Distributed Stochastic AC Optimal Power Flow based on Polynomial Chaos Expansion

Distributed optimization methods for Optimal Power Flow (tIPE) problems are of importance in reducing coordination complexity and ensuring economic grid operation. Renewable feed -ins and demands are intrinsically uncertain and often follow non-Gaussian distributions. The present paper combines uncertainty propagation via Polynomial Chaos Expansion (PCE) with the Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) method to solve stochastic OPF problems with non-Gaussian uncertainties in a distributed setting. Moreover, using ALADIN and PCE we obtain fast convergence while avoiding computationally expensive sampling. A numerical example illustrates the performance of the proposed approach.