Blockchain-Orchestrated Autonomous EnergyTrading Systems: A Firefly Optimization-based Multi-Agent Learning Framework

Abstract

The paper will suggest a decentralized energy trading system based on blockchain and multi-agent system with Firefly Optimization to solve inefficiencies, absence of transparency, and high operational expenses in the conventional centralized energy markets. The methodology involves the use of autonomous agents to trade peers, blockchain as a security and transparent transaction management system, and Firefly Optimization to improve decision-making and optimization of trading strategies. Evaluation of the system is done via simulation in a smart grid setting. The results reveal the high advancement with 93% trading efficiency, 30% cost reduction, accelerated convergence (12 iterations) and system reliability (96%) in comparison to the current techniques. Moreover, the model suggested is highly scaled and can withstand dynamic conditions of energy. In general, the framework offers an effective, safe, and flexible response to the present-day decentralized energy trading systems.