A HYBRID ARTIFICIAL NEURAL NETWORK-DIFFERENTIAL DYNAMIC-PROGRAMMING APPROACH FOR SHORT-TERM HYDRO SCHEDULING

In this paper, a hybrid artificial neural network-differential dynamic programming (ANN-DDP) method for the scheduling of short-term hydro generation is developed. The purpose of short-term hydro scheduling is to find the optimal amounts of generated powers for the hydro units in the system for the next N (N = 24 in this work) hours in the future. In the proposed method, the DDP procedures are performed offline on historical load data. The results are compiled and valuable information is obtained by using ANN algorithms. The DDP algorithm is then performed online according to the obtained information to give the hydro generation schedule for the forecasted load. Two types of ANN algorithm, the supervised learning neural network by Rumelhart et al. and the unsupervised learning neural network by Kohonen, are employed and compared in this paper. The effectiveness of the proposed approach is demonstrated by the short-term hydro scheduling of Taiwan power system which consists of ten hydro plants. It is concluded from the results that the proposed approach can significantly reduce the execution time of the conventional differential dynamic programming algorithm which is required to reach proper hydro generation schedules.