Real-World Security-Constrained AC Optimal Power Flow Analysis Using Artificial Gorilla Troops Optimizer

The problem of Alternating Current Optimal Power Flow (ACOPF) pertains to the strategic identification of the most advantageous operating conditions for electricity generating facilities. The objective here is to fulfill the demand across a power transmission network while simultaneously curbing operating costs. The significance of the Optimal Power Flow (OPF) challenge has rapidly escalated in recent years, impacting the financial operations of modern power plants. The complexity of the OPF issue lies in its non-convex and high-dimensional nature. This makes it an intricate optimization challenge that requires the application of sophisticated metaheuristic algorithms for its resolution. The focus of this paper is on the utilization of an advanced algorithm, the Artificial Gorilla Troops Optimizer (AGTO), for scrutinizing some of the benchmark functions and addressing the real-world ACOPF problem. To ensure rigorous testing and validation, the study takes into account the IEEE 30-bus test system. We then compare our simulation results with other renowned optimization methodologies available in the existing literature. This comparative study demonstrates that the application of AGTO could potentially yield high-quality OPF solutions, exhibiting its potency in addressing the ACOPF problem.