Peer-to-peer energy trading of solar and energy storage: A networked multiagent reinforcement learning approach

Utilizing distributed renewable energy resources, particularly solar and energy storage, in local distribution networks via peer-to-peer (P2P) energy trading has long been touted as a solution to improve energy systems' resilience and sustainability. Consumers and prosumers (that is, those with solar PV and/or energy storage), however, do not have the expertise to engage in repeated P2P trading, and the zero-marginal costs of renewables present challenges in determining fair market prices. To address these issues, we propose multi- agent reinforcement learning (MARL) frameworks to help automate consumers' bidding and management of their solar PV and energy storage resources, under a specific P2P clearing mechanism that utilizes the socalled supply-demand ratio. In addition, we show how the MARL frameworks can integrate physical network constraints, ensuring the physical feasibility of P2P energy trading and providing a possible pathway for practical deployment.