Exploring the effectiveness of adaptive randomized sine cosine algorithm in wind integrated scenario based power system optimization with FACTS devices.

In modern power systems, increasing transmission efficiency and responsiveness is necessary to accommodate rising demand and constrained infrastructure development. In this study, the Adaptive Randomized Sine Cosine Algorithm (ARSCA) is introduced to solve the problem of optimal placement and settings of Flexible AC Transmission System (FACTS) devices-Thyristor-Controlled Series Capacitors (TCSC), Thyristor-Controlled Phase Shifters (TCPS), and Static VAR Compensators (SVC)-in the IEEE 30-bus test system. With dynamic load scenarios, ARSCA was shown to perform better by minimizing active power losses to 1.7655 MW, achieving a minimum generation cost of 807.17 $/h, and reducing the gross system cost to 883.53 $/h. The results of these experiments show faster convergence and consistent solution accuracy compared to benchmark algorithms such as Sine Cosine Algorithm (SCA), Improved Grey Wolf Optimization (IGWO), Whale Optimization Algorithm (WOA), and others. The algorithm also improved voltage stability and reactive power management. ARSCA combines robust exploration and exploitation mechanisms to provide an efficient and scalable solution to power system optimization problem that is cost effective and operationally stable. Its scalability in larger networks and adaptability under expanded uncertainty conditions should be investigated in future studies.