Optimized Hybrid Renewable Energy Systems for Sustainable Electric Vehicle Charging Infrastructure: A Modified Metaheuristic-Based Techno-Economic Analysis Across Geographically Diverse Indian Regions

Optimized Hybrid Renewable Energy Systems for Sustainable Electric Vehicle Charging Infrastructure: A Modified Metaheuristic-Based Techno-Economic Analysis Across Geographically Diverse Indian Regions | 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 Article Optimized Hybrid Renewable Energy Systems for Sustainable Electric Vehicle Charging Infrastructure: A Modified Metaheuristic-Based Techno-Economic Analysis Across Geographically Diverse Indian Regions ANKIT SRIVASTAVA, Dashrath Nishad, Ayush Kumar, Ashutosh Kumar Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8952977/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract This is an all-inclusive study examining the technical feasible and economic possible possibilities of grid-connected hybrid renewable energy systems with electric vehicle charging stations (EVCS) installed in three geographically dispersive areas in India. The energy-efficient algorithm (MSSA) a newmodified salp swarm algorithm (MSSA) is applied and optimizedwith Levy Flight distribution and optimized particle swarm optimization (PSO) to optimize the system component sizingand minimizes the total net present cost (TNPC) and levelized cost of electricity (LCOE). System modeling and optimization is done by using the MATLAB-based simulation platform. The research fills critically important research gaps by comparing the complementary wind and solar resources in New Delhi,Ahmedabad, and Madurai, which have conflicting potentials of renewable energy. Findings indicate New Delhi gives the most cost-effective set-up with TNPC of 14,853.63 and LCOE of 0.0051/kWh with MSSA. Optimized PSO method has an equivalent performance with improved convergence properties with 35 percent reduction of the number of computational iterations than MSSA without compromising the quality of the solution. The best system has 120 solar photovoltaic panels (325W rated capacity) and 310 wind turbines (650 W rated capacity)with a production of 591,117 kWh/annually with 64.5 percent contribution of the wind and 33.5 percent contribution of the solar energy. When comparing Ahmedabad with Madurai, it can be concluded that the city of Ahmedabad needs significantly varied optimization settings when it comes to the exceptional coastal wind strengths, whereas Madurai is portrayed with balanced solar-wind complementary. Both MSSA and optimizedPSO show better convergence properties than the traditional algorithms. Through sensitivity analysis, it can be seen that feed-in tariff structure and discount rates play a very crucial role in the economic performance. The study offers evidence-based policy formulation aid to policymakers and utility providers intending on building sustainable EV charging infrastructures in developing countries, whose findings can be applied to renewable energy penetration in transport electrification projects. Physical sciences/Energy science and technology Physical sciences/Engineering electric vehicle charging hybrid renewable energy system solar photo-voltaic wind turbine battery energy storage metaheuristic optimization modified salp swarm algorithm optimized particle swarm optimization MATLAB simulation levelized cost of electricity net present cost sustainable transportation India Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 31 Mar, 2026 Reviews received at journal 25 Mar, 2026 Reviews received at journal 21 Mar, 2026 Reviews received at journal 21 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers agreed at journal 05 Mar, 2026 Reviewers agreed at journal 05 Mar, 2026 Reviewers agreed at journal 03 Mar, 2026 Reviewers invited by journal 03 Mar, 2026 Editor invited by journal 03 Mar, 2026 Editor assigned by journal 24 Feb, 2026 Submission checks completed at journal 24 Feb, 2026 First submitted to journal 24 Feb, 2026 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. 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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-8952977","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":601217840,"identity":"1f58f52c-c3e1-420f-a584-0aa82591aae3","order_by":0,"name":"ANKIT 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