Metaheuristic-Based Optimization for Electric Vehicle Charging Station Deployment in the Electrical Distribution Networks

Metaheuristic-Based Optimization for Electric Vehicle Charging Station Deployment in the Electrical Distribution Networks | 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 Metaheuristic-Based Optimization for Electric Vehicle Charging Station Deployment in the Electrical Distribution Networks Sanip S. Yeole, Prakash G. Burade This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6162079/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 May, 2025 Read the published version in Smart Grids and Sustainable Energy → Version 1 posted 11 You are reading this latest preprint version Abstract The transportation sector is one of the largest consumers of fossil fuels globally. To alleviate the environmental impact of hazardous emissions and mitigate dependence on conventional fossil fuels, electrifying the transportation network, including the adoption of electrical vehicles (EVs), has become imperative. For the successful deployment of EVs, a robust and well-planned charging infrastructure is essential. Among the challenges, the optimum placement of charging stations (CSs) stands out as a critical issue. This study proposes an effective approach to determine the optimum locations of electric vehicle charging stations (EVCSs) in the East Delta Network (EDN). Transition to electric mobility significantly impacts the electric distribution system parameters. Thus, key considerations in deploying EVCSs include real and reactive power loss, and installation costs. Additionally, integrating EVCSs into the electrical system increases energy demand on the grid. To address this, the research recommends incorporating renewable energy sources (RESs) at strategic locations within the electrical system to alleviate the additional load imposed by EVCSs, thereby enhancing system reliability and sustainability. This paper also evaluates the electric distribution system reliability after deploying EVCSs and RESs. Six cases are proposed to analyze the deployment of EVCSs, with and without RESs integration. As a result, the total loss is reduced from 1021.34 kW to 832.23 kW in all cases. Optimization Giza Pyramid Construction Algorithm Optimal location reliability electric vehicle Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 May, 2025 Read the published version in Smart Grids and Sustainable Energy → Version 1 posted Editorial decision: Revision requested 27 Mar, 2025 Reviews received at journal 26 Mar, 2025 Reviewers agreed at journal 26 Mar, 2025 Reviews received at journal 25 Mar, 2025 Reviewers agreed at journal 19 Mar, 2025 Reviewers agreed at journal 19 Mar, 2025 Reviewers agreed at journal 19 Mar, 2025 Reviewers invited by journal 19 Mar, 2025 Editor assigned by journal 19 Mar, 2025 Submission checks completed at journal 18 Mar, 2025 First submitted to journal 05 Mar, 2025 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-6162079","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":434700722,"identity":"283fa8db-43d6-43ff-9cb5-a397eca226d6","order_by":0,"name":"Sanip S. 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