## 7 Conclusions  
The main aim of this research is to contribute to the long-term sustainability of ferry transportation. As a rule, this type of transport is characterized by short routes with a predetermined sailing order. Furthermore, ferry docks are often located in sheltered ports, on islands, and in city ports that do not fulfill the energy needs for charging of a group of ferries. Therefore, it is necessary to optimize the charging process of the electrical ESSs of the ferry fleet. When conducting energy system optimization of different transport technologies, various techniques and methods are used. However, the most commonly used are particle swarm optimization (PSO), deep learning (DL), genetic algorithm (GA), fuzzy logic (FL), multi-objective optimization (MOO), mixed integer linear programming (MILP), and petri network (PN). In similar energy system optimization problems, PSO is often used. However, to the best of our knowledge and the available literature, we have not found an example of the application of this method in the optimization of the charging schedule of a group of electric ferries. The main objective of this paper is to develop a mathematical model based on the application of the PSO method in order to propose optimal solutions for the charging arrangement of the vessel. The objective function of this optimization task is to reduce the amount of peak charging power.  
This model can be used as a tool for the optimization of various transport, energy, and production processes. Also, technical aspects and limitations are not considered when optimizing the peak load following the repetitive navigation profile of the ships. Namely, for charging the entire fleet of ships to be feasible, all docks should have sufficient power connections. However, this research assumes that the mentioned requirement is fulfilled.  
The proposed model reduces the peak charging power in different operating conditions by up to 24%. Furthermore, robustness has been proven for states that differ significantly in daily energy profile compared to the initial state without optimization. Optimizing the charging process, peak charging power has been reduced without additional energy storage.  
Possibilities for additional improvement of the algorithm and increasing its robustness include the following:  
* Implementation of the algorithm for reducing the charging power during the entire charging period and not only for the peak load;
* Test the model under different conditions of exploitation that affect the load profile (number of ships, sailing schedule, energy needs).  
Recommendations for future research include the determination of the available energy capacities of the ships' ESSs when they are not sailing. Such ESS capacities can be used to reduce the peak charging power, regulate the load of the shore power grid, and regulate the voltage and frequency of the network.  
Conceptualization, T.P., M.K., G.K., and J.S.; methodology, T.P., M.K., and J.S.; software, T.P.; validation, T.P.; formal analysis, T.P.; investigation, T.P.; resources, T.P. and M.K.; data curation, T.P.; writing--original draft preparation, T.P. and G.K.; writing--review and editing, T.P., M.K., G.K., and J.S.; visualization, T.P.; supervision, M.K. and J.S.; project administration, M.K. and J.S.; funding acquisition, M.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.