arXiv:2606. 07583v1 Announce Type: cross Abstract: Self-healing smart grids can quickly adjust their network configuration during outages to minimize power disruptions.
arXiv:2609.16738v1 Announce Type: cross Abstract: Power Flow (PF), Optimal Power Flow (OPF), and State Estimation (SE) are fundamental problems in power system analysis, but solving them is computati...
The paper introduces a method that uses a single deep neural network, trained only on basecase AC power flow data, to predict post-contingency operating states for any single-line outage. It frames this prediction as a fixed-point iteration and provides sufficient convergence conditions, certifying them via semidefinite programming. Numerical experiments on the IEEE 118-bus system show that the certified conditions hold for all tested contingencies and that accurate state estimates are achieved in only a few iterations.
arXiv:2606. 13848v1 Announce Type: cross Abstract: Mobile networks continue to grow in complexity and next generation networks are expected to support both increasing traffic loads and more diverse services.
The paper presents a graph-based framework for large-scale railway network management, combining a hierarchical Bayesian model with a Gaussian Process on a graph kernel to infer spatially correlated maintenance environments from Swiss Federal Railways data. It introduces a topology-aware Multi-Agent Reinforcement Learning system that uses graph neural networks and Transformers to optimize network-level policies. The approach demonstrates scalability via zero-shot transfer learning, enabling agents trained on small network segments to perform effectively on unseen large networks, outperforming heuristics and standard MARL baselines while reducing training time.
arXiv:2608. 16494v1 Announce Type: cross Abstract: Modern power systems face growing operational complexity driven by the integration of renewable energy sources, decentralization, and the need for real-time decision-making across a wide range of timescales.
arXiv:2405. 00742v2 Announce Type: replace-cross Abstract: Mitigating cybersecurity risk in electric vehicle (EV) charging demand forecasting plays a crucial role in the safe operation of collective EV chargings, the stability of the power grid, and the cost-effective infrastructure expansion.
arXiv:2511. 13103v2 Announce Type: replace Abstract: Multi-agent reinforcement learning (MARL) has shown promise for large-scale network control, yet existing methods face two major limitations.
The paper presents a graph-based framework for large-scale railway network management that combines a hierarchical Bayesian model with a Gaussian Process on a graph kernel to model spatially correlated maintenance environments, and a topology-aware Multi-Agent Reinforcement Learning system using graph neural networks and Transformers to optimize network-level policies. It demonstrates scalability by training agents on small network segments and deploying them zero-shot on larger, unseen networks, achieving superior performance over heuristics and standard MARL baselines while reducing training time. The approach addresses the computational challenges of centralized methods and the coordination gaps of decentralized methods in complex, long-horizon infrastructure asset management.
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.
The paper introduces an AI-driven Learning-to-Optimise framework that uses Graph Neural Networks to select real‑time multi‑hop relays in NR‑V2X networks for smart cities. By modelling the vehicular network as a graph and training a GINE model with MILP‑derived optimal decisions, the method achieves near‑optimal connectivity, improving connectivity by up to 11.3% and speeding up computation by up to 100×. This enables scalable, real‑time network control suitable for Industry 4.0 and smart‑city deployments.
arXiv:2511. 05420v2 Announce Type: replace-cross Abstract: As smart grids evolve to meet growing energy demands and modern operational challenges, the ability to accurately predict faults becomes increasingly critical.
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.