## 1 Introduction
### Literature Review  
To enable the effective integration and intelligent dispatch of WTs in MGs, it is essential to develop advanced power management strategies [15]. These strategies must ensure the optimal utilization of generation resources while observing the fundamental technical constraints, such as the active and reactive power balance, voltage and current limits, and the operational boundaries of generation units [16]. Below is a review of the most relevant studies that address the optimal operation of WTs, with a specific focus on strategies involving the coordinated control of active and reactive power. In [17], the authors aim to reduce energy loss in 84- and 32-node test systems by implementing a PSO-based energy management strategy for the intelligent integration and operation of WTs. Different case scenarios are analyzed by varying the number of installed turbines--each with a heuristically assigned nominal power. This study reports energy loss reductions of up to 50.72% in the 84-node system and approximately 73% in the 32-node system. However, the WTs are operated at their maximum power point while considering a fixed power factor without adjusting the active and reactive power according to system demand, which limits the adaptability and practical applicability of the approach. In [18], the focus shifts to minimizing operating costs, emissions, and losses in a 30-node system by replacing diesel generators with wind farms at specific nodes. The proposed strategy includes fixed-step active and reactive power control, considering the network's technical constraints. Despite achieving substantial improvements, the fixed-step nature of the control limits the responsiveness of generation units. A continuous control framework could enhance adaptability to load variations and better support the technical, economic, and environmental performance of the system. The authors of [19] address the optimal power dispatch problem through a day-ahead WT power injection strategy based on the NSGA-II algorithm [20]. Their multi-objective formulation accounts for a detailed hourly power flow analysis and multiple operational constraints, including voltage profiles, transformer tap positions, and harmonic distortion. Implemented on 33-, 69-, and 29-node test systems, the methodology consistently improves technical performance but leaves room for future work on economic and environmental optimization, as well as on the exploration of alternative metaheuristic algorithms. In [21], a multi-objective approach is proposed to enhance the technical performance of networks integrating PV, WT, and electric vehicle resources. Using the cooperative dual population evolutionary algorithm (c-DPEA), collective cell migration optimization (CCMO), and the weighted optimization framework (WOF), the authors optimize the reactive power dispatch of systems with 14, 33, 39, and 118 nodes. The results demonstrate that intelligent reactive power control significantly reduces losses and voltage deviations, with the CDPEA outperforming other methods in dynamic environments. In the work by [22], the objective is to reduce the operating costs and energy losses of an MG. To this effect, a Monte Carlo algorithm is used in conjunction with an interval-based optimal power flow model that accounts for wind variability. The test system considered is the IEEE 118-node benchmark feeder, which includes eight WTs. The power injection of each turbine is managed through the intelligent adjustment of their corresponding power output intervals. The results demonstrate that the proposed strategy effectively addresses the problem, although performance could be further improved by directly optimizing the WTs' power injections rather than merely managing their allowable power intervals through the Monte Carlo approach. This could lead to higher efficiency and increased benefits.