[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84745-en":3,"doc-seo-84745-105":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},84745,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Storage as a Transmission Asset (SATA) for Large-Load Congestion Relief","Hyperscale data centers and other large concentrated loads can strain existing transmission networks, forcing operators to curtail load, delay interconnections, or reinforce infrastructure when import corridors lack transfer capability. Storage-as-transmission asset (SATA) provides a non-wires alternative by offering operator-directed support to constrained corridors. The study quantifies day-ahead operator-directed SATA reliability via DC optimal power flow co-optimizing generation, ESS dispatch, and load curtailment across Monte Carlo scenarios, using EENS, LOLH, and CVaR metrics.","Storage as a Transmission Asset (SATA) for Large-Load Congestion Relief  \nAbanish Tiwari, Chandan Chaudhary, Student Member, IEEE, Yansong Pei, Member, IEEE, Mohammed Ben-Idris, Senior Member, IEEE, and Joydeep Mitra, Fellow, IEEE  \nElectrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA E-mails: [tiwariab@msu.edu](tiwariab@msu.edu) ; [chaud152@msu.edu](chaud152@msu.edu) ; [peiyanso@msu.edu](peiyanso@msu.edu) ; [benidris@msu.edu](benidris@msu.edu) ; [mitraj@msu.edu](mitraj@msu.edu)  \narXiv :2607 .04545v1 [ ee ss . SY] 5 Jul 2026  \nAbstract—Hyperscale data centers and other large concentrated loads can impose substantial new demand on existing transmission networks. If import corridors lack sufficient transfer capability, operators may need to curtail load, delay interconnection, or reinforce the network to maintain reliable service. An energy storage system (ESS) deployed as a storage-astransmission asset (SATA) offers a non-wires alternative by providing operator-directed support to constrained import corridors. However, the operating-level reliability value of SATA dispatch remains insufficiently quantified. This paper evaluates operatordirected SATA using a day-ahead DC optimal power flow that cooptimizes generation, ESS dispatch, and load curtailment across Monte Carlo scenarios of demand and generator availability. Operating reliability is assessed using expected energy not served (EENS), loss-of-load hours (LOLH), and the conditional value at risk (CVaR) of daily unserved energy. Congestion-price and flow-sensitivity metrics are used to identify the limiting corridor and storage location. The interconnection is then screened to determine whether SATA is suitable, reinforcement is required, or storage would provide little transmission value. Results show that operator-directed SATA reduces average unserved energy, loss-of-load exposure, and tail risk compared with deploying the same ESS for pure arbitrage. These results demonstrate that the operating designation of storage is a primary driver of its transmission value.  \nIndex Terms—conditional value-at-risk (CVaR), congestion relief, data centers, energy storage system (ESS), large-load, reliability, storage as transmission asset (SATA).  \nI. INTRODUCTION  \nRapid growth in large, concentrated loads, especially AI data centers, is creating new challenges for transmission systems. Data center electricity consumption is projected to represent 6.7–12% of national electricity demand by 2028 [1] . In the United States, transmission infrastructure often requires several years, and in some cases nearly a decade, to plan, permit, and construct, whereas large loads are typically developed and interconnected within two to three years. Thermal limitson existing corridors therefore delay load interconnection and constrain system adequacy.  \nEnergy storage systems (ESS) deployed at a thermally constrained corridor can provide temporary congestion relief. The ESS charges during off-peak hours and discharges during peak demand, which reduces corridor loading and raises the transferable power capacity of the existing infrastructure. This capability allows a large load to be served on the existing corridor while long-term transmission upgrades are planned or constructed.  \nThe regulatory framework for this application has been established in recent years [2] . Storage operated as a transmission asset (SATA) is dispatched under the direction of the  \ntransmission operator, maintains a sufficient state of charge to perform its transmission function, and recovers costs through regulated transmission rates [2] . By contrast, storage operated in place of a transmission asset (SIPTA) participates in electricity markets and recovers costs through market revenues, while dual-use storage combines both functions [2], [3] . Transmission-only storage tariffs filed by MISO, ISO-NE, and SPP between 2020 and 2023 restrict these designated assets to transmission servic","cbCaiaAVptQqnjWE","https://ap.wps.com/l/cbCaiaAVptQqnjWE","pdf",465732,3,1,6,"English","en",105,"# Introduction\n## Motivation: large concentrated loads and transmission constraints\n## SATA vs SIPTA vs dual-use storage\n## Related work and research gaps","[{\"question\":\"What problem does SATA aim to solve for large-load connections?\",\"answer\":\"SATA targets congestion and limited transfer capability on import corridors, helping operators serve large loads without immediately reinforcing the network.\"},{\"question\":\"How is operator-directed SATA evaluated in the study?\",\"answer\":\"Using a day-ahead DC optimal power flow that co-optimizes generation, ESS dispatch, and load curtailment across Monte Carlo scenarios of demand and generator availability.\"},{\"question\":\"Which reliability metrics are used to quantify SATA dispatch value?\",\"answer\":\"The paper assesses reliability with expected energy not served (EENS), loss-of-load hours (LOLH), and the conditional value at risk (CVaR) of daily unserved energy.\"}]",1784198006,15,{"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},"storage-as-a-transmission-asset-sata-for-large-load-congestion-relief","",{"@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/storage-as-a-transmission-asset-sata-for-large-load-congestion-relief/84745/",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","2026-07-22","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 problem does SATA aim to solve for large-load connections?","Question",{"text":75,"@type":76},"SATA targets congestion and limited transfer capability on import corridors, helping operators serve large loads without immediately reinforcing the network.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is operator-directed SATA evaluated in the study?",{"text":80,"@type":76},"Using a day-ahead DC optimal power flow that co-optimizes generation, ESS dispatch, and load curtailment across Monte Carlo scenarios of demand and generator availability.",{"name":82,"@type":73,"acceptedAnswer":83},"Which reliability metrics are used to quantify SATA dispatch value?",{"text":84,"@type":76},"The paper assesses reliability with expected energy not served (EENS), loss-of-load hours (LOLH), and the conditional value at risk (CVaR) of daily unserved 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