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 …

Uncertainties from deepening penetration of renewable energy resources have posed
critical challenges to the secure and reliable operations of future electric grids. Among …

The scenario approach is a general methodology for data-driven optimization that has
attracted a great deal of attention in the past few years. It prescribes that one collects a …

In this paper, we discuss the model predictive control algorithms that are tailored for
uncertain systems. Robustness notions with respect to both deterministic (or set based) and …

This paper presents a new approach to efficiently integrate long prediction horizons subject
to uncertainty into a stochastic model predictive control (MPC) framework for the energy …

Scenario optimization is a broad methodology to perform optimization based on empirical
knowledge. One collects previous cases, called “scenarios”, for the set-up in which …

In the eyes of many control scientists, the theory of the scenario approach is a tool for
determining the sample size in certain randomized control-design methods, where an …

We provide a data-driven approach equipped with a formal guarantee for verifying the safety
of stochastic systems with unknown dynamics. First, using a notion of barrier certificates, the …

This paper introduces an empirical approach to dispatch resources in real-time power
system operation with growing levels of uncertainties emerging from intermittent and …

This book is about optimizing in the presence of uncertainty. Due to uncertainty, one needs
to exercise caution, and optimization must accommodate the uncertain elements that are …