基于变尺度混沌算法的混联水电站水库群优化调度

Optimal operation for a large-scale hydropower station system based on mutative scale chaos optimization algorithm

提出一种求解混联水电站水库群中长期优化调度问题的方法—变尺度混沌优化算法(Mutative Scale Chaos Optimization Algorithm,MSCOA)。算法利用混沌运动的内在随机性、遍历性和规律性来寻找混联水电站水库群中长期最优调度计划。算法利用混沌运动的特点,将混沌变量映射到待寻优变量区间,通过尺度变换不断缩小优化变量的搜索空间,利用改变"二次搜索"的调节系数提高搜索精度以获取全局最优解。实例计算结果表明,算法可以求解具有复杂约束条件的非线性混联水电站水库群优化调度问题。算法求解精度高、收敛速度快,为解决混联水电站水库群中长期优化调度问题提供了一种新的方法。

This paper proposes a mutative scale chaos optimization algorithm （MSCOA） to optimize the long- term operation of large scale hydroelectric power system of multiple-mixed-connected reservoirs. Based on the natures are taken of chaotic movement, i.e. randomness, ergodicity and regularity, an optimal schedule can be obtained by three MSCOA steps： map the chaotic variable to the range of variables optimization, reduce the search space of optimized variable in succession through scale transformation, and improve the search accuracy by adjusting the coefficient of quadratic search. A case study indicates that this method is effective in optimizing a nonlinear system with complex constraints, and shows advantages of high accuracy and quick convergence. MSCOA is a new efficient method for optimal operation of large scale hydropower system of multiple- mixed-connected reservoirs.