Optimal power utilization in hybrid microgrid systems with IoT-based battery-sustained energy management using RSA-PFGAN approach

Abstract

The quantity of power electronics converters that interface with the various components of a hybrid microgrid system has a major impact on its efficiency. Minimizing power conversion stages and increasing system efficiency requires integrating a photovoltaic system with micro grids while maximizing the number of converters. This paper presents a hybrid approach for utilizing power in microgrid system with an Internet of Things (IoT) based battery sustained energy management scheme. The proposed hybrid technique combines the Reptile Search Algorithm (RSA) and Progressive Fusion Generative Adversarial Network (PFGAN). Thus, it is referred to as the RSA-PFGAN technique. The principal aim of the proposed approach is to minimize operating costs, improve voltage profiles, and reduce computation time and errors. The discharging and charging strategy of the battery is optimized by the RSA approach. The load demand is predicted using the PFGAN approach. Using MATLAB, the proposed method is evaluated and contrasted to other existing methods. The proposed approach determines better outcomes contrasted to existing techniques such as Wild Horse Optimization (WHO), Particle Swarm Optimization (PSO) and Seeker Optimization Algorithm (SOA). The proposed method achieves an efficiency of 85 %, significantly higher than the PSO's 55 %, WHO's 65 %, and SOA's 75 %. Additionally, the proposed approach exhibits a computation time of just 0.21 s, demonstrating its efficiency compared to PSO at 2.95 s, WHO at 0.87 s, and SOA at 0.43 s. These results indicates that the RSA-PFGAN method offers better performance in terms of cost, efficiency, and computation time for hybrid microgrid systems.