Improved Finite Control Set Model Predictive Estimation Voltage Control in DC-Microgrids

Finite control set model predictive control (FCS-MPC) has recently been applied to solve the real-time measurement problems of DC-Microgrid (DC MG). The details of DC MG stochastic modeling have not been presented through the discretization of DC/DC converters that approximate the DC MG stochastic model. Additionally, a detailed description of the simulations and real-time verifications is not included. In this paper, we propose an improved FCS-MPC that provides details stochastic model of DC MG via validations. The proposed approach utilizes the linear Kalman filtering algorithm (LKFA) via the DC MG stochastic average dynamic equation model. The model has been approximated using LKFA and the DC MG stochastic model. The stochastic FCS-MPC handles the real-time information required to achieve the proposed approach. A highly effective algorithm is presented that allows a sample time to be executed for the operation of the DC MGs in the second prediction model. A DC grid estimated voltage reference has been developed using LKFA, state feedback regulation, and integral action. By using the reference, DC grid voltage estimation and currents are regulated via a weighted expected cost function, and provide equal power sharing capability. Improved FCS-MPC effectiveness was evaluated through Simulink simulation and real-time validation.