Snake-Optimized Fuzzy Control for Three-Area Power Systems with EV Integration

Abstract

The growing penetration of renewable energy sources and electric vehicles (EVs) into modern power systems has introduced significant challenges for Automatic Generation Control (AGC), including nonlinearities, communication delays, and load uncertainties that conventional Proportional-Integral-Derivative (PID) controllers struggle to handle. This paper proposes a hybrid Fuzzy Fractional-Order Proportional-Integral (Fuzzy FOPI) and Tilt-Integral-Derivative (TID) controller for load frequency control (LFC) in a three-area interconnected power system comprising thermal, hydro, gas turbine, wind, solar photovoltaic, and EV resources. Controller parameters are optimally tuned using the Snake Optimization (SO) metaheuristic algorithm, with the Integral Squared Error (ISE) of frequency and tie-line power deviations adopted as the fitness function. A comprehensive mathematical model of the multi-source system is developed, and the proposed SO-tuned Fuzzy FOPI+TID controller is benchmarked against classical PID and Differential-Evolution (DE)-tuned Fuzzy FOPI+TID controllers under step load disturbances, random load variations, variable renewable penetration levels, and tie-line power exchange scenarios. Simulation results demonstrate that the proposed controller achieves the lowest ISE value (1.19 × 10⁻⁴), substantially outperforming the SO-tuned PID (358.5 × 10⁻³) and DE-tuned Fuzzy FOPI+TID (7.9 × 10⁻²) controllers, while also reducing settling time, overshoot, and undershoot across all three areas. The findings confirm that intelligent, optimization-driven, fractional-order control provides superior robustness and resilience for frequency regulation in EV-integrated, renewable-rich, multi-source power systems