## 1 Introduction
### Literature Review  
The problem regarding the optimal placement and sizing of FACTS in medium- and low-voltage distribution networks has been widely explored in the specialized literature. This section summarizes the most recent approaches in this research area.  
The authors of [11] applied the hunter-prey algorithm to determine the optimal location and sizing of photovoltaic-SVC systems in electrical distribution networks, with the aim to minimize grid power losses and improve system voltage profiles. The main characteristic of a photovoltaic-SVC system is that it can use the converter interfacing with the photovoltaic (PV) system to add reactive power injection capabilities via control design [24]. The numerical results presented in [11] for the IEEE 33- and 69-bus grids demonstrated the effectiveness of the proposed hunter-prey optimizer when compared to different combinatorial methods, such as the differential evolution algorithm, particle swarm optimization, the artificial rabbits algorithm, and the golden search optimizer.  
The work by [25] presented a complete comparative analysis of multiple combinatorial optimizers in locating and sizing TCSCs and SVCs in power systems while aiming to minimize the total grid operating costs associated with energy losses. The voltage profiles were kept within an acceptable range. Numerical validations in the IEEE 30- and 57-bus systems were used to test the effectiveness of the whale optimization algorithm in comparison with multiple combinatorial optimizers.  
In [10], the black widow optimization algorithm was proposed as a solution method to locate and size FACTS in medium-voltage distribution networks. The exact MINLP model was solved using a leader-follower optimization strategy, combining a discrete-continuous codification in the leader stage with a black widow optimizer using the successive approximations of power flow in the follower stage. Numerical results in the IEEE 33-, 69-, and 85-bus grids demonstrated the effectiveness of the leader-follower solution compared to that of the vortex search algorithm reported in [26]. All this, while considering the minimization of investment and operating costs as objective functions.  
The authors of [27] presented a complete review of the optimal location and sizing of SVCs in electrical distribution networks while considering the technical and economic aspects of the objective function. Five approaches regarding solution technologies were reviewed, which include artificial neural network techniques, analytical methods, combinatorial methods, and sensitivity approaches. This review demonstrated that SVCs are suitable FACTS for improving grid voltage profiles, enhancing stability indices, and reducing grid power losses with reasonable investments by utility companies. Thus, SVCs are a feasible economic solution for compensating reactive power in these networks.  
The study by [28] presented a solution methodology to locate and size SVCs in distribution networks while considering load variations via a nodal sensitivity approach. Efficient numerical validation in practical distribution networks composed of 38 nodes demonstrated important improvements with regard to different objective functions, including energy losses cost reduction, voltage profile improvements, and stability margin enhancement.  
Other solution methods for locating and sizing FACTS in distribution networks are the tabu search algorithm [29; 30], the fractional levy flight bat algorithm [31], the particle swarm optimizer [32], the krill herd algorithm [33], and the barnacles mating optimization [34], among others.  
All of the works reviewed above yielded good solutions. However, none of them can guarantee the optimal solution to the problem, and some require tuning parameters, which indicates that, if the test systems are changed, they may not reach reasonable solutions and thus require adjustments.