Comparative Analysis of Meta-Heuristic Algorithms for Optimal Sizing of Hybrid Renewable Energy System

This study analyzes and evaluates five meta-heuristic algorithms, the Particle Swarm Optimization (PSO), the Harmony Search (HS), the Firefly Algorithm (FA), the Cultural Algorithm (CA), and the flower pollination algorithm (FPA), to address optimal configuration of Hybrid Renewable Energy System (HRES). The proposed hybrid system is comprised of a Photovoltaic (PV), wind turbine, and biomass generator designed to meet the electrical load of a remote village located in Saudi Arabia. The objective of optimizing the system is to identify an optimum system configuration with minimum total net present costs (TNPC). Per each technique, simulations have been made for 30 independent runs to verify the proposed methodology. The results of the five algorithms (minimum TNPC) were examined using an independent t-test sample. The analysis demonstrates that the FPA was the best algorithm followed by the FA.