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Genetic algorithm-based non-synchronous unit-specific optimal load-following control of multi-unit small modular reactor

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  • Byun, Hyeon-Ho
  • Yim, Man-Sung

Abstract

Small modular reactors (SMRs) serve as low-carbon energy sources with the advantage of flexible construction within small-scale power grids. Particularly, they are promising candidates for replacing existing coal-based power plants and supporting renewable energy (RE). The load-following operation of multi-unit SMR (muSMR) is essential in this regard to compensate for the intermittency of renewable energy production. To simulate realistic operations, this study assumes that muSMRs are composed of cores with varying burnup levels and applies genetic algorithm to allocate the optimal power output for each unit along with T-avg control, thereby minimizing the control rod movements. A reactor system code was developed to assign optimal power outputs for each unit, considering the core characteristics based on burnup levels. The proposed methodology was applied to replace coal in South Kalimantan, Indonesia, which is a region relying heavily on coal power for energy with limited external power transmission and consistent load fluctuation patterns, under the conditions of increased renewable energy penetration. The results showed feasible SMR deployment supporting renewables with a reduction in control rod movements and replacement costs compared to conventional methods that synchronize the power output across all SMR units.

Suggested Citation

  • Byun, Hyeon-Ho & Yim, Man-Sung, 2025. "Genetic algorithm-based non-synchronous unit-specific optimal load-following control of multi-unit small modular reactor," Energy, Elsevier, vol. 314(C).
    • Handle: RePEc:eee:energy:v:314:y:2025:i:c:s0360544224038696
    DOI: 10.1016/j.energy.2024.134091
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    1. Zhang, Songyang & Chen, Weiran & Zhang, Yuzhong & Dinavahi, Venkata, 2025. "AI-accelerated physics-informed transient real-time digital-twin of SMR-based multi-domain submarine power distribution," Energy, Elsevier, vol. 338(C).
    2. Liu, Hongliang & Song, Yingming & Xiao, Qizhen & Xu, Qiming, 2025. "Neural networks and adaptive finite-time state observer-based preassigned-time fault-tolerant control of load following for a PWR-SMR under CRDM faults and sensor noises," Energy, Elsevier, vol. 323(C).

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