[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124704-en":3,"doc-seo-124704-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124704,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Achieving High Renewable Energy Integration in Smart Grids with Machine Learning - Doctoral Dissertation","Integration of high renewable energy levels into smart grids is essential for a sustainable, efficient energy infrastructure, yet it introduces major technical and operational difficulties due to the intermittency and uncertainty of renewable energy sources. Because renewable energy must rely on energy storage systems, charge/discharge control becomes central, while added storage and renewable capabilities expand control, communication, monitoring, and cybersecurity attack surface. This dissertation investigates reinforcement learning methods to optimize energy storage system behavior and generator/grid components, evaluating performance using simulation studies on real-world datasets.","Achieving High Renewable Energy Integration in Smart Grids with Machine Learning  \nA dissertation submitted in partial fulfillment of the requirements for the degree of  \nDoctor of Philosophy in Engineering with a concentration in Electrical Engineering  \nby  \nYaze Li  \nTsinghua University  \nBachelor of Science in Electronic Engineering, 2017  \nAugust 2023  \nUniversity of Arkansas  \nThis dissertation is approved for recommendation to the Graduate Council.  \n\n| Jingxian Wu, Ph.D.\u003Cbr>Dissertation Director |\n| --- |\n| Jeff Dix, Ph.D.\u003Cbr>Committee member |\n\nRoy McCann, Ph.D. Committee member  \nQinghua Li, Ph.D. Committee member  \nYanjun Pan, Ph.D. Committee member  \nABSTRACT  \nThe integration of high levels of renewable energy into smart grids is crucial for achieving a sustainable and efficient energy infrastructure. However, this integration presents significant technical and operational challenges due to the intermittent nature and inherent uncertainty of renewable energy sources (RES) . Therefore, the energy storage system (ESS) has always been bound to renewable energy, and its charge and discharge control has become an important part of the integration. The addition of RES and ESS comes with their complex control, communication, and monitor capabilities, which also makes the grid more vulnerable to attacks, brings new challenges to the cybersecurity. A large number of works have been devoted to the optimization integration of the RES and ESS system to the traditional grid, along with combining the ESS scheduling control with the traditional Optimal Power Flow (OPF) control. Cybersecurity problem focusing on the RES integrated grid has also gradually aroused researchers’ interest.  \nIn recent years, machine learning techniques have emerged in different research field including optimizing renewable energy integration in smart grids. Reinforcement learning (RL), which trains agent to interact with the environment by making sequential decisions to maximize the expected future reward, is used as an optimization tool. This dissertation explores the application of RL algorithms and models to achieve high renewable energy integration in smart grids.  \nThe research questions focus on the effectiveness, benefits of renewable energy integration to individual consumers and electricity utilities, applying machine learning techniques in optimizing the behaviors of the ESS and the generators and other components in the grid.  \nThe objectives of this research are to investigate the current algorithms of renewable energy integration in smart grids, explore RL algorithms, develop novel RL-based models and algorithms for optimization control and cybersecurity, evaluate their performance through simulations on real-world data set, and provide practical recommendations for implementation.  \nThe research approach includes a comprehensive literature review to understand the challenges and opportunities associated with renewable energy integration. Various optimization algorithms, such as linear programming (LP), dynamic programming (DP) and various RL algorithms, such as Deep Q-Learning (DQN) and Deep Deterministic Policy Gradient (DDPG), are applied to solve problems during renewable energy integration in smart grids.  \nSimulation studies on real-world data, including different types of loads, solar and wind energy profiles, are used to evaluate the performance and effectiveness of the proposed machine learning techniques. The results provide insights into the capabilities and limitations of machine learning in solving the optimization problems in the power system. Compared with traditional optimization tools, the RL approach has the advantage of real-time implementation, with the cost being the training time and unguaranteed model performance. Recommendations and guidelines for practical implementation of RL algorithms on power systems are provided in the appendix.  \nACKNOWLEDGEMENTS  \nI would like to express my gratefulness to my advisor, Dr. J","cbCaitJ2CkKtd3Gp","https://ap.wps.com/l/cbCaitJ2CkKtd3Gp","pdf",4195016,1,199,"English","en",105,"# Introduction\n## Background and Motivation\n## Research Objectives\n## Dissertation outline","[{\"question\":\"Why is integrating high renewable energy into smart grids challenging?\",\"answer\":\"It is challenged by the intermittency and inherent uncertainty of renewable energy sources, which complicate technical and operational control in the grid.\"},{\"question\":\"What role does an energy storage system play in renewable integration?\",\"answer\":\"Because energy storage is coupled to renewable generation, its charge and discharge control becomes a key part of successfully integrating renewable energy into smart grids.\"},{\"question\":\"How does the dissertation use machine learning in this context?\",\"answer\":\"It applies reinforcement learning algorithms to optimize the behaviors of energy storage systems and other grid components, and then evaluates them through simulations using real-world data.\"}]","Achieving High Renewable Energy Integration in Smart Grids with Machine Learning - Doctoral Dissertation | PDF",1785894015,501,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"achieving-high-renewable-energy-integration-in-smart-grids-with-machine-learning-doctoral-dissertation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/achieving-high-renewable-energy-integration-in-smart-grids-with-machine-learning-doctoral-dissertation/124704/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is integrating high renewable energy into smart grids challenging?","Question",{"text":75,"@type":76},"It is challenged by the intermittency and inherent uncertainty of renewable energy sources, which complicate technical and operational control in the grid.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role does an energy storage system play in renewable integration?",{"text":80,"@type":76},"Because energy storage is coupled to renewable generation, its charge and discharge control becomes a key part of successfully integrating renewable energy into smart grids.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the dissertation use machine learning in this context?",{"text":84,"@type":76},"It applies reinforcement learning algorithms to optimize the behaviors of energy storage systems and other grid components, and then evaluates them through simulations using real-world data.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]