We study sample average approximations (SAA) of chance constrained programs. SAA methods typically approximate the actual distribution in constraint using an empirical constructed from random samples assumed to be independent and identically distributed according distribution. In this paper, we consider a nonstationary variant problem, where are independently drawn sequential fashion unknown po...

Increasing integration of renewable generation poses significant challenges to ensure robustness guarantees in real-time energy system decision-making. This work aims develop a robust optimal transmission switching (OTS) framework that can effectively relieve grid congestion and mitigate curtailment. We formulate two-stage distributionally chance-constrained (DRCC) problem assures limited const...

In this article, a fast chance-constrained trajectory generation strategy incorporating convex optimization and approximation of chance constraints is designed so as to solve the unmanned vehicle path planning problem. A path-length-optimal model constructed with consideration pitch angle constraint, curvature radius probabilistic control actuation collision avoidance constraint. Subsequently, ...

In the paper we consider the chance-constrained version of an affinely perturbed linear matrix inequality (LMI) constraint, assuming the primitive perturbations to be independent with light-tail distributions (e.g., bounded or Gaussian). Constraints of this type, playing a central role in chance-constrained linear/conic quadratic/semidefinite programming, are typically computationally intractab...