IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v191y2022icp510-534.html
   My bibliography  Save this article

A novel two-objective optimization computational framework for a two-body heaving wave energy converter

Author

Listed:
  • Pavlidou, Lamprini
  • Angelides, Demos C.

Abstract

The present research introduces a novel two-objective optimization computational framework, named H∞ optimization criterion-constrained Two-Objective Optimization Genetic Algorithm (H∞ − cTOOGA). Within this optimization framework, the objectives considered are: (i) the sprung mass isolation, and (ii) the wave energy extraction of a heaving buoy coupled with a sprung (overlying) mass. In such a two-body heaving wave energy converter (WEC), the satisfaction of both objectives is pursued by proper selection of the buoy geometry (i.e., radius r and draft Dr) and the characteristics of the power take-off (PTO). The interrelation among potential buoy geometries and PTO characteristics is performed on the basis of a critical condition. The critical condition refers to the formation of equal double peaks in the sprung mass acceleration response curve and is imposed as the sprung mass acceleration limit that is not allowed to be exceeded throughout the entire frequency range of interest. The two-objective optimization is realized with r, Dr and the tuning ratio δOPT being the decision variables. The value of δOPT is constrained by the value of the critical tuning ratio δcrit, that is, δOPT <δcrit. This ensures the minimization of the maximum sprung mass acceleration response. Then, the Pareto-optimal solutions result to optimal wave energy extraction.

Suggested Citation

  • Pavlidou, Lamprini & Angelides, Demos C., 2022. "A novel two-objective optimization computational framework for a two-body heaving wave energy converter," Renewable Energy, Elsevier, vol. 191(C), pages 510-534.
    • Handle: RePEc:eee:renene:v:191:y:2022:i:c:p:510-534
    DOI: 10.1016/j.renene.2022.04.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148122004670
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2022.04.006?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Michailides, Constantine & Gao, Zhen & Moan, Torgeir, 2016. "Experimental study of the functionality of a semisubmersible wind turbine combined with flap-type Wave Energy Converters," Renewable Energy, Elsevier, vol. 93(C), pages 675-690.
    2. McCabe, A.P., 2013. "Constrained optimization of the shape of a wave energy collector by genetic algorithm," Renewable Energy, Elsevier, vol. 51(C), pages 274-284.
    3. Gaspar, J.F. & Kamarlouei, M. & Thiebaut, F. & Guedes Soares, C., 2021. "Compensation of a hybrid platform dynamics using wave energy converters in different sea state conditions," Renewable Energy, Elsevier, vol. 177(C), pages 871-883.
    4. Konak, Abdullah & Coit, David W. & Smith, Alice E., 2006. "Multi-objective optimization using genetic algorithms: A tutorial," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 992-1007.
    5. Zhang, Haicheng & Xu, Daolin & Zhao, Huai & Xia, Shuyan & Wu, Yousheng, 2018. "Energy extraction of wave energy converters embedded in a very large modularized floating platform," Energy, Elsevier, vol. 158(C), pages 317-329.
    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. Garcia-Teruel, Anna & DuPont, Bryony & Forehand, David I.M., 2021. "Hull geometry optimisation of wave energy converters: On the choice of the objective functions and the optimisation formulation," Applied Energy, Elsevier, vol. 298(C).
    2. Zhou, Binzhen & Hu, Jianjian & Jin, Peng & Sun, Ke & Li, Ye & Ning, Dezhi, 2023. "Power performance and motion response of a floating wind platform and multiple heaving wave energy converters hybrid system," Energy, Elsevier, vol. 265(C).
    3. Jin, Peng & Zheng, Zhi & Zhou, Zhaomin & Zhou, Binzhen & Wang, Lei & Yang, Yang & Liu, Yingyi, 2023. "Optimization and evaluation of a semi-submersible wind turbine and oscillating body wave energy converters hybrid system," Energy, Elsevier, vol. 282(C).
    4. Gupta, Pankaj & Mittal, Garima & Mehlawat, Mukesh Kumar, 2013. "Expected value multiobjective portfolio rebalancing model with fuzzy parameters," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 190-203.
    5. Weifan Zhong & Lijing Du, 2023. "Predicting Traffic Casualties Using Support Vector Machines with Heuristic Algorithms: A Study Based on Collision Data of Urban Roads," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
    6. Mehdi Neshat & Nataliia Y. Sergiienko & Erfan Amini & Meysam Majidi Nezhad & Davide Astiaso Garcia & Bradley Alexander & Markus Wagner, 2020. "A New Bi-Level Optimisation Framework for Optimising a Multi-Mode Wave Energy Converter Design: A Case Study for the Marettimo Island, Mediterranean Sea," Energies, MDPI, vol. 13(20), pages 1-23, October.
    7. Ahmadi, Mohammad H. & Amin Nabakhteh, Mohammad & Ahmadi, Mohammad-Ali & Pourfayaz, Fathollah & Bidi, Mokhtar, 2017. "Investigation and optimization of performance of nano-scale Stirling refrigerator using working fluid as Maxwell–Boltzmann gases," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 337-350.
    8. Hausken, Kjell & Levitin, Gregory, 2009. "Minmax defense strategy for complex multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 577-587.
    9. Akhlaque Ahmad Khan & Ahmad Faiz Minai & Rupendra Kumar Pachauri & Hasmat Malik, 2022. "Optimal Sizing, Control, and Management Strategies for Hybrid Renewable Energy Systems: A Comprehensive Review," Energies, MDPI, vol. 15(17), pages 1-29, August.
    10. Janssens, Jochen & Van den Bergh, Joos & Sörensen, Kenneth & Cattrysse, Dirk, 2015. "Multi-objective microzone-based vehicle routing for courier companies: From tactical to operational planning," European Journal of Operational Research, Elsevier, vol. 242(1), pages 222-231.
    11. H. Liao & Q. Wu, 2013. "Multi-objective optimization by learning automata," Journal of Global Optimization, Springer, vol. 55(2), pages 459-487, February.
    12. Wang, LiGuo & Ringwood, John V., 2021. "Control-informed ballast and geometric optimisation of a three-body hinge-barge wave energy converter using two-layer optimisation," Renewable Energy, Elsevier, vol. 171(C), pages 1159-1170.
    13. Ahmadi, Mohammad H. & Ahmadi, Mohammad-Ali & Maleki, Akbar & Pourfayaz, Fathollah & Bidi, Mokhtar & Açıkkalp, Emin, 2017. "Exergetic sustainability evaluation and multi-objective optimization of performance of an irreversible nanoscale Stirling refrigeration cycle operating with Maxwell–Boltzmann gas," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 80-92.
    14. Cheng, Zhengshun & Wen, Ting Rui & Ong, Muk Chen & Wang, Kai, 2019. "Power performance and dynamic responses of a combined floating vertical axis wind turbine and wave energy converter concept," Energy, Elsevier, vol. 171(C), pages 190-204.
    15. Abokersh, Mohamed Hany & Vallès, Manel & Cabeza, Luisa F. & Boer, Dieter, 2020. "A framework for the optimal integration of solar assisted district heating in different urban sized communities: A robust machine learning approach incorporating global sensitivity analysis," Applied Energy, Elsevier, vol. 267(C).
    16. Nizami, M.S.H. & Hossain, M.J. & Amin, B.M. Ruhul & Fernandez, Edstan, 2020. "A residential energy management system with bi-level optimization-based bidding strategy for day-ahead bi-directional electricity trading," Applied Energy, Elsevier, vol. 261(C).
    17. Briš, Radim & Byczanski, Petr & Goňo, Radomír & Rusek, Stanislav, 2017. "Discrete maintenance optimization of complex multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 80-89.
    18. Schmidt, Adam & Albert, Laura A. & Zheng, Kaiyue, 2021. "Risk management for cyber-infrastructure protection: A bi-objective integer programming approach," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    19. Zio, E. & Pedroni, N., 2010. "An optimized Line Sampling method for the estimation of the failure probability of nuclear passive systems," Reliability Engineering and System Safety, Elsevier, vol. 95(12), pages 1300-1313.
    20. Juan Carlos Bravo-Rodríguez & Juan Carlos del-Pino-López & Pedro Cruz-Romero, 2019. "A Survey on Optimization Techniques Applied to Magnetic Field Mitigation in Power Systems," Energies, MDPI, vol. 12(7), pages 1-20, April.

    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:eee:renene:v:191:y:2022:i:c:p:510-534. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.