The Optimal Configuration of Wave Energy Conversions Respective to the Nearshore Wave Energy Potential

Alireza Shadmani, Mohammad Reza Nikoo (), Riyadh I. Al-Raoush, Nasrin Alamdari and Amir H. Gandomi ()
Additional contact information
Alireza Shadmani: Department of Maritime Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran P.O. Box 15875-4413, Iran
Mohammad Reza Nikoo: Department of Civil and Architectural Engineering, College of Engineering, Sultan Qaboos University, Muscat 123, Oman
Riyadh I. Al-Raoush: Department of Civil and Architectural Engineering, Qatar University, Doha P.O. Box 2713, Qatar
Nasrin Alamdari: Department of Civil and Environmental Engineering, Florida State University, Tallahassee, FL 32306, USA
Amir H. Gandomi: Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia

Energies, 2022, vol. 15, issue 20, 1-29

Abstract: Ocean energy is one potential renewable energy alternative to fossil fuels that has a more significant power generation due to its better predictability and availability. In order to harness this source, wave energy converters (WECs) have been devised and used over the past several years to generate as much energy and power as is feasible. While it is possible to install these devices in both nearshore and offshore areas, nearshore sites are more appropriate places since more severe weather occurs offshore. Determining the optimal location might be challenging when dealing with sites along the coast since they often have varying capacities for energy production. Constructing wave farms requires determining the appropriate location for WECs, which may lead us to its correct and optimum design. The WEC size, shape, and layout are factors that must be considered for installing these devices. Therefore, this review aims to explain the methodologies, advancements, and effective hydrodynamic parameters that may be used to discover the optimal configuration of WECs in nearshore locations using evolutionary algorithms (EAs).

Keywords: wave energy; wave energy converters; optimal configuration; nearshore sites; evolutionary algorithms (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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