Optimal Sizing of Hybrid Renewable Energy Based Microgrid System

Abstract

With the decline of fossil fuel reserves and the escalating global average temperature, the quest for environmentally friendly and renewable energy sources has gained significant momentum. Recent interest has focused on wind and photovoltaic and biogas-based energy conversion processes. However, due to the unpredictable nature of their inputs, incorporating energy storage devices is essential to ensure uninterrupted power supply. Furthermore, for hybrid renewable power generation to be economically viable, careful optimization of the participating generating units is imperative. This thesis presents an optimal sizing approach for a Wind-Photovoltaic-Biogas Battery system using a single objective optimization (SOO) method. The study compares the performance of seven metaheuristic optimizers: Particle Swarm Optimization (PSO), Aquila Optimizer (AO), Pelican Optimization Algorithm (POA), Dandelion Optimizing Algorithm (DOA), Gazelle Optimization Algorithm (GOA), Zebra Optimization Algorithm (ZOA), and Osprey Optimization Algorithm (OOA). A comprehensive comparative analysis is conducted, evaluating the convergence speed and objective mean (for minimization) of the applied metaheuristic algorithms. The results demonstrate that the Pelican Optimization Algorithm (POA) outperforms other existing algorithms, exhibiting faster convergence and lower objective mean. These findings highlight the efficacy of POA for optimizing the sizing of hybrid renewable energy systems. This research contributes to advancing renewable energy systems by addressing intermittent input challenges and facilitating the design optimization of hybrid systems. The findings can serve as valuable insights for energy scientists, vi engineers, and policymakers, enabling them to make well-informed choices regarding the deployment and functioning of hybrid renewable energy systems. This will contribute to the promotion of a sustainable and resilient energy landscape, supporting a future that prioritizes environmental sustainability and adaptability.