Adjustable Robust Two-Stage Polynomial Optimization with Application to AC Optimal Power Flow

In this work, we consider two-stage polynomial optimization problems under uncertainty. In the first stage, one needs to decide upon the values of a subset of optimization variables (control variables). In the second stage, the uncertainty is revealed and the rest of optimization variables (state variables) are set up as a solution to a known system of possibly non-linear equations. This type of problem occurs, for instance, in optimization for dynamical systems, such as electric power systems. We combine tools from polynomial and robust optimization to provide a framework for general adjustable robust polynomial optimization problems. In particular, we propose an iterative algorithm to build a sequence of (approximately) robustly feasible solutions with an improving objective value and verify robust feasibility or infeasibility of the resulting solution under a semialgebraic uncertainty set. At each iteration, the algorithm optimizes over a subset of the feasible set and uses affine approximations of the second-stage equations while preserving the non-linearity of other constraints. The algorithm allows for additional simplifications in case of possibly non-convex quadratic problems under ellipsoidal uncertainty. We implement our approach for AC Optimal Power Flow and demonstrate the performance of our proposed method on Matpower instances.Comment: 28 pages, 3 table

Inner and outer approximations of existentially quantified equality constraints

Abstract. We propose a branch and prune algorithm that is able to compute inner and outer approximations of the solution set of an existentially quantified constraint where existential parameters are shared between several equations. While other techniques that handle such constraints need some preliminary formal simplification of the problem or only work on simpler special cases, our algorithm is the first pure numerical algorithm that can approximate the solution set of such constraints in the general case. Hence this new algorithm allows computing inner approximations that were out of reach until today.