Abstract

The research and development of Maritime Autonomous Surface Ships (MASS) is underway in several countries, with operations either remotely controlled from a Shore Control Center (SCC) or fully autonomous, without the need for Officer of the Watch (OOW) supervision. This study focuses on integrating renewable energy systems, alternative fuels, and energy management strategies (EMS) to enhance the efficiency and sustainability of both conventional and fully autonomous vessels. In response to rising fuel costs and stringent International Maritime Organization (IMO) regulations, the research aims to optimize vessel performance, reduce emissions, and improve energy efficiency across various ship types.
The study begins by assessing conventional vessels before transitioning to fully autonomous operations. The research then examines the optimization of a hybrid renewable energy system (HRES) that incorporates photovoltaic (PV) arrays, vertical axis wind turbines (VAWTs), and battery storage into the existing ship power system. A comparative analysis is conducted between conventional and fully autonomous vessels using an artificial bee colony (ABC) algorithm. The optimal configuration for both vessel types is identified as Genset/PV/VAWT/Battery, minimizing the annualized cost of the system (ACS), while maximizing the renewable energy fraction and reducing carbon emissions. Notably, autonomous vessels demonstrate superior performance in terms of cost and emissions when compared to conventional vessels.
Further, the study investigates optimal marine alternative fuels for short-sea shipping, including hydrogen, LNG, and traditional fuels. Mathematical modeling in Python is used to evaluate key performance indicators (KPIs), with LNG proving to deliver the highest Net Present Value (NPV), especially for autonomous vessels. This provides insights for optimizing fuel selection and ensuring compliance with environmental regulations.
Finally, a multi-objective predictive energy management system is developed using nonlinear model predictive control (NMPC) combined with grey wolf optimization (GWO) to optimize energy distribution in autonomous vessels under dynamic wave conditions. The NMPC-GWO algorithm demonstrates robustness and adaptability, ensuring reliable performance in varying environmental and operational conditions.
In summary, this research offers a comprehensive framework for optimizing energy systems and fuel selection, driving improvements in operational efficiency and environmental sustainability in the maritime industry.