A wake-induced two-phase planning framework for offshore wind farm maintenance with stochastic mixed-integer program
Offshore wind farms are essential in fulfilling global renewable energy targets; yet, their maintenance poses substantial challenges due to remote marine locations and harsh environmental conditions. Effective maintenance strategies and implementation are crucial to enhance operational efficiency an...
Uloženo v:
| Vydáno v: | Applied energy Ročník 380; s. 124976 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
15.02.2025
|
| Témata: | |
| ISSN: | 0306-2619 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Offshore wind farms are essential in fulfilling global renewable energy targets; yet, their maintenance poses substantial challenges due to remote marine locations and harsh environmental conditions. Effective maintenance strategies and implementation are crucial to enhance operational efficiency and reduce costs. Current models frequently overlook an integrated perspective on periodic factors and uncertainties inherent in metocean conditions and failure rates, leading to suboptimal planning and increased costs. This study bridges this gap by introducing a comprehensive maintenance planning framework that incorporates these uncertainties. We formulate an annual planning model as a stochastic mixed-integer linear programming problem. The annual planning process aims to minimize operations and maintenance costs, including losses from downtime, by employing wake model constraints and accounting for stochastic scenarios. By tackling the scheme challenge, we garner strategic allocations of maintenance resources, which specifies the requisite number of operational vehicles and teams to be deployed over the year. Tentatively, we establish a long-term strategy and devise a short-term program that encompasses failure parameters and weather-related conditions. To further refine planning, we address weekly short-term scheduling problems to elaborate detailed maintenance schedules. Each week, maintenance tasks are adjusted based on actual stochastic conditions, yielding precise, real-world schedules. Our weekly scheduling considers not only preventive and corrective maintenance but also opportunistic maintenance. In particular, we harness a flexible scheduling approach to accommodate the efficiency of maintenance vessels. Computational tests demonstrate that our framework remarkably reduces downtime losses by 19.0% and recovery delays by 38.2%, leveraging scheduling flexibility.
•We address mathematical optimization for maintenance planning in offshore wind farms.•Our framework reduces downtime losses (19%) and recovery delays (38.2%).•Unified model incorporates long-term resource planning and short-term scheduling.•Annual planning with weather and failure scenarios enables robust wake modeling.•Weekly scheduling with task-focused flexibility enhances maintenance efficiency.
[Display omitted] |
|---|---|
| AbstractList | Offshore wind farms are essential in fulfilling global renewable energy targets; yet, their maintenance poses substantial challenges due to remote marine locations and harsh environmental conditions. Effective maintenance strategies and implementation are crucial to enhance operational efficiency and reduce costs. Current models frequently overlook an integrated perspective on periodic factors and uncertainties inherent in metocean conditions and failure rates, leading to suboptimal planning and increased costs. This study bridges this gap by introducing a comprehensive maintenance planning framework that incorporates these uncertainties. We formulate an annual planning model as a stochastic mixed-integer linear programming problem. The annual planning process aims to minimize operations and maintenance costs, including losses from downtime, by employing wake model constraints and accounting for stochastic scenarios. By tackling the scheme challenge, we garner strategic allocations of maintenance resources, which specifies the requisite number of operational vehicles and teams to be deployed over the year. Tentatively, we establish a long-term strategy and devise a short-term program that encompasses failure parameters and weather-related conditions. To further refine planning, we address weekly short-term scheduling problems to elaborate detailed maintenance schedules. Each week, maintenance tasks are adjusted based on actual stochastic conditions, yielding precise, real-world schedules. Our weekly scheduling considers not only preventive and corrective maintenance but also opportunistic maintenance. In particular, we harness a flexible scheduling approach to accommodate the efficiency of maintenance vessels. Computational tests demonstrate that our framework remarkably reduces downtime losses by 19.0% and recovery delays by 38.2%, leveraging scheduling flexibility.
•We address mathematical optimization for maintenance planning in offshore wind farms.•Our framework reduces downtime losses (19%) and recovery delays (38.2%).•Unified model incorporates long-term resource planning and short-term scheduling.•Annual planning with weather and failure scenarios enables robust wake modeling.•Weekly scheduling with task-focused flexibility enhances maintenance efficiency.
[Display omitted] |
| ArticleNumber | 124976 |
| Author | Lee, Namkyoung Joung, Seulgi Lee, Hyuntae |
| Author_xml | – sequence: 1 givenname: Namkyoung orcidid: 0009-0008-6035-3031 surname: Lee fullname: Lee, Namkyoung organization: Power Technology Research Institute, KEPCO Engineering & Construction Company. INC, 269, Hyeoksin-ro, Gimcheon-si, 39660, Gyeongsangbuk-do, Republic of Korea – sequence: 2 givenname: Hyuntae surname: Lee fullname: Lee, Hyuntae organization: Department of Industrial Engineering, Chonnam National University, 77, Yongbong-ro, Buk-gu, 61186, Gwangju, Republic of Korea – sequence: 3 givenname: Seulgi orcidid: 0000-0001-7608-2266 surname: Joung fullname: Joung, Seulgi email: sgjoung@ajou.ac.kr organization: Department of Industrial Engineering, Ajou University, 206, Worldcup-ro, Yeongtong-gu, 16499, Suwon, Republic of Korea |
| BookMark | eNqFkMtOwzAQRb0oEm3hF5B_IMGPNI8dVcVLqsQG1pZrj1O3jR3ZhtC_J1FhzWqkO7pHM2eBZs47QOiOkpwSWt4fctmDg9Cec0ZYkVNWNFU5Q3PCSZmxkjbXaBHjgRDCKCNzlNZ4kEfIrNOfCjROg8_6vYyA-5N0zroWmyA7GHw4YuMD9sbEvQ-Ah7GCjQwd7qR1CZx0akrTHsfk1chIVuHOfoPOpn0LAffBtyPtBl0ZeYpw-zuX6OPp8X3zkm3fnl83622m2KpJGZWsYjXsoFgZIA2rJQHFC82Nami1K7SUTNW0LmShKK84UMMVa9iK0ga43vElKi9cFXyMAYzog-1kOAtKxORLHMSfLzH5EhdfY_HhUoTxui8LQURlYfxP2wAqCe3tf4gfxd9-aw |
| Cites_doi | 10.1007/s43253-022-00082-7 10.3390/jmse5010011 10.1016/j.apenergy.2021.117189 10.1002/we.2815 10.1002/we.348 10.1260/0309-524X.39.1.15 10.1016/j.oceaneng.2023.114041 10.1016/j.ymssp.2019.02.012 10.1016/j.trc.2015.01.005 10.1016/j.epsr.2020.106298 10.1016/j.renene.2020.06.030 10.1016/j.apenergy.2022.119284 10.1016/j.rser.2015.12.229 10.1029/98JC02622 10.1016/j.renene.2020.07.065 10.1016/j.ymssp.2017.10.035 10.1016/j.marpol.2020.104134 10.1016/j.apenergy.2024.124431 10.1016/j.renene.2015.11.022 10.1016/j.renene.2016.07.037 10.1016/j.ejor.2019.04.020 10.1016/j.energy.2017.02.174 10.1016/j.apenergy.2022.119429 10.1002/we.1887 10.1016/j.rser.2021.110886 10.1016/j.energy.2019.07.019 |
| ContentType | Journal Article |
| Copyright | 2024 Elsevier Ltd |
| Copyright_xml | – notice: 2024 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.apenergy.2024.124976 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Environmental Sciences |
| ExternalDocumentID | 10_1016_j_apenergy_2024_124976 S0306261924023602 |
| GroupedDBID | --K --M .~1 0R~ 1B1 1~. 1~5 23M 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAHBH AAHCO AAIKJ AAKOC AALRI AAOAW AAQFI AARJD AAXKI AAXUO ABJNI ABMAC ACDAQ ACGFS ACRLP ADBBV ADEZE ADTZH AEBSH AECPX AEKER AENEX AFJKZ AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHIDL AHJVU AIEXJ AIKHN AITUG AJOXV AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AXJTR BELTK BJAXD BKOJK BLXMC CS3 EBS EFJIC EO8 EO9 EP2 EP3 FDB FIRID FNPLU FYGXN G-Q GBLVA IHE J1W JARJE JJJVA KOM LY6 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 ROL RPZ SDF SDG SES SEW SPC SPCBC SSR SST SSZ T5K TN5 ~02 ~G- 9DU AAQXK AATTM AAYWO AAYXX ABEFU ABFNM ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADMUD ADNMO AEIPS AEUPX AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EFLBG EJD FEDTE FGOYB G-2 HVGLF HZ~ R2- SAC WUQ ZY4 ~HD |
| ID | FETCH-LOGICAL-c259t-1a2728ebe45fe0928a0ec34d3fc917b4daa2c8184a4c1373e1f3c2925119e3db3 |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001375081200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0306-2619 |
| IngestDate | Sat Nov 29 06:10:05 EST 2025 Sat Jan 11 15:48:57 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Mixed-integer program Stochastic program Maintenance scheduling Offshore wind farm Wake effect |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c259t-1a2728ebe45fe0928a0ec34d3fc917b4daa2c8184a4c1373e1f3c2925119e3db3 |
| ORCID | 0000-0001-7608-2266 0009-0008-6035-3031 |
| ParticipantIDs | crossref_primary_10_1016_j_apenergy_2024_124976 elsevier_sciencedirect_doi_10_1016_j_apenergy_2024_124976 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-02-15 |
| PublicationDateYYYYMMDD | 2025-02-15 |
| PublicationDate_xml | – month: 02 year: 2025 text: 2025-02-15 day: 15 |
| PublicationDecade | 2020 |
| PublicationTitle | Applied energy |
| PublicationYear | 2025 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Raknes, Ødeskaug, Stålhane, Hvattum (b8) 2017; 5 Zhang, Chowdhury, Zhang, Tong, Messac (b9) 2023; 26 Cui, Geng, Zhu, Han (b33) 2017; 125 Tusar, Sarker (b6) 2023; 274 Johnston, Foley, Doran, Littler (b3) 2020; 160 Ehler (b1) 2021; 132 Dai, Stålhane, Utne (b7) 2015; 39 Shields, Beiter, Nunemaker, Cooperman, Duffy (b2) 2021; 298 Carroll, McDonald, McMillan (b16) 2016; 19 Ren, Verma, Li, Teuwen, Jiang (b28) 2021; 144 Shakoor, Hassan, Raheem, Wu (b29) 2016; 58 Zhong, Pantelous, Goh, Zhou (b30) 2019; 124 Zhong, Pantelous, Beer, Zhou (b11) 2018; 104 Lee, Woo, Kim (b18) 2025; 377 Maples, Saur, Hand, Van De Pietermen, Obdam (b25) 2013 Liu, Qin, Lu, Zhang, Zhu, Faber (b4) 2022; 322 Katic, Højstrup, Jensen (b21) 1986; Vol. 1 Yang, Kwak, Cho, Huh (b22) 2019; 183 Gundegjerde, Halvorsen, Halvorsen-Weare, Hvattum, Nonås (b13) 2015; 52 Barthelmie, Hansen, Frandsen, Rathmann, Schepers, Schlez, Phillips, Rados, Zervos, Politis (b17) 2009; 12 Jensen (b20) 1983 Miettinen (b32) 1999 Gutierrez-Alcoba, Hendrix, Ortega, Halvorsen-Weare, Haugland (b14) 2019; 279 Booij, Ris, Holthuijsen (b27) 1999; 104 Li, Ouelhadj, Song, Jones, Wall, Howell, Igwe, Martin, Song, Pertin (b5) 2016; 99 Wolsey, Nemhauser (b23) 1999 Mathews, Thurbon, Kim, Tan (b26) 2023; 4 Li, Jiang, Carroll, Negenborn (b15) 2022; 321 Zhou, Miao, Yan, Zhang (b12) 2020; 160 Ge, Chen, Fu, Chung, Mi (b10) 2020; 184 Bak, Zahle, Bitsche, Kim, Yde, Henriksen, Natarajan, Hansen (b19) 2013 Gurobi Optimization (b24) 2023 Abdollahzadeh, Atashgar, Abbasi (b31) 2016; 88 Zhou (10.1016/j.apenergy.2024.124976_b12) 2020; 160 Mathews (10.1016/j.apenergy.2024.124976_b26) 2023; 4 Bak (10.1016/j.apenergy.2024.124976_b19) 2013 Jensen (10.1016/j.apenergy.2024.124976_b20) 1983 Raknes (10.1016/j.apenergy.2024.124976_b8) 2017; 5 Booij (10.1016/j.apenergy.2024.124976_b27) 1999; 104 Johnston (10.1016/j.apenergy.2024.124976_b3) 2020; 160 Abdollahzadeh (10.1016/j.apenergy.2024.124976_b31) 2016; 88 Liu (10.1016/j.apenergy.2024.124976_b4) 2022; 322 Barthelmie (10.1016/j.apenergy.2024.124976_b17) 2009; 12 Maples (10.1016/j.apenergy.2024.124976_b25) 2013 Lee (10.1016/j.apenergy.2024.124976_b18) 2025; 377 Yang (10.1016/j.apenergy.2024.124976_b22) 2019; 183 Shields (10.1016/j.apenergy.2024.124976_b2) 2021; 298 Carroll (10.1016/j.apenergy.2024.124976_b16) 2016; 19 Ren (10.1016/j.apenergy.2024.124976_b28) 2021; 144 Li (10.1016/j.apenergy.2024.124976_b15) 2022; 321 Zhong (10.1016/j.apenergy.2024.124976_b30) 2019; 124 Li (10.1016/j.apenergy.2024.124976_b5) 2016; 99 Gurobi Optimization (10.1016/j.apenergy.2024.124976_b24) 2023 Ge (10.1016/j.apenergy.2024.124976_b10) 2020; 184 Tusar (10.1016/j.apenergy.2024.124976_b6) 2023; 274 Gundegjerde (10.1016/j.apenergy.2024.124976_b13) 2015; 52 Zhang (10.1016/j.apenergy.2024.124976_b9) 2023; 26 Ehler (10.1016/j.apenergy.2024.124976_b1) 2021; 132 Katic (10.1016/j.apenergy.2024.124976_b21) 1986; Vol. 1 Dai (10.1016/j.apenergy.2024.124976_b7) 2015; 39 Shakoor (10.1016/j.apenergy.2024.124976_b29) 2016; 58 Miettinen (10.1016/j.apenergy.2024.124976_b32) 1999 Cui (10.1016/j.apenergy.2024.124976_b33) 2017; 125 Gutierrez-Alcoba (10.1016/j.apenergy.2024.124976_b14) 2019; 279 Zhong (10.1016/j.apenergy.2024.124976_b11) 2018; 104 Wolsey (10.1016/j.apenergy.2024.124976_b23) 1999 |
| References_xml | – volume: 184 year: 2020 ident: b10 article-title: Optimization of maintenance scheduling for offshore wind turbines considering the wake effect of arbitrary wind direction publication-title: Electr Power Syst Res – volume: Vol. 1 start-page: 407 year: 1986 end-page: 410 ident: b21 article-title: A simple model for cluster efficiency publication-title: European wind energy association conference and exhibition – year: 2023 ident: b24 article-title: Gurobi optimizer reference manual – year: 1999 ident: b23 publication-title: Integer and combinatorial optimization – volume: 58 start-page: 1048 year: 2016 end-page: 1059 ident: b29 article-title: Wake effect modeling: A review of wind farm layout optimization using Jensen’s model publication-title: Renew Sustain Energy Rev – volume: 104 start-page: 7649 year: 1999 end-page: 7666 ident: b27 article-title: A third-generation wave model for coastal regions 1. Model description and validation publication-title: J Geophys Res – volume: 274 year: 2023 ident: b6 article-title: Developing the optimal vessel fleet size and mix model to minimize the transportation cost of offshore wind farms publication-title: Ocean Eng – volume: 26 start-page: 1103 year: 2023 end-page: 1122 ident: b9 article-title: Optimal selection of time windows for preventive maintenance of offshore wind farms subject to wake losses publication-title: Wind Energy – volume: 322 year: 2022 ident: b4 article-title: Towards resilience of offshore wind farms: A framework and application to asset integrity management publication-title: Appl Energy – volume: 39 start-page: 15 year: 2015 end-page: 30 ident: b7 article-title: Routing and scheduling of maintenance fleet for offshore wind farms publication-title: Wind Eng – volume: 321 year: 2022 ident: b15 article-title: A multi-objective maintenance strategy optimization framework for offshore wind farms considering uncertainty publication-title: Appl Energy – volume: 279 start-page: 124 year: 2019 end-page: 131 ident: b14 article-title: On offshore wind farm maintenance scheduling for decision support on vessel fleet composition publication-title: European J Oper Res – volume: 125 start-page: 681 year: 2017 end-page: 704 ident: b33 article-title: Multi-objective optimization methods and application in energy saving publication-title: Energy – volume: 4 start-page: 27 year: 2023 end-page: 48 ident: b26 article-title: Gone with the wind: how state power and industrial policy in the offshore wind power sector are blowing away the obstacles to east Asia’s green energy transition publication-title: Rev Evol Political Econ – volume: 144 year: 2021 ident: b28 article-title: Offshore wind turbine operations and maintenance: A state-of-the-art review publication-title: Renew Sustain Energy Rev – volume: 19 start-page: 1107 year: 2016 end-page: 1119 ident: b16 article-title: Failure rate, repair time and unscheduled O&M cost analysis of offshore wind turbines publication-title: Wind Energy – volume: 183 start-page: 983 year: 2019 end-page: 995 ident: b22 article-title: Wind farm layout optimization for wake effect uniformity publication-title: Energy – year: 2013 ident: b19 article-title: Description of the DTU 10 MW reference wind turbine – volume: 160 start-page: 1136 year: 2020 end-page: 1147 ident: b12 article-title: Bio-objective long-term maintenance scheduling for wind turbines in multiple wind farms publication-title: Renew Energy – volume: 88 start-page: 247 year: 2016 end-page: 261 ident: b31 article-title: Multi-objective opportunistic maintenance optimization of a wind farm considering limited number of maintenance groups publication-title: Renew Energy – volume: 104 start-page: 347 year: 2018 end-page: 369 ident: b11 article-title: Constrained non-linear multi-objective optimisation of preventive maintenance scheduling for offshore wind farms publication-title: Mech Syst Signal Process – volume: 99 start-page: 784 year: 2016 end-page: 799 ident: b5 article-title: A decision support system for strategic maintenance planning in offshore wind farms publication-title: Renew Energy – volume: 52 start-page: 74 year: 2015 end-page: 92 ident: b13 article-title: A stochastic fleet size and mix model for maintenance operations at offshore wind farms publication-title: Transp Res C – year: 1983 ident: b20 publication-title: A note on wind generator interaction – volume: 12 start-page: 431 year: 2009 end-page: 444 ident: b17 article-title: Modelling and measuring flow and wind turbine wakes in large wind farms offshore publication-title: Wind Energy – volume: 377 year: 2025 ident: b18 article-title: A deep reinforcement learning ensemble for maintenance scheduling in offshore wind farms publication-title: Appl Energy – volume: 124 start-page: 643 year: 2019 end-page: 663 ident: b30 article-title: A reliability-and-cost-based fuzzy approach to optimize preventive maintenance scheduling for offshore wind farms publication-title: Mech Syst Signal Process – volume: 5 start-page: 11 year: 2017 ident: b8 article-title: Scheduling of maintenance tasks and routing of a joint vessel fleet for multiple offshore wind farms publication-title: J Mar Sci Eng – year: 2013 ident: b25 article-title: Installation, operation, and maintenance strategies to reduce the cost of offshore wind energy – volume: 160 start-page: 876 year: 2020 end-page: 885 ident: b3 article-title: Levelised cost of energy, a challenge for offshore wind publication-title: Renew Energy – volume: 132 year: 2021 ident: b1 article-title: Two decades of progress in marine spatial planning publication-title: Mar Policy – year: 1999 ident: b32 publication-title: Nonlinear multiobjective optimization – volume: 298 year: 2021 ident: b2 article-title: Impacts of turbine and plant upsizing on the levelized cost of energy for offshore wind publication-title: Appl Energy – volume: 4 start-page: 27 issue: 1 year: 2023 ident: 10.1016/j.apenergy.2024.124976_b26 article-title: Gone with the wind: how state power and industrial policy in the offshore wind power sector are blowing away the obstacles to east Asia’s green energy transition publication-title: Rev Evol Political Econ doi: 10.1007/s43253-022-00082-7 – volume: 5 start-page: 11 issue: 1 year: 2017 ident: 10.1016/j.apenergy.2024.124976_b8 article-title: Scheduling of maintenance tasks and routing of a joint vessel fleet for multiple offshore wind farms publication-title: J Mar Sci Eng doi: 10.3390/jmse5010011 – volume: 298 year: 2021 ident: 10.1016/j.apenergy.2024.124976_b2 article-title: Impacts of turbine and plant upsizing on the levelized cost of energy for offshore wind publication-title: Appl Energy doi: 10.1016/j.apenergy.2021.117189 – volume: 26 start-page: 1103 issue: 11 year: 2023 ident: 10.1016/j.apenergy.2024.124976_b9 article-title: Optimal selection of time windows for preventive maintenance of offshore wind farms subject to wake losses publication-title: Wind Energy doi: 10.1002/we.2815 – volume: 12 start-page: 431 issue: 5 year: 2009 ident: 10.1016/j.apenergy.2024.124976_b17 article-title: Modelling and measuring flow and wind turbine wakes in large wind farms offshore publication-title: Wind Energy doi: 10.1002/we.348 – year: 1999 ident: 10.1016/j.apenergy.2024.124976_b32 – volume: 39 start-page: 15 issue: 1 year: 2015 ident: 10.1016/j.apenergy.2024.124976_b7 article-title: Routing and scheduling of maintenance fleet for offshore wind farms publication-title: Wind Eng doi: 10.1260/0309-524X.39.1.15 – year: 2013 ident: 10.1016/j.apenergy.2024.124976_b25 – volume: 274 year: 2023 ident: 10.1016/j.apenergy.2024.124976_b6 article-title: Developing the optimal vessel fleet size and mix model to minimize the transportation cost of offshore wind farms publication-title: Ocean Eng doi: 10.1016/j.oceaneng.2023.114041 – volume: 124 start-page: 643 year: 2019 ident: 10.1016/j.apenergy.2024.124976_b30 article-title: A reliability-and-cost-based fuzzy approach to optimize preventive maintenance scheduling for offshore wind farms publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2019.02.012 – volume: 52 start-page: 74 year: 2015 ident: 10.1016/j.apenergy.2024.124976_b13 article-title: A stochastic fleet size and mix model for maintenance operations at offshore wind farms publication-title: Transp Res C doi: 10.1016/j.trc.2015.01.005 – volume: 184 year: 2020 ident: 10.1016/j.apenergy.2024.124976_b10 article-title: Optimization of maintenance scheduling for offshore wind turbines considering the wake effect of arbitrary wind direction publication-title: Electr Power Syst Res doi: 10.1016/j.epsr.2020.106298 – volume: 160 start-page: 876 year: 2020 ident: 10.1016/j.apenergy.2024.124976_b3 article-title: Levelised cost of energy, a challenge for offshore wind publication-title: Renew Energy doi: 10.1016/j.renene.2020.06.030 – volume: 321 year: 2022 ident: 10.1016/j.apenergy.2024.124976_b15 article-title: A multi-objective maintenance strategy optimization framework for offshore wind farms considering uncertainty publication-title: Appl Energy doi: 10.1016/j.apenergy.2022.119284 – volume: Vol. 1 start-page: 407 year: 1986 ident: 10.1016/j.apenergy.2024.124976_b21 article-title: A simple model for cluster efficiency – volume: 58 start-page: 1048 year: 2016 ident: 10.1016/j.apenergy.2024.124976_b29 article-title: Wake effect modeling: A review of wind farm layout optimization using Jensen’s model publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2015.12.229 – year: 1983 ident: 10.1016/j.apenergy.2024.124976_b20 – volume: 104 start-page: 7649 issue: C4 year: 1999 ident: 10.1016/j.apenergy.2024.124976_b27 article-title: A third-generation wave model for coastal regions 1. Model description and validation publication-title: J Geophys Res doi: 10.1029/98JC02622 – volume: 160 start-page: 1136 year: 2020 ident: 10.1016/j.apenergy.2024.124976_b12 article-title: Bio-objective long-term maintenance scheduling for wind turbines in multiple wind farms publication-title: Renew Energy doi: 10.1016/j.renene.2020.07.065 – volume: 104 start-page: 347 year: 2018 ident: 10.1016/j.apenergy.2024.124976_b11 article-title: Constrained non-linear multi-objective optimisation of preventive maintenance scheduling for offshore wind farms publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2017.10.035 – volume: 132 year: 2021 ident: 10.1016/j.apenergy.2024.124976_b1 article-title: Two decades of progress in marine spatial planning publication-title: Mar Policy doi: 10.1016/j.marpol.2020.104134 – volume: 377 year: 2025 ident: 10.1016/j.apenergy.2024.124976_b18 article-title: A deep reinforcement learning ensemble for maintenance scheduling in offshore wind farms publication-title: Appl Energy doi: 10.1016/j.apenergy.2024.124431 – volume: 88 start-page: 247 issn: 0960-1481 year: 2016 ident: 10.1016/j.apenergy.2024.124976_b31 article-title: Multi-objective opportunistic maintenance optimization of a wind farm considering limited number of maintenance groups publication-title: Renew Energy doi: 10.1016/j.renene.2015.11.022 – volume: 99 start-page: 784 year: 2016 ident: 10.1016/j.apenergy.2024.124976_b5 article-title: A decision support system for strategic maintenance planning in offshore wind farms publication-title: Renew Energy doi: 10.1016/j.renene.2016.07.037 – volume: 279 start-page: 124 issue: 1 year: 2019 ident: 10.1016/j.apenergy.2024.124976_b14 article-title: On offshore wind farm maintenance scheduling for decision support on vessel fleet composition publication-title: European J Oper Res doi: 10.1016/j.ejor.2019.04.020 – volume: 125 start-page: 681 year: 2017 ident: 10.1016/j.apenergy.2024.124976_b33 article-title: Multi-objective optimization methods and application in energy saving publication-title: Energy doi: 10.1016/j.energy.2017.02.174 – year: 2023 ident: 10.1016/j.apenergy.2024.124976_b24 – year: 2013 ident: 10.1016/j.apenergy.2024.124976_b19 – volume: 322 year: 2022 ident: 10.1016/j.apenergy.2024.124976_b4 article-title: Towards resilience of offshore wind farms: A framework and application to asset integrity management publication-title: Appl Energy doi: 10.1016/j.apenergy.2022.119429 – volume: 19 start-page: 1107 issue: 6 year: 2016 ident: 10.1016/j.apenergy.2024.124976_b16 article-title: Failure rate, repair time and unscheduled O&M cost analysis of offshore wind turbines publication-title: Wind Energy doi: 10.1002/we.1887 – year: 1999 ident: 10.1016/j.apenergy.2024.124976_b23 – volume: 144 year: 2021 ident: 10.1016/j.apenergy.2024.124976_b28 article-title: Offshore wind turbine operations and maintenance: A state-of-the-art review publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2021.110886 – volume: 183 start-page: 983 year: 2019 ident: 10.1016/j.apenergy.2024.124976_b22 article-title: Wind farm layout optimization for wake effect uniformity publication-title: Energy doi: 10.1016/j.energy.2019.07.019 |
| SSID | ssj0002120 |
| Score | 2.453522 |
| Snippet | Offshore wind farms are essential in fulfilling global renewable energy targets; yet, their maintenance poses substantial challenges due to remote marine... |
| SourceID | crossref elsevier |
| SourceType | Index Database Publisher |
| StartPage | 124976 |
| SubjectTerms | Maintenance scheduling Mixed-integer program Offshore wind farm Stochastic program Wake effect |
| Title | A wake-induced two-phase planning framework for offshore wind farm maintenance with stochastic mixed-integer program |
| URI | https://dx.doi.org/10.1016/j.apenergy.2024.124976 |
| Volume | 380 |
| WOSCitedRecordID | wos001375081200001&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 issn: 0306-2619 databaseCode: AIEXJ dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0002120 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Nb9MwFLdKxwEOCAYT40s-cIs8UtupnWOFisYOFRJD6i1yXId1W5MqTdfuv-c5jtOsTMAOXKLIil8-3k_P7znv9x5CH41hoaKxIelQQ4BCs4jEVCsCoUgGC0rIhXDNJsRkIqfT-Fuvt_FcmJtrkedyu42X_1XVMAbKttTZB6i7FQoDcA5KhyOoHY7_pPhRsFFXhkCsvbb_9qtNQZYXsFbZhtF1f6Ig8wlZdY5hkWWri6I0wQamBJkqF8FC2SoSec0mcDnsVaFBhi3uuphvzYzUVSZM6dO7ui6u92tNzSrcS_iZqMXVrTUwe-Ont-u8Ui3IzrwN-m7W1z_n3a0JWlO9HTnTU7LCIbEhWtfcMte5qTGYtve1awDzmy132wqXJ2rpHhiCecpPdhPuFs_eW9TaVEOfxXaZeDmJlZM4OY_QARVRLPvoYPR1PD1rF3HaVPT0b9Ahl9__RPf7NR1f5fw5etYEGXjkwPEC9Ux-iJ52Sk8eoqPxjuEIlzYmfvUSVSPcxQ9u8YM9fnCLHwz4wR4_2OIHW_zgDn6wxQ_e4QffwQ9u8PMK_fgyPv98SprWHERDvFyRgaKCSjAAPMpMGFOpQqMZn7FMQ_yf8plSVIMvyBXXAyaYGWRM09jGs7Fhs5QdoX5e5OY1wsIWCRwaIyWPODNCKiNTyaRII7iUD4_RJ_9hk6WrwJL8WanHKPbfP2n8SOcfJgCtv8x98-C7vUVPdth_h_pVuTbv0WN9U81X5YcGV78Au_ec2A |
| 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=A+wake-induced+two-phase+planning+framework+for+offshore+wind+farm+maintenance+with+stochastic+mixed-integer+program&rft.jtitle=Applied+energy&rft.au=Lee%2C+Namkyoung&rft.au=Lee%2C+Hyuntae&rft.au=Joung%2C+Seulgi&rft.date=2025-02-15&rft.issn=0306-2619&rft.volume=380&rft.spage=124976&rft_id=info:doi/10.1016%2Fj.apenergy.2024.124976&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_apenergy_2024_124976 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0306-2619&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0306-2619&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0306-2619&client=summon |