Metaheuristic-based automatic generation controller in interconnected power systems with renewable energy sources

Abstract

It is necessary to minimize fluctuations in system frequency and tie-line power deviations to provide high-quality, reliable, and stable electrical power. Automatic Generation Control (AGC) is the essential control process to keep the frequency and tie-line power change between acceptable values in interconnected power systems. Problems such as stability, peak deviation, and transient response are the general disadvantages of studies in the literature. To eliminate these disadvantages and to improve parameters such as maximum/minimum overshoot, and settling time, we propose a novel controller named PID-(1+I) for AGC in a two-area nonreheat thermal power system integrated with renewable energy sources (RESs) such as photovoltaic (PV) panels and wind turbines (WTs). The gain parameters of the proposed controller are optimally tuned by newly developed metaheuristic algorithms such as Gorilla Troops Optimizer (GTO), African Vulture Optimization Algorithm (AVOA), and Honey Badger Algorithm (HBA). To examine the effectiveness of the proposed controller, the system is tested under conditions such as random load change, RES generation, and system parameter change. The results show that the proposed controller gives remarkable results in terms of overshoot and settling time values