Optimization Scheduling of Microgrid Systems and Enhance-ment of Green Economic Benefits through Particle Swarm Al-gorithm

Optimization Scheduling of Microgrid Systems and Enhance-ment of Green Economic Benefits through Particle Swarm Al-gorithm | 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 Optimization Scheduling of Microgrid Systems and Enhance-ment of Green Economic Benefits through Particle Swarm Al-gorithm Jinsong Zhang, Yan Huang, Kaiman Li, Taofei Ku, Hongyan Xin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9211369/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract With the transformation of energy structure and the promotion of carbon peak target, microgrid, a regional energy system integrating various renewable energy sources, has become increasingly important in promoting green and low-carbon development. However, microgrid scheduling faces many challenges, including heterogeneity of energy sources, severe load fluctuation and complex operation constraints. In order to solve these problems, an improved particle swarm optimization (IPSO) algorithm is proposed, which combines the bacterial foraging mechanism to enhance the global search ability and improve the convergence stability in complex scheduling environment. IPSO algorithm is based on the classical particle swarm optimization (PSO) framework, and introduces the disturbance component based on chemotaxis and the dynamic inertia weight strategy. These improvements improve the ability of the algorithm to jump out of the local optimal solution and adapt to the dynamic changes of the system. In this study, a multi-objective optimization model is established for a typical microgrid system, taking into account economic costs, carbon emissions, energy storage life and dynamic electricity prices. The proposed scheduling framework can ensure power balance, meet output constraints and maintain operational safety. Simulation experiments under typical summer and winter load conditions in a certain area of Zhejiang Province show that IPSO algorithm is significantly superior to standard PSO, genetic algorithm (GA) and bee colony optimization (BCO) in terms of cost efficiency, carbon dioxide emission reduction and system stability. Specifically, in the summer working conditions, the IPSO algorithm reduces the operating cost by 16.7%, carbon emissions by 18.4% and energy storage cycles by 37.5%. In winter, the algorithm reduces the cost by 14.5% and the carbon emission by 15.1%. Ablation experiments confirmed the synergistic effect of chemotaxis-based disturbance and dynamic weighting mechanism. In addition, in 30 independent simulation runs, IPSO algorithm always shows fast convergence, low fluctuation of optimal results and no convergence failure. Compared with traditional optimization methods (such as linear programming (LP) and sequential quadratic programming (SQP)), IPSO shows better scalability and robustness under nonlinear constraints. Generally speaking, this study expands the theoretical application of PSO in green energy dispatching, and provides a practical and extensible way for intelligent low-carbon dispatching and valuable reference for engineering application and policy formulation. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Microgrid particle swarm optimization algorithm optimization scheduling green economy carbon emissions Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 27 Apr, 2026 Reviews received at journal 26 Apr, 2026 Reviews received at journal 07 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviews received at journal 05 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers invited by journal 04 Apr, 2026 Editor invited by journal 31 Mar, 2026 Editor assigned by journal 25 Mar, 2026 Submission checks completed at journal 25 Mar, 2026 First submitted to journal 24 Mar, 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-9211369","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":619115560,"identity":"3aee7393-84c6-4597-b3f7-e8782ebbc450","order_by":0,"name":"Jinsong Zhang","email":"","orcid":"","institution":"China Southern Power Grid","correspondingAuthor":false,"prefix":"","firstName":"Jinsong","middleName":"","lastName":"Zhang","suffix":""},{"id":619115561,"identity":"68844db6-ac16-4c61-8f61-6c3333cfdec9","order_by":1,"name":"Yan Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBACAyQ24wMgIcPAwEa8FmYgx4CHJC1sEkRpMZdIfvbwa5tdHn//8WsVH9v+8PCztyUw/KjYhlOL5Yw0c2PZtuRiiRs5ZTdnthnwSPYcO8DYc+Y2bofdSDCTlmxjTtwgwZN2mxeoxeBGegMzYxs+LenfgFrqEzfwn0krJlJLjpnkx7bDiRsY0o8xQ7SkHcCv5cybMmmGc8cTZ9zIYZaccc4Y5JeEg3j9cjx9m+SPsurE/v7jDz98KJOTA4aY4YMfFbi1gAAzLzgmeBBxdACveiBg/PEHRLE/IKRwFIyCUTAKRigAAMYUV3b6u7cnAAAAAElFTkSuQmCC","orcid":"","institution":"China Southern Power Grid","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Huang","suffix":""},{"id":619115562,"identity":"71d03aac-f419-4c8b-8cb9-7b8a0dae4554","order_by":2,"name":"Kaiman Li","email":"","orcid":"","institution":"China Southern Power Grid","correspondingAuthor":false,"prefix":"","firstName":"Kaiman","middleName":"","lastName":"Li","suffix":""},{"id":619115564,"identity":"a1edd7d7-78fd-4f91-8eb4-94b928d1d4a3","order_by":3,"name":"Taofei Ku","email":"","orcid":"","institution":"China Southern Power Grid","correspondingAuthor":false,"prefix":"","firstName":"Taofei","middleName":"","lastName":"Ku","suffix":""},{"id":619115566,"identity":"b54af05a-ff40-4092-8fc0-ec3a3245cc28","order_by":4,"name":"Hongyan Xin","email":"","orcid":"","institution":"China Southern Power Grid","correspondingAuthor":false,"prefix":"","firstName":"Hongyan","middleName":"","lastName":"Xin","suffix":""}],"badges":[],"createdAt":"2026-03-24 11:39:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9211369/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9211369/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106768803,"identity":"038351d2-4e69-45db-9e70-aadb74de3b8c","added_by":"auto","created_at":"2026-04-13 09:51:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":989687,"visible":true,"origin":"","legend":"","description":"","filename":"OptimizationSchedulingofMicrogridSystemsandEnhancementofGreenEconomicBenefitsthroughParticleSwarmAlgorithm.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9211369/v1_covered_b38d3654-f859-4c33-9184-5e609ef9bb35.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimization Scheduling of Microgrid Systems and Enhance-ment of Green Economic Benefits through Particle Swarm Al-gorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Microgrid, particle swarm optimization algorithm, optimization scheduling, green economy, carbon emissions","lastPublishedDoi":"10.21203/rs.3.rs-9211369/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9211369/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the transformation of energy structure and the promotion of carbon peak target, microgrid, a regional energy system integrating various renewable energy sources, has become increasingly important in promoting green and low-carbon development. However, microgrid scheduling faces many challenges, including heterogeneity of energy sources, severe load fluctuation and complex operation constraints. In order to solve these problems, an improved particle swarm optimization (IPSO) algorithm is proposed, which combines the bacterial foraging mechanism to enhance the global search ability and improve the convergence stability in complex scheduling environment. IPSO algorithm is based on the classical particle swarm optimization (PSO) framework, and introduces the disturbance component based on chemotaxis and the dynamic inertia weight strategy. These improvements improve the ability of the algorithm to jump out of the local optimal solution and adapt to the dynamic changes of the system. In this study, a multi-objective optimization model is established for a typical microgrid system, taking into account economic costs, carbon emissions, energy storage life and dynamic electricity prices. The proposed scheduling framework can ensure power balance, meet output constraints and maintain operational safety. Simulation experiments under typical summer and winter load conditions in a certain area of Zhejiang Province show that IPSO algorithm is significantly superior to standard PSO, genetic algorithm (GA) and bee colony optimization (BCO) in terms of cost efficiency, carbon dioxide emission reduction and system stability. Specifically, in the summer working conditions, the IPSO algorithm reduces the operating cost by 16.7%, carbon emissions by 18.4% and energy storage cycles by 37.5%. In winter, the algorithm reduces the cost by 14.5% and the carbon emission by 15.1%. Ablation experiments confirmed the synergistic effect of chemotaxis-based disturbance and dynamic weighting mechanism. In addition, in 30 independent simulation runs, IPSO algorithm always shows fast convergence, low fluctuation of optimal results and no convergence failure. Compared with traditional optimization methods (such as linear programming (LP) and sequential quadratic programming (SQP)), IPSO shows better scalability and robustness under nonlinear constraints. Generally speaking, this study expands the theoretical application of PSO in green energy dispatching, and provides a practical and extensible way for intelligent low-carbon dispatching and valuable reference for engineering application and policy formulation.\u003c/p\u003e","manuscriptTitle":"Optimization Scheduling of Microgrid Systems and Enhance-ment of Green Economic Benefits through Particle Swarm Al-gorithm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-13 09:50:47","doi":"10.21203/rs.3.rs-9211369/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-27T06:55:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-26T13:25:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-07T16:48:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147609447730298698088237859389299284973","date":"2026-04-07T16:11:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"258501396862801020611269962381547779903","date":"2026-04-05T11:27:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-05T08:21:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186823450476324799199854130250616053620","date":"2026-04-05T07:47:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-04T23:25:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-31T15:30:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-25T11:47:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-25T11:47:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-24T11:30:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d5dff935-30a2-4bf7-a276-2fec9c3aed87","owner":[],"postedDate":"April 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":65881491,"name":"Physical sciences/Energy science and technology"},{"id":65881492,"name":"Physical sciences/Engineering"},{"id":65881493,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-04-27T07:56:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-13 09:50:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9211369","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9211369","identity":"rs-9211369","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}