Peer to Peer Energy Trading

Peer-to-peer (P2P) energy trading facilitates the direct exchange of electricity between consumers and prosumers (consumers with distributed generation, like solar panels), aiming to improve grid resilience, reduce reliance on centralized grids, and enhance the integration of renewable energy sources. Current research heavily utilizes multi-agent reinforcement learning (MARL) and game theory, often incorporating machine learning models like deep Q-networks and graph convolutional networks, to optimize energy trading strategies and pricing mechanisms within various market structures (centralized vs. decentralized). These advancements offer significant potential for cost savings, peak demand reduction, and increased renewable energy utilization, impacting both the efficiency of energy systems and the economic viability of distributed generation.