A bi-level optimization model and hybrid evolutionary algorithm for wind farm layout with different turbine types

A bi-level optimization model and hybrid evolutionary algorithm for wind farm layout with different turbine types | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A bi-level optimization model and hybrid evolutionary algorithm for wind farm layout with different turbine types Song Erping This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4876813/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In order to reduce the influence of wake effect between wind turbines under complex terrain in wind farm layout, it is feasible to adopt a deployment scheme of multiple types of wind turbines to maximize the benefits. In this paper, both the cost function and the output power of the wind farm are considered, where the cost functions include of different diameter, hub-height, and types of wind turbines. Therefore, a mode of bi-level constrained optimization is established, where decision variable at the upper-level is the deployment of wind turbine type, and decision variable at the lower-level is the deployment of wind turbine micro-location. Then, according to the characteristics of the decision variables, a hybrid algorithm based on genetic algorithm and differential evolution is proposed, and the above evolution operations and parameter values are improved to improve the performance of the algorithm. Finally, the feasibility of the proposed scheme is verified by extensive simulations. Physical sciences/Energy science and technology Physical sciences/Mathematics and computing/Information technology Physical sciences/Mathematics and computing/Scientific data Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4876813","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":345595294,"identity":"a38c68d3-725c-4d0f-b20c-e4525c3e301c","order_by":0,"name":"Song Erping","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIie3PsWrDMBCA4UsFziLi9QzBeYUDg8mWV5EwZEpKoIuHUFxa7CEOfZWOGWUMmhSyenTIC9Rbhgz13hC5Wwd9k4b7OR2A4/xD3vi9utwIQx/qqhXp1p5MuE6Ib+ZR8NY/WqPtSYirGPl3Kqk2cXD+YAM+xlWMSChAqziVmQd+sROWW7JkToTPo1ItG3mYAprjl21L1QjCF4ZKN9J4QLi2JCgzVIQyn53zjczZkCR5CrI+KaH2YFjCNYv6sQhBMxRGc+sts+Kzu8DtNVyoU9dd023oF/vHyS/8b+OO4zjOXT88eUtphwBokwAAAABJRU5ErkJggg==","orcid":"","institution":"Qinghai University","correspondingAuthor":true,"prefix":"","firstName":"Song","middleName":"","lastName":"Erping","suffix":""}],"badges":[],"createdAt":"2024-08-07 20:01:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4876813/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4876813/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67196320,"identity":"c5abe0d6-4520-420c-9c77-2e825093a6c1","added_by":"auto","created_at":"2024-10-22 09:09:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":510553,"visible":true,"origin":"","legend":"","description":"","filename":"Temeplate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4876813/v1_covered_c01fd938-e665-4cbb-9a13-00913d584a87.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A bi-level optimization model and hybrid evolutionary algorithm for wind farm layout with different turbine types","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4876813/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4876813/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In order to reduce the influence of wake effect between wind turbines under complex terrain in wind farm layout, it is feasible to adopt a deployment scheme of multiple types of wind turbines to maximize the benefits. In this paper, both the cost function and the output power of the wind farm are considered, where the cost functions include of different diameter, hub-height, and types of wind turbines. Therefore, a mode of bi-level constrained optimization is established, where decision variable at the upper-level is the deployment of wind turbine type, and decision variable at the lower-level is the deployment of wind turbine micro-location. Then, according to the characteristics of the decision variables, a hybrid algorithm based on genetic algorithm and differential evolution is proposed, and the above evolution operations and parameter values are improved to improve the performance of the algorithm. Finally, the feasibility of the proposed scheme is verified by extensive simulations.","manuscriptTitle":"A bi-level optimization model and hybrid evolutionary algorithm for wind farm layout with different turbine types","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-13 06:03:37","doi":"10.21203/rs.3.rs-4876813/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4a0dafe1-6b1d-45cf-95be-34d7558b21ae","owner":[],"postedDate":"September 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":36633264,"name":"Physical sciences/Energy science and technology"},{"id":36633265,"name":"Physical sciences/Mathematics and computing/Information technology"},{"id":36633266,"name":"Physical sciences/Mathematics and computing/Scientific data"}],"tags":[],"updatedAt":"2024-10-22T09:08:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-13 06:03:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4876813","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4876813","identity":"rs-4876813","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}