arXiv Machine Learning
Sep 3

A Comparative Study of Graph Representations for GNN-Based Power Grid Control in L2RPN

The paper investigates how different graph representations affect graph neural network (GNN) performance in power grid control tasks within the L2RPN environment. It compares physical topology, electrical-sensitivity, and hybrid graph variants in a controlled experimental setting. The results show that aligning graph complexity with task granularity yields better outcomes than simply increasing representational richness.

By Adrian Degenkolb, Qiong Huang, Benjamin Sch\"afer
arXiv Machine Learning
Aug 17

Learning to Run Power Networks: Effective AlphaZero-inspired Topological Control

arXiv:2608. 14114v1 Announce Type: new Abstract: As the integration of volatile renewable energy sources increases the strain on modern power grids, the use of Reinforcement Learning (RL) for autonomous topological reconfiguration has emerged as a promising research field to keep strained grids stable and operational.

By Lukas Zetto, Benjamin Sch\"afer, Qiong Huang
arXiv AI
Jul 7

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems

arXiv:2607. 03324v1 Announce Type: cross Abstract: Eco-friendly energy management for artificial intelligence data centers (AIDCs) is crucial because of the significant increase in energy consumption-induced carbon emissions from AIDCs resulting from the rapid expansion of AI applications.

By Hyunsoo Lee, Panggah Prabawa, Dae-Hyun Choi, Joongheon Kim