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

Chengwei Lou, Zekai Jin, Yue Zhou, Wei Tang, Lu Zhang and Jin Yang

Applied Energy, 2025, vol. 384, issue C, No S0306261925001394

Abstract: 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.

Keywords: P2P energy trading; Distributed optimization; Distributionally robust chance-constrained; Asymmetric nash bargaining; KL divergence; Privacy protection; Carbon emission trading (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125409

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