Improved chaos grasshopper optimizer and its application to HRES techno-economic evaluation

Current political and economic trends are moving more and more toward the use of renewable and clean energy as a result of rising energy use and diminishing fossil fuel supplies. In this paper, an improved chaos-based grasshopper optimizer used for techno-economic evaluation in integrated green power systems is investigated. The integrated system consists of a fuel cell system, a wind farm, and solar energy. The integrated solar, wind, and hydrogen fuel cell architectures increase the effectiveness and electrical output of the system while needing less energy storage in structures that are unconnected from the grid. The grasshopper optimization technique and chaos theory have been combined to create the suggested chaotic grasshopper optimizer in this study. The performance, precision, and robustness of this optimization were then assessed, using four benchmark tasks. The ICGO model is utilized to assign suitable ratings to all system devices, thereby guaranteeing the attainment of optimal performance and efficiency. The Net Present Cost (NPC) analysis revealed that the ICGO algorithm attained the lowest minimum NPC value of 274.541E4 USD and the highest maximum NPC value of 311.94E4 USD. The average NPC value of the ICGO algorithm (289.176E4 USD) was found to be comparable to the other algorithms examined in the study. These findings indicate that the ICGO algorithm outperformed other optimization algorithms in minimizing the cost of the renewable energy system. The chaotic grasshopper optimizer can handle several targets, restrictions, and variables with ease, and the results demonstrate that it is substantially more efficient and precise than standard optimization techniques. It is also quite durable, with minimal performance degradation as compared to the benchmark solutions. This study demonstrates the effectiveness of the chaos grasshopper optimizer as an HRES technique.