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- W2361574265 abstract "The unit commitment has commonly been formulated as a mixed-integer,nonlinear optimization problem.To find an efficient and stable method to solve this problem is important.A novel discrete particle swarm optimization to solve unit commitment was proposed in this paper.A novel identification method for the integer variables was proposed to reduce the dimensions.Besides,according to the characteristics of unit commitment and the security constraints,an improved self-learning strategy based on novel particle swarm optimization was proposed.This method can solve security constrained unit commitment well.In addition,a method to produce initial particles in the feasible region was proposed in order to improve the quality of the solution.The feasibility and effectiveness of the proposed method are demonstrated by two test systems of IEEE30 and IEEE118,and the computational results are compared with the custom benders decomposition and the commercial software CPLEX.The result shows that this method can find the optimum or suboptimum solution quickly,which proves the feasibility and validity of this method." @default.
- W2361574265 created "2016-06-24" @default.
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- W2361574265 date "2014-01-01" @default.
- W2361574265 modified "2023-09-23" @default.
- W2361574265 title "Unit Commitment Model Based on Self-learning Particle Swarm Algorithm" @default.
- W2361574265 hasPublicationYear "2014" @default.
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