24. DEEP REINFORCEMENT LEARNING FOR REAL.pdf

Real-time energy pricing and peer-to-peer (P2P) energy trading are key elements in the transition toward decentralized energy systems. Traditional market mechanisms struggle to respond to rapid fluctuations in supply and demand, while security concerns hinder the adoption of P2P transactions. Deep reinforcement learning (DRL) offers a dynamic approach to optimizing energy prices in real time while ensuring secure, transparent transactions when integrated with blockchain and cloud-based platforms. This paper explores DRL architectures tailored for energy pricing and P2P trading, presenting implementation strategies, performance metrics, and integration with secure transaction frameworks. Two tables summarize DRL model types and their application outcomes in decentralized markets. The findings show that DRL enables adaptive, profitable, and secure energy exchanges, enhancing efficiency and trust in emerging energy ecosystems.