IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v6y2013i6p2840-2858d26421.html
   My bibliography  Save this article

Can Tidal Current Energy Provide Base Load?

Author

Listed:
  • Simone Giorgi

    (Department of Electronic Engineering, National University of Ireland, Maynooth, Co. Kildare, Ireland)

  • John V. Ringwood

    (Department of Electronic Engineering, National University of Ireland, Maynooth, Co. Kildare, Ireland)

Abstract

Tidal energy belongs to the class of intermittent but predictable renewable energy sources. In this paper, we consider a compact set of geographically diverse locations, which have been assessed to have significant tidal stream energy, and attempt to find the degree to which the resource in each location should be exploited so that the aggregate power from all locations has a low variance. An important characteristic of the locations chosen is that there is a good spread in the peak tidal flow times, though the geographical spread is relatively small. We assume that the locations, all on the island of Ireland, can be connected together and also assume a modular set of tidal turbines. We employ multi-objective optimisation to simultaneously minimise variance, maximise mean power and maximise minimum power. A Pareto front of optimal solutions in the form of a set of coefficients determining the degree of tidal energy penetration in each location is generated using a genetic algorithm. While for the example chosen the total mean power generated is not great (circa 100 MW), the case study demonstrated a methodology that can be applied to other location sets that exhibit similar delays between peak tidal flow times.

Suggested Citation

  • Simone Giorgi & John V. Ringwood, 2013. "Can Tidal Current Energy Provide Base Load?," Energies, MDPI, vol. 6(6), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:6:p:2840-2858:d:26421
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/6/6/2840/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/6/6/2840/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Garrett, Chris & Cummins, Patrick, 2008. "Limits to tidal current power," Renewable Energy, Elsevier, vol. 33(11), pages 2485-2490.
    2. 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.
    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. Eva Segura & Rafael Morales & José A. Somolinos, 2017. "Cost Assessment Methodology and Economic Viability of Tidal Energy Projects," Energies, MDPI, vol. 10(11), pages 1-27, November.
    2. Eva Segura & Rafael Morales & José A. Somolinos, 2019. "Influence of Automated Maneuvers on the Economic Feasibility of Tidal Energy Farms," Sustainability, MDPI, vol. 11(21), pages 1-22, October.
    3. Widén, Joakim & Carpman, Nicole & Castellucci, Valeria & Lingfors, David & Olauson, Jon & Remouit, Flore & Bergkvist, Mikael & Grabbe, Mårten & Waters, Rafael, 2015. "Variability assessment and forecasting of renewables: A review for solar, wind, wave and tidal resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 356-375.
    4. Stuart Walker & Lorenzo Cappietti, 2017. "Experimental Studies of Turbulent Intensity around a Tidal Turbine Support Structure," Energies, MDPI, vol. 10(4), pages 1-21, April.
    5. Driscoll, Honora & Salmon, Nicholas & Bañares-Alcántara, Rene, 2024. "Exploiting the temporal characteristics of tidal stream power for green ammonia production," Renewable Energy, Elsevier, vol. 226(C).

    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. 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.
    2. 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.
    3. Lilia Flores Mateos & Michael Hartnett, 2020. "Hydrodynamic Effects of Tidal-Stream Power Extraction for Varying Turbine Operating Conditions," Energies, MDPI, vol. 13(12), pages 1-23, June.
    4. Zhang, Yue & Zhang, Qi & Farnoosh, Arash & Chen, Siyuan & Li, Yan, 2019. "GIS-Based Multi-Objective Particle Swarm Optimization of charging stations for electric vehicles," Energy, Elsevier, vol. 169(C), pages 844-853.
    5. J. Octavio Gutierrez-Garcia & Kwang Mong Sim, 2012. "GA-based cloud resource estimation for agent-based execution of bag-of-tasks applications," Information Systems Frontiers, Springer, vol. 14(4), pages 925-951, September.
    6. Cai, Yuhao & Qian, Xin & Su, Ruihang & Jia, Xiongjie & Ying, Jinhui & Zhao, Tianshou & Jiang, Haoran, 2024. "Thermo-electrochemical modeling of thermally regenerative flow batteries," Applied Energy, Elsevier, vol. 355(C).
    7. Myers, L.E. & Bahaj, A.S., 2012. "An experimental investigation simulating flow effects in first generation marine current energy converter arrays," Renewable Energy, Elsevier, vol. 37(1), pages 28-36.
    8. Villalón, V. & Watts, D. & Cienfuegos, R., 2019. "Assessment of the power potential extraction in the Chilean Chacao channel," Renewable Energy, Elsevier, vol. 131(C), pages 585-596.
    9. 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.
    10. 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.
    11. 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.
    12. Alarcon-Rodriguez, Arturo & Ault, Graham & Galloway, Stuart, 2010. "Multi-objective planning of distributed energy resources: A review of the state-of-the-art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(5), pages 1353-1366, June.
    13. Prina, Matteo Giacomo & Lionetti, Matteo & Manzolini, Giampaolo & Sparber, Wolfram & Moser, David, 2019. "Transition pathways optimization methodology through EnergyPLAN software for long-term energy planning," Applied Energy, Elsevier, vol. 235(C), pages 356-368.
    14. 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.
    15. H. Liao & Q. Wu, 2013. "Multi-objective optimization by learning automata," Journal of Global Optimization, Springer, vol. 55(2), pages 459-487, February.
    16. Huan Yu & Jun Yang & Yu Zhao, 2018. "Reliability of nonrepairable phased-mission systems with common bus performance sharing," Journal of Risk and Reliability, , vol. 232(6), pages 647-660, December.
    17. Li, Yuqiang & Liu, Gang & Liu, Xianping & Liao, Shengming, 2016. "Thermodynamic multi-objective optimization of a solar-dish Brayton system based on maximum power output, thermal efficiency and ecological performance," Renewable Energy, Elsevier, vol. 95(C), pages 465-473.
    18. 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.
    19. Nayara R. M. Sakiyama & Joyce C. Carlo & Leonardo Mazzaferro & Harald Garrecht, 2021. "Building Optimization through a Parametric Design Platform: Using Sensitivity Analysis to Improve a Radial-Based Algorithm Performance," Sustainability, MDPI, vol. 13(10), pages 1-25, May.
    20. 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).

    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:6:y:2013:i:6:p:2840-2858:d:26421. 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.