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SUMMARY:Seminar by Bismark Singh\, University of Texas at Austin
DTSTART:20161103T120000
DTEND:20161103T133000
DTSTAMP:20260916T044020Z
UID:72e4e913915e742b2bd86289a08e95630fdaece4abd03dc123144eb1
CATEGORIES:Conferences - Seminars
DESCRIPTION:Bismark Singh\, University of Texas at Austin\n"An Adaptive Mo
 del with Joint Chance Constraints for a Hybrid Wind-Conventional Generator
  System"  \n\nAbstract\nWe study the problem of scheduling a hybrid wind-
 conventional generator system to make it dispatchable\, with the aim of pr
 ofit maximization. Our models ensure that with high probability we satisfy
  the day-ahead energy promised by the model\, using the combined output of
  the conventional and wind generators. We consider two scenarios\, which d
 iffer in whether the conventional generator must commit to a generation sc
 hedule prior to observing the wind-power realizations or has the flexibili
 ty to adapt in near real-time to these observations. The adaptive model is
  a two-stage stochastic integer program with joint-chance constraints. We 
 develop an iterative regularization scheme in which we solve a sequence of
  sample average approximations under a growing sample size\, to dramatical
 ly reduce computational effort.\n\nBio\nBismark Singh obtained his PhD and
  Masters from The University of Texas at Austin in 2016 and 2013\, respect
 ively. He spent two semesters of his PhD working at Sandia National Labora
 tories as a research intern. His research interests include stochastic opt
 imization with applications to public health and renewable energy.
LOCATION:EPFL\, ODY 4.03\, VIP Room http://plan.epfl.ch/?zoom=19&recenter_
 y=5863800.12869&recenter_x=731560.22521&layerNodes=fonds\,batiments\,label
 s\,information\,parkings_publics\,arrets_metro\,transports_publics&floor=4
 &q=ODY_4.03
STATUS:CONFIRMED
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