set of 5970 possible distinct locations which represent the available global area where a H₂ production facility could be constructed. The Python library Rasterio was used to apply the geographic masks and retrieve the annual solar energy potential (kWp) of each possible location. (Mapbox. Rasterio, 2016) Wind power density (W/m²) was obtained for each identified production location from Global Wind Atlas. (Technical University of Denmark, 2021) A map of European industrial emitters provided by the project Carbon4PUR was used to determine important locations in Europe that might require renewable H₂ in the future. (Carbon4PUR, 2020)

Creation of Shipping Network

In order to calculate the most cost-effective method of transportation from production sites to the desired end location, the shipping distance between the production and end locations had to be determined. A shapefile of world shipping routes based on Automatic Identification System (AIS) data was obtained from NCEAS. (Halpern et al., 2015) A network was created from this shapefile using the Python module created by Benita et al. (2019) although several changes were required to the module code for it to produce a working network of shipping routes, such as not simplifying the resulting network by removing intermediate nodes, and the connections between nodes that crossed over the International Date Line had to be manually added. A list of global ports and their associated port codes and global positioning system (GPS) locations was obtained from Novikov (2019) which was used to find the closest port to every possible production location as well as the desired end location. These ports represented the possible start and end nodes of the shipping network. It should be noted that the nodes are positioned 20 km offshore from the ports to ensure shipping paths don’t overlap the coastline. Therefore, 40 km is added to the distance of every shipping route. The finished network was then used to determine the shortest path between the two desired ports and is shown in Figure 3.

FIGURE 3

FIGURE 3. Global network of shipping routes created from AIS data, used to evaluate the shortest shipping distance between ports.

Model Description

A model in Python was constructed that takes as input the H₂ demand in kt/y, the year that the facility will be constructed, GPS coordinates of the desired end location, the type of electrolyser, if pipelines are permitted (and if so, the maximum pipeline length), and if the distribution centres are centralised or decentralised. As well, many of the economic parameters can be adjusted if desired, such as the interest rate and the full load hours of the electrolyser per year. The model calculates the cost to produce H₂ at each of the possible H₂ production locations derived in Data Collection and Assumptions, followed by the transport cost from each of these locations to the desired end location. The location with the minimum sum of production and transportation cost is therefore the most cost-effective location to produce H₂ for the desired end location.

A Monte-Carlo simulation was conducted on the Python model to effectively cover the reasonable range of cost uncertainties. Particularly in future scenarios, uncertainties for electrolyser investment and operating costs are relatively large, with high range estimates being up to 600% of low range estimates. (Glenk and Reichelstein, 2019) Using a Monte-Carlo simulation, the uncertainties of these variables are accounted for using probability distributions. The probability density functions of each variable were determined using the methodology presented by Hawer et al. (Hawer et al., 2018)

Production Cost

Firstly, the capacity of the electrolyser required (S) was calculated from the H₂ demand in tons per hour (F) and the electrolyser efficiency (η_E):

$S=\frac{39F}{{\eta }_E} in \left[MW\right]$

The coefficient 39 represents the amount of electricity a theoretical 100% efficient electrolyser takes to produce H₂ (39 kWh/kg H₂). (Christensen, 2020) The total amount of electricity required (E_R) is then equal to the electrolyser capacity (S) plus the electricity required for H₂ compression (C):

$E_R=S+C in \left[MW\right]$

For each location, the levelized cost of electricity (LCOE) for the required electricity amount was determined for both solar and wind power, and the cheaper of the two power sources was used as the electricity source for the electrolyser. The LCOE was calculated by dividing the total life cycle cost by the total lifetime power produced (Branker et al., 2011):

$LCOE= \frac{{\displaystyle \sum \frac{I_t+M_t}{{(1+r)}^t}}}{{\displaystyle \sum \frac{E_t}{{(1+r)}^t}}} in \left[\frac{€}{kWh}\right]$

Where I_t is the initial investment required (CapEx), M_t is maintenance and operational expenditures (OpEx), E_t is the total electricity generated, r is the discount rate of the project, and t is the lifetime of the system. The individual wind turbine power (P_W) was calculated using the following formula:

$P_W=WPD\ast {\eta }_W\ast L_B^2\ast \pi in \left[W\right]$

Where WPD is the wind power density [W/m²], η_W is the wind turbine efficiency, and L_B is the length of the turbine blades. The solar array size required (S_A) [kWp] was calculated by dividing the yearly electricity demand (E_t) [kWh] by the solar energy potential (S_P) [kWh/kWp] for each production location:

$S_A=E_t{S}_P in \left[kWp\right]$

From here, the CapEx of the wind and solar power required can be directly calculated by multiplying with the appropriate CapEx factors (Table 1) and then used in the LCOE formula above.

TABLE 1

TABLE 1. Parameters and distributions used in Monte-Carlo simulation of the production cost of H₂ from electrolysis in 2020.

Once the LCOE has been determined, the H₂ production cost was evaluated by summing the electrolyser OpEx (mostly consisting of the renewable electricity production cost) and annualised electrolyser CapEx. (Chiuta et al., 2016) The change of parameters over time was calculated using the expected change per year to the power of the number of years after the base year (2020):

$Va{r}_x=Va{r}_{2020}\ast {\Delta }_{Yr}^{(x-2020)}$

Where Var is a variable that is expected to undergo yearly changes, x is the year of the study, and Δ_Yr is the yearly change of the variable. The parameters used in the Monte-Carlo simulation are shown along with the lower and upper bounds and associated distribution in Table 1. A full list of economic assumptions relating to the production cost of H₂ is shown in Table 2.

TABLE 2

TABLE 2. Static economic parameters assumed for the production cost of H₂ in 2020. “Growth per year” refers to the assumed increase of the parameter, per year from 2020, if the electrolyser was to be built in a future year.

Transport Cost

Four different transport mediums are considered: gaseous H₂, LH₂, ammonia (NH₃), and a sample LOHC (toluene). Cost functions for shipping, trucking and piping these mediums as a function of distance were derived from IEA, (International Energy Agency, 2019) which includes the costs of any intermittent storage that is required. Not each of the three transport methods can be used for all four transport mediums; while NH₃ can be shipped, trucked or piped, LOHCs and LH₂ can only be trucked or shipped, and gaseous H₂ can only be trucked or piped. Driving distances between locations were calculated using the OSRM API. (OSRM, 2020) Pipeline distances were determined as straight-line distances over the Earth’s surface, using the Python library GeoPy, (GeoPy Contributors, 2018) with 20% of the straight line distance added on to account for necessary deviations over unsuitable land such as private land or areas of rough terrain. Shipping distances were evaluated using the created shipping network (see Creation of Shipping Network); firstly, the nearest port to each production site as well as the desired end location was determined from a list of global ports; then, a “closest node” search of the shipping network revealed the nearest node to the port. Dijkstra’s algorithm was implemented over the shipping network using the Python package NetworkX (NetworkX Developers, 2014) to find the shortest path between the start port of each production site and the end port, as well as the length of the path.

The most cost-effective transport method between the possible H₂ production locations and the desired end location had to be determined. The possible transportation options are shown in Figure 4 for example production and usage locations, namely a solar power plant in Spain to a steel mill in Germany. Firstly, the driving distance between the production and end location was calculated. If the route existed and was less than 1,000 km, a direct truck between the production and end location was considered an option, and the cost for trucking the required distance was calculated. Secondly, the pipeline distance was also calculated as the direct distance between the production location and the usage location, with an additional 20% added to account for the fact that most pipelines cannot be built in a straight line, and the associated pipeline cost determined. Next, the cost for shipping was evaluated, which in reality is a three-step transportation process; the produced H₂ must be either piped or trucked from the production location to the nearest port, then shipped to the port closest to the end location, and finally trucked or piped from the end port to the end location.

FIGURE 4

FIGURE 4. Example showing the possible transport options from the production locations to the desired usage location. H₂ can be trucked or piped directly to the usage location, or it can be trucked or piped to the nearest port and shipped to the port closest to the usage location, from where it is trucked or piped to the usage location. The gaseous H₂ produced can be converted into other mediums at the production location or start port and re-converted at the end port or usage location.

To determine which transportation option and medium was the cheapest, each complete ‘production site to end location route’ was calculated for each medium possibility, with two possible conversion and re-conversion points. The gaseous H₂ produced can be converted to other mediums either at the production site or the start port, and re-conversion could occur at the end port or the desired end location. The assumed conversion, re-conversion and export costs are shown in Table 3. It should also be noted that there are also technological and environmental differences between the different transport mediums. As the model only accounts for the economic cost of the transportation medium, these are discussed in Transport Cost Analysis.

TABLE 3

TABLE 3. Costs of conversion and re-conversion from and to gaseous H₂, as well as export costs for each medium (International Energy Agency, 2019).

Model Assumptions

It is assumed that the electrolyser is directly connected to the renewable energy source and is not connected to the grid. Consequently, the operating time of the electrolyser is the same as the capacity factor of the renewable electricity source. Additionally, no assumptions are made as to the political stability and safety of areas; as the study considers years until 2050, it is difficult to predict the stability of regions into the future. It is assumed that terrain suitable for building solar and wind plants is also appropriate for construction of electrolysers and their associated operations, as the terrain is usually flat and with enough space for a comparatively small electrolysis plant.

Regarding the shipping network, it is assumed that the nearest port to the production site can be used for shipping H₂. While this is likely the case for most ports, there may be some cases where the port lacks the infrastructure required to ship industrial chemicals. In other situations, the nearest port may lie in a different country, which could add extra tariffs to trade (or in the most extreme cases, deny access to the port). However, as this data is both highly uncertain and impractical to obtain for every possible production site, the authors assume that each production site can freely ship from the port closest to it. Due to the high costs associated with the conversion of H₂, it is assumed that converting and re-converting more than once is not economically viable and is therefore not considered.

Results

Global Production and Transport Cost Results

This section discusses the cost of producing H₂ for the baseline scenario, including transport to each of the five locations specified in Scenario Definition. Firstly, the H₂ production cost at each of the possible production sites selected (described in Data Collection and Assumptions) was calculated and is shown in Figure 5. The production cost ranges from 6.7—11.4 €/kg H₂ (average standard deviation 0.37 €/kg H₂), with the lowest cost areas being northern Africa and the Middle East (7.0—7.5 €/kg), the central Andes around southern Peru, Bolivia and northern Chile (6.7—7.8 €/kg), the south-west of the United States and western Mexico (7.0—7.4 €/kg), south-west Africa around Namibia and western South Africa (7.0—7.4 €/kg), Mongolia and northern China (7.2—7.7 €/kg), and north-west Australia (7.2—7.3 €/kg) all offering large areas with production costs lower than 7.5 €/kg H₂. More expensive production areas include most of Europe and central Asia, as well as south-east and central China, coastal west Africa and the eastern half of the United States and Canada, due to their relative lack of either solar or wind power potential.

FIGURE 5

FIGURE 5. Production cost of H₂ at each of the 5970 studied locations for the baseline scenario (100 kt/y AE electrolyser in 2030, centralised reconversion). Results are from a Monte Carlo simulation with 1,000 iterations.

The transport cost from each of the production locations to Cologne is shown in Figure 6. As expected, the area around Cologne has the lowest transport cost, slowly increasing as the distance from Cologne increases, with the closest measured location having a transport cost of 0.56 €/kg H₂. Once the distance from the production site to Cologne surpasses around 1,000 km, in most cases becomes more economical to transport the H₂ to the nearest port and ship it to Cologne rather than truck it directly, unless the production location is far from a port. It should be noted that all mediums are considered in Figure 6. For example, for a production location in South Africa, it might be more economically feasible to convert the H₂ to NH₃, whereas in India converting to a LOHC might make more financial sense.

FIGURE 6

FIGURE 6. The transport cost of H₂ to Cologne, Germany, in the baseline scenario (100 kt/y AE electrolyser in 2030, centralised reconversion), from each of the 5970 selected global locations. Black dots represent locations where possible transport routes could not be found. Pipelines are not considered in this scenario. All transport mediums are considered. Results are from a Monte Carlo simulation with 1,000 iterations.

The most cost-effective transport medium for each of the production locations is shown in Figure 7. For the baseline scenario with H₂ required in Cologne, LOHCs are the most cost-effective transport medium for 64% of the production locations, followed by NH₃ being the cheapest for 34% of the production locations. H₂ gas is the cheapest only for 1.2% of production locations, all of which are locations closely surrounding Cologne, as the costs for trucking gaseous H₂ increase quickly with respect to distance. The locations where LOHCs are cheaper are closer to the coast, whereas locations where NH₃ is cheaper are further inland. This is because it is slightly cheaper to ship LOHCs than NH₃, but more expensive to truck them, meaning that for locations far inland that must be trucked a long distance, it is cheaper to use NH₃. For other locations close to the coast, LOHCs are the more cost-effective medium. LH₂ is not preferred from a cost perspective for any production location, due to the combination of high conversion and export costs as well as high shipping and trucking costs per kilometre.

FIGURE 7

FIGURE 7. The most cost-effective transport medium for H₂ required in Cologne, Germany, in the baseline scenario (100 kt/y AE electrolyser in 2030, centralised reconversion), from each of the 5970 selected global locations. Pipelines are not considered as a transport method in this scenario. Results are from a Monte Carlo simulation with 1,000 iterations.

Case Studies for Particular H₂ Usage Locations

Figure 8 shows the total delivered cost of producing and transporting H₂ to Cologne, Germany, from each possible H₂ production location. The mean of the Monte Carlo simulation for each location was taken for the visualizations in this section. The costs range from 9.4—16.9 €/kg H₂ (average standard deviation 0.31 €/kg H₂), with the cheapest overall location being at (28, 34) near Sharm El-Sheikh, Egypt, with the H₂ being converted to a LOHC, shipped to the closest port to Cologne and trucked the rest of the way before being reconverted. In general, there are an abundance of locations that could supply cheap renewable H₂ to Cologne, with the cheapest production locations being in coastal north Africa (9.8—10.5 €/kg), the Middle East (9.8—11.0 €/kg), southern Spain (9.6—10.6 €/kg), and the area directly around Cologne (9.9—10.9 €/kg), closely followed by the other aforementioned low-cost generation areas. Western Germany’s relatively high H₂ production costs (9.2—9.4 €/kg) are offset by the very low transportation distance to the usage location.

FIGURE 8

FIGURE 8. The total delivered cost (production and transport cost) of H₂ required in Cologne, Germany, in the baseline scenario (100 kt/y AE electrolyser in 2030, centralised reconversion), from each of the 5970 selected global locations. Black dots represent locations where possible transport routes could not be found. All transport mediums are considered. Results are from a Monte Carlo simulation with 1,000 iterations.

The total delivered cost for the other studied locations is shown in Figure 9. For Abu Dhabi, the total costs reach lower values when compared to Cologne, with costs as low as 8.4 €/kg. The cheapest locations are directly around Abu Dhabi in the UAE, closely followed by Oman and southern Iran (8.9—9.8 €/kg). In the case of UAE and Oman, the H₂ is transported by truck either in gaseous form, which although is more expensive than transporting NH₃ or LH₂, avoids the expensive conversion and re-conversion costs. From southern Iran, the H₂ is transported in gaseous form to the nearest port, where it is converted to NH₃ or LOHCs, shipped across the Persian Gulf to Abu Dhabi, and re-converted. No locations outside the Middle East offer costs under 9.4 €/kg. For Abu Dhabi and indeed most locations in the Middle East, using renewable H₂ produced in the Middle East is the most desirable solution. As most of the region is very sunny, most countries will be able to produce renewable H₂ at low costs themselves, reducing their energy reliance on other countries, as well as transport costs. Most countries in the region have a strong chemical and energy industry already and that expertise and infrastructure could be useful for renewable H₂ generation.

FIGURE 9

FIGURE 9. The total (delivered) cost per kg of producing and transporting H₂ to Abu Dhabi (A), Tokyo (B), Guangzhou (C), and Houston (D), from each of the 5970 selected global locations. Black dots represent locations where possible transport routes could not be found. The rest of the parameters remain the same as for the baseline scenario (100 kt/y AE electrolyser in 2030). Results are from a Monte Carlo simulation with 1,000 iterations.

The minimum cost to produce and transport renewable H₂ to Tokyo-Yokohama is 9.0 €/kg, from a location to the north of Tokyo utilizing wind power. However, most other areas in Japan are considerably more expensive, with the bulk ranging from 10.0—11.0 €/kg. It is possible that these smaller areas with strong renewable potential within Japan will not be able to produce enough H₂ for the country’s requirements. Also, Japan might utilize these areas renewable potential to produce electricity for its population, instead of renewable H₂. The safest most cost-effective options for the Tokyo-Yokohama region are north-west Australia and north Chile, with both having costs ranging from 9.3—9.8 €/kg. Indeed, Japan has already signed a partnership agreement with Australia to secure a supply of renewable H₂ in the future. (FuelCellsWorks. Australia, 2020; Taylor, 2021) The Middle East (9.4—10.0 €/kg) and the west coast of Mexico (9.3—9.9 €/kg) are also strong alternatives.

For Guangzhou, the cheapest production location including transport is the coastal regions of south-west US and west Mexico (8.6—9.6 €/kg). Again, northern Chile (8.7—8.9 €/kg), north-west Australia (9.0—9.4 €/kg) and the Middle East (9.0—9.7 €/kg) are also very promising production locations. From these regions, the H₂ is converted into either NH₃ or LOHCs and shipped to Guangzhou. Costs in the region directly around Guangzhou range from 9.3—10. €/kg. The region is relatively cloudy and lacks consistent strong winds, resulting in locally renewably produced H₂ being significantly more expensive than importing it, despite the additional transportation costs.

Lastly, the cheapest production locations for H₂ required in Houston are in the western Gulf of Mexico, in a relatively large area directly around Houston with total costs ranging from 8.6—9.4 €/kg, from where the H₂ can be trucked in gaseous form to the desired usage location. This area is relatively sunny, and although it has higher production costs than coastal south-west United States, the proximity to the usage location and thereby low transport cost outweigh the difference in production cost. The next cheapest locations include the coastal regions of southwest US and west Mexico (9.1—9.8 €/kg) and northern Chile (9.1—9.6 €/kg). The other low production cost areas such as the Middle East (9.7—10.2 €/kg) and north-west Australia (9.4—9.8 €/kg) are slightly more expensive than for the other usage locations studied, due to the large shipping distances required to transport the H₂ to Houston. Therefore, it is likely that renewable H₂ for the Houston area could be produced within Texas, allowing for energy independence.

Distribution of Results

The Monte Carlo simulation resulted in a distribution of costs for each production location analysed. Shown in Figure 10 are the distributions of the four major cost components calculated for the production site near Cairo with the lowest total cost per kg of H₂ for the baseline scenario; the total cost of H₂ (including transport to Cologne) (subplot A), the production cost of H₂ (subplot B), and the cost of wind (subplot C) and solar (subplot D) electricity. All distributions resemble normal distributions with differing variances. Producing H₂ at this location near Cairo and transporting it to Cologne has a mean total cost of 9.4 €/kg with a standard deviation of 0.43 €/kg. The production cost has a mean of 6.6 €/kg and a standard deviation of 0.39/kg. The distribution of the production cost is the same as that of the total cost but shifted by 2.2 €/kg, which is the transportation cost. As the transportation cost parameters are not included in the Monte Carlo simulation, the shape of the distribution of the total cost and production cost are the same. The cost of wind electricity at this location averages 32 €/MWh with a standard deviation of 1.5 €/MWh, while solar electricity costs on average 48 €/MWh with a standard deviation of 7.8 €/MWh. Wind electricity is the cheaper option at this location, although it has a much lower standard deviation due to the greater certainty of wind power costs. Therefore, the electricity cost from wind power is also more certain than the electricity cost from solar power.

FIGURE 10

FIGURE 10. Distributions of the total (delivered) H₂ cost including transport (A), the production cost (B), and the cost of wind (C) and solar (D) electricity from the Monte Carlo analysis (1,000 iterations) of the baseline scenario (100 kt/y AE electrolyser in 2030, H₂ usage location in Cologne, centralised reconversion). The distributions represent the costs of the production site with the lowest total cost of H₂ per kg, in Cairo, Egypt.

Parameter-Specific Results

The effect of changing other model parameters, such as the type of electrolyser used, the year the plant will be constructed, and the size of the plant was also analysed. In Figure 11, how the total cost in €/kg H₂ is expected to change over time is shown for the three major electrolyser types for usage locations in Cologne and Houston. The mean value of the ‘total cost’ distribution in Figure 10 is shown. The study found that costs are expected to decrease in the future for all electrolyser types due to improvements in electrolyser design and efficiencies. Using AE electrolysers, the lowest calculated total cost for H₂ required in Cologne decreases from 10.2 €/kg in 2020 to 8.6 €/kg by 2050, a drop of about 16%. PEM electrolysers are currently slightly more expensive (10.3 €/kg in 2020) but are projected to have a stronger reduction in costs of 26% to 7.6 €/kg by 2050. SOEs are the most expensive but are expected to have the sharpest decrease in costs over the next 30 years (33%), going from 12.9 €/kg in 2020 to 8.7 €/kg by 2050. The costs for Houston are about 1 €/kg less than for Cologne for all scenarios because it is cheaper to produce H₂ close to Houston than close to Cologne, meaning the transport costs for H₂ required in Cologne are higher. The transport costs are the same irrespective of the type of electrolyser used, as the H₂ produced by different electrolysers is the same.

FIGURE 11

FIGURE 11. Cost per kg of producing and transporting H₂ to Cologne and Houston for each electrolyser type from 2020–2050. The other parameters are the same as for the baseline scenario (100 kt/y capacity, centralised reconversion). Results are the average from a Monte Carlo simulation with 1,000 iterations.

The cost comparison between centralised and decentralised reconversion is shown in Figure 12 for H₂ required in Cologne, Houston and Guangzhou. It should be noted that only the cheapest total production and transport option is shown in each case; if H₂ is being produced and transported from another location, the costs and their relative difference could change. For Cologne, where it is cheapest to get H₂ shipped as a LOHC from Egypt when conversion is centralised, the transport (and therefore also total) cost is about 0.4 €/kg H₂ higher when conversion is decentralised. When conversion is decentralised, it is cheaper to produce the H₂ close by to Cologne and transport it directly in trucks as gaseous H₂ than to obtain it from Egypt in the form of LOHCs. Likewise, Guangzhou achieves a cost reduction of 0.95 €/kg H₂ when conversion is centralised, a more significant cost reduction than in Cologne. When reconversion is decentralised in Guangzhou, it is more economically viable to transport using NH₃ as the medium, due to the lower reconversion costs for decentralised conversion (see Figure 9). For Houston, no difference between centralised and decentralised reconversion is apparent; this is because for Houston, the cheapest H₂ would be already transported in gaseous form by trucks from a production site close by; therefore, conversion is not necessary. In this case, the trucks could be directly dispatched to even small-scale end consumers directly, saving costs and time transporting the H₂ first to a large-scale reconversion centre before distributing it to end consumers.

FIGURE 12

FIGURE 12. Lowest cost of producing and transporting H₂ to Cologne, Houston and Guangzhou for the baseline scenario (100 kt/y AE electrolyser in 2030), showing both centralised and decentralised reconversion to gaseous H₂. Results are from a Monte Carlo simulation with 1,000 iterations.

Use of Pipelines for Large Scale Transport

While pipelines are impractical for smaller H₂ consumers, for large industrial consumers the cheaper operating cost of pipelines at large H₂ volumes could be worth the high investment cost. Pipelines could also be used to cheaply supply large areas or even small countries if the H₂ is piped to a central point before being distributed from there to smaller end users. Two particular pipeline scenarios were investigated for either large consumers or groups of consumers located in the areas of Cologne and Houston. When the use of large-volume (1,000 kt/y H₂) pipelines is considered, the cheapest production location for H₂ required in Cologne and its associated cost changes significantly. As well as direct pipelines between the production locations and the desired H₂ usage location, pipelines were also considered between the production location and the closest port, from where H₂ could still be shipped to the usage location, as well as between the usage location and the closest port. Three production locations offer the most cost-effective H₂ to Cologne at similar prices: northern Scotland, the south coast of Ireland, and southern France close to Spain on the Mediterranean Sea, which all have strong wind resources. As the potential production location in France is better situated in terms of closeness to other potential industry and population centres and does not require an undersea pipeline, it will be investigated over the locations in Ireland and Scotland. The lowest-cost location is a place called Parc Eolien de Port la Nouvelle, which is actually already home to a wind park. If H₂ was to be produced from the wind power generated in Parc Eolien de Port la Nouvelle, the estimated pipeline length required to transport H₂ to Cologne would be about 1,120 km. Having such a pipeline would significantly reduce the costs for H₂ in Cologne to 7.6 €/kg, as compared to the cheapest obtainable H₂ without using pipelines, which is 9.4 €/kg for H₂ shipped as a LOHC from Egypt and re-converted at Cologne. The total delivered cost per kg for H₂ required in Cologne if pipelines are considered is shown in Figure 13 for all studied locations.

FIGURE 13

FIGURE 13. The total (delivered) cost per kg of producing and transporting H₂ to Cologne, Germany, for large demand (1,000 kt/y) and allowing pipelines from production sites to Cologne. Other parameters follow the baseline scenario (AE electrolyser in 2030, centralised reconversion). Black dots represent locations where possible transport routes could not be found. Results are from a Monte Carlo simulation with 1,000 iterations.

As well as the three locations mentioned, other low-cost production locations for H₂ needed in Cologne include southeast Spain, Italy, and the north coasts of Morocco, Algeria and Tunisia. Locations such as the Middle East, northern Chile and the southwest US, which are cost-competitive if no pipelines are built, are no longer economically viable compared to more local options if H₂ pipelines are constructed. If a pipeline was built from the production site near Sharm El-Sheikh, Egypt, that was determined to be the most optimal production site for H₂ required in Cologne if no pipelines are to be constructed, H₂ required in Cologne would cost 8.4 €/kg, which is considerably more than H₂ produced in Parc Eolien de Port la Nouvelle and piped to Cologne. The pure production cost of H₂ in Parc Eolien de Port la Nouvelle is 7.2 €/kg H₂, which is slightly higher than the production cost of 7.0 €/kg H₂ for Sharm El-Sheikh. However, the transport cost using pipeline from Parc Eolien de Port la Nouvelle is only around 0.4 €/kg H₂, considerably lower than the pipeline cost of 1.4/kg H₂ to transport H₂ from Sharm El-Sheikh to Cologne, and outweighing the slightly cheaper production cost at Sharm El-Sheikh. Another advantage of obtaining H₂ from France as opposed to Egypt is geopolitical stability; France is also in the EU and a close ally of Germany, making it a more secure energy source. It could be possible that areas in the EU with high renewable energy resources, such as France, Ireland, or Spain could produce enough H₂ to supply the future needs of the EU, reducing the energy reliance of the EU on foreign providers.

If pipelines are considered for the scenario of H₂ required in Houston, the cheapest production location changes to (27, -104), a sunny location in north-central Mexico requiring a pipeline length of about 1,080 km, decreasing the minimum total cost to 7.6 €/kg H₂ from a cost of 8.6 €/kg H₂ if pipelines are not allowed. In general, a variety of locations across Mexico and the southern US are strong candidates for production sites, as shown in Figure 14. As with the Cologne scenario, it makes more sense for the US to produce H₂ within the country to ensure a greater degree of energy self-sufficiency. A production site on the New Mexico/Texas border at (32, -105) could provide H₂ to Houston at only 0.04 €/kg H₂ more than the aforementioned site in Mexico while ensuring control over the production facilities. Other low-cost production locations that are competitive if no pipelines are allowed, such as northern Chile, are considerably more costly than closer locations in the southwest US and Mexico if pipelines are permitted. For these further away locations, shipping is still more cost-effective than a pipeline when large volumes are considered, while for closer locations, a pipeline becomes a much more economically viable option.

FIGURE 14

FIGURE 14. The total (delivered) cost per kg of producing and transporting H₂ to Houston, United States, for large demand (1,000 kt/y) and allowing pipelines from production sites to Houston. Other parameters follow the baseline scenario (AE electrolyser in 2030, centralised reconversion). Black dots represent locations where possible transport routes could not be found. Results are from a Monte Carlo simulation with 1,000 iterations.

The most cost-effective transport medium for H₂ required in Cologne when pipelines are considered is shown in Figure 15. H₂ gas is now the cheapest medium for 42% of cases, as large-scale pipelines allow for much cheaper transport without expensive conversion and re-conversion to other mediums. However, the cost of pipelines increases more quickly per kilometre than shipping, and therefore shipping becomes more cost-effective for this scenario at distances greater than 6–7,000 km. Beyond that distance, LOHCs are a cheaper medium to use, being the most cost-effective medium for 58% of all production locations. LOHCs are favoured over NH₃ in this scenario because as the produced amount is so large, pipelines are also considered from the production location to the closest port, and from the port closest to Cologne to the final usage location. LOHC pipelines are cheaper than NH₃ pipelines, and so they are favoured for every production location where shipping is still cheaper than a pipeline.

FIGURE 15

FIGURE 15. The most cost-effective transport medium for H₂ required in Cologne, Germany, for large demand (1,000 kt/y) and allowing pipelines, from each of the 5970 selected global locations. Results are from a Monte Carlo simulation with 1,000 iterations.

Discussion

Production Cost Analysis

As expected, there is a strong correlation between locations with good wind or solar resources and low H₂ production costs, as production cost variables other than electricity price remain constant between locations. In 85.7% of the studied locations, it is cheaper to generate electricity from solar power than from wind power; however, there are still many locations where wind power is the cheaper option, for example locations at far northerly or southerly latitudes, many coastal locations, or locations that have strong winds due to unique geographical circumstances, such as northern and western Europe (particularly the British Isles), the plains of central North America, and the Kazakh and Russian steppe. The average cost of wind power across all locations studied was 432 €/MWh, significantly higher than the 55 €/MWh for solar power, but also with much greater variance (standard deviation of 672 €/MWh for wind compared to 10 €/MWh for solar). The high mean of wind power is a result of many locations with very little wind in central and western Africa, South America and south-east Asia. In these locations, solar power is much more economically viable than wind power. The costs of generating both solar and wind power are shown for all locations in the Supplementary Figures S1, S2. In general, it is promising that a variety of regions have the potential to produce cheap renewable H₂, as many countries could benefit that do not currently profit from an abundance of industry or resources.

The location-based trends in production cost over the world calculated in this study (shown in Figure 5) correlate strongly with the IEA (International Energy Agency, 2019) map of production costs of renewable H₂ worldwide, which also shows northern Chile, south-west Africa, south-west United States and Mexico, the Middle East and Australia having the lowest H₂ production cost. As the IEA did not exclude locations based on physical or human features, they also show Tibet as the cheapest region to produce renewable H₂; however, this (and many of the other locations shown on their map) are unrealistic due to altitude, tough terrain, distance from industrial and urban centres, and a variety of other man-made and natural features. The Hydrogen Council (Hydrogen Council, 2021) also notes similar areas as having the best solar/wind resources for renewable H₂ production. While the overarching trends between this study and the IEA report look very similar, it should be noted the costs reported by the IEA, as well as several other agencies, are markedly lower than those calculated here. For example, the IEA reports a H₂ production cost of 2.0—3.4 €/kg H₂ in the EU ‘in the future’ (assumed to be 2050 for the purposes of comparison). Bloomberg (Bloomberg New Energy Finance, 2020) has an even more aggressive estimation of a delivered cost of 2.0 USD/kg H₂ in western Europe by 2030 and 1.0 USD/kg H₂ by 2050, while IRENA (International Renewable Energy Agency, 2020) assumes costs of 2.5—3.5 USD/kg H₂ in 2030 to 1.4 USD/kg H₂ by 2050. For comparison, this study finds production costs of 7.5—9.7 €/kg H₂ in western Europe in 2030, and 6.5—8.3 €/kg H₂ in 2050, with the cheapest production costs worldwide being 6.0 €/kg H₂ in 2050, which is similar to those reported by Christensen (Christensen, 2020) (7.5—16.0 USD/kg H₂ in the EU in 2020 and 5.8—10.5 USD/kg H₂ in 2050). In order to benchmark the results of this study against others, an overview of the H₂ production costs from various studies is shown in Table 4, along with short notes about the factors considered in each study.

TABLE 4

TABLE 4. Comparison of the H₂ production cost in 2050 from various studies.

The IEA’s calculations for the levelized cost of hydrogen (LCOH) are unclear as to what year the costs are calculated for, what capacity the hybrid solar-wind power system achieves and most importantly, what is included in their calculations for “production cost”. Christensen (Christensen, 2020) was “able to reproduce the IEA data... but only if we neglected all system costs beyond the electricity and electrolyser CAPEX costs”. The Bloomberg (Bloomberg New Energy Finance, 2020) and IRENA (Gielen et al., 2019) studies also seem to neglect these extra systems costs. In this study, the H₂ production cost is calculated including the required compression, storage, OpEx and other maintenance, piping, and dispense costs required to ensure the H₂ is ready to be transported to its required destination, which explains the discrepancy between the production costs of the aforementioned agencies and those presented here. The costs calculated in this study are at this stage not cost-competitive with the benchmark of H₂ produced by SMR (2.2 €/kg H₂). (Gielen et al., 2019) However, with a combination of CO₂ taxes and technological development, it is possible that renewable H₂ could be cost-competitive in the future.

Transport Cost Analysis

Although the transport costs are almost always lower than the production cost, they still make up a significant portion of the total cost. For production locations, the distance from the nearest port is important—coastal regions usually have lower transport costs, whereas far inland regions such as Mongolia have very high transport costs. If the production location is close enough to the usage location to truck the H₂ in gaseous form, the huge cost of conversion and re-conversion is saved. Interesting to note is that a large increase in shipping distance results in only a slight increase to the transport cost; for example, transport to Cologne from a coastal location in southern France has a transport cost of around 2.7 €/kg H₂, but transport from Brazil costs only 2.9—3.0 €/kg H₂. The bulk of these costs is the conversion and re-conversion into either NH₃ or a LOHC, which is expensive but drastically reduces the cost of shipping. This is expected; ships are the most cost-effective transport method for transporting goods long distances, especially when large volumes of cargo are transported, and are currently used to transport oil, natural gas, minerals, and many other goods (International Transport Forum, 2019). A bigger contributor to cost is the distance from the coast—it can be seen from Figure 6 that across the world the transport costs increase as the production locations go further inland. It can then be inferred that trucking from the production location to the port, either for gaseous H₂ or any transport medium, is a large cost contributor. Therefore, having production locations on the coast, and in particular close to ports, would heavily reduce transport costs to far-away locations that require H₂.

Other studies, such as those done by the IEA (International Energy Agency, 2019) and the Hydrogen Council, (Hydrogen Council, 2021) estimate the transport cost for a few particular scenarios. The Hydrogen Council calculates the transport costs for three similarly distanced shipping routes (7000–8500 km); one using NH₃ as a medium, one using LH₂, and the last being converted to a LOHC, with the resulting transport costs ranging from 1.8—2.7 €/kg H₂. The IEA calculates a transport cost of 1.8—3.6 €/kg H₂ from Australia to Japan. While there are some differences, the costs are comparable to the transport costs calculated in this study for similar routes, as highlighted in Table 5, and show much more similarity than the production costs from the same studies. The largest discrepancy is for LH₂ and gaseous H₂ transport; this is probably due to differing assumptions on how fast transport technology for LH₂ will progress. As it is still in a relatively early stage of development, the transport costs for LH₂ and gaseous H₂ are quite uncertain, and it is therefore natural to have greater discrepancies than for LOHCs and NH₃. The transport costs calculated in this study for NH₃ and LOHCs are slightly higher than those from the IEA and Hydrogen Council, but they are generally more similar than the H₂ transport costs and within the realms of normal uncertainty.

TABLE 5

TABLE 5. Comparison of H₂ transport costs of a variety of mediums and modes from similar studies.

While the most cost-effective transport mediums for each production location (Figure 7) currently heavily favour LOHCs and NH₃ due to the ease of transportation and storage, this could change in the future due to differences in energy conversion efficiency between the different mediums. Comparing the energy efficiency between different mediums is complex, as not only must the energy losses of conversion and re-conversion be considered but also the energy losses during storage and transportation, which differ for every medium. Such calculations would be different for every production location and could be the subject of a complete study on its own. However, as the energy losses for H₂ gas (5–15% for compression (Energy Transition Institute, 2014)) are lower than for LH₂ (9–22% round trip efficiency (Baker and Shaner, 1978)), NH₃ (11—19% round trip efficiency (Giddey et al., 2017)), and LOHCs (26—43% round trip efficiency (Modisha et al., 2019)), it could end up being the most cost-effective transport medium if energy efficiency can be preserved along the transport route. For comparison, Bøe et al. (Bøe et al., 2021) find average total supply chain efficiencies of 20—30% for LH₂, 15—27% for NH₃, and 15—25% for a sample LOHC. These efficiencies suggest pipelines of gaseous H₂ could be the most energy-efficient transport method when the production site is close enough, and demand high enough, to justify the high CapEx of the pipeline.

It should also be mentioned that although this study focuses only on the economic cost of storage and transportation, there are many physical and technological factors to be considered, such as H₂ density of the carrier, leakage, and corrosivity. For example, NH₃ has a relatively high H₂ density (17.7 wt%) and has a low leakage rate, making it an attractive transport and storage medium from a technological perspective. (Wijayanta et al., 2019) While it is corrosive and toxic, it can be stored in low-cost tanks currently used for LPG, and transport and storage of NH₃ is already widespread. Therefore, it is the most technologically ready transport medium.

LOHCs have a lower H₂ density (6.16 wt% for toluene), requiring notably larger tank sizes to transport the same amount of H₂. (Wijayanta et al., 2019) As this is stoichiometrically set, this places inherent space and weight limitations on the transport and storage of H₂ using LOHCs. LH₂, while naturally being very pure, requires immense energy for cooling (-250°C) and has relatively high boil off (0.2—0.3%/d), while is problematic for long-term storage and long-haul transportation. (Wijayanta et al., 2019) While NH₃ and LOHCs can already be shipped, the first LH₂ ship has only just been launched in 2022, and gaseous H₂ pipelines still require further research and development. (Carbon capture needed for expansion, 2020) Hence, LOHCs and especially NH₃ might be more beneficial transport mediums in the near future. LOHCs and NH₃ could also use existing pipeline infrastructure, whereas new or heavily retrofitted pipelines would have to be installed to accommodate H₂. (International Energy Agency, 2019)

Parameter Analysis

In general, CapEx costs for electrolysers are predicted to decrease significantly in the next few decades, primarily due to advances in technology and efficiency as the number of electrolysers constructed increases dramatically, but also due to the increasing capacity of electrolyser systems, which can take advantage of economy of scale effects. The difference in cost reductions over the next 30 years is likely due to the differing levels of industrial development of the three electrolyser types; AE electrolysers, as the most commonly used industrially in the present day, have already undergone the most development and there are therefore less cost reduction breakthroughs to be made in the future. PEM electrolysers are expected to have a greater cut in cost as their market share increases and natural development continues, and even more so for SOEs, which are currently the least industrially proven option. Especially in the short term, over the next decade, SOEs are expected to have a drastic reduction in costs due to further industrial development. PEM electrolysers are believed to offer the cheapest H₂ from around 2023 onwards due to their high scalability and efficiency, as well as the fact that it is possible to obtain already compressed H₂ out of the process, heavily reducing the H₂ compression costs that are required for storage or transport.

Pipeline Analysis

As H₂ can be piped in gaseous form without the need for conversion into a different medium or liquefaction, this can drastically reduce transport costs, especially for larger H₂ demands. Capital investment per unit of product generally decreases as the amount of product increases due to economies of scale effects. (Gim and Yoon, 2012) As pipelines have by far the most capital investment required of the transport methods studied, the scaling effect has a more pronounced cost reduction for pipelines compared to trucks and ships, of which the bulk of the costs come from operational expenditures and do not decrease much as the amount transported increases.

It is still cheaper to ship H₂ in the form of a chemical medium long distances than to pipe it in gaseous form, despite the conversion and re-conversion costs. However, it is much cheaper to pipe H₂ short distances than ship H₂, meaning the inclusion of pipelines favours more locally based H₂ production sites, at least for large-scale H₂ demand. Using pipelines would be particularly attractive for countries or regions with strong renewable resources looking to be energy independent, provided they have a large enough demand for H₂ to justify the high capital investment of pipelines.

A weakness of the pipeline calculations is the assumption that the pipeline distance is 20% greater than the straight-line distance between the locations. In reality, this number might often be quite different; in some scenarios, it might be possible to build a pipeline directly from the production location to the usage location, and in other situations, the pipeline could have to circumvent a variety of obstacles such as other properties or rough terrain. However, as assessing optimal pipeline routes is a complex task for just a single pipeline, it is impractical to assume different values for different routes in this study.

Data, Uncertainty and Model Analysis

There are some limitations imposed by the scope of the model. For some unique locations that have both good wind and solar resources, such as south-west Europe and northern Africa, the Middle East, Western Australia, and northern Chile, hybrid systems could potentially allow for an increase in the capacity factor of the electrolyser, as there would be a greater fraction of time that electricity could be generated compared to a single renewable electricity generation system. Nevertheless, building a hybrid wind-solar system would likely result in a slightly higher cost of electricity than a single system due to capital investment “economy of scale” effects. (Gim and Yoon, 2012) Additionally, offshore wind could be the most cost-effective electricity generation source for some H₂ usage locations, such as countries like the UK or Denmark.

Labour and infrastructure costs could vary in different countries. Additionally, there could be instances of the closest port to a production site being in another country. In many scenarios, this may not make a difference, but it could in some scenarios where the neighbouring country does not allow the production site use of the port.

There are likely some cases where NH₃ or LOHCs are desired as the final product instead of H₂. In this case, the high re-conversion costs can be avoided, which would lower the cost. As the market share of NH₃, in particular, is comparatively large, a relatively high amount of renewable H₂ could be used to produce NH₃ (Fortune Business Insights, 2020) Although the cost is greater than conventional NH₃, it could become competitive as CO₂ prices increase. (ChemAnalyst. Ammonia Price, 2020)

Grid-connected electrolysis systems could likely reduce the H₂ production cost and increase the capacity factor, as electricity can be sold to the grid when there is an oversupply of renewable power and drawn from the grid when not enough is produced. (Christensen, 2020) It should be noted, however, that grid power in most countries is not yet fully renewable, which would increase GHG emissions of H₂ production. As grids become more and more renewable, however, most electrolysers will likely be grid-connected to maximize their capacity factor. In general, estimating the capacity factor for different locations is difficult. In this study, a constant value was assumed, which would likely vary in reality for different locations, as well as being dependent on whether solar or wind power is used. Other ways to increase the electrolyser capacity factor include oversizing the renewable electricity generator, adding batteries as an energy storage, and hybrid PV-wind systems. (Christensen, 2020) However, these have associated extra costs and research is required to determine their economic plausibility.

The parameters of the cost calculations with the biggest impact on the final cost are the CapEx of the electrolyser and the electricity price. The solar and wind data have very low uncertainty as they were obtained from reputable public sources and were cross-checked against other databases to ensure an acceptable degree of similarity. The low, medium and high electrolyser CapEx were taken from Christensen’s (Christensen, 2020) analysis of the study conducted by Glenk et al., (Glenk and Reichelstein, 2019) who thoroughly investigated a large range of original data sources of electrolyser investment costs. While the sample size of electrolyser costs taken is large (n = 52 for AE electrolysers), the standard deviation is also comparably high (±345 €/kW or ±29% for AE electrolysers), which infers the data is quite uncertain. As H₂ electrolysis is a technology still undergoing development at larger industrial scales, cost data is likely to both become more certain and decrease in the next few decades. As the uncertainty of each electrolysis CapEx obtained from literature is high, taking the mean of all obtained values from reputable studies is the most certain option available at the moment. The uncertainty of transport cost data is difficult to ascertain due to the various ways it is reported in different studies; many papers only report the cost for a particular case study. Transport costs were taken from the IEA report (International Energy Agency, 2019) and were verified against other reports and data to ensure costs were similar. Costs for transportation of NH₃ and LOHCs were more similar than for LH₂ and gaseous H₂, largely because transportation of H₂ is much less developed and therefore cost data are by nature more uncertain. Overall, the delivered costs reported in the study have a moderate uncertainty, largely due to the aforementioned uncertainty regarding future electrolysis and H₂ transport costs.

The production locations were selected at intervals of one-degree latitude and longitude, resulting in 5970 locations once the masked areas were excluded. Selecting points with smaller intervals, for example every 0.5°, would increase the resolution of the study and potentially find some slightly more advantageous locations, but at the expense of a vastly increased computation time. For example, decreasing the intervals to 0.5° would increase the number of production locations and therefore also the computation time by a factor of four, which is unfeasible in this study for the relatively small preciseness gain that would result.

1,000 iterations were completed for each scenario in the Monte Carlo simulation. Although more iterations would have slightly improved the accuracy of the predictions, it would drastically increase the computation time and render it unfeasible within the limitations of the study. For example, increasing the number of iterations from 1,000 to 10,000 would likely only increase the accuracy of the simulation by about 0.5%, but would take 10 times longer to compute. (Oberle, 2015)

Conclusion

Replacing fossil fuels or fossil-fuel-based H₂ with renewable H₂ is a promising solution to reduce GHG emissions in industries such as steel and chemicals. For stakeholders wishing to use renewable H₂, the cost, as well as the production location and transport route, are essential information for process development. A model was created to determine the minimum delivered cost of renewable H₂ to any location worldwide, as well as the associated cheapest production location, transport route and transport medium.

Locations with high population and heavy industry, such as Cologne, Germany, and Houston, United States, were selected as case studies. Total delivered H₂ costs range from 9.4—11.0 €/kg in Cologne and 8.6—10.2 €/kg in Houston, with the most promising production locations being the Middle East for Cologne and either the southwest United States or northern Chile for Houston. South-west Africa and north-west Australia also have low H₂ production costs, and along with the aforementioned production locations are the best suited to offer relatively low-cost H₂ worldwide. For small demands, when pipelines are not economically feasible, LOHCs are the most commonly preferred transport medium, except for production locations close (<1,000 km) to the usage location, for which trucking gaseous H₂ is more economically feasible. For larger demands such as industrial areas, large pipelines transporting H₂ gas similar to those used for natural gas today can be considered, which could reduce the cost significantly to 7.6 €/kg H₂ for both Cologne and Houston, as well as favouring closer production locations in southern France and Texas, respectively. In general, NH₃ and LOHCs are the preferred transport medium for longer distances, while H₂ gas pipelines are a more cost-effective option for short to medium distances, particularly if there is a large demand. (Hydrogen Council, 2020) In the short term, NH₃ and LOHCs are likely to be more attractive transport mediums, as they can be transported by currently existing infrastructure. LH₂ ships and H₂ pipelines require time to be built, as well as further technological development. However, in the future, they may become more attractive due to high conversion efficiencies. H₂ production costs are expected to decrease over the next few decades by up to 1.8 €/kg, largely due to decreasing electrolyser CapEx and renewable energy costs, particularly for PEM and SOEs.

Needed further studies include investigating the effect of rising CO₂ prices on the economic feasibility of renewable H₂ (Gerbelová, 2014) and evaluating the full-trip energy efficiency of different transport mediums to determine the potential for cost reduction. (Cerniauskas et al., 2019) As well, examining particular case studies in more depth, such as considering hybrid PV-wind systems, offshore wind generation, and scenarios where NH₃ and LOHCs are desired as an end product would be a logical next step.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author. The model used in this study can be found at the following link: https://github.com/jooooohannes/h2-mapping.

Author Contributions

JC performed literature research, envisaged and developed the model, and was the main author of all text. RS provided guidance and structure to the manuscript.

Funding

This research was carried out within the project BAC-2-FUEL, which received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 825999. The European Commission is neither responsible nor liable for the content of this document.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors would also like to thank Till Strunge for proof-reading the manuscript.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenrg.2022.909298/full#supplementary-material

References

Aditiya, H. B., and Aziz, M. (2021). Prospect of Hydrogen Energy in Asia-Pacific: A Perspective Review on Techno-Socio-Economy Nexus. Int. J. Hydrogen Energy 46, 35027–35056. doi:10.1016/j.ijhydene.2021.08.070

CrossRef Full Text | Google Scholar

Andaloro, L., Ferraro, M., Brunaccini, G., Sergi, F., and Antonucci, V. (2019). From Distributed Generation to Smart Grids: Integration of H2 and Renewable Energy towards Zero Emissions Home. ECS Trans. 17, 673–684.

Google Scholar

Baker, C., and Shaner, R. L. (1978). A Study of the Efficiency of Hydrogen Liquefaction. Int. J. Hydrogen Energy 3, 321–334. doi:10.1016/0360-3199(78)90037-x

CrossRef Full Text | Google Scholar

Baufumé, S. (2013). GIS-based Scenario Calculations for a Nationwide German Hydrogen Pipeline Infrastructure. Int. J. Hydrogen Energy 38, 3813–3829.

Google Scholar

Bellevrat, E., and Menanteau, P. (2009). Introducing Carbon Constraint in the Steel Sector: ULCOS Scenarios and Economic Modeling. Rev. Metall. Paris 106, 318–324. doi:10.1051/metal/2009059

CrossRef Full Text | Google Scholar

Benita, F., Bansal, G., Piliouras, G., and Tunçer, B. (2019). Short-distance Commuters in the Smart City.

Google Scholar

Bhandari, R., Trudewind, C. A., and Zapp, P. (2014). Life Cycle Assessment of Hydrogen Production via Electrolysis - A Review. J. Clean. Prod. 85, 151–163. doi:10.1016/j.jclepro.2013.07.048

CrossRef Full Text | Google Scholar

Bhaskar, A., Assadi, M., and Somehsaraei, H. N. (2020). Decarbonization of the Iron and Steel Industry with Direct Reduction of Iron Ore with Green Hydrogen. Energies 13, 1–23. doi:10.3390/en13030758

CrossRef Full Text | Google Scholar

Bloomberg New Energy Finance, (2020). Hydrogen Economy Outlook.

Google Scholar

Bøe, A. V., Gullbrå, D. O., and Reinertsen, T. A. (2021). An Efficiency Comparison of Liquid Hydrogen, Ammonia, and Liquid Organic Hydrogen Carriers for Maritime Use. ACS Sustain. Chem. Eng.

Google Scholar

Botelho, A., Ferreira, P., Lima, F., Pinto, L. M. C., and Sousa, S. (2017). Assessment of the Environmental Impacts Associated with Hydropower. Renew. Sustain. Energy Rev. 70, 896–904. doi:10.1016/j.rser.2016.11.271

CrossRef Full Text | Google Scholar

Branker, K., Pathak, M. J. M., and Pearce, J. M. (2011). A Review of Solar Photovoltaic Levelized Cost of Electricity. Renew. Sustain. Energy Rev. 15, 4470–4482. doi:10.1016/j.rser.2011.07.104

CrossRef Full Text | Google Scholar

Brauns, J., and Turek, T. (2020). Alkaline Water Electrolysis Powered by Renewable Energy: A Review. Processes 8. doi:10.3390/pr8020248

CrossRef Full Text | Google Scholar

Brennan, D. (2020). Process Industry Economics - Principles, Concepts and Applications. Elsevier.

Google Scholar

Brynolf, S., Taljegard, M., Grahn, M., and Hansson, J. (2018). Electrofuels for the Transport Sector: A Review of Production Costs. Renew. Sustain. Energy Rev. 81, 1887–1905. doi:10.1016/j.rser.2017.05.288

CrossRef Full Text | Google Scholar

Cai, L., Jacobs, S., Langer, R., vom Lehn, F., Heufer, K. A., and Pitsch, H. (2020). Auto-ignition of Oxymethylene Ethers (OMEn, N = 2-4) as Promising Synthetic E-Fuels from Renewable Electricity: Shock Tube Experiments and Automatic Mechanism Generation. Fuel 264, 116711. doi:10.1016/j.fuel.2019.116711

CrossRef Full Text | Google Scholar

Carbon capture needed for expansion, (2020). World-first Hydrogen Tanker Arrives in Victoria to Test Potential for Exporting Fuel Source to Japan - ABC News. Available at: https://www.abc.net.au/news/2022-01-21/world-first-hydrogen-tanker-docks-at-port-of-hastings/100769138.

Google Scholar

Carbon4Pur, (2020). CO2/CO Sources and Consumer Map. Available at: https://carbon4pur.github.io/mapping/index.html.

Google Scholar

Carmo, M., and Fritz, D. L. (2013). A Comprehensive Review on PEM Water Electrolysis. Hydrog. Energy 8. doi:10.1016/j.ijhydene.2013.01.151

CrossRef Full Text | Google Scholar

Cerniauskas, S., Grube, T., Praktiknjo, A., Stolten, D., and Robinius, M. (2019). Future Hydrogen Markets for Transportation and Industry: The Impact of CO2 Taxes. Energies 12. doi:10.3390/en12244707

CrossRef Full Text | Google Scholar

ChemAnalyst. Ammonia Price, (2020). Prices, Pricing, Market & Analysis. Available at: https://www.chemanalyst.com/Pricing-data/ammonia-37.

Google Scholar

Chiuta, S., Engelbrecht, N., Human, G., and Bessarabov, D. G. (2016). Techno-economic Assessment of Power-To-Methane and Power-To-Syngas Business Models for Sustainable Carbon Dioxide Utilization in Coal-To-Liquid Facilities. J. CO2 Util. 16, 399–411. doi:10.1016/j.jcou.2016.10.001

CrossRef Full Text | Google Scholar

Christensen, A. (2020). Assessment of Hydrogen Production Costs from Electrolysis: United States and Europe. Berlin: International Council on Clean Transportation. Available at: https://theicct.org/sites/default/files/publications/final_icct2020_assessment_of _hydrogen_production_costs v2.pdf.

Google Scholar

Christopher, K., and Dimitrios, R. (2012). A Review on Exergy Comparison of Hydrogen Production Methods from Renewable Energy Sources. Energy Environ. Sci. 5, 6640–6651. doi:10.1039/c2ee01098d

CrossRef Full Text | Google Scholar

Collis, J., Strunge, T., Steubing, B., Zimmermann, A., and Schomäcker, R. (2021). Deriving Economic Potential and GHG Emissions of Steel Mill Gas for Chemical Industry. Front. Energy 9, 1–22. doi:10.3389/fenrg.2021.642162

CrossRef Full Text | Google Scholar

De-León Almaraz, S., Azzaro-Pantel, C., Montastruc, L., Pibouleau, L., and Senties, O. B. (2012). Design of a Hydrogen Supply Chain Using Multiobjective Optimisation. Comput. Aided Chem. Eng. 30, 292–296.

Google Scholar

Dufour, J., Serrano, D. P., Gálvez, J. L., Moreno, J., and González, A. (2011). Hydrogen Production from Fossil Fuels: Life Cycle Assessment of Technologies with Low Greenhouse Gas Emissions. Energy fuels. 25, 2194–2202. doi:10.1021/ef200124d

CrossRef Full Text | Google Scholar

Element Energy Ltd (2018). Hydrogen Supply Chain Evidence Base.

Google Scholar

Energy Transition Institute, (2014). Hydrogen-based Energy Conversion.

Google Scholar

Esmap. Global, (2020). Photovoltaic Power Potential by Country. Washington, DC: World Bank. Available at: https://globalsolaratlas.info/global-pv-potential-study.

Google Scholar

Fortune Business Insights (2020). Ammonia Market Size & Share | Industry Analysis Report. Available at: https://www.fortunebusinessinsights.com/industry-reports/ammonia-market-101716.

Google Scholar

FuelCellsWorks. Australia, (2020). Japan Sign Hydrogen Agreement: An Exciting Step towards Hydrogen Future. Available at: https://fuelcellsworks.com/news/australia-japan-sign-hydrogen-agreement-an-exciting-step-towards-hydrogen-future/.

Google Scholar

GeoPy Contributors (2018). GeoPy. Available at: https://geopy.readthedocs.io/en/stable/#.

Google Scholar

Gerbelová, H. (2014). Potential of CO2 (Carbon Dioxide) Taxes as a Policy Measure towards Low-Carbon Portuguese Electricity Sector by 2050. Energy 69, 113–119.

Google Scholar

Germeshuizen, L. M., and Blom, P. W. E. (2013). A Techno-Economic Evaluation of the Use of Hydrogen in a Steel Production Process, Utilizing Nuclear Process Heat. Int. J. Hydrogen Energy 38, 10671–10682. doi:10.1016/j.ijhydene.2013.06.076

CrossRef Full Text | Google Scholar

Giddey, S., Badwal, S. P. S., Munnings, C., and Dolan, M. (2017). Ammonia as a Renewable Energy Transportation Media. ACS Sustain. Chem. Eng. 5, 10231–10239. doi:10.1021/acssuschemeng.7b02219

CrossRef Full Text | Google Scholar

Gielen, D., Taibi, E., and Miranda, R. (2019). Hydrogen: A Renewable Energy Perspective. Abu Dhabi: International Renewable Energy Agency. Available at: www.irena.org.

Google Scholar

Gim, B., and Yoon, W. L. (2012). Analysis of the Economy of Scale and Estimation of the Future Hydrogen Production Costs at On-Site Hydrogen Refueling Stations in Korea. Int. J. Hydrogen Energy 37, 19138–19145. doi:10.1016/j.ijhydene.2012.09.163

CrossRef Full Text | Google Scholar

Glenk, G., and Reichelstein, S. (2019). Economics of Converting Renewable Power to Hydrogen. Nat. Energy 4, 216–222. doi:10.1038/s41560-019-0326-1

CrossRef Full Text | Google Scholar

Green, R., and Vasilakos, N. (2011). The Economics of Offshore Wind. Energy Policy 39, 496–502. doi:10.1016/j.enpol.2010.10.011

CrossRef Full Text | Google Scholar

Halpern, B., Frazier, M., Potapenko, J., Casey, K., and Koenig, K. (2015). Cumulative Human Impacts: Raw Stressor Data (2008 and 2013). Knowl. Netw. Biocomplexity. doi:10.5063/F1S180FS

CrossRef Full Text | Google Scholar

Han, J.-H., Ryu, J.-H., and Lee, I.-B. (2012). Modeling the Operation of Hydrogen Supply Networks Considering Facility Location. Int. J. Hydrogen Energy 37, 5328–5346. doi:10.1016/j.ijhydene.2011.04.001

CrossRef Full Text | Google Scholar

Hawer, S., Schönmann, A., and Reinhart, G. (2018). Guideline for the Classification and Modelling of Uncertainty and Fuzziness. Procedia CIRP 67, 52–57. doi:10.1016/j.procir.2017.12.175

CrossRef Full Text | Google Scholar

Ho, M. T., Bustamante, A., and Wiley, D. E. (2013). Comparison of CO2 Capture Economics for Iron and Steel Mills. Int. J. Greenh. Gas Control 19, 145–159. doi:10.1016/j.ijggc.2013.08.003

CrossRef Full Text | Google Scholar

Hoffmann, C., Hoey, M. Van., and Zeumer, B. (2020). Decarbonization Challenge for Steel. Available at: https://www.mckinsey.com/∼/media/McKinsey/Industries/Metals and Mining/Our Insights/Decarbonization challenge for steel/Decarbonization-challenge-for-steel.pdf.

Google Scholar

Hörsch, J., and Brown, T. (2017). The Role of Spatial Scale in Joint Optimisations of Generation and Transmission for European Highly Renewable Scenarios. Frankfurt am Main: Frankfurt Institiute for Advanced Studies.

Google Scholar

Hwangbo, S., Lee, I.-B., and Han, J. (2017). Mathematical Model to Optimize Design of Integrated Utility Supply Network and Future Global Hydrogen Supply Network under Demand Uncertainty. Appl. Energy 195, 257–267. doi:10.1016/j.apenergy.2017.03.041

CrossRef Full Text | Google Scholar

Hydrogen Council (2021). Hydrogen Insights. Available at: https://hydrogencouncil.com/wp-content/uploads/2021/02/Hydrogen-Insights-2021.pdf.

Google Scholar

Hydrogen Council (2020). Path to Hydrogen Competitiveness: A Cost Perspective. Available at: ww.hydrogencouncil.com.

Google Scholar

Iea. Global, (2017). Energy and CO2 Status Report. Paris: IEA.

Google Scholar

International Energy Agency, (2020). Iron and Steel Technology Roadmap. Paris: Iron and Steel Technology Roadmap. doi:10.1787/3dcc2a1b-en

CrossRef Full Text | Google Scholar

International Energy Agency, (2019). The Future of Hydrogen. Paris: The Future of Hydrogen. doi:10.1787/1e0514c4-en

CrossRef Full Text | Google Scholar

International Renewable Energy Agency, (2020). Green Hydrogen Cost Reduction. Available at: https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2020/Dec/IRENA_Green_hydrogen_cost_2020.pdf.

Google Scholar

International Renewable Energy Agency, (2016). The Power to Change: Solar and Wind Cost Reduction Potential to 2025.

Google Scholar

International Renewable Energy Agency, (2012). Wind Power, 20. Abu Dhabi: Green Energy and Technology.

Google Scholar

International Transport Forum, (2019). ITF Transport Outlook 2019. Paris: OECD. doi:10.1787/transp_outlook-en-2019-enITF Transport Outlook 2019

CrossRef Full Text | Google Scholar

Jäger-Waldau, A. (2019). PV Status Report 2019. Brussels: JRC Science for Policy Report. doi:10.2760/326629

CrossRef Full Text | Google Scholar

Jarraud, M., and Steiner, A. (2014). Climate Change 2014: Synthesis Report. Geneva: IPCC, 9781107025.

Google Scholar

Kaiwen, L., Bin, Y., and Tao, Z. (2018). Economic Analysis of Hydrogen Production from Steam Reforming Process: A Literature Review. Energy Sources, Part B Econ. Plan. Policy 13, 109–115. doi:10.1080/15567249.2017.1387619

CrossRef Full Text | Google Scholar

Kakoulaki, G., Kougias, I., Taylor, N., Dolci, F., Moya, J., and Jäger-Waldau, A. (2021). Green Hydrogen in Europe - A Regional Assessment: Substituting Existing Production with Electrolysis Powered by Renewables. Energy Convers. Manag. 228, 113649. doi:10.1016/j.enconman.2020.113649

CrossRef Full Text | Google Scholar

Khojasteh Salkuyeh, Y., Saville, B. A., and MacLean, H. L. (2017). Techno-economic Analysis and Life Cycle Assessment of Hydrogen Production from Natural Gas Using Current and Emerging Technologies. Int. J. Hydrogen Energy 42, 18894–. doi:10.1016/j.ijhydene.2017.05.219

CrossRef Full Text | Google Scholar

Laguna-Bercero, M. A. (2012). Recent Advances in High Temperature Electrolysis Using Solid Oxide Fuel Cells: A Review. J. Power Sources 203, 4–16. doi:10.1016/j.jpowsour.2011.12.019

CrossRef Full Text | Google Scholar

Lahnaoui, A., Wulf, C., and Dalmazzone, D. (2021). Optimization of Hydrogen Cost and Transport Technology in france and germany for Various Production and Demand Scenarios. Energies 14. doi:10.3390/en14030744

CrossRef Full Text | Google Scholar

Leimkühler, H. (2010). Managing CO2 Emissions in the Chemical Industry. Wiley-VCH Verlag GmbH & Co. KGaA.

Google Scholar

Levene, J. I., Mann, M. K., Margolis, R. M., and Milbrandt, A. (2007). An Analysis of Hydrogen Production from Renewable Electricity Sources. Sol. Energy 81, 773–780. doi:10.1016/j.solener.2006.10.005

CrossRef Full Text | Google Scholar

Lindstad, E., Lagemann, B., Rialland, A., Gamlem, G. M., and Valland, A. (2021). Reduction of Maritime GHG Emissions and the Potential Role of E-Fuels. Transp. Res. Part D Transp. Environ. 101, 103075. doi:10.1016/j.trd.2021.103075

CrossRef Full Text | Google Scholar

Mapbox. Rasterio, (2016). Access to Geospatial Raster Data. Available at: https://rasterio.readthedocs.io/en/latest/index.html.

Google Scholar

Mayer, J. N., Philipps, S., Hussein, N. S., Schlegl, T., and Senkpiel, C. (2015). Current and Future Cost of Photovoltaics Long-Term Scenarios for Market Development, 82.

Google Scholar

Modisha, P. M., Ouma, C. N. M., Garidzirai, R., Wasserscheid, P., and Bessarabov, D. (2019). The Prospect of Hydrogen Storage Using Liquid Organic Hydrogen Carriers. Energy Fuels 33. doi:10.1021/acs.energyfuels.9b00296

CrossRef Full Text | Google Scholar

Muradov, N. (2017). Low to Near-Zero CO2 Production of Hydrogen from Fossil Fuels: Status and Perspectives. Int. J. Hydrogen Energy 42, 14058–14088. doi:10.1016/j.ijhydene.2017.04.101

CrossRef Full Text | Google Scholar

Net Zero Tracker, (2021). Hydrogen Production from Renewables - Renewable Energy Focus. Available at: http://www.renewableenergyfocus.com/view/3157/hydrogen-production-from-renewables/.

Google Scholar

NetworkX Developers, (2014). NetworkX — NetworkX Documentation. Available at: https://networkx.org/.

Google Scholar

Novikov, A. (2019). Creating Sea Routes from the Sea of AIS Data. Towards Data Sci. Available at: https://towardsdatascience.com/creating-sea-routes-from-the-sea-of-ais-data-30bc68d8530e.

Google Scholar

Oberle, W. (2015). Monte Carlo Simulations: Number of Iterations and Accuracy. Adelphi: US Army Research Laboratory.

Google Scholar

Office of Energy Efficiency and Renewable Energy, (2020). Hydrogen Production Processes. Washington, D.C.: US Department of Energy. Available at: https://www.energy.gov/eere/fuelcells/hydrogen-production-processes.

Google Scholar

Osrm, (2020). Project OSRM. Available at: http://project-osrm.org/.

Google Scholar

Parks, G., Boyd, R., Cornish, J., and Remick, R. (2014). Hydrogen Station Compression, Storage, and Dispensing Technical Status and Costs: Systems Integration. Golden: National Renewable Energy Laboratory Publication. Available at: http://www.osti.gov/scitech//servlets/purl/1130621/.

Google Scholar

Preuster, P., Papp, C., and Wasserscheid, P. (2016). Liquid Organic Hydrogen Carriers (LOHCs): Toward a Hydrogen-free Hydrogen Economy. Acc. Chem. Res. 50, 74–85. doi:10.1021/acs.accounts.6b00474

PubMed Abstract | CrossRef Full Text | Google Scholar

Rashid, M. M., Mesfer, M. K. Al., Naseem, H., and Danish, M. (2015). Hydrogen Production by Water Electrolysis: A Review of Alkaline Water Electrolysis, PEM Water Electrolysis and High Temperature Water Electrolysis. Int. J. Eng. Adv. Technol., 2249–8958.

Google Scholar

Rogelj, J. (2018). Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development. Special Report. Geneva: Intergovernmental Panel on Climate Change. Available at: https://www.ipcc.ch/site/assets/uploads/sites/2/2019/02/SR15_Chapter2_Low_Res.pdf.

Google Scholar

Scheelhaase, J., Maertens, S., and Grimme, W. (2019). Synthetic Fuels in Aviation - Current Barriers and Potential Political Measures. Transp. Res. Procedia 43, 21–30. doi:10.1016/j.trpro.2019.12.015

CrossRef Full Text | Google Scholar

Shiva Kumar, S., and Himabindu, V. (2019). Hydrogen Production by PEM Water Electrolysis - A Review. Mater. Sci. Energy Technol. 2, 442–454. doi:10.1016/j.mset.2019.03.002

CrossRef Full Text | Google Scholar

Spath, P. L., Mann, M. K., and Mann, M. K. (2001). Life Cycle Assessment of Hydrogen Production via Natural Gas Steam Reforming.

Google Scholar

Steilen, M., and Jörissen, L. (2015). Electrochemical Energy Storage for Renewable Sources and Grid Balancing, 143–158. doi:10.1016/B978-0-444-62616-5.00010-3Hydrogen Conversion into Electricity and Thermal Energy by Fuel Cells

CrossRef Full Text | Google Scholar

Taylor, A. (2021). Japan-Australia Partnership on Decarbonisation through Technology. Minist. Dep. Industry, Sci. Energy Resour. Available at: https://www.minister.industry.gov.au/ministers/taylor/media-releases/japan-australia-partnership-decarbonisation-through-technology.

Google Scholar

Technical University of Denmark, (2021). Global Wind Atlas. Available at: https://globalwindatlas.info.

Google Scholar

Ueckerdt, F., Bauer, C., Dirnaichner, A., Everall, J., Sacchi, R., and Luderer, G. (2021). Potential and Risks of Hydrogen-Based E-Fuels in Climate Change Mitigation. Nat. Clim. Chang. 11, 384–393. doi:10.1038/s41558-021-01032-7

CrossRef Full Text | Google Scholar

Wang, Y., Li, W., Ma, L., Li, W., and Liu, X. (2020). Degradation of Solid Oxide Electrolysis Cells: Phenomena, Mechanisms, and Emerging Mitigation Strategies-A Review. J. Mater. Sci. Technol. 55, 35–55. doi:10.1016/j.jmst.2019.07.026

CrossRef Full Text | Google Scholar

Wijayanta, A. T., Oda, T., Purnomo, C. W., Kashiwagi, T., and Aziz, M. (2019). Liquid Hydrogen, Methylcyclohexane, and Ammonia as Potential Hydrogen Storage: Comparison Review. Int. J. Hydrogen Energy 44, 15026–15044. doi:10.1016/j.ijhydene.2019.04.112

CrossRef Full Text | Google Scholar

Wilberforce, T., El-Hassan, Z., Khatib, F. N., Al Makky, A., Baroutaji, A., Carton, J. G., et al. (2017). Developments of Electric Cars and Fuel Cell Hydrogen Electric Cars. Int. J. Hydrogen Energy 42, 25695–25734. doi:10.1016/j.ijhydene.2017.07.054

CrossRef Full Text | Google Scholar

Woo, Y.-B., and Kim, B. S. (2019). A Genetic Algorithm-Based Matheuristic for Hydrogen Supply Chain Network Problem with Two Transportation Modes and Replenishment Cycles. Comput. Industrial Eng. 127, 981–997. doi:10.1016/j.cie.2018.11.027

CrossRef Full Text | Google Scholar

Zang, G., Sun, P., Elgowainy, A. A., Bafana, A., and Wang, M. (2021). Performance and Cost Analysis of Liquid Fuel Production from H2 and CO2 Based on the Fischer-Tropsch Process. J. CO2 Util. 46, 101459. doi:10.1016/j.jcou.2021.101459

CrossRef Full Text | Google Scholar

Zhao, Y., McDonell, V., and Samuelsen, S. (2019). Influence of Hydrogen Addition to Pipeline Natural Gas on the Combustion Performance of a Cooktop Burner. Int. J. Hydrogen Energy 44, 12239–12253. doi:10.1016/j.ijhydene.2019.03.100

CrossRef Full Text | Google Scholar

Nomenclature

Abbreviations

AE Alkaline

CapEx Capital expenditures

CCS Carbon capture and storage

GHG Greenhouse gas

GPS Global positioning system

LCOE Levelized cost of electricity

LCOH Levelized cost of hydrogen

LH₂ Liquid hydrogen

LOHC Liquid organic hydrogen carriers

OpEx Operational expenditures

PEM Proton-exchange membrane

PV Photovoltaic

SMR Steam methane reforming

SOE Solid oxide electrolysis