A parallel distributed bargaining mechanism for joint electricity-carbon trading in multi-energy manufacturing plant networks

Abstract

Manufacturing plants (MPs) are energy-consuming and carbon-intensive industrial entities facing significant challenges in reducing energy costs and carbon emissions. Enabling local electricity and carbon trading among MPs offers a promising solution to address these challenges. However, existing research lacks effective trading mechanisms that support proactive local trading while ensuring fair profit distribution among MPs. To address this gap, this paper proposes a computation-efficient, privacy-preserving, and fair local trading mechanism for multi-energy MPs. Specifically, a novel parallel distributed bargaining mechanism is developed to facilitate local electricity and carbon trading among MPs. The trading process is formulated as a Nash bargaining problem, which is decomposed into two subproblems: a local electricity and carbon trading problem and a payment bargaining problem. To protect the privacy of MPs, we design an accelerated-adaptive alternating direction method of multipliers (AA-ADMM)-based distributed algorithm to solve the subproblems. Meanwhile, a diagonal quadratic approximation (DQA) method is introduced to enable parallel computation of the subproblems. The novelty of the proposed method lies in its ability to enable parallel and distributed solving of the formulated problems, while ensuring fast convergence and privacy protection without relying on any mediator or aggregator. Simulation results demonstrate the effectiveness of the proposed mechanism in conducting local electricity and carbon trading among MPs. Compared to the independent operation mode, the proposed framework reduces the total operation cost and carbon emissions of MPs by 57.75 % and 11.56 %, respectively. Moreover, compared to standard ADMM, the proposed method reduces the total solution time by 46.93 %.