Insights into trade-offs encountered when designing a green hydrogen supply chain

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Master Supply Chain Management

Focus area: energy

Master thesis

Insights into trade-offs encountered when

designing a green hydrogen supply chain

From an offshore wind farm to different demand categories on land

D.J. Nijnens – S3214435

Supervision university:

Prof. Dr. Ir. J.C. Wortmann

Dr. M.J. Land

Supervision field of study:

Mr. H. Zwetsloot MSc

June 22nd 2020

joeynijnens@student.rug.nl Acknowledgement

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Abstract

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1. Introduction

The 2015 Paris agreements set the goal to achieve a reduction of greenhouse gas emissions to keep global warming under 2°C (Fawcett et al., 2015). All targets in the European Commission’s ‘2030 climate & energy framework’ include the use of renewable energy sources (RES) (2030 Climate & Energy Framework, 2020; Rogelj et al., 2016). A challenging characteristic of these sources is the intermitted character of sunshine and wind which causes fluctuating electricity output (Mueller, 2016). In combination with the use of RES, hydrogen is a promising energy carrier when aiming to reduce greenhouse gasses due to the lack of CO2 or toxic gas formation in its use (Chaubey et al., 2013).

The process of electrolysis with RES as feedstock has green hydrogen as output (Maggio et al., 2019). This green method of producing hydrogen ensures that the global warming potential of the process is very low (Baykara, 2018). In this thesis, the RES used for electrolysis is offshore wind. Due to the use of RES, the operation of a green hydrogen plant is by definition intermittent (Troncoso & Newborough (2010). The electrolysis can take place close to the offshore wind park or on land. In the first situation, the energy is transported to land as a gas through (existing) pipelines and in the second situation, it is transported as electricity through a cable (Mulder et al., 2019). The electrolysers that form the total capacity can be set up in modules of certain sizes (Muyeen et al., 2011). The hydrogen produced by a system as mentioned above can be used for multiple end purposes such as heating, transport, feedstock for industry and electricity generation (Rooijers, 2017).

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energy transportation infrastructures are not yet clear for all the projects. The different options for these supply chain design questions deserve a thorough study due to their possible implications for the rest of the supply chain in combination with the rare scale and level of integration of these projects.

From the status review by Thema et al. (2019) can be concluded that a considerable amount of hydrogen projects have been analyzed, but there is a need to add new long term and high capacity cases to this literature. The current hydrogen literature, based on mathematical modeling, does lack research which sees the hydrogen supply chain as a part of the integrated energy system. A move towards this integration is an important research direction (Li et al., 2019). The energy system analyzed in this research will stretch from the RES offshore wind power up to the different demand categories for hydrogen. The specific trade-offs that make this research interesting from a supply chain management perspective become apparent in the implications of supply chain characteristics on hydrogen output and storage needs. Energy flow restrictions that are part of a chosen supply chain configuration such as energy transport capacity or electrolyser capacity seem to have a trade-off with the amount of hydrogen produced. Secondly, the trade-off might exist between the volume produced and the variability in hydrogen output. These trade-offs are also likely to have implications for the storage needs to match the demand for hydrogen. When combining the previously mentioned characteristics, the following research questions arise:

What are the trade-offs between supply chain characteristics restricting energy flow and hydrogen output?

What are the trade-offs between hydrogen output and the needed storage capacity to match demand?

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results are presented, in the fifth section these results are discussed and in the last section a conclusion will be formulated.

2. Literature review

A wide array of green hydrogen literature can be found that is based on simulation or optimization research methods. Li et al. (2019) and De-León Almaraz et al. (2015) present literature reviews indicating relevant previous studies modeling and designing hydrogen infrastructures. They make clear that existing models are based on several variables. Examples of these variables are energy supply for hydrogen production, hydrogen demand, energy transportation features and analysis of future development. The scope of these models was regional, national and both autonomous and grid-connected. There is a lack of research into the subject of hydrogen supply chains as part of entire energy systems. From this gap in the research arises the need for analysis of the effects of the characteristics of points where successive supply chains meet. They are located where the electrolyser is supplied with electricity from a RES and where the electrolyser supplies to end-users (Li et al., 2019). In this case, the end-users will be composed out of the expected hydrogen user categories in the area.

2.1 Wind energy

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the southern North Sea. When looking at the average Dutch capacity factors per month for the year 2017 reported by ‘Wind Europe’, fluctuations per month can be found. The factors differ between approximately 26% and 61% (Remy & Mbistrova, 2018). When analyzing the power output of a wind farm based on wind speeds, it is important to transfer the wind speed to the hub height of the turbines (Tambke et al., 2005; Vincent et al., 2010). The power generation curves of these turbines start at the cut-in speed of the wind and end at the cut-out speed of the wind. The rated speed is the speed between the cut-in and cut out points at which the turbine generates maximum power (Manwell et al., 2010).

2.2 Hydrogen production

Renewable electricity such as the previously discussed electricity from wind power can be converted from power into gas using an electrolyser system (Gahleitner, 2013). In the context of energy collection for the use of electrolysis, the availability of RES is a possible resource constraint for a hydrogen economy based on renewable electricity sources (Kleijn & Van Der Voet, (2010). Production methods for hydrogen can be divided into three categories, blue, grey and green hydrogen production. Grey hydrogen production methods use non-renewable energy sources. Blue methods are similar to grey methods, except that the harmful byproducts from grey hydrogen production are captured and stored. Green hydrogen can be generated from renewable resources in the process of electrolysis using wind or solar energy (Abdalla et al., 2018; Dincer, 2012). The production method considered in this thesis is green hydrogen production. This green power to gas electrolysis process has currently an efficiency of 54-72 % (Azzaro-Pantel, 2018). According to Troncoso & Newborough (2010), the operation of the green hydrogen plant is by definition intermittent because of the use of RES.

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optimum results in more moments when a full load for the electrolyser is achieved. For the future, three types of electrolysers are considered. These types are alkaline water electrolysis, PEM (polymer electrolyte membrane) and SOEC (solid oxide electrolyser cell). In general, these devices are reliable and do not require continuous maintenance because they hardly include mobile elements (Ursúa et al., 2011). All different types of electrolysers can handle load changes. This does not mean that load changes do not have consequences. The load changes can affect the operation of the electrolysers negatively (Azzaro-Pantel, 2018; Bourasseau & Guinot, 2015). Due to the negative effects in a variable load operation, a choice can be made to work with various smaller units (‘modules’) that only work at their rated load (Muyeen et al., 2011). More information on modularity and variable load operation is presented in appendix B.

2.3 Hydrogen supply and demand balance

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depleted gas fields as gas storage demonstrates the possible future feasibility of their use. It has to be noted that there is no experience with pure hydrogen in depleted gas fields. For the continuous and safe operation of such storage means, further investigation is needed. When this technology could be deployed in the future, the average working gas volumes could range between one million m3 to several thousand million m3 (Kruck et al., 2013). Another solution would be the import and export of hydrogen when globally a sufficiently large volume of hydrogen is produced (Wijk, 2017). Jepma et al. (2019) describe scenarios where in case of short supply, the hydrogen is imported and in case of a surplus of hydrogen, the hydrogen is exported. To ensure the security of the supply, one can choose to establish responsive backup suppliers (Sting & Huchzermeier, 2010). During the simulation, the demand and supply imbalance is measured over time and based on the case the storage needs will be related to a salt cavern storage solution.

2.4 End-uses

Green hydrogen and hydrogen in general can be used for different purposes. In this research, the purposes of feedstock for industry, transport, electricity and heating will be considered. The selection of these purposes is argued in the methodology section.

2.4.1. Hydrogen as feedstock for industry: Currently, produced hydrogen is mostly used as a

feedstock for industry. It is used among other things in oil refining and ammonia production for fertilizers. (Durville et al., 2015).

2.4.2. Hydrogen for transportation: Alongside biofuel and electric vehicles, hydrogen is one

of three options for low carbon transport. (Brandon & Kurban, 2017). Especially in sectors with greater vehicle weight and driving range, the use of hydrogen seems more suitable than the use of batteries. There are already hydrogen passenger cars commercially available, but they are still expensive. The distribution of hydrogen refueling stations is essential for these cars to achieve market penetration (Alazemi & Andrews, 2015; Staffell et al., 2019).

2.4.3. Hydrogen for electricity & heating: The electricity that is converted to hydrogen and

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From the information in this literature review can be concluded that the supply chain factors analyzed in this review have properties that could influence the output volume of the electrolysers, the electrolyser output variance and the resulting storage needs for hydrogen. These influencing properties are due to the proven fluctuations in offshore wind energy, the advantages and disadvantages of the electrolyser location, the electrolyser properties such as rated capacity and modularity and end-user demand profiles. The next section will explain the method used to conduct this research.

3. Method

This section explains the methods used to answer the research questions of this thesis on the trade-offs between supply chain characteristics restricting energy flow, hydrogen output and the needed storage capacity to match demand.

3.1. Type of research

The research method applied in this thesis is a simulation-based quantitative study. This is the right type of research method for this thesis because of the exploratory and predictive character regarding operational processes within the research objectives (Karlsson, 2016). The choice for a simulation-based quantitative study was made because of the interconnectedness of the model and stochastic characteristics of the model inputs. The use of simulation models is explicitly suitable to represent interconnectedness and variability in a system (Robinson, 2004). Due to the aforementioned characteristics of this model, the use of a simulation-based model with heuristics is widely accepted, according to Karlsson (2016). The use of simulation will make it possible to determine the effect of alternative policies on system performance, to compare alternative system designs and to predict system performance (Robinson, 2004).

3.2. Case selection and situational overview

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decided upon. The electrolyser can be located at sea near an offshore wind farm or on land. There is also a possibility for electrolyser capacity both at sea and on land. The electrolyser capacity is fed with electricity generated by the offshore wind farm at the North Sea with a given capacity of 10 GW. The wind electricity generated will either be transformed into hydrogen at sea and transported via partly existing pipeline connections to the seaport, or the electricity is transported by cable to land where it is transformed into hydrogen. The amounts of hydrogen produced are dependent on the character of the wind, offshore energy transport capacity and the capacity of the electrolyser. The hydrogen produced by the electrolysers will be consumed for different end-uses. Since supply and demand for hydrogen are likely to be out of balance, where possible the amount of hydrogen that cannot be used or cannot be supplied needs to be stored or supplied from storage respectively.

This case has been selected because of the clear plans for the future development of the region and the fact that a complete hydrogen supply chain from the offshore wind farm to end-user is implemented within this compact case. The elements in this case illustrate the typical questions future energy transition initiatives will encounter. The case stays in the scope of reality and incorporates integrations with the broader energy system at the hydrogen demand side.

3.3. Experimental design

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Figure 3.1. Model used in simulation: 1 Offshore wind, 2 Windfarm, 3 Offshore energy

transportation, 4 Electrolyser capacity, 5 Electrolyser output, 6 Hydrogen supply and demand balance/storage, 7 Hydrogen use.

3.3.1. Offshore wind

The wind at the North Sea is the energy source of the hydrogen supply chain. The exact location of the electricity generation is yet unclear, but (Geijp, 2020) indicates that a location near the ‘Gemini’ wind farm is likely. The input for the model is the average wind speed per hour (m/s) at the ‘Gemini’ wind farm at a height of 10 meters in the year 2019 (KNMI, n.d.). The wind speed at the North Sea needs to be measured at the hub height at which the wind farm generates power. Since these wind speeds are not accessible, the power-law wind profile as described by Hsu et al. (1994) will be applied to calculate the speed of the wind at the hub height of the turbines, i.e. at a height of 167.9 meters (Jensen et al., 2017).

3.3.2. Windfarm output

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3.3.3. Offshore energy transportation

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3.3.4. Electrolyser capacity

In the NortH2 project no decision has been made on the configuration and size of the electrolyser capacity. The electrolyser capacity in the model is based on two variables in this thesis. The first variable is the size of the different electrolyser modules. The different values for this variable are modules of 20 MW (currently technologically feasible based upon Gasunie (2020)) and modules of 50 MW (feasible due to high technological development (interviewee 3)). These modules will always run on rated capacity and an extra module will only be added when it can run at rated capacity to preserve efficiency and lifespan. The second variable is the total number of electrolyser modules that form the electrolysis capacity. This capacity is concluded from an analysis of the wind data to make sure the chosen electrolyser capacities in percentage of the wind farm capacity yield comparable results for the research. Based on this analysis, electrolyser capacities of approximately 34, 87 and 100% of the wind farm capacity were chosen. The model assumes that the electrolysers will only run on the renewable electricity from a dedicated North Sea wind park as is described by the NortH2 project (Reuters, 2020).

3.3.5. Electrolyser hydrogen output

The value of this variable in the model is the actual hydrogen output in kg hydrogen. The model converts 0.0333 MWh of electricity into 1 kg of hydrogen before efficiency losses. This output is constrained by the capacity of the electrolyser, the generated electricity from the wind turbines, the type of electrolyser, the corresponding efficiency and the capacity of the mode of transport through the sea. The used electrolyser type is an alkaline electrolyser with an efficiency at rated capacity in 2030 of 71% (IEA, 2019). This electrolyser type is chosen based upon the consultation of interviewee 2.

3.3.6. Hydrogen supply and demand balance/storage

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3.3.7. Hydrogen demand

The value of the demand variable in the model is the expected demand of hydrogen per hour. This consists of an expected hydrogen demand scenario for the Northern Netherlands in 2040. The scenario is built up out of expected hydrogen demand categories with an accompanied share of the total hydrogen demand as displayed in figure 3.2. The categories are feedstock for industry, transportation, electricity, low-temperature heating and high-temperature heating. These categories are selected from reports on the expected Dutch energy supply and hydrogen economy in 2050 (Jepma et al., 2019; Rooijers, 2017). The selected categories were chosen based upon the ‘Investment agenda hydrogen northern Netherlands’ (SNN, 2019). The

information on the hourly demand profiles per category is presented in appendix E.

Figure 3.2. Percentage of total demand for hydrogen per category (Jepma et al., 2019)

Since the author sees no added value for the research objective in using absolute demand forecasts and to make the model not unnecessarily complex, the assumption is made that the yearly cumulative demand is equal to the yearly cumulative supply.

3.4. Scenarios

The different supply chain configuration scenarios are determined by variations in the electrolyser location, offshore energy transportation (OET), electrolyser module sizes and electrolyser capacities (EL) in percentages of the total wind farm capacity. The different variations for these variables are visible in table 3.1.

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

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Table 3.1. Experimental variables Experimental variables Variations Electrolyser location Land

Sea

Dual location (DL)

Offshore energy transport (OET) Cable 7200MW Cable 10000MW Pipeline

Cable 3000MW & pipeline

Electrolyser module size 20MW 50MW

Electrolyser capacity % (EL) 33.96% 87.27% 100%

OET = offshore energy transport, EL = Electrolyser capacity in percentage of wind farm capacity

The combination of these variables results in 24 scenarios as can be seen in table 3.2. Table 3.2. also shows the six groups that appear based on the type of energy flow restrictions in the scenarios. Within these groups, the implications for energy flow through the simulation are the same, but the module sizes can be 20 MW or 50MW.

Table 3.2. Scenario and group overview Scenario Group name Electrolyser

location Offshore energy transport (OET) Electrolyser module size Electrolyser capacity % (EL) 1 1. Limited OET &

Low EL

Land 7200 MW 20 MW 34

2 2. Limited OET & Middle EL

Land 7200 MW 20 MW 87

3 3. Limited OET & High EL Land 7200 MW 20 MW 100 4 4. Unlimited OET & Low EL Land 10000 MW 20 MW 34 5 Sea Pipeline 20 MW 34

6 DL Pipeline & cable 20 MW 34

7 5. Unlimited OET

& Middle EL

Land 10000 MW 20 MW 87

8 Sea Pipeline 20 MW 87

10 6. Unlimited OET & High EL

Land 10000 MW 20 MW 100

13 1. Limited OET & Low EL

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14 2. Limited OET & Middle EL

Land 7200 MW 50 MW 87

15 3. Limited OET & High EL Land 7200 MW 50 MW 100 16 4. Unlimited OET & Low EL Land 10000 MW 50 MW 34 17 Sea Pipeline 50 MW 34

19 5. Unlimited OET & Middle EL

Land 10000 MW 50 MW 87

22 6. Unlimited OET & High EL

Land 10000 MW 50 MW 100

OET = offshore energy transport, EL = Electrolyser capacity in percentage of wind farm capacity

3.5. Data analysis

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4. Results

The results from the simulation model will be displayed and analysed in this section. First, the results of the individual supply chain stages are presented. Second, the results regarding the whole supply chain, taking into account the different configurations (table 3.2), are presented. In this section, the effects of the scenarios on storage, electrolyser output and output variability will be discussed. The results of the sensitivity analysis will be shown in appendix I. The simulation is performed according to the scenarios as described in table 3.2. The scenarios within the defined groups (table 3.2) are represented in this analysis as being one. This generalization is possible because the energy flow restrictions in these scenarios have the same implications for the outcomes.

4.1. Results for individual supply chain stages

This first part of the results section presents the results of the individual supply chain stages. The stages windfarm, offshore energy transportation, electrolyser and the demand are discussed without consideration of energy flow restrictions caused by previous supply chain stages. These results will be described using the terminology from table 3.1. and group names from table 3.2.

4.1.1. Stage: Wind farm

The first supply chain stage discussed in this results section is the wind farm. The absolute hourly output of the wind farm is fluctuating heavily within days in all months. This is visible in figure 4.1. This figure is displaying the hourly energy output of the windfarm in MWh.

Figure 4.1. Output windfarm per hour in MWh

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The differences in average hourly output per month are visualized in figure 4.2. This figure displays that a significant difference in energy output exists per month and per season. From March until May, the electricity output decreases and from September until December the electricity output increases. The months February and November show lower average wind speeds than the other months in respectively winter and autumn.

Figure 4.2. Average hourly wind farm output per month in MWh

The wind farm does not always produce at full capacity. This is measured by calculating the capacity factor (CF, the ratio between the actual output and the potential output of the total capacity). This wind farm only generates electricity at full capacity for approximately 27% of the year (appendix G). The total electricity generated based upon the year 2019 is approximately 49 million MWh with an average hourly electricity generation of approximately 5595 MWh and a capacity factor of 0,56.

4.1.2. Stage: Offshore energy transportation

The electricity generated by the wind farm is transported over sea via the offshore energy transportation stage. If the electrolysers are based at sea, they do not encounter any limitations in this stage due to the hydrogen transport via pipelines. If the electrolysers are based on land, the electricity supply to the shore can be limited due to cable restrictions. The hourly effect of offshore electricity transport restrictions is visualized in figure 4.3. The blue curve shows an

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a maximum capacity of 7200 MW and the green curve shows the electricity transport with a maximum of 3000 MW. The maximum cable capacity of 3000 MW is described in the dual location scenario (3000 MW cable capacity combined with unlimited pipeline capacity). The result of these cable capacities is a situation where the 7200 MW cable reaches maximum capacity 56% of the time and where the 3000 MW cable reaches maximum capacity 83% of the time.

Figure 4.3. Hourly effect of cable restrictions

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The only configuration in the simulation where offshore energy transportation causes a need to curtail energy is the 7200 MW electricity cable configuration (Limited OET groups). This conclusion is formulated based on the assumption that there is no alternative use for the excess electricity at sea. The yearly curtailed amount of electricity is about 9.5 million MWh. This happens mostly in the winter months according to figure 4.4. The left vertical axis in figure 4.4. displays with the orange curve the curtailed electricity per month and the right vertical axis displays with the blue curve the curtailed electricity per day. The blue curve shows that the amount of electricity that cannot be transported also fluctuates highly on a daily basis. It can be concluded that more electricity is curtailed in winter than in summer, which is in line with the conclusions on the output profile of the wind farm (figure 4.2.).

Figure 4.4. configuration with 7200 MW cable capacity: electricity curtailed per day and per month

4.1.3. Stage: Electrolyser

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The yearly hydrogen output from the ‘unlimited’ groups in table 3.2. is visible in figure 4.5. This yearly hydrogen output of the groups is split into the results from electrolysers comprised of 50 MW (green bars) modules and 20 MW(orange bars) modules. Figure 4.5. illustrates clearly that the electrolyser module size does not have a big impact on the hydrogen output. The electrolyser capacities with a module size of 20MW produce slightly more due to their ability to add 20 or 40 MW worth of electrolyser capacity if supply allows, whereas the electrolysers with 50 MW modules do not have these possibilities. Since the difference between 20 MW modules and 50 MW modules is small compared to the total electrolyser capacity, adding both modules to the further analyses does not yield additional value for this research. The remaining part of the analyses will only focus on the 20MW module scenarios (scenarios 1-12).

Figure 4.5. Yearly hydrogen output comparison of different electrolyser configurations with unlimited

OET and electrolyser modules of 20MW or 50 MW

0 million 200 million 400 million 600 million 800 million 1000 million 1200 million

High EL Middle EL Low EL

Kg

h

2

supply chain configurations

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The group ‘Unlimited OET & Low EL’ produces 474 million Kg of hydrogen yearly ‘Unlimited OET & Middle EL’ produces 966 million Kg of hydrogen yearly and ‘Unlimited OET & High EL’ produces 1004 million Kg of hydrogen yearly. Figure 6 is used to visualise the course of the cumulative hydrogen output for the different groups. The grey curve representing ‘Unlimited OET & High EL’, shows more fluctuation than the blue curve representing ‘Unlimited OET & Low EL’. This indicates that there are fewer fluctuations in output for lower electrolyser capacities. The effect and quantification of this difference in fluctuations is explained in section 4.2.2. and 4.2.3.

Figure 4.6. Hourly cumulative output of hydrogen per group

0 million 200 million 400 million 600 million 800 million 1000 million 1200 million 1-1-19 3-12-19 5-21-19 7-30-19 10-8-19 12-17-19 Kg o f h yd ro ge n Time

Unlimited OET & Low EL and Limited OET & Low EL Unlimited OET & Middle EL

Unlimited OET & High EL

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The yearly output per MW of electrolysis capacity for the different groups is presented in figure 4.7. This makes it possible to compare the hydrogen output to the energy flow restriction of the different groups (in this case, the capacity of the electrolyser). The yellow bars (showing the unlimited OET groups) indicate that the electrolysers with a smaller capacity compared to the windfarm capacity produce more hydrogen per MWh of capacity. It is interesting to see that a higher electrolyser capacity will yield a higher absolute output but a lower output per MW installed electrolyser capacity.

Figure 4.7. Yearly output per MW of electrolyser capacity.

This paragraph concludes that smaller electrolyser modules create a better match with the supplied electricity. Smaller electrolyser sizes yield lower absolute hydrogen output but when the restriction to the energy flow increases, the hydrogen output relative to the energy flow restriction becomes higher.

0 20000 40000 60000 80000 100000 120000 140000 160000

Kg

H

2

EL configurations

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4.1.4. Demand

The last individual stage in this analysis is the demand stage. The hydrogen demand is built up of the categories: hydrogen for low-temperature heat, high-temperature heat, feedstock, mobility and electricity. The values of the categories with a constant demand are displayed as described in the methodology section. The fluctuating categories, hydrogen for electricity and low-temperature heat, have a specific profile shown by the yellow and pink curves in figure 4.8. The demand for hydrogen for low-temperature heat (pink curve) is partly dependent on the outside temperature. This causes a strong seasonal profile. The demand for hydrogen for electricity (yellow curve) is high when the availability of wind and solar power is low. This causes a pattern of more fluctuation in demand levels during winter compared to summer. The combined demand is shown by the orange curve.

Figure 4.8. Example of profile total and separate demand curves of all categories

0 10000 20000 30000 40000 50000 60000 70000 80000 90000 0 1000 2000 3000 4000 5000 6000 7000 8000 Kg H 2 Time

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4.2. Results regarding the whole supply chain

This second part of the results section presents the results regarding the whole supply chain. The effects of the groups (table 3.2.) on electrolyser output, output variability and storage will be discussed.

4.2.1. Electrolyser output

The electrolyser output of the groups with ‘Unlimited OET’ was discussed in the section on the electrolyser stage (3.1.3.) This section will analyse the results of the groups with ‘Limited OET’ and compare the 6 groups. Figure 4.9. and table 4.1. show the yearly hydrogen output for all groups. The green bars in figure 4.9. show the ‘Limited OET’ groups and the blue bars show the ‘Unlimited OET’ groups. figure 4.9. shows an identical yearly hydrogen output for multiple groups. This occurs when two groups both incorporate a supply chain stage of the same capacity that causes the biggest restriction to the flow of energy. Per group, the most restrictive stages are presented in table 4.1.

Figure 4.9. Yearly hydrogen output comparison supply chain configurations with 20 MW electrolyser

modules

0 million 200 million 400 million 600 million 800 million 1000 million 1200 million

Kg

h

2

supply chain configurations

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Table 4.1. Yearly hydrogen output and biggest energy flow restriction per group

When comparing all groups, the conclusion can be drawn that the configuration with the smallest energy flow restrictions will yield the highest total hydrogen output. Figure 4.10. visualizes the yearly hydrogen output of groups as well. In this figure, the vertical axis shows the yearly hydrogen output and the horizontal axis shows the biggest energy restrictions in the supply chain per group. The blue curve shows a relationship slightly decreasing in intensity. This observation is important because it shows that the effect of a higher energy flow restriction on the actual output of an electrolyser as described in section 4.1.3. is present but not very strong.

Figure 4.10. Graphical representation of the relationship between yearly hydrogen output and

biggest energy flow restriction per group

Group Yearly hydrogen

output

Yearly output per MW of electrolyser capacity

Biggest energy flow restriction in supply chain

Limited OET & High EL 843 million 84.3 thousand 7200 MW OET

Limited OET & Middle EL 843 million 96.7 thousand 7200 MW OET

Limited OET & Low EL 474 million 140.2 thousand 34% EL

Unlimited OET & High EL 1043 million 104.3 thousand No restriction

Unlimited OET & Middle EL 966 million 110.7 thousand 87% EL

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4.2.2. Output variability

Section 4.1.3. on the electrolyser stage describes a visible difference in variability in the course of the cumulative electrolyser output (figure 4.6.). Due to offshore sea transport and electrolyser capacity, the variability per hydrogen supply chain group changes throughout the supply chain stages. This is quantified by measuring the standard deviation of the hourly data as a percentage of the average output or throughput per hour for the stages: wind farm, offshore energy transportation and electrolyser. Figure 4.11. shows this information for the ‘Unlimited OET’ groups and figure 4.12. shows the same information for the ‘Limited OET’ groups. The different supply chain stages from the wind farm up to the electrolyser are displayed on the x-axis of these figures. Figure 4.11. shows with the yellow curve the ‘Unlimited OET & High EL’ group. The variability in the output of the wind farm is 71% and stays at that level up to and including the electrolyser stage. The dotted green curve shows the ‘Unlimited OET & Low EL’ group. The variability in this data declines to 53% at the electrolyser. In conclusion, the variability in the output data declines at smaller electrolyser capacities.

Figure 4.11. Unlimited OET: Standard deviation in percentage of the average output at stages in the

green hydrogen supply chain

50% 55% 60% 65% 70% 75% 1 2 3 % St D ev of a ver ag e

1: WF 2: Electricity cable restrictions 3: Electrolyser land/sea

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Figure 4.12. shows that the orange interrupted curve and the blue curve are following an identical pattern. These curves represent ‘Limited OET & High EL’ and ‘Limited OET & middle EL’. The two above mentioned groups have an identical pattern of variability for the same reason that they produce the same hydrogen output. The offshore energy transport capacity of these groups is lower than their electrolyser capacity. The black ‘Limited OET & Low EL’ curve follows a similar pattern as the orange and blue curve but declines even more towards the electrolyser because of its low electrolyser capacity. It can be concluded that when the restriction for offshore energy transport is stronger than the electrolyser capacity restriction, the electrolyser capacity will not influence the variability. When the electrolyser capacity is lower than the offshore energy restriction, the electrolyser capacity will be of influence.

Figure 4.12. Limited OET: Standard deviation in percentage of the average output at stages in the

green hydrogen supply chain

50% 55% 60% 65% 70% 75% 1 2 3 % St D ev of a ver ag e

1: WF 2: Electricity cable restrictions 3:Electrolyser land/sea

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From the variability results of the dual location scenarios, two main conclusions can be drawn. The land-based electrolysers with an energy priority over the sea-based electrolysers have very low variability. This is caused by their maximum transport and electrolyser capacity of 3000 MW in combination with their energy priority. The sea-based electrolysers have a very high variability because they are only supplied with electricity when the land-based electrolysers are running at full capacity. This is the case because the land-based electrolysers have priority for electricity over the sea-based electrolysers. This difference in fluctuation of energy supply is also visualised in figure 4.3. where the green curve shows the supply for land-based electrolysers and the remaining energy forms the supply for sea-based electrolysers. When the two flows of hydrogen are combined, the combined variability of the electrolysers at sea and on land is the same as for the other scenarios in the groups to which they belong (For further analysis of the dual location scenarios see appendix H). This section concludes that energy flow restrictions lower the variability in the energy flow considerably.

4.2.3. Storage needs

This last section of the results will elaborate on the storage needs. As we have seen in the previous sections, the 6 distinguished groups of scenarios lead to 4 different electrolyser outputs. Table 4.2. shows the needed storage to fulfill all demand at all times. The table also shows the storage needs as a percentage of the total yearly demand. Notice that the demand per group is different and that the yearly supply and demand within each group are equal.

Table 4.2. Absolute and relative storage need to satisfy demand at all times

Group Storage needed absolute in Kg Salt caverns needed Storage needed in % of total demand Total yearly demand

Unlimited OET & High EL

79 million 13 7.6% 1043 million

Unlimited OET & Middle EL

65 million 11 6.7% 966 million

Limited OET & High EL

Limited OET & Middle EL

49 million 8 5.9% 843 million

Limited OET & Low EL

Unlimited OET & Low EL

17 million 3 3.6% 474 million

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& Low EL’ have the lowest absolute and relative storage need. Most of the storage is built up

from September until May and the storage is largely drained in the summer as can be seen in figure 4.13. This figure shows the shortages and surpluses per hour for hydrogen cumulated over time, or in other words, the difference of the inventory level at some point in time compared to the starting inventory level. There are fluctuations in this pattern that differ per group. ‘Unlimited OET & High EL’ shows the biggest fluctuations in storage and ‘Limited

OET & Low EL’ and ‘Unlimited OET & Low EL’ show the smallest fluctuations in storage.

From this analysis, it can be concluded that the storage need in absolute and relative values is the highest when the electrolyser capacity is high in combination with no other restrictions to the energy flow. This need for storage is lower when the flow of energy through the supply chain is restricted.

Figure 4.13. Storage needs of scenarios with different results

The next section will relate the conclusions from this result section to the research questions as stated in the introduction. The following section will also discuss the applied research

-50 million -40 million -30 million -20 million -10 million 0 million 10 million 20 million 30 million 01-2019 03-2019 05-2019 06-2019 08-2019 10-2019 12-2019 Kg of h yd rog en Time

Limited OET & Middle EL and Limited OET & High EL Unlimited OET & High EL

Unlimited OET & Middle EL

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method, directions for further research, implications for practice and the contribution to the existing literature.

5. Discussion

This section will interpret and discuss the used research method and the results presented in the previous section. The insights presented in this discussion could contribute to green hydrogen supply chain research and in practice to the design of projects with a setup comparable to the situation in the Northern Netherlands. The answers formulated in this thesis will apply to hydrogen supply chains with configurations similar to the ones presented in this research.

5.1. Results in relation to the research question

The research questions stated in the introduction of this thesis can only be addressed when considering the entire supply chain. Based on this research, a trade-off can be defined between the supply chain characteristics restricting energy flow and the electrolyser output. A high restriction of the flow of energy to and through the electrolyser results in consequences for the hydrogen output volume as well as for the hydrogen output relative to the size of the energy flow restrictions in the supply chain and the variability of the electrolyser output. The research question on the trade-off between hydrogen output and the needed storage capacity is answered by looking at hydrogen output volume and variability. A high hydrogen output is consequently also very variable. This high output causes the absolute and relative storage need to be high, compared to a scenario with lower hydrogen output volumes. Table 4 summarizes the answer to the research questions. An important remark has to be made on the relationship between energy flow restrictions and hydrogen output (presented by the column ‘hydrogen output relative’). Although the results indicate an increasing relative hydrogen output the bigger the energy flow restriction gets, the relationship is shown to be only of limited strength.

Table 5.1. A comparison of supply chain configuration consequences on a yearly basis between a

hydrogen supply chain with low energy flow restrictions and high energy flow restrictions.

Energy flow restriction Hydrogen output absolute Hydrogen output relative * Variability electrolyser output Storage need Redundant energy

Low High Low High High Low

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From table 5.1. can be concluded that the research question elements of energy flow restrictions, hydrogen output and needed storage size are closely connected.

5.2. Implications of the main findings

This section elaborates on the implications of the findings for organisations such as the ones in the consortium leading the NortH2 project. The implications of the findings should be interpreted with the knowledge that this research is conducted considering a fixed wind farm capacity and while keeping the limitations presented in section 5.4. in mind. The following information could help practitioners during the feasibility study and the supply chain design. The theoretical implications are based on results from the simulation study, the theory in section 2 and the conducted interviews (appendix C).

5.2.1. hydrogen output and energy flow restricting stages

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5.2.2. Land or sea

The choice where to locate the electrolyser is likely to have bigger managerial implications than suggested by the results of the simulation. Interviewee 1 indicates that as long as there is cable capacity available for energy transport to land, it is most likely that the electrolyser will be located on land due to the high cost of offshore operations. When there is no cable capacity, the possibility to use existing pipelines in combination with an electrolyser at sea might become favourable. These pipelines could decrease the needed storage of hydrogen. It has to be noted that offshore operations come at a very high cost. In a situation where energy is redundant at sea, there is little chance of finding a purpose for the energy. When the electrolyser is located on land, there is a significant chance of high infrastructure costs because there is not yet any usable offshore energy transportation infrastructure present (interviewee 1 and 2). When the energy flow is restricted by the land-based electrolyser capacity, there is a higher possibility to find an alternative use for the redundant electricity in the system. This might improve the business case of a land-based electrolyser (Interviewee 2).

5.2.3. Implications storage

The second trade-off described in section 5.1 occurs between the electrolyser output and the needed storage size to match all demand. Consequently, when a practitioner aims for a high hydrogen output, a higher storage requirement in percentages of total yearly demand will be required to always satisfy the hourly demand. Next to storage, there is also the alternative to import or export hydrogen. This could affect the needed hydrogen storage. Interviewee 3 indicates that the more the supply of hydrogen is guaranteed by the use of storage, the higher the value of the business case becomes. The possibility exists that one general storage in a geographical cavity is unsuitable for the different demand categories (Interviewee 1, 2). This demand for different qualities of hydrogen can lead to a situation where other types of storage facilities are needed.

5.2.4. Directions for further research

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research a suggestion towards a favourable supply chain configuration can be given. The final configuration will likely aim to limit the variability in the output up to an equilibrium between hydrogen produced and needed infrastructure such as energy transportation capacity, electrolyser capacity and storage. Based on the available information, it seems that the most favourable location for the electrolyser is at sea, because of high needed energy transportation in projects similar to the case in this thesis.

5.3. Academic contribution

The results and conclusions presented in this thesis add to the literature on green hydrogen supply chains in multiple ways. A contribution is achieved by providing insight into the implications of energy flow restrictions while carrying out a high capacity green hydrogen project situated in the near future. The particular characteristics of the case discussed in this thesis: the unprecedented scale of the case analysed, the move towards integration with the energy system and the dedicated wind farm of fixed capacity, provide for an addition to the existing knowledge. The conclusions on the implications of energy flow restrictions in this particular case are an addition to the current knowledge by providing insight into the trade-offs that arise between different supply chain configurations and hydrogen output, output variability and storage needs.

5.4. Critical reflection on the research method

The conclusions of this thesis have to be read with the knowledge of possible limitations of the research method. The model upon which this research is based is an abstraction from reality to make it possible to use a simulation-based quantitative research method. The consequence of this decision is that caution is required when applying the results to reality.

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sensitivity analysis (appendix I) concluded that other wind measurement years result in a similar seasonal pattern. However, there are differences on a monthly basis. There are also monthly differences with the pattern described by Coelingh et al. (1996) (presented in 2.1.), but overall the offshore wind pattern described in the presented literature is comparable to the results from the research.

6. Conclusion

The main aim of thesis is to find insights into the trade-offs encountered when designing a green hydrogen supply chain that will be operational in the year 2040. This supply chain incorporates a dedicated offshore wind farm of fixed size and a demand profile considering integrations with the Dutch energy system.

To accomplish the aim of this thesis as described above, a simulation-based quantitative study analysing potential supply chain configurations was conducted. These supply chain configurations are based upon an abstraction of the expected reality of the ‘NortH2’ project which aims to be fully operational in 2040. The supply chain configurations are variated based on supply chain decisions able to restrict energy flow (I.e. energy transportation and electrolyser capacity). The research resulted in the possibility to answer the question of trade-offs between supply chain characteristics restricting the flow of energy, hydrogen output and storage needs as presented in the introduction.

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The insights gained from the results of the simulation study are supplemented by information from interviews with industry experts. This additional information facilitates the realistic application of the results of the simulation to practical situations. From this information can be concluded that the decision where to locate the electrolyser capacity highly depends on costs connected to offshore operations and availability of modes of energy transportation. In practice, this research will be most significant for practitioners attempting to find the most favourable supply chain design. The question during this attempt will likely be about finding a degree of variability in the hydrogen output where an equilibrium arises between hydrogen produced and infrastructure needed (e.g. electrolyser capacity, hydrogen storage and energy transportation). Further research on economic consequences, risks involved and other scenarios decreasing the need for hydrogen storage are needed to come closer to finding an optimal configuration.

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Appendices

Appendix A, Visualisation of the model per electrolyser location configuration

Figure A.1. Configuration with electrolyser at sea: 1 Offshore wind, 2 Windfarm, 3 Offshore energy

transportation, 4 Electrolyser capacity, 5 Electrolyser output, 6 Hydrogen supply and demand balance/storage, 7 Hydrogen use.

Figure A.2. Configuration with electrolyser on land: 1 Offshore wind, 2 Windfarm, 3 Offshore energy

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Figure A.3. Configuration with electrolyser on land and at sea: 1 Offshore wind, 2 Windfarm, 3

Offshore energy transportation, 4 Electrolyser capacity, 5 Electrolyser output, 6 Hydrogen supply and demand balance/storage, 7 Hydrogen use.

Appendix B, The effects of variable electrolyser load and modular electrolysers

Modularity allows tailoring of the water electrolysis process based on the variable energy input (Aricò et al., 2013). This is an interesting option to avoid the load fluctuations that affect temperature and pressure levels in the electrolyser. Optimal temperature and pressure levels are needed to achieve maximum electrolyser efficiency. Next to the losses in efficiency due to fluctuations of temperature and pressure, the material used for the construction of the electrolyser and the electrolysis process will experience a faster degradation as well. Using multiple smaller electrolyser units that only work at their rated load might thus increase overall efficiency and life span (Azzaro-Pantel, 2018; Bourasseau & Guinot, 2015).

Appendix C, Interviewees and relevant takeaways from the interviews Interviewee 1: Pipeline engineer

- When there is a possibility to transport electricity to land in its original form you will try to do that, when the existing connection is operating at its full capacity you have to choose between the investment between a new cable or other options such as the use of gas through pipelines.