利用数学优化管理洪水的进展,以及利用案例研究评估可能的效益

Nesa Ilich, Ashoke Basistha
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摘要

本文介绍了在假定可获得短期径流预报的基础上利用数学优化进行水库运行的好处。新颖之处在于将 SSARR 水文路由作为优化约束条件纳入多时步优化,其中路由系数作为渠道流量的函数进行动态调整。论文显示,即使预报期只有 2 天,洪水易发区的下游峰值流量也会明显减少,论文还包括测试不同预报期长度对模型结果影响的结果。案例研究的对象是印度西孟加拉邦的达莫达尔河流域,该流域的开发始于 20 世纪 50 年代,除供水和水力发电外,下游河谷的防洪保护也是管理的重中之重。本文介绍的解决方法和模型结果为最终引入水库出流的自动化管理铺平了道路,假定径流预报能力不断提高,这可能会像自动驾驶和无人驾驶汽车彻底改变交通运输业一样,彻底改变水利行业。
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Advances in using mathematical optimization to manage floods with assessment of possible benefits using a case study
This paper presents the benefits of using mathematical optimization for reservoir operation based on the assumed availability of short-term runoff forecasts. The novelty is the inclusion of the SSARR hydrological routing as optimization constraints in multiple time step optimization, where the routing coefficients are adjusted dynamically as functions of the channel flows. The paper shows significant reduction to downstream peak flows in flood-prone areas even with a forecast horizon of only 2 days, and it also includes the results of testing the effects of different lengths of forecasting horizons on model results. The case study is conducted on the Damodar River Basin in the Indian State of West Bengal, where basin development started in the 1950s, with flood protection of the downstream river valley as the highest management priority, in addition to water supply and hydro power. The solution methodology and the model results presented in this paper pave the way for eventual introduction to automated management of reservoir outflows that could revolutionize water resources industry in much the same way that auto-pilot and driverless cars are revolutionizing the transportation industry, assuming that runoff forecasting capabilities continue to improve.
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