Reactive Power Planning in Microgrids with Renewable Energy Sources Using Starfish Optimization Algorithm  

Reactive Power Planning in Microgrids with Renewable Energy Sources Using Starfish Optimization Algorithm | 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 Reactive Power Planning in Microgrids with Renewable Energy Sources Using Starfish Optimization Algorithm Umesh Kumar Saket bitu, Himmat Singh Himmat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8686135/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 (RESs) in microgrids introduces significant challenges to reactive power planning (RPP) due to their intermittent and uncertain nature. Effective reactive power management is essential to maintain voltage stability, minimize power losses, and enhance overall system performance. This paper presents a novel application of the Starfish Optimization Algorithm (SFOA), a recent bio-inspired metaheuristic, for optimal reactive power planning in microgrids with integrated renewable energy sources such as solar photovoltaic and wind generation. A comprehensive objective function is formulated to minimize real power losses and voltage deviation while improving voltage profiles across the network. The effectiveness of the proposed SFOA-based RPP method is validated on standard IEEE distribution test systems under different loading conditions and renewable penetration levels. Simulation results demonstrate that the proposed algorithm achieves superior convergence characteristics, improved voltage stability, and lower power losses compared to conventional optimization techniques The results confirm that SFOA is a robust and efficient tool for reactive power planning in modern microgrids with high renewable energy integration. Validation is performed on modified IEEE 37-bus and IEEE 85-bus distribution networks integrated with renewable energy components. Comprehensive comparisons with established optimization methods demonstrate SFOA's superior performance, achieving voltage profile enhancement and IEEE37-bus 21.4% & IEEE85-bus 19.6% power loss reduction compared to traditional approaches. Starfish Optimization Algorithm Reactive Power Planning Microgrids Renewable Energy Integration Voltage Optimization Loss Minimization Full Text Additional Declarations Competing interest reported. Declaration of Interest Statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article. The authors declare the following financial or non-financial interests, which may be considered as potential conflicts of interest 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. 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-8686135","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584940684,"identity":"321d109b-ec16-4b7e-b278-9cca8c8afbaf","order_by":0,"name":"Umesh Kumar Saket bitu","email":"","orcid":"","institution":"Madhav Institute of Technology\u0026 Science, Gwalior, Pin-: 474005Madhya Pradesh, India.","correspondingAuthor":false,"prefix":"","firstName":"Umesh","middleName":"Kumar Saket","lastName":"bitu","suffix":""},{"id":584940686,"identity":"48313a94-7343-4776-beed-895d80dc8f6a","order_by":1,"name":"Himmat Singh Himmat","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYDACCcYGMG3ADKZsgJix8QAJWhLSQFoaCGiB0gZgMuEwmMKrhX92c9tnnpp7DObs3Kkbfv44b7e2/TDQlhqbaJyW3DnYPJvnWDGDZTPvtps9CbeTt51JBGo5lpbbgEvPjcRm5hy2BAaDw7zbbvAAtZgdAGphbDiMU4s8WMs/iJabfxLOJZudf4hfiwFIS24bRMttnoQDdmY3CNhiCNLyty+BB6xFJi05wewG0JYEPH6Ru5H+mHHGtwQ5g/Nnt918Y2Nnb3Y+/eGDDzU2uL0PBTwwRiJYZQIB5SjAnhTFo2AUjIJRMDIAANWOZZo4NF/ZAAAAAElFTkSuQmCC","orcid":"","institution":"Madhav Institute of Technology\u0026 Science, Gwalior, Pin-: 474005Madhya Pradesh, India.","correspondingAuthor":true,"prefix":"","firstName":"Himmat","middleName":"Singh","lastName":"Himmat","suffix":""}],"badges":[],"createdAt":"2026-01-24 11:08:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8686135/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8686135/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107218008,"identity":"a39f65f7-040a-4fab-8327-d299316db1d6","added_by":"auto","created_at":"2026-04-18 12:10:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":946463,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8686135/v1_covered_c5d229e8-8283-4709-80d5-eed62ce2f7f8.pdf"}],"financialInterests":"Competing interest reported. Declaration of Interest Statement\nThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article. \nThe authors declare the following financial or non-financial interests, which may be considered as potential conflicts of interest\n\nBest regards\nDr Himmat Singh","formattedTitle":"Reactive Power Planning in Microgrids with Renewable Energy Sources Using Starfish Optimization Algorithm ","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":"Starfish Optimization Algorithm, Reactive Power Planning, Microgrids, Renewable Energy Integration, Voltage Optimization, Loss Minimization","lastPublishedDoi":"10.21203/rs.3.rs-8686135/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8686135/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe increasing penetration of renewable energy sources (RESs) in microgrids introduces significant challenges to reactive power planning (RPP) due to their intermittent and uncertain nature. Effective reactive power management is essential to maintain voltage stability, minimize power losses, and enhance overall system performance. This paper presents a novel application of the Starfish Optimization Algorithm (SFOA), a recent bio-inspired metaheuristic, for optimal reactive power planning in microgrids with integrated renewable energy sources such as solar photovoltaic and wind generation. A comprehensive objective function is formulated to minimize real power losses and voltage deviation while improving voltage profiles across the network. The effectiveness of the proposed SFOA-based RPP method is validated on standard IEEE distribution test systems under different loading conditions and renewable penetration levels. Simulation results demonstrate that the proposed algorithm achieves superior convergence characteristics, improved voltage stability, and lower power losses compared to conventional optimization techniques The results confirm that SFOA is a robust and efficient tool for reactive power planning in modern microgrids with high renewable energy integration. Validation is performed on modified IEEE 37-bus and IEEE 85-bus distribution networks integrated with renewable energy components. Comprehensive comparisons with established optimization methods demonstrate SFOA's superior performance, achieving voltage profile enhancement and IEEE37-bus 21.4% \u0026amp; IEEE85-bus 19.6% power loss reduction compared to traditional approaches.\u003c/p\u003e","manuscriptTitle":"Reactive Power Planning in Microgrids with Renewable Energy Sources Using Starfish Optimization Algorithm ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 06:30:15","doi":"10.21203/rs.3.rs-8686135/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":"2ea29cfd-ebf6-4f08-ac21-8cd25d84cd63","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-18T12:09:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 06:30:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8686135","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8686135","identity":"rs-8686135","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}