Load Frequency Control in Hybrid Power Systems Using ANFIS-PSO with EV Support

The increasing reliance on renewable energy sources (RES), such as photovoltaic (PV) and wind power, has introduced significant challenges in maintaining the frequency stability of modern power grids due to their intermittent and variable nature. This study proposes a hybrid approach that combines particle swarm optimization (PSO) and adaptive neuro-fuzzy inference system (ANFIS) for frequency regulation in hybrid power systems. The method leverages electric vehicles (EVs) operating in vehicle-to-grid (V2G) mode to enhance grid stability. PSO is utilized to optimize the parameters of the ANFIS model, enabling intelligent and adaptive control of power flows between the grid, renewable sources, and EVs. The proposed system mitigates frequency deviations caused by PV and wind power generation fluctuations by dynamically coordinating EVs' charging and discharging rates. Simulation results under various scenarios, including sudden changes in renewable output and load demand, demonstrate that the ANFIS-PSO approach effectively reduces frequency deviations and maintains stability within permissible limits.