AUQ–ADMM Algorithm-Based Peer-to-Peer Trading Strategy in Large-Scale Interconnected Microgrid Systems Considering Carbon Trading
Chun Wei, Yudie Wang, Zhuzheng Shen, Dongliang Xiao, Xiaoqing Bai, Haoyong Chen
Abstract
In the context of dual carbon target, this article proposes an optimal dispatch strategy for peer-to-peer (P2P) trading of large-scale interconnected microgrid (MG) systems considering carbon trading cost so that the individual MG can minimize its operation cost and earn profits through active trading. A robust optimization approach is adopted to cope with the uncertain renewable power outputs, and an adaptive uncertainty quantification–alternative direction method of multipliers (AUQ–ADMM) algorithm is used to solve the game problem of large-scale MGs participating in trading, which encourages individual MG to trade power and share benefit fairly proactively. Simulation analysis is conducted for large-scale interconnected MGs. The proposed AUQ–ADMM algorithm-based P2P trading scheme is verified to be economical, fair, and environmentally friendly, which also has superior convergence performance for large-scale MG P2P trading problems compared with the traditional ADMM algorithm.