电力系统经济负荷分配的改进混沌粒子群算法

Improved chaotic particle swarm optimization algorithm for power system economic load dispatch

提出一种用于电力系统经济负荷分配的改进混沌粒子群算法.算法中采用自适应外罚函数法解决目标函数的约束问题,考虑了机组的系统平衡、出力上下限、爬坡速率和工作死区等约束条件;在粒子群算法中引入混沌机制,使算法能快速跳出局部极值区,提高算法的全局寻优性能;针对变惯性权重系数和变最大搜索速度改进措施的不足,提出依据机组爬坡速率约束来缩小最优解的搜索区域.仿真结果表明,改进的混沌粒子群算法对于解决带约束条件的经济负荷分配问题是可行和高效的,与改进前的计算方法相比,降低了运行费用,提高了寻优速度.

An improved chaotic particle swarm optimization （PSO） algorithm is introduced in order to resolve the power system economic load dispatch （ELD） problem. The new algorithm applies outer-penalty function to solve the constraints of objective function, such as system balance limits, upper and lower output limits, ramp rate limits and prohibited zones of generator units. By integrating chaotic optimization into particle swarm optimization, it can jump out of the local extreme zone quickly and improve particles＇searching performance of the global best solution. To overcome the disadvantage brought by alterable inertia weight coefficient and alterable maximal hunting rate, a method using the ramp rate constraint of generator units is put forward to narrow search region. Simulation result shows that the improved chaotic particle swarm optimization algorithm is efficient and feasible when it is applied to solve the economic dispatch prob- lems with constraints. Compared with the previous improved algorithm, it can reduce the operation cost and improve the searching rate.