Optimal Scheduling of Grid-Connected Microgrid Based on Improved Artificial Protozoa Optimizer

Introduction: As energy crises and environmental pollution issues grow more acute, new energy technologies are being continuously developed, leading to a large-scale integration of renewable energy and electric vehicles into the grid. The variability in renewable energy generation and the unpredictable integration of electric vehicles complicate the optimal scheduling of the microgrid. Methods: An optimal scheduling model for a microgrid was developed, taking into account both economic operational costs and environmental governance costs. The population initialization employs an improved Tent chaotic map, while an adjustment factor based on the Sigmoid function was incorporated, along with a disturbance mutation strategy derived from hybrid opposition- based learning to refine the Artificial Protozoa Optimizer (APO). Three operational scenarios were established for an optimal scheduling model of the microgrid, which corresponded to different objective functions. Simulation analyses were performed for each scenario. Results: The results indicate that IAPO outperforms other comparative algorithms, demonstrating superior economic efficiency. The outcomes of the multi-objective optimization scheduling are found to be more rational than those of single-objective optimization scheduling. Discussion: The IAPO algorithm's superiority stems from integrating an improved population initialization, Sigmoid-based adjustment factor, and disturbance mutation strategy. These enhancements boost its exploration and exploitation capabilities, enabling effective search in the complex microgrid scheduling solution space. The multi-objective optimization model's effectiveness underscores the necessity of considering economic and environmental factors simultaneously for more sustainable scheduling. However, uncertainties in renewable energy and complex electric vehicle load patterns may impact the model and algorithm performance, warranting further research. Conclusion: This study validated the accuracy of the multi-objective optimization model and the effectiveness and advantages of the proposed algorithm. By employing the method developed herein for analyzing the microgrid, more rational strategies and lower operational costs can be achieved.