A hierarchical fusion framework for vehicle to grid energy management using predictive intelligence and learning...

Area of Science:

• Electrical Engineering
• Computer Science
• Energy Systems

Background:

• Grid stability and energy efficiency are critical challenges.
• Electric Vehicles (EVs) offer a potential solution through Vehicle-to-Grid (V2G) technology.
• Optimizing EV charging and discharging based on driver needs and grid demands is complex.

Purpose of the Study:

• To compare and integrate advanced methods for V2G system optimization.
• To develop a hierarchical collaborative fusion approach for V2G control.
• To enhance energy management, economic viability, and sustainability of V2G systems.

Main Methods:

• Artificial Intelligence (AI) for predictive charging and demand forecasting.
• Game Theory for dynamic pricing strategies.
• Reinforcement Learning (RL) for adaptive real-time control.
• A hierarchical fusion model integrating AI-driven price signals with RL control.

Main Results:

• The proposed hierarchical fusion method enables consistent strategic, financial, and operational decisions in V2G systems.
• AI-based forecasting informs Game Theory pricing, which constrains the RL control policy.
• RL provides adaptive, real-time optimal charging and discharging decisions.
• Simulated data validates the methodologies' effectiveness for real-world applications.

Conclusions:

• Combining AI, Game Theory, and RL offers a robust solution for V2G optimization.
• The integrated approach improves energy management, reduces grid congestion, and enhances economic benefits.
• This research demonstrates a pathway towards more efficient and sustainable V2G integration.