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Dynamic Incentive Strategies for Smart EV Charging Stations: An LLM-Driven User Digital Twin Approach

Yichen Sun, Chenggang Cui, Chuanlin Zhang, Chunyang Gong

TL;DR

The paper tackles the challenge of efficiently integrating vehicle-to-grid services into power systems by modeling heterogeneous EV user behavior through a large language model–driven multi-agent digital twin. It introduces a dynamic incentive mechanism and a distributed optimization framework that jointly optimize EVCS revenue, user costs, and grid stability under V2G. Key contributions include a three-agent user profiling system, a minimum-reward decision model with delay costs, and an expert-evaluation, RAG-based decision framework that validates actions. Simulation results indicate improved peak-shave performance and grid balance, with incentives needing careful tuning to avoid unintended peak shifts, yielding a scalable tool for DR policy and EV grid integration.

Abstract

This paper presents an enhanced electric vehicle demand response system based on large language models, aimed at optimizing the application of vehicle-to-grid technology. By leveraging an large language models-driven multi-agent framework to construct user digital twins integrated with multidimensional user profile features, it enables deep simulation and precise prediction of users' charging and discharging decision-making patterns. Additionally, a data- and knowledge-driven dynamic incentive mechanism is proposed, combining a distributed optimization model under network constraints to optimize the grid-user interaction while ensuring both economic viability and security. Simulation results demonstrate that the approach significantly improves load peak-valley regulation and charging/discharging strategies. Experimental validation highlights the system's substantial advantages in load balancing, user satisfaction and grid stability, providing decision-makers with a scalable V2G management tool that promotes the sustainable, synergistic development of vehicle-grid integration.

Dynamic Incentive Strategies for Smart EV Charging Stations: An LLM-Driven User Digital Twin Approach

TL;DR

The paper tackles the challenge of efficiently integrating vehicle-to-grid services into power systems by modeling heterogeneous EV user behavior through a large language model–driven multi-agent digital twin. It introduces a dynamic incentive mechanism and a distributed optimization framework that jointly optimize EVCS revenue, user costs, and grid stability under V2G. Key contributions include a three-agent user profiling system, a minimum-reward decision model with delay costs, and an expert-evaluation, RAG-based decision framework that validates actions. Simulation results indicate improved peak-shave performance and grid balance, with incentives needing careful tuning to avoid unintended peak shifts, yielding a scalable tool for DR policy and EV grid integration.

Abstract

This paper presents an enhanced electric vehicle demand response system based on large language models, aimed at optimizing the application of vehicle-to-grid technology. By leveraging an large language models-driven multi-agent framework to construct user digital twins integrated with multidimensional user profile features, it enables deep simulation and precise prediction of users' charging and discharging decision-making patterns. Additionally, a data- and knowledge-driven dynamic incentive mechanism is proposed, combining a distributed optimization model under network constraints to optimize the grid-user interaction while ensuring both economic viability and security. Simulation results demonstrate that the approach significantly improves load peak-valley regulation and charging/discharging strategies. Experimental validation highlights the system's substantial advantages in load balancing, user satisfaction and grid stability, providing decision-makers with a scalable V2G management tool that promotes the sustainable, synergistic development of vehicle-grid integration.

Paper Structure

This paper contains 10 sections, 13 equations, 8 figures, 1 table.

Table of Contents

  1. Introduction
  2. SYSTEM DESCRIPTION AND PROBLEM STATEMENT
  3. Electric Vehicle Charging Station Model
  4. Demand Response Model
  5. LLM-Driven Digital Twin of EV Users for Enhanced Behavior Modeling
  6. User Profile Generation Agent
  7. User Behavioral Decision Agent
  8. Expert Evaluation Agents System
  9. Simulation results
  10. CONCLUSION

Figures (8)

  • Figure 1: Electric vehicle charging station model.
  • Figure 2: Demand response model.
  • Figure 3: User profile generation with large language model-based multi-agent systems.
  • Figure 4: User behavioral decision with large language model-based multi-agent systems.
  • Figure 5: The variation of electric vehicle discharged energy under different incentive amounts across various time periods.
  • ...and 3 more figures