Optimal Control and Optimization of Grid-Connected PV and Wind Turbine Hybrid Systems Using Electric Eel Foraging...

Area of Science:

• Renewable Energy Systems
• Electrical Engineering
• Optimization Techniques

Background:

• Renewable energy sources like wind and photovoltaics (PV) are intermittent, posing challenges for stable electricity generation.
• Hybrid systems integrating multiple renewable sources are crucial for reliable, emissions-free energy.
• Addressing intermittency requires advanced control and optimization for seamless grid integration.

Purpose of the Study:

• To explore a hybrid wind-PV energy system for reliable, emissions-free electricity.
• To optimize power regulation, grid integration, and load management using advanced algorithms.
• To assess the effectiveness of Electric Eel Foraging Optimization (EEFO) in hybrid system operation.

Main Methods:

• Utilized Particle Swarm Optimization (PSO) and Electric Eel Foraging Optimization (EEFO) for system optimization.
• Employed mathematical modeling and simulation techniques in MATLAB/SIMULINK.
• Evaluated optimization strategies through diverse operating scenarios and grid-connected system simulations.

Main Results:

• EEFO effectively optimizes the operation of grid-connected wind-PV hybrid systems.
• Proposed optimization strategies precisely and swiftly compensate for linked loads.
• The system demonstrates effective control of energy supply to maintain desired load power.

Conclusions:

• The hybrid wind-PV system with EEFO offers a sustainable and reliable solution for power demands.
• Optimization strategies enhance system performance, maximize energy yield, and improve grid integration.
• This approach contributes to the advancement of clean energy technologies and sustainable energy systems.