Modeling and Real-Time Scheduling of DC Platform Supply Vessel for Fuel Efficient Operation

DC marine architecture integrated with variable speed diesel generators (DGs) has garnered the attention of the researchers primarily because of its ability to deliver fuel efficient operation. This paper aims in modeling and to autonomously perform real-time load scheduling of dc platform supply vessel (PSV) with an objective to minimize specific fuel oil consumption (SFOC) for better fuel efficiency. Focus has been on the modeling of various components and control routines, which are envisaged to be an integral part of dc PSVs. Integration with photovoltaic-based energy storage system (ESS) has been considered as an option to cater for the short time load transients. In this context, this paper proposes a real-time transient simulation scheme, which comprises of optimized generation scheduling of generators and ESS using dc optimal power flow algorithm. This framework considers real dynamics of dc PSV during various marine operations with possible contingency scenarios, such as outage of generation systems, abrupt load changes, and unavailability of ESS. The proposed modeling and control routines with real-time transient simulation scheme have been validated utilizing the real-time marine simulation platform. The results indicate that the coordinated treatment of renewable based ESS with DGs operating with optimized speed yields better fuel savings. This has been observed in improved SFOC operating trajectory for critical marine missions. Furthermore, SFOC minimization at multiple suboptimal points with its treatment in the real-time marine system is also highlighted

Prognostics and Health Management for the Optimization of Marine Hybrid Energy Systems

Decarbonization of marine transport is a key global issue, with the carbon emissions of international shipping projected to increase 23% to 1090 million tonnes by 2035 in comparison to 2015 levels. Optimization of the energy system (especially propulsion system) in these vessels is a complex multi-objective challenge involving economical maintenance, environmental metrics, and energy demand requirements. In this paper, data from instrumented vessels on the River Thames in London, which includes environmental emissions, power demands, journey patterns, and variance in operational patterns from the captain(s) and loading (passenger numbers), is integrated and analyzed through automatic, multi-objective global optimization to create an optimal hybrid propulsion configuration for a hybrid vessel. We propose and analyze a number of computational techniques, both for monitoring and remaining useful lifetime (RUL) estimation of individual energy assets, as well as modeling and optimization of energy use scenarios of a hybrid-powered vessel. Our multi-objective optimization relates to emissions, asset health, and power performance. We show that, irrespective of the battery packs used, our Relevance Vector Machine (RVM) algorithm is able to achieve over 92% accuracy in remaining useful life (RUL) predictions. A k-nearest neighbors algorithm (KNN) is proposed for prognostics of state of charge (SOC) of back-up lead-acid batteries. The classifier achieved an average of 95.5% accuracy in a three-fold cross validation. Utilizing operational data from the vessel, optimal autonomous propulsion strategies are modeled combining the use of battery and diesel engines. The experiment results show that 70% to 80% of fuel saving can be achieved when the diesel engine is operated up to 350 kW. Our methodology has demonstrated the feasibility of combination of artificial intelligence (AI) methods and real world data in decarbonization and optimization of green technologies for maritime propulsion

Utilization of Batteries in The Momentary Load Variations of a Cruise Ship

The shipping and cruising industry is considered one of the most important and cheapest transportation, however, it is considered responsible for almost 2.89% of global emissions in 2018. Due to the new regulations provided by IMO, the need to reduce fuel consumption and emissions from the shipping industry becomes imperative. Several technologies have been applied to achieve those challenges, but the main focus of this thesis will be on the utilization of batteries as one of the most promising energy storage technologies, to handle the load variation rather than the operation of the auxiliary diesel engines at an economical loading range. In cruise ship applications, the auxiliary diesel engines are utilized to supply the power required for the auxiliary loads and thruster motors, usually, thruster motors operate close to harbors. So, to ensure power availability, the auxiliary diesel engines usually run at low loading levels. The optimum operating point for the diesel engines is at 80% of loading, if that percentage decreases, both fuel consumption, and NOx emissions increased exponentially, moreover, the engine’s lifetime will be reduced and more maintenance will be required. By utilizing batteries, it will be capable of providing the required power for the operation of thruster motors or during peak loading periods rather than the operation of all available auxiliary diesel engines at low loading levels. The presented study focused on four different scenarios with different battery-pack sizes, showing the space required for each scenario and the operating profile of each diesel engine indicating the fuel consumption with and without the presence of batteries. The first scenario utilized a 940-kWh battery pack, which increased the efficiency of the running engines close to the optimum operating level. The last scenario utilized a 3240-kWh battery pack, which enables the shutdown of the auxiliary engines during the operation of thruster motors or peak loading. By using the large battery model scenario, half the number of diesel engines will not be required in the future new builds of a cruise ship. This will not only improve the fuel consumption efficiency and reduce emissions, moreover, the maintenance and overall build cost will also be reduced. Technical and economic analysis is presented showing the payback period of the batteries with different fuel and battery price options. The payback period is highly affected by the saving associated with fuel costs and the price of batteries

Power management optimization of hybrid power systems in electric ferries

The integration of more-electric technologies, such as energy storage systems (ESSs) and electric propulsion, has gained attention in recent years as a promising approach to reduce fuel consumption and emissions in the maritime industry. In this context, hybrid power systems (HPSs) with direct current (DC) distribution are currently gaining a commendable interest in research and industrial applications. This paper examines the impact of using HPS with DC distribution and a battery energy storage system (BESS) over a conventional AC power system for short haul roll-on/roll-off (RORO) ferries. An electric ferry with a HPS is modeled in this study and the power management system is simulated using the Matlab/Simulink software. The result is validated using measured load profile of a ferry. The performance of the DC HPS is compared with the conventional AC system based on fuel consumption and emission reductions. An approach to estimate the fuel consumption of the diesel engine through calculation of specific fuel oil consumption (SFOC) is also presented. This study uses two optimization techniques: a classical power management method namely Rule-Based control (RB) and a meta-heuristic power management method known as Grey Wolf Optimization (GWO) to optimally manage the power sharing of the proposed HPS. Fuel consumption and emission indicators are also used to assess the performance of the two power management methods. The simulation results show that the HPS provides a 2.91% and 7.48% fuel consumption reduction using RB method and GWO method respectively. It is apparent from the result that the HPS has more fuel savings while running the diesel generator sets (DGs) at higher operational efficiency. It is interesting that the proposed HPS using both power management methods provided a 100% emission reduction at berth. Finally, it was found that using a meta-heuristic optimization algorithm provides better fuel and emission reductions than a classical method