Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/22893
Title: Layout optimization of heaving Wave Energy Converters linear arrays in front of a vertical wall
Authors: Loukogeorgaki, Eva 
Michailides, Constantine 
Lavidas, George 
Chatjigeorgiou, Ioannis K. 
Major Field of Science: Engineering and Technology
Field Category: Environmental Engineering
Keywords: Wave energy converters;Array;Vertical wall;Optimization;Genetic algorithms;Greece
Issue Date: Dec-2021
Source: Renewable Energy, 2021, vol. 179, pp. 189-203
Volume: 179
Start page: 189
End page: 203
Journal: Renewable Energy 
Abstract: The present paper focuses on the determination of optimum layouts for linear arrays of heaving Wave Energy Converters (WECs) in front of a vertical wall. Optimum layouts maximize the annual averaged absorbed energy at a given marine site and satisfy spatial constraints. For achieving this goal, we developed an efficient optimization numerical framework, where a genetic algorithm solver is appropriately coupled with a frequency-domain hydrodynamic model, while, furthermore, a numerical wave model is utilized to determine the local wave climate conditions at the site of interest. The context is applied for an array of five semi-immersed, oblate spheroidal heaving WECs deployed at five near-shore sites of mild wave environments in the Aegean Sea, Greece. For each site, different optimization cases are solved, facilitating the investigation of different aspects of the examined problem. The largest annual energy absorption ability is observed for optimum layouts, characterized by the placement of the array close to the wall and the formation of clusters of closely-positioned WECs near the wall edges. Compared to arrays employed at sites in south-eastern Aegean, optimally-arranged arrays at central Aegean locations showed reduced energy absorption ability due to milder local wave conditions and/or the existence of quite limited water depths.
URI: https://hdl.handle.net/20.500.14279/22893
ISSN: 09601481
DOI: 10.1016/j.renene.2021.07.040
Rights: © Elsevier
Attribution-NonCommercial-NoDerivatives 4.0 International
Type: Article
Affiliation : Aristotle University of Thessaloniki 
Cyprus University of Technology 
Delft University of Technology 
National Technical University Of Athens 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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