2010Unpublished venueRequires access

A enhanced multiple predictor-corrector interior point method for optimal power flow

Liang Xie, Hsiao‐Dong Chiang

Open publisher page 10 citations

Abstract

Interior point method (IPM), as one of the most effecient methods, is being extended to solve different types of optimization problems in electric power domain. In this paper, the nonlinear OPF problem, formulated with a rectangular coordinate form, is solved using the enhanced multiple predictor-corrector interior point method, which is combined with the selection of the optimal composite direction. An two stage linesearch strategy is also employed to obtain an optimal composite direction to improve the convergence property of MPC. The proposed method is then simulated for several test system ranging in size from 57 buses to 2790 buses. Numerical results demonstrate that the proposed method can lead to convergence with a smaller number of iterations and better computational time. Moreover, the comparison with different methods shows that the proposed method can be faster and robuster than that traditonal predictor-corrector interior point method and its variant.

About this research paper

What this paper is about

Interior point method (IPM), as one of the most effecient methods, is being extended to solve different types of optimization problems in electric power domain. In this paper, the nonlinear OPF problem, formulated with a rectangular coordinate form, is solved using the enhanced multiple predictor-corrector interior point method, which is combined with the selection of the optimal composite direction. An two stage linesearch strategy is also employed to obtain an optimal composite direction to improve the convergence property of MPC. The proposed method is then simulated for several test system ranging in size from 57 buses to 2790 buses. Numerical results demonstrate that the proposed method can lead to convergence with a smaller number of iterations and better computational time. Moreover, the comparison with different methods shows that the proposed method can be faster and robuster than that traditonal predictor-corrector interior point method and its variant.

Why it matters

OpenAlex reports 10 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Interior point method (IPM), as one of the most effecient methods, is being extended to solve different types of optimization problems in electric power domain. In this paper, the nonlinear OPF problem, formulated with a rectangular coordinate form, is solved using the enhanced multiple predictor-corrector interior point method, which is combined with the selection of the optimal composite direction. An two stage linesearch strategy is also employed to obtain an optimal composite direction to improve the convergence property of MPC. The proposed method is then simulated for several test system ranging in size from 57 buses to 2790 buses. Numerical results demonstrate that the proposed method can lead to convergence with a smaller number of iterations and better computational time. Moreover, the comparison with different methods shows that the proposed method can be faster and robuster than that traditonal predictor-corrector interior point method and its variant.

Key concepts: Interior point method, Predictor–corrector method, Convergence (economics), Mathematical optimization, Point (geometry), Computer science, Nonlinear system, Power flow

Related papers

Back to paper searchBrowse research topicsOriginal source
A enhanced multiple predictor-corrector interior point method for optimal power flow — Research Paper | ScholarLens