Research on Optimization Methods for Multi-Energy Expansion Supply Plans in Industrial Parks Based on Genetic Algorithms

Research on Optimization Methods for Multi-Energy Expansion Supply Plans in Industrial Parks Based on Genetic Algorithms | 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 Research on Optimization Methods for Multi-Energy Expansion Supply Plans in Industrial Parks Based on Genetic Algorithms Shengwei Guo, Hua Wei, Feng Li, Meng Wang, Dejian Wang, Zixin Hong, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7172321/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract In response to the problem of global warming, the factories are actively adjusting their energy use structure and significantly introducing zero-carbon energy sources such as wind and solar energy to reduce carbon dioxide emissions. Integrated energy systems significantly increase the difficulty and cost of designing energy supply systems. Currently, many researchers have designed some simulation software for optimization of integrated energy systems in industrial factories. However, these approaches are specific to single sites (i.e., not generalizable) and are typically not designed to anticipate capacity expansion of facilities. Herein, an optimization modeling of Multi-energy Expansion Supply system has been developed based on the Genetic Algorithm (GA) to optimize the cost of energy supply systems. This model has been used for optimization of multi-energy system in the new energy supply systems. The proposed method was verified against commercial software results, showing a higher total cost saving (23.19%) and faster payback time (5 years comparing to 9 years). Additional case was studied by comparing the dynamic installation and fixed installation, demonstrating 8.4% more total cost saving and faster payback time (2 years and 4 years). Furthermore, the same demand was fulfilled by different amount of CHP units, achieving 40% initial investment and 36% higher utilization rate. This model will promote the green transformation of the energy structure of traditional industrial factories and the optimization of multi-energy supply systems in new factories. Physical sciences/Energy science and technology Physical sciences/Engineering Multi-Energy Expansion Industrial Parks Genetic Algorithm Optimization Method Full Text Additional Declarations No competing interests reported. Supplementary Files 7.21supplementary.docx Cite Share Download PDF Status: Published Journal Publication published 14 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 Sep, 2025 Reviews received at journal 21 Sep, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviews received at journal 20 Aug, 2025 Reviews received at journal 20 Aug, 2025 Reviewers agreed at journal 20 Aug, 2025 Reviewers agreed at journal 20 Aug, 2025 Reviewers invited by journal 20 Aug, 2025 Editor assigned by journal 07 Aug, 2025 Editor invited by journal 07 Aug, 2025 Submission checks completed at journal 03 Aug, 2025 First submitted to journal 02 Aug, 2025 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-7172321","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":504071397,"identity":"ba5debbc-dfc3-463a-887a-55ebdb0cb331","order_by":0,"name":"Shengwei Guo","email":"","orcid":"","institution":"China National Offshore Oil Corporation","correspondingAuthor":false,"prefix":"","firstName":"Shengwei","middleName":"","lastName":"Guo","suffix":""},{"id":504071398,"identity":"05379856-7ef7-4a5d-ab1a-55d74da2748c","order_by":1,"name":"Hua Wei","email":"","orcid":"","institution":"China National Offshore Oil 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