Maximizing hybrid microgrid system performance: A comparative analysis and optimization using a gradient pelican algorithm

Abstract

The development of hybrid systems that combine different energy sources has become increasingly important to meet the growing demand for clean and sustainable energy. These systems can offer a reliable and efficient solution to reduce dependence on conventional energy sources and address the issues of climate change and environmental degradation. This study focuses on two types of hybrid systems, namely PV/biomass and PV/diesel, which are commonly used in off-grid areas or places with limited access to electricity. The proposed optimization framework aims to determine the optimal sizing of each component in the system, including PV panels, batteries, and backup sources, to maximize the performance and efficiency of the hybrid system. The Gradient Pelican Optimization Algorithm (GPOA), which is an advanced optimization technique, is proposed to solve the optimization problem. The algorithm is modified to include a local escaping operator, which allows the algorithm to escape local optima and achieve better solutions. The outcomes of the simulation show that the GPOA algorithm outperforms other well-known algorithms in terms of accuracy and computational efficiency. The simulation results also demonstrate that the PV/biomass hybrid system outperforms the PV/diesel hybrid system in terms of cost and environmental impact. The proposed framework and optimization methodology can be useful for designing and optimizing other hybrid systems and can be applied to various applications, including off-grid and remote areas, distributed power systems, and microgrids. The findings of this study can also contribute to the development and promotion of renewable energy and sustainable development.