## 2 Related Work  
Various techniques and methods are used to optimize the energy system of different transport technologies. 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) [5; 6]. In paper [7], the PSO is used to optimize the charging and discharging process of the composite ESS of a fuel cell ship under maneuvering conditions. The amplitude of the voltage fluctuation is reduced, the power quality of the marine power grid is improved, and the battery service life is extended. The same method is used in research [8] to optimize the ESS size efficiently and enable the stable operation of the fuel cell ship. Another example is the schedule optimization of drone routes to prevent collisions during charging flights [9]. The presented methodology achieves good results in optimizing the drone charging schedule. Further, the paper [10] proposes a novel energy management strategy for a ship's solar -diesel hybrid generator system and fully considers the ship's efficiency. By applying the PSO algorithm, reduced fuel consumption and greater efficiency were achieved in the operation of the ship generator. Paper [11] proposed a multi-objective random black-hoe PSO algorithm for an optimal peak load shaving strategy aiming to achieve the best peak load shaving effect with the minimum electricity cost for the charging schedule of EVs and battery ESSs. The PSO method was used in paper [12] with the aim of minimizing operational costs considering capital costs, maintenance costs, as well as the performance and system size of a hybrid freight train. A modified multi-objective PSO was used in paper [13] to optimize the size of renewable energy systems. The function goal was to minimize losses and energy prices. In paper [14], the GA is utilized for location optimization of electric vehicle charging stations. A proposed algorithm for the optimal distribution of charging stations is presented to reduce investment costs. A DL-based optimization scheduling method is proposed for the ship power system, generator, and ESS [15]. The cost function correlates the dynamic optimization scheduling decision of energy with the goal of economy. The paper [16] uses an improved FL to achieve an optimal output power distribution and online control. The object of optimization is the hybrid power system with multiple power sources on a tourist ship. The MILP model with discrete time is employed to find the optimal number of batteries, docking stations, and locations to make the vessel's demand effectively powered by electric energy [17]. A model based on MILP is used to minimize the total cost, which includes charging costs, the construction cost of charging stations, and the fixed cost of ships [18]. Optimization was achieved using a cost-efficient and environmentally friendly service network for electric ships. Paper [19] proposes a PN scheduling framework model of the automated guided vehicle to optimize vehicle scheduling. The optimal schedule of charging samples of electric vehicles and buses is mainly carried out to minimize charging costs [20,21]. The cost is minimized so that the electric ESS is charged when the price of electricity is lower at a certain period of the day. The paper [22] proposes using smart control of the battery ESS in the harbor area. Due to the limited capacity of the shore connection and the non-periodic need for electricity, the use of a land-based ESS is suggested, which ensures a stable and reliable supply of electricity to ships. Peak loads of the port infrastructure arise due to cargo handling, ship consumption, and the needs of the port itself. The energy management system can be optimized by predicting the consumption schedule at a particular time [23]. By studying the presented literature, it can be concluded that optimizing the charging process in the port of call of the ferry fleet is technically justified and necessary.