[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128196-en":3,"doc-seo-128196-105":30,"detail-sidebar-cat-0-en-105":92},{"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":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},128196,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","Enhancing Power System and Market Operations through Stochastic Modeling, Inverse Optimization, and Machine Learning - Dissertation","Modern power system and market operations face intertwined challenges and opportunities driven by uncertainty, information limitations, and model complexity. The dissertation studies how renewable and distributed resources introduce stochasticity, how information asymmetry can be partially resolved via data mining, and how machine learning may reduce explainability. It proposes a chance-constrained stochastic market framework with co-optimization and pricing, including virtual inertia provision, and develops a data-driven inverse optimization method to recover private offer prices efficiently. It further introduces adversarial conditional generative modeling for extreme scenarios and distribution-level incentive-based voltage regulation using online learning.","ENHANCING POWER SYSTEM AND MARKET OPERATIONS THROUGH STOCHASTIC MODELING, INVERSE OPTIMIZATION, AND  \nMACHINE LEARNING  \nby  \nZhirui Liang  \nA dissertation submitted to The Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy  \nBaltimore, Maryland  \nFebruary 2025  \n© 2025 Zhirui Liang  \nAll rights reserved  \nAbstract  \nModern power system and market operations are characterized by both challenges and opportunities, often in paradoxical ways. First, the increasing integration of renewable energy sources (RES) and distributed energy resources (DERs) introduces stochasticity, which can reduce system efficiency but also provide additional flexibility to support operations. Second, while information asymmetry among power market participants continues to hinder operational efficiency, advancements in data availability and data mining create opportunities for information recovery. Third, while machine learning offers promising solutions to manage the complexity of operational models, it may also compromise their explainability. This dissertation explores these paradoxes, aiming to leverage emerging opportunities while addressing critical challenges in power system and market operations.  \nThe research begins by tackling challenges in power transmission systems and wholesale markets. First, to efficiently use flexible resources like energy storage, a chanceconstrained stochastic market framework is proposed, enabling the co-optimization and pricing of energy, reserves, and a new service called virtual inertia provision. This framework mitigates high uncertainty and inertia shortages in RES-dominated systems. Second, to address information asymmetry among power producers and improve market participant strategy design, a data-driven inverse optimization model is developed to recover private offer prices from public market-clearing results. This method is computationally efficient, robust, and offers strong performance guarantees for information recovery. Additionally, an operational-adversarial conditional generative  \nadversarial network is introduced to enhance grid-awareness in scenario generation for extreme operational conditions. By integrating feedback from downstream operational models through modified gradient descent, this model identifies critical scenarios for system operations, enabling effective scenario-based reserve scheduling.  \nThe focus then shifts to power distribution systems, examining incentive-based voltage regulation using grid-edge DERs in environments with uncertainty and partial information. A distributionally robust incentive design model with online learning is introduced, enabling the distribution system operator (DSO) to effectively incentivize DER aggregators to participate in voltage regulation. Aggregators respond to these incentives by adjusting their DER settings, while the DSO dynamically adjusts its conservativeness level based on aggregator responses. This work highlights the potential of combining stochastic modeling, inverse optimization, and machine learning to tackle diverse challenges in power system and market operations.  \nKeywords: Power system operation, wholesale power market, stochastic modeling, inverse optimization, machine learning  \nPrimary reader and thesis advisor  \nDr. Yury Dvorkin Associate Professor  \nDepartment of Electrical and Computer Engineering Department of Civil and System Engineering Johns Hopkins University, Baltimore MD  \nSecondary reader  \nDr. Sijia Geng Assistant Professor  \nDepartment of Electrical and Computer Engineering Johns Hopkins University, Baltimore, MD  \nOther committee members  \nDr. Enrique Mallada Associate Professor  \nDepartment of Electrical and Computer Engineering Johns Hopkins University, Baltimore, MD  \nDr. Benjamin Hobbs  \nTheodore M. and Kay W. Schad Professor of Environmental Management Department of Environmental Health and Engineering  \nJohns Hopkins University, Baltimore, MD  \nDr. Kimia Ghobadi","cbCaiajPR4jQvlpk","https://ap.wps.com/l/cbCaiajPR4jQvlpk","pdf",8280334,1,244,"English","en",105,"# Abstract\n# Research scope and challenges\n## Stochastic modeling for transmission and wholesale markets\n## Inverse optimization for information recovery\n## Machine learning for extreme scenario generation\n## Distribution systems and incentive-based voltage regulation","[{\"question\":\"How does the dissertation address uncertainty introduced by renewable and distributed energy resources?\",\"answer\":\"It proposes a chance-constrained stochastic market framework that supports co-optimization and pricing for energy, reserves, and virtual inertia provision, mitigating high uncertainty and inertia shortages in RES-dominated systems.\"},{\"question\":\"What problem does inverse optimization solve in the dissertation?\",\"answer\":\"It recovers private offer prices from public market-clearing results using a data-driven inverse optimization model that is computationally efficient, robust, and provides strong performance guarantees.\"},{\"question\":\"How is machine learning used for scenario generation and operational reserves?\",\"answer\":\"An operational-adversarial conditional generative adversarial network enhances grid awareness in scenario generation for extreme conditions, and modified gradient descent feedback helps identify critical scenarios for effective scenario-based reserve scheduling.\"}]","Enhancing Power System and Market Operations through Stochastic Modeling, Inverse Optimization, and Machine Learning - Dissertation | PDF",1785945487,615,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"enhancing-power-system-and-market-operations-through-stochastic-modeling-inverse-optimization-and-machine-learning-dissertation","",{"@graph":36,"@context":86},[37,54,69],{"@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/enhancing-power-system-and-market-operations-through-stochastic-modeling-inverse-optimization-and-machine-learning-dissertation/128196/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"How does the dissertation address uncertainty introduced by renewable and distributed energy resources?","Question",{"text":76,"@type":77},"It proposes a chance-constrained stochastic market framework that supports co-optimization and pricing for energy, reserves, and virtual inertia provision, mitigating high uncertainty and inertia shortages in RES-dominated systems.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What problem does inverse optimization solve in the dissertation?",{"text":81,"@type":77},"It recovers private offer prices from public market-clearing results using a data-driven inverse optimization model that is computationally efficient, robust, and provides strong performance guarantees.",{"name":83,"@type":74,"acceptedAnswer":84},"How is machine learning used for scenario generation and operational reserves?",{"text":85,"@type":77},"An operational-adversarial conditional generative adversarial network enhances grid awareness in scenario generation for extreme conditions, and modified gradient descent feedback helps identify critical scenarios for effective scenario-based reserve scheduling.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]