JAYA Algorithm-Based Energy Management for a Grid-Connected Micro-Grid with PV-Wind-Microturbine-Storage Energy System

Abstract

In this study, the Jaya optimization algorithm is used to address the micro-grid energy management optimization problem using a hybrid PV-wind-microturbine-storage energy system. The main goals of this study are to reduce environmental pollution, increase microturbine operating efficiency, and minimize the cost of power generated. The overall objective of the proposed optimization method employed in the PV-WECS system is to run the PV-WECS systems at full capacity while running the microturbine when the PV-WECS systems are unable to produce all of the required power. The amount of emissions and costs of generated energy are reduced when BESS is used in the microgrid system. Furthermore, it is observed from the results that there is about 61.39% cost saving in the micro-grid operational costs and 38% carbon emissions reductions using the proposed optimization algorithm compared to the other metaheuristic algorithms used in this study. To demonstrate the appropriateness and supremacy of the proposed algorithm over the various optimization techniques for energy management of the proposed micro-grid systems, simulation results from the proposed algorithm are compared with those from other population-based metaheuristic algorithms, such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Teaching Learning Based Optimization (TLBO), and Genetic Algorithms (GA). It is clear that the proposed algorithm outperforms and produces better results than the existing metaheuristic optimization techniques. More importantly, it illustrates the viability and efficacy of the proposed JAYA optimization approach in addressing the issue of energy management for large-scale power systems.