Privacy-protected P2P electricity and carbon emission trading markets based on distributionally robust proximal atomic coordination algorithm

As global power systems modernize towards intelligent infrastructures, peer-to-peer (P2P) energy trading is increasingly adopted worldwide as an innovative electricity market mechanism. This paper explores the decision-making behaviors of diverse agents, market mechanisms, and privacy protections in fully decentralized P2P electricity and carbon emission trading (CET), accounting for uncertainties from renewable energy sources. A novel P2P energy trading mechanism is proposed based on asymmetric Nash bargaining theory. The P2P electricity and carbon market models are decomposed into a cooperative alliance operation problem and an asymmetric cost distribution problem. Additionally, a contribution factor calculation method is introduced, considering both P2P electricity trading and CET marginal effect contribution. To manage renewable energy output uncertainties, a distributionally robust model using Kullback–Leibler (KL) divergence is reformulated as a chance-constrained problem. A proximal atomic coordination (PAC) algorithm is implemented to enhance privacy protection within a fully decentralized framework. Case studies demonstrate that P2P energy trading can reduce total costs by 10.29% and carbon quotas by 11.86% for cooperative alliances. Furthermore, the PAC algorithm decreases total computational time by 12.65% compared to the ADMM algorithm, highlighting its effectiveness in improving computational efficiency and safeguarding user privacy.