arXiv:2606. 02049v1 Announce Type: new Abstract: The increasing integration of renewable energy sources into power systems, particularly in buildings equipped with photovoltaic (PV) panels and energy storage systems, introduces significant complexity in energy systems.
arXiv:2607. 12763v1 Announce Type: cross Abstract: Federated Reinforcement Learning (FedRL) enables coordination of distributed energy resources without sharing raw local data, but standard aggregation methods such as FedAvg do not account for system-level constraints, often leading to unsafe global behavior.
arXiv:2606. 31347v1 Announce Type: new Abstract: The electrification of transportation through electric vehicles introduces new challenges for power grid management, such as increased peak demand, voltage fluctuations, line overloads, and the integration of variable renewable energy sources.
arXiv:2606. 24947v1 Announce Type: new Abstract: The increasing integration of distributed energy resources (DERs) is crucial for power system decarbonization, yet unlocking DERs' flexibility is challenged by their inherent uncertainties and modelling complexity.
arXiv:2604. 25848v2 Announce Type: replace Abstract: We study city-scale control of electric-vehicle (EV) ride-hailing fleets where dispatch, repositioning, and charging decisions must respect charger and feeder limits under uncertain, spatially correlated demand and travel times.
arXiv:2606. 00561v1 Announce Type: cross Abstract: Deep reinforcement learning (RL) offers a promising route to real-time power grid operation, yet large neural policies are costly to evaluate, hard to deploy on constrained hardware, and opaque to operators.
arXiv:2510. 21127v2 Announce Type: replace-cross Abstract: Despite rapid advancements in sensor networks, conventional battery-powered sensor networks suffer from limited operational lifespans and frequent maintenance requirements that severely constrain their deployment in remote and inaccessible environments.
arXiv:2607. 05683v1 Announce Type: new Abstract: Battery charging of Autonomous Mobile Robots (AMRs) in warehouses is a critical operational challenge that heavily impacts both order processing times and throughput.
arXiv:2608. 03878v1 Announce Type: new Abstract: Synthetic power-grid scenarios are essential for planning, resilience assessment, contingency analysis, and data-driven power-system applications.
The paper compares rule‑based and reinforcement‑learning (RL) pricing mechanisms for peer‑to‑peer electricity trading in residential photovoltaic communities. Rule‑based benchmarks—bill‑sharing, mid‑market rate, and supply‑demand‑ratio pricing—outperform the best RL policy in a PV‑only setup, while RL policies achieve higher community savings when battery storage is added. Across both configurations, SDR‑shaped pricing outperforms multiplier‑based parameterization, but benefit distribution remains heterogeneous among households.
arXiv:2607. 16004v1 Announce Type: cross Abstract: Increases in photovoltaic generation, charging of electric vehicles and heat-pump demand challenge operating limits in low-voltage distribution grids.
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.