This research investigates the effectiveness of four metaheuristic algorithms, the Population-Based Genetic Algorithm, Particle Swarm Optimization, JAYA, and Generalized Normal Distribution Optimizer, for managing the energy production of wind-based distributed generators (DGs). The aim is to reduce operational costs in a 33-node microgrid (MG) operating under both connected and isolated configurations. The study seeks to identify the most efficient algorithm for minimizing operational expenses in distributed generation systems, specifically in terms of energy production and purchasing costs, as well as the maintenance costs of DGs. Due to limited statistical validation and unrealistic operational constraints in previous studies, we propose a novel framework that offers a robust, reproducible solution for optimizing the management of wind-based distributed generators in microgrids. Through 100 independent trials for each algorithm and configuration, rigorous statistical analyses are conducted, including ANOVA and Tukey’s post hoc test, to assess performance consistency and the significance of cost reduction outcomes across algorithms. The results indicate that the PGA demonstrates superior cost efficiency and stability, particularly in the connected MG configuration.

This research evaluates the performance of six metaheuristic algorithms in the active and reactive power management of wind turbines (WTs) integrated into an AC microgrid (MG). The population-based genetic algorithm (PGA) is proposed as the primary optimization strategy and is rigorously compared against five benchmark techniques: Monte Carlo (MC), particle swarm optimization (PSO), the JAYA algorithm, the generalized normal distribution optimizer (GNDO), and the multiverse optimizer (MVO). This study aims to minimize, through independent optimization scenarios, the operating costs, power losses, or CO₂ emissions of the microgrid during both grid-connected and islanded modes. To achieve this, a coordinated control strategy for distributed generators is proposed, offering flexible adaptation to economic, technical, or environmental priorities while accounting for the variability of power generation and demand. The proposed optimization model includes active and reactive power constraints for both conventional generators and WTs, along with technical and regulatory limits imposed on the MG, such as current thresholds and nodal voltage boundaries. To validate the proposed strategy, two scenarios are considered: one involving 33 nodes and another one featuring 69. These configurations allow evaluation of the aforementioned optimization strategies under different energy conditions while incorporating the power generation and demand variability corresponding to a specific region of Colombia. The analysis covers two-time horizons (a representative day of operation and a full week) in order to capture both short-term and weekly fluctuations. The variability is modeled via an artificial neural network to forecast renewable generation and demand. Each optimization method undergoes a statistical evaluation based on multiple independent executions, allowing for a comprehensive assessment of its effectiveness in terms of solution quality, average performance, repeatability, and computation time. The proposed methodology exhibits the best performance for the three objectives, with excellent repeatability and computational efficiency across varying microgrid sizes and energy behavior scenarios.

This study tackles the energy management problem for wind distributed generators in AC microgrids (MGs) operating in both connected and isolated modes. A mathematical formulation is proposed to minimize energy losses and $CO$\(_2\) emissions, incorporating technical and regulatory constraints to reflect real-world MG operations. The solution methodology combines the Population-Based Genetic Algorithm (PGA) with an hourly power flow analysis based on the successive approximation (SA) method. To validate the proposed approach, a comprehensive comparison is conducted against three widely used metaheuristic algorithms: Particle Swarm Optimization (PSO), JAYA, and the Generalized Normal Distribution Optimizer (GNDO). Employing a rigorous statistical framework, including ANOVA and Tukey HSD tests, the algorithms' performance is evaluated through 100 independent runs per objective and configuration, using a 33-node AC microgrid with variable generation and demand as the test scenario. Results demonstrate that PGA consistently outperforms other algorithms, achieving lower mean values and variance in both energy loss and emission minimization. GNDO, by contrast, shows higher variability and less effective optimization. This work not only underscores the robustness and adaptability of PGA for sustainable microgrid management but also establishes a standardized framework for evaluating optimization algorithms in energy systems.