当前位置: X-MOL 学术J. Hydrol. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Real-time reservoir flood control operation enhanced by data assimilation
Journal of Hydrology ( IF 6.4 ) Pub Date : 2021-05-07 , DOI: 10.1016/j.jhydrol.2021.126426
Jingwen Zhang , Ximing Cai , Xiaohui Lei , Pan Liu , Hao Wang

Real world reservoir operations are usually not fully automatic based on computer models; instead, reservoir operators conduct the operations based on their experiences, professional justification, as well as modeling support for some cases due to unavoidable gap between computer modeling and real world reservoir operation conditions. In this paper, we propose a human-machine interactive method, namely Real-time Optimization Model Enhanced by Data Assimilation (ROMEDA) tested with simple but reasonable rules for direct interaction between operators and a computer model for reservoirs which have complex storage and stage relations (e.g. long and narrow reservoirs). ROMEDA couples 1) an optimization model to search for optimal releases, 2) a reservoir storage-stage simulation and data assimilation schedule to update the storage based on real-time reservoir stage observations, and 3) reservoir operators’ choices based on the optimization model solutions, as well as their experiences, knowledge, and behaviors. For every time period and based on the updated storage, ROMEDA provides optimal releases as recommendations, actual releases made by operators, as well as a warning of flood risk when the storage exceeds a threshold level. ROMEDA does not assume that operators strictly accept the recommendations, and storage will be updated based on actual release at each time period. The data assimilation procedure plays a key linkage between human and machine in ROMEDA. Via a case study on-channel reservoir, it is found that for both small and large flood events, ROMEDA, which integrates the advantages of both machine and human, shows better performance on flood risk mitigation and water use (hydropower) benefit than the case with historical operation records (HOR) or optimization with single/multi-objective. ROMEDA is one of the first attempts of a human-machine interactive method for online use of an optimization model for real-time reservoir operation based on integrated modeling, observation, and operators’ choice.



中文翻译:

通过数据同化增强实时水库防洪运营

基于计算机模型,现实世界中的储层操作通常不是完全自动的。取而代之的是,由于计算机建模与现实世界中的储层运行条件之间存在不可避免的差距,因此储层运营商将根据他们的经验,专业理由以及对某些情况的建模支持来进行运营。在本文中,我们提出了一种人机交互方法,即通过数据同化增强的实时优化模型(ROMEDA),该方法已通过简单但合理的规则进行了操作员之间直接交互的测试,并采用了具有复杂存储和阶段关系的储层计算机模型(例如,狭长的水库)。ROMEDA结合了1)用于搜索最佳版本的优化模型,2)基于实时油藏阶段观测结果的油藏存储阶段模拟和数据同化计划,以更新存储; 3)基于优化模型解决方案的油藏运营商选择以及他们的经验,知识和行为。对于每个时间段并基于更新后的存储,ROMEDA会提供建议的最佳释放,操作员的实际释放以及存储超过阈值水平时的洪水风险警告。ROMEDA不假设运营商严格接受建议,并且存储将根据每个时间段的实际发布进行更新。数据同化过程在ROMEDA中的人机之间起着关键的联系。通过对河道水库进行案例研究,发现无论大小洪水事件,ROMEDA,结合了机器和人的优点,与历史操作记录(HOR)或具有单/多目标优化的情况相比,它在减轻洪水风险和降低用水(水电)收益方面表现出更好的性能。ROMEDA是人机交互方法的首次尝试之一,该方法是基于集成建模,观测和操作员选择,在线使用实时油藏优化模型的优化模型。

更新日期:2021-05-07
down
wechat
bug