DocumentCode
2053350
Title
An Affine Arithmetic method to solve the stochastic power flow problem based on a mixed complementarity formulation
Author
Pirnia, M. ; Canizares, C.A. ; Bhattacharya, K. ; Vaccaro, A.
Author_Institution
Dept. of Electr. & Comput. Eng., Univ. of Waterloo, Waterloo, ON, Canada
fYear
2012
fDate
22-26 July 2012
Firstpage
1
Lastpage
7
Abstract
An affine-based stochastic power flow problem is proposed in this paper. First, a novel optimization-based model of the power flow problem using complementarity conditions to properly represent generator bus voltage controls, including reactive power limits and voltage recovery is presented. This model is then used to solve the stochastic power flow problem to obtain operational intervals for power flow variables based on an Affine Arithmetic (AA) method to consider active and reactive power demand uncertainties. The proposed AA algorithm is tested on a 14-bus test system and the results are then compared with the Monte-Carlo Simulation (MCS) results. The AA method shows slightly more conservative bounds; however, it is faster and does not need any information regarding statistical distributions of random variables.
Keywords
Monte Carlo methods; load flow; optimisation; stochastic processes; wind power plants; 14-bus test system; AA method; MCS; Monte-Carlo simulation; active power demand uncertainty; affine arithmetic method; affine-based stochastic power flow problem; complementarity conditions; generator bus voltage controls; mixed complementarity formulation; optimization-based model; power flow variables; reactive power demand uncertainty; reactive power limits; statistical distributions; voltage recovery; Generators; Load flow; Noise; Reactive power; Stochastic processes; Uncertainty; Vectors; Power flow problem; affine arithmetic; mixed complementarity problem; monte-carlo simulation; stochastic power flow problem;
fLanguage
English
Publisher
ieee
Conference_Titel
Power and Energy Society General Meeting, 2012 IEEE
Conference_Location
San Diego, CA
ISSN
1944-9925
Print_ISBN
978-1-4673-2727-5
Electronic_ISBN
1944-9925
Type
conf
DOI
10.1109/PESGM.2012.6345100
Filename
6345100
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