基于模拟退火遗传算法的水电站优化调度研究

Study on the Optimal Operation of Hydropower Station Based on Simulated Annealing Genetic Algorithm

采用模拟退火遗传算法(SAGA)研究了水电站优化调度问题,与GA和经典的DP算法相比,结果表明该算法具有较强的局部搜索能力和较好的收敛能力,能以较快的速度找到全局最优解,是一种有效的搜索方法,可用于水电站优化调度中。

Simulated Annealing Genetic Algorithms （GASA） is used to study the optimal operation problem of hydropower station. Comparing with Genetic Algorithms and the traditional Dynamic Programming, the proposed algorithm has much stronger ability of local search as well as better convergence property and can find the global optimization solution quickly. It is shows that the GASA is an effective optimal algorithm and can be applied to the optimal operation of hydropower station.