Large-scale Ev Charging Coordination for Grid-aware Valley-filling

Large-scale Ev Charging Coordination for Grid-aware Valley-filling | 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Article Large-scale Ev Charging Coordination for Grid-aware Valley-filling Việt Trần Lê Quốc This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8201415/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 rapid proliferation of Electric Vehicles (EVs) presents a significant challenge to the stability of existing power distribution networks, primarily due to the risk of severe demand peaks from uncoordinated charging. This paper proposes a hybrid two-layer optimization framework to manage large-scale EV charging, ensuring grid stability while minimizing costs. The framework integrates a micro-simulation layer-which stochastically models realistic driving patterns and State of Charge (SoC) to determine heterogeneous energy demands-with a macro-optimization layer. This macro-layer utilizes a Particle Swarm Optimization (PSO) algorithm to solve a multi-objective problem. The optimization objectives are twofold: (1) minimizing the total electricity procurement cost for the aggregator, and (2) maximizing the grid load factor (i.e., load flattening or "valley-filling") by minimizing the variance of the total load profile. The model is validated through a large-scale case study of an urban area with 2.2 million EVs, segmented into "Home" and "Workplace" charging clusters. We utilize realistic technical specifications for battery capacity, energy efficiency, and charger power derived from a specific manufacturer's fleet (e.g., VinFast). Simulation results demonstrate that the proposed strategy successfully avoids peak-hour charging and strategically distributes the EV load across both low-price (night-time) and medium-price (midday) periods. This multi-objective approach achieves significant valley-filling, resulting in a much flatter total load profile compared to a naive cost-only optimization, thereby enhancing grid reliability and reducing operational expenditures. Electric Vehicles (EVs) Smart Charging Multi-Objective Optimization Valley-Filling Load Flattening Particle Swarm Optimization (PSO) Hybrid Simulation Full Text Additional Declarations No competing interests reported. Supplementary Files CostFunctionEVHSOVT.m HybridEVSoCHSOOptimizationVT.m 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-8201415","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":551035482,"identity":"1e3357a4-cecf-400c-8c09-6ef39dee8f81","order_by":0,"name":"Việt Trần Lê 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This paper proposes a hybrid two-layer optimization framework to manage large-scale EV charging, ensuring grid stability while minimizing costs. The framework integrates a micro-simulation layer-which stochastically models realistic driving patterns and State of Charge (SoC) to determine heterogeneous energy demands-with a macro-optimization layer. This macro-layer utilizes a Particle Swarm Optimization (PSO) algorithm to solve a multi-objective problem. The optimization objectives are twofold: (1) minimizing the total electricity procurement cost for the aggregator, and (2) maximizing the grid load factor (i.e., load flattening or \"valley-filling\") by minimizing the variance of the total load profile. The model is validated through a large-scale case study of an urban area with 2.2\u0026nbsp;million EVs, segmented into \"Home\" and \"Workplace\" charging clusters. We utilize realistic technical specifications for battery capacity, energy efficiency, and charger power derived from a specific manufacturer's fleet (e.g., VinFast). Simulation results demonstrate that the proposed strategy successfully avoids peak-hour charging and strategically distributes the EV load across both low-price (night-time) and medium-price (midday) periods. This multi-objective approach achieves significant valley-filling, resulting in a much flatter total load profile compared to a naive cost-only optimization, thereby enhancing grid reliability and reducing operational expenditures.\u003c/p\u003e","manuscriptTitle":"Large-scale Ev Charging Coordination for Grid-aware Valley-filling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-27 07:50:49","doi":"10.21203/rs.3.rs-8201415/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":"2df72a25-2684-49a1-a845-0f0cf0165566","owner":[],"postedDate":"November 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-03T14:24:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-27 07:50:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8201415","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8201415","identity":"rs-8201415","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}