Optimizing Energy Management in AC Microgrids: A Comparative Study of Metaheuristic Algorithms for Minimizing Energy Losses and CO₂ Emissions

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.