The purpose of this paper is to clarify some important issues as regards ship speed optimization at the operational level and develop models that optimize ship speed for a spectrum of routing scenarios in a single ship setting. The paper’s main contribution is the incorporation of those fundamental parameters and other considerations that weigh heavily in a ship owner’s or charterer’s speed decision and in his routing decision, wherever relevant. Various examples are given so as to illustrate the properties of the optimal solution and the various trade-offs that are involved.
Due to the increasing impacts of ships pollutants on the environment and the preventive laws that are tightening every day, the utilization of all-electric ships is a recent emerging technology. Being a promising technology, the usage of fuel cells as the main energy resource of marine vessels is an interesting choice. In this article, an all-electric hybrid energy system with zero emission based on fuel cell, battery, and cold-ironing is proposed and analyzed. To this end, actual data of a ferry boat, including load profiles and paths, are considered to assess the feasibility of the proposed energy system. The configuration of the boat and energy resources as well as the problem constraints are modeled and analyzed. Finally, the boat's energy management in hourly form for a one-day period is implemented. The improved sine cosine algorithm is used for the power dispatch optimization, and all models are implemented in MATLAB software. Based on the analysis results, the proposed hybrid system and the energy management method have high performance as an applicable method for the marine vessels. In addition, to be a zero-emission ship, the proposed system has an acceptable energy cost.
The maritime industry is a crucial hard-to-abate sector that is expected to depend on high-energy density renewable liquid fuels in the future. Traditionally, decarbonization pathways have been assessed assuming exogenous cost trajectories for renewable liquid fuels based on an exogenous learning curve. While past studies have looked at the impact of endogenizing learning curves for a specific technology utilizing linear approximation, a fully endogenous direct non-linear implementation of learning curves in a detailed sectoral model (maritime industry) that explores dynamics concerning sensitive parameters does not yet exist. Here, we apply an open-source optimization model for decarbonizing the maritime industry and further develop the model by encompassing a nonconvex mixed-integer quadratically constrained programming approach to analyze the impact of endogenized learning curves for renewable fuel costs following an experience curve approach. We find that global greenhouse gas emissions are significantly lower (up to 25% over a 30 year horizon) when utilizing endogenously modeled prices for renewable fuels compared to commonly used exogenous learning frameworks. Furthermore, we find that conventional modeling approaches overestimate the cost of climate mitigation, which can have significant policy implication related to carbon pricing and fuel efficiency requirements. In a broader context, this emphasizes the potential opportunities that can be achieved if policymakers and companies accelerate investments that drive down the costs of renewable technologies efficiently and thus trigger endogenous experience-based learning in real life.
Driven by increased integration of renewable energy sources, the widespread decarbonization of power systems has led to energy price fluctuations that require greater adaptability and flexibility from grid users in order to maximize profits. Industrial loads equipped with flexible resources can optimize energy consumption rather than merely reacting to immediate events, thereby capitalizing on volatile energy prices. However, the absence of sufficient measured data in industrial processes limits the ability to fully harness this flexibility. To address this challenge, we present a black-box optimization model for optimizing the energy consumption of cooling systems in the aquaculture industry using Extreme Gradient Boosting (XGBoost) and Bayesian Optimization (BO). XGBoost is employed to establish a nonlinear relationship between cooling system power consumption and available measured data. Based on this model, Bayesian Optimization with the Lower Confidence Bound (LCB) acquisition function is used to determine the optimal discharge temperature of water into breeding pools, minimizing day-ahead electricity costs. The proposed approach is validated using real-world data from a case study at the Port of Hirtshals, Denmark based on measurements from 2023. Our findings illustrate that leveraging the inherent flexibility of industrial processes can yield financial benefits while providing valuable signals for grid operators to adjust consumption behaviors through appropriate price mechanisms. Furthermore, machine learning techniques prove effective in optimizing energy consumption for industries with limited measured data, delivering accurate and practical estimates.
To achieve IMO’s goal of a 50% reduction of GHG emission by 2050 (compared to the 2008 levels), shipping must not only work towards an optimization of each ship and its components but aim for an optimization of the complete marine transport system, including fleet planning, harbour logistics, route planning, speed profiles, weather routing and ship design. ShipCLEAN, a newly developed model, introduces a coupling of a marine transport economics model to a sophisticated ship energy systems model – it provides a leap towards a holistic optimization of marine transport systems. This paper presents how the model is applied to propose a reduction in fuel consumption and environmental impact by speed reduction of a container ship on a Pacific Ocean trade and the implementation of wind assisted propulsion on a MR Tanker on a North Atlantic trade. The main conclusions show that an increase of the fuel price, for example by applying a bunker levy, will lead to considerable, economically motivated speed reductions in liner traffic. The case study sowed possible yearly fuel savings of almost 21 300 t if the fuel price would be increased from 300 to 1000 USD/t. Accordingly, higher fuel prices can motivate the installation of wind assisted propulsion, which potentially saves up to 500 t of fuel per year for the investigated MR Tanker on a transatlantic route.
An efficient extreme ship response prediction approach in a given short-term sea state is devised in the paper. The present approach employs an active learning reliability method, named as the active learning Kriging + Markov Chain Monte Carlo (AK-MCMC), to predict the exceedance probability of extreme ship response. Apart from that, the Karhunen-Loève (KL) expansion of stochastic ocean wave is adopted to reduce the number of stochastic variables and to expedite the AK-MCMC computations. Weakly and strongly nonlinear vertical bending moments (VBMs) in a container ship, where the former only accounts for the nonlinearities in the hydrostatic and Froude-Krylov forces, while the latter also accounts for the nonlinearities in the radiation and diffraction forces together with slamming and hydroelastic effects, are studied to demonstrate the efficiency and accuracy of the present approach. The nonlinear strip theory is used for time domain VBM computations. Validation and comparison against the crude Monte Carlo Simulation (MCS) and the First Order Reliability Method (FORM) are made. The present approach demonstrates superior efficiency and accuracy compared to FORM. Moreover, methods for estimating the Mean-out-crossing rate of VBM based on reliability indices derived from the present approach are proposed and are validated against long-time numerical simulations.
This paper presents an assessment of the energy harvesting potential from wave-induced motions when producing electricity by linear generators installed on ships. The study estimates an upper maximum energy extraction potential by not considering the electro-mechanical coupling; neither is mechanical and electrical dissipation considered. The analysis of the harvested energy is made using simulated data in a case study investigating three different ships (by size). Specifically, the case study reveals that, in moderate to mildly severe sea states, the power harvested from the environment using linear generators may reach values around 1–2 kW/tons of seismic mass. Thus, it is unrealistic to imagine ship designs where linear generators are thought to provide a ship's necessary propulsion power but, on the other hand, they may serve to supplement the main engine for auxiliary power generation.
The purpose of this paper is to investigate a multiple ship routing and speed optimization problem under time, cost and environmental objectives. A branch and price algorithm as well as a constraint programming model are developed that consider (a) fuel consumption as a function of payload, (b) fuel price as an explicit input, (c) freight rate as an input, and (d) in-transit cargo inventory costs. The alternative objective functions are minimum total trip duration, minimum total cost and minimum emissions. Computational experience with the algorithm is reported on a variety of scenarios.
In order to enhance sustainability in maritime shipping, shipping companies spend good efforts in improving the operational energy efficiency of existing ships. Accurate fuel consumption prediction model is a prerequisite of such operational improvements. Existing grey-box models (GBMs) are found with significant performance potential for ship fuel consumption prediction, although having a limitation of separating weather directions. Aiming to overcome this limitation, we propose a novel genetic algorithm-based GBM (GA-based GBM), where ship fuel consumption is modelled in a procedure based on basic principles of ship propulsion and the unknown parameters in this model are estimated with a GA-based procedure. Real ship operation data from a crude oil tanker over a 7-year sailing period are used to demonstrate the accuracy and reliability of the proposed model. To highlight the contribution of this work, we compare the proposed model against the latest GBM. The results show that the fitting performance of the proposed model is remarkably better, especially for oblique weather directions. The proposed model can be employed as a basis of ship energy efficiency management programs to reduce fuel consumption and greenhouse gas (GHG) emissions of a ship. This is beneficial to achieve the goal of sustainable shipping.
Enhancing environmental sustainability in maritime shipping has emerged as an important topic for both firms in shipping-related industries and policy makers. Speed optimization has been proven to be one of the most effective operational measures to achieve this goal, as fuel consumption and greenhouse gas (GHG) emissions of a ship are very sensitive to its sailing speed. Existing research on ship speed optimization does not differentiate speed through water (STW) from speed over ground (SOG) when formulating the fuel consumption function and the sailing time function. Aiming to fill this research gap, we propose a speed optimization model for a fixed ship route to minimize the total fuel consumption over the whole voyage, in which the influence of ocean currents is taken into account. As the difference between STW and SOG is mainly due to ocean currents, the proposed model is capable of distinguishing STW from SOG. Thus, in the proposed model, the ship’s fuel consumption and sailing time can be determined with the correct speed. A case study on a real voyage for an oil products tanker shows that: (a) the average relative error between the estimated SOG and the measured SOG can be reduced from 4.75% to 1.36% across sailing segments, if the influence of ocean currents is taken into account, and (b) the proposed model can enable the selected oil products tanker to save 2.20% of bunker fuel and reduce 26.12 MT of CO2 emissions for a 280-h voyage. The proposed model can be used as a practical and robust decision support tool for voyage planners/managers to reduce the fuel consumption and GHG emissions of a ship