Real-time simulation of enhanced smell agent optimization based fuzzy-assisted fractional order PID controller for efficient load frequency control in interconnected microgrids

Independent microgrids face significant challenges in maintaining frequency stability due to demand fluctuations and the intermittent nature of renewable energy sources, resulting in reduced system inertia and limited external support. Conventional control approaches struggle to maintain dynamic performance under such nonlinear and uncertain operating conditions. To address this, a Fuzzy Logic Controller (FLC)-based Load Frequency Control (LFC) strategy optimized through a Diversity Preserved Multi-Swarm Chaotic Smell Agent Optimization with Adaptive Control Strategy algorithm is proposed for interconnected microgrids. The framework integrates Plug-in Hybrid Electric Vehicle (PHEV) coordination using a State-of-Charge-based deviation mechanism to balance bidirectional energy flow via Vehicle-to-Grid operation. The proposed algorithm simultaneously tunes Fuzzy-assisted Fractional Order Proportional Integral Derivative (PID) and PID controllers, enhancing robustness against parameter uncertainties, nonlinearities, and communication delays. Comprehensive simulations on a two-area microgrid were conducted under four critical scenarios: sudden load disturbances, renewable intermittency, parameter variations, and PHEV participation and validated through real-time OPAL-RT experiments. Results demonstrate that the proposed FLC-based scheme achieves improved frequency regulation, offering a 30–40% reduction in IAE and faster settling times compared with traditional controllers. Overall, the developed control strategy provides a highly responsive, adaptive, and implementation-feasible solution for resilient LFC in modern renewable-integrated microgrids.