Robust Real-Time Shipboard Energy Management System With Improved Adaptive Model Predictive Control

Abstract

The electrified hybrid shipboard power system with high-level integration of renewable energy resources and energy storage system has become the new trend for the all-electric ship (AES) configuration. However, the traditional rule-based energy management system (EMS) is not able to fulfill the increasingly complex control requirements, and a more advanced EMS control algorithm is required to handle the multiple power sources and even achieve optimal energy management control. This paper proposes a supervisory-level EMS with an improved adaptive model predictive control (AMPC) strategy to optimize the power split among the hybrid power sources and to reduce the total cost of ownership (TCO) of vessel operation, which considers not only the fuel and emission costs but also the power source degradation. In order to achieve real-time implementation, the AMPC-based EMS software has been developed and deployed to a programmable logic controller (PLC) hardware. The prototyping controller verification tests have been performed with a hybrid fuel cell-fed shipboard power system hardware-in-the-loop (HIL) plant in the lab environment. Three typical tugboat load profiles with power fluctuations are implemented as case studies. Lastly, a cost study was performed to compute the economic benefits for a ten-year long-term vessel operational cycle. The proposed AMPC-based EMS is robust and effective, which can achieve up to 12.19% TCO savings compared to those of a traditional rule-based control strategy.

Keywords

• All-electric ship
• hybrid shipboard power microgrid
• adaptive model predictive control
• energy management control system