Dual-track optimization system for energy markets based on heterogeneous blockchain and federated reinforcement learning: a synergistic mechanism for carbon footprint traceability and decentralized trading

This study develops a blockchain-FRL hybrid system for decentralized energy trading, achieving dual objectives of secure transactions (200 + TPS via PBFT consensus) and precise carbon tracking (<0.5% error). The framework's five-layer architecture enables real-time optimization while preserving data privacy through federated averaging. Key results show Consumer 5 and Supplier 3 gained ¥15.05M and ¥374.67M respectively, demonstrating 40% higher efficiency than conventional systems. The solution uniquely combines: (1) tamper-proof transaction records, (2) GDPR-compliant emission monitoring, and (3) adaptive multi-energy coordination - representing a 62% improvement in eco-economic performance over cap-and-trade models.