Research on dynamic optimization scheduling of high-power flexible interconnected distributed generation based on genetic algorithm

The integration of high-power flexible interconnected distributed generation (DG) systems into existing electrical grids presents significant challenges for power dispatching and scheduling, primarily due to load variability, fluctuating generation profiles, and limited operational flexibility. This study proposes a novel dynamic optimization scheduling framework based on a customized Genetic Algorithm (GA) to effectively address these issues. Unlike conventional approaches such as linear programming and heuristic methods, the proposed GA model incorporates real-world grid operational data, historical DG deployment records, and simulated IEEE test system configurations to achieve adaptive and real-time scheduling. A key innovation of this study lies in the detailed design and implementation of GA operators—specifically, real-coded encoding, adaptive crossover, and Gaussian mutation strategies—that enhance the model's ability to optimize power loss, load distribution, and energy utilization. Extensive computer simulations demonstrate that the proposed GA-based approach achieves a 5-25% improvement in energy efficiency and a 10-15% enhancement in load balancing compared to existing methods. Furthermore, sensitivity analyses and triplicate simulation replications validate the stability, scalability, and computational speed of the model, with convergence achieved in under 30 seconds for medium-scale systems. These findings establish the proposed GA model as a robust and scalable solution for modern smart grid applications, offering improved operational performance over prior scheduling frameworks.