Application of dynamic adjustment strategy of map service resources combined with reinforcement learning in power supply network visualization

Application of dynamic adjustment strategy of map service resources combined with reinforcement learning in power supply network visualization | 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 Application of dynamic adjustment strategy of map service resources combined with reinforcement learning in power supply network visualization Zhu Meiying, Zheng Sida, Hu Hao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8351834/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 16 You are reading this latest preprint version Abstract Decentralized generation, varying demand, and the integration of renewable energy sources are all posing challenges to conventional grid control systems, making modern power grids more complicated. Due to their inability to effectively handle dynamic and unexpected grid circumstances, these traditional systems—which are built on static rule-based methodologies create instability and inefficiency. The increased issues in power grid management brought on by dispersed renewable energy sources, variable system conditions, and rising energy demand are discussed in this study. Static rule-based optimization is the foundation of traditional power grid control systems, which find it difficult to adjust to these complicated and dynamic circumstances. A dynamic map service resource adjustment technique is put forth to get over these restrictions. It combines transformer networks with Reinforcement Learning (RL) to optimize power supply network control and boost overall efficiency. The strategy incorporates the FEDformer forecasting model, which makes precise predictions about future power demand and allows the system to react proactively to variations in energy output and consumption. In order to optimize power generation, distribution, and grid stability, a RL-based resource allocation technique is used to dynamically modify grid resources. In order to give operators a real-time picture of the grid, a GIS-based visualization dashboard is also created. It shows important metrics including resource distribution, grid status, and dynamic modifications performed by the RL agent. The suggested approach effectively combines geographic visualization, RL, and forecasting to maximize power grid management. The system's capacity to accurately forecast power demand, dynamically modify resources, and improve grid performance is demonstrated by the results, which also show notable gains in operational efficiency, stability, and resource utilization. Important measures like success rates and cumulative incentives show that the RL adjusts well to changing grid circumstances, maximizing grid performance and reducing energy waste. Power Grid Optimization Reinforcement Learning Transformer Networks FEDformer Forecasting Model Dynamic Resource Allocation GIS Visualization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 11 May, 2026 Reviews received at journal 09 May, 2026 Reviews received at journal 09 May, 2026 Reviewers agreed at journal 08 May, 2026 Reviews received at journal 07 May, 2026 Reviews received at journal 05 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers invited by journal 30 Apr, 2026 Editor assigned by journal 30 Apr, 2026 Submission checks completed at journal 14 Dec, 2025 First submitted to journal 13 Dec, 2025 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-8351834","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":635920602,"identity":"45765652-859c-4835-90ac-c1e62216e4d0","order_by":0,"name":"Zhu Meiying","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYDACCSjJxsDA+OBDBZDNDmQ1EKmF2XDGGQYGHmbitIABmzRvGxFa5Gf3GDDdqLBI7JNuvyDNO88mcT8z88GHMxjs5HRx6GOcc8aAOeeMRGKbzJkCw7nb0hJ7mNmSDTcwJBubHcCuhVkix4A5tw2oRSInIeHttsNALTxmkg8YDiRuw6GFDazlH0TLAd45RGjhAWtpAGlJP9jI2wDVsgGPFgmJtALmnGMSxkBbmBlnHEsz7jkM9MsMA9x+kZ+RvIE5p6ZOdv6M9Oc/PtTYyLa3Nx982FNhJ4dLCxCw/wASjg0MPAZIgga4VCOAPVDrA8LKRsEoGAWjYEQCAKm5WAHPX07TAAAAAElFTkSuQmCC","orcid":"","institution":"State Grid Jibei Electric Power Company Limited Center of Metrology,Beijing, 100055, Beijing, China.","correspondingAuthor":true,"prefix":"","firstName":"Zhu","middleName":"","lastName":"Meiying","suffix":""},{"id":635920603,"identity":"bc3842c7-fac0-4110-b15e-1c6cb4f53dbd","order_by":1,"name":"Zheng Sida","email":"","orcid":"","institution":"State Grid Jibei Electric Power Company Limited Center of Metrology,Beijing, 100055, Beijing, China.","correspondingAuthor":false,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Sida","suffix":""},{"id":635920604,"identity":"cef46b3d-f753-4deb-8eea-eb92ce12b923","order_by":2,"name":"Hu Hao","email":"","orcid":"","institution":"State Grid Jibei Electric Power Company Limited Center of Metrology,Beijing, 100055, Beijing, China.","correspondingAuthor":false,"prefix":"","firstName":"Hu","middleName":"","lastName":"Hao","suffix":""}],"badges":[],"createdAt":"2025-12-13 09:53:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8351834/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8351834/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109067568,"identity":"ba57efd6-4cd1-4b84-ab7e-e87d9ed91d43","added_by":"auto","created_at":"2026-05-12 09:56:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":693491,"visible":true,"origin":"","legend":"","description":"","filename":"Applicationofdynamicadjustmentstrategyofmapservice.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8351834/v1_covered_2a531e91-0d40-475d-958a-e1ee973a5819.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application of dynamic adjustment strategy of map service resources combined with reinforcement learning in power supply network visualization","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"wireless-networks","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wine","sideBox":"Learn more about [Wireless Networks](http://link.springer.com/journal/11276)","snPcode":"11276","submissionUrl":"https://submission.nature.com/new-submission/11276/3","title":"Wireless Networks","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Power Grid Optimization, Reinforcement Learning, Transformer Networks, FEDformer Forecasting Model, Dynamic Resource Allocation, GIS Visualization ","lastPublishedDoi":"10.21203/rs.3.rs-8351834/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8351834/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Decentralized generation, varying demand, and the integration of renewable energy sources are all posing challenges to conventional grid control systems, making modern power grids more complicated. 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