基于C-PSO的水火电混合电力系统电源规划

Generation expansion planning of hydro-thermal mixed power system based on C-PSO

提出了一种粒子群算法与遗传算法结合的组合粒子群算法,并将其用于求解复杂的、非线性的水火电混合电力系统电源规划问题。该结合算法引入的遗传算法成功地提高了基本粒子群算法的全局搜索能力,同时也比基本遗传算法的收敛速度更快。算例结果表明:对于短期规划,该算法能可靠、快速地收敛到全局最优解,对于大型电力系统的中长期电源规划问题也可得到较好解。

A composite particle swarm optimization algorithm（C-PSO） in which the particle swarm optimization （PSO） is integrated with genetic algorithm （GA） is proposed and applied to a complicated and nonlinear generation expansion planning of hydro-thermal mixed power system. This integrated algorithm improves the global search ability of PSO, and the convergence speed is faster than GA. Case analysis result shows clearly that this algorithm can reliably and fast search for global optimal solution in short-term planning. To solve the problems of large scale and long-term generation expansion planning of power system, it is feasible as well.