[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118142-en":3,"doc-seo-118142-105":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},118142,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Improving Renewable Energy Operations in Smart Grids through Machine Learning","This paper reviews how machine learning strengthens renewable energy operations in smart grids, addressing the growing reliance on eco-friendly sources such as wind and solar. Renewable generation remains highly uncertain, causing curtailment and operational difficulties when dispatch systems cannot match rapidly changing supply and demand. The paper examines learning techniques that enable automated decision-making and improved planning, including energy-efficiency enhancements, seamless integration, data understanding in smart grids, consumption forecasting, and improved power-system security.","Improving Renewable Energy Operations in Smart Grids through Machine Learning  \nDr. P. Muralidharan1, Dr K Subramani2, Mohammed I. Habelalmateen3, Rajesh Pant4, Aishwarya Mishra5, and Sharayu Ikhar6  \n*Assistant Professor ,School of Business and Management , Christ university yeshwanthpur campus Bangalore.  \n†Assistant Professor ,School of Business and Management , CHRIST(Deemed to be University )  \nBangalore Yeshwantpur Campus ‡The Islamic university, Najaf, Iraq  \n§ Uttaranchal Institute of Management , Uttaranchal University Uttarakhand, India  \n**Department of Computer Science & Engineering, IES College of Technology,IES University, Bhopal, Madhya Pradesh 462044 India.  \n6Researcher, Yashika Journal Publications Pvt Ltd, Wardha, Maharashtra, India Email: [sharyu.ikhar@gmail.com](sharyu.ikhar@gmail.com)  \nAbstract.This paper reviews the work in the areas of machine learning's role in bolstering renewable energy within smart grids. As the global shift towards eco-friendly energy sources such as wind and solar gains momentum, the challenge lies in managing these unpredictable energy sources efficiently. Innovative learning techniques are emerging as potential solutions to these challenges, optimising the use and benefits of renewable energies. Furthermore, the landscape of energy distribution is evolving, with a growing emphasis on automated decision-making software. Central to this evolution is machine learning, with its applications spanning a range of sectors. These include enhancing energy efficiency, seamlessly integrating green energy sources, making sense of vast data sets within smart grids, forecasting energy consumption patterns, and fortifying the security of power systems. Through a comprehensive review of these areas, this paper highlights the potential of machine  \nlearning in paving the way for a greener, more efficient energy future.  \n*Corresponding Authour : [muralidharan.p@christuniversity.in](muralidharan.p@christuniversity.in)  \n†[Subramani.k@christuniversity.in](Subramani.k@christuniversity.in)  \n‡[mohammed.ha@iunajaf.edu.iq](mohammed.ha@iunajaf.edu.iq)  \n§[rajeshpant.mech@gmail.com](rajeshpant.mech@gmail.com)  \n**[research@iesbpl.ac.in](research@iesbpl.ac.in)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \n1 Introduction  \nThe Growing Importance of Renewable Energy and Smart Grids  \nThe global shift towards renewable energy sources like solar and wind power is not only a response to environmental concerns but also a significant trend in modern energy systems. According to the International Renewable Energy Agency (IRENA), the global renewable generation capability reached 2351 GW by the end of 2018, with solar and wind energy accounting for approximately 89.1% of new energy capabilities. However, the integration of these renewable sources into energy systems is not without challenges. High penetrationsof wind and solar energy have led to severe curtailment issues, reflecting the extent to which these renewable sources can be effectively utilized. The intrinsic uncertainty in renewable energy generation, due to factors like time-varying wind speed and sunlight intensity, further complicates the design of effective accommodation mechanisms.  \nThe Challenge of Accommodation and Dispatch  \nOne of the main reasons for the curtailment of wind and solar energy is the mismatch between the rapid growth of renewable energy capabilities and the limitations of existing dispatch systems. Traditional energy resources from plants continue to dominate system planning, while dispatch strategies for renewable energy often fall short in matching the load with generated power. This is especially true during operations when high curtailment occurs at lower loads due to the absence of fast and effective dispatch strategies. The","cbCaiefdoanwHmAH","https://ap.wps.com/l/cbCaiefdoanwHmAH","pdf",1775818,1,9,"English","en",105,"# Introduction\n## The Growing Importance of Renewable Energy and Smart Grids\n## The Challenge of Accommodation and Dispatch\n## Machine Learning as a Solution\n## Objectives and Structure of This Study\n# Review and discussion","[{\"question\":\"为什么可再生能源在并网运行中会出现削减（curtailment）问题？\",\"answer\":\"当风能和太阳能的装机增长速度超过现有调度系统的能力时，就会导致供给与负荷匹配不足，尤其在低负荷时更明显。再加上风速与日照的时间变化带来不确定性，使得有效消纳与调度更难实现。\"},{\"question\":\"机器学习如何被用来缓解可再生能源调度与接纳能力不足？\",\"answer\":\"论文指出，传统线性或二次规划方法难以适应最新数据，而深度强化学习能够根据电力系统的变化动态调整策略，从而提升能源与经济调度效果。\"},{\"question\":\"论文重点讨论了哪些机器学习在智能电网中的应用方向？\",\"answer\":\"文中涵盖提升能源效率、整合绿色能源来源、对智能电网中的海量数据进行理解、预测能耗模式，以及增强电力系统安全等内容。\"}]","Improving Renewable Energy Operations in Smart Grids through Machine Learning | PDF",1785681861,23,{"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},"improving-renewable-energy-operations-in-smart-grids-through-machine-learning","",{"@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/improving-renewable-energy-operations-in-smart-grids-through-machine-learning/118142/",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","2026-08-02",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},"为什么可再生能源在并网运行中会出现削减（curtailment）问题？","Question",{"text":75,"@type":76},"当风能和太阳能的装机增长速度超过现有调度系统的能力时，就会导致供给与负荷匹配不足，尤其在低负荷时更明显。再加上风速与日照的时间变化带来不确定性，使得有效消纳与调度更难实现。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"机器学习如何被用来缓解可再生能源调度与接纳能力不足？",{"text":80,"@type":76},"论文指出，传统线性或二次规划方法难以适应最新数据，而深度强化学习能够根据电力系统的变化动态调整策略，从而提升能源与经济调度效果。",{"name":82,"@type":73,"acceptedAnswer":83},"论文重点讨论了哪些机器学习在智能电网中的应用方向？",{"text":84,"@type":76},"文中涵盖提升能源效率、整合绿色能源来源、对智能电网中的海量数据进行理解、预测能耗模式，以及增强电力系统安全等内容。","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]