AI-Driven P2P Renewable Energy Trading System

The rapid integration of renewable energy sources into residential power systems has increased the need for efficient, decentralized energy management solutions. Peer-to-peer energy trading has emerged as a promising approach to enable house- holds to exchange surplus renewable energy directly, reducing de- pendency on centralized grids and improving energy utilization. This literature survey reviews existing research on peer-to-peer energy markets, blockchain-based energy trading frameworks, smart contracts, and decentralized grid architectures. It also examines the role of artificial intelligence in forecasting energy generation and consumption to enhance trading efficiency and grid stability. Additionally, the survey analyzes simulation-based approaches used to model virtual energy communities for evalu- ating system performance and scalability. The review highlights key challenges such as interoperability, real-time coordination, pricing mechanisms, and security, while identifying research gaps that motivate the development of an integrated virtual peer-to- peer renewable energy trading and simulation system.