Application of improved ant-lion algorithm for power systems

Area of Science:

• Electrical Engineering
• Optimization Algorithms
• Environmental Science

Background:

• Load allocation in power systems is crucial for efficiency.
• Existing algorithms often optimize economic goals solely.
• Environmental impact of power generation requires simultaneous consideration.

Purpose of the Study:

• To propose an improved ant-lion algorithm for power system load allocation.
• To achieve dual optimization of economic and environmental objectives.
• To enhance the global search capability and performance of the ant-lion algorithm.

Main Methods:

• Incorporation of elite weights and chaotic search mechanisms into the ant-lion algorithm.
• Testing the algorithm's performance on benchmark functions (Sphere and Griebank).
• Application to economic load dispatch (ELD) and environmental/economic load dispatch (EELD) problems, including valve point effects.

Main Results:

• The improved ant-lion algorithm demonstrated faster convergence and fewer iterations than other algorithms.
• Achieved fuel cost reductions of 0.10%-2.39% (3-unit) and 6% (6-unit) in ELD simulations.
• In EELD, the proposed scheme resulted in a total fuel cost of 622.46 $/hr and emissions of 0.20 tons/h, with an average reduction of 53.55 $/hr in the total objective function.

Conclusions:

• The enhanced ant-lion algorithm significantly improves optimization performance for power system load allocation.
• The proposed method effectively balances economic and environmental considerations.
• This approach offers a superior solution for efficient and sustainable power system operation.