Comparative Performance Study of Genetic Algorithm and Particle Swarm Optimization Applied on Off-grid Renewable Hybrid Energy System

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Abstract

This paper focuses on unit sizing of stand-alone hybrid energy system using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) and comparative performance study of these two meta-heuristic techniques on hybrid energy system. The hybrid system is designed focusing on the viability and combining different renewable energy sources like wind turbines, solar panels along with micro-hydro plant as well as fuel cells to compensate the deficit generation in different hours. Apart from the non-conventional sources, the system has been optimized with converters, electrolyzers and hydrogen tanks. Net present cost (NPC), cost of energy (COE) and generation cost (GC) for power generation have been considered while optimal unit sizing of the system are performed. Feasibility of the system is made based on net present cost (NPC). The performances of two algorithms have been checked for different values of variants of the respective algorithms and a comparative study has been carried out based on number of iterations taken to find optimal solution, CPU utilization time and also quality of solutions. The comparative analysis shows that the Particle Swarm Optimization technique performs better than Genetic Algorithm when applied for the sizing problem.