Efficient Energy Management Control Strategy by Model Predictive Control for Standalone DC Micro Grids

This paper deals about Energy Management Strategy that dynamically optimizes the operation of stand-alone dc microgrids, consisting of wind, photovoltaic (PV) and battery branches, and coordinately manage all energy flows in order to achieve four control objectives i.e regulating dc bus voltage level of microgrids; proportional power sharing between generators as a local droop control realization; charging batteries as close to IU regime as possible; and tracking MPPs of wind and PV branches during their normal operations. Non-linear model predictive control (NMPC) strategies are inherently multivariable and handle constraints and delays. In this paper, the above mentioned EMS is developed as an NMPC strategy to extract the optimal control signals, which are duty cycles of three DC-DC converters and pitch angle of a wind turbine. The variable load demands are also shared accurately between generators in proportion to their ratings. Moreover, the DC bus voltage is regulated within a predefined range, as a design parameter.