IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i1p159-d126002.html
   My bibliography  Save this article

Cost Optimization of Mooring Solutions for Large Floating Wave Energy Converters

Author

Listed:
  • Jonas Bjerg Thomsen

    (Department of Civil Engineering, Aalborg University, Thomas Manns Vej 23, 9220 Aalborg ∅st , Denmark)

  • Francesco Ferri

    (Department of Civil Engineering, Aalborg University, Thomas Manns Vej 23, 9220 Aalborg ∅st , Denmark)

  • Jens Peter Kofoed

    (Department of Civil Engineering, Aalborg University, Thomas Manns Vej 23, 9220 Aalborg ∅st , Denmark)

  • Kevin Black

    (Tension Technology International Ltd., 69 Parkway, Eastbourne, East Sussex BN20 9DZ, UK)

Abstract

The increasing desire for using renewable energy sources throughout the world has resulted in a considerable amount of research into and development of concepts for wave energy converters. By now, many different concepts exist, but still, the wave energy sector is not at a stage that is considered commercial yet, primarily due to the relatively high cost of energy. A considerable amount of the wave energy converters are floating structures, which consequently need mooring systems in order to ensure station keeping. Despite being a well-known concept, mooring in wave energy application has proven to be expensive and has a high rate of failure. Therefore, there is a need for further improvement, investigation into new concepts and sophistication of design procedures. This study uses four Danish wave energy converters, all considered as large floating structures, to investigate a methodology in order to find an inexpensive and reliable mooring solution for each device. The study uses a surrogate-based optimization routine in order to find a feasible solution in only a limited number of evaluations and a constructed cost database for determination of the mooring cost. Based on the outcome, the mooring parameters influencing the cost are identified and the optimum solution determined.

Suggested Citation

  • Jonas Bjerg Thomsen & Francesco Ferri & Jens Peter Kofoed & Kevin Black, 2018. "Cost Optimization of Mooring Solutions for Large Floating Wave Energy Converters," Energies, MDPI, vol. 11(1), pages 1-23, January.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:159-:d:126002
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/1/159/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/1/159/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jonas Bjerg Thomsen & Francesco Ferri & Jens Peter Kofoed, 2017. "Screening of Available Tools for Dynamic Mooring Analysis of Wave Energy Converters," Energies, MDPI, vol. 10(7), pages 1-18, June.
    2. Luis Rios & Nikolaos Sahinidis, 2013. "Derivative-free optimization: a review of algorithms and comparison of software implementations," Journal of Global Optimization, Springer, vol. 56(3), pages 1247-1293, July.
    3. Josh Davidson & John V. Ringwood, 2017. "Mathematical Modelling of Mooring Systems for Wave Energy Converters—A Review," Energies, MDPI, vol. 10(5), pages 1-46, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gomes, Rui P.F. & Gato, Luís M.C. & Henriques, João C.C. & Portillo, Juan C.C. & Howey, Ben D. & Collins, Keri M. & Hann, Martyn R. & Greaves, Deborah M., 2020. "Compact floating wave energy converters arrays: Mooring loads and survivability through scale physical modelling," Applied Energy, Elsevier, vol. 280(C).
    2. 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).
    3. Perera, A.T.D. & Wickramasinghe, P.U. & Nik, Vahid M. & Scartezzini, Jean-Louis, 2019. "Machine learning methods to assist energy system optimization," Applied Energy, Elsevier, vol. 243(C), pages 191-205.
    4. Garcia-Teruel, A. & Forehand, D.I.M., 2021. "A review of geometry optimisation of wave energy converters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    5. Peña-Sanchez, Yerai & García-Violini, Demián & Penalba, Markel & Zarketa-Astigarraga, Ander & Ferri, Francesco & Nava, Vincenzo & Ringwood, John V., 2024. "Control co-design for wave energy farms: Optimisation of array layout and mooring configuration in a realistic wave climate," Renewable Energy, Elsevier, vol. 227(C).
    6. Li, Demin & Sharma, Sanjay & Borthwick, Alistair G.L. & Huang, Heao & Dong, Xiaochen & Li, Yanni & Shi, Hongda, 2023. "Experimental study of a floating two-body wave energy converter," Renewable Energy, Elsevier, vol. 218(C).
    7. 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.
    8. 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.
    9. Xu, Sheng & Wang, Shan & Guedes Soares, C., 2019. "Review of mooring design for floating wave energy converters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 595-621.
    10. Luca Martinelli & Barbara Zanuttigh, 2018. "Effects of Mooring Compliancy on the Mooring Forces, Power Production, and Dynamics of a Floating Wave Activated Body Energy Converter," Energies, MDPI, vol. 11(12), pages 1-24, December.
    11. Dongsheng Qiao & Rizwan Haider & Jun Yan & Dezhi Ning & Binbin Li, 2020. "Review of Wave Energy Converter and Design of Mooring System," Sustainability, MDPI, vol. 12(19), pages 1-31, October.

    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. Gomes, Rui P.F. & Gato, Luís M.C. & Henriques, João C.C. & Portillo, Juan C.C. & Howey, Ben D. & Collins, Keri M. & Hann, Martyn R. & Greaves, Deborah M., 2020. "Compact floating wave energy converters arrays: Mooring loads and survivability through scale physical modelling," Applied Energy, Elsevier, vol. 280(C).
    2. Luca Martinelli & Barbara Zanuttigh, 2018. "Effects of Mooring Compliancy on the Mooring Forces, Power Production, and Dynamics of a Floating Wave Activated Body Energy Converter," Energies, MDPI, vol. 11(12), pages 1-24, December.
    3. Xu, Sheng & Wang, Shan & Guedes Soares, C., 2019. "Review of mooring design for floating wave energy converters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 595-621.
    4. Gabriela Simonet & Julie Subervie & Driss Ezzine-De-Blas & Marina Cromberg & Amy Duchelle, 2015. "Paying smallholders not to cut down the amazon forest: impact evaluation of a REDD+ pilot project," Working Papers 1514, Chaire Economie du climat.
    5. Somayeh Moazeni & Warren B. Powell & Boris Defourny & Belgacem Bouzaiene-Ayari, 2017. "Parallel Nonstationary Direct Policy Search for Risk-Averse Stochastic Optimization," INFORMS Journal on Computing, INFORMS, vol. 29(2), pages 332-349, May.
    6. Zhang, Jincheng & Zhao, Xiaowei & Greaves, Deborah & Jin, Siya, 2023. "Modeling of a hinged-raft wave energy converter via deep operator learning and wave tank experiments," Applied Energy, Elsevier, vol. 341(C).
    7. Jakubik, Johannes & Binding, Adrian & Feuerriegel, Stefan, 2021. "Directed particle swarm optimization with Gaussian-process-based function forecasting," European Journal of Operational Research, Elsevier, vol. 295(1), pages 157-169.
    8. Christophe Gouel & Nicolas Legrand, 2017. "Estimating the Competitive Storage Model with Trending Commodity Prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(4), pages 744-763, June.
    9. Zhao, Jake, 2020. "Accounting for the corporate cash increase," European Economic Review, Elsevier, vol. 123(C).
    10. Hannes Schwarz & Valentin Bertsch & Wolf Fichtner, 2018. "Two-stage stochastic, large-scale optimization of a decentralized energy system: a case study focusing on solar PV, heat pumps and storage in a residential quarter," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(1), pages 265-310, January.
    11. Breitmoser, Yves & Valasek, Justin, 2017. "A rationale for unanimity in committees," Discussion Papers, Research Unit: Economics of Change SP II 2017-308, WZB Berlin Social Science Center.
    12. Krese, Gorazd & Lampret, Žiga & Butala, Vincenc & Prek, Matjaž, 2018. "Determination of a Building's balance point temperature as an energy characteristic," Energy, Elsevier, vol. 165(PB), pages 1034-1049.
    13. Tavakol Aghaei, Vahid & Ağababaoğlu, Arda & Bawo, Biram & Naseradinmousavi, Peiman & Yıldırım, Sinan & Yeşilyurt, Serhat & Onat, Ahmet, 2023. "Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm," Applied Energy, Elsevier, vol. 341(C).
    14. Andrzej Wędzik & Tomasz Siewierski & Michał Szypowski, 2019. "The Use of Black-Box Optimization Method for Determination of the Bus Connection Capacity in Electric Power Grid," Energies, MDPI, vol. 13(1), pages 1-21, December.
    15. Oikonomou, C.L.G. & Gomes, R.P.F. & Gato, L.M.C. & Falcão, A.F.O., 2020. "On the dynamics of an array of spar-buoy oscillating water column devices with inter-body mooring connections," Renewable Energy, Elsevier, vol. 148(C), pages 309-325.
    16. Linas Stripinis & Remigijus Paulavičius, 2023. "Novel Algorithm for Linearly Constrained Derivative Free Global Optimization of Lipschitz Functions," Mathematics, MDPI, vol. 11(13), pages 1-19, June.
    17. Pál, László & Sándor, Zsolt, 2023. "Comparing procedures for estimating random coefficient logit demand models with a special focus on obtaining global optima," International Journal of Industrial Organization, Elsevier, vol. 88(C).
    18. Qihong Feng & Kuankuan Wu & Jiyuan Zhang & Sen Wang & Xianmin Zhang & Daiyu Zhou & An Zhao, 2022. "Optimization of Well Control during Gas Flooding Using the Deep-LSTM-Based Proxy Model: A Case Study in the Baoshaceng Reservoir, Tarim, China," Energies, MDPI, vol. 15(7), pages 1-14, March.
    19. Luca Riboldi & Lars O. Nord, 2017. "Lifetime Assessment of Combined Cycles for Cogeneration of Power and Heat in Offshore Oil and Gas Installations," Energies, MDPI, vol. 10(6), pages 1-23, May.
    20. Satyajith Amaran & Nikolaos V. Sahinidis & Bikram Sharda & Scott J. Bury, 2016. "Simulation optimization: a review of algorithms and applications," Annals of Operations Research, Springer, vol. 240(1), pages 351-380, May.

    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:11:y:2018:i:1:p:159-:d:126002. 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.