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Impacts of Inflow Variations on the Long Term Operation of a Multi-Hydropower-Reservoir System and a Strategy for Determining the Adaptable Operation Rule
Water Resources Management ( IF 4.3 ) Pub Date : 2020-03-21 , DOI: 10.1007/s11269-020-02515-6
Saiyan Liu , Yangyang Xie , Hongyuan Fang , Qiang Huang , Shengzhi Huang , Jingcai Wang , Zhen Li

Obvious inflow variations resulting from changing environments bring big challenges to the operations of hydropower reservoirs. This study reveals the impacts of average annual inflow volume (AAIV) variations on the long term operation of a multi-hydropower-reservoir (MHR) system, and presents a strategy for determining the adaptable operation rule. The strategy includes two parts. One part is making different inflow scenarios based on the change points of AAIVs. Another part is applying the principle of cross validation to select the adaptable rule from the formulated operation rules in various inflow scenarios. Specifically, the change points of AAIVs are identified by three statistical methods. An optimization operation model of an MHR system is built, and three evolutionary and meta-heuristic algorithms are applied to resolve the model in different inflow scenarios. Based on the optimal operation results, two machine learning algorithms are employed to formulate operation rules in each inflow scenario. The MHR system at the upstream of Yellow River basin is taken as a case study. The results show that (1) the long term operation of an MHR system is sensitive to the AAIV variations; and (2) the presented strategy is feasible in determining the adaptable operation rule for an MHR system under the AAIV variations. The findings of the study are helpful for the long term operation of an MHR system under the AAIV variations.



中文翻译:

流量变化对多水库系统长期运行的影响及适应性规则的确定策略

由于环境变化而导致的明显流量变化给水力发电库的运行带来了巨大挑战。这项研究揭示了平均年流入量(AAIV)变化对多水电水库(MHR)系统的长期运行的影响,并提出了确定适应性运行规则的策略。该策略包括两个部分。一方面是根据AAIV的变化点制定不同的流入方案。另一部分是应用交叉验证的原理,从各种流入情况下,从制定的操作规则中选择适应性规则。具体地,通过三种统计方法识别AAIV的变化点。建立了MHR系统的优化运行模型,分别采用三种进化算法和元启发式算法对模型进行了解析。基于最佳运行结果,两种机器学习算法用于制定每种流入情况下的运行规则。以黄河流域上游的MHR系统为例。结果表明:(1)MHR系统的长期运行对AAIV变化敏感;(2)提出的策略在确定AAIV变化下MHR系统的适应性运行规则时是可行的。该研究的发现对于AAIV变化下的MHR系统的长期运行很有帮助。以黄河流域上游的MHR系统为例。结果表明:(1)MHR系统的长期运行对AAIV变化敏感;(2)提出的策略在确定AAIV变化下MHR系统的适应性运行规则时是可行的。该研究的发现对于AAIV变化下的MHR系统的长期运行很有帮助。以黄河流域上游的MHR系统为例。结果表明:(1)MHR系统的长期运行对AAIV变化敏感;(2)提出的策略在确定AAIV变化下MHR系统的适应性运行规则时是可行的。该研究的发现对于AAIV变化下的MHR系统的长期运行很有帮助。

更新日期:2020-04-22
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