Hydro Plant Dispatch Using Artificial Neural Network and Genetic Algorithm

This paper presents a novel approach to solve the hydro plant dispatch problem based on the artificial neural network (ANN) and genetic algorithm (GA). In this work, the difficult water balance constraints are embedded and satisfied throughout the proposed encoding and decoding algorithms. The ANN is used as a pre-dispatch tool to generate raw hydro output for each hour temporarily ignoring time-dependent constraints. Then, the proposed decoding algorithm decodes the raw schedule of each plant into a feasible one. Finally, a GA is used to find the optimal schedule. The proposed approach is applied to an actual utility system of four hydro plants and 22 thermal units with great success. Results show that the new approach obtains a more highly optimal solution than the conventional dynamic programming method.