  Utilizing distributed renewable and energy storage resources via peer-to-peer
(P2P) energy trading has long been touted as a solution to improve energy
system's resilience and sustainability. Consumers and prosumers (those who have
energy generation resources), however, do not have 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 a multi-agent reinforcement learning (MARL) framework to help automate
consumers' bidding and management of their solar PV and energy storage
resources, under a specific P2P clearing mechanism that utilizes the so-called
supply-demand ratio. In addition, we show how the MARL framework can integrate
physical network constraints to realize decentralized voltage control, hence
ensuring physical feasibility of the P2P energy trading and paving ways for
real-world implementations.
