1353. Multistage Distributionally Robust Optimization under Stochastic Disruptions
Invited abstract in session TA-27: Applications of Optimization under Uncertainty, stream Stochastic and Robust optimization.
Tuesday, 8:30-10:00Room: Maurice Keyworth G.02
Authors (first author is the speaker)
| 1. | Haoxiang Yang
|
| School of Data Science, The Chinese University of Hong Kong, Shenzhen |
Abstract
A stochastic disruption is a type of infrequent event in which the timing and the magnitude are random. We introduce the concept of stochastic disruptions, and a stochastic optimization framework is proposed for such problems. In this talk, we discuss two possibilities where we do not know the exact probabilistic distribution of the uncertainty under the stochastic disruption setting, one for the uncertainty magnitude and the other for the timing. We formulate a multi-stage distributionally robust optimization model while considering potential stochastic disruptions. To solve such complex models, we propose stochastic programming models for each case and solve them using cutting-plane methods. We present the computational results of our approach applied to an optimal power flow problem with N-1 contingencies.
Keywords
- Programming, Stochastic
- Robust Optimization
- OR in Energy
Status: accepted
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