[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122101-en":3,"doc-seo-122101-105":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},122101,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Leveraging machine learning for efficient EV integration as mobile battery energy storage systems - Exploring strategic frameworks and incentives","Electric vehicles are reshaping the energy landscape and require efficient integration mechanisms that can engage prosumers, yet active community involvement faces major barriers. Key obstacles include system uncertainties, time-related issues, optimized charging, real-time decision needs, privacy concerns, and battery degradation driven by unpredictable driver behavior and traffic conditions. The review surveys supervised and unsupervised learning, covering prediction, clustering, dimensionality reduction, and generative modeling, and highlights reinforcement learning for real-time control, then evaluates strategic operational frameworks for integration.","Journal of Energy Storage 92 (2024) 112151  \nContents lists available at ScienceDirect Journal of Energy Storage  \njournal [homepage: www.elsevier.com/locate/est](homepage: www.elsevier.com/locate/est)  \n| Review article\u003Cbr>Leveraging machine learning for efficient EV integration as mobile battery energy storage systems: Exploring strategic frameworks and incentives Mohammad Javad Salehpour *, M.J. Hossain\u003Cbr>School of Electrical and Data Engineering, University of Technology Sydney, Ultimo NSW 2007, New South Wales, Australia |  |  |  |\n| --- | --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Electric vehicles\u003Cbr>Machine learning Power systems Pricing Vehicle-to-grid |  | The emergence of electric vehicles is reshaping the energy landscape, requiring the development of innovative energy integration mechanisms to engage prosumers. However, current methods face numerous challenges when actively involving communities. Some key challenges include system uncertainties and time issues, optimizing charging strategies, real-time decision-making needs, privacy concerns, and battery degradation. For instance, the unpredictability of driver behavior and traffic conditions introduces complexities in devising efficient energy integration strategies and economic incentive models. The intricate interplay of these factors necessitates advanced computational techniques, making machine learning an invaluable tool. This paper concisely reviews prominent machine learning algorithms, encompassing supervised and unsupervised learning, focusing on their distinctive capabilities in prediction, clustering, dimensionality reduction, and generative modeling. Additionally, it explores reinforcement learning, emphasizing its aptitude for real-time decision-making. The focus of the study lies in the application of advanced algorithms, specifically examining their effectiveness in various strategic operational frameworks. The aim is to integrate electric vehicles into power systems efficiently. These frameworks include bargaining, contracts, auctions, game theory, and economic incentives such as pricing and cost-profit optimization. Each application includes a concise overview of the general methodology and investigates in-depth discussions regarding the suitability and challenges of deploying machine learning techniques. This paper will guide industry professionals in implementing solutions for electric vehicle dispatching problems and provide valuable insights to academics for further research and development. |  |\n\n1. Introduction  \nThe widespread adoption of electric vehicles (EVs) has the potential to revolutionize the transportation sector. From an environmental perspective, EVs powered by renewable energy sources significantly decrease greenhouse gas emissions, which is crucial in addressing climate change. Moreover, they eliminate tailpipe emissions, resulting in cleaner air in urban areas and better public health outcomes. Traditional combustion engine vehicles release pollutants like nitrogen oxides (NOx), particulate matter (PM), and volatile organic compounds (VOCs), which are linked to respiratory illnesses, cardiovascular diseases, and other health issues. By substituting these vehicles with EVs, cities can experience a decrease in harmful air pollutants, leading to cleaner and healthier environments for residents. This transition also reduces dependence on fossil fuels, enhances energy security, and mitigates environmental damage associated with their extraction and  \ntransportation.  \nFrom a social perspective, EV integration offers a quieter future with reduced traffic noise pollution, creating a more peaceful and livable urban environment. Additionally, vehicle-to-grid (V2G) technology facilitates a more stable and efficient power grid, potentially reducing the need for additional power plants. The transition to EVs can also create new design, manufacturing, and servicing jobs, revitalize economies, and f","cbCaic742CfXrEah","https://ap.wps.com/l/cbCaic742CfXrEah","pdf",3078017,1,26,"English","en",105,"# Introduction\n## Challenges in EV integration\n## Incentives and strategic frameworks\n# Machine learning approaches\n## Supervised and unsupervised learning\n## Reinforcement learning for real-time decisions\n# Deployment frameworks and incentives","[{\"question\":\"Why is machine learning important for integrating electric vehicles into power systems?\",\"answer\":\"Machine learning helps address uncertainties and time-critical requirements in EV integration, including optimized charging and real-time decision-making, while also supporting economic incentive modeling.\"},{\"question\":\"What machine learning categories does the review focus on?\",\"answer\":\"It reviews prominent supervised and unsupervised learning methods for prediction, clustering, dimensionality reduction, and generative modeling, and it also discusses reinforcement learning for real-time decision-making.\"},{\"question\":\"Which strategic frameworks for EV integration are highlighted?\",\"answer\":\"The review explores bargaining, contracts, auctions, game theory, and economic incentives such as pricing and cost-profit optimization, linking each framework to deployment methodology and challenges.\"}]","Leveraging machine learning for efficient EV integration as mobile battery energy storage systems - Exploring strategic frameworks and incentives | PDF",1785808827,66,{"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},"leveraging-machine-learning-for-efficient-ev-integration-as-mobile-battery-energy-storage-systems-exploring-strategic-frameworks-and-incentives","",{"@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/leveraging-machine-learning-for-efficient-ev-integration-as-mobile-battery-energy-storage-systems-exploring-strategic-frameworks-and-incentives/122101/",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","2026-08-04",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 machine learning important for integrating electric vehicles into power systems?","Question",{"text":75,"@type":76},"Machine learning helps address uncertainties and time-critical requirements in EV integration, including optimized charging and real-time decision-making, while also supporting economic incentive modeling.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning categories does the review focus on?",{"text":80,"@type":76},"It reviews prominent supervised and unsupervised learning methods for prediction, clustering, dimensionality reduction, and generative modeling, and it also discusses reinforcement learning for real-time decision-making.",{"name":82,"@type":73,"acceptedAnswer":83},"Which strategic frameworks for EV integration are highlighted?",{"text":84,"@type":76},"The review explores bargaining, contracts, auctions, game theory, and economic incentives such as pricing and cost-profit optimization, linking each framework to deployment methodology and challenges.","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"]