Proximal policy optimization-driven decentralized peer-to-peer energy trading model for optimal real-time operations in smart energy communities

The transition towards sustainable energy solutions has driven significant advancements in smart grid systems, with a focus on decentralized energy management and innovative trading mechanisms. This article introduces a novel decentralized peer-to-peer (P2P) energy trading model driven by Proximal Policy Optimization (PPO), a state-of-the-art deep reinforcement learning (DRL) algorithm. The model addresses the dynamic pricing, consumer preferences, and intermittent nature of renewable energy sources, enhancing energy transaction efficiency and adaptability to real-time market fluctuations. The proposed framework incorporates historical consumption data and real-time market signals to develop optimal energy trading policies, ensuring faster convergence during training and adaptive trading strategies that align with varying market dynamics. Experimental evaluations demonstrate the model's transformative impact, achieving an average reduction of 45% in energy expenses for participating households. The article also provides a detailed comparison with existing models, highlighting the superior performance and scalability of the proposed approach. Additionally, it discusses the mathematical modeling of single-home energy management systems and local energy communities, offering insights into the dynamics of energy trading within smart energy communities. The simulation analysis presents the performance of the proposed model under various conditions, underscoring its robustness and adaptability across diverse market scenarios.