A multi-objective approach for location and layout optimization of wave energy converters

Shadmani, A; Reza Nikoo, M; Etri, T; Gandomi, AH

A multi-objective approach for location and layout optimization of wave energy converters

Shadmani, A Reza Nikoo, M Etri, T Gandomi, AH

Permalink

Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Applied Energy, 2023, 347
Issue Date:: 2023-10-01

Closed Access

	Filename	Description	Size
	1-s2.0-S0306261923007614-main.pdf	Published version	18.65 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Shadmani, A
dc.contributor.author	Reza Nikoo, M
dc.contributor.author	Etri, T
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2024-03-17T22:27:14Z
dc.date.available	2024-03-17T22:27:14Z
dc.date.issued	2023-10-01
dc.identifier.citation	Applied Energy, 2023, 347
dc.identifier.issn	0306-2619
dc.identifier.issn	1872-9118
dc.identifier.uri	http://hdl.handle.net/10453/176819
dc.description.abstract	Wave Energy Converters (WECs) have been increasingly installed in various coastal regions due to the higher energy density of waves than other renewable sources. Regarding coastal regions’ high potential, it is remarkably better to emplace multiple devices concerning a layout of the arrays with different configurations. Although WECs can capture the highest energy in hotspots, the location for installing these devices must be optimized. Purposefully, the wave propagation model, SWAN, was first employed respected to stochastic wind data to assess the wave energy potential and compute the annual energy production (AEP). The Latin Hypercube Sampling (LHS) technique was used to generate samples. Finally, the optimal location and layout of the arrays were determined through the multi-objective optimization (MOOP) algorithm, NSGA-III, based on SWAN's sequential outputs. The optimized layouts contained arrays with 4-, 8-, and 16-devices with regard to devices’ initial state. Almost 20 hotspots were located by solving the Pareto-front. It was found that the best arrangement for the 4-device arrays is linear. However, the optimal arrangement of the 8- and 16-device arrays widely varies and depends on the AEP of the region. Nevertheless, it seems best to position the 16-device array in a diagonal layout with one to three rows.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Applied Energy
dc.relation.isbasedon	10.1016/j.apenergy.2023.121397
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	09 Engineering, 14 Economics
dc.subject.classification	Energy
dc.subject.classification	33 Built environment and design
dc.subject.classification	38 Economics
dc.subject.classification	40 Engineering
dc.title	A multi-objective approach for location and layout optimization of wave energy converters
dc.type	Journal Article
utslib.citation.volume	347
utslib.for	09 Engineering
utslib.for	14 Economics
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-17T22:27:05Z
pubs.publication-status	Published
pubs.volume	347

Abstract:

Wave Energy Converters (WECs) have been increasingly installed in various coastal regions due to the higher energy density of waves than other renewable sources. Regarding coastal regions’ high potential, it is remarkably better to emplace multiple devices concerning a layout of the arrays with different configurations. Although WECs can capture the highest energy in hotspots, the location for installing these devices must be optimized. Purposefully, the wave propagation model, SWAN, was first employed respected to stochastic wind data to assess the wave energy potential and compute the annual energy production (AEP). The Latin Hypercube Sampling (LHS) technique was used to generate samples. Finally, the optimal location and layout of the arrays were determined through the multi-objective optimization (MOOP) algorithm, NSGA-III, based on SWAN's sequential outputs. The optimized layouts contained arrays with 4-, 8-, and 16-devices with regard to devices’ initial state. Almost 20 hotspots were located by solving the Pareto-front. It was found that the best arrangement for the 4-device arrays is linear. However, the optimal arrangement of the 8- and 16-device arrays widely varies and depends on the AEP of the region. Nevertheless, it seems best to position the 16-device array in a diagonal layout with one to three rows.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/176819