Cuckoo optimization algorithm via Grey Wolf Optimizer for usage in engineering optimization and optimal power flow...

Area of Science:

• Electrical Engineering
• Computational Intelligence
• Optimization Techniques

Background:

• Optimal Power Flow (OPF) is crucial for efficient power distribution management.
• Existing optimization techniques face challenges with complex power systems and renewable energy integration.
• Resilient and efficient optimization methods are needed for modern power grids.

Purpose of the Study:

• To introduce COGWO, a hybrid metaheuristic combining GWO and COA, for enhanced OPF solutions.
• To validate COGWO's performance against standard engineering problems and state-of-the-art methods.
• To apply COGWO to OPF issues in large-scale power systems with fluctuating renewable energy sources.

Main Methods:

• Developed COGWO by integrating Grey Wolf Optimizer (GWO) and Cuckoo Optimization Algorithm (COA).
• Validated COGWO on CEC2020 benchmark problems, demonstrating superior performance.
• Applied COGWO to IEEE 30-bus and 118-bus systems, considering renewable energy source (RES) fluctuations.

Main Results:

• COGWO achieved superior convergence quality and solution resilience compared to GWO, COA, and other metaheuristics.
• The method effectively minimized fuel costs, power loss, voltage variation, and emissions.
• COGWO demonstrated an optimal balance between exploration and exploitation for non-convex and non-smooth optimization functions.

Conclusions:

• COGWO offers a computationally efficient, flexible, and resilient solution for large-scale power system optimization.
• The hybrid approach significantly improves solution stability and convergence speed in OPF.
• COGWO is a promising technique for managing modern power grids with integrated renewable energy.