The optimization study of user and renewable energy integration scheme in medium and low-voltage distribution networks based on deep learning

Abstract

In response to the growing integration of renewable energy and electric vehicle loads in distribution networks, this paper presents an optimized access scheme leveraging deep learning. We propose a Multi-Scale Topology-Aware Graph Neural Network (MT-GNN) to capture the spatial and electrical characteristics of the network, coupled with a spatiotemporal feature fusion module utilizing a dual attention mechanism to handle dynamic load and generation uncertainties. An end-to-end multitask learning framework integrates access location, capacity, and timing decisions, enhanced by a Soft Actor-Critic reinforcement learning module for adaptive strategy optimization. Experimental results demonstrate superior reliability and economic performance under uncertain conditions.