Resilience-Aware Energy Management of Microgrids Incorporating Dynamic Thermal Line Rating

Resilience-Aware Energy Management of Microgrids Incorporating Dynamic Thermal Line Rating | 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 Resilience-Aware Energy Management of Microgrids Incorporating Dynamic Thermal Line Rating Mohit Kumar, Deepesh Sharma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8838433/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 growing infiltration of renewable energy sources, these bidirectional flows, and disturbance-induced operation have posed a considerable challenge in the distribution-level microgrids, where the constraints of the typical energy management system (EMS) based on static network constraints have been exposed. Specifically, the thermal characteristics of distribution lines are so frequently overlooked. The paper suggests a resilience-conscious microgrid energy management system that directly includes dynamic thermal line rating (TLR) as one of the key operational constraints. A hybrid learning-optimization approach is designed, with the integration of Long Short-Term Memory (LSTM) based forecasting of renewable generation, load demand, and conductor temperature with a mixed-integer linear program (MILP) based EMS. The suggested formulation combines time-varying thermal limits that are associated with normal, emergency, and recovery operating conditions and allows consistent conditions. Extensive simulation experiments are undertaken on a distribution level of the microgrid during grid outages, renewable overload, generator failure, and post fault recovery conditions. The proposed solution removes thermal violations completely, serves more total load during disturbances by up to 9% and sheds non-critical loads fewer times by about 40% than conventional EMS formulations. In addition, the proposed scheme reduces the realistic total cost of operation by 21% of the proposed EMS over the approach of applying the static limits to the problem. The results determine thermal line rating as a key enabler of resilient microgrid energy management. Microgrid energy control thermal line rating mixed-integer linear programming deep learning distribution networks dynamic thermal constraints 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. 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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-8838433","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607159335,"identity":"6bd60ec4-9949-46b1-b710-c49185b0e7bd","order_by":0,"name":"Mohit Kumar","email":"data:image/png;base64,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","orcid":"","institution":"Deenbandhu Chhotu Ram University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Mohit","middleName":"","lastName":"Kumar","suffix":""},{"id":607159337,"identity":"e28ee8c1-fc49-47b3-9929-75d473e3473e","order_by":1,"name":"Deepesh Sharma","email":"","orcid":"","institution":"Deenbandhu Chhotu Ram University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Deepesh","middleName":"","lastName":"Sharma","suffix":""}],"badges":[],"createdAt":"2026-02-10 08:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8838433/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8838433/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105033691,"identity":"2336000a-1e2f-4641-b7ef-2e14fbd506d7","added_by":"auto","created_at":"2026-03-20 07:21:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1811987,"visible":true,"origin":"","legend":"","description":"","filename":"finalRPMohitwithauthor.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8838433/v1_covered_59e8e282-3613-446c-9017-ee0210a9c052.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Resilience-Aware Energy Management of Microgrids Incorporating Dynamic Thermal Line Rating","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":"Microgrid energy control, thermal line rating, mixed-integer linear programming, deep learning, distribution networks, dynamic thermal constraints","lastPublishedDoi":"10.21203/rs.3.rs-8838433/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8838433/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe growing infiltration of renewable energy sources, these bidirectional flows, and disturbance-induced operation have posed a considerable challenge in the distribution-level microgrids, where the constraints of the typical energy management system (EMS) based on static network constraints have been exposed. 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