The concepts of risk-aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. Statistical learning community has also …

This paper reviews recent advances in the field of optimization under uncertainty via a
modern data lens, highlights key research challenges and promise of data-driven …

Many decision problems in science, engineering, and economics are affected by uncertain
parameters whose distribution is only indirectly observable through samples. The goal of …

The concepts of risk aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. The statistical learning community has …

In this paper, we survey the primary research on the theory and applications of
distributionally robust optimization (DRO). We start with reviewing the modeling power and …

Aggregations of electric loads can provide reserves to power systems, but their available
reserve capacities are time-varying and not perfectly known when the system operator …

This paper studies a distributionally robust chance constrained program (DRCCP) with
Wasserstein ambiguity set, where the uncertain constraints should be satisfied with a …

We provide an exact deterministic reformulation for data-driven, chance-constrained
programs over Wasserstein balls. For individual chance constraints as well as joint chance …

Most of classical stochastic programming assumes that the distribution of uncertain
parameters are known, and this distribution is an input to the model. In many applications …

With the development of smart grid, energy management becomes critical for reliable and
efficient operation of power systems. In this paper, we develop a chance-constrained energy …