Scenario-Based Hybrid Optimization and Exponential Smoothing Forecasting for Enhanced Microgrid Energy Management

Scenario-Based Hybrid Optimization and Exponential Smoothing Forecasting for Enhanced Microgrid Energy Management | 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 Scenario-Based Hybrid Optimization and Exponential Smoothing Forecasting for Enhanced Microgrid Energy Management Bassam Omri, Radhia Garraoui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6639486/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 presents a hybrid energy management strategy for microgrids that integrates long-term global optimization with real-time dynamic adaptive control enhanced by predictive forecasting using exponential smoothing. The proposed method is designed to maximize self-consumption and minimize grid dependency while ensuring the battery's state-of-charge remains within safe operational limits. Simulation results across multiple scenarios demonstrate that the hybrid approach improves energy efficiency and reduces overall operational costs compared to baseline strategies. The method achieves a high self-consumption ratio and maintains battery stability, even under rapidly changing conditions. These findings highlight the potential of the hybrid approach to provide robust, cost-effective energy management for microgrids. Microgrid Energy Management Global Optimization Dynamic Adaptive Control Exponential Smoothing Self-Consumption Grid Dependency Battery Storage Forecasting 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-6639486","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":468439276,"identity":"070fefbd-c4b6-44ce-9965-993ee0366faa","order_by":0,"name":"Bassam Omri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYJCCA4wNYJrxAZANpYnUwmwA0QKiCQGoFjYJorTwi519eIBxh429fHt3WjVPzR05fgZmtg/4tEjOTjc4wHgmLXHDmbPbbvMce2Ys2cDMPAOfFoPbaUC/tB1OMJDIBWphO5y44QD/YbwOg2r5by8//+22Yp5/IC3MzMRoAQbaDd5tzLxtRGiRnA3SciYZ6JfczZJz+w4bSzYT0MIvncb8gXGHHTDEzm788ObbYTl+9mb8WkCA+Q+UwcQD5hLUgAQYf5CiehSMglEwCkYMAAAVU0ueEWEiJwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Gafsa","correspondingAuthor":true,"prefix":"","firstName":"Bassam","middleName":"","lastName":"Omri","suffix":""},{"id":468439277,"identity":"d903787d-c070-4275-992f-067f8fcde5d1","order_by":1,"name":"Radhia Garraoui","email":"","orcid":"","institution":"University of Gafsa","correspondingAuthor":false,"prefix":"","firstName":"Radhia","middleName":"","lastName":"Garraoui","suffix":""}],"badges":[],"createdAt":"2025-05-11 12:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6639486/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6639486/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84362335,"identity":"58e6814e-4ecf-403a-a819-ecbc8edf474c","added_by":"auto","created_at":"2025-06-11 05:01:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":727723,"visible":true,"origin":"","legend":"","description":"","filename":"articleOmriBassam.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6639486/v1_covered_72cf4588-7ebb-4275-98a2-89c5c89351ee.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Scenario-Based Hybrid Optimization and Exponential Smoothing Forecasting for Enhanced Microgrid Energy Management","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":"Microgrid, Energy Management, Global Optimization, Dynamic Adaptive Control, Exponential Smoothing, Self-Consumption, Grid Dependency, Battery Storage, Forecasting","lastPublishedDoi":"10.21203/rs.3.rs-6639486/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6639486/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper presents a hybrid energy management strategy for microgrids that integrates long-term global optimization with real-time dynamic adaptive control enhanced by predictive forecasting using exponential smoothing. 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