Loading…

Optimization of regular offshore wind-power plants using a non-discrete evolutionary algorithm

Offshore wind farms (OWFs) often present a regular configuration mainly due to aesthetical considerations. This paper presents a new evolutionary algorithm that optimizes the location, configuration and orientation of a rhomboid-shape OWF. Existing optimization algorithms were based on dividing the...

Full description

Saved in:
Bibliographic Details
Published in:AIMS energy 2017-01, Vol.5 (2), p.173-192
Main Authors: G. Gonzalez-Rodriguez, Angel, Burgos Payan, Manuel, Riquelme Santos, Jesús, Serrano Gonzalez, Javier
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c354t-de61d236474db938be1807d08b8fba42eb855eb62bfb61e386750c6cbc6bfd433
cites cdi_FETCH-LOGICAL-c354t-de61d236474db938be1807d08b8fba42eb855eb62bfb61e386750c6cbc6bfd433
container_end_page 192
container_issue 2
container_start_page 173
container_title AIMS energy
container_volume 5
creator G. Gonzalez-Rodriguez, Angel
Burgos Payan, Manuel
Riquelme Santos, Jesús
Serrano Gonzalez, Javier
description Offshore wind farms (OWFs) often present a regular configuration mainly due to aesthetical considerations. This paper presents a new evolutionary algorithm that optimizes the location, configuration and orientation of a rhomboid-shape OWF. Existing optimization algorithms were based on dividing the available space into a mess of cells and forcing the turbines to be located in the centre of a cell. However, the presented algorithm searches for the optimum within a continuous range of the eight parameters that define the OWF, which allows including a gradient-based local search operator to improve the optimization process. The study starts from a review of the economic data available in the bibliography relative to the most significant issues influencing the profitability of the investment in terms of the Internal Rate of Return (IRR). In order to address the distinctive characteristics of OWFs, specific issues arise which have been solved. The most important ones are: interpretation of nautical charts, utilization of the seabed map with different load-bearing capacities, and location of the shoreline transition.
doi_str_mv 10.3934/energy.2017.2.173
format article
fullrecord <record><control><sourceid>doaj_cross</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_ff03ac25f72344bc8b1e43aaf0d0d1f2</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_ff03ac25f72344bc8b1e43aaf0d0d1f2</doaj_id><sourcerecordid>oai_doaj_org_article_ff03ac25f72344bc8b1e43aaf0d0d1f2</sourcerecordid><originalsourceid>FETCH-LOGICAL-c354t-de61d236474db938be1807d08b8fba42eb855eb62bfb61e386750c6cbc6bfd433</originalsourceid><addsrcrecordid>eNo90EtuwjAQgOEsWqmIcoDufIGktsdJzLJCfSAhsWm3tfwYB6MQIzsU0dMXStXVjGbxafQXxQOjFcxBPOKAqTtVnLK24hVr4aaYcAAoJYC4K2Y5bymljNftXMhJ8bnej2EXvvUY4kCiJwm7Q6_TefV5ExOSYxhcuY9HTGTf62HM5JDD0BFNhjiULmSbcESCX7E_XBCdTkT3XUxh3Ozui1uv-4yzvzktPl6e3xdv5Wr9ulw8rUoLtRhLhw1zHBrRCmfmIA0ySVtHpZHeaMHRyLpG03DjTcMQZNPW1DbW2MZ4JwCmxfLquqi3ap_C7vyGijqo30NMndJpDLZH5T0FbXntWw5CGCsNQwFae-qoY56fLXa1bIo5J_T_HqPq0lhdG6tLY8XVuTH8AM_9dig</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Optimization of regular offshore wind-power plants using a non-discrete evolutionary algorithm</title><source>IngentaConnect Journals</source><creator>G. Gonzalez-Rodriguez, Angel ; Burgos Payan, Manuel ; Riquelme Santos, Jesús ; Serrano Gonzalez, Javier</creator><creatorcontrib>G. Gonzalez-Rodriguez, Angel ; Burgos Payan, Manuel ; Riquelme Santos, Jesús ; Serrano Gonzalez, Javier ; 1 Department of Electronic Engineering and Automation, University of Jaen, Jaen, Spain</creatorcontrib><description>Offshore wind farms (OWFs) often present a regular configuration mainly due to aesthetical considerations. This paper presents a new evolutionary algorithm that optimizes the location, configuration and orientation of a rhomboid-shape OWF. Existing optimization algorithms were based on dividing the available space into a mess of cells and forcing the turbines to be located in the centre of a cell. However, the presented algorithm searches for the optimum within a continuous range of the eight parameters that define the OWF, which allows including a gradient-based local search operator to improve the optimization process. The study starts from a review of the economic data available in the bibliography relative to the most significant issues influencing the profitability of the investment in terms of the Internal Rate of Return (IRR). In order to address the distinctive characteristics of OWFs, specific issues arise which have been solved. The most important ones are: interpretation of nautical charts, utilization of the seabed map with different load-bearing capacities, and location of the shoreline transition.</description><identifier>ISSN: 2333-8334</identifier><identifier>DOI: 10.3934/energy.2017.2.173</identifier><language>eng</language><publisher>AIMS Press</publisher><subject>continuous evolutionary algorithm ; gradient-based local search ; IRR ; non-discrete evolutionary algorithm ; offshore ; optimal configuration ; regular patterns ; wind energy</subject><ispartof>AIMS energy, 2017-01, Vol.5 (2), p.173-192</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c354t-de61d236474db938be1807d08b8fba42eb855eb62bfb61e386750c6cbc6bfd433</citedby><cites>FETCH-LOGICAL-c354t-de61d236474db938be1807d08b8fba42eb855eb62bfb61e386750c6cbc6bfd433</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>G. Gonzalez-Rodriguez, Angel</creatorcontrib><creatorcontrib>Burgos Payan, Manuel</creatorcontrib><creatorcontrib>Riquelme Santos, Jesús</creatorcontrib><creatorcontrib>Serrano Gonzalez, Javier</creatorcontrib><creatorcontrib>1 Department of Electronic Engineering and Automation, University of Jaen, Jaen, Spain</creatorcontrib><title>Optimization of regular offshore wind-power plants using a non-discrete evolutionary algorithm</title><title>AIMS energy</title><description>Offshore wind farms (OWFs) often present a regular configuration mainly due to aesthetical considerations. This paper presents a new evolutionary algorithm that optimizes the location, configuration and orientation of a rhomboid-shape OWF. Existing optimization algorithms were based on dividing the available space into a mess of cells and forcing the turbines to be located in the centre of a cell. However, the presented algorithm searches for the optimum within a continuous range of the eight parameters that define the OWF, which allows including a gradient-based local search operator to improve the optimization process. The study starts from a review of the economic data available in the bibliography relative to the most significant issues influencing the profitability of the investment in terms of the Internal Rate of Return (IRR). In order to address the distinctive characteristics of OWFs, specific issues arise which have been solved. The most important ones are: interpretation of nautical charts, utilization of the seabed map with different load-bearing capacities, and location of the shoreline transition.</description><subject>continuous evolutionary algorithm</subject><subject>gradient-based local search</subject><subject>IRR</subject><subject>non-discrete evolutionary algorithm</subject><subject>offshore</subject><subject>optimal configuration</subject><subject>regular patterns</subject><subject>wind energy</subject><issn>2333-8334</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNo90EtuwjAQgOEsWqmIcoDufIGktsdJzLJCfSAhsWm3tfwYB6MQIzsU0dMXStXVjGbxafQXxQOjFcxBPOKAqTtVnLK24hVr4aaYcAAoJYC4K2Y5bymljNftXMhJ8bnej2EXvvUY4kCiJwm7Q6_TefV5ExOSYxhcuY9HTGTf62HM5JDD0BFNhjiULmSbcESCX7E_XBCdTkT3XUxh3Ozui1uv-4yzvzktPl6e3xdv5Wr9ulw8rUoLtRhLhw1zHBrRCmfmIA0ySVtHpZHeaMHRyLpG03DjTcMQZNPW1DbW2MZ4JwCmxfLquqi3ap_C7vyGijqo30NMndJpDLZH5T0FbXntWw5CGCsNQwFae-qoY56fLXa1bIo5J_T_HqPq0lhdG6tLY8XVuTH8AM_9dig</recordid><startdate>20170101</startdate><enddate>20170101</enddate><creator>G. Gonzalez-Rodriguez, Angel</creator><creator>Burgos Payan, Manuel</creator><creator>Riquelme Santos, Jesús</creator><creator>Serrano Gonzalez, Javier</creator><general>AIMS Press</general><scope>AAYXX</scope><scope>CITATION</scope><scope>DOA</scope></search><sort><creationdate>20170101</creationdate><title>Optimization of regular offshore wind-power plants using a non-discrete evolutionary algorithm</title><author>G. Gonzalez-Rodriguez, Angel ; Burgos Payan, Manuel ; Riquelme Santos, Jesús ; Serrano Gonzalez, Javier</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c354t-de61d236474db938be1807d08b8fba42eb855eb62bfb61e386750c6cbc6bfd433</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>continuous evolutionary algorithm</topic><topic>gradient-based local search</topic><topic>IRR</topic><topic>non-discrete evolutionary algorithm</topic><topic>offshore</topic><topic>optimal configuration</topic><topic>regular patterns</topic><topic>wind energy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>G. Gonzalez-Rodriguez, Angel</creatorcontrib><creatorcontrib>Burgos Payan, Manuel</creatorcontrib><creatorcontrib>Riquelme Santos, Jesús</creatorcontrib><creatorcontrib>Serrano Gonzalez, Javier</creatorcontrib><creatorcontrib>1 Department of Electronic Engineering and Automation, University of Jaen, Jaen, Spain</creatorcontrib><collection>CrossRef</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>AIMS energy</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>G. Gonzalez-Rodriguez, Angel</au><au>Burgos Payan, Manuel</au><au>Riquelme Santos, Jesús</au><au>Serrano Gonzalez, Javier</au><aucorp>1 Department of Electronic Engineering and Automation, University of Jaen, Jaen, Spain</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimization of regular offshore wind-power plants using a non-discrete evolutionary algorithm</atitle><jtitle>AIMS energy</jtitle><date>2017-01-01</date><risdate>2017</risdate><volume>5</volume><issue>2</issue><spage>173</spage><epage>192</epage><pages>173-192</pages><issn>2333-8334</issn><abstract>Offshore wind farms (OWFs) often present a regular configuration mainly due to aesthetical considerations. This paper presents a new evolutionary algorithm that optimizes the location, configuration and orientation of a rhomboid-shape OWF. Existing optimization algorithms were based on dividing the available space into a mess of cells and forcing the turbines to be located in the centre of a cell. However, the presented algorithm searches for the optimum within a continuous range of the eight parameters that define the OWF, which allows including a gradient-based local search operator to improve the optimization process. The study starts from a review of the economic data available in the bibliography relative to the most significant issues influencing the profitability of the investment in terms of the Internal Rate of Return (IRR). In order to address the distinctive characteristics of OWFs, specific issues arise which have been solved. The most important ones are: interpretation of nautical charts, utilization of the seabed map with different load-bearing capacities, and location of the shoreline transition.</abstract><pub>AIMS Press</pub><doi>10.3934/energy.2017.2.173</doi><tpages>20</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2333-8334
ispartof AIMS energy, 2017-01, Vol.5 (2), p.173-192
issn 2333-8334
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_ff03ac25f72344bc8b1e43aaf0d0d1f2
source IngentaConnect Journals
subjects continuous evolutionary algorithm
gradient-based local search
IRR
non-discrete evolutionary algorithm
offshore
optimal configuration
regular patterns
wind energy
title Optimization of regular offshore wind-power plants using a non-discrete evolutionary algorithm
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-02T20%3A12%3A17IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-doaj_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Optimization%20of%20regular%20offshore%20wind-power%20plants%20using%20a%20non-discrete%20evolutionary%20algorithm&rft.jtitle=AIMS%20energy&rft.au=G.%20Gonzalez-Rodriguez,%20Angel&rft.aucorp=1%20Department%20of%20Electronic%20Engineering%20and%20Automation,%20University%20of%20Jaen,%20Jaen,%20Spain&rft.date=2017-01-01&rft.volume=5&rft.issue=2&rft.spage=173&rft.epage=192&rft.pages=173-192&rft.issn=2333-8334&rft_id=info:doi/10.3934/energy.2017.2.173&rft_dat=%3Cdoaj_cross%3Eoai_doaj_org_article_ff03ac25f72344bc8b1e43aaf0d0d1f2%3C/doaj_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c354t-de61d236474db938be1807d08b8fba42eb855eb62bfb61e386750c6cbc6bfd433%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true