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Ensemble of Markovian stochastic dynamic programming models in different time scales for long term hydropower scheduling

机译：长期水电调度中不同时间尺度的马尔可夫随机动态规划模型的集合

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This paper presents a new approach for long term hydropower scheduling. In opposition to the standard Markovian stochastic dynamic programming approach, where monthly inflows are modeled according to probability distribution functions, conditioned to some occurrence of inflow in the previous month, in the proposed approach the monthly inflows are aggregated in different time scales and then submitted to the Markovian model. The discharge decisions are then calculated by a deterministic model that optimizes the problem for one year ahead according to inflows provided by a combination of each Markovian model. Tests were conducted on hypothetical single-reservoirs hydrothermal systems using data from four real Brazilian hydro plants, with distinct hydrological regimes. The performance of the proposed method was evaluated through simulation, using the historical inflow data, in comparison with the standard Markovian model. The results have shown that the proposed approach has provided spillage reduction and increase on hydro productivity as well as power generation, which incurred in up to 2.1% reduction in operational costs. (C) 2017 Elsevier B.V. All rights reserved.
机译：本文提出了一种新的长期水电调度方法。与标准的马尔可夫随机动态规划方法相反，后者根据概率分布函数对月流入量进行建模，并以上个月出现的某些流入为条件，在建议的方法中，将月流入量汇总到不同的时间范围内，然后提交给马尔可夫模型。然后由确定性模型计算排放决策，该模型根据每种马尔可夫模型的组合所提供的流入量将问题提前一年进行优化。使用来自巴西四个真实水电厂的数据，采用不同的水文制度，对假设的单水库热液系统进行了测试。与历史马尔可夫模型相比，使用历史流入数据通过仿真评估了所提出方法的性能。结果表明，所提出的方法减少了溢漏，提高了水力生产率和发电量，从而使运营成本降低了2.1％。 （C）2017 Elsevier B.V.保留所有权利。

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