Multi-Timescale Coordinated Optimization for Adaptive Scheduling of Industrial Park CCHP Systems with Integrated Thermal and Cold Storage

Multi-Timescale Coordinated Optimization for Adaptive Scheduling of Industrial Park CCHP Systems with Integrated Thermal and Cold Storage | 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 Multi-Timescale Coordinated Optimization for Adaptive Scheduling of Industrial Park CCHP Systems with Integrated Thermal and Cold Storage Xiaomin Wu, Changhui Hou, Weixin Lei, Peng Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7975338/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 In order to addresses operation limitations in industrial park CCHP systems, including single load patterns and the disconnect between storage capacity planning and operational strategies. A multi-time-scale collaborative optimization framework with multi-energy complementary storage is proposed. An enhanced CCHP system model integrates gas engines, waste heat recovery units, and absorption chillers with coupled electricity-heat-cooling flow dynamics. Equipment scheduling and power allocation are optimized using an improved Branch-and-Bound algorithm to minimize annual costs covering investment, operation, and dynamic energy pricing. For thermal and cold storage capacity configuration, an Adaptive Weighted Particle Swarm Optimization algorithm determines the optimal chilled/hot water tank capacities by maximizing revenue through valley electricity storage and peak discharge. The system achieves a peak comprehensive efficiency of 71.38% via full daytime waste heat utilization for cooling and partial nighttime surplus absorption. Results demonstrate that adaptive operational strategy, rather than capacity expansion, is the primary driver for elevating systemic efficiency. Physical sciences/Energy science and technology Physical sciences/Engineering 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-7975338","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":544159241,"identity":"55e15c30-2977-4b65-b3fd-8a7a07508e69","order_by":0,"name":"Xiaomin Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApklEQVRIiWNgGAWjYHACxgcQOoF4LcwGJGthkyBNi8GN7LSKn221DPzsOQYMP3cQo+XM2W03e9uOM0j2vDFg7D1DhBaz473bbjO2HQNal2PAzNhGjJbDvNuKQVrsidcCtAWosobBQIJYLfZnzm6W7Dl3gEfizLOCg73EaJGckbvxw4+yOjn+9uSND34SowUKDvOAyAPEa2BgqCNF8SgYBaNgFIw0AACAMDYKdJEPtwAAAABJRU5ErkJggg==","orcid":"","institution":"School of Information and Artificial Intelligence, Nanchang Institute of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Xiaomin","middleName":"","lastName":"Wu","suffix":""},{"id":544159242,"identity":"609a14e3-06c7-4a2e-92f1-21b1e03e6392","order_by":1,"name":"Changhui Hou","email":"","orcid":"","institution":"School of Information and Artificial Intelligence, Nanchang Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Changhui","middleName":"","lastName":"Hou","suffix":""},{"id":544159243,"identity":"fd283fc4-9244-42b5-ba9f-3593ac50851b","order_by":2,"name":"Weixin Lei","email":"","orcid":"","institution":"School of Computer and Artificial Intelligence, Hubei University Of Education","correspondingAuthor":false,"prefix":"","firstName":"Weixin","middleName":"","lastName":"Lei","suffix":""},{"id":544159244,"identity":"6070948a-9875-4965-9463-2f1d2198faf1","order_by":3,"name":"Peng Yu","email":"","orcid":"","institution":"Wuhan Fiberhome Fuhua Electric Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2025-10-29 04:14:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7975338/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7975338/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96259764,"identity":"a53f02a9-4018-40b0-9d9f-3a3d726e9f63","added_by":"auto","created_at":"2025-11-19 07:56:04","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5584,"visible":true,"origin":"","legend":"","description":"","filename":"bf28a687c8b24c9dbf58016bf38b6ada.json","url":"https://assets-eu.researchsquare.com/files/rs-7975338/v1/b10829e246101a1e66e611f2.json"},{"id":100796602,"identity":"61f6addf-e87b-40aa-8fa7-2c2565b60859","added_by":"auto","created_at":"2026-01-21 13:44:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":21255433,"visible":true,"origin":"","legend":"","description":"","filename":"MultiTimescaleCoordinatedOptimizationforAdaptiveSchedulingofIndustrialParkCCHPSystemswithIntegratedThermalandColdStorage.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7975338/v1_covered_030e2f85-92e0-4eca-936a-b51b658d1a3d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-Timescale Coordinated Optimization for Adaptive Scheduling of Industrial Park CCHP Systems with Integrated Thermal and Cold Storage","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":"","lastPublishedDoi":"10.21203/rs.3.rs-7975338/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7975338/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In order to addresses operation limitations in industrial park CCHP systems, including single load patterns and the disconnect between storage capacity planning and operational strategies. 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