Marine Predators Algorithm for Optimizing GENCO Surplus in Power Markets: A Metaheuristic-Based Approach

Marine Predators Algorithm for Optimizing GENCO Surplus in Power Markets: A Metaheuristic-Based Approach | 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 Marine Predators Algorithm for Optimizing GENCO Surplus in Power Markets: A Metaheuristic-Based Approach Vankadara Sampath Kumar, Saibal Chatterjee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7743320/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 Optimizing bidding strategies of generating companies (GENCOs) in competitive electricity markets is crucial for maximizing surplus while ensuring efficient market operation. This paper presents a novel metaheuristic framework based on the Marine Predators Algorithm (MPA), designed to address the complex optimization landscape of power bidding in deregulated markets. Inspired by the foraging behaviour of marine predators, MPA is employed to generate strategic bids that enhance GENCO profitability. The proposed method is benchmarked against state-of-the-art optimization techniques, including Genetic Algorithm (GA), Intelligent Programmed Genetic Algorithm (IPGA), Simulated Annealing (SA), Particle Swarm Optimization (PSO), Teaching–Learning-Based Optimization (TLBO), and Biogeography-Based Optimization (BBO). Simulation studies involving six GENCOs and two consumer agents confirm that MPA exhibits superior convergence speed and surplus maximization compared to traditional methods. These findings underscore the effectiveness of MPA in supporting decision-making under competitive energy trading scenarios. Marine Predators Algorithm GENCO bidding power market optimization metaheuristic algorithms strategic decision-making convergence analysis Full Text Additional Declarations No competing interests reported. Supplementary Files Appendix1.docx 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-7743320","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":529197143,"identity":"59836ff4-9066-46c2-ac0e-4060eca53a42","order_by":0,"name":"Vankadara Sampath 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