A Novel Approach to Multi-Objective Control for Battery-Enabled Hybrid Micro Grid

Abstract: In this study, we present a new technique for overcoming the operational difficulties faced by Microgrid (MG) systems, which are capable of functioning in both islanding and grid-connected states. As the expenses and requirements of traditional energy sources rise, there's been a growing focus on renewable energy alternatives. A key issue in the operation of MG systems is the efficient management of control to ensure the highest possible power output despite the variability in generation. To tackle this, we suggest a power-sharing strategy that is optimized for the components of Solar Photovoltaic (PV), wind, and Battery Energy Storage Systems (BESS). Our approach is environmentally sustainable, reducing pollution and greenhouse gas emissions, which in turn benefits the natural environment. We formulate a multi-faceted objective function designed to enhance system efficiency while keeping costs to a minimum. By employing the Genetic Wolf Optimization (GWO) algorithm, we achieve outstanding outcomes in lowering the financial expenses associated with microgrid electricity production, surpassing traditional optimization techniques such as Particle Swarm Optimization (PSO) and Bacteria Foraging Optimization (BFO). Furthermore, we introduce a control framework that organizes the operation of each subsystem to ensure the stability of frequency in the face of unpredictable generation and demand. A comparative study validates the effectiveness of our approach, highlighting the GWO algorithm's advantage in optimizing microgrid performance