A green shipping corridor fuel demand prediction method and device

Abstract

This invention discloses a method and apparatus for predicting fuel demand in green shipping corridors. First, it acquires basic fleet and fuel data for the target green shipping corridor, and sets the average annual capacity growth rate and fuel-dominant proportion. Using the minimization of the annual total cost per ship as the objective function, a single-ship fuel optimization model is constructed to obtain the optimal solution set for fuel consumption and fuel selection decision variables for each ship size in the predicted year. Then, based on the average annual capacity growth rate, the number of new ships built each year to supplement capacity gaps is calculated, and clean energy ship quotas and conventional fuel oil ship quotas are calculated based on the fuel-dominant proportion. Finally, the maximum balance method is used to calculate the specific integer number of ships of each size and power type, and the weighted summation yields the total fuel demand for the green shipping corridor. This invention overcomes the limitations of traditional static accounting, realizing a systematic transmission from micro-level single-ship MINLP decision-making to macro-level fleet demand extrapolation.

Description

Technical Field

[0001] This application belongs to the field of carbon emission reduction and energy planning technology in the shipping industry, and in particular relates to a method and device for predicting fuel demand for green shipping corridors. Background Technology

[0002] Decarbonization of the shipping industry is crucial to achieving global climate goals. To accelerate the achievement of the Paris Agreement's emission reduction targets, the concept of Green Shipping Corridors (GSCs) has emerged, aiming to integrate clean energy technologies and policy support on specific shipping routes to build low-carbon or even zero-carbon transportation networks. Meanwhile, the International Maritime Organization's (IMO) Net-Zero Framework (NZF) has introduced a two-tiered greenhouse gas fuel intensity (GFI) target as a core regulatory tool, including a "baseline target" and a more stringent "direct compliance target." Driven by these multiple factors, replacing traditional heavy oil with low-carbon or zero-carbon fuels (such as green methanol, green LNG, and green ammonia) has become an inevitable path.

[0003] However, the green fuel transition is a complex systems engineering project involving multiple technological paths and highly complex cost and compliance constraints. Currently, existing technologies used in the industry to predict shipping fuel demand and assess emission reduction pathways have the following limitations:

[0004] 1. Traditional statistical forecasting techniques based on macroeconomic historical data: These techniques mainly rely on historical fuel consumption to extrapolate trends. Lacking underlying economic logic, these methods cannot characterize the complex two-way economic mechanisms in the IMO's new regulations (such as the high penalties for purchasing remedial units and the potential rewards for using near-zero carbon fuels under the GFI system), resulting in extremely low forecasting accuracy when facing disruptive energy transitions.

[0005] 2. Isolated static life-cycle cost accounting model for a single ship: Some existing technologies can calculate the fuel and conversion costs of a single ship, but their analysis units are usually static and isolated. They fail to be mathematically coupled with the dynamic evolution of the macro fleet capacity (such as capacity growth based on the macro economy, the rate of retirement of old ships and the filling of capacity gaps by new ships). This results in the micro-optimal decision not being accurately extended to the real annual total demand of the macro corridor.

[0006] 3. Lack of operational optimization tools to handle high-dimensional and nonlinear constraints: When faced with constraints on the combination of multiple fossil and clean fuels, rigid constraints on the proportion of pilot fuel, and complex compliance deficit / surplus calculations, existing conventional scenario analysis tools are unable to perform global economic optimization and cannot accurately simulate the optimal fuel allocation behavior of rational ship operators under complex technical and operational constraints.

[0007] Therefore, there is an urgent need for a comprehensive forecasting method that can couple micro-level mixed integer nonlinear programming (MINLP) with macro-level fleet dynamics to systematically and accurately assess the scale of long-term fuel demand under the premise of meeting the IMO net-zero emission target. Summary of the Invention

[0008] The purpose of this application is to provide a method and apparatus for predicting fuel demand in green shipping corridors, so as to overcome the limitations of existing technologies in terms of insufficient accuracy in fuel demand prediction and lack of global decision-making guidance when dealing with complex emission reduction regulations and multidimensional uncertainty scenarios.

[0009] To achieve the above objectives, the technical solution of this application is as follows:

[0010] A method for forecasting fuel demand in green shipping corridors includes:

[0011] Obtain basic fleet and fuel data for the target green shipping corridor, and set the average annual capacity growth rate and fuel dominance ratio;

[0012] With the objective function of minimizing the total annual cost per ship, a fuel optimization model for a single ship is constructed, and the optimal solution set for the fuel consumption and fuel selection decision variables of each ship type in the predicted year is obtained by solving the model.

[0013] The number of new ships built each year to supplement the capacity gap is calculated based on the average annual capacity growth rate. The quotas for clean energy ships and conventional fuel oil ships are calculated based on the proportion of fuel dominance. Finally, the specific integer number of ships of each size and power type is calculated using the maximum balance method.

[0014] The total fuel demand for the green shipping corridor is obtained by weighted summarization based on the optimal unit fuel consumption and fuel configuration scheme for each ship type in the forecast year and the integer number matrix of ships.

[0015] Preferably, the single-ship fuel optimization model has an objective function of minimizing the total annual cost, expressed by the following formula:

[0016] ;

[0017] in, This represents the total cost in year t. Let f be the unit price of fuel in year t. and The prices for first-level and second-level remedial units are respectively. Price per unit of surplus; A Level 1 compliance deficit, For Level 2 compliance deficit, For compliant earnings; This represents the ZNZ reward value. Let k be the set of fuels that are allowed to be used for power type k.

[0018] Preferably, the calculation of the number of new vessels built each year to supplement the capacity gap based on the average annual capacity growth rate includes:

[0019] Based on the baseline total capacity and the set average annual capacity growth rate, calculate the target total capacity for the forecast year;

[0020] The capacity gap for the forecast year is obtained by subtracting the previous year's existing capacity from the target total capacity for the forecast year, and the number of new ships built in the forecast year is estimated based on the capacity gap.

[0021] Preferably, the calculation of the number of new vessels built each year to supplement the capacity gap based on the average annual capacity growth rate includes:

[0022] Based on the baseline total capacity and the set average annual capacity growth rate, calculate the target total capacity for year t and year t+1;

[0023] Based on the capacity gap in year t and the actual new capacity added in year t, the remaining capacity that was not fully utilized in year t can be calculated.

[0024] The remaining capacity not filled in year t is carried over to year t+1. Combining the target total capacity in year t and year t+1, the capacity gap in year t+1 is calculated, and the number of new ships built in year t+1 is estimated based on the capacity gap.

[0025] Preferably, the calculation of clean energy vessel quotas and conventional fuel oil vessel quotas based on fuel dominance includes:

[0026] The total number of ships for the year is calculated by summing up the number of existing ships from the previous year and the number of new ships built in the current year.

[0027] Calculate the quota for traditional fuel-powered vessels by combining the macro target proportion of traditional fuel-powered vessels;

[0028] Then, the quota for clean energy vessels is calculated by subtracting the quota for traditional fuel oil vessels from the total number of vessels in that year.

[0029] This application also proposes a fuel demand forecasting device for green shipping corridors, including a processor and a memory storing a number of computer instructions, which, when executed by the processor, implement the steps of the above method.

[0030] This application proposes a method and apparatus for forecasting fuel demand in green shipping corridors. It innovatively introduces the penalty and reward mechanism of the IMO net-zero framework into a single-ship optimization model, accurately quantifying the economic impact of two-tiered GFI targets on the micro-level fuel selection behavior of ships. Breaking through the limitations of traditional static accounting, it achieves a systematic transmission from micro-level single-ship MINLP decisions to macro-level fleet demand projection. The maximum balance method operator is used to handle capacity changes and fuel allocation, ensuring the logical consistency of forecast results under complex dynamic capacity evolution. A scenario analysis is constructed covering multi-dimensional variables such as macro-level trade, energy technology pathways, market price elasticity, and biofuel blending limits, comprehensively covering highly uncertain future market and technology evolution paths. This application can be directly applied to real-world shipping scenarios with multi-fuel coexistence and strong policy drivers (such as the China-US green shipping corridor), providing quantitative decision support for setting energy consumption limits and energy suppliers investing in infrastructure. Attached Figure Description

[0031] Figure 1 This is a flowchart of the fuel demand forecasting method for the green shipping corridor in this application. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0033] One embodiment of this application, such as Figure 1 As shown, a method for predicting fuel demand in green shipping corridors is proposed, including:

[0034] Step S1: Obtain basic fleet and fuel data for the target green shipping corridor, and set the average annual capacity growth rate and fuel dominance ratio.

[0035] The fleet and fuel baseline data read in this embodiment includes: the annual total energy demand baseline for each typical ship type s. The low calorific value of each candidate fuel f and whole life cycle (WtW) carbon emission factors Forecast annual fuel market price (t) .

[0036] For example, the obtained basic fuel data is shown in Table 1:

[0037] Table 1

[0038]

[0039] The average annual capacity growth rate is set by user input. And the dominant share of various clean energy sources (methanol, LNG, ammonia) in the target year (e.g., 2050). ).

[0040] For example, the annual average capacity growth rate is set as shown in Table 2:

[0041] Table 2

[0042]

[0043] Table 2 sets out two scenarios for the average annual capacity growth rate: low speed and high speed.

[0044] In one specific embodiment, the fuel dominance is shown in Table 3:

[0045] Table 3

[0046]

[0047] Table 3 presents four energy technology dominance scenarios for 2050, quantifying the direct impact of different clean fuel price trends on their future shipping market share. **Single Fuel Dominance Scenario (M1 and G1):** This scenario assumes a significant price decrease (up to 50%) for a specific single fuel, resulting in a unipolar dominance by 2050. In scenario M1, a sharp drop in methanol prices leads to approximately 50% market share; in scenario G1, a sharp drop in LNG prices leads to approximately 50%. In these scenarios, other alternative fuels serve only as supplementary components, with relatively low market shares. **Dual Fuel Coexistence Scenario (M2 and G2):** This scenario assumes a simultaneous moderate price decrease (30%) for both clean fuels, resulting in a dual-core equilibrium by 2050. Scenario M2 assumes simultaneous price decreases for methanol and ammonia, leading to each holding approximately 35% market share; scenario G2 assumes simultaneous price decreases for LNG and ammonia, also with each holding approximately 35% market share. By pre-setting the long-term price decline characteristics of specific fuels, this table clearly defines the macro-quota weights of different power technology paths in the target year (2050), providing precise scenario boundary conditions for subsequent fleet fuel structure simulations.

[0048] Step S2: With minimizing the total annual cost of a single ship as the objective function, construct a single-ship fuel optimization model and solve for the optimal solution set of fuel consumption and fuel selection decision variables for each ship size in the predicted year.

[0049] For a typical ship of size s, define the decision variables. Let f be the fuel consumption (tons) in year t. This is a 0-1 binary variable representing whether or not to use this fuel.

[0050] The objective function is constructed as follows, aiming to minimize the total annual cost, including fuel procurement, emissions penalties, and compliance incentives:

[0051] ;

[0052] in, This represents the total cost in year t. Let f be the unit price of fuel in year t. and The prices for first-level and second-level remedial units are respectively. Price per unit of surplus; A Level 1 compliance deficit, For Level 2 compliance deficit, For compliant earnings; This represents the ZNZ reward value. Let k be the set of fuels that are allowed to be used for power type k.

[0053] Specifically, the fuel set This covers a wide range of fuels currently in service and planned for the shipping industry, primarily including: traditional fossil fuels (heavy fuel oil HFO, light fuel oil LFO), biomass clean fuels (B100 biofuel, bio-methanol, bio-LNG), and electro-generated zero-carbon fuels (electro-generated methanol, electro-generated ammonia, electro-generated LNG). The specific power technology of the vessel will be used for configuration. The scope, for example, methanol dual-fuel ships. It includes fossil methanol, bio-methanol, electro-generated methanol, and heavy / light oil used as pilot fuel.

[0054] The latter part of the formula is the compliance reward and penalty calculation item for introducing a tiered carbon pricing mechanism. "Remedial measures" or "remedy measures" refer to the mandatory economic compensation behavior that requires ship owners to pay a penalty at a specific rate to make up for the compliance shortfall and regain legal operating qualifications when the actual greenhouse gas emission intensity of a single ship exceeds the regulatory limit. Based on this definition, the specific physical and economic meanings of each professional term are briefly described below:

[0055] (1) Compliance earnings and the price per unit of surplus Compliance surplus refers to the positive environmental benefits generated when a single ship's actual greenhouse gas fuel intensity (GFI) is lower than the direct emission reduction target set by regulations (i.e., the portion exceeding the emission reduction target). This refers to the unit revenue price that the surplus can be realized under market or regulatory mechanisms.

[0056] (2) Level 1 compliance deficit and the price of a first-level remedial unit A Level 1 compliance deficit refers to a single ship's actual GFI exceeding the direct emission reduction target set by regulations, but not yet reaching the historical emission baseline (i.e., a less severe level of non-compliance) carbon emissions. This refers to the base penalty rate (set at 100 USD / tCO2eq) applicable when requiring the ship to take primary remedial measures for this minor violation.

[0057] (3) Level 2 compliance deficit and secondary remedial unit price Level 2 compliance deficit refers to a single ship's actual GFI significantly exceeding the limit, or even surpassing the historical emission baseline of severe carbon emissions without any emission reduction measures; This refers to the punitive high penalty rate (set at 380 USD / tCO2eq) that applies when requiring the ship to take secondary remedial measures for this serious violation.

[0058] By introducing the aforementioned tiered deficit definition and remediation mechanism, the objective function can accurately simulate the real economic driving force of extremely stringent environmental regulations on shipowners' choice of low-carbon and zero-carbon fuels.

[0059] To achieve the nonlinear solution for the aforementioned deficit and surplus, this application employs a continuously differentiable smooth absolute value function for mathematical approximation calculation. The specific calculation logic is as follows:

[0060] (1) Total calorific value constraint: Based on the ship fuel demand in the baseline year, ensure that the annual total calorific value supply of ships is not lower than the preset demand.

[0061] ;

[0062] in, This represents the baseline of the typical annual total energy demand for a ship of size s. This represents the set of candidate fuels allowed for power type k.

[0063] (2) Fuel usage restrictions: Each dual-fuel vessel can only choose between conventional fuel and the fuel corresponding to its conversion type. Unconverted vessels can freely choose between conventional fuel and biofuel. At the same time, to reflect actual operation, a single vessel is limited to using a maximum of 3 different types of fuel at the same time.

[0064] ;

[0065] (3) Emission constraints: Considering the CII hard constraint (CII Compliance), the carbon emission intensity of a single ship must be lower than the annual CII emission reduction target. In addition, penalties and rewards are calculated in segments, and auxiliary variables are introduced to divide the carbon emissions of the entire life cycle into compliance segment, baseline segment and above-baseline segment to facilitate the application of graded penalties and the calculation of surplus benefits.

[0066] ;

[0067] ;

[0068] ;

[0069] ;

[0070] ;

[0071] ;

[0072] ;

[0073] ;

[0074] ;

[0075] ;

[0076] in, Let represent the total annual greenhouse gas emissions of a single ship of size s and power type k in year t. The carbon dioxide mass emission factor representing fuel f; This represents the life-cycle greenhouse gas emission factor of fuel f, calculated based on its lower heating value. This represents the annual CII total carbon emission cap threshold allowed for a ship of size s in year t. This represents the direct compliance total emissions limit for a single ship of size s in year t. This represents the direct compliance greenhouse gas fuel intensity target limit stipulated by regulations for year t; This represents the baseline total emissions limit for a single ship of size s in year t. This represents the baseline greenhouse gas fuel intensity target limit stipulated by regulations for year t.

[0077] (4) Decision variable constraints: Guarantee through the Big M constraint and Semantic continuity is ensured, while limiting the number of fuel types and ensuring consistent variable meanings.

[0078] ;

[0079] in .

[0080] (5) ZNZ Incentive: Calculate the fuel incentive for using ZNZ-compliant fuels. For ease of calculation, define the fuel emission reduction intensity difference. It represents the fuel determination value of a certain fuel relative to the ZNZ fuel value of that year. The clean advantage. When fuel emission intensity If the fuel level is below the threshold, it indicates that the fuel is cleaner than the standard and deserves a reward; otherwise, no reward is given. Reward Value Defined as the relative emission reduction and incentive coefficient of all clean fuels The product of the two. The relative emission reduction is based on the fuel difference. Compared with the actual consumption of the fuel and calorific value The product is calculated as follows:

[0081] ;

[0082] ;

[0083] The reward value calculated above This directly corresponds to the total cost deduction item in the aforementioned single-ship objective function (i.e. ).

[0084] In the solution process aimed at minimizing the total annual cost, this reward represents policy subsidies or carbon asset trading revenue obtained from using ultra-low-carbon fuels. Mathematically, this mechanism effectively hedges against the high initial purchase price of zero-carbon fuels (such as those produced by electro-methanol and electro-ammonia). By establishing this objective function correlation, this model can accurately reflect the underlying economic driving force of green shipping incentive policies on shipowners' energy decisions.

[0085] (6) Pilot fuel constraints: For methanol, LNG or ammonia fueled ships, the calorific value of pilot fuel (HFO / LFO) must be kept in a fixed ratio r with that of the main fuel.

[0086] ;

[0087] in, This represents the set of pilot fuels that are allowed to be used under a specific power type k (such as heavy oil HFO and light oil LFO); This represents the set of low-carbon or zero-carbon primary fuels used under a specific power type k (such as methanol, LNG, or ammonia fuel in a dual-fuel mechanism); This represents the actual consumption of a specific pilot fuel p by a single ship of size s and power type k in year t. This indicates the lower heating value of the corresponding pilot fuel oil and p; This represents the actual consumption of a specific main fuel m by a single ship of size s and power type k in year t. represents the lower heating value of the corresponding main fuel m; r represents the fixed ratio coefficient of the pilot fuel's heating value to the main fuel's heating value as required by regulations or engine physical characteristics.

[0088] This embodiment's single-ship fuel optimization model belongs to the mixed integer nonlinear programming (MINLP) optimization problem. It can utilize a pre-built computer numerical solution engine (such as the Ipopt solver) to output the optimal solution values ​​for each decision variable in the model for a specific forecast year, specifically including: the fuel consumption of each candidate variable. The numerical value; a binary decision variable regarding whether to use a specific fuel. The value.

[0089] Based on the optimal solutions of the decision variables obtained by direct calculation above, and through the determined algebraic derivation formulas, the values ​​of various derived indicators required for rendering the front-end stacked chart and annual financial assessment report are further calculated:

[0090] (1) Total fuel procurement cost ( The optimal fuel consumption is calculated by multiplying the optimal consumption of each fuel by its corresponding unit price, using the following formula:

[0091] ;

[0092] (2) Fines for exceeding emission limits in compliance with regulations ( The formula is derived by algebraically adding the optimal solutions of the Level 1 and Level 2 compliance deficit variables to the corresponding penalty rates, as shown in the following formula:

[0093]

[0094] (3) The actual greenhouse gas fuel intensity (GFI) level ultimately achieved by a single ship is derived from the ratio of total life-cycle greenhouse gas emissions to total calorific value supply.

[0095] .

[0096] The continuous decision variables and objective function sub-terms extracted from each year are encapsulated into structured data records. After iterating through all years, these are aggregated into a two-dimensional data matrix using a data analysis engine. The matrix is ​​then sliced ​​and exported to generate a table of micro-optimization results. It should be noted that, to maintain the simplicity and readability of the data report, Table 4 does not have a separate binary column for the 0-1 binary decision variables. Discrete mapping is performed on the solution states. Based on the large M constraint (the decision variable set in the model)... The binary decision variables The 0-1 Boolean states are fully and definitively implied in the corresponding fuel consumption (i.e., each Use_ column in Table 4). Specifically, when the annual optimal consumption value for a certain fuel is greater than 0, it means that the fuel has been selected for that year. The implicit value is 1; when the optimal consumption value is strictly 0.00, it means that the fuel was not selected that year. The implicit value is 0. Therefore, Table 4, through the real-valued expression of physical consumption, fully and consistently encapsulates the collaborative optimization results of all decision variables at the underlying level of the model. Taking a 16000 TEU methanol dual-fuel ship as an example, its optimization results under the DNV price scenario are shown in Table 4:

[0097] Table 4

[0098]

[0099] Table 4 visually reflects the dynamic evolution of fuel allocation strategies over time under the objective of minimizing the total annual compliance cost per ship.

[0100] First, the rigid constraint characteristics of the ignition fuel: from 2026 to 2048, the ignition ratio constraint of 5% of the calorific value of the main fuel will be strictly adhered to to stably consume heavy oil, and in the later period (2049-2050), it will automatically switch to light oil according to extreme value optimization.

[0101] Second, the three-stage evolution of the main fuel: the initial transition stage (2026-2029) adopts a mixed strategy of fossil methanol and bio-methanol; the radical compliance and dynamic balance stage (2030-2041) the system dynamically adjusts between achieving zero penalties for using bio-methanol and incurring certain penalties for reintroducing fossil methanol; the deep decarbonization stage (2042-2050) completely eliminates bio-methanol and ultimately achieves 100% electric methanol dominance, reducing the actual GFI to 11.36.

[0102] Third, the trade-off between compliance costs and penalties: The optimal solution of the model does not mean an absolute "zero penalty". The system conducts a precise marginal cost trade-off between the direct fuel procurement cost (Cost_Fuel) and the over-emission penalty (Cost_Penalty) (e.g., allowing a penalty of 862.81 in 2040 in exchange for a lower overall cost), which proves the global optimization capability of this model in taking into account both shipping economic laws and the goal of deep decarbonization.

[0103] Step S3: Calculate the number of new ships built each year to supplement the capacity gap based on the average annual capacity growth rate, and calculate the quota for clean energy ships and traditional fuel oil ships based on the fuel dominance ratio. Finally, use the maximum balance method to calculate the specific integer number of ships of each size and power type.

[0104] In one embodiment, the calculation of the number of new vessels built each year to supplement the capacity gap, based on the average annual capacity growth rate, includes:

[0105] Step 3.1.1: Calculate the target total capacity for the forecast year based on the baseline total capacity and the set average annual capacity growth rate.

[0106] First, calculate the baseline total transport capacity. For example, the actual total capacity in 2025 can be used as the baseline total capacity, and the baseline total capacity can be used as the base for growth in all subsequent years.

[0107] ;

[0108] in, This represents the baseline fleet size in 2025, which is the starting year (baseline year) for the entire long-term projection. It represents the known number of existing vessels actually operating on the target green shipping corridor within that year. It serves as the starting point for the evolution of the entire fleet's capacity, providing a unique and deterministic evolutionary base for calculating capacity growth and new shipbuilding gaps in subsequent years (2026-2050). S represents the set of vessel sizes s, and K represents the set of power types k. This indicates the standard container capacity per ship for dimension s.

[0109] Then, based on the calculated baseline total capacity linear growth rate of the set average annual capacity Calculate the target total transport capacity for the predicted year t:

[0110] .

[0111] Step 3.1.2: Subtract the previous year's stock capacity from the target total capacity for the forecast year to obtain the capacity gap for the forecast year, and estimate the number of new ships to be built for the forecast year based on the capacity gap.

[0112] The capacity gap for the projected year is calculated by subtracting the previous year's existing capacity from the target total capacity for year t.

[0113] ;

[0114] in, This represents the number of ships of size s and power type k in year t.

[0115] This embodiment assumes that the replacement of large ships only allows the construction of two types: 14,000 TEU and 16,000 TEU. The solution is to find non-negative integer solutions that make the actual increase in capacity closest to the capacity gap:

[0116] ;

[0117] in, These represent the number of newly built vessels with capacities of 14,000 TEU and 16,000 TEU, respectively.

[0118] In another embodiment, the number of new vessels built each year to supplement the capacity gap is calculated based on the average annual capacity growth rate, including:

[0119] Step 3.2.1: Based on the baseline total capacity and the set average annual capacity growth rate, calculate the target total capacity for year t and year t+1.

[0120] Using the same method as in the previous embodiment, the target total transport capacity for year t can be calculated. and the target total capacity in year t+1 .

[0121] Step 3.2.2: Based on the capacity gap in year t and the actual new capacity in year t, calculate the remaining capacity that was not fully utilized in year t.

[0122] Based on the capacity gap in year t and the actual increase in transport capacity in year t The remaining transport capacity that was not fully utilized in year t is calculated as follows:

[0123] .

[0124] Step 3.2.3: Carry forward the remaining capacity not filled in year t to year t+1. Combine the target total capacity in year t and year t+1 to calculate the capacity gap in year t+1, and estimate the number of new ships built in year t+1 based on the capacity gap.

[0125] ;

[0126] in, This represents the capacity gap in year t+1. This represents the target total transport capacity for year t+1. Let represent the target total capacity in year t. Through the above mechanism, the remaining capacity can be incorporated into the capacity gap calculation for the following year, constructing a closed-loop negative feedback adjustment mechanism. That is, the remaining capacity... A multi-stage time-stepping cycle is used as the constraint control variable for the next cycle.

[0127] In this embodiment, the estimation of the number of new ships to be built in year t+1 based on the capacity gap is the same as in the previous embodiment, and will not be repeated here.

[0128] It should be noted that the two embodiments described above for calculating the number of newly built ships yield the same result. In the first embodiment, it is necessary to calculate the existing capacity of the previous year, while in the second embodiment, only the capacity gap calculated in year t and the actual newly added capacity are needed to calculate the capacity gap in year t+1, without having to calculate the existing capacity of the previous year, thus reducing the amount of calculation.

[0129] Finally, the calculated number of newly built ships is incorporated into the corresponding new ship quantity matrix. This is for future allocation.

[0130] Specifically, clean energy ship quotas are calculated based on the proportion of fuel used for power, including:

[0131] The number of new ships built this year is calculated based on the previous year's existing fleet and previous steps. Sum the total number of ships for that year:

[0132] ;

[0133] in, This represents the total number of ships in year t. This represents the number of newly built ships of size s in year t.

[0134] Subsequently, in conjunction with the established macro target for the proportion of traditional fuel-powered ships... And, constrained by the rigid limitations of last year's existing stock, calculate the quota for traditional fuel-powered vessels:

[0135] ;

[0136] Finally, the clean energy vessel quota is calculated by subtracting the traditional fuel oil vessel quota from the total number of vessels in that year:

[0137]

[0138] Specifically, the exact integer quantity of ships of each size and power type is calculated using the maximum balance method, including:

[0139] Define operator Let w be the maximum balance allocation function, where N is the total number of units to be allocated and w is the weight vector.

[0140] First, the operator is invoked to allocate the target quota for conventional fuel tankers to specific size categories:

[0141] ;

[0142] Secondly, based on the set proportion of each macro-level clean energy target, this operator is invoked to divide the total clean energy quota into intermediate allocations corresponding to specific power types k:

[0143] ;

[0144] Finally, using the total number of ships of each size in the previous year plus the number of new ships built this year as the weight base, the operator is called to map the intermediate allocation of each clean energy source to specific sizes, and calculates and outputs the final integer number matrix of ships of each size s and power type k in a specific year t:

[0145] .

[0146] Step S4: Based on the optimal unit fuel consumption and fuel configuration scheme of each ship type in the forecast year and the integer number matrix of ships, the total fuel demand of the green shipping corridor is obtained by weighted summation.

[0147] Specifically, for the predicted year t and a specific fuel f, iterate through all ship sizes s and power types k output in step S3, and extract the corresponding optimal fuel consumption per ship obtained in step S2. The total demand for the target corridor is obtained by multiplying the consumption per ship by the corresponding total number of ships and summing the results globally. The calculation formula is as follows:

[0148]

[0149] in, Let S be the total demand for a specific fuel f for the target shipping route corridor in year t; S be the set of ship sizes; and K be the set of ship power types. Let be the total number of ships of size s and power type k in year t; Let f be the optimal fuel consumption for a typical single ship of size s and power type k in year t.

[0150] For example, the annual total demand forecast for methanol fuel for the target green shipping corridor is shown in Table 5 (unit: tons):

[0151] Table 5

[0152]

[0153] Table 5 visually illustrates the macro-evolution of the overall methanol fuel demand structure over time within the target green shipping corridor under specific constraints and scenarios. The data not only reflects the increase in total demand resulting from the expansion of corridor capacity, but also reveals the three core macro-stages of fuel transformation within the corridor driven by the IMO's dual-level GFI targets:

[0154] 1. Transition and Exploration Period (2026-2029): During this period, the demand for methanol fuel in the corridor will be mainly based on fossil methanol (methanol (by LNG), with total demand climbing from about 101,000 tons to 276,000 tons. At the same time, bio-methanol will begin to be used in small quantities as a supplement to emission reduction.

[0155] 2. Compliance Pressure and Cost Game Period (2030-2041): Constrained by the tightening GFI regulations in 2030, the corridor will experience an explosive demand for bio-methanol in 2030-2031 (peaking at 540,000 tons) to completely replace fossil methanol and achieve compliance; subsequently (2032-2041), driven by the optimization of overall costs, the corridor will enter a state of large-scale mixed demand for fossil methanol and bio-methanol, with both growing in parallel.

[0156] 3. Deep Decarbonization Period (2042-2050): To cope with extremely stringent long-term emission targets, the demand for bio-methanol will rapidly shrink until it reaches zero, while the demand for electro-methanol, which has better zero-carbon properties, will explode. From 2048 onwards, fossil methanol will also be completely phased out, and the corridor will be entirely dominated by electro-methanol; by 2050, the total demand for electro-methanol will exceed 3.08 million tons, marking the complete completion of the zero-carbon fuel transition for this green shipping corridor. The system front-end renders stacked bar charts based on this data, allowing users to intuitively understand the macro-level emission reduction process.

[0157] This application extrapolates from micro-level single-ship data (as shown in Table 4) to macro-level corridor data (as shown in Table 5). For example, it first extracts the specific fuel consumption floating-point values ​​for single ships of a particular size and power type from the micro-optimization results (as shown in Table 4) on an annual basis. For instance, it extracts the bio-methanol consumption of a 16,000 TEU methanol dual-fuel ship in 2028 from Table 4. tons, fossil methanol consumption Tons. Then from the ship distribution matrix In this context, a cross-table query is performed to obtain the macroscopic ship inventory in terms of year, size, and power type. Assume the actual number of 16,000 TEU methanol dual-fuel ships operating in the corridor in 2028 is... The number of 14,000 TEU methanol dual-fuel ships is Finally, the consumption per ship of each size is multiplied by the corresponding macroscopic number of ships and then summed. Taking the total bio-methanol demand of 38,375.07 tons in 2028 as an example, its underlying calculation logic strictly follows: Total demand = (i.e., consumption of 16,000 TEU per ship) + (i.e., 14,000 TEU consumption per ship). During this accumulation process, other power types within the corridor, such as traditional fuel oil ships or LNG dual-fuel ships, are automatically eliminated in the formula calculation because their corresponding methanol fuel consumption per ship (x-value) is 0. Through the above process, the micro-level single-ship multi-fuel data series are accurately aggregated and calculated by year and by fuel type, ultimately transforming and outputting the corridor-level macro-total demand forecast matrix as shown in Table 5.

[0158] In another embodiment of this application, a green shipping corridor fuel demand forecasting device is also provided, including a processor and a memory storing a plurality of computer instructions, which, when executed by the processor, implement the steps of the above method.

[0159] Specific limitations regarding the fuel demand forecasting device for green shipping corridors can be found in the limitations of the fuel demand forecasting method for green shipping corridors mentioned above, and will not be repeated here. The aforementioned fuel demand forecasting device for green shipping corridors can be implemented entirely or partially through software, hardware, or a combination thereof. It can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0160] The memory and processor are electrically connected directly or indirectly to enable data transmission or interaction. For example, these components can be electrically connected to each other via one or more communication buses or signal lines. The memory stores a computer program that can run on the processor, which implements the green shipping corridor fuel demand forecasting method in this embodiment of the invention by running the computer program stored in the memory.

[0161] The memory may be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory stores the program, and the processor executes the program upon receiving an execution instruction.

[0162] The processor may be an integrated circuit chip with data processing capabilities. The aforementioned processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor.

[0163] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for predicting fuel demand in green shipping corridors, characterized in that, The method for forecasting fuel demand for the green shipping corridor includes: Obtain basic fleet and fuel data for the target green shipping corridor, and set the average annual capacity growth rate and fuel dominance ratio; With the objective function of minimizing the total annual cost per ship, a fuel optimization model for a single ship is constructed, and the optimal solution set for the fuel consumption and fuel selection decision variables of each ship size in the predicted year is obtained by solving the model. The number of new ships built each year to supplement the capacity gap is calculated based on the average annual capacity growth rate. The quotas for clean energy ships and conventional fuel oil ships are calculated based on the proportion of fuel dominance. Finally, the specific integer number of ships of each size and power type is calculated using the maximum balance method. The total fuel demand for the green shipping corridor is obtained by weighted summarization based on the optimal unit fuel consumption and fuel configuration scheme for each ship type in the forecast year and the integer number matrix of ships.

2. The method for predicting fuel demand for green shipping corridors according to claim 1, characterized in that, The single-ship fuel optimization model has the objective function of minimizing the total annual cost, expressed by the following formula: ； in, This represents the total cost in year t. Let f be the unit price of fuel in year t. and The prices for first-level and second-level remedial units are respectively. Price per unit of surplus; A Level 1 compliance deficit, For Level 2 compliance deficit, For compliant earnings; This represents the ZNZ reward value. Let k be the set of fuels that are allowed to be used for power type k.

3. The method for predicting fuel demand for green shipping corridors according to claim 1, characterized in that, The calculation of the number of new vessels built each year to supplement the capacity gap based on the average annual capacity growth rate includes: Based on the baseline total capacity and the set average annual capacity growth rate, calculate the target total capacity for the forecast year; The capacity gap for the forecast year is obtained by subtracting the previous year's existing capacity from the target total capacity for the forecast year, and the number of new ships built in the forecast year is estimated based on the capacity gap.

4. The method for predicting fuel demand for green shipping corridors according to claim 1, characterized in that, The calculation of the number of new vessels built each year to supplement the capacity gap based on the average annual capacity growth rate includes: Based on the baseline total capacity and the set average annual capacity growth rate, calculate the target total capacity for year t and year t+1; Based on the capacity gap in year t and the actual new capacity added in year t, the remaining capacity that was not fully utilized in year t can be calculated. The remaining capacity not filled in year t is carried over to year t+1. Combining the target total capacity in year t and year t+1, the capacity gap in year t+1 is calculated, and the number of new ships built in year t+1 is estimated based on the capacity gap.

5. The method for predicting fuel demand for green shipping corridors according to claim 1, characterized in that, The calculation of clean energy vessel quotas and conventional fuel oil vessel quotas based on fuel dominance includes: The total number of ships for the year is calculated by summing up the number of existing ships from the previous year and the number of new ships built in the current year. Calculate the quota for traditional fuel-powered vessels by combining the macro target proportion of traditional fuel-powered vessels; Then, the quota for clean energy vessels is calculated by subtracting the quota for traditional fuel oil vessels from the total number of vessels in that year.

6. A fuel demand forecasting device for green shipping corridors, comprising a processor and a memory storing a plurality of computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 5.

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