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

Mehdi Neshat, Nataliia Y. Sergiienko, Erfan Amini, Meysam Majidi Nezhad, Davide Astiaso Garcia, Bradley Alexander and Markus Wagner
Additional contact information
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

Energies, 2020, vol. 13, issue 20, 1-23

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.

Keywords: bi-level optimisation method; evolutionary algorithms; renewable energy; wave energy converter; geometric parameters; power take-off; levelised cost of energy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/20/5498/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/20/5498/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:20:p:5498-:d:431953

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().