摘要
为优化伦潭水电站调度运行方式,分析水库下游社会经济和河道内生态环境需水过程,基于发电优化调度结果采用逐步回归法提取考虑下游需水的逐月发电调度函数。结果表明,伦潭水库对下游需水过程发挥较大调蓄补给作用,主要补水时段为7月至次年2月,直接影响了伦潭水电站的发电引水流量过程;基于多元线性回归模型和BP神经网络提取的伦潭水电站发电调度函数大部分月份的纳什效率系数均在0.90以上,考虑模型结构简单且操作方便,伦潭水电站选取基于多元线性回归模型的发电调度函数;对于2017年3月至2019年2月,伦潭水电站发电调度函数较实际发电调度和发电调度图可分别增加发电量338.4×10^(4)、183.8×10^(4)kW·h/月,调度函数可明显提高伦潭水库的发电运行水位。研究成果可为伦潭水电站实际发电调度提供技术支撑,也可为其他水电站完善发电调度运行方式提供参考。
In order to optimize the dispatching mode of Luntan hydropower plant, the process of socio-economic and ecological water demand in the downstream were analyzed. Considering the downstream water demand, the monthly generation dispatching function were extracted from the results of optimal generation dispatching by use of stepwise regression method. The results show that the dispatching of Luntan reservoir plays a great role in the downstream water demand process. The main period of water replenishment is from July to the following February, which directly changes the process of power generation diversion flow of Luntan hydropower plant. The NSE coefficients of the generation dispatching function of Luntan hydropower plant based on multiple linear regression model and BP neural network are greater than 0.90 in most months. Considering the advantages of simple structure and convenient implementation, the generation dispatching function based on multiple linear regression model is selected for Luntan hydropower plant. Compared with the results of actual dispatching and dispatching diagram of Luntan hydropower plant, the generation capacity of the dispatching function is increased by 338.4×10^(4) kW·h/month and 183.8×10^(4) kW·h/month from March 2017 to February 2019, respectively. As a result, the water level of Luntan Reservoir can be significantly increased by use of the dispatching function. The research can provide a support for the generation optimal dispatching process of Luntan hydropower plant, and provide a useful reference for many other hydropower plants to improve the practical way of the generation dispatching.
作者
温天福
鄢笑宇
刘章君
许新发
WEN Tian-fu;YAN Xiao-yu;LIU Zhang-jun;XU Xin-fa(Jiangxi Academy of Water Science and Engineering,Nanchang 330029,China;Jiangxi Provincial Key Laboratory of Water Resources and Environment of Poyang Lake,Nanchang 330029,China)
出处
《水电能源科学》
北大核心
2023年第1期87-91,共5页
Water Resources and Power
基金
国家自然科学基金项目(52169001)
江西省科技厅重大科技研发专项“揭榜挂帅”制项目(20213AAG01012)
江西水利科技项目(202325ZDKT14,201922ZDKT14)。
关键词
发电调度函数
BP神经网络
DP优化调度
发电调度图
伦潭水电站
generation dispatching function
BP neural networks
DP optimal dispatch
generation dispatching diagram
Luntan hydropower plant