Energy Optimal Dispatch of the Data Center Microgrid Based on Stochastic Model Predictive Control - Zhu, Yixin; Wang, Jingyun; Bi, Kaitao; Sun, Qingzhu; Zong, Yu; Zong, Chenxi (Frontiers in Energy Research, 2022)
Renewable energy outputs such as wind turbines and photovoltaics, as well as data center workloads are both random and uncertain. In order to enhance the stability and economy of the data center in actual operation effectively, a multi-time scale optimal dispatch method for the data center microgrid based on stochastic model predictive control is proposed in this paper. In the day-ahead scheduling stage, the characteristic is considered that batch workloads of the data center allow to be served delayed. And the scenario analysis method is used to describe the uncertainty of loads and renewable energy outputs, based on which an economic optimal scheduling model is established to minimize the system operating cost. The intra-day scheduling utilizes the rolling optimization and feedback correction of model predictive control to correct the deviation of loads and renewable energy outputs and adjust the day-ahead dispatch plan in real time, which ensures the effectiveness of day-ahead plan and the stability of system operating. Through the simulation results of a typical data center microgrid, the effectiveness of the proposed method is verified.