Multi-fidelity Graph Neural Networks for Efficient Power Flow Analysis under High-Dimensional Demand and Renewable Generation Uncertainty

Multi-fidelity Graph Neural Networks for Efficient Power Flow Analysis under High-Dimensional Demand and Renewable Generation Uncertainty | 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 Multi-fidelity Graph Neural Networks for Efficient Power Flow Analysis under High-Dimensional Demand and Renewable Generation Uncertainty Mehdi Taghizadeh, Kamiar Khayambashi, Md Abul Hasnat, Negin Alemazkoor This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4745466/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 modernization of power systems faces uncertainties due to fluctuating renewable energy sources, electric vehicle expansion, and demand response initiatives. These uncertainties require consideration in power flow analyses by calculating power flow across a spectrum of generation and load values. Traditional power flow methods, which rely on iterative solutions of non-linear equation sets, become computationally burdensome under these uncertain conditions. Surrogate models have been used to reduces the computational cost of power flow analysis under uncertainty. Recently, graph neural networks (GNNs) have gained increasing attention as surrogates for power system simulations. However, GNNs, similar to other machine learning models, require significant amounts of training data. This paper proposes an innovative approach combining a multi-fidelity methodology with a GNN-based surrogate model for efficient power flow calculations. This model is trained using both high-fidelity power flow simulations and low-fidelity power flow simulations. Our multi-fidelity GNN model not only reduces the cost of generating training data, but also outperforms both single-fidelity GNN models trained solely on high-fidelity data and conventional neural network models. Additionally, the proposed model exhibits robustness to minor topology changes and achieves reasonable performance with unseen topologies. The model’s effectiveness is validated through simulations on standard IEEE systems. Artificial Intelligence and Machine Learning Electrical Engineering Power Flow Analysis Graph Neural Network Multi-fidelity Data Fusion Varying Topology Renewable Energy Generation Full Text Additional Declarations The authors declare no competing interests. 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. 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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-4745466","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":327353340,"identity":"b38633f3-4c4c-4fa5-9334-ff4014785262","order_by":0,"name":"Mehdi Taghizadeh","email":"","orcid":"https://orcid.org/0000-0001-7228-6114","institution":"University of Virginia","correspondingAuthor":false,"prefix":"","firstName":"Mehdi","middleName":"","lastName":"Taghizadeh","suffix":""},{"id":327353341,"identity":"7669eaad-86e2-4ab4-bb3f-1189511a6ee9","order_by":1,"name":"Kamiar Khayambashi","email":"","orcid":"","institution":"University of Virginia","correspondingAuthor":false,"prefix":"","firstName":"Kamiar","middleName":"","lastName":"Khayambashi","suffix":""},{"id":327353342,"identity":"b54c912f-54cc-4816-9147-2bc82123f46a","order_by":2,"name":"Md Abul Hasnat","email":"","orcid":"","institution":"University of Virginia","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Abul","lastName":"Hasnat","suffix":""},{"id":327353343,"identity":"18db5e5c-9c01-470c-ab17-c8b0ea75ceca","order_by":3,"name":"Negin Alemazkoor","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYFAC/g8fGCoYeBgYGBsYEkACBwjrMZzBcIZkLYxtyHxCWszZDyQ2F847LGPev7jtwYMaBjm+Gwn4tVj2JBxsnrntMI/MjYftBgnHGIwlCWkxOJDY/ph3WxqPhMTBNokENobEDQS1nH/M2Mw7B6blH0M9YS030oBaGmx4JPgb2yQS2xgSDAhrecPYPOMYUIsEI1BLn4ThzDMPCDksh7G5oEbCXoL/+DPJH99s5PmOE7AFBJjBpARYpQRh5Qgt/AeIUz0KRsEoGAUjDwAAC29GrsitrFMAAAAASUVORK5CYII=","orcid":"","institution":"University of Virginia","correspondingAuthor":true,"prefix":"","firstName":"Negin","middleName":"","lastName":"Alemazkoor","suffix":""}],"badges":[],"createdAt":"2024-07-15 20:58:09","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-4745466/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4745466/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60462006,"identity":"917cb888-beca-4443-92c1-d6b3b56d4681","added_by":"auto","created_at":"2024-07-17 04:08:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11087436,"visible":true,"origin":"","legend":"","description":"","filename":"Preprint.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4745466/v1_covered_b85303b2-96f9-4609-b544-8b767cf945dc.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eMulti-fidelity Graph Neural Networks for Efficient Power Flow Analysis under High-Dimensional Demand and Renewable Generation Uncertainty\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Virginia","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Power Flow Analysis, Graph Neural Network, Multi-fidelity Data Fusion, Varying Topology, Renewable Energy Generation","lastPublishedDoi":"10.21203/rs.3.rs-4745466/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4745466/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe modernization of power systems faces uncertainties due to fluctuating renewable energy sources, electric vehicle expansion, and demand response initiatives. 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