BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20250309T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20251102T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20250620T182413Z UID:F9A2AE05-8B43-4D30-90C4-B061E90D4C8F DTSTART;TZID=America/New_York:20250411T120000 DTEND;TZID=America/New_York:20250411T130000 DESCRIPTION:Seminar by Dr. Nurcin Celik\, Professor\, The University of Mia mi\nAbstract: Energy security and resilience are critical to our daily act ivities\, yet the current electricity systems\nare vulnerable and fragile against natural or human-made hazards and disasters as well as other abnor malities\nin the system. Motivated by these challenges\, this project inve stigates a new dynamic data driven monitoring\nand control framework for s mart energy systems with a focus on the critical infrastructures of microg rids and\ntheir connection to the main power grid as it is becoming more a nd more vital to realize economic security and\nreliability. However\, rea l-time monitoring and inferencing in a large-scale system such as a power system under\nlimited computational resources is very challenging due to t he excessive number of parameters and massive\ndata loads involved. New an d effective methodologies that are capable of dealing with massive\, highl y\ncomplex\, and rapid moving data are needed to make useful discoveries a nd achieve punctual control over\nthese networks. Addressing this need\, w e focused our research efforts on data driven knowledge discovery by\nreal -time inferencing. To this end\, the essentials of the developed decision making framework include 1) new\nand advanced optimization models and algo rithms for operation and control including a comprehensive\noptimization m odel and a decomposition algorithm for the operation of off-grid AC microg rids\, 2) multi-scale\nadaptive simulation models (component level and bat tery load simulation models operating at high- and lowfidelities)\, and 3) instrumentation methods for decision making in a smart grid network inclu ding a demand\nresponse model. The capabilities and performance of the inv estigated framework were initially tested on IEEE9\, IEEE-30\, and IEEE-11 8 test systems. Next\, we will work on tractable robust optimization model s for demand\nresponse solutions\, and stochastic optimization models for operation re-scheduling and topology reconfiguration problems.\nBio: NURCI N CELIK\, Ph.D.\, is a Professor in the Department of Industrial and Syste ms\nEngineering at the University of Miami (UM). She received her M.S. and Ph.D. degrees in\nSystems and Industrial Engineering from the University of Arizona with magna cum laude.\nSponsored by AFOSR\, Department of Defen se\, DOE\, and the City of Coral Gables she has\nworked on the development of integrated modeling and decision-making methodologies\nfor large-scale \, complex\, and dynamic systems with a focus on smart grids. Dr. Celik\nr eceived the Presidential Early Career Award for Scientists and Engineers f rom the White\nHouse in 2017. She also received the UM Provost’s Award f or Scholarly Activity 2011\, 2015\,\n2019 & 2023\, 2018 IISE CIS Best Pape r Award\, 2013 AFOSR Young Investigator Research\nAward\, and 2011 WSC Bes t Paper Award amongst others. She has served in the editorial board of aca demic\njournals and conference organizations\, and has been an ad-hoc revi ewer for more than 15 journals. For\npublication citations and impacts\, s ee\nWebsite: https://people.miami.edu/profile/88cee39f55138037196cc4ea916f 8bdd\nGoogle Scholar: https://scholar.google.com/citations?user=qXyC7fEAAA AJ&hl=en\n\nCo-sponsored by: Wayne State University Industrial and Systems Engineering\n\nBldg: Engineering Building\, 5050 Anthony Wayne Drive\, De troit\, Michigan\, United States\, Virtual: https://events.vtools.ieee.org /m/480598 LOCATION:Bldg: Engineering Building\, 5050 Anthony Wayne Drive\, Detroit\, Michigan\, United States\, Virtual: https://events.vtools.ieee.org/m/48059 8 ORGANIZER:unluturk@msu.edu SEQUENCE:76 SUMMARY:Seminar @Wayne State University on Dynamic Data-Driven Simulation M odeling for Resilient Microgrid Systems URL;VALUE=URI:https://events.vtools.ieee.org/m/480598 X-ALT-DESC:Description: <br /><p>Seminar by Dr. Nurcin Celik\, Professor\, The University of Miami<br>Abstract: Energy security and resilience are cr itical to our daily activities\, yet the current electricity systems<br>ar e vulnerable and fragile against natural or human-made hazards and disaste rs as well as other abnormalities<br>in the system. Motivated by these cha llenges\, this project investigates a new dynamic data driven monitoring<b r>and control framework for smart energy systems with a focus on the criti cal infrastructures of microgrids and<br>their connection to the main powe r grid as it is becoming more and more vital to realize economic security and<br>reliability. However\, real-time monitoring and inferencing in a la rge-scale system such as a power system under<br>limited computational res ources is very challenging due to the excessive number of parameters and m assive<br>data loads involved. New and effective methodologies that are ca pable of dealing with massive\, highly<br>complex\, and rapid moving data are needed to make useful discoveries and achieve punctual control over<br >these networks. Addressing this need\, we focused our research efforts on data driven knowledge discovery by<br>real-time inferencing. To this end\ , the essentials of the developed decision making framework include 1) new <br>and advanced optimization models and algorithms for operation and cont rol including a comprehensive<br>optimization model and a decomposition al gorithm for the operation of off-grid AC microgrids\, 2) multi-scale<br>ad aptive simulation models (component level and battery load simulation mode ls operating at high- and lowfidelities)\, and 3) instrumentation methods for decision making in a smart grid network including a demand<br>response model. The capabilities and performance of the investigated framework wer e initially tested on IEEE9\, IEEE-30\, and IEEE-118 test systems. Next\, we will work on tractable robust optimization models for demand<br>respons e solutions\, and stochastic optimization models for operation re-scheduli ng and topology reconfiguration problems.<br>Bio: NURCIN CELIK\, Ph.D.\, i s a Professor in the Department of Industrial and Systems<br>Engineering a t the University of Miami (UM). She received her M.S. and Ph.D. degrees in <br>Systems and Industrial Engineering from the University of Arizona with magna cum laude.<br>Sponsored by AFOSR\, Department of Defense\, DOE\, an d the City of Coral Gables she has<br>worked on the development of integra ted modeling and decision-making methodologies<br>for large-scale\, comple x\, and dynamic systems with a focus on smart grids. Dr. Celik<br>received the Presidential Early Career Award for Scientists and Engineers from the White<br>House in 2017. She also received the UM Provost&rsquo\;s Award f or Scholarly Activity 2011\, 2015\,<br>2019 &amp\; 2023\, 2018 IISE CIS Be st Paper Award\, 2013 AFOSR Young Investigator Research<br>Award\, and 201 1 WSC Best Paper Award amongst others. She has served in the editorial boa rd of academic<br>journals and conference organizations\, and has been an ad-hoc reviewer for more than 15 journals. For<br>publication citations an d impacts\, see<br>Website: https://people.miami.edu/profile/88cee39f55138 037196cc4ea916f8bdd<br>Google Scholar: https://scholar.google.com/citation s?user=qXyC7fEAAAAJ&amp\;hl=en</p> END:VEVENT END:VCALENDAR