2009Journal of Yili Normal UniversityRequires access

Research for Unit Commitment Problem Algorithm in Power System

Zhao Jinquan

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Abstract

Unit commitment(UC) is a very important link of generation scheduling in power system economy dispatch, the experience indicated that the optimized unit commitment is more economical than the optimized load distribution. But this problem is very complicated to solve, and it is very hard to find the most optimization conclusion in theory. This paper presents a hybrid method for solving UC problem between Lagrangian relaxation (LR) and the genetic algorithm(GA). Numerical results showed that the feature of easy implementation, better convergence, and highly near-optimal solution to the UC problem can be achieved by the method. It is more robust and adaptive than the traditional methods.

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What this paper is about

Unit commitment(UC) is a very important link of generation scheduling in power system economy dispatch, the experience indicated that the optimized unit commitment is more economical than the optimized load distribution. But this problem is very complicated to solve, and it is very hard to find the most optimization conclusion in theory. This paper presents a hybrid method for solving UC problem between Lagrangian relaxation (LR) and the genetic algorithm(GA). Numerical results showed that the feature of easy implementation, better convergence, and highly near-optimal solution to the UC problem can be achieved by the method. It is more robust and adaptive than the traditional methods.

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Available abstract

Unit commitment(UC) is a very important link of generation scheduling in power system economy dispatch, the experience indicated that the optimized unit commitment is more economical than the optimized load distribution. But this problem is very complicated to solve, and it is very hard to find the most optimization conclusion in theory. This paper presents a hybrid method for solving UC problem between Lagrangian relaxation (LR) and the genetic algorithm(GA). Numerical results showed that the feature of easy implementation, better convergence, and highly near-optimal solution to the UC problem can be achieved by the method. It is more robust and adaptive than the traditional methods.

Key concepts: Power system simulation, Lagrangian relaxation, Computer science, Mathematical optimization, Convergence (economics), Relaxation (psychology), Genetic algorithm, Power (physics)

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