EV Charging System Considering Power Dispatching Based on Multi-Agent LLMs and CGAN

Zeyuan Niu, Jiamei Li, Qian Ai, Jiamei Jiang, Qiunan Yang, Haojie Zhou

Abstract: The growing adoption of electric vehicles (EVs) has placed significant demands on power grids, necessitating coordination between EV charging and power dispatching. This paper proposes a novel EV charging system using Multi-Agent Large Language Models (LLMs) to enhance recommendations, optimize decision-making, and dynamically adapt to user behaviors and grid conditions. The system includes a User Agent and an EV Charging Station (EVCS) Agent, connected through a Negotiation Platform for secure data sharing. The User Agent provides personalized recommendations based on historical data, while the EVCS Agent adjusts prices in real time using fine-tuned LLMs. A Conditional Generative Adversarial Network (CGAN) model is used to generate user behavior and pricing data to fine-tune the LLMs. The proposed system effectively adapts to dynamic user behaviors and grid conditions by combining Multi-Agent coordination with fine-tuned LLMs and CGAN-generated data. A case study demonstrates the system’s ability to balance user preferences with power dispatching, offering scalable, efficient, and intelligent solutions for modern EV ecosystems.

External IDs:dblp:journals/tits/NiuLAJYZ25