基于改进PSO算法的电力系统机组优化组合

机组组合优化问题是一个大规模、多约束、非线性的混合整数规划问题，因此求解非常困难．粒子群优化（PSO）算法是一类随机全局优化技术，它通过粒子间的相互作用发现复杂搜索空间中的最优区域．采用二进制粒子群优化方法解决机组状态组合问题，用遗传算法结合启发式技术解决经济分配问题，并对最小开停机时间及启停费用进行了处理，使得运算速度大大加快．方法的可行性在10台机组系统中检验．模拟结果表明文章所提出的算法具有收敛速度快及解的质量高等优点．

Improved PSO Algorithms for Unit Commitment of Power System

Unit commitment(UC) is a large scale,multi-constraint and nonlinear hybrid integer programming problem.Particle swarm optimization is a stochastic global optimization technique.It finds optimal regions of complex searching spaces through the interaction of individuals in a population of particles.The start-up of unit can be solved by the BPSO method;and the economic dispatch(ED) problem can be solved by the genetic algorithm(GA) combined with heuristic method for the minimization of the production cost.At the same time,a new method for transition and minimum start-stop time.The feasibility of the proposed method is demonstrated by solving a 10 units system.The simulation results show that the proposed method possesses high convergence speed and higher quality solutions.