Optimization of Electric vehicle charging or discharging scheduling and energy storage in multi-objective market transactions based on quantum genetic algorithm

Optimization of Electric vehicle charging or discharging scheduling and energy storage in multi-objective market transactions based on quantum genetic 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 Optimization of Electric vehicle charging or discharging scheduling and energy storage in multi-objective market transactions based on quantum genetic algorithm Siva Shankar S This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4452226/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 Electric Vehicles (AEVs) may play an essential role in the future of transportation as the use of electric vehicles grows and new transportation network services evolve. AEVs can automatically plan their routes, park charging stations, and provide vehicle-to-grid (V2G) services. However, V2G services may at disappoint customers due to work delays. EVs hold massive promise for future transportation systems, and effective charge scheduling tactics are vital to growing EV profitability. Two difficulties arise when charging/discharging EVs: how to reduce load and charging costs. The goal is to discover the most convenient EV charging station using VANET. This paper uses Monarch Butterfly African Vulture Optimization Algorithm (MBAVOA) for charge scheduling in EVs. The initial stage is to simulate EVs in the Vehicular Ad-hoc Network (VANET) model. Here, the shifting requests from EVs and accessible charging stations are identified. In addition, the load is computed using a Quantum Genetic Algorithm (QGA). Moreover, the multi-objective fitness parameters, like distance, charging cost, and user preference is considered for a charge or discharging schedule. The QGA-MBAVOA outperformed with the lowest charging cost of 66%, fitness of 0.010, and user convenience of 0.779. Monarch Butterfly optimization Charging or discharging scheduling Quantum Genetic Algorithm Electric Vehicle Energy Management Full Text 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-4452226","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495548296,"identity":"01b04b82-f0dd-4514-bb05-7211c051818e","order_by":0,"name":"Siva Shankar S","email":"data:image/png;base64,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","orcid":"","institution":"KG Reddy College of Engineering \u0026 Technology","correspondingAuthor":true,"prefix":"","firstName":"Siva","middleName":"Shankar","lastName":"S","suffix":""}],"badges":[],"createdAt":"2024-05-21 05:01:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4452226/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4452226/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89230065,"identity":"809f581b-d738-4450-8649-8b5ed323ecfa","added_by":"auto","created_at":"2025-08-17 14:09:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":574712,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4452226/v1_covered_f156a6c3-3456-4b60-ac56-8864806d1697.pdf"}],"financialInterests":"","formattedTitle":"Optimization of Electric vehicle charging or discharging scheduling and energy storage in multi-objective market transactions based on quantum genetic 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":"Monarch Butterfly optimization, Charging or discharging scheduling, Quantum Genetic Algorithm, Electric Vehicle, Energy Management","lastPublishedDoi":"10.21203/rs.3.rs-4452226/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4452226/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eElectric Vehicles (AEVs) may play an essential role in the future of transportation as the use of electric vehicles grows and new transportation network services evolve. 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