Electric power systems and the companies and customers that interact with them are
experiencing increasing levels of uncertainty due to factors such as renewable energy …

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 …

In this paper, we study data-driven chance constrained stochastic programs, or more
specifically, stochastic programs with distributionally robust chance constraints (DCCs) in a …

The objective of uncertainty quantification is to certify that a given physical, engineering or
economic system satisfies multiple safety conditions with high probability. A more ambitious …

We investigate the control of constrained stochastic linear systems when faced with limited
information regarding the disturbance process, ie, when only the first two moments of the …

Uncertainty arising from renewable energy results in considerable challenges in optimal
power flow (OPF) analysis. Various chance-constrained approaches are proposed to …

In light of a reliable and resilient power system under extreme weather and natural disasters,
networked microgrids integrating local renewable resources have been adopted extensively …

We study distributionally robust optimization (DRO) with Sinkhorn distance--a variant of
Wasserstein distance based on entropic regularization. We derive convex programming …