Dynamic power management based on model predictive control for hybrid-energy-storage-based grid-connected microgrids

Abstract

In this study, an efficient and reliable dynamic power management system (PMS) is proposed for microgrids (μGs) based on hybrid energy storage systems. Owing to the differences in the response times of the different components (i.e., the battery, supercapacitor, and fuel cell) of the μG, efficiently allocating the power between the different devices is a challenging task for system designers. To address this issue and optimize the operation of the μG, a dynamic PMS is necessary. The contribution of this study is the application of model predictive control (MPC) for operating and controlling such μGs. The MPC method is used to manage power electronic devices such as DC-DC converters (for the battery, fuel cell, and supercapacitor) and grid-connected inverters. After the proposed PMS is successfully implemented in simulations, its real-time performance is validated under different operating scenarios that could occur in actual situations. The proposed PMS is found to be effective in controlling the control and operation of the μG. It can regulate the DC bus voltage during transient power imbalances, control the fuel cell current slope as per the requirement, and smoothen the operation between isolated and grid-connected modes.