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.
作者
郭玉雪
方国华
闻昕
黄显峰
GUO Yuxue;FANG Guohua;WEN Xin;HUANG Xianfeng(College of Water Conservancy and Hydropower Engineering,Hohai University,Nanjing 210098)
出处
《水力发电学报》
EI
CSCD
北大核心
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)
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
grey relational analysis
Bayesian model averaging
staged dispatching rules
hydropower station
power production
model uncertainty