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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.01423 |
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| _version_ | 1866916671413813248 |
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| author | Sun, Yichen Cui, Chenggang Zhang, Chuanlin Gong, Chunyang |
| author_facet | Sun, Yichen Cui, Chenggang Zhang, Chuanlin Gong, Chunyang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01423 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamic Incentive Strategies for Smart EV Charging Stations: An LLM-Driven User Digital Twin Approach Sun, Yichen Cui, Chenggang Zhang, Chuanlin Gong, Chunyang Systems and Control 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. |
| title | Dynamic Incentive Strategies for Smart EV Charging Stations: An LLM-Driven User Digital Twin Approach |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2504.01423 |