关键词: 火电机组组合问题, 等微增率, 混沌离散粒子群优化, Kuhn-Tucker最优性条件

Abstract: In this paper, considering the characteristics of the unit commitment problems such as nonlinearity, discreteness, randomness, high-dimension and non-convexity, an improved chaotic discrete particle swarm optimization(CDPSO) algorithm, suitable for solving large-capacity thermal power unit combinatorial optimization issues, is proposed . The improved discrete particle swarm algorithm is used to determine the unit commitment decision variables. Furthermore, by tracking load changes and introducing amendments, the variables are revised to improve the efficiency of the algorithm and the accuracy of the solutions. The equal incremental rate principle is then improved by using Kuhn-Tucker optimality conditions to satisfy the climbing requirements as well as the upper and lower limits of generations. The unit startup and shutdown variables in the outer loop and the load dispatch in the inner loop are optimized through alternating iterations by embedding the improved chaotic particle swarm algorithm with the equal incremental rate principle. The simulation examples show that the proposed algorithm has strong global searching capability and adaptability in solving the unit commitment problems.

Key words: thermal power combinatorial problem, the equal incremental rate, chaotic discrete particle swarm optimization, Kuhn-Tucker optimality condition