Energy-aware intelligent controller for Dynamic Energy Management on smart microgrid

Abstract

A Dynamic Energy Management (DEM) controller which is capable of taking decisions based on the status of the grid-connected smart microgrid has been developed using Support Vector Machine (SVM) and Artificial Neural Networks (ANN). The proposed control strategy involves the decisions for the dynamic charge-discharge transactions in the energy storage systems like battery and pumped hydro (PH) units connected to the smart microgrid in order to maintain a real time balance of generation and load. A comparison has been made based on the realizations of both SVM model and ANN model on SPARTAN 3AN Field Programmable Gate Array (FPGA) and the results show that SVM implementation is better than ANN implementation. The projected DEM system when tested with the existing laboratory model of a smart microgrid results in sustainable supply of power as the SVM based DEM controller monitors power flow in the lines and provides an optimal solution.