Optimization of Solar-Wind-Battery Hybrid Energy System Using Artificial Bee Colony Algorithm

Access to reliable and sustainable power generation in remote areas is a critical challenge faced by many regions worldwide. In such areas, hybrid energy systems integrating solar, wind, and battery components have emerged as a promising solution to overcome the lack of electricity infrastructure. This research primarily focuses on modeling the power supply for houses in remote areas of Iraq. It discusses a hybrid system to meet the electrical demand of a small area. The simulations and optimal sizing of the system are performed in MATLAB 2023a using a swarm-based artificial bee colony (ABC) algorithm. The findings have been compared with results from HOMER, a program commonly used to optimize combinations of electric and renewable energy sources, to confirm the effectiveness of the proposed approach. The main goals of this optimization project are to optimize the system component sizes and obtain the minimum cost of energy (LCOE). The results from the ABC algorithm show that the system exhibits good performance and convergence. This study also evaluates the proposed system's performance in the event of a failure from any source. The results confirm that the proposed method and optimal configuration can handle an efficient and reliable system.