Multi-time Scale Hierarchical Predictive Control for Energy Management of Microgrid System with Smart Users

In this paper, a multi-time scale hierarchal model predictive control strategy is proposed to optimize energy management problem of a microgrid with multiple smart users. According to the power flow among different energy modules, a hierarchical system model and a multi-time scale hierarchal energy optimization management problem are established. The centralized controller in the upper layer is to optimize the charge/discharge time and energy of storage devices, controllable supply power adjustment and dispatch of the aggregators. The optimization problem in the lower layer is to meet users' demands in real time. Meanwhile, in order to improve the disturbances caused by the randomness of renewable energy and variant loads, a multi-time scale optimization scheme is applied. At the slow scale, the upper optimization problem is solved, and the optimal energy scheduling in the long-term can be achieved. At the fast scale, the energy balance between supply and demand of smart users can be realized in the short-term. Finally, simulation results illustrate the effectiveness of proposed method.