基于混沌进化算法的梯级水电群实时优化调度方法

Real-time Optimal Dispatching Method of Cascade Hydropower Group Based on Chaotic Evolution Algorithm

传统的梯级水电群优化调度方法存在最优解质量差、收敛过早、效率低下等问题,因此,研究通过混沌优化方法的随机遍历性与非周期性的特征来增强全局搜索能力,避免目标函数值过早的进行收敛。本研究还采用动态概率的方式来选择新个体的优化解是否为最优解,对不同阶层的个体进行择优处理,并通过多线程并行计算提升优化效率。本文将对代号为O流域的梯级水电群作为研究对象,从主干流上选取了4级水电站进行调度实验,与其他算法相比优化调度效果好,运算耗时短。

Aiming at the problems of poor optimal solution quality,premature convergence,and low efficiency of traditional cascade hydropower group optimization scheduling methods,this paper studies the random ergodicity and non-periodic characteristics of the chaotic optimization method,which can enhance the global search ability and avoid the objective function value and early convergence.This research also uses dynamic probabilistic methods to select the optimal solution,selects the optimal solution for individuals of different levels,and uses multi-threaded parallel computing to improve optimization efficiency.This paper takes the cascade hydropower group code-named O watershed as the research object,and selects four-level hydropower stations from the main stream for dispatching experiments.Compared with other algorithms,the optimal dispatching effect is better and the calculation time is shorter.