Robust load-frequency control of islanded urban microgrid using 1PD-3DOF-PID controller including mobile EV energy...

Area of Science:

• Electrical Engineering
• Control Systems
• Renewable Energy Integration

Background:

• Islanded Urban Microgrids (IUMGs) rely on Renewable Energy Sources (RES), facing frequency instability due to RES intermittency and low inertia.
• Electrical Energy Storage Systems (EESs) are crucial for mitigating frequency deviations, but high costs and power density needs necessitate alternative solutions.
• Electric Vehicle (EV) batteries are explored as Mobile EV Energy Storage (MEVES) to enhance frequency regulation in IUMGs.

Purpose of the Study:

• To introduce a robust, high-order cascade controller, the 1PD-3DOF-PID, for Load Frequency Control (LFC) in IUMGs integrated with MEVES.
• To optimize the controller's parameters using the Coati Optimization Algorithm (COA), applied for the first time in IUMG LFC.
• To evaluate the controller's performance and robustness against classical methods under diverse operating conditions and parameter variations.

Main Methods:

• Development and implementation of a 1PD-3DOF-PID controller for LFC in IUMGs with MEVES.
• Optimization of controller parameters using the Coati Optimization Algorithm (COA).
• Comparative simulation studies in MATLAB-SIMULINK against PID, 3DOF-PID, Reptile Search Algorithm, and White Shark Optimizer under various IUMG scenarios.

Main Results:

• The proposed COA-optimized 1PD-3DOF-PID controller demonstrated superior performance in mitigating frequency fluctuations compared to other controllers.
• The controller exhibited robust stability and resilience under ±25% variations in IUMG parameters.
• Statistical analyses confirmed the consistent and reliable performance of the COA-based control method.

Conclusions:

• The 1PD-3DOF-PID controller optimized by the COA is highly effective for LFC in IUMGs with MEVES integration.
• This approach offers a promising solution for enhancing the stability and reliability of renewable-rich microgrids.
• The study highlights the potential of novel metaheuristic algorithms and advanced control strategies in modern power systems.