Modeling and Simulation Analysis of Multi-Scenario Air Conditioning Cluster Response for Intelligent Load Management in Distribution Networks

Modeling and Simulation Analysis of Multi-Scenario Air Conditioning Cluster Response for Intelligent Load Management in Distribution Networks | 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 Article Modeling and Simulation Analysis of Multi-Scenario Air Conditioning Cluster Response for Intelligent Load Management in Distribution Networks zhiyong zhang, HaiYun Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5878697/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 May, 2025 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract This paper proposes a dynamic multi-scenario modeling approach for air conditioning (AC) cluster loads, integrating occupant behavior, spatiotemporal activity distributions, and meteorological factors. A refined unregulated load baseline is established to better isolate and evaluate the effects of AC usage on overall distribution network loads. Simulation results under various scenarios indicate that the proposed framework accurately captures cluster-level load responses, effectively reflecting the interplay among occupant activities, temperature variations, and regional characteristics. The outcomes demonstrate the model’s potential to enhance load forecasting and support intelligent demand-side management in smart grids, offering both theoretical and practical insights for future load regulation strategies. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Energy science and technology/Renewable energy Human Activity Modeling Intelligent Load Management Dynamic Scenarios Air Conditioning Load Response Cluster Response Modeling Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 May, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Accepted 23 Apr, 2025 Reviews received at journal 21 Apr, 2025 Reviewers agreed at journal 21 Apr, 2025 Reviews received at journal 16 Apr, 2025 Reviewers agreed at journal 16 Apr, 2025 Reviewers agreed at journal 16 Apr, 2025 Reviewers invited by journal 16 Apr, 2025 Submission checks completed at journal 15 Apr, 2025 First submitted to journal 08 Apr, 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. 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-5878697","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":443670159,"identity":"7ad16c44-2b45-4bdf-87e0-7cd8bfe95c6c","order_by":0,"name":"zhiyong zhang","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"prefix":"","firstName":"zhiyong","middleName":"","lastName":"zhang","suffix":""},{"id":443670160,"identity":"c712b3db-874b-4a56-a3ad-1b8723072160","order_by":1,"name":"HaiYun Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYBACefb+Dwc+VNjI8bM3EKnFsOeA4cMZZ9KMJXsOEGvNjQRjY962w4kbbiQQqYOxISFNgucMM+OGm4833mCosYkmqIWd4cAxCYkKNmbJ22nFFgzH0nIbCNrS2NgmYXCGh43vdo6ZBGPDYcJaGA4zs0kktknwMNw8Q6yWY2zMBgfbDCQEbvAQqcWwh4fxYcOZBAPJHqBfEojxi7z8G4bDfyr+1/ezH95440ONDREOQwIGEgmkKIdoIVXHKBgFo2AUjAwAAHONQx5LufQ9AAAAAElFTkSuQmCC","orcid":"","institution":"Xinjiang University","correspondingAuthor":true,"prefix":"","firstName":"HaiYun","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-01-22 07:53:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5878697/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5878697/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-99816-w","type":"published","date":"2025-05-14T15:57:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83067692,"identity":"5e3e4b15-8ac2-4c4b-96ae-f2b2901142c1","added_by":"auto","created_at":"2025-05-19 16:03:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3453693,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReports48.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5878697/v1_covered_84b97268-3b9b-441b-be2a-64d6079c3d6d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modeling and Simulation Analysis of Multi-Scenario Air Conditioning Cluster Response for Intelligent Load Management in Distribution Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Human Activity Modeling, Intelligent Load Management, Dynamic Scenarios, Air Conditioning Load Response, Cluster Response Modeling","lastPublishedDoi":"10.21203/rs.3.rs-5878697/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5878697/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper proposes a dynamic multi-scenario modeling approach for air conditioning (AC) cluster loads, integrating occupant behavior, spatiotemporal activity distributions, and meteorological factors. 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