Hybrid GOA and PSO optimization for load frequency control in renewable multi source dual area power systems

Area of Science:

• Electrical Engineering
• Control Systems
• Renewable Energy Integration

Background:

• Integrating solar, wind, and electric vehicles (EVs) into power grids challenges frequency stability and tie-line power flow.
• Fluctuations from unconventional sources can degrade power quality and reliability for end-users.

Purpose of the Study:

• To propose a novel Proportional-Integral-Derivative (PID) controller optimized via a hybrid Grasshopper Optimization Algorithm-Particle Swarm Optimization (GOA-PSO) for enhanced load frequency control (LFC).
• To evaluate the controller's performance in single-area and dual-area interconnected power systems with diverse renewable energy sources and EVs.

Main Methods:

• Developed a hybrid GOA-PSO algorithm to optimize PID controller parameters, leveraging GOA's exploration and PSO's exploitation.
• Integrated fuzzy-based MPPT PV, P&O MPPT PMSG wind, and EV models within single and dual-area power system networks.
• Utilized Integral Time Absolute Error (ITAE) as the fitness function for controller tuning.

Main Results:

• The GOA-PSO-PID controller demonstrated superior performance over conventional PSO-PID, achieving significant reductions in overshoot (up to 79.95%) and undershoot (up to 92.78%), and improvements in settling time (up to 98.91%) in a single-area system.
• In a dual-area system, the controller provided substantial reductions in overshoot (up to 76.73%) and undershoot (up to 87.62%), alongside improved rise time (up to 75.68%).
• The controller proved robust across ±40% parameter variations and load fluctuations.

Conclusions:

• The GOA-PSO-PID controller offers a robust and adaptable solution for managing frequency stability in renewable-dominated power networks.
• This optimized control strategy significantly enhances LFC performance, ensuring reliable power delivery in modern smart grids.