动态粒子群算法在经济负荷分配中的应用

Application of Dynamic Particle Swarm Optimization to Economic Dispatch

将一种惯性权重动态调整的粒子群算法用于求解复杂的不连续、非凸、非线性电力系统的经济负荷分配(ED)问题,使其在满足各机组负荷和运行约束的条件下总的发电费用最小。算法惯性权重随不同粒子距全局最优点的距离不同而动态调整,从而提高了基本粒子群算法的收敛速度,避免其容易陷入局部极值。将算法应用到经济负荷分配问题的Matlab仿真结果表明,所提出的方法不仅提高了解的寻优能力和收敛速度,而且随着问题规模的增大,其优化结果要好于其它方法。

An improved particle swarm optimization （DPSO） is proposed to solve the discontinuous, nonconvex and nonlinear economic dispatch （ED） problem, to determine the unit power so that the system＇s production cost is minimized while the power demand and other constraints are met. In DPSO, the inertia weight of each particle changes dynamically according to its distance to the global optimal particle. DPSO can avoid trapping into local optimal position. The convergence speed of DPSO is improved. Experiments of ED show that the proposed algorithm can get better result when the scale of problem increases.