Two-sided linear chance constraints and extensions

Abstract

We examine the convexity and tractability of the two-sided linear chance constraint model under Gaussian uncertainty. We show that these constraints can be applied directly to model a larger class of nonlinear chance constraints as well as provide a reasonable approximation for a challenging class of quadratic chance constraints of direct interest for applications in power systems. With a view towards practical computations, we develop a second-order cone outer approximation of the two-sided chance constraint with provably small approximation error.

References in corpus (1)

• A Robust Approach to Chance Constrained Optimal Power Flow with Renewable Generation

Cited by in corpus (3)

• Chance Constraints for Improving the Security of AC Optimal Power Flow
• Revenue Adequate Prices for Chance-Constrained Electricity Markets with Variable Renewable Energy Sources
• On the Relation between the Extended Supporting Hyperplane Algorithm and Kelley's Cutting Plane Algorithm