Hybrid cheetah particle swarm optimization based optimal hierarchical control of multiple microgrids

Area of Science:

• Electrical Engineering
• Computer Science
• Artificial Intelligence

Background:

• Microgrids are increasingly integrating renewable energy resources (RES) and energy storage systems (ESSs) into distribution networks (DNs).
• Coordinating multiple microgrids (MMGs) during energy transition presents significant control challenges for traditional methods.
• Artificial Intelligence (AI) offers a promising solution for enhancing the dynamic operation and control of MMGs in smart grids.

Purpose of the Study:

• To introduce an innovative hybrid optimization algorithm, HYCHOPSO, for improved MMG control.
• To evaluate HYCHOPSO's performance against existing optimization techniques in microgrid applications.
• To enhance the efficiency, reliability, and scalability of microgrid operations through advanced control strategies.

Main Methods:

• Development of a hybrid optimization technique, HYCHOPSO, combining Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO).
• Extensive benchmark testing to validate HYCHOPSO's convergence performance and superiority over individual CHO and PSO.
• Comparative analysis of HYCHOPSO against various metaheuristic optimization approaches for optimizing Proportional-Integral (PI) controller parameters in microgrid hierarchical control systems.

Main Results:

• HYCHOPSO demonstrates superior convergence performance, achieving optimal scores in fewer than 50 iterations compared to other algorithms stabilizing around 200 iterations.
• HYCHOPSO consistently achieves lower mean values and scores closer to optimal values across benchmark functions, indicating robust convergence.
• The HYCHOPSO-optimized PI controller effectively minimizes errors, enhancing system reliability, power sharing accuracy, voltage/frequency stability, and seamless transitions during dynamic MMG operations.

Conclusions:

• The HYCHOPSO algorithm offers a significant advancement in optimizing control parameters for MMGs.
• This hybrid approach enhances microgrid reliability, flexibility, scalability, and robustness in real-world applications.
• HYCHOPSO provides a practical and efficient solution for the complex challenges in microgrid energy management and control.