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

Lou, Chengwei, Jin, Zekai, Zhou, Yue ORCID: https://orcid.org/0000-0002-6698-4714, Tang, Wei, Zhang, Lu and Yang, Jin 2025. Privacy-protected P2P electricity and carbon emission trading markets based on distributionally robust proximal atomic coordination algorithm. Applied Energy 384 , 125409. 10.1016/j.apenergy.2025.125409

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/, Start Date: 2025-02-02
Publisher:	Elsevier
ISSN:	0306-2619
Date of First Compliant Deposit:	11 February 2025
Date of Acceptance:	20 January 2025
Last Modified:	11 Feb 2025 15:45
URI:	https://orca.cardiff.ac.uk/id/eprint/176095

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