{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101499"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101499","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Formulations and methods for wind farm layout optimization","abstract":"The use of wind energy in electricity generation around the world has increased steadily over the past few years as the world seeks to reduce the use of fossil fuels in response to concerns over climate change and air pollution. Utility scale wind power is generated at large wind farms with as many as a hundred wind turbines. The layout of turbines in the wind farm has important implications regarding maintenance costs, electrical infrastructure costs, and most importantly, wind farm power generation. The wind farm layout optimization problem seeks to find the optimal layout of turbines that minimizes power loss from placing turbines in the wake cones of upstream turbines. The problem has received plenty of attention from researchers, but there remains significant room for improvement in terms of dealing with non-convexity, use of heuristics, and robust layouts which are resistant to errors in wind predictions. The first part of this work proposes a novel mixed integer linear programming formulation that allows for unrestricted placement of turbines within the wind farm, while at the same time eliminating solution dependence on the initial layout common to other continuous formulations. The second part introduces a dual-decomposition method for getting a close bound on the optimal solutions to discrete formulations, thereby facilitating the use of heuristics by giving an objective estimate of solution quality. The final part presents a robust layout optimization formulation with minimal data requirements, as well as a modified greedy algorithm with feasibility guarantees for finding robust solutions.","abstract_html":"The use of wind energy in electricity generation around the world has increased steadily over the past few years as the world seeks to reduce the use of fossil fuels in response to concerns over climate change and air pollution. Utility scale wind power is generated at large wind farms with as many as a hundred wind turbines. The layout of turbines in the wind farm has important implications regarding maintenance costs, electrical infrastructure costs, and most importantly, wind farm power generation. The wind farm layout optimization problem seeks to find the optimal layout of turbines that minimizes power loss from placing turbines in the wake cones of upstream turbines. The problem has received plenty of attention from researchers, but there remains significant room for improvement in terms of dealing with non-convexity, use of heuristics, and robust layouts which are resistant to errors in wind predictions. The first part of this work proposes a novel mixed integer linear programming formulation that allows for unrestricted placement of turbines within the wind farm, while at the same time eliminating solution dependence on the initial layout common to other continuous formulations. The second part introduces a dual-decomposition method for getting a close bound on the optimal solutions to discrete formulations, thereby facilitating the use of heuristics by giving an objective estimate of solution quality. The final part presents a robust layout optimization formulation with minimal data requirements, as well as a modified greedy algorithm with feasibility guarantees for finding robust solutions.","abstract_has_math":false,"creators":["Quan, Ning"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Industrial Engineering","degree_department":null,"school":null,"contributors":["Kim, Harrison","Thurston, Deborah L.","Ouyang, Yanfeng","Ho, Koki"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-27T16:17:30Z","date_published":"2018-09-27T16:17:30Z","updated_at":"2026-07-22T22:24:40Z","subjects":["wind farm layout","layout optimization","robust wind farm","robust layout","wind farm design","wind farm optimization","wind farm micro-siting","turbine placement"],"languages":["en"],"rights":["Copyright 2018 Ning Quan"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101499","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kim, Harrison","Thurston, Deborah L.","Ouyang, Yanfeng","Ho, Koki"]},{"key":"dc:creator","label":"Author","values":["Quan, Ning"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-27T16:17:30Z","2018-06-25","2018-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["wind farm layout","layout optimization","robust wind farm","robust layout","wind farm design","wind farm optimization","wind farm micro-siting","turbine placement"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Ning Quan"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101499"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The use of wind energy in electricity generation around the world has increased steadily over the past few years as the world seeks to reduce the use of fossil fuels in response to concerns over climate change and air pollution. Utility scale wind power is generated at large wind farms with as many as a hundred wind turbines. The layout of turbines in the wind farm has important implications regarding maintenance costs, electrical infrastructure costs, and most importantly, wind farm power generation. The wind farm layout optimization problem seeks to find the optimal layout of turbines that minimizes power loss from placing turbines in the wake cones of upstream turbines. The problem has received plenty of attention from researchers, but there remains significant room for improvement in terms of dealing with non-convexity, use of heuristics, and robust layouts which are resistant to errors in wind predictions. The first part of this work proposes a novel mixed integer linear programming formulation that allows for unrestricted placement of turbines within the wind farm, while at the same time eliminating solution dependence on the initial layout common to other continuous formulations. The second part introduces a dual-decomposition method for getting a close bound on the optimal solutions to discrete formulations, thereby facilitating the use of heuristics by giving an objective estimate of solution quality. The final part presents a robust layout optimization formulation with minimal data requirements, as well as a modified greedy algorithm with feasibility guarantees for finding robust solutions.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-09-27 without embargo terms","The student, Ning Quan, accepted the attached license on 2018-06-20 at 14:27.","The student, Ning Quan, submitted this Dissertation for approval on 2018-06-20 at 14:55.","This Dissertation was approved for publication on 2018-06-25 at 09:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12651 on 2018-09-27 at 10:45:19","Made available in DSpace on 2018-09-27T16:17:30Z (GMT). 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Utility scale wind power is generated at large wind farms with as many as a hundred wind turbines. The layout of turbines in the wind farm has important implications regarding maintenance costs, electrical infrastructure costs, and most importantly, wind farm power generation. The wind farm layout optimization problem seeks to find the optimal layout of turbines that minimizes power loss from placing turbines in the wake cones of upstream turbines. The problem has received plenty of attention from researchers, but there remains significant room for improvement in terms of dealing with non-convexity, use of heuristics, and robust layouts which are resistant to errors in wind predictions. The first part of this work proposes a novel mixed integer linear programming formulation that allows for unrestricted placement of turbines within the wind farm, while at the same time eliminating solution dependence on the initial layout common to other continuous formulations. The second part introduces a dual-decomposition method for getting a close bound on the optimal solutions to discrete formulations, thereby facilitating the use of heuristics by giving an objective estimate of solution quality. The final part presents a robust layout optimization formulation with minimal data requirements, as well as a modified greedy algorithm with feasibility guarantees for finding robust solutions.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-09-27 without embargo terms","The student, Ning Quan, accepted the attached license on 2018-06-20 at 14:27.","The student, Ning Quan, submitted this Dissertation for approval on 2018-06-20 at 14:55.","This Dissertation was approved for publication on 2018-06-25 at 09:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12651 on 2018-09-27 at 10:45:19","Made available in DSpace on 2018-09-27T16:17:30Z (GMT). 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