Plugged-in Electric Vehicle-Assisted Demand Response Strategy for Residential Energy Management

Plugged-in Electric Vehicle-Assisted Demand Response Strategy for Residential Energy Management | 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 Plugged-in Electric Vehicle-Assisted Demand Response Strategy for Residential Energy Management Khaldoon Alfaverh, Fayiz Alfaverh, Laszlo Szamel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2424104/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Mar, 2023 Read the published version in Energy Informatics → Version 1 posted 7 You are reading this latest preprint version Abstract Demand response (DR) management systems are a potentially growing market due to their ability to maximize energy savings by allowing customers to manage their energy consumption at times of peak demand in response to financial incentives from the electricity supplier. Successful execution of a demand response program requires an effective management system where the home energy management system (HEMS) is a promising solution nowadays. HEMS is developed to manage energy use in households and to conduct the management of energy supply, either from the grid or the alternative energy sources like solar or wind power plants. With the increase of vehicle electrification, in order to achieve a more reliable and efficient smart grid (SG), cooperation between electric vehicles (EVs) and residential systems is required. This cooperation could involve not only vehicle to grid (V2G) operation but a vehicle to home (V2H) too. V2H operation is used to transfer the power and relevant data between EVs and residential systems. This paper provides an efficient HEMS enhanced by smart scheduling and an optimally designed charging and discharging strategy for plugged-in electric vehicles (PEVs). The proposed design uses a fuzzy logic controller (FLC) for smart scheduling and to take the charging (from the grid)/discharging (supply the household appliances) decision without compromising the driving needs. Simulations are presented to demonstrate how the proposed strategies can help to reduce electricity costs by 19.28% and 14.27% with 30% and 80% state of charge (SOC) of the PEV respectively compared to the case where G2V operation only used along with the photovoltaic (PV) production, improve energy utilization by smoothing the energy consumption profile and satisfy the user’s needs by ensuring enough EV battery SOC for each planned trip. Fuzzy Logic Controller Home Energy Management System Energy Storage Systems Vehicle-to-Home Smart Home Smart Grid Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Mar, 2023 Read the published version in Energy Informatics → Version 1 posted Editorial decision: Major revision 01 Feb, 2023 Reviews received at journal 22 Jan, 2023 Reviewers agreed at journal 06 Jan, 2023 Reviewers invited by journal 05 Jan, 2023 Editor assigned by journal 30 Dec, 2022 Submission checks completed at journal 30 Dec, 2022 First submitted to journal 29 Dec, 2022 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-2424104","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":163782669,"identity":"def62a3b-9f8b-402b-b936-4291d7e8a70a","order_by":0,"name":"Khaldoon Alfaverh","email":"data:image/png;base64,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","orcid":"","institution":"Budapest University of Technology and Economics","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Khaldoon","middleName":"","lastName":"Alfaverh","suffix":""},{"id":163782670,"identity":"86192d5e-a4d3-4653-a0c2-05cafa912f4d","order_by":1,"name":"Fayiz Alfaverh","email":"","orcid":"","institution":"University of Hertfordshire","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fayiz","middleName":"","lastName":"Alfaverh","suffix":""},{"id":163782671,"identity":"d77270f0-1b9a-4a4e-b5d1-f993552dcc4d","order_by":2,"name":"Laszlo Szamel","email":"","orcid":"","institution":"Budapest University of Technology and Economics","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Laszlo","middleName":"","lastName":"Szamel","suffix":""}],"badges":[],"createdAt":"2022-12-29 06:29:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2424104/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2424104/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s42162-023-00260-9","type":"published","date":"2023-03-16T20:02:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":44722737,"identity":"1c724f98-e1b4-4103-b808-b4f7accf1c51","added_by":"auto","created_at":"2023-10-16 20:07:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1271372,"visible":true,"origin":"","legend":"","description":"","filename":"PEVAssistedDRStrategyforResidentialEnergyManagement.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2424104/v1_covered_4fdc1036-f45e-4cac-b47d-0db60b4967ad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Plugged-in Electric Vehicle-Assisted Demand Response Strategy for Residential Energy Management","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":"energy-informatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"einf","sideBox":"Learn more about [Energy Informatics](https://energyinformatics.springeropen.com)","snPcode":"42162","submissionUrl":"https://submission.nature.com/new-submission/42162/3","title":"Energy Informatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Fuzzy Logic Controller, Home Energy Management System, Energy Storage Systems, Vehicle-to-Home, Smart Home, Smart Grid","lastPublishedDoi":"10.21203/rs.3.rs-2424104/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2424104/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDemand response (DR) management systems are a potentially growing market due to their ability to maximize energy savings by allowing customers to manage their energy consumption at times of peak demand in response to financial incentives from the electricity supplier. Successful execution of a demand response program requires an effective management system where the home energy management system (HEMS) is a promising solution nowadays. HEMS is developed to manage energy use in households and to conduct the management of energy supply, either from the grid or the alternative energy sources like solar or wind power plants. With the increase of vehicle electrification, in order to achieve a more reliable and efficient smart grid (SG), cooperation between electric vehicles (EVs) and residential systems is required. This cooperation could involve not only vehicle to grid (V2G) operation but a vehicle to home (V2H) too. V2H operation is used to transfer the power and relevant data between EVs and residential systems. This paper provides an efficient HEMS enhanced by smart scheduling and an optimally designed charging and discharging strategy for plugged-in electric vehicles (PEVs). 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