Optimal operation of multireservoir power systems with stochastic inflows
Optimal operation of multireservoir power systems with stochastic inflows
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DOI：
10.1029/wr016i002p00275
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发表时间：
1980-04
影响因子：
5.4
通讯作者：
A. Turgeon
中科院分区：
文献类型：
--
作者：
A. Turgeon
水库群水电系统周调度策略的优化是一个随机非线性规划问题。对于小系统，这个问题可以解决的动态规划，但对于大系统，还没有直接解决这个问题的方法，所以，人们必须诉诸数学操作，以解决它。本文提出并比较了两种可能的操纵方法来解决这个问题。第一种方法，称为一次一个方法，包括将原来的多变量问题分解成一系列的单状态变量子问题，这些子问题通过动态规划来解决。最终的结果是每个水库的最优局部反馈操作策略。第二种方法，称为聚合/分解方法，包括在打破了原来的n-状态变量随机优化问题成n个随机优化子问题的两个状态变量，也解决了动态规划。最后的结果是一个次优的全局反馈操作策略的系统的n水库。这两种方法，然后应用到一个网络的六个水电站复杂的，并报告所获得的结果。结果表明，次优的全局反馈操作策略比最优的局部反馈操作策略给出更好的结果。
The optimization of the weekly operating policy of multireservoir hydroelectric power systems is a stochastic nonlinear programing problem. For small systems this problem can be solved by dynamic programing, but for large systems there is yet no method of solving this problem directly, so that one must resort to mathematical manipulations in order to solve it. This paper presents and compares two possible manipulation methods for solving this problem. The first, called the one-at-a-time method, consists in breaking up the original multivariable problem into a series of one-state variable subproblems that are solved by dynamic programing. The final result is an optimal local feedback operating policy for each reservoir. The second method, called the aggregation/decomposition method, consists in breaking up the original n-state variable stochastic optimization problem into n stochastic optimization subproblems of two-state variables that are also solved by dynamic programing. The final result is a suboptimal global feedback operating policy for the system of n reservoirs. The two methods are then applied to a network of six reservoir-hydroplant complexes, and the results obtained are reported. It is shown that the suboptimal global feedback operating policy gives better results than the optimal local feedback operating policy.