Distributed stochastic AC optimal power flow based on polynomial chaos expansion

Publikationstyp
Conference Paper
Publikationsdatum
2018-08-09
Sprache
English
Author
Engelmann, Alexander
Mühlpfordt, Tillmann
Jiang, Yuning
Houska, Boris
Faulwasser, Timm 
TORE-URI
https://hdl.handle.net/11420/46277
Enthalten in
Proceedings of the American Control Conference 
Volume
2018-June
Start Page
6188
End Page
6193
Article Number
8431090
Citation
Proceedings of the American Control Conference 2018: 6188-6193 (2018)
Contribution to Conference
American Control Conference, ACC 2018 
Publisher DOI
10.23919/ACC.2018.8431090
Scopus ID
2-s2.0-85052598507
Publisher
American Automatic Control Council
ISBN
9781538654286
Distributed 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.
Schlagworte
Load flow
Mathematical model
Nickel
Optimization
Random variables
Stochastic processes
Uncertainty
DDC Class
004: Computer Sciences
510: Mathematics