Integrating Distributed Model Predictive Control into Multi-time Scale Optimization of Energy Systems

Integrating Distributed Model Predictive Control into Multi-time Scale Optimization of Energy Systems | 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 Integrating Distributed Model Predictive Control into Multi-time Scale Optimization of Energy Systems Xuli Wang, Tiancheng Shi, Zhiwei Li, Fan Zhou, Fei Jiao, Jun Xu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6400921/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 adoption of model predictive control with rolling optimization and feedback correction is one of the key techniques to realize multi-timescale optimal scheduling of integrated energy systems. In view of the complexity of model predictive control for overall online optimization, this paper proposes a multi-timescale optimal scheduling method based on distributed model predictive control for integrated energy systems. To further reduce the power fluctuation caused by the prediction error in the real-time phase and the difficulty in solving the real-time optimization model, the overall optimization problem is decomposed by an optimization scheduling strategy based on distributed model predictive control. The overall optimization problem is decomposed by an optimization scheduling strategy based on the predictive control of a distributed model. By coordinating and controlling each subsystem, the whole system can be optimized online to meet its dynamic adjustment requirements. The simulation results show that the method can improve the control performance of the system and at the same time increase the economy of the system operation. Integrating Distributed Energy Systems Model Predictive Control Multi-time Scale Optimization 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-6400921","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":455591041,"identity":"e3f4c1a4-2b77-4a19-9196-a8c083142e5c","order_by":0,"name":"Xuli Wang","email":"","orcid":"","institution":"State Grid Anhui Electric Power Co., Ltd., Economic and Technical Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Xuli","middleName":"","lastName":"Wang","suffix":""},{"id":455591042,"identity":"26ba27f0-f0b3-493a-9a09-83433b2b09ad","order_by":1,"name":"Tiancheng Shi","email":"","orcid":"","institution":"State Grid 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