基于随机模型预测控制的数据中心微电网能量优化调度研究

针对数据中心负荷的随机性和可再生能源出力的不确定性，为保证数据中心在实际运行中的稳定性和经济性，提出了一种基于随机模型预测控制的数据中心微电网能量优化调度方法。引入场景分析法描述不确定因素，以数据中心运行成本最低为目标建立优化模型，通过模型预测控制的滚动优化和反馈校正，协调系统内各设备的出力情况，减小数据中心的运行成本，并降低不确定因素对其运行产生的不良影响。以某个典型数据中心微电网系统为例进行仿真分析，仿真结果验证了所提方法的有效性。

Energy Optimization Dispatch of Data Center Microgrid Based on Stochastic Model Predictive Control

Data center workloads and renewable energy outputs are random and uncertain. In order to ensure the stability and economy of data center in actual operation，an energy optimal dispatch method of data center microgrid based on stochastic model predictive control is proposed. The scenarios analysis method is introduced to describe the uncertain factors. An optimization model is established with the objective of the lowest operating cost of data center. Through the rolling optimization and feedback correction of model predictive control，the outputs of each system equipment are regulated，which reduce the operating cost and negative impact of uncertain factors on the operation. Taking a typical data center microgrid as an example，the simulation results verify the effectiveness of the proposed method.