Energetic operation planning using genetic algorithms

This paper investigates the application of genetic algorithms to optimize large, nonlinear complex systems, particularly the optimization of the operation planning of hydrothermal power systems. Several of the current studies to solve this kind of problem are based on nonlinear programming. This approach presents some deficiencies, such as difficult convergence, oversimplification of the original problem or difficulties related to the objective function approximation. Aiming to find more efficient solutions for this class of problems, this paper proposes and investigates the use of a genetic approach. The characteristics of the GAs such as simplicity, parallelism, and generality, can provide an effective solution to these problems. The paper presents an adaptation of the technique and an actual application on the optimization of the operation planning for a cascade system composed of interconnected hydroelectric plants.