Research on Key Technologies of AI-Based Source-Load Coordinated Regulation at the Edge of Distribution Networks

INTRODUCTION: The integration of a high proportion of renewable energy into distribution networks introduces significant challenges, including increased source-load volatility, uncertainty, and operational difficulties.OBJECTIVES: This study aims to address these challenges by proposing an artificial intelligence-based framework for source-load coordination at the grid edge.METHODS: The proposed framework integrates deep reinforcement learning, federated learning, and edge computing technologies. It employs multi-timescale optimization and distributed cooperative control methods to enhance the distribution network's capability to accommodate renewable energy.RESULTS: Experimental results on the IEEE 33-node system demonstrate that the proposed framework can reduce the wind and solar curtailment rate by 65.5% and decrease operational costs by 12.7% compared to traditional methods, while simultaneously satisfying all distribution network security constraints.CONCLUSION: This research provides a theoretical foundation and a technical pathway for building a new data-driven intelligent control system for distribution networks.