[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128723-en":3,"doc-seo-128723-105":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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":11,"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},128723,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","A Two-Level Machine Learning Framework for Managing EV Charging and Renewable Energy Curtailment in Smart Grids","Increasing integration of electric vehicles (EVs) and renewable energy sources (RES) into power grids creates operational difficulties in forecasting dynamic demand and maintaining stability. The framework proposes a two-level machine learning (ML) and optimisation approach for intelligent energy management in EV- and RES-integrated smart grids. Ensemble predictors using Random Forest and Gradient Boosting forecast EV charging demand and renewable generation, then feed a multi-objective particle swarm optimisation routine. The optimisation jointly minimises power losses, improves EV charging scheduling, reduces renewable curtailment, and maintains voltage stability, validated on a modified IEEE 14-bus system.","A Two-Level Machine Learning Framework for Managing EV Charging and Renewable Energy Curtailment in Smart Grids  \nFatemeh Nasr Esfahani School of Engineering Lancaster University  \nLancaster, UK  \n[f.nasresfahani@lancaster.ac.uk](f.nasresfahani@lancaster.ac.uk)  \nNeeraj Suri  \nSchool of Computing and Communications Lancaster University  \nLancaster, UK  \n[neeraj.suri@lancaster.ac.uk](neeraj.suri@lancaster.ac.uk)  \nXiandong Ma School of Engineering Lancaster University  \nLancaster, UK  \n[xiandong.ma@lancaster.ac.uk](xiandong.ma@lancaster.ac.uk)  \nAbstract—The increasing integration of electric vehicles (EVs) and renewable energy sources (RES) into power grids introduces significant challenges in managing dynamic energy demands and ensuring grid stability. This paper proposes a comprehensive twolevel machine learning (ML) and optimisation framework for intelligent energy management in EV-and RES-integrated smart grids. In the prediction layer, supervised ML models, including Random Forest (RF) and Gradient Boosting (GB), accurately forecast EV charging demand and renewable generation. These forecasts are then fed into the optimisation layer, where a multi-objective particle swarm optimisation (PSO) algorithm minimises power losses, optimises EV charging schedules, and reduces renewable curtailment while ensuring voltage stability. The framework is evaluated on a modified IEEE 14-bus system incorporating EV charging stations, photovoltaics (PV), and wind turbines. Simulation results validate the effectiveness of the proposed framework, demonstrating a reduction in renewable energy curtailment and improved computational efficiency compared to benchmark optimisation methods.  \nIndex Terms—Electric Vehicles, Smart Grids, Renewable Energy Integration, Machine Learning.  \nI. INTRODUCTION  \nAs modern power systems evolve to meet the increasing demand for cleaner and more efficient energy solutions, electric vehicles (EVs) and renewable energy sources (RESs) play an essential role in decarbonising both the electricity and transportation sectors [1]–[3] . However, the large-scale integration of EVs and RESs introduces complex technical challenges for modern power grids [4]–[6] . In particular, the rapid adoption of EVs is revolutionising the transportation sector while simultaneously reshaping power systems by introducing dynamic, decentralised, and unpredictable energy demands [4] . Unlike traditional loads, EV charging behaviour is driven by consumer preferences and travel habits, resulting in demand spikes that are difficult to predict and manage. High penetration of RESs further compounds these challenges due to their variable and intermittent generation [5], [6] .  \nThe simultaneous variability of the demand for EV charging and the generation of RES disrupts the stability of the grid, causing voltage fluctuations and frequency deviations [4], [6],[7] . Traditional centralised energy management systems are of-  \nten inadequate for managing these dynamics, prompting a shift towards distributed, intelligent energy management strategies aimed at improving reliability and operational efficiency [8] .  \nVarious distributed optimisation frameworks have been developed to enhance grid stability under high RES and EV penetration. For example, a joint distributed optimisation approach in [4] addresses voltage control and coordinated scheduling of energy storage and EV charging, but it lacks predictive capabilities for proactive management. More advanced strategies, including hierarchical control methods in [9], [10] and twostage optimisations combining particle swarm optimisation (PSO) and mixed-integer linear programming (MILP) in [7], offer improvements in scalability, resilience, and computational efficiency. However, even these approaches largely rely on fixed control structures, limiting adaptability to dynamic, real-time grid conditions.  \nTo address the limitations of fixed and reactive control  \nstrategies, machine learning (ML) techniq","cbCaibx9xKTduaHf","https://ap.wps.com/l/cbCaibx9xKTduaHf","pdf",1635324,3,1,"English","en",105,"# Abstract\n# Index Terms\n# I. Introduction\n## Challenges of EV and RES integration\n## Distributed optimisation approaches\n## ML-driven proactive energy management\n## Research gap and contribution","[{\"question\":\"What problem does the two-level framework address in smart grids?\",\"answer\":\"It addresses the difficulty of managing dynamic EV charging demand and intermittent RES generation while preserving grid stability, including voltage stability and reduced curtailment.\"},{\"question\":\"How does the framework use machine learning in the prediction layer?\",\"answer\":\"It employs supervised ensemble models—Random Forest and Gradient Boosting—to forecast EV charging demand and renewable generation.\"},{\"question\":\"What does the optimisation layer optimise, and how?\",\"answer\":\"A multi-objective particle swarm optimisation algorithm minimises power losses, improves EV charging schedules, reduces renewable energy curtailment, and ensures voltage stability.\"}]","A Two-Level Machine Learning Framework 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problem does the two-level framework address in smart grids?","Question",{"text":75,"@type":76},"It addresses the difficulty of managing dynamic EV charging demand and intermittent RES generation while preserving grid stability, including voltage stability and reduced curtailment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the framework use machine learning in the prediction layer?",{"text":80,"@type":76},"It employs supervised ensemble models—Random Forest and Gradient Boosting—to forecast EV charging demand and renewable generation.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the optimisation layer optimise, and how?",{"text":84,"@type":76},"A multi-objective particle swarm optimisation algorithm minimises power losses, improves EV charging schedules, reduces renewable energy curtailment, and ensures voltage 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