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Modular reconfiguration of hybrid PV-TEG systems via artificial rabbit algorithm: Modelling, design and HIL validation

Author

Listed:
  • Yang, Bo
  • Li, Yulin
  • Huang, Jianxiang
  • Li, Miwei
  • Zheng, Ruyi
  • Duan, Jinhang
  • Fan, Tingsheng
  • Zou, He
  • Liu, Tao
  • Wang, Jingbo
  • Shu, Hongchun
  • Jiang, Lin

Abstract

To further improve the power generation efficiency of traditional photovoltaic (PV) systems, this paper designs a theoretical model of a hybrid power generation system that consists of the individual PV system and thermoelectric generation (TEG) system. Meanwhile, partial shielding condition (PSC) is a common but serious problem during operation that might lead to power loss and component mismatch in hybrid PV-TEG system. Therefore, a reconfiguration method for the hybrid PV-TEG system based on artificial rabbit optimization (ARO) algorithm is proposed in this study to alleviate the negative impact caused by PSC and thus improve the power generation efficiency of the hybrid system. ARO algorithm is applied to adjust the switching matrix of the hybrid system to change the electrical connection among PV arrays and TEG arrays, and thus further to reduce the adverse effect of PSC and maximize the output power of the hybrid system. To verify the effectiveness of the proposed method, simulation tests are carried out on 4 × 4 and 20 × 15 arrays, respectively. For a quantitative and fair comparison, this work employs maximum output power, average output power, mismatch loss, and standard deviation as evaluation indexes, upon which four different algorithms including GA, PSO, WOA, AOA and ACO are thoroughly compared. Simulation results show that the output power of the hybrid system after ARO algorithm based reconfiguration is improved by 34.05% in the 4 × 4 array and 23.10% in the 20 × 15 array, respectively. In addition, hardware-in-the-loop (HIL) experiments are carried out based on RTLAB platform to verify the hardware feasibility of the proposed reconfiguration strategy.

Suggested Citation

  • Yang, Bo & Li, Yulin & Huang, Jianxiang & Li, Miwei & Zheng, Ruyi & Duan, Jinhang & Fan, Tingsheng & Zou, He & Liu, Tao & Wang, Jingbo & Shu, Hongchun & Jiang, Lin, 2023. "Modular reconfiguration of hybrid PV-TEG systems via artificial rabbit algorithm: Modelling, design and HIL validation," Applied Energy, Elsevier, vol. 351(C).
    • Handle: RePEc:eee:appene:v:351:y:2023:i:c:s0306261923012321
    DOI: 10.1016/j.apenergy.2023.121868
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    References listed on IDEAS

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    1. Yang, Bo & Zeng, Chunyuan & Li, Danyang & Guo, Zhengxun & Chen, Yijun & Shu, Hongchun & Cao, Pulin & Li, Zilin, 2022. "Improved immune genetic algorithm based TEG system reconfiguration under non-uniform temperature distribution," Applied Energy, Elsevier, vol. 325(C).
    2. Kornelakis, Aris & Marinakis, Yannis, 2010. "Contribution for optimal sizing of grid-connected PV-systems using PSO," Renewable Energy, Elsevier, vol. 35(6), pages 1333-1341.
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