Offshore wind farm layout optimization using mathematical programming techniques

Offshore wind power is a renewable energy of growing relevance in current electric energy systems, presenting favorable wind conditions in comparison with the sites on land. However, the higher energy yield has to compensate the increment in installation and maintenance costs, thus the importance of...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Renewable energy Ročník 53; s. 389 - 399
Hlavní autori:	Pérez, Beatriz, Mínguez, Roberto, Guanche, Raúl
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Oxford Elsevier Ltd 01.05.2013 Elsevier
Predmet:	Applied sciences Electric power generation electricity Energy Exact sciences and technology Germany Mathematical analysis Mathematical programming methodology Natural energy North Sea Offshore Offshore engineering Offshore structures Optimization prices profitability wind wind farms Wind power North Sea Germany ANE, North Sea ANE, Germany Offshore Windfarm Renewable energy Heuristic optimization Layout optimization Wake effect Offshore wind farm Wake Optimization Mathematical programming
ISSN:	0960-1481, 1879-0682
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	Offshore wind power is a renewable energy of growing relevance in current electric energy systems, presenting favorable wind conditions in comparison with the sites on land. However, the higher energy yield has to compensate the increment in installation and maintenance costs, thus the importance of optimizing resources. One relevant aspect to increase profitability is the wind farm layout. The aim of this paper is to propose a new method to maximize the expected power production of offshore wind farms by setting the appropriate layout, i.e. minimizing the wake effects. The method uses a sequential procedure for global optimization consisting of two steps: i) an heuristic method to set an initial random layout configuration, and ii) the use of nonlinear mathematical programming techniques for local optimization, which use the random layout as an initial solution. The method takes full advantage of the most up-to-date mathematical programming techniques while performing a global optimization approach, which can be easily parallelized. The performance of the proposed procedure is tested using the German offshore wind farm Alpha Ventus, located in the North Sea, yielding an increment of expected annual power production of 3.52% with respect to the actual configuration. According to current electricity prices in Germany, this constitutes an expected profit increment of almost 1 M€ per year.
AbstractList	Offshore wind power is a renewable energy of growing relevance in current electric energy systems, presenting favorable wind conditions in comparison with the sites on land. However, the higher energy yield has to compensate the increment in installation and maintenance costs, thus the importance of optimizing resources. One relevant aspect to increase profitability is the wind farm layout. The aim of this paper is to propose a new method to maximize the expected power production of offshore wind farms by setting the appropriate layout, i.e. minimizing the wake effects. The method uses a sequential procedure for global optimization consisting of two steps: i) an heuristic method to set an initial random layout configuration, and ii) the use of nonlinear mathematical programming techniques for local optimization, which use the random layout as an initial solution. The method takes full advantage of the most up-to-date mathematical programming techniques while performing a global optimization approach, which can be easily parallelized. The performance of the proposed procedure is tested using the German offshore wind farm Alpha Ventus, located in the North Sea, yielding an increment of expected annual power production of 3.52% with respect to the actual configuration. According to current electricity prices in Germany, this constitutes an expected profit increment of almost 1 M€ per year. Offshore wind power is a renewable energy of growing relevance in current electric energy systems, presenting favorable wind conditions in comparison with the sites on land. However, the higher energy yield has to compensate the increment in installation and maintenance costs, thus the importance of optimizing resources. One relevant aspect to increase profitability is the wind farm layout. The aim of this paper is to propose a new method to maximize the expected power production of offshore wind farms by setting the appropriate layout, i.e. minimizing the wake effects. The method uses a sequential procedure for global optimization consisting of two steps: i) an heuristic method to set an initial random layout configuration, and ii) the use of nonlinear mathematical programming techniques for local optimization, which use the random layout as an initial solution. The method takes full advantage of the most up-to-date mathematical programming techniques while performing a global optimization approach, which can be easily parallelized. The performance of the proposed procedure is tested using the German offshore wind farm Alpha Ventus, located in the North Sea, yielding an increment of expected annual power production of 3.52% with respect to the actual configuration. According to current electricity prices in Germany, this constitutes an expected profit increment of almost 1 M€ per year. Offshore wind power is a renewable energy of growing relevance in current electric energy systems, presenting favorable wind conditions in comparison with the sites on land. However, the higher energy yield has to compensate the increment in installation and maintenance costs, thus the importance of optimizing resources. One relevant aspect to increase profitability is the wind farm layout. The aim of this paper is to propose a new method to maximize the expected power production of offshore wind farms by setting the appropriate layout, i.e. minimizing the wake effects. The method uses a sequential procedure for global optimization consisting of two steps: i) an heuristic method to set an initial random layout configuration, and ii) the use of nonlinear mathematical programming techniques for local optimization, which use the random layout as an initial solution. The method takes full advantage of the most up-to-date mathematical programming techniques while performing a global optimization approach, which can be easily parallelized. The performance of the proposed procedure is tested using the German offshore wind farm Alpha Ventus, located in the North Sea, yielding an increment of expected annual power production of 3.52% with respect to the actual configuration. According to current electricity prices in Germany, this constitutes an expected profit increment of almost 1 Man per year.
Author	Guanche, Raúl Mínguez, Roberto Pérez, Beatriz
Author_xml	– sequence: 1 fullname: Pérez, Beatriz – sequence: 2 fullname: Mínguez, Roberto – sequence: 3 fullname: Guanche, Raúl
BackLink	http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=26925801$$DView record in Pascal Francis
BookMark	eNqNkV1LBCEUhiUK2j7-QdDcBN3Mpo7jR3cRfUFQUF2L4-quy4xu6hL163Pauumm8OABfd5zDu_ZA9s-eAPAEYJTBBE9W06j8eVMMUR4WgJCtgUmiDNRQ8rxNphAQWGNCEe7YC-lJYSo5YxMwOODtWkRoqnenJ9VVsWh6tV7WOcqrLIb3IfKLvhqnZyfV4PKC1Mup1VfrWKYRzUM40c2euHd69qkA7BjVZ_M4XfeBy_XV8-Xt_X9w83d5cV9rQkkucbadpQ3rGWtaWBDupkgCBvaWUGoxgxRLmxnKOOk5aZpaceoIIpiNcOsKS_74HRTt4wx9s1ycEmbvlfehHWSpQBlmAiM_4ESjAXCgv6NtsXawjFU0JNvVKVih43Ka5fkKrpBxXeJqcAthyN3vuF0DClFY6V2-cvUHJXrJYJyXKJcys0S5bhEWaJ0KmLyS_xT_w_Z8UZmVZBqHstcL08FKNMjBhnizSdzg62n
CitedBy_id	crossref_primary_10_1016_j_engappai_2017_01_010 crossref_primary_10_1093_ijlct_ctz016 crossref_primary_10_3390_en7116930 crossref_primary_10_1016_j_enconman_2022_116077 crossref_primary_10_1016_j_energy_2019_07_019 crossref_primary_10_3390_jmse11081566 crossref_primary_10_1016_j_ifacol_2017_08_025 crossref_primary_10_1016_j_eswa_2013_05_054 crossref_primary_10_1155_2024_9406519 crossref_primary_10_1016_j_energy_2022_125376 crossref_primary_10_1016_j_energy_2018_11_073 crossref_primary_10_1016_j_renene_2021_12_057 crossref_primary_10_1002_we_2664 crossref_primary_10_1002_er_5336 crossref_primary_10_1016_j_asoc_2016_10_022 crossref_primary_10_1016_j_apenergy_2023_120925 crossref_primary_10_1007_s10668_024_05278_x crossref_primary_10_1007_s40565_019_0550_5 crossref_primary_10_1088_1742_6596_2626_1_012054 crossref_primary_10_1016_j_renene_2021_10_032 crossref_primary_10_1016_j_enconman_2016_01_020 crossref_primary_10_1016_j_renene_2013_09_004 crossref_primary_10_1016_j_renene_2020_12_043 crossref_primary_10_1016_j_ijepes_2019_04_003 crossref_primary_10_1016_j_renene_2023_01_016 crossref_primary_10_1016_j_jweia_2014_03_012 crossref_primary_10_1016_j_renene_2017_02_036 crossref_primary_10_1002_we_1840 crossref_primary_10_1016_j_renene_2023_119240 crossref_primary_10_3390_en13040865 crossref_primary_10_1016_j_apenergy_2018_06_027 crossref_primary_10_1016_j_jweia_2015_07_009 crossref_primary_10_1049_iet_rpg_2015_0340 crossref_primary_10_1016_j_renene_2025_123730 crossref_primary_10_1016_j_enconman_2016_08_080 crossref_primary_10_1016_j_ifacol_2018_09_323 crossref_primary_10_3390_en16031471 crossref_primary_10_1016_j_energy_2024_132443 crossref_primary_10_1038_s41467_023_37536_3 crossref_primary_10_1080_09544828_2024_2347820 crossref_primary_10_1080_0305215X_2018_1509962 crossref_primary_10_1016_j_apenergy_2014_02_070 crossref_primary_10_1109_ACCESS_2020_3009046 crossref_primary_10_1109_TSTE_2015_2415037 crossref_primary_10_1016_j_energy_2016_07_062 crossref_primary_10_1016_j_rser_2014_07_120 crossref_primary_10_3390_app9040639 crossref_primary_10_1016_j_apenergy_2018_07_076 crossref_primary_10_1016_j_jweia_2021_104548 crossref_primary_10_1016_j_seta_2016_10_004 crossref_primary_10_1016_j_jweia_2015_01_018 crossref_primary_10_1063_5_0207111 crossref_primary_10_1109_TIA_2017_2737399 crossref_primary_10_1016_j_esr_2022_101016 crossref_primary_10_1016_j_apenergy_2017_05_071 crossref_primary_10_1007_s11831_019_09316_0 crossref_primary_10_1016_j_apenergy_2019_114426 crossref_primary_10_1016_j_apenergy_2016_06_085 crossref_primary_10_1002_we_2116 crossref_primary_10_1016_j_epsr_2022_107877 crossref_primary_10_1016_j_renene_2023_119785 crossref_primary_10_1002_we_2199 crossref_primary_10_1016_j_oceaneng_2022_112959 crossref_primary_10_1007_s40095_019_00332_1 crossref_primary_10_1016_j_energy_2024_132159 crossref_primary_10_1109_TII_2022_3217282 crossref_primary_10_3390_app14177916 crossref_primary_10_1007_s42452_019_1611_0 crossref_primary_10_1016_j_enconman_2014_11_005 crossref_primary_10_1109_TSTE_2014_2369432 crossref_primary_10_1016_j_renene_2025_123429 crossref_primary_10_1016_j_compchemeng_2025_109346 crossref_primary_10_1109_ACCESS_2019_2944888 crossref_primary_10_1016_j_apenergy_2017_08_126 crossref_primary_10_1049_iet_rpg_2015_0052 crossref_primary_10_3390_app8122660 crossref_primary_10_1007_s11356_023_27849_7 crossref_primary_10_1051_e3sconf_202233600013 crossref_primary_10_1016_j_apenergy_2017_09_064 crossref_primary_10_1016_j_rser_2015_03_066 crossref_primary_10_1016_j_renene_2017_07_057 crossref_primary_10_1109_TSTE_2016_2614266 crossref_primary_10_1016_j_apenergy_2024_125165 crossref_primary_10_1016_j_apor_2025_104537 crossref_primary_10_3390_ijgi13110409 crossref_primary_10_1016_j_seta_2020_100970 crossref_primary_10_1109_TSTE_2019_2932409 crossref_primary_10_1088_1742_6596_2767_3_032036 crossref_primary_10_3390_app142311331 crossref_primary_10_1016_j_energy_2014_02_060 crossref_primary_10_1016_j_apenergy_2016_06_101 crossref_primary_10_1088_1742_6596_753_9_092005 crossref_primary_10_1016_j_apenergy_2017_10_120 crossref_primary_10_1016_j_energy_2020_117899 crossref_primary_10_1109_ACCESS_2017_2657638 crossref_primary_10_1007_s11831_017_9222_7 crossref_primary_10_1016_j_renene_2025_123775 crossref_primary_10_1016_j_oceaneng_2022_112807 crossref_primary_10_1177_0309524X231165484 crossref_primary_10_1016_j_renene_2020_05_003 crossref_primary_10_1109_TSTE_2015_2429912 crossref_primary_10_1016_j_renene_2018_09_085 crossref_primary_10_1080_0305215X_2017_1316844 crossref_primary_10_1016_j_enconman_2016_07_017 crossref_primary_10_1515_cait_2017_0007 crossref_primary_10_1057_s41274_016_0021_6 crossref_primary_10_1016_j_oceaneng_2022_110859 crossref_primary_10_1016_j_apenergy_2016_11_083
Cites_doi	10.1016/j.renene.2011.06.033 10.1016/j.renene.2011.04.018 10.1016/j.epsr.2003.12.006 10.1016/j.renene.2004.05.007 10.1016/j.renene.2009.08.019 10.1007/BF01582221 10.1016/0167-6105(94)90080-9 10.1145/235815.235821 10.1260/030952408784305877 10.1016/j.renene.2010.01.010 10.1137/0806023 10.2172/947422 10.1016/j.renene.2011.12.013
ContentType	Journal Article
Copyright	2014 INIST-CNRS
Copyright_xml	– notice: 2014 INIST-CNRS
DBID	FBQ AAYXX CITATION IQODW 7ST 7TN 7U6 C1K F1W H96 L.G SOI 7SU 7TB 8FD FR3 H8D KR7 L7M 7S9 L.6
DOI	10.1016/j.renene.2012.12.007
DatabaseName	AGRIS CrossRef Pascal-Francis Environment Abstracts Oceanic Abstracts Sustainability Science Abstracts Environmental Sciences and Pollution Management ASFA: Aquatic Sciences and Fisheries Abstracts Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources Aquatic Science & Fisheries Abstracts (ASFA) Professional Environment Abstracts Environmental Engineering Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database Aerospace Database Civil Engineering Abstracts Advanced Technologies Database with Aerospace AGRICOLA AGRICOLA - Academic
DatabaseTitle	CrossRef Aquatic Science & Fisheries Abstracts (ASFA) Professional Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources Oceanic Abstracts Sustainability Science Abstracts ASFA: Aquatic Sciences and Fisheries Abstracts Environment Abstracts Environmental Sciences and Pollution Management Aerospace Database Civil Engineering Abstracts Technology Research Database Mechanical & Transportation Engineering Abstracts Environmental Engineering Abstracts Engineering Research Database Advanced Technologies Database with Aerospace AGRICOLA AGRICOLA - Academic
DatabaseTitleList	AGRICOLA Aquatic Science & Fisheries Abstracts (ASFA) Professional Aerospace Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Applied Sciences
EISSN	1879-0682
EndPage	399
ExternalDocumentID	26925801 10_1016_j_renene_2012_12_007 US201500170718
GeographicLocations	North Sea Germany ANE, North Sea ANE, Germany
GeographicLocations_xml	– name: North Sea – name: Germany – name: ANE, North Sea – name: ANE, Germany
GroupedDBID	--K --M .~1 0R~ 123 1B1 1RT 1~. 1~5 29P 4.4 457 4G. 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAHCO AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARJD AAXUO ABFNM ABMAC ABPIF ABPTK ABXDB ABYKQ ACDAQ ACGFS ACNNM ACRLP ADBBV ADEZE ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHIDL AHJVU AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ASPBG AVWKF AXJTR AZFZN BELTK BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EJD EO8 EO9 EP2 EP3 FBQ FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HMC HVGLF HZ~ IHE J1W JARJE JJJVA K-O KOM LY6 LY9 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SAC SDF SDG SDP SEN SES SET SEW SPC SPCBC SSR SST SSZ T5K TN5 WUQ ZCA ~02 ~G- 9DU AAHBH AATTM AAXKI AAYWO AAYXX ABJNI ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEGFY AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS EFLBG ~HD BNPGV IQODW SSH 7ST 7TN 7U6 C1K F1W H96 L.G SOI 7SU 7TB 8FD FR3 H8D KR7 L7M 7S9 L.6
ID	FETCH-LOGICAL-c404t-2cfb6837575e3034bd9412e6bf946c271689fbe678458e356b7694a62ad2738e3
ISICitedReferencesCount	142
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000315539000046&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0960-1481
IngestDate	Thu Oct 02 06:09:00 EDT 2025 Tue Oct 07 09:49:42 EDT 2025 Tue Oct 07 11:13:41 EDT 2025 Wed Apr 02 07:25:20 EDT 2025 Sat Nov 29 06:21:07 EST 2025 Tue Nov 18 21:49:54 EST 2025 Wed Dec 27 19:18:03 EST 2023
IsPeerReviewed	true
IsScholarly	true
Keywords	Offshore Windfarm Renewable energy Heuristic optimization Layout optimization Wake effect Offshore wind farm Wake Optimization Mathematical programming
Language	English
License	https://www.elsevier.com/tdm/userlicense/1.0 CC BY 4.0
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-c404t-2cfb6837575e3034bd9412e6bf946c271689fbe678458e356b7694a62ad2738e3
Notes	http://dx.doi.org/10.1016/j.renene.2012.12.007 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
PQID	1500796371
PQPubID	23462
PageCount	11
ParticipantIDs	proquest_miscellaneous_1686724922 proquest_miscellaneous_1642291296 proquest_miscellaneous_1500796371 pascalfrancis_primary_26925801 crossref_citationtrail_10_1016_j_renene_2012_12_007 crossref_primary_10_1016_j_renene_2012_12_007 fao_agris_US201500170718
PublicationCentury	2000
PublicationDate	2013-05-01
PublicationDateYYYYMMDD	2013-05-01
PublicationDate_xml	– month: 05 year: 2013 text: 2013-05-01 day: 01
PublicationDecade	2010
PublicationPlace	Oxford
PublicationPlace_xml	– name: Oxford
PublicationTitle	Renewable energy
PublicationYear	2013
Publisher	Elsevier Ltd Elsevier
Publisher_xml	– name: Elsevier Ltd – name: Elsevier
References	(10.1016/j.renene.2012.12.007_bib37) 2011 Eroğlu (10.1016/j.renene.2012.12.007_bib14) 2012; 44 Saavedra-Moreno (10.1016/j.renene.2012.12.007_bib13) 2011; 36 Drud (10.1016/j.renene.2012.12.007_bib31) 1996 Mosetti (10.1016/j.renene.2012.12.007_bib2) 1994; 51 Mínguez (10.1016/j.renene.2012.12.007_bib7) 2011 Menéndez (10.1016/j.renene.2012.12.007_bib28) 2011 Espejo (10.1016/j.renene.2012.12.007_bib1) 2011 Elkinton (10.1016/j.renene.2012.12.007_bib5) 2006 Coleman (10.1016/j.renene.2012.12.007_bib33) 1994; 67 Ozturk (10.1016/j.renene.2012.12.007_bib3) 2004; 70 Murtagh (10.1016/j.renene.2012.12.007_bib30) 1998 Bazaraa (10.1016/j.renene.2012.12.007_bib16) 1993 Coleman (10.1016/j.renene.2012.12.007_bib34) 1996; 6 (10.1016/j.renene.2012.12.007_bib19) 2011 Donovan (10.1016/j.renene.2012.12.007_bib8) 2005 Elkinton (10.1016/j.renene.2012.12.007_bib11) 2008; 32 10.1016/j.renene.2012.12.007_bib18 10.1016/j.renene.2012.12.007_bib17 Vanderplaats (10.1016/j.renene.2012.12.007_bib15) 1984 10.1016/j.renene.2012.12.007_bib38 (10.1016/j.renene.2012.12.007_bib21) 2011 Kusiak (10.1016/j.renene.2012.12.007_bib9) 2010; 35 Castillo (10.1016/j.renene.2012.12.007_bib26) 2001 Conejo (10.1016/j.renene.2012.12.007_bib27) 2006 Serrano (10.1016/j.renene.2012.12.007_bib12) 2010; 35 Rademakers (10.1016/j.renene.2012.12.007_bib6) 2003 Barber (10.1016/j.renene.2012.12.007_bib36) 1996; 22 G. Hassan, P. Ltd., Gh (10.1016/j.renene.2012.12.007_bib25) 2004 10.1016/j.renene.2012.12.007_bib20 Katic (10.1016/j.renene.2012.12.007_bib24) 1986 Brooke (10.1016/j.renene.2012.12.007_bib32) 1998 10.1016/j.renene.2012.12.007_bib23 Grady (10.1016/j.renene.2012.12.007_bib4) 2005; 30 10.1016/j.renene.2012.12.007_bib22 Chowdhury (10.1016/j.renene.2012.12.007_bib10) 2012; 38 10.1016/j.renene.2012.12.007_bib29 Byrd (10.1016/j.renene.2012.12.007_bib35) 2006
References_xml	– volume: 38 start-page: 16 year: 2012 ident: 10.1016/j.renene.2012.12.007_bib10 article-title: Unrestricted wind farm layout optimization (UWFLO): Investigating key factors influencing the maximum power generation publication-title: Renewable Energy doi: 10.1016/j.renene.2011.06.033 – year: 2011 ident: 10.1016/j.renene.2012.12.007_bib37 – volume: 36 start-page: 2338 year: 2011 ident: 10.1016/j.renene.2012.12.007_bib13 article-title: Seeding evolutionary algorithms with heuristics for optimal wind turbines positioning in wind farms publication-title: Renewable Energy doi: 10.1016/j.renene.2011.04.018 – year: 2004 ident: 10.1016/j.renene.2012.12.007_bib25 – volume: 70 start-page: 179 issue: 3 year: 2004 ident: 10.1016/j.renene.2012.12.007_bib3 article-title: Heuristic methods for wind energy conversion system positioning publication-title: Electric Power Systems Research doi: 10.1016/j.epsr.2003.12.006 – volume: 30 start-page: 259 issue: 2 year: 2005 ident: 10.1016/j.renene.2012.12.007_bib4 article-title: Placement of wind turbines using genetic algorithms publication-title: Renewable Energy doi: 10.1016/j.renene.2004.05.007 – year: 2006 ident: 10.1016/j.renene.2012.12.007_bib5 – volume: 35 start-page: 685 issue: 3 year: 2010 ident: 10.1016/j.renene.2012.12.007_bib9 article-title: Design of wind farm layout for maximum wind energy capture publication-title: Renewable Energy doi: 10.1016/j.renene.2009.08.019 – year: 1986 ident: 10.1016/j.renene.2012.12.007_bib24 – ident: 10.1016/j.renene.2012.12.007_bib18 – year: 2005 ident: 10.1016/j.renene.2012.12.007_bib8 – year: 1993 ident: 10.1016/j.renene.2012.12.007_bib16 – year: 1996 ident: 10.1016/j.renene.2012.12.007_bib31 – volume: 67 start-page: 189 issue: 2 year: 1994 ident: 10.1016/j.renene.2012.12.007_bib33 article-title: On the convergence of reflective Newton methods for large-scale nonlinear minimization subject to bounds publication-title: Mathematical Programming doi: 10.1007/BF01582221 – year: 2006 ident: 10.1016/j.renene.2012.12.007_bib27 – year: 2003 ident: 10.1016/j.renene.2012.12.007_bib6 article-title: Assessment and optimization of operation and maintenance of offshore wind turbines – year: 1984 ident: 10.1016/j.renene.2012.12.007_bib15 – volume: 51 start-page: 105 issue: 1 year: 1994 ident: 10.1016/j.renene.2012.12.007_bib2 article-title: Optimization of wind turbine positioning in large wind farms by means of a genetic algorithm publication-title: Journal of Wind Engineering and Industrial Aerodynamics doi: 10.1016/0167-6105(94)90080-9 – ident: 10.1016/j.renene.2012.12.007_bib23 – ident: 10.1016/j.renene.2012.12.007_bib29 – volume: 22 start-page: 469 issue: 4 year: 1996 ident: 10.1016/j.renene.2012.12.007_bib36 article-title: The quickhull algorithm for convex hulls publication-title: ACM Transactions on Mathematical Software doi: 10.1145/235815.235821 – volume: 32 start-page: 67 issue: 1 year: 2008 ident: 10.1016/j.renene.2012.12.007_bib11 article-title: Algorithms for offshore wind farm layout optimization publication-title: Wind Engineering doi: 10.1260/030952408784305877 – volume: 35 start-page: 1671 year: 2010 ident: 10.1016/j.renene.2012.12.007_bib12 article-title: Optimization of wind farm turbines layout using an evolutive algorithm publication-title: Renewable Energy doi: 10.1016/j.renene.2010.01.010 – start-page: 1 year: 2011 ident: 10.1016/j.renene.2012.12.007_bib28 article-title: A methodology to evaluate regional-scale offshore wind energy resources – year: 2011 ident: 10.1016/j.renene.2012.12.007_bib19 – ident: 10.1016/j.renene.2012.12.007_bib17 – volume: 6 start-page: 418 year: 1996 ident: 10.1016/j.renene.2012.12.007_bib34 article-title: An interior, trust region approach for nonlinear minimization subject to bounds publication-title: SIAM Journal on Optimization doi: 10.1137/0806023 – year: 1998 ident: 10.1016/j.renene.2012.12.007_bib30 – ident: 10.1016/j.renene.2012.12.007_bib38 doi: 10.2172/947422 – volume: 44 start-page: 53 year: 2012 ident: 10.1016/j.renene.2012.12.007_bib14 article-title: Design of wind farm layout using ant colony algorithm publication-title: Renewable Energy doi: 10.1016/j.renene.2011.12.013 – start-page: 1 year: 2011 ident: 10.1016/j.renene.2012.12.007_bib7 article-title: Component failure simulation tool for optimal electrical configuration and repair strategy of off-shore wind farms – year: 1998 ident: 10.1016/j.renene.2012.12.007_bib32 – ident: 10.1016/j.renene.2012.12.007_bib20 – year: 2011 ident: 10.1016/j.renene.2012.12.007_bib21 – ident: 10.1016/j.renene.2012.12.007_bib22 – year: 2001 ident: 10.1016/j.renene.2012.12.007_bib26 – start-page: 35 year: 2006 ident: 10.1016/j.renene.2012.12.007_bib35 article-title: KNITRO: an integrated package for nonlinear optimization – start-page: 1 year: 2011 ident: 10.1016/j.renene.2012.12.007_bib1 article-title: Directional calibrated wind and wave reanalysis databases using instrumental data for optimal design of off-shore wind farms
SSID	ssj0015874
Score	2.4764352
Snippet	Offshore wind power is a renewable energy of growing relevance in current electric energy systems, presenting favorable wind conditions in comparison with the...
SourceID	proquest pascalfrancis crossref fao
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	389
SubjectTerms	Applied sciences Electric power generation electricity Energy Exact sciences and technology Germany Mathematical analysis Mathematical programming methodology Natural energy North Sea Offshore Offshore engineering Offshore structures Optimization prices profitability wind wind farms Wind power
Title	Offshore wind farm layout optimization using mathematical programming techniques
URI	https://www.proquest.com/docview/1500796371 https://www.proquest.com/docview/1642291296 https://www.proquest.com/docview/1686724922
Volume	53
WOSCitedRecordID	wos000315539000046&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1879-0682 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0015874 issn: 0960-1481 databaseCode: AIEXJ dateStart: 19950201 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db9MwELfYxgM8ID618jEZibcqKHUcfzx2MAQIjWlsUt8iJ7E3pjaZkoYN_nrOdpKmVAx4QKqiKnGcyHe5L9_9DqFXGRNKyYgFjKk0oJrmQaoZDUKeTrgyscw9iOsnfngoZjN51G4X1K6dAC8KcX0tL_8rqeEcENuWzv4DuftJ4QT8B6LDEcgOx78i_Gdj6vOy0uMrcLfHRlWL8Vx9t-nHJYiHRVt3OW5ckGDRo7baiiyfq7VwFVQdtms9NF-PQTJeuWIr7WoGe8Hq99srH47e1xb3_0dPS3fxbXHW-Ms-mbvsM38aYLxzF1c9Vnbo_nQ-jEVMBpl_PkDWFcmsMpJcpJGFAXhdfpz2clZwC34g1gRxHA0kaeQ7C7VKOfJdlDbkvQ89XLy28J-FRT2dEBfd9Z10f0HSPv1CbHzHIgaBRt5CO4THEoT5zvTDwexjv_cUC4_d3b12V3DpsgI3H7Rm0GwZVdr0WlUD2YxvjbKh5Z3pcnIf3Wt9Djz1vPIA3dLFQ3R3gET5CB11XIMt12DLNdhzDR5yDXZcg4dcgwdcg1dc8xidvjs4efM-aJttBBkN6TIgmUmZiDiY7xrMGprmkk6IZqmRlGUE3GohDXzGXNBY6ChmKWeSKkZUbqu7dPQEbRdloXcRZjTLQ7hbhxk4DErAQmYpKFXKeaSJNiMUdSuWZC0SvW2IMk-6lMOLxK9zYtc5gR-s8wgF_V2XHonlD-N3gRiJOgNlmayTfoT21ijUz0eYJDHYbCP0siNZAuLW7qGpQpdNndhJOCgtftMY8OmJBEOa3TRGMG7ROsnT37_nM3Rn9ZU9R9vLqtEv0O3s2_JrXe21fPsTe3GyFg
linkProvider	Elsevier
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Offshore+wind+farm+layout+optimization+using+mathematical+programming+techniques&rft.jtitle=Renewable+energy&rft.au=P%C3%A9rez%2C+Beatriz&rft.au=M%C3%ADnguez%2C+Roberto&rft.au=Guanche%2C+Ra%C3%BAl&rft.date=2013-05-01&rft.pub=Elsevier+Ltd&rft.issn=0960-1481&rft.eissn=1879-0682&rft.volume=53&rft.spage=389&rft.epage=399&rft_id=info:doi/10.1016%2Fj.renene.2012.12.007&rft.externalDocID=US201500170718
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0960-1481&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0960-1481&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0960-1481&client=summon