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Multi-objective optimization application for a coupled light water small modular reactor-combined heat and power cycle (cogeneration) systems

  • Seong Woo Kang (Risk Assessment Research Division, Korea Atomic Energy Research Institute (KAERI)) ;
  • Man-Sung Yim (Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology (KAIST))
  • Received : 2023.09.20
  • Accepted : 2023.12.07
  • Published : 2024.05.25

Abstract

The goal of this research is to propose a way to maximize small modular reactor (SMR) utilization to gain better market feasibility in support of carbon neutrality. For that purpose, a comprehensive tool was developed, combining off-design thermohydraulic models, economic objective models (levelized cost of electricity, annual profit), non-economic models (saved CO2), a parameter input sampling method (Latin hypercube sampling, LHS), and a multi-objective evolutionary algorithm (Non-dominated Sorting Algorithm-2, NSGA2 method) for optimizing a SMR-combined heat and power cycle (CHP) system design. Considering multiple objectives, it was shown that NSGA2+LHS method can find better optimal solution sets with similar computational costs compared to a conventional weighted sum (WS) method. Out of multiple multi-objective optimal design configurations for a 105 MWe design generation rating, a chosen reference SMR-CHP system resulted in its levelized cost of electricity (LCOE) below $60/MWh for various heat prices, showing economic competitiveness for energy market conditions similar to South Korea. Examined economic feasibility may vary significantly based on CHP heat prices, and extensive consideration of the regional heat market may be required for SMR-CHP regional optimization. Nonetheless, with reasonable heat market prices (e.g. district heating prices comparable to those in Europe and Korea), SMR can still become highly competitive in the energy market if coupled with a CHP system.

Keywords

Acknowledgement

This research was supported by National Nuclear R&D Program through the National Research Foundation of the Republic of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF2021M2D1A1084847). It was also supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20224B10200050).

References

  1. L. Mez, Nuclear energy-Any solution for sustainability and climate protection? Energy Pol. 48 (2012) 56-63.
  2. N. Muellner, et al., Nuclear energy-The solution to climate change? Energy Pol. 155 (2021), 112363.
  3. IEA, Combined Heat and Power: Evaluating the Benefits of Greater Global Investment, International Energy Agency, 2008.
  4. IAEA, Opportunities for Cogeneration with Nuclear Energy. IAEA Nuclear Energy Series No. NP-T-4.1, International Atomic Energy Agency, 2017.
  5. P. Breeze, Combined Heat and Power, Academic Press, Elsevier Ltd, 2018.
  6. J. Wang, et al., Flexibility of combined heat and power plants: a review of technologies and operation strategies, Appl. Energy 252 (2019), 113445.
  7. IAEA, Nuclear-Renewable Hybrid Energy Systems for Decarbonized Energy Production and Cogeneration, International Atomic Energy Agency, 2019. IAEATECDOC-1885.
  8. S.W. Kang, M.S. Yim, Coupled system model analysis for a small modular reactor cogeneration (combined heat and power) application, Energy 262 (2023), 125481.
  9. OECD, The NEA Small Modular Reactor Dashboard (NEA No. 7650), Organisation for Economic Co-Operation and Development, 2023.
  10. J.M. Schmidt, V.G. Gude, Nuclear cogeneration for cleaner desalination and power generation-a feasibility study, Clean Eng Technol 2 (2021), 100044.
  11. R. Grimes, et al., Nuclear Cogeneration: Civil Nuclear Energy in a Low Carbon Future, Royal Society, 2020, 978-1-78252-494-6.
  12. J.E. Nielsen, P.A. Sorensen, Renewable district heating and cooling technologies with and without seasonal storage, in: Renewable Heating and Cooling - Technologies and Applications, Woodhead Publishing, 2016, pp. 197-220.
  13. KDHC, Standard for Heat-Using Facility, Korea District Heating Corporation, 2022. Report (in Korean).
  14. IAEA, Introduction of Nuclear Desalination, International Atomic Energy Agency, 2000. Technical Reports Series No. 400.
  15. I. Khamis, Overview of Nuclear Desalination Technologies & Costs, Department of Nuclear Energy, Division of Nuclear Power, International Atomic Energy Agency, 2013.
  16. H. Cherif, J. Belhadj, Environmental life cycle analysis of water desalination processes, in: Sustainable Desalination Handbook, Butterworth-Heinemann, 2018, pp. 527-559.
  17. B. Mignacca, G. Locatelli, Economics and finance of Small Modular Reactors: a systematic review and research agenda, Renew. Sustain. Energy Rev. 118 (2020), 109519.
  18. A. Vaya Soler, et al., Small Modular Reactors: Challenges and Opportunities, No. NEA-7560, Organisation for Economic Co-Operation and Development, 2021.
  19. D. Schilissel, Eye-popping New Cost Estimates Released for NuScale Small Modular Reactor, Institute for Energy Economics and Financial Analysis, 2023. Last retrieved 5 Jun 2023, https://ieefa.org/resources/eye-popping-new-cost-estimates-released-nuscale-small-modular-reactor.
  20. IEA, Projected Costs of Generating Electricity, 2020 Edition, International Energy Agency, 2020.
  21. A. Kpx, Study on Non-AGC Generator Economic Dispatch Power Output Method, 2015. Final Project Report [in Korean], Korea Power Exchange.
  22. N. Norouzi, M. Fani, S. Talebi, Exergetic design and analysis of a nuclear SMR reactor tetrageneration (combined water, heat, power, and chemicals) with designed PCM energy storage and a CO2 gas turbine inner cycle, Nucl. Eng. Technol. 53 (2) (2021) 677-687.
  23. N. Norouzi, et al., Exergy and exergoeconomic analysis of hydrogen and power cogeneration using an HTR plant, Nucl. Eng. Technol. 53 (8) (2021) 2753-2760.
  24. K. Karlsson, P. Meibom, Optimal investment paths for future renewable based energy systems-using the optimisation model Balmorel, Int. J. Hydrogen Energy 33 (7) (2008) 1777-1787.
  25. M.R. Akhtari, M. Baneshi, Techno-economic assessment and optimization of a hybrid renewable co-supply of electricity, heat and hydrogen system to enhance performance by recovering excess electricity for a large energy consumer, Energy Convers. Manag. 188 (2019) 131-141.
  26. S. Bragg-Sitton, et al., Reimagining future energy systems: overview of the US program to maximize energy utilization via integrated nuclear-renewable energy systems, Int. J. Energy Res. 44 (10) (2020) 8156-8169.
  27. A. Epiney, et al., Economic analysis of a nuclear hybrid energy system in a stochastic environment including wind turbines in an electricity grid, Appl. Energy 260 (2020), 114227.
  28. K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms, Wiley, 2009.
  29. P.R. Wilding, N.R. Murray, M.J. Memmott, The use of multi-objective optimization to improve the design process of nuclear power plant systems, Ann. Nucl. Energy 137 (2020), 107079.
  30. R.H. Stewart, T.S. Palmer, B. DuPont, A survey of multi-objective optimization methods and their applications for nuclear scientists and engineers, Prog. Nucl. Energy 138 (2021), 103830.
  31. M.G.C. Tapia, C.A. Coello Coello, Applications of Multi-Objective Evolutionary Algorithms in Economics and Finance: A Survey, 2007 IEEE Congr Evol Comput, IEEE, 2007.
  32. M. Caramia, P. Dell'Olmo, Multi-objective Management in Freight Logistics, Springer, London, 2008.
  33. Y. Cui, et al., Multi-objective optimization methods and application in energy saving, Energy 125 (2017) 681-704.
  34. M.J. Mayer, A. Szilagyi, G. Grof, Environmental and economic multi-objective optimization of a household level hybrid renewable energy system by genetic algorithm, Appl. Energy 269 (2020), 115058.
  35. A.J. Nebro, et al., Is NSGA-II ready for large-scale multi-objective optimization? Math. Comput. Appl. 27 (6) (2022) 103.
  36. K. Deb, et al., A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput. 6 (2) (2002) 182-197.
  37. S. Ling, et al., Analysis and optimization research on latch life of control rod drive mechanism based on approximate model, Nucl. Eng. Technol. 53 (12) (2021) 4166-4178.
  38. W. Zhou, et al., An Intelligent Optimization Method for the HCSB Blanket Based on an Improved Multi-Objective NSGA-III Algorithm and an Adaptive BP Neural Network, Nucl Eng and Technol, 2023.
  39. OECD, Current Status, Current Status, Technical Feasibility, and Economics of Small Nuclear Reactors, Organisation for Economic Co-Operation and Development, 2011.
  40. OECD, Small Modular Reactors: Nuclear Energy Market Potential for Near-Term Deployment (No. NEA-7213), Organisation for Economic Co-Operation and Development, 2016.
  41. L. Blank, A. Tarquin, Engineering Economy, McGraw-Hill, 2005.
  42. G.F. Hewitt, S.J. Pugh, Approximate design and costing methods for heat exchangers, Heat Tran. Eng. 28 (2) (2007) 76-86.
  43. KDHC, Annual Business Report, Korea District Heating Corporation, 2022. Report (in Korean).
  44. EPA, Inventory of U.S, Greenhouse Gas Emissions and Sinks: 1990-2019. Annex 2 (Methodology for Estimating CO2 Emissions from Fossil Fuel Combustion), Table A-28, U.S. Environmental Protection Agency, Washington, DC, 2021. U.S. EPA #430-R-20-002.
  45. L. Van Wortswinkel, W. Nijs, Industrial Combustion Boilers, vol. 101, IEA ETSAP - Technology Brief, May 2010.
  46. N. Wu, J.E. Parsons, K.R. Polenske, The impact of future carbon prices on CCS investment for power generation in China, Energy Pol. 54 (2013) 160-172.
  47. Khnp, Kaeri, Kacare, SMART100 Standard Safety Analysis Report, 2019.
  48. A. Solomykov, J. Zhao, Comparison of Nuclear District Heating Technologies in Russia and China, the International Symposium on Heating, Ventilation and Air Conditioning, Springer, Singapore, 2019.
  49. S. Werner, European district heating price series, energiforsk, Report 316 (2016) (2016).