{"639735":{"#nid":"639735","#data":{"type":"event","title":"PhD Defense by Paritosh Ramanan ","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EThesis Title:\u003C\/strong\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDecentralized Optimization and Analytics for Large Scale Power System Problems\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAdvisors:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Nagi Gebraeel, School of Industrial and Systems Engineering,\u003C\/p\u003E\r\n\r\n\u003Cp\u003EGeorgia Tech\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Edmond Chow, School of Computational Science and Engineering,\u003C\/p\u003E\r\n\r\n\u003Cp\u003EGeorgia Tech\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee members:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Santanu Dey, School of Industrial and Systems Engineering, Georgia\u003C\/p\u003E\r\n\r\n\u003Cp\u003ETech\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Andy Sun, School of Industrial and Systems Engineering, Georgia\u003C\/p\u003E\r\n\r\n\u003Cp\u003ETech\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Michael Rabbat, Facebook AI Research, Montreal\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EDate: Thursday, October 8th, 2020\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETime: 830am - 10am, EST\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EMeeting URL (for BlueJeans):\u003C\/strong\u003Ehttps:\/\/bluejeans.com\/520087903\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EMeeting ID (for BlueJeans):\u003C\/strong\u003E520\u003C\/p\u003E\r\n\r\n\u003Cp\u003E087 903\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003ELarge scale power networks form the backbone of the global energy infrastructure. Power system optimization problems aim at better utilization\u003C\/p\u003E\r\n\r\n\u003Cp\u003Eof system resources and form a significant part of power systems research. However, large scale planning and optimization problems demand efficient computational schemes that respect the data privacy of asset owners and operators as well. As a result, decentralized\u003C\/p\u003E\r\n\r\n\u003Cp\u003Ecomputational paradigms for large scale planning problems in power systems are gaining popularity.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn this thesis, we explore novel ways to solve computationally challenging planning and analytics problems in a decentralized manner using\u003C\/p\u003E\r\n\r\n\u003Cp\u003Esynchronous as well as asynchronous computational models. We specifically focus on decentralized formulations of unit commitment, joint operations and maintenance, differential privacy based unit commitment and maintenance as well as a blockchain based, decentralized\u003C\/p\u003E\r\n\r\n\u003Cp\u003Eanalytics methodology for replay attack detection.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn Chapter 2, we establish a synchronous solution mechanism to the decentralized sensor driven optimization problem that provides a comparable\u003C\/p\u003E\r\n\r\n\u003Cp\u003Esolution to the centralized method. In Chapter 3 we tackle the more fundamental decentralized UC (DUC) problem and explore an asynchronous solution for its computational benefit. We show that the asynchronous approach presented in Chapter 3 potentially leads\u003C\/p\u003E\r\n\r\n\u003Cp\u003Eto faster solution times showing good computational promise.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn Chapter 4, we strengthen our solution to the DUC problem with the help of privacy preserving valid inequalities that are computed asynchronously\u003C\/p\u003E\r\n\r\n\u003Cp\u003Eleading to a significant improvement in solution quality. We introduce an interleaved binary approach to our asynchronous method that improves convergence times for the asynchronous solution drastically.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn Chapter 5, we present our work that is driven by a subgradient based solution to the integrated sensor driven, decentralized maintenance\u003C\/p\u003E\r\n\r\n\u003Cp\u003Eand UC problem for planning horizons spanning many months. In this approach, by dualizing the maintenance limit constraint that is coupled across weeks, we parallelize weekly operations locally with the help of the subgradient method. We orchestrate multithreading\u003C\/p\u003E\r\n\r\n\u003Cp\u003Efor every subproblem while employing the distributed memory paradigm to communicate among regions.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn Chapter 6 we propose a differential privacy driven approach geared towards decentralized formulations of mixed integer operations and maintenance\u003C\/p\u003E\r\n\r\n\u003Cp\u003Eoptimization problems that protects network flow estimates. We prove strong privacy guarantees by leveraging the linear relationship between the phase angles and the flow. To address the challenges associated with the mixed integer and dynamic nature of the\u003C\/p\u003E\r\n\r\n\u003Cp\u003Eproblem, we introduce an exponential moving average based consensus mechanism to enhance convergence, coupled with a control chart based convergence criteria to improve stability.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn Chapter 7, we propose a blockchain based decentralized framework for detecting coordinated replay attacks with full data privacy. We develop\u003C\/p\u003E\r\n\r\n\u003Cp\u003Ea Bayesian inference mechanism employing locally reported attack probabilities that is tailor made for a blockchain framework. We compare our framework to a traditional decentralized algorithm based on the broadcast gossip framework both theoretically as well\u003C\/p\u003E\r\n\r\n\u003Cp\u003Eas empirically. With the help of experiments on a private Ethereum blockchain, we show that our approach achieves good detection quality and significantly outperforms gossip driven approaches in terms of accuracy, timeliness and scalability\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Decentralized Optimization and Analytics for Large Scale Power System Problems"}],"uid":"27707","created_gmt":"2020-09-30 16:52:04","changed_gmt":"2020-09-30 16:52:04","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2020-10-08T09:30:00-04:00","event_time_end":"2020-10-08T11:30:00-04:00","event_time_end_last":"2020-10-08T11:30:00-04:00","gmt_time_start":"2020-10-08 13:30:00","gmt_time_end":"2020-10-08 15:30:00","gmt_time_end_last":"2020-10-08 15:30:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}