A Novel Meta-Heuristic Algorithm Based $\text{PI}(1+\text{PI})$ Controller for Automatic Generation of Power System Integrated with Photovoltaic System

The affordability and long-term sustainability of renewable energy have garnered significant attention in recent times. The large-scale integration of a photovoltaic (PV) system into an existing power infrastructure is a difficult task. It is difficult to regulate frequency because of the inconsistent irradiance and inertia-less system. Thus for multi-area power systems with PV and thermal plant sources, load frequency control is proposed in this article. In a two-area hybrid power system that combines a PV system and a reheat thermal system, the aim of this research is to investigate how the AGC operates. A PI(1 + PI) controller is implemented in order to enhance system performance. To fine-tune controller parameters, GCRA, a recently developed optimization technique is adopted. In AGC processes, this method has never been investigated before. The integral of time-multiplied absolute error (ITAE) objective function is used in the optimization process. This article provides a comparative analysis between firefly algorithm (FA), salp swarm algorithm (SSA), genetic algorithm (GA) and shuffled frog leaping algorithm (SFLA) which are well-known methodologies found in the literature and the performance of the GCRA based PI(1+PI) controller is analyzed under various scenarios. According to comparison analysis, the proposed controller performs better than cutting-edge methods in terms of undershoot and overshoot values for changes in tie-line power as well as system frequency variations. This research offers important insights into how well the GCRA tuned PI(1+PI) controller manages AGC operations in intricate hybrid power systems.