Research on Multi-time Scale Active Power Coordinated Control Strategy of Grid-connected Microgrid based on EEMD-MPC method

Abstract

Grid-connected microgrid contains controllable micro-sources (CMS) which have different time characteristics. In order to dispatch the energy in microgrid more effectively, a multi-time scale control strategy based on model predictive control (MPC) method is proposed. MPC is a finite-time closed-loop optimization control algorithm. It can effectively cope with the variation of time and uncertainty of the system, and widely used in complex industrial process control. In grid-connected microgrid, the power sequences of photovoltaic (PV) system and load appear to fluctuate and have no regularity, but they do have the components with different frequency scales. In this paper, ensemble empirical mode decomposition (EEMD) is used to decompose the predicted PV power and load sequences of a day to four sub-sequences with different frequency scales. Based on these sub-sequences, a combined control model is put forward which includes a long time scale optimization control model and a short time scale optimization control model. Each model optimizes the output of the CMSs with different time scale characteristics. After that, add the results of different CMSs together respectively, and the final optimal results of each CMS will be yielded. At the last of this paper, the effectiveness of the proposed method is proved by simulation results.