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Multi-objective optimization design of the wind-to-heat system blades based on the Particle Swarm Optimization algorithm

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  • Qian, Jing
  • Sun, Xiangyu
  • Zhong, Xiaohui
  • Zeng, Jiajun
  • Xu, Fei
  • Zhou, Teng
  • Shi, Kezhong
  • Li, Qingan

Abstract

A novel wind turbine direct driving heat pump (WTDHP) system is proposed, in which the heat pump is employed to replace the wind turbine's generator. The impacts of the blades with different tip speed ratios on WTDHP system are analyzed in this study. The inverse design method, verified by importing the design shape into FAST for simulation and comparing the simulated tip speed ratio and axial induction factor, was employed to create blades with different tip speed ratios. The heating performances of different blade shape designs at various turbulence and wind speeds are understood. The conclusion is drawn that the blades with a low tip speed ratio (λ=6–8) are clearly superior for heating in the turbulence categories IEC A at wind speeds between 6 and 14 m/s. However, the dynamic responses of the system are weakened by low turbulence and wind speeds. Taking into account the cost and manufacturing process, the blades with a high tip speed ratio can be chosen. Furthermore, the multi-objective optimization of blades was carried out using the enhanced Particle Swarm Optimization (PSO) algorithm, considering heating capacity, flapping load and blade mass. Based on the Wilson method, initial blade of PSO optimization algorithm is generated. Obtaining the heating capacity, flapping load and blade mass in real time from the simulation, the objective functions is calculated to update the status of particle swarm. After optimization using the improved multi-objective algorithm under turbulent wind with an average wind speed of 7 m/s, the blade mass is reduced by 2.96%, the flapping moment is reduced by 3.17%, and the heating capacity is increased by 1.22%. The results show that the optimization effect is more obvious at higher wind speeds.

Suggested Citation

  • Qian, Jing & Sun, Xiangyu & Zhong, Xiaohui & Zeng, Jiajun & Xu, Fei & Zhou, Teng & Shi, Kezhong & Li, Qingan, 2024. "Multi-objective optimization design of the wind-to-heat system blades based on the Particle Swarm Optimization algorithm," Applied Energy, Elsevier, vol. 355(C).
    • Handle: RePEc:eee:appene:v:355:y:2024:i:c:s0306261923015507
    DOI: 10.1016/j.apenergy.2023.122186
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    References listed on IDEAS

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    1. Ruhnau, Oliver & Hirth, Lion & Praktiknjo, Aaron, 2020. "Heating with wind: Economics of heat pumps and variable renewables," Energy Economics, Elsevier, vol. 92(C).
    2. Cao, Karl-Kiên & Nitto, Alejandro Nicolás & Sperber, Evelyn & Thess, André, 2018. "Expanding the horizons of power-to-heat: Cost assessment for new space heating concepts with Wind Powered Thermal Energy Systems," Energy, Elsevier, vol. 164(C), pages 925-936.
    3. Sun, X.Y. & Zhong, X.H. & Zhang, M.Y. & Zhou, T., 2022. "Experimental investigation on a novel wind-to-heat system with high efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    4. Rieck, Jenny & Taube, Lina & Behrendt, Frank, 2020. "Feasibility analysis of a heat pump powered by wind turbines and PV- Applications for detached houses in Germany," Renewable Energy, Elsevier, vol. 162(C), pages 1104-1112.
    5. Wang, Zheng & Zeng, Tiansheng & Chu, Xuening & Xue, Deyi, 2023. "Multi-objective deep reinforcement learning for optimal design of wind turbine blade," Renewable Energy, Elsevier, vol. 203(C), pages 854-869.
    6. Yudong Zhang & Shuihua Wang & Genlin Ji, 2015. "A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-38, October.
    7. Nimmanterdwong, Prathana & Chalermsinsuwan, Benjapon & Piumsomboon, Pornpote, 2023. "Optimizing utilization pathways for biomass to chemicals and energy by integrating emergy analysis and particle swarm optimization (PSO)," Renewable Energy, Elsevier, vol. 202(C), pages 1448-1459.
    8. Okazaki, Toru & Shirai, Yasuyuki & Nakamura, Taketsune, 2015. "Concept study of wind power utilizing direct thermal energy conversion and thermal energy storage," Renewable Energy, Elsevier, vol. 83(C), pages 332-338.
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