Unit commitment problem using enhanced particle swarm optimization algorithm

Authors:
Xiaohui Yuan;Anjun Su;Hao Nie;Yanbin Yuan;Liang Wang
Affiliations:
Huazhong University of Science and Technology, School of Hydropower and Information Engineering, 430074, Wuhan, China;Huazhong University of Science and Technology, School of Hydropower and Information Engineering, 430074, Wuhan, China;Huazhong University of Science and Technology, School of Hydropower and Information Engineering, 430074, Wuhan, China;Wuhan University of Technology, School of Resource and Environmental Engineering, 430070, Wuhan, China;Huazhong University of Science and Technology, School of Hydropower and Information Engineering, 430074, Wuhan, China
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Bio-inspired Learning and Intelligent Systems
Year:
2011

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Abstract

This paper proposes an enhanced PSO (EPSO) approach to solve the unit commitment (UC) problem in electric power system, which is an integrated improved discrete binary particle swarm optimization (DBPSO) with the Lambda-iteration method. The EPSO is enhanced by priority list based on the unit characteristics and heuristic search strategies to repair the spinning reserve and minimum up/down time constraints. The implementation of EPSO for UC problem consists of three stages. First, the DBPSO based on priority list is applied for unit scheduling when neglecting the minimum up/down time constraints. Second, heuristic search strategies are used to handle the minimum up/down time constraints and decommit excess spinning reserve units. Finally, Lambda-iteration method is adopted to solve economic load dispatch based on the obtained unit schedule. To verify the advantages of the EPSO method, the EPSO is tested and compared to the other methods on the systems with the number of units in the range of 10 to 100. Numerical results demonstrate that the EPSO is superior to other methods reported in the literature in terms of lower production cost and shorter computational time.