Multi-Agent RL-Based Dynamic Feeder Acceptance Orchestration Using the FeederBW Dataset and 236-Bus Low-Voltage Distribution Network.

Abstract

The extensive integration of distributed renewable energy sources and the rapid expansion of low-voltage (LV) feeders have created operational challenges related to feeder acceptance and congestion coordination. Existing voltage-control and feeder-management approaches may have limited scalability and adaptability under intermittent renewable generation and changing network conditions. To address these limitations, this study proposes a Dynamic Orchestration Framework for Low-Voltage Feeder Acceptance based on intelligent-agent collaboration within a multi-agent reinforcement learning approach. Unlike voltage-control strategies focused primarily on voltage stabilization, the proposed framework supports dynamic feeder acceptance through the coordinated operation of agents responsible for feeder monitoring, congestion management, and voltage-sensitivity-based prioritization. The agents independently observed feeder states, evaluated congestion and voltage sensitivities, and collaborated on feeder-acceptance decisions. The framework was evaluated through simulations using the FeederBW dataset and the 236-bus low-voltage distribution-network dataset under varying renewable-integration levels and load conditions. The proposed framework achieved a Feeder Acceptance Rate of 97%, a Voltage Compliance Index of 98%, an orchestration latency of 98 ms measured as the wall-clock execution time of the orchestration algorithm on the reported hardware, and a convergence speed of 98.5%, outperforming the evaluated centralized coordination methods. The results also indicated improved adaptability, operational resilience, and decentralized decision-making efficiency under the simulated low-voltage distribution-network conditions.