Optimization of Offshore Wind and Wave Energy Co-Generation System Based on Improved Seagull Optimization Algorithm

Xiaoshi Zhuang, Honglue Wan, Dongran Song (), Xinyu Fan (), Yuchen Wang, Qian Huang and Jian Yang
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Xiaoshi Zhuang: School of Automation, Central South University, Changsha 410083, China
Honglue Wan: School of Automation, Central South University, Changsha 410083, China
Dongran Song: School of Automation, Central South University, Changsha 410083, China
Xinyu Fan: School of Automation, Central South University, Changsha 410083, China
Yuchen Wang: School of Automation, Central South University, Changsha 410083, China
Qian Huang: School of Automation, Central South University, Changsha 410083, China
Jian Yang: School of Automation, Central South University, Changsha 410083, China

Energies, 2025, vol. 18, issue 11, 1-22

Abstract: To address the high complexity layout optimization problem of an offshore wind and wave energy co-generation system, an improved seagull optimization algorithm-based method is proposed. Firstly, the levelized cost of electricity (LCOE) model, based on the whole-life-cycle cost, serves as the optimization objective. Therein, the synergistic effect between wind turbines and wave energy generators is taken into consideration to decouple the problem and establish a two-layer optimization framework. Secondly, the seagull optimization algorithm is enhanced by integrating three strategies: the nonlinear adjustment strategy for control factors, the Gaussian–Cauchy hybrid variational strategy, and the multiple swarm strategy, thereby improving the global search capability. Finally, a case study in the South China Sea validates the effectiveness of the model and algorithm. Using the improved algorithm, the optimal layout of the co-generation system and the optimal wind turbine parameters are obtained. The results indicate that the optimized system achieves a LCOE of 0.6561 CNY/kWh, which is 0.29% lower than that achieved by traditional algorithms. The proposed method provides a reliable technical solution for the economic optimization of the co-generation system.

Keywords: offshore wind and wave energy co-generation; whole-life-cycle cost; improved seagull optimization algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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