水电站分期发电调度规则提取方法  

Deriving rules for staged dispatching of hydropower stations

作　　者：郭玉雪[1] 方国华[1] 闻昕[1] 黄显峰[1] GUO Yuxue;FANG Guohua;WEN Xin;HUANG Xianfeng(College of Water Conservancy and Hydropower Engineering,Hohai University,Nanjing 210098)

机构地区：[1]河海大学水利水电学院,南京210098

出　　处：《水力发电学报》2019年第1期20-31,共12页Journal of Hydroelectric Engineering

基　　金：江苏省研究生科研与实践创新计划项目(KYZZ16_0287);江苏省高校优势学科建设工程资助项目(PAPD)

摘　　要：针对水电站发电优化调度需求,提出了结合灰色关联度(GRA)和贝叶斯模型平均法(BMA)提取水电站水库分期发电调度规则方法。在确定性优化调度模型基础上,首先确定决策变量和影响因子属性集,基于GRA筛选分期影响因子;然后分别采用多元线性回归模型、支持向量机模型及BP神经网络模型拟合得到分期水电站水库发电调度规则;最后应用BMA进行多模型结果加权平均获取最终分期水电站水库发电调度规则。以新安江水电站为例,对本文的方法进行了验证。研究结果表明,基于GRA和BMA结合的调度规则提取方法不仅可以提供精度较高的均值模拟,而且能较好地保持确定性优化调度的发电效益。Applying the grey relational analysis(GRA) and Bayesian model averaging(BMA) method, this paper develops a new method for dispatching the power production of a hydropower station. We first determine decision variables and impact factor sets using GRA and the results of a deterministic optimal dispatch model, and then obtain rules for staged hydropower production dispatching using a multivariate linear regression model, a support vector machine, and a back propagation neural networks. Finally, the rules for monthly power dispatching are derived using BMA to take weighted average of the models’ results. Application in a case study of the Xinanjiang hydropower station shows that our method is more accurate and can achieve an efficiency of hydropower production comparable to that of deterministic optimal dispatch.

关 键 词：灰色关联度 贝叶斯模型平均 分期规则 水电站水库 发电调度 模型不确定性 

分 类 号：TV697.1[水利工程—水利水电工程]