Table 2 Research gaps identified along with the proposed solution.

From: A hierarchical fusion framework for vehicle to grid energy management using predictive intelligence and learning based pricing

Gap

Proposed Solution

1. No hierarchical integration

Hierarchical fusion design: Three-layer architecture with bidirectional feedback

2. Forecast uncertainty ignored in pricing

Forecast-conditioned Nash equilibrium: Prices become functions of forecast state

3. Price-blind RL

Coupled optimization with feedback: Closed-loop price-aware control policy

4. No synergy demonstration

Fusion benefits quantification: Comparative evaluation framework

5. Static incentives

Multi-source adaptation: Real-time pricing from all three layers

6. Single-aggregator focus

Scalable market mechanism: Multi-aggregator coordination via unified pricing

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