Day-Ahead Optimal Scheduling of Large-Scale Renewable Energy Bases Based on Rime Optimization Algorithm

Day-Ahead Optimal Scheduling of Large-Scale Renewable Energy Bases Based on Rime Optimization Algorithm | 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 Day-Ahead Optimal Scheduling of Large-Scale Renewable Energy Bases Based on Rime Optimization Algorithm HUANG Zeyi, Xiayang LI, Hang ZHOU, LU Bo, XU Dongsheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7586817/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 The integration of large-scale renewable energy (RE) poses complex challenges for day-ahead scheduling, characterized by high-dimensional, nonlinear, and tightly constrained optimization problems. Traditional optimization methods often fail to efficiently handle such complexity, leading to suboptimal solutions and high computational costs. Inspired by the growing potential of artificial intelligence (AI) in clean energy systems, this paper introduces an AI-powered scheduling framework based on the Rime Optimization Algorithm (RIME) to address these issues. We develop a high-fidelity Mixed-Integer Nonlinear Programming (MINLP) model that incorporates wind, photovoltaic, hydro, thermal, nuclear, and pumped storage units, with dual objectives of minimizing operating costs and maximizing RE utilization. Leveraging RIME’s bio-inspired mechanisms—soft rime search for adaptive exploration and hard rime piercing for escaping local optima—the algorithm demonstrates superior performance in balancing exploration and exploitation. Extensive simulations on a real-world regional energy base (comprising 470 + units) under four operational modes show that RIME achieves a 6–10% reduction in operating costs, 100% RE utilization, and 10–15% faster computation compared to benchmark methods. These results highlight RIME’s potential as an efficient AI-driven tool for the smart and sustainable management of complex renewable-rich power systems. AI Renewable energy integration Smart grid scheduling Rime optimization algorithm Multi-energy systems Mixed-integer nonlinear programming 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. 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Algorithm","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":"AI, Renewable energy integration, Smart grid scheduling, Rime optimization algorithm, Multi-energy systems, Mixed-integer nonlinear programming","lastPublishedDoi":"10.21203/rs.3.rs-7586817/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7586817/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe integration of large-scale renewable energy (RE) poses complex challenges for day-ahead scheduling, characterized by high-dimensional, nonlinear, and tightly constrained optimization problems. Traditional optimization methods often fail to efficiently handle such complexity, leading to suboptimal solutions and high computational costs. Inspired by the growing potential of artificial intelligence (AI) in clean energy systems, this paper introduces an AI-powered scheduling framework based on the Rime Optimization Algorithm (RIME) to address these issues. We develop a high-fidelity Mixed-Integer Nonlinear Programming (MINLP) model that incorporates wind, photovoltaic, hydro, thermal, nuclear, and pumped storage units, with dual objectives of minimizing operating costs and maximizing RE utilization. Leveraging RIME\u0026rsquo;s bio-inspired mechanisms\u0026mdash;soft rime search for adaptive exploration and hard rime piercing for escaping local optima\u0026mdash;the algorithm demonstrates superior performance in balancing exploration and exploitation. Extensive simulations on a real-world regional energy base (comprising 470\u0026thinsp;+\u0026thinsp;units) under four operational modes show that RIME achieves a 6\u0026ndash;10% reduction in operating costs, 100% RE utilization, and 10\u0026ndash;15% faster computation compared to benchmark methods. These results highlight RIME\u0026rsquo;s potential as an efficient AI-driven tool for the smart and sustainable management of complex renewable-rich power systems.\u003c/p\u003e","manuscriptTitle":"Day-Ahead Optimal Scheduling of Large-Scale Renewable Energy Bases Based on Rime Optimization Algorithm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 08:30:49","doi":"10.21203/rs.3.rs-7586817/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":"6fa25ce8-3a8d-43c6-80f1-4841ae6ec8c7","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-14T12:25:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 08:30:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7586817","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7586817","identity":"rs-7586817","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}