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

Area of Science:

• Renewable Energy Systems
• Optimization Algorithms
• Techno-economic Analysis

Background:

• Global shift towards renewable and clean energy due to rising consumption and dwindling fossil fuels.
• Integrated green power systems (solar, wind, fuel cell) enhance efficiency and output with reduced storage needs.
• Need for advanced optimization techniques for effective techno-economic evaluation of these complex systems.

Purpose of the Study:

• To introduce and evaluate an improved chaos-based grasshopper optimizer (ICGO) for techno-economic assessment of integrated green power systems.
• To enhance the performance, precision, and robustness of optimization for renewable energy systems.
• To demonstrate the ICGO's capability in assigning optimal ratings to system devices for maximum efficiency.

Main Methods:

• Integration of chaos theory with the grasshopper optimization technique to develop the Chaotic Grasshopper Optimizer (ICGO).
• Application of the ICGO model for techno-economic evaluation of an integrated system comprising solar, wind, and fuel cell power sources.
• Performance assessment of the ICGO using four benchmark tasks to evaluate its precision and robustness.

Main Results:

• The ICGO algorithm achieved the lowest minimum Net Present Cost (NPC) of 274.541E4 USD and a high maximum NPC of 311.94E4 USD.
• The average NPC of the ICGO algorithm (289.176E4 USD) was competitive with other examined algorithms.
• The ICGO demonstrated superior efficiency and precision compared to standard optimization techniques, handling multiple targets, constraints, and variables effectively.

Conclusions:

• The developed Chaotic Grasshopper Optimizer is a highly effective technique for Hybrid Renewable Energy Systems (HRES).
• The ICGO significantly outperforms traditional optimization methods in minimizing the overall cost of renewable energy systems.
• The algorithm exhibits robustness with minimal performance degradation, making it suitable for complex, multi-objective optimization tasks in the renewable energy sector.