Recent Trends in Optimization Objectives for Power System Operation Improvement Using FACTS

Abstract

The increasing penetration of renewable energy sources, extensive deployment of power-electronic interfaces, and growing operational uncertainty have significantly intensified the challenges associated with secure and efficient power system operation. Flexible AC Transmission Systems (FACTS) have emerged as key enabling technologies for enhancing system controllability, power quality, and operational flexibility. This article provides a comprehensive and structured review of recent trends in optimization objectives for power system operation improvement using FACTS devices, with particular emphasis on the evolution of control and optimization paradigms in modern power grids. First, the study presents a systematic classification of FACTS devices, shunt, series, combined, and hybrid configurations, along with the associated optimization techniques employed for their control, placement, and coordination. Second, the paper examines the principal optimization objectives for power system performance improvement, encompassing voltage profile enhancement, loss minimization, congestion management, economic and emission optimization, frequency and transient stability, power quality, reliability, and renewable energy integration. Third, recent research trends are analyzed, highlighting the transition from single-objective, steady-state formulations toward multi-objective, security-constrained, uncertainty-aware, and data-driven optimization frameworks. The growing adoption of meta-heuristic algorithms, artificial intelligence, and distributed optimization strategies for FACTS-based operation is critically discussed in the context of renewable-dominated and smart grid environments. The article concludes by identifying key research gaps and future directions, emphasizing the need for coordinated, adaptive, and resilient FACTS optimization frameworks to support next-generation power system operation under increasing complexity and variability.