An Efficient Technique-Based Distributed Energy Management for Hybrid MG System: A Hybrid RFCFA Technique

Abstract

This paper presents an efficient hybrid approach-based energy management strategy for grid-connected microgrid (MG) system. The proposed hybrid technique is the combination of both random forest (RF) and cuttlefish algorithm (CFA) named as RFCFA. The proposed hybrid technique is utilized to decrease the electricity cost and increase the power flow between the source and load side. The MG system is tracked by the RF technique. The CFA is optimized based on the MG with the predicted load demand. MG employs two energy management strategies to reduce the impact of renewable energy prediction errors. The first strategy seeks at minimizing electricity costs during MG’s operation. And the second strategy is aimed at balancing the power flow and reducing forecast error effects. In the grid-connected MG system, the objective function of the proposed technique is characterized with the inclusion of fuel cost, grid power variation, operation and maintenance cost. Battery energy storage systems (BESSs) can stabilize the output power and allow renewable power system units to operate at stable rate of output power. The proposed hybrid technique is executed in the working platform of MATLAB/Simulink, and the execution is evaluated using existing techniques such as GA, CFA and RBFNBBMO.