A New Hybrid Algorithm for Multi-Objective Reactive Power Planning via FACTS Devices and Renewable Wind Resources

Area of Science:

• Electrical Engineering
• Power Systems Engineering
• Optimization Algorithms

Background:

• Modern power systems face complex planning challenges due to integrating renewable energy sources like wind power.
• Flexible AC Transmission System (FACTS) devices are crucial for enhancing stability and reliability in reactive power planning.
• The stochastic nature of cost, loss, and voltage functions in power systems introduces non-linear constraints and conflicts.

Purpose of the Study:

• To develop a multi-objective hybrid algorithm for optimizing power system planning.
• To address the complexities introduced by wind resources and conflicting objective functions.
• To improve system loss, voltage profile, FACTS device cost, and stability.

Main Methods:

• A hybrid optimization algorithm combining Particle Swarm Optimization (PSO) and Virus Colony Search (VCS) was developed.
• The algorithm incorporates linear and non-linear constraints relevant to power system planning.
• Non-dominated sorting based on the Pareto criterion was used for data sorting.

Main Results:

• The hybrid PSO-VCS algorithm demonstrated improved convergence time and voltage stability.
• The method effectively reduced the absolute magnitude of voltage deviation and total transmission line losses.
• The positive impact of wind resource integration on power system planning parameters was confirmed.

Conclusions:

• The proposed hybrid algorithm offers a robust solution for multi-objective power system planning, particularly with renewable energy integration.
• The combined optimization approach effectively balances conflicting objectives, leading to enhanced system performance.
• The study highlights the significance of advanced optimization techniques for reliable and efficient modern power systems.