Harbor comprehensive energy system whole-process logistics-energy combined dispatching method and system

Abstract

The application discloses a kind of sea port comprehensive energy system whole process logistics-energy joint scheduling method and system, it is related to sea port comprehensive energy system technical field, including: constructing sea port comprehensive energy system model and sea port logistics system model;With the minimum comprehensive energy system cost and ship average in port length as optimization goal, according to ship arrival information, improved multi-objective differential evolution algorithm is used to solve optimization goal, obtain multiple Pareto solution, according to analytic hierarchy process combination entropy weight method to multiple Pareto solution is authorized, to in multiple Pareto solution set screening optimal solution, obtain including ship berth allocation and each logistics equipment operating state logistics scheduling plan, and the output of each energy equipment, to complete logistics-energy joint scheduling.In improving logistics scheduling efficiency, reduce the energy consumption and carbon emission of sea port comprehensive energy system.

Description

Technical Field

[0001] This invention relates to the field of integrated energy systems for seaports, and in particular to a method and system for the joint scheduling of logistics and energy throughout the entire process of an integrated energy system for seaports. Background Technology

[0002] As hubs of maritime transportation and key nodes of global trade, seaports also generate significant carbon emissions. To promote energy conservation and emission reduction in seaport areas, traditional fossil fuel-powered ships, port machinery, and transport vehicles are gradually being replaced by electrified equipment. However, simply altering seaport energy scheduling plans for the sake of energy conservation and emission reduction will inevitably affect normal logistics and transportation. Therefore, to unlock the potential of logistics system scheduling in terms of energy saving and efficiency improvement, joint logistics-energy scheduling in seaport areas has become a popular solution.

[0003] The main equipment in a container logistics system includes shore power, quay cranes, automated guided vehicles (AGVs), and yard cranes. The logistics system coordinates these devices to complete the scheduling of container logistics in the seaport. Therefore, the seaport container logistics system is an important component of the energy-consuming side of the integrated energy system of the seaport. The logistics scheduling plan determines the load demand of the seaport and greatly affects the quality of the integrated energy system's energy management strategy.

[0004] A comprehensive energy system for a seaport integrates multiple energy forms, including electricity, heat, cooling, and hydrogen. It comprises equipment such as photovoltaics, wind turbines, gas turbines, gas-fired boilers, electric refrigeration, and energy storage, capable of meeting the diverse energy needs of seaport infrastructure (electricity, heating, and cooling). Typically, the scheduling plans for the energy system and the logistics system are independent. This scheduling method overlooks the potential for reducing energy consumption and improving energy efficiency in the logistics system. By improving the scheduling scheme for logistics equipment, peak shaving and valley filling can be achieved, enhancing the system's economic efficiency and reducing carbon emissions.

[0005] However, the joint scheduling of logistics and energy in a port integrated energy system is a complex, high-dimensional mixed-integer nonlinear programming problem, and it suffers from the inconsistency in time scales between the logistics and energy systems, making accurate solutions extremely difficult. Furthermore, existing port logistics-energy joint scheduling methods often focus on a specific part of the logistics system, such as ship berth allocation and quay crane scheduling, neglecting the transportation processes of container trucks, yard cranes, and warehousing; or they focus on container scheduling between container trucks and yard cranes, ignoring berth allocation and quay crane scheduling constraints. This results in the energy system's supply side only reflecting a portion of the logistics system's energy demand. To perform full-process modeling and optimization scheduling of the logistics-energy coupled system requires setting a large number of 0-1 variables for logistics equipment, and the coupling constraints between various logistics links exponentially increase the difficulty of the solution, rendering existing models and solution methods inapplicable. Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes a method and system for the joint scheduling of logistics and energy throughout the entire process of a port integrated energy system. By modeling the entire process of the port logistics system and considering the thermodynamic dynamic model of cold chain equipment, a multi-objective optimization algorithm is designed to solve multiple optimization objectives, such as ship port stay time, integrated energy system cost, and carbon emissions. This improves logistics scheduling efficiency while reducing energy consumption and carbon emissions of the port integrated energy system.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides a method for the joint scheduling of logistics and energy throughout the entire process of a seaport integrated energy system, comprising:

[0009] A comprehensive energy system model for a seaport is constructed, taking into account the energy load of energy equipment including cold containers and cold storage, and a seaport logistics system model is constructed, taking into account the scheduling of ship berths and various logistics equipment.

[0010] With the optimization objectives of minimizing the integrated energy system cost and the average port dwell time of ships, an improved multi-objective differential evolution algorithm is used to solve the optimization objectives based on ship arrival information, resulting in multiple Pareto solutions. The multiple Pareto solutions are then weighted using the analytic hierarchy process combined with the entropy weight method to select the optimal solution from the multiple Pareto solution set. This yields a logistics scheduling plan that includes ship berth allocation and the operating status of each logistics equipment, as well as the output of each energy equipment, thereby completing the logistics-energy joint scheduling.

[0011] Among them, the improved multi-objective differential evolution algorithm switches between constrained and unconstrained processing when selecting the optimal individual using fast non-dominated sorting and crowded distance sorting, based on the constraint processing switching mechanism.

[0012] As an alternative implementation method, a thermodynamic dynamic model of the cold box is constructed in the integrated energy system model of the seaport:

[0013] ;

[0014] in, The scheduling period is per unit. Let t be the internal temperature of the cold box; External ambient temperature; To account for the correction factor introduced by solar radiation; A is the outer surface area of ​​the cold box; Thermal conductivity; For the weight of the goods inside the container; Specific heat capacity; For cooling capacity, when the compressor is not working, ;

[0015] The total cooling load of a cold storage facility includes the cooling load of the building envelope, the cooling load of ventilation, the cooling load of personnel operations, the cooling load of incoming goods, and the cooling load of electrical equipment.

[0016] As an alternative implementation method, the overall energy system cost This includes electricity purchase costs, gas purchase costs, equipment operation and maintenance costs, energy storage degradation costs, and carbon emission costs;

[0017] The optimization objective is to minimize the average time ships spend in port. for:

[0018] ;

[0019] ;

[0020] in, This refers to the average time a vessel spends in port. The total number of ships; This refers to the number of quay cranes; Let i be the departure time of the i-th ship; Let i be the arrival time of the i-th ship; Total quantity of goods; Let q be the state of the quay crane at time t and j be the berth. T represents the unloading efficiency of the quay crane; T represents the total time.

[0021] As an alternative implementation method, the operating status of each logistics device includes:

[0022] The quay crane performs unloading operations, waits at a berth, and departs from one berth to another.

[0023] The automated guided vehicle (AGV) begins loading and transporting goods at the quay crane, returns empty from one quay crane to another, and is in a charging state.

[0024] The yard crane is used for unloading operations at the yard location, waiting at the yard location, and moving from one yard location to another.

[0025] As an alternative implementation, the improved multi-objective differential evolution algorithm includes differential evolution operators and an environment selection strategy:

[0026] ; ;

[0027] in, This represents the offspring produced by the differential evolution operator. It is a random number between 0 and 1. , and They are different individuals. It is the most adaptable individual. and Indicates the mutation probability and crossover probability; For individuals after crossover; for Elements in;

[0028] The constraint handling switching mechanism is as follows: if there is no feasible solution in the population, then search based on constraint handling techniques; if there is a feasible solution in the population, then search according to the unconstrained multi-objective optimization problem.

[0029] As an alternative implementation method, the process of assigning weights to multiple Pareto solutions using the analytic hierarchy process (AHP) combined with the entropy weight method includes: using the weights determined by the AHP and the weights determined by the entropy weight method as the basic weight vectors, constructing a comprehensive weight vector by combining it with linear combination coefficients, and determining the optimal linear combination coefficients with the objective of minimizing the deviation between the comprehensive weights and the basic weights. and optimal comprehensive weight ;

[0030] Specifically:

[0031] The overall weight vector is: ;

[0032] Optimal linear combination coefficients for: ;

[0033] Optimal overall weight for: ;

[0034] in, This is the basic weight vector; The weights determined by the Analytic Hierarchy Process (AHP) for the i-th optimization objective; The weights are determined by the entropy weight method for the i-th optimization objective; m is the number of optimization objectives. The coefficients are linear combination coefficients. .

[0035] Secondly, the present invention provides a full-process logistics-energy joint dispatching system for a seaport integrated energy system, comprising:

[0036] The model building module is configured to build a comprehensive energy system model of the port considering the load of energy equipment including cold boxes and cold storage, and to build a port logistics system model considering the scheduling of ship berths and various logistics equipment.

[0037] The joint scheduling module is configured to minimize the integrated energy system cost and the average port dwell time of ships as optimization objectives. Based on ship arrival information, an improved multi-objective differential evolution algorithm is used to solve the optimization objectives, resulting in multiple Pareto solutions. The analytic hierarchy process (AHP) combined with the entropy weight method is used to assign weights to the multiple Pareto solutions in order to select the optimal solution from the set of multiple Pareto solutions. This yields a logistics scheduling plan that includes ship berth allocation and the operating status of each logistics equipment, as well as the output of each energy equipment, thereby completing the logistics-energy joint scheduling.

[0038] Among them, the improved multi-objective differential evolution algorithm switches between constrained and unconstrained processing when selecting the optimal individual using fast non-dominated sorting and crowded distance sorting, based on the constraint processing switching mechanism.

[0039] Thirdly, the present invention provides an electronic device including a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the first aspect.

[0040] Fourthly, the present invention provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in the first aspect.

[0041] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0043] This invention proposes a joint logistics-energy scheduling method for the entire process of a port integrated energy system. By modeling the entire port logistics system and considering the thermodynamic dynamic model of cold chain equipment, a multi-objective optimization algorithm is designed to solve multiple optimization objectives, such as ship port dwell time, integrated energy system cost, and carbon emissions. This is because the model of the method in this invention has extremely high model complexity and a large number of coupled variables, and precise solution algorithms such as nonlinear mixed integer programming cannot solve the problem or have extremely long solution times. Therefore, an improved multi-objective differential evolution algorithm is proposed for fast solution, which improves logistics scheduling efficiency while reducing energy consumption and carbon emissions of the port integrated energy system.

[0044] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0046] Figure 1 This is a flowchart of the integrated logistics-energy scheduling method for a seaport's integrated energy system provided in Embodiment 1 of the present invention.

[0047] Figure 2 This is a schematic diagram of the integrated energy system structure of a seaport provided in Embodiment 1 of the present invention;

[0048] Figure 3 The flowchart of the improved multi-objective differential evolution algorithm provided in Embodiment 1 of the present invention is shown below;

[0049] Figure 4 This is a diagram showing the power dispatching plan results provided in Embodiment 1 of the present invention;

[0050] Figure 5 This is a diagram showing the thermal energy dispatching plan results provided in Embodiment 1 of the present invention;

[0051] Figure 6 This is a diagram showing the ship berthing plan results provided in Embodiment 1 of the present invention;

[0052] Figure 7 This is a diagram showing the scheduling plan results of the quay crane provided in Embodiment 1 of the present invention;

[0053] Figure 8 This is a diagram showing the scheduling results of the automated guided vehicle (AGV) system provided in Embodiment 1 of the present invention.

[0054] Figure 9 This is a diagram showing the scheduling plan results of the yard crane provided in Embodiment 1 of the present invention;

[0055] Figure 10 This is a Pareto front result diagram provided in Embodiment 1 of the present invention. Detailed Implementation

[0056] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0057] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0058] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form as well. Furthermore, it should be understood that the terms “comprising” and “including”, and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0059] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0060] Example 1

[0061] This embodiment provides a method for the joint scheduling of logistics and energy throughout the entire process of a seaport integrated energy system, such as... Figure 1 As shown, it includes:

[0062] A comprehensive energy system model for a seaport is constructed, taking into account the energy load of energy equipment including cold containers and cold storage, and a seaport logistics system model is constructed, taking into account the scheduling of ship berths and various logistics equipment.

[0063] With the optimization objectives of minimizing the integrated energy system cost and the average port dwell time of ships, an improved multi-objective differential evolution algorithm is used to solve the optimization objectives based on ship arrival information, resulting in multiple Pareto solutions. The multiple Pareto solutions are then weighted using the analytic hierarchy process combined with the entropy weight method to select the optimal solution from the multiple Pareto solution set. This yields a logistics scheduling plan that includes ship berth allocation and the operating status of each logistics equipment, as well as the output of each energy equipment, thereby completing the logistics-energy joint scheduling.

[0064] Among them, the improved multi-objective differential evolution algorithm switches between constrained and unconstrained processing when selecting the optimal individual using fast non-dominated sorting and crowded distance sorting, based on the constraint processing switching mechanism.

[0065] like Figure 2The diagram shows the structure of a port integrated energy system, which is divided into a port integrated energy system and a port logistics system. The port integrated energy system integrates various forms of energy, including electricity, heat, and cooling, covering energy production, storage, conversion, transportation, and utilization. The energy supply side mainly consists of the external power grid, natural gas grid, and port-supporting new energy facilities such as wind power, photovoltaics, and energy storage. The energy consumption side consists of daily electricity loads for port offices and lighting, logistics equipment loads, heating loads, and cooling loads. It also includes combined heat and power (CHP), gas boilers (GB), electric refrigeration, and energy storage equipment.

[0066] The energy balance equation for the integrated energy system of a seaport is:

[0067] (1);

[0068] (2);

[0069] (3);

[0070] in, This refers to the power generation capacity of wind power. Photovoltaic power generation; For the power generation of combined heat and power units; Power for charging electric energy storage; Power purchased from the upper-level power grid; Output power for electrical energy storage; Power consumption for cold storage; This refers to shore power. Basic electrical load; The electrical power used to charge the AGV at the charging station; Let r be the electrical power of the r-th cold box stack. This refers to the number of cold box storage yards; Power of logistics cranes; For the number of logistics cranes; For the heat output of combined heat and power units; The heat output of the gas-fired boiler; Power for thermal energy storage; Output power for thermal energy storage; Based on the basic heat load; Refrigeration capacity of the cold storage; For the refrigeration capacity of the cold box storage yard; Electric cooling power; For cold storage cooling load; To reduce the cooling load on the cold container yard; Based on the basic cooling load.

[0071] The output of the CHP unit is: , .

[0072] GB gas-fired boilers are: .

[0073] in, Input natural gas to CHP to provide the corresponding power. Input the power corresponding to natural gas for GB; For the electrical efficiency of the CHP unit; For the thermal efficiency of the CHP unit; This refers to the electrical efficiency of GB generator units.

[0074] The output of the electric cooling system is: ;in, For electric cooling efficiency; It provides electrical power for electric cooling.

[0075] The energy storage model is as follows: , ;

[0076] in, The remaining energy storage capacity at time t+1; Let be the remaining energy stored at time t; The self-loss coefficient is the electrical self-loss coefficient. and For charging efficiency and discharging efficiency; and For charging power and discharging power; The remaining energy stored at the initial moment; The remaining energy stored at the time of termination.

[0077] The thermal energy storage model is as follows: , ;

[0078] in, The remaining energy stored at time t+1; Let be the remaining energy stored at time t; This is the self-loss coefficient; and For charging efficiency and discharging efficiency; and For charging power and discharging power; The remaining energy stored at the initial moment; The remaining energy stored at the time of termination.

[0079] In this embodiment, considering the influence of factors such as ambient temperature, solar radiation intensity, and the type of goods inside the container, a thermodynamic dynamic model of the refrigerated container (i.e., cold box) is established based on the principle of heat balance:

[0080] (4);

[0081] in, The scheduling period is in seconds; Indicates the internal temperature of the cold box; External ambient temperature; To account for the correction factor introduced by solar radiation; A is the outer surface area of ​​the cold box, in square meters; Thermal conductivity, ; The weight of the goods inside the box is in kilograms. For specific heat capacity, ; For cooling capacity, When the compressor is not working, .

[0082] The actual power consumption of the cold box is:

[0083] (5);

[0084] in, This refers to the power consumption of the cold box; This is the minimum power consumption of the cold box; This is the maximum power consumption of the cold box; This indicates the operating status of the cold box compressor; 1 indicates cooling, and 0 indicates no cooling. Different set temperatures are used to adjust the cooling efficiency ratio. Different. Because goods stored in refrigerated containers require specific temperature controls, the internal temperature needs to be maintained within a certain range to prevent damage. .

[0085] Total cooling load of cold storage Including building envelope cooling load , ventilation cooling load Cooling load of personnel operation , cold load of incoming goods and electrical equipment cooling load ;

[0086] Therefore, the total cooling load of the cold storage for:

[0087] (6).

[0088] Specifically:

[0089] ;

[0090] ;

[0091] ;

[0092] ;

[0093] ;

[0094] in; Outdoor temperature; The temperature inside the cold storage; The heat transfer coefficient of the building envelope. Take 0.2; For the heat transfer area, take 5000. ; The temperature difference correction factor on both sides of the building envelope is set to 1; The density of air inside the cold storage, in units of ; This refers to the volume inside the cold storage. ; Air exchange rate; The specific enthalpy of the air outside the cold storage; The specific enthalpy of the air inside the cold storage; Number of cold storage operators; The heat generated for personnel is 395W; For the quality of goods received, Enthalpy prior to goods revenue Specific enthalpy of the goods after cooling; The thermal conversion coefficient is taken as 1. This refers to the rated power of the motor. Cooling energy consumption power. Cold storage refrigeration energy efficiency ratio It is a temperature-related function. The actual power consumption of the cold storage is: .

[0095] In this embodiment, the optimization objective is established with the goal of minimizing the operating cost of the integrated energy system:

[0096] (7);

[0097] in, The integrated energy system cost function is derived from the electricity purchase cost. Gas purchase cost Equipment operation and maintenance costs Energy storage degradation cost and carbon emission costs composition.

[0098] Specifically:

[0099] ;

[0100] ;

[0101] ; ; ;

[0102] ;

[0103] ;

[0104] in, For time-of-use electricity pricing, For gas prices, The unit time maintenance cost of the equipment. This is the energy storage loss coefficient. This represents the tiered carbon trading cost coefficient.

[0105] The power upper and lower limits constraints related to the integrated energy system of a seaport include:

[0106] (8);

[0107] (9);

[0108] (10);

[0109] (11);

[0110] (12);

[0111] (13);

[0112] (14);

[0113] (15);

[0114] in, and The upper and lower limits of power generation for combined heat and power units; The power output of the combined heat and power unit at time t-1; The upper and lower limits of heat output for gas-fired boilers; The upper and lower limits of the power for electric cooling; The upper and lower limits of remaining energy storage capacity; The upper and lower limits of the remaining energy for energy storage; The upper and lower limits of power for cold box storage yards; The upper and lower limits of power consumption for cold storage; and The upper and lower limits of the ramp-up power for the CHP unit.

[0115] In this embodiment, the port logistics system includes onshore power supply (OPS), quayside container cranes (QC), automated guided vehicles (AGVs), yantian cranes (YC), reefer containers (RF), and cold storage (CS), etc., and handles port container logistics tasks through these logistics equipment. The scheduling process of the logistics system is as follows: When a ship is about to arrive at the port, its arrival information is sent to the port logistics dispatch center in advance. The ship arrival information includes the ship's arrival time, berthing time, departure time, and the type and quantity of cargo carried. The dispatch center allocates berths to the ship and provides shore power services, while simultaneously assigning QC, AGV, and YC to carry out container logistics unloading tasks. The QC unloads the containers from the ship to the port, and then the AGV transports them to the yard or warehouse for storage. The YC is responsible for stacking the containers in the yard. After the containers are unloaded, the ship departs the port, and the empty berth awaits the arrival of the next ship.

[0116] Therefore, the modeling and related constraints of the seaport logistics system are as follows:

[0117] (1) Ship berth allocation and shore power.

[0118] Total number of berths For each berth Distributing shore power and quay cranes / port machinery serves as a connecting hub for energy and logistics. Arriving ships... Is it docked at a berth? state Shore power is allocated when the ship is docked, and the shore power connection status is maintained. Ship arrival time Stop time Departure time Before the ship arrives, it must report its arrival time and cargo information. The dispatch center will then... Each berth is allocated to a vessel. .

[0119] The relevant constraints are as follows:

[0120] , (16);

[0121] (17);

[0122] , (18);

[0123] (19);

[0124] (20);

[0125] (twenty one);

[0126] in, The latest departure time; The total number of ships; This refers to the total shore power capacity, including the ship's basic power consumption. Electricity for cold boxes ; and These represent the upper and lower limits of shore power.

[0127] Equation (16) describes the berthing status of a ship; Equation (17) restricts the berthing time to be later than the arrival time and the departure time to be no later than the latest departure time; Equation (18) ensures that each ship is assigned a berth and that the same berth can only serve one ship at any given time; Equation (19) restricts the total number of berthed ships to not exceeding the number of berths; Equation (20) describes the shore power, including the base load during berthing and the load of unloaded refrigerated containers. Under normal operation, the shore power connection status is the same as the ship's berthing status, i.e. Equation (21) limits the upper and lower limits of shore power.

[0128] (2) Shore crane.

[0129] Using three binary variables , , Describe the operating status of the QC (Quality Control) system for the quay crane, especially when QC is performing unloading operations. When QC is at the berth While waiting When QC starts from the berth Depart for berth hour .

[0130] The relevant constraints are as follows:

[0131] (twenty two);

[0132] (twenty three);

[0133] (twenty four);

[0134] (25);

[0135] (26);

[0136] (27);

[0137] (28);

[0138] in, This is the state at the previous QC time step; To improve QC unloading efficiency; and These are adjacent QC states; Total QC power; For QC operating power; For QC mobile power; The QC unloading operation status at time t-1; QC at berth at time t-1 A state of waiting.

[0139] Equation (22) ensures that a QC cannot move to another berth before waiting at one berth or before the operation is completed. Equation (23) represents the state logic constraint of each QC in each time period, ensuring that a QC can choose at most one action from operation, waiting, or moving. Equation (24) links the QC to the state of the ship and restricts the allocation of berths. QC quantity No more than the maximum number ,in Equation (25) represents the completion of the ship's QC work by several QC personnel before the ship departs. The unloading task For the ship The number of refrigerated containers, Equation (26) stipulates that all QCs serving on the same ship are adjacent, which means that when working on the same ship, unassigned QCs are not allowed between occupied QCs. Equation (27) describes the QC power, and Equation (28) limits the upper and lower limits of QC power.

[0140] (3) AGV transportation model.

[0141] The AGV transportation process begins with unloading and loading at QC (Quality Control Center) and ends with storing the goods in the yard or warehouse and then returning to QC. By default, after loading at QC, the AGV transports the goods to its destination (yard or cold storage) for unloading according to the route arranged by the dispatch center. This is achieved using three binary variables. , , Describe the operating status of the Automated Guided Vehicle (AGV) when the AGV is in QC (Quality Control) phase. When loading and transportation begin When the AGV comes from Returning empty to QC hour When the AGV is charging .

[0142] The relevant constraints are as follows:

[0143] (29);

[0144] (30);

[0145] (31);

[0146] (32);

[0147] (33);

[0148] (34);

[0149] (35);

[0150] (36);

[0151] (37);

[0152] (38);

[0153] (39);

[0154] in, Number of destinations; Number of AGVs; This represents the maximum capacity of the AGV in the QC (Quality Control Center). To improve AGV unloading efficiency; To maximize the unloading efficiency of AGVs; The remaining power of the AGV; Total charging power for the charging station.

[0155] Equation (29) ensures that the AGV cannot perform another operation before the end of one operation. Equation (30) represents the state logic constraint of each AGV in each time period, ensuring that the AGV can choose one action from loading, returning, and charging at most. Equation (31) restricts the vehicle from taking the returning action after loading begins. Equation (32) restricts the vehicle from taking the charging action after loading begins. Equation (33) limits the maximum number of AGVs loading at QC at the same time. Equation (34) indicates that the upper limit of QC's unloading efficiency cannot exceed the AGV's transportation efficiency. Equation (35) indicates the upper limit of AGV's unloading efficiency. Equation (36) indicates the upper limit of the first unloading efficiency. The SOC status of each AGV. Improve AGV charging efficiency. For charging power, For cargo transport power, The power of an AGV is the power of an empty vehicle. An AGV that cannot complete a full transportation task needs to be charged. Equation (37) represents the charging status of the AGV, Equation (38) represents the total charging power of the AGV, and Equation (39) represents the upper and lower limits of the charging pile power.

[0156] (4) Yard crane.

[0157] Using three binary variables , , To describe the operating status of the yard crane YC, when YC is in the yard location When unloading operations When YC is in the yard location While waiting When YC starts from position Departure location hour .

[0158] The relevant constraints are as follows:

[0159] (40);

[0160] (41);

[0161] (42);

[0162] (43);

[0163] (44);

[0164] (45);

[0165] in, The number of yard bridges; This represents the total power of the field bridge; This refers to the operating power of the field bridge. This refers to the power of the field bridge movement.

[0166] Equation (40) ensures that YC cannot move to another location before waiting or completing an operation at a location. Equation (41) represents the state logic constraint of each YC in each time period, ensuring that YC can choose one action from operation, waiting, or moving at most. Equation (42) restricts YC from waiting in place after unloading operation. Equation (43) restricts YC from moving to the next unloading location after unloading operation. Equation (44) indicates that only one YC can perform unloading or waiting operation at the same location at the same time. Equation (45) represents the power of YC.

[0167] In this embodiment, the scheduling cost of the seaport logistics system is:

[0168] (46);

[0169] in, These are QC costs, AGV scheduling costs, cold box operation and maintenance costs, and cold storage operation and maintenance costs. QC costs include operating costs, relocation costs, and maintenance costs.

[0170] Specifically:

[0171] ,

[0172] ;

[0173] ;

[0174] ;

[0175] in, This is the QC operation cost coefficient. This is the QC movement cost coefficient. This is the AGV transportation cost coefficient. and This represents the cost coefficient for the operation and maintenance of cold storage containers and cold storage facilities.

[0176] In this embodiment, the logistics efficiency of the logistics system scheduling is measured by the average time ships spend in port, and the optimization objective is to minimize the average time ships spend in port.

[0177] (47);

[0178] (48).

[0179] in, This refers to the average time a vessel spends in port. For QC quantity; The time of departure of the ship; The arrival time of the ship; This refers to the total volume of goods.

[0180] Therefore, the multi-objective optimization function with the optimization objectives of integrated energy system cost and logistics efficiency is:

[0181] (49).

[0182] In this embodiment, when solving unconstrained multi-objective optimization problems, the convergence and diversity of the population are key factors in evaluating the quality of the final solution obtained by the algorithm. However, when dealing with constrained multi-objective optimization problems in equation (49), in addition to the above two important performance characteristics, the feasibility of the solution must also be considered, which can be measured by the degree of constraint violation. Decision variables The degree of constraint violation of normalization is calculated using equation (50):

[0183] (50);

[0184] In the formula, and This represents the number of inequality constraints and equality constraints. and This represents the maximum degree of constraint violation. If but A feasible solution, otherwise An infeasible solution. The larger the value, the greater the degree of constraint violation.

[0185] Define the set of feasible solutions as If for each individual objective function, , , The number of objective functions is called the number of solutions. Dominant Solution If given a set of solutions, where If it is not governed by any other solution, then define This is the Pareto optimal solution in this set of solutions. The Pareto optimal solutions constitute the Pareto solution set. Define the Pareto frontier for The target space is defined. The proportion of feasible solutions in the population is also defined. Used to measure the feasibility of a population. It is the number of feasible solutions in the current iteration. Population size.

[0186] Differential Evolution (DE) is an algorithm with fast global search capabilities. It was originally proposed to solve unconstrained single-objective optimization problems. However, for multi-objective optimization problems with complex constraints, DE must improve the elite selection strategy and add a constraint handling technique (CHT) to balance the convergence, dispersion, and feasibility of the solution in order to generate a true and well-distributed Pareto optimal solution.

[0187] The improved multi-objective differential evolution (IMODE) algorithm proposed in this embodiment selects elite individuals through fast non-dominated sorting and crowding distance sorting, and improves this process by adding a constraint handling rules (CHS) mechanism to avoid the algorithm getting trapped in local optima or failing to find a suitable feasible solution during the search process. The algorithm flowchart is as follows. Figure 3 As shown.

[0188] The population evolution of IMODE is accomplished by two genetic operators: differential evolution operator and environmental selection strategy.

[0189] (51);

[0190] (52);

[0191] in, This represents the offspring produced by the differential evolution operator; for elements in It is a random number between 0 and 1. , and They are different individuals. It is the most adaptable individual. and Indicates the mutation probability and crossover probability; For the individuals after crossover.

[0192] In the constraint handling technique CHT, a criterion for non-dominated sorting is added, namely, solving under the following conditions. Also govern the solution :

[0193] (53);

[0194] threshold The value retrieval mechanism is as follows:

[0195] (54);

[0196] In the formula, To constrain the degree of violation in descending order of the first Individual; The first infeasible individual in the population The constraint of the generation violates the minimum value; It is a positive number less than 1; G is the iterative algebra.

[0197] exist In the value retrieval mechanism, The value gradually decreased until The value is less than ,if The value is lower than ,but Reinitialization allows for the retention of more infeasible solutions with good objective functions in the population, thereby enhancing population diversity. Simultaneously, with... As the population size gradually decreases, these infeasible solutions with good diversity will move towards the region of their nearest feasible solution. Therefore, the CHT-based search process can maintain population diversity, which can drive the population to find feasible solutions over a wide area.

[0198] The constraint handling switching mechanism is as follows: if there is no feasible solution in the population, then a CHT search is performed; if there is a feasible solution in the population, then a search is performed as an unconstrained multi-objective optimization problem. This is because during the optimization process, CHT emphasizes satisfying the constraints in the search process, which may unintentionally trap the population in a local optimal feasible region and may lead to premature convergence.

[0199] As mentioned above, unconstrained search can help the population move out of the locally feasible region and towards Movement. It is evident that unconstrained search focuses more on convergence, while CHT is relatively more focused on feasibility. Therefore, CHS maximizes the convergence of the population when a feasible solution is obtained.

[0200] The algorithm described above yields the final Pareto solution set for the multi-objective optimization problem (Equation 49). However, a weighting scheme is still needed to select a solution that balances logistics scheduling efficiency and system operating cost from all Pareto optimal solutions. Therefore, this embodiment uses a combined weighting method of Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) to determine the optimal solution.

[0201] Equation (49) can be rewritten as a dimensionless single-valued function as follows:

[0202] (55);

[0203] in, and These are the weighting coefficients;

[0204] In AHP, the primary criterion is to reduce system operating costs, while the secondary criterion is to improve logistics efficiency. The weights determined for AHP, The weights determined for EWM are based on a comprehensive weight determination method grounded in game theory. The distance function is:

[0205] (56);

[0206] Where m is the number of optimization objectives, It passed the consistency check.

[0207] Construct the comprehensive weight vector:

[0208] (57);

[0209] Among them, the basic weight vector , ; These are the coefficients of the comprehensive weight vector, i.e., the coefficients of the linear combination.

[0210] Based on the principles of game theory, we need to find the optimal linear combination coefficients. and optimal comprehensive weight The goal is to minimize the deviation between the overall weight and the basic weight, that is:

[0211] (58);

[0212] The optimal linear combination coefficients are calculated according to equation (58):

[0213] (59);

[0214] The optimal overall weight is:

[0215] (60).

[0216] This embodiment uses real data from the harbor area as a case study to analyze the effectiveness of the proposed model and algorithm. To verify this, three schemes were designed for comparison.

[0217] Option 1: The port cold chain logistics system and the integrated energy system are scheduled separately. First, the scheduling plan for each logistics equipment is obtained based on the ship arrival information, and the output of each energy equipment is obtained based on the logistics scheduling plan. Then, the scheduling status of the integrated energy system is calculated.

[0218] Option 2: Joint scheduling of logistics and energy systems, but directly transforming the multi-objective optimization function into a single-objective solution using the comprehensive weighting method.

[0219] Option 3: Joint scheduling of the logistics system and energy system, and multi-objective optimization solution according to the algorithm in this embodiment.

[0220] The optimization scheduling results of different schemes are shown in Table 1.

[0221] Table 1. Optimized scheduling results for different schemes;

[0222] .

[0223] As shown in Table 1, when the port cold chain logistics system and the integrated energy system are scheduled separately, the logistics efficiency is the highest, but the corresponding logistics costs, energy purchase costs, and carbon emission costs are also the highest, failing to fully tap the potential of the logistics system to reduce energy consumption and carbon emissions. When the logistics and energy systems are jointly scheduled with a single objective, neither the system operating cost nor the logistics efficiency is optimal. This is because the selection of weighting coefficients for directly weighting multiple objectives into a single objective function is never ideal. Scheme 3, the multi-objective joint scheduling of the logistics and energy systems, achieves the best cost and the logistics efficiency only increases by about 3% compared to separate scheduling. Figures 4-9 As shown, the solution in this embodiment can achieve reasonable optimization of logistics efficiency, system cost, and carbon emissions.

[0224] To verify the effectiveness of the proposed improved multi-objective differential evolution algorithm, it was compared and analyzed with Multi-Objective Particle Swarm Optimization (MOPSO), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Differential Evolution (MODE). The results are shown in Table 2. Figure 10 As shown.

[0225] Table 2. Performance metrics of different algorithms;

[0226] .

[0227] It can be seen that IMODE outperforms other multi-objective algorithms in both IGD and HV metrics, meaning it is optimal in terms of convergence, solution diversity, and solution efficiency.

[0228] Example 2

[0229] This embodiment provides a comprehensive logistics-energy joint dispatching system for a seaport integrated energy system, including:

[0230] The model building module is configured to build a comprehensive energy system model of the port considering the load of energy equipment including cold boxes and cold storage, and to build a port logistics system model considering the scheduling of ship berths and various logistics equipment.

[0231] The joint scheduling module is configured to minimize the integrated energy system cost and the average port dwell time of ships as optimization objectives. Based on ship arrival information, an improved multi-objective differential evolution algorithm is used to solve the optimization objectives, resulting in multiple Pareto solutions. The analytic hierarchy process (AHP) combined with the entropy weight method is used to assign weights to the multiple Pareto solutions in order to select the optimal solution from the set of multiple Pareto solutions. This yields a logistics scheduling plan that includes ship berth allocation and the operating status of each logistics equipment, as well as the output of each energy equipment, thereby completing the logistics-energy joint scheduling.

[0232] Among them, the improved multi-objective differential evolution algorithm switches between constrained and unconstrained processing when selecting the optimal individual using fast non-dominated sorting and crowded distance sorting, based on the constraint processing switching mechanism.

[0233] It should be noted that the above modules correspond to the steps described in Embodiment 1, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1. It should also be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0234] In further embodiments, the following is also provided:

[0235] An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in Embodiment 1. For brevity, further details are omitted here.

[0236] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in Embodiment 1.

[0237] A computer program product includes a computer program that, when executed by a processor, implements the method described in Embodiment 1.

[0238] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for the joint scheduling of logistics and energy throughout the entire process of a seaport integrated energy system, characterized in that, include: A comprehensive energy system model for a seaport is constructed, taking into account the energy load of energy equipment including cold containers and cold storage, and a seaport logistics system model is constructed, taking into account the scheduling of ship berths and various logistics equipment. With the optimization objectives of minimizing the integrated energy system cost and the average port dwell time of ships, an improved multi-objective differential evolution algorithm is used to solve the optimization objectives based on ship arrival information, resulting in multiple Pareto solutions. The multiple Pareto solutions are then weighted using the analytic hierarchy process combined with the entropy weight method to select the optimal solution from the multiple Pareto solution set. This yields a logistics scheduling plan that includes ship berth allocation and the operating status of each logistics equipment, as well as the output of each energy equipment, thereby completing the logistics-energy joint scheduling. Among them, the improved multi-objective differential evolution algorithm switches between constrained and unconstrained processing when selecting the optimal individual using fast non-dominated sorting and crowding distance sorting, according to the constraint processing switching mechanism. The improved multi-objective differential evolution algorithm includes differential evolution operators and an environment selection strategy: ； ； in, This represents the offspring produced by the differential evolution operator. It is a random number between 0 and 1. , and They are different individuals. It is the most adaptable individual. and Indicates the mutation probability and crossover probability; For individuals after crossover; for Elements in; The constraint handling switching mechanism is as follows: if there is no feasible solution in the population, then search based on constraint handling techniques; if there is a feasible solution in the population, then search according to the unconstrained multi-objective optimization problem.

2. The method for joint logistics and energy scheduling of a comprehensive energy system for a seaport as described in claim 1, characterized in that, In the integrated energy system model of a seaport, a thermodynamic dynamic model of the cold box is constructed: ； in, The scheduling period is per unit. Let t be the internal temperature of the cold box; External ambient temperature; To account for the correction factor introduced by solar radiation; A is the outer surface area of ​​the cold box; Thermal conductivity; For the weight of the goods inside the container; Specific heat capacity; For cooling capacity, when the compressor is not working, ; The total cooling load of a cold storage facility includes the cooling load of the building envelope, the cooling load of ventilation, the cooling load of personnel operations, the cooling load of incoming goods, and the cooling load of electrical equipment.

3. The method for joint logistics and energy scheduling of a comprehensive energy system for a seaport as described in claim 1, characterized in that, Integrated energy system cost This includes electricity purchase costs, gas purchase costs, equipment operation and maintenance costs, energy storage degradation costs, and carbon emission costs; The optimization objective is to minimize the average time ships spend in port. for: ； ； in, This refers to the average time a vessel spends in port. The total number of ships; This refers to the number of quay cranes; Let i be the departure time of the i-th ship; Let i be the arrival time of the i-th ship; Total quantity of goods; Let q be the state of the quay crane at time t and j be the berth. T represents the unloading efficiency of the quay crane; T represents the total time.

4. The method for joint logistics and energy scheduling of a comprehensive energy system for a seaport as described in claim 1, characterized in that, The operational status of each logistics device includes: The quay crane performs unloading operations, waits at a berth, and departs from one berth to another. The automated guided vehicle (AGV) begins loading and transporting goods at the quay crane, returns empty from one quay crane to another, and is in a charging state. The yard crane is used for unloading operations at the yard location, waiting at the yard location, and moving from one yard location to another.

5. The method for joint logistics and energy scheduling of a comprehensive energy system for a seaport as described in claim 1, characterized in that, The process of assigning weights to multiple Pareto solutions using the analytic hierarchy process (AHP) combined with the entropy weight method includes: using the weights determined by the AHP and the entropy weight method as the basic weight vectors, constructing a comprehensive weight vector by combining it with linear combination coefficients, and determining the optimal linear combination coefficients with the objective of minimizing the deviation between the comprehensive weights and the basic weights. and optimal comprehensive weight ; Specifically: The overall weight vector is: ; Optimal linear combination coefficients for: ; Optimal overall weight for: ; in, This is the basic weight vector; The weights determined by the Analytic Hierarchy Process (AHP) for the i-th optimization objective; The weights are determined by the entropy weight method for the i-th optimization objective; m is the number of optimization objectives. The coefficients are linear combination coefficients. .

6. A comprehensive logistics-energy scheduling system for a seaport integrated energy system, characterized in that, include: The model building module is configured to build a comprehensive energy system model of the port considering the load of energy equipment including cold boxes and cold storage, and to build a port logistics system model considering the scheduling of ship berths and various logistics equipment. The joint scheduling module is configured to minimize the integrated energy system cost and the average port dwell time of ships as optimization objectives. Based on ship arrival information, an improved multi-objective differential evolution algorithm is used to solve the optimization objectives, resulting in multiple Pareto solutions. The analytic hierarchy process (AHP) combined with the entropy weight method is used to assign weights to the multiple Pareto solutions in order to select the optimal solution from the set of multiple Pareto solutions. This yields a logistics scheduling plan that includes ship berth allocation and the operating status of each logistics equipment, as well as the output of each energy equipment, thereby completing the logistics-energy joint scheduling. Among them, the improved multi-objective differential evolution algorithm switches between constrained and unconstrained processing when selecting the optimal individual using fast non-dominated sorting and crowding distance sorting, according to the constraint processing switching mechanism. The improved multi-objective differential evolution algorithm includes differential evolution operators and an environment selection strategy: ； ； in, This represents the offspring produced by the differential evolution operator. It is a random number between 0 and 1. , and They are different individuals. It is the most adaptable individual. and Indicates the mutation probability and crossover probability; For individuals after crossover; for Elements in; The constraint handling switching mechanism is as follows: if there is no feasible solution in the population, then search based on constraint handling techniques; if there is a feasible solution in the population, then search according to the unconstrained multi-objective optimization problem.

7. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, perform the method described in any one of claims 1-5.

9. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the method described in any one of claims 1-5.

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