Comparison and modeling of hybrid green energy systems tied to unhealthy grid utilizing different algorithms

Developing Nations encounter numerous issues affecting their energy sector, spanning from insufficient energy production to unreliable electricity grids. Instances of electricity blackouts rank as one of the most prevalent problems. Employing sustainable energy sources integrated into the grid helps to tackle this challenge and compensate for power shortages from internal grids. This paper conducts a comparison of the performance of four commonly utilized optimization algorithms, namely Artificial Gorilla Troops Optimization (AGTO), Artificial Rabbits Optimization (ARO), Particle Swarm Optimization (PSO) and Chaos Game Optimization (CGO) in ascertaining the sizing of a hybrid renewable energy system (HRES) that offers the optimal cost-effective levelized cost of energy (LCOE) to provide the energy requirements of a large hospital in Egypt facing an unreliable grid, with average load demand 8809 kilowatt-hour per day. CGO emerges as the most effective algorithm, suggesting an HRES with an LCOE of $0.0347 per kilowatt-hour, and a net present cost (NPC) of $3.75 million. The proposed HRES is identified as capable of mitigating power interruptions and boosting the proportion of environmentally friendly energy generation, resulting in a noteworthy reduction in emissions of greenhouse gases (GHGs). Pertaining to outcomes and convergence time, the CGO performance is deemed adequate when compared with the findings obtained through the utilization of commercially accessible hybrid optimization software such as HOMER Pro in the previous study.