基于水循环算法的梯级水电站短期优化调度

鉴于梯级水电站优化运行的高复杂度、强非线性、多约束等特点,构建了基于峰谷分时电价下的梯级水电站日最大发电效益模型。针对遗传算法(GA)等传统智能算法对复杂模型求解易陷入局部最优的问题,提出一种水循环算法(WCA)与水位廊道约束耦合、降低约束复杂度、规范寻优空间的方法,并以湖北某梯级短期优化调度为背景进行建模仿真,将计算结果分别与GA和粒子群算法(PSO)所得结果进行比较。实例研究表明,WCA计算的总效益在丰、平、枯典型日分别比GA和PSO计算值约高5.65%、3.15%、0.80%,迭代收敛速度更快,求解能力更强,为解决梯级水电站优化调度问题提供了新思路。

Short-term Optimal Scheduling of Cascade Hydropower Stations Based on Water Cycle Algorithm

In view of the characteristics of optimal operation of cascade hydropower stations, such as high complexity, strong non-linearity and multiple constraints, taking peak-load regulation and time-of-use tariff as research background, this paper established a daily scheduling model of cascade hydropower stations. At the same time, the traditional intelligent algorithms can easily trap into local extremum for solving complex models, water cycle algorithm (WCA) coupled with water level corridor constraint was proposed to reduce the constraint complexity and standardize the optimization space. The method was applied to short-term optimal scheduling of cascade hydropower station in Hubei. The calculation results are compared with the results obtained by GA and particle swarm optimization (PSO). The case study shows that the total benefit by WCA in the typical day of abundant water period, average water period and dry period is respectively larger 5.65%, 3.15%, and 0.80% than that of GA and PSO; Convergence speed of the iteration is faster, and its solving ability is stronger, which provides a new idea for solving the optimal scheduling problem of cascade hydropower stations.