Seminar by Bismark Singh, University of Texas at Austin
Event details
| Date | 03.11.2016 |
| Hour | 12:00 › 13:30 |
| Speaker | Bismark Singh, University of Texas at Austin |
| Location | |
| Category | Conferences - Seminars |
"An Adaptive Model with Joint Chance Constraints for a Hybrid Wind-Conventional Generator System"
Abstract
We study the problem of scheduling a hybrid wind-conventional generator system to make it dispatchable, with the aim of profit 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 differ in whether the conventional generator must commit to a generation schedule prior to observing the wind-power realizations or has the flexibility 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 dramatically reduce computational effort.
Bio
Bismark Singh obtained his PhD and Masters from The University of Texas at Austin in 2016 and 2013, respectively. He spent two semesters of his PhD working at Sandia National Laboratories as a research intern. His research interests include stochastic optimization with applications to public health and renewable energy.
Abstract
We study the problem of scheduling a hybrid wind-conventional generator system to make it dispatchable, with the aim of profit 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 differ in whether the conventional generator must commit to a generation schedule prior to observing the wind-power realizations or has the flexibility 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 dramatically reduce computational effort.
Bio
Bismark Singh obtained his PhD and Masters from The University of Texas at Austin in 2016 and 2013, respectively. He spent two semesters of his PhD working at Sandia National Laboratories as a research intern. His research interests include stochastic optimization with applications to public health and renewable energy.
Practical information
- General public
- Free