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首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >Effect of Inflow Class Selection on Multi-Objective Reservoir Operation Using Stochastic Dynamic Programming
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Effect of Inflow Class Selection on Multi-Objective Reservoir Operation Using Stochastic Dynamic Programming

机译：流入级别选择对使用随机动态规划的多目标储层运行的影响

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摘要

A combined simulation optimization model was developed to derive optimal operational policies for a multiobjective reservoir in a semiarid environment. Stochastic dynamic programmingwas selected as the optimization technique. Different scenarios were considered to optimize the current operational policy as well as different possible future uses of the reservoir. The first scenario was used tomaximize hydropower production within the framework of the current operational policy. Other scenarios were used to address multiple objective operation of the reservoir including hydropower, agricultural, and domestic uses. Generated policies for all the scenarios were simulated in real time using historical inflow data for the Qar’awn reservoir within the Litani Basin in Lebanon. Sensitivity analysis on number of inflow classeswas performed. Results showed that the newly derived policies decrease failure by a range of threefold to sixfold and improve hydropower production by more than 15%. The model was able to derive policies that decreased system failure and shortages to less than 10%. Best inflow classes were found to be in the range of 3-5.
机译：开发了一个组合的仿真优化模型，用于在半干旱环境中获得多目标水库的最佳运行策略。选择随机动态编程WAS作为优化技术。考虑了不同的情景，优化当前的运营政策以及水库的不同可能的未来使用。第一个情景在当前运营政策的框架内使用截巨大水电生产。其他情况用于解决储层的多个客观运作，包括水电，农业和国内使用。使用Litanon盆地内的Qar'awn水库的历史流入数据来实时模拟所有方案的生成策略。流入类别措施的敏感性分析。结果表明，新导出的政策将失效减少了三倍到六倍，并提高水电产量超过15％。该模型能够导出减少系统故障和短缺至小于10％的政策。发现最好的流入课程在3-5的范围内。

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