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

Yanqian Lu, Weilin Liu, Tianlin Wang, Hanyang Xie and Xuan He

International Journal of Low-Carbon Technologies, 2025, vol. 20, 1092-1103

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.

Keywords: distribution network optimization; graph neural networks; reinforcement learning; renewable energy integration (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:20:y:2025:i::p:1092-1103.

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