LLM boosts multi-agent control of EV charging for grid and station balance
LLM-Enhanced Multi-Agent Reinforcement Learning for Unified Electric Vehicles-Charging Station-Grid Optimization in Public Charging Systems
Artificial Intelligence
Summary
Coordinating when and how electric vehicles charge is tricky because it involves many goals like keeping electric car owners happy, helping charging stations make money, and keeping the electric grid stable. The authors show how large language models (LLMs), like advanced AI that understands language, can help pick the most important factors from lots of data and balance these goals better than before. Their method learns faster and produces better results than other similar approaches. This could help cities manage electric car charging more efficiently and sustainably.
What this means in practice
- •For urban infrastructure planners: Manage complex EV charging scheduling that balances user satisfaction, station profits, and grid stability more effectively using AI-enhanced solutions.
- •For smart grid operators: Improve real-time grid load management by dynamically adapting to EV charging patterns with AI-guided multi-objective control.
Authors
Yang Zhang, Lindong Xie, Chongyu Wang, Gaojunjie Li, Siqi Bu, Edward Chung
Abstract
In the era of the Internet of Things (IoT), coordinating connected electric vehicle (EV) charging scheduling to balance EV charging satisfaction, station profitability, and smart grid stability presents a complex multi-objective challenge. Existing Multi-Agent Reinforcement Learning (MARL) approaches often struggle with high-dimensional state spaces generated by massive IoT sensing data and conflicting stakeholder interests. This paper proposes a novel LLM-enhanced MARL framework that, for the first time, simultaneously optimizes the Grid, EVs, and Stations within a unified loop. By integrating Large Language Model (LLM), we address two critical bottlenecks: interpretable feature selection and adaptive multi-objective balancing. The LLM analyzes real-time IoT-collected environmental states to extract physically significant features and dynamically assigns weights to conflicting objectives-including profit, user satisfaction, and grid load-using semantic reasoning instead of complex manual tuning. Extensive experiments demonstrate that our framework significantly outperforms state-of-the-art baselines, achieving superior market efficiency while reducing training time by over 70%. This approach offers a scalable, transparent solution for efficient and sustainable IoT-enabled urban charging infrastructure management.