DocumentCode
135245
Title
Security Constrained Optimal Power Flow via proximal message passing
Author
Chakrabarti, Subit ; Kraning, Matt ; Chu, Eric ; Baldick, Ross ; Boyd, Stephen
Author_Institution
Dept. of Electr. & Comput. Eng., Univ. of Texas at Austin, Austin, TX, USA
fYear
2014
fDate
11-14 March 2014
Firstpage
1
Lastpage
8
Abstract
In this paper, we propose a distributed algorithm to solve the Security Constrained Optimal Power Flow (SC-OPF) Problem. We consider a network of devices, each with its own dynamic constraints and objective, subject to reliability constraints across multiple scenarios. Each scenario corresponds to the failure or degradation of a set of devices and has an associated probability of occurrence. The network objective is to minimize the cost of operation of all devices, over a given time horizon, across all scenarios subject to the constraints of transmission limit, upper and lower generating limits, generation-load balance etc. This is a large optimization problem, with variables for consumption and generation for each device, in each scenario. In this paper, we extend the proximal message passing framework to handle reliability constraints across scenarios. The resulting algorithm is extremely scalable with respect to both network size and the number of scenarios.
Keywords
load flow; message passing; optimisation; power engineering computing; power system reliability; power system security; SC-OPF problem; dynamic constraints; generation-load balance; optimization problem; proximal message passing framework; reliability constraints; security constrained optimal power flow; Convergence; Generators; Mathematical model; Message passing; Nickel; Power transmission lines; Security; Alternating Direction Method of Multipliers (ADMM); Augmented Lagrangian; Locational Marginal Price (LMP); Shift Factor Matrix;
fLanguage
English
Publisher
ieee
Conference_Titel
Power Systems Conference (PSC), 2014 Clemson University
Conference_Location
Clemson, SC
Type
conf
DOI
10.1109/PSC.2014.6808131
Filename
6808131
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