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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 Fo Electronic Supplementary Material (ESI) for Energy & Environmental Science. This journal is © The Royal Society of Chemistry 2020 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 푃퐶푢푟푡푎푙 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 Please do not adjust margins Journal Name ARTICLE 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 This journal is © The Royal Society of Chemistry 2020 R.M.Nayak-Luke and R.Bañares-Alcántara 2020 | 11 Please do not adjust margins Please do not adjust margins 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. 12 | R.M.Nayak-Luke and R.Bañares-Alcántara 2020 This journal is © The Royal Society of Chemistry 2020 Please do not adjust margins Please do not adjust margins Journal Name ARTICLE 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 40 ASU specific energy consumption 0.110kWh/kg 8, 41 Energy losses in the compression of Hydrogen for storage 6.6% of LHV 42 Hydrogen fuel cell efficiency 50% 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. This journal is © The Royal Society of Chemistry 2020 R.M.Nayak-Luke and R.Bañares-Alcántara 2020 | 13 Please do not adjust margins Please do not adjust margins ARTICLE Journal Name 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).
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