{"paper_id":"278102b3-475a-41e7-95c2-e8995aed1715","body_text":"Adaptive Multi-Objective Optimization of Microgrid Energy Management Using Deep Reinforcement Learning Considering Battery Degradation and Renewable Uncertainty | 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 Adaptive Multi-Objective Optimization of Microgrid Energy Management Using Deep Reinforcement Learning Considering Battery Degradation and Renewable Uncertainty Mohammad Rashed M. Altimania, Ali Basem, Bakhodir Saydullaev, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6744762/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Microgrids offer enhanced resilience and efficiency but require sophisticated energy management systems (EMS) to balance conflicting objectives like cost minimization, renewable energy utilization, and component longevity, especially under uncertainty. Traditional optimization methods often rely on precise forecasts and may struggle with real-time adaptation and complex trade-offs like battery degradation. This research aimed to develop a Deep Reinforcement Learning (DRL) based EMS for optimizing microgrid operation considering operational cost, battery degradation, and renewable generation uncertainty. A Deep Q-Network (DQN) based reinforcement learning agent was trained to manage energy flows within a simulated microgrid comprising solar PV, battery storage, controllable loads, and a grid connection. The reward function incorporated operational costs, battery degradation, and renewable utilization objectives, with the agent learning control policies through environment interaction. The DRL-based EMS demonstrated effective adaptive control, achieving a 12.01% reduction in overall operational costs compared to the Model Predictive Control benchmark. The DRL agent implicitly learned strategies that reduced battery degradation by 8.19% while increasing renewable energy utilization by 10.39%. Most notably, the approach maintained robust performance under uncertainty, with only 8.9% cost increase under severe forecast errors compared to 21.5% for conventional methods. This study demonstrates the efficacy of DRL for adaptive multi-objective microgrid energy management, successfully balancing economic operation, battery health preservation, and renewable energy integration under uncertainty. Physical sciences/Energy science and technology Physical sciences/Energy science and technology/Energy infrastructure Battery Degradation Deep Reinforcement Learning Energy Management System Microgrid Optimization Multi-Objective Optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 28 Jul, 2025 Reviews received at journal 26 Jul, 2025 Reviewers agreed at journal 26 Jul, 2025 Reviews received at journal 29 Jun, 2025 Reviewers agreed at journal 19 Jun, 2025 Reviewers agreed at journal 08 Jun, 2025 Reviewers invited by journal 30 May, 2025 Editor assigned by journal 30 May, 2025 Editor invited by journal 30 May, 2025 Submission checks completed at journal 29 May, 2025 First submitted to journal 25 May, 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. 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-6744762\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":465436769,\"identity\":\"091e52c4-886c-457a-9e1a-aeefeaca8a25\",\"order_by\":0,\"name\":\"Mohammad Rashed M. 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