AI-Optimized Smart Local Grid for Peer-to-Peer Energy Trading System

This paper has presented the AI-Optimized Smart Local Grid (AI-SLG), a comprehensive framework integrating mul-tiple AI methodologies with blockchain-based P2P energy trading, IoT infrastructure, and agentic AI orchestration. The proposed system addresses the multidimensional challenges of P2P energy trading across virtual and physical network layers through four principal contributions: (1) a Fuzzy Q-learning algorithm for continuous, model-free optimization of household energy management and community trading; (2) a Stackelberg game-theoretic double auction mechanism implemented via Ethereum smart contracts ensuring individual rationality, incentive compatibility, and privacy preservation; (3) an agentic AI cluster zooming framework leveraging LoRaWAN spreading factor adaptation for dynamic virtual microgrid formation; and (4) an integrated fault detection and voltage regulation system enabling autonomous grid self-healing. Simulation results validate that the AI-SLG achieves 40–50% reduction in per-prosumer energy trading costs, 21–38% OPEX savings for network operators, blockchain throughput of approximately 500 transactions per second, and sub-second voltage restoration following fault events. Over 70% of traded energy volume occurs within local low-SF clusters, promoting community self-sufficiency and minimizing network charges. Future research directions include federated learning for privacy-preserving model training, co-optimization of trading decisions with physical network constraints, quantum com-puting applications for complex grid optimization, and inte-gration with 5G/6G communication infrastructure to further enhance real-time responsiveness and scalability. The AI-SLG framework represents a significant step toward the vision of fully autonomous, resilient, and sustainable smart energy communities that empower prosumers while strengthening grid reliability.

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