## 5 Stability Enhancement Using FACTS Devices
### Load Frequency Control (LFC)
The IPFC also proves its performance in a deregulated environment, as explained in [93]. Table 3 also shows the role of various FACTS devices and their pros and cons. There is no doubt that FACTS devices enhance LFC. However, their performance becomes more efficient by combining various energy storage devices and various optimization techniques. Optimization techniques for deregulated power networks include bacterial foraging [94] and the genetic algorithm (GA) [95; 96], simulated annealing algorithm (SA) [97], back propagation algorithm (BPA) [98], strength Pareto honey bee mating optimization (SPHBMO) algorithm [99], partial swarm optimization (PSO) [100], back propagation through time algorithm [101], fuzzy logic [102], hybrid salp swarm algorithm and pattern search algorithm [103], and many more. Generally, meta-heuristics and hybrid optimization techniques are popular nowadays. Meta-heuristics techniques are those optimization techniques that are inspired by natural processes, such as PSO, GA, grey wolf optimization (GWO), etc., used to find optimum solutions of complex optimization problems in a relatively short time [104]. Moreover, meta-heuristic optimization techniques are highly considered for the optimal placement of FACTS devices. Based on these advantages a list of highly adopted meta-heuristic techniques is presented in [47]. Similarly, hybrid optimization techniques, which are a combination of more than two types of optimization technique, have their own importance. Hybrid methods are useful to compensate for the limitations of each other, e.g., a hybrid method may use a classical optimization technique to generate an initial solution and then refine the solution using a meta-heuristic method or analytical method. Moreover, in recent years, machine learning (ML) algorithms have been increasingly adopted to address challenges within power systems. ML algorithms, particularly reinforcement learning (RL) and deep learning (DL) techniques, provide robust solutions by facilitating data-driven, adaptive control strategies [105; 106]. These algorithms are capable of learning optimal control policies directly from system interactions, continuously refining their strategies to accommodate uncertainties, non-linear dynamics, and operational constraintswithout necessitating detailed system modeling. For example, the authors of [105] used RL techniques to implement load frequency control (LFC) without requiring a central authority. Similarly, in [107] IEEE-14, IEEE-57, and IEEE-118 bus testing systems are used to provide LFC using RL. However, there may be a very limited amount of research conducted that jointly represents the ML techniques for LFC studies using FACTS controllers in deregulated power networks. But some significant references that have discussed the various ML techniques in detail can be found in the literature [108; 109; 110]. However, a detailed discussion about algorithms' pros and cons is out of the scope of this paper. However, a detailed discussion about optimization techniques for FACTS devices is presented in [104]. Conclusions regarding the role of different FACTS controllers in a deregulated environment are presented below. 1. STATCOM performed better than the SVC for LFC problems. 2. The G-UPFC is superior to the UPFC because it can control the power flow of more than one line or even of subnetworks. 3. Trends to adopt UPFCs and G-UPFCs have increased in recent decades, but SVCs and TCSCs are preferred due to lower installation costs. In other words, between the UPFC and G-UPFC, the UPFC is not an economically viable solution in terms of cost for LFC problems. 4. For LFC problems, the IPFC is preferable to the UPFC. 5. When comparing 2 nd-generation FACTS controllers, the SSSC is preferable in series while STATCOM is preferable in shunt for LFC. 6. By comparing 3 rd-generation FACTS controllers, IPFC is more beneficial. 7. FACTS controllers combined with energy storage devices perform better than alone. 8.