Method and system for determining construction investment of shore power system

Abstract

The invention provides a method and a system for determining construction investment of a shore power system. The method comprises the following steps: acquiring shore power system equipment data based on pre-constructed parameter indexes; based on the shore power system equipment data, a pre-constructed shore power system mathematical model is utilized, a multi-target genetic algorithm is adopted for solving, and shore power system construction investment of the government is obtained; wherein the mathematical model of the shore power system is constructed by taking the minimum cost recovery age limit of the harbor shore power system, the minimum cost recovery age limit of the ship shore power transformation and the minimum cost of the government as optimization targets and combining constraint conditions set for the optimization targets; the shore power system equipment data comprises shore power system equipment capacity, ship auxiliary engine total power and ship auxiliary engine utilization rate; the construction investment of the government on the shore power system is used for determining the construction and use of the shore power system. According to the method, factors influencing construction and use of the shore power system are comprehensively considered.

Description

Technical Field

[0001] The present invention belongs to the field of multi-objective optimization of shore power systems, and in particular relates to a method and system for determining construction investment of a shore power system. Background Art

[0002] With the country's call for energy conservation, emission reduction, low carbon emissions and green travel, the construction of port shore power systems is imperative. Since various domestic regulations on shore power systems have not yet been perfected, shore power systems are often difficult to maintain operation, so it is necessary to analyze the economic feasibility of commercial operations of shore power systems.

[0003] Traditional analysis methods mostly use government costs, port costs and ship costs as objective functions to analyze the economic efficiency of port shore power operations and obtain the decision factors that affect the objective function. However, this economic analysis method has great limitations.

[0004] A Chinese patent document with publication number CN107944689A and publication date April 20, 2018, discloses a shore power operation economic analysis method, which analyzes the actual situation of port shore power operation according to the composition of the high-voltage shore power system, quantifies the parameter indicators in the system, and constructs the port cost recovery period model and the ship cost benefit recovery period model according to the parameter indicators, as well as the relationship model between the quantified Internet technology and the berth utilization rate. The cost of port and ship users, the benefits after shore power transformation, and the number of years for port cost recovery are determined through economic models. After selecting appropriate model data for the constructed mathematical model, the optimal result is obtained through simulation analysis of the model according to the relationship between subsidies and ports and ships, as well as the impact of shore power service fees and berth utilization rates on the corresponding recovery period, and a reasonable shore power usage price is found.

[0005] This economic analysis method has great limitations. Using the port's cost recovery period to analyze the economic benefits of the commercial operation of the shore power system can only be limited to the static situation of the entire shore power system. The berth utilization rate can be used to evaluate the demand for shore power facilities at the port or terminal, thereby obtaining the shore power usage price. However, the berth utilization rate cannot reflect the overall operating benefits of the port, resulting in the inability of this analysis method to comprehensively evaluate the investment in the shore power system.

[0006] In addition, existing analysis methods often transform the objective function into the weighted sum of various objectives, and then use single-objective optimization techniques. However, there are inconsistencies in the units between objectives of different natures, and it is not appropriate to set appropriate weights. In addition, the allocation of weighted values ​​for each objective is highly subjective. The optimization objective is only the weighted sum of various objectives. Different weight settings will produce completely different optimal solutions. The quality of the solution results depends heavily on the quality of the objective function weight setting. Summary of the invention

[0007] In order to solve the problem that the commercial operation benefits of the shore power system cannot be fully analyzed in the prior art, and the quality of the obtained results depends on the weight setting of the objective function, the present invention proposes a method for determining the construction investment of the shore power system, including:

[0008] Obtain shore power system equipment data based on pre-built parameter indicators;

[0009] Based on the shore power system equipment data, a pre-built shore power system mathematical model is used to solve the problem using a multi-objective genetic algorithm to obtain the government's investment in the shore power system construction;

[0010] The mathematical model of the shore power system is constructed by taking the cost recovery period of the port shore power system, the cost recovery period of the ship shore power transformation and the minimum government cost as the optimization objectives, combined with the constraint conditions set for the optimization objectives;

[0011] The shore power system equipment data includes: shore power system equipment capacity, total power of ship auxiliary engines and utilization rate of ship auxiliary engines;

[0012] The government's investment in the construction of the shore power system is used to determine the construction and use of the shore power system.

[0013] Preferably, the construction of the mathematical model of the shore power system includes:

[0014] The optimization variables are the government's percentage subsidy for shore power system construction, the government's percentage subsidy for ship shore power transformation, the government's subsidy for shore power electricity price and the shore power service fee.

[0015] Building constraints based on shore power system operating restrictions;

[0016] The objective function of optimizing the life of the system is constructed with the goal of minimizing the cost recovery period of the port shore power system.

[0017] The objective function for optimizing the transformation period is constructed with the goal of minimizing the cost recovery period of ship shore power transformation;

[0018] Constructing the economic optimization objective function with the goal of minimizing government costs;

[0019] Constructing a shore power system mathematical model based on the optimization variables, the constraint conditions, the system life optimization objective function, the transformation life optimization objective function and the economic optimization objective function;

[0020] The constraints include: port net profit constraint and port berthing cost constraint after ship shore power transformation;

[0021] The government's investment in shore power system construction includes: government percentage subsidies for shore power system construction, government percentage subsidies for ship shore power transformation, government subsidies for shore power electricity prices and shore power service fees.

[0022] Preferably, the parameter indicators include:

[0023] The price of large industrial electricity, the types of ships using shore power and the number of each type, the cost of converting different types of ships, the auxiliary engine utilization rate and total auxiliary engine power of different ships, the number of port calls per year for each ship, the time of a single port call, the time when the ship is not connected to the port shore power equipment during a single port call, the total capacity of the shore power system, the total construction cost of the shore power system, the annual shore power labor cost, the main types of pollutants generated by burning fuel, the emission of each pollutant, the tax rate levied on different pollutants, the auxiliary engine fuel consumption and the auxiliary engine fuel price.

[0024] Preferably, the system life optimization objective function is as follows:

[0025]

[0026] In the above formula, PT port is the cost recovery period of the port shore power system, N1 is the number of shore power systems in the port, C sp_o is the total construction cost of the shore power system o, S port is the percentage subsidy of the government for the construction of shore power system, N2 is the number of ships at the port using shore power, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship_i is the utilization rate of ship auxiliary engines, p s is the shore power service fee, P sp_o is the capacity of the shore power system o, C l_per is the annual labor cost converted into 1kW shore power capacity, o is the shore power system, and i is the i-th ship.

[0027] Preferably, the transformation period optimization objective function is as follows:

[0028]

[0029] In the above formula, PT ship is the recovery period of the cost of shore power transformation for ships, N2 is the number of ships at the port using shore power, C ship_i is the shore power transformation cost of ship i, S ship is the government's percentage subsidy for ship shore power transformation, m iis the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at the port. ship_i is the total power of auxiliary engines of ship i, r ship_i is the utilization rate of ship auxiliary engines, r f is the auxiliary engine fuel consumption per 1 kWh, p f is the auxiliary engine fuel price, N3 is the number of types of pollutants emitted by the auxiliary engine, E s is the emission of the sth pollutant per 1 kWh, T s is the tax rate imposed on the sth pollutant, p p is the shore power price, S price is the government subsidy for shore power price, i is the i-th ship, and s is the s-th pollutant.

[0030] Preferably, the economic optimization objective function is as follows:

[0031]

[0032] In the above formula, C g is the government cost, N1 is the number of shore power systems in the port, C sp_o is the total construction cost of the shore power system o, S port is the percentage subsidy of the government for the construction of shore power system, N2 is the number of ships at the port using shore power, C ship_i is the shore power transformation cost of ship i, S ship is the government's percentage subsidy for ship shore power transformation, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship_i is the utilization rate of ship auxiliary engines, S price is the government subsidy for shore power price, o is the shore power system, and i is the i-th ship.

[0033] Based on the same inventive concept, the present invention also proposes a shore power system construction investment determination system, comprising:

[0034] A data acquisition module, used to acquire shore power system equipment data based on pre-built parameter indicators;

[0035] A model solving module is used to solve the shore power system equipment data using a pre-built shore power system mathematical model and a multi-objective genetic algorithm to obtain the government's investment in the shore power system construction;

[0036] The mathematical model of the shore power system is constructed by taking the cost recovery period of the port shore power system, the cost recovery period of the ship shore power transformation and the minimum government cost as the optimization objectives, combined with the constraint conditions set for the optimization objectives;

[0037] The shore power system equipment data includes: shore power system equipment capacity, total power of ship auxiliary engines and utilization rate of ship auxiliary engines;

[0038] The government's investment in the construction of the shore power system is used to determine the construction and use of the shore power system.

[0039] Preferably, the system further comprises a model building module, wherein the model building module comprises:

[0040] The variable setting submodule is used to optimize the variables by taking the government's percentage subsidy for shore power system construction, the government's percentage subsidy for ship shore power transformation, the government's subsidy for shore power electricity price and shore power service fee in the government's investment in shore power system construction;

[0041] The constraint building submodule is used to build constraint conditions based on the shore power system operation restrictions;

[0042] The system life function construction submodule is used to construct the system life optimization objective function with the minimum cost recovery period of the port shore power system as the goal;

[0043] The transformation period function construction submodule is used to construct the transformation period optimization objective function with the minimum cost recovery period of ship shore power transformation as the goal;

[0044] The economic function construction submodule is used to construct the economic optimization objective function with the goal of minimizing government costs;

[0045] A mathematical model construction submodule, used to construct a shore power system mathematical model based on the optimization variables, the constraint conditions, the system life optimization objective function, the transformation life optimization objective function and the economic optimization objective function;

[0046] The constraints include: port net profit constraint and port berthing cost constraint after ship shore power transformation;

[0047] The government's investment in shore power system construction includes: government percentage subsidies for shore power system construction, government percentage subsidies for ship shore power transformation, government subsidies for shore power electricity prices and shore power service fees.

[0048] Preferably, the parameter indicators include:

[0049] The price of large industrial electricity, the types of ships using shore power and the number of each type, the cost of converting different types of ships, the auxiliary engine utilization rate and total auxiliary engine power of different ships, the number of port calls per year for each ship, the time of a single port call, the time when the ship is not connected to the port shore power equipment during a single port call, the total capacity of the shore power system, the total construction cost of the shore power system, the annual shore power labor cost, the main types of pollutants generated by burning fuel, the emission of each pollutant, the tax rate levied on different pollutants, the auxiliary engine fuel consumption and the auxiliary engine fuel price.

[0050] Preferably, the system life optimization objective function is as follows:

[0051]

[0052] In the above formula, PT port is the cost recovery period of the port shore power system, N1 is the number of shore power systems in the port, C sp_o is the total construction cost of the shore power system o, S port is the percentage subsidy of the government for the construction of shore power system, N2 is the number of ships at the port using shore power, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship_i is the utilization rate of ship auxiliary engines, p s is the shore power service fee, P sp_o is the capacity of the shore power system o, C l_per is the annual labor cost converted into 1kW shore power capacity, o is the shore power system, and i is the i-th ship.

[0053] Preferably, the transformation period optimization objective function is as follows:

[0054]

[0055] In the above formula, PT ship is the recovery period of the cost of shore power transformation for ships, N2 is the number of ships at the port using shore power, C ship_i is the shore power transformation cost of ship i, S ship is the government's percentage subsidy for ship shore power transformation, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship_i is the utilization rate of ship auxiliary engines, r fis the auxiliary engine fuel consumption per 1 kWh, p f is the auxiliary engine fuel price, N3 is the number of types of pollutants emitted by the auxiliary engine, E s is the emission of the sth pollutant per 1 kWh, T s is the tax rate imposed on the sth pollutant, p p is the shore power price, S price is the government subsidy for shore power price, i is the i-th ship, and s is the s-th pollutant.

[0056] Preferably, the economic optimization objective function is as follows:

[0057]

[0058] In the above formula, C g is the government cost, N1 is the number of shore power systems in the port, C sp_o is the total construction cost of the shore power system o, S port is the percentage subsidy of the government for the construction of shore power system, N2 is the number of ships at the port using shore power, C ship_i is the shore power transformation cost of ship i, S ship is the government's percentage subsidy for ship shore power transformation, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship_i is the utilization rate of ship auxiliary engines, S price is the government subsidy for shore power price, o is the shore power system, and i is the i-th ship.

[0059] In yet another aspect, the present invention further provides a computer device, comprising: one or more processors;

[0060] The processor is used to store one or more programs;

[0061] When the one or more programs are executed by the one or more processors, the above-mentioned method for determining construction investment of the shore power system is implemented.

[0062] On the other hand, the present invention further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed, the above-mentioned method for determining the construction investment of the shore power system is implemented.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] The present invention provides a method and system for determining the construction investment of a shore power system, including: obtaining shore power system equipment data based on pre-constructed parameter indicators; based on the shore power system equipment data, using a pre-constructed shore power system mathematical model, using a multi-objective genetic algorithm to solve, and obtaining the government's construction investment in the shore power system; wherein the shore power system mathematical model is constructed with the port shore power system cost recovery period, the ship shore power transformation cost recovery period and the minimum government cost as optimization objectives, combined with the constraints set for the optimization objectives; the shore power system equipment data includes: shore power system equipment capacity, ship auxiliary engine total power and ship auxiliary engine utilization rate; the government's construction investment in the shore power system is used to determine the construction and use of the shore power system. The present invention adopts the minimum port shore power cost recovery period as the objective function, more comprehensively considers the factors affecting the shore power system investment, and more comprehensively analyzes the benefits of the shore power system, so that the analyzed data and the obtained results are more convincing, and more adaptable and targeted. The government investment in shore power system construction obtained by using a multi-objective genetic algorithm gets rid of the problem of relying on the objective function to set weights, making it more reliable to determine the construction of the shore power system through the government investment in shore power system construction, thereby improving the adaptability of the shore power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A flow chart of a method for determining construction investment of a shore power system provided by the present invention;

[0066] Figure 2 A flowchart of a multi-objective genetic algorithm provided by the present invention;

[0067] Figure 3 This is a graph showing the impact of the government subsidy amount on the shore power price of the port according to an embodiment of the present invention;

[0068] Figure 4 This is a graph showing the impact of the amount of government subsidies to ports on the recovery period of the cost of the port shore power system according to an embodiment of the present invention;

[0069] Figure 5 This is a graph showing the impact of the amount of government subsidies to ports on the recovery period of the cost of ship shore power transformation according to an embodiment of the present invention;

[0070] Figure 6 This is a main structural block diagram of a shore power system construction investment determination system provided by the present invention. DETAILED DESCRIPTION

[0071] The present invention takes the cost recovery period of ship shore power transformation, government cost and port shore power system cost recovery period as the objective function, and uses the improved NSGA-II algorithm to obtain the optimal solution set.

[0072] Embodiment 1:

[0073] The present invention provides a method for determining the construction investment of a shore power system. Figure 1 As shown, including:

[0074] Step S101: Acquire shore power system equipment data based on pre-built parameter indicators;

[0075] Step S102: Based on the shore power system equipment data, a pre-built shore power system mathematical model is used to solve the problem using a multi-objective genetic algorithm to obtain the government's investment in the construction of the shore power system.

[0076] The parameter index pre-constructed in step S101 is determined according to the composition of the shore power system and the actual situation of the port shore power operation;

[0077] The shore power system includes: shore-based power supply system, ship power sales system and ship-shore connection equipment.

[0078] The specific execution process of obtaining shore power system equipment data based on the pre-built parameter indicators in step S101 is:

[0079] Statistics are collected on the historical data and the port information required by the objective function is sorted out:

[0080] Shore power service fee; large industrial price; types of ships using shore power and the number of each type; the cost of converting different types of ships; auxiliary engine utilization rate and total auxiliary engine power of different ships; annual port calls for each ship; single port call time; time not connected to port shore power equipment during a single port call; total capacity of shore power system; total construction cost of shore power system; annual shore power labor cost; government subsidy for shore power electricity price; government percentage subsidy for ship shore power conversion; government percentage subsidy for shore power system construction; several major types of pollutants generated by burning fuel; auxiliary engine fuel price; emission of each pollutant per 1kW·h; tax rates levied on different pollutants and auxiliary engine fuel consumption per 1kW·h.

[0081] Among them, the unit of emission of each pollutant per 1kW·h is g / kW·h, and the unit of tax rate levied on different pollutants is yuan / ton.

[0082] Before executing step S102, the mathematical model of the shore power system is constructed, which specifically includes:

[0083] Step A1: Taking the government's percentage subsidy for shore power system construction, the government's percentage subsidy for ship shore power transformation, the government's subsidy for shore power electricity price and shore power service fee as optimization variables, the specific implementation process is as follows:

[0084] A 4-dimensional decision vector is composed of four decision variables: the government's percentage subsidy for shore power system construction, the government's percentage subsidy for ship shore power transformation, the government's subsidy for shore power electricity price and the shore power service fee.

[0085] Step A2: Construct constraints based on the shore power system operation restrictions. The specific implementation process is as follows:

[0086] The constraints required should include: the net profit of the port is positive and the cost of the ship's berthing after the shore power transformation is less than before the transformation. This process includes the following steps:

[0087] (1) The port net profit is required to be positive, and the mathematical expression is:

[0088]

[0089] In the above formula, N2 is the number of ships at the port using shore power, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship_i is the utilization rate of ship auxiliary engines, p s is the shore power service fee, P sp_o is the capacity of the shore power system o, C l_per is the annual labor cost converted into 1kW shore power capacity, i is the i-th ship, and N1 is the number of shore power systems in the port.

[0090] The constraints can be simplified and ships can be classified by tonnage: first-class ships: over 1,600 gross tonnage; second-class ships: over 600 gross tonnage and under 1,600 gross tonnage; third-class ships: over 200 gross tonnage and under 600 gross tonnage; fourth-class ships: over 50 gross tonnage or under 200 gross tonnage; fifth-class ships: under 50 gross tonnage.

[0091] The original constraints are simplified to:

[0092]

[0093] In the above formula, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship is the fixed value of the utilization rate of ship auxiliary engines, p s is the shore power service fee, P sp_o is the capacity of the shore power system o, C l_peris the annual labor cost converted into 1kW shore power capacity, o is the shore power system, and i is the i-th ship.

[0094] (2) The cost of berthing after the ship shore power transformation is required to be less than that before the transformation. The mathematical expression is:

[0095]

[0096] In the above formula, N2 is the number of ships at the port using shore power, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship_i is the utilization rate of ship auxiliary engines, r f is the auxiliary engine fuel consumption per 1 kWh, p f is the auxiliary engine fuel price, E s is the emission of the sth pollutant per 1 kWh, T s is the tax rate imposed on the sth pollutant, p p is the shore power price, S price is the government subsidy for shore power price, s is the sth pollutant, and N3 is the number of types of pollutants emitted by the auxiliary engine.

[0097] The original constraints are simplified to:

[0098]

[0099] In the above formula, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship is the fixed value of the utilization rate of ship auxiliary engines, r f is the auxiliary engine fuel consumption per 1 kWh, p f is the auxiliary engine fuel price, E s is the emission of the sth pollutant per 1 kWh, T s is the tax rate imposed on the sth pollutant, p p is the shore power price, S price is the government subsidy for shore power price, s is the sth pollutant, N3 is the number of types of pollutants emitted by auxiliary engines, and i is the i-th ship.

[0100] Step A3: Construct the system life optimization objective function with the goal of minimizing the cost recovery period of the port shore power system. The specific implementation process is as follows:

[0101] Construct an objective function, which includes three objective functions: the cost recovery period of the port shore power system, the cost recovery period of the ship shore power transformation, and the government cost. The cost recovery period of the port shore power system is:

[0102] Cost recovery period of port shore power system:

[0103]

[0104] The mathematical expression is:

[0105]

[0106] In the above formula, PT port is the cost recovery period of the port shore power system, N1 is the number of shore power systems in the port, C sp_o is the total construction cost of the shore power system o, S port is the percentage subsidy of the government for the construction of shore power system, N2 is the number of ships at the port using shore power, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship_i is the utilization rate of ship auxiliary engines, p s is the shore power service fee, P sp_o is the capacity of the shore power system o, C l_per is the annual labor cost converted into 1kW shore power capacity, o is the shore power system, and i is the i-th ship.

[0107] Among them, shore power service fee p s Use the formula to solve: p p =p s +p it

[0108] In the above formula, p p is the shore power price, p s is the shore power service fee, p it For large industrial electricity prices.

[0109] The objective function can be simplified and ships can be classified by tonnage: first-class ships: over 1,600 gross tonnage; second-class ships: over 600 gross tonnage and under 1,600 gross tonnage; third-class ships: over 200 gross tonnage and under 600 gross tonnage; fourth-class ships: over 50 gross tonnage or under 200 gross tonnage; fifth-class ships: under 50 gross tonnage.

[0110] Let i be the ship class, P ship_i is the average power of auxiliary engines of ship i, m iis the number of port calls per year for ships of class i, t i is the average berthing time of a ship of type i, r ship_i is the utilization rate of ship auxiliary engines, and each grade of ship takes a fixed value r ship .

[0111] The original objective function is simplified to:

[0112]

[0113] In the above formula, PT port is the cost recovery period of the port shore power system, N1 is the number of shore power systems in the port, C sp_o is the total construction cost of the shore power system o, S port is the percentage subsidy provided by the government for shore power system construction, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship is the fixed value of the utilization rate of ship auxiliary engines, p s is the shore power service fee, P sp_o is the capacity of the shore power system o, C l_per is the annual labor cost converted into 1kW shore power capacity, o is the shore power system, and i is the i-th ship.

[0114] Step A4: Construct the optimization objective function of the transformation period with the goal of minimizing the cost recovery period of ship shore power transformation. The specific execution process is as follows:

[0115] Cost recovery period for ship shore power transformation:

[0116]

[0117] The mathematical expression is:

[0118]

[0119] In the above formula, PT ship is the recovery period of the cost of shore power transformation for ships, N2 is the number of ships at the port using shore power, C ship_i is the shore power transformation cost of ship i, S ship is the government's percentage subsidy for ship shore power transformation, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship_i is the utilization rate of ship auxiliary engines, rf is the auxiliary engine fuel consumption per 1 kWh, p f is the auxiliary engine fuel price, N3 is the number of types of pollutants emitted by the auxiliary engine, E s is the emission of the sth pollutant per 1 kWh, T s is the tax rate imposed on the sth pollutant, p p is the shore power price, S price is the government subsidy for shore power price, i is the i-th ship, and s is the s-th pollutant.

[0120] The original objective function is simplified:

[0121]

[0122] In the above formula, PT ship is the recovery period of the cost of shore power transformation for ships, N2 is the number of ships at the port using shore power, C ship_i is the shore power transformation cost of ship i, S ship is the government's percentage subsidy for ship shore power transformation, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship is the fixed value of the utilization rate of ship auxiliary engines, r f is the auxiliary engine fuel consumption per 1 kWh, p f is the auxiliary engine fuel price, N3 is the number of types of pollutants emitted by the auxiliary engine, E s is the emission of the sth pollutant per 1 kWh, T s is the tax rate imposed on the sth pollutant, p p is the shore power price, S price is the government subsidy for shore power price, i is the i-th ship, and s is the s-th pollutant.

[0123] Step A5: Construct an economic optimization objective function with the goal of minimizing government costs. The specific implementation process of Step A5 is as follows:

[0124] Cost to government:

[0125] Government cost = Government subsidies for port shore power system construction

[0126] +Government subsidies for ship shore power transformation

[0127] +Government subsidies for shore power prices

[0128] The mathematical expression is:

[0129]

[0130] In the above formula, C g is the government cost, N1 is the number of shore power systems in the port, C sp_o is the total construction cost of the shore power system o, S port is the percentage subsidy of the government for the construction of shore power system, N2 is the number of ships at the port using shore power, C ship_i is the shore power transformation cost of ship i, S ship is the government's percentage subsidy for ship shore power transformation, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship_i is the utilization rate of ship auxiliary engines, S price It is the government’s subsidy for shore electricity prices.

[0131] C ship is the average shore power transformation cost of ships. The average shore power transformation cost of inland ships is different from that of riverine ships. The original objective function can be simplified as follows:

[0132]

[0133] In the above formula, C g is the government cost, N1 is the number of shore power systems in the port, C sp_o is the total construction cost of the shore power system o, S port is the percentage subsidy of the government for the construction of shore power system, N2 is the number of ships at the port using shore power, C ship is the average shore power conversion cost of ships, S ship is the government's percentage subsidy for ship shore power transformation, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship is the fixed value of the utilization rate of ship auxiliary engines, S price It is the government’s subsidy for shore electricity prices.

[0134] Step A6: constructing a shore power system mathematical model based on the optimization variables, the constraint conditions, the system life optimization objective function, the transformation life optimization objective function and the economic optimization objective function. The specific execution process is as follows:

[0135] The steps to construct a mathematical model of the economic benefits of a complete shore power system are as follows:

[0136] A 4-dimensional decision vector is composed of four decision variables: the government's percentage subsidy for shore power system construction, the government's percentage subsidy for ship shore power transformation, the government's subsidy for shore power electricity price and the shore power service fee.

[0137] x=(S port ,S ship ,S price ,p s )

[0138] Specifies the domain of the decision vector.

[0139] In the above formula, x is the decision vector, S port S is the percentage subsidy provided by the government for shore power system construction. ship is the government's percentage subsidy for ship shore power transformation, S price is the government subsidy for shore power prices, p s Shore power service fee.

[0140] The three objective functions of port shore power system cost recovery period, ship shore power transformation cost recovery period and government cost are taken as the three-dimensional objective vector, and minimizing these three objective functions is the best.

[0141] F(x)=(f1(x),f2(x),f3(x))

[0142] f1(x)=PT port

[0143] f2(x)=PT ship

[0144] f3(x)=C g

[0145] In the above formula, F(x) is the optimal value of the objective function, f1(x) is the objective function of the cost recovery period of the port shore power system, f2(x) is the objective function of the cost recovery period of the ship shore power transformation, f3(x) is the government cost objective function, PT port The cost recovery period of the port shore power system is PT ship is the recovery period of the cost of ship shore power transformation, C g Cost to the government.

[0146] The net profit of the port is positive and the cost of the ship at the port after the shore power transformation is less than before the transformation as two constraints:

[0147] g1(x)>0

[0148] g2(x)>0

[0149] In the above formula, g1(x) is the net profit of the port, and g2(x) is the cost of the ship at the port after the shore power conversion.

[0150] In the step of constructing the mathematical model of the shore power system, the factors affecting the investment in the shore power system are fully considered, making the analyzed data and the obtained solutions more convincing; and compared with the existing objective function, the cost recovery period of the port shore power system is considered as the objective function, which makes the analysis of the investment in the shore power system more adaptable and targeted.

[0151] The equipment data in step S102 includes: the capacity of shore power system equipment, the total power of ship auxiliary machinery and the utilization rate of ship auxiliary machinery.

[0152] The government's investment in the construction of the shore power system in step S102 is used to determine the construction and use of the shore power system.

[0153] In step S102, based on the shore power system equipment data, a pre-built shore power system mathematical model is used to solve the problem using a multi-objective genetic algorithm to obtain the government's investment in the construction of the shore power system. The specific execution process is as follows:

[0154] Combination Figure 2 ,The steps of using the genetic algorithm based on NSGA-II to process the mathematical model of the shore power system and obtain the Pareto solution set are as follows:

[0155] 1. Set the initial population, that is, the initial parent population P0, within the specified decision vector definition domain, and randomly generate N decision vectors x1,…,x1 under the two constraints. N , the individuals that make up the parent population.

[0156] 2. Select the parent population P1 for mating, the population number is N. This embodiment adopts the binary selection method, randomly selects 2 individuals from the N initial parent populations, selects the individual with the best fitness and puts it into the offspring population, and after repeating the above steps N times, a parent population P1 with a size of N is generated.

[0157] In particular, parent individuals with good fitness are likely to be repeatedly selected.

[0158] The fitness is determined by the non-dominated level and crowding degree of the decision vector x of the parent population. The basis for judging the fitness is as follows:

[0159] (1) If the non-dominated rank of x1 is better than that of x2, that is, if the non-dominated rank of x1 is less than that of x2, that is, Rank_x1<Rank_x2, then the fitness of x1 is better than that of x2.

[0160] (2) If Rank_x1 = Rank_x2, but the crowding distance of x1 is larger than that of x2, that is, d_x1 > d_x2, the fitness of x1 is better than that of x2.

[0161] The non-dominated level of x is calculated as follows:

[0162] Step 1: From the decision vector set C: {x1,...,x N}Select the first-level non-dominated solution set.

[0163] Select the first element x i As the benchmark mid-bound, x i Compare with other N-1 individuals, perform the first round of sorting, and exclude those that are x i The dominated individuals and the excluded individuals form the set W1, and the remaining individuals form C1. i is not dominated by individuals in C1, x i is the first-level non-dominated solution. Select the first element of C1 as the benchmark mid-bound, repeat the above operation for the second round of sorting, and obtain the set W2, C2. After the mth sorting, C m There is only one individual in , and if there are n non-dominated solutions among the m benchmark intermediate bounds of the m-times sorting, then these n non-dominated solutions constitute the first-level non-dominated solution set.

[0164] Among them, x i Compare based on the size of F(x).

[0165] Step 2: Repeat the first step with the remaining individual sets to select the second-level non-dominated solutions. The non-dominated solution sets of the remaining levels can be obtained in turn by repeating the above operations.

[0166] The crowding degree of x is calculated as follows:

[0167]

[0168] In the above formula, j is 1, 2, or 3; when j is 1, f j (x i+1 ) is the system life optimization objective function value of the i+1th individual, f j (x i-1 ) is the system life optimization objective function value of the i-1th individual; when j is 2, f j (x i+1 ) is the optimization objective function value of the transformation period of the i+1th individual, f j (x i-1 ) is the optimization objective function value of the transformation period of the i-1th individual; when j is 3, f j (x i+1 ) is the value of the economic optimization objective function of the i+1th individual, f j (x i-1 ) is the value of the economic optimization objective function of the i-1th individual; d_x i is the crowding value of the ith individual.

[0169] Among them, xi+1 and x i-1 is the distance x i The two closest individuals are set as the two individuals on the boundary x0, x N The crowding degree is infinite;

[0170] The distance sorting rule between individuals is:

[0171] f j (x i-2 )>f j (x i-1 )>f j (x i )>f j (x i+1 )>f j (x i+2 )

[0172] In the above formula, j = 1, 2, 3, i = [3, N-2]

[0173] 3. Parent population P1 (P k ) to generate a progeny population by crossover mutation, the number of which is N. First, set the crossover probability P c , mutation probability P v . Randomly generate a random number a in [0,1], if a<P c , indicating that the offspring Q1(Q k ) are two new individuals from the parent P1(P k ) are generated by crossover of two random individuals, and the above operation is repeated several times until the new individual population S1(S k )The population size is N.

[0174] It is also necessary to determine the population S1(S k ) Whether the individual will mutate, randomly generate a random number b in [0,1], if b<P v , individuals mutate, affecting the population S1(S k ) performs the above operation on each individual, and finally generates the offspring population Q1(Q k ). Among them, the crossover operator is:

[0175]

[0176] in,

[0177] In the above formula, q k,o is the population Q k The oth individual in, β is the crossover distribution factor, p k,j For population P k The jth individual in q k,o+1 is the population Q kThe o+1th individual, u c is a random number in [0,1), η c is the cross-distribution index, p k,i For population P k The i-th individual in , k is the set number of iterations, and K is the set final number of iterations.

[0178] Among them, the mutation operator is a polynomial mutation operator, and the mutation operator is δ:

[0179]

[0180] in,

[0181] In the above formula, δ is the variation factor, u v is a random number in [0,1), η v is the adaptive mutation distribution index, k is the set number of iterations, and K is the set final number of iterations.

[0182]

[0183] For population S k All individuals in k The maximum value of the decision variable; For population S k All individuals in k The minimum value of the decision variable.

[0184] 4. Use the elite strategy to generate a new parent population P2 (P k+1 ). The parent population P1(P k ) and the offspring population Q1(Q k ) merged to form population R1(R k ), R1(R k ) population is 2N. k ) individuals to calculate the non-dominated hierarchy and crowding degree (only individuals in the offspring population need to be calculated), from R1 (R k ) select N individuals (with better fitness first) from all individuals to generate a new parent population P2 (P k+1 ).

[0185] 5. Use the new parent population P2 (P k+1 ) Repeat 6) until the number of iterations reaches our requirement, and finally the Pareto front of the objective function is obtained.

[0186] 6. Use the obtained Pareto frontier to analyze the economic benefits of shore power system operation and obtain the best scenario that meets the requirements.

[0187] Based on the constructed mathematical model and selected model data, and with the addition of government subsidies, the optimal recovery period of the port shore power system cost and the ship shore power transformation cost is calculated. The required data is calculated according to the formula and brought into the mathematical model, see the attached Figure 3 , 4 and 5. Figure 3 This reflects the impact of government subsidies on the shore power price of ports. Figure 4 This reflects the impact of the amount of government subsidies to ports on the cost recovery period of port shore power systems. Figure 5 It reflects the impact of the amount of government subsidies to ports on the recovery period of the cost of ship shore power transformation.

[0188] From the mathematical model of port cost recovery period and ship cost recovery period model, it can be seen that with the increase of government subsidy amount, the recovery period of port shore power system cost and ship shore power transformation cost will be significantly shortened. When the port wants to recover the transformation cost within five years, Figure 3 It can be seen that as the amount of government subsidies increases, the price of shore power in the port will decrease. When the amount of government subsidies is 72.1549 million yuan, the electricity cost of the ship before and after the shore power transformation is the same. Therefore, when the government subsidy amount is less than 72.1549 million yuan, if the port transformation is expected to recover the cost in five years, the fee charged to the docked ships will be more than 0.7 yuan / (kW·h), and the electricity cost of the ship will be greater than the power generation cost of the ship that has not been transformed. When the government subsidy amount is greater than 72.1549 million yuan, the port charges less than 0.7 yuan / (kW·h) to docked ships within the expected cost recovery period. At this time, the cost to the ship users is lower than the power generation cost of the ship before the transformation. From Figure 4 and Figure 5 It can be seen that when the government subsidizes the port, as the subsidy amount increases, the recovery period of the port shore power system cost and the recovery period of the ship shore power transformation cost will decrease, indicating that the government subsidy to the port can also effectively reduce the recovery period of the port shore power system cost and the recovery period of the ship shore power transformation cost. If the port is expected to recover the cost in the fifth year, then when the government subsidy is 25% of the port and ship costs respectively, the shore power usage price is 0.67 yuan / (kW·h), at which time the recovery period of the port shore power system cost and the recovery period of the ship shore power transformation cost are 4.3 years and 3.7 years respectively.

[0189] Let the decision vector set have three elements

[0190] Decision vector 1 (x1, x2, x3, x4), corresponding to the objective function vector 1 (a1, b1, c1, d1)

[0191] Decision vector 2 (y1, y2, y3, y4), corresponding to the objective function vector 2 (a2, b2, c2, d2)

[0192] Decision vector 3 (z1, z2, z3, z4), corresponding to the objective function vector 3 (a3, b3, c3, d3)

[0193] If c1=c2=c3, d1=d2=d3, a1>a2>a3, b1<b2, b2>b3.

[0194] Larger is better, decision vector 2 is better than decision vector 3, and decision vector 3 does not belong to the Pareto optimal set.

[0195] Decision vector 1 and decision vector 2 cannot be compared. They have their own advantages in objective functions a and b. Decision vector 1 and decision vector 2 belong to the Pareto optimal set.

[0196] In this step, a multi-objective genetic algorithm is used to solve the problem, which avoids inconsistent units between objectives of different natures, makes it inappropriate to set appropriate weights, and the allocation of weighted values ​​of each objective is highly subjective. Different weights produce different optimal solutions, and the Pareto optimal solution set can be found in one algorithm process. It is not limited to the shape and continuity of the Pareto frontier, and is easy to handle discontinuous and concave Pareto fronts, which facilitates the construction investment analysis of the shore power operation system, making the results more comprehensive and convincing, which is conducive to the construction and use of the shore power system.

[0197] Embodiment 2:

[0198] The present invention provides a system for determining the construction investment of a shore power system. Figure 6 As shown, including:

[0199] A data acquisition module, used to acquire shore power system equipment data based on pre-built parameter indicators;

[0200] The model solving module is used to solve the shore power system equipment data using a pre-built shore power system mathematical model and a multi-objective genetic algorithm to obtain the government's investment in the construction of the shore power system.

[0201] Among them, the parameter indicators pre-built in the data acquisition module are determined according to the composition of the shore power system and the actual situation of the port shore power operation;

[0202] The shore power system includes: shore-based power supply system, ship power sales system and ship-shore connection equipment.

[0203] The specific execution process of the data acquisition module is as follows:

[0204] Statistics are collected on the historical data and the port information required by the objective function is sorted out:

[0205] Shore power service fee; large industrial price; types of ships using shore power and the number of each type; the cost of converting different types of ships; auxiliary engine utilization rate and total auxiliary engine power of different ships; annual port calls for each ship; single port call time; time not connected to port shore power equipment during a single port call; total capacity of shore power system; total construction cost of shore power system; annual shore power labor cost; government subsidy for shore power electricity price; government percentage subsidy for ship shore power conversion; government percentage subsidy for shore power system construction; several major types of pollutants generated by burning fuel; auxiliary engine fuel price; emission of each pollutant per 1kW·h; tax rates levied on different pollutants and auxiliary engine fuel consumption per 1kW·h.

[0206] Among them, the unit of emission of each pollutant per 1kW·h is g / kW·h, and the unit of tax rate levied on different pollutants is yuan / ton.

[0207] The system also includes a model building module, which specifically includes:

[0208] The variable setting submodule is used to optimize the variables by taking the government's percentage subsidy for shore power system construction, the government's percentage subsidy for ship shore power transformation, the government's subsidy for shore power electricity price and shore power service fee in the government's investment in shore power system construction;

[0209] The constraint building submodule is used to build constraint conditions based on the shore power system operation restrictions;

[0210] The system life function construction submodule is used to construct the system life optimization objective function with the minimum cost recovery period of the port shore power system as the goal;

[0211] The transformation period function construction submodule is used to construct the transformation period optimization objective function with the minimum cost recovery period of ship shore power transformation as the goal;

[0212] The economic function construction submodule is used to construct the economic optimization objective function with the goal of minimizing government costs;

[0213] The mathematical model construction submodule is used to construct a mathematical model of the shore power system based on the optimization variables, the constraint conditions, the system life optimization objective function, the transformation life optimization objective function and the economic optimization objective function.

[0214] Among them, the specific execution process of the variable setting submodule is:

[0215] A 4-dimensional decision vector is composed of four decision variables: the government's percentage subsidy for shore power system construction, the government's percentage subsidy for ship shore power transformation, the government's subsidy for shore power electricity price and the shore power service fee.

[0216] The specific execution process of the constraint construction submodule is as follows:

[0217] The constraints required should include: the port net profit is positive and the port cost after the ship shore power transformation is less than before the transformation. This process includes the following steps:

[0218] (1) The port net profit is required to be positive, and the mathematical expression is:

[0219]

[0220] In the above formula, N2 is the number of ships at the port using shore power, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship_i is the utilization rate of ship auxiliary engines, p s is the shore power service fee, P sp_o is the capacity of the shore power system o, C l_per is the annual labor cost converted into 1kW shore power capacity, i is the i-th ship, and N1 is the number of shore power systems in the port.

[0221] The constraints can be simplified and ships can be classified by tonnage: first-class ships: over 1,600 gross tonnage; second-class ships: over 600 gross tonnage and under 1,600 gross tonnage; third-class ships: over 200 gross tonnage and under 600 gross tonnage; fourth-class ships: over 50 gross tonnage or under 200 gross tonnage; fifth-class ships: under 50 gross tonnage.

[0222] The original constraints are simplified to:

[0223]

[0224] In the above formula, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship is the fixed value of the utilization rate of ship auxiliary engines, p s is the shore power service fee, P sp_o is the capacity of the shore power system o, C l_peris the annual labor cost converted into 1kW shore power capacity, o is the shore power system, and i is the i-th ship.

[0225] (2) The cost of berthing after the ship shore power transformation is required to be less than that before the transformation. The mathematical expression is:

[0226]

[0227] In the above formula, N2 is the number of ships at the port using shore power, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship_i is the utilization rate of ship auxiliary engines, r f is the auxiliary engine fuel consumption per 1 kWh, p f is the auxiliary engine fuel price, E s is the emission of the sth pollutant per 1 kWh, T s is the tax rate imposed on the sth pollutant, p p is the shore power price, S price is the government subsidy for shore power price, s is the sth pollutant, and N3 is the number of types of pollutants emitted by the auxiliary engine.

[0228] The original constraints are simplified to:

[0229]

[0230] In the above formula, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship is the fixed value of the utilization rate of ship auxiliary engines, r f is the auxiliary engine fuel consumption per 1 kWh, p f is the auxiliary engine fuel price, E s is the emission of the sth pollutant per 1 kWh, T s is the tax rate imposed on the sth pollutant, p p is the shore power price, S price is the government subsidy for shore power price, s is the sth pollutant, N3 is the number of types of pollutants emitted by auxiliary engines, and i is the i-th ship.

[0231] The specific execution process of the system age function construction submodule is as follows:

[0232] The objective functions constructed include three objective functions: the cost recovery period of the port shore power system, the cost recovery period of the ship shore power transformation, and the government cost. This process includes the following steps:

[0233] Cost recovery period of port shore power system:

[0234]

[0235] The mathematical expression is:

[0236]

[0237] In the above formula, PT port is the cost recovery period of the port shore power system, N1 is the number of shore power systems in the port, C sp_o is the total construction cost of the shore power system o, S port is the percentage subsidy of the government for the construction of shore power system, N2 is the number of ships at the port using shore power, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship_i is the utilization rate of ship auxiliary engines, p s is the shore power service fee, P sp_o is the capacity of the shore power system o, C l_per is the annual labor cost converted into 1kW shore power capacity, o is the shore power system, and i is the i-th ship.

[0238] Among them, shore power service fee p s Use the formula to solve: p p =p s +p it

[0239] In the above formula, p p is the shore power price, p s is the shore power service fee, p it For large industrial electricity prices.

[0240] The objective function can be simplified and ships can be classified by tonnage: first-class ships: over 1,600 gross tonnage; second-class ships: over 600 gross tonnage and under 1,600 gross tonnage; third-class ships: over 200 gross tonnage and under 600 gross tonnage; fourth-class ships: over 50 gross tonnage or under 200 gross tonnage; fifth-class ships: under 50 gross tonnage.

[0241] Let i be the ship class, P ship_i is the average power of auxiliary engines of ship i, m i is the number of port calls per year for ships of class i, t iis the average berthing time of a ship of type i, r ship_i is the utilization rate of ship auxiliary engines, and each grade of ship takes a fixed value r ship .

[0242] The original objective function is simplified to:

[0243]

[0244] In the above formula, PT port is the cost recovery period of the port shore power system, N1 is the number of shore power systems in the port, C sp_o is the total construction cost of the shore power system o, S port is the percentage subsidy provided by the government for shore power system construction, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship is the fixed value of the utilization rate of ship auxiliary engines, p s is the shore power service fee, P sp_o is the capacity of the shore power system o, C l_per is the annual labor cost converted into 1kW shore power capacity, o is the shore power system, and i is the i-th ship.

[0245] The specific execution process of the transformation years function construction submodule is as follows:

[0246] Cost recovery period for ship shore power transformation:

[0247]

[0248] The mathematical expression is:

[0249]

[0250] In the above formula, PT ship is the recovery period of the cost of shore power transformation for ships, N2 is the number of ships at the port using shore power, C ship_i is the shore power transformation cost of ship i, S ship is the government's percentage subsidy for ship shore power transformation, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship_i is the utilization rate of ship auxiliary engines, r f is the auxiliary engine fuel consumption per 1 kWh, p fis the auxiliary engine fuel price, N3 is the number of types of pollutants emitted by the auxiliary engine, E s is the emission of the sth pollutant per 1 kWh, T s is the tax rate imposed on the sth pollutant, p p is the shore power price, S price is the government subsidy for shore power price, i is the i-th ship, and s is the s-th pollutant.

[0251] The original objective function is simplified:

[0252]

[0253] In the above formula, PT ship is the recovery period of the cost of shore power transformation for ships, N2 is the number of ships at the port using shore power, C ship_i is the shore power transformation cost of ship i, S ship is the government's percentage subsidy for ship shore power transformation, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship is the fixed value of the utilization rate of ship auxiliary engines, r f is the auxiliary engine fuel consumption per 1 kWh, p f is the auxiliary engine fuel price, N3 is the number of types of pollutants emitted by the auxiliary engine, E s is the emission of the sth pollutant per 1 kWh, T s is the tax rate imposed on the sth pollutant, p p is the shore power price, S price is the government subsidy for shore power price, i is the i-th ship, and s is the s-th pollutant.

[0254] The specific execution process of the economic function construction submodule is as follows:

[0255] Cost to government:

[0256] Government cost = Government subsidies for port shore power system construction

[0257] +Government subsidies for ship shore power transformation

[0258] +Government subsidies for shore power prices

[0259] The mathematical expression is:

[0260]

[0261] In the above formula, C gis the government cost, N1 is the number of shore power systems in the port, C sp_o is the total construction cost of the shore power system o, S port is the percentage subsidy of the government for the construction of shore power system, N2 is the number of ships at the port using shore power, C ship_i is the shore power transformation cost of ship i, S ship is the government's percentage subsidy for ship shore power transformation, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship_i is the utilization rate of ship auxiliary engines, S price It is the government’s subsidy for shore electricity prices.

[0262] C ship is the average shore power transformation cost of ships. The average shore power transformation cost of inland ships is different from that of riverine ships. The original objective function can be simplified as follows:

[0263] The original objective function is simplified:

[0264]

[0265] In the above formula, C g is the government cost, N1 is the number of shore power systems in the port, C sp_o is the total construction cost of the shore power system o, S port is the percentage subsidy of the government for the construction of shore power system, N2 is the number of ships at the port using shore power, C ship is the average shore power conversion cost of ships, S ship is the government's percentage subsidy for ship shore power transformation, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship is the fixed value of the utilization rate of ship auxiliary engines, S price It is the government’s subsidy for shore electricity prices.

[0266] The specific execution process of the mathematical model construction submodule is as follows:

[0267] The steps to construct a mathematical model of the economic benefits of a complete shore power system are as follows:

[0268] A 4-dimensional decision vector is composed of four decision variables: the government's percentage subsidy for shore power system construction, the government's percentage subsidy for ship shore power transformation, the government's subsidy for shore power electricity price and the shore power service fee.

[0269] x=(S port ,S ship ,S price ,p s )

[0270] Specifies the domain of the decision vector.

[0271] In the above formula, x is the decision vector, S port S is the percentage subsidy provided by the government for shore power system construction. ship is the government's percentage subsidy for ship shore power transformation, S price is the government subsidy for shore power prices, p s Shore power service fee.

[0272] The three objective functions of port shore power system cost recovery period, ship shore power transformation cost recovery period and government cost are taken as the three-dimensional objective vector, and minimizing these three objective functions is the best.

[0273] F(x)=(f1(x),f2(x),f3(x))

[0274] f1(x)=PT port

[0275] f2(x)=PT ship

[0276] f3(x)=C g

[0277] In the above formula, F(x) is the optimal value of the objective function, f1(x) is the objective function of the cost recovery period of the port shore power system, f2(x) is the objective function of the cost recovery period of the ship shore power transformation, f3(x) is the government cost objective function, PT port The cost recovery period of the port shore power system is PT ship is the recovery period of the cost of ship shore power transformation, C g Cost to the government.

[0278] The net profit of the port is positive and the cost of the ship at the port after the shore power transformation is less than before the transformation as two constraints:

[0279] g1(x)>0

[0280] g2(x)>0

[0281] In the above formula, g1(x) is the net profit of the port, and g2(x) is the cost of the ship at the port after the shore power conversion.

[0282] In the process of using the model building module to build the mathematical model of the shore power system, the factors affecting the investment in the shore power system are fully considered, making the analyzed data and the resulting solutions more convincing; and compared with the existing objective function, the cost recovery period of the port shore power system is considered as the objective function, which makes the analysis of the investment in the shore power system more adaptable and targeted.

[0283] The equipment data in the model solving module include: shore power system equipment capacity, total power of ship auxiliary engines and utilization rate of ship auxiliary engines.

[0284] The government's investment in the shore power system in the model solution module is used to determine the construction and use of the shore power system.

[0285] The specific execution process of the model solving module is as follows:

[0286] Combination Figure 2 ,The process of using the genetic algorithm based on NSGA-II to process the mathematical model of the shore power system and obtain the Pareto solution set is as follows:

[0287] 1. Set the initial population, that is, the initial parent population P0, within the specified decision vector definition domain, and randomly generate N decision vectors x1,…,x1 under the two constraints. N , the individuals that make up the parent population.

[0288] 2. Select the parent population P1 for mating, the population number is N. This embodiment adopts the binary selection method, randomly selects 2 individuals from the N initial parent populations, selects the individual with the best fitness and puts it into the offspring population, and after repeating the above steps N times, a parent population P1 with a size of N is generated.

[0289] In particular, parent individuals with good fitness are likely to be repeatedly selected.

[0290] The fitness is determined by the non-dominated level and crowding degree of the decision vector x of the parent population. The basis for judging the fitness is as follows:

[0291] 1. If the non-dominated rank of x1 is better than that of x2, that is, the non-dominated rank of x1 is less than that of x2, that is, Rank_x1<Rank_x2, then the fitness of x1 is better than that of x2.

[0292] 2. If Rank_x1=Rank_x2, but the crowding distance of x1 is larger than that of x2, that is, d_x1>d_x2, the fitness of x1 is better than that of x2.

[0293] The non-dominated level of x is calculated as follows:

[0294] Step 1: From the decision vector set C: {x1,...,x N}Select the first level non-dominated solution set

[0295] Select the first element x1 as the base mid-bound, x i Compare with other N-1 individuals, perform the first round of sorting, and exclude those that are x i The dominated individuals and excluded individuals form the set W1, and the remaining individuals form C1. If x1 is not dominated by an individual in C1, x i is the first-level non-dominated solution. Select the first element of C1 as the benchmark mid-bound, repeat the above operation for the second round of sorting, and obtain the set W2, C2. After the mth sorting, C m There is only one individual in . If n of the m benchmark intermediate bounds of the m-ordering are non-dominated solutions, then these n non-dominated solutions constitute the first-level non-dominated solution set.

[0296] Among them, x i Compare based on the size of F(x).

[0297] Step 2: Repeat the first step with the remaining individual sets to select the second-level non-dominated solutions. The non-dominated solution sets of the remaining levels can be obtained in turn by repeating the above operations.

[0298] The crowding degree of x is calculated as follows:

[0299]

[0300] In the above formula, j is 1, 2, or 3; when j is 1, f j (x i+1 ) is the system life optimization objective function value of the i+1th individual, f j (x i-1 ) is the system life optimization objective function value of the i-1th individual; when j is 2, f j (x i+1 ) is the optimization objective function value of the transformation period of the i+1th individual, f j (x i-1 ) is the optimization objective function value of the transformation period of the i-1th individual; when j is 3, f j (x i+1 ) is the value of the economic optimization objective function of the i+1th individual, f j (x i-1 ) is the value of the economic optimization objective function of the i-1th individual; d_x i is the crowding value of the ith individual.

[0301] Among them, x i+1 and x i-1 is the distance x i The two closest individuals are set as the two individuals on the boundary x0, x N The crowding degree is infinite;

[0302] The distance sorting rule between individuals is:

[0303] f j (x i-2 )>f j (x i-1 )>f j (x i )>f j (x i+1 )>f j (x i+2 )

[0304] In the above formula, j = 1, 2, 3, i = [3, N-2]

[0305] 3. Parent population P1 (P k ) to generate a progeny population by crossover mutation, the number of which is N. First, set the crossover probability P c , mutation probability P v . Randomly generate a random number a in [0,1], if a<P c , indicating that the offspring Q1(Q k ) are two new individuals from the parent P1(P k ) are generated by crossover of two random individuals, and the above operation is repeated several times until the new individual population S1(S k )The population size is N.

[0306] It is also necessary to determine the population S1(S k ) Whether the individual will mutate, randomly generate a random number b in [0,1], if b<P v , individuals mutate, affecting the population S1(S k ) performs the above operation on each individual, and finally generates the offspring population Q1(Q k ). Among them, the crossover operator is:

[0307]

[0308] in,

[0309] In the above formula, q k,o is the population Q k The oth individual in, β is the crossover distribution factor, p k,j For population P k The jth individual in q k,o+1 is the population Q k The o+1th individual, u c is a random number in [0,1), η c is the cross-distribution index, p k,i For population P kThe i-th individual in , k is the set number of iterations, and K is the set final number of iterations.

[0310] Among them, the mutation operator is a polynomial mutation operator, and the mutation operator is δ:

[0311]

[0312] in,

[0313] In the above formula, δ is the variation factor, u v is a random number in [0,1), η v is the adaptive mutation distribution index, k is the set number of iterations, and K is the set final number of iterations.

[0314]

[0315] For population S k All individuals in k The maximum value of the decision variable; For population S k All individuals in k The minimum value of the decision variable.

[0316] 4. Use the elite strategy to generate a new parent population P2 (P k+1 ). The parent population P1(P k ) and the offspring population Q1(Q k ) merged to form population R1(R k ), R1(R k ) population is 2N. k ) individuals to calculate the non-dominated hierarchy and crowding degree (only individuals in the offspring population need to be calculated), from R1 (R k ) select N individuals (with better fitness first) from all individuals to generate a new parent population P2 (P k+1 ).

[0317] 5. Use the new parent population P2 (P k+1 ) Repeat 6) until the number of iterations reaches our requirement, and finally the Pareto front of the objective function is obtained.

[0318] 6. Use the obtained Pareto frontier to analyze the economic benefits of shore power system operation and obtain the best scenario that meets the requirements.

[0319] Based on the constructed mathematical model and selected model data, and with the addition of government subsidies, the optimal recovery period of the port shore power system cost and the ship shore power transformation cost is calculated. The required data is calculated according to the formula and brought into the mathematical model, see the attached Figure 3 , 4 and 5. Figure 3 This reflects the impact of government subsidies on the shore power price of ports. Figure 4 This reflects the impact of the amount of government subsidies to ports on the cost recovery period of port shore power systems. Figure 5 It reflects the impact of the amount of government subsidies to ports on the recovery period of the cost of ship shore power transformation.

[0320] From the mathematical model of port cost recovery period and ship cost recovery period model, it can be seen that with the increase of government subsidy amount, the recovery period of port shore power system cost and ship shore power transformation cost will be significantly shortened. When the port wants to recover the transformation cost within five years, Figure 3 It can be seen that as the amount of government subsidies increases, the price of shore power in the port will decrease. When the amount of government subsidies is 72.1549 million yuan, the electricity cost of the ship before and after the shore power transformation is the same. Therefore, when the government subsidy amount is less than 72.1549 million yuan, if the port transformation is expected to recover the cost in five years, the fee charged to the docked ships will be more than 0.7 yuan / (kW·h), and the electricity cost of the ship will be greater than the power generation cost of the ship that has not been transformed. When the government subsidy amount is greater than 72.1549 million yuan, the port charges less than 0.7 yuan / (kW·h) to docked ships within the expected cost recovery period. At this time, the cost to the ship users is lower than the power generation cost of the ship before the transformation. From Figure 4 and Figure 5 It can be seen that when the government subsidizes the port, as the subsidy amount increases, the recovery period of the port shore power system cost and the recovery period of the ship shore power transformation cost will decrease, indicating that the government subsidy to the port can also effectively reduce the recovery period of the port shore power system cost and the recovery period of the ship shore power transformation cost. If the port is expected to recover the cost in the fifth year, then when the government subsidy is 25% of the port and ship costs respectively, the shore power usage price is 0.67 yuan / (kW·h), at which time the recovery period of the port shore power system cost and the recovery period of the ship shore power transformation cost are 4.3 years and 3.7 years respectively.

[0321] Let the decision vector set have three elements

[0322] Decision vector 1 (x1, x2, x3, x4), corresponding to the objective function vector 1 (a1, b1, c1, d1)

[0323] Decision vector 2 (y1, y2, y3, y4), corresponding to the objective function vector 2 (a2, b2, c2, d2)

[0324] Decision vector 3 (z1, z2, z3, z4), corresponding to the objective function vector 3 (a3, b3, c3, d3)

[0325] If c1=c2=c3, d1=d2=d3, a1>a2>a3, b1<b2, b2>b3.

[0326] Larger is better, decision vector 2 is better than decision vector 3, and decision vector 3 does not belong to the Pareto optimal set.

[0327] Decision vector 1 and decision vector 2 cannot be compared. They have their own advantages in objective functions a and b. Decision vector 1 and decision vector 2 belong to the Pareto optimal set.

[0328] In this embodiment, a multi-objective genetic algorithm is used for solving, which avoids inconsistent units between objectives of different natures, is not suitable for setting appropriate weights, and the allocation of weighted values ​​of each objective is relatively subjective. Different weights produce different optimal solutions, and the Pareto optimal solution set can be found in one algorithm process. It is not limited to the shape and continuity of the Pareto frontier, and is easy to handle discontinuous and concave Pareto fronts, which facilitates the construction investment analysis of the shore power operation system, making the obtained results more comprehensive and more convincing, which is conducive to the construction and use of the shore power system.

[0329] Embodiment 3:

[0330] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of the construction investment determination method of a shore power system in the above embodiment.

[0331] Embodiment 4:

[0332] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of a method for determining the construction investment of a shore power system in the above embodiment.

[0333] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0334] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0335] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0336] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0337] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A method for determining the construction investment of a shore power system, characterized in that: include: Obtain shore power system equipment data based on pre-built parameter indicators; Based on the shore power system equipment data, a pre-built shore power system mathematical model is used to solve the problem using a multi-objective genetic algorithm to obtain the government's investment in the shore power system construction; The mathematical model of the shore power system is constructed by taking the cost recovery period of the port shore power system, the cost recovery period of the ship shore power transformation and the minimum government cost as the optimization objectives, combined with the constraint conditions set for the optimization objectives; The shore power system equipment data includes: shore power system equipment capacity, total power of ship auxiliary engines and utilization rate of ship auxiliary engines; The government's investment in the construction of the shore power system is used to determine the construction and use of the shore power system.

2. The method according to claim 1, characterized in that The construction of the mathematical model of the shore power system includes: The optimization variables are the government's percentage subsidy for shore power system construction, the government's percentage subsidy for ship shore power transformation, the government's subsidy for shore power electricity price and the shore power service fee. Building constraints based on shore power system operating restrictions; The objective function of optimizing the life of the system is constructed with the goal of minimizing the cost recovery period of the port shore power system. The objective function for optimizing the transformation period is constructed with the goal of minimizing the cost recovery period of ship shore power transformation; Constructing the economic optimization objective function with the goal of minimizing government costs; Constructing a shore power system mathematical model based on the optimization variables, the constraint conditions, the system life optimization objective function, the transformation life optimization objective function and the economic optimization objective function; The constraints include: port net profit constraint and ship berthing cost constraint after shore power transformation; The government's investment in shore power system construction includes: government percentage subsidies for shore power system construction, government percentage subsidies for ship shore power transformation, government subsidies for shore power electricity prices and shore power service fees.

3. The method according to claim 1, characterized in that The parameter indicators include: The price of large industrial electricity, the types of ships using shore power and the number of each type, the cost of converting different types of ships, the auxiliary engine utilization rate and total auxiliary engine power of different ships, the number of port calls per year for each ship, the time of a single port call, the time when the ship is not connected to the port shore power equipment during a single port call, the total capacity of the shore power system, the total construction cost of the shore power system, the annual shore power labor cost, the main types of pollutants generated by burning fuel, the emission of each pollutant, the tax rate levied on different pollutants, the auxiliary engine fuel consumption and the auxiliary engine fuel price.

4. The method according to claim 2, characterized in that The system life optimization objective function is as follows: In the above formula, PT port is the cost recovery period of the port shore power system, N1 is the number of shore power systems in the port, C sp_o is the total construction cost of the shore power system o, S port is the percentage subsidy of the government for the construction of shore power system, N2 is the number of ships at the port using shore power, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship_i is the utilization rate of ship auxiliary engines, p s is the shore power service fee, P sp_o is the capacity of the shore power system o, C l_per is the annual labor cost converted into 1kW shore power capacity, o is the shore power system, and i is the i-th ship.

5. The method according to claim 2, characterized in that The transformation period optimization objective function is as follows: In the above formula, PT ship is the recovery period of the cost of shore power transformation for ships, N2 is the number of ships at the port using shore power, C ship_i is the shore power transformation cost of ship i, S ship is the government's percentage subsidy for ship shore power transformation, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship_i is the utilization rate of ship auxiliary engines, r f is the auxiliary engine fuel consumption per 1 kWh, p f is the auxiliary engine fuel price, N3 is the number of types of pollutants emitted by the auxiliary engine, E s is the emission of the sth pollutant per 1 kWh, T s is the tax rate imposed on the sth pollutant, p p is the shore power price, S price is the government subsidy for shore power price, i is the i-th ship, and s is the s-th pollutant.

6. The method according to claim 2, characterized in that The economic optimization objective function is as follows: In the above formula, C g is the government cost, N1 is the number of shore power systems in the port, C sp_o is the total construction cost of the shore power system o, S port is the percentage subsidy of the government for the construction of shore power system, N2 is the number of ships at the port using shore power, C ship_i is the shore power transformation cost of ship i, S ship is the government's percentage subsidy for ship shore power transformation, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship_i is the utilization rate of ship auxiliary engines, S price is the government subsidy for shore power price, o is the shore power system, and i is the i-th ship.

7. A system for determining the construction investment of a shore power system, characterized in that: include: A data acquisition module, used to acquire shore power system equipment data based on pre-built parameter indicators; A model solving module is used to solve the shore power system equipment data using a pre-built shore power system mathematical model and a multi-objective genetic algorithm to obtain the government's investment in the shore power system construction; The mathematical model of the shore power system is constructed by taking the cost recovery period of the port shore power system, the cost recovery period of the ship shore power transformation and the minimum government cost as the optimization objectives, combined with the constraint conditions set for the optimization objectives; The shore power system equipment data includes: shore power system equipment capacity, total power of ship auxiliary engines and utilization rate of ship auxiliary engines; The government's investment in the construction of the shore power system is used to determine the construction and use of the shore power system.

8. The system according to claim 7, characterized in that The system also includes a model building module, and the model building module includes: The variable setting submodule is used to optimize the variables by taking the government's percentage subsidy for shore power system construction, the government's percentage subsidy for ship shore power transformation, the government's subsidy for shore power electricity price and shore power service fee in the government's investment in shore power system construction; The constraint building submodule is used to build constraint conditions based on the shore power system operation restrictions; The system life function construction submodule is used to construct the system life optimization objective function with the minimum cost recovery period of the port shore power system as the goal; The transformation period function construction submodule is used to construct the transformation period optimization objective function with the minimum cost recovery period of ship shore power transformation as the goal; The economic function construction submodule is used to construct the economic optimization objective function with the goal of minimizing government costs; A mathematical model construction submodule, used to construct a shore power system mathematical model based on the optimization variables, the constraint conditions, the system life optimization objective function, the transformation life optimization objective function and the economic optimization objective function; The constraints include: port net profit constraint and ship berthing cost constraint after shore power transformation; The government's investment in shore power system construction includes: government percentage subsidies for shore power system construction, government percentage subsidies for ship shore power transformation, government subsidies for shore power electricity prices and shore power service fees.

9. The system according to claim 7, characterized in that The parameter indicators include: The price of large industrial electricity, the types of ships using shore power and the number of each type, the cost of converting different types of ships, the auxiliary engine utilization rate and total auxiliary engine power of different ships, the number of port calls per year for each ship, the time of a single port call, the time when the ship is not connected to the port shore power equipment during a single port call, the total capacity of the shore power system, the total construction cost of the shore power system, the annual shore power labor cost, the main types of pollutants generated by burning fuel, the emission of each pollutant, the tax rate levied on different pollutants, the auxiliary engine fuel consumption and the auxiliary engine fuel price.

10. The system according to claim 8, characterized in that The system life optimization objective function is as follows: In the above formula, PT port is the cost recovery period of the port shore power system, N1 is the number of shore power systems in the port, C sp_o is the total construction cost of the shore power system o, S port is the percentage subsidy of the government for the construction of shore power system, N2 is the number of ships at the port using shore power, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship_i is the utilization rate of ship auxiliary engines, p s is the shore power service fee, P sp_o is the capacity of the shore power system o, C l_per is the annual labor cost converted into 1kW shore power capacity, o is the shore power system, and i is the i-th ship.

11. The system according to claim 8, characterized in that The transformation period optimization objective function is as follows: In the above formula, PT ship is the recovery period of the cost of shore power transformation for ships, N2 is the number of ships at the port using shore power, C ship_i is the shore power transformation cost of ship i, S ship is the government's percentage subsidy for ship shore power transformation, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship_i is the utilization rate of ship auxiliary engines, r f is the auxiliary engine fuel consumption per 1 kWh, p f is the auxiliary engine fuel price, N3 is the number of types of pollutants emitted by the auxiliary engine, E s is the emission of the sth pollutant per 1 kWh, T s is the tax rate imposed on the sth pollutant, p p is the shore power price, S price is the government subsidy for shore power price, i is the i-th ship, and s is the s-th pollutant.

12. The system of claim 8, wherein: The economic optimization objective function is as follows: In the above formula, C g is the government cost, N1 is the number of shore power systems in the port, C sp_o is the total construction cost of the shore power system o, S port is the percentage subsidy of the government for the construction of shore power system, N2 is the number of ships at the port using shore power, C ship_i is the shore power transformation cost of ship i, S ship is the government's percentage subsidy for ship shore power transformation, m i is the number of port calls of the ship in year i, t i is the single berthing time of ship i, t l P is the time when the ship is not connected to the port shore power equipment when it is at port. ship_i is the total power of auxiliary engines of ship i, r ship_i is the utilization rate of ship auxiliary engines, S price is the government subsidy for shore power price, o is the shore power system, and i is the i-th ship.

13. A computer device, characterized in that: include: one or more processors; The processor is used to store one or more programs; When the one or more programs are executed by the one or more processors, the method for determining construction investment of a shore power system according to any one of claims 1 to 6 is implemented.

14. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the method for determining the construction investment of the shore power system as claimed in any one of claims 1 to 6 is implemented.

Images