IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i11p2846-d1667852.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xiaoshi Zhuang & Honglue Wan & Dongran Song & Xinyu Fan & Yuchen Wang & Qian Huang & Jian Yang, 2025. "Optimization of Offshore Wind and Wave Energy Co-Generation System Based on Improved Seagull Optimization Algorithm," Energies, MDPI, vol. 18(11), pages 1-22, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:11:p:2846-:d:1667852
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/11/2846/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/11/2846/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Song, Dongran & Yan, Jiaqi & Gao, Yang & Wang, Lei & Du, Xin & Xu, Zhiliang & Zhang, Zhihong & Yang, Jian & Dong, Mi & Chen, Yang, 2023. "Optimization of floating wind farm power collection system using a novel two-layer hybrid method," Applied Energy, Elsevier, vol. 348(C).
    2. Moreno, Sinvaldo Rodrigues & Pierezan, Juliano & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2021. "Multi-objective lightning search algorithm applied to wind farm layout optimization," Energy, Elsevier, vol. 216(C).
    3. Clark, Caitlyn E. & Miller, Annalise & DuPont, Bryony, 2019. "An analytical cost model for co-located floating wind-wave energy arrays," Renewable Energy, Elsevier, vol. 132(C), pages 885-897.
    4. Astariz, S. & Perez-Collazo, C. & Abanades, J. & Iglesias, G., 2015. "Co-located wave-wind farms: Economic assessment as a function of layout," Renewable Energy, Elsevier, vol. 83(C), pages 837-849.
    5. Zilong, Ti & Xiao Wei, Deng, 2022. "Layout optimization of offshore wind farm considering spatially inhomogeneous wave loads," Applied Energy, Elsevier, vol. 306(PA).
    6. Song, Dongran & Xu, Shanmin & Huang, Lingxiang & Xia, E. & Huang, Chaoneng & Yang, Jian & Hu, Yang & Fang, Fang, 2022. "Multi-site and multi-objective optimization for wind turbines based on the design of virtual representative wind farm," Energy, Elsevier, vol. 252(C).
    7. Maienza, C. & Avossa, A.M. & Ricciardelli, F. & Coiro, D. & Troise, G. & Georgakis, C.T., 2020. "A life cycle cost model for floating offshore wind farms," Applied Energy, Elsevier, vol. 266(C).
    8. Wen, Yi & Kamranzad, Bahareh & Lin, Pengzhi, 2022. "Joint exploitation potential of offshore wind and wave energy along the south and southeast coasts of China," Energy, Elsevier, vol. 249(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gao, Qiang & Yuan, Rui & Ertugrul, Nesimi & Ding, Boyin & Hayward, Jennifer A. & Li, Ye, 2023. "Analysis of energy variability and costs for offshore wind and hybrid power unit with equivalent energy storage system," Applied Energy, Elsevier, vol. 342(C).
    2. Jaan Rönkkö & Ali Khosravi & Sanna Syri, 2023. "Techno-Economic Assessment of a Hybrid Offshore Wind–Wave Farm: Case Study in Norway," Energies, MDPI, vol. 16(11), pages 1-24, May.
    3. Gao, Qiang & Hayward, Jennifer A. & Sergiienko, Nataliia & Khan, Salman Saeed & Hemer, Mark & Ertugrul, Nesimi & Ding, Boyin, 2024. "Detailed mapping of technical capacities and economics potential of offshore wind energy: A case study in South-eastern Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    4. Ophelie Choupin & Michael Henriksen & Amir Etemad-Shahidi & Rodger Tomlinson, 2021. "Breaking-Down and Parameterising Wave Energy Converter Costs Using the CapEx and Similitude Methods," Energies, MDPI, vol. 14(4), pages 1-27, February.
    5. Teixeira-Duarte, Felipe & Rosa-Santos, Paulo & Taveira-Pinto, Francisco, 2025. "Multi-objective optimization of co-located wave-wind farm layouts supported by genetic algorithms and numerical models," Renewable Energy, Elsevier, vol. 241(C).
    6. Chenglong Guo & Wanan Sheng & Dakshina G. De Silva & George Aggidis, 2023. "A Review of the Levelized Cost of Wave Energy Based on a Techno-Economic Model," Energies, MDPI, vol. 16(5), pages 1-30, February.
    7. Clemente, D. & Rosa-Santos, P. & Taveira-Pinto, F., 2021. "On the potential synergies and applications of wave energy converters: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    8. Choupin, Ophelie & Henriksen, Michael & Tomlinson, Rodger, 2022. "Interrelationship between variables for wave direction-dependent WEC/site-configuration pairs using the CapEx method," Energy, Elsevier, vol. 248(C).
    9. Cao, Feifei & Yu, Mingqi & Han, Meng & Liu, Bing & Wei, Zhiwen & Jiang, Juan & Tian, Huiyuan & Shi, Hongda & Li, Yanni, 2023. "WECs microarray effect on the coupled dynamic response and power performance of a floating combined wind and wave energy system," Renewable Energy, Elsevier, vol. 219(P2).
    10. Gao, Qiang & Bechlenberg, Alva & Jayawardhana, Bayu & Ertugrul, Nesimi & Vakis, Antonis I. & Ding, Boyin, 2024. "Techno-economic assessment of offshore wind and hybrid wind–wave farms with energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    11. Cao, Lichao & Ge, Mingwei & Gao, Xiaoxia & Du, Bowen & Li, Baoliang & Huang, Zhi & Liu, Yongqian, 2022. "Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines," Applied Energy, Elsevier, vol. 323(C).
    12. Ho, Lip-Wah & Lie, Tek-Tjing & Leong, Paul TM & Clear, Tony, 2018. "Developing offshore wind farm siting criteria by using an international Delphi method," Energy Policy, Elsevier, vol. 113(C), pages 53-67.
    13. Dalton, Gordon & Bardócz, Tamás & Blanch, Mike & Campbell, David & Johnson, Kate & Lawrence, Gareth & Lilas, Theodore & Friis-Madsen, Erik & Neumann, Frank & Nikitas, Nikitakos & Ortega, Saul Torres &, 2019. "Feasibility of investment in Blue Growth multiple-use of space and multi-use platform projects; results of a novel assessment approach and case studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 107(C), pages 338-359.
    14. Castro-Santos, Laura & Martins, Elson & Guedes Soares, C., 2017. "Economic comparison of technological alternatives to harness offshore wind and wave energies," Energy, Elsevier, vol. 140(P1), pages 1121-1130.
    15. Javier Serrano González & Manuel Burgos Payán & Jesús Manuel Riquelme Santos & Ángel Gaspar González Rodríguez, 2021. "Optimal Micro-Siting of Weathervaning Floating Wind Turbines," Energies, MDPI, vol. 14(4), pages 1-19, February.
    16. Zilong, Ti & Yubing, Song & Xiaowei, Deng, 2022. "Spatial-temporal wave height forecast using deep learning and public reanalysis dataset," Applied Energy, Elsevier, vol. 326(C).
    17. Zhang, Lijun & Li, Ye & Xu, Wenhao & Gao, Zhiteng & Fang, Long & Li, Rongfu & Ding, Boyin & Zhao, Bin & Leng, Jun & He, Fenglan, 2022. "Systematic analysis of performance and cost of two floating offshore wind turbines with significant interactions," Applied Energy, Elsevier, vol. 321(C).
    18. Rusu, Liliana, 2019. "Evaluation of the near future wave energy resources in the Black Sea under two climate scenarios," Renewable Energy, Elsevier, vol. 142(C), pages 137-146.
    19. Jonasson, Erik & Fjellstedt, Christoffer & Temiz, Irina, 2024. "Grid impact of co-located offshore renewable energy sources," Renewable Energy, Elsevier, vol. 230(C).
    20. Liu, Min & Qin, Jianjun & Lu, Da-Gang & Zhang, Wei-Heng & Zhu, Jiang-Sheng & Faber, Michael Havbro, 2022. "Towards resilience of offshore wind farms: A framework and application to asset integrity management," Applied Energy, Elsevier, vol. 322(C).

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:11:p:2846-:d:1667852. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.