Mechanism-Data Driven Improved ADP-Prediction Control for Renewable Energy Utilization in Microgrids

Mechanism-Data Driven Improved ADP-Prediction Control for Renewable Energy Utilization in Microgrids | 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 Mechanism-Data Driven Improved ADP-Prediction Control for Renewable Energy Utilization in Microgrids Jun Yuan, Zhizhao Fang, Rongjun Chen, Jiayu Zhou, Qun Liu, Xianxian Zeng, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8512011/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 With the continuous development of renewable energy, enhancing its utilization efficiency has become a critical technical bottleneck restricting the optimal operation of microgrids. To address this challenge, this paper proposes a novel control algorithm for improving local renewable energy consumption. Firstly, an improved transformer network is adopted to establish a high-precision prediction model, which realizes accurate forecasting of photovoltaic (PV) output power and load demand. On this basis, an Adaptive Dynamic Programming (ADP)-based energy management controller is designed to optimize the charging/discharging scheduling of battery energy storage systems, thereby maximizing the absorption of local renewable energy. Furthermore, a mechanism-driven abnormal state regulation module is proposed to rapidly adjust the battery State of Charge (SOC) back to the safe operating range when abnormal conditions occur, while ensuring smooth mode switching of the microgrid to maintain stable system operation. Experimental validation results demonstrate that the proposed algorithm achieves a PV utilization rate of 99.41%, which effectively improves the economic benefits of the microgrid and enhances the overall system reliability. Smart grid energy storage control renewable energy utilization adaptive dynamic programming mechanism-data hybrid approach 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-8512011","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":572893893,"identity":"fba0c411-6f01-47c7-ae72-748e80c13809","order_by":0,"name":"Jun Yuan","email":"","orcid":"","institution":"Guangdong Polytechnic Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Yuan","suffix":""},{"id":572893894,"identity":"6e421e9f-2d34-452a-b62c-2697db893f23","order_by":1,"name":"Zhizhao Fang","email":"","orcid":"","institution":"Guangdong Polytechnic Normal 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Microgrids","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":"Smart grid, energy storage control, renewable energy utilization, adaptive dynamic programming, mechanism-data hybrid approach","lastPublishedDoi":"10.21203/rs.3.rs-8512011/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8512011/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the continuous development of renewable energy, enhancing its utilization efficiency has become a critical technical bottleneck restricting the optimal operation of microgrids. To address this challenge, this paper proposes a novel control algorithm for improving local renewable energy consumption. Firstly, an improved transformer network is adopted to establish a high-precision prediction model, which realizes accurate forecasting of photovoltaic (PV) output power and load demand. On this basis, an Adaptive Dynamic Programming (ADP)-based energy management controller is designed to optimize the charging/discharging scheduling of battery energy storage systems, thereby maximizing the absorption of local renewable energy. Furthermore, a mechanism-driven abnormal state regulation module is proposed to rapidly adjust the battery State of Charge (SOC) back to the safe operating range when abnormal conditions occur, while ensuring smooth mode switching of the microgrid to maintain stable system operation. Experimental validation results demonstrate that the proposed algorithm achieves a PV utilization rate of 99.41%, which effectively improves the economic benefits of the microgrid and enhances the overall system reliability.\u003c/p\u003e","manuscriptTitle":"Mechanism-Data Driven Improved ADP-Prediction Control for Renewable Energy Utilization in Microgrids","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 18:34:00","doi":"10.21203/rs.3.rs-8512011/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":"0554b411-72f7-47d7-b5c7-ff72f7980c9e","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-07T13:56:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 18:34:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8512011","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8512011","identity":"rs-8512011","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}