Single and Multi-Objective Optimal Power Flow Based on JAYA Algorithm with Teaching-Learning Based Optimization

Single and Multi-Objective Optimal Power Flow Based on JAYA Algorithm with Teaching-Learning Based Optimization | 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 Research Article Single and Multi-Objective Optimal Power Flow Based on JAYA Algorithm with Teaching-Learning Based Optimization Oğuz TAŞDEMİR, Salih ERMİŞ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6017780/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 This paper deals with the Optimal Power Flow (OPF) in an IEEE standard bus (30-bus) power system and presents a multi-objective optimization approach to minimize generation costs, active power losses and voltage deviations. The OPF problem is of critical importance for the reliable, efficient and economical operation of power systems. However, the solution to this problem is complex due to its nonlinear nature and large number of constraints. Conventional methods are often insufficient to overcome the nonlinear challenges inherent in OPF. In addressing these challenges, this study employs metaheuristic algorithms, namely Teaching-Learning Based Optimisation (TLBO), JAYA and hybrid TLBO-JAYA, to enhance the efficiency and convergence speed of the solution process. To manage the multi-objective nature of the problem, Pareto optimisation is utilised to identify a solution set that balances conflicting objectives. The outcomes demonstrate that the hybrid TLBO-JAYA algorithm offers a balanced enhancement in terms of generation cost, active power loss and voltage stability, thereby providing a versatile and efficient solution framework for contemporary power systems. These findings underscore the potential of hybrid metaheuristic algorithms in addressing complex multi-objective optimisation problems in power systems. Multi-objective optimization Optimal Power Flow problem Teaching-Learning-Based Optimization (TLBO) algorithm JAYA algorithm Pareto optimal technique 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. 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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-6017780","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":415168058,"identity":"ec1a359b-1688-4d39-a6e7-01c40a4d6285","order_by":0,"name":"Oğuz TAŞDEMİR","email":"","orcid":"","institution":"Kırşehir Ahi Evran University","correspondingAuthor":false,"prefix":"","firstName":"Oğuz","middleName":"","lastName":"TAŞDEMİR","suffix":""},{"id":415168059,"identity":"de5cbcba-6ee5-46b9-9c81-fa092ea98106","order_by":1,"name":"Salih ERMİŞ","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYLCCBAYbBgNmJC4xWtLQtBwgrOkwgwGyCXi1yLf3Pt3w4M95eXN25mPSFTV3GPjZcwyYP+7BrcXgzHGzG4lttw13NrOlSZ459oxBsueNAcOBZ3i0SKSx3UhsuM244TCPsWEDG9CFN3KAWvC4TH7+M7YbCX/O2W84zP/ZsOHfYQZ7QloYbrABtbAdSATawviwsQ1oiwQBLQZnQA5rS07ecJjN8GFj32EeiTPPCg6cweew9mNsN3/8sbPdcP7wg4MN3w7L8bcnb3xQgc9h6IAHRJCiYRSMglEwCkYBFgAAmYJZdHaOsvQAAAAASUVORK5CYII=","orcid":"","institution":"Kırşehir Ahi Evran University","correspondingAuthor":true,"prefix":"","firstName":"Salih","middleName":"","lastName":"ERMİŞ","suffix":""}],"badges":[],"createdAt":"2025-02-12 19:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6017780/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6017780/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78070769,"identity":"7a18c8a7-2516-4b8f-9ebf-f280a2216ab5","added_by":"auto","created_at":"2025-03-09 06:31:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1022961,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6017780/v1_covered_81d947e5-c40b-4869-b473-697794a5860d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Single and Multi-Objective Optimal Power Flow Based on JAYA Algorithm with Teaching-Learning Based Optimization","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":"Multi-objective optimization, Optimal Power Flow problem, Teaching-Learning-Based Optimization (TLBO) algorithm, JAYA algorithm, Pareto optimal technique","lastPublishedDoi":"10.21203/rs.3.rs-6017780/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6017780/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper deals with the Optimal Power Flow (OPF) in an IEEE standard bus (30-bus) power system and presents a multi-objective optimization approach to minimize generation costs, active power losses and voltage deviations. 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