Optimal power flow using hybrid firefly and particle swarm optimization algorithm.

Area of Science:

• Electrical Engineering
• Computational Intelligence
• Optimization Techniques

Background:

• Optimal Power Flow (OPF) problems are crucial for efficient power system operation.
• Traditional optimization methods face challenges with non-linear and complex OPF scenarios.
• Enhancing exploration, exploitation, and convergence speed in meta-heuristics is an ongoing research area.

Purpose of the Study:

• To introduce and evaluate a novel Hybrid Firefly Particle Swarm Optimization (HFPSO) algorithm.
• To apply HFPSO to solve various non-linear and convex Optimal Power Flow (OPF) problems.
• To demonstrate the algorithm's effectiveness in addressing multiple objective functions.

Main Methods:

• Hybridization of Firefly Optimization (FFO) and Particle Swarm Optimization (PSO) to create HFPSO.
• Implementation of HFPSO in MATLAB for solving OPF.
• Testing the algorithm on the standard IEEE 30-bus system.
• Evaluation based on five objective functions: cost minimization, voltage profile, voltage stability, active power loss, and reactive power loss reduction.

Main Results:

• HFPSO demonstrated superior performance compared to standard PSO and other state-of-the-art techniques.
• The algorithm achieved optimal, feasible, and global solutions for the tested OPF problems.
• Fast convergence rates were observed during the optimization process.
• HFPSO effectively handled the complexities and challenges inherent in OPF problems.

Conclusions:

• The proposed HFPSO algorithm is a powerful and effective meta-heuristic for solving diverse OPF problems.
• HFPSO offers enhanced exploration and exploitation capabilities, leading to improved solution quality and faster convergence.
• This method provides a robust approach for optimizing power systems with multiple objectives.