Peer-to-Peer Energy Trading of Carbon-Aware Prosumers: An Online Accelerated Distributed Approach With Differential Privacy
Xuan Wei, Yinliang Xu, Hongbin Sun, Wai Kin Chan
Abstract
Peer-to-peer (P2P) energy trading offers a promising way for prosumers to achieve multi-bilateral trades, further aids the integration of distributed energy resources into distribution networks and facilitates the low-carbon operation of the system. But realizing this potential requires overcoming challenges in model formulation and distributed optimization. This paper presents a novel P2P energy trading framework of carbon-aware prosumers based on carbon intensity analysis, where explicit emission cap constraints are embedded. To alleviate the computational complexity of each prosumer and accelerate the convergence process, an improved distributed algorithm based on the heavy-ball method is proposed, in which the distribution system operator handles the global constraints. Furthermore, a forgetting-based average online learning method and differential privacy mechanism are integrated into the proposed approach to jointly address the uncertainties and privacy issues. Case studies demonstrate that the proposed methodology can mitigate system carbon emissions while ensuring economics, and simultaneously enhances the algorithm’s performance in terms of convergence speed, uncertainty, and privacy.