[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83826-en":3,"doc-seo-83826-105":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},83826,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Large-Load Demand Flexibility as Virtual Storage","Water electrolysis plants, hyperscale data centers, and aluminum potlines provide gigawatt-scale demand-side flexibility for grid balancing, operational planning, and procurement services. These resources are scheduled through per-interval power bounds and horizon energy windows, while co-located battery energy storage systems follow state-of-charge dynamics. The work proves a virtual storage equivalence that maps any feasible large-load trajectory to a storage charging trajectory under unity accounting efficiency. Curtailment opportunity costs drive dispatch, and aggregation reduces constraint complexity from O(NT) to O(T), enabling co-dispatch pricing with dominant joint savings on IEEE RTS-GMLC.","Large-Load Demand Flexibility as Virtual Storage  \nChandan Chaudhary, Student Member, IEEE,  \nMohammed Benidris, Senior Member, IEEE, and Joydeep Mitra, Fellow, IEEE  \nElectrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA  \nEmails: [chaud152@msu.edu](chaud152@msu.edu), [benidris@msu.edu](benidris@msu.edu), [and mitraj@msu.edu](and mitraj@msu.edu)  \narXiv :2607 .04564v1 [ ee ss . SY] 6 Jul 2026  \nAbstract—Water electrolysis plants, hyperscale data centers, and aluminum potlines represent gigawatts of demand-side flexibility for bulk power system balancing, operational planning, and procurement services. Such loads are scheduled through per-interval power bounds and horizon energy windows, whereas co-located battery energy storage systems (BESS) operate understate-of-charge dynamics. The two formulations share no common mathematical structure, and the joint procurement value of co-located loads and storage goes unrealized as a result. This paper establishes the connection between the two formulations through a virtual storage (VS) equivalence. Every feasible largeload trajectory under power-bound and energy-window constraints is a valid charge trajectory of a VS device that operates at unity accounting efficiency in the grid power balance. Production and service-level costs lie outside this abstraction and enter the dispatch through curtailment opportunity costs. For a portfolio co-located with a BESS, aggregation reduces the constraint count from O (NT) to O (T) and yields a co-dispatch price for both resources. Validation on the IEEE RTS-GMLC with three representative load classes shows that virtual storage delivers the dominant share of joint procurement savings. In the tested case, savings are additive because the two resources dispatch to nonoverlapping intervals, and the curtailment shadow price tracks the peak-price band onset rather than the daily peak price.  \nIndex Terms—demand flexibility, energy storage, large loads, virtual storage, co-dispatch, operational planning, RTS-GMLC  \nI. INTRODUCTION  \nThe growth of distributed energy resources (DER) and the changing role of dispatchable synchronous generation are reshaping the mix of resources used for balancing and ancillary services in bulk power systems [1], [2] . Battery energy storage systems (BESS) and large flexible industrial loads have emerged as the two principal responses to the resulting adequacy and flexibility gap. However, these resources are scheduled through structurally different formulations. Large flexible loads are managed through per-interval power boundsand horizon energy windows within day-ahead market-clearing and resource-adequacy models, while BESS are operated through state-of-charge dynamics [3] . The two formulations are incompatible and do not admit a unified dispatch, so significant system value goes unrealized. The joint operation of BESS and large flexible loads, particularly at the transmission level, remains an open problem.  \nResearch on grid-scale storage has concentrated on optimizing charge and discharge decisions against uncertain DER output. On the demand side, flexibility models for residential, commercial, and industrial loads have been surveyed in [4] . The aggregate flexibility of thermostatically controlled loads can be represented as a convex polytope in powertime space [5], and this result extends to a polymatroid characterization for large populations [6] . Flexibility envelopeshave also been proposed as a planning tool that integrates supply-side and demand-side contributions [7] . However, none of these works establishes a formal equivalence between large-  \nload curtailment trajectories and storage charge trajectories, nor do they provide a unified formulation to dispatch flexible loads jointly with co-located BESS.  \nLarge industrial and commercial loads occupy a position in this landscape that neither strand of prior literature addresses. Water electrolysis plants adjust power wit","cbCaipyDhoV0yscq","https://ap.wps.com/l/cbCaipyDhoV0yscq","pdf",367063,3,1,6,"English","en",105,"# I. Introduction\n## Background and scheduling mismatch\n## Prior research on grid-scale storage and demand flexibility\n## Motivation from large-load flexibility characteristics\n## Practical consequences and need for unified dispatch","[{\"question\":\"What does the paper claim about large-load flexibility scheduling versus battery storage scheduling?\",\"answer\":\"Large flexible loads and BESS are scheduled using structurally different formulations, so they cannot be jointly dispatched in a unified way under the existing modeling approaches.\"},{\"question\":\"How does the paper connect large-load curtailment trajectories to storage behavior?\",\"answer\":\"It establishes a virtual storage equivalence showing that any feasible large-load trajectory under power-bound and energy-window constraints corresponds to a valid virtual storage charge trajectory with unity accounting efficiency in grid power balance.\"},{\"question\":\"What impact does virtual storage have on joint procurement and co-dispatch?\",\"answer\":\"For a portfolio co-located with BESS, aggregation reduces the constraint count and yields a co-dispatch price for both resources. Validation on IEEE RTS-GMLC indicates virtual storage delivers the dominant share of joint procurement savings.\"}]",1784190702,15,{"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},"large-load-demand-flexibility-as-virtual-storage","",{"@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/large-load-demand-flexibility-as-virtual-storage/83826/",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","2026-07-24","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},"What does the paper claim about large-load flexibility scheduling versus battery storage scheduling?","Question",{"text":75,"@type":76},"Large flexible loads and BESS are scheduled using structurally different formulations, so they cannot be jointly dispatched in a unified way under the existing modeling approaches.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper connect large-load curtailment trajectories to storage behavior?",{"text":80,"@type":76},"It establishes a virtual storage equivalence showing that any feasible large-load trajectory under power-bound and energy-window constraints corresponds to a valid virtual storage charge trajectory with unity accounting efficiency in grid power balance.",{"name":82,"@type":73,"acceptedAnswer":83},"What impact does virtual storage have on joint procurement and co-dispatch?",{"text":84,"@type":76},"For a portfolio co-located with BESS, aggregation reduces the constraint count and yields a co-dispatch price for both resources. 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