Model Predictive Control of Renewable Energy Sources in DC Microgrid for Power Flow Control

Abstract

This paper aims to integrate and control renewable energy sources for power management and operation of a standalone hybrid DC microgrid. The system consists of photovoltaic arrays, wind turbine and fuel cells with storage batteries as backup. It proposes a Model Predictive Control (MPC) scheme that accurately tracks the desired load current and output voltage for relative power sharing among multiple distributed sustainable energy resources. Sustainable energy sources are controlled to deliver maximum power using DC-DC boost converters. MPPT control strategy is designed based on model predictive control, which evaluates the suitable power references at each sampling time with optimal cost function, in order to achieve desired results under varying conditions of renewable energy sources. Commonly used Incremental Conductance algorithm is used as a base framework along with MPC to design MPPT controller. For power flow control, MPC controller with discrete time Kalman filter has been designed for modifying voltage and current references depending upon the input/output power variations from sources and loads respectively. The proposed MPC scheme has fast tracking response that can achieve the optimal power management between the Distributed Energy Resources (DERs) units, and loads connected to DC microgrid. The results are validated using MATLAB/SIMULINK simulation and experimental studies.