Techno-Economic Assessment of Fuel Cycle Facility of System Integrated...

[Full title: Techno-Economic Assessment of Fuel Cycle Facility of System Integrated Modular Advanced Reactor (SMART)]

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sustainability Article Techno-Economic Assessment of Fuel Cycle Facility of System Integrated Modular Advanced Reactor (SMART) Salah Ud-Din Khan 1,2, * , Zeyad Almutairi 1,2,3,4 and Meshari Alanazi 1,2 Citation: Khan, S.U.-D.; Almutairi, Z.; Alanazi, M. Techno-Economic Assessment of Fuel Cycle Facility of System Integrated Modular Advanced Reactor (SMART) Sustainability 2021 , 13 , 11815. https:// doi.org/10.3390/su 132111815 Academic Editor: Lin Li Received: 30 May 2021 Accepted: 19 August 2021 Published: 26 October 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations Copyright: © 2021 by the authors Licensee MDPI, Basel, Switzerland This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/) 1 Sustainable Energy Technologies Center, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia; zaalmutairi@ksu.edu.sa (Z.A.); 438105244@student.ksu.edu.sa (M.A.) 2 K.A.CARE Energy Research and Innovation Center, Riyadh 12244, Saudi Arabia 3 Mechanical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia 4 King Abdullah Institute of Nanotechnology, King Saud University, Riyadh 11451, Saudi Arabia * Correspondence: drskhan@ksu.edu.sa Abstract: The economic assessment of advanced nuclear power reactors is very important, specifically during the early stages of concept design. Therefore, a study was performed to calculate the total cost estimation of fuel cycle supply for a system modular advanced reactor (SMART) by using the Generation-IV economic program called G 4-ECONS (Generation 4 Excel-based Calculation of Nuclear Systems). In this study, the detailed description of each model and methodology are presented including facility, operations, construction matrix, post-production model, and fuel cycle cost estimation model. Based on these models, six Generation-III+ and Generation-IV nuclear reactors were simulated, namely System 80+ with benchmark data, System 80+ with uranium oxide (UOx) and mixed oxide (MOx) fuel assemblies, fast reactor, PBMR (Pebble Bed Modular Reactor), and PWR (Pressurized Water Reactor), with partially closed and benchmarked cases. The total levelized costs of these reactors were obtained, and it was observed that PBMR showed the lowest cost. The research was extended to work on the SMART reactor to calculate the total levelized fuel cycle cost, capital cost, capital component cost, fraction of capital spent, and sine curve spent pattern. To date, no work is being reported to calculate these parameters for the SMART reactor. It was observed that SMART is the most cost-effective reactor system among other proven and advanced pressurized water-based reactor systems. The main objective of the research is to verify and validate the G 4-ECONS model to be used for other innovative nuclear reactors Keywords: SMART; fuel cycle; total cost; Gen III+/IV reactors; verification; validation 1. Introduction Today, Generation-III, III+, and IV nuclear power reactors, due to their unique and novel features, are struggling to increase continuous improvement in the areas of sustainability, reliability, safety and proliferation resistance, protection, and economics [ 1 ]. With these criteria, advanced reactors have turned out to be a revolution in the nuclear industry, in which highly sophisticated and novel methods and concepts are being implemented These incremental technologies are considered to significantly improve the safety, reliability, and economics of nuclear power reactors along with other safety features. In this way, economic assessment is an important factor in the development of Generation-IV nuclear systems because the decision on funding is based on the economic assessment report [ 2 ]. The main goal of the research is to include the proliferation, protection, safety, reliability, and economics of the plant. This way, it substantially upgrades safety and enhances public confidence by adding inherent safety features and reducing core damage frequency, which is governed by offsite emergency response. These standards develop a methodology that would allow for safety performance and evaluation of various nuclear power plant concepts. Over the last couple of years, the safety performance of nuclear Sustainability 2021 , 13 , 11815. https://doi.org/10.3390/su 132111815 https://www.mdpi.com/journal/sustainability

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Sustainability 2021 , 13 , 11815 2 of 11 power plants has been increased by using the best estimate deterministic approach in conjunction with the probabilistic approach, despite using conservative assumptions and approaches. Probabilistic safety assessment (PSA) identifies potential accident scenarios and helps in the design and licensing assessment of any nuclear power plant by using a deterministic approach and defense-in-depth analysis [ 1 ]. The Generation-IV economic program includes both deterministic and probabilistic approaches, along with the defensein-depth principle. Economics is an important parameter that is considered and defined in two terms, (1) total capital investment capital cost, which is used to determine the comparison between advanced energy systems and other associated systems, and (2) levelized energy cost unit, which is used to determine the comparison between life cycle cost of advanced and other energy systems [ 3 ]. To counter such issues, the International Atomic Energy Agency (IAEA), in collaboration with an international project on innovative nuclear reactors and fuel cycles (INPRO) and Generation-IV international forum through an economic modeling working group, has developed an economic analysis program to assess the economic sector of advanced nuclear reactor systems [ 4 ]. Various other computational tools have been developed to perform the economic assessment of different types of nuclear energy systems, and among them is Economic Modeling Working Group (EMWG). This best estimate economical assessment tool identifies the methodology and provides support for the analysis of the wide variety of reactor technologies. This working group, after reviewing the existing economic methodologies, developed a Generation-IV Excel-based Calculation of Nuclear Systems (G 4-ECONS). Megan et al. [ 5 ] conducted the benchmark analysis of G 4-ECONS, and the nuclear energy support tool (NEST) was used for the high-performance light water reactor and fast reactor to calculate the levelized unit cost of electricity and capital investment cost. The group also conducted an economic analysis of a Canadian-based supercritical water-cooled reactor [ 6 ]. Both technical and economic analyses were performed by M. Jaskolski et al. [ 7 ] for the cogeneration of nuclear power plant producing electricity and heat. The specific cost of heat was calculated to be 10.3–12.7 EUR/GJ. Various simulation techniques have been developed and are practiced in many countries to find the economic assessment of any nuclear or thermal plant [ 8 – 11 ]. Among them is the WASP model, which was designed by the Tennessee Valley Authority (TVA) and the Oak Ridge National Laboratory (ORNL) to find the economic competitiveness of nuclear power reactors. Several studies were performed using the WASP tool, particularly in South Korea [ 12 ]. Similar work was performed by B. Ali and S. Omid [ 12 ] to determine the energy demand, generation-wise, of nuclear power plants, as well as combined nuclear power plants. They concluded that Generation-IV nuclear power plants are considered as cost effective as any other nuclear power plants, generation-wise. A very important and clear demonstration tool for the cost analysis decision, in case of the continuation of the construction of nuclear power plants, was developed by S. Jain et al. [ 13 ]. Another study is reported to assess the economic feasibility of a nuclear-powered hydrogen plant by using discounted cash flow analysis [ 14 ]. The cost estimation scenario of nuclear power reactors in the context of Europe, particularly for Gen-IV nuclear reactor designs, was conducted by R.F and colleagues [ 15 ]. These studies show that there are many existing simulation techniques for cost analysis of nuclear power plants and for analyzing generation wise nuclear power plant [ 16 ]. However, no study has reported on the assessment of fuel supply cost or has concluded the optimal generation for a specific plant. Therefore, the current study was conducted to discover the most economic and optimal reactor type 2. Research Methodology In the current research, the G 4-ECONS program was used, which is a Microsoft Excel-based program used to conduct an economic analysis of an advanced nuclear reactor system [ 17 ]. The tool covers four main pillars of the economic model development, which are simplicity, transparency, universality, and adaptability. This dictates that the tool has the ability to accept projected and actual plant input data, open and closed fuel cycles, and

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Sustainability 2021 , 13 , 11815 3 of 11 can also govern international laws as well. In this tool, different cases were provided that included both Generation-III and IV nuclear reactor systems. The program consisted of five parts, which are construction, production, fuel cycle, energy products, and modularization. The life cycle of any nuclear reactor system includes many categories such as research development and demonstration, commercial design, commissioning, operations, fueling, and decommissioning, as presented in Figure 1 . The economic model of G 4-ECONS is particularly generic, in that it can calculate the levelized unit production cost of the facility as same as the levelized cost of electrical energy from the reactor plant, as given in Figure 1 . IAEA has concluded a comprehensive account model for addressing the capital, operation, maintenance, and fuel cycle cost from nuclear power to individual systems. The model can be used for all types of plans, either single or dual-purpose, along with various contract and deployment approaches. This guideline helps in the bidding process for the construction from the vendors Sustainability 2021 , 13 , x FOR PEER REVIEW 3 of 12 the ability to accept projected and actual plant input data, open and closed fuel cycles, and can also govern international laws as well In this tool, different cases were provided that included both Generation ‐ III and IV nuclear reactor systems The program consisted of five parts, which are construction, production, fuel cycle, energy products, and modular ‐ ization The life cycle of any nuclear reactor system includes many categories such as re ‐ search development and demonstration, commercial design, commissioning, operations, fueling, and decommissioning, as presented in Figure 1 The economic model of G 4 ‐ ECONS is particularly generic, in that it can calculate the levelized unit production cost of the facility as same as the levelized cost of electrical energy from the reactor plant, as given in Figure 1 IAEA has concluded a comprehensive account model for addressing the capital, operation, maintenance, and fuel cycle cost from nuclear power to individual systems The model can be used for all types of plans, either single or dual ‐ purpose, along with various contract and deployment approaches This guideline helps in the bidding process for the construction from the vendors The G 4 ‐ ECONS model is based on three models, namely reactor model, fuel cycle model, and non ‐ electricity production model, as illustrated in Figure 1 Figure 1. Flow diagram of the G 4 ‐ ECONS model The investment cost of a nuclear power plant, or part of it, includes engineering, con ‐ struction, commissioning, and testing commercial operation While the basic cost covers design, installation, equipment, structure, material, and supply, other costs include super ‐ vision, indirect costs, initial cost, spare part cost, financial cost, owner’s cost, contingency cost, and other financial costs The total capital investment cost (TCIC) represents the cost of the building and bringing it into commercial operation Figure 2 presents the structure and model used to calculate the construction and production cost Figure 1. Flow diagram of the G 4-ECONS model The G 4-ECONS model is based on three models, namely reactor model, fuel cycle model, and non-electricity production model, as illustrated in Figure 1 . The investment cost of a nuclear power plant, or part of it, includes engineering, construction, commissioning, and testing commercial operation. While the basic cost covers design, installation, equipment, structure, material, and supply, other costs include supervision, indirect costs, initial cost, spare part cost, financial cost, owner’s cost, contingency cost, and other financial costs. The total capital investment cost (TCIC) represents the cost of the building and bringing it into commercial operation. Figure 2 presents the structure and model used to calculate the construction and production cost The fuel cycle model of G 4-ECONS includes fuel material, project burnup cycle, enrichment, total fuel mass, and full reactor core model, as illustrated in Figure 3 . The model requires an input that includes fuel needed for the initial core, along with fissile enrichment of uranium or plutonium. Some reactors, such as very high temperature reactors, require a higher temperature particle fuel, and fast reactors may require innovative pyrometallurgical and pyrochemical facilities for fabrication/re-fabrication and processing/re-processing. In such reactor systems, the fuel cost data are not available, and the unit cost of fuel cycle, such as $/kg, needs to be calculated by using a methodology similar to the calculation of levelized cost of electricity for any reactor system.

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Sustainability 2021 , 13 , 11815 4 of 11 Sustainability 2021 , 13 , x FOR PEER REVIEW 4 of 12 Figure 2. Flow chart of construction and production model The fuel cycle model of G 4 ‐ ECONS includes fuel material, project burnup cycle, en ‐ richment, total fuel mass, and full reactor core model, as illustrated in Figure 3 The model requires an input that includes fuel needed for the initial core, along with fissile enrich ‐ ment of uranium or plutonium Some reactors, such as very high temperature reactors, require a higher temperature particle fuel, and fast reactors may require innovative pyro ‐ metallurgical and pyrochemical facilities for fabrication/re ‐ fabrication and processing/re ‐ processing In such reactor systems, the fuel cost data are not available, and the unit cost of fuel cycle, such as $/kg, needs to be calculated by using a methodology similar to the calculation of levelized cost of electricity for any reactor system The levelized fuel cycle cost can be estimated by the following equation: {?} {?} 1 {?} where ‘ F i ’ is the fuel cost of each component, ‘ L ’ is the lifetime of reactor, ‘ T 1 and T 2 ′ are the maximum lag and lead time (front and back end), ‘ r ’ is the discount rate, and ‘ t 0 ′ is the reference date The quantities and specifications of the fuel were derived from the reactor characteristics The cost of each component can be calculated by simply multiplying the quantity of material by the unit price Figure 2. Flow chart of construction and production model Sustainability 2021 , 13 , x FOR PEER REVIEW 5 of 12 Figure 3. Flow diagram of spent fuel model The two applications of G 4 ‐ ECONS tools were to demonstrate its suitability for SCWR (supercritical water reactor) In this way, an economic analysis was first performed for a benchmark case of six Generation ‐ IV reactor systems against light water reactors of Generation ‐ III types It is based on the guidelines provided by the Generation ‐ IV interna ‐ tional forum (GIF) to use the G 4 ‐ ECONS model to conduct a comparison of various tech ‐ nologies However, some concerns still exist regarding the capital cost estimation meth ‐ odology The applicability of this model is to compare a diversified set of various nuclear energy systems that are at different stages of development These results were presented in the annual report of the 2012 GIF symposium [6] For the second application of the G 4 ‐ ECONS model, economic analysis of the European high ‐ performance pressurized water reactor (HPPWR) was considered, and the report was published in 2012 [18] The analysis showed that sensitivity analysis is considered a major input parameter The analysis was later confirmed by other simulation programs [19] Figure 3. Flow diagram of spent fuel model.

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Sustainability 2021 , 13 , 11815 5 of 11 The levelized fuel cycle cost can be estimated by the following equation: ∑ i t = t 0 + L + T 2 ∑ t = t 0 − T 1 F i ( t ) ( 1 + r ) ( t − t 0 ) where ‘ F i ’ is the fuel cost of each component, ‘ L ’ is the lifetime of reactor, ‘ T 1 and T 2 ’ are the maximum lag and lead time (front and back end), ‘ r ’ is the discount rate, and ‘ t 0 ’ is the reference date. The quantities and specifications of the fuel were derived from the reactor characteristics. The cost of each component can be calculated by simply multiplying the quantity of material by the unit price The two applications of G 4-ECONS tools were to demonstrate its suitability for SCWR (supercritical water reactor). In this way, an economic analysis was first performed for a benchmark case of six Generation-IV reactor systems against light water reactors of Generation-III types. It is based on the guidelines provided by the Generation-IV international forum (GIF) to use the G 4-ECONS model to conduct a comparison of various technologies. However, some concerns still exist regarding the capital cost estimation methodology. The applicability of this model is to compare a diversified set of various nuclear energy systems that are at different stages of development. These results were presented in the annual report of the 2012 GIF symposium [ 6 ]. For the second application of the G 4-ECONS model, economic analysis of the European high-performance pressurized water reactor (HPPWR) was considered, and the report was published in 2012 [ 18 ]. The analysis showed that sensitivity analysis is considered a major input parameter. The analysis was later confirmed by other simulation programs [ 19 ]. 3. Results and Discussion To help assess the cost calculation for the Generation- IV nuclear power systems, the consortium of Gen-IV reactor systems created guidelines that provide standardized cost-estimating protocol for such reactor systems in comparison to future energy systems. It provides a code of accounts, assumptions, cost estimation guidelines, set of equations, and a Generation-IV excel-based calculation model for nuclear energy systems. It is a userfriendly program, which employs simple and relatively fundamental economic algorithms The program is independent of the country, which allows the user to ignore cost accounting, depreciation, interest rate, discount rate, taxation, and capital cost recovery issues. The prime assumption of the program includes constant dollar levelized annual cost, capital and financing costs, levelized cash flow, operation of the plant, and the annual electricity production over the entire life of the plant. One of the main parameters denoted by Levelized Unit Electricity Cost (LUEC) and Levelized non-Electricity Unit Product Cost (LUPC) calculates the levelized unit product cost of other energy products, such as the recovery of capital cost including financing, non-fuel operation and maintenance costs, decontamination and decommissioning cost, and fuel cycle cost, as presented above in Figure 3 . The total capital cost consists of two main components, which are overnight cost (direct and indirect cost) and interest (part of total cost, duration and other activities, and discount rate) during construction. The program utilizes a simple sine-wave quarter function (S- Curve) to approximate the cumulative expenditures. Further, the model converts total capital cost into annual amortization ($M/year). In Table 1 , it can be observed that the system 80+ PWR based reactor, with the benchmark data and the cycle of reprocessing uranium into uranium dioxide and mixed oxide fuel assemblies, gives approximately comparable results and even lower results than the already published work [ 20 ]. This demonstrates that selection of the G 4-ECONS model used in this study gives accurate results. For other reactors, as listed in Table 1 , namely fast reactors with plutonium mixed oxide fuel, MIT-based high-temperature reactor (PBMR), and PWR partially closed (based on EMWG analysis), there was no study reported. However, we have already confirmed

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Sustainability 2021 , 13 , 11815 6 of 11 our model by carrying out the comparative study with the System 80+ reactor, as presented in Table 1 . Table 1. Comparison between total investment cost of various nuclear reactors Cost Parameters Reactor (s) This work This work This work This work Ref. [ 20 ] Capital ($/MWh) Fast reactor (Pu-MOX) MIT PBMR (HTR) PWR Partially Closed (EMWG July 07 FC Cost Data & 1 st Core in Capital) Sys 80+ PWR (Benchmark Data) + Sys 80 + PWR (Recycle of RepU and Pu into UOX and MOX FAs Sys 80+ PWR (Benchmark Data) + Sys 80 + PWR (Recycle of RepU and Pu into UOX and MOX FAs O&M ($/MWh) 222.66 23.00 34.61 116.55 212.08 Fuel cycle ($/MWh) 134.45 6.51 8.88 67.86 95.78 D&D ($/MWh) 59.67 13.05 8.21 57.39 75.85 Total ($/MWh) 1.08 0.26 0.07 0.74 2.90 TCIC ($/MWh) 417.85 42.81 51.76 242.53 399.23 The direct cost includes structural components, reactor, turbine, and electrical equipment in addition to miscellaneous components and main condenser heat rejection system Indirect cost covers engineering services, construction labor cost, field trip, and supervision. Supporting cost includes total capital cost and owners cost, or some percentage of total capital cost Korean Atomic Energy Institute (KAIST) developed a dual-purpose nuclear reactor system called SMART (System integrated modular advanced reactor) in 1997 with an electricity production of 100 Mwe and a water production that corresponds to 40,000 m 3 /day [ 21 ] The reactor consists of many active and safety features to achieve higher safety and reduce construction time. Various parameters of this reactor are presented in Table 2 that have been used to perform a cost analysis of the reactor Table 2. Summary of the SMART [ 21 ] reactor Reactor Summary Data Reactor design data Reactor plant description SMART Reactor electricity capacity 330 MWe Average reactor capacity 80% Annual electrical production 2.313 × 10 9 Thermodynamic efficiency 33% Plant operational life 60 years Construction period 3 years Cumulative spending profile during construction S-Curve Non-Fuel data for reactor Discount rate Regularity rate as per regulations Real discount rate for interest 5% Estimated cost at end-of-life 300 $M On-site staffing cost 23.531 $M/year Pension and benefits 6.286 $M/year Consumables 18.636 $M/year Repair cost 4.559 $M/year Purchase services and subcontracts 6.375 $M/year Insurance premium and taxes 7.04 $M/year Regulatory fees 4.075 $M/year Other general and administrative 7.965 $M/year The capital preand post-construction cost, direct cost, and fuel cost with other parameters are presented in Table 3 . In the historical context, the cost of fuel has been accounted for as a very small portion of the levelized cost of electricity from nuclear power. However, this is not inconsistent with advanced nuclear reactor concepts because fuel prices are becoming higher in such reactor technology. For the economic viability of

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Sustainability 2021 , 13 , 11815 7 of 11 advanced reactors, total capital investment cost (TCIC) is one of the main factors to consider This factor measures the financial risk that involves the overnight capital cost, construction time, and interest rate [ 19 ]. The total TCIC as calculated by G 4-ECONS is 7833.96 $/k We included the interest rate of 5% during the construction to set up a stable operating environment for financing. However, for other proven and operational reactors such as SCWR and ABWR, the TCIC is estimated to be 3863 $/kWe and 3591 $/kWe, respectively (as of 2003) [ 22 ]. Table 3. Parameters of the total capitalized cost (TCIC) for SMART reactor SMART Description Total Cost ($M) Specific Cost ($/Kwe) Capital pre-construction cost 5.0$ 15.15 Land rights 5.0$ Capitalized direct cost 1249.6$ 3786.67 Building, structures, and improvements on site 338.60$ Reactor plant equipment 349.30$ Turbine/generator plant equipment 331.40$ Electrical equipment 96.60$ Water intake and heat rejection plant 70.30$ Miscellaneous plant equipment 63.40$ Capitalized support services 473.30$ 1434.24 Design services 74.30$ Design services 107.60$ Construction supervision 291.40$ Capital operations cost 240.50$ 728.79 Other capital investment cost 240.50$ First fuel load Total contingency 294.50$ 892.42 Overnight cost 2262.90 6857.27 First fuel load 131.038$ 399.81 Total overnight cost with fuel loading 2394.838 7257.08 Financial costs 190.364$ 576.86 Real escalation 0.0 Interest during construction 190.364$ Total capitalized cost (TCIC) 2585.20$ 7833.95 However, these reactors do not cover new regulations and safety requirements as inferred by the post-Fukushima accident. Therefore, the costs calculated for SCWR and ABWR will be underestimated of the present-day calculations [ 23 ]. LUEC is the factor initiated by the Generation-IV consortium to measure the economic viability of advanced reactors (precisely Gen-IV). This factor calculates the life cycle cost of the reactor in $/MWh, as shown in Tables 4 and 5 . The comparison among various nuclear reactor systems was performed to calculate total LEUC. It was observed that Gen-IV nuclear reactor systems show the lowest cost while Gen-III+ nuclear power plants show higher cost. For instance, different power ranges of integral molten salt reactor (IMSR), which is a Gen-IV nuclear reactor, were compared with AP 1000 and SMART, which are Gen-III+ reactor systems, and concluded that AP 1000 gives the lowest levelized cost of electricity, equivalent to 39.38 $/MWh, while the highest value is obtained from IMSR 80. This trend dictates that there is an exponential decline trend observed for the LUEC in generation-wise nuclear power plants due to advancement in the technologies and vice versa During construction, the interest rate of the total capital cost depends upon the frontend activities, time span, and other discount options. To make the model simpler, the peak in the middle of the project capital campaign and sine wave function that covers the total front-end project duration gives an acceptable mathematical estimation, as presented in Figure 4 .

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Sustainability 2021 , 13 , 11815 8 of 11 Table 4. Amortization of capital cost for SMART reactor Amortization of Capital Cost Values Real Discount Rate 5.00% Operating/economic life of plant 60 years Baseline capacity factor 80.00% Contingency on capacity factor (perf reduction) 0% Adjusted capacity factor 80.00% Annual power production (adjusted) 2.31 × 10 9 kWh/Year Amount to be amortized (TCIC) 2585.20 $ M Fixed charge rate 0.052828185 per year Annual capital recovery 136.57 $M/Year Capital Component of LUEC 0.0591 $/kWh Table 5. Total levelized unit of various reactors with fuel cycle systems Parameters Values ($/MWh) SMART (This Work) AP 1000 [ 20 ] IMSR 600 [ 20 ] IMSR 300 [ 20 ] IMSR 80 [ 20 ] Capital (Including 1 st Core and Financing) 19.02 20.79 21.92 28.60 70.48 Operation 21.03 9.23 13.85 17.15 44.73 Fuel Cycle–Front End 9.04 7.95 7.01 7.44 9.25 Fuel Cycle–Back End 4.52 1.24 1.20 1.21 1.24 D&D Sinking Fund 0.16 0.16 0.15 0.17 0.35 Total LUEC 53.77 39.38 44.13 54.58 126.05 Sustainability 2021 , 13 , x FOR PEER REVIEW 9 of 12 Annual capital recovery 136.57 $M/Year Capital Component of LUEC 0.0591 $/kWh Table 5. Total levelized unit of various reactors with fuel cycle systems Parameters Values ($/MWh) SMART (This Work) AP 1000 [20] IMSR 600 [20] IMSR 300 [20] IMSR 80 [20] Capital (Including 1 st Core and Financing) 19.02 20.79 21.92 28.60 70.48 Operation 21.03 9.23 13.85 17.15 44.73 Fuel Cycle–Front End 9.04 7.95 7.01 7.44 9.25 Fuel Cycle–Back End 4.52 1.24 1.20 1.21 1.24 D&D Sinking Fund 0.16 0.16 0.15 0.17 0.35 Total LUEC 53.77 39.38 44.13 54.58 126. 05 During construction, the interest rate of the total capital cost depends upon the front ‐ end activities, time span, and other discount options To make the model simpler, the peak in the middle of the project capital campaign and sine wave function that covers the total front ‐ end project duration gives an acceptable mathematical estimation, as presented in Figure 4 Figure 4. Quarter by quarter fraction of capital spent Interest rate calculations can be performed by using cumulative expenditures, which are represented by an S ‐ shaped curve To provide modeling and fidelity, the payments related to the interest rate can be estimated on a quarterly basis, as shown in Figure 5 Generally, the interest rate is started from the mid ‐ point of each quarter up to the begin ‐ ning of commercial electricity generation Hence, the sum of all interest payments is the total interest during the construction Therefore, an S ‐ curve is typically used for many projects, thus avoiding entering capital cash flow data manually Figure 4. Quarter by quarter fraction of capital spent Interest rate calculations can be performed by using cumulative expenditures, which are represented by an S-shaped curve. To provide modeling and fidelity, the payments related to the interest rate can be estimated on a quarterly basis, as shown in Figure 5 . Generally, the interest rate is started from the mid-point of each quarter up to the beginning of commercial electricity generation. Hence, the sum of all interest payments is the total interest during the construction. Therefore, an S-curve is typically used for many projects, thus avoiding entering capital cash flow data manually.

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Sustainability 2021 , 13 , 11815 9 of 11 Sustainability 2021 , 13 , x FOR PEER REVIEW 10 of 12 Figure 5. S ‐ curve cumulative spend pattern 4. Conclusions Research has concluded that uncertainty lies in the future cost of advanced nuclear reactor concepts It has been concluded that among proven and design phase reactors, Gen ‐ III+ reactor systems are the most cost ‐ effective design reactors, which validates the investigation Therefore, the SMART reactor was tested for the cost performance analysis, and it was observed that total LUEC and TCIC are calculated as 53.7$/MWh and 2585.20$, respectively, which is less than other design reactor concepts The S ‐ curve calculations give other associated cost parameters as well, and it is important to understand that the input variable needs to be identified as deterministic so as to present the great impact on the total capital and fuel cost Therefore, it is recommended that uncertainty should be rectified for future research and development of any other advanced nuclear power plant Author Contributions: Conceptualization, S.U ‐ D.K and Z.A.; methodology, S.U ‐ D.K and M.A.; software, M.A.; validation, S.U ‐ D.K and M.A.; formal analysis, Z.A.; investigation, S.U ‐ D.K and Z.A.; resources, Z.A.; data curation, S.U ‐ D.K and M.A.; writing—original draft preparation, S.U ‐ D.K and Z.A.; writing—review and editing, S.U ‐ D.K., Z.A and M.A.; visualization, M.A.; supervi ‐ sion, Z.A.; project administration, S.U ‐ D.K.; funding acquisition, S.U ‐ D.K and M.A All authors have read and agreed to the published version of the manuscript Funding: This research was funded by Deanship of Scientific Research, grant number RGP ‐ 255 Acknowledgments : The authors sincerely appreciate the deanship of scientific research at King Saud University for its funding of this research through the research group project no RGP ‐ 255 Further, Meshari wants to thank scholarship program of King Abdullah City for Atomic and Re ‐ newable Energy (K.A.CARE) Conflicts of Interest: The authors declare no conflict of interest Nomenclature SMART System Modular Advanced Reactor G 4 ‐ ECONS Generation 4 Excel ‐ based Calculation of Nuclear Systems (G 4 ‐ ECONS) Gen ‐ III+/IV Generation ‐ III+ and Generation ‐ IV UO x Uranium oxide MO x Mixed oxide PBMR Pebble Bed Modular Reactor PWR Pressurized Water Reactor PSA Probabilistic safety assessment IAEA International Atomic Energy Agency Figure 5. S-curve cumulative spend pattern 4. Conclusions Research has concluded that uncertainty lies in the future cost of advanced nuclear reactor concepts. It has been concluded that among proven and design phase reactors, Gen-III+ reactor systems are the most cost-effective design reactors, which validates the investigation. Therefore, the SMART reactor was tested for the cost performance analysis, and it was observed that total LUEC and TCIC are calculated as 53.7 $/MWh and 2585.20$, respectively, which is less than other design reactor concepts. The S-curve calculations give other associated cost parameters as well, and it is important to understand that the input variable needs to be identified as deterministic so as to present the great impact on the total capital and fuel cost. Therefore, it is recommended that uncertainty should be rectified for future research and development of any other advanced nuclear power plant Author Contributions: Conceptualization, S.U.-D.K. and Z.A.; methodology, S.U.-D.K. and M.A.; software, M.A.; validation, S.U.-D.K. and M.A.; formal analysis, Z.A.; investigation, S.U.-D.K. and Z.A.; resources, Z.A.; data curation, S.U.-D.K. and M.A.; writing—original draft preparation, S.U.-D.K. and Z.A.; writing—review and editing, S.U.-D.K., Z.A. and M.A.; visualization, M.A.; supervision, Z.A.; project administration, S.U.-D.K.; funding acquisition, S.U.-D.K. and M.A. All authors have read and agreed to the published version of the manuscript Funding: This research was funded by Deanship of Scientific Research, grant number RGP-255 Acknowledgments: The authors sincerely appreciate the deanship of scientific research at King Saud University for its funding of this research through the research group project no. RGP-255. Further, Meshari wants to thank scholarship program of King Abdullah City for Atomic and Renewable Energy (K.A.CARE) Conflicts of Interest: The authors declare no conflict of interest Nomenclature SMART System Modular Advanced Reactor G 4-ECONS Generation 4 Excel-based Calculation of Nuclear Systems (G 4-ECONS) Gen-III+/IV Generation-III+ and Generation-IV UO x Uranium oxide MO x Mixed oxide PBMR Pebble Bed Modular Reactor

[Find the meaning and references behind the names: Di Maio, Eng, Liu, Mode, Malik, Leung, Zio, Eds, Antony, Mix, Ashraf, Move, Int, Kannan, Sci, Ann, Nice, Maio, Austria, Whale, Vienna, Moore, Nawaz, Multi, Smith, Christa, Ramarao, Siano, Zamani, Roelofs, France, Pandiyan, Free, Chen, Maheshwari, Woodhead]

Sustainability 2021 , 13 , 11815 10 of 11 PWR Pressurized Water Reactor PSA Probabilistic safety assessment IAEA International Atomic Energy Agency INPRO innovative nuclear reactors and fuel cycles EMWG Economic Modeling Working Group NEST Nuclear energy support tool TCIC Total capital investment cost SCWR Supercritical water reactor LWR Light water reactor GIF Generation IV International Forum HPLWR High-Performance light-water reactor LUPC Levelized non-Electricity Unit Product Cost LUEC Levelized Unit Electricity Cost Sys 80+ System 80+ reactor system RepU Reprocessing uranium Pu Plutonium FA Fuel assembly HTR High temperature reactor O&M Operation and Maintenance D&D Dismantling and Disposal ABWR Advanced boiling water reactor PM/CM Project management/construction management References 1 Salah Ud-Din Khan, S.; Nakhabov, A. (Eds.) Nuclear Reactor Technology Development and Utilization , 1 st ed.; Woodhead Publishing: Sawston, UK, 2020; ISBN 9780128189436 2 IAEA Report, Global Development of Advanced Nuclear Power Plants, and Related IAEA Activities. 2006. Available online: https://inis.iaea.org/collection/NCLCollectionStore/_Public/38/050/38050730.pdf?r=1&r=1 (accessed on 3 August 2021) 3 Di Maio, F.; Zio, E.; Smith, C.; Rychkov, V. Integrated Deterministic and Probabilistic Safety Analysis for Safety Assessment of Nuclear Power Plants Sci. Technol. Nucl. Install 2015 , 2015 , 136940. [ CrossRef ] 4 International Atomic Energy Agency Approaches for Assessing the Economic Competitiveness of Small and Medium Sized Reactors ; Nuclear Energy Series No. NP-T-3.7; International Atomic Energy Agency: Vienna, Austria, 2013 5 Moore, M.; Korinny, A.; Shropshire, D.; Sadhankar, R. Benchmarking of nuclear economic tools Ann. Nucl. Energy 2017 , 103 , 122–129. [ CrossRef ] 6 Moore, M.; Leung, L.; Sadhankar, R. An economic analysis of the Canadian SCWR concept using G 4-ECONS CNL Nucl. Rev 2016 , 5 , 363–372. [ CrossRef ] 7 Jaskolski, M.; Renski, A.; Minkiewicz, T. Thermodynamic and economic analysis of nuclear power unit operating in partial cogeneration mode to produce electricity and district heat Energy 2017 , 141 , 2470–2483. [ CrossRef ] 8 Nawaz, U.; Malik, T.N.; Ashraf, M.M. Least-cost generation expansion planning using whale optimization algorithm incorporating emission reduction and renewable energy sources Int. Trans. Electr. Energ. Syst 2019 , 30 , e 12238. [ CrossRef ] 9 Bhuvanesh, A.; Christa, S.J.; Kannan, S.; Pandiyan, M.K. Aiming towards pollution free future by high penetration of renewable energy sources in electricity generation expansion planning Futures 2018 , 104 , 25 e 36. [ CrossRef ] 10 Chen, S.; Liu, P.; Li, Z. Multi-regional power generation expansion planning with air pollutants emission constraints Renew Sustain. Energy Rev 2019 , 112 , 382 e 394. [ CrossRef ] 11 Alhelou, H.H.; Mirjalili, S.J.; Zamani, R.; Siano, P. Assessing the optimal generation technology mix determination considering demand response and EVs Int. J. Electr. Power Energy Syst 2020 , 119 , 105871. [ CrossRef ] 12 Ali, B.; Omid, S. Effects of the move towards Gen IV reactors in capacity expansion planning by total generation cost and environmental impact optimization Nucl. Eng. Technol 2021 , 53 , 1369–1377 13 Jain, S.; Roelofs, F.; Oosterlee, C.W. Decision-support tool for assessing future nuclear reactor generation portfolios Energy Econ 2014 , 44 , 99–112. [ CrossRef ] 14 Antony, A.; Maheshwari, N.K.; RamaRao, A. A generic methodology to evaluate economics of hydrogen production using energy from nuclear power plants Int. J. Hydrog. Energy 2017 , 42 , 25813–25823. [ CrossRef ] 15 Roelofs, F.; Van Heek, A. Nuclear technology cost assessments using G 4 Econs and its cost accounting system. In Proceedings of the ICAPP 2011: Performance and Flexibility—The Power of Innovation, Nice, France, 2–5 May 2011 16 International Atomic Energy Agency Small and Medium Sized Reactors ; International Atomic Energy Agency: Vienna, Austria, 2013; ISBN 978-92-0-144210-9.

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Sustainability 2021 , 13 , 11815 11 of 11 17 Tech Doc. GIF/EMWG/2007/004. Cost Estimating Guidelines for Generation IV Nuclear Energy Systems, Revision 4.2. 2007 Available online: https://www.gen-4.org/gif/upload/docs/application/pdf/2013-09/emwg_guidelines.pdf (accessed on 26 September 2007) 18 van Heek, A.I.; Roelofs, F.; Ehlert, A. Cost Estimation with G 4-ECONS for Generation IV Reactor Designs. In Proceedings of the GIF Symposium Proceedings 2012 Annual Report, San Diego, CA, USA, 14–15 November 2012; NEA No. 7141. pp. 29–33 19 Schulenberg, T.; Starflinger, J High Performance Light Water Reactor—Design and Analyses ; KIT Scientific Publishing: Hamburg, Germany, 2012; Available online: https://www.ksp.kit.edu/site/books/e/10.5445/KSP/1000025989/ (accessed on 12 June 2012) 20 Samalova, L.; Chvala, O.; Maldonado, G.I. Comparative economic analysis of the Integral Molten Salt Reactor and an advanced PWR using the G 4-ECONS methodology Ann. Nucl. Energy 2017 , 99 , 258–265. [ CrossRef ] 21 Ud-Din Khan, S.; Almutairi, Z.; Alanizi, M.; Khan, S. Safety analysis of pool-type double containment of system-integrated modular advanced reactor: A case study for Saudi Arabia Int. J. Energy Res 2020 , 45 , 12047–12058. [ CrossRef ] 22 MMoore, M.; Pencer, J.; Leung, L.K.H.; Sadhankar, R. Knowledge Gaps in Economic Analyses of Advanced Reactor Concepts. In Proceedings of the 19 th Pacific Basin Nuclear Conference (PBNC 2014), Vancouver, BC, Canada, 24–28 August 2014. Paper PBNC 2014-419 23 Salah Ud-Din Khan, S. Economic assessment of ABWR, SCWR and SMART by using G 4-ECONS code Trans. Am. Nucl. Soc 2020 , 123 , 1043–1044.

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