Optimal operation of multireservoir power systems with stochastic inflows

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.