Techno-Economic Multi-Objective Optimization of DG, Soft Open Points, and Demand Response for EV Integrated Distribution Networks

Techno-Economic Multi-Objective Optimization of DG, Soft Open Points, and Demand Response for EV Integrated 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 Techno-Economic Multi-Objective Optimization of DG, Soft Open Points, and Demand Response for EV Integrated Distribution Networks Hemant Patel, Aashish Kumar Bohre, Omkar Yadav This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8641403/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 With the widespread proliferation of electric vehicle charging stations in the modern distribution networks, several operational challenges have emerged, including increased power losses, voltage instability, harmonic distortion, and higher substation loading. Conventional distribution systems were not originally designed to accommodate such dynamic and concentrated loads; therefore, coordinated planning, and advanced control strategies are essential to ensure reliable and efficient operation. In this paper proposes a comprehensive multi-objective optimization framework for the IEEE 33 bus radial distribution system; that integrates distributed generation (DG), soft open points (SOPs), and demand response (DR) in a coordinated manner. The proposed framework simultaneously addresses multiple technical and economic objectives, including minimization of power losses, enhancement of voltage profiles, reduction of harmonic distortion, and effective management of substation capacity. To achieve optimal placement and sizing of distributed resources under various operating scenarios, Harris Hawks Optimization (HHO) and Particle Swarm Optimization (PSO) are employed. The coordinated utilization of DG and SOPs significantly enhances power flow flexibility and voltage regulation, while demand response effectively reshapes load profiles and mitigates peak demand stress caused by electric vehicle charging station (EVCS). The results demonstrate that uncoordinated integration of EV charging infrastructure can substantially degrade network performance. In contrast, coordinated deployment of DG, SOPs, and DR leads to notable improvements in system efficiency, voltage stability, and power quality. Furthermore, HHO consistently outperforms PSO by providing superior convergence characteristics and more balanced trade-offs among competing objectives. Overall, the proposed multi-objective framework offers a robust and scalable solution for future smart distribution networks with high electric vehicle penetration, underscoring the critical role of coordinated resource planning and advanced optimization techniques in achieving safe, economical, and sustainable power system operation. Distributed Generation Electric Vehicle Charging Station Soft Open Point Particle Swarm Optimization Harris Hawks Optimization Demand Response Multi-Objective Distribution Network Full Text Additional Declarations No competing interests reported. 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. 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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-8641403","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":577402645,"identity":"e3bb2f5f-32a6-4015-a817-55b6043f8e40","order_by":0,"name":"Hemant Patel","email":"data:image/png;base64,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","orcid":"","institution":"National Institute of Technology Durgapur","correspondingAuthor":true,"prefix":"","firstName":"Hemant","middleName":"","lastName":"Patel","suffix":""},{"id":577402646,"identity":"502bd66b-6d6f-4b8b-88a5-59b5c1600573","order_by":1,"name":"Aashish Kumar Bohre","email":"","orcid":"","institution":"National Institute of Technology Durgapur","correspondingAuthor":false,"prefix":"","firstName":"Aashish","middleName":"Kumar","lastName":"Bohre","suffix":""},{"id":577402647,"identity":"c5fc3d90-16eb-4895-9461-ad14e41f38cd","order_by":2,"name":"Omkar Yadav","email":"","orcid":"","institution":"National Institute of Technology Durgapur","correspondingAuthor":false,"prefix":"","firstName":"Omkar","middleName":"","lastName":"Yadav","suffix":""}],"badges":[],"createdAt":"2026-01-19 16:23:09","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8641403/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8641403/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102047167,"identity":"89b30458-cb20-4369-87d7-b3426821400b","added_by":"auto","created_at":"2026-02-06 14:12:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":906502,"visible":true,"origin":"","legend":"","description":"","filename":"FINALHEMANTPATEL.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8641403/v1_covered_b3a2b558-799e-459d-a7a2-61067bb084ae.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Techno-Economic Multi-Objective Optimization of DG, Soft Open Points, and Demand Response for EV Integrated Distribution Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Distributed Generation, Electric Vehicle Charging Station, Soft Open Point, Particle Swarm Optimization, Harris Hawks Optimization, Demand Response, Multi-Objective, Distribution Network","lastPublishedDoi":"10.21203/rs.3.rs-8641403/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8641403/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the widespread proliferation of electric vehicle charging stations in the modern distribution networks, several operational challenges have emerged, including increased power losses, voltage instability, harmonic distortion, and higher substation loading. Conventional distribution systems were not originally designed to accommodate such dynamic and concentrated loads; therefore, coordinated planning, and advanced control strategies are essential to ensure reliable and efficient operation. In this paper proposes a comprehensive multi-objective optimization framework for the IEEE 33 bus radial distribution system; that integrates distributed generation (DG), soft open points (SOPs), and demand response (DR) in a coordinated manner. The proposed framework simultaneously addresses multiple technical and economic objectives, including minimization of power losses, enhancement of voltage profiles, reduction of harmonic distortion, and effective management of substation capacity.\u003c/p\u003e\n\u003cp\u003eTo achieve optimal placement and sizing of distributed resources under various operating scenarios, Harris Hawks Optimization (HHO) and Particle Swarm Optimization (PSO) are employed. The coordinated utilization of DG and SOPs significantly enhances power flow flexibility and voltage regulation, while demand response effectively reshapes load profiles and mitigates peak demand stress caused by electric vehicle charging station (EVCS). The results demonstrate that uncoordinated integration of EV charging infrastructure can substantially degrade network performance. In contrast, coordinated deployment of DG, SOPs, and DR leads to notable improvements in system efficiency, voltage stability, and power quality. Furthermore, HHO consistently outperforms PSO by providing superior convergence characteristics and more balanced trade-offs among competing objectives. 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