Neural control for a microgrid

Due to energy demand increasing and environmental issues, which are becoming more critical with time, integration of clean energy resources has become an important subject of research. Microgrids can be defined as an interconnection of energy sources at distribution level, which preferably includes renewable ones, and which are key in the evolution of distributed generation systems. This work deals with the neural control of a photovoltaic panel, a battery bank system, and a doubly-fed induction generator connected to a wind turbine, all of which work together as components of a microgrid. The neural controllers follow a reference given by an optimization algorithm in order to use the different sources efficiently.