Dynamic Stochastic Multi-objective Optimal Power Flow incorporating Solar-Wind Energy using Hybrid Gbest-guided Artificial Bee Colony–NSGA-II Algorithm

Dynamic Stochastic Multi-objective Optimal Power Flow incorporating Solar-Wind Energy using Hybrid Gbest-guided Artificial Bee Colony–NSGA-II Algorithm | 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 Dynamic Stochastic Multi-objective Optimal Power Flow incorporating Solar-Wind Energy using Hybrid Gbest-guided Artificial Bee Colony–NSGA-II Algorithm Abhishek Bajirao Katkar, Dr. Himmat Tukaram Jadhav This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7714779/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 Integrating wind and solar resources into power systems has created significant variability and uncertainty, rendering traditional Optimal Power Flow (OPF) formulations insufficient for effective operational planning. This work advances existing research by incorporating dynamic load demand scenarios and renewable intermittency into a comprehensive Dynamic Stochastic Multi-Objective Optimal Power Flow (DS-MOOPF) framework, moving beyond the focus on deterministic or fixed stochastic OPF models. Renewable energy uncertainties are modeled with probability distributions: wind speed uses a Weibull distribution, solar irradiance follows a lognormal distribution, and dynamic load variations are represented by normal distributions to reflect real-time demand. An improved hybrid gbestABC–NSGA-II algorithm is introduced to tackle the complex optimization problem, merging the exploration-exploitation balance of the Artificial Bee Colony with the elitist non-dominated sorting and diversity-preserving features of NSGA-II. Diversity-Enhanced Tri-Stage Repair Constraint-Handling Method an effective constraint-handling method guarantees feasible decisions amid various system limits, while Pareto archive management with crowding-distance sorting improves convergence and maintains solution diversity. A modified TOPSIS-based decision analysis module is used to find the Best Compromise Solution (BCS), providing scalable and practical support for system operators. Experiments on IEEE 30, 57, and 118-bus test systems show that the proposed hybrid framework offers faster convergence, better diversity, and greater robustness than benchmark algorithms like MOEA/D, NSGA-III, MOABC, NSGA-II, and MOPSO. Statistical tests with Inverted Generational Distance (IGD), Hypervolume (HV), and the Wilcoxon rank-sum method confirm the approach’s superiority. The results show that the DS-MOOPF model is a scalable and resilient tool for managing large-scale uncertainty in power systems, supporting sustainable development and net-zero emission goals. Multi-objective optimal power flow global best guided Artificial Bee Colony NSGA-II Diversity-EnhancedTri-Stage Model Dynamic Load Probability distribution function Modified- TOPSIS Inverted Generational Distance Hypervolume Wilcoxon rank-sum tests 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. 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-7714779","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":537404667,"identity":"0796ca8f-d64e-47ba-abe0-eebe23084bc7","order_by":0,"name":"Abhishek Bajirao Katkar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIie2RsWrDMBBAzwjkRYlXQ0j9CycypvRbZALuVgxdAgVXoaApkLWlQ3+hU2YVg7O0eyAZnKVzp9LBQ0/p1GITspWgh0DScY+7kwA8nv9IzPdboIFZAATl7qhcjB1SOFdHKwLdQR3sK3o0ik0bGC6S+ecgz4urfjyp8xouEgh7trXItrLBm4HRg+ktB/dYXvM4Q2psIjXrt5dcX+pgpiF9rkgRaFMjrFMYrZ9W/5I4RTdOEe+kFKSsPki57VRwnVkafa9wUlhqwrmrUnYqclupl5mJaZZsNBZYOiVHhStpOpSzjZE73ZwPF6zcbURTpE934VJ+TW+SKHptVRyWHu5XgCP9KfCu/DZYfUy2x+PxnDzfWCJVrx8B0Q8AAAAASUVORK5CYII=","orcid":"","institution":"Shivaji University","correspondingAuthor":true,"prefix":"","firstName":"Abhishek","middleName":"Bajirao","lastName":"Katkar","suffix":""},{"id":537404670,"identity":"63a97a59-363d-4826-b365-931d554b3bfe","order_by":1,"name":"Dr. Himmat Tukaram Jadhav","email":"","orcid":"","institution":"Shreemati Nathibai Damodar Thackersey Women's University","correspondingAuthor":false,"prefix":"Dr.","firstName":"Himmat","middleName":"Tukaram","lastName":"Jadhav","suffix":""}],"badges":[],"createdAt":"2025-09-25 16:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7714779/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7714779/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95505262,"identity":"88d6f2f6-489e-48fd-9b30-d29bcc00dd23","added_by":"auto","created_at":"2025-11-10 05:58:18","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5017,"visible":true,"origin":"","legend":"","description":"","filename":"f51ae91a6c2546ef818bd0c8251c08be.json","url":"https://assets-eu.researchsquare.com/files/rs-7714779/v1/85511c902c5a9e8fd766fb9d.json"},{"id":95505269,"identity":"975c25c7-bc19-432c-baf7-9cd0e9115387","added_by":"auto","created_at":"2025-11-10 05:58:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2352316,"visible":true,"origin":"","legend":"","description":"","filename":"DynamicMOOPFSOLWind.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7714779/v1/929841bc30ee329792f2cbeb.pdf"},{"id":95505263,"identity":"fdeb4b2a-a3f7-4698-8564-a8080a449edf","added_by":"auto","created_at":"2025-11-10 05:58:18","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":509767,"visible":true,"origin":"","legend":"","description":"","filename":"IEEE118.png","url":"https://assets-eu.researchsquare.com/files/rs-7714779/v1/c433331a4e9a97534433b679.png"},{"id":95505266,"identity":"d386de46-a99a-4f0e-9880-3620054eaed8","added_by":"auto","created_at":"2025-11-10 05:58:18","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1016435,"visible":true,"origin":"","legend":"","description":"","filename":"IEEE30.png","url":"https://assets-eu.researchsquare.com/files/rs-7714779/v1/526b6e7038696f5a24ea5d6e.png"},{"id":95505267,"identity":"47375b66-cfb4-4408-acdc-865c3f5094d1","added_by":"auto","created_at":"2025-11-10 05:58:18","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":482911,"visible":true,"origin":"","legend":"","description":"","filename":"IEEE57.png","url":"https://assets-eu.researchsquare.com/files/rs-7714779/v1/4f9a17922d5584f63bb6f610.png"},{"id":95505264,"identity":"572ab853-0a4d-4030-9423-7c8045b2f7b4","added_by":"auto","created_at":"2025-11-10 05:58:18","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":223107,"visible":true,"origin":"","legend":"","description":"","filename":"coverletter.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7714779/v1/29adb7a6c8626425b10f5cb2.pdf"},{"id":95505268,"identity":"8d955465-3ff4-4131-bbca-9a5355c952e7","added_by":"auto","created_at":"2025-11-10 05:58:18","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":544288,"visible":true,"origin":"","legend":"","description":"","filename":"moopfflowredraw.png","url":"https://assets-eu.researchsquare.com/files/rs-7714779/v1/f09147b6cb502b00337e040b.png"},{"id":95505265,"identity":"e73b7ba6-b996-4ec7-9bbc-ca97157f3402","added_by":"auto","created_at":"2025-11-10 05:58:18","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":221098,"visible":true,"origin":"","legend":"","description":"","filename":"f51ae91a6c2546ef818bd0c8251c08be1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7714779/v1/612374ddb383251b06c533a9.xml"},{"id":95529406,"identity":"27852e65-a661-4c4d-970d-7dc891dea784","added_by":"auto","created_at":"2025-11-10 10:17:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1444681,"visible":true,"origin":"","legend":"","description":"","filename":"DynamicMOOPFSOLWind.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7714779/v1_covered_53d14283-12ea-4ce7-8201-7a43a8324fae.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDynamic Stochastic Multi-objective Optimal Power Flow incorporating Solar-Wind Energy using Hybrid Gbest-guided Artificial Bee Colony–NSGA-II Algorithm\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Multi-objective optimal power flow, global best guided Artificial Bee Colony, NSGA-II, Diversity-EnhancedTri-Stage Model, Dynamic Load, Probability distribution function,Modified- TOPSIS, Inverted Generational Distance, Hypervolume,Wilcoxon rank-sum tests","lastPublishedDoi":"10.21203/rs.3.rs-7714779/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7714779/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntegrating wind and solar resources into power systems has created significant variability and uncertainty, rendering traditional Optimal Power Flow (OPF) formulations insufficient for effective operational planning. This work advances existing research by incorporating dynamic load demand scenarios and renewable intermittency into a comprehensive Dynamic Stochastic Multi-Objective Optimal Power Flow (DS-MOOPF) framework, moving beyond the focus on deterministic or fixed stochastic OPF models. Renewable energy uncertainties are modeled with probability distributions: wind speed uses a Weibull distribution, solar irradiance follows a lognormal distribution, and dynamic load variations are represented by normal distributions to reflect real-time demand. An improved hybrid \u003cem\u003egbestABC–NSGA-II \u003c/em\u003ealgorithm is introduced to tackle the complex optimization problem, merging the exploration-exploitation balance of the Artificial Bee Colony with the elitist non-dominated sorting and diversity-preserving features of NSGA-II. Diversity-Enhanced Tri-Stage Repair Constraint-Handling Method an effective constraint-handling method guarantees feasible decisions amid various system limits, while Pareto archive management with crowding-distance sorting improves convergence and maintains solution diversity. A modified TOPSIS-based decision analysis module is used to find the Best Compromise Solution (BCS), providing scalable and practical support for system operators. Experiments on IEEE 30, 57, and 118-bus test systems show that the proposed hybrid framework offers faster convergence, better diversity, and greater robustness than benchmark algorithms like MOEA/D, NSGA-III, MOABC, NSGA-II, and MOPSO. Statistical tests with Inverted Generational Distance (IGD), Hypervolume (HV), and the Wilcoxon rank-sum method confirm the approach’s superiority. The results show that the DS-MOOPF model is a scalable and resilient tool for managing large-scale uncertainty in power systems, supporting sustainable development and net-zero emission goals.\u003c/p\u003e","manuscriptTitle":"Dynamic Stochastic Multi-objective Optimal Power Flow incorporating Solar-Wind Energy using Hybrid Gbest-guided Artificial Bee Colony–NSGA-II Algorithm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 05:58:14","doi":"10.21203/rs.3.rs-7714779/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":"16ba621e-e49d-482c-92ce-2152face52b2","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T05:58:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-10 05:58:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7714779","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7714779","identity":"rs-7714779","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}