AI-Powered Microgrid Networks: Multi-Agent Deep Reinforcement Learning for Optimized Energy Trading in Interconnected Systems

Intelligent smart microgrids have been identified as a subject of significant research interest, given their potential to optimize energy consumption in residential contexts. The growing utilization of intelligent appliances and the integration of renewable energy sources, including distributed generation (DG) and electric vehicles (EVs), have increased energy demand. This paper presents an artificial intelligence (AI) system that employs deep reinforcement learning to facilitate efficient device scheduling and peer-to-peer (P2P) energy trading within microgrids. The system accommodates users with varying access levels to distributed generation (DG), battery storage, and electric vehicles (EVs). The real-time pricing and demand response mechanisms enable the system to adapt to fluctuating energy requirements. In contrast, surplus energy is shared through a peer-to-peer network, reducing grid dependency. The approach was validated using an experimental database from Saudi Arabia, demonstrating a notable reduction in electricity costs for participants.