Communication Data Delivery Optimization for Distribution Automation Based on Multi-Agent Reinforcement Learning

The rapid advancement of smart grid technologies requires real-time monitoring and dynamic control, but legacy protocols like TCP lack real-time adaptive routing capabilities. This critical gap in network responsiveness leads to chronic congestion and degraded data transmission efficiency in modern power distribution systems. This paper proposes a communication data delivery optimization of distribution automation based on multi-agent reinforcement learning. By establishing a self-behavior pattern recognition layer through multi-agent reinforcement learning, the mechanism dynamically adjusts link route selection by real-time sensing of traffic distribution states across multiple links, thereby enhancing the overall throughput of the network. Experimental results demonstrate that the proposed algorithm achieves throughput enhancements of 23.7% and 17.2% over conventional distribution automation protocols and Shortest Path First (SPF) routing respectively, while simultaneously establishing balanced traffic distribution across parallel transmission paths - a critical requirement for modern smart grid communication systems.