Simulations of Hybrid Energy System for a more Sustainable Naval Navigation
Maritime transport is responsible for more than 18% of some air pollutants and 3% of total worldwide greenhouse gas (GHG) emissions [1] (Figure 1). Being a key part of the international supply chain [2], it is crucial to decrease the environment impact of shipping in the presence of the tremendous threat of the climate crisis [3].
Navigation using renewable energy sources is one of the ways that would allow for a significant drop in emissions associated with shipping. However, the transition from non-renewable to renewable energy sources requires time and a hybrid energy system could be the nowadays option for the transition to more sustainable vessel navigation.
Hybrid Energy Systems (HES) are such energy systems that can satisfy the power demand with both non-renewable and renewable energy sources [4] (Figure 2). They play one of the central roles in solving the challenge of reducing our dependence on non-renewable energy sources when an immediate transition to renewables is not feasible. At the same, a clever way of managing the energy system is central in order to obtain a substantial reduction in emissions.
Thus, we are solving the optimization problem whose goal is to minimize the consumption of fuel and associated with it emissions for the navigation of a vessel from point A to point B using HES (Figure 3). The challenge comes from the fact that the battery charge is limited and usually is not sufficient to provide the energy for the full mission. Additionally, there is a number of constraints that are presented in the problem in order to obtain the solution that would be applicable to the real-world scenario.
As a solution, we devised a workflow as follows as it is shown in Figure 4. First, the data were collected from the sensors installed on the vessel after which data analysis has been performed. Then, machine learning methods have been applied in order to approximate the cost function and some of the constraints of the optimization problem. Finally, mathematical optimization was implemented using the Simulated Annealing method.
As the outcome, we obtained the values of control variables such that minimize the values of the key performance indicators (KPIs). The results presented in Figure 5 show a reduction of up to 31% for different KPIs. In Figure 5, we illustrate energy management system (EMS) and Smart Hybrid EMS (SHEMS). The x-axis indicates the emissions type and fuel consumption. The y-axis does not include any value indication for the data protection reason. Nevertheless, it is noticeable from the figure that the SHEMS results are significantly lower than in the current EMS.
Thus, we can conclude that even though in the work highly heterogeneous examples of the missions were presented, a significant reduction in the emission was obtained. Hence, one may conclude that similar approaches can be applied to real-world scenarios when variability and uncertainty are present.
References
[1] Issa, M., Ilinca, A., & Martini, F. “Ship Energy Efficiency and Maritime Sector Initiatives to Reduce Carbon Emissions.” Energies, 15(21), (2022): 7910.
[2] European Maritime Transport Environmental Report, launched by the European Environment Agency and the European Maritime Safety Agency https://www.eea.europa.eu/publications/maritime-transport/.
[3] Masson-Delmotte, Valérie, et al. “Climate change 2021: the physical science basis.” Contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change 2 (2021).
[4] Manwell, J. F. “Hybrid energy systems.” Encyclopedia of energy3.2004 (2004): 215–229.
