Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17288
Title: Decomposed predictor-corrector interior point method for dynamic optimal power flow
Authors: Chung, CY
Yan, W
Liu, F
Keywords: Dynamic constraint
interior point method
optimal power flow
Issue Date: 2011
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on power systems, 2011, v. 26, no. 3, 5599890, p. 1030-1039 How to cite?
Journal: IEEE transactions on power systems 
Abstract: In this paper, a decomposed predictor-corrector interior point method (DPCIPM) is proposed for solving the dynamic optimal power flow (DOPF) problem, which is a large-scale nonlinear optimization problem. The Karush-Kuhn-Tucker (KKT) system in DPCIPM is decomposed into many subsystems based on its special block structure, where the size of each subsystem depends on the network size only. In the iterative process, slack variables and Lagrange multipliers of dynamic constraints are first predicted and corrected, and then other variables in each time interval are predicted and corrected. The parameters, such as step length and barrier parameter, are independently estimated in each subsystem. Besides, an inequality iteration strategy is introduced to avoid unnecessary computation. Implementation of the proposed DPCIPM is described in detail. The effectiveness of the proposed method has been demonstrated on the IEEE 14-bus and IEEE 118-bus systems with up to 24 time intervals. It has been found that compared with a decomposed pure primal dual interior point method (DIPM), the proposed DPCIPM is more attractive, especially when dynamic constraints become active.
URI: http://hdl.handle.net/10397/17288
ISSN: 0885-8950
EISSN: 1558-0679
DOI: 10.1109/TPWRS.2010.2080326
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