Study on the Application of Simulated Evolutionary Optimization Algorithm in Optimizing the Combination of Unit Commitments in Power System

Abstract:

This study adopted a simulated evolutionary optimization algorithm, ant colony optimization algorithm to find the optimal unit commitment operation. The concepts such as status, strategy, and path, etc. were introduced to devise the optimization of unit commitment operation by ant colony optimization algorithm mode, so that the optimal unit commitment operation could be found by ant colony optimization algorithm. To cope with different constraints by additional penalties and restrict the statuses not satisfying the constraints by tabu table, the retrieval of ant colony optimization algorithm could always be performed in feasible region and the retrieval process of the algorithm was effectively conducted. It is feasible and efficient to find the optimal unit commitment operation by ant colony optimization algorithm, which was proved by stimulation.

• Periodical: Applied Mechanics and Materials (Volumes 48-49)
• Pages: 1186-1190
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.48-49.1186
• Online since: February 2011
• Authors: Qing Song Liu, Jia Tong, Yi Feng Li
• Keywords: Ant Colony Optimization Algorithm (ACOA), Evolutionary Optimization Algorithm, Optimal Operation, Optimal Unit Commitment Operation, Power System

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