Distributed optimization for joint peer-to-peer electricity and carbon trading among multi-energy microgrids considering renewable generation uncertainty

The increasing penetration of renewable energy and the further coupling of the electricity and carbon markets have hindered the realization of efficient and low-carbon transformation processes in new power systems. This study addresses the optimization problems of joint peer-to-peer (P2P) electricity and carbon trading in multi-energy microgrids (MEMGs), taking into account the risks associated with renewable generation in a distributed manner. First, a coordinated operation model is developed to describe the joint P2P electricity and carbon trading issues among MEMGs, aiming to minimize operating costs, mitigate potential risk losses, and reduce renewable energy wastage. Second, the conditional value-at-risk technique, paired with stochastic programming, is employed to quantify potential risk losses arising from uncertainties. Finally, a distributed optimization approach is developed based on the alternating direction method of multipliers to maintain the privacy and independence of decision-making in individual MEMGs. During the trading processes, the Lagrangian multipliers are used as price signals to ensure fairness in optimal trading schemes among MEMGs. Moreover, a parallel solution mechanism is implemented to improve overall operational efficiency with minimal calculation expenditure. The simulation results demonstrate that the proposed method can reduce operation costs and carbon emissions while also preventing a significant amount of renewable energy abandonment.