arXiv (All)
open access
Reinforcement Learning for Vehicle-to-Grid Voltage Regulation: Single-Hub to Multi-Hub Coordination with Battery-Aware Constraints
This paper presents a Vehicle-to-Grid (V2G) coordination framework using reinforcement learning (RL). {An intelligent control strategy based on the soft actor-critic algorithm is developed for voltage regulation through single and multi-hub charging systems while respecting realistic fleet constrain
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- LLM-Enhanced Multi-Agent Reinforcement Learning for Unified Electric Vehicles-Charging Station-Grid Optimization in Public Charging Systems#1083303
- Grid-aware charging for shared autonomous electric vehicles: a bi-Level optimization approach for fleet operations and grid resilience#377536
- Graph neural network-based multi-agent reinforcement learning for active voltage control: Performance and topology robustness / Chijioke Eze, Chetan Prakash, Antonello Monti#780262
- ENHANCING LOGISTICS BATTLE COMMAND FOR REINFORCEMENT LEARNING–ENABLED DECISION SUPPORT IN CONTESTED LOGISTICS ENVIRONMENTS#674611
- OPTIMAL CONTROL OF MULTI-LEVEL BOOST-BUCK AC/DC CONVERTER FOR HIGH EFFICIENCY, WIDE-RANGE EV CHARGERS#174111
Record · ID 1141496
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive