Distributed stochastic AC optimal power flow based on polynomial chaos expansion

Authors

A. Engelmann, T. Mühlpfordt, Y. Jiang, B. Houska, T. Faulwasser

Reference

In Proceedings of the
American Control Conference,
pages 6188 - 6193, Milwaukee, USA, June, 2018.

Abstract

Distributed optimization methods for Optimal Power Flow (OPF) problems are of importance in order to reduce 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 method to solve stochastic OPF problems with non-Gaussian uncertainties in a distributed setting obtaining convergence guarantees and fast convergence while avoiding computationally expensive sampling. The present paper appears to be the first approach solving stochastic OPF problems in a distributed fashion. A numerical example illustrates the performance of the proposed approach.

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Bibtex

@INPROCEEDINGS{Engelmann2018,
author = {A. Engelmann and T. Mühlpfordt and Y. Jiang and B. Houska and T. Faulwasser},
title = {Distributed stochastic AC optimal power flow based on polynomial chaos expansion},
booktitle = {In Proceedings of the American Control Conference, Milwaukee, USA},
year = {2018},
pages = {6188–6193},
}