BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Eversource Energy Center - ECPv6.15.17.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Eversource Energy Center
X-ORIGINAL-URL:https://www.eversource.uconn.edu
X-WR-CALDESC:Events for Eversource Energy Center
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:UTC
BEGIN:STANDARD
TZOFFSETFROM:+0000
TZOFFSETTO:+0000
TZNAME:UTC
DTSTART:20200101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=UTC:20211210T122000
DTEND;TZID=UTC:20211210T132000
DTSTAMP:20260415T055417
CREATED:20211213T155549Z
LAST-MODIFIED:20211213T155549Z
UID:7865-1639138800-1639142400@www.eversource.uconn.edu
SUMMARY:Joint Seminar by the ECE Department & Eversource Energy Center at UConn Part of the ECE 6094 Power & Energy Seminar Series
DESCRIPTION:Abstract: With the increasing integration of renewable energy into power systems\, the scheduling and operation of power systems becomes more challenging\, in particular because of the intermittency of wind speed. Thus\, appropriate optimization tools are needed to efficiently manage the operation of modern power systems. My research considers the unit commitment problem of power systems under high wind energy penetration. To mitigate the potential negative effects of inaccurate wind energy forecast\, a chance constrained model is employed to guarantee that supply and demand for energy be balanced at each time period over a finite planning horizon. As joint chance constraint is known to be difficult to handle analytically\, two approximation approaches\, namely quantile-based and p-efficiency-based\, are proposed and benchmarked against the well-known scenario-based approximation. The results indicate that the proposed schemes provide feasible solutions and tight upper bounds within a much shorter time frame than the scenario-based counterpart. \nBio: Dr. Claire Guo is an Assistant Professor in the Department of Information Systems and Decision Science at the University of Baltimore’s Merrick School of Business. Her research focuses on optimization modeling and solution algorithm development for large-scale optimization\, stochastic optimization\, and integer optimization\, in applications on power systems and manufacturing systems. Prior to joining the University of Baltimore\, Dr. Guo was a Postdoctoral Associate in Systems Engineering at Cornell University. She received her Ph.D. in Operations Research from Iowa State University and received her M.S. in Industrial Engineering from Texas A&M University. Dr. Guo currently serves on the Editorial Board of the Journals Production and Operations Management and Humanities & Social Science Communications (Operations Research track) and serves as a peer reviewer for many impactful journals. Her research works are published in leading journals including International Journal of Production Research\, Computers and Operations Research\, IEEE Transactions on Power Systems\, and Operations Research Letters.
URL:https://www.eversource.uconn.edu/event/joint-seminar-by-the-ece-department-eversource-energy-center-at-uconn-part-of-the-ece-6094-power-energy-seminar-series/
LOCATION:ITEB 336
CATEGORIES:Seminar
END:VEVENT
END:VCALENDAR