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A New Bi-Level Optimisation Framework for Optimising a Multi-Mode Wave Energy Converter Design: A Case Study for the Marettimo Island, Mediterranean Sea

Author

Listed:
  • Mehdi Neshat

    (Optimization and Logistics Group, School of Computer Science, The University of Adelaide, 5005 Adelaide, Australia)

  • Nataliia Y. Sergiienko

    (School of Mechanical Engineering, The University of Adelaide, 5005 Adelaide, Australia)

  • Erfan Amini

    (Coastal and offshore structures engineering group, School of Civil Engineering, University of Tehran, 13145-1384 Tehran, Iran)

  • Meysam Majidi Nezhad

    (Department of Astronautics, Electrical and Energy Engineering (DIAEE), Sapienza University of Rome, 00184 Rome, Italy)

  • Davide Astiaso Garcia

    (Department of Planning, Design and Technology of Architecture, Sapienza University of Rome, 00197 Rome, Italy)

  • Bradley Alexander

    (Optimization and Logistics Group, School of Computer Science, The University of Adelaide, 5005 Adelaide, Australia)

  • Markus Wagner

    (Optimization and Logistics Group, School of Computer Science, The University of Adelaide, 5005 Adelaide, Australia)

Abstract

To advance commercialisation of ocean wave energy and for the technology to become competitive with other sources of renewable energy, the cost of wave energy harvesting should be significantly reduced. The Mediterranean Sea is a region with a relatively low wave energy potential, but due to the absence of extreme waves, can be considered at the initial stage of the prototype development as a proof of concept. In this study, we focus on the optimisation of a multi-mode wave energy converter inspired by the CETO system to be tested in the west of Sicily, Italy. We develop a computationally efficient spectral-domain model that fully captures the nonlinear dynamics of a wave energy converter (WEC). We consider two different objective functions for the purpose of optimising a WEC: (1) maximise the annual average power output (with no concern for WEC cost), and (2) minimise the levelised cost of energy (LCoE). We develop a new bi-level optimisation framework to simultaneously optimise the WEC geometry, tether angles and power take-off (PTO) parameters. In the upper-level of this bi-level process, all WEC parameters are optimised using a state-of-the-art self-adaptive differential evolution method as a global optimisation technique. At the lower-level, we apply a local downhill search method to optimise the geometry and tether angles settings in two independent steps. We evaluate and compare the performance of the new bi-level optimisation framework with seven well-known evolutionary and swarm optimisation methods using the same computational budget. The simulation results demonstrate that the bi-level method converges faster than other methods to a better configuration in terms of both absorbed power and the levelised cost of energy. The optimisation results confirm that if we focus on minimising the produced energy cost at the given location, the best-found WEC dimension is that of a small WEC with a radius of 5 m and height of 2 m.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5498-:d:431953
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    References listed on IDEAS

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    Cited by:

    1. Mehdi Neshat & Nataliia Y. Sergiienko & Seyedali Mirjalili & Meysam Majidi Nezhad & Giuseppe Piras & Davide Astiaso Garcia, 2021. "Multi-Mode Wave Energy Converter Design Optimisation Using an Improved Moth Flame Optimisation Algorithm," Energies, MDPI, vol. 14(13), pages 1-17, June.
    2. Erfan Amini & Danial Golbaz & Fereidoun Amini & Meysam Majidi Nezhad & Mehdi Neshat & Davide Astiaso Garcia, 2020. "A Parametric Study of Wave Energy Converter Layouts in Real Wave Models," Energies, MDPI, vol. 13(22), pages 1-23, November.
    3. Majidi Nezhad, M. & Heydari, A. & Pirshayan, E. & Groppi, D. & Astiaso Garcia, D., 2021. "A novel forecasting model for wind speed assessment using sentinel family satellites images and machine learning method," Renewable Energy, Elsevier, vol. 179(C), pages 2198-2211.
    4. Siyavash Filom & Soheil Radfar & Roozbeh Panahi & Erfan Amini & Mehdi Neshat, 2021. "Exploring Wind Energy Potential as a Driver of Sustainable Development in the Southern Coasts of Iran: The Importance of Wind Speed Statistical Distribution Model," Sustainability, MDPI, vol. 13(14), pages 1-24, July.
    5. Erfan Amini & Rojin Asadi & Danial Golbaz & Mahdieh Nasiri & Seyed Taghi Omid Naeeni & Meysam Majidi Nezhad & Giuseppe Piras & Mehdi Neshat, 2021. "Comparative Study of Oscillating Surge Wave Energy Converter Performance: A Case Study for Southern Coasts of the Caspian Sea," Sustainability, MDPI, vol. 13(19), pages 1-21, October.
    6. Foteinis, Spyros, 2022. "Wave energy converters in low energy seas: Current state and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    7. Benedetto Nastasi & Massimiliano Manfren & Michel Noussan, 2021. "Open Data and Models for Energy and Environment," Energies, MDPI, vol. 14(15), pages 1-2, July.

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