LEARNING DISAGGREGATION TECHNIQUE FOR THE OPERATION OF LONG-TERM HYDROELECTRIC POWER-SYSTEMS

This paper describes a nonlinear disaggregation technique for the oper ation of multireservoir systems. The disaggregation is done by trainin g a neural network to give, for an aggregated storage level, the stora ge level of each reservoir of the system. The training set is obtained by solving the deterministic operating problem of a large number of e qually likely flow sequences. The training is achieved using the back propagation method, and the minimization of the quadratic error is com puted by a variable step gradient method. The aggregated storage level can be determined by stochastic dynamic programming in which all hydr oelectric installations are aggregated to form one equivalent reservoi r. The results of applying the learning disaggregation technique to Qu ebec's La Grande river are reported, and a comparison with the princip al component analysis disaggregation technique is given.