Engelmann, AlexanderAlexanderEngelmannMühlpfordt, TillmannTillmannMühlpfordtJiang, YuningYuningJiangHouska, BorisBorisHouskaFaulwasser, TimmTimmFaulwasser2024-03-062024-03-062018-08-09Proceedings of the American Control Conference 2018: 6188-6193 (2018)9781538654286https://hdl.handle.net/11420/46277Distributed optimization methods for Optimal Power Flow (OPF) 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.en0743-1619201861886193American Automatic Control CouncilLoad flowMathematical modelNickelOptimizationRandom variablesStochastic processesUncertaintyComputer SciencesMathematicsConference Paper10.23919/ACC.2018.8431090Other