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Evaluation of Two Lagrangian Dual Optimization Algorithms for Large-Scale Unit Commitment Problems
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 Title & Authors
Evaluation of Two Lagrangian Dual Optimization Algorithms for Large-Scale Unit Commitment Problems
Fan, Wen; Liao, Yuan; Lee, Jong-Beom; Kim, Yong-Kab;
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 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.
 Keywords
Dual optimization;Lagrangian relaxation;Resource scheduling;Unit commitment;
 Language
English
 Cited by
1.
A hybrid biased random key genetic algorithm approach for the unit commitment problem, Journal of Combinatorial Optimization, 2014, 28, 1, 140  crossref(new windwow)
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