## 1 Introduction
### Identified Research Needs and Opportunities  
Our review of the most relevant works on the optimal operation of WTs in MGs (Table 1) reveals several common patterns in the existing methodologies. Most proposals employ well-established metaheuristic algorithms such as PSO, GAs, and variants of evolutionary techniques that are primarily focused on active power control. The widespread use of these strategies is due to their ease of implementation, their ability to handle multiple de \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Research** & **Proposed Methodology** & **Test System** & **Objective Function** & **Power Management Approach** \\ \hline
[27] & Effective whale optimization & IEEE 6-, 14-, 30-, 118-node systems & Operating costs, voltage stability & Active power \\
[28] & Chaotic-based big bang-big crunch & IEEE 37-node system & Energy losses, voltage stability & Active and reactive power \\  & algorithm (CBCA) & IEEE 118-node system & Operating costs, energy losses & Active and reactive power \\
[22] & Monte Carlo algorithm (MC) & IEEE 118-node system & Operating costs, energy losses & Active power \\
[23] & Genetic algorithm (GA) & IEEE 30-node system & Operating costs, energy losses & Active and reactive power \\
[29] & Particle swarm optimization (PSO) & IEEE 30-node system & Operating costs, energy losses & Active power \\
[24] & Generalized normal distribution & IEEE 14-node system & Operating costs & Active power \\
[25] & JOYA algorithm & IEEE 28-node system & Energy losses & Active power \\
[17] & PSO & IEEE 32-, 84-node systems & Energy losses & Active power \\
[26] & Multiverse optimizer (MVO) & IEEE 30-node system & Operating costs, energy losses & Active power \\
[30] & PSO & IEEE 14, 30 node systems & Operating costs, energy losses & Active power \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of recent research on optimization problems involving intelligent WTs management. cision variables, and their strong performance when solving complex problems in contexts where exact methods or commercial tools often fail or show limited effectiveness [31]. Additionally, the test systems commonly used in these studies feature small-scale topologies, e.g., 14-, 33-, 34-, or 69-node networks, mostly under grid-connected conditions. Regarding the objective functions considered, there is a strong emphasis on minimizing operating costs and technical losses, with a limited integration of environmental indicators. Moreover, the validation methods employed are generally deterministic, with little or no consideration of statistical repeatability and, in many cases, with no assessment of computation times. This limits the understanding of the robustness and applicability of these methodologies in real-world scenarios. The key gaps observed in this comparative analysis have motivated this study. Firstly, only a few works address the coordinated control of both active and reactive power in WTs within MG environments, despite these devices' great potential for providing voltage support through power electronic converters, especially in scenarios where active power injection is limited or curtailed (i.e., renewable energy curtailment events). Secondly, most of the existing methodologies fail to simultaneously consider the three fundamental pillars of energy system performance: technical indicators (e.g., voltage quality and power losses), economic indicators (e.g., operating costs), and environmental indicators (e.g., \(CO_{2}\) emissions). This limits their applicability in addressing the multifaceted challenges currently faced by modern power systems. Furthermore, it is evident that operational strategies must be adaptive and capable of effectively responding to the shifting priorities of grid operators and asset owners, which can vary depending on system conditions and external factors. At times, economic efficiency may be the main concern.