Techno-economic optimization framework of renewable hybrid photovoltaic/wind turbine/fuel cell energy system using artificial rabbits algorithm

In order to maximize the electricity supply from clean energy sources, the goal of the smart power system is to unite all renewable energy sources. The goal of the present study is to use three optimization techniques, artificial rabbits optimization algorithm (ARO), grey wolf optimizer (GWO), and whale optimization algorithm (WOA), to reduce the cost of electricity (COE) while improving the reliability of the power supply for rural areas. While using the same control variables for the optimization methods and load profile, various hybrid system configurations are explored. Photovoltaic, wind turbine, fuel cell, and electrolyser systems are all involved in the proposed hybrid renewable system. The ARO methodology is more effective than the GWO, WOA, and PSO procedures in terms of net present cost (NPC) and cost of energy (COE) generation, according to data comparing the three optimization techniques with the traditional Particle Swarm Optimization (PSO) method. The proposed ARO reached a value of COE of 0.4412$/kWh compared to 0.4438$/kWh for GWO, 0.4443$/kWh for WOA, and 0.44378$/kWh for PSO.