Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2018 | 2018 ANNUAL AMERICAN CONTROL CONFERENCE (ACC) | Convergence (routing),Mathematical optimization,Random variable,Propagation of uncertainty,Control theory,Computer science,Stochastic process,Polynomial chaos,Augmented Lagrangian method,Sampling (statistics),Grid |
DocType | ISSN | Citations |
Conference | 0743-1619 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexander Engelmann | 1 | 1 | 3.74 |
| Tillmann Mühlpfordt | 2 | 1 | 2.74 |
| Yuning Jiang | 3 | 411 | 21.30 |
| Boris Houska | 4 | 214 | 26.14 |
| Timm Faulwasser | 5 | 194 | 27.39 |