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Multi-objective optimisation of buoyancy energy storage technology using transit search algorithm

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  • Ng, Jian Yew
  • Tan, Wen-Shan
  • Chan, Ping Yi
  • Elias, Azimin

Abstract

Implementing energy storage solutions is crucial to address the intermittency challenges of marine renewable energy. Buoyancy energy storage technology (BEST) holds potential, but its development remains in its infancy. Additionally, optimisation has not been implemented to improve the design. Therefore, a transit search (TS)-based optimisation model is developed to optimise both the small-scale (volume of tank less than 500 m3) and large-scale (volume of tank less than 30,000 m3) fabric BEST to maximise the power density and energy density. The optimisation results obtained through the transit search (TS) algorithm surpass those from the gray wolf (GW), whale, and artificial bee colony (ABC) algorithms in terms of consistency. The optimised large-scale fabric BEST design demonstrates improvements, with a 56% increase in power density from 345 W/m3 to 539 W/m3 and a 58% increase in energy density from 201 Wh/m3 to 318 Wh/m3 compared to the pre-optimised design from the literature. Moreover, linear relationships between the tank's submerged volume-to-water volume ratio and the extra weight limit were investigated through sensitivity analysis. The optimised fabric BEST shows competitive energy density when compared to other emerging energy storage technologies. Lithium-sulphur has higher energy density, but fabric BEST offers better safety and ease of installation.

Suggested Citation

  • Ng, Jian Yew & Tan, Wen-Shan & Chan, Ping Yi & Elias, Azimin, 2024. "Multi-objective optimisation of buoyancy energy storage technology using transit search algorithm," Renewable Energy, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:renene:v:225:y:2024:i:c:s0960148124004075
    DOI: 10.1016/j.renene.2024.120342
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    References listed on IDEAS

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    1. Liu, Zhan & Liu, Xu & Yang, Shanju & Hooman, Kamel & Yang, Xiaohu, 2021. "Assessment evaluation of a trigeneration system incorporated with an underwater compressed air energy storage," Applied Energy, Elsevier, vol. 303(C).
    2. Zhang, Aibo & Yin, Zhaoyuan & Wu, Zhiying & Xie, Min & Liu, Yiliu & Yu, Haoshui, 2023. "Investigation of the compressed air energy storage (CAES) system utilizing systems-theoretic process analysis (STPA) towards safe and sustainable energy supply," Renewable Energy, Elsevier, vol. 206(C), pages 1075-1085.
    3. Chen, Wei & Qin, Haoxuan & Zhu, Qing & Bai, Jianshu & Xie, Ningning & Wang, Yazhou & Zhang, Tong & Xue, Xiaodai, 2024. "Optimal design and performance assessment of a proposed constant power operation mode for the constant volume discharging process of advanced adiabatic compressed air energy storage," Renewable Energy, Elsevier, vol. 221(C).
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