Optimal Sizing of Hybrid Renewable Energy Based Microgrid System

TL;DR

This paper develops an optimal sizing method for a hybrid renewable energy microgrid combining wind, photovoltaic, biogas, and batteries, using metaheuristic algorithms to improve efficiency and reliability.

Contribution

It introduces a novel optimization approach for hybrid microgrid sizing and compares multiple metaheuristic algorithms to identify the most effective one.

Findings

Pelican Optimization Algorithm (POA) outperforms others in convergence speed.

POA achieves lower objective mean, indicating better optimization.

The proposed method enhances microgrid reliability and economic viability.

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. Focus has turned to wind and photovoltaic energy, but their variable inputs necessitate energy storage for reliable power. Economic viability of hybrid renewable power requires meticulous optimization of generating units to ensure uninterrupted and efficient energy production. This paper presents an optimal sizing approach for a Wind-Photovoltaic-Biogas-Battery system using a single objective optimization (SOO) method. A comprehensive comparative analysis is conducted, evaluating the convergence speed and objective mean (for minimization) 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). The results demonstrate that the Pelican Optimization Algorithm (POA) outperforms other existing algorithms, exhibiting faster convergence and lower objective mean.