Accurate model predictive control of bidirectional DC-DC converters for DC distributed power systems

This paper presents accurate model predictive control (MPC) of bidirectional DC-DC converters for DC distribution power systems. The different four model predictive control synthesis frameworks are proposed to the bidirectional converter to achieve stability and desired performance for wide range operations. The converter consists of bidirectional modes; Buck mode for charging the battery or energy storage bank, and Boost mode for discharging to the distribution generation (DG) loads from the battery. The performances of model predictive control strategies which make use of different forms of linearized models are compared. These linear models are ranging from a simple fixed model, linearized about a reference steady state to a weighted sum of different local models called multi model predictive control. A more complicated choice is represented by the extended dynamic matrix control in which the control input is determined based on the local linear model approximation of the converter that is updated during each sampling interval by making use of a nonlinear model. Simulation results show an excellent transient response and a good tracking for a wide operating range and uncertainties in modeling.