Research on optimizing the networking mode of low-voltage distribution network in Internet of Things system based on improved ACO

Optimizing the stability and transmission efficiency of low-voltage distribution networks remains a critical challenge in smart grid systems. This study proposes an AI-enhanced optimization framework that integrates an improved Ant Colony Optimization (ACO) algorithm with intelligent Internet of Things (IoT) sensing technologies. The ACO algorithm, inspired by swarm intelligence principles, dynamically adjusts network paths to minimize power loss, while IoT devices provide real-time data on grid load and voltage distribution, enabling adaptive decision-making. Experimental results demonstrate that the AI-driven model reduces power loss by 61.8835 kW (30.53%) and 91.9952 kW (45.39%) in pre- and post-reconstruction scenarios, respectively. Additionally, the minimum node voltage improves from 0.9133 p.u. to 0.9291 p.u. (pre-reconstruction) and 0.9453 p.u. (post-reconstruction), with system-wide voltage stability enhancements. Theoretical and simulation analyses confirm that the synergy of AI-based optimization and IoT intelligence significantly improves transmission efficiency and operational robustness in low-voltage grids.