Optimal Reactive Power Flow in RES Based System Using Hybrid Grey Wolf with PSO

Optimal Reactive Power Flow in RES Based System Using Hybrid Grey Wolf with PSO | 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 Optimal Reactive Power Flow in RES Based System Using Hybrid Grey Wolf with PSO Umesh Kumar Saket, Himmat Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8806872/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 The increasing penetration of renewable energy sources (RES) such as solar and wind introduces significant uncertainty and nonlinearity in power systems, making optimal reactive power flow (ORPF) a challenging optimization problem. ORPF aims to minimize active power losses while maintaining voltage stability and satisfying system constraints. Conventional optimization and standalone metaheuristic techniques often suffer from premature convergence and reduced performance under high renewable penetration levels. To overcome these limitations, this paper proposes a hybrid Grey Wolf Optimizer–Particle Swarm Optimization (GWO–PSO) algorithm for efficient ORPF in RES-based power systems.The proposed hybrid approach combines the strong global exploration capability of the Grey Wolf Optimizer with the fast local exploitation characteristics of Particle Swarm Optimization through a sequential embedded strategy. PSO is adaptively activated in the later stages of the optimization process to refine solutions obtained by GWO, ensuring improved convergence and solution quality. The effectiveness of the proposed method is validated on IEEE 33-bus and 69-bus distribution systems with varying renewable penetration levels and loading conditions. Simulation results demonstrate significant reductions in active power losses and notable improvements in voltage profiles compared to PSO, Genetic Algorithm, Simulated Annealing, and standalone GWO methods. The hybrid GWO–PSO also exhibits faster convergence and robust performance under high renewable penetration, making it suitable for practical implementation in energy management systems for modern RES-integrated power networks. Grey Wolf Optimizer Simulated Annealing Optimal Reactive Power Flow Renewable Energy Integration Distribution Networks Hybrid Metaheuristic Algorithm Voltage Stability Power Loss Minimization Full Text Additional Declarations Competing interest reported. 06/02/2026 Respected Editor-in-Chief, Please find my joint research paper, “Optimal Reactive Power Flow in RES Based System Using Hybrid Grey Wolf with PSO”, for its review and possible publication in this prestigious journal. The work presented in this paper is original and not submitted elsewhere for its consideration. There is no conflict of interest with any of the suggested reviewers. Thanking you Best regards Dr Himmat Singh 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. 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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-8806872","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587454664,"identity":"cf46fba9-6b4a-4949-887c-b40e1a475291","order_by":0,"name":"Umesh Kumar Saket","email":"","orcid":"","institution":"Madhav Institute of Technology\u0026 Science","correspondingAuthor":false,"prefix":"","firstName":"Umesh","middleName":"Kumar","lastName":"Saket","suffix":""},{"id":587454665,"identity":"dcc41dde-7ef4-43d5-92b1-ff58d100dbd1","order_by":1,"name":"Himmat Singh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYDACCcYGMG3ADKZsgJix8QAJWhLSQFoaCGiB0gZgMuEwmMKrhX92c9tnnpp7DObs3Kkbfv44b7e2/TDQlhqbaJyW3DnYPJvnWDGDZTPvtps9CbeTt51JBGo5lpbbgEvPjcRm5hy2BAaDw7zbbvAAtZgdAGphbDiMU4s8WMs/iJabfxLOJZudf4hfiwFIS24bRMttnoQDdmY3CNhiCNLyty+BB6xFJi05wewG0JYEPH6Ru5H+mHHGtwQ5g/Nnt918Y2Nnb3Y+/eGDDzU2uL0PBTwwRiJYZQIB5SjAnhTFo2AUjIJRMDIAANWOZZo4NF/ZAAAAAElFTkSuQmCC","orcid":"","institution":"Madhav Institute of Technology\u0026 Science","correspondingAuthor":true,"prefix":"","firstName":"Himmat","middleName":"","lastName":"Singh","suffix":""}],"badges":[],"createdAt":"2026-02-06 12:10:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8806872/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8806872/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106587991,"identity":"aecb62f9-7e10-408e-a273-b54dee18617b","added_by":"auto","created_at":"2026-04-10 07:58:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1107806,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8806872/v1_covered_38b6e0fb-785b-45f8-9be5-d3a85cef25c5.pdf"}],"financialInterests":"Competing interest reported. 06/02/2026\nRespected Editor-in-Chief,\nPlease find my joint research paper, “Optimal Reactive Power Flow in RES Based System Using Hybrid Grey Wolf with PSO”, for its review and possible publication in this prestigious journal. The work presented in this paper is original and not submitted elsewhere for its consideration.\nThere is no conflict of interest with any of the suggested reviewers.\nThanking you\nBest regards\nDr Himmat Singh","formattedTitle":"Optimal Reactive Power Flow in RES Based System Using Hybrid Grey Wolf with PSO","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Grey Wolf Optimizer, Simulated Annealing, Optimal Reactive Power Flow, Renewable Energy Integration, Distribution Networks, Hybrid Metaheuristic Algorithm, Voltage Stability, Power Loss Minimization","lastPublishedDoi":"10.21203/rs.3.rs-8806872/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8806872/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe increasing penetration of renewable energy sources (RES) such as solar and wind introduces significant uncertainty and nonlinearity in power systems, making optimal reactive power flow (ORPF) a challenging optimization problem. ORPF aims to minimize active power losses while maintaining voltage stability and satisfying system constraints. Conventional optimization and standalone metaheuristic techniques often suffer from premature convergence and reduced performance under high renewable penetration levels. To overcome these limitations, this paper proposes a hybrid Grey Wolf Optimizer\u0026ndash;Particle Swarm Optimization (GWO\u0026ndash;PSO) algorithm for efficient ORPF in RES-based power systems.The proposed hybrid approach combines the strong global exploration capability of the Grey Wolf Optimizer with the fast local exploitation characteristics of Particle Swarm Optimization through a sequential embedded strategy. PSO is adaptively activated in the later stages of the optimization process to refine solutions obtained by GWO, ensuring improved convergence and solution quality. The effectiveness of the proposed method is validated on IEEE 33-bus and 69-bus distribution systems with varying renewable penetration levels and loading conditions. Simulation results demonstrate significant reductions in active power losses and notable improvements in voltage profiles compared to PSO, Genetic Algorithm, Simulated Annealing, and standalone GWO methods. 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