Supplementary Material Techno-Economic Viability of Islanded Green Ammonia As a Carbon-Free Energy Vector and As a Substitute Fo

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Supplementary Material

Techno-economic viability of islanded green ammonia as a carbon-free energy vector and as a substitute for conventional production

Richard Michael Nayak-Luke and René Bañares-Alcántara

Acronyms ASU Air separation unit CAGR Compound annual growth rate CAPEX Discounted capital expenditure CCGT Combined cycle gas turbine CR Geometric series common ratio FLH Full load hours equivalent GHG Greenhouse gases HB Haber-Bosch LCOA Levelised cost of ammonia LCOE Levelised cost of electricity LCOH Levelised cost of hydrogen LHV Lower heating value LNG Liquified natural gas NOLA New Orleans, Louisiana, USA OPEX Operational expenditure PV Photovoltaics RE Renewable energy SMR Steam methane reforming UK United Kingdom of Great Britain and Northern Ireland USA United States of America

Nomenclature .∗ Rated power .‡ Revised value (i.e. after re-allocation of energy) $ United States Dollar

훾 Fraction of 푃퐻퐵/퐴푆푈 allocated to synthesis 휂퐿퐻푉 Efficiency relative to the lower heating value  ∆퐸푡,푅푒−푎푙푙표푐푎푡푒 Potential impact on the amount of energy to re-allocate at index t for a given method 퐸푡,푀푒푡ℎ표푑 푥 Amount of energy that can be re-allocated for index t by method x H Height [m]

mNH3 Discounted mass of ammonia M Molar Mass 푃퐸푙푒푐 Electrolyser power 푃퐸푙푒푐퐸푥푐푒푠푠 Excess electroliser power (i.e. power allocated for excess production of hydrogen)

푃퐻2 Hydrogen storage power 푃퐻퐵/퐴푆푈 Haber-Bosch synthesis and air separation unit power

푃퐶푢푟푡푎𝑖푙 Curtailment power r Discount rate

푆푝푒푐퐶퐴푆푈 Specific energy consumption of the air separation unit 푆푝푒푐퐶퐸푙푒푐 Specific energy consumption of the electrolyser 푆푝푒푐퐶퐻2푆푡표푟푒 Specific energy consumption of the hydrogen storage 푆푝푒푐퐶퐻퐵 Specific energy consumption of the Haber-Bosch synthesis (including compression) U Wind speed [m/s] z Roughness length

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Note 1 Optimisation of the three decision variables (the rated power of the electrolyser, rated power of the HB synthesis and ASU components, and the mix of renewable energy sources as a fraction of total energy provided as shown in Figure 6) to minimise the LCOA was achieved using the MATLAB function written by Seshadri , A. based on the multi-objective non-dominated sorting genetic algorithm (NSGA-II) 32, 33. The inputs that used with this genetic algorithm can be seen in Table 1. A detailed description and evaluation of the NSGA-II methodology can be found in 33. The control parameters, seen in Table 1, that govern the operation of the genetic algorithm were defined after review of literature and initial trial. Convergence was checked by comparing the results for over 20 locations with a brute force approach. In practice, the optimal solution was commonly converged upon within 80 generations.

Variable Value Number of generations 200 Number of population 100 Number of crossover 20 Probability of crossover 80% Number of mutation 20 Probability of mutation 10% Table 1: Inputs to the genetic algorithm

The model of the production process encompasses its system design and operating schedule. The system considered consists of a solar photovoltaic power source, a wind power source, an electrolyser, hydrogen storage, hydrogen fuel cell, cryogenic air separation unit, and a Haber-Bosch synthesis unit (including compression and separation).

Figure 6: Overview of the optimisation of the RE resources, plant design and operation to minimise LCOA.

Taking into account the control panel inputs and assumptions three decision variables are optimised using the NSGA-II genetic algorithm using the inputs in Table 1) with the objective function of minimising the LCOA. Part of this analysis is determining the optimal operating schedule for each system design trialled. The decision variables defined in a chromosome define the fraction of energy from wind (i.e. the mix of RE sources), the rated power of the electrolyser, and the rated power of HB synthesis and the ASU. This provides the design of the process being considered. If it is identified as a viable production process (i.e. there is a viable operation schedule) then the optimal

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ARTICLE Journal Name production schedule is determined (Supplementary Material, Note 4). This therefore enables the plant and its operation to be costed for the location considered and the LCOA determined.

The technical process being modelled is shown in Figure 7. The compression required for the recycle loop and feed-streams for ammonia synthesis are included in the Haber-Bosch synthesis block. The hydrogen compression required for storage is included in the hydrogen storage block.

Figure 7: Process block diagram including energy, chemical and information flows.

Note 2 The wind and solar power profiles were calculated from wind speed and global solar irradiance data from Meteonorm 18. To calculate the wind power profile, the wind speed provided (FF parameter) was converted to a hub height of 80m using Equation 3. This enabled the calculation of the power using the power profile of the Vestas V90 3.0 MW turbine with a cut-in speed of 4 m/s, cut-out speed of 25 m/s and an air density of 1.225 kg/m3 34. The solar power profile was taken as the horizontal global irradiance (Gh parameter).

퐻 ln 1 푈 = 푈 ( 푧 ) 1 2 퐻 (3) ln 2 푧

Note 3

This model has been designed to operate using wind and solar energy data with 30-minute or hourly resolution. The electrolyser, HB and the ASU are assumed to be able to ramp instantaneously and to operate in steady state conditions with constant specific energy consumption (as defined in Table 2) for the duration of the time interval. The mechanism for the allocation of available energy (Supplementary Material, Note 4) within the assessment of a given process design, assumed perfect forecasting of the energy supply 35.

The main variable about which the plant design is optimised is the average energy supplied to the plant. It is defined as 100MW. The total rated power of the RE sources used or the mass flow rate of ammonia produced could have been used but were opted against. Use of rated power was opted against as it makes it more difficult to see the impact of the RE profiles on the RE selection and plant design. The average mass produced, instead of scaling the plant size, scales the rated power capacity of the RE sources without regard for the availability of the RE resource at that magnitude.

For the optimisation the rated power of the ASU and HB processes were lumped together as one of the decision variables that is optimised by the genetic algorithm. This is replicated during the power allocation (within the process assessment) by allocated in power to the combination of these components. This is necessary as there is no nitrogen “buffer” considered in this analysis and therefore these processes must operate stoichiometrically.

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Variable Value Reference

Electrolyser lifetime 30 years 20-22, 36, 37

Wind farm lifetime 30 years

Solar farm lifetime 30 years

Hydrogen fuel cell lifetime 5 years 38

HB synthesis and ASU lifetime 30 years

Maintenance downtime 20 days per year

Electrolyser specific energy consumption calculated from 21, 36 47.571kWh/kg 휂퐿퐻푉 = 70%

8, 39 HB synthesis specific energy consumption 0.532kWh/kg

ASU specific energy consumption 0.110kWh/kg 40

Energy losses in the compression of Hydrogen for storage 6.6% of LHV 8, 41

Hydrogen fuel cell efficiency 50% 42

21, 36 Minimum operating power of Electrolyser 0% of Prated

43, 44 Minimum operating power of HB synthesis & ASU 20% of Prated

21 Electrolyser CAPEX (2019 scenario) 700$/kW of Prated

21, 22, 45, 46 Electrolyser CAPEX (2030 scenario) 341$/kW of Prated

8, 24 HB synthesis CAPEX 6,467$/kW of Prated

8 ASU CAPEX 13,182$/kW of Prated

47 Hydrogen storage CAPEX 500$/kg

38 Hydrogen fuel cell CAPEX 960$/kW of Prated

Operation and maintenance of all components 2% CAPEX per year 8

Cost of water feedstock 2 $/t 48

Table 2: Technical and economic parameters in the model.

Taking into account the capacity factor of the electrolyser, the electrolyser lifetime assumption equates to an average of 89,041 and 95,181 full load hour equivalent in the 2019 and 2030 scenarios for a multi-national corporation. For the best 10 locations by geographic region, the averages shift to 102,598 and 101,218 respectively.

Note 4 The allocation of power starts from the position of producing the maximum amount of hydrogen. The method of re-allocation is based on identifying the time intervals that should be considered, calculating their potential for each method of re-allocation (defined in the Methods section), then re-allocating the available energy to these indices sequentially.

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푇

∫ 퐸푅푒−푎푙푙표푐푎푡푒 푑푡 = Total net hydrogen mass . (푆푝푒푐퐶퐸푙푒푐 + 푆푝푒푐퐶퐻2 푆푡표푟푒) (4) 푡=1

Figure 8: Cumulative net hydrogen produced with time. Illustration of before and after brute force energy re-allocation. Abashiri, Japan. ∗ ∗ 휃 = 0.7513, 푃퐸푙푒푐 = 409.6874 푀푊 and 푃퐻퐵/퐴푆푈 = 5.5953 푀푊. Reduction of the hydrogen storage size required from 5,910t to 327t.

The amount of energy that is available to re-allocate is dependent on the amount of excess hydrogen currently produced (Equation 4). The first time interval to consider is the index with the minimum in the cumulative sum of the net hydrogen produced. The final time interval to consider is the maximum in the cumulative sum of the net hydrogen produced after the first time interval. Equations 9 – 13 determine the amount of energy that can be re-allocated from the curtailment and hydrogen storage to HB synthesis, excess hydrogen production to HB synthesis, stoichiometric hydrogen production to HB synthesis and hydrogen production to curtailment for every interval in the range.

푆푝푒푐퐶퐸푙푒푐 + 푆푝푒푐퐶퐻2 푆푡표푟푒 훼 = = 1.0462 (5) 푆푝푒푐퐶퐸푙푒푐

푆푝푒푐퐶퐻퐵 훾 = = 0.8569 푀푁2 1 (6) 푆푝푒푐퐶퐻퐵 + (푆푝푒푐퐶퐴푆푈 . . ) 푀푁퐻3 2

푆퐹. 푆푝푒푐퐶퐻2 푆푡표푟푒 퐶푅 = = 0.62856 (7) 푆푝푒푐퐶퐸푙푒푐

푀퐻2 3 푆푝푒푐퐶퐸푙푒푐 푆퐹 = γ. . . = 13.6049 (8) 푀푁퐻3 2 푆푝푒푐퐶퐻퐵

푃푡,퐶푢푟푡푎𝑖푙 퐸 = 푚푎푥 (푚𝑖푛 ((푃 + 푃 ), , (푃∗ − 푃 )) , 0) (9) 푡,푀푒푡ℎ표푑 1 푡,퐶푢푟푡푎𝑖푙 푡,퐻2 1 − 퐶푅 퐻퐵/퐴푆푈 푡,퐻퐵/퐴푆푈

‡ ‡ 푃 + 푃푡,퐻2 퐸 = 푚푎푥 (푚𝑖푛 (( 푡,퐸푙푒푐퐸푥푐푒푠푠 ) , (푃∗ − 푃‡ )) , 0) 푡,푀푒푡ℎ표푑 2 푆퐹 + 1 퐻퐵/퐴푆푈 푡,퐻퐵/퐴푆푈 (10)

‡ ‡ ∗ ‡ 퐸푡,푀푒푡ℎ표푑 3 = 푚푎푥 (푚𝑖푛 ((푃푡,퐸푙푒푐 − 푃푡,퐸푙푒푐퐸푥푐푒푠푠), (푃퐻퐵/퐴푆푈 − 푃푡,퐻퐵/퐴푆푈)) , 0) (11)

‡ ‡ 퐸푡,푀푒푡ℎ표푑 4 = 푃푡,퐸푙푒푐퐸푥푐푒푠푠 + 푃푡,퐻2 (12)

‡ ‡ 퐸푡,푀푒푡ℎ표푑 5 = 푃푡,퐸푙푒푐 − 푃푡,퐸푙푒푐퐸푥푐푒푠푠 (13)

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퐸푡,푀푒푡ℎ표푑 2 is the amount of energy that can be shifted to the HB/ASU. 퐸푡,푀푒푡ℎ표푑 2. (푆퐹 + 1) is removed from the electrolyser excess and the hydrogen storage, the rest is allocated to electrolyser stoichiometric production. This is necessary due to the increased stoichiometric demand for hydrogen.

Actual re-allocation of energy is conducted by considering each interval in the time period of interest sequentially for method 1 (curtailment and hydrogen storage to HB synthesis) then method 2, etc. until all of the available energy that can be re-allocated has been. Equation 14  determines the potential impact on the amount of energy that can be re-allocated (∆퐸푡,푅푒−푎푙푙표푐푎푡푒) by shifting all of the energy that can be by this method for that index (퐸푡,푀푒푡ℎ표푑 푥). Equation 15 determines that maximum actual impact on the amount of energy that can be re- allocated (∆퐸푡,푅푒−푎푙푙표푐푎푡푒) that is possible. This takes into account the total amount of energy left to be allocated and ensures that the re- allocation does not increase the amount of hydrogen storage required. The amount of energy re-allocated to each individual process is determined using this result, Equation 16 and the context of the re-allocation method used.

퐸 . 훼. 푆퐹 푡,푀푒푡ℎ표푑 1 퐸푡,푀푒푡ℎ표푑 2. (푆퐹 + 1)  ∆퐸푡,푅푒−푎푙푙표푐푎푡푒 = 퐸푡,푀푒푡ℎ표푑 3. 훼. (푆퐹 + 1) (14)

퐸푡,푀푒푡ℎ표푑 4 { 퐸푡,푀푒푡ℎ표푑 5. 훼

∆퐸푡,푅푒−푎푙푙표푐푎푡푒 푇 푇  (15) = 푚푎푥 (푚𝑖푛 (∆퐸푡,푅푒−푎푙푙표푐푎푡푒, ∫ 퐸푅푒−푎푙푙표푐푎푡푒 푑푡 , 푚𝑖푛 ((∑ 퐸푅푒−푎푙푙표푐푎푡푒) ) ) , 0) 푡=1 푡=1 푡:퐿푎푠푡 퐼푛푑푒푥

∆퐸푡,푅푒−푎푙푙표푐푎푡푒 = (∆푃퐻퐵. 푆퐹 − ∆푃퐸푙푒푐) . 훼 (16)

Note 5 The estimation of the reduction in LCOE by 2030, based on two-degree exponential functions fitted to 2009-2018 data (Equations 17 and 18, Figure 8). This function was used in the location specific calculation of the achievable LCOE for solar photovoltaics and wind turbines in 2030 23.

Figure 9: Second-order exponential functions fitted to global weighted average LCOE for wind and solar PV to predict the reduction by 2030 17.

−0.4682푡 −0.0451푡 푊𝑖푛푑 퐿퐶푂퐸2008+푡 = 121.8 푒 + 65.7 푒 1 ≤ 푡 ≤ 22 (17)

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−0.5478푡 −0.09022푡 푆표푙푎푟 퐿퐶푂퐸2008+푡 = 460.6 푒 + 102.9 푒 1 ≤ 푡 ≤ 22 (18)

Note 6 The cost of flexibility calculation compares the LCOA achievable with the actual power profile from RE against an ideal profile (Figure 9). To determine the ideal power profile, the RE mix determined in the optimisation is used to calculate the supply load factor. The ideal power profile is then defined as the rated power of the wind and solar components multiplied by the load factor and accounting for the maintenance period. Comparison of the plant design and operation enables the identification of the impact that RE intermittency has. In the ideal case there is no need to provide a “hydrogen buffer” and therefore hydrogen storage capacity, the hydrogen fuel cell and oversizing of the electrolyser are not required. Moreover, there is no curtailment of energy supplied and the HB and ASU can be sized differently to maximise the amount of ammonia that is produced.

Attributing the change in the LCOA to its components is difficult as both the numerator (cost) and denominator (mass of ammonia produced) in Equation 2 change. Defining the percentage change in the total discounted cost, discounted mass of ammonia produced and LCOA as x, y and z respectively (Equations 19 – 21), enables the impact of the two components on the change of LCOA to be identified (Equations 22 and 23). Assuming the initial percentage change of each component is zero (i.e. 푥 = 푦 = 0) identifies that there is a component (∆푥∆푦⁄1 + ∆푦) that needs to be ‘fairly’ removed from the components. A scale factor is defined (Equation 25) so that this component is achieved (Equations 26 and 27).

퐶표푠푡퐴푐푡푢푎푙 = 퐶표푠푡퐼푑푒푎푙 × (1 + 푥) (19)

푀푎푠푠퐴푐푡푢푎푙 = 푀푎푠푠퐼푑푒푎푙 × (1 + 푦) (20)

퐿퐶푂퐴퐴푐푡푢푎푙 = 퐿퐶푂퐴퐼푑푒푎푙 × (1 + 푧) (21)

FigureTable 103: :Illustrative Illustrative actual actual and and `ideal' `ideal' power power profiles. profiles.

푥 + ∆푥 − 푦 푥 − 푦 ∆ 푧 = − (22) 푥 1 + 푦 1 + 푦

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푥 − (푦 + ∆푦) 푥 − 푦 ∆ 푧 = − (23) 푦 1 + (푦 + ∆푦) 1 + 푦

푥 + ∆푥 − (푦 + ∆푦) 푥 − 푦 ∆푧 = − (24) 1 + (푦 + ∆푦) 1 + 푦

푥 − 푦 1 + 푦 푆푐푎푙푒 퐹푎푐푡표푟 = 푦 (25) 푥 − 1 + 푦

푧 푎푡푡푟𝑖푏푢푡푎푏푙푒 푡표 푐ℎ푎푛𝑔푒 𝑖푛 푐표푠푡 = 푥 × (푆푐푎푙푒 퐹푎푐푡표푟) (26)

−푦 푧 푎푡푡푟𝑖푏푢푡푎푏푙푒 푡표 푐ℎ푎푛𝑔푒 𝑖푛 푚푎푠푠 = × (푆푐푎푙푒 퐹푎푐푡표푟) (27) 1 + 푦

Note 7

Variable Value Reference

Energy storage lifetime 30 years

Ammonia storage CAPEX 1,302 $/t 8, 21

49 Gas turbine CAPEX 565 $/kW of Prated

Operation and maintenance 5% CAPEX per year

Ammonia-to-power efficiency of the gas turbine 29.7% 50

Natural gas-to-power carbon dioxide emissions 0.509 – 0.763 t/MWh 25

Maximum flowrate of the gas turbine 1% of storage per hour

Operations per year 12

19 Energy storage discount factor 7.14%

Table 4: Technical and economic assumptions for the LCOE calculations.

Note 8 The achievable LCOA in the 2019 scenario for a multi-national corporation has a strong second-order exponential relationship (Equation 28) with the renewable energy supply capacity factor (SCF), with an R2 of 0.7132. The correlation in 2030 remains strong with an R2 of 0.7267 (Equation 29). These functions have been fitted to quantify the relationship seen in the results and should be used with caution and only as a rough approximation of the achievable LCOA. Firstly, as shown in Figure 11 there is error in these functions, particularly with solar dominated systems. Secondly, and more importantly, the SCF values used are the result from the optimisation where the objective function was to minimise the LCOA. Most of these locations could have achieved a higher SCF by using wind sources instead of solar, but this only resulted in a higher LCOA.

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−0.1673 푆퐶퐹 −0.004057푆퐶퐹 퐿퐶푂퐴2019 = 6709 푒 + 692.7 푒 9 ≤ 푆퐶퐹 ≤ 65 (28)

−0.275 푆퐶퐹 −0.003815 푆퐶퐹 퐿퐶푂퐴2030 = 8894 푒 + 417.2 푒 8 ≤ 푆퐶퐹 ≤ 60 (29)

Figure 11: The achievable LCOA for a multi-national company against the renewable energy supply capacity factor (SCF) in a) 2019 and b) 2030 Scenario. The functions fitted to these results (Equations 28 and 29) are shown as red lines in each both figures.

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Note 9 The LCOA estimates for the 2019 scenario when plotted against the electrolyser FLH are readily groupable by the proportion of RE provided by wind. Figure 10a shows that the locations with the lowest LCOA are those with a highly dependent on wind and therefore able to achieve a higher electrolyser load factor (up to 5643 FLH equivalent). Those locations dependent on solar resource instead can only achieve a lower electrolyser load factor (less than 3100 FLH equivalent) and in this scenario higher LCOA. Within the locations dependent on solar resource the LCOA achieved can be seen in Figure 10b to have a strong correlation with the installed cost of solar PV.

However, in the 2030 scenario the dramatic reduction in the cost of solar PV has driven most locations to be less dominant on wind regardless of its commonly higher capacity factor. Comparison of Figure 10a with 11a shows that even those locations previously highly dependent on wind have shifted towards a greater dependence on solar. Driven by the locations with the lowest installed solar PV cost, Figure 11b shows that those locations that are solar dependent have ‘caught-up’ to the LCOA estimates achievable at the wind favouring locations able to achieve a higher electrolyser load factor. While the lowest LCOA estimates are still in wind favouring locations by 2030, the majority of the best locations are now solar dependent.

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Figure 12: 2019 Scenario: Range of the optimal achievable LCOA and the respective electrolyser FLH for a multi-national corporation grouped by a) the RE mix and b) installed CAPEX of solar PV.

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Figure 13: 2030 Scenario: Range of the optimal achievable LCOA and the respective electrolyser FLH for a multi-national corporation grouped by a) the RE mix and b) installed CAPEX of solar PV.

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Note 10: Comparison of previous investigations into ‘green’ ammonia production, their inputs / constraints and results. Arranged by date of publication.

Inputs / Constraints Results

Authors & reference Electrolyser Location RE sources Grid connection / Plant size Discount LCOAi Electrolyser specific energy considered considered Plant purpose [t/day] rate [%] [$/t] size [MW ] consumption e Tunå, P. et al. 48 Not specified General Constant Grid dependent / 18.5 5 – 12%ii 1,015 – 2,328 10* (specifies cost) (4.25kWh/Nm3) Commodity 5.6 1,078 – 2,392 3* Beerbühl, S. et al. 51 Germany N.A. Non-linear (3.54- Grid dependent / Not specified Not 591 34.0 – 34.76 4.40kWh/Nm3) Commodity specified Bañares-Alcántara, R. et al. Victoria, Wind Constant (47- Islanded / 48 8% 655 33.31 52 Australia 64.5kWh/kg) Commodity or ES ISPT (Goeree-Overflakkee: Middelharnis, Wind Constant Semi-islanded / 109.6 7% 526 – 861 (2023) 20* 24 Case 2) Netherlands (53kWh/kg) Commodity (40MWe) 431 – 931 (2030) 53 iii * Wang, G. et al. Germany N.A. RSOFC: Not Grid dependent / 100MWe 7.49% 713 – 1,457 10 specified Diurnal ES Morgan, E.R. et al. 54 Gulf of Maine, Wind Constant Semi-islanded / 300 7% 1,224 Not specified 3 USA (370MWe) (4.8kWh/Nm ) Commodity (580 minimum) Sánchez, A. et al. 55 South of Wind and Solar Constant Islanded / 300 Not 1,535 – 1,569 Not specified Europe PV (53.15kWh/kg) Commodity specified Ikäheimo, J. et al. 8 Northern Wind, Solar PV Piecewise Semi-islanded / Not specified 7% 490 – 600 Not specified Europe and Hydro (41-47kWh/kg) Commodity Nayak-Luke, R. et al. 13 Lerwick, Wind and Solar Constant Islanded / 228 0% 750 (2025/2030) 196 Scotland PV (53.4kWh/kg) Commodity (202MWe) Eichhammer, W. et al. 56 Morocco Wind and Solar Constant Islanded / 77.55 2 – 6%ii 769 – 1,243 (2017) Not specified PV (specifies (45.4, 42.0 & Commodity (802 – 1,426 473 – 762 (2030) FLH) 41.0kWh/kg) 724 – 1,287 326 – 561 (2050) MWe) Armijo, J. et al. 57 Chile and Wind and Solar Constant Semi-islanded / 95.9 7% 462 – 506 60.2 Argentina PV (48.0kWh/kg) Commodity (87.8MWe) 10% 462 – 571 Not specified Palys, M.J. et al. 58 America (15 Wind and Solar Constant Islanded / ES using 0.30–2.33 iv 10% 391 – 644 v 0.80 – 3.50 iv locations) PV (45 & 50kWh/kg hydrogen and PEM and alkaline) ammonia as vectors Allman, A. et al. 59 Minnesota, Wind Constant Semi-islanded / 0.1812 8.3% Not specified 0.250 USA (60.0kWh/kg) Commodity & ES (batteries, hydrogen, and ammonia)

Table 5: Abbreviation: ES – Energy Storage; *: Defined variable 60 i LCOA has been converted from other currencies (when appropriate) and rounded to the nearest United States Dollar using the following exchange rates: 1.00000 USD = 0.87930 EUR = 0.75722 GBP 60. ii Stated as the “interest rate”. iii Back calculated from the 0.23 – 0.47 $/kWh estimates and η_LHV=60%. Unlike all other studies, these LCOA estimates include the cost of ammonia-to-power. iv Results for the hybrid hydrogen and ammonia energy storage systems . The ammonia-only energy storage system design was not specified. v Back calculated from the 0.17 – 0.28 $/kWh estimates and ICE genset energy production of 2.3 kWh/kgNH3 (despite SOFCs with 3.1 kWh/khNH3 also being considered the plant design for ammonia only systems was not specified).

Additional literature that could be of interest but is partially tangential to the work presented here includes: 61 for schedule optimisation with a receding 48-hour horizon, 62 for optimising process design of ammonia production for use as a commodity and as part of an energy storage system 63, for optimising power-to-X-to-power systems, and 64 optimising process selection when using ammonia as an energy vector in combination with batteries.

Note 11: Cost of flexibility

The scaling factor outlined in the Results section is only a multiplier that should be used to provide a more realistic result of the LCOA achievable if one has estimated the LCOA achievable at a location assuming that there is an idealised power supply as shown in Figure 10, Supplementary Material Note 6. Use of such a profile would neglect the process flexibility required (both in the plant design and its operation). This, as shown below, underestimates many cost components while overestimating the amount of ammonia produced. The scale factor mentioned in results is therefore simply a multiplier calculated from the actual LCOA divided by the “ideal” LCOA from the results (i.e. 735⁄470 = 1.56). This changes depending on the locations and scenario considered, however considering the groupings below this varies from 1.56 to 1.91.

2019 Scenario 2030 Scenario Mean of lowest 10 LCOA Mean of lowest 10 LCOA Mean of all 534 Mean of all 534 estimates for each estimates for each locations locations geographic region geographic region Ideal supply profile LCOA [$/t] 470 623 221 273 CAPEX Electrolyser [% of the LCOA increase] 32.3% 25.2% 22.3% 14.8% CAPEX HB synthesis & ASU [% of the LCOA increase] 13.6% 9.8% 15.0% 11.2% CAPEX Hydrogen Storage [% of the LCOA increase] 8.1% 13.5% 9.5% 14.0% CAPEX Hydrogen Fuel Cell [% of the LCOA increase] 2.4% 1.7% 3.5% 2.4% OPEX Electrolyser Electricity [% of the LCOA increase] -14.3% -18.8% -14.3% -18.6% OPEX HB synthesis & ASU Electricity [% of the LCOA increase] -1.2% -1.6% -1.2% -1.5% OPEX Curtailed Electricity [% of the LCOA increase] 15.5% 20.2% 15.3% 20.0% OPEX Hydrogen Storage Electricity [% of the LCOA increase] 1.2% 1.3% 0.8% 0.7% OPEX Operation & Maintenance [% of the LCOA increase] 13.4% 12.0% 11.7% 10.0% OPEX Water [% of the LCOA increase] -0.1% -0.1% -0.4% -0.4% Mass of Ammonia produced [% of the LCOA increase] 29.3% 36.7% 37.9% 47.3% Actual supply profile LCOA [$/t] 735 1,014 388 521

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Note 12: Simulation inputs by country

Renewable Energy fixed costs Domestic Corporation variables

Country region Geographic locations of Number simulated installed cost Solar [$/kW] installed Wind [$/kW] cost Utilities Factor Discount [%] Plant Ammonia Factor Discount [%]

Argentina Lat. America & Caribbean 5 823 1,829 10.24 14.69 Australia Oceania 37 1,550 2,124 3.64 7.49 Austria Europe 6 921 1,868 3.76 7.60 Belgium Europe 4 921 1,868 3.87 7.72 Bosnia & Herzegovina Europe 1 921 1,868 9.34 13.17 Botswana Sub-Saharan Africa 1 805 2,040 4.92 8.88 Brazil Lat. America & Caribbean 5 823 1,829 5.33 9.31 Bulgaria Europe 4 921 1,868 5.44 9.25 Canada Northern America 11 955 1,718 3.68 7.48 Chile Lat. America & Caribbean 7 823 1,829 4.33 8.20 China Eastern Asia 51 1,005 1,197 4.43 8.24 Colombia Lat. America & Caribbean 2 823 1,829 5.12 8.94 Congo Sub-Saharan Africa 1 805 2,040 6.71 10.76 Croatia Europe 2 921 1,868 5.81 9.66 Czech Republic Europe 5 921 1,868 4.43 8.29 Denmark Europe 7 921 1,868 3.73 7.53 Egypt Northern Africa 5 1,201 1,320 10.67 15.01 El Salvador Lat. America & Caribbean 1 1,319 2,184 6.13 9.97 Estonia Europe 1 921 1,868 4.43 8.34 Finland Europe 5 921 1,868 3.88 7.71 France Europe 39 921 1,868 3.57 7.37

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Renewable Energy fixed costs Domestic Corporation variables

Country region Geographic locations of Number simulated installed cost Solar [$/kW] installed cost Wind [$/kW] Discount Utilities [%] Factor Plant Ammonia Factor [%] Discount

Germany Europe 9 921 1,868 3.62 7.43 Ghana Sub-Saharan Africa 1 805 2,040 8.53 12.84 Greece Europe 3 921 1,868 9.11 13.32 Hungary Europe 2 921 1,868 5.59 9.53 India Southern Asia 12 661 1,097 5.50 9.65 Indonesia South-Eastern Asia 1 832 1,221 5.52 9.52 Ireland Europe 5 921 1,868 5.14 8.98 Italy Europe 16 921 1,868 4.60 8.52 Japan Eastern Asia 62 832 1,221 3.80 7.62 Kazakhstan Northern Asia 1 1,463 1,605 5.44 9.41 Kenya Sub-Saharan Africa 3 805 2,040 6.79 10.84 Republic of Korea, (South Korea) Eastern Asia 8 832 1,221 4.18 8.03 Latvia Europe 1 921 1,868 5.18 8.94 Lithuania Europe 1 921 1,868 5.23 9.03 Republic of Macedonia Europe 2 921 1,868 6.90 10.80 Malaysia South-Eastern Asia 4 832 1,221 4.85 8.67 Mexico Lat. America & Caribbean 9 1,319 2,184 4.52 8.51 Mongolia Eastern Asia 5 1,463 1,605 8.35 12.57 Morocco Northern Africa 1 805 2,040 5.51 9.34 Mozambique Sub-Saharan Africa 3 805 2,040 6.67 10.69 Netherlands Europe 4 921 1,868 3.78 7.64 New Zealand Oceania 4 1,550 2,124 3.65 7.46 Nicaragua Lat. America & Caribbean 1 1,319 2,184 9.08 13.30

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Renewable Energy fixed costs Domestic Corporation variables

Country region Geographic locations of Number simulated installed cost Solar [$/kW] installed Wind [$/kW] cost Discount Utilities [%] Factor Plant Ammonia Factor Discount [%]

Norway Europe 2 921 1,868 3.71 7.55 Oman Western Asia 2 1,201 1,320 4.75 8.55 Pakistan Southern Asia 2 832 1,221 10.02 14.35 Papua New Guinea Oceania 1 1,550 2,124 6.75 10.76 Peru Lat. America & Caribbean 1 823 1,829 4.76 8.59 Philippines South-Eastern Asia 1 832 1,221 5.21 9.07 Poland Europe 3 921 1,868 4.59 8.44 Portugal Europe 6 921 1,868 5.36 9.26 Romania Europe 7 921 1,868 5.64 9.56 Russian Federation Europe 18 832 1,221 5.40 9.40 Saudi Arabia Western Asia 6 1,201 1,320 4.58 8.44 Senegal Sub-Saharan Africa 2 805 2,040 6.47 10.32 Singapore South-Eastern Asia 1 832 1,221 4.13 7.94 South Africa Sub-Saharan Africa 8 805 2,040 5.13 9.08 Spain Europe 16 921 1,868 4.66 8.58 Sri Lanka Southern Asia 1 832 1,221 6.94 11.03 Sweden Europe 7 921 1,868 3.77 7.54 Switzerland Europe 7 921 1,868 3.87 7.64 Thailand South-Eastern Asia 2 832 1,221 5.16 9.00 Tunisia Northern Africa 2 805 2,040 6.62 10.66 Turkey Western Asia 14 921 1,868 5.50 9.64 Ukraine Europe 2 1,463 1,605 11.14 15.07 UK Europe 16 921 1,868 3.98 7.85

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Discount Discount

Country region Geographic locations of Number simulated installed cost Solar [$/kW] installed Wind [$/kW] cost Utilities [%] Factor Plant Ammonia Factor Discount [%]

USA Northern America 47 850 1,648 3.33 7.14 Venezuela Lat. America & Caribbean 1 823 1,829 13.08 18.74 Zambia Sub-Saharan Africa 1 805 2,040 6.70 10.80 Abbreviation: Lat. America & Caribbean – Latin America and the Caribbean

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Note 13: 2019 Scenario, Multinational Corporation – Simulation results by country

Decision variables Resulting variables Objective

synthesis load load synthesis

Bosch Bosch

Country simulated locations of Number Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of energy [%] by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Equivalent Load Full energyLCOE supplied of [$/MWh] Percentage RE of energy [%] curtailed [%] factor Electrolyser load Haber [%] factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Argentina 5 198 – 273 11.1 – 14.5 0 – 98 0 – 93 1,516 – 3,136 38 – 41 7 – 28 30 – 42 39 – 49 28 – 111 16 – 38 757 – 896 Australia 37 137 – 307 8.8 – 16.1 0 – 100 0 – 100 1,363 – 5,637 28 – 91 0 – 19 28 – 64 37 – 69 20 – 93 3 – 105 473 – 1,325 Austria 6 152 – 241 9.9 – 12.9 0 – 92 0 – 75 1,116 – 3,995 30 – 70 3 – 30 30 – 59 40 – 63 43 – 334 6 – 61 518 – 1,411 Belgium 4 186 – 262 10 – 13.2 36 – 80 34 – 57 1,059 – 2,333 46 – 86 6 – 10 32 – 47 49 – 61 52 – 132 11 – 92 737 – 1,269 Bosnia & 1 211 11.6 0 0 1,263 53 29 31 40 123 34 1,193 Herzegovina Botswana 1 238 12.8 0 0 1,692 35 15 33 44 17 37 748 Brazil 5 246 – 294 10.7 – 14.6 0 – 1 0 – 2 1,209 – 1,711 36 – 50 13 – 17 28 – 32 39 – 53 36 – 61 37 – 77 766 – 989 Bulgaria 4 200 – 252 11.2 – 13.7 0 – 6 0 – 9 1,120 – 1,357 50 – 66 19 – 30 30 – 33 39 – 42 90 – 195 30 – 35 1,047 – 1,336 Canada 11 179 – 281 9 – 14.7 22 – 84 29 – 65 862 – 2,416 44 – 107 4 – 18 30 – 48 41 – 65 48 – 815 9 – 88 729 – 2,013 Chile 7 191 – 318 11.5 – 15.6 0 – 98 0 – 94 926 – 3,328 37 – 75 8 – 27 27 – 45 39 – 51 31 – 105 15 – 50 720 – 1,369 China 51 174 – 381 9.5 – 16.5 0 – 67 0 – 56 886 – 1,740 42 – 85 7 – 33 23 – 45 35 – 64 24 – 151 16 – 132 801 – 1,462 Colombia 2 299 – 311 11.6 – 12 0 – 0 0 – 0 1,234 – 1,383 44 – 49 13 – 14 26 – 27 48 – 50 76 – 78 89 – 90 907 – 989 Congo 1 333 15.6 0 0 1,459 40 7 26 39 44 57 864 Croatia 2 195 – 219 9.1 – 13.2 0 – 30 0 – 26 1,205 – 1,541 55 – 56 12 – 34 32 – 37 45 – 48 33 – 339 22 – 58 925 – 1,455 Czech Republic 5 190 – 258 9.5 – 14.5 24 – 70 23 – 46 1,069 – 1,758 56 – 81 8 – 17 32 – 45 41 – 65 46 – 173 6 – 94 874 – 1,273 Denmark 7 159 – 283 9.9 – 13.1 41 – 98 37 – 91 1,074 – 4,712 28 – 88 1 – 7 31 – 58 49 – 66 59 – 150 9 – 105 528 – 1,288 Egypt 5 188 – 279 10.2 – 14.7 0 – 53 0 – 49 1,834 – 2,132 41 – 48 6 – 12 31 – 44 41 – 60 22 – 32 7 – 78 759 – 896 El Salvador 1 322 12.1 1 3 1,707 57 5 27 52 74 96 988 Estonia 1 264 10.2 44 39 1,054 89 9 32 60 193 91 1,351 Finland 5 259 – 422 10.2 – 12.3 24 – 53 28 – 45 691 – 1,136 87 – 139 3 – 12 21 – 33 52 – 58 176 – 638 79 – 152 1,309 – 2,090 France 39 170 – 344 9.4 – 14.7 0 – 69 0 – 51 1,016 – 2,498 41 – 80 5 – 30 24 – 50 39 – 67 18 – 413 6 – 109 668 – 1,425 Germany 9 193 – 394 9.8 – 14.3 6 – 42 9 – 34 905 – 1,321 67 – 101 5 – 26 22 – 38 40 – 64 103 – 419 21 – 125 1,083 – 1,696 Ghana 1 281 11.2 1 1 1,668 36 11 29 53 56 84 770 Greece 3 187 – 228 10.6 – 12.7 0 – 3 0 – 5 1,472 – 1,639 41 – 48 22 – 31 32 – 35 41 – 43 67 – 97 28 – 31 938 – 1,051

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Decision variables Resulting variables Objective

met by wind [%] met by wind

Bosch synthesis load load synthesis Bosch

Country simulated locations of Number Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of energy [%] by wind supplied ratedPercentage RE of power energySupply profile Hours Equivalent Load Full energyLCOE supplied of [$/MWh] Percentage RE of energy [%] curtailed [%] factor Electrolyser load Haber [%] factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Hungary 2 267 – 322 10.7 – 11.5 6 – 13 12 – 19 1,094 – 1,129 67 – 73 8 – 18 26 – 29 51 – 53 264 – 362 83 – 105 1,301 – 1,391 India 12 218 – 294 10.7 – 14.7 0 – 0 0 – 0 1,356 – 1,957 25 – 36 8 – 19 29 – 35 41 – 52 14 – 53 33 – 67 636 – 823 Indonesia 1 285 13.9 0 0 1,310 47 15 28 40 42 51 953 Ireland 5 168 – 277 10.4 – 13.8 49 – 98 40 – 90 1,106 – 4,210 31 – 86 2 – 6 32 – 54 46 – 61 58 – 150 9 – 96 554 – 1,248 Italy 16 180 – 266 9.4 – 15.1 0 – 43 0 – 32 1,126 – 1,917 41 – 60 7 – 32 30 – 39 39 – 67 33 – 382 21 – 80 777 – 1,480 Japan 62 139 – 332 8.7 – 16.6 0 – 74 0 – 56 1,130 – 2,568 30 – 62 6 – 25 26 – 55 37 – 65 13 – 114 4 – 104 562 – 1,105 Kazakhstan 1 218 12.6 10 22 1,214 90 21 34 42 182 27 1,567 Kenya 3 250 – 281 11.8 – 13.4 0 – 1 0 – 2 1,802 – 1,885 32 – 33 7 – 14 31 – 32 45 – 53 25 – 48 46 – 75 697 – 732 Korea, Republic of (South 8 223 – 316 9.6 – 16 0 – 29 0 – 31 1,183 – 1,357 46 – 59 10 – 16 26 – 35 37 – 61 32 – 87 41 – 94 860 – 1,022 Korea) Latvia 1 302 11.5 13 24 868 97 15 26 49 676 93 1,929 Lithuania 1 213 9.5 41 36 1,037 89 16 37 60 119 66 1,350 Macedonia, 2 197 – 258 10.9 – 14 0 – 6 0 – 9 1,262 – 1,306 52 – 58 17 – 32 30 – 32 39 – 41 194 – 252 32 – 36 1,231 – 1,254 Republic of Malaysia 4 262 – 340 10.8 – 15.7 0 – 0 0 – 0 1,356 – 1,709 36 – 45 5 – 18 26 – 29 40 – 51 30 – 76 52 – 99 788 – 917 Mexico 9 270 – 321 10.1 – 15.6 0 – 13 0 – 13 1,345 – 1,962 49 – 72 5 – 16 26 – 31 38 – 62 24 – 61 40 – 99 896 – 1,231 Mongolia 5 223 – 325 11 – 14.3 0 – 4 0 – 7 1,225 – 1,519 71 – 88 9 – 24 26 – 32 40 – 55 37 – 312 27 – 117 1,171 – 1,573 Morocco 1 227 12.6 0 0 1,825 32 18 34 43 44 32 757 Mozambique 3 244 – 270 13.2 – 14.3 0 – 0 0 – 1 1,595 – 1,714 34 – 37 10 – 14 31 – 33 42 – 44 17 – 23 36 – 42 739 – 777 Netherlands 4 182 – 249 12.3 – 14.2 32 – 72 31 – 49 1,002 – 1,968 51 – 89 7 – 11 33 – 47 42 – 51 41 – 141 9 – 29 796 – 1,374 New Zealand 4 195 – 238 12.8 – 13.5 14 – 100 17 – 100 1,162 – 4,011 39 – 104 2 – 17 33 – 47 41 – 52 59 – 109 12 – 30 673 – 1,599 Nicaragua 1 317 12.3 0 0 1,491 65 7 27 50 74 89 1,087 Norway 2 188 – 314 11 – 12.8 48 – 88 41 – 63 895 – 2,471 45 – 107 5 – 5 28 – 47 51 – 58 58 – 219 8 – 111 731 – 1,540 Oman 2 144 – 270 10.3 – 14.1 0 – 69 0 – 59 1,929 – 2,291 41 – 46 9 – 20 32 – 51 43 – 52 22 – 27 3 – 41 735 – 856 Pakistan 2 233 – 246 10 – 12.7 0 – 6 0 – 9 1,706 – 1,778 36 – 36 12 – 18 33 – 33 43 – 58 33 – 35 35 – 80 716 – 793

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Decision variables Resulting variables Objective

power

Bosch synthesis load load synthesis Bosch

Country simulated locations of Number Electrolyser rated power [MW] rated & ASU HB [MW] Percentage RE of energy [%] by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Equivalent Load Full energyLCOE supplied of [$/MWh] Percentage RE of energy [%] curtailed [%] factor Electrolyser load Haber [%] factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Papua New 1 317 12.2 0 0 1,625 70 7 27 50 54 91 1,129 Guinea Peru 1 276 11.5 0 0 1,845 33 8 31 53 47 76 709 Philippines 1 305 14.9 0 0 1,439 42 11 27 39 41 50 894 Poland 3 221 – 242 9.3 – 14.1 33 – 46 32 – 41 1,062 – 1,133 83 – 85 12 – 13 34 – 36 42 – 62 131 – 163 27 – 79 1,282 – 1,350 Portugal 6 180 – 260 11.9 – 13.5 0 – 60 0 – 45 1,482 – 2,104 37 – 55 6 – 25 30 – 46 41 – 51 29 – 222 12 – 41 767 – 1,108 Romania 7 178 – 235 10 – 13 0 – 9 0 – 14 1,222 – 1,424 48 – 62 20 – 33 31 – 36 40 – 52 45 – 338 22 – 73 1,043 – 1,357 Russian 18 162 – 341 8.5 – 13.7 0 – 98 0 – 93 768 – 3,389 26 – 92 5 – 37 26 – 48 43 – 65 66 – 1579 12 – 124 565 – 2,464 Federation Saudi Arabia 6 243 – 271 10 – 12.9 0 – 10 0 – 14 1,615 – 2,240 39 – 55 6 – 14 31 – 33 44 – 61 13 – 56 38 – 98 741 – 919 Senegal 2 275 – 289 11 – 11.4 0 – 2 0 – 2 1,677 – 2,000 30 – 35 8 – 9 29 – 31 53 – 56 34 – 62 84 – 86 661 – 758 Singapore 1 273 11.1 0 0 1,423 43 15 29 51 70 81 886 South Africa 8 170 – 262 8.8 – 13.6 0 – 99 0 – 96 1,470 – 4,626 32 – 43 0 – 18 31 – 55 42 – 65 20 – 74 8 – 85 552 – 830 Spain 16 191 – 278 8.9 – 15.4 0 – 41 0 – 35 1,189 – 2,046 38 – 62 8 – 30 30 – 40 39 – 70 38 – 169 19 – 102 687 – 1,207 Sri Lanka 1 260 9.7 25 22 1,682 40 6 33 65 36 98 715 Sweden 7 197 – 355 10.5 – 13.2 18 – 65 18 – 46 879 – 1,693 59 – 106 4 – 21 25 – 42 44 – 60 68 – 434 18 – 119 896 – 1,625 Switzerland 7 158 – 292 10.7 – 12.1 0 – 70 0 – 49 1,124 – 2,439 42 – 63 9 – 31 26 – 53 40 – 57 33 – 465 7 – 89 675 – 1,464 Thailand 2 261 – 293 10.9 – 14.1 0 – 0 0 – 0 1,695 – 1,727 35 – 36 9 – 13 29 – 31 43 – 53 46 – 66 53 – 76 764 – 790 Tunisia 2 212 – 252 12.1 – 13.8 0 – 1 0 – 1 1,676 – 1,726 35 – 35 17 – 23 31 – 34 40 – 42 32 – 98 28 – 34 798 – 852 Turkey 14 175 – 279 10.1 – 15.2 0 – 6 0 – 7 1,220 – 1,647 41 – 55 14 – 37 29 – 34 37 – 42 38 – 111 24 – 37 867 – 1,285 Ukraine 2 272 – 292 14.8 – 16.2 39 – 57 45 – 52 1,070 – 1,436 78 – 104 5 – 8 29 – 33 38 – 43 94 – 163 32 – 35 1,172 – 1,559 UK 16 151 – 326 9.7 – 14.1 21 – 98 22 – 91 843 – 4,102 32 – 99 3 – 18 27 – 56 45 – 63 43 – 329 7 – 119 584 – 1,638 USA 47 152 – 278 8.5 – 14.6 0 – 67 0 – 50 966 – 2,315 34 – 79 7 – 31 30 – 53 38 – 70 24 – 551 7 – 95 668 – 1,678 Venezuela 1 273 13.6 0 0 1,732 35 11 30 43 22 47 758 Zambia 1 296 12.6 0 0 1,463 40 9 29 48 68 72 836

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Note 14: 2019 Scenario, Multinational Corporation – Simulation results by location. Lowest 10 LCOA estimates for each geographic region Information about location Decision variables Resulting variables Objective

Bosch synthesis load load synthesis Bosch

Location Country region Geographic Latitude Longitude Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of energy [%] by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Equivalent Load Full energyLCOE supplied of [$/MWh] [%] energySupply curtailed [%] factor Electrolyser load Haber [%] factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Arrecife Lanzarote Spain Northern Africa 28.95 -13.60 243 8.9 22 14 2,046 38 8 35 70 41 102 687 Casablanca Morocco Northern Africa 33.57 -7.67 227 12.6 0 0 1,825 32 18 34 43 44 32 757 Aswan Egypt Northern Africa 23.97 32.78 247 10.2 9 13 2,080 43 9 34 60 32 78 759 Mersa Matruh Egypt Northern Africa 31.33 27.22 188 12.9 53 49 1,958 47 10 44 47 22 7 779 El Kharga Egypt Northern Africa 25.45 30.53 279 14.4 0 0 2,132 41 6 32 43 22 42 796 Tunis Tunisia Northern Africa 36.83 10.23 212 12.1 1 1 1,726 35 23 34 42 32 28 798 Sidi Bouzid Tunisia Northern Africa 35.00 9.48 252 13.8 0 0 1,676 35 17 31 40 98 34 852 El Arish Egypt Northern Africa 31.12 33.75 272 14.7 0 0 1,910 46 10 31 41 27 36 877 El Natroon Egypt Northern Africa 30.40 30.35 257 14.1 0 1 1,834 48 12 32 42 24 35 896 Funchal Portugal Northern Africa 32.63 -16.90 260 13.5 0 0 1,482 46 16 30 41 66 41 940 Marion Island South Africa S-Saharan Africa -46.88 37.87 170 11.6 99 96 4,626 32 0 55 59 62 8 552 Dakar Senegal S-Saharan Africa 14.73 -17.47 275 11.0 2 2 2,000 30 8 31 56 34 84 661 Gillot France S-Saharan Africa -20.89 55.53 170 11.8 69 51 2,498 41 8 50 53 18 6 668 Garissa Kenya S-Saharan Africa -0.47 39.63 281 11.8 1 2 1,885 32 7 31 53 48 75 697 Voi Kenya S-Saharan Africa -3.40 38.57 267 13.4 1 1 1,872 32 10 32 45 25 46 706 Upington South Africa S-Saharan Africa -28.43 21.27 239 13.2 0 0 1,771 33 14 34 43 20 33 729 Mombasa Airport Kenya S-Saharan Africa -4.03 39.62 250 12.5 0 0 1,802 33 14 32 45 28 47 732 Beira Mozambique S-Saharan Africa -19.83 34.85 262 13.5 0 0 1,714 34 11 32 44 20 42 739 Maun Botswana S-Saharan Africa -19.98 23.42 238 12.8 0 0 1,692 35 15 33 44 17 37 748 Gough Island UK S-Saharan Africa -40.35 9.88 181 10.7 79 58 2,322 46 8 47 59 74 24 753 Piura Peru Latin America -5.17 -80.60 276 11.5 0 0 1,845 33 8 31 53 47 76 709 Punta Arenas Chile Latin America -53.00 -70.85 191 12.2 98 94 3,328 39 8 45 51 99 15 720 Caracas Maiquetia Venezuela Latin America 10.60 -66.98 273 13.6 0 0 1,732 35 11 30 43 22 47 758 Ushuaia Airport Argentina Latin America -54.80 -68.32 204 13.0 98 93 3,136 41 7 42 49 111 16 757 Petrolina Brazil Latin America -9.38 -40.50 252 10.7 1 2 1,711 36 14 32 53 54 73 766

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Information about location Decision variables Resulting variables Objective

Equivalent

Bosch synthesis load load synthesis Bosch

Location Country region Geographic Latitude Longitude Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of energy [%] by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Load Full energyLCOE supplied of [$/MWh] [%] energySupply curtailed [%] factor Electrolyser load Haber [%] factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Le Lamentin France Latin America 14.60 -61.00 286 10.8 5 5 1,751 41 7 30 57 44 94 770 Antofagasta Chile Latin America -23.43 -70.43 250 13.1 0 1 1,648 37 15 32 43 37 40 797 Raizet France Latin America 16.27 -61.52 257 13.3 0 0 1,662 41 13 32 43 20 42 819 Brasilia Brazil Latin America -15.78 -47.93 257 10.7 0 0 1,517 40 14 31 53 61 77 821 Mendoza Airport Argentina Latin America -32.83 -68.78 228 12.6 0 0 1,579 38 20 33 42 29 33 827 Dodge City USA N. America 37.77 -99.97 164 11.3 61 46 2,276 39 11 50 53 24 7 668 Kahului USA N. America 20.90 -156.43 233 8.8 26 18 2,000 36 9 36 70 40 94 669 Lihue Kauai USA N. America 21.98 -159.35 152 10.4 67 50 2,315 39 14 53 56 28 7 674 Churchill Canada N. America 58.75 -94.07 179 12.0 81 60 2,342 44 8 48 52 51 9 729 Honolulu Oahu USA N. America 21.33 -157.92 233 9.7 21 18 1,777 41 12 35 61 25 77 739 New York USA N. America 40.65 -73.78 194 12.6 53 40 1,854 46 7 45 50 30 14 746 Inukjuak Canada N. America 58.45 -78.12 186 12.2 84 65 2,416 44 8 46 51 67 11 747 Tucson USA N. America 32.12 -110.93 245 13.7 0 0 1,808 34 14 33 41 24 31 759 Oklahoma City USA N. America 35.40 -97.60 189 12.5 51 40 1,894 45 11 44 49 27 11 761 Fort Worth USA N. America 32.83 -97.05 235 9.1 28 25 1,778 43 9 36 67 67 90 764 Kwajalein Island Japan Eastern Asia 8.73 167.73 156 11.0 71 54 2,568 30 12 53 54 21 4 562 Iwojima Japan Eastern Asia 24.78 141.31 179 12.4 67 54 2,290 33 8 48 50 22 7 609 Minamitorishima Japan Eastern Asia 24.30 153.97 139 9.9 62 51 2,218 34 17 55 57 13 4 611 Choshi Japan Eastern Asia 35.73 140.87 192 12.4 74 56 2,317 33 6 46 51 58 13 628 Nemuro Japan Eastern Asia 43.33 145.58 178 12.2 67 50 1,973 38 10 47 50 34 7 676 Naha Japan Eastern Asia 26.20 127.68 175 11.7 69 54 2,004 38 11 47 52 33 10 676 Ishigakijima Japan Eastern Asia 24.33 124.17 169 11.3 64 53 1,933 39 15 47 51 28 9 704 Omaezaki Japan Eastern Asia 34.60 138.22 178 12.2 54 44 1,810 41 12 46 49 34 8 717 Hachijojima Japan Eastern Asia 33.10 139.78 197 13.2 65 46 1,820 41 8 43 47 42 9 723 Tanegashima Japan Eastern Asia 30.73 131.00 177 12.3 63 49 1,775 42 12 46 49 27 6 729 Chiang Mai Airport Thailand S-Eastern Asia 18.78 98.98 261 10.9 0 0 1,727 35 13 31 53 66 76 764

32 | R.M.Nayak-Luke and R.Bañares-Alcántara 2020 This journal is © The Royal Society of Chemistry 2020

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Information about location Decision variables Resulting variables Objective

Bosch synthesis load load synthesis Bosch

Location Country region Geographic Latitude Longitude Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of energy [%] by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Equivalent Load Full energyLCOE supplied of [$/MWh] [%] energySupply curtailed [%] factor Electrolyser load Haber [%] factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Penang Malaysia S-Eastern Asia 5.30 100.27 308 14.9 0 0 1,709 36 8 28 41 30 52 788 Bangkok Thailand S-Eastern Asia 13.73 100.57 293 14.1 0 0 1,695 36 9 29 43 46 53 790 Kota Kinabalu Malaysia S-Eastern Asia 5.93 116.05 330 12.2 0 0 1,547 39 6 26 51 76 99 831 Kota Bahru Malaysia S-Eastern Asia 6.17 102.28 340 15.7 0 0 1,523 40 5 26 40 40 59 847 Singapore Airport Singapore S-Eastern Asia 1.37 103.98 273 11.1 0 0 1,423 43 15 29 51 70 81 886 Science Garden Philippines S-Eastern Asia 14.63 121.01 305 14.9 0 0 1,439 42 11 27 39 41 50 894 Kuching Malaysia S-Eastern Asia 1.48 110.33 262 10.8 0 0 1,356 45 18 29 51 57 79 917 Bukit Kototabang Indonesia S-Eastern Asia -0.20 100.32 285 13.9 0 0 1,310 47 15 28 40 42 51 953 New Delhi India Southern Asia 28.58 77.20 218 12.0 0 0 1,957 25 19 35 45 15 33 638 Jodhpur India Southern Asia 26.30 73.02 223 12.2 0 0 1,933 25 18 35 45 14 34 636 Vishakhapatnam India Southern Asia 17.72 83.23 244 10.7 0 0 1,869 26 15 32 52 53 67 656 Ahmedabad India Southern Asia 23.07 72.63 227 12.2 0 0 1,892 26 18 34 44 22 36 657 Thiruvananthapura India Southern Asia 8.48 76.95 242 12.3 0 0 1,828 26 16 33 45 24 44 665 m Poona India Southern Asia 18.53 73.85 226 12.0 0 0 1,796 27 19 33 44 23 38 678 Santacruz Bombay India Southern Asia 19.12 72.85 294 14.7 0 0 1,790 27 8 29 41 41 47 693 Madras India Southern Asia 13.00 80.18 268 13.4 0 0 1,711 28 11 31 44 40 47 693 Goa India Southern Asia 15.48 73.82 240 12.3 0 0 1,726 28 17 32 45 25 43 689 Nagpur Sonegaon India Southern Asia 21.10 79.05 252 13.3 0 0 1,665 29 14 32 43 25 39 702 Masira Oman Western Asia 20.67 58.90 144 10.3 69 59 2,291 41 20 51 52 27 3 735 Sharura Saudi Arabia Western Asia 17.47 47.12 271 11.4 0 0 2,240 39 6 32 54 25 74 741 Wadi Al Dawaser Saudi Arabia Western Asia 20.50 45.20 265 11.0 4 8 2,140 41 7 33 57 28 77 751 Jeddah Saudi Arabia Western Asia 21.68 39.15 249 10.4 4 7 2,093 42 10 33 58 33 76 773 Madinah Saudi Arabia Western Asia 24.55 39.70 250 10.5 3 10 1,932 46 10 33 57 56 76 831 Al Ahsa Saudi Arabia Western Asia 25.28 49.48 243 12.9 0 0 1,954 45 14 33 44 13 38 856 Silifke Turkey Western Asia 36.38 33.93 262 14.1 0 0 1,639 41 14 31 40 53 37 867

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ARTICLE Journal Name

Information about location Decision variables Resulting variables Objective

[%]

Bosch synthesis load load synthesis Bosch

Location Country region Geographic Latitude Longitude Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of energy by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Equivalent Load Full energyLCOE supplied of [$/MWh] [%] energySupply curtailed [%] factor Electrolyser load Haber [%] factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Buraimi Oman Western Asia 24.23 55.78 270 14.1 0 0 1,929 46 9 32 43 22 41 856 Mugla Turkey Western Asia 37.20 28.35 231 13.2 0 0 1,647 41 20 32 40 47 29 891 Taif Saudi Arabia Western Asia 21.48 40.55 270 10.0 10 14 1,615 55 9 31 61 49 98 919 Sonnblick Austria Europe 47.05 12.95 152 10.5 92 75 3,995 30 3 59 63 43 6 518 List Denmark Europe 55.02 8.42 159 10.3 98 91 4,712 28 1 58 66 107 14 528 Malin Head Ireland Europe 55.37 -7.33 168 11.4 98 90 4,210 31 2 54 58 63 9 554 Russian Dickson Island Europe 73.50 80.23 175 11.5 98 93 3,389 26 10 48 53 70 12 565 Federation Lerwick UK Europe 60.13 -1.18 151 10.3 98 91 4,102 32 10 56 60 63 8 584 Skagen Fyr Denmark Europe 57.73 10.63 180 12.1 92 76 3,332 36 3 50 54 60 9 622 Aberporth UK Europe 52.13 -4.57 164 11.2 89 69 2,973 39 8 52 56 43 7 649 Guetsch Switzerland Europe 46.65 8.62 158 10.9 70 49 2,439 42 9 53 57 33 7 675 Valley UK Europe 53.25 -4.53 182 12.2 88 69 2,834 41 5 49 53 52 10 677 Belmullet Ireland Europe 54.23 -10.00 197 13.0 91 74 2,880 41 3 46 51 64 12 694 Cape Grim Australia Oceania -40.66 144.68 144 10.0 100 100 5,637 28 0 64 68 44 6 473 Willis Island Australia Oceania -16.30 149.98 137 9.8 99 99 5,115 30 7 63 65 38 3 519 Glenmore Australia Oceania -33.69 115.02 187 10.6 97 93 3,834 40 4 48 61 93 29 677 Wellington New Zealand Oceania -41.32 174.77 195 12.8 100 100 4,011 39 2 47 52 84 12 673 Carnarvon Airport Australia Oceania -24.88 113.67 161 11.2 79 66 2,955 48 11 51 54 42 6 768 Noumea France Oceania -22.28 166.45 242 9.4 32 22 1,773 47 7 36 67 47 88 775 Koumac France Oceania -20.57 164.28 246 13.0 0 0 1,580 43 15 32 43 23 39 853 Cook Australia Oceania -30.62 130.40 199 13.3 78 67 2,554 55 6 44 48 57 10 858 Ils Des Pins Moue France Oceania -22.60 167.45 250 9.6 26 18 1,499 54 9 33 64 41 93 871 Geraldton Airport Australia Oceania -28.78 114.70 180 12.3 64 53 2,307 59 10 46 50 44 8 899 Abbreviation: S-Saharan Africa – Sub-Saharan Africa; Lat. America – Latin America and the Caribbean; N. America – Northern America; S-Eastern Asia – South-Eastern Asia.

34 | R.M.Nayak-Luke and R.Bañares-Alcántara 2020 This journal is © The Royal Society of Chemistry 2020

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Note 15: 2019 Scenario, Multinational Corporation – Components of LCOA by location. Lowest 10 LCOA estimates for each geographic region

[%]

Separation CAPEX Separation

Location Country Latitude Longitude Electrolyser OPEX Electrolyser CAPEX [%] Hydrogen Storage [%] CAPEX Hydrogen Storage OPEX [%] Synthesis Ammonia OPEX [%] Synthesis Ammonia [%] CAPEX & Operation [%] Maintenance OPEXEnergy Curtailed [%] Cell Hydrogen Fuel [%] CAPEX OPEX Separation Air [%] Air [%] Water OPEX [%] Arrecife Lanzarote Spain 28.95 -13.6 46.5 23.1 3.1 0.7 2.9 6.7 8.7 4.4 0.7 0.5 2.3 0.5 Casablanca Morocco 33.57 -7.67 37.5 22.5 3.4 0.5 2.3 9.9 9.7 9.0 1.0 0.4 3.4 0.4 Aswan Egypt 23.97 32.78 48.3 21.5 2.2 0.7 3.0 7.1 8.2 5.0 0.7 0.5 2.4 0.4 Mersa Matruh Egypt 31.33 27.22 51.7 16.1 1.5 0.3 3.2 8.7 7.3 6.3 0.9 0.5 3.0 0.4 El Kharga Egypt 25.45 30.53 45.6 22.9 1.4 0.6 2.8 9.4 9.1 3.3 1.0 0.5 3.2 0.4 Tunis Tunisia 36.83 10.23 37.8 21.1 2.5 0.5 2.3 9.6 9.0 12.2 1.0 0.4 3.3 0.4 Sidi Bouzid Tunisia 35 9.48 36.4 21.8 6.7 0.5 2.2 9.5 10.2 7.8 1.0 0.4 3.2 0.4 El Arish Egypt 31.12 33.75 46.3 21.0 1.7 0.6 2.8 9.0 8.6 5.2 0.9 0.5 3.1 0.4 El Natroon Egypt 30.4 30.35 47.0 19.9 1.5 0.6 2.8 8.6 8.1 6.7 0.9 0.5 2.9 0.4 Funchal Portugal 32.63 -16.9 42.6 20.2 4.0 0.6 2.6 8.3 8.7 8.6 0.9 0.4 2.8 0.4 Marion Island South Africa -46.88 37.87 48.5 18.4 5.3 0.2 3.0 9.9 9.1 0.1 1.0 0.5 3.4 0.6 Dakar Senegal 14.73 -17.47 39.6 27.4 2.7 0.6 2.4 8.7 10.3 3.6 0.9 0.4 3.0 0.5 Gillot France -20.89 55.53 52.5 16.5 1.4 0.2 3.3 9.0 7.4 4.7 0.9 0.5 3.1 0.5 Garissa Kenya -0.47 39.63 39.9 26.4 3.6 0.5 2.5 8.8 10.3 3.3 0.9 0.4 3.0 0.5 Voi Kenya -3.4 38.57 39.5 25.5 1.9 0.5 2.4 10.1 10.1 4.6 1.1 0.4 3.5 0.5 Upington South Africa -28.43 21.27 40.2 23.4 1.5 0.5 2.4 10.2 9.5 6.9 1.1 0.4 3.5 0.5 Mombasa Airport Kenya -4.03 39.62 39.2 24.4 2.1 0.5 2.4 9.7 9.7 6.9 1.0 0.4 3.3 0.5 Beira Mozambique -19.83 34.85 40.9 24.4 1.5 0.5 2.5 10.0 9.7 5.3 1.0 0.4 3.4 0.4 Maun Botswana -19.98 23.42 40.9 22.9 1.3 0.5 2.5 9.8 9.2 7.7 1.0 0.4 3.3 0.4 Gough Island UK -40.35 9.88 52.3 15.5 5.0 0.2 3.3 7.3 7.5 4.7 0.8 0.5 2.5 0.4 Piura Peru -5.17 -80.6 40.2 26.0 3.5 0.6 2.5 8.6 10.1 3.9 0.9 0.4 2.9 0.5 Punta Arenas Chile -53 -70.85 46.0 17.2 7.1 0.2 2.9 8.7 8.9 4.2 0.9 0.5 3.0 0.4 Caracas Maiquetia Venezuela 10.6 -66.98 40.3 24.8 1.6 0.5 2.4 9.8 9.8 5.5 1.0 0.4 3.3 0.4 Ushuaia Airport Argentina -54.8 -68.32 46.1 17.3 7.4 0.3 2.9 8.7 9.0 3.5 0.9 0.5 3.0 0.4 Petrolina Brazil -9.38 -40.5 40.8 23.3 3.9 0.6 2.5 7.8 9.3 7.4 0.8 0.4 2.7 0.4 Le Lamentin France 14.6 -61 45.2 24.2 3.0 0.7 2.8 7.3 9.1 3.7 0.8 0.5 2.5 0.4

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ARTICLE Journal Name

OPEX

Location Country Latitude Longitude Electrolyser OPEX [%] Electrolyser CAPEX [%] Hydrogen Storage CAPEX [%] Hydrogen Storage OPEX [%] Synthesis Ammonia [%] Synthesis Ammonia [%] CAPEX & Operation [%] Maintenance OPEXEnergy Curtailed [%] CAPEX Cell Hydrogen Fuel [%] OPEX [%] Separation Air CAPEX [%] Separation Air Water OPEX [%] Antofagasta Chile -23.43 -70.43 40.4 22.6 2.6 0.5 2.5 9.4 9.3 7.6 1.0 0.4 3.2 0.4 Raizet France 16.27 -61.52 43.6 22.1 1.4 0.6 2.6 9.1 8.8 7.0 0.9 0.4 3.1 0.4 Brasilia Brazil -15.78 -47.93 42.2 22.3 4.2 0.6 2.6 7.4 9.0 7.6 0.8 0.4 2.5 0.4 Mendoza Airport Argentina -32.83 -68.78 40.6 21.0 2.1 0.5 2.5 9.2 8.7 10.5 1.0 0.4 3.1 0.4 Dodge City USA 37.77 -99.97 49.9 16.5 1.9 0.2 3.1 9.0 7.5 6.8 0.9 0.5 3.1 0.5 Kahului USA 20.9 -156.43 46.1 23.0 3.1 0.7 2.9 6.9 8.7 4.7 0.7 0.5 2.3 0.5 Lihue Kauai USA 21.98 -159.35 49.8 15.6 2.3 0.2 3.1 8.4 7.2 8.6 0.9 0.5 2.9 0.5 Churchill Canada 58.75 -94.07 51.5 15.9 3.5 0.2 3.2 8.4 7.6 4.8 0.9 0.5 2.9 0.4 Honolulu Oahu USA 21.33 -157.92 47.3 21.4 1.8 0.6 2.9 7.1 8.1 6.7 0.7 0.5 2.4 0.4 New York USA 40.65 -73.78 52.6 16.7 2.0 0.3 3.3 8.6 7.5 4.2 0.9 0.5 2.9 0.4 Inukjuak Canada 58.45 -78.12 50.1 16.2 4.6 0.2 3.1 8.4 7.9 4.8 0.9 0.5 2.9 0.4 Tucson USA 32.12 -110.93 39.9 23.1 1.8 0.5 2.4 10.2 9.5 7.1 1.1 0.4 3.5 0.4 Oklahoma City USA 35.4 -97.6 50.7 16.6 1.8 0.3 3.2 8.7 7.4 6.5 0.9 0.5 3.0 0.4 Fort Worth USA 32.83 -97.05 48.3 20.3 4.6 0.6 3.0 6.2 8.2 5.1 0.6 0.5 2.1 0.4 Kwajalein Island Japan 8.73 167.73 45.0 18.7 2.0 0.2 2.8 10.5 8.6 6.4 1.1 0.5 3.6 0.6 Iwojima Japan 24.78 141.31 46.6 19.2 1.8 0.2 2.9 10.5 8.7 4.5 1.1 0.5 3.6 0.5 Minamitorishima Japan 24.3 153.97 47.3 16.4 1.2 0.2 3.0 9.2 7.4 10.4 1.0 0.5 3.1 0.5 Choshi Japan 35.73 140.87 45.0 19.5 4.6 0.2 2.8 10.0 9.2 3.3 1.0 0.5 3.4 0.5 Nemuro Japan 43.33 145.58 48.0 17.5 2.6 0.2 3.0 9.5 8.1 5.9 1.0 0.5 3.2 0.5 Naha Japan 26.2 127.68 47.9 17.4 2.6 0.2 3.0 9.2 8.0 6.6 1.0 0.5 3.1 0.5 Ishigakijima Japan 24.33 124.17 47.6 16.8 2.2 0.2 3.0 8.9 7.6 8.8 0.9 0.5 3.0 0.5 Omaezaki Japan 34.6 138.22 48.4 16.8 2.6 0.2 3.0 9.1 7.8 7.0 0.9 0.5 3.1 0.4 Hachijojima Japan 33.1 139.78 48.2 17.8 3.0 0.3 3.0 9.5 8.3 4.7 1.0 0.5 3.2 0.4 Tanegashima Japan 30.73 131 49.4 16.5 2.0 0.2 3.1 9.1 7.6 7.2 0.9 0.5 3.1 0.4 Chiang Mai Airport Thailand 18.78 98.98 40.3 23.9 4.7 0.6 2.5 7.9 9.6 6.3 0.8 0.4 2.7 0.4 Penang Malaysia 5.3 100.27 39.9 26.1 2.0 0.6 2.4 10.0 10.2 3.6 1.0 0.4 3.4 0.4 Bangkok Thailand 13.73 100.57 40.0 25.0 3.1 0.5 2.4 9.5 10.1 4.3 1.0 0.4 3.2 0.4 Kota Kinabalu Malaysia 5.93 116.05 41.4 25.9 4.7 0.6 2.5 7.6 10.0 3.0 0.8 0.4 2.6 0.4 Kota Bahru Malaysia 6.17 102.28 41.7 26.0 2.4 0.6 2.5 9.5 10.1 2.2 1.0 0.4 3.2 0.4

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Cell CAPEX Cell

Location Country Latitude Longitude Electrolyser OPEX [%] Electrolyser CAPEX [%] Hydrogen Storage CAPEX [%] Hydrogen Storage OPEX [%] OPEX Synthesis Ammonia [%] Synthesis Ammonia [%] CAPEX & Operation [%] Maintenance OPEXEnergy Curtailed [%] Hydrogen Fuel [%] OPEX [%] Separation Air CAPEX [%] Separation Air Water OPEX [%] Singapore Airport Singapore 1.37 103.98 42.2 22.1 4.5 0.6 2.6 7.1 8.9 8.0 0.7 0.4 2.4 0.4 Science Garden Philippines 14.63 121.01 41.8 23.6 2.5 0.6 2.5 9.1 9.5 5.6 0.9 0.4 3.1 0.4 Kuching Malaysia 1.48 110.33 42.8 21.2 3.6 0.6 2.6 6.9 8.4 9.9 0.7 0.4 2.4 0.4 Bukit Kototabang Indonesia -0.2 100.32 43.0 21.6 2.5 0.6 2.6 8.3 8.7 8.2 0.9 0.4 2.8 0.3 New Delhi India 28.58 77.2 34.0 25.9 1.4 0.4 2.1 11.3 10.5 8.5 1.2 0.3 3.8 0.5 Jodhpur India 26.3 73.02 34.6 26.1 1.3 0.4 2.1 11.3 10.5 7.9 1.2 0.3 3.8 0.5 Vishakhapatnam India 17.72 83.23 34.5 26.8 4.5 0.5 2.1 9.3 10.8 6.6 1.0 0.4 3.2 0.5 Ahmedabad India 23.07 72.63 34.2 25.9 2.0 0.4 2.1 11.0 10.5 8.1 1.1 0.3 3.8 0.5 Thiruvananthapuram India 8.48 76.95 34.9 26.5 2.1 0.4 2.1 10.7 10.6 7.0 1.1 0.4 3.6 0.5 Poona India 18.53 73.85 34.9 25.2 2.0 0.4 2.1 10.7 10.3 8.8 1.1 0.4 3.6 0.5 Santacruz Bombay India 19.12 72.85 34.3 28.3 3.1 0.5 2.1 11.2 11.5 3.2 1.2 0.3 3.8 0.5 Madras India 13 80.18 35.8 26.6 3.1 0.5 2.2 10.5 10.8 4.8 1.1 0.4 3.6 0.5 Goa India 15.48 73.82 35.7 25.6 2.1 0.5 2.2 10.4 10.3 7.7 1.1 0.4 3.5 0.5 Nagpur Sonegaon India 21.1 79.05 36.4 25.7 2.0 0.5 2.2 10.8 10.4 6.4 1.1 0.4 3.7 0.5 Masira Oman 20.67 58.9 47.1 14.7 2.1 0.2 2.9 8.3 6.9 13.0 0.9 0.5 2.8 0.4 Sharura Saudi Arabia 17.47 47.12 46.2 23.8 1.7 0.7 2.8 8.0 8.9 3.4 0.8 0.5 2.7 0.4 Wadi Al Dawaser Saudi Arabia 20.5 45.2 47.4 22.9 1.9 0.7 2.9 7.5 8.6 3.8 0.8 0.5 2.6 0.4 Jeddah Saudi Arabia 21.68 39.15 47.1 21.7 2.2 0.7 2.9 7.2 8.3 5.8 0.7 0.5 2.4 0.4 Madinah Saudi Arabia 24.55 39.7 47.7 20.4 3.6 0.7 2.9 6.8 8.1 5.9 0.7 0.5 2.3 0.4 Al Ahsa Saudi Arabia 25.28 49.48 46.2 20.3 0.8 0.6 2.8 8.5 8.0 8.1 0.9 0.5 2.9 0.4 Silifke Turkey 36.38 33.93 41.8 21.5 3.4 0.6 2.5 9.1 9.2 7.0 0.9 0.4 3.1 0.4 Buraimi Oman 24.23 55.78 46.9 21.3 1.3 0.6 2.8 8.8 8.5 5.0 0.9 0.5 3.0 0.4 Mugla Turkey 37.2 28.35 40.5 19.9 3.2 0.6 2.4 9.0 8.7 10.9 0.9 0.4 3.1 0.4 Taif Saudi Arabia 21.48 40.55 51.5 19.6 2.8 0.8 3.2 5.7 7.4 5.5 0.6 0.5 2.0 0.4 Sonnblick Austria 47.05 12.95 48.9 18.1 4.0 0.1 3.1 9.9 8.7 1.7 1.0 0.5 3.4 0.6 List Denmark 55.02 8.42 44.5 18.1 9.6 0.2 2.8 9.3 9.9 0.3 1.0 0.5 3.2 0.6 Malin Head Ireland 55.37 -7.33 47.2 18.5 5.4 0.2 3.0 9.9 9.2 1.1 1.0 0.5 3.4 0.6 Russian Dickson Island 73.5 80.23 38.8 20.6 6.5 0.2 2.4 10.7 10.2 4.8 1.1 0.4 3.6 0.6 Federation

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ARTICLE Journal Name

Synthesis OPEX Synthesis

Location Country Latitude Longitude Electrolyser OPEX [%] Electrolyser CAPEX [%] Hydrogen Storage CAPEX [%] Hydrogen Storage OPEX [%] Ammonia [%] Synthesis Ammonia [%] CAPEX & Operation [%] Maintenance OPEXEnergy Curtailed [%] CAPEX Cell Hydrogen Fuel [%] OPEX [%] Separation Air CAPEX [%] Separation Air Water OPEX [%] Lerwick UK 60.13 -1.18 46.2 17.0 5.6 0.2 2.9 9.2 8.6 5.3 1.0 0.5 3.1 0.5 Skagen Fyr Denmark 57.73 10.63 49.3 17.8 4.7 0.2 3.1 9.5 8.7 1.6 1.0 0.5 3.2 0.5 Aberporth UK 52.13 -4.57 50.7 16.3 3.4 0.2 3.2 8.9 7.8 4.6 0.9 0.5 3.0 0.5 Guetsch Switzerland 46.65 8.62 52.3 15.4 2.5 0.2 3.3 8.4 7.2 5.9 0.9 0.5 2.9 0.5 Valley UK 53.25 -4.53 51.0 16.9 3.8 0.2 3.2 9.0 8.1 2.9 0.9 0.5 3.0 0.5 Belmullet Ireland 54.23 -10 50.4 17.5 4.5 0.2 3.2 9.1 8.4 1.7 0.9 0.5 3.1 0.5 Cape Grim Australia -40.66 144.68 49.5 18.2 4.4 0.1 3.1 10.0 8.9 0.2 1.0 0.5 3.4 0.7 Willis Island Australia -16.3 149.98 49.4 16.8 3.7 0.1 3.1 9.5 8.2 3.8 1.0 0.5 3.2 0.6 Glenmore Australia -33.69 115.02 49.9 17.2 6.7 0.3 3.1 7.7 8.4 2.2 0.8 0.5 2.6 0.5 Wellington New Zealand -41.32 174.77 49.1 17.6 6.0 0.2 3.1 9.2 8.9 0.9 1.0 0.5 3.1 0.5 Carnarvon Airport Australia -24.88 113.67 53.0 14.2 2.9 0.2 3.3 7.8 6.8 7.4 0.8 0.6 2.6 0.4 Noumea France -22.28 166.45 51.2 20.1 3.1 0.6 3.2 6.2 7.8 4.0 0.6 0.5 2.1 0.4 Koumac France -20.57 164.28 44.0 20.9 1.5 0.6 2.7 8.7 8.4 8.5 0.9 0.4 3.0 0.4 Cook Australia -30.62 130.4 55.0 14.8 3.3 0.3 3.4 7.8 7.1 3.9 0.8 0.6 2.7 0.4 Ils Des Pins Moue France -22.6 167.45 52.3 19.1 2.4 0.7 3.3 5.8 7.2 5.8 0.6 0.5 2.0 0.4 Geraldton Airport Australia -28.78 114.7 55.7 13.4 2.6 0.3 3.5 7.2 6.3 6.9 0.7 0.6 2.4 0.4

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Note 16: 2019 Scenario, Domestic Corporation – Simulation results by country

Decision variables Resulting variables Objective

Bosch synthesis load load synthesis Bosch

ogen storageogen size [t]

Country simulated locations of Number Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of energy [%] by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Equivalent Load Full energyLCOE supplied of [$/MWh] Percentage RE of energy [%] curtailed [%] factor Electrolyser load Haber [%] factor Hydr factor Electrolyser excess [%] LCOA [$/t] Argentina 5 193 – 287 11.7 – 15 0 – 97 0 – 90 1,517 – 3,068 66 – 72 9 – 26 29 – 44 39 – 50 26 – 104 14 – 40 1,292 – 1,527 Australia 37 140 – 332 8.8 – 16.6 0 – 100 0 – 100 1,364 – 5,636 28 – 96 1 – 18 27 – 65 37 – 69 20 – 89 3 – 108 487 – 1,363 Austria 6 159 – 251 9.8 – 10.9 0 – 95 0 – 84 1,116 – 4,303 30 – 68 2 – 32 30 – 58 41 – 62 53 – 272 7 – 83 537 – 1,466 Belgium 4 180 – 271 9.3 – 12.9 34 – 79 31 – 56 1,056 – 2,302 49 – 89 7 – 13 32 – 48 49 – 63 50 – 133 10 – 89 774 – 1,337 Bosnia & 1 210 11.5 0 0 1,263 88 29 32 40 120 34 1,898 Herzegovina Botswana 1 259 13.7 0 0 1,692 40 11 32 43 19 39 854 Brazil 5 241 – 284 11 – 14.7 0 – 1 0 – 1 1,209 – 1,730 42 – 60 12 – 19 29 – 32 40 – 53 29 – 67 34 – 81 912 – 1,177 Bulgaria 4 226 – 253 9.9 – 13.7 0 – 5 0 – 8 1,126 – 1,357 60 – 78 19 – 23 29 – 32 39 – 53 91 – 214 30 – 86 1,247 – 1,532 Canada 11 182 – 259 8.7 – 13.7 9 – 88 13 – 72 879 – 2,555 44 – 104 4 – 28 32 – 47 44 – 63 47 – 575 8 – 84 753 – 2,108 Chile 7 204 – 276 11.2 – 14.1 0 – 99 0 – 98 928 – 3,436 40 – 80 2 – 28 29 – 41 39 – 49 27 – 115 18 – 44 786 – 1,497 China 51 175 – 373 9.7 – 17.9 0 – 68 0 – 57 893 – 1,740 47 – 93 7 – 31 23 – 45 34 – 64 26 – 196 15 – 125 880 – 1,660 Colombia 2 280 – 293 11.2 – 11.6 0 – 0 0 – 0 1,234 – 1,383 51 – 57 15 – 16 27 – 28 48 – 50 68 – 72 84 – 85 1,053 – 1,146 Congo 1 315 15.1 0 0 1,459 54 9 27 40 40 54 1,134 Croatia 2 180 – 249 8.6 – 14.5 0 – 28 0 – 25 1,205 – 1,537 68 – 70 8 – 37 33 – 34 42 – 48 44 – 285 26 – 54 1,140 – 1,786 Czech Republic 5 192 – 307 9.1 – 14.6 29 – 71 28 – 46 1,069 – 1,767 62 – 90 4 – 15 29 – 45 41 – 64 47 – 193 6 – 122 961 – 1,402 Denmark 7 158 – 250 9.5 – 13.6 42 – 98 35 – 91 1,075 – 4,707 29 – 91 1 – 10 34 – 58 47 – 66 54 – 133 11 – 90 546 – 1,339 Egypt 5 185 – 286 10.4 – 14.7 0 – 54 0 – 50 1,835 – 2,132 74 – 86 5 – 12 31 – 45 41 – 61 22 – 39 7 – 88 1,329 – 1,569 El Salvador 1 290 11.5 1 2 1,709 73 9 29 53 63 86 1,247 Estonia 1 232 9.6 45 40 1,057 99 13 35 61 161 78 1,498 Finland 5 241 – 467 10.3 – 12.6 27 – 54 31 – 45 691 – 1,138 91 – 146 2 – 11 19 – 34 52 – 58 159 – 593 72 – 173 1,376 – 2,203 France 39 166 – 328 9.4 – 14.6 0 – 70 0 – 52 1,014 – 2,505 41 – 80 6 – 33 25 – 51 40 – 66 18 – 370 5 – 103 681 – 1,455 Germany 9 216 – 394 9.8 – 13.9 9 – 50 13 – 38 942 – 1,331 69 – 90 5 – 23 22 – 37 42 – 54 76 – 378 24 – 125 1,112 – 1,660 Ghana 1 311 11.8 1 1 1,667 56 7 28 52 66 94 1,173 Greece 3 199 – 218 11.3 – 12.2 0 – 1 0 – 1 1,497 – 1,639 66 – 74 25 – 28 32 – 34 41 – 42 50 – 137 28 – 31 1,470 – 1,651

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Decision variables Resulting variables Objective

wind [%] wind

Bosch synthesis load load synthesis Bosch

Country simulated locations of Number Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of energy [%] by wind supplied ratedPercentage RE of metpower by energySupply profile Hours Equivalent Load Full energyLCOE supplied of [$/MWh] Percentage RE of energy [%] curtailed [%] factor Electrolyser load Haber [%] factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Hungary 2 256 – 275 10.5 – 10.8 6 – 7 10 – 10 1,125 – 1,138 80 – 81 16 – 20 28 – 29 51 – 52 269 – 345 79 – 87 1,559 – 1,672 India 12 224 – 284 10.7 – 14.8 0 – 0 0 – 0 1,356 – 1,957 30 – 43 7 – 20 29 – 34 41 – 52 19 – 52 38 – 66 768 – 992 Indonesia 1 265 13.2 0 0 1,310 57 18 29 41 38 48 1,152 Ireland 5 157 – 250 9.9 – 13.6 47 – 99 37 – 94 1,096 – 4,338 36 – 100 4 – 8 34 – 56 47 – 63 55 – 142 8 – 86 648 – 1,454 Italy 16 187 – 279 8.8 – 15.3 0 – 39 0 – 30 1,126 – 1,867 44 – 75 8 – 33 30 – 42 39 – 67 41 – 312 22 – 77 871 – 1,646 Japan 62 142 – 331 9.3 – 15.9 0 – 71 0 – 54 1,129 – 2,544 31 – 65 7 – 26 26 – 55 37 – 65 14 – 128 4 – 95 585 – 1,139 Kazakhstan 1 200 11.7 8 17 1,251 105 26 35 42 154 25 1,880 Kenya 3 233 – 267 11.6 – 12.5 0 – 0 0 – 1 1,802 – 1,891 42 – 44 10 – 16 32 – 34 45 – 52 20 – 48 43 – 69 924 – 973 Korea, Republic of (South 8 243 – 288 10 – 15.1 0 – 29 0 – 31 1,183 – 1,363 50 – 63 9 – 20 28 – 33 37 – 61 34 – 93 37 – 97 921 – 1,099 Korea) Latvia 1 226 9.7 9 17 903 104 27 30 50 544 71 2,241 Lithuania 1 266 10.7 43 38 1,041 107 8 32 58 158 83 1,567 Macedonia, 2 208 – 250 11.5 – 13.7 0 – 5 0 – 9 1,264 – 1,306 70 – 79 19 – 29 30 – 32 40 – 41 210 – 232 34 – 35 1,633 – 1,666 Republic of Malaysia 4 272 – 285 11 – 13.8 0 – 0 0 – 0 1,356 – 1,709 41 – 52 12 – 14 28 – 30 42 – 52 26 – 63 47 – 81 886 – 1,036 Mexico 9 275 – 335 10.3 – 16 0 – 13 0 – 13 1,345 – 1,962 55 – 80 4 – 10 25 – 31 37 – 61 26 – 85 42 – 100 1,003 – 1,369 Mongolia 5 228 – 403 10.4 – 14.4 0 – 16 0 – 17 1,225 – 1,495 111 – 133 4 – 23 22 – 34 40 – 52 50 – 301 21 – 140 1,763 – 2,368 Morocco 1 208 11.7 0 0 1,825 39 23 35 44 28 30 905 Mozambique 3 249 – 267 10.4 – 14.2 0 – 4 0 – 5 1,595 – 1,714 46 – 52 10 – 12 31 – 33 42 – 57 20 – 43 39 – 77 971 – 1,022 Netherlands 4 197 – 281 10 – 13.8 37 – 74 36 – 50 1,004 – 1,998 53 – 96 5 – 10 31 – 45 45 – 62 48 – 199 11 – 106 829 – 1,426 New Zealand 4 192 – 254 12.7 – 14.8 17 – 100 20 – 100 1,154 – 4,002 40 – 109 2 – 14 32 – 47 41 – 52 63 – 136 11 – 31 691 – 1,640 Nicaragua 1 317 12.3 0 0 1,491 104 7 27 50 74 89 1,716 Norway 2 187 – 249 9.9 – 12.7 48 – 88 41 – 63 896 – 2,474 46 – 111 5 – 12 33 – 47 51 – 60 58 – 179 8 – 84 756 – 1,609 Oman 2 172 – 306 12.1 – 15.4 0 – 65 0 – 54 1,929 – 2,243 47 – 52 4 – 13 29 – 47 41 – 49 25 – 40 4 – 46 822 – 967 Pakistan 2 242 – 247 10 – 13.3 0 – 6 0 – 9 1,706 – 1,778 62 – 62 13 – 15 32 – 33 42 – 58 33 – 38 37 – 79 1,201 – 1,322

40 | R.M.Nayak-Luke and R.Bañares-Alcántara 2020 This journal is © The Royal Society of Chemistry 2020

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Decision variables Resulting variables Objective

d factor [%] factor d

Bosch synthesis load load synthesis Bosch

Country simulated locations of Number Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of energy [%] by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Equivalent Load Full energyLCOE supplied of [$/MWh] Percentage RE of energy [%] curtailed Electrolyser loa Haber [%] factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Papua New 1 335 12.5 0 0 1,625 94 6 26 50 58 96 1,497 Guinea Peru 1 274 11.5 0 0 1,845 37 9 31 53 47 75 799 Philippines 1 304 14.9 0 0 1,439 50 11 27 39 41 50 1,045 Poland 3 248 – 278 9.9 – 14.3 33 – 47 33 – 42 1,063 – 1,134 94 – 95 6 – 11 31 – 33 42 – 62 159 – 177 28 – 92 1,424 – 1,505 Portugal 6 180 – 273 11.4 – 14.1 0 – 60 0 – 45 1,482 – 2,105 45 – 66 9 – 28 30 – 47 40 – 51 29 – 161 9 – 43 911 – 1,286 Romania 7 178 – 224 9.7 – 11.7 0 – 10 0 – 16 1,226 – 1,423 60 – 74 24 – 35 31 – 36 41 – 52 47 – 272 22 – 70 1,266 – 1,625 Russian 18 179 – 351 8.4 – 14.4 0 – 99 0 – 94 775 – 3,415 31 – 109 7 – 37 24 – 47 40 – 64 29 – 2082 12 – 120 674 – 2,984 Federation Saudi Arabia 6 245 – 307 10.3 – 15.3 0 – 10 0 – 14 1,614 – 2,240 44 – 62 4 – 12 29 – 34 42 – 60 13 – 63 40 – 113 824 – 1,026 Senegal 2 252 – 259 10.5 – 10.7 0 – 2 0 – 2 1,677 – 2,001 40 – 46 12 – 13 31 – 32 54 – 56 30 – 54 77 – 79 852 – 978 Singapore 1 272 11.0 0 0 1,423 46 15 29 51 70 81 948 South Africa 8 161 – 258 8.5 – 13.8 0 – 99 0 – 96 1,470 – 4,617 37 – 51 3 – 18 31 – 56 41 – 67 19 – 70 7 – 80 645 – 968 Spain 16 172 – 280 11.3 – 15.5 0 – 63 0 – 49 1,201 – 2,549 44 – 67 9 – 29 30 – 48 39 – 51 28 – 172 5 – 39 768 – 1,370 Sri Lanka 1 244 9.4 24 21 1,678 54 8 35 66 33 92 964 Sweden 7 208 – 343 9.9 – 14.7 20 – 66 20 – 46 917 – 1,696 61 – 111 6 – 18 25 – 42 40 – 61 66 – 448 17 – 109 929 – 1,711 Switzerland 7 170 – 241 9.3 – 13.4 0 – 73 0 – 53 1,136 – 2,513 43 – 70 7 – 35 30 – 51 40 – 55 38 – 258 8 – 62 705 – 1,468 Thailand 2 245 – 264 10.5 – 13.1 0 – 0 0 – 0 1,695 – 1,727 42 – 42 13 – 16 31 – 32 44 – 54 41 – 55 49 – 72 890 – 918 Tunisia 2 206 – 253 11.8 – 14.2 0 – 3 0 – 3 1,676 – 1,721 47 – 48 15 – 26 31 – 34 40 – 41 52 – 63 28 – 31 1,060 – 1,112 Turkey 14 205 – 267 11.2 – 14.7 0 – 9 0 – 11 1,220 – 1,647 50 – 69 13 – 31 30 – 34 38 – 41 33 – 190 27 – 37 1,047 – 1,550 Ukraine 2 266 – 280 14.6 – 15.8 32 – 57 37 – 52 1,088 – 1,437 146 – 190 5 – 10 30 – 33 38 – 44 93 – 171 30 – 34 2,107 – 2,809 UK 16 162 – 369 10.7 – 14 24 – 99 26 – 94 841 – 4,228 33 – 108 3 – 12 24 – 54 42 – 59 41 – 405 9 – 137 613 – 1,767 USA 47 160 – 325 8.8 – 16.3 0 – 67 0 – 50 965 – 2,318 34 – 79 6 – 30 27 – 51 38 – 69 23 – 559 6 – 100 669 – 1,680 Venezuela 1 312 15.1 0 0 1,732 73 6 28 41 26 52 1,581 Zambia 1 299 12.7 0 0 1,463 54 9 29 48 69 73 1,105

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Note 17: 2019 Scenario, Domestic Corporation – Simulation results by location. Lowest 10 LCOA for multi-national corporation by geographic region Information about location Decision variables Resulting variables Objective

energy energy

Bosch synthesis load load synthesis Bosch

Location Country region Geographic Latitude Longitude Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of [%] by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Equivalent Load Full energyLCOE supplied of [$/MWh] [%] energySupply curtailed [%] factor Electrolyser load Haber [%] factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Arrecife Lanzarote Spain Northern Africa 28.95 -13.60 172 12.0 63 49 2,549 45 10 48 51 28 5 768 Casablanca Morocco Northern Africa 33.57 -7.67 208 11.7 0 0 1,825 39 23 35 44 28 30 905 Aswan Egypt Northern Africa 23.97 32.78 266 10.4 10 15 2,060 78 6 33 61 39 88 1,329 Mersa Matruh Egypt Northern Africa 31.33 27.22 185 12.7 54 50 1,960 85 11 45 47 22 7 1,367 El Kharga Egypt Northern Africa 25.45 30.53 286 14.7 0 0 2,132 74 5 31 43 23 43 1,390 Tunis Tunisia Northern Africa 36.83 10.23 253 14.2 3 3 1,721 48 15 31 40 63 31 1,060 Sidi Bouzid Tunisia Northern Africa 35.00 9.48 206 11.8 0 0 1,676 47 26 34 41 52 28 1,112 El Arish Egypt Northern Africa 31.12 33.75 261 14.2 0 0 1,910 83 11 32 41 25 35 1,534 El Natroon Egypt Northern Africa 30.40 30.35 255 13.9 0 1 1,835 86 12 32 42 25 35 1,569 Funchal Portugal Northern Africa 32.63 -16.90 273 14.1 0 0 1,482 55 14 30 40 69 43 1,114 Marion Island South Africa S-Saharan Africa -46.88 37.87 161 11.1 99 96 4,617 37 3 56 60 59 7 645 Dakar Senegal S-Saharan Africa 14.73 -17.47 252 10.5 2 2 2,001 40 12 32 56 30 77 852 Gillot France S-Saharan Africa -20.89 55.53 166 11.6 70 52 2,505 42 9 51 54 18 5 681 Garissa Kenya S-Saharan Africa -0.47 39.63 267 11.6 0 1 1,891 43 10 32 52 48 69 924 Voi Kenya S-Saharan Africa -3.40 38.57 233 12.0 0 0 1,875 42 16 34 46 20 43 942 Upington South Africa S-Saharan Africa -28.43 21.27 229 12.7 0 0 1,771 39 16 34 44 19 32 852 Mombasa Airport Kenya S-Saharan Africa -4.03 39.62 249 12.5 0 0 1,802 44 15 32 45 27 47 973 Beira Mozambique S-Saharan Africa -19.83 34.85 264 13.6 0 0 1,714 46 10 32 44 20 43 971 Maun Botswana S-Saharan Africa -19.98 23.42 259 13.7 0 0 1,692 40 11 32 43 19 39 854 Gough Island UK S-Saharan Africa -40.35 9.88 190 12.0 77 55 2,273 49 6 46 54 70 17 797 Piura Peru Latin America -5.17 -80.60 274 11.5 0 0 1,845 37 9 31 53 47 75 799 Punta Arenas Chile Latin America -53.00 -70.85 219 13.6 99 98 3,436 42 2 41 49 115 18 786 Caracas Maiquetia Venezuela Latin America 10.60 -66.98 312 15.1 0 0 1,732 73 6 28 41 26 52 1,581 Ushuaia Airport Argentina Latin America -54.80 -68.32 193 12.4 97 90 3,068 72 9 44 50 104 14 1,292

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Journal Name ARTICLE

Information about location Decision variables Resulting variables Objective

Bosch synthesis load load synthesis Bosch

of energy supplied of

Location Country region Geographic Latitude Longitude Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of energy [%] by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Equivalent Load Full LCOE [$/MWh] [%] energySupply curtailed [%] factor Electrolyser load Haber [%] factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Petrolina Brazil Latin America -9.38 -40.50 241 12.4 0 0 1,730 42 17 32 44 29 42 912 Le Lamentin France Latin America 14.60 -61.00 277 10.7 5 5 1,751 41 8 31 57 43 91 785 Antofagasta Chile Latin America -23.43 -70.43 242 12.8 0 1 1,647 40 16 32 43 35 39 870 Raizet France Latin America 16.27 -61.52 251 13.0 0 0 1,662 41 14 32 44 20 41 837 Brasilia Brazil Latin America -15.78 -47.93 270 11.0 0 0 1,517 47 12 30 53 67 81 971 Mendoza Airport Argentina Latin America -32.83 -68.78 216 12.0 0 0 1,579 67 22 34 43 26 32 1,419 Dodge City USA N. America 37.77 -99.97 160 11.1 60 45 2,260 39 12 51 54 23 6 669 Kahului USA N. America 20.90 -156.43 247 9.1 27 19 2,008 36 6 35 69 44 100 670 Lihue Kauai USA N. America 21.98 -159.35 169 11.4 67 50 2,318 39 10 50 54 32 9 670 Churchill Canada N. America 58.75 -94.07 210 13.7 87 71 2,555 44 4 43 48 63 13 753 Honolulu Oahu USA N. America 21.33 -157.92 242 9.8 21 19 1,779 41 10 34 62 28 81 738 New York USA N. America 40.65 -73.78 191 12.5 54 40 1,858 46 7 45 50 29 12 746 Inukjuak Canada N. America 58.45 -78.12 202 10.6 88 72 2,554 44 6 43 60 110 40 771 Tucson USA N. America 32.12 -110.93 259 14.3 0 0 1,808 34 12 32 41 26 34 759 Oklahoma City USA N. America 35.40 -97.60 213 9.3 36 27 1,779 44 10 39 65 66 68 762 Fort Worth USA N. America 32.83 -97.05 226 9.0 28 25 1,777 43 10 37 68 62 86 763 Kwajalein Island Japan Eastern Asia 8.73 167.73 163 11.5 70 53 2,544 31 10 52 53 22 4 585 Iwojima Japan Eastern Asia 24.78 141.31 165 11.5 67 54 2,292 35 11 50 52 20 6 632 Minamitorishima Japan Eastern Asia 24.30 153.97 142 10.0 62 51 2,218 35 16 55 57 14 4 635 Choshi Japan Eastern Asia 35.73 140.87 159 10.7 71 53 2,259 35 13 51 55 39 9 648 Nemuro Japan Eastern Asia 43.33 145.58 177 12.1 67 50 1,971 40 10 47 50 33 7 704 Naha Japan Eastern Asia 26.20 127.68 172 11.5 70 54 2,008 40 12 48 52 32 10 704 Ishigakijima Japan Eastern Asia 24.33 124.17 182 12.1 63 52 1,929 41 12 45 49 31 10 733 Omaezaki Japan Eastern Asia 34.60 138.22 169 11.6 55 45 1,811 42 14 47 50 32 7 748 Hachijojima Japan Eastern Asia 33.10 139.78 177 12.0 66 48 1,836 42 13 46 49 36 8 753 Tanegashima Japan Eastern Asia 30.73 131.00 179 12.4 63 49 1,775 44 11 46 48 28 6 759 Chiang Mai Airport Thailand S-Eastern Asia 18.78 98.98 245 10.5 0 0 1,727 42 16 32 54 55 72 890

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Information about location Decision variables Resulting variables Objective

Hours Equivalent

Bosch synthesis load load synthesis Bosch

Location Country region Geographic Latitude Longitude Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of energy [%] by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Load Full energyLCOE supplied of [$/MWh] [%] energySupply curtailed [%] factor Electrolyser load Haber [%] factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Penang Malaysia S-Eastern Asia 5.30 100.27 276 13.7 0 0 1,709 41 12 30 42 26 48 886 Bangkok Thailand S-Eastern Asia 13.73 100.57 264 13.1 0 0 1,695 42 13 31 44 41 49 918 Kota Kinabalu Malaysia S-Eastern Asia 5.93 116.05 272 11.0 0 0 1,547 45 13 30 52 63 81 938 Kota Bahru Malaysia S-Eastern Asia 6.17 102.28 276 13.8 0 0 1,523 46 12 30 42 27 47 944 Singapore Airport Singapore S-Eastern Asia 1.37 103.98 272 11.0 0 0 1,423 46 15 29 51 70 81 948 Science Garden Philippines S-Eastern Asia 14.63 121.01 304 14.9 0 0 1,439 50 11 27 39 41 50 1,045 Kuching Malaysia S-Eastern Asia 1.48 110.33 285 11.7 0 0 1,356 52 14 28 48 58 79 1,036 Bukit Kototabang Indonesia S-Eastern Asia -0.20 100.32 265 13.2 0 0 1,310 57 18 29 41 38 48 1,152 New Delhi India Southern Asia 28.58 77.20 261 13.9 0 0 1,957 30 11 32 43 19 38 768 Jodhpur India Southern Asia 26.30 73.02 284 14.8 0 0 1,933 30 7 31 42 20 41 773 Vishakhapatnam India Southern Asia 17.72 83.23 242 10.7 0 0 1,869 31 15 33 52 52 66 793 Ahmedabad India Southern Asia 23.07 72.63 280 14.4 0 0 1,892 31 8 31 42 31 43 796 Thiruvananthapura India Southern Asia 8.48 76.95 224 11.6 0 0 1,828 32 20 34 46 23 43 807 m Poona India Southern Asia 18.53 73.85 244 12.8 0 0 1,796 33 16 32 44 25 40 816 Santacruz Bombay India Southern Asia 19.12 72.85 253 13.1 0 0 1,790 33 14 32 43 32 42 826 Madras India Southern Asia 13.00 80.18 279 13.8 0 0 1,711 34 9 30 43 43 49 841 Goa India Southern Asia 15.48 73.82 282 13.9 0 0 1,726 34 9 30 43 31 49 832 Nagpur Sonegaon India Southern Asia 21.10 79.05 260 13.7 0 0 1,665 35 13 31 42 26 40 848 Masira Oman Western Asia 20.67 58.90 172 12.1 65 54 2,243 47 13 47 49 40 4 822 Sharura Saudi Arabia Western Asia 17.47 47.12 269 11.4 0 0 2,240 44 7 32 54 24 73 824 Wadi Al Dawaser Saudi Arabia Western Asia 20.50 45.20 259 10.9 4 8 2,138 46 8 33 57 25 74 836 Jeddah Saudi Arabia Western Asia 21.68 39.15 245 10.3 3 6 2,101 47 11 34 58 31 75 860 Madinah Saudi Arabia Western Asia 24.55 39.70 301 15.3 0 0 2,065 48 4 30 42 22 45 907 Al Ahsa Saudi Arabia Western Asia 25.28 49.48 257 13.5 0 0 1,954 50 12 32 43 13 40 947 Silifke Turkey Western Asia 36.38 33.93 261 14.1 0 0 1,639 50 14 31 41 52 37 1,047

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Information about location Decision variables Resulting variables Objective

power

actor [%] actor

Bosch synthesis load load synthesis Bosch

Location Country region Geographic Latitude Longitude Electrolyser rated [MW] rated power & ASU HB [MW] Percentage RE of energy [%] by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Equivalent Load Full energyLCOE supplied of [$/MWh] [%] energySupply curtailed f Electrolyser load Haber [%] factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Buraimi Oman Western Asia 24.23 55.78 306 15.4 0 0 1,929 52 4 29 41 25 46 967 Mugla Turkey Western Asia 37.20 28.35 245 13.8 0 0 1,647 50 18 32 39 44 30 1,067 Taif Saudi Arabia Western Asia 21.48 40.55 307 10.6 10 14 1,614 62 5 29 60 63 113 1,026 Sonnblick Austria Europe 47.05 12.95 159 10.9 95 84 4,303 30 2 58 62 53 7 537 List Denmark Europe 55.02 8.42 158 10.3 98 91 4,707 29 1 58 66 107 13 546 Malin Head Ireland Europe 55.37 -7.33 157 10.7 99 94 4,338 36 6 56 60 61 8 648 Russian Dickson Island Europe 73.50 80.23 179 11.7 99 94 3,415 31 9 47 53 73 12 674 Federation Lerwick UK Europe 60.13 -1.18 162 10.9 99 94 4,228 33 6 54 58 72 9 613 Skagen Fyr Denmark Europe 57.73 10.63 193 12.8 96 87 3,636 36 1 48 53 71 11 643 Aberporth UK Europe 52.13 -4.57 183 12.4 88 67 2,909 41 3 49 53 49 9 683 Guetsch Switzerland Europe 46.65 8.62 170 11.6 73 53 2,513 43 7 51 55 38 8 705 Valley UK Europe 53.25 -4.53 174 11.7 86 65 2,738 44 6 50 54 50 9 718 Belmullet Ireland Europe 54.23 -10.00 192 12.7 90 72 2,827 49 4 47 51 60 11 807 Cape Grim Australia Oceania -40.66 144.68 143 9.9 100 100 5,636 28 1 65 68 44 6 487 Willis Island Australia Oceania -16.30 149.98 140 10.0 100 99 5,138 31 6 63 64 39 3 532 Glenmore Australia Oceania -33.69 115.02 199 12.7 99 97 3,937 40 1 46 53 87 15 695 Wellington New Zealand Oceania -41.32 174.77 192 12.7 100 100 4,002 40 2 47 52 82 11 691 Carnarvon Airport Australia Oceania -24.88 113.67 186 12.5 76 63 2,901 50 6 47 51 50 10 787 Noumea France Oceania -22.28 166.45 228 9.5 35 24 1,797 48 8 37 66 48 77 791 Koumac France Oceania -20.57 164.28 281 14.4 0 0 1,580 44 10 30 41 27 43 867 Cook Australia Oceania -30.62 130.40 189 12.8 75 64 2,513 57 8 46 49 50 9 881 Ils Des Pins Moue France Oceania -22.60 167.45 256 9.6 26 18 1,499 55 9 33 64 41 95 888 Geraldton Airport Australia Oceania -28.78 114.70 206 13.5 66 55 2,332 60 6 42 47 55 12 925 Abbreviation: S-Saharan Africa – Sub-Saharan Africa; Lat. America – Latin America and the Caribbean; N. America – Northern America; S-Eastern Asia – South-Eastern Asia.

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ARTICLE Journal Name

Note 18: 2019 Scenario, Domestic Corporation – Components of LCOA by location. Lowest 10 LCOA for multi-national corporation by geographic region

Location Country Latitude Longitude Electrolyser OPEX [%] Electrolyser CAPEX [%] Hydrogen Storage [%] CAPEX Hydrogen Storage OPEX [%] Synthesis Ammonia OPEX [%] Synthesis Ammonia [%] CAPEX & Operation [%] Maintenance OPEXEnergy Curtailed [%] Cell Hydrogen Fuel [%] CAPEX OPEX Separation Air [%] CAPEX Separation Air [%] Water OPEX [%] Arrecife Lanzarote Spain 28.95 -13.6 49.8 17.1 2.2 0.2 3.1 9.5 6.9 6.2 0.9 0.5 3.2 0.4 Casablanca Morocco 33.57 -7.67 38.0 22.3 2.4 0.5 2.3 10.0 7.6 11.9 0.9 0.4 3.4 0.4 Aswan Egypt 23.97 32.78 50.3 23.9 2.8 0.7 3.1 7.4 4.8 3.2 0.5 0.5 2.5 0.2 Mersa Matruh Egypt 31.33 27.22 53.1 17.0 1.6 0.3 3.3 9.2 4.1 6.9 0.6 0.6 3.1 0.2 El Kharga Egypt 25.45 30.53 47.1 24.8 1.6 0.6 2.8 10.1 5.3 2.8 0.7 0.5 3.4 0.2 Tunis Tunisia 36.83 10.23 39.1 23.3 4.6 0.5 2.4 10.4 7.5 7.1 0.9 0.4 3.5 0.3 Sidi Bouzid Tunisia 35 9.48 37.1 21.0 4.2 0.5 2.2 9.5 6.8 13.9 0.8 0.4 3.2 0.3 El Arish Egypt 31.12 33.75 47.7 21.9 1.6 0.6 2.9 9.5 4.8 6.4 0.6 0.5 3.2 0.2 El Natroon Egypt 30.4 30.35 48.3 21.1 1.6 0.6 2.9 9.1 4.6 7.1 0.6 0.5 3.1 0.2 Funchal Portugal 32.63 -16.9 43.2 21.3 4.3 0.6 2.6 8.7 7.5 7.4 0.8 0.4 3.0 0.3 Marion Island South Africa -46.88 37.87 48.8 18.3 5.3 0.2 3.1 10.0 7.6 1.5 0.9 0.5 3.4 0.5 Dakar Senegal 14.73 -17.47 40.3 27.1 2.5 0.6 2.5 8.9 7.7 5.8 0.8 0.4 3.0 0.4 Gillot France -20.89 55.53 52.6 16.2 1.4 0.2 3.3 9.0 7.1 5.3 0.9 0.6 3.0 0.5 Garissa Kenya -0.47 39.63 40.0 27.0 3.9 0.5 2.4 9.3 7.7 4.6 0.8 0.4 3.2 0.4 Voi Kenya -3.4 38.57 39.5 25.1 1.7 0.5 2.4 10.2 7.2 8.3 0.8 0.4 3.5 0.3 Upington South Africa -28.43 21.27 40.3 23.5 1.5 0.5 2.4 10.3 8.0 8.2 0.9 0.4 3.5 0.4 Mombasa Airport Kenya -4.03 39.62 39.8 25.4 2.2 0.5 2.4 10.1 7.3 7.2 0.8 0.4 3.4 0.3 Beira Mozambique -19.83 34.85 41.6 25.6 1.5 0.5 2.5 10.4 7.4 5.2 0.9 0.4 3.6 0.3 Maun Botswana -19.98 23.42 41.4 24.7 1.4 0.5 2.5 10.3 8.4 5.5 1.0 0.4 3.5 0.4 Gough Island UK -40.35 9.88 52.7 16.1 4.7 0.2 3.3 8.0 7.3 3.3 0.8 0.6 2.7 0.4 Piura Peru -5.17 -80.6 40.6 26.3 3.5 0.6 2.5 8.7 8.9 4.2 0.8 0.4 3.0 0.4 Punta Arenas Chile -53 -70.85 45.9 19.0 7.8 0.3 2.9 9.3 8.8 1.2 0.9 0.5 3.2 0.4 Caracas Maiquetia Venezuela 10.6 -66.98 40.8 29.8 1.9 0.6 2.5 11.4 5.0 2.8 0.7 0.4 3.9 0.2 Ushuaia Airport Argentina -54.8 -68.32 47.5 17.9 7.6 0.3 3.0 9.1 5.1 5.0 0.6 0.5 3.1 0.2 Petrolina Brazil -9.38 -40.5 40.1 23.7 2.3 0.5 2.4 9.7 7.9 8.5 0.9 0.4 3.3 0.4 Le Lamentin France 14.6 -61 45.2 23.8 2.9 0.7 2.8 7.3 8.8 4.4 0.7 0.5 2.5 0.4 Antofagasta Chile -23.43 -70.43 40.6 22.5 2.6 0.5 2.5 9.4 8.4 8.6 0.9 0.4 3.2 0.4

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Curtailed OPEX Curtailed

Location Country Latitude Longitude Electrolyser OPEX [%] Electrolyser CAPEX [%] Hydrogen Storage CAPEX [%] Hydrogen Storage OPEX [%] OPEX Synthesis Ammonia [%] Synthesis Ammonia [%] CAPEX & Operation [%] Maintenance Energy [%] CAPEX Cell Hydrogen Fuel [%] OPEX [%] Separation Air CAPEX [%] Separation Air Water OPEX [%] Raizet France 16.27 -61.52 43.6 21.9 1.4 0.6 2.6 9.0 8.5 7.7 0.9 0.4 3.1 0.4 Brasilia Brazil -15.78 -47.93 42.7 23.7 4.6 0.6 2.6 7.6 7.8 6.4 0.7 0.4 2.6 0.3 Mendoza Airport Argentina -32.83 -68.78 41.4 21.8 2.1 0.5 2.5 9.6 5.0 12.5 0.7 0.4 3.3 0.2 Dodge City USA 37.77 -99.97 49.8 16.3 1.8 0.2 3.1 8.9 7.4 7.5 0.9 0.5 3.0 0.5 Kahului USA 20.9 -156.43 46.1 23.7 3.3 0.7 2.9 6.9 8.9 3.5 0.7 0.5 2.4 0.5 Lihue Kauai USA 21.98 -159.35 50.1 16.7 2.5 0.2 3.1 8.9 7.7 5.9 0.9 0.5 3.0 0.5 Churchill Canada 58.75 -94.07 50.0 17.9 4.2 0.2 3.1 9.2 8.2 2.1 0.9 0.5 3.1 0.4 Honolulu Oahu USA 21.33 -157.92 47.5 21.9 2.0 0.6 3.0 7.0 8.2 5.7 0.7 0.5 2.4 0.4 New York USA 40.65 -73.78 52.7 16.5 2.0 0.3 3.3 8.6 7.4 4.5 0.9 0.5 2.9 0.4 Inukjuak Canada 58.45 -78.12 49.0 17.2 7.4 0.3 3.1 7.2 8.2 3.5 0.7 0.5 2.4 0.4 Tucson USA 32.12 -110.93 40.0 23.8 1.9 0.5 2.4 10.4 9.8 5.8 1.1 0.4 3.5 0.4 Oklahoma City USA 35.4 -97.6 48.9 18.7 4.6 0.5 3.1 6.5 7.9 6.1 0.7 0.5 2.2 0.4 Fort Worth USA 32.83 -97.05 48.3 19.9 4.3 0.6 3.0 6.2 8.0 5.9 0.6 0.5 2.1 0.4 Kwajalein Island Japan 8.73 167.73 45.4 19.3 2.1 0.2 2.8 10.8 8.5 5.3 1.1 0.5 3.7 0.5 Iwojima Japan 24.78 141.31 46.8 18.4 1.7 0.2 2.9 10.1 8.0 6.4 1.0 0.5 3.5 0.5 Minamitorishima Japan 24.3 153.97 47.5 16.7 1.3 0.2 3.0 9.3 7.2 9.9 0.9 0.5 3.2 0.5 Choshi Japan 35.73 140.87 46.1 17.6 3.4 0.2 2.9 9.4 7.9 7.5 0.9 0.5 3.2 0.5 Nemuro Japan 43.33 145.58 48.2 17.6 2.6 0.2 3.0 9.5 7.8 6.0 1.0 0.5 3.2 0.5 Naha Japan 26.2 127.68 48.0 17.3 2.5 0.2 3.0 9.2 7.6 7.1 0.9 0.5 3.1 0.5 Ishigakijima Japan 24.33 124.17 47.7 17.7 2.4 0.2 3.0 9.3 7.7 7.0 0.9 0.5 3.2 0.4 Omaezaki Japan 34.6 138.22 48.4 16.5 2.4 0.2 3.0 9.0 7.3 8.3 0.9 0.5 3.1 0.4 Hachijojima Japan 33.1 139.78 48.1 16.9 2.7 0.3 3.0 9.1 7.5 7.5 0.9 0.5 3.1 0.4 Tanegashima Japan 30.73 131 49.5 16.7 2.0 0.2 3.1 9.2 7.3 6.9 0.9 0.5 3.1 0.4 Chiang Mai Airport Thailand 18.78 98.98 40.7 23.7 4.2 0.6 2.5 8.0 8.0 8.0 0.7 0.4 2.7 0.4 Penang Malaysia 5.3 100.27 40.6 25.1 1.9 0.5 2.5 9.9 8.6 5.9 0.9 0.4 3.4 0.4 Bangkok Thailand 13.73 100.57 40.5 24.2 2.9 0.5 2.5 9.5 8.3 6.7 0.9 0.4 3.2 0.4 Kota Kinabalu Malaysia 5.93 116.05 42.0 23.7 4.3 0.6 2.6 7.6 8.1 7.0 0.7 0.4 2.6 0.3 Kota Bahru Malaysia 6.17 102.28 42.7 23.6 1.9 0.6 2.6 9.3 8.1 6.3 0.9 0.4 3.2 0.4 Singapore Airport Singapore 1.37 103.98 42.4 22.4 4.5 0.6 2.6 7.2 8.3 8.1 0.7 0.4 2.4 0.3 Science Garden Philippines 14.63 121.01 42.3 24.1 2.6 0.6 2.5 9.3 8.1 5.7 0.9 0.4 3.2 0.3

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Fuel Cell CAPEX Cell Fuel

Location Country Latitude Longitude Electrolyser OPEX [%] Electrolyser CAPEX [%] Hydrogen Storage CAPEX [%] Hydrogen Storage OPEX [%] OPEX Synthesis Ammonia [%] Synthesis Ammonia [%] CAPEX & Operation [%] Maintenance OPEXEnergy Curtailed [%] Hydrogen [%] OPEX [%] Separation Air CAPEX [%] Separation Air Water OPEX [%] Kuching Malaysia 1.48 110.33 43.5 22.7 3.7 0.6 2.6 7.4 7.7 7.8 0.7 0.4 2.5 0.3 Bukit Kototabang Indonesia -0.2 100.32 43.2 21.4 2.4 0.6 2.6 8.4 7.0 10.0 0.7 0.4 2.9 0.3 New Delhi India 28.58 77.2 34.3 29.4 1.7 0.5 2.1 12.3 9.3 4.3 1.1 0.3 4.2 0.4 Jodhpur India 26.3 73.02 34.6 30.3 1.7 0.5 2.1 12.5 9.6 2.6 1.1 0.3 4.3 0.4 Vishakhapatnam India 17.72 83.23 34.6 27.8 4.7 0.5 2.1 9.7 8.9 6.8 0.9 0.4 3.3 0.4 Ahmedabad India 23.07 72.63 34.3 29.7 2.6 0.5 2.1 12.1 9.5 3.4 1.1 0.3 4.1 0.4 Thiruvananthapuram India 8.48 76.95 34.9 26.6 2.1 0.4 2.1 10.9 8.5 9.1 1.0 0.4 3.7 0.4 Poona India 18.53 73.85 35.2 27.4 2.2 0.5 2.1 11.4 8.8 6.9 1.0 0.4 3.9 0.4 Santacruz Bombay India 19.12 72.85 34.9 27.6 2.8 0.5 2.1 11.3 8.9 6.3 1.0 0.4 3.9 0.4 Madras India 13 80.18 35.9 28.3 3.4 0.5 2.2 11.1 9.1 4.0 1.0 0.4 3.8 0.4 Goa India 15.48 73.82 36.0 28.9 2.5 0.5 2.2 11.3 9.1 4.0 1.0 0.4 3.8 0.4 Nagpur Sonegaon India 21.1 79.05 36.6 27.2 2.1 0.5 2.2 11.3 8.7 5.8 1.0 0.4 3.9 0.4 Masira Oman 20.67 58.9 48.7 16.4 3.0 0.3 3.0 9.2 6.8 7.8 0.9 0.5 3.1 0.4 Sharura Saudi Arabia 17.47 47.12 46.5 24.2 1.7 0.7 2.8 8.1 8.0 3.6 0.8 0.5 2.8 0.4 Wadi Al Dawaser Saudi Arabia 20.5 45.2 47.7 23.0 1.8 0.7 3.0 7.7 7.6 4.3 0.7 0.5 2.6 0.4 Jeddah Saudi Arabia 21.68 39.15 47.2 22.0 2.2 0.7 2.9 7.3 7.4 6.3 0.7 0.5 2.5 0.4 Madinah Saudi Arabia 24.55 39.7 46.3 24.0 1.4 0.6 2.8 9.6 8.3 1.9 0.9 0.5 3.3 0.4 Al Ahsa Saudi Arabia 25.28 49.48 46.8 21.3 0.9 0.6 2.8 8.9 7.4 6.6 0.8 0.5 3.0 0.3 Silifke Turkey 36.38 33.93 42.0 22.3 3.5 0.6 2.5 9.5 7.6 7.1 0.8 0.4 3.2 0.3 Buraimi Oman 24.23 55.78 47.2 23.3 1.5 0.6 2.9 9.3 8.0 2.3 0.9 0.5 3.2 0.3 Mugla Turkey 37.2 28.35 41.1 21.5 3.1 0.6 2.5 9.6 7.4 9.4 0.8 0.4 3.3 0.3 Taif Saudi Arabia 21.48 40.55 51.7 21.6 3.5 0.8 3.2 5.9 7.2 2.8 0.6 0.5 2.0 0.3 Sonnblick Austria 47.05 12.95 47.9 18.7 4.9 0.1 3.0 10.1 8.8 0.9 1.0 0.5 3.5 0.6 List Denmark 55.02 8.42 44.7 18.2 9.7 0.2 2.8 9.3 9.6 0.4 0.9 0.5 3.2 0.6 Malin Head Ireland 55.37 -7.33 47.0 18.3 5.6 0.2 2.9 9.9 7.7 3.2 0.9 0.5 3.4 0.5 Russian Dickson Island 73.5 80.23 38.9 21.6 6.9 0.2 2.4 11.2 8.7 4.3 1.0 0.4 3.8 0.5 Federation Lerwick UK 60.13 -1.18 46.2 18.0 6.3 0.2 2.9 9.6 8.6 3.2 0.9 0.5 3.3 0.5 Skagen Fyr Denmark 57.73 10.63 48.0 18.8 5.4 0.2 3.0 9.8 8.9 0.5 1.0 0.5 3.3 0.5

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[%]

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Note 19: 2030 Scenario, Multinational Corporation – Simulation results by location. Lowest 10 LCOA estimates for each geographic region

Decision variables Resulting variables Objective

Bosch synthesis load load synthesis Bosch

Country simulated locations of Number Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of energy [%] by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Equivalent Load Full energyLCOE supplied of [$/MWh] Percentage RE of energy [%] curtailed [%] factor Electrolyser load Haber [%] factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Argentina 5 168 – 236 10.5 – 13.1 0 – 68 0 – 39 1,517 – 1,881 13 – 22 10 – 32 32 – 50 40 – 55 30 – 89 10 – 35 415 – 461 Australia 37 136 – 291 7.9 – 15.2 0 – 95 0 – 82 1,357 – 4,848 16 – 38 2 – 21 30 – 66 38 – 71 15 – 56 2 – 100 310 – 636 Austria 6 121 – 210 8 – 11.7 0 – 83 0 – 56 1,116 – 3,320 16 – 27 7 – 52 32 – 63 40 – 65 23 – 228 4 – 55 318 – 728 Belgium 4 161 – 213 8.9 – 11.2 32 – 67 27 – 40 1,054 – 1,957 23 – 37 8 – 17 37 – 51 53 – 63 44 – 113 11 – 76 426 – 672 Bosnia & 1 185 10.4 0 0 1,263 18 35 33 41 76 30 573 Herzegovina Botswana 1 216 11.8 0 0 1,692 11 20 35 45 17 35 378 Brazil 5 193 – 269 10.2 – 14.2 0 – 0 0 – 1 1,209 – 1,730 11 – 16 14 – 28 30 – 35 40 – 53 20 – 61 32 – 77 388 – 480 Bulgaria 4 141 – 214 9.3 – 12 0 – 4 0 – 7 1,131 – 1,357 16 – 23 26 – 45 32 – 37 39 – 43 43 – 132 11 – 32 539 – 672 Canada 11 136 – 188 8.4 – 12.1 8 – 66 8 – 41 883 – 1,937 21 – 44 8 – 43 36 – 57 43 – 60 26 – 537 5 – 57 423 – 1,234 Chile 7 150 – 268 9.4 – 14 0 – 67 0 – 37 931 – 1,914 12 – 27 14 – 37 29 – 54 40 – 58 29 – 88 8 – 41 408 – 682 China 51 123 – 296 8.2 – 15.4 0 – 47 0 – 36 902 – 1,740 14 – 30 9 – 48 26 – 40 37 – 60 16 – 123 10 – 106 407 – 699 Colombia 2 220 – 235 10.1 – 11.3 0 – 0 0 – 0 1,234 – 1,383 14 – 16 24 – 25 30 – 32 44 – 50 23 – 47 44 – 72 444 – 473 Congo 1 203 9.4 0 0 1,459 13 27 33 52 42 60 426 Croatia 2 134 – 184 7.8 – 11.3 0 – 13 0 – 11 1,205 – 1,494 18 – 19 26 – 49 36 – 37 43 – 44 26 – 121 20 – 26 481 – 714 Czech Republic 5 157 – 189 10 – 11.4 12 – 53 12 – 29 1,066 – 1,469 26 – 31 16 – 33 36 – 50 43 – 51 26 – 59 4 – 22 487 – 653 Denmark 7 148 – 287 8.6 – 10.2 42 – 86 29 – 56 1,075 – 3,316 16 – 41 4 – 14 30 – 58 62 – 68 38 – 150 7 – 106 352 – 708 Egypt 5 185 – 265 8.4 – 14.4 0 – 23 0 – 20 1,840 – 2,132 14 – 18 11 – 21 32 – 41 41 – 65 19 – 26 32 – 65 374 – 436 El Salvador 1 272 11.2 0 0 1,729 18 12 30 52 64 78 471 Estonia 1 207 9.0 45 40 1,057 42 16 37 62 142 69 756 Finland 5 179 – 460 10.7 – 12.5 26 – 38 29 – 45 691 – 1,074 36 – 66 2 – 25 20 – 39 44 – 55 119 – 522 17 – 175 724 – 1,249 France 39 130 – 241 8.3 – 12.7 0 – 38 0 – 29 1,044 – 1,911 13 – 32 10 – 41 32 – 50 41 – 73 22 – 124 10 – 83 359 – 732 Germany 9 117 – 229 7.3 – 11.3 7 – 37 9 – 27 967 – 1,266 26 – 32 12 – 50 31 – 41 43 – 65 52 – 242 17 – 85 573 – 873 Ghana 1 228 9.9 0 1 1,670 12 21 32 53 40 69 388 Greece 3 135 – 196 8.9 – 11.1 0 – 1 0 – 1 1,497 – 1,623 14 – 15 28 – 46 34 – 38 40 – 43 33 – 71 11 – 30 484 – 541

50 | R.M.Nayak-Luke and R.Bañares-Alcántara 2020 This journal is © The Royal Society of Chemistry 2020

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Decision variables Resulting variables Objective

power

Bosch synthesis load load synthesis Bosch

Country simulated locations of Number Electrolyser rated [MW] rated power & ASU HB [MW] Percentage RE of energy [%] by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Equivalent Load Full energyLCOE supplied of [$/MWh] Percentage RE of energy [%] curtailed [%] factor Electrolyser load Haber [%] factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Hungary 2 130 – 172 8.7 – 10.4 2 – 9 4 – 13 1,116 – 1,177 21 – 26 33 – 48 36 – 37 40 – 43 66 – 115 10 – 22 660 – 748 India 12 168 – 240 9.4 – 12.3 0 – 2 0 – 7 1,356 – 1,957 8 – 12 17 – 35 32 – 38 45 – 54 11 – 39 30 – 62 338 – 417 Indonesia 1 229 11.8 0 0 1,310 15 24 31 43 30 43 464 Ireland 5 125 – 201 8.8 – 12.5 36 – 81 27 – 46 1,052 – 2,610 18 – 36 9 – 19 39 – 64 45 – 67 30 – 73 5 – 18 355 – 668 Italy 16 136 – 232 7.8 – 13.3 0 – 28 0 – 20 1,126 – 1,773 13 – 20 17 – 48 32 – 43 40 – 68 28 – 134 10 – 61 407 – 703 Japan 62 152 – 314 7.5 – 15.5 0 – 51 0 – 33 1,131 – 1,997 13 – 25 7 – 36 27 – 49 38 – 72 20 – 81 8 – 103 324 – 550 Kazakhstan 1 172 10.3 5 11 1,301 30 33 36 43 88 22 729 Kenya 3 203 – 242 9.8 – 12.2 0 – 1 0 – 2 1,802 – 1,886 10 – 11 16 – 23 33 – 35 46 – 54 16 – 26 40 – 56 354 – 375 Korea, Republic of (South 8 179 – 223 8.7 – 12.4 0 – 19 0 – 22 1,183 – 1,370 15 – 22 19 – 33 33 – 36 42 – 60 27 – 60 29 – 71 433 – 501 Korea) Latvia 1 104 6.4 8 16 911 34 55 40 46 120 19 1,087 Lithuania 1 208 12.8 35 31 1,027 39 17 37 43 91 19 725 Macedonia, 2 146 – 147 8.5 – 8.8 0 – 5 0 – 8 1,267 – 1,306 17 – 21 41 – 44 36 – 37 44 – 45 71 – 97 23 – 27 627 – 631 Republic of Malaysia 4 211 – 267 10.9 – 12.6 0 – 0 0 – 0 1,356 – 1,709 12 – 15 17 – 24 29 – 34 43 – 49 20 – 52 42 – 74 394 – 447 Mexico 9 214 – 311 9.9 – 15.1 0 – 4 0 – 4 1,345 – 1,962 16 – 24 6 – 26 27 – 34 38 – 56 22 – 76 37 – 90 440 – 572 Mongolia 5 177 – 228 10.3 – 13.2 0 – 1 0 – 1 1,225 – 1,526 23 – 29 19 – 34 32 – 35 41 – 43 24 – 89 25 – 33 536 – 708 Morocco 1 189 10.7 0 0 1,825 11 27 36 45 26 29 387 Mozambique 3 225 – 230 12.1 – 12.4 0 – 0 0 – 0 1,595 – 1,714 11 – 12 17 – 18 34 – 34 44 – 45 16 – 19 35 – 37 375 – 390 Netherlands 4 158 – 224 9.1 – 11.8 20 – 58 20 – 34 998 – 1,686 24 – 33 11 – 31 37 – 51 46 – 66 32 – 119 7 – 80 451 – 713 New Zealand 4 174 – 217 7.9 – 12 2 – 83 3 – 62 1,191 – 2,973 23 – 40 4 – 25 32 – 51 41 – 69 44 – 98 12 – 89 428 – 738 Nicaragua 1 276 11.4 0 0 1,491 21 12 30 51 60 78 504 Norway 2 137 – 270 9.5 – 10.3 49 – 71 36 – 42 898 – 1,758 24 – 51 9 – 14 31 – 58 59 – 62 37 – 185 6 – 93 435 – 852 Oman 2 223 – 246 8.6 – 13.1 0 – 22 0 – 15 1,849 – 1,929 15 – 18 12 – 13 33 – 36 44 – 68 19 – 33 38 – 90 390 – 417 Pakistan 2 210 – 232 9.8 – 11.7 0 – 5 0 – 7 1,706 – 1,786 12 – 13 15 – 23 34 – 34 44 – 58 27 – 32 32 – 74 364 – 402

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Decision variables Resulting variables Objective

Bosch synthesis load load synthesis Bosch

Country simulated locations of Number Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of energy [%] by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Equivalent Load Full energyLCOE supplied of [$/MWh] Percentage RE of energy [%] curtailed [%] factor Electrolyser load Haber [%] factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Papua New 1 293 11.7 0 0 1,625 23 10 29 51 49 84 514 Guinea Peru 1 226 10.2 0 0 1,845 11 18 34 54 37 64 362 Philippines 1 222 11.6 0 0 1,439 14 24 32 43 28 41 441 Poland 3 156 – 171 9.9 – 10.7 25 – 28 22 – 25 1,060 – 1,088 32 – 34 27 – 31 40 – 41 46 – 47 65 – 101 16 – 18 686 – 732 Portugal 6 152 – 270 7.2 – 13.9 0 – 27 0 – 16 1,449 – 1,835 12 – 22 12 – 37 30 – 44 41 – 67 24 – 108 26 – 77 403 – 557 Romania 7 123 – 221 8.3 – 12.9 0 – 8 0 – 15 1,198 – 1,422 16 – 26 21 – 49 33 – 39 41 – 43 19 – 167 9 – 30 538 – 719 Russian 18 100 – 242 3.6 – 11.1 0 – 90 0 – 65 783 – 2,592 15 – 40 13 – 67 24 – 55 43 – 66 17 – 1069 6 – 148 369 – 1,687 Federation Saudi Arabia 6 210 – 271 9 – 13.4 0 – 6 0 – 9 1,643 – 2,240 13 – 19 6 – 21 32 – 35 44 – 59 17 – 31 37 – 74 363 – 446 Senegal 2 224 – 241 9.8 – 10.1 0 – 2 0 – 2 1,677 – 2,000 10 – 12 14 – 20 33 – 33 54 – 57 28 – 42 69 – 75 339 – 382 Singapore 1 214 9.6 0 0 1,423 14 26 32 51 39 64 432 South Africa 8 133 – 268 8.3 – 14.3 0 – 77 0 – 44 1,468 – 2,712 11 – 18 9 – 27 31 – 64 41 – 71 16 – 43 12 – 63 345 – 413 Spain 16 147 – 233 8.7 – 12.4 0 – 22 0 – 14 1,201 – 2,045 12 – 22 10 – 40 32 – 38 37 – 70 19 – 138 9 – 97 353 – 597 Sri Lanka 1 239 9.4 18 16 1,665 15 11 34 64 31 88 368 Sweden 7 130 – 218 8.3 – 11.7 20 – 57 20 – 42 893 – 1,508 27 – 47 9 – 42 37 – 42 45 – 67 100 – 231 14 – 69 504 – 954 Switzerland 7 133 – 205 8.2 – 11.4 0 – 34 0 – 17 1,106 – 1,777 16 – 24 17 – 46 31 – 44 39 – 69 41 – 279 8 – 67 389 – 800 Thailand 2 206 – 218 9.3 – 11.3 0 – 0 0 – 0 1,695 – 1,727 12 – 12 22 – 24 33 – 35 46 – 54 31 – 46 42 – 62 388 – 401 Tunisia 2 148 – 185 9.8 – 10.7 0 – 1 0 – 1 1,676 – 1,726 12 – 12 29 – 41 36 – 37 40 – 44 26 – 33 11 – 27 413 – 454 Turkey 14 132 – 243 8.1 – 13.7 0 – 5 0 – 6 1,220 – 1,647 13 – 19 18 – 49 32 – 36 36 – 44 27 – 87 9 – 31 432 – 635 Ukraine 2 191 – 226 9.6 – 11.8 23 – 46 27 – 41 1,109 – 1,401 34 – 40 10 – 25 37 – 37 43 – 63 83 – 125 19 – 72 623 – 792 UK 16 138 – 235 8 – 11.7 13 – 88 14 – 56 848 – 2,818 19 – 39 4 – 32 35 – 58 44 – 66 28 – 190 5 – 83 364 – 855 USA 47 133 – 258 6.5 – 14.2 0 – 56 0 – 35 1,035 – 1,953 11 – 26 11 – 47 30 – 46 39 – 70 18 – 284 10 – 78 347 – 938 Venezuela 1 222 11.6 0 0 1,732 11 21 33 46 17 41 383 Zambia 1 204 9.5 0 0 1,463 13 25 34 52 50 58 423

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Note 20: 2030 Scenario, Multinational Corporation – Simulation results by location. Lowest 10 LCOA estimates for each geographic region Information about location Decision variables Resulting variables Objective

storage size [t]

Bosch synthesis load load synthesis Bosch

Location Country region Geographic Latitude Longitude Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of energy [%] by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Equivalent Load Full energyLCOE supplied of [$/MWh] [%] energySupply curtailed [%] factor Electrolyser load Haber [%] factor Hydrogen factor Electrolyser excess [%] LCOA [$/t] Arrecife Lanzarote Spain Northern Africa 28.95 -13.60 233 8.7 22 14 2,045 15 10 36 70 37 97 353 Aswan Egypt Northern Africa 23.97 32.78 221 9.8 7 10 2,103 15 14 36 58 21 65 374 Casablanca Morocco Northern Africa 33.57 -7.67 189 10.7 0 0 1,825 11 27 36 45 26 29 387 El Kharga Egypt Northern Africa 25.45 30.53 246 13.1 0 0 2,132 14 12 34 45 19 38 394 Mersa Matruh Egypt Northern Africa 31.33 27.22 185 8.4 23 20 1,864 18 19 41 65 26 61 402 Tunis Tunisia Northern Africa 36.83 10.23 185 10.7 1 1 1,726 12 29 36 44 33 27 413 El Arish Egypt Northern Africa 31.12 33.75 265 14.4 0 0 1,910 15 11 32 41 25 35 433 El Natroon Egypt Northern Africa 30.40 30.35 211 11.8 0 0 1,840 16 21 35 44 20 32 436 Sidi Bouzid Tunisia Northern Africa 35.00 9.48 148 9.8 0 0 1,676 12 41 37 40 26 11 454 Funchal Portugal Northern Africa 32.63 -16.90 270 13.9 0 0 1,482 15 14 30 41 68 43 477 Dakar Senegal S-Saharan Africa 14.73 -17.47 241 10.1 2 2 2,000 10 14 33 57 28 75 339 Marion Island South Africa S-Saharan Africa -46.88 37.87 133 8.7 77 44 2,712 18 9 64 71 43 12 345 Garissa Kenya S-Saharan Africa -0.47 39.63 207 9.8 1 2 1,886 11 21 35 54 25 56 354 Voi Kenya S-Saharan Africa -3.40 38.57 203 10.7 0 0 1,875 10 23 35 48 16 40 362 Gillot France S-Saharan Africa -20.89 55.53 204 8.3 33 19 1,911 17 10 40 73 27 81 359 Upington South Africa S-Saharan Africa -28.43 21.27 205 11.5 0 0 1,771 11 21 36 45 16 31 371 Mombasa Airport Kenya S-Saharan Africa -4.03 39.62 242 12.2 0 0 1,802 11 16 33 46 26 46 375 Beira Mozambique S-Saharan Africa -19.83 34.85 225 12.1 0 0 1,714 11 18 34 45 17 37 375 Maun Botswana S-Saharan Africa -19.98 23.42 216 11.8 0 0 1,692 11 20 35 45 17 35 378 Bloemfontein South Africa S-Saharan Africa -29.10 26.30 268 14.3 0 0 1,676 12 10 31 41 27 38 395 Piura Peru Latin America -5.17 -80.60 226 10.2 0 0 1,845 11 18 34 54 37 64 362 Caracas Maiquetia Venezuela Latin America 10.60 -66.98 222 11.6 0 0 1,732 11 21 33 46 17 41 383 Le Lamentin France Latin America 14.60 -61.00 210 9.1 3 4 1,752 14 21 35 58 31 69 386 Petrolina Brazil Latin America -9.38 -40.50 193 10.2 0 0 1,730 11 27 35 47 20 38 388 Antofagasta Chile Latin America -23.43 -70.43 193 10.6 0 0 1,652 12 27 35 46 29 34 408

This journal is © The Royal Society of Chemistry 2020 R.M.Nayak-Luke and R.Bañares-Alcántara 2020 | 53

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Information about location Decision variables Resulting variables Objective

[%]

Bosch synthesis load load synthesis Bosch

Location Country region Geographic Latitude Longitude Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of energy [%] by wind supplied ratedPercentage RE of metpower by wind energySupply profile Hours Equivalent Load Full energyLCOE supplied of [$/MWh] [%] energySupply curtailed [%] factor Electrolyser load Haber [%] factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Raizet France Latin America 16.27 -61.52 215 9.5 2 3 1,638 15 21 34 56 43 66 412 Mendoza Airport Argentina Latin America -32.83 -68.78 236 12.9 0 0 1,579 13 18 33 42 30 34 415 Brasilia Brazil Latin America -15.78 -47.93 257 10.7 0 0 1,517 13 14 31 53 61 77 410 Cayenne-Rochea France Latin America 4.83 -52.37 204 10.7 0 0 1,663 13 27 34 45 23 39 423 Arica-Chacalluta Chile Latin America -18.33 -70.33 262 13.7 0 0 1,503 13 15 30 41 38 41 435 Kahului USA N. America 20.90 -156.43 188 7.7 21 14 1,953 14 19 40 70 24 78 347 Lihue Kauai USA N. America 21.98 -159.35 196 8.4 29 17 1,794 16 15 40 68 24 70 362 Dodge City USA N. America 37.77 -99.97 185 8.4 22 13 1,829 15 19 40 65 31 62 365 Tucson USA N. America 32.12 -110.93 202 11.5 0 0 1,808 11 23 36 44 18 30 385 Honolulu Oahu USA N. America 21.33 -157.92 207 9.4 7 6 1,741 13 21 35 57 28 62 378 Las Vegas USA N. America 36.08 -115.17 192 11.0 0 0 1,793 11 26 36 45 22 28 391 Albuquerque USA N. America 35.05 -106.62 208 11.9 0 0 1,711 12 23 35 43 24 28 402 Fort Worth USA N. America 32.83 -97.05 173 7.8 17 15 1,748 16 25 40 65 34 64 399 Corpus Christi USA N. America 27.77 -97.50 180 8.1 22 15 1,616 17 22 40 65 25 64 398 Airport Hilo USA N. America 19.72 -155.07 207 9.3 0 0 1,537 13 25 34 53 37 63 411 Kwajalein Island Japan Eastern Asia 8.73 167.73 212 9.3 37 22 1,997 13 9 40 67 23 68 324 Minamitorishima Japan Eastern Asia 24.30 153.97 199 8.2 30 21 1,917 14 12 41 72 31 78 331 Iwojima Japan Eastern Asia 24.78 141.31 195 8.4 31 21 1,902 14 14 41 69 29 70 339 Choshi Japan Eastern Asia 35.73 140.87 210 9.1 31 17 1,666 15 13 38 65 39 70 367 Omaezaki Japan Eastern Asia 34.60 138.22 200 8.8 24 18 1,614 16 16 39 65 28 68 373 Nemuro Japan Eastern Asia 43.33 145.58 165 11.2 51 33 1,744 17 14 49 52 20 8 382 Agana Guam Japan Eastern Asia 13.48 144.80 209 8.5 19 16 1,551 16 17 37 66 28 81 381 Mariana Naha Japan Eastern Asia 26.20 127.68 224 9.8 27 16 1,534 16 13 36 60 30 68 386 Ishigakijima Japan Eastern Asia 24.33 124.17 185 8.1 23 16 1,613 15 22 39 64 28 67 383

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Information about location Decision variables Resulting variables Objective

Bosch synthesis load load synthesis Bosch

Location Country region Geographic Latitude Longitude Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of energy [%] by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Equivalent Load Full energyLCOE supplied of [$/MWh] [%] energySupply curtailed [%] factor Electrolyser load Haber [%] factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Tanegashima Japan Eastern Asia 30.73 131.00 205 8.7 28 18 1,452 18 15 38 66 33 74 392 Chiang Mai Airport Thailand S-Eastern Asia 18.78 98.98 206 9.3 0 0 1,727 12 24 35 54 46 62 388 Penang Malaysia S-Eastern Asia 5.30 100.27 224 11.6 0 0 1,709 12 21 33 45 21 42 394 Bangkok Thailand S-Eastern Asia 13.73 100.57 218 11.3 0 0 1,695 12 22 33 46 31 42 401 Kota Kinabalu Malaysia S-Eastern Asia 5.93 116.05 211 10.9 0 0 1,547 13 24 34 46 20 42 411 Kota Bahru Malaysia S-Eastern Asia 6.17 102.28 247 12.6 0 0 1,523 13 17 31 43 24 44 414 Singapore Airport Singapore S-Eastern Asia 1.37 103.98 214 9.6 0 0 1,423 14 26 32 51 39 64 432 Science Garden Philippines S-Eastern Asia 14.63 121.01 222 11.6 0 0 1,439 14 24 32 43 28 41 441 Kuching Malaysia S-Eastern Asia 1.48 110.33 267 11.3 0 0 1,356 15 17 29 49 52 74 447 Bukit Kototabang Indonesia S-Eastern Asia -0.20 100.32 229 11.8 0 0 1,310 15 24 31 43 30 43 464 Jodhpur India Southern Asia 26.30 73.02 168 9.5 0 0 1,933 8 32 38 47 11 30 340 New Delhi India Southern Asia 28.58 77.20 189 10.7 0 0 1,957 8 26 37 46 12 30 338 Ahmedabad India Southern Asia 23.07 72.63 195 10.9 0 0 1,892 8 26 36 45 17 32 349 Vishakhapatnam India Southern Asia 17.72 83.23 204 9.5 0 0 1,869 9 24 35 53 39 58 347 Poona India Southern Asia 18.53 73.85 209 11.3 0 0 1,796 9 23 35 45 21 36 357 Thiruvananthapura India Southern Asia 8.48 76.95 193 10.3 0 0 1,828 9 27 35 47 19 38 351 m Santacruz Bombay India Southern Asia 19.12 72.85 214 9.7 2 7 1,704 11 22 34 54 37 62 365 Madras India Southern Asia 13.00 80.18 216 11.4 0 0 1,711 9 21 34 46 26 39 362 Goa India Southern Asia 15.48 73.82 240 12.3 0 0 1,721 9 17 32 45 24 43 362 Nagpur Sonegaon India Southern Asia 21.10 79.05 201 11.0 0 0 1,665 10 25 35 45 20 35 366 Sharura Saudi Arabia Western Asia 17.47 47.12 271 11.4 0 0 2,240 13 6 32 54 25 74 363 Wadi Al Dawaser Saudi Arabia Western Asia 20.50 45.20 232 10.4 2 4 2,182 14 14 34 55 19 64 368 Jeddah Saudi Arabia Western Asia 21.68 39.15 224 12.0 0 0 2,170 13 16 35 46 17 37 390 Masira Oman Western Asia 20.67 58.90 223 8.6 22 15 1,849 18 12 36 68 33 90 390 Madinah Saudi Arabia Western Asia 24.55 39.70 250 13.4 0 0 2,065 14 11 33 44 17 37 399

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Information about location Decision variables Resulting variables Objective

supplied

Bosch synthesis load load synthesis Bosch

Location Country region Geographic Latitude Longitude Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of energy [%] by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Equivalent Load Full energyLCOE of [$/MWh] [%] energySupply curtailed [%] factor Electrolyser load Haber [%] factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Al Ahsa Saudi Arabia Western Asia 25.28 49.48 227 9.8 3 8 1,845 17 17 34 57 25 70 412 Buraimi Oman Western Asia 24.23 55.78 246 13.1 0 0 1,929 15 13 33 44 19 38 417 Silifke Turkey Western Asia 36.38 33.93 214 12.0 0 0 1,639 14 22 34 43 33 31 432 Gaziantep Turkey Western Asia 37.08 37.37 200 11.5 0 0 1,631 14 26 35 43 35 29 438 Mugla Turkey Western Asia 37.20 28.35 243 13.7 0 0 1,647 13 18 32 39 44 30 449 Sonnblick Austria Europe 47.05 12.95 138 9.7 83 56 3,320 16 7 63 65 23 4 318 Malin Head Ireland Europe 55.37 -7.33 125 8.8 81 46 2,610 18 13 64 67 30 5 355 List Denmark Europe 55.02 8.42 155 9.6 86 56 3,316 16 4 58 68 86 18 352 Lerwick UK Europe 60.13 -1.18 158 10.8 88 56 2,818 19 4 56 60 41 7 364 Russian Dickson Island Europe 73.50 80.23 136 9.4 90 65 2,592 16 19 55 58 44 6 369 Federation Skagen Fyr Denmark Europe 57.73 10.63 148 10.1 73 42 2,330 20 8 58 62 38 7 376 Aberporth UK Europe 52.13 -4.57 143 10.0 69 37 2,096 21 10 58 61 33 5 385 Faro Portugal Europe 37.02 -7.97 200 11.4 0 0 1,835 12 24 36 44 24 28 403 Guetsch Switzerland Europe 46.65 8.62 174 8.2 34 17 1,777 18 17 44 69 41 57 389 Valley UK Europe 53.25 -4.53 138 9.5 65 37 2,028 21 14 58 62 35 7 400 Cape Grim Australia Oceania -40.66 144.68 137 9.6 95 82 4,848 16 2 66 69 33 5 310 Willis Island Australia Oceania -16.30 149.98 136 9.8 85 67 4,054 18 6 64 66 15 2 323 Noumea France Oceania -22.28 166.45 221 8.9 27 18 1,734 19 11 37 67 40 83 400 Koumac France Oceania -20.57 164.28 241 12.7 0 0 1,580 14 16 33 43 22 39 418 Glenmore Australia Oceania -33.69 115.02 151 10.1 68 46 2,716 23 10 55 60 33 10 411 Wellington New Zealand Oceania -41.32 174.77 174 11.4 83 62 2,973 23 4 51 57 61 12 428 Carnarvon Airport Australia Oceania -24.88 113.67 193 7.9 21 12 2,081 21 17 39 71 28 80 425 Ouanaham France Oceania -20.77 167.23 228 9.4 6 7 1,461 18 18 33 58 53 79 447 Tennant Creek Australia Oceania -19.63 134.18 247 10.8 4 4 1,909 21 11 34 55 26 68 440 Airport

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Bosch synthesis load load synthesis Bosch

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Note 21: 2030 Scenario, Multi-national Corporation – Components of LCOA by location. Lowest 10 LCOA for multi-national corporation by geographic region

[%] ance

Location Country Latitude Longitude Electrolyser OPEX [%] Electrolyser CAPEX [%] Hydrogen Storage [%] CAPEX Hydrogen Storage OPEX [%] Synthesis Ammonia OPEX [%] Synthesis Ammonia [%] CAPEX & Operation Mainten OPEXEnergy Curtailed [%] Cell Hydrogen Fuel [%] CAPEX OPEX Separation Air [%] CAPEX Separation Air [%] Water OPEX [%] Arrecife Lanzarote Spain 28.95 -13.6 35.1 21.6 5.5 0.5 2.2 13.0 11.0 4.0 1.3 0.4 4.4 0.9 Aswan Egypt 23.97 32.78 34.4 20.5 3.1 0.5 2.1 14.7 10.7 6.2 1.5 0.4 5.0 0.9 Casablanca Morocco 33.57 -7.67 24.1 20.2 4.4 0.3 1.5 18.5 12.2 9.7 1.9 0.2 6.3 0.9 El Kharga Egypt 25.45 30.53 30.2 21.3 2.6 0.4 1.8 18.2 12.0 4.2 1.9 0.3 6.2 0.8 Mersa Matruh Egypt 31.33 27.22 38.9 16.8 3.8 0.5 2.4 12.3 9.2 9.6 1.3 0.4 4.2 0.8 Tunis Tunisia 36.83 10.23 24.3 19.0 5.4 0.3 1.5 17.7 11.9 10.9 1.8 0.2 6.0 0.8 El Arish Egypt 31.12 33.75 30.8 20.7 3.2 0.4 1.9 18.1 11.9 3.9 1.9 0.3 6.2 0.8 El Natroon Egypt 30.4 30.35 31.7 18.5 2.7 0.4 1.9 16.6 10.8 9.0 1.7 0.3 5.6 0.8 Sidi Bouzid Tunisia 35 9.48 22.5 16.7 4.7 0.3 1.4 17.8 11.2 16.6 1.9 0.2 6.1 0.7 Funchal Portugal 32.63 -16.9 27.7 19.9 8.1 0.4 1.7 16.6 12.4 4.9 1.7 0.3 5.6 0.7 Dakar Senegal 14.73 -17.47 26.4 24.6 4.5 0.4 1.6 16.6 12.7 4.5 1.7 0.3 5.7 1.0 Marion Island South Africa -46.88 37.87 44.0 12.4 6.5 0.1 2.8 13.1 9.0 4.9 1.4 0.5 4.5 0.9 Garissa Kenya -0.47 39.63 26.5 22.2 4.2 0.3 1.6 16.8 12.1 7.6 1.7 0.3 5.7 0.9 Voi Kenya -3.4 38.57 25.0 22.0 2.8 0.3 1.5 18.6 12.3 8.1 1.9 0.3 6.3 0.9 Gillot France -20.89 55.53 40.4 18.7 4.0 0.5 2.5 12.3 9.7 5.1 1.3 0.4 4.2 0.9 Upington South Africa -28.43 21.27 25.9 21.1 2.6 0.3 1.6 19.1 12.2 7.5 2.0 0.3 6.5 0.9 Mombasa Airport Kenya -4.03 39.62 25.1 23.0 4.0 0.3 1.5 18.7 12.9 5.0 1.9 0.3 6.4 0.9 Beira Mozambique -19.83 34.85 26.4 21.9 2.6 0.3 1.6 19.0 12.4 6.2 2.0 0.3 6.5 0.9 Maun Botswana -19.98 23.42 26.6 21.4 2.6 0.3 1.6 18.8 12.2 7.0 1.9 0.3 6.4 0.9 Bloemfontein South Africa -29.1 26.3 25.8 22.9 3.7 0.3 1.6 19.6 13.1 3.2 2.0 0.3 6.7 0.8 Piura Peru -5.17 -80.6 25.9 22.8 5.9 0.4 1.6 16.5 12.6 5.9 1.7 0.3 5.6 0.9 Caracas Maiquetia Venezuela 10.6 -66.98 26.2 21.9 2.8 0.3 1.6 18.4 12.2 7.3 1.9 0.3 6.3 0.9 Le Lamentin France 14.6 -61 30.7 20.5 4.9 0.4 1.9 14.3 11.0 8.8 1.5 0.3 4.9 0.8 Petrolina Brazil -9.38 -40.5 25.8 20.5 3.4 0.3 1.6 17.5 11.7 10.2 1.8 0.3 6.0 0.8 Antofagasta Chile -23.43 -70.43 25.8 19.6 4.6 0.3 1.6 17.3 11.7 10.3 1.8 0.3 5.9 0.8 Raizet France 16.27 -61.52 30.6 19.7 6.3 0.4 1.9 14.0 11.1 8.7 1.5 0.3 4.8 0.8 Mendoza Airport Argentina -32.83 -68.78 26.6 20.9 4.2 0.4 1.6 18.4 12.4 6.3 1.9 0.3 6.3 0.8

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Synthesis Synthesis

Location Country Latitude Longitude Electrolyser OPEX [%] Electrolyser CAPEX [%] Hydrogen Storage CAPEX [%] Hydrogen Storage OPEX [%] OPEX Synthesis Ammonia [%] Ammonia [%] CAPEX & Operation [%] Maintenance OPEXEnergy Curtailed [%] CAPEX Cell Hydrogen Fuel [%] OPEX [%] Separation Air CAPEX [%] Separation Air Water OPEX [%] Brasilia Brazil -15.78 -47.93 27.8 22.0 8.4 0.4 1.7 14.7 12.4 5.0 1.5 0.3 5.0 0.8 Cayenne-Rochea France 4.83 -52.37 27.6 19.7 3.5 0.3 1.7 16.7 11.3 10.6 1.7 0.3 5.7 0.8 Arica-Chacalluta Chile -18.33 -70.33 26.7 21.5 5.0 0.4 1.6 18.0 12.6 5.1 1.9 0.3 6.1 0.8 Kahului USA 20.9 -156.43 34.0 19.8 4.1 0.5 2.1 13.2 10.3 8.9 1.4 0.4 4.5 0.9 Lihue Kauai USA 21.98 -159.35 37.3 18.8 3.7 0.4 2.3 13.1 9.9 7.4 1.4 0.4 4.5 0.9 Dodge City USA 37.77 -99.97 34.4 18.5 4.9 0.4 2.2 13.5 10.3 8.7 1.4 0.4 4.6 0.9 Tucson USA 32.12 -110.93 25.8 20.6 3.0 0.3 1.6 18.8 12.1 8.3 2.0 0.3 6.4 0.9 Honolulu Oahu USA 21.33 -157.92 30.2 20.6 4.4 0.4 1.9 15.0 11.2 8.6 1.6 0.3 5.1 0.9 Las Vegas USA 36.08 -115.17 25.6 19.8 3.7 0.3 1.6 18.3 11.9 9.5 1.9 0.3 6.2 0.8 Albuquerque USA 35.05 -106.62 26.2 20.1 3.7 0.3 1.6 18.5 12.1 8.2 1.9 0.3 6.3 0.8 Fort Worth USA 32.83 -97.05 34.1 17.1 5.4 0.4 2.1 12.3 9.7 12.2 1.3 0.4 4.2 0.8 Corpus Christi Airport USA 27.77 -97.5 36.5 17.2 3.9 0.4 2.3 12.4 9.3 11.2 1.3 0.4 4.2 0.8 Hilo USA 19.72 -155.07 28.3 20.2 5.7 0.4 1.7 14.7 11.3 10.2 1.5 0.3 5.0 0.8 Kwajalein Island Japan 8.73 167.73 35.3 21.1 3.6 0.4 2.2 14.9 11.1 3.6 1.5 0.4 5.1 1.0 Minamitorishima Japan 24.3 153.97 35.7 20.1 4.9 0.4 2.2 13.4 10.6 5.2 1.4 0.4 4.6 1.0 Iwojima Japan 24.78 141.31 35.1 19.7 4.7 0.4 2.2 13.8 10.6 6.1 1.4 0.4 4.7 0.9 Choshi Japan 35.73 140.87 35.2 19.4 5.7 0.4 2.2 13.5 10.7 5.6 1.4 0.4 4.6 0.9 Omaezaki Japan 34.6 138.22 36.1 18.9 4.2 0.4 2.3 13.3 10.1 7.5 1.4 0.4 4.5 0.9 Nemuro Japan 43.33 145.58 38.9 14.7 2.9 0.2 2.4 16.1 9.7 6.7 1.7 0.4 5.5 0.8 Agana Guam Mariana Japan 13.48 144.8 36.1 19.5 4.2 0.5 2.2 12.7 10.1 7.8 1.3 0.4 4.3 0.8 Naha Japan 26.2 127.68 36.1 19.6 4.2 0.4 2.3 13.9 10.5 5.7 1.4 0.4 4.7 0.8 Ishigakijima Japan 24.33 124.17 34.2 18.3 4.4 0.4 2.1 13.0 9.9 10.6 1.3 0.4 4.4 0.8 Tanegashima Japan 30.73 131 38.2 18.2 4.7 0.5 2.4 12.4 9.8 7.2 1.3 0.4 4.2 0.8 Chiang Mai Airport Thailand 18.78 98.98 26.0 20.8 7.5 0.4 1.6 15.2 12.0 8.6 1.6 0.3 5.2 0.8 Penang Malaysia 5.3 100.27 26.1 21.7 3.3 0.3 1.6 18.1 12.2 7.5 1.9 0.3 6.2 0.8 Bangkok Thailand 13.73 100.57 25.9 21.0 4.8 0.3 1.6 17.5 12.2 7.9 1.8 0.3 6.0 0.8 Kota Kinabalu Malaysia 5.93 116.05 27.6 20.4 3.2 0.3 1.7 17.1 11.5 9.5 1.8 0.3 5.8 0.8 Kota Bahru Malaysia 6.17 102.28 27.9 21.6 3.4 0.4 1.7 17.8 12.1 6.1 1.9 0.3 6.1 0.8 Singapore Airport Singapore 1.37 103.98 28.5 20.0 5.8 0.4 1.7 14.4 11.2 10.5 1.5 0.3 4.9 0.8 Science Garden Philippines 14.63 121.01 27.7 20.0 4.0 0.4 1.7 16.8 11.5 9.4 1.7 0.3 5.7 0.7

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Location Country Latitude Longitude Electrolyser OPEX [%] Electrolyser CAPEX [%] Hydrogen Storage CAPEX [%] Hydrogen Storage OPEX [%] OPEX Synthesis Ammonia [%] Synthesis Ammonia [%] CAPEX & Operation [%] Maintenance OPEXEnergy Curtailed [%] CAPEX Cell Hydrogen Fuel [%] OPEX [%] Separation Air CAPEX [%] Separation Air Water OPEX [%] Kuching Malaysia 1.48 110.33 28.9 21.6 6.8 0.4 1.8 14.7 11.9 6.3 1.5 0.3 5.0 0.7 Bukit Kototabang Indonesia -0.2 100.32 29.0 19.5 4.1 0.4 1.8 16.2 11.2 9.7 1.7 0.3 5.5 0.7 Jodhpur India 26.3 73.02 21.2 21.8 2.2 0.2 1.3 19.9 12.6 10.7 2.1 0.2 6.8 1.0 New Delhi India 28.58 77.2 21.1 22.8 2.3 0.3 1.3 20.7 13.1 8.1 2.2 0.2 7.1 1.0 Ahmedabad India 23.07 72.63 21.2 22.7 3.2 0.3 1.3 20.3 13.2 7.8 2.1 0.2 6.9 0.9 Vishakhapatnam India 17.72 83.23 21.4 23.2 7.1 0.3 1.3 17.4 13.3 7.2 1.8 0.2 5.9 0.9 Poona India 18.53 73.85 21.8 22.9 3.7 0.3 1.3 19.9 13.2 6.9 2.1 0.2 6.8 0.9 Thiruvananthapuram India 8.48 76.95 21.7 22.6 3.6 0.3 1.3 19.3 12.9 8.5 2.0 0.2 6.6 0.9 Santacruz Bombay India 19.12 72.85 25.0 22.3 6.2 0.3 1.5 16.3 12.5 7.4 1.7 0.3 5.6 0.9 Madras India 13 80.18 22.6 22.8 4.4 0.3 1.4 19.4 13.2 6.4 2.0 0.2 6.6 0.9 Goa India 15.48 73.82 22.5 24.0 3.8 0.3 1.4 19.8 13.5 4.8 2.1 0.2 6.7 0.9 Nagpur Sonegaon India 21.1 79.05 22.9 22.0 3.4 0.3 1.4 19.4 12.7 8.1 2.0 0.2 6.6 0.9 Sharura Saudi Arabia 17.47 47.12 31.0 23.9 3.6 0.4 1.9 16.3 12.2 2.3 1.7 0.3 5.5 0.9 Wadi Al Dawaser Saudi Arabia 20.5 45.2 32.1 21.8 2.9 0.4 2.0 15.8 11.3 5.5 1.6 0.3 5.4 0.9 Jeddah Saudi Arabia 21.68 39.15 29.9 20.6 2.6 0.4 1.8 17.8 11.7 6.2 1.8 0.3 6.1 0.8 Masira Oman 20.67 58.9 38.7 19.3 4.6 0.6 2.4 12.0 9.9 5.9 1.2 0.4 4.1 0.8 Madinah Saudi Arabia 24.55 39.7 30.8 21.2 2.4 0.4 1.9 18.3 11.9 4.0 1.9 0.3 6.2 0.8 Al Ahsa Saudi Arabia 25.28 49.48 35.3 19.7 3.5 0.5 2.2 13.7 10.3 7.5 1.4 0.4 4.7 0.8 Buraimi Oman 24.23 55.78 31.6 20.5 2.5 0.4 1.9 17.5 11.5 5.2 1.8 0.3 6.0 0.8 Silifke Turkey 36.38 33.93 27.6 19.3 4.7 0.4 1.7 17.4 11.7 8.5 1.8 0.3 5.9 0.8 Gaziantep Turkey 37.08 37.37 27.3 18.5 5.1 0.4 1.7 17.0 11.5 10.0 1.8 0.3 5.8 0.8 Mugla Turkey 37.2 28.35 26.5 19.9 5.8 0.4 1.6 18.2 12.4 6.2 1.9 0.3 6.2 0.7 Sonnblick Austria 47.05 12.95 43.2 13.6 3.7 0.1 2.7 15.5 9.4 3.5 1.6 0.5 5.3 1.0 Malin Head Ireland 55.37 -7.33 44.1 11.9 4.5 0.1 2.8 13.5 8.5 7.3 1.4 0.5 4.6 0.9 List Denmark 55.02 8.42 39.1 13.4 12.0 0.2 2.4 13.4 10.7 1.7 1.4 0.4 4.6 0.9

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Cell CAPEX Cell

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Note 22: 2030 Scenario, Domestic Corporation – Simulation results by country

Decision variables Resulting variables Objective

Bosch synthesis load load synthesis Bosch

Country simulated locations of Number Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of energy [%] by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Equivalent Load Full energyLCOE supplied of [$/MWh] Percentage RE of energy [%] curtailed [%] factor Electrolyser load Haber [%] factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Argentina 5 135 – 225 9.3 – 12.7 0 – 65 0 – 35 1,517 – 1,798 22 – 39 20 – 30 33 – 55 41 – 59 25 – 68 8 – 34 694 – 787 Australia 37 129 – 307 8.4 – 15.1 0 – 90 0 – 69 1,357 – 4,278 18 – 38 4 – 21 29 – 68 38 – 70 13 – 66 2 – 101 320 – 654 Austria 6 133 – 181 8 – 10.2 0 – 84 0 – 58 1,116 – 3,389 17 – 24 6 – 48 33 – 62 39 – 65 24 – 193 5 – 31 330 – 738 Belgium 4 154 – 197 9 – 11.9 19 – 64 17 – 37 1,030 – 1,879 24 – 35 11 – 33 37 – 54 45 – 66 39 – 86 9 – 54 446 – 712 Bosnia & 1 187 10.5 0 0 1,263 29 34 33 41 79 31 900 Herzegovina Botswana 1 216 11.7 0 0 1,692 13 20 35 45 17 35 432 Brazil 5 200 – 236 9 – 12.3 0 – 9 0 – 10 1,209 – 1,730 14 – 22 20 – 26 31 – 35 42 – 60 23 – 63 32 – 78 457 – 568 Bulgaria 4 143 – 249 9.2 – 13.6 0 – 4 0 – 6 1,132 – 1,357 20 – 27 20 – 44 30 – 37 39 – 43 41 – 85 11 – 34 623 – 782 Canada 11 132 – 445 8.7 – 12.6 8 – 70 12 – 50 827 – 2,016 23 – 60 2 – 42 20 – 55 44 – 58 25 – 734 6 – 159 432 – 1,325 Chile 7 125 – 236 8.1 – 12.8 0 – 73 0 – 43 928 – 2,069 13 – 31 8 – 49 31 – 49 39 – 54 25 – 93 11 – 37 442 – 746 China 51 170 – 303 9.1 – 15.1 0 – 48 0 – 37 902 – 1,740 15 – 33 9 – 35 25 – 40 37 – 60 19 – 127 22 – 108 445 – 778 Colombia 2 213 – 270 10.9 – 11 0 – 0 0 – 0 1,234 – 1,383 17 – 19 19 – 27 28 – 32 44 – 49 22 – 59 43 – 80 513 – 551 Congo 1 210 11.1 0 0 1,459 18 26 33 44 22 39 559 Croatia 2 93 – 217 6.2 – 13 0 – 14 0 – 12 1,205 – 1,498 23 – 24 19 – 61 35 – 39 42 – 42 37 – 62 11 – 23 591 – 956 Czech Republic 5 158 – 230 10 – 13.2 21 – 54 21 – 30 1,067 – 1,479 29 – 35 15 – 28 33 – 49 42 – 51 27 – 72 4 – 28 535 – 723 Denmark 7 132 – 265 8.8 – 11.6 40 – 80 27 – 46 1,072 – 2,902 17 – 42 6 – 14 32 – 64 51 – 70 41 – 152 10 – 96 362 – 731 Egypt 5 198 – 273 8.7 – 14.2 0 – 21 0 – 19 1,839 – 2,132 24 – 33 7 – 23 32 – 39 41 – 65 21 – 33 30 – 76 651 – 752 El Salvador 1 250 10.6 1 3 1,707 25 15 31 53 45 74 586 Estonia 1 211 12.8 35 31 1,037 42 17 37 43 140 21 833 Finland 5 147 – 355 9.4 – 11.8 26 – 37 25 – 39 692 – 1,063 36 – 67 7 – 35 24 – 41 45 – 55 107 – 590 15 – 121 757 – 1,284 France 39 137 – 256 8.2 – 12.9 0 – 39 0 – 29 1,021 – 1,906 14 – 32 10 – 46 31 – 43 39 – 73 19 – 124 10 – 84 366 – 748 Germany 9 140 – 205 6.9 – 12.4 3 – 32 4 – 22 974 – 1,255 23 – 33 17 – 43 32 – 39 43 – 66 72 – 234 17 – 71 594 – 875 Ghana 1 213 9.5 0 0 1,672 18 24 33 54 36 66 584 Greece 3 188 – 201 10.7 – 11.5 0 – 2 0 – 4 1,482 – 1,639 22 – 26 27 – 30 33 – 35 42 – 43 51 – 101 28 – 29 720 – 845

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Decision variables Resulting variables Objective

[t]

[%]

Bosch synthesis load load synthesis Bosch

Country simulated locations of Number Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of energy by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Equivalent Load Full energyLCOE supplied of [$/MWh] Percentage RE of energy [%] curtailed [%] factor Electrolyser load Haber [%] factor Hydrogen storage size factor Electrolyser excess [%] LCOA [$/t] Hungary 2 105 – 174 6.9 – 10.5 0 – 10 0 – 14 1,113 – 1,198 23 – 33 32 – 57 36 – 38 41 – 43 66 – 67 11 – 22 800 – 886 India 12 178 – 232 8.7 – 12.3 0 – 0 0 – 0 1,356 – 1,957 10 – 14 18 – 31 32 – 37 43 – 54 11 – 33 30 – 54 407 – 502 Indonesia 1 231 11.8 0 0 1,310 19 23 31 43 31 43 557 Ireland 5 145 – 173 10 – 11 29 – 83 22 – 50 1,028 – 2,754 22 – 39 6 – 24 41 – 60 46 – 64 31 – 57 6 – 16 405 – 773 Italy 16 106 – 264 7 – 14.7 0 – 27 0 – 19 1,126 – 1,773 14 – 23 13 – 57 31 – 43 39 – 68 29 – 129 12 – 66 458 – 824 Japan 62 142 – 297 7.5 – 14.7 0 – 57 0 – 38 1,131 – 2,018 14 – 26 13 – 35 26 – 54 38 – 73 16 – 92 6 – 89 337 – 581 Kazakhstan 1 142 8.8 4 9 1,314 35 42 38 44 35 18 867 Kenya 3 197 – 238 9.1 – 11.4 0 – 3 0 – 3 1,802 – 1,886 15 – 15 14 – 24 33 – 36 47 – 56 23 – 34 42 – 63 470 – 493 Korea, Republic of (South 8 195 – 247 8.6 – 13.2 0 – 20 0 – 22 1,183 – 1,377 16 – 24 18 – 25 30 – 37 40 – 61 25 – 55 31 – 68 465 – 538 Korea) Latvia 1 98 6.1 7 14 918 39 57 40 47 116 18 1,278 Lithuania 1 180 11.3 30 26 1,017 43 24 39 45 73 17 840 Macedonia, 2 168 – 170 9.7 – 9.8 0 – 5 0 – 9 1,264 – 1,306 23 – 29 36 – 38 34 – 36 42 – 44 105 – 137 25 – 29 843 – 850 Republic of Malaysia 4 197 – 257 8.8 – 13 0 – 0 0 – 0 1,356 – 1,709 13 – 17 15 – 30 31 – 33 43 – 53 21 – 38 41 – 64 446 – 508 Mexico 9 206 – 299 9.7 – 14.6 0 – 4 0 – 4 1,345 – 1,962 18 – 26 8 – 25 27 – 35 40 – 56 14 – 72 36 – 87 485 – 631 Mongolia 5 157 – 251 9.1 – 14.1 0 – 1 0 – 1 1,225 – 1,527 35 – 44 15 – 39 32 – 37 40 – 45 34 – 93 26 – 33 803 – 1,141 Morocco 1 203 11.5 0 0 1,825 13 24 35 44 25 30 458 Mozambique 3 214 – 233 11.8 – 12.6 0 – 0 0 – 0 1,595 – 1,714 15 – 16 17 – 21 34 – 35 44 – 45 16 – 20 34 – 36 491 – 510 Netherlands 4 153 – 222 9.1 – 10.6 20 – 57 20 – 33 998 – 1,673 25 – 35 11 – 31 37 – 52 46 – 66 31 – 121 7 – 79 470 – 742 New Zealand 4 160 – 220 7.9 – 13.3 2 – 79 3 – 54 1,191 – 2,771 24 – 39 6 – 26 32 – 54 41 – 69 53 – 91 10 – 90 437 – 756 Nicaragua 1 242 10.4 0 0 1,491 34 18 32 53 53 72 791 Norway 2 173 – 303 10.8 – 11.6 48 – 72 38 – 41 895 – 1,808 25 – 52 4 – 6 29 – 51 56 – 58 49 – 211 10 – 106 445 – 884 Oman 2 204 – 226 8.3 – 9.9 2 – 21 6 – 14 1,845 – 1,859 19 – 20 16 – 17 34 – 38 56 – 68 28 – 39 68 – 81 441 – 470 Pakistan 2 213 – 228 9.8 – 12 0 – 4 0 – 6 1,706 – 1,792 20 – 21 16 – 22 34 – 34 43 – 57 29 – 31 30 – 72 601 – 670

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ARTICLE Journal Name

Decision variables Resulting variables Objective

energy energy

Bosch synthesis load load synthesis Bosch

[%]

Country simulated locations of Number Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of [%] by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Equivalent Load Full energyLCOE supplied of [$/MWh] Percentage RE of energy [%] curtailed [%] factor Electrolyser load Haber factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Papua New 1 304 11.9 0 0 1,625 31 9 28 51 52 87 679 Guinea Peru 1 207 9.6 0 0 1,845 12 22 35 54 32 59 407 Philippines 1 224 11.7 0 0 1,439 16 24 32 43 28 41 514 Poland 3 166 – 267 10.1 – 12.2 25 – 41 24 – 37 1,060 – 1,109 38 – 44 8 – 29 32 – 40 45 – 61 92 – 175 17 – 93 773 – 814 Portugal 6 173 – 218 8.3 – 12.4 0 – 32 0 – 20 1,482 – 1,835 15 – 27 18 – 33 33 – 44 42 – 67 26 – 126 29 – 71 476 – 664 Romania 7 110 – 198 7.3 – 11.7 0 – 7 0 – 14 1,205 – 1,425 21 – 31 28 – 55 34 – 39 40 – 45 31 – 163 6 – 27 626 – 871 Russian 18 103 – 283 3.1 – 12.6 0 – 90 0 – 65 791 – 2,598 18 – 47 8 – 72 24 – 54 41 – 66 21 – 1153 6 – 141 438 – 2,025 Federation Saudi Arabia 6 208 – 255 9.4 – 13.4 0 – 9 0 – 13 1,620 – 2,240 14 – 22 9 – 20 33 – 36 44 – 61 17 – 37 37 – 87 400 – 491 Senegal 2 245 – 263 10.3 – 10.7 0 – 2 0 – 2 1,677 – 2,000 14 – 15 10 – 16 32 – 32 54 – 56 32 – 49 75 – 80 436 – 490 Singapore 1 247 10.5 0 0 1,423 15 19 30 51 56 74 465 South Africa 8 131 – 271 8.6 – 14.4 0 – 77 0 – 43 1,470 – 2,672 13 – 21 10 – 23 31 – 64 41 – 71 19 – 43 12 – 74 402 – 485 Spain 16 130 – 256 8.8 – 12.6 0 – 21 0 – 13 1,202 – 2,039 14 – 24 6 – 48 32 – 41 39 – 70 23 – 101 9 – 108 401 – 672 Sri Lanka 1 199 8.3 20 18 1,669 21 18 38 66 24 76 494 Sweden 7 132 – 344 8.5 – 11.7 20 – 55 20 – 36 876 – 1,557 28 – 46 4 – 41 26 – 49 46 – 61 45 – 377 10 – 117 523 – 937 Switzerland 7 131 – 207 7.6 – 11.8 0 – 58 0 – 36 1,146 – 2,170 17 – 25 15 – 50 33 – 57 41 – 60 23 – 133 6 – 29 404 – 789 Thailand 2 250 – 266 10.6 – 13.1 0 – 0 0 – 0 1,695 – 1,727 14 – 14 13 – 15 31 – 32 44 – 53 41 – 59 49 – 73 451 – 470 Tunisia 2 217 – 226 12.4 – 12.6 0 – 1 0 – 1 1,676 – 1,725 15 – 15 22 – 22 33 – 34 41 – 42 34 – 64 28 – 31 534 – 573 Turkey 14 136 – 234 7.9 – 13.2 0 – 6 0 – 7 1,220 – 1,647 16 – 22 18 – 47 32 – 36 37 – 44 30 – 91 9 – 32 520 – 747 Ukraine 2 204 – 235 9.8 – 12.4 24 – 47 28 – 41 1,108 – 1,403 64 – 74 9 – 23 35 – 36 42 – 63 99 – 130 21 – 76 1,111 – 1,420 UK 16 135 – 248 6.8 – 11.8 9 – 87 10 – 54 851 – 2,742 20 – 46 8 – 42 33 – 61 46 – 65 33 – 206 6 – 88 390 – 919 USA 47 109 – 241 6.8 – 13.5 0 – 52 0 – 32 1,028 – 1,953 11 – 25 14 – 54 32 – 48 40 – 70 16 – 307 8 – 83 348 – 943 Venezuela 1 252 12.7 0 0 1,732 24 15 32 44 20 45 787 Zambia 1 226 10.2 0 0 1,463 18 21 33 52 54 62 551

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Note 23: 2030 Scenario, Domestic Corporation – Simulation results by location. Lowest 10 LCOA for multi-national corporation by geographic region Information about location Decision variables Resulting variables Objective

Bosch synthesis load load synthesis Bosch

Location Country region Geographic Latitude Longitude Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of energy [%] by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Equivalent Load Full energyLCOE supplied of [$/MWh] [%] energySupply curtailed [%] factor Electrolyser load Haber [%] factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Arrecife Lanzarote Spain Northern Africa 28.95 -13.60 256 9.0 21 13 2,039 16 6 34 70 46 108 401 Aswan Egypt Northern Africa 23.97 32.78 244 10.2 8 12 2,084 28 9 34 60 30 76 651 Casablanca Morocco Northern Africa 33.57 -7.67 203 11.5 0 0 1,825 13 24 35 44 25 30 458 El Kharga Egypt Northern Africa 25.45 30.53 273 14.2 0 0 2,132 24 7 32 43 22 41 684 Mersa Matruh Egypt Northern Africa 31.33 27.22 198 8.7 21 19 1,860 33 16 39 65 33 67 699 Tunis Tunisia Northern Africa 36.83 10.23 217 12.4 1 1 1,725 15 22 34 42 34 28 534 El Arish Egypt Northern Africa 31.12 33.75 203 11.5 0 0 1,910 27 23 36 44 21 30 748 El Natroon Egypt Northern Africa 30.40 30.35 261 14.2 0 0 1,839 28 11 32 41 23 35 752 Sidi Bouzid Tunisia Northern Africa 35.00 9.48 226 12.6 0 0 1,676 15 22 33 41 64 31 573 Funchal Portugal Northern Africa 32.63 -16.90 199 10.9 0 0 1,482 18 28 34 44 46 35 557 Dakar Senegal S-Saharan Africa 14.73 -17.47 263 10.7 2 2 2,000 14 10 32 56 32 80 436 Marion Island South Africa S-Saharan Africa -46.88 37.87 131 8.6 77 43 2,672 21 10 64 71 43 12 402 Garissa Kenya S-Saharan Africa -0.47 39.63 238 10.7 1 2 1,886 15 14 33 53 34 63 470 Voi Kenya S-Saharan Africa -3.40 38.57 197 9.1 3 3 1,869 15 24 36 56 30 59 481 Gillot France S-Saharan Africa -20.89 55.53 200 8.2 33 18 1,906 17 11 41 73 26 79 366 Upington South Africa S-Saharan Africa -28.43 21.27 229 12.7 0 0 1,771 13 16 34 44 19 32 433 Mombasa Airport Kenya S-Saharan Africa -4.03 39.62 220 11.4 0 0 1,802 15 20 34 47 23 42 493 Beira Mozambique S-Saharan Africa -19.83 34.85 221 11.9 0 0 1,714 15 19 34 45 16 36 491 Maun Botswana S-Saharan Africa -19.98 23.42 216 11.7 0 0 1,692 13 20 35 45 17 35 432 Bloemfontein South Africa S-Saharan Africa -29.10 26.30 271 14.4 0 0 1,676 14 10 31 41 28 39 459 Piura Peru Latin America -5.17 -80.60 207 9.6 0 0 1,845 12 22 35 54 32 59 407 Caracas Maiquetia Venezuela Latin America 10.60 -66.98 252 12.7 0 0 1,732 24 15 32 44 20 45 787 Le Lamentin France Latin America 14.60 -61.00 237 9.8 4 5 1,751 14 15 33 58 34 77 391 Petrolina Brazil Latin America -9.38 -40.50 207 10.9 0 0 1,730 14 24 34 47 23 40 457

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ARTICLE Journal Name

Information about location Decision variables Resulting variables Objective

RE energy RE energy

Bosch synthesis load load synthesis Bosch

ogen storageogen size [t]

Location Country region Geographic Latitude Longitude Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage of [%] by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Equivalent Load Full energyLCOE supplied of [$/MWh] [%] energySupply curtailed [%] factor Electrolyser load Haber [%] factor Hydr factor Electrolyser excess [%] LCOA [$/t] Antofagasta Chile Latin America -23.43 -70.43 213 11.5 0 0 1,652 13 23 34 45 32 36 442 Raizet France Latin America 16.27 -61.52 240 12.6 0 0 1,662 14 16 33 44 19 40 412 Mendoza Airport Argentina Latin America -32.83 -68.78 208 11.6 0 0 1,579 22 24 34 44 25 32 694 Brasilia Brazil Latin America -15.78 -47.93 200 9.1 0 0 1,517 16 26 35 54 42 61 486 Cayenne-Rochea France Latin America 4.83 -52.37 256 12.9 0 0 1,663 14 16 31 43 34 46 435 Arica-Chacalluta Chile Latin America -18.33 -70.33 206 11.2 0 0 1,503 14 26 34 44 27 35 467 Kahului USA N. America 20.90 -156.43 182 7.6 20 14 1,953 14 21 40 70 23 76 348 Lihue Kauai USA N. America 21.98 -159.35 203 8.7 29 17 1,798 16 14 39 67 25 72 362 Dodge City USA N. America 37.77 -99.97 184 8.4 23 14 1,835 15 19 41 65 31 61 365 Tucson USA N. America 32.12 -110.93 210 11.9 0 0 1,808 11 21 35 44 19 30 385 Honolulu Oahu USA N. America 21.33 -157.92 237 10.0 7 6 1,742 13 15 33 57 38 73 379 Las Vegas USA N. America 36.08 -115.17 241 13.5 0 0 1,793 11 15 33 42 29 32 395 Albuquerque USA N. America 35.05 -106.62 192 11.2 0 0 1,712 12 26 36 44 21 27 402 Fort Worth USA N. America 32.83 -97.05 230 9.2 14 12 1,739 15 14 35 63 68 83 404 Corpus Christi USA N. America 27.77 -97.50 200 8.5 23 16 1,628 17 17 38 65 32 72 396 Airport Hilo USA N. America 19.72 -155.07 201 9.2 0 0 1,537 13 26 34 53 35 61 412 Kwajalein Island Japan Eastern Asia 8.73 167.73 164 8.1 35 20 1,974 14 19 46 68 16 48 337 Minamitorishima Japan Eastern Asia 24.30 153.97 191 8.1 30 21 1,917 15 13 42 73 29 74 344 Iwojima Japan Eastern Asia 24.78 141.31 191 8.6 33 23 1,921 15 14 42 68 28 63 352 Choshi Japan Eastern Asia 35.73 140.87 142 9.8 57 38 2,018 17 18 54 57 23 6 373 Omaezaki Japan Eastern Asia 34.60 138.22 208 9.0 25 18 1,616 17 15 38 64 29 71 388 Nemuro Japan Eastern Asia 43.33 145.58 166 11.2 51 33 1,747 18 13 49 52 21 8 397 Agana Guam Japan Eastern Asia 13.48 144.80 211 8.4 19 16 1,551 17 16 37 67 30 84 397 Mariana Naha Japan Eastern Asia 26.20 127.68 188 9.2 32 20 1,579 17 17 41 61 28 50 399 Ishigakijima Japan Eastern Asia 24.33 124.17 196 8.4 23 16 1,616 16 20 38 65 31 72 398

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Journal Name ARTICLE

Information about location Decision variables Resulting variables Objective

energy supplied

Bosch synthesis load load synthesis Bosch

Location Country region Geographic Latitude Longitude Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of energy [%] by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Equivalent Load Full LCOE of [$/MWh] [%] energySupply curtailed [%] factor Electrolyser load Haber [%] factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Tanegashima Japan Eastern Asia 30.73 131.00 194 9.2 31 20 1,473 19 16 40 62 26 56 408 Chiang Mai Airport Thailand S-Eastern Asia 18.78 98.98 250 10.6 0 0 1,727 14 15 32 53 59 73 451 Penang Malaysia S-Eastern Asia 5.30 100.27 218 11.4 0 0 1,709 13 22 33 45 21 41 446 Bangkok Thailand S-Eastern Asia 13.73 100.57 266 13.1 0 0 1,695 14 13 31 44 41 49 470 Kota Kinabalu Malaysia S-Eastern Asia 5.93 116.05 220 11.3 0 0 1,547 15 23 33 45 21 43 464 Kota Bahru Malaysia S-Eastern Asia 6.17 102.28 257 13.0 0 0 1,523 15 15 31 43 26 45 470 Singapore Airport Singapore S-Eastern Asia 1.37 103.98 247 10.5 0 0 1,423 15 19 30 51 56 74 465 Science Garden Philippines S-Eastern Asia 14.63 121.01 224 11.7 0 0 1,439 16 24 32 43 28 41 514 Kuching Malaysia S-Eastern Asia 1.48 110.33 197 8.8 0 0 1,356 17 30 33 53 38 64 508 Bukit Kototabang Indonesia S-Eastern Asia -0.20 100.32 231 11.8 0 0 1,310 19 23 31 43 31 43 557 Jodhpur India Southern Asia 26.30 73.02 179 10.2 0 0 1,933 10 29 37 46 11 30 408 New Delhi India Southern Asia 28.58 77.20 192 10.8 0 0 1,957 10 26 36 46 12 31 407 Ahmedabad India Southern Asia 23.07 72.63 212 11.6 0 0 1,892 10 22 35 45 20 34 421 Vishakhapatnam India Southern Asia 17.72 83.23 182 8.7 0 0 1,869 10 30 36 54 33 54 420 Poona India Southern Asia 18.53 73.85 232 12.3 0 0 1,796 11 18 33 44 23 39 432 Thiruvananthapura India Southern Asia 8.48 76.95 197 10.4 0 0 1,828 11 26 35 47 20 39 422 m Santacruz Bombay India Southern Asia 19.12 72.85 184 10.1 0 0 1,790 11 30 36 46 19 34 435 Madras India Southern Asia 13.00 80.18 188 10.1 0 0 1,711 11 28 36 47 17 36 431 Goa India Southern Asia 15.48 73.82 178 9.7 0 0 1,726 11 31 36 47 14 35 435 Nagpur Sonegaon India Southern Asia 21.10 79.05 185 10.2 0 0 1,665 12 29 36 46 18 33 443 Sharura Saudi Arabia Western Asia 17.47 47.12 255 11.1 0 0 2,240 14 9 33 55 21 69 400 Wadi Al Dawaser Saudi Arabia Western Asia 20.50 45.20 221 10.1 2 3 2,192 15 17 35 55 18 60 410 Jeddah Saudi Arabia Western Asia 21.68 39.15 208 9.4 2 3 2,138 16 20 36 57 20 62 420 Masira Oman Western Asia 20.67 58.90 204 8.3 21 14 1,845 20 16 38 68 28 81 441 Madinah Saudi Arabia Western Asia 24.55 39.70 249 13.4 0 0 2,065 16 11 33 44 17 37 443

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Information about location Decision variables Resulting variables Objective

[%]

Bosch synthesis load load synthesis Bosch

Location Country region Geographic Latitude Longitude Electrolyser rated power [MW] rated power & ASU HB [MW] Percentage RE of energy [%] by wind supplied ratedPercentage RE of [%] metpower by wind energySupply profile Hours Equivalent Load Full energyLCOE supplied of [$/MWh] [%] energySupply curtailed factor Electrolyser load Haber [%] factor Hydrogen storage size [t] factor Electrolyser excess [%] LCOA [$/t] Al Ahsa Saudi Arabia Western Asia 25.28 49.48 232 9.9 3 8 1,846 19 15 34 57 29 72 458 Buraimi Oman Western Asia 24.23 55.78 226 9.9 2 6 1,859 19 17 34 56 39 68 470 Silifke Turkey Western Asia 36.38 33.93 216 12.1 0 0 1,639 16 22 34 43 33 32 520 Gaziantep Turkey Western Asia 37.08 37.37 234 13.2 0 0 1,631 16 18 33 41 46 30 535 Mugla Turkey Western Asia 37.20 28.35 190 11.1 0 0 1,647 16 29 35 42 39 26 544 Sonnblick Austria Europe 47.05 12.95 140 9.9 84 58 3,389 17 6 62 65 24 5 330 Malin Head Ireland Europe 55.37 -7.33 145 10.1 83 50 2,754 22 6 60 64 35 6 405 List Denmark Europe 55.02 8.42 132 8.8 80 46 2,902 17 9 64 70 70 11 362 Lerwick UK Europe 60.13 -1.18 135 9.4 87 54 2,742 20 12 61 64 34 6 390 Russian Dickson Island Europe 73.50 80.23 147 10.2 90 65 2,598 19 15 54 57 48 6 438 Federation Skagen Fyr Denmark Europe 57.73 10.63 160 10.7 75 45 2,428 20 6 55 60 41 10 390 Aberporth UK Europe 52.13 -4.57 144 9.9 69 38 2,105 22 10 58 62 33 7 406 Faro Portugal Europe 37.02 -7.97 210 12.0 0 0 1,835 15 22 35 43 26 29 476 Guetsch Switzerland Europe 46.65 8.62 139 9.6 58 36 2,170 21 15 57 60 23 6 404 Valley UK Europe 53.25 -4.53 148 10.1 66 38 2,051 22 11 56 60 38 7 422 Cape Grim Australia Oceania -40.66 144.68 130 9.2 90 69 4,278 18 5 68 70 24 4 320 Willis Island Australia Oceania -16.30 149.98 129 9.3 84 65 3,990 18 8 66 67 13 2 333 Noumea France Oceania -22.28 166.45 225 9.0 27 18 1,734 19 10 37 67 41 84 409 Koumac France Oceania -20.57 164.28 227 12.2 0 0 1,580 14 19 33 44 20 37 426 Glenmore Australia Oceania -33.69 115.02 150 10.0 68 46 2,709 23 11 56 61 32 10 423 Wellington New Zealand Oceania -41.32 174.77 160 10.8 79 54 2,771 24 6 54 59 53 10 437 Carnarvon Airport Australia Oceania -24.88 113.67 137 9.7 59 44 2,587 24 19 55 57 22 4 453 Ouanaham France Oceania -20.77 167.23 226 12.2 0 0 1,478 15 21 33 43 24 36 451 Tennant Creek Australia Oceania -19.63 134.18 251 10.9 4 4 1,909 21 10 33 56 27 69 452 Airport

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Information about location Decision variables Resulting variables Objective

storage size [t]

Bosch synthesis load load synthesis Bosch

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ARTICLE Journal Name

Note 24: 2030 Scenario, Domestic Corporation – Components of LCOA by location. Lowest 10 LCOA for multi-national corporation by geographic region

Location Country Latitude Longitude Electrolyser OPEX [%] Electrolyser CAPEX [%] Hydrogen Storage [%] CAPEX Hydrogen Storage OPEX [%] Synthesis Ammonia OPEX [%] Synthesis Ammonia [%] CAPEX & Operation [%] Maintenance OPEXEnergy Curtailed [%] Cell Hydrogen Fuel [%] CAPEX OPEX Separation Air [%] CAPEX Separation Air [%] Water OPEX [%] Arrecife Lanzarote Spain 28.95 -13.6 34.7 23.1 6.7 0.5 2.2 13.1 10.2 2.6 1.2 0.4 4.5 0.8 Aswan Egypt 23.97 32.78 36.6 22.9 4.6 0.5 2.3 15.4 6.4 4.1 1.0 0.4 5.2 0.5 Casablanca Morocco 33.57 -7.67 24.7 21.5 4.3 0.3 1.5 19.6 10.5 8.2 1.8 0.2 6.7 0.7 El Kharga Egypt 25.45 30.53 31.5 24.1 3.1 0.4 1.9 20.2 7.2 2.5 1.4 0.3 6.9 0.5 Mersa Matruh Egypt 31.33 27.22 40.0 18.6 5.0 0.5 2.5 13.2 5.5 8.4 0.9 0.4 4.5 0.5 Tunis Tunisia 36.83 10.23 25.3 21.4 5.3 0.3 1.5 19.7 9.6 7.6 1.6 0.3 6.7 0.6 El Arish Egypt 31.12 33.75 32.0 19.7 3.2 0.4 1.9 18.0 6.3 10.3 1.2 0.3 6.1 0.4 El Natroon Egypt 30.4 30.35 33.3 22.0 3.1 0.5 2.0 19.3 6.8 4.5 1.3 0.3 6.6 0.4 Sidi Bouzid Tunisia 35 9.48 23.7 20.8 9.4 0.3 1.4 18.7 10.0 7.0 1.6 0.2 6.4 0.6 Funchal Portugal 32.63 -16.9 28.3 18.2 6.8 0.4 1.7 15.9 9.4 11.6 1.4 0.3 5.4 0.6 Dakar Senegal 14.73 -17.47 26.8 26.6 5.2 0.4 1.7 17.5 10.3 3.1 1.5 0.3 6.0 0.7 Marion Island South Africa -46.88 37.87 44.3 12.7 6.6 0.1 2.8 13.4 7.7 5.3 1.2 0.5 4.6 0.8 Garissa Kenya -0.47 39.63 26.9 24.5 5.7 0.4 1.7 17.8 9.6 4.9 1.5 0.3 6.1 0.7 Voi Kenya -3.4 38.57 27.5 22.2 5.5 0.4 1.7 16.5 8.9 9.3 1.4 0.3 5.6 0.7 Gillot France -20.89 55.53 40.4 18.6 3.9 0.5 2.5 12.3 9.4 5.6 1.3 0.4 4.2 0.9 Upington South Africa -28.43 21.27 26.1 22.7 3.0 0.3 1.6 20.3 10.9 5.2 1.8 0.3 6.9 0.8 Mombasa Airport Kenya -4.03 39.62 25.8 23.4 3.9 0.3 1.6 19.4 9.5 7.0 1.6 0.3 6.6 0.7 Beira Mozambique -19.83 34.85 27.1 22.9 2.7 0.3 1.6 19.9 9.4 6.7 1.6 0.3 6.8 0.7 Maun Botswana -19.98 23.42 26.8 22.1 2.8 0.3 1.6 19.3 10.7 7.1 1.8 0.3 6.6 0.8 Bloemfontein South Africa -29.1 26.3 26.1 23.7 3.9 0.4 1.6 20.3 11.3 3.1 1.8 0.3 6.9 0.7 Piura Peru -5.17 -80.6 26.2 22.5 5.5 0.4 1.6 16.7 10.9 7.9 1.6 0.3 5.7 0.8 Caracas Maiquetia Venezuela 10.6 -66.98 26.9 26.1 3.4 0.3 1.6 21.3 6.2 5.0 1.3 0.3 7.2 0.4 Le Lamentin France 14.6 -61 31.7 21.7 5.0 0.5 2.0 14.5 11.2 6.0 1.5 0.3 4.9 0.8 Petrolina Brazil -9.38 -40.5 26.2 21.8 3.8 0.3 1.6 18.4 10.2 8.7 1.7 0.3 6.3 0.7 Antofagasta Chile -23.43 -70.43 26.1 20.7 5.0 0.3 1.6 18.0 11.2 8.1 1.7 0.3 6.1 0.7 Raizet France 16.27 -61.52 29.1 21.4 2.7 0.4 1.8 18.1 11.7 6.0 1.8 0.3 6.1 0.8 Mendoza Airport Argentina -32.83 -68.78 27.8 21.6 4.1 0.4 1.7 19.4 7.0 9.2 1.3 0.3 6.6 0.5

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Fuel Cell CAPEX Cell Fuel

Location Country Latitude Longitude Electrolyser OPEX [%] Electrolyser CAPEX [%] Hydrogen Storage CAPEX [%] Hydrogen Storage OPEX [%] OPEX Synthesis Ammonia [%] Synthesis Ammonia [%] CAPEX & Operation [%] Maintenance OPEXEnergy Curtailed [%] Hydrogen [%] OPEX [%] Separation Air CAPEX [%] Separation Air Water OPEX [%] Brasilia Brazil -15.78 -47.93 28.0 20.4 6.9 0.4 1.7 15.0 9.6 10.6 1.3 0.3 5.1 0.7 Cayenne-Rochea France 4.83 -52.37 27.5 21.7 4.7 0.4 1.7 17.5 12.1 5.7 1.8 0.3 6.0 0.8 Arica-Chacalluta Chile -18.33 -70.33 27.2 19.9 4.2 0.3 1.6 17.4 10.6 10.3 1.7 0.3 5.9 0.7 Kahului USA 20.9 -156.43 33.9 19.5 4.0 0.5 2.1 13.1 10.2 9.6 1.4 0.4 4.5 0.9 Lihue Kauai USA 21.98 -159.35 37.4 19.2 3.7 0.5 2.3 13.2 10.1 6.5 1.4 0.4 4.5 0.9 Dodge City USA 37.77 -99.97 34.5 18.4 4.9 0.4 2.2 13.5 10.2 8.7 1.4 0.4 4.6 0.9 Tucson USA 32.12 -110.93 25.8 20.9 3.1 0.3 1.6 19.0 12.3 7.5 2.0 0.3 6.5 0.9 Honolulu Oahu USA 21.33 -157.92 30.3 21.7 5.6 0.4 1.9 14.9 11.7 5.7 1.5 0.3 5.1 0.8 Las Vegas USA 36.08 -115.17 25.5 21.7 4.2 0.3 1.5 19.5 12.9 4.7 2.0 0.3 6.6 0.8 Albuquerque USA 35.05 -106.62 26.1 19.5 3.4 0.3 1.6 18.2 11.7 9.9 1.9 0.3 6.2 0.8 Fort Worth USA 32.83 -97.05 32.0 19.6 9.4 0.5 2.0 12.7 11.4 5.7 1.3 0.3 4.3 0.8 Corpus Christi Airport USA 27.77 -97.5 37.3 18.0 4.7 0.5 2.3 12.4 9.7 8.4 1.3 0.4 4.2 0.8 Hilo USA 19.72 -155.07 28.2 19.9 5.5 0.4 1.7 14.6 11.2 10.9 1.5 0.3 5.0 0.8 Kwajalein Island Japan 8.73 167.73 35.2 18.4 2.9 0.3 2.2 14.7 9.7 8.7 1.5 0.4 5.0 0.9 Minamitorishima Japan 24.3 153.97 35.9 19.8 4.9 0.4 2.2 13.5 10.1 6.0 1.4 0.4 4.6 0.9 Iwojima Japan 24.78 141.31 35.5 19.5 4.5 0.4 2.2 14.1 10.1 6.2 1.4 0.4 4.8 0.9 Choshi Japan 35.73 140.87 37.7 14.2 3.7 0.1 2.4 15.8 9.2 8.7 1.6 0.4 5.4 0.9 Omaezaki Japan 34.6 138.22 36.3 19.4 4.4 0.4 2.3 13.5 9.9 6.7 1.4 0.4 4.6 0.8 Nemuro Japan 43.33 145.58 39.1 14.8 3.0 0.2 2.4 16.2 9.4 6.5 1.6 0.4 5.5 0.8 Agana Guam Mariana Japan 13.48 144.8 36.2 19.8 4.5 0.5 2.3 12.7 9.7 7.6 1.3 0.4 4.3 0.8 Naha Japan 26.2 127.68 36.9 17.5 4.2 0.3 2.3 13.8 9.5 8.1 1.4 0.4 4.7 0.8 Ishigakijima Japan 24.33 124.17 34.5 19.0 4.9 0.4 2.2 13.1 9.8 9.2 1.3 0.4 4.5 0.8 Tanegashima Japan 30.73 131 38.6 17.5 3.8 0.4 2.4 13.3 9.2 7.8 1.3 0.4 4.5 0.8 Chiang Mai Airport Thailand 18.78 98.98 26.4 23.2 8.8 0.4 1.6 15.9 11.1 4.8 1.5 0.3 5.4 0.7 Penang Malaysia 5.3 100.27 26.4 22.0 3.4 0.3 1.6 18.4 10.7 8.1 1.7 0.3 6.3 0.7 Bangkok Thailand 13.73 100.57 26.0 23.3 5.8 0.3 1.6 18.6 11.2 4.2 1.7 0.3 6.3 0.7 Kota Kinabalu Malaysia 5.93 116.05 28.1 21.3 3.3 0.3 1.7 17.6 10.3 8.7 1.6 0.3 6.0 0.7 Kota Bahru Malaysia 6.17 102.28 28.2 22.5 3.7 0.4 1.7 18.3 10.9 5.4 1.7 0.3 6.2 0.7 Singapore Airport Singapore 1.37 103.98 28.4 21.4 7.7 0.4 1.7 14.6 11.1 7.2 1.4 0.3 5.0 0.7 Science Garden Philippines 14.63 121.01 28.2 20.6 4.1 0.4 1.7 17.3 9.9 9.4 1.6 0.3 5.9 0.6

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Location Country Latitude Longitude Electrolyser OPEX [%] Electrolyser CAPEX [%] Hydrogen Storage CAPEX [%] Hydrogen Storage OPEX [%] OPEX Synthesis Ammonia [%] Synthesis Ammonia [%] CAPEX & Operation [%] Maintenance OPEXEnergy Curtailed [%] CAPEX Cell Hydrogen Fuel [%] OPEX [%] Separation Air CAPEX [%] Separation Air Water OPEX [%] Kuching Malaysia 1.48 110.33 29.1 19.3 5.9 0.4 1.8 13.9 9.4 13.3 1.3 0.3 4.7 0.6 Bukit Kototabang Indonesia -0.2 100.32 29.3 20.3 4.3 0.4 1.8 16.8 9.4 9.6 1.5 0.3 5.7 0.6 Jodhpur India 26.3 73.02 21.5 23.4 2.2 0.3 1.3 21.3 10.7 9.3 1.9 0.2 7.3 0.8 New Delhi India 28.58 77.2 21.2 24.0 2.4 0.3 1.3 21.8 10.9 7.8 1.9 0.2 7.4 0.8 Ahmedabad India 23.07 72.63 21.3 24.4 3.7 0.3 1.3 21.5 11.2 6.2 1.9 0.2 7.3 0.8 Vishakhapatnam India 17.72 83.23 21.5 23.3 6.8 0.3 1.3 17.9 10.6 9.7 1.6 0.2 6.1 0.8 Poona India 18.53 73.85 21.9 24.8 4.0 0.3 1.3 21.2 11.3 5.1 1.9 0.2 7.2 0.8 Thiruvananthapuram India 8.48 76.95 21.9 23.8 3.8 0.3 1.3 20.3 10.8 8.1 1.8 0.2 6.9 0.8 Santacruz Bombay India 19.12 72.85 21.7 22.8 3.9 0.3 1.3 20.1 10.5 9.8 1.8 0.2 6.8 0.8 Madras India 13 80.18 22.9 22.9 3.3 0.3 1.4 19.9 10.4 9.5 1.7 0.2 6.8 0.8 Goa India 15.48 73.82 22.5 22.6 2.8 0.3 1.4 19.8 10.2 10.9 1.7 0.2 6.7 0.8 Nagpur Sonegaon India 21.1 79.05 23.0 22.3 3.4 0.3 1.4 19.8 10.3 10.1 1.7 0.2 6.7 0.7 Sharura Saudi Arabia 17.47 47.12 31.5 23.9 3.1 0.4 1.9 16.7 10.8 3.4 1.6 0.3 5.7 0.8 Wadi Al Dawaser Saudi Arabia 20.5 45.2 31.9 21.8 2.8 0.4 2.0 16.1 10.1 6.8 1.5 0.3 5.5 0.8 Jeddah Saudi Arabia 21.68 39.15 32.0 20.9 3.2 0.4 2.0 15.3 9.8 8.7 1.5 0.3 5.2 0.8 Masira Oman 20.67 58.9 38.9 18.7 4.1 0.6 2.4 12.2 8.5 8.1 1.2 0.4 4.2 0.7 Madinah Saudi Arabia 24.55 39.7 31.1 21.6 2.4 0.4 1.9 18.6 10.7 4.1 1.8 0.3 6.3 0.7 Al Ahsa Saudi Arabia 25.28 49.48 35.5 20.3 4.0 0.5 2.2 13.9 9.4 7.0 1.3 0.4 4.7 0.7 Buraimi Oman 24.23 55.78 34.2 19.8 5.4 0.5 2.1 13.9 9.5 7.5 1.3 0.4 4.7 0.7 Silifke Turkey 36.38 33.93 27.7 20.2 4.9 0.4 1.7 18.2 9.8 8.4 1.6 0.3 6.2 0.6 Gaziantep Turkey 37.08 37.37 27.2 20.4 6.5 0.4 1.6 18.5 10.2 6.5 1.6 0.3 6.3 0.6 Mugla Turkey 37.2 28.35 26.4 18.7 6.1 0.3 1.6 17.5 9.5 11.5 1.5 0.3 6.0 0.6 Sonnblick Austria 47.05 12.95 43.2 13.9 3.8 0.1 2.7 15.7 9.2 3.1 1.6 0.5 5.3 1.0 Malin Head Ireland 55.37 -7.33 45.0 13.3 5.2 0.1 2.8 14.8 8.0 3.1 1.4 0.5 5.1 0.8 List Denmark 55.02 8.42 40.2 12.3 10.5 0.1 2.5 13.1 9.6 4.5 1.3 0.4 4.5 0.9 Lerwick UK 60.13 -1.18 44.0 12.3 5.0 0.1 2.8 13.8 8.3 6.5 1.4 0.5 4.7 0.8 Russian Dickson Island 73.5 80.23 36.1 14.3 7.5 0.1 2.3 15.9 8.7 7.1 1.4 0.4 5.4 0.7 Federation Skagen Fyr Denmark 57.73 10.63 44.4 13.3 5.5 0.1 2.8 14.3 9.1 3.0 1.4 0.5 4.9 0.8

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CAPEX CAPEX

Location Country Latitude Longitude Electrolyser OPEX [%] Electrolyser CAPEX [%] Hydrogen Storage [%] Hydrogen Storage OPEX [%] OPEX Synthesis Ammonia [%] Synthesis Ammonia [%] CAPEX & Operation [%] Maintenance OPEXEnergy Curtailed [%] CAPEX Cell Hydrogen Fuel [%] OPEX [%] Separation Air CAPEX [%] Separation Air Water OPEX [%] Aberporth UK 52.13 -4.57 45.7 12.3 4.5 0.1 2.9 13.7 8.1 5.4 1.4 0.5 4.7 0.8 Faro Portugal 37.02 -7.97 26.8 20.7 4.1 0.3 1.6 19.1 10.2 8.0 1.7 0.3 6.5 0.7 Guetsch Switzerland 46.65 8.62 43.3 12.5 3.4 0.1 2.7 14.0 8.2 8.4 1.4 0.5 4.8 0.8 Valley UK 53.25 -4.53 45.1 12.3 5.0 0.1 2.8 13.5 8.2 5.7 1.3 0.5 4.6 0.8 Cape Grim Australia -40.66 144.68 46.4 12.9 3.8 0.1 2.9 14.7 8.7 2.5 1.5 0.5 5.0 1.0 Willis Island Australia -16.3 149.98 46.5 12.7 2.1 0.1 2.9 14.8 8.3 4.5 1.5 0.5 5.0 1.0 Noumea France -22.28 166.45 39.8 18.6 5.4 0.5 2.5 12.0 9.7 5.0 1.2 0.4 4.1 0.8 Koumac France -20.57 164.28 29.6 20.3 2.9 0.4 1.8 17.5 11.3 7.4 1.8 0.3 6.0 0.8 Glenmore Australia -33.69 115.02 46.7 12.0 4.1 0.1 2.9 13.0 8.0 6.1 1.3 0.5 4.4 0.8 Wellington New Zealand -41.32 174.77 47.2 11.8 6.3 0.1 3.0 12.8 8.5 3.4 1.3 0.5 4.3 0.7 Carnarvon Airport Australia -24.88 113.67 44.7 11.3 2.9 0.2 2.8 12.9 7.5 11.0 1.3 0.5 4.4 0.7 Ouanaham France -20.77 167.23 29.9 19.5 3.3 0.4 1.8 17.0 11.1 8.4 1.7 0.3 5.8 0.7 Tennant Creek Airport Australia -19.63 134.18 40.4 18.9 3.2 0.5 2.5 13.2 9.5 4.7 1.3 0.4 4.5 0.7 Ils Des Pins Moue France -22.6 167.45 38.7 18.0 4.5 0.5 2.4 12.2 9.4 7.8 1.2 0.4 4.1 0.7 Please note that summation of the components not equalling 100% is due to rounding.

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Note 25: 2019 Scenario, Multi-national Corporation – Impact of a carbon tax on the LCOA required for fossil-fuel based production to still be competitive against ‘green’ domestic production

Lowest Carbon Tax = 25 USD/t Carbon Tax = 50 USD/t Carbon Tax = 75 USD/t Country ‘green’ LCOA Fuel Oil Fuel Oil Fuel Oil SMR [USD/t] Coal [USD/t] SMR [USD/t] Coal [USD/t] SMR [USD/t] Coal [USD/t] [USD/t] [USD/t] [USD/t] [USD/t] Argentina 757 717 682 667 677 607 577 637 532 487 Australia 473 433 398 383 393 323 293 353 248 203 Austria 518 478 443 428 438 368 338 398 293 248 Belgium 737 697 662 647 657 587 557 617 512 467 Bosnia & 1,193 1,153 1,118 1,103 1,113 1,043 1,013 1,073 968 923 Herzegovina Botswana 748 708 673 658 668 598 568 628 523 478 Brazil 766 726 691 676 686 616 586 646 541 496 Bulgaria 1,047 1,007 972 957 967 897 867 927 822 777 Canada 729 689 654 639 649 579 549 609 504 459 Chile 720 680 645 630 640 570 540 600 495 450 China 801 761 726 711 721 651 621 681 576 531 Colombia 907 867 832 817 827 757 727 787 682 637 Congo 864 824 789 774 784 714 684 744 639 594 Croatia 925 885 850 835 845 775 745 805 700 655 Czech 874 834 799 784 794 724 694 754 649 604 Republic Denmark 528 488 453 438 448 378 348 408 303 258 Egypt 759 719 684 669 679 609 579 639 534 489 El Salvador 988 948 913 898 908 838 808 868 763 718 Estonia 1,351 1,311 1,276 1,261 1,271 1,201 1,171 1,231 1,126 1,081 Finland 1,309 1,269 1,234 1,219 1,229 1,159 1,129 1,189 1,084 1,039 France 668 628 593 578 588 518 488 548 443 398 Germany 1,083 1,043 1,008 993 1,003 933 903 963 858 813 Ghana 770 730 695 680 690 620 590 650 545 500 Greece 938 898 863 848 858 788 758 818 713 668 Hungary 1,301 1,261 1,226 1,211 1,221 1,151 1,121 1,181 1,076 1,031 India 636 596 561 546 556 486 456 516 411 366 Indonesia 953 913 878 863 873 803 773 833 728 683 Ireland 554 514 479 464 474 404 374 434 329 284 Italy 777 737 702 687 697 627 597 657 552 507 Japan 562 522 487 472 482 412 382 442 337 292 Kazakhstan 1,567 1,527 1,492 1,477 1,487 1,417 1,387 1,447 1,342 1,297 Kenya 697 657 622 607 617 547 517 577 472 427

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Lowest Carbon Tax = 25 USD/t Carbon Tax = 50 USD/t Carbon Tax = 75 USD/t Country ‘green’ LCOA Fuel Oil Fuel Oil Fuel Oil SMR [USD/t] Coal [USD/t] SMR [USD/t] Coal [USD/t] SMR [USD/t] Coal [USD/t] [USD/t] [USD/t] [USD/t] [USD/t] Korea, Republic of 860 820 785 770 780 710 680 740 635 590 (South Korea) Latvia 1,929 1,889 1,854 1,839 1,849 1,779 1,749 1,809 1,704 1,659 Lithuania 1,350 1,310 1,275 1,260 1,270 1,200 1,170 1,230 1,125 1,080 Macedonia, 1,231 1,191 1,156 1,141 1,151 1,081 1,051 1,111 1,006 961 Republic of Malaysia 788 748 713 698 708 638 608 668 563 518 Mexico 896 856 821 806 816 746 716 776 671 626 Mongolia 1,171 1,131 1,096 1,081 1,091 1,021 991 1,051 946 901 Morocco 757 717 682 667 677 607 577 637 532 487 Mozambique 739 699 664 649 659 589 559 619 514 469 Netherlands 796 756 721 706 716 646 616 676 571 526 New Zealand 673 633 598 583 593 523 493 553 448 403 Nicaragua 1,087 1,047 1,012 997 1,007 937 907 967 862 817 Norway 731 691 656 641 651 581 551 611 506 461 Oman 735 695 660 645 655 585 555 615 510 465 Pakistan 716 676 641 626 636 566 536 596 491 446 Papua New 1,129 1,089 1,054 1,039 1,049 979 949 1,009 904 859 Guinea Peru 709 669 634 619 629 559 529 589 484 439 Philippines 894 854 819 804 814 744 714 774 669 624 Poland 1,282 1,242 1,207 1,192 1,202 1,132 1,102 1,162 1,057 1,012 Portugal 767 727 692 677 687 617 587 647 542 497 Romania 1,043 1,003 968 953 963 893 863 923 818 773 Russian 565 525 490 475 485 415 385 445 340 295 Federation Saudi Arabia 741 701 666 651 661 591 561 621 516 471 Senegal 661 621 586 571 581 511 481 541 436 391 Singapore 886 846 811 796 806 736 706 766 661 616 South Africa 552 512 477 462 472 402 372 432 327 282 Spain 687 647 612 597 607 537 507 567 462 417 Sri Lanka 715 675 640 625 635 565 535 595 490 445 Sweden 896 856 821 806 816 746 716 776 671 626 Switzerland 675 635 600 585 595 525 495 555 450 405 Thailand 764 724 689 674 684 614 584 644 539 494 Tunisia 798 758 723 708 718 648 618 678 573 528 Turkey 867 827 792 777 787 717 687 747 642 597 Ukraine 1,172 1,132 1,097 1,082 1,092 1,022 992 1,052 947 902

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Lowest Carbon Tax = 25 USD/t Carbon Tax = 50 USD/t Carbon Tax = 75 USD/t Country ‘green’ LCOA Fuel Oil Fuel Oil Fuel Oil SMR [USD/t] Coal [USD/t] SMR [USD/t] Coal [USD/t] SMR [USD/t] Coal [USD/t] [USD/t] [USD/t] [USD/t] [USD/t] UK 584 544 509 494 504 434 404 464 359 314 USA 668 628 593 578 588 518 488 548 443 398 Venezuela 758 718 683 668 678 608 578 638 533 488 Zambia 836 796 761 746 756 686 656 716 611 566

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Note 25: 2030 Scenario, Multi-national Corporation – Impact of carbon tax on the competitiveness of fossil-fuel based production by country

Lowest Carbon Tax = 25 USD/t Carbon Tax = 50 USD/t Carbon Tax = 75 USD/t Country ‘green’ LCOA Fuel Oil Fuel Oil Fuel Oil SMR [USD/t] Coal [USD/t] SMR [USD/t] Coal [USD/t] SMR [USD/t] Coal [USD/t] [USD/t] [USD/t] [USD/t] [USD/t] Argentina 415 375 340 325 335 265 235 295 190 145 Australia 310 270 235 220 230 160 130 190 85 40 Austria 318 278 243 228 238 168 138 198 93 48 Belgium 426 386 351 336 346 276 246 306 201 156 Bosnia & 573 533 498 483 493 423 393 453 348 303 Herzegovina Botswana 378 338 303 288 298 228 198 258 153 108 Brazil 388 348 313 298 308 238 208 268 163 118 Bulgaria 539 499 464 449 459 389 359 419 314 269 Canada 423 383 348 333 343 273 243 303 198 153 Chile 408 368 333 318 328 258 228 288 183 138 China 407 367 332 317 327 257 227 287 182 137 Colombia 444 404 369 354 364 294 264 324 219 174 Congo 426 386 351 336 346 276 246 306 201 156 Croatia 481 441 406 391 401 331 301 361 256 211 Czech 487 447 412 397 407 337 307 367 262 217 Republic Denmark 352 312 277 262 272 202 172 232 127 82 Egypt 374 334 299 284 294 224 194 254 149 104 El Salvador 471 431 396 381 391 321 291 351 246 201 Estonia 756 716 681 666 676 606 576 636 531 486 Finland 724 684 649 634 644 574 544 604 499 454 France 359 319 284 269 279 209 179 239 134 89 Germany 573 533 498 483 493 423 393 453 348 303 Ghana 388 348 313 298 308 238 208 268 163 118 Greece 484 444 409 394 404 334 304 364 259 214 Hungary 660 620 585 570 580 510 480 540 435 390 India 338 298 263 248 258 188 158 218 113 68 Indonesia 464 424 389 374 384 314 284 344 239 194 Ireland 355 315 280 265 275 205 175 235 130 85 Italy 407 367 332 317 327 257 227 287 182 137 Japan 324 284 249 234 244 174 144 204 99 54 Kazakhstan 729 689 654 639 649 579 549 609 504 459 Kenya 354 314 279 264 274 204 174 234 129 84

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Lowest Carbon Tax = 25 USD/t Carbon Tax = 50 USD/t Carbon Tax = 75 USD/t Country ‘green’ LCOA Fuel Oil Fuel Oil Fuel Oil SMR [USD/t] Coal [USD/t] SMR [USD/t] Coal [USD/t] SMR [USD/t] Coal [USD/t] [USD/t] [USD/t] [USD/t] [USD/t] Korea, Republic of 433 393 358 343 353 283 253 313 208 163 (South Korea) Latvia 1,087 1,047 1,012 997 1,007 937 907 967 862 817 Lithuania 725 685 650 635 645 575 545 605 500 455 Macedonia, 627 587 552 537 547 477 447 507 402 357 Republic of Malaysia 394 354 319 304 314 244 214 274 169 124 Mexico 440 400 365 350 360 290 260 320 215 170 Mongolia 536 496 461 446 456 386 356 416 311 266 Morocco 387 347 312 297 307 237 207 267 162 117 Mozambique 375 335 300 285 295 225 195 255 150 105 Netherlands 451 411 376 361 371 301 271 331 226 181 New Zealand 428 388 353 338 348 278 248 308 203 158 Nicaragua 504 464 429 414 424 354 324 384 279 234 Norway 435 395 360 345 355 285 255 315 210 165 Oman 390 350 315 300 310 240 210 270 165 120 Pakistan 364 324 289 274 284 214 184 244 139 94 Papua New 514 474 439 424 434 364 334 394 289 244 Guinea Peru 362 322 287 272 282 212 182 242 137 92 Philippines 441 401 366 351 361 291 261 321 216 171 Poland 686 646 611 596 606 536 506 566 461 416 Portugal 403 363 328 313 323 253 223 283 178 133 Romania 538 498 463 448 458 388 358 418 313 268 Russian 369 329 294 279 289 219 189 249 144 99 Federation Saudi Arabia 363 323 288 273 283 213 183 243 138 93 Senegal 339 299 264 249 259 189 159 219 114 69 Singapore 432 392 357 342 352 282 252 312 207 162 South Africa 345 305 270 255 265 195 165 225 120 75 Spain 353 313 278 263 273 203 173 233 128 83 Sri Lanka 368 328 293 278 288 218 188 248 143 98 Sweden 504 464 429 414 424 354 324 384 279 234 Switzerland 389 349 314 299 309 239 209 269 164 119 Thailand 388 348 313 298 308 238 208 268 163 118 Tunisia 413 373 338 323 333 263 233 293 188 143 Turkey 432 392 357 342 352 282 252 312 207 162 Ukraine 623 583 548 533 543 473 443 503 398 353

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Lowest Carbon Tax = 25 USD/t Carbon Tax = 50 USD/t Carbon Tax = 75 USD/t Country ‘green’ LCOA Fuel Oil Fuel Oil Fuel Oil SMR [USD/t] Coal [USD/t] SMR [USD/t] Coal [USD/t] SMR [USD/t] Coal [USD/t] [USD/t] [USD/t] [USD/t] [USD/t] UK 364 324 289 274 284 214 184 244 139 94 USA 347 307 272 257 267 197 167 227 122 77 Venezuela 383 343 308 293 303 233 203 263 158 113 Zambia 423 383 348 333 343 273 243 303 198 153

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