Mixed-Stage Energy Management for Decentralized Microgrid Cluster Based on Enhanced Tube Model Predictive Control
Peng Xie, Youwei Jia, Hongkun Chen, Jun Wu, Zexiang Cai
Abstract
In view of the ineluctable uncertainties induced by renewables and load demand, it becomes challenging to realize reliable online energy scheduling for microgrid clusters. To overcome this challenge, this paper proposes a novel energy management framework based on tube-based model predictive control for off-grid microgrid clusters, which enables the robustness against system uncertainties in the energy scheduling strategy with less sacrifice in economic performance and computational efficiency. The proposed energy management framework adopts a mixed-stage optimization structure, where the concerned problem is progressively optimized along with diverse energy management scales and various prediction horizons. A novel decentralized decomposition and coordination algorithm based on the alternating direction method of multipliers is developed, which enhances privacy preserving and overall convergency. Case studies in presence of high system uncertainties on a typical microgrid cluster demonstrate the effectiveness and computational efficiency of the proposed framework and solving algorithm.