## 6 Conclusions
This increase is expected as the solution space becomes more constrained due to the limited power capacity of the diesel generator, which imposes stricter feasibility requirements and increases the computational effort required to find viable solutions. Notably, the proposed PGA demonstrated a consistently competitive performance, ranking among the fastest methods in nearly all objective functions. Its ability to maintain low computation times, even under the tighter constraints of islanded operation, underscores its practical suitability for real-time or near-real-time energy management in MGs operating under both grid-connected and isolated conditions. An evaluation of the PGA on a 69-node MG over a one-week period, using demand and generation forecasts generated by ANNs, confirms the robustness of the proposed methodology. In the grid-connected mode, this methodology achieved reductions of 112.6609%, 92.5971%, and 119.9285% in the three objective functions under study (\(FO_{1}\), \(FO_{2}\), and \(FO_{3}\), respectively), with corresponding standard deviations of 1.9558%, 0.2696%, and 1.9008%. In the islanded mode, forecasting enabled adaptive, optimized planning, achieving average daily values of USD 7212.0110 (cost), 171.7586 kWh (losses), and 6416.4515 kg\(CO_{2}\) (emissions), with standard deviations of 1.7022%, 3.20%, and 0.0140%, respectively. The low standard deviations observed across all objectives indicate that the proposed strategy maintains a highly stable and predictable performance, even under varying daily conditions. This consistency is critical for MG operation, as it reduces the risk of unexpected outcomes, facilitates short-term planning, and ensures a reliable operation regardless of fluctuations in demand or renewable generation. In this test scenario, the use of ANN forecasts improved the responsiveness and proactivity of the strategy, reinforcing the role of intelligent, predictive control in enhancing MG autonomy, efficiency, and scalability under variable conditions. In the 69-node MG, the execution times remained consistent across the three objectives analyzed, averaging 412.0521 s per day (\(\approx\)7 min). This validates the strategy's feasibility for day-ahead scheduling. Compared with the 33-node MG, where the average times were 217.4016 s (islanded) and 241.2056 s (grid-connected), this case saw increases of 89.53% and 80.20%, respectively, reflecting greater complexity, albeit within the practical limits. Although the results presented in this study are based on simulation data, the proposed method exhibits characteristics that support its potential application in real-world MGs. The relatively low computational burden and convergence times observed make it suitable for integration into scheduling schemes with hourly or day-ahead horizons. Additionally, the codification strategy and the use of the SA power flow method ensure numerical stability and compatibility with embedded control platforms or supervisory systems. As future work, an evaluation of the algorithm's real-time performance is proposed, considering practical constraints such as processor capacity, memory availability, and data acquisition rates, which are typical of MG controllers. Experimental validation using real measurement data or hardware-in-the-loop (HIL) platforms is also considered essential to confirm our proposal's feasibility and operational robustness under real operating conditions. Based on the findings presented in this work, a potential avenue for future research is the integration of complementary technologies alongside WTs, such as energy storage systems. This combination could enhance the efficiency of WTs by expanding their operational range and potentially lead to greater reductions in operating costs, energy losses, and \(CO_{2}\) emissions. The proposed framework could be extended to a multi-objective optimization setting by simultaneously incorporating two or more of the objective functions considered herein.