Research on Power System Optimization Dispatching and Power Market Trading Strategies Based on Deep Reinforcement Learning

Research on Power System Optimization Dispatching and Power Market Trading Strategies Based on Deep Reinforcement Learning | 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 Research on Power System Optimization Dispatching and Power Market Trading Strategies Based on Deep Reinforcement Learning Zhi Bin Jing, Shao Qing Yuan, Xiao Fan Lv, Hong Wei Kang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6788531/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 integration of renewable energy sources into power systems has made the optimization of dispatching and market trading approaches vital for preserving system dependability and economic efficacy. The research presents optimization tactics for power system dispatch and market trading through Deep Reinforcement Learning (DRL). A proposed model integrates key mechanisms of the power system, including power generation units, energy storage systems, external grids, and market-based instruments. A main challenge in power systems is the uncertainty of renewable energy generation, significant to unproductive dispatching and market volatility. The examination uses the Power System Dispatch and Market Trading dataset, with data preprocessing performed using Min-Max normalization to advance training performance. To address this issue, a dual approach is presented for optimizing power system dispatch, involving direct control of Thermostatically Controlled Loads (TCLs) and indirect control of price-responsive loads. An advanced deep reinforcement learning algorithm, the Optimal Power Reinforced Twin Deterministic Policy Gradient (OPRTDPG), is used to solve the optimization problem in the dynamic and uncertain situation. The OPRTDPG technique adjusts to real-time fluctuations in renewable energy output and market conditions, providing an optimal decision-making approach that reduces costs and improves system dependability. The results demonstrate that the OPRTDPG algorithm enhanced the power supply system stability by decreasing market price variations by 10%, improving energy use by 15%, and decreasing the system cost by 12%. The outcomes the efficiency of OPRTDPG in optimizing power system dispatch and market trading tactics in real-world power systems. Power System Optimization Deep Reinforcement Learning (DRL) Wind-Storage Cooperation Market Trading Strategies Optimal Power Reinforced Twin Deterministic Policy Gradient (OPRTDPG) Renewable Energy Uncertainty Energy Storage Systems 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-6788531","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472160267,"identity":"d87f33b9-6626-49b7-b757-b4afcaeddf65","order_by":0,"name":"Zhi Bin Jing","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIiWNgGAWjYBAC+xmM7Q8SGOR4GBiYj3/+UwEUYmdgYGzAo4VxBmObQQIDG1ALWxoDzxkGBh5mgloY2CSAyoFMHjUG3jYitDBLN7cZPNzBJmPOv4btgeQ8m8T9zMwHH85gsJPTxaGPTeZg+4PEM2w8ljPeHjcw3JaW2MPMlmy4gSHZ2OwAdi08EoltBoltbDwGN84lSCRuOwzUwmMm+YDhQOI2HFokgFokIFrOGEgcnEOEFgOIFjceg/M9ZpKNDVAtGwhoATosB2gLW7Ixw7E0457DQL/MMMDtF/sZ6c8f/myzsDc4f/jgY4YaG9n29uaDD3sq7ORwaUHyVQKK7YSUgwA/QUNHwSgYBaNgpAIAztZc65ynV6UAAAAASUVORK5CYII=","orcid":"","institution":"Power Dispatch Control Branch of Inner Mongolia Power(Group) Co., Ltd. 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The research presents optimization tactics for power system dispatch and market trading through Deep Reinforcement Learning (DRL). A proposed model integrates key mechanisms of the power system, including power generation units, energy storage systems, external grids, and market-based instruments. A main challenge in power systems is the uncertainty of renewable energy generation, significant to unproductive dispatching and market volatility. The examination uses the Power System Dispatch and Market Trading dataset, with data preprocessing performed using Min-Max normalization to advance training performance. To address this issue, a dual approach is presented for optimizing power system dispatch, involving direct control of Thermostatically Controlled Loads (TCLs) and indirect control of price-responsive loads. 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