Dual Model Predictive Controlled Hybrid Energy Storage System for Naval DC Microgrids

Abstract

Hybrid energy storage system (HESS) is an effective measure to improve the electrical performance of naval dc microgrids supplying pulsed power loads (PPLs). Coordination control scheme and capacity configuration of the HESS are two key issues to meet multiple control objectives and constraints. In response to the requirements of optimal operation for HESS under various complex scenarios, a dual model predictive control (D-MPC) strategy is proposed for the HESS integrated with the superconducting magnetic energy storage (SMES) and battery in this article. Firstly, the current reference of battery is obtained through the MPC integrated adaptive low-pass filtering (ALPF). Then, the local MPC controller of HESS converter is utilized to track the current reference of battery and SMES as well as the bus voltage reference to achieve transient power allocation of HESS and energy balancing of dc microgrid. Meanwhile, a cost function with adaptive weighting factor is designed to make tradeoff of conflict control objectives. Furthermore, the HESS capacity configuration and superconducting magnet optimal design according to its actual operation condition are presented. Comparative case studies are conducted in hardware-in-the-loop (HIL) experiment to demonstrate the superiority of the proposed scheme in terms of improving system operation performance under various PPLs.