Carbon-Aware Peer-to-Peer Energy Trading in an Unbalanced Distribution Network via a Nash Equilibrium Discovery Deep Reinforcement Learning Approach

Peer-to-peer (P2P) energy trading offers an innova-tive platform for prosumers to engage in bilateral electricity and carbon trades, enabling promoting the integration of distributed energy resources and supporting low-carbon operations in distri-bution networks. However, existing studies often rely on single-phase approximations, leading to inaccuracies in emission ac-counting and potential economic inefficiencies loss. To address these challenges, this paper proposes a carbon-aware P2P energy trading mechanism for unbalanced distribution networks. A novel joint electricity and carbon trading market framework is devel-oped, integrating carbon permits trading with a continuous double auction mechanism. The trading process is formulated as a coop-erative Markov game to capture the stochastic nature of market dynamics and to enable intelligent bidding decisions. A Nash equi-librium (NE) discovery multi-agent deep reinforcement learning framework is employed, utilizing a trust region policy optimiza-tion-based algorithm to enhance trading reliability and policy in-terpretability. Simulations on a modified IEEE-123 unbalanced distribution network demonstrate the proposed effectiveness in re-ducing operational costs and carbon emissions, providing rigorous NE validation and showcasing its practical applicability in com-plex energy markets.