Performance Assessment of Metaheuristic Algorithms Applied for the Optimal Hybrid Renewable Energy Design: Zagora City as Case Study, Morocco

Abstract

This study focuses on the optimization and comparative analysis of hybrid renewable energy systems (HRES) in the Zagora region of Morocco, using three recent optimization algorithms: the Dream Optimization Algorithm (DOA), the Horned Lizard Optimization Algorithm (HLOA), and Particle Swarm Optimization (PSO). Two system configurations were evaluated: a PV/Wind/Battery/Diesel hybrid system and a PV/Wind/Battery-only system. The primary objective was to minimize the Cost of Energy (COE) while ensuring system reliability, measured by the Loss of Power Supply Probability (LPSP). The results indicate that HLOA achieved the lowest COE of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.33018 \$ / \text{kWh}$</tex> for the PV/Wind/Battery/Diesel system, while PSO provided the best balance between cost, reliability, and renewable energy integration. For the PV/Wind/Battery system, HLOA and PSO showed similar performance, with COE values of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.4301 \$ / \text{kWh}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.43087 \$ / \text{kWh}$</tex>, respectively. DOA, however, exhibited higher costs and greater variability, making it less efficient. Statistical analysis confirmed the stability and reliability of HLOA and PSO, highlighting their suitability for sustainable energy applications.