Neural networks approach for wind-solar energy system with complex networks

Because large demand for electricity due to rapid increasing of population growth, using power generation systems renewable energies have widely been studied, and introduction of the power generation systems into many fields such as houses or buildings is accelerating. Essentially, supplying electric power by the renewable energies often becomes unstable because the amount of the electric power depends on the weather conditions. Then, we need to introduce a sophisticated control method to maintain supply systems stably. From this view point, M. E. Gamez et al. proposed an optimal control method using recurrent neural networks for a wind solar power energy generation system. In the conventional control method, the optimization problems for the wind solar energy power generation system are regarded as the linear programming problems, and they solved the problems by the recurrent neural networks. Then, results indicate that the control method has much possibility to apply into the real power generation systems. However, only small sizes of the systems are evaluated for the control method. Then, we evaluate the control method using more realistic power generation systems using the complex network theory in this paper. In this model, the wind solar power generation systems are connected by electric power lines. From the results of the numerical simulations, the control method with the recurrent neural networks exhibits good performance even if more realistic conditions are introduced.