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Evaluation of Two Lagrangian Dual Optimization Algorithms for Large-Scale Unit Commitment Problems

  • Fan, Wen (Department of Electrical and Computer Engineering, University of Kentucky) ;
  • Liao, Yuan (Department of Electrical and Computer Engineering, University of Kentucky) ;
  • Lee, Jong-Beom (Department of Electrical Engineering, Wonkwang University) ;
  • Kim, Yong-Kab (Department of Information and Communication Engineering)
  • Received : 2011.01.05
  • Accepted : 2011.09.19
  • Published : 2012.01.01

Abstract

Lagrangian relaxation is the most widely adopted method for solving unit commitment (UC) problems. It consists of two steps: dual optimization and primal feasible solution construction. The dual optimization step is crucial in determining the overall performance of the solution. This paper intends to evaluate two dual optimization methods - one based on subgradient (SG) and the other based on the cutting plane. Large-scale UC problems with hundreds of thousands of variables and constraints have been generated for evaluation purposes. It is found that the evaluated SG method yields very promising results.

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