Optimization of generation capacity at the incoming microgrid in an interconnected microgrid system using ANN

To enhance the reliability of transmission and distribution system centralized grid is split into a number of microgrids with an emphasis on the utility enhancement of green energy sources. As the renewable energy sources depend on the climatic conditions, their generation capacity is uncertain and unpredictable. To maintain the reliability of power supply overcoming the stochastic characteristics of hybrid generation system interconnected microgrid system is desired. In order to achieve the economic installation level, the optimization of the generation capacity should be carried out under variable load conditions. Although the concept of interconnected microgrid system has been conceived already but the generation optimization of an incoming microgrid is virtually non-existent. In this paper an extensive analysis of an interconnected microgrid system is presented under different generation capacities and load demands using DC load flow study. In spite of the fact that absolute dependence on the renewable resources is unreliable but to make the interconnected system independent, the interaction with the AC utility grid must be reduced. To meet the self-sufficient optimization, the output data set of DC load flow study is further analysed using Multilayer Supervised learning Artificial Neural Network algorithm.