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Solving stochastic AC power flow via polynomial chaos expansion
Publikationstyp
Conference Paper
Date Issued
2016
Sprache
English
Author(s)
Start Page
70
End Page
76
Article Number
7587824
Citation
2016 IEEE Conference on Control Applications (CCA) : part of 2016 IEEE Multi-Conference on Systems and Control : September 19-22, 2016, Buenos Aires, Argentina. - Piscataway, NJ, 2016. - Seite 70-76
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
IEEE
ISBN
978-1-5090-0755-4
978-1-5090-0756-1
The present contribution demonstrates the applicability of polynomial chaos expansion to stochastic (optimal) AC power flow problems that arise in the operation of power grids. For rectangular power flow, polynomial chaos expansion together with Galerkin projection yields a deterministic reformulation of the stochastic power flow problem that is solved numerically in a single run. From its solution, approximations of the true posterior probability density functions are obtained. The presented approach does not require linearization. Furthermore, the IEEE 14 bus serves as an example to demonstrate that the proposed approach yields accurate approximations to the probability density functions for low orders of polynomial bases, and that it is computationally more efficient than Monte Carlo sampling.
Subjects
optimal power flow
polynomial chaos expansion
stochastic uncertainty
DDC Class
004: Computer Sciences
510: Mathematics