An Ordinal Optimization Theory-Based Algorithm for Solving the Optimal Power Flow Problem With Discrete Control Variables

Shin Yeu Lin*, Yu Chi Ho, Ch'i Hsin Lin

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

83 Scopus citations

Abstract

The optimal power flow (OPF) problem with discrete control variables is an NP-hard problem in its exact formulation. To cope with the immense computational-difficulty of this problem, we propose an ordinal optimization theory-based algorithm to solve for a good enough solution with high probability. Aiming for hard optimization problems, the ordinal optimization theory, in contrast to heuristic methods, guarantee to provide a top n% solution among all with probability more than 0.95. The approach of our ordinal optimization theory-based algorithm consists of three stages. First, select heuristically a large set of candidate solutions. Then, use a simplified model to select a subset of most promising solutions. Finally, evaluate the candidate promising-solutions of the reduced subset using the exact model. We have demonstrated the computational efficiency of our algorithm and the quality of the obtained solution by comparing with the competing methods and the conventional approach through simulations.

Original languageEnglish
Pages (from-to)276-286
Number of pages11
JournalIEEE Transactions on Power Systems
Volume19
Issue number1
DOIs
StatePublished - 02 2004
Externally publishedYes

Keywords

  • Discrete control variables
  • Nonlinear programming
  • Optimal power flow
  • Ordinal optimization

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