arXiv AI
Physics-Informed Graph-Based Safe RL with Constraint-Specific Critics for Community Battery Coordination in Distribution Network
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jun 2
Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildings
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 AI
Jul 15
Constraint-Aware Aggregation for Federated Reinforcement Learning in Microgrid Energy Coordination
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 AI
Jul 1
Smart charging of large fleets of Electric Vehicles: Independent Multi-Agent Reinforcement Learning approaches
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 Machine Learning
Jun 25
Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources
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 AI
Jun 18
A Distributionally Robust Reinforcement Learning Framework for Constrained Urban EV Dispatch
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 AI
Jun 2
Interpretable Policy Distillation for Power Grid Topology Control
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.