[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83554-en":3,"doc-seo-83554-105":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":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83554,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Deadline-Aware Electric Vehicles Charging with Distribution Transformer Overload Mitigation","High electric-vehicle (EV) adoption can overload distribution transformers when charging requests with heterogeneous departure deadlines compete for limited capacity. Existing coordination often enforces hard deadlines and strict transformer limits, which can become infeasible during severe congestion. The proposed deadline-aware charging framework explicitly trades transformer thermal aging against charging service quality under capacity constraints. It models transformer stress via a convex aging proxy and softens deadlines using penalty-weighted unmet energy. An online low-complexity policy prioritizes EVs with a marginal-cost-aware urgency index, achieving near-offline benchmark performance using only real-time information.","Deadline-Aware Electric Vehicles Charging with Distribution Transformer Overload Mitigation  \nB Hari Kiran Reddy  \narXiv :2607 .00935v1 [ ee ss . SY] 1 Jul 2026  \nAbstract—High adoption of electric vehicles (EVs) can overload distribution transformers when charging requests with heterogeneous departure deadlines compete for limited capacity. Most existing coordination schemes enforce hard deadlines and strict transformer limits, implicitly assuming feasibility and failing under severe congestion. We propose a deadline-aware EV charging framework that explicitly trades off transformer thermal aging and charging service quality under capacity-constrained operation. We model transformer stress using a convex aging proxy and soften charging deadlines via penalty-weighted unmet energy at departure. We further develop a low-complexity online charging policy that prioritizes EVs based on a marginal-costaware urgency index. We demonstrate through case studies under increasing EV penetration that the proposed approach reduces transformer aging while preferentially allocating limited capacity to time-critical EVs, closely approximating offline benchmark performance using only real-time information.  \nIndex Terms—Electric vehicle charging, distribution transformer aging, soft deadline scheduling, online optimization, assetaware demand management  \nI. INTRODUCTION  \nThe rapid growth of EV adoption is placing increasing stress on distribution-level infrastructure, particularly residential and commercial transformers that were not originally designed for large, coincident charging loads. While EV charging is inherently time-flexible, clustering of arrivals and heterogeneous departure deadlines can lead to short-duration but severe overloads, accelerating transformer insulation aging and increasing the risk of premature asset failure [1], [2] . As EV penetration continues to rise, utilities face a fundamental operational trade-off between protecting distribution assets and meeting customer charging expectations.  \nA substantial body of literature has investigated coordinated EV charging to mitigate distribution network constraints, including feeder congestion, voltage violations, and transformer overload [3]–[5] . Most existing approaches enforce EV charging deadlines and network limits as hard constraints, implicitly assuming that sufficient capacity exists to satisfy all demands. Under high EV penetration or constrained infrastructure, however, such formulations become infeasible and fail to describe realistic operating conditions in which not all charging requests can be fully served.  \nTo address this issue, several transformer-aware charging strategies have been proposed that strictly cap transformer loading to avoid overload [6], [7] . While effective at asset protection, these approaches typically curtail charging uniformly or proportionally across EVs, without accounting for heterogeneous urgency or service priorities. As a result, vehicles with imminent departures or higher operational importance may experience significant unmet charging demand, even when limited flexibility remains.  \nIn contrast, soft-deadline scheduling frameworks, widely studied in communication networks and real-time computing, allow controlled service degradation when system resources are insufficient, by penalizing deadline violations rather than forbidding them outright [8], [9] . Despite their conceptual relevance, such soft-deadline formulations have seen limited adoption in distribution-level EV charging, particularly in conjunction with asset-health metrics such as transformer thermal aging. Therefore, we study the following open critical question: How should limited distribution transformer capacity be allocated among EVs with heterogeneous departure deadlines when it is impossible to satisfy all charging requests without overloading the transformer?  \nIn this paper, we propose a deadline-aware EV charging framework that explicitly captures the operatio","cbCaibeRnDX0ABhy","https://ap.wps.com/l/cbCaibeRnDX0ABhy","pdf",282971,3,1,6,"English","en",105,"# Introduction\n# Offline Problem Formulation\n## EV Model","[{\"question\":\"Why do distribution transformers get overloaded during EV charging?\",\"answer\":\"Charging requests can arrive close together while having heterogeneous departure deadlines, which leads to short-duration but severe overloads at the distribution-transformer level. This accelerates insulation aging and increases premature asset-failure risk.\"},{\"question\":\"What problem do the authors identify with existing EV charging coordination schemes?\",\"answer\":\"Most schemes treat deadlines and transformer limits as hard constraints and assume feasibility. Under high EV penetration or constrained infrastructure, not all charging requests can be fully satisfied, so such formulations can fail.\"},{\"question\":\"How does the proposed framework balance transformer aging and charging service quality?\",\"answer\":\"It penalizes transformer stress using a convex overload/aging proxy and softens charging deadlines by penalizing unmet energy at departure rather than forbidding violations outright. This preserves graceful degradation and supports an online policy that approximates offline benchmarks using real-time information.\"}]",1784188787,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"deadline-aware-electric-vehicles-charging-with-distribution-transformer-overload-mitigation","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"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":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/deadline-aware-electric-vehicles-charging-with-distribution-transformer-overload-mitigation/83554/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-26","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why do distribution transformers get overloaded during EV charging?","Question",{"text":75,"@type":76},"Charging requests can arrive close together while having heterogeneous departure deadlines, which leads to short-duration but severe overloads at the distribution-transformer level. This accelerates insulation aging and increases premature asset-failure risk.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem do the authors identify with existing EV charging coordination schemes?",{"text":80,"@type":76},"Most schemes treat deadlines and transformer limits as hard constraints and assume feasibility. Under high EV penetration or constrained infrastructure, not all charging requests can be fully satisfied, so such formulations can fail.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed framework balance transformer aging and charging service quality?",{"text":84,"@type":76},"It penalizes transformer stress using a convex overload/aging proxy and softens charging deadlines by penalizing unmet energy at departure rather than forbidding violations outright. 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