Implementation of Modified Effective Butterfly Optimizer in Solving Multi-Objective Pareto Optimal Power Flow Problem...

Area of Science:

• Electrical Engineering
• Computational Intelligence
• Optimization Algorithms

Background:

• The power flow problem is critical for power system efficiency and energy quality.
• Optimal Power Flow (OPF) introduces complexity by requiring system variable and parameter optimization.
• Multi-objective OPF (MOOPF) presents significant challenges in balancing competing goals.

Purpose of the Study:

• To adapt the Modified Effective Butterfly Optimizer (MEBO) for solving multi-objective optimal power flow (MOOPF) problems.
• To evaluate MEBO's performance in handling trade-offs between objectives such as cost, loss, emission, and voltage deviation.
• To establish a novel application of MEBO in the field of MOOPF.

Main Methods:

• Utilized the Modified Effective Butterfly Optimizer (MEBO), an algorithm featuring population reduction and parameter learning.
• Applied MEBO to solve four distinct multi-objective problems: cost-loss, cost-voltage, cost-emission, and emission-loss.
• Tested the proposed technique on IEEE 30 and 57 bus systems, comparing results with existing literature methods.

Main Results:

• MEBO achieved effective compromised solutions for the cost-emission MOOPF problem, with specific examples provided.
• Demonstrated MEBO's capability in balancing conflicting objectives, such as achieving low emissions and power loss simultaneously.
• Presented Pareto curves illustrating MEBO's performance in achieving MO problems and compared compromised solutions with literature values.

Conclusions:

• The Modified Effective Butterfly Optimizer (MEBO) is effectively applied for the first time to solve MOOPF problems.
• MEBO demonstrates superior performance compared to most alternative methods for MOOPF.
• The proposed approach shows significant potential for further advancements in addressing MOOPF challenges.