Distributed Model Predictive Control for On-Connected Microgrid Power Management

This paper considers the energy dispatching optimization of a grid-connected microgrid in a park in the city of Shanghai in a distributed framework, in order to improve its economic and environment-friendly performance. The microgrid is composed of distributed generations (DGs), energy storage systems, and shiftable loads. Some properties of the microgrid make the dispatching problem difficult. For example, the disturbance in renewable energy is unpredictable; the operation of shiftable loads is discrete and the relationship between the generated power and the total operation cost is nonlinear. A novel distributed model predictive control (MPC) is proposed for the power dispatching optimization of the microgrid in this paper, where there is a local MPC for each of the following entities: DGs, storage batteries, and shiftable loads. In this method, the centralized mix-integer programming problem of microgrid energy dispatching is converted into several interacted nonlinear programming problems and integer programming problems, and subsystem-based MPCs coordinate with each other via iteratively minimizing the cost over the entire system. In this way, the realization of plug-and-play property becomes easier and the computational load is reduced. The numerical results show the effectiveness of the proposed method.