Dual-fuel ship route fuel filling, switching and navigational speed collaborative optimization method and system

Abstract

The invention provides a dual-fuel ship route fuel filling, switching and navigational speed collaborative optimization method and system, and the method comprises the steps: firstly obtaining route parameters and port parameters of a dual-fuel ship, and dividing a leg between two ports into a discharge control region leg, a canal region leg and a plurality of common legs according to a discharge control region and a canal region; averagely dividing a common leg with the sailing distance greater than a preset distance threshold into a plurality of interval legs, and establishing a mixed integer nonlinear optimization model taking the minimum fuel filling cost and the minimum sailing period delay cost as objective functions under a plurality of specific constraint conditions; carrying out linearization processing on the mixed integer nonlinear optimization model by adopting a linearization method to obtain a mixed integer linear optimization model; and finally, a solver is adopted to calculate an optimal solution of the mixed integer linear optimization model, so that an optimal fuel filling, fuel switching and navigational speed strategy of each sub-route segment is obtained, and collaborative optimization of fuel filling, fuel switching and navigational speed of the dual-fuel ship route is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of ship shipping management and optimization, and in particular to a method and system for collaborative optimization of fuel filling, switching and speed of a dual-fuel ship route. Background Art

[0002] Shipping carries over 80% of international freight trade and is the primary mode of international cargo transport, playing a vital role in international trade and global cargo flows. However, the shipping industry also faces serious environmental pollution issues, primarily due to the significant carbon and sulfur emissions released during the combustion of traditional marine fuels (such as HFO). To address this, the International Maritime Organization (IMO) and other organizations have implemented a series of measures to promote green development in the shipping industry. The IMO has established Emission Control Areas (ECAs) to address sulfur emissions, with a maximum sulfur content of 0.1% m / m for fuel oil within these areas. Starting January 1, 2023, the IMO will implement mandatory measures such as the annual Carbon Intensity Indicator (CII) rating to strictly control carbon emissions from ship operations. These measures are forcing shipping companies to shift from traditional fuels to more expensive low-sulfur, low-carbon fuels, posing a challenge to controlling operating costs. Therefore, it is crucial for shipping companies to conduct research on minimizing operating costs while meeting emission limits.

[0003] To meet shipping industry emission reduction requirements, such as those in Emission Control Areas (ECAs), liner companies have deployed dual-fuel vessels. However, compared to conventional fuel vessels, dual-fuel liner routes present greater decision-making challenges than traditional fuel-based liner routes, as they require consideration of the proportion of different fuels used in each leg, bunkering strategies, and vessel speed. Existing methods often optimize single factors and lack systematic models, resulting in inefficient cost control or excessive emissions. Therefore, a comprehensive optimization approach is needed to achieve multi-objective collaborative optimization. Summary of the Invention

[0004] To address the current problems of traditional fuel ship operations, which often focus on optimizing a single factor, leading to inefficient cost control or excessive emissions, the present invention provides a method for collaboratively optimizing fuel refueling, fuel switching, and speed for dual-fuel ship routes. With the goal of minimizing fuel costs and voyage delay costs, and with constraints such as port call time and speed, the ship simultaneously optimizes speed, fuel switching, and refueling strategies to minimize the sum of fuel refueling costs and voyage delay compensation. This effectively reduces costs and provides a decision-making optimization solution for ship operators. The present invention also relates to a system for collaboratively optimizing fuel refueling, fuel switching, and speed for dual-fuel ship routes.

[0005] The technical solutions of the present invention are as follows:

[0006] A method for collaboratively optimizing fuel filling, switching, and navigation speed of a dual-fuel ship route, characterized by comprising the following steps:

[0007] Parameter acquisition step: obtaining the route parameters and port parameters of the dual-fuel ship, wherein the route parameters include the sections within and outside the emission control area, the sections within and outside the canal area, and the sailing distance of each section; and the port parameters include the price of different types of fuel at the port, the amount of fuel to be refueled upon arrival at the port, the delay penalty cost at the port, the time of arrival of the ship at the port, and the expected time window for arrival at the port;

[0008] Segment division step: Based on the route parameters, the route segment between the two ports is divided into an emission control area segment, a canal area segment, and multiple ordinary segments according to the emission control area and the canal area; the ordinary segments with a sailing distance greater than a preset distance threshold are evenly divided into multiple interval segments, and the emission control area segment, canal area segment, and interval segment are used as sub-segments;

[0009] Model establishment steps: Under multiple constraints established by speed and fuel restrictions for each sub-segment, high-sulfur fuel restrictions within the emission control area, fuel inventory and refueling quantity restrictions, ship arrival and departure time restrictions, sailing time restrictions, and ship carbon intensity index level compliance restrictions, a mixed integer nonlinear optimization model is established with the objective function of minimizing fuel refueling cost and voyage delay cost based on the fuel price at a certain port, the amount of fuel a ship must refuel at that port, the delay penalty cost at that port, the time the ship arrives at that port, and the expected time window for arrival at that port;

[0010] Linearization processing step: linearizing the established mixed integer nonlinear optimization model using a linearization method to obtain a mixed integer linear optimization model;

[0011] Optimal solution calculation steps: Use the solver to calculate the optimal solution of the mixed integer linear optimization model, and then obtain the optimal fuel filling, fuel switching and speed strategy for each sub-segment, realizing the coordinated optimization of fuel filling, fuel switching and speed for the dual-fuel ship route.

[0012] Preferably, in the model building step, the multiple constraints include a first constraint and a second constraint based on the speed and fuel restrictions of each sub-segment, a third constraint based on the high-sulfur fuel restriction in the emission control area, a fourth constraint and a fifth constraint based on the fuel inventory and refueling quantity restriction, a sixth constraint and a seventh constraint based on the time restriction for the ship to arrive at and leave the port, an eighth constraint based on the sailing time restriction, and a ninth constraint based on the compliance of the ship's carbon intensity index level.

[0013] Preferably, the ship parameters of the dual-fuel ship are also obtained, including the ship's fuel tank capacity, minimum fuel storage capacity, consumption rate of different types of fuel in each segment, ship deadweight tonnage, carbon emissions generated in the year, carbon intensity index rating target value, and optional speed set, and a decision variable is constructed based on the judgment result of whether to sail at a certain speed and use a certain fuel in each sub-segment.

[0014] The first constraint, the second constraint and the third constraint are constructed based on the decision variables;

[0015] The fourth constraint is constructed based on the amount of fuel that the ship refuels upon arrival at the port;

[0016] The fifth constraint is constructed based on the fuel inventory of the ship when it arrives at the starting port, the amount of fuel the ship refuels at the port, and the maximum fuel capacity of the ship, to ensure that the fuel inventory of the ship at the port plus the refueling amount does not exceed the maximum fuel capacity of the ship;

[0017] The sixth constraint is constructed based on the time when the ship arrives at the starting port;

[0018] The seventh constraint is constructed based on the time when the ship leaves the port, the time when the ship arrives at the port, the expected time window for arrival at the port, and the working time of the ship at the port;

[0019] The eighth constraint is constructed based on the time when the ship leaves the port, the decision variable, the sailing time required for the ship to pass through a sub-segment of the route segment at a certain speed, and the time when the ship arrives at the port;

[0020] The ninth constraint is constructed based on the carbon emissions generated by the ship in the year, the fuel consumption rate of the ship when sailing at a certain speed in a sub-segment of the route, the decision variables, the sailing time required for the ship to pass through a sub-segment of the route at a certain speed, the ratio of the carbon intensity index value actually achieved by the ship on each route to the target value of the carbon intensity index level, the carbon emissions of the voyage, and the deadweight tonnage of the ship.

[0021] Preferably, in the model building step, the plurality of constraints further include a tenth constraint, an eleventh constraint, and a twelfth constraint established based on the fuel inventory and the refueling amount limit.

[0022] The tenth constraint is constructed based on the fuel inventory of the ship when it arrives at the starting port and the initial fuel inventory;

[0023] The eleventh constraint condition is constructed based on the fuel inventory of the ship when it arrives at the port and the minimum fuel storage capacity of the ship;

[0024] The twelfth constraint is constructed based on the fuel inventory of the ship when it arrives at the port, the amount of fuel the ship refuels when it arrives at the port, the fuel consumption rate of the ship when it sails at a certain speed in a sub-segment of the route, the sailing time required for the ship to pass through a sub-segment of the route at a certain speed, and the decision variables.

[0025] Preferably, in the optimal solution calculation step, the solver uses a branch and bound method and a cutting plane method to calculate the optimal solution of the mixed integer linear optimization model.

[0026] A dual-fuel ship route fuel filling, switching and speed collaborative optimization system is characterized by comprising a parameter acquisition module, a segment division module, a model building module, a linearization processing module and an optimal solution calculation module connected in sequence.

[0027] The parameter acquisition module acquires the route parameters and port parameters of the dual-fuel ship, wherein the route parameters include the sections within and outside the emission control area, the sections within and outside the canal area, and the sailing distance of each section; the port parameters include the price of different types of fuel at the port, the amount of fuel the ship needs to refuel at the port, the delay penalty cost at the port, the time the ship arrives at the port, and the expected time window for arrival at the port;

[0028] The segment division module divides the segment between the two ports into an emission control area segment, a canal area segment, and a plurality of ordinary segments based on the route parameters according to the emission control area and the canal area, and evenly divides the ordinary segment with a sailing distance greater than a preset distance threshold into a plurality of sub-segments;

[0029] The model building module establishes a mixed integer nonlinear optimization model with the objective function of minimizing the fuel bunkering cost and the voyage delay cost based on the fuel price at a certain port, the amount of fuel a ship must bunker at the port, the delay penalty cost at the port, the time the ship arrives at the port, and the expected time window for arrival at the port, under multiple constraints established based on the speed and fuel restrictions of each sub-segment, the high-sulfur fuel restrictions in the emission control area, the fuel inventory and refueling quantity restrictions, the time restrictions for the ship to arrive at and leave the port, the sailing time restrictions, and the ship's carbon intensity index level compliance restrictions;

[0030] The linearization processing module uses a linearization method to perform linearization processing on the established mixed integer nonlinear optimization model to obtain a mixed integer linear optimization model;

[0031] The optimal solution calculation module uses a solver to calculate the optimal solution of the mixed integer linear optimization model, and then obtains the optimal fuel filling, fuel switching and speed strategy for each control area section, canal area section and sub-section, realizing the coordinated optimization of fuel filling, fuel switching and speed of the dual-fuel ship route.

[0032] Preferably, in the model building module, the multiple constraints include a first constraint and a second constraint based on the speed and fuel restrictions of each sub-segment, a third constraint based on the high-sulfur fuel restriction in the emission control area, a fourth constraint and a fifth constraint based on the fuel inventory and refueling quantity restriction, a sixth constraint and a seventh constraint based on the time restriction for the ship to arrive at and leave the port, an eighth constraint based on the sailing time restriction, and a ninth constraint based on the compliance of the ship's carbon intensity index level.

[0033] Preferably, the parameter acquisition module further acquires ship parameters of the dual-fuel ship, the ship parameters including the ship's fuel tank capacity, minimum fuel storage capacity, consumption rate of different types of fuel in each section, ship deadweight tonnage, carbon emissions generated in the year, carbon intensity index rating target value, and optional speed set;

[0034] The model building module also constructs decision variables based on the judgment result of whether to sail at a certain speed and use a certain fuel in each sub-segment. At this time,

[0035] The first constraint, the second constraint and the third constraint are constructed based on the decision variables;

[0036] The fourth constraint is constructed based on the amount of fuel that the ship refuels upon arrival at the port;

[0037] The fifth constraint is constructed based on the fuel inventory of the ship when it arrives at the starting port, the amount of fuel the ship refuels at the port, and the maximum fuel capacity of the ship, to ensure that the fuel inventory of the ship at the port plus the refueling amount does not exceed the maximum fuel capacity of the ship;

[0038] The sixth constraint is constructed based on the time when the ship arrives at the starting port;

[0039] The seventh constraint is constructed based on the time when the ship leaves the port, the time when the ship arrives at the port, the expected time window for arrival at the port, and the working time of the ship at the port;

[0040] The eighth constraint is constructed based on the time when the ship leaves the port, the decision variable, the sailing time required for the ship to pass through a sub-segment of the route segment at a certain speed, and the time when the ship arrives at the port;

[0041] The ninth constraint is constructed based on the carbon emissions generated by the ship in the year, the fuel consumption rate of the ship when sailing at a certain speed in a sub-segment of the route, the decision variables, the sailing time required for the ship to pass through a sub-segment of the route at a certain speed, the ratio of the carbon intensity index value actually achieved by the ship on each route to the target value of the carbon intensity index level, the carbon emissions of the voyage, and the deadweight tonnage of the ship.

[0042] Preferably, in the model building module, the plurality of constraints further include a tenth constraint, an eleventh constraint, and a twelfth constraint established based on the fuel inventory and the refueling amount limit.

[0043] The tenth constraint is constructed based on the fuel inventory of the ship when it arrives at the starting port and the initial fuel inventory;

[0044] The eleventh constraint condition is constructed based on the fuel inventory of the ship when it arrives at the port and the minimum fuel storage capacity of the ship;

[0045] The twelfth constraint is constructed based on the fuel inventory of the ship when it arrives at the port, the amount of fuel the ship refuels when it arrives at the port, the fuel consumption rate of the ship when it sails at a certain speed in a sub-segment of the route, the sailing time required for the ship to pass through a sub-segment of the route at a certain speed, and the decision variables.

[0046] Preferably, in the optimal solution calculation module, the solver uses the branch and bound method and the cutting plane method to calculate the optimal solution of the mixed integer linear optimization model.

[0047] The beneficial effects of the present invention are:

[0048] The present invention provides a method for collaboratively optimizing fuel refueling, switching, and speed for dual-fuel ship routes. The method first obtains the ship parameters, route parameters, and port parameters of the dual-fuel ship, and comprehensively collects key information associated with the optimization process of the present invention. These parameters cover the ship's fuel characteristics, the geographical and environmental characteristics of the route, and the port's operating and cost information, providing a solid data foundation for subsequent optimization decisions. The parameters obtained by the present invention are targeted and can meet the optimization needs of dual-fuel ships under different environments and conditions. For example, the fuel tank capacity and minimum fuel storage volume in the ship parameters provide a basis for formulating fuel refueling strategies; the division of routes into and out of emission control areas in the route parameters provides a basis for formulating fuel switching strategies; and the fuel price and delay penalty cost in the port parameters provide a basis for cost optimization. The carbon intensity index (CII) target value and emission control area (ECA) restrictions are incorporated into the parameters to ensure that the optimization results comply with environmental regulations. The method can also have dynamic adaptability, and by obtaining updated data (such as fuel price fluctuations and port time window adjustments) in real time or periodically, it can ensure that the constructed model always reflects the actual operating environment. Furthermore, the sections between two adjacent ports on the route are divided into emission control area sections, canal area sections and multiple ordinary sections according to emission control areas and canal areas, and ordinary sections with a sailing distance greater than the preset distance threshold are evenly divided into multiple sub-segments. This can enable fine-grained optimization of fuel usage restrictions and navigation conditions in different areas, ensuring that ships can comply with corresponding emission regulations and navigation requirements in different sections, providing a more detailed division basis for subsequent optimization decisions, and thus being able to adapt to different types of routes and navigation tasks, while improving fuel usage efficiency.Then, under multiple constraints established with the speed and fuel restrictions of each control area section, canal area section and sub-section, high sulfur fuel restrictions in the emission control area, fuel inventory and refueling quantity restrictions, time restrictions for ships to arrive at and leave the port, sailing time restrictions and ship carbon intensity index level compliance restrictions as constraints, a mixed integer nonlinear optimization model with the objective function of minimizing the sum of fuel refueling cost and voyage delay cost is established based on the obtained ship parameters, route parameters and port parameters of the dual-fuel ship. This model can comprehensively consider fuel cost, voyage delay cost, fuel refueling quantity, consumption rate of different types of fuel in each section, ship deadweight tonnage and carbon emissions generated in the year, carbon intensity index rating objectives The mixed integer nonlinear optimization model under multiple constraints with specific restrictions is constructed based on multiple key factors such as standard value and speed. It is also a mixed integer nonlinear optimization model. Nonlinear modeling can directly deal with the nonlinear relationship between speed and fuel consumption (such as the nonlinear correlation between fuel consumption rate and speed), improve model accuracy, and has a clear goal. The objective function is to minimize the sum of fuel refueling cost and voyage delay cost. The objective function is constructed with the optimization goal clearly defined, which can guide optimization decisions, make the optimization results more in line with actual needs, improve the practicality and economy of the optimization scheme, achieve global optimization, and ensure that the optimization strategy minimizes fuel cost and voyage delay cost under the premise of meeting various constraints. The linearization method is then used to perform nonlinear processing on the established mixed integer nonlinear optimization model to obtain a mixed integer linear optimization model. The complex nonlinear optimization problem is converted into a mixed integer linear optimization model through the linearization method, which can significantly reduce the difficulty of solving the model, reduce the calculation time, meet the real-time or rapid response requirements in practical applications, provide timely decision support for ship operators, improve accuracy and reliability, and the linear optimization model is easier to expand (such as adding new constraints or adjusting parameters) to adapt to different routes or ship types. At the same time, it avoids the local optimal problem in nonlinear solution, ensures the global optimality of the solution, and enhances the robustness of the model. Finally, the solver is used to calculate the optimal solution of the mixed integer linear optimization model, and then the optimal fuel filling, fuel switching and speed strategy for each sub-segment is obtained, realizing the coordinated optimization of fuel filling, fuel switching and speed of the dual-fuel ship route. The optimal fuel filling, fuel switching and speed strategy for each segment can be quickly obtained. These strategies can be directly applied to actual ship operations, providing specific operational guidance for ship operators, ensuring that ships can minimize costs and maximize efficiency during navigation, thereby helping shipping companies reduce operating costs, improve competitiveness, and meet environmental protection requirements.

[0049] The overall solution of the present invention is based on comprehensive parameter acquisition, refined segment division, systematic nonlinear optimization model establishment, linearization processing and optimal solution calculation, providing a scientific and reasonable optimization decision-making method. By comprehensively considering the coordinated optimization of fuel refueling, fuel switching and speed of dual-fuel ship routes, it can significantly reduce fuel costs and voyage delay costs, improve the economic benefits of ship operations, and through refined segment division and optimization models, by considering fuel usage restrictions in emission control areas, canal areas and various sub-segments, it can effectively improve the navigation efficiency of ships, reduce unnecessary fuel consumption and delay time, ensure that ships meet environmental protection requirements during navigation, and reduce environmental pollution.

[0050] The multiple constraints modeled by the present invention include a first constraint and a second constraint established based on the speed and fuel limit of each sub-segment, wherein the first constraint is the speed limit, that is, limiting the speed of the ship in each sub-segment to ensure that the ship's navigation speed meets the safety standards and the speed limit requirements of the port or waterway, ensuring safety and compliance, and by controlling the speed, avoiding excessive speed leading to increased fuel consumption, optimizing fuel consumption, and thus reducing fuel costs. Reasonable speed control helps ships maintain optimal navigation efficiency in different segments, reduce unnecessary delays, and improve navigation efficiency; the second constraint is the fuel consumption limit, which ensures that the fuel consumption of the ship in each sub-segment is within the allowable range, avoiding increased operating costs due to excessive fuel consumption. By precisely controlling fuel consumption, optimizing the fuel management strategy of the ship, and improving fuel utilization efficiency, limiting fuel consumption helps reduce carbon emissions, in line with the environmental protection requirements of the International Maritime Organization; the third constraint condition established with the high-sulfur fuel restriction in the emission control area as the constraint ensures that the ship does not use high-sulfur fuel in the emission control area, in line with the emission control area regulations of the International Maritime Organization, reduces sulfur oxide emissions, and reduces operational risks; the fourth and fifth constraints conditions established with the fuel inventory and refueling quantity restrictions as the constraints, the fourth constraint condition is the fuel refueling quantity restriction, which limits the amount of fuel that the ship refuels at the port, avoids overloading of the fuel tank, and ensures the safe operation of the ship. Based on the port's fuel refueling capacity and the ship's fuel tank capacity, the fuel refueling quantity is reasonably planned to reduce Low fuel costs, optimize the operational efficiency of ships, and reduce unnecessary docking and refueling; the fifth constraint is the fuel inventory limit (based on the fuel tank capacity), which ensures that the fuel inventory of the ship does not exceed the fuel tank capacity after refueling at the port, avoids fuel overflow or overloading, and optimizes the fuel management of the ship by setting the upper limit of the fuel tank capacity, ensuring the stable fuel supply of the ship in different sections, avoiding the operational risks caused by excessive fuel inventory, and improving the operational safety of the ship; the sixth and seventh constraints established based on the time limit of the ship's arrival and departure from the port can optimize the scheduling, ensure that the arrival time of the ship at the starting port meets the scheduling requirements, and the stay time and departure time at the port meet the scheduling requirements, improve the operational efficiency of the ship, and reduce Reduce port congestion and unnecessary delays, improve the punctuality of ships, ensure that ships arrive at ports on time, improve customer satisfaction, and enhance the market competitiveness of shipping companies; the eighth constraint condition, established with the sailing time limit as the constraint, ensures that the time arrangement of ships during navigation meets the scheduling requirements, optimizes the speed strategy, and contributes to the efficient operation of ships and reduces unnecessary stops and delays by reasonably arranging sailing time; the ninth constraint condition, established with the ship carbon intensity index level as the constraint, ensures that the carbon emissions of ships meet the requirements of the Carbon Intensity Index (CII) of the International Maritime Organization, reduces carbon emissions, avoids high fines for violating the carbon intensity index regulations, reduces operational risks, and improves the environmental image of shipping companies and enhances their market competitiveness.

[0051] The present invention also relates to a dual-fuel ship route fuel filling, switching and speed collaborative optimization system. The system corresponds to the above-mentioned dual-fuel ship route fuel filling, switching and speed collaborative optimization method, and can be understood as a system for implementing the above-mentioned dual-fuel ship route fuel filling, switching and speed collaborative optimization method. The system includes a parameter acquisition module, a segment division module, a model building module, a linearization processing module and an optimal solution calculation module connected in sequence, and each module works in coordination with each other. By comprehensively considering the coordinated optimization of fuel filling, fuel switching and speed of the dual-fuel ship route, the present invention can significantly reduce fuel costs and voyage delay costs, minimize the fuel costs and voyage delay costs of the dual-fuel ship route, and improve the economic benefits of ship operation. By refining the segment division and considering the fuel use restrictions in the emission control area, the canal area and each sub-segment, the navigation efficiency of the ship can be effectively improved, and unnecessary fuel consumption and delay time can be reduced. In addition, the scheme takes into account constraints such as high-sulfur fuel restrictions in the emission control area and ship carbon intensity index level compliance restrictions, which helps to reduce the carbon emissions of ships, improve environmental benefits, ensure that the ship meets environmental protection requirements during navigation, and reduce environmental pollution. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 The present invention is a flow chart of the method for coordinated optimization of fuel filling, switching and speed of dual-fuel ship routes.

[0053] Figure 2 It is a schematic diagram of the sub-segment division method between two ports of the present invention. DETAILED DESCRIPTION

[0054] The present invention will be described below with reference to the accompanying drawings.

[0055] To meet shipping industry emission reduction requirements, such as those in Emission Control Areas (ECAs), liner companies have deployed dual-fuel vessels on their routes. However, compared to traditional fuel vessels, dual-fuel liner routes require consideration of the fuel usage ratios, bunkering strategies, and vessel speeds for each leg of the voyage. Therefore, dual-fuel liner routes present greater decision-making challenges than traditional fuel-based liner routes.

[0056] Sailing speed, fuel switching, and refueling strategies are crucial decisions during a ship's voyage. Under emission limits, these three factors interact and collectively determine sailing costs. Carbon emissions during a ship's voyage depend on fuel consumption, which is closely related to sailing speed. Sail at lower speeds, which reduces fuel consumption, fuel costs, and carbon emissions. However, slowing down the speed can lead to voyage delays. These delays can have a cumulative effect, leading to continued delays at subsequent port calls if a ship cannot catch up by increasing speed or skipping ports.

[0057] Furthermore, due to port operating hours, even small delays can lead to larger delays. For example, if a vessel arrives shortly before a port closes and cannot complete loading and unloading operations on time, it may be idle overnight or over the weekend. If a voyage delay occurs, the shipowner will have to pay compensation to the shipper, incurring additional costs.

[0058] Fuel switching decisions determine which fuel to use at each stage of a voyage. This decision, along with speed, determines carbon emissions and impacts fuel costs. The most straightforward fuel switching occurs based on Emission Control Area (ECA) designations. When a ship enters an ECA, it must use low-sulfur fuel. Beyond fuel switching driven by ECA restrictions, multi-fuel vessels can achieve a trade-off between emissions and costs by switching fuels during navigation.

[0059] The refueling strategy needs to decide the location and amount of fuel refueling. The choice of location is related to price and refueling conditions, while the refueling amount is related to fuel consumption, and is coupled with speed and fuel switching decisions. The relationship between the three decisions of speed, fuel switching and refueling strategy is complex, and they are also related to the emission restrictions and navigation costs focused on by the present invention. Manual decision-making is difficult. In order to solve the problem of fuel switching, fuel refueling and speed collaborative optimization of dual-fuel liner routes, an algorithm needs to be designed for joint optimization. The present invention relates to a method for collaborative optimization of fuel refueling, switching and speed of dual-fuel ship routes. The flow chart of the method is as follows Figure 1 As shown, the following steps are included in sequence:

[0060] 1. Parameter Acquisition Step: Acquire the route parameters and port parameters of the dual-fuel vessel. The route parameters include the segments within and outside the emission control area, the segments within and outside the canal area, and the sailing distance of each segment. The port parameters include the price of different types of fuel at the port, the amount of fuel the ship must refuel upon arrival at the port, the port's delay penalty cost, the time the ship arrives at the port, and the expected arrival time window. Preferably, the dual-fuel vessel's ship parameters are also acquired. The ship parameters include the ship's fuel tank capacity, minimum fuel storage capacity, the consumption rate of different types of fuel in each segment, the ship's deadweight tonnage, the carbon emissions generated in the year, the carbon intensity index rating target value, and the set of optional speeds.

[0061] 2. Segment division step: Based on the route parameters, the route segment between the two ports is divided into an emission control area segment, a canal area segment, and multiple ordinary segments according to the emission control area and canal area. Then, the ordinary segments with a sailing distance greater than the preset distance threshold are evenly divided into multiple interval segments, and the emission control area segment, canal area segment, and interval segment are used as sub-segments.

[0062] Specifically, since fuel switching usually occurs between pairs of ports (segments) that are far apart on the route, in order to facilitate the decision-making of fuel switching and speed on the segment, the segment between the two ports can be divided into emission control area segments, canal area segments, and multiple ordinary segments according to the emission control area and canal area. Therefore, the segment division method between adjacent ports with a long port distance is first given: the first step is to divide the segment between two adjacent ports on the route into emission control area segments, canal area segments, and multiple ordinary segments according to geographical factors such as emission control areas (ECAs) and canal areas; the second step is to further divide the ordinary segments with a sailing distance greater than the preset distance threshold into multiple sub-segments (i.e., long-distance secondary division). At this point, each segment on the route is divided into emission control area segments, canal area segments, and several sub-segments. Figure 2 This demonstrates how to divide routes. With information about Emission Control Area (ECA) routes, Canal Zone routes, and several sub-segments, it's possible to determine the fuel usage and corresponding speed for each ECA, Canal Zone, and sub-segment, thereby obtaining the fuel usage percentages and corresponding speeds for long routes. This simplifies problem solving.

[0063] 3. Model establishment steps: Under multiple constraints established based on the speed and fuel restrictions for each control area section, canal area section and sub-section, high-sulfur fuel restrictions within the emission control area, fuel inventory and refueling quantity restrictions, time restrictions for ships to arrive at and leave the port, sailing time restrictions, and ship carbon intensity index level compliance restrictions, a mixed integer nonlinear optimization model is established with the objective function of minimizing fuel refueling cost and voyage delay cost based on the fuel price at a certain port, the amount of fuel refueled by the ship upon arriving at the port, the delay penalty cost of the port, the time of arrival of the ship at the port, and the expected time window for arrival at the port.

[0064] Specifically, in order to construct the mixed integer nonlinear optimization model, the following assumptions need to be made before the optimization: 1) only the static water power of the main engine is considered; 2) there is an agreed fuel bunkering price between the shipping company and the port of call, that is, the fuel bunkering price at the port remains unchanged; 3) the impact of fuel bunkering on draft is not considered; 4) the ship serves the same route throughout the year; 5) fuel bunkering is only carried out at the port of call.

[0065] Then, under the constraints of the speed and fuel restrictions of each control area section, canal area section and sub-section, high sulfur fuel restrictions in the emission control area, fuel inventory and refueling restrictions, time restrictions for ships to arrive and leave the port, sailing time restrictions and ship carbon intensity index level compliance restrictions, the fuel price of a certain port i is calculated. The amount of fuel e that the ship refuels when arriving at the port i The delay penalty cost θ for port ii , the time when the ship arrives at port i The mixed integer nonlinear optimization model is established based on the expected time window for arrival at the port. The mixed integer nonlinear optimization model for speed, fuel switching and refueling strategy is expressed as follows:

[0066]

[0067] In the above formula, represents the fuel price at port i; represents the amount of fuel e added when the ship arrives at port i; θ i represents the delay penalty cost of port i (USD / hour); represents the time when the ship arrives at port i; represents the upper limit of the time window for the expected arrival at port i, that is, the latest time the ship plans or expects to arrive at a specific port i.

[0068] Then, the decision variables are constructed based on the judgment results of whether to sail at a certain speed and use a certain fuel in each control area section, canal area section and sub-section. That is, in the control area section / canal area section / sub-section s, s∈S ij Whether the vessel is sailing at speed v and using fuel e; wherein the multiple constraints include first and second constraints based on the speed and fuel limits of each sub-segment, a third constraint based on the high-sulfur fuel limit within the emission control area, fourth and fifth constraints based on fuel inventory and refueling quantity limits, sixth and seventh constraints based on the time limits for the vessel to arrive at and leave the port, an eighth constraint based on the sailing time limit, and a ninth constraint based on the compliance of the vessel's carbon intensity index level. Preferably, the constraints also include tenth, eleventh, and twelfth constraints based on fuel inventory and refueling quantity limits.

[0069] The first constraint is constructed based on the decision variables and the preset numerical threshold, which constrains the speed and fuel selected for each sub-segment and is expressed as follows:

[0070]

[0071] In the above formula, Indicates that in sub-segment s, s∈S ij Whether to sail at speed v and fuel e; s represents sub-segment; L represents segment set; S ij Represents the sub-segment set of segment (i, j).

[0072] The second constraint, constructed based on the decision variables and the preset numerical threshold, constrains the speed limit when passing through the canal and is expressed as follows:

[0073]

[0074] In the above formula, Represents the set of sub-segments of the route segment (i, j) passing through the canal.

[0075] The third constraint is constructed based on the decision variables and the preset numerical threshold. The constraint is limited to the emission control area (ECA) and the use of high-sulfur fuel is not allowed. It is expressed as follows:

[0076]

[0077] In the above formula, Indicates the sub-segment set of the flight segment (e, j) passing through ECA, e indicates the fuel type, E H Indicates the high sulfur fuel aggregate.

[0078] The fourth constraint is constructed based on the amount of fuel that the ship refuels at the port and the preset numerical threshold. The constraint restricts the ship to refuel only at port e, which belongs to the set E. i The fuel is expressed as follows:

[0079]

[0080] in, E represents the amount of fuel e added when the ship arrives at port i; i represents the set of fuels that can be refueled at port i.

[0081] The fifth constraint is constructed based on the fuel inventory of the ship when it arrives at the starting port, the amount of fuel the ship refuels at the port, and the maximum fuel capacity of the ship. The constraint restricts the fuel inventory of the ship at the port plus the refueling amount to not exceed the maximum fuel capacity of the ship, as shown in the following formula:

[0082]

[0083] in, represents the amount of fuel e when the ship arrives at port i; represents the amount of fuel e that a ship refuels when arriving at port i; Indicates the maximum tank capacity of ship fuel e.

[0084] The sixth constraint is constructed based on the time when the ship arrives at the starting port. The constraint sets the initial time to 0, as shown in the following formula:

[0085]

[0086] in, represents the time when the ship arrives at port i.

[0087] The seventh constraint is constructed based on the time when the ship leaves the port, the time when the ship arrives at the port, the expected time window for arrival at the port, and the working time of the ship at the port. The time when the ship arrives at each port is constrained and calculated according to the following formula:

[0088]

[0089] in, represents the lower limit of the time window for expected arrival at port i, i.e., the earliest time the ship plans or expects to arrive at a specific port i; represents the working time of the ship at port i.

[0090] The eighth constraint is constructed based on the time when the ship leaves the port, the decision variable, the sailing time required for the ship to pass a sub-segment of the route segment at a certain speed, and the time when the ship arrives at the port. It constrains the calculation of the time when the ship leaves each port and is calculated according to the following formula:

[0091]

[0092] in, represents the time when the ship leaves port i; represents the decision variable; It represents the time required for a ship to pass through the sub-segment s of the segment (i, j) at a speed of v; represents the time when the ship arrives at port j.

[0093] The ninth constraint is constructed based on the carbon emissions generated by the ship in the year, the fuel consumption rate of the ship when sailing at a certain speed in a sub-segment of the route, the decision variables, the sailing time required for the ship to pass through a sub-segment of the route at a certain speed, the ratio of the carbon intensity index value actually achieved by the ship on each route to the carbon intensity index grade target value, the carbon emissions of the voyage, and the deadweight tonnage of the ship. The constraint restricts the ship to meet the expected carbon intensity index CII rating requirements, as shown in the following formula:

[0094]

[0095] Where C0 represents the initial reserve of fuel e; represents the carbon conversion coefficient of fuel e; c k represents the carbon emissions of voyage k; η represents the ratio of the carbon intensity index target value Attained CII to the actual carbon intensity index value Required CII; Z represents the CII correction coefficient related to the year; α,c represents the CII reference line correlation coefficient, CIIreference line = αG-c G represents the ship's deadweight tonnage, k represents the kth voyage, and K represents the set of voyages planned for the year but not yet started, excluding the current voyage.

[0096] The tenth constraint is constructed based on the fuel inventory when the ship arrives at the start port and the initial fuel inventory. The constraint limits the fuel inventory of the ship at the start port to the initial value and is expressed as follows:

[0097]

[0098] in, represents the amount of fuel e when the ship arrives at port i; It represents the initial reserve of fuel e, e represents the fuel type, and E represents the fuel set.

[0099] The eleventh constraint is constructed based on the fuel inventory of the ship when it arrives at the port and the minimum fuel storage capacity of the ship. The constraint stipulates that the fuel inventory of the ship at each port must be greater than or equal to the minimum storage capacity, which can be expressed as follows:

[0100]

[0101] in, represents the minimum storage capacity of ship fuel e; P represents the port set.

[0102] The twelfth constraint is constructed based on the fuel inventory of the ship when it arrives at the port, the amount of fuel the ship refuels at the port, the fuel consumption rate of the ship when it sails at a certain speed in a sub-segment of the route segment, the sailing time required for the ship to pass a sub-segment of the route segment at a certain speed, and the decision variables. It constrains the change in the fuel inventory of the ship from port i to port j, as shown in the following formula:

[0103]

[0104] in, represents the amount of fuel e when the ship arrives at port j; represents the fuel e consumption rate of the ship when it sails at speed v in the sub-segment s of the segment (i, j); is the decision variable, It represents the time required for a ship to pass through the sub-segment s of the segment (i, j) at a speed of v; L represents the segment set; e represents the fuel type; and E represents the fuel set.

[0105] The value ranges of each variable are as follows:

[0106]

[0107] Among them, S ijRepresents the sub-segment set of segment (i, j); v represents the speed, v∈V C , V C A set of optional speeds for canal sub-sections.

[0108] 4. Linearization processing steps: Use the linearization method to linearize the established mixed integer nonlinear optimization model to obtain a mixed integer linear optimization model.

[0109] Specifically, since the established mixed integer nonlinear optimization model and the seventh constraint, that is, the nonlinear term in Equation (8), cannot be solved directly using the solver, the linearization method is used to process the established mixed integer nonlinear optimization model to obtain a mixed integer linear optimization model. Then formula (1) can be written as:

[0110]

[0111] The seventh constraint, equation (8), can be written as the following equivalent linear constraint:

[0112]

[0113] After the above processing, the original mixed integer nonlinear optimization model is converted into a mixed integer linear optimization model, which can be solved using the solver CPLEX.

[0114] V. Optimal Solution Calculation Steps: A solver is used to calculate the optimal solution to the mixed-integer linear optimization model. This solver then derives the optimal fueling, fuel switching, and speed strategies for each control area segment, canal area segment, and sub-segment, achieving coordinated optimization of fueling, fuel switching, and speed for dual-fuel vessel routes. Preferably, the solver utilizes a branch-and-bound and cutting plane method to calculate the optimal solution to the mixed-integer linear optimization model.

[0115] Example:

[0116] Taking a liner route from a specific shipping company as an example, a case study based on data collected from actual ships was conducted to analyze the feasibility of the model and the operational characteristics of dual-fuel liner routes. The mathematical model was solved using the CPLEX 12.8.0 solver.

[0117] This liner route is equipped with a 12,000 TEU container vessel with a deadweight tonnage of 146,500 DWT, capable of simultaneously loading HFO, MGO, and LNG. The corresponding fuel tank capacities, minimum storage capacities, and initial fuel reserves are shown in Table 1. This vessel serves North American routes and can complete five voyages annually. Port information for the route is shown in Table 2; the route segments are divided into 10 equally spaced sub-segments, including the long Busan-New York (non-inland section) and Savannah-Ningbo (non-inland section) segments. Fuel bunkering information for each port is shown in Table 4. Fuel consumption rates are calculated based on the vessel's loading manual and ship model tests.

[0118] Table 1 Marine fuel parameters

[0119]

[0120] Table 2 Route information

[0121]

[0122] Table 3 Flight segment information on the route

[0123]

[0124] Table 4 Agreement bunkering prices for different types of fuel at ports of call

[0125]

[0126] Based on the above parameters, the problem is optimized using the CPLEX solver, and the results are as follows:

[0127] Table 5 Emission and cost information of routes

[0128]

[0129] Table 5 shows the emissions, bunkering costs, and penalty costs for calling later than the arrival window for a single voyage. Table 6 shows the call times, departure times, and bunkering for each port on the route. It shows that the vessel's second call at Ningbo Port was 0.5 hours late, incurring a delay cost of $1,000. Furthermore, the vessel bunkered 566 tons of HFO at Ningbo Port and 2,197 tons at Norfolk Port, respectively.

[0130] Table 6 Fuel type used in each leg and speed (unit: knots)

[0131]

[0132] Table 7 Fuel type used in each leg and speed (unit: knots)

[0133]

[0134] Table 7 shows the ship's speed and fuel usage for each leg. The first row shows the leg information for the entire route. The second row indicates the fuel type used during the corresponding leg. Some longer legs may involve fuel switching, such as the "Pusan-New York (non-inland waterway)" leg, which uses both HFO and LNG. The third row shows the speed during the leg. If a fuel switch occurs during a leg, the speed before and after the fuel switch may be different. For example, the speed before and after the fuel switch for the "Pusan-New York (non-inland waterway)" leg is 17.8 and 18.0 knots, respectively. The fourth row shows the proportion of each fuel used in each leg. As can be seen from the table, in areas with strict emission regulations (such as the route between Shanghai and Ningbo), ships typically operate at lower speeds to reduce fuel consumption and, therefore, emissions. However, in areas with relatively low emission regulations, such as the open sea, ships typically use higher-emission HFO and operate at higher speeds. Furthermore, to reduce overall bunkering costs, ships often switch fuels during the open sea leg.

[0135] From the above, we can conclude that, compared to container liner routes based on conventional fuel vessels, dual-fuel liner routes present greater decision-making challenges due to the need to consider the proportion of different fuels used in each voyage, fuel bunkering strategies, and vessel speed. To address the coordinated optimization of fuel switching, bunkering, and speed for dual-fuel liner routes, a mixed-integer linear programming model was established, with the objective of minimizing fuel cost and delay costs, and with port call times and speed as constraints. Secondly, the model's effectiveness was verified using the CPLEX solver, using a specific liner route as an example. Numerical results show that in areas with high emission requirements, ships typically choose lower-emission fuels such as MGO or LNG and sail at lower speeds. In areas with lower emission requirements, such as offshore routes, ships typically choose higher-emission HFO fuels and sail at higher speeds. In offshore routes, ships tend to switch fuels to reduce bunkering costs.

[0136] The present invention also relates to a dual-fuel ship route fuel filling, switching and speed collaborative optimization system, which corresponds to the dual-fuel ship route fuel filling, switching and speed collaborative optimization method, and can be understood as a system for implementing the above method. The system includes a parameter acquisition module, a segment division module, a model building module, a linearization processing module and an optimal solution calculation module connected in sequence. Specifically,

[0137] The parameter acquisition module acquires the route parameters and port parameters of the dual-fuel ship, wherein the route parameters include the sections within and outside the emission control area, the sections within and outside the canal area, and the sailing distance of each section; the port parameters include the price of different types of fuel at the port, the amount of fuel the ship needs to refuel at the port, the delay penalty cost at the port, the time the ship arrives at the port, and the expected time window for arrival at the port;

[0138] The segment division module divides the segment between the two ports into an emission control area segment, a canal area segment, and a plurality of ordinary segments based on the route parameters according to the emission control area and the canal area, and evenly divides the ordinary segment with a sailing distance greater than a preset distance threshold into a plurality of sub-segments;

[0139] The model building module establishes a mixed integer nonlinear optimization model with the objective function of minimizing the fuel bunkering cost and the voyage delay cost based on the fuel price at a certain port, the amount of fuel a ship must bunker at the port, the delay penalty cost at the port, the time the ship arrives at the port, and the expected time window for arrival at the port, under multiple constraints established based on the speed and fuel restrictions of each sub-segment, the high-sulfur fuel restrictions in the emission control area, the fuel inventory and refueling quantity restrictions, the time restrictions for the ship to arrive at and leave the port, the sailing time restrictions, and the ship's carbon intensity index level compliance restrictions;

[0140] The linearization processing module uses a linearization method to perform linearization processing on the established mixed integer nonlinear optimization model to obtain a mixed integer linear optimization model;

[0141] The optimal solution calculation module uses a solver to calculate the optimal solution of the mixed integer linear optimization model, and then obtains the optimal fuel filling, fuel switching and speed strategy for each control area section, canal area section and sub-section, realizing the coordinated optimization of fuel filling, fuel switching and speed of the dual-fuel ship route.

[0142] Preferably, in the model building module, the multiple constraints include a first constraint and a second constraint based on the speed and fuel restrictions of each sub-segment, a third constraint based on the high-sulfur fuel restriction in the emission control area, a fourth constraint and a fifth constraint based on the fuel inventory and refueling quantity restriction, a sixth constraint and a seventh constraint based on the time restriction for the ship to arrive at and leave the port, an eighth constraint based on the sailing time restriction, and a ninth constraint based on the compliance of the ship's carbon intensity index level.

[0143] Preferably, the parameter acquisition module further acquires the ship parameters of the dual-fuel ship, which include the ship's fuel tank capacity, minimum fuel storage capacity, consumption rate of different types of fuel in each segment, ship deadweight tonnage, carbon emissions generated in the year, carbon intensity index rating target value, and optional speed set; the model building module further constructs decision variables based on the judgment result of whether to sail at a certain speed and use a certain fuel in each sub-segment,

[0144] The first constraint, the second constraint and the third constraint are constructed based on the decision variables;

[0145] The fourth constraint is constructed based on the amount of fuel that the ship refuels upon arrival at the port;

[0146] The fifth constraint is constructed based on the fuel inventory of the ship when it arrives at the starting port, the amount of fuel the ship refuels at the port, and the maximum fuel capacity of the ship, to ensure that the fuel inventory of the ship at the port plus the refueling amount does not exceed the maximum fuel capacity of the ship;

[0147] The sixth constraint is constructed based on the time when the ship arrives at the starting port;

[0148] The seventh constraint is constructed based on the time when the ship leaves the port, the time when the ship arrives at the port, the expected time window for arrival at the port, and the working time of the ship at the port;

[0149] The eighth constraint is constructed based on the time when the ship leaves the port, the decision variable, the sailing time required for the ship to pass through a sub-segment of the route segment at a certain speed, and the time when the ship arrives at the port;

[0150] The ninth constraint is constructed based on the carbon emissions generated by the ship in the year, the fuel consumption rate of the ship when sailing at a certain speed in a sub-segment of the route, the decision variables, the sailing time required for the ship to pass through a sub-segment of the route at a certain speed, the ratio of the carbon intensity index value actually achieved by the ship on each route to the target value of the carbon intensity index level, the carbon emissions of the voyage, and the deadweight tonnage of the ship.

[0151] Preferably, in the model building module, the plurality of constraints further include a tenth constraint, an eleventh constraint, and a twelfth constraint established based on the fuel inventory and the refueling amount limit.

[0152] The tenth constraint is constructed based on the fuel inventory of the ship when it arrives at the starting port and the initial fuel inventory;

[0153] The eleventh constraint condition is constructed based on the fuel inventory of the ship when it arrives at the port and the minimum fuel storage capacity of the ship;

[0154] The twelfth constraint is constructed based on the fuel inventory of the ship when it arrives at the port, the amount of fuel the ship refuels when it arrives at the port, the fuel consumption rate of the ship when it sails at a certain speed in a sub-segment of the route, the sailing time required for the ship to pass through a sub-segment of the route at a certain speed, and the decision variables.

[0155] Preferably, in the optimal solution calculation module, the solver uses the branch and bound method and the cutting plane method to calculate the optimal solution of the mixed integer linear optimization model.

[0156] The present invention provides an objective and scientific method and system for the coordinated optimization of fuel filling, switching and speed of dual-fuel ship routes. By comprehensively considering the coordinated optimization of fuel filling, fuel switching and speed of dual-fuel ship routes, it can significantly reduce fuel costs and voyage delay costs, improve the economic benefits of ship operations, and through refined segment division and consideration of fuel usage restrictions in emission control areas, canal areas and various sub-segments, effectively improve the navigation efficiency of ships, reduce unnecessary fuel consumption and delay time, ensure that ships meet environmental protection requirements during navigation, and reduce environmental pollution.

[0157] It should be noted that the specific embodiments described above can enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although this specification has described the present invention in detail with reference to the drawings and embodiments, those skilled in the art should understand that the present invention can still be modified or replaced with equivalents. In short, all technical solutions and improvements that do not depart from the spirit and scope of the present invention should be included in the scope of protection of the patent for the present invention.

Claims

1. A method for collaborative optimization of fuel filling, switching and speed of dual-fuel ship routes, characterized in that: The following steps are involved: Parameter acquisition step: obtaining the route parameters and port parameters of the dual-fuel ship, wherein the route parameters include the sections within and outside the emission control area, the sections within and outside the canal area, and the sailing distance of each section; and the port parameters include the price of different types of fuel at the port, the amount of fuel to be refueled upon arrival at the port, the delay penalty cost at the port, the time of arrival of the ship at the port, and the expected time window for arrival at the port; Segment division step: Based on the route parameters, the route segment between the two ports is divided into an emission control area segment, a canal area segment, and multiple ordinary segments according to the emission control area and the canal area; the ordinary segments with a sailing distance greater than a preset distance threshold are evenly divided into multiple interval segments, and the emission control area segment, canal area segment, and interval segment are used as sub-segments; Model establishment steps: Under multiple constraints established by speed and fuel restrictions for each sub-segment, high-sulfur fuel restrictions within the emission control area, fuel inventory and refueling quantity restrictions, ship arrival and departure time restrictions, sailing time restrictions, and ship carbon intensity index level compliance restrictions, a mixed integer nonlinear optimization model is established with the objective function of minimizing fuel refueling cost and voyage delay cost based on the fuel price at a certain port, the amount of fuel a ship must refuel at that port, the delay penalty cost at that port, the time the ship arrives at that port, and the expected time window for arrival at that port; Linearization processing step: linearizing the established mixed integer nonlinear optimization model using a linearization method to obtain a mixed integer linear optimization model; Optimal solution calculation steps: Use the solver to calculate the optimal solution of the mixed integer linear optimization model, and then obtain the optimal fuel filling, fuel switching and speed strategy for each sub-segment, realizing the coordinated optimization of fuel filling, fuel switching and speed for the dual-fuel ship route.

2. The method for coordinated optimization of fuel filling, switching and speed of dual-fuel ship routes according to claim 1 is characterized in that: In the model establishment step, the multiple constraints include a first constraint and a second constraint based on the speed and fuel restrictions of each sub-segment, a third constraint based on the high-sulfur fuel restriction in the emission control area, a fourth constraint and a fifth constraint based on the fuel inventory and refueling quantity restriction, a sixth constraint and a seventh constraint based on the time restriction for the ship to arrive at and leave the port, an eighth constraint based on the sailing time restriction, and a ninth constraint based on the compliance of the ship's carbon intensity index level.

3. The method for coordinated optimization of fuel filling, switching and speed of dual-fuel ship routes according to claim 2 is characterized in that: The ship parameters of the dual-fuel ship are also obtained, including the ship's fuel tank capacity, minimum fuel storage capacity, consumption rate of different types of fuel in each segment, ship deadweight tonnage, carbon emissions generated in the year, carbon intensity index rating target value, and optional speed set. Decision variables are constructed based on the judgment results of whether to sail at a certain speed and use a certain fuel in each sub-segment. The first constraint, the second constraint and the third constraint are constructed based on the decision variables; The fourth constraint is constructed based on the amount of fuel that the ship refuels upon arrival at the port; The fifth constraint is constructed based on the fuel inventory of the ship when it arrives at the starting port, the amount of fuel the ship refuels at the port, and the maximum fuel capacity of the ship, to ensure that the fuel inventory of the ship at the port plus the refueling amount does not exceed the maximum fuel capacity of the ship; The sixth constraint is constructed based on the time when the ship arrives at the starting port; The seventh constraint is constructed based on the time when the ship leaves the port, the time when the ship arrives at the port, the expected time window for arrival at the port, and the working time of the ship at the port; The eighth constraint is constructed based on the time when the ship leaves the port, the decision variable, the sailing time required for the ship to pass through a sub-segment of the route segment at a certain speed, and the time when the ship arrives at the port; The ninth constraint is constructed based on the carbon emissions generated by the ship in the year, the fuel consumption rate of the ship when sailing at a certain speed in a sub-segment of the route, the decision variables, the sailing time required for the ship to pass through a sub-segment of the route at a certain speed, the ratio of the carbon intensity index value actually achieved by the ship on each route to the target value of the carbon intensity index level, the carbon emissions of the voyage, and the deadweight tonnage of the ship.

4. The method for coordinated optimization of fuel filling, switching and speed of dual-fuel ship routes according to claim 3 is characterized in that: In the model building step, the plurality of constraints further include a tenth constraint, an eleventh constraint, and a twelfth constraint established based on the fuel inventory and the refueling amount limit. The tenth constraint is constructed based on the fuel inventory of the ship when it arrives at the starting port and the initial fuel inventory; The eleventh constraint condition is constructed based on the fuel inventory of the ship when it arrives at the port and the minimum fuel storage capacity of the ship; The twelfth constraint is constructed based on the fuel inventory of the ship when it arrives at the port, the amount of fuel the ship refuels when it arrives at the port, the fuel consumption rate of the ship when it sails at a certain speed in a sub-segment of the route, the sailing time required for the ship to pass through a sub-segment of the route at a certain speed, and the decision variables.

5. The method for coordinated optimization of fuel filling, switching and speed of dual-fuel ship routes according to any one of claims 1 to 4, characterized in that: In the optimal solution calculation step, the solver uses the branch and bound method and the cutting plane method to calculate the optimal solution of the mixed integer linear optimization model.

6. A dual-fuel ship route fuel filling, switching and speed coordinated optimization system, characterized by: It includes the parameter acquisition module, flight segment division module, model building module, linearization processing module and optimal solution calculation module connected in sequence. The parameter acquisition module acquires the route parameters and port parameters of the dual-fuel ship, wherein the route parameters include the sections within and outside the emission control area, the sections within and outside the canal area, and the sailing distance of each section; the port parameters include the price of different types of fuel at the port, the amount of fuel the ship needs to refuel at the port, the delay penalty cost at the port, the time the ship arrives at the port, and the expected time window for arrival at the port; The segment division module divides the segment between the two ports into an emission control area segment, a canal area segment, and a plurality of ordinary segments based on the route parameters according to the emission control area and the canal area, and evenly divides the ordinary segment with a sailing distance greater than a preset distance threshold into a plurality of sub-segments; The model building module establishes a mixed integer nonlinear optimization model with the objective function of minimizing the fuel bunkering cost and the voyage delay cost based on the fuel price at a certain port, the amount of fuel a ship must bunker at the port, the delay penalty cost at the port, the time the ship arrives at the port, and the expected time window for arrival at the port, under multiple constraints established based on the speed and fuel restrictions of each sub-segment, the high-sulfur fuel restrictions in the emission control area, the fuel inventory and refueling quantity restrictions, the time restrictions for the ship to arrive at and leave the port, the sailing time restrictions, and the ship's carbon intensity index level compliance restrictions; The linearization processing module uses a linearization method to perform linearization processing on the established mixed integer nonlinear optimization model to obtain a mixed integer linear optimization model; The optimal solution calculation module uses a solver to calculate the optimal solution of the mixed integer linear optimization model, and then obtains the optimal fuel filling, fuel switching and speed strategy for each control area section, canal area section and sub-section, realizing the coordinated optimization of fuel filling, fuel switching and speed of the dual-fuel ship route.

7. The dual-fuel ship route fuel filling, switching and speed coordinated optimization system according to claim 6 is characterized in that: In the model establishment module, the multiple constraints include a first constraint and a second constraint based on the speed and fuel restrictions of each sub-segment, a third constraint based on the high-sulfur fuel restriction in the emission control area, a fourth constraint and a fifth constraint based on the fuel inventory and refueling quantity restriction, a sixth constraint and a seventh constraint based on the time restriction for the ship to arrive at and leave the port, an eighth constraint based on the sailing time restriction, and a ninth constraint based on the compliance of the ship's carbon intensity index level.

8. The dual-fuel ship route fuel filling, switching and speed coordinated optimization system according to claim 7 is characterized in that: The parameter acquisition module further acquires ship parameters of the dual-fuel ship, including the ship's fuel tank capacity, minimum fuel storage capacity, consumption rates of different types of fuel in each voyage segment, ship deadweight tonnage, carbon emissions generated in the year, carbon intensity index rating target value, and optional speed set; The model building module also constructs decision variables based on the judgment result of whether to sail at a certain speed and use a certain fuel in each sub-segment. At this time, The first constraint, the second constraint and the third constraint are constructed based on the decision variables; The fourth constraint is constructed based on the amount of fuel that the ship refuels upon arrival at the port; The fifth constraint is constructed based on the fuel inventory of the ship when it arrives at the starting port, the amount of fuel the ship refuels at the port, and the maximum fuel capacity of the ship, to ensure that the fuel inventory of the ship at the port plus the refueling amount does not exceed the maximum fuel capacity of the ship; The sixth constraint is constructed based on the time when the ship arrives at the starting port; The seventh constraint is constructed based on the time when the ship leaves the port, the time when the ship arrives at the port, the expected time window for arrival at the port, and the working time of the ship at the port; The eighth constraint is constructed based on the time when the ship leaves the port, the decision variable, the sailing time required for the ship to pass through a sub-segment of the route segment at a certain speed, and the time when the ship arrives at the port; The ninth constraint is constructed based on the carbon emissions generated by the ship in the year, the fuel consumption rate of the ship when sailing at a certain speed in a sub-segment of the route, the decision variables, the sailing time required for the ship to pass through a sub-segment of the route at a certain speed, the ratio of the carbon intensity index value actually achieved by the ship on each route to the target value of the carbon intensity index level, the carbon emissions of the voyage, and the deadweight tonnage of the ship.

9. The dual-fuel ship route fuel filling, switching and speed coordinated optimization system according to claim 8 is characterized in that: In the model building module, the plurality of constraints further include a tenth constraint, an eleventh constraint, and a twelfth constraint established based on the fuel inventory and the refueling amount limit. The tenth constraint is constructed based on the fuel inventory of the ship when it arrives at the starting port and the initial fuel inventory; The eleventh constraint condition is constructed based on the fuel inventory of the ship when it arrives at the port and the minimum fuel storage capacity of the ship; The twelfth constraint is constructed based on the fuel inventory of the ship when it arrives at the port, the amount of fuel the ship refuels when it arrives at the port, the fuel consumption rate of the ship when it sails at a certain speed in a sub-segment of the route, the sailing time required for the ship to pass through a sub-segment of the route at a certain speed, and the decision variables.

10. The dual-fuel ship route fuel filling, switching and speed coordinated optimization system according to any one of claims 6 to 9, characterized in that: In the optimal solution calculation module, the solver uses the branch and bound method and the cutting plane method to calculate the optimal solution of the mixed integer linear optimization model.

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