基于混合蝙蝠算法的梯级水电站群优化调度研究

Research on the Optimal Operation of Cascade Hydropower Station Based on the Hybrid Bat Algorithm

为进一步提高梯级水电站水库优化效果,提高水电站年发电量,本文作者以梯级电站为例,研究了蝙蝠算法的混合改进;混合了粒子群算法,以及加入小生境排挤技术;并将改进的蝙蝠算法应用于梯级水电站中长期优化调度。通过以粒子群算法、蝙蝠算法和提出的改进算法对某梯级电站进行优化计算,结果显示,改进的算法优于粒子群算法和蝙蝠算法,发电量高于其他两种算法,弃水更少,并证明本文提出的混合蝙蝠算法在求解具有复杂约束条件的非线性的梯级水库优化调度问题时,具有求解结果更优、收敛速度快的优点,为解决梯级水电站中长期优化调度问题提供了一种新的有效方法。

In order to improve the reservoir optimization effect, and the annual power generation of cascade hydropower station, this paper studied the bat algorithm of the improved hybrid; hybrid particle swarm optimization algorithm by the cascade hydropower stations as an example, and joined the crowding niche technology; and also bats and the improved algorithm were applied in the cascade hydropower station in long-term optimal scheduling. Examples were presented to calculate the optimization of a cascade station by the PSO algorithm, the bat algorithm and the improved algorithm. The results showed that the improved algorithm was better than the particle swarm algorithm and bat algorithm, and the power generation was higher than the other two algorithms, and the water was less. Results showed that hybrid bat algorithm was proposed in this paper for solving with complex constraints nonlinear cascade reservoirs optimal scheduling problem, with better solution results, the advantages of fast convergence speed, solve cascade hydropower station in long-term optimal scheduling problem provides a new and effective method.