Multi-constraint-based port comprehensive energy coordinated optimization method and system

Abstract

The invention provides a port comprehensive energy coordinated optimization method and system based on multiple constraints, and the method comprises the steps: obtaining ship arrival data, and building a port comprehensive energy coordinated optimization model based on the ship arrival data; the target function of the model comprises carbon emission generated by fuel consumption, carbon emission generated by main online electricity shopping, total fuel cost, main online electricity shopping cost, energy power supply unit start-stop cost and penalty cost of uncompleted loading and unloading after the end of a scheduling period; utilizing ship and berthing station distribution, loading and unloading subsystem quay crane distribution, electric container truck operation logic, ship electricity, cold and heat loads, a ship unit and load demand balance to construct constraint conditions of an objective function; calling a Gurobi solver to solve the objective function to obtain a port comprehensive energy coordinated optimization strategy; and executing port comprehensive energy coordination according to the port comprehensive energy coordination optimization strategy. According to the method, the use efficiency of port resources can be improved, and greener and sustainable operation is achieved.

Description

Technical Field

[0001] This invention relates to the field of port management and coordination optimization technology, specifically to a port integrated energy coordination optimization method and system based on multiple constraints. Background Technology

[0002] As crucial hubs for global trade and logistics, ports face significant challenges in optimizing their energy systems. With increasing global trade volumes, port energy demands continue to rise, particularly in areas such as vessel coordination, cargo transshipment operations, loading and unloading subsystems, and electric truck transportation, where energy consumption and carbon footprint are becoming increasingly apparent. Port energy coordination involves coordinating various energy sources, including electricity, fuel, wind power, and renewable energy sources such as solar power. However, existing port coordination methods often rely on traditional, rigid time constraints and rules. These methods fail to adequately account for unforeseen factors in vessel coordination, such as vessels failing to complete cargo transshipment on time or the impact of unforeseen events. This leads to inflexible resource coordination, energy waste, and increased carbon footprint.

[0003] Therefore, there is an urgent need for a port integrated energy coordination optimization method and system based on multiple constraints to address the shortcomings of existing technologies. Summary of the Invention

[0004] The purpose of this invention is to provide a port integrated energy coordination optimization method and system based on multiple constraints, so as to solve the technical problems mentioned in the background art.

[0005] To achieve the above objectives, the first aspect of this invention proposes a port integrated energy coordination optimization method based on multiple constraints, comprising the following steps: Step 1: Obtain ship arrival data and establish a port integrated energy coordination and optimization model based on the ship arrival data. The objective function of the port integrated energy coordination and optimization model includes carbon emissions from fuel consumption, carbon emissions from electricity purchased from the main grid, total fuel cost, main grid electricity purchase cost, start-up and shutdown costs of energy supply units, and penalty costs for unloaded cargo after the end of the scheduling cycle. The constraints of the objective function are constructed using ship berthing scheduling, port quay crane operation scheduling, electric truck operation logic, ship electricity, cooling, and heating loads, and the balance of ship unit and load demand. Step 2: Use the Gurobi solver to solve the objective function of the port integrated energy coordination optimization model constructed in Step 1 to obtain the port integrated energy coordination optimization strategy, with constraints participating in the solution; Step 3: Implement port integrated energy coordination according to the port integrated energy coordination and optimization strategy.

[0006] Furthermore, the formula for calculating the objective function of the port integrated energy coordination optimization model is as follows: (1) in, The objective function is d; d is the fuel emission coefficient. Fuel consumed by the PGU; ... To consume fuel costs; Main grid electricity purchase cost; Costs of starting and stopping energy supply units; This represents the penalty cost coefficient for goods not fully loaded or unloaded by the end of the scheduling cycle; t represents time; T represents the scheduling cycle.

[0007] Furthermore, constraints established through ship berthing scheduling ensure that each ship can berth in an orderly manner and complete its berthing task. Specific constraints include: (2) Formula (2) represents the consistency constraint between the ship's berthing status and the berth allocation decision; st It is a 0-1 variable, indicating whether ship k is moored; The variable is 0-1, indicating whether ship k occupies berth b at time t; T represents the scheduling cycle; B is the number of berths. (3) Wherein, formula (3) represents the constraints on berth service; The variable is 0-1, indicating whether ship k occupies berth b at time t; N is the number of ships; B is the number of berths; T represents the scheduling cycle. (4) Formula (4) represents the berth allocation constraint during the scheduling cycle; The variable is 0-1, representing whether ship k starts berthing at time t when it is at berth b; N is the number of ships; B is the number of berths; T represents the scheduling cycle. (5) Wherein, formula (5) represents the continuity constraint of the berthing state; It is a 0-1 variable, indicating whether the ship k occupies the berth at time t when it is berthed at the berth b; It is a 0-1 variable, indicating whether ship k starts berthing at time t when it is at berthing position b; The variable is 0-1, representing whether ship k has finished berthing at time t when it is at berth b; N is the number of ships; B is the number of berths; T is the scheduling cycle. (6) Formula (6) means ensuring that the berthing time of a vessel is no earlier than its actual arrival time. The variable is 0-1, representing whether ship k begins berthing at time t when it is at berth b; N is the number of ships; B is the number of berths; a k The estimated berthing time for vessel k; (7) Formula (7) represents the total number of vessels that a port can serve; This represents the total number of docking stations. The variable is 0-1, indicating whether ship k is docked at time t; T represents the scheduling period; N is the number of ships. (8) Formula (8) represents the constraint on the amount of cargo unloaded by the ship, and its total demand must be met by both the completed and uncompleted portions. The number of loading and unloading subsystems used by vessel k at time t; crane_rate is the loading and unloading efficiency of a single crane quay crane at each time step. It is a 0-1 variable, indicating whether ship k is anchored at time t; This represents the amount of cargo that ship k has not yet completed at the end of the current time domain; Let T represent the cargo transfer demand of ship k; T represents the scheduling period. (9) (10) (11) Among them, formulas (9)-(11) represent the continuity constraints of the berthing time of ship k; It is a 0-1 variable, indicating whether ship k begins berthing at time t; It is a 0-1 variable, indicating whether ship k is anchored at time t; The variable is 0-1, indicating whether ship k finishes berthing at time t; N is the number of ships; T is the scheduling period. (12) Formula (12) represents the constraints on ship arrival operations; The variable is 0-1, representing whether ship k is anchored at time t; N is the number of ships; a k The estimated berthing time for vessel k; (13) (14) Among them, formulas (13)-(14) represent single berth single ship constraints, which are mutually exclusive; It is a 0-1 variable, indicating whether the ship k occupies the berth at time t when it is berthed at the berth b; The variable is 0-1, representing whether berth b is occupied at time t; B is the number of berths; T is the scheduling cycle; and N is the number of ships.

[0008] Furthermore, constraints established using port quay crane operation scheduling are used to systematically allocate loading and unloading tasks to ships based on the actual situation of quay crane and ship berthing operations. Specific constraints include: (15) Wherein, formula (15) represents the product linearization constraint; Let t be the number of loading and unloading subsystems assigned to ship k at time t when it is berthed at work position b; M represents the number of loading and unloading subsystems used by vessel k at time t; M represents the total number of electric trucks. The variable is 0-1, representing whether ship k occupies berth b at time t; N is the number of ships; B is the number of berths; and T is the scheduling cycle. (16) Formula (16) represents the consistency constraint for the allocation of ship cranes; Let t be the number of loading and unloading subsystems assigned to ship k at time t when it is berthed at work position b; Let N be the number of times vessel k uses the loading and unloading subsystem at time t; T be the number of vessels; B be the scheduling cycle; and B be the number of berths. (17) Formula (17) indicates that cranes are prohibited from being assigned when the ship is not berthed. The number of loading and unloading subsystems used by ship k at time t; This represents the maximum number of total loading and unloading subsystems. The variable is 0-1, representing whether ship k is docked; N is the number of ships; T is the scheduling period. (18) Among them, formula (18) represents the closed-loop constraint on the number of cranes; The number of loading and unloading subsystems used at berthing station b at time t; Let B be the number of loading and unloading subsystems assigned to vessel k at time t when it is berthed at berth b; T be the scheduling cycle; and N be the number of vessels. (19) (20) (twenty one) (twenty two) (twenty three) Among them, formulas (19)-(23) represent the continuous distribution constraint of cranes; formula (19) is the total capacity constraint of cranes; formula (20) represents the initialization of the first berth crane, that is, the first berth starts from crane number 0; formula (21) represents the berth continuity constraint, that is, the starting number of the crane at berth b+1 is immediately after the end of berth b; formula (22) represents the allocation constraint that can only be allocated when occupied, that is, the quay crane will be allocated to work only when it is parked at the berth; formula (23) represents the boundary constraint of continuous blocks, that is, the number of quay cranes called by the berth will not exceed the upper limit. (twenty four) Wherein, formula (24) represents the indicator constraint; h is a 0-1 variable, indicating whether the docking station b is occupied at time t; b,t B is the starting quay crane number for berth b at time t; B is the number of berths; and T is the scheduling cycle.

[0009] Furthermore, constraints constructed using the electric truck operation logic are used to rationally allocate the demand for transporting containers to the yard using electric trucks based on ship loading and unloading tasks and quay crane operations. Specific constraints include: (25) Wherein, formula (25) represents the working state constraint of the electric truck; The variable is 0-1, indicating whether the electric truck m is in operation at time t; The variable is 0-1, indicating whether electric truck m is in a charging state at time t; M is the total number of electric trucks; T is the scheduling period. (26) Wherein, formula (26) represents the electric truck power state constraint; This represents the remaining battery power of electric truck m at time t; The variable is 0-1, indicating whether the electric truck m is in a charging state at time t; The variable is 0-1, indicating whether the electric truck m is in operation at time t; Indicates the power consumption efficiency per unit time; Indicates the charging power per unit time; Indicates the total battery capacity; Indicates charging efficiency; T is the scheduling cycle; M is the total number of electric trucks; (27) (28) (29) (30) (31) (32) Among them, formulas (27)-(32) represent the operating logic constraints of electric trucks; This represents the remaining battery power of electric truck m at time t; This refers to the charging threshold. The discharge threshold; It is a 0-1 variable, indicating whether charging should be performed; It is a 0-1 variable, indicating whether or not to discharge; The variable is 0-1, indicating whether the electric truck m is in operation at time t; The variable is 0-1, representing whether the electric truck m is in a charging state at time t, according to... and value settings and ; This refers to the total battery capacity. (33) Formula (33) represents the matching constraint between the loading and unloading speed of the quay crane and the transfer speed of the electric truck; Y represents the cargo transfer rate for each loading / unloading subsystem; k,c,t The variable is 0-1, indicating whether ship k uses crane c at time t; For the transport rate of each electric truck; The variable is 0-1, indicating whether the electric container truck m is in operation at time t; T is the scheduling cycle; N is the number of ships; C is the total number of loading and unloading subsystems; M is the total number of electric container trucks. (34) Formula (34) represents the addition of a total loading and unloading power constraint for the quay crane; Let t be the total electrical power consumption of the quay crane system. Y represents the power consumption per unit of each loading / unloading subsystem. k,c,t The variable is 0-1, representing whether ship k uses crane c at time t; T is the scheduling cycle; C is the total number of loading and unloading subsystems; N is the number of ships. (35) Wherein, formula (35) represents the addition of a total charging power constraint for electric trucks; The total charging power of all electric trucks at time t; The variable is 0-1, indicating whether the electric truck m is in a charging state at time t; The charging power per unit time is T; the scheduling cycle is M; and the total number of electric trucks is M.

[0010] Furthermore, constraints are established using the ship's electrical, cooling, and heating loads to ensure the balance of electrical and cooling / heating loads for each ship at any given moment. Specific constraints include: (36) Formula (36) represents the ship's electrical load demand; This represents the total electrical load demand for all ships. Whether cruise ship C is docked at time t; For cruise ships Electricity load demand; Refrigerated ship Electricity load demand; Refrigerated ship Whether the vessel is stopped at time t; C is the total number of loading and unloading subsystems; T is the scheduling period; (37) Formula (37) represents the ship's heat load demand; For cruise ships Heat load requirements; Total heat load requirements for all ships; Indicates whether cruise ship c is docked at time t; C is the total number of loading and unloading subsystems; (38) Wherein, formula (38) represents the cooling load demand constraint; Refrigerated ship The cooling load demand; Total cooling load requirements for all ships; Indicates refrigerated ship Whether the vessel is docked at time t; C represents the total number of loading and unloading subsystems.

[0011] Furthermore, the constraints imposed by the ship's engine unit configuration ensure that each unit outputs power as required. Specific constraints include: (39) (40) (41) Among them, formulas (39)-(41) represent PGU constraints, i.e. generator set constraints; Fuel consumption of PGU; Power output for PGU; for Electrical efficiency; Waste heat recovery power; for Thermal efficiency; for maximum output; It is a 0-1 variable, indicating whether the PGU is contributing power; (42) Wherein, formula (42) represents the power purchase constraint of the main grid; This is the upper limit for electricity purchased from the power grid. This indicates the power purchased by the main grid at time t; (43) (44) Among them, formulas (43)-(44) represent EB constraints, i.e., energy storage device constraints; for Heat output; for Output power; for Heating efficiency; Indicates the maximum output power limit of EB; It is a 0-1 variable, indicating whether the EB unit is working; (45) (46) Among them, formulas (45)-(46) represent AC constraints, i.e., absorption chiller constraints; for Output cooling power; for Consumes heat power; Indicates the AC unit coefficient; for Output upper limit; It is a 0-1 variable, indicating whether AC is exerting force; (47) (48) Among them, formulas (47)-(48) represent EC constraints, i.e., electric chiller constraints; for Output cooling power; for Consumes electrical power; express Unit coefficient; This is the upper limit of EC's output; It is a 0-1 variable, indicating whether EC is contributing power; (49) Wherein, formula (49) represents the HX constraint, i.e., the heat exchanger constraint; 0-1 variables represent Whether or not to exert effort; express Maximum output of the unit; (50) Wherein, formula (50) represents the TES charge-discharge state constraint; for Injected thermal power; for Release heat power; (51) (52) Wherein, formulas (51)-(52) represent the power constraints of the TES equipment; Q TES,in,t Injecting thermal power into the TES; Q TES,dr,t Release heat power for TES; Maximum limit of thermal power injected into TES; for Maximum limit of heat output; for Efficiency coefficient of heat release power; for Injected thermal power; for Release heat power; (53) (54) Among them, formulas (53)-(54) represent the capacity constraints of TES equipment; for Heat storage capacity; This is the minimum limit for heat storage; for Maximum limit for heat storage; The efficiency coefficient for releasing heat power in TES; Inject thermal power efficiency coefficient into TES; express Injected thermal power; express Release heat power; (55) (56) (57) (58) (59) Among them, formulas (55)-(59) represent EES constraints, i.e., energy storage device constraints; for Discharge state; for Charging status; for Discharge power; for Maximum discharge power limit; EES discharge efficiency; for Charging efficiency; for Battery level; This is the maximum limit for EES power. for Minimum battery capacity; (60) Wherein, formula (60) represents the constraint protecting the EES device; for Charging efficiency; for Discharge efficiency; for Discharge power; for Charging power; T is the scheduling period; (61) (62) Among them, formulas (61)-(62) represent the power output constraints of wind and solar power; for Power generation capacity; Indicates the upper limit of light output; for Power generation capacity; This indicates the upper limit of wind power output.

[0012] Furthermore, the constraints constructed using load demand balancing ensure that the load demand of each ship is balanced at every moment. Specific constraints include: (63) Formula (63) represents the power balance; Power output for PGU; This indicates that electricity was purchased from the main network. for Power generation capacity; for Power generation capacity; express Discharge power; express The unit consumes electrical power; express Output power; express Charging power; This represents the total electrical load demand of all ships; This indicates the total power consumed by the quay crane; This indicates the total charging power of the electric truck; (64) Formula (64) represents the thermal power balance; This refers to the power of waste heat recovery. This indicates the heat output of the TES unit; 0-1 variables represent Whether or not to exert effort; for Consumes heat power; This indicates the heat power absorbed by the TES unit; (65) Among them, formula (65) represents the heat load supply and demand balance constraint; 0-1 variables represent Whether or not to exert effort; for Heat output; Total cooling load requirements for all ships; (66) Among them, formula (66) represents the cooling load supply and demand balance constraint; The AC consumes heat power; The EC consumes heat power; This represents the total cooling load requirement for all ships.

[0013] A second aspect of this invention proposes a port integrated energy coordination and optimization system based on multiple constraints, comprising: The first module is used to acquire ship arrival data and establish a port integrated energy coordination and optimization model based on the ship arrival data. The objective function of the port integrated energy coordination and optimization model includes carbon emissions from fuel consumption, carbon emissions from electricity purchased from the main grid, total fuel cost, main grid electricity purchase cost, start-up and shutdown costs of energy power supply units, and penalty costs for unloaded cargo after the end of the scheduling cycle. The constraints of the objective function are constructed by using ship and berthing position allocation, quay crane allocation of loading and unloading subsystem, electric truck operation logic, ship electrical, cooling, and heating loads, and ship unit and load demand balance. The second module is used to call the Gurobi solver to solve the objective function of the port integrated energy coordination optimization model constructed in step 1 to obtain the port integrated energy coordination optimization strategy, with constraints participating in the solution. The third module is used to implement port integrated energy coordination based on the port integrated energy coordination optimization strategy.

[0014] The beneficial effects of this invention include: by introducing integrated energy system optimization, it can not only reduce energy waste and lower carbon consumption costs, but also improve the overall resource utilization efficiency of the port, achieve green and sustainable operation, and more flexibly handle the uncertainties in ship coordination optimization, especially in the case of cross-time zone coordination optimization and incomplete cargo transfer, ensuring the efficiency and adaptability of port energy coordination optimization. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating an embodiment of a port integrated energy coordination optimization method based on multiple constraints. Figure 2 This is a schematic diagram of the arrival times of various ships in the port area, based on an embodiment of a port integrated energy coordination optimization method with multiple constraints. Figure 3 This is a schematic diagram of berthing operations, representing an embodiment of a port integrated energy coordination optimization method based on multiple constraints. Figure 4 This is a schematic diagram of the spatial distribution of quay crane resources in an embodiment of a port integrated energy coordination optimization method based on multiple constraints. Detailed Implementation

[0016] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] First, some of the technical terms used in this invention will be explained to help those skilled in the art understand the invention.

[0018] Gurobi Solver is an optimization software used to solve various mathematical programming and integer programming problems. It provides a high-performance mathematical programming solver capable of handling linear programming, mixed integer programming, quadratic programming, and constrained programming problems to maximize or minimize the objective function. It boasts powerful solving capabilities and efficient algorithms, enabling it to handle large-scale complex problems. Its solver employs advanced optimization techniques such as the simplex method and interior-point method for linear programming, and the branch-and-bound method and cutting plane method for integer programming.

[0019] Example 1 The first aspect of this invention proposes a multi-constraint-based method for coordinated optimization of integrated energy resources in ports, such as... Figure 1 As shown, it includes the following steps: Step 1: Obtain ship arrival data and establish a port integrated energy coordination and optimization model based on the ship arrival data. The objective function of the port integrated energy coordination and optimization model includes carbon emissions from fuel consumption, carbon emissions from electricity purchased from the main grid, total fuel cost, main grid electricity purchase cost, start-up and shutdown costs of energy supply units, and penalty costs for unloaded cargo after the end of the scheduling cycle. The constraints of the objective function are constructed using ship berthing scheduling, port quay crane operation scheduling, electric truck operation logic, ship electricity, cooling, and heating loads, and the balance of ship unit and load demand. Specifically, the parameter settings for the specific scenario are as follows: the scheduling cycle for the simulation is set to 24 hours. In order to accurately depict the rapid dynamic processes such as berth occupancy, quay crane operations, energy status of electric container trucks, and changes in energy system power, the scheduling cycle is discretized into 72 time periods, that is, each hour is divided into 3 discrete time periods, representing a scheduling time period of 15 minutes.

[0020] The port area comprises 5 berths, each capable of accommodating only one vessel at any given time. The example involves scheduling 22 vessels, considering their arrival times, maximum permissible berthing durations, loading / unloading workloads, and vessel types. Container ships involve not only loading and unloading operations, but each vessel also has electrical load requirements. Refrigerated container ships numbered 12 to 22 have continuous cooling load requirements during berthing, while other container ships numbered 1 to 11 generate both electrical and thermal loads, making their overall energy consumption characteristics more complex. Container ships are categorized into three types based on the number of containers: large, medium, and small. The thermal or cooling load requirements for large, medium, and small container ships are set at 50MW, 30MW, and 10MW, respectively. Correspondingly, the electrical load requirements for the three types of ships are set at 20MW, 10MW, and 4MW, respectively. Finally, the load requirements of all vessels throughout the scheduling cycle are obtained through a normal distribution.

[0021] The port area is equipped with a total of 20 quay cranes, with a maximum of 4 cranes per berth. Each quay crane has a rated power consumption of 300 kW. The quay crane loading and unloading efficiency is set at 50 containers per hour.

[0022] To simulate a port horizontal transport system, the example uses 100 electric container trucks with a transport capacity of 5 TEU / hour. The types of vessels required for the experiment are shown in Table 1. Table 1 Ship Types Step 2: Use the Gurobi solver to solve the objective function of the port integrated energy coordination optimization model constructed in Step 1 to obtain the port integrated energy coordination optimization strategy, with constraints participating in the solution; Step 3: Implement port integrated energy coordination according to the port integrated energy coordination and optimization strategy.

[0023] The formula for calculating the objective function of the port integrated energy coordination optimization model is as follows: (1) Where min z is the objective function; d is the fuel emission coefficient; Fuel consumed by the PGU; ... To consume fuel costs; Main grid electricity purchase cost; Costs of starting and stopping energy supply units; This represents the penalty cost coefficient for goods not fully loaded or unloaded by the end of the scheduling cycle; t represents time; T represents the scheduling cycle.

[0024] Furthermore, constraints established through ship berthing scheduling ensure that each ship can berth in an orderly manner and complete its berthing task. Specific constraints include: (2) Formula (2) represents the constraint that establishes a consistency relationship between the ship's berthing status and berth allocation decisions; st It is a 0-1 variable, indicating whether ship k is moored; The variable is 0-1, indicating whether ship k occupies berth b at time t; T represents the scheduling cycle; B is the number of berths. (3) Wherein, formula (3) represents the constraints on berth service; The variable is 0-1, indicating whether ship k occupies berth b at time t; N is the number of ships; B is the number of berths; T represents the scheduling cycle. (4) Formula (4) represents the berth allocation constraint during the scheduling cycle; The variable is 0-1, representing whether ship k starts berthing at time t when it is at berth b; N is the number of ships; B is the number of berths; T represents the scheduling cycle. (5) Wherein, formula (5) represents the continuity constraint of the berthing state; It is a 0-1 variable, indicating whether the ship k occupies the berth at time t when it is berthed at the berth b; It is a 0-1 variable, indicating whether ship k starts berthing at time t when it is at berthing position b; The variable is 0-1, representing whether ship k has finished berthing at time t when it is at berth b; N is the number of ships; B is the number of berths; T is the scheduling cycle. (6) Formula (6) means ensuring that the berthing time of a vessel is no earlier than its actual arrival time. The variable is 0-1, representing whether ship k begins berthing at time t when it is at berth b; N is the number of ships; B is the number of berths; a k The estimated berthing time for vessel k; (7) Formula (7) represents the total number of vessels that a port can serve; This represents the total number of docking stations. The variable is 0-1, indicating whether ship k is docked at time t; T represents the scheduling period; N is the number of ships. (8) Formula (8) represents the constraint on the amount of cargo unloaded by the ship, and its total demand must be met by both the completed and uncompleted portions. The number of loading and unloading subsystems used by vessel k at time t; crane_rate is the loading and unloading efficiency of a single crane quay crane at each time step. It is a 0-1 variable, indicating whether ship k is anchored at time t; This represents the amount of cargo that ship k has not yet completed at the end of the current time domain; Let T represent the cargo transfer demand of ship k; T represents the scheduling period. (9) (10) (11) Among them, formulas (9)-(11) represent the continuity constraints of the berthing time of ship k; It is a 0-1 variable, indicating whether ship k begins berthing at time t; It is a 0-1 variable, indicating whether ship k is anchored at time t; The variable is 0-1, indicating whether ship k finishes berthing at time t; N is the number of ships; T is the scheduling period. (12) Formula (12) represents the constraints on ship arrival operations; The variable is 0-1, representing whether ship k is anchored at time t; N is the number of ships; a k The estimated berthing time for vessel k; (13) (14) Among them, formulas (13)-(14) represent single berth single ship constraints, which are mutually exclusive; It is a 0-1 variable, indicating whether the ship k occupies the berth at time t when it is berthed at the berth b; The variable is 0-1, representing whether berth b is occupied at time t; B is the number of berths; T is the scheduling cycle; and N is the number of ships.

[0025] Specifically, embodiments of the present invention introduce a penalty-type flexible docking station constraint mechanism. This is achieved through variables... Indicates a ship The model sets a penalty cost coefficient for any unfinished cargo transfers at the end of the current time period. When a ship fails to complete cargo transfers on time, the model allows it to continue occupying a berth across time periods, but it must bear the penalty cost coefficient for the delay. This mechanism transforms "time default" into a quantifiable optimization variable, ensuring both the flexibility of overall coordination optimization and maintaining constraint control over delays, thus overcoming the limitations of traditional "hard time window" coordination optimization. Secondly, a cross-time period berth status transfer mechanism is established. This is achieved by transferring the berth occupancy status at the end of the previous coordination optimization window. and uncompleted cargo transshipment volume The initial state is passed to the next window, enabling continuous coordinated optimization across time periods. This mechanism ensures the temporal continuity of berthing space resources, allowing rolling optimization to naturally connect coordinated optimization tasks across different time periods, avoiding task interruptions or energy waste. Furthermore, a coupled constraint structure of berthing space, energy, and carbon energy consumption costs is constructed. This invention establishes a correlation between ship berthing space occupancy time and shore power system energy consumption and carbon energy consumption cost factors, ensuring that berthing space allocation not only affects operational efficiency but also directly impacts energy system operation and carbon energy consumption cost levels. Through collaborative optimization, the system automatically balances berthing space occupancy time and carbon energy consumption costs, achieving green integrated coordinated optimization of operations and the energy system.

[0026] like Figure 2 As shown, Figure 2The data shows a distinctly discrete distribution of arrival times for vessels within the port area, with vessels arriving successively throughout the morning, noon, and evening. Based on this characteristic, the optimization model of this invention uniformly schedules the berthing start times of each vessel, resulting in a relatively balanced distribution of berthing intervals across the time axis. Therefore, this model can effectively reduce the risk of berth resource conflicts during peak hours and improve the balance of berth utilization, especially when multiple vessels arrive at the port simultaneously.

[0027] Furthermore, the berthing durations of different vessels in the figure show significant differences, reflecting the varying loading and unloading workloads of each vessel. For example, the berthing durations for some vessels, such as vessels 12 and 14, are significantly longer than those for others, while the berthing periods for some vessels with smaller workloads are relatively shorter. The optimization results of this invention ensure that berthing operations for each vessel continue uninterrupted after securing berth resources, thus meeting the requirements for operational continuity in actual port operations.

[0028] Furthermore, during the middle of the scheduling cycle, approximately 25–40 seconds, is a typical period of concentrated ship arrivals, resulting in high berth capacity at port. Even during this period, Figure 2 The berthing Gantt chart shown still demonstrates an orderly berthing arrangement. The optimization model of this invention, under conditions of berth shortages, can avoid prolonged queuing by coordinating resource allocation among different vessels, enabling multiple vessels to operate smoothly under limited berth conditions. This demonstrates the model's resource scheduling capability and adaptability under complex port conditions.

[0029] like Figure 3 As shown, Figure 3 The diagram shows that berth 3 was sequentially assigned to vessels 7, 17, 5, and 20 for berthing operations throughout the entire scheduling cycle. As can be seen from the diagram, vessel 7 occupied the berth approximately from time 17 to 24, and its berthing duration matched its operational needs, with no premature departures or abnormal delays. Subsequently, vessel 17 berthed at time 24, with almost no idle time between the two vessels. This demonstrates that the scheduling model of this invention can achieve a close connection between vessel arrival times and berth resources, improving berth utilization efficiency.

[0030] Vessel 17's operations continued until approximately time 40, with a relatively long berthing interval, corresponding to its large loading and unloading volume. Subsequently, Vessel 5 arrived at time 45 and successfully entered its berth, with stable and continuous berthing start and end times, and no resource conflicts or scheduling interruptions occurred throughout the entire operation. Finally, Vessel 20 completed its berthing in the latter part of the scheduling cycle, approximately time 67–72. The scheduling sequence and duration of all vessels in this berth met the continuity requirements of the port operation process.

[0031] Furthermore, constraints established using port quay crane operation scheduling are used to systematically allocate loading and unloading tasks to ships based on the actual situation of quay crane and ship berthing operations. Specific constraints include: (15) Wherein, formula (15) represents the product linearization constraint; Let t be the number of loading and unloading subsystems assigned to ship k at time t when it is berthed at work position b; M represents the number of loading and unloading subsystems used by vessel k at time t; M represents the total number of electric trucks. The variable is 0-1, representing whether ship k occupies berth b at time t; N is the number of ships; B is the number of berths; and T is the scheduling cycle. (16) Formula (16) represents the consistency constraint for the allocation of ship cranes; Let t be the number of loading and unloading subsystems assigned to ship k at time t when it is berthed at work position b; Let N be the number of times vessel k uses the loading and unloading subsystem at time t; T be the number of vessels; B be the scheduling cycle; and B be the number of berths. (17) Formula (17) indicates that cranes are prohibited from being assigned when the ship is not berthed. The number of loading and unloading subsystems used by ship k at time t; This represents the maximum number of total loading and unloading subsystems. The variable is 0-1, representing whether ship k is docked; N is the number of ships; T is the scheduling period. (18) Among them, formula (18) represents the closed-loop constraint on the number of cranes; The number of loading and unloading subsystems used at berthing station b at time t; Let B be the number of loading and unloading subsystems assigned to vessel k at time t when it is berthed at berth b; T be the scheduling cycle; and N be the number of vessels. (19) (20) (twenty one) (twenty two) (twenty three) Among them, formulas (19)-(23) represent the continuous distribution constraint of cranes; formula (19) is the total capacity constraint of cranes; formula (20) represents the initialization of the first berth crane, that is, the first berth starts from crane number 0; formula (21) represents the berth continuity constraint, that is, the starting number of the crane at berth b+1 is immediately after the end of berth b; formula (22) represents the allocation constraint that can only be allocated when occupied, that is, the quay crane will be allocated to work only when it is parked at the berth; formula (23) represents the boundary constraint of continuous blocks, that is, the number of quay cranes called by the berth will not exceed the upper limit. (twenty four) Wherein, formula (24) represents the indicator constraint; h is a 0-1 variable, indicating whether the docking station b is occupied at time t; b,t B is the starting quay crane number for berth b at time t; B is the number of berths; and T is the scheduling cycle.

[0032] Specifically, traditional port loading and unloading subsystem coordination optimization models are usually based on fixed berthing positions and fixed vessel operation plans, allocating loading and unloading subsystems only based on total quantity constraints, failing to fully consider the spatial continuity, energy consumption characteristics, and carbon energy consumption cost differences of quay cranes. This invention improves upon these aspects by introducing a dynamic quay crane allocation and start-stop optimization mechanism. In traditional coordination optimization, once a loading and unloading subsystem is allocated, it runs continuously throughout the entire operation period, without considering energy consumption fluctuations caused by equipment start-stop. This invention improves upon these aspects by introducing a dynamic quay crane allocation and start-stop optimization mechanism in equations (15)–(19). Status of docking station The system employs a linkage constraint mechanism to optimize the dynamic start-up and shutdown of the loading and unloading subsystem and the load distribution. When no ships are operating at the berth, the system automatically puts the corresponding quay crane into standby mode, reducing ineffective energy consumption and carbon emissions. The mechanism links quay crane numbers with spatial locations through variables... Indicates docking station At any moment This invention introduces spatial continuity constraints into the model, ensuring that multiple loading and unloading subsystems assigned to the same berth are physically arranged continuously, avoiding the cross-berth operation planning or redundant allocation of loading and unloading subsystems in traditional models. This constraint not only guarantees the feasibility of actual operations but also effectively reduces the additional power consumption and equipment wear caused by the cross-rail movement of quay cranes; it also optimizes the linkage between the energy consumption and carbon consumption costs of the loading and unloading subsystems. This invention couples the allocation results of the loading and unloading subsystems with the energy consumption model, incorporating the carbon consumption cost contribution of each quay crane into the overall objective function of the system based on its operating power and emission factor. By optimizing the start-up and shutdown plans and allocation quantities of the loading and unloading subsystems, the invention achieves the synchronous minimization of carbon consumption costs and energy consumption during the operation period. This innovation realizes the integrated optimization of operation planning and energy consumption control.

[0033] Specifically, such as Figure 4 As shown, the spatial distribution of quay crane resources indicates that the scheduling model of this invention strictly satisfies the physical constraint of continuous numbering allocation. That is, the quay crane numbers assigned to the same ship at any given time are always continuous, thereby ensuring that the quay crane scheduling scheme is practically feasible and meets the requirements of terminal operations for equipment continuity.

[0034] Furthermore, the number of quay cranes used at different berths during the scheduling cycle exhibits phased characteristics depending on their loading and unloading loads. During peak periods of concentrated ship arrivals, such as 21–41 h and 46–61 h, multiple berths are simultaneously allocated a large number of quay cranes, reflecting peak resource input characteristics; while during periods when some berths are idle or under low load, the corresponding number of quay cranes occupied decreases. Through this dynamic allocation mechanism, the model can achieve balanced utilization and real-time adjustment of quay crane resources globally, improving the overall operational efficiency of the port area.

[0035] The constraints constructed using the electric container truck operation logic are used to rationally allocate the demand for transporting containers to the yard using electric container trucks based on ship loading and unloading tasks and quay crane operations. Specific constraints include: (25) Wherein, formula (25) represents the working state constraint of the electric truck; The variable is 0-1, indicating whether the electric truck m is in operation at time t; The variable is 0-1, indicating whether electric truck m is in a charging state at time t; M is the total number of electric trucks; T is the scheduling period. (26) Wherein, formula (26) represents the electric truck power state constraint; This represents the remaining battery power of electric truck m at time t; The variable is 0-1, indicating whether the electric truck m is in a charging state at time t; The variable is 0-1, indicating whether the electric truck m is in operation at time t; Indicates the power consumption efficiency per unit time; Indicates the charging power per unit time; Indicates the total battery capacity; Indicates charging efficiency; T is the scheduling cycle; M is the total number of electric trucks; (27) (28) (29) (30) (31) (32) Among them, formulas (27)-(32) represent the operating logic constraints of electric trucks; This represents the remaining battery power of electric truck m at time t; This refers to the charging threshold. The discharge threshold; It is a 0-1 variable, indicating whether charging should be performed; It is a 0-1 variable, indicating whether or not to discharge; The variable is 0-1, indicating whether the electric truck m is in operation at time t; The variable is 0-1, representing whether the electric truck m is in a charging state at time t, according to... and value settings and ; This refers to the total battery capacity. (33) Formula (33) represents the matching constraint between the loading and unloading speed of the quay crane and the transfer speed of the electric truck; Y represents the cargo transfer rate for each loading / unloading subsystem; k,c,t The variable is 0-1, indicating whether ship k uses crane c at time t; For the transport rate of each electric truck; The variable is 0-1, indicating whether the electric container truck m is in operation at time t; T is the scheduling cycle; N is the number of ships; C is the total number of loading and unloading subsystems; M is the total number of electric container trucks. (34) Formula (34) represents the addition of a total loading and unloading power constraint for the quay crane; Let t be the total electrical power consumption of the quay crane system. Y represents the power consumption per unit of each loading / unloading subsystem. k,c,t The variable is 0-1, representing whether ship k uses crane c at time t; T is the scheduling cycle; C is the total number of loading and unloading subsystems; N is the number of ships. (35) Wherein, formula (35) represents the addition of a total charging power constraint for electric trucks; The total charging power of all electric trucks at time t; The variable is 0-1, indicating whether the electric truck m is in a charging state at time t; The charging power per unit time is T; the scheduling cycle is M; and the total number of electric trucks is M.

[0036] Furthermore, constraints are established using the ship's electrical, cooling, and heating loads to ensure the balance of electrical and cooling / heating loads for each ship at any given moment. Specific constraints include: (36) Formula (36) represents the ship's electrical load demand; This represents the total electrical load demand for all ships. Whether cruise ship C is docked at time t; For cruise ships Electricity load demand; Refrigerated ship Electricity load demand; Refrigerated ship Whether the vessel is stopped at time t; C is the total number of loading and unloading subsystems; T is the scheduling period; (37) Formula (37) represents the ship's heat load demand; For cruise ships Heat load requirements; Total heat load requirements for all ships; Indicates whether cruise ship c is docked at time t; C is the total number of loading and unloading subsystems; (38) Wherein, formula (38) represents the cooling load demand constraint; Refrigerated ship The cooling load demand; Total cooling load requirements for all ships; Indicates refrigerated ship Whether the vessel is docked at time t; C represents the total number of loading and unloading subsystems.

[0037] Furthermore, the constraints imposed by the ship's engine unit configuration ensure that each unit outputs power as required. Specific constraints include: (39) (40) (41) Among them, formulas (39)-(41) represent PGU constraints, i.e. generator set constraints; Fuel consumption of PGU; Power output for PGU; for Electrical efficiency; Waste heat recovery power; for Thermal efficiency; for maximum output; It is a 0-1 variable, indicating whether the PGU is contributing power; (42) Wherein, formula (42) represents the power purchase constraint of the main grid; This is the upper limit for electricity purchased from the power grid. This indicates the power purchased by the main grid at time t; (43) (44) Among them, formulas (43)-(44) represent EB constraints, i.e., energy storage device constraints; for Heat output; for Output power; for Heating efficiency; Indicates the maximum output power limit of EB; It is a 0-1 variable, indicating whether the EB unit is working; (45) (46) Among them, formulas (45)-(46) represent AC constraints, i.e., absorption chiller constraints; for Output cooling power; for Consumes heat power; Indicates the AC unit coefficient; for Output upper limit; It is a 0-1 variable, indicating whether AC is exerting force; (47) (48) Among them, formulas (47)-(48) represent EC constraints, i.e., electric chiller constraints; for Output cooling power; for Consumes electrical power; express Unit coefficient; This is the upper limit of EC's output; It is a 0-1 variable, indicating whether EC is contributing power; (49) Wherein, formula (49) represents the HX constraint, i.e., the heat exchanger constraint; 0-1 variables represent Whether or not to exert effort; express Maximum output of the unit; (50) Wherein, formula (50) represents the TES charge-discharge state constraint; for Injected thermal power; for Release heat power; (51) (52) Wherein, formulas (51)-(52) represent the power constraints of the TES equipment; Q TES,in,t Injecting thermal power into the TES; Q TES,dr,t Release heat power for TES; Maximum limit of thermal power injected into TES; for Maximum limit of heat output; for The efficiency coefficient of heat release power; for Injected thermal power; for Release heat power; (53) (54) Among them, formulas (53)-(54) represent the capacity constraints of TES equipment; for Heat storage capacity; This is the minimum limit for heat storage; for Maximum limit for heat storage; The efficiency coefficient for releasing heat power in TES; Inject thermal power efficiency coefficient into TES; express Injected thermal power; express Release heat power; (55) (56) (57) (58) (59) Among them, formulas (55)-(59) represent EES constraints, i.e., energy storage device constraints; for Discharge state; for Charging status; for Discharge power; for Maximum discharge power limit; EES discharge efficiency; for Charging efficiency; for Battery level; This is the maximum limit for EES power. for Minimum battery capacity; (60) Wherein, formula (60) represents the constraint protecting the EES device; for Charging efficiency; for Discharge efficiency; for Discharge power; for Charging power; T is the scheduling period; (61) (62) Among them, formulas (61)-(62) represent the power output constraints of wind and solar power; for Power generation capacity; Indicates the upper limit of light output; for Power generation capacity; This indicates the upper limit of wind power output.

[0038] Furthermore, the constraints constructed using load demand balancing ensure that the load demand of each ship is balanced at every moment. Specific constraints include: (63) Formula (63) represents the power balance; Power output for PGU; This indicates that electricity was purchased from the main network. for Power generation capacity; for Power generation capacity; express Discharge power; express The unit consumes electrical power; express Output power; express Charging power; This represents the total electrical load demand of all ships; This indicates the total power consumed by the quay crane; This indicates the total charging power of the electric truck; (64) Formula (64) represents the thermal power balance; This refers to the power of waste heat recovery. 0-1 variables represent Whether or not to exert effort; for Consumes heat power; This indicates the heat output of the TES unit; This indicates the heat power absorbed by the TES unit; (65) Among them, formula (65) represents the heat load supply and demand balance constraint; 0-1 variables represent Whether or not to exert effort; for Heat output; Total cooling load requirements for all ships; (66) Among them, formula (66) represents the cooling load supply and demand balance constraint; The AC consumes heat power; The EC consumes heat power; This represents the total cooling load requirement for all ships.

[0039] Example 2 The second aspect proposes a port integrated energy coordination and optimization system based on multiple constraints, including: The first module is used to acquire ship arrival data and establish a port integrated energy coordination and optimization model based on the ship arrival data. The objective function of the port integrated energy coordination and optimization model includes carbon emissions from fuel consumption, carbon emissions from electricity purchased from the main grid, total fuel cost, main grid electricity purchase cost, start-up and shutdown costs of energy power supply units, and penalty costs for unloaded cargo after the end of the scheduling cycle. The constraints of the objective function are constructed by using ship and berthing position allocation, quay crane allocation of loading and unloading subsystem, electric truck operation logic, ship electrical, cooling, and heating loads, and ship unit and load demand balance. The second module is used to call the Gurobi solver to solve the objective function of the port integrated energy coordination optimization model constructed in step 1 to obtain the port integrated energy coordination optimization strategy, with constraints participating in the solution. The third module is used to implement port integrated energy coordination based on the port integrated energy coordination optimization strategy.

[0040] The contents not described in detail in this specification are prior art known to those skilled in the art. Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.

[0041] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0042] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0043] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0044] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims of the invention.

Claims

1. A port integrated energy coordination optimization method based on multiple constraints, characterized in that, Includes the following steps: Step 1: Obtain ship arrival data and establish a port integrated energy coordination and optimization model based on the ship arrival data. The objective function of the port integrated energy coordination and optimization model includes carbon emissions from fuel consumption, carbon emissions from electricity purchased from the main grid, total fuel cost, main grid electricity purchase cost, start-up and shutdown costs of energy supply units, and penalty costs for unloaded cargo after the end of the scheduling cycle. The constraints of the objective function are constructed using ship berthing scheduling, port quay crane operation scheduling, electric truck operation logic, ship electricity, cooling, and heating loads, and the balance of ship unit and load demand. Step 2: Use the Gurobi solver to solve the objective function of the port integrated energy coordination optimization model constructed in Step 1 to obtain the port integrated energy coordination optimization strategy, with constraints participating in the solution; Step 3: Implement port integrated energy coordination according to the port integrated energy coordination and optimization strategy.

2. The port integrated energy coordination optimization method based on multiple constraints according to claim 1, characterized in that, The formula for calculating the objective function of the port integrated energy coordination optimization model is as follows: (1) Where min z is the objective function; d is the fuel emission coefficient; Fuel consumed by the PGU; ... To consume fuel costs; Main grid electricity purchase cost; Costs of starting and stopping energy supply units; This represents the penalty cost coefficient for goods not fully loaded or unloaded by the end of the scheduling cycle; t represents time; T represents the scheduling cycle.

3. The port integrated energy coordination optimization method based on multiple constraints according to claim 1, characterized in that, The constraints established by ship berthing scheduling ensure that each ship can berth in an orderly manner and complete its berthing task. Specific constraints include: (2) Formula (2) represents the consistency constraint between the ship's berthing status and the berth allocation decision; It is a 0-1 variable, indicating whether ship k is moored; The variable is 0-1, indicating whether ship k occupies berth b at time t; T represents the scheduling cycle; B is the number of berths. (3) Wherein, formula (3) represents the constraints on berth service; The variable is 0-1, indicating whether ship k occupies berth b at time t; N is the number of ships; B is the number of berths; T represents the scheduling cycle. (4) Formula (4) represents the berth allocation constraint during the scheduling cycle; The variable is 0-1, representing whether ship k starts berthing at time t when it is at berth b; N is the number of ships; B is the number of berths; T represents the scheduling cycle. (5) Wherein, formula (5) represents the continuity constraint of the berthing state; It is a 0-1 variable, indicating whether the ship k occupies the berth at time t when it is berthed at the berth b; It is a 0-1 variable, indicating whether ship k starts berthing at time t when it is at berthing position b; The variable is 0-1, representing whether ship k has finished berthing at time t when it is at berth b; N is the number of ships; B is the number of berths; T is the scheduling cycle. (6) Formula (6) means ensuring that the berthing time of a vessel is no earlier than its actual arrival time. The variable is 0-1, representing whether ship k begins berthing at time t when it is at berth b; N is the number of ships; B is the number of berths; a k The estimated berthing time for vessel k; (7) Formula (7) represents the total number of vessels that a port can serve; This represents the total number of docking stations. The variable is 0-1, indicating whether ship k is docked at time t; T represents the scheduling period; N is the number of ships. (8) Formula (8) represents the constraint on the amount of cargo unloaded by the ship, and its total demand must be met by both the completed and uncompleted portions. The number of loading and unloading subsystems used by vessel k at time t; crane_rate is the loading and unloading efficiency of a single crane quay crane at each time step. It is a 0-1 variable, indicating whether ship k is anchored at time t; This represents the amount of cargo that ship k has not yet completed at the end of the current time domain; Let T represent the cargo transfer demand of ship k; T represents the scheduling period. (9) (10) (11) Among them, formulas (9)-(11) represent the continuity constraints of the berthing time of ship k; It is a 0-1 variable, indicating whether ship k begins berthing at time t; It is a 0-1 variable, indicating whether ship k is anchored at time t; The variable is 0-1, indicating whether ship k finishes berthing at time t; N is the number of ships; T is the scheduling period. (12) Formula (12) represents the constraints on ship arrival operations; The variable is 0-1, representing whether ship k is anchored at time t; N is the number of ships; a k The estimated berthing time for vessel k; (13) (14) Among them, formulas (13)-(14) represent single berth single ship constraints, which are mutually exclusive; It is a 0-1 variable, indicating whether the ship k occupies the berth at time t when it is berthed at the berth b; The variable is 0-1, representing whether berth b is occupied at time t; B is the number of berths; T is the scheduling cycle; and N is the number of ships.

4. The port integrated energy coordination optimization method based on multiple constraints according to claim 1, characterized in that, The constraints established by the port quay crane operation scheduling are used to orderly allocate loading and unloading tasks to ships based on the actual situation of quay crane and ship berthing operations. Specific constraints include: (15) Wherein, formula (15) represents the product linearization constraint; Let t be the number of loading and unloading subsystems assigned to ship k at time t when it is berthed at work position b; M represents the number of loading and unloading subsystems used by vessel k at time t; M represents the total number of electric trucks. The variable is 0-1, representing whether ship k occupies berth b at time t; N is the number of ships; B is the number of berths; and T is the scheduling cycle. (16) Formula (16) represents the consistency constraint for the allocation of ship cranes; Let t be the number of loading and unloading subsystems assigned to ship k at time t when it is berthed at work position b; Let N be the number of times vessel k uses the loading and unloading subsystem at time t; T be the number of vessels; B be the scheduling cycle; and B be the number of berths. (17) Formula (17) indicates that cranes are prohibited from being assigned when the ship is not berthed. The number of loading and unloading subsystems used by ship k at time t; This represents the maximum number of total loading and unloading subsystems. The variable is 0-1, representing whether ship k is docked; N is the number of ships; T is the scheduling period. (18) Among them, formula (18) represents the closed-loop constraint on the number of cranes; The number of loading and unloading subsystems used at berthing station b at time t; Let B be the number of loading and unloading subsystems assigned to vessel k at time t when it is berthed at berth b; T be the scheduling cycle; and N be the number of vessels. (19) (20) (21) (22) (23) Among them, formulas (19)-(23) represent the continuous distribution constraint of cranes; formula (19) is the total capacity constraint of cranes; formula (20) represents the initialization of the first berth crane, that is, the first berth starts from crane number 0; formula (21) represents the berth continuity constraint, that is, the starting number of the crane at berth b+1 is immediately after the end of berth b; formula (22) represents the allocation constraint that can only be allocated when occupied, that is, the quay crane will be allocated to work only when it is parked at the berth; formula (23) represents the boundary constraint of continuous blocks, that is, the number of quay cranes called by the berth will not exceed the upper limit. (24) Wherein, formula (24) represents the indicator constraint; h is a 0-1 variable, indicating whether the docking station b is occupied at time t; b,t B is the starting quay crane number for berth b at time t; B is the number of berths; and T is the scheduling cycle.

5. The port integrated energy coordination optimization method based on multiple constraints according to claim 1, characterized in that, The constraints constructed using the electric container truck operation logic are used to rationally allocate the demand for transporting containers to the yard using electric container trucks based on ship loading and unloading tasks and quay crane operations. Specific constraints include: (25) Wherein, formula (25) represents the working state constraint of the electric truck; The variable is 0-1, indicating whether the electric truck m is in operation at time t; The variable is 0-1, indicating whether electric truck m is in a charging state at time t; M is the total number of electric trucks; T is the scheduling period. (26) Wherein, formula (26) represents the electric truck power state constraint; This represents the remaining battery power of electric truck m at time t; The variable is 0-1, indicating whether the electric truck m is in a charging state at time t; The variable is 0-1, indicating whether the electric truck m is in operation at time t; Indicates the power consumption efficiency per unit time; Indicates the charging power per unit time; Indicates the total battery capacity; Indicates charging efficiency; T is the scheduling cycle; M is the total number of electric trucks; (27) (28) (29) (30) (31) (32) Among them, formulas (27)-(32) represent the operating logic constraints of electric trucks; This represents the remaining battery power of electric truck m at time t; This refers to the charging threshold. The discharge threshold; It is a 0-1 variable, indicating whether charging should be performed; It is a 0-1 variable, indicating whether or not to discharge; The variable is 0-1, indicating whether the electric truck m is in operation at time t; The variable is 0-1, representing whether the electric truck m is in a charging state at time t, according to... and value settings and ; This refers to the total battery capacity. (33) Formula (33) represents the matching constraint between the loading and unloading speed of the quay crane and the transfer speed of the electric truck; Y represents the cargo transfer rate for each loading / unloading subsystem; k,c,t The variable is 0-1, indicating whether ship k uses crane c at time t; For the transport rate of each electric truck; The variable is 0-1, indicating whether the electric container truck m is in operation at time t; T is the scheduling cycle; N is the number of ships; C is the total number of loading and unloading subsystems; M is the total number of electric container trucks. (34) Formula (34) represents the addition of a total loading and unloading power constraint for the quay crane; Let t be the total electrical power consumption of the quay crane system. Y represents the power consumption per unit of each loading / unloading subsystem. k,c,t The variable is 0-1, representing whether ship k uses crane c at time t; T is the scheduling cycle; C is the total number of loading and unloading subsystems; N is the number of ships. (35) Wherein, formula (35) represents the addition of a total charging power constraint for electric trucks; The total charging power of all electric trucks at time t; The variable is 0-1, indicating whether the electric truck m is in a charging state at time t; The charging power per unit time is T; the scheduling cycle is M; and the total number of electric trucks is M.

6. The port integrated energy coordination optimization method based on multiple constraints according to claim 1, characterized in that, The constraints established by utilizing the ship's electrical, cooling, and heating loads ensure the balance of electrical and cooling load requirements for each ship at any given moment. Specific constraints include: (36) Formula (36) represents the ship's electrical load demand; This represents the total electrical load demand for all ships. Whether cruise ship C is docked at time t; For cruise ships Electricity load demand; Refrigerated ship Electricity load demand; Refrigerated ship Whether the vessel is stopped at time t; C is the total number of loading and unloading subsystems; T is the scheduling period; (37) Formula (37) represents the ship's heat load demand; For cruise ships Heat load requirements; Total heat load requirements for all ships; Indicates whether cruise ship c is docked at time t; C is the total number of loading and unloading subsystems; (38) Wherein, formula (38) represents the cooling load demand constraint; Refrigerated ship The cooling load demand; Total cooling load requirements for all ships; Indicates refrigerated ship Whether the vessel is docked at time t; C represents the total number of loading and unloading subsystems.

7. The port integrated energy coordination optimization method based on multiple constraints according to claim 1, characterized in that, The constraints imposed by the ship's generator sets ensure that each unit outputs power as required. Specific constraints include: (39) (40) (41) Among them, formulas (39)-(41) represent PGU constraints, i.e. generator set constraints; Fuel consumption of PGU; Power output for PGU; for Electrical efficiency; Waste heat recovery power; for Thermal efficiency; for maximum output; It is a 0-1 variable, indicating whether the PGU is contributing power; (42) Wherein, formula (42) represents the power purchase constraint of the main grid; This is the upper limit for electricity purchased from the power grid. This indicates the power purchased by the main grid at time t; (43) (44) Among them, formulas (43)-(44) represent EB constraints, i.e., energy storage device constraints; for Heat output; for Output power; for Heating efficiency; Indicates the maximum output power limit of EB; It is a 0-1 variable, indicating whether the EB unit is working; (45) (46) Among them, formulas (45)-(46) represent AC constraints, i.e., absorption chiller constraints; for Output cooling power; for Consumes heat power; Indicates the AC unit coefficient; for Output upper limit; It is a 0-1 variable, indicating whether AC is exerting force; (47) (48) Among them, formulas (47)-(48) represent EC constraints, i.e., electric chiller constraints; for Output cooling power; for Consumes electrical power; express Unit coefficient; This is the upper limit of EC's output; It is a 0-1 variable, indicating whether EC is contributing power; (49) Wherein, formula (49) represents the HX constraint, i.e., the heat exchanger constraint; 0-1 variables represent Whether or not to exert effort; express Maximum output of the unit; (50) Wherein, formula (50) represents the TES charge-discharge state constraint; for Injected thermal power; for Release heat power; (51) (52) Wherein, formulas (51)-(52) represent the power constraints of the TES equipment; Q TES,in,t Injecting thermal power into the TES; Q TES,dr,t Release heat power for TES; Maximum limit of thermal power injected into TES; for Maximum limit of heat output; for The efficiency coefficient of heat release power; for Injected thermal power; for Release heat power; (53) (54) Among them, formulas (53)-(54) represent the capacity constraints of TES equipment; for Heat storage capacity; This is the minimum limit for heat storage; for Maximum limit for heat storage; The efficiency coefficient for releasing heat power in TES; Inject thermal power efficiency coefficient into TES; express Injected thermal power; express Release heat power; (55) (56) (57) (58) (59) Among them, formulas (55)-(59) represent EES constraints, i.e., energy storage device constraints; for Discharge state; for Charging status; for Discharge power; for Maximum discharge power limit; EES discharge efficiency; for Charging efficiency; for Battery level; This is the maximum limit for EES power. for Minimum battery capacity; (60) Wherein, formula (60) represents the constraint protecting the EES device; for Charging efficiency; for Discharge efficiency; for Discharge power; for Charging power; T is the scheduling period; (61) (62) Among them, formulas (61)-(62) represent the power output constraints of wind and solar power; for Power generation capacity; Indicates the upper limit of light output; for Power generation capacity; This indicates the upper limit of wind power output.

8. The port integrated energy coordination optimization method based on multiple constraints according to claim 1, characterized in that, The constraints constructed using load demand balancing ensure that the load demand of each ship is balanced at every moment. Specific constraints include: (63) Formula (63) represents the power balance; Power output for PGU; This indicates that electricity was purchased from the main network. for Power generation capacity; for Power generation capacity; express Discharge power; express The unit consumes electrical power; express Output power; express Charging power; This represents the total electrical load demand of all ships; This indicates the total power consumed by the quay crane; This indicates the total charging power of the electric truck; (64) Formula (64) represents the thermal power balance; This refers to the power of waste heat recovery. This indicates the heat output of the TES unit; 0-1 variables represent Whether or not to exert effort; for Consumes heat power; This indicates the heat power absorbed by the TES unit; (65) Among them, formula (65) represents the heat load supply and demand balance constraint; 0-1 variables represent Whether or not to exert effort; for Heat output; Total cooling load requirements for all ships; (66) Among them, formula (66) represents the cooling load supply and demand balance constraint; The AC consumes heat power; The EC consumes heat power; This represents the total cooling load requirement for all ships.

9. A port integrated energy coordination and optimization system based on multiple constraints, characterized in that, include: The first module is used to acquire ship arrival data and establish a port integrated energy coordination and optimization model based on the ship arrival data. The objective function of the port integrated energy coordination and optimization model includes carbon emissions from fuel consumption, carbon emissions from electricity purchased from the main grid, the total cost of fuel consumption during the entire scheduling cycle, the cost of electricity purchased from the main grid, the start-up and shutdown costs of energy power supply units, and the penalty cost for unfinished loading and unloading after the scheduling cycle ends. The constraints of the objective function are constructed using ship and berthing allocation, quay crane allocation in the loading and unloading subsystem, electric truck operation logic, ship heating and cooling demand, and power load balance. The second module is used to call the Gurobi solver to solve the objective function of the port integrated energy coordination optimization model constructed in step 1 to obtain the port integrated energy coordination optimization strategy, with constraints participating in the solution. The third module is used to implement port integrated energy coordination based on the port integrated energy coordination optimization strategy.

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