Voltage model of medium-voltage distribution network with source load based on improved global genetic algorithm

The growing use of renewable energy is increasing Distributed Generation (DG), making it difficult for conventional optimization methods like Genetic Algorithms (GA) to manage dynamic challenges such as voltage fluctuations in medium-voltage networks. This paper proposes a voltage optimization method for medium-voltage distribution networks based on an enhanced Global Genetic Improved Genetic Algorithm (GGIGA), which innovatively uses a dynamic fitness second-order function to associate the voltage sensitivity matrix with gene fitness. Compared with traditional GA, the global search efficiency is improved by 42%. A genome crossover strategy is adopted, enabling directional crossover through GenBank clustering analysis, thereby mitigating the local optima issue inherent in conventional algorithms. Furthermore, the convergence iteration count is reduced by 31% in the IEEE 33-node test. A coordinated "Source-Load-Network" model is developed, explicitly incorporating DG power output characteristics, dynamic load variations, and network topology constraints. This integrated model facilitates voltage deviation minimization, power loss reduction, and decreased reliance on external grid supply under specific load distributions. Compared to traditional methods, GGIGA achieves a 30% reduction in voltage deviation and a 15% decrease in power losses, significantly enhancing power supply reliability. The dynamic fitness achieves 97.8% voltage control accuracy, while genome crossover strategy accelerates convergence by 19.2%. In practical deployment, network losses are reduced by 15%, and the voltage qualification rate increases to 97.3%. This research provides an innovative technical approach for improving voltage stability in distribution networks. The proposed GGIGA methodology offers a practical and effective solution for real-world engineering challenges associated with high DG penetration.