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Optimization of a Nuclear–CSP Hybrid Energy System Through Multi-Objective Evolutionary Algorithms

Author

Listed:
  • Chenxiao Ji

    (Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Xueying Nie

    (Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Shichao Chen

    (Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China)

  • Maosong Cheng

    (Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Zhimin Dai

    (Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

Combining energy storage with base-load power sources offers an effective way to cover the fluctuation of renewable energy. This study proposes a nuclear–solar hybrid energy system (NSHES), which integrates a small modular thorium molten salt reactor (smTMSR), concentrating solar power (CSP), and thermal energy storage (TES). Two operation modes are designed and analyzed: constant nuclear power (mode 1) and adjusted nuclear power (mode 2). The nondominated sorting genetic algorithm II (NSGA-II) is applied to minimize both the deficiency of power supply probability (DPSP) and the levelized cost of energy (LCOE). The decision variables used are the solar multiple (SM) of CSP and the theoretical storage duration (TSD) of TES. The criteria importance through inter-criteria correlation (CRITIC) method and the technique for order preference by similarity to ideal solution (TOPSIS) are utilized to derive the optimal compromise solution. The electricity curtailment probability (ECP) is calculated, and the results show that mode 2 has a lower ECP compared with mode 1. Furthermore, the configuration with an installed capacity of nuclear and CSP (100:100) has the lowest LCOE and ECP when the DPSP is satisfied with certain conditions. Optimizing the NSHES offers an effective approach to mitigating the mismatch between energy supply and demand.

Suggested Citation

  • Chenxiao Ji & Xueying Nie & Shichao Chen & Maosong Cheng & Zhimin Dai, 2025. "Optimization of a Nuclear–CSP Hybrid Energy System Through Multi-Objective Evolutionary Algorithms," Energies, MDPI, vol. 18(9), pages 1-22, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:9:p:2189-:d:1642100
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    References listed on IDEAS

    as
    1. Cyril Martin de Lagarde & Frédéric Lantz, 2018. "How renewable production depresses electricity prices: Evidence from the German market," Post-Print hal-01985024, HAL.
    2. Heydari, Ali & Askarzadeh, Alireza, 2016. "Optimization of a biomass-based photovoltaic power plant for an off-grid application subject to loss of power supply probability concept," Applied Energy, Elsevier, vol. 165(C), pages 601-611.
    3. Son, In Woo & Jeong, Yongju & Son, Seongmin & Park, Jung Hwan & Lee, Jeong Ik, 2022. "Techno-economic evaluation of solar-nuclear hybrid system for isolated grid," Applied Energy, Elsevier, vol. 306(PA).
    4. Cyril Martin de Lagarde & Frédéric Lantz, 2018. "How renewable production depresses electricity prices: Evidence from the German market," Post-Print hal-01986207, HAL.
    5. Popov, Dimityr & Borissova, Ana, 2017. "Innovative configuration of a hybrid nuclear-solar tower power plant," Energy, Elsevier, vol. 125(C), pages 736-746.
    6. Sharafi, Masoud & ElMekkawy, Tarek Y., 2015. "Stochastic optimization of hybrid renewable energy systems using sampling average method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1668-1679.
    7. Zhang, Maolong & Xu, Chao & Du, Xiaoze & Amjad, Muhammad & Wen, Dongsheng, 2017. "Off-design performance of concentrated solar heat and coal double-source boiler power generation with thermocline energy storage," Applied Energy, Elsevier, vol. 189(C), pages 697-710.
    8. Garcia, Humberto E. & Chen, Jun & Kim, Jong S. & Vilim, Richard B. & Binder, William R. & Bragg Sitton, Shannon M. & Boardman, Richard D. & McKellar, Michael G. & Paredis, Christiaan J.J., 2016. "Dynamic performance analysis of two regional Nuclear Hybrid Energy Systems," Energy, Elsevier, vol. 107(C), pages 234-258.
    9. Fares, Dalila & Fathi, Mohamed & Mekhilef, Saad, 2022. "Performance evaluation of metaheuristic techniques for optimal sizing of a stand-alone hybrid PV/wind/battery system," Applied Energy, Elsevier, vol. 305(C).
    10. Zhao, Bing-chen & Cheng, Mao-song & Liu, Chang & Dai, Zhi-min, 2018. "System-level performance optimization of molten-salt packed-bed thermal energy storage for concentrating solar power," Applied Energy, Elsevier, vol. 226(C), pages 225-239.
    11. Martin de Lagarde, Cyril & Lantz, Frédéric, 2018. "How renewable production depresses electricity prices: Evidence from the German market," Energy Policy, Elsevier, vol. 117(C), pages 263-277.
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