摘要
为提高水库群联合调度时的水资源利用率,重新审核水库群系统中原有单库调度图的有效性,本文提出了一种解决库群联合调度多目标、多变量的智能优化新方法—混合模拟退火遗传算法。该方法将遗传算法的全局搜索能力和模拟退火算法的局部搜索能力相结合,提高了计算效率和精度,避免了手工修正调度图的随意性。在以实际生产项目为依托的应用与检验中,在满足各类边界条件及保证率要求的前提下,该方法对梯级水电站水库调度图的优化可行、有效,为优化梯级水库调度图提供了一种新的有效算法。
To improve the water resources utilization rate of reservoir group,we reassesse effectiveness of single reservoir operation graph according to conventional methods.We put forward an intelligent optimization method—hybrid simulated annealing genetic algorithm to united dispatch cascaded hydropower stations with multiple targets and variables.By combining the global search ability of genetic algorithm and local search ability of simulated annealing algorithm,the algorithm can improve calculation efficiency and accuracy and avoid the arbitrariness of manual operation graph correction.In the practical dispatch,the algorithm is proved effective and feasible in optimization of operation graph of cascade reservoirs when various boundary conditions and probabilities are met.It provides a new and effective algorithm to optimize the operation graph of the cascade reservoirs.
出处
《人民长江》
北大核心
2010年第3期34-37,共4页
Yangtze River
基金
国家自然科学基金资助项目(50609007)
关键词
水库调度图
水电站经济运行
调度优化
混合模拟退火遗传算法
reservoir operation chart
economical operation of hydropower station
operation optimization
hybrid genetic algorithm and simulated annealing
