考虑动态水流滞时的梯级水库群日优化调度

Short-term optimal operation of cascade reservoirs considering dynamic water flow hysteresis

梯级水电站水库群短期优化调度中,通常忽略梯级水库间水流滞时的影响或将其作为常数进行考虑,实际上水流滞时是随着上级水库出库流量大小、河道槽蓄状态、区间入流等因素动态变化,想要准确预测下级水库入流是存在困难的。因此,采用多种方法筛选出影响下级水库入库流量的主要因素作为输入,利用神经网络建立输入与下级水库入库流量之间的动态函数关系。以锦东和官地水库梯级为例,建立考虑动态滞时的梯级水电站水库群日优化调度模型,并采用逐次优化法对采用动态滞时与固定滞时的优化方案进行求解和对比分析。结果表明:与固定滞时相比,动态滞时下能够更准确地描述梯级水库间的水流联系,同时能够在一定程度上增加梯级水电站水库群发电效益。

In short-term optimal operation of a cascade hydropower system,the influence of water flow hysteresis between cascade reservoirs is usually neglected or the time delay considered as a constant.However,water flow hysteresis changes dynamically with upstream reservoir outflow,river channel storage,interval inflow and other factors.It is difficult to accurately predict downstream reservoir inflow in the cases of dynamic water flows.In this study,we screen out main factors affecting the inflow of lower reservoirs using a variety of methods,and develop a prediction model of the lower reservoir inflow by employing artificial neural networks with these factors as inputs.In a case study of the Jindong and Guandi hydropower stations,a daily optimization scheduling model of cascade reservoirs is constructed considering the dynamic time delay and solved with a progressive optimization algorithm.The results show that compared with the constant time delay models,a dynamic time delay model of a cascade reservoir system is more accurate in modeling the water flow connection between the reservoirs and can give an optimized scheme that increases the system’s power generation efficiency to a certain degree.