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An aggregation-disaggregation approach to long-term reservoir management

机译:一种长期储层管理的聚集-分解方法

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The problem is to find the monthly operating policy of Hydro-Quebec's 26 large reservoirs that maximizes the utility's expected profits over a period of several years. The problem is solved in a hierarchical way. First, the optimal operating policy of the whole system, represented by an aggregate model, is found by stochastic dynamic programming (SDP). This gives not only the hydroelectric energy to produce in a month but also, as is very important in a deregulated market, the expected marginal value of the hydroelectric energy produced. At the second level the expected marginal value of the potential energy stored in each liver is determined by solving a SDP problem with two state variables: one for the energy content of the river and the other for the energy content of all the other rivers combined. These marginal values are used afterward to divide the hydroelectric production among the rivers. At the third level the production assigned to each river is distributed between the reservoirs so as to minimize the spillages first, and then the square of the deviations of the reservoir levels from the target level. The targets are adjusted to maximize the expected long-term production of the river. The monthly inflows to the reservoirs are the only random variables in this problem. [References: 14]
机译:问题在于找到魁北克水电公司26个大型水库的月度运营政策,该政策可以在几年内最大限度地提高公用事业公司的预期利润。问题以分层方式解决。首先,利用随机动态规划(SDP)找到以聚合模型表示的整个系统的最优运行策略;这不仅提供了一个月内生产的水力发电量,而且在放松管制的市场中非常重要,提供了水力发电量的预期边际价值。在第二层次上,通过求解具有两个状态变量的SDP问题来确定每个肝脏中储存的势能的预期边际值:一个是河流的能量含量,另一个是所有其他河流的能量含量的总和。这些边际值随后用于划分河流之间的水力发电量。在第三级,分配给每条河流的产量分布在水库之间,以便首先最大限度地减少溢出,然后是水库水位与目标水位偏差的平方。对目标进行了调整,以最大限度地提高河流的预期长期产量。每月流入水库是这个问题中唯一的随机变量。[参考资料: 14]

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