VOLT: Vehicle-to-Grid Optimization with Language-Guided Transfer Learning for Dynamic Power Management
Abstract
A novel framework combining LLM-guided reinforcement learning with dynamic wireless power transfer systems for optimizing vehicle-to-grid (V2G) energy distribution. The system uses natural language interfaces to coordinate between human operators, electric vehicles, and power grid infrastructure while incorporating real-time power transfer optimization.
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Research Gap Analysis
Current systems lack effective integration between human operators and complex V2G systems, while existing AI solutions don't fully leverage the potential of natural language interfaces for power management.
VOLT: Vehicle-to-Grid Optimization with Language-Guided Transfer Learning
Motivation
Current V2G systems face significant challenges in coordinating between multiple stakeholders - grid operators, EV owners, and power infrastructure. While recent advances in wireless power transfer and AI-driven energy management show promise, there remains a gap in creating intuitive, efficient interfaces between human operators and complex power distribution systems. Additionally, existing solutions often struggle with real-time optimization across varying grid conditions and user preferences.
Proposed Approach
The VOLT framework consists of three main components:
1. Language-Guided Interface Layer
- Utilizes LLMs to translate natural language instructions from grid operators and EV owners into actionable power management policies
- Maintains a context-aware knowledge base of grid conditions, vehicle states, and historical performance
- Enables dynamic adjustment of charging/discharging strategies based on human feedback
2. Transfer Learning Optimization Core
- Implements a hybrid reinforcement learning architecture combining DDPG with memory functions
- Transfers learned policies across different grid configurations and vehicle types
- Incorporates DC-controlled variable inductor techniques for efficient wireless power transfer
3. Dynamic Coordination System
- Manages real-time power flow between vehicles and grid infrastructure
- Optimizes charging schedules based on predicted demand and available capacity
- Implements adaptive threshold event-triggered mechanisms for efficient resource utilization
Expected Outcomes
- 15-20% improvement in overall grid energy efficiency
- Reduced coordination overhead between operators and systems
- More intuitive control interfaces for both technical and non-technical users
- Enhanced adaptation to varying grid conditions and user requirements
Potential Applications
- Smart city power management systems
- Electric vehicle fleet operations
- Renewable energy integration
- Emergency power distribution scenarios
- Mobile charging infrastructure deployment
Proposed Methodology
Combine LLM-guided interfaces with transfer learning and dynamic wireless power transfer optimization to create a comprehensive V2G management system.
Potential Impact
Could significantly improve grid efficiency, reduce operational complexity, and accelerate the adoption of V2G technology while making power management more accessible to non-technical operators.