Port integrated energy system and container logistics system collaborative scheduling method

Abstract

The application discloses a kind of port comprehensive energy systems and container logistics system collaborative scheduling method, steps are as follows: the collaborative scheduling architecture of port comprehensive energy system and port container logistics system is constructed;Based on the shortest average time of ship in port and the lowest logistics scheduling cost, port container logistics system joint optimization scheduling model and operation energy model are established;Based on the lowest system operation cost, port comprehensive energy system multi-energy flow coupling optimization scheduling model and energy output model are established;Port container logistics scheduling process model is constructed based on the principle of flexible assembly line workshop scheduling;The energy supply and demand coupling model of port comprehensive energy system and port container logistics system is constructed, considering double uncertainty;ADMM is used, and the improved non-dominated sorting genetic algorithm MNSGA-II is combined to solve, and energy flow and logistics scheduling plan are obtained.The collaborative scheduling of the application promotes the economic, efficient, low-carbon operation and green development of port.

Description

Technical Field

[0001] This invention relates to the field of port energy systems and logistics systems, specifically a method for the coordinated scheduling of a port integrated energy system and a container logistics system. Background Technology

[0002] The shipping industry's heavy reliance on fossil fuels has led to severe environmental pollution. As key hubs in shipping networks, ports are increasingly focusing on energy efficiency management and pollution reduction. my country is the world's largest port nation, with annual carbon emissions from fossil fuel consumption exceeding 14 million tons. To promote "green port" construction and improve port energy efficiency and pollution reduction, multi-energy integration technologies and their applications, as well as facility electrification and energy substitution technologies, are considered key directions for my country's future port construction. In recent years, my country's marine and inland river ports have accelerated the deployment of electrification equipment such as shore power charging piles, electric quay cranes, yard cranes, refrigerated containers, and automated guided vehicles (AGVs) to provide power supply, container loading and unloading, transportation, stacking, and cargo refrigeration services for arriving vessels. To meet the rapidly increasing electricity demand of ports and the diversified energy needs for heat and cooling brought about by the rapid development of port infrastructure, integrated port energy systems that combine new energy sources, power generation / heat / cooling / gas generation units, and energy storage equipment, enabling the production, conversion, transmission, storage, and utilization of multiple types of energy, are gradually being built. The integrated development of port integrated energy systems and port container logistics systems has led to ports exhibiting characteristics of "electricity as the mainstay, coexistence of multiple energy flows, and coupling of energy and logistics." Against this backdrop, conducting coordinated scheduling of port integrated energy systems and port container logistics systems is of great significance for improving the quality and efficiency of port logistics energy utilization and fully supporting the realization of the "carbon neutrality" vision.

[0003] Currently, research on the coordinated scheduling of port integrated energy systems and port container logistics systems mainly comes from the logistics and transportation sector, focusing on highly electrified ports. This includes improving logistics efficiency and thus port energy efficiency through logistics management methods such as berth scheduling optimization, electric-driven quay crane scheduling optimization, and automated guided container truck scheduling optimization; or reducing port energy consumption through energy management methods such as electric-driven quay cranes combined with energy storage operation, intermittent power supply operation for refrigerated containers, and optimized charging and discharging of automated guided container trucks. However, most studies focus on single or a few links in the logistics operation process, without comprehensively considering the energy consumption characteristics of equipment throughout the entire logistics process. Related research from the energy scheduling field largely fails to consider the scheduling characteristics of the logistics side. It simplifies the scheduling model of the logistics side, treating the energy consumption of logistics work as a constant, or only optimizes the scheduling of a single or a few logistics links to obtain the energy required for a portion of the logistics process, prioritizing the fulfillment of logistics energy demands in port energy system scheduling. A review of existing research reveals that most studies on the coordinated scheduling of port integrated energy systems and port container logistics systems treat the energy management issues of port integrated energy systems and container logistics management issues independently, neglecting the role of coordinated scheduling of the two systems in promoting port economic, efficient, low-carbon operation and green development. Summary of the Invention

[0004] The purpose of this invention is to address the problems existing in the prior art by providing a method for the coordinated scheduling of a port integrated energy system and a container logistics system.

[0005] The objective of this invention is achieved through the following technical solution:

[0006] A method for coordinated scheduling of a port integrated energy system and a container logistics system, characterized by the following steps:

[0007] A. Clarify the composition of the port integrated energy system, the composition of the port container logistics system and the logistics operation process, as well as the coupling relationship between energy flow and logistics, and construct a collaborative scheduling architecture for the port integrated energy system and the port container logistics system.

[0008] B. Establish a joint optimization scheduling model and operational energy consumption model for the port container logistics system based on minimizing the average ship time in port and the lowest logistics scheduling cost;

[0009] C. Establish a multi-energy flow coupling optimization scheduling model and energy output model for the port integrated energy system based on the lowest system operating cost;

[0010] D. Construct a port container logistics scheduling process model based on the principle of flexible assembly line workshop scheduling;

[0011] E. Construct a coupling model of energy supply and demand between the port integrated energy system and the port container logistics system, taking into account dual uncertainties;

[0012] F. The alternating direction multiplier method (ADMM) combined with the improved non-dominated sorting genetic algorithm (MNSGA-II) is used to solve the model in steps B, C, D, and E, and the port energy flow and logistics scheduling plan is obtained.

[0013] The port integrated energy system in step A covers the production and consumption of electricity, heat, and cold energy.

[0014] In step A, the port container logistics system schedules shore power, quay cranes, trucks, yard cranes, and refrigerated containers within the logistics operation area based on the arrival and departure times of each container ship and the volume of containers to be loaded and unloaded, to jointly complete the logistics operation process, including ship berthing, container loading and unloading, transportation, stacking, and refrigeration.

[0015] The coupling relationship between energy flow and logistics in step A is manifested in the fact that the energy consumed by the port container logistics system in scheduling shore power, quay cranes, container trucks, yard cranes, and refrigerated container operations is met by the output of the port's integrated energy system.

[0016] The joint optimization scheduling model and operational energy consumption model of the port container logistics system in step B include the overall scheduling objective function of the port container logistics system, the constraints of the port container logistics system scheduling, and the power demand model of the entire container logistics process. The overall scheduling objective function of the port container logistics system includes the objective function for berths with shore power and the joint scheduling objective function for quay cranes, yard cranes, trucks, and refrigerated containers. The constraints of the port container logistics system scheduling include constraints on vessel berthing / departure time and berthing position, logistics equipment scheduling constraints, and logistics equipment operation constraints. The power demand model of the entire container logistics process covers shore power, quay cranes, trucks, yard cranes, and refrigerated containers.

[0017] The objective function for scheduling berths with shore power is: to minimize the average berthing time of all vessels in port, i.e.:

[0018]

[0019] In formula (1): B ship This represents the average time all vessels spend in port within a scheduling cycle; r ship The number of ships arriving in port within a period; t arr,i t ber,i and t SPS,i These represent the planned arrival time, berthing time, and shore power usage time of vessel i, respectively.

[0020] Objective function for joint scheduling of quay cranes, yard cranes, trucks, and refrigerated containers: The logistics system schedules various types of equipment with the objective function of minimizing the total cost of scheduling quay cranes, yard cranes, trucks, and refrigerated containers, expressed as:

[0021] minC PCLS =C QC +C GC +C AGV +C RC (2)

[0022] In equation (2): C PCLS C represents the total scheduling cost of a logistics system over a scheduling cycle; QC C GC C AGV and C RC The dispatch costs for quay cranes, yard cranes, container trucks, and refrigerated containers are as follows:

[0023] The cost of quay crane scheduling includes the startup cost of quay cranes serving each vessel and the cost of loading and unloading operations, expressed as:

[0024]

[0025] In equation (3): c QC,on and c QC,w These represent the startup cost of a single quay crane and the average hourly loading and unloading operation cost, respectively; QC r represents the total number of quay cranes. C,i T represents the number of containers to be loaded or unloaded on the i-th ship; L This refers to the scheduling cycle of the logistics system. This represents a 0-1 state variable indicating the work of the k-th quay crane serving the i-th vessel in loading and unloading containers during time period t, with 1 indicating work and 0 otherwise.

[0026] The yard crane scheduling cost includes the yard crane start-up cost for each vessel and the container stacking operation, and is expressed as:

[0027]

[0028] In equation (4): c GC,on and c GC,w These represent the startup cost of a single quay crane and the average hourly container stacking cost, respectively; r GC This represents the total number of yard bridges; This represents a 0-1 state variable indicating the operation of the nth yard bridge serving the i-th ship's container stacking during time period t, with 1 indicating operation and 0 indicating otherwise.

[0029] Truck dispatching costs include the startup costs and transportation costs of the trucks serving each vessel, expressed as:

[0030]

[0031] In equation (5): c AGV,on and c AGV,w These represent the startup cost of a single container truck and the average hourly container transport cost, respectively. AGV Indicates the total number of trucks; This represents the 0-1 state variable indicating the m-th container truck's service to the i-th vessel for container transport during time period t, with 1 for outage charging and 0 for container transport.

[0032] The cold box scheduling cost is the sum of the cold box's start-up and refrigeration costs, which can be expressed as:

[0033]

[0034] In equation (6): c RC,on This represents the startup cost of a single cold box; r RC This represents the total number of cold boxes; This represents the refrigeration status of the f-th cold box, a variable ranging from 0 to 1, where 1 indicates refrigeration and 0 indicates otherwise.

[0035] Berthing / departure time and berthing position constraints: Each vessel can only berth after it arrives, and the actual departure time of the vessel cannot be later than the planned latest departure time. The berthing times of any two vessels cannot conflict, as shown in Equation (7); the berthing positions of all vessels cannot exceed the port coastline, and the berthing positions and berthing times of any two vessels cannot conflict, as shown in Equation (8).

[0036] t arr,i ≤t ber,i ,t ber,i +t SPS,i ≤t dep,i ,t ber,i +t SPS,i ≤t ber,q +M(1-h i,q (7)

[0037] l 0,i +l i ≤L,l 0,i +l i ≤l q +M(1-z i,q ),z i,q +z q,i +h i,q +h q,i ≥1 (8)

[0038] In equations (7) and (8): t dep,i Indicates the planned departure time of vessel i; l 0,i and l i These represent the starting position of vessel i at its berth and the vessel length including the safe berthing distance between vessels, respectively; L is the shoreline length; zi,q h represents a 0-1 state variable indicating the berthing position of any two ships. A value of 1 indicates ship i is berthed to the left of ship q on the shoreline, and 0 otherwise. i,q Let M be a 0-1 state variable representing the berthing time of any two ships. If ship i berths before ship q, the value is 1; otherwise, it is 0. M is an infinite constant.

[0039] Logistics equipment scheduling constraints: Each shore power, quay crane, yard crane and container truck can only provide services to one ship in each time period, which can be expressed by equation (9); the number of each type of equipment in operation in each time period cannot exceed its total number, as shown in equation (10); the number of quay cranes serving each ship is limited by the total number of quay cranes that can be allocated to the ship, which can be expressed as equation (11); assuming that the loading and unloading tasks of each ship can be evenly allocated to the quay cranes, the time that each ship occupies the shore power at the berth is the sum of the loading and unloading preparation time and the time used to load and unload all containers, as shown in equation (12);

[0040]

[0041]

[0042]

[0043]

[0044] In equations (9)-(12): This indicates that the j-th shore power supply is a 0-1 variable for the i-th ship during time period t, where 1 represents power supply and 0 represents otherwise; r SPS The total number of shore power charging stations; r QC,i,max and r QC,i,min These represent the upper and lower limits of the number of quay cranes that can serve the loading and unloading operations of vessel i, respectively.

[0045] Operational constraints of logistics equipment: The loading and unloading efficiency of a single quay crane, the stacking efficiency of a yard crane, and the container transport efficiency of a container truck are all subject to upper and lower limits during each time period, which can be expressed by equation (13); the power consumption of the ship during berthing is subject to upper and lower limits of the shore power supply, as shown in equation (14); the charging power and state of charge of each container truck are subject to upper and lower limits, and the state of charge of each container truck at the end of the scheduling cycle must be the same as at the beginning of the scheduling, as shown in equation (15); the refrigeration power and the change in temperature inside each cold container are also subject to upper and lower limits, and the internal temperature of the cold container at the end of the scheduling cycle should be the same as at the beginning of the scheduling, as shown in equation (16).

[0046]

[0047]

[0048]

[0049]

[0050] In equations (13)-(16): and Let n represent the container loading / unloading efficiency of the k-th quay crane, the container stacking efficiency of the n-th yard crane, and the container transport efficiency of the m-th truck during time period t, respectively; n QC,k,max n AGV,m,max With n GC,n,max These represent the maximum operating efficiency of a single quay crane, container truck, and yard crane, respectively. The power supply capacity of the j-th shore power unit; and These are the upper and lower limits of the power supply capacity of the j-th shore power unit, respectively. Let m be the charging power of the m-th truck during time period t; and Let represent the maximum and minimum charging power of the m-th truck, respectively; Indicates the state of charge of the SoC during time period t; AGV,m,max SoC AGV,m,min , and These are the maximum and minimum state of charge values ​​of the m-th truck, as well as the state of charge values ​​at the beginning of the scheduling and the end of the scheduling cycle, respectively. This represents the cooling power of the f-th cold box; and These represent the maximum and minimum cooling power of the f-th cold box, respectively. ΔT represents the change in temperature inside the f-th cold box after a time interval following the start of refrigeration. RC,f,max ΔT RC,f,min , and These represent the maximum and minimum temperature changes inside the f-th cold box, as well as the temperatures inside the box at the beginning of the scheduling process and at the end of the scheduling cycle.

[0051] Electricity demand model for the entire container logistics process: The total electricity demand of the port container logistics system in time period t is the sum of the power of all types of logistics equipment in that time period, expressed as:

[0052]

[0053] In equation (17): This represents the total electricity demand of the port container logistics system during time period t; and The total power of shore power, shore crane, yard crane, container truck, and refrigerated container during time period t are respectively as follows:

[0054] The total shore power during time period t is the sum of the shore power supply provided to ships during that time period, and also the sum of the power of berthed ships, expressed as:

[0055]

[0056] In equation (18): Let be a 0-1 variable representing the berthing status of the i-th ship; 1 is the value when the ship is berthed and connected to shore power, and 0 is the value otherwise. This represents the electricity demand of the i-th ship in port during time period t;

[0057] The total power of the quay cranes during time period t is the sum of the operating power of the quay cranes serving the loading and unloading operations of all ships during that time period, as shown in equation (19). The operating power of a single quay crane is expressed by the working power of the frame structure, the lifting mechanism and the trolley transport mechanism, as well as the loading and unloading rate, as shown in equation (20):

[0058]

[0059]

[0060] In equations (19) and (20): H represents the operating power of the k-th quay crane during time period t; QC,k R QC,k and D QC,k These represent the lifting height, outreach, and horizontal span of the k-th quay crane, respectively. and These are the average power and operating speed of the kth quay crane lifting motor, respectively; and The average power and operating speed of the horizontally running trolley;

[0061] The total power of the yard cranes during time period t is the operating power of all yard cranes providing container stacking services to each ship during the time period, as shown in equation (21); the power of each yard crane depends on the working power of its frame structure, lifting mechanism and trolley transport mechanism and the container stacking rate, as shown in equation (22):

[0062]

[0063]

[0064] In equations (21) and (22): H represents the power of the nth field bridge during time period t; GC,n and D GC,n These represent the lifting height and horizontal span of the crane for the nth yard bridge, respectively. and These are the average power and operating speed of the lifting motor and the horizontal trolley of the nth yard bridge, respectively.

[0065] The total power of the trucks in time period t is the sum of the charging power of all trucks that are in the charging state during that time period, which can be expressed by equation (23); the charging / discharging process of each truck can be expressed by the state of charge, as shown in equation (24), where the first term represents the state of charge of the truck in time period t-1, and the second term represents the change in the state of charge of the truck after it has been in the charging state or started transportation for a period of time.

[0066]

[0067]

[0068] In equations (23) and (24): This indicates the state of charge of the truck during time period t-1; and These are the charging and discharging efficiencies of the m-th truck, respectively. This represents the power of the m-th truck carrying a single container; Δt represents the rated battery capacity of the m-th truck; L This refers to the unit time interval for logistics scheduling in a logistics system.

[0069] The total power of the cold box during time period t is the sum of the cooling power of all the cold boxes that are started during the time period, as shown in equation (25); the change in the internal temperature of each cold box is determined by the cooling power, which can be expressed by equation (26), where the first term is the change in the internal temperature caused by the ambient temperature, and the second term is the change in the internal temperature after the power is supplied for a period of time.

[0070]

[0071]

[0072] In equations (25) and (26): Let f be the internal temperature of the f-th cold box during time period t; Let A be the ambient temperature during time period t; RC,f and m RC,f Let z represent the outer surface area of ​​the f-th refrigerated container and the weight of the cargo loaded thereon, respectively; RC and c RC These are the thermal conductivity coefficient and specific heat capacity of the cold box, respectively.

[0073] The multi-energy flow coupling optimization scheduling model and energy output model of the port integrated energy system in step C include the multi-energy flow scheduling objective function of the port integrated energy system, the scheduling constraints of the port integrated energy system, and the multi-energy flow coupling output model of the port integrated energy system.

[0074] Among them, the multi-energy flow scheduling objective function of the port integrated energy system aims to minimize operating costs, and the objective function can be expressed as follows:

[0075]

[0076] In equation (27): C PIES Indicates the total operating cost of the port's integrated energy system; C net C om C loss , and These are, respectively, the energy purchase cost, energy equipment operation and maintenance cost, energy storage loss cost, CO2 transmission and storage cost, and emission treatment cost for a single dispatch cycle; S er and S pc The revenue from reducing carbon emissions and participating in carbon market transactions, and the revenue from participating in peak shaving and valley filling demand response in the power system ancillary services market, are respectively as follows:

[0077]

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084] In equations (28)-(34): and These refer to the electricity price and natural gas price for time period t, respectively. and These represent the power purchased by the energy system from the external power grid and natural gas grid during time period t, respectively; c ω,om ω represents the unit operating and maintenance cost of each energy device, where ω∈{photovoltaic, wind power, gas turbine, waste heat boiler, gas boiler, absorption chiller, electric refrigeration equipment, electric-to-gas equipment, carbon capture equipment, storage battery, thermal storage tank, ...}; Let τ represent the power generation / heat production / cooling / gas production of each energy device during time period t, where τ∈{e,h,c,g}; The difference in power purchased by ports from the grid before and after peak shaving and valley filling requirements; c HS,loss and c BS,loss These represent the unit consumption costs of the battery and the heat storage tank, respectively. and These represent the charging and discharging power of the battery and the storage and releasing power of the heat storage tank, respectively. and These are the charging and discharging efficiencies of the battery and the heat storage tank, respectively. and These represent the unit cost of CO2 transmission and storage, and the unit cost of CO2 emission treatment, respectively. and These are the unit carbon emissions for electricity consumption and natural gas combustion, respectively; This represents the amount of CO2 captured by the electro-gas conversion device during time period t; The gas production capacity of the electro-gas conversion equipment; Δt E T is the unit time interval for the scheduling of the port's integrated energy system. p and T v These represent the peak shaving and valley filling demand periods set by the power grid, respectively. Subsidized unit price for peak shaving and valley filling;

[0085] The scheduling constraints of the port integrated energy system include energy supply and demand balance constraints, energy network supply constraints, and energy equipment operation constraints. Among them, the energy supply and demand balance constraints are that the power generation of the port integrated energy system during time period t must meet the total power demand of the port infrastructure and logistics system, as shown in Equation (35); the system's heat and cold generation power must meet the heat and cold energy demand of the port infrastructure, as shown in Equations (36) and (37), respectively; the external natural gas network and the gas generation equipment in the port work together to supply gas to the gas turbine units, which can be represented by Equation (38).

[0086]

[0087]

[0088]

[0089]

[0090] In equations (35)-(38): and These represent the electricity, heat, and cooling load values ​​of the port infrastructure during time period t. and These represent the power generation capacities of wind power, photovoltaic power, and gas turbines, respectively. and These are the power consumption of electric refrigeration, electric-to-gas conversion equipment, and carbon capture, respectively. and These represent the heat production power of the waste heat boiler, the heat absorption power of the gas boiler, and the heat absorption power of the absorption chiller, respectively. and The cooling capacity of absorption chillers and electric chillers; and These are the gas consumption power of the gas boiler and the gas turbine, respectively.

[0091] Energy network supply constraints, i.e., the scheduling of the port's integrated energy system, are subject to upper and lower limits of energy supply from the external power grid and natural gas network, as shown in equations (39) and (40), respectively:

[0092]

[0093]

[0094] The operating constraints of energy equipment are that the operation of each energy equipment must meet the output constraint conditions, which can be expressed as Equation (41); the gas turbine unit must also meet the ramp constraint, as shown in Equation (42); the energy of the energy storage device must meet the capacity constraint and the energy at the end of the scheduling should be the same as that at the beginning of the scheduling, as shown in Equation (43).

[0095]

[0096]

[0097]

[0098] In equations (41)-(43): and G net,max These represent the maximum power supplied to the port by the external power grid and the natural gas grid, respectively. and These are the maximum and minimum production capacity of the gas turbine and gas boiler, respectively; R GT,max and R GT,min R GB,max and R GB,min These are the maximum and minimum ramp rates for the gas turbine and gas boiler, respectively. E represents the remaining energy in the battery and heat storage tank at the end of time period t; BS / HS,max E BS / HS,min , and These represent the upper and lower limits of the energy that the battery and the thermal storage tank can store, respectively, as well as the energy stored at the start and end of the scheduling process.

[0099] Multi-energy flow coupled output model of port integrated energy system: Combining the supply and demand balance relationship of multi-energy flow in port integrated energy system and the output model of each energy equipment, scheduling factors α, β, are introduced. θ and γ represent the proportions of input power from the power grid, natural gas grid, and internal heating network allocated to the multi-energy coupling conversion equipment. The multi-energy flow coupling output model of the port integrated energy system based on the energy hub is shown in Equation (44).

[0100]

[0101] in,

[0102] In equation (44): A and B are the multi-energy coupling conversion matrix and the energy storage charging and discharging state matrix, respectively; and These are the power generation efficiency and heat production efficiency of the gas turbine, respectively. This indicates the efficiency of a waste heat boiler in recovering the waste heat from the high-temperature flue gas discharged from a gas turbine and generating usable heat. For the heat production efficiency of gas-fired boilers; and H represents the refrigeration efficiency of the electric chiller and the absorption chiller, respectively; g The calorific value of natural gas; α represents the proportion of gas turbine power consumption to the total power output of the external natural gas supply network and the power generated by the power-to-gas conversion equipment, β, Let θ and θ represent the proportions of the power consumed by the electric chiller, the electric-to-gas conversion equipment, and the carbon capture equipment to the power input from the external power grid, respectively. Let γ represent the proportion of the heat power absorbed by the absorption chiller to the total heat output of the waste heat boiler and the gas boiler. The expressions are as follows:

[0103]

[0104] The construction of the port container logistics scheduling process model based on the principle of flexible assembly line workshop scheduling in step D is based on the continuous characteristics of the ship berthing, container loading and unloading, transportation, stacking and refrigeration operations. The whole process scheduling problem of logistics is abstracted into a multi-stage operation scheduling problem of assembly line. The port container logistics scheduling process model based on the principle of flexible assembly line workshop scheduling is established. The port container logistics scheduling process includes five stages: ship berthing and access to shore power, quay crane unloading, truck transport of containers, yard crane stacking of containers and refrigerated container refrigeration. The amount of containers to be unloaded by the ship is the amount of work to be processed in each stage. Each stage is carried out synchronously under the constraints of sequence and equipment operation.

[0105] The specific steps are as follows: Organize the logistics work for each ship {r C,1 ,r C,2 ,…,r C,i The time windows for each stage of the work are divided as follows:

[0106] W i,s =[t str,i,s ,t end,i,s ]s∈{1,2,...,5} (46)

[0107] In equation (46): W i,s Indicates ship logistics work r C,i The corresponding task time window for the s-th stage; t str,i,s and t end,i,s For ship logistics work C,iThe earliest start time and latest end time of the corresponding s-th stage;

[0108] The time window boundaries for each stage are as follows:

[0109]

[0110] Logistics equipment at each stage of the service can only operate within its corresponding time window, as shown below:

[0111]

[0112] In equations (47) and (48): t str,i,1 t str,i,2 t str,i,3 t str,i,4 and t str,i,5 These represent the ship logistics work r. C,i The earliest start time corresponding to stages 1 through 5; t end,i,1 t end,i,2 t end,i,3 t end,i,4 and t end,i,5 For ship logistics work C,i The latest completion time for each of the five phases; and These represent the 0-1 state variables for the j-th shore power unit, k-th shore crane, m-th container truck, and n-th yard crane serving the logistics work of the i-th ship during time period t, with 1 indicating work and 0 indicating otherwise. This represents the 0-1 refrigeration state variable of the f-th cold box during time period t, where 1 indicates refrigeration and 0 indicates otherwise.

[0113] Since the planned port arrival time of the vessel determines the available time range for Phase 1, and the unloading end time of Phase 2 determines the vessel's berthing end time, i.e., the end time of Phase 1, both Phase 1 and Phase 2 must be carried out within the planned port arrival time window, as shown below:

[0114]

[0115] In equation (49): W i,1 and W i,2 These represent the time windows for vessel i to perform the first and second phases of operations, respectively; W i For ship logistics work time window.

[0116] The specific steps in step E for constructing the energy supply and demand coupling model of the port integrated energy system and the port container logistics system that takes into account dual uncertainties are as follows: considering the uncertainty of ship arrival time and the uncertainty of new energy output, establish a probability distribution model of actual ship arrival time and a probability distribution model of actual wind power and photovoltaic output. On this basis, construct the energy supply and demand coupling model of the port integrated energy system and the port container logistics system that takes into account dual uncertainties.

[0117] Among them, the probability distribution model of the actual arrival time of the ship is that the actual arrival time of the ship can be regarded as a uniform distribution with the planned arrival time as the mean, as shown in Equation (50); in order to enhance the robustness of the logistics scheduling plan and reduce the impact of uncertainty factors on the scheduling plan, a delay time parameter δ is introduced, which stipulates that the berthing resources of the ship are still occupied by the ship during the delay time, so as to absorb the impact of uncertainty. The maximum value and the sign of the delay time can be determined according to the range of variation of the ship arrival time, as shown in Equations (51) and (52):

[0118]

[0119] δ max =2μ (51)

[0120]

[0121] In equations (51) and (52): Indicates the actual arrival time of ship i; U is an uncertain set; Δt arr,i denoted as , where μ is the range of arrival times for vessel i; and μ is the standard deviation of planned arrival times for all vessels.

[0122] Therefore, equation (49) is modified as follows:

[0123]

[0124] The probability distribution model of actual output of wind power and photovoltaic power adopts a box-type uncertainty set to characterize the actual output of wind power and photovoltaic power, as shown in Equation (54); and introduces an uncertainty parameter Γ to adjust the conservatism and robustness of the probability distribution model of actual output of wind power and photovoltaic power, and makes the uncertainty variables satisfy the uncertainty constraint conditions as shown in Equation (55), that is, at most Γ uncertain parameters can reach the boundary.

[0125]

[0126]

[0127] In equations (54) and (55): and These represent the actual output of wind power and solar power, respectively. and These are the predicted output values ​​for wind power and solar power, respectively. and For the disturbance range of wind power and photovoltaic output; Γ WT and Γ PV The uncertainties in wind power and solar power output, respectively;

[0128] Based on the probability distribution model of actual wind and solar power output, the energy supply and demand balance constraint in the port integrated energy system scheduling constraint expressed in equation (35) is modified as follows:

[0129]

[0130] In equation (56): This indicates the electricity demand for the entire container logistics process under actual ship arrival conditions.

[0131] Based on the probability distribution model of actual ship arrival time and the probability distribution model of actual wind and solar power output, an energy supply and demand coupling model of the port integrated energy system and the port container logistics system, taking into account dual uncertainties, is established based on the multi-energy flow coupling output model of the port integrated energy system described in equation (44):

[0132]

[0133] The solution method in step F takes into account the superiority of non-dominated sorting and congestion comparison sorting in terms of individual classification and selection of superior individuals. MNSGA-II, which has dual comparison sorting, is selected to solve the port container logistics scheduling process model based on the flexible assembly line workshop scheduling principle. Since the initial parent population and the new parent population used for each iteration are obtained through non-dominated sorting and congestion comparison sorting, the impact of randomly generated initial parent population on the convergence of subsequent iterations is effectively reduced. On this basis, considering that the port integrated energy system and the port container logistics system may not be scheduled and managed by a single operator, and the information is not completely open and shared, the distributed optimization algorithm ADMM, which has the advantage of partial information consensus, is selected to solve the collaborative scheduling problem of the port integrated energy system and the port container logistics system.

[0134] In the solution method of step F, the collaborative scheduling problem is decomposed into sub-problems based on the supply and demand balance constraints of electricity, heat, and cold energy. Electricity, heat, and cold coupling variables are introduced into the balance constraints where energy supply and demand relationships exist. and To represent the coupling relationship between the energy output of the port's integrated energy system and the port's total energy demand, the consensus constraint on the supply and demand relationship of electricity, heat, and cooling energy is simplified and expressed as the energy consensus constraint formula (58):

[0135]

[0136] Based on the constraints of equation (58), the Lagrange relaxation method is used to increase the convergence consistency of the objective functions of multi-energy flow scheduling of the port integrated energy system, the objective function of shore-powered berth scheduling, and the objective function of joint scheduling of quay cranes, yard cranes, container trucks, and refrigerated containers. The augmented Lagrange function of the coordinated scheduling optimization problem of the port integrated energy system and the port container logistics system is expressed as the overall energy flow-logistics scheduling objective function of equation (59):

[0137]

[0138] In Equation (59): a, b, and c are the weights of the three objectives of the port integrated energy system scheduling total cost, average ship time in port, and port container logistics system scheduling total cost, respectively; the latter three terms represent the synergistic effect between the electricity, heat, and cooling output of the port integrated energy system and the electricity, heat, and cooling demand of the port container logistics system and port infrastructure; T is the coordinated scheduling period. and These are the Lagrange multipliers for the consensus constraints on the supply and demand of electricity, heat, and cooling energy, respectively; ρ e ρ h and ρ c These are the constant step sizes of the consensus constraints;

[0139] The coupling variables are calculated using equation (60), and the Lagrange multipliers are updated using equation (61) based on the latest coupling variables obtained through iteration.

[0140]

[0141]

[0142] In equations (60) and (61): and Let represent the coupling variables obtained after the (v+1)th iteration; and These are the Lagrange multipliers after the vth iteration; Let represent the Lagrange multipliers obtained after the (v+1)th iteration;

[0143] Each iteration uses coupling variables and Lagrange multipliers to solve the overall energy flow-material flow scheduling objective function (59). The iteration stops when the coupling variables are sufficiently close, i.e. when the judgment criterion of equation (62) is met.

[0144]

[0145] In equation (62): E is the residual constraint value;

[0146] Subsequently, the port's integrated energy system will share the coupled variable information with the port's container logistics system to determine the final port energy flow and logistics scheduling plan.

[0147] In the solution method of step F, the solution steps for the coordinated scheduling of the port integrated energy system and the port container logistics system based on ADMM and MNSGA-II are as follows:

[0148] 1) Input parameters of the port integrated energy system and port container logistics system, data on ships scheduled to arrive on a certain day, and forecast data of wind power, photovoltaic power output and basic electricity, heat and cooling loads;

[0149] 2) Establish a consensus constraint formula for energy supply and demand between the port integrated energy system and the port container logistics system (58), and construct an overall scheduling objective function formula for energy flow and logistics (59);

[0150] 3) Initialize the optimization variables, the initial population size and maximum number of iterations of MNSGA-II, the ADMM energy coupling variables, the objective function weights, the consensus constraint Lagrange multipliers and step size, the residual constraint values, and the maximum number of cooperative scheduling solutions;

[0151] 4) Randomly generate N initial populations, calculate the objective function of the port container logistics system's whole-process scheduling in the joint optimization scheduling model and operation energy consumption model, sort the calculation results by non-dominated sorting and congestion comparison sorting, and use a bidding method to select the most suitable population as the initial parent population, and enter the iteration;

[0152] 5) Cross over and mutate the parent population to form the offspring population, and calculate the objective function of the port container logistics system's whole-process scheduling in the joint optimization scheduling model and operation energy consumption model of the port container logistics system.

[0153] 6) Merge the offspring population with the parent population, perform non-dominated sorting and crowding sorting on the merged population, and use an elite strategy to select the most suitable individuals to form a new population; when the maximum number of iterations is reached, the iteration stops and proceeds to step 7); otherwise, let the new population be the parent population and return to step 5).

[0154] 7) Obtain feasible logistics scheduling schemes based on Pareto solution sets. Obtain power consumption schemes from the power demand model of the entire container logistics process in the joint optimization scheduling model and operation energy consumption model of the port container logistics system. Let the initial energy coupling variable of ADMM be the power demand of the better solution and enter the iteration.

[0155] 8) Calculate the overall energy flow-material flow scheduling objective function (59), update the energy coupling variables by equation (60), and update the Lagrange multipliers by equation (61); when equation (62) is satisfied, the iteration stops and proceeds to step 9); otherwise, repeat step 8);

[0156] 9) If the maximum number of collaborative scheduling solutions is not reached, return to step 7); otherwise, terminate the iteration and output the final port energy flow and logistics scheduling plan.

[0157] The present invention has the following advantages over the prior art:

[0158] The collaborative scheduling method of this invention combines the energy production units within the port integrated energy system and the logistics operation links within the port container logistics system. It analyzes the energy coupling relationship between energy flow scheduling and logistics scheduling throughout the entire process, and considers the uncertainty of the port's new energy output and the uncertainty of container ship arrival time. It optimizes the overall scheduling of the port integrated energy system and the port container logistics system from the aspects of economic benefits, environmental benefits, energy efficiency, and logistics efficiency. Attached Figure Description

[0159] Appendix Figure 1 This is a flowchart of the collaborative scheduling method for the port integrated energy system and container logistics system of the present invention;

[0160] Appendix Figure 2 This invention provides a collaborative scheduling architecture for the port integrated energy system and the port container logistics system.

[0161] Appendix Figure 3 This is a structural diagram of the port container logistics scheduling process model based on the flexible assembly line workshop scheduling principle of the present invention;

[0162] Appendix Figure 4 This is a schematic diagram of the collaborative scheduling solution process for the port integrated energy system and port container logistics system based on ADMM and MNSGA-II of the present invention.

[0163] Appendix Figure 5 The port infrastructure load and wind and solar power output prediction curves are shown in the embodiments of the present invention.

[0164] Appendix Figure 6 A comparison chart of power purchases using existing technologies and the method of this invention in port energy flow scheduling plans;

[0165] Appendix Figure 7 A comparison chart of gas turbine power generation output using existing technologies and the method of this invention in port energy flow scheduling plans;

[0166] Appendix Figure 8 A comparison chart of battery charging / discharging power using existing technologies and the method of this invention in port energy flow scheduling;

[0167] Appendix Figure 9 A comparison chart of power consumption for electricity-to-gas conversion using existing technologies and the method of this invention in port energy flow scheduling plans;

[0168] Appendix Figure 10 A comparison chart of the electrical power consumption for carbon capture using existing technologies and the method of this invention in port energy flow scheduling plans;

[0169] Appendix Figure 11 A comparison chart of total port power demand using existing technologies and the method of this invention in port energy flow scheduling plans;

[0170] Appendix Figure 12 A comparison chart of shore power consumption using existing technologies and the method of this invention in port logistics scheduling plans;

[0171] Appendix Figure 13 A comparison chart of the power consumption of quay cranes using existing technologies and the method of this invention in port logistics scheduling plans;

[0172] Appendix Figure 14 A comparison chart of the power consumption of yard cranes using existing technologies and the method of this invention in port logistics scheduling plans;

[0173] Appendix Figure 15 A comparison chart of the charging power of container trucks using existing technologies and the method of this invention in port logistics scheduling plans;

[0174] Appendix Figure 16 A comparison chart of the power consumption of refrigerated containers using existing technologies and the method of this invention in port logistics scheduling plans;

[0175] Appendix Figure 17 This is a comparison chart of the total power consumption of port container logistics systems using existing technologies and the methods of this invention in port logistics scheduling plans. Detailed Implementation

[0176] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are illustrative and not limiting, and the scope of protection of the present invention should not be limited by the following embodiments.

[0177] The collaborative scheduling method of this invention targets the application of port integrated energy Internet of Things technology, electricity substitution technology, and logistics automation technology. Based on the energy coupling relationship of the entire operation process of port integrated energy system and port container logistics system, it proposes a collaborative scheduling method for port integrated energy system and container logistics system that takes into account energy supply reliability, economic and environmental benefits, and logistics and energy efficiency.

[0178] This invention discloses a method for coordinated scheduling of a port integrated energy system and a container logistics system, such as... Figure 1 As shown, the steps of this cooperative scheduling method are as follows:

[0179] A. Clarify the composition of the port integrated energy system, the composition of the port container logistics system and the logistics operation process, as well as the coupling relationship between energy flow and logistics, and construct a collaborative scheduling architecture for the port integrated energy system and the port container logistics system.

[0180] B. Establish a joint optimization scheduling model and operational energy consumption model for the port container logistics system based on minimizing the average ship time in port and the lowest logistics scheduling cost;

[0181] C. Establish a multi-energy flow coupling optimization scheduling model and energy output model for the port integrated energy system based on the lowest system operating cost;

[0182] D. Construct a port container logistics scheduling process model based on the principle of flexible assembly line workshop scheduling;

[0183] E. Construct a coupling model of energy supply and demand between the port integrated energy system and the port container logistics system, taking into account dual uncertainties;

[0184] F. The alternating direction multiplier method (ADMM) combined with the improved non-dominated sorting genetic algorithm (MNSGA-II) is used to solve the model in steps B, C, D, and E, and the port energy flow and logistics scheduling plan is obtained.

[0185] In step A, the collaborative scheduling architecture comprises two main scheduling entities: the port integrated energy system and the port container logistics system. 1) The port integrated energy system encompasses the production and consumption of electricity, heat, and cold energy. It coordinates with the external power grid and natural gas network to meet the diverse energy needs of port logistics equipment and port office infrastructure, including photovoltaic and wind power, gas turbines, waste heat boilers, gas boilers, lithium bromide absorption chillers, electric chillers, and other power generation / heat / cooling units, electricity-to-gas and carbon capture combined gas production devices, and energy storage equipment such as batteries and thermal storage tanks. 2) The port container logistics system, based on the arrival and departure times of each container ship and the volume of containers to be loaded and unloaded, schedules logistics equipment within the logistics operation area, including shore power (port shore power charging piles), quay cranes (shore-side container cranes), container trucks, yard cranes (yard container cranes), and refrigerated containers, to jointly complete logistics operations including ship berthing, container loading and unloading, transportation, stacking, and refrigeration. After entering the port, the vessel berths at the designated berth, shutting down its own fuel-powered auxiliary engines and connecting to shore power to obtain the necessary electricity during berthing. Quay cranes are deployed at the port terminal to provide container loading and unloading services for berthed vessels. Once loading and unloading operations are completed, the vessel can depart. Container trucks transport containers between the terminal and the yard. Yard cranes provide container stacking services in the yard. Refrigerated containers within the yard provide refrigerated storage services for perishable goods. During logistics operations, the quay cranes and yard cranes are powered on. Electric container trucks consume their own battery power when transporting containers and are charged by dedicated charging stations when not transporting containers. Refrigerated containers activate refrigeration after being powered on to maintain the refrigerated temperature of the goods inside.

[0186] Since the total energy consumption of the entire logistics process determines the total energy demand of the port's container logistics system, which is supplied uniformly by the port's integrated energy system, the port's integrated energy system and the port's container logistics system have an energy coupling relationship. To maximize the overall scheduling efficiency of energy flow and logistics at the port, it is necessary to comprehensively consider the scheduling objectives and constraints of both the port's integrated energy system and the port's container logistics system, and to carry out coordinated scheduling of energy flow and logistics "dual flows" based on their energy coupling relationship throughout the entire process. The coordinated scheduling architecture of the port's integrated energy system and the port's container logistics system is as follows: Figure 2 As shown.

[0187] During the coordinated scheduling process, the port container logistics system formulates a full-process logistics scheduling plan based on its own scheduling objectives and constraints, and sends the corresponding electricity application plan to the port integrated energy system. The port integrated energy system assesses the logistics electricity demand, combines its own benefits, formulates an energy flow scheduling plan, and transmits the power supply plan to the port container logistics system. Subsequently, the port container logistics system comprehensively considers available electricity and its own benefits, adjusts the logistics scheduling plan, and sends the electricity demand back to the port integrated energy system. This process is repeated until the overall scheduling efficiency of energy flow and logistics is optimized, thus determining the final port energy flow and logistics scheduling plan.

[0188] In step B, the joint optimization scheduling model and operational energy consumption model of the port container logistics system include: the objective function and constraints for the whole-process scheduling of the port container logistics system, and the power demand model for the whole logistics process. Details are as follows:

[0189] 1) Objective function for the whole-process scheduling of port container logistics system

[0190] ① Objective function for scheduling berths with shore power: The objective of scheduling berths with shore power is to minimize the average berthing time of all vessels in port, i.e.:

[0191]

[0192] In formula (1): B ship This represents the average time all vessels spend in port within a scheduling cycle; r ship The number of ships arriving in port within a period; t arr,i t ber,i and t SPS,i These represent the planned arrival time, berthing time, and shore power usage time of vessel i, respectively.

[0193] ② Objective function for joint scheduling of quay cranes, yard cranes, trucks, and refrigerated containers: The logistics system schedules various types of equipment with the objective function of minimizing the total cost of scheduling quay cranes, yard cranes, trucks, and refrigerated containers, expressed as:

[0194] minC PCLS =C QC +C GC +C AGV +C RC (2)

[0195] In equation (2): C PCLS C represents the total scheduling cost of a logistics system over a scheduling cycle; QC C GC C AGV and C RC The dispatch costs for quay cranes, yard cranes, container trucks, and refrigerated containers are as follows:

[0196] The cost of quay crane scheduling includes the startup cost of quay cranes serving each vessel and the cost of loading and unloading operations, expressed as:

[0197]

[0198] In equation (3): c QC,on and c QC,w These represent the startup cost of a single quay crane and the average hourly loading and unloading operation cost, respectively; QC r represents the total number of quay cranes. C,i T represents the number of containers to be loaded or unloaded on the i-th ship;L This refers to the scheduling cycle of the logistics system. This represents a 0-1 state variable indicating the work of the k-th quay crane serving the i-th ship in loading and unloading containers during time period t, where 1 indicates work and 0 indicates otherwise.

[0199] The yard crane scheduling cost includes the yard crane start-up cost for each vessel and the container stacking operation, and is expressed as:

[0200]

[0201] In equation (4): c GC,on and c GC,w These represent the startup cost of a single quay crane and the average hourly container stacking cost, respectively; r GC This represents the total number of yard bridges; This represents a 0-1 state variable indicating the operation of the nth yard bridge serving the i-th ship's container stacking during time period t, where 1 indicates operation and 0 indicates otherwise.

[0202] Truck dispatching costs include the startup costs and transportation costs of the trucks serving each vessel, expressed as:

[0203]

[0204] In equation (5): c AGV,on and c AGV,w These represent the startup cost of a single container truck and the average hourly container transport cost, respectively. AGV Indicates the total number of trucks; The variable represents the 0-1 state of the m-th container truck serving the i-th ship during time period t, with 1 for charging when the truck is out of service and 0 for transporting containers.

[0205] The cold box scheduling cost is the sum of the cold box's start-up and refrigeration costs, which can be expressed as:

[0206]

[0207] In equation (6): c RC,on This represents the startup cost of a single cold box; r RC This represents the total number of cold boxes; This represents the refrigeration status of the f-th cold box, a variable ranging from 0 to 1, where 1 indicates refrigeration and 0 indicates otherwise.

[0208] 2) Constraints on the scheduling of port container logistics system

[0209] ① Constraints on berthing / departure time and berthing position: The continuous berth allocation method, which is commonly used in ports, is adopted. The port shoreline is regarded as a continuous whole. Berths are allocated according to the arrival time sequence and length of each ship. Shore power within the berth range provides shore power service to each ship. Assuming that the water depth of each berth meets the berthing requirements of all ships, the following constraints are imposed on the berthing / departure time and berthing position: each ship can only berth after arrival, and the actual departure time of the ship cannot be later than the planned latest departure time. The berthing time of any two ships cannot conflict, as shown in Equation (7); the berthing position of all ships cannot exceed the port shoreline range, and the berthing position and berthing time of any two ships cannot conflict, as shown in Equation (8).

[0210] t arr,i ≤t ber,i ,t ber,i +t SPS,i ≤t dep,i ,t ber,i +t SPS,i ≤t ber,q +M(1-h i,q (7)

[0211] l 0,i +l i ≤L,l 0,i +l i ≤l q +M(1-z i,q ),z i,q +z q,i +h i,q +h q,i ≥1 (8)

[0212] In equations (7) and (8): t dep,i Indicates the planned departure time of vessel i; l 0,i and l i These represent the starting position of vessel i at its berth and the vessel length including the safe berthing distance between vessels, respectively; L is the shoreline length; z i,q h represents a 0-1 state variable indicating the berthing position of any two ships. A value of 1 indicates ship i is berthed to the left of ship q on the shoreline, and 0 otherwise. i,q Let M be a 0-1 state variable representing the berthing time of any two ships. If ship i berths before ship q, the value is 1; otherwise, it is 0. M is an infinite constant.

[0213] ② Logistics equipment scheduling constraints: Each shore power, quay crane, yard crane and container truck can only provide services to one ship in each time period, which can be expressed by equation (9); the number of each type of equipment in operation in each time period cannot exceed its total number, as shown in equation (10); the number of quay cranes serving each ship is limited by the total number of quay cranes that can be allocated to the ship, which can be expressed as equation (11); assuming that the loading and unloading tasks of each ship can be evenly allocated to the quay cranes, the time that each ship occupies the shore power at the berth is the sum of the loading and unloading preparation time and the time used to load and unload all containers, as shown in equation (12);

[0214]

[0215]

[0216]

[0217]

[0218] In equations (9)-(12): This indicates that the j-th shore power supply is a 0-1 variable for the i-th ship during time period t, where 1 represents power supply and 0 represents otherwise; r SPS The total number of shore power charging stations; r QC,i,max and r QC,i,min These represent the upper and lower limits of the number of quay cranes that can serve the loading and unloading operations of vessel i.

[0219] ③ Constraints on logistics equipment operation: The loading and unloading efficiency of a single quay crane, the stacking efficiency of a yard crane, and the container transport efficiency of a container truck are all subject to upper and lower limits during each time period, which can be expressed by equation (13); the power consumption of the ship during berthing is subject to upper and lower limits of the shore power supply, as shown in equation (14); the charging power and state of charge of each container truck are subject to upper and lower limits, and the state of charge of each container truck at the end of the scheduling cycle must be the same as at the beginning of the scheduling, as shown in equation (15); the refrigeration power and the change in temperature inside each cold container are also subject to upper and lower limits, and the internal temperature of the cold container at the end of the scheduling cycle should be the same as at the beginning of the scheduling, as shown in equation (16).

[0220]

[0221]

[0222]

[0223]

[0224] In equations (13)-(16): and Let n represent the container loading / unloading efficiency of the k-th quay crane, the container stacking efficiency of the n-th yard crane, and the container transport efficiency of the m-th truck during time period t, respectively; n QC,k,maxn AGV,m,max With n GC,n,max These represent the maximum operating efficiency of a single quay crane, container truck, and yard crane, respectively. The power supply capacity of the j-th shore power unit; and These are the upper and lower limits of the power supply capacity of the j-th shore power unit, respectively. Let m be the charging power of the m-th truck during time period t; and Let represent the maximum and minimum charging power of the m-th truck, respectively; Indicates the state of charge of the SoC during time period t; AGV,m,max SoC AGV,m,min , and These are the maximum and minimum state of charge values ​​of the m-th truck, as well as the state of charge values ​​at the beginning of the scheduling and the end of the scheduling cycle, respectively. This represents the cooling power of the f-th cold box; and These represent the maximum and minimum cooling power of the f-th cold box, respectively. ΔT represents the change in temperature inside the f-th cold box after a time interval following the start of refrigeration. RC,f,max ΔT RC,f,min , and These represent the maximum and minimum temperature changes inside the f-th cold box, as well as the box temperatures at the beginning of the scheduling process and at the end of the scheduling cycle.

[0225] 3) Electricity demand model for the entire container logistics process

[0226] The total power demand of the port container logistics system during time period t is the sum of the power consumption of all types of logistics equipment during that time period, expressed as:

[0227]

[0228] In equation (17): This represents the total electricity demand of the port container logistics system during time period t; and The total power of shore power, shore crane, yard crane, container truck, and refrigerated container during time period t are respectively as follows:

[0229] ① Shore power: The total shore power during time period t is the sum of the shore power supply provided to ships during that time period, and also the sum of the power of berthed ships, expressed as:

[0230]

[0231] In equation (18): Let be a 0-1 variable representing the berthing status of the i-th ship; 1 is the value when the ship is berthed and connected to shore power, and 0 is the value otherwise. This represents the electricity demand of the i-th ship in port during time period t.

[0232] ② Quay cranes: The total power of quay cranes during time period t is the sum of the operating power of quay cranes serving the loading and unloading operations of all ships during that time period, as shown in equation (19). The operating power of a single quay crane can be expressed by the working power of the frame structure, lifting mechanism and trolley transport mechanism, as well as the loading and unloading rate, as shown in equation (20);

[0233]

[0234]

[0235] In equations (19) and (20): H represents the operating power of the k-th quay crane during time period t; QC,k R QC,k and D QC,k These represent the lifting height, outreach, and horizontal span of the k-th quay crane, respectively. and These are the average power and operating speed of the kth quay crane lifting motor, respectively; and The average power and operating speed of the horizontally running trolley.

[0236] ③ Yard bridge: The total power of the yard bridge during time period t is the operating power of all yard bridges that provide container stacking services for each ship during the time period, as shown in equation (21); the power of each yard bridge also depends on the working power of its frame structure, lifting mechanism and trolley transport mechanism and the container stacking rate. However, since the bridge arm of the yard bridge cannot be extended, the influence of the outrigger does not need to be considered, and it can be expressed as equation (22).

[0237]

[0238]

[0239] In equations (21) and (22): H represents the power of the nth field bridge during time period t; GC,n and D GC,n These represent the lifting height and horizontal span of the crane for the nth yard bridge, respectively. and These represent the average power and operating speed of the lifting motor of the nth yard bridge and the horizontal running trolley, respectively.

[0240] ④ Trucks: The total power of trucks in time period t is the sum of the charging power of all trucks that are in the charging state during that time period, which can be expressed by formula (23); the charging / discharging process of each truck can be expressed by the state of charge, as shown in formula (24), where the first term represents the state of charge of the truck in time period t-1, and the second term represents the change in the state of charge of the truck after it has been in the charging state or started transportation for a period of time.

[0241]

[0242]

[0243] In equations (23) and (24): This indicates the state of charge of the truck during time period t-1; and These are the charging and discharging efficiencies of the m-th truck, respectively. This represents the power of the m-th truck carrying a single container; Δt represents the rated battery capacity of the m-th truck; L This refers to the unit time interval for logistics scheduling in a logistics system.

[0244] ⑤ Cold Box: The total power of the cold box during time period t is the sum of the cooling power of all cold boxes that are started during the time period, as shown in Equation (25); the change in the internal temperature of each cold box is determined by the cooling power, which can be expressed by Equation (26), where the first term is the change in the internal temperature caused by the ambient temperature, and the second term is the change in the internal temperature after the power supply has been used for cooling for a period of time.

[0245]

[0246]

[0247] In equations (25) and (26): Let f be the internal temperature of the f-th cold box during time period t; Let A be the ambient temperature during time period t; RC,f and m RC,f Let z represent the outer surface area of ​​the f-th refrigerated container and the weight of the cargo loaded thereon, respectively; RC and c RC These are the thermal conductivity coefficient and specific heat capacity of the cold box, respectively.

[0248] In step C, the multi-energy flow coupling optimization scheduling model and energy output model of the port integrated energy system include the multi-energy flow scheduling objective function of the port integrated energy system, the scheduling constraints of the port integrated energy system, and the multi-energy flow coupling output model of the port integrated energy system.

[0249] 1) Objective function of multi-energy flow scheduling for port integrated energy system

[0250] The port integrated energy system operates with the goal of minimizing operating costs. The objective function can be expressed as:

[0251]

[0252] In equation (27): C PIES Indicates the total operating cost of the port's integrated energy system; C net C om C loss , and These are, respectively, the energy purchase cost, energy equipment operation and maintenance cost, energy storage loss cost, CO2 transmission and storage cost, and emission treatment cost for a single dispatch cycle; S er and S pc The revenue from reducing carbon emissions and participating in carbon market transactions, and the revenue from participating in peak shaving and valley filling demand response in the power system ancillary services market, are respectively as follows:

[0253]

[0254]

[0255]

[0256]

[0257]

[0258]

[0259]

[0260] In equations (28)-(34): and These refer to the electricity price and natural gas price for time period t, respectively. and These represent the power purchased by the energy system from the external power grid and natural gas grid during time period t, respectively; c ω,om ω represents the unit operating and maintenance cost of each energy device, where ω∈{photovoltaic, wind power, gas turbine, waste heat boiler, gas boiler, absorption chiller, electric refrigeration equipment, electric-to-gas equipment, carbon capture equipment, storage battery, thermal storage tank, ...}; Let τ represent the power generation / heat production / cooling / gas production of each energy device during time period t, where τ∈{e,h,c,g}; The difference in power purchased by ports from the grid before and after peak shaving and valley filling requirements; c HS,loss and c BS,loss These represent the unit consumption costs of the battery and the heat storage tank, respectively. and These represent the charging and discharging power of the battery and the storage and releasing power of the heat storage tank, respectively. and These are the charging and discharging efficiencies of the battery and the heat storage tank, respectively. and These represent the unit cost of CO2 transmission and storage, and the unit cost of CO2 emission treatment, respectively. and These are the unit carbon emissions for electricity consumption and natural gas combustion, respectively; This represents the amount of CO2 captured by the electro-gas conversion device during time period t; The gas production capacity of the electro-gas conversion equipment; Δt E T is the unit time interval for the scheduling of the port's integrated energy system. p and T v These represent the peak shaving and valley filling demand periods set by the power grid, respectively. Subsidize unit price for peak shaving and valley filling.

[0261] 2) Port Integrated Energy System Dispatch Constraints

[0262] ① Energy supply and demand balance constraints: The power generation of the port's integrated energy system during time period t must meet the total power demand of the port infrastructure and logistics system, as shown in equation (35); the system's heat and cold generation power must meet the heat and cold energy demand of the port infrastructure, as shown in equations (36) and (37) respectively; the external natural gas network and the gas production equipment in the port work together to supply gas to the gas turbine units, which can be represented by equation (38);

[0263]

[0264]

[0265]

[0266]

[0267] In equations (35)-(38): and These represent the electricity, heat, and cooling load values ​​of the port infrastructure during time period t. and These represent the power generation capacities of wind power, photovoltaic power, and gas turbines, respectively. and These are the power consumption of electric refrigeration, electric-to-gas conversion equipment, and carbon capture, respectively. and These represent the heat production power of the waste heat boiler, the heat absorption power of the gas boiler, and the heat absorption power of the absorption chiller, respectively. and The cooling capacity of absorption chillers and electric chillers; and These are the gas consumption power of the gas boiler and the gas turbine, respectively.

[0268] ② Energy network supply constraints: The scheduling of the port's integrated energy system is constrained by the upper and lower limits of the external power grid and natural gas network supply, as shown in equations (39) and (40), respectively;

[0269]

[0270]

[0271] ③ Energy equipment operation constraints: The operation of each energy equipment must meet the output constraint conditions, which can be expressed as Equation (41); the gas turbine unit must also meet the ramp constraint, as shown in Equation (42); the energy of the energy storage device must meet the capacity constraint, and the energy at the end of the scheduling should be the same as that at the beginning of the scheduling, as shown in Equation (43).

[0272]

[0273]

[0274]

[0275] In equations (41)-(43): and G net,max These represent the maximum power supplied to the port by the external power grid and the natural gas grid, respectively. and These are the maximum and minimum production capacity of the gas turbine and gas boiler, respectively; R GT,max and R GT,min R GB,max and R GB,min These are the maximum and minimum ramp rates for the gas turbine and gas boiler, respectively. E represents the remaining energy in the battery and heat storage tank at the end of time period t; BS / HS,max E BS / HS,min , and These represent the upper and lower limits of the energy that the battery and the thermal storage tank can store, respectively, as well as the energy stored at the start and end of the scheduling process.

[0276] 3) Multi-energy flow coupled output model of port integrated energy system

[0277] Combining the supply and demand balance equations of the multi-energy flow in the port's integrated energy system and the output models of various energy equipment, scheduling factors α, β, and are introduced. θ and γ represent the proportions of input power from the power grid, natural gas grid, and internal heating network allocated to the multi-energy coupling conversion equipment. The multi-energy flow coupling output model of the port integrated energy system based on the energy hub is shown in Equation (44).

[0278]

[0279] in,

[0280] In equation (44): A and B are the multi-energy coupling conversion matrix and the energy storage charging and discharging state matrix, respectively; and These are the power generation efficiency and heat production efficiency of the gas turbine, respectively. This indicates the efficiency of a waste heat boiler in recovering the waste heat from the high-temperature flue gas discharged from a gas turbine and generating usable heat. For the heat production efficiency of gas-fired boilers; and H represents the refrigeration efficiency of the electric chiller and the absorption chiller, respectively; g The calorific value of natural gas; α represents the proportion of gas turbine power consumption to the total power output of the external natural gas supply network and the power generated by the power-to-gas conversion equipment, β, Let θ and θ represent the proportions of the power consumed by the electric chiller, the electric-to-gas conversion equipment, and the carbon capture equipment to the power input from the external power grid, respectively. Let γ represent the proportion of the heat power absorbed by the absorption chiller to the total heat output of the waste heat boiler and the gas boiler. The expressions are as follows:

[0281]

[0282] In step D, based on the continuous nature of ship berthing, container loading and unloading, transportation, stacking, and refrigeration operations, the whole-process logistics scheduling problem is abstracted into a multi-stage assembly line operation scheduling problem. A port container logistics scheduling process model based on the flexible assembly line workshop scheduling principle is established, as follows: Figure 3 As shown, the port container logistics scheduling process includes five stages: ship berthing and access to shore power, quay crane unloading, truck transport of containers, yard crane stacking, and refrigerated container refrigeration. The amount of containers waiting to be unloaded from the ship is the amount of work to be processed in each stage. Each stage is carried out synchronously under the constraints of sequence and equipment operation.

[0283] Logistics work for each ship {r C,1 ,r C,2 ,…,r C,i The time windows for each stage of the work are divided as follows:

[0284] W i,s =[t str,i,s ,t end,i,s ]s∈{1,2,...,5} (46)

[0285] In equation (46): W i,s Indicates ship logistics work r C,i The corresponding task time window for the s-th stage; tstr,i,s and t end,i,s For ship logistics work C,i The earliest start time and latest end time of the corresponding s-th stage.

[0286] The time window boundaries for each stage are as follows:

[0287]

[0288] Logistics equipment at each stage of the service can only operate within its corresponding time window, as shown below:

[0289]

[0290] In equations (47) and (48): t str,i,1 t str,i,2 t str,i,3 t str,i,4 and t str,i,5 These represent the ship logistics work r. C,i The earliest start time corresponding to stages 1 through 5; t end,i,1 t end,i,2 t end,i,3 t end,i,4 and t end,i,5 For ship logistics work C,i The latest completion time for each of the five phases; and These represent the 0-1 state variables for the j-th shore power unit, k-th shore crane, m-th container truck, and n-th yard crane serving the logistics work of the i-th ship during time period t, with 1 indicating work and 0 indicating otherwise. This represents the 0-1 refrigeration status variable of the f-th cold container during time period t, where 1 indicates refrigeration and 0 indicates otherwise. Since the planned port arrival time of the vessel determines the available time interval for Phase 1, and the unloading end time of Phase 2 determines the vessel's berthing end time (i.e., the end time of Phase 1), both Phase 1 and Phase 2 must be carried out within the planned port arrival time window, as shown below:

[0291]

[0292] In equation (49): W i,1 and W i,2 These represent the time windows for vessel i to perform the first and second phases of operations, respectively; W i For ship logistics work time window.

[0293] Following this process, based on given indicators such as the planned arrival and departure times of each vessel, vessel length, and container workload, the vessel logistics work is divided into operational stages and time windows. With the average vessel time in port and the total scheduling cost of the port container logistics system as optimization objectives, the optimal start sequence of logistics work and the best scheduling scheme for berths and equipment at each stage are solved.

[0294] In step E, considering the uncertainty of ship arrival time and the uncertainty of new energy output, a probability distribution model of actual ship arrival time and a probability distribution model of actual wind and solar power output are established. Based on this, an energy supply and demand coupling model of the port integrated energy system and the port container logistics system that takes into account the dual uncertainties is constructed.

[0295] 1) Probability distribution model of actual arrival time of ships: The actual arrival time of ships can be regarded as following a uniform distribution with the planned arrival time as the mean, as shown in equation (50). In order to enhance the robustness of the logistics scheduling plan and reduce the impact of uncertainties on the scheduling plan, a delay time parameter δ is introduced, which stipulates that the berth and the corresponding quay crane and other related resources are still occupied by the ship during the delay time, so as to absorb the impact of uncertainty. The maximum value and the sign of the delay time can be determined according to the range of variation of the ship arrival time, as shown in equations (51) and (52):

[0296]

[0297] δ max =2μ (51)

[0298]

[0299] In equations (51) and (52): Indicates the actual arrival time of ship i; U is an uncertain set; Δt arr,i denoted as , where is the range of arrival time for vessel i; μ is the standard deviation of planned arrival times for all vessels.

[0300] Therefore, equation (49) is modified as follows:

[0301]

[0302] 2) Probability distribution model of actual output of wind power and photovoltaic power: The actual output of wind power and photovoltaic power is affected by the light intensity and wind speed respectively, and has the characteristics of randomness and intermittency. The actual output of wind power and photovoltaic power can be characterized by the box uncertainty set, as shown in Equation (54). In order to avoid the model being too conservative, the uncertainty parameter Γ is introduced to adjust the conservatism and robustness of the model. The uncertainty variables should satisfy the uncertainty constraint conditions as shown in Equation (55), that is, at most Γ uncertain parameters can reach the boundary.

[0303]

[0304]

[0305] In equations (54) and (55): and These represent the actual output of wind power and solar power, respectively. and These are the predicted output values ​​for wind power and solar power, respectively. and For the disturbance range of wind power and photovoltaic output; Γ WT and Γ PV These represent the uncertainties in wind power and photovoltaic output, respectively. Therefore, equation (35) can be modified as follows:

[0306]

[0307] In equation (56): This indicates the electricity demand for the entire container logistics process when a ship actually arrives at the port.

[0308] 3) Energy supply and demand coupling model of port integrated energy system and port container logistics system considering dual uncertainties: Based on the above analysis and modeling of the uncertainty of actual ship arrival time and the uncertainty of wind power and photovoltaic output, an energy supply and demand coupling model of port integrated energy system and port container logistics system considering dual uncertainties is established based on the multi-energy flow coupling output model of port integrated energy system described in equation (44):

[0309]

[0310] In step F, the Alternating Direction Multiplier Method (ADMM) combined with the improved non-dominated sorting genetic algorithm (MNSGA-II) is used to solve the above-established joint optimization scheduling model and operation energy consumption model of the port container logistics system, the multi-energy flow coupling optimization scheduling model and energy output model of the port integrated energy system, and the energy supply and demand coupling model of the port integrated energy system and port container logistics system considering dual uncertainties. The solution results are analyzed comprehensively to obtain the energy and logistics scheduling plan.

[0311] Considering the superiority of non-dominated sorting and crowding comparison sorting in individual classification and selection of superior individuals, MNSGA-II, which has dual comparison sorting, is selected to solve the port container logistics scheduling process model based on the flexible assembly line workshop scheduling principle. Since the initial parent population and the new parent population used for each iteration are obtained through non-dominated sorting and crowding comparison sorting, the impact of randomly generated initial parent population on the convergence of subsequent iterations is effectively reduced. On this basis, considering that the port integrated energy system and the port container logistics system may not be scheduled and managed by a single operator, and the information is not completely open and shared, the distributed optimization algorithm ADMM, which has the advantage of partial information consensus, is selected to solve the collaborative scheduling problem of the port integrated energy system and the port container logistics system. The collaborative scheduling problem of the port integrated energy system and the port container logistics system is decomposed into a sub-problem based on the supply and demand balance constraints of electricity, heat and cold energy. There are balance constraints of energy supply and demand relationship as shown in equations (56), (36) and (37), and electricity, heat and cold coupling variables are introduced. and To represent the coupling relationship between the energy output of the port's integrated energy system and the port's total energy demand, the consensus constraint on the supply and demand relationship of electricity, heat, and cooling energy is simplified and expressed as the energy consensus constraint formula (58):

[0312]

[0313] Based on the constraints of equation (58), the Lagrange relaxation method is used to increase the consistency of the convergent objective functions (1), (2), and (27), and the augmented Lagrange function of the coordinated scheduling optimization problem of the port integrated energy system and the port container logistics system is expressed as the overall energy-logistics scheduling objective function of equation (59):

[0314]

[0315] In Equation (59): a, b, and c are the weights of the three objectives of the port integrated energy system scheduling total cost, average ship time in port, and port container logistics system scheduling total cost, respectively; the latter three terms represent the synergistic effect between the electricity, heat, and cooling output of the port integrated energy system and the electricity, heat, and cooling demand of the port container logistics system and port infrastructure; T is the coordinated scheduling period. and These are the Lagrange multipliers for the consensus constraints on the supply and demand of electricity, heat, and cooling energy, respectively; ρ e ρ h and ρ c These are the constant step size of the consensus constraint.

[0316] The coupling variables are calculated using equation (60), and the Lagrange multipliers are updated using equation (61) based on the latest coupling variables obtained through iteration.

[0317]

[0318]

[0319] In equations (60) and (61): and Let represent the coupling variables obtained after the (v+1)th iteration; and These are the Lagrange multipliers after the vth iteration; Let represent the Lagrange multipliers obtained after the (v+1)th iteration.

[0320] Each iteration uses coupling variables and Lagrange multipliers to solve the overall energy flow-material flow scheduling objective function (59). The iteration stops when the coupling variables are close enough, i.e. when equation (62) holds.

[0321]

[0322] In equation (62): E is the residual constraint value; subsequently, the port integrated energy system will share the coupling variable information with the port container logistics system to determine the final port energy flow and logistics scheduling plan.

[0323] The solution process for the collaborative scheduling of the port integrated energy system and the port container logistics system based on ADMM and MNSGA-II is as follows: Figure 4 As shown, the solution steps are as follows:

[0324] 1) Input parameters of the port integrated energy system and port container logistics system, data on ships scheduled to arrive on a certain day, and forecast data of wind power, photovoltaic power output and basic electricity, heat and cooling loads;

[0325] 2) Establish a consensus constraint on energy supply and demand for the port integrated energy system and the port container logistics system as shown in equation (58), and construct the overall scheduling objective function of energy flow and logistics (59);

[0326] 3) Initialize the optimization variables, the initial population size and maximum number of iterations of MNSGA-II, the ADMM energy coupling variables, the objective function weights, the consensus constraint Lagrange multipliers and step size, the residual constraint values, and the maximum number of cooperative scheduling solutions;

[0327] 4) Randomly generate N initial populations, calculate the objective function of shore power berth scheduling in equation (1) and the joint scheduling objective function of quay crane, yard crane, container truck and cold container in equation (2), sort the calculation results by non-dominated sorting and congestion comparison sorting, and use the bidding method to select the most suitable population as the initial parent population and enter the iteration;

[0328] 5) Cross over and mutate the parent population to form the offspring population, and calculate the objective function for scheduling shore power berths in equation (1) and the objective function for joint scheduling of quay cranes, yard cranes, container trucks and cold containers in equation (2);

[0329] 6) Merge the offspring population with the parent population, perform non-dominated sorting and crowding sorting on the merged population, and use an elite strategy to select the most suitable individuals to form a new population. When the maximum number of iterations is reached, the iteration stops and proceeds to step 7); otherwise, let the new population be the parent population and return to step 5).

[0330] 7) Obtain feasible logistics scheduling schemes based on Pareto solution sets. Obtain power consumption schemes from equations (17)-(26) involved in the power demand model of the whole process of container logistics. Let the initial energy coupling variables of ADMM be the power demand of the better solution and enter the iteration.

[0331] 8) Calculate the overall energy flow and logistics scheduling objective function (59), update the energy coupling variables by equation (60), and update the Lagrange multipliers by equation (61); when equation (62) is satisfied, the iteration stops and proceeds to step 9); otherwise, repeat step 8);

[0332] 9) If the maximum number of collaborative scheduling solutions is not reached, return to step 7); otherwise, terminate the iteration and output the final port energy flow and logistics scheduling plan.

[0333] Example

[0334] The following specific embodiment further illustrates the implementation effect of the port integrated energy system and port container logistics system collaborative scheduling method provided by the present invention.

[0335] Taking a port in my country as an example, the container terminal has a 1500m long shoreline, equipped with 8 shore power units, 12 quay cranes, and 40 container trucks. The yard houses 25 yard cranes and 1000 refrigerated containers. Table 1 shows the planned arrival data for a certain day. Following a first-come, first-served principle, the time taken for a vessel to berth and connect to shore power is counted as 1.5 hours in the vessel's port time. The predicted curves for the port's infrastructure electricity, heat, and cooling loads, as well as wind and solar power output for that day are shown below. Figure 5 As shown, the price of natural gas in this region is 3.1 yuan / m³. 3Electricity prices are implemented using a peak-shaving-valley time-of-use pricing system, with rates of 1.46 yuan / kWh for peak hours (9:00–13:00 and 19:00–23:00), 0.87 yuan / kWh for normal hours (13:00–19:00), and 0.39 yuan / kWh for valley hours (0:00–9:00 and 23:00–24:00). The grid and enterprise agreement stipulates peak shaving and valley filling response periods of 19:00–21:00 and 23:00–5:00 the next day, with subsidy prices of 4.0 yuan / kWh and 1.2 yuan / kWh respectively. The carbon market trading price is based on the national carbon market's trading price of 52 yuan / tCO2e on a specific day after the market's opening. SO2 and NO... x The pollution equivalent value is 0.95, and the pollution tax rate is 6 yuan / kgCO2e. The method provided by this invention is used to solve for the port's energy flow and scheduling plan for that day, and a comparison is made with a comparative example based on existing technologies.

[0336] Table 1. Planned Arrival Vessel Data

[0337]

[0338] 1) Comparison of port scheduling results

[0339] Table 2 Comparison of port scheduling target results between existing technologies and the method of this invention.

[0340]

[0341] Compared with the port scheduling objectives of existing technologies and the method of this invention, the method provided by this invention reduces the average port time of ships by 0.3167 hours, increases pollution and carbon reduction benefits and peak shaving and valley filling benefits by 0.10% and 15.63% respectively, and reduces the overall scheduling cost of the port integrated energy system and the port container logistics system by 3.29%. It is evident that the method of this invention can reduce the overall scheduling cost of port energy and logistics while ensuring the efficiency of port container logistics.

[0342] 2) Comparison of port scheduling plans

[0343] ① Comparison of Port Energy Flow Scheduling Plans

[0344] Existing technologies and the port energy flow scheduling methods of this invention, for example Figures 6-11 As shown, the port energy flow scheduling plan obtained based on the method of this invention increases the power generation of gas turbines and the power discharge of batteries during high electricity price periods, thereby reducing the power purchased from the grid, thus reducing energy purchase costs and reducing gas pollution emissions. In addition, the battery charging period is adjusted to the low electricity price period, and the power consumption for electricity-to-gas conversion and carbon capture increases during the low electricity price period, resulting in increased electricity demand during the low electricity price period. This increases the benefits of pollution reduction and carbon reduction as well as peak shaving and valley filling, thereby further reducing the port energy flow scheduling cost.

[0345] ② Comparison of Port Logistics Scheduling Plans

[0346] Existing technologies and the port logistics scheduling methods of this invention, for example Figures 12-17 As shown, the port logistics scheduling plan obtained by the method of this invention increases the power consumption of quay cranes and yard cranes during periods of low and flat electricity prices, and reduces the charging power due to container trucks transporting containers. As container logistics efficiency improves, some ships complete loading and unloading and leave the port earlier, resulting in a reduction in shore power consumption, which effectively reduces the total power consumption of the port container logistics system during peak shaving periods. In addition, the delay in completing the container stacking task by yard cranes until the valley filling period increases the total power consumption of the port container logistics system during the valley filling period, thereby reducing the peak-valley difference in power consumption of the port container logistics system.

[0347] In summary, the analysis shows that, compared with the existing technology, the method provided by the present invention can effectively improve the efficiency of port container logistics, shorten the average time of ships in port by 0.3167 hours, reduce the overall scheduling cost of port energy flow and logistics by 3.29%, reduce the peak-valley difference of port electricity demand and reduce pollution emissions, thereby increasing the benefits of peak shaving and valley filling and the benefits of pollution reduction and carbon reduction by 0.10% and 15.63%, respectively.

[0348] The above embodiments are merely illustrative of the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solutions based on the technical concept proposed in this invention shall fall within the scope of protection of this invention. Technologies not covered in this invention can be implemented using existing technologies.

Claims

1. A method for coordinating scheduling of a port integrated energy system and a container logistics system, characterized in that: The steps of the coordinated scheduling method are as follows: A, the composition of the port comprehensive energy system, the composition of the port container logistics system, the logistics operation process, and the coupling relationship between energy flow and logistics are determined, and the coordinated scheduling architecture of the port comprehensive energy system and the port container logistics system is constructed; B, based on the shortest average time of the ship in the port and the lowest logistics scheduling cost, a joint optimization scheduling model of the port container logistics system and an operation energy model are established; C, based on the lowest system operation cost, a multi-energy flow coupled optimization scheduling model of the port comprehensive energy system and an energy output model are established; D, a port container logistics scheduling process model based on the principle of flexible flow line workshop scheduling is constructed; E, a port comprehensive energy system and port container logistics system energy supply and demand coupling model considering double uncertainty is constructed; F, the models in steps B, C, D, and E are solved by using the alternating direction multiplier method ADMM combined with the improved non-dominated sorting genetic algorithm MNSGA-II to obtain the port energy flow and logistics scheduling plan; The multi-energy flow coupled optimization scheduling model and the energy output model of the port comprehensive energy system in step C include the multi-energy flow scheduling objective function of the port comprehensive energy system, the scheduling constraint conditions of the port comprehensive energy system, and the multi-energy flow coupled output model of the port comprehensive energy system; The scheduling constraint conditions of the port comprehensive energy system include energy supply and demand balance constraints, energy network energy supply constraints, and energy device operation constraints, wherein the energy supply and demand balance constraints are that the power generation of the port comprehensive energy system at time t needs to meet the total power demand of the port infrastructure and the logistics system, as shown in formula (35); the system heat and cold power needs to meet the port infrastructure heat and cold energy demand, as shown in formulas (36) and (37) respectively; the external natural gas network and the gas production device in the port cooperate to supply gas to the gas turbine unit, which can be represented by formula (38); in formulas (35)-(38) are the electric, thermal, and cold load values of the port infrastructure at time t, respectively; and are the electric, thermal, and cold load values of the port infrastructure at time t, respectively; and are the electric, thermal, and cold load values of the port infrastructure at time t, respectively; and are the electric, thermal, and cold load values of the port infrastructure at time t, respectively; and are the electric, thermal, and cold load values of the port infrastructure at time t, respectively; and are the electric, thermal, and cold load values of the port infrastructure at time t, respectively; and are the electric, thermal, and cold load values of the port infrastructure at time t, respectively; are the electric, thermal, and cold load values of the port infrastructure at time t, respectively; The multi-energy flow coupling output model of the port comprehensive energy system: combined with the multi-energy flow supply and demand balance relationship of the port comprehensive energy system and the output model of each energy device, the dispatching factors α, β, θ and γ represent the proportion of the input power of the power grid, the natural gas grid and the internal heat grid allocated to the multi-energy coupling conversion device, and the multi-energy flow coupling output model of the port comprehensive energy system based on the energy hub is established as shown in formula (44); In formula (44): is the electrical load value of the port infrastructure for the time period t; is the total electrical power demand of the port container logistics system for the time period t; is the thermal load value of the port infrastructure for the time period t; is the cold load value of the port infrastructure for the time period t; is the wind power output disturbance range for the time period t; is the photovoltaic power output disturbance range for the time period t; is the energy system purchased power from the external power grid for the time period t; is the energy system gas purchase rate from the external natural gas grid for the time period t; is the battery discharge power for the time period t; is the battery charging power for the time period t; is the heat release power of the heat storage tank for the time period t; is the heat storage power of the heat storage tank for the time period t;A and B are the multi-energy coupling conversion matrix and the energy storage charging and discharging state matrix, respectively, and are the power generation efficiency and heat generation efficiency of the gas turbine, respectively; represents the efficiency of the waste heat boiler recovering the high-temperature flue gas waste heat discharged by the gas turbine and generating usable heat in turn; is the gas production power of the electric gas conversion equipment; is the heat generation efficiency of the gas boiler; and represent the refrigeration efficiency of the electric refrigerator and the absorption refrigerator, respectively;H g is the natural gas heat value;α represents the proportion of the gas consumption power of the gas turbine to the total power of the external natural gas grid input and the electric gas conversion equipment gas production power,β, and θ are the proportion of the power consumption of the electric refrigerator, the electric gas conversion equipment, and the carbon capture equipment to the input power of the external power grid, respectively, and γ represents the proportion of the heat absorption power of the absorption refrigerator to the total heat generation power of the waste heat boiler and the gas boiler, and the expressions are as follows: in formula (45), is the gas consumption power of the t-period gas turbine; is the gas production power of the t-period external natural gas network input; is the gas production power of the t-period electric gas conversion device; is the electricity consumption power of the t-period electric refrigerator; is the electricity consumption power of the t-period electric gas conversion device; is the electricity consumption power of the t-period carbon capture device; is the electricity purchase power of the t-period energy system from the external power grid; and respectively represent the heat production power of the t-period waste heat boiler, the heat production power of the t-period gas boiler, and the heat absorption power of the t-period absorption refrigerator. The specific steps of constructing the port comprehensive energy system and port container logistics system energy supply and demand coupling model considering double uncertainty in step E are as follows: considering the uncertainty of ship arrival time and the uncertainty of new energy output, a ship actual arrival time probability distribution model and a wind power and photovoltaic actual output probability distribution model are established, and on this basis, a port comprehensive energy system and port container logistics system energy supply and demand coupling model considering double uncertainty is constructed; Among them, the ship actual arrival time probability distribution model is that the ship actual arrival time can be regarded as subject to a uniform distribution with the planned arrival time as the mean value, as shown in formula (50); in order to enhance the robustness of the logistics scheduling plan and reduce the impact of uncertainty factors on the scheduling plan, a delay time parameter δ is introduced, which stipulates that within the delay time, the ship berth resource is still occupied by the ship, thereby absorbing the impact of uncertainty, and the maximum value of the delay time and the positive and negative delay time can be determined according to the variation range of the ship arrival time, as shown in formulas (51) and (52): δ max = 2μ (51) in formulas (51), (52): denotes the actual arrival time of the ship i; U is an uncertain set; Δt arr,i is the range of variation of the arrival time of ship i; μ is the standard deviation of the planned arrival times of all ships; The first stage represents the time interval of the ship's berthing and shore power connection, and the second stage represents the time interval of the shore crane unloading containers from the ship. The available time interval of the first stage is determined by the ship's planned time in port, and the end time of the second stage determines the end time of the ship's berthing, i.e. the end time of the first stage. Therefore, the first stage and the second stage need to be carried out within the ship's planned time window, which is represented as: In formula (53), W i,1 and W i,2 respectively represent the time window of ship i to carry out stage one and stage two operations; W i is the logistics work time window of ship i; t dep,i represents the planned latest departure time of ship i; The actual output probability distribution model of wind power and photovoltaic power is represented by a box-type uncertain set, as shown in equation (54). An uncertainty budget parameter Γ is introduced to adjust the conservatism and robustness of the actual output probability distribution model of wind power and photovoltaic power. The uncertainty variable should satisfy the uncertainty budget constraint as shown in equation (55), i.e. at most Γ uncertain parameters can reach the boundary. (54), (55) in the formula: and respectively represent the actual output of wind power and photovoltaic at time t; and respectively are the predicted values of wind power and photovoltaic output at time t; and are the disturbance ranges of wind power and photovoltaic output at time t; Γ WT and Γ PV respectively are the uncertainty budgets of wind power and photovoltaic output at time t; Based on the actual output probability distribution model of wind power and photovoltaic power, the energy supply and demand balance constraint in the port comprehensive energy system scheduling constraint condition expressed by equation (35) is modified as: in formula (56): represents the power purchased from the external grid by the energy system at time t; is the power generated by the gas turbine at time t; and are the charging and discharging power of the battery at time t, respectively; and are the power consumption of the electric refrigeration, electric gas conversion device and carbon capture at time t, respectively; is the electrical load value of the port infrastructure at time t; represents the power demand for the whole process of container logistics scheduling under the actual arrival of the ship. According to the ship's actual arrival time probability distribution model of equation (50), the actual output probability distribution model of wind power and photovoltaic power of equation (54), and the port comprehensive energy system multi-energy flow coupling output model of equation (44), an energy supply and demand coupling model of the port comprehensive energy system and the port container logistics system considering double uncertainty is established:

2. The method of claim 1, wherein the port integrated energy system and container logistics system collaborative scheduling method is characterized in that: The port comprehensive energy system in step A covers the production and consumption of electric energy, thermal energy, and cold energy.

3. The method of claim 1, wherein: The port container logistics system in step A schedules shore power, shore cranes, trucks, yard cranes, and cold boxes in the logistics operation area according to the arrival and departure times of each container ship and the amount of containers to be loaded or unloaded, and jointly completes the logistics operation process including ship berthing, container loading and unloading, transportation, stacking, and refrigeration.

4. The method of claim 1, wherein: The coupling relationship between energy flow and logistics in step A is that the energy consumed by the port container logistics system in scheduling shore power, shore cranes, trucks, yard cranes, and cold boxes is satisfied by the output of the port comprehensive energy system.

5. The method of claim 1, wherein: The joint optimization scheduling model and operation energy model of the port container logistics system in step B include the port container logistics system whole-process scheduling objective function, the port container logistics system scheduling constraint condition, and the container logistics whole-process power demand model. The port container logistics system whole-process scheduling objective function includes the shore power berth scheduling objective function and the shore crane, yard crane, truck, and cold box joint scheduling objective function. The port container logistics system scheduling constraint condition includes the ship berthing and unberthing time and berthing position constraint, logistics equipment scheduling constraint, and logistics equipment operation constraint. The container logistics whole-process power demand model covers shore power, shore cranes, trucks, yard cranes, and cold boxes. The shore power berth scheduling objective function is to minimize the average time of all ships in port, i.e. In formula (1): B ship represents the average time of all ships in port within a scheduling period; r ship is the number of ships arriving at the port within a period; t arr,i , t ber,i , and t SPS,i represent the planned arrival time, berthing time, and shore power occupation time of ship i, respectively; The shore crane, yard crane, truck, and cold box joint scheduling objective function is to schedule all types of equipment with the lowest total cost of shore cranes, yard cranes, trucks, and cold boxes as the objective function, which is represented as: minC PCLS = C QC + C GC + C AGV + C RC (2) In formula (2): C PCLS represents the total cost of the logistics system scheduling for a scheduling period; C QC , C GC , C AGV , and C RC are the scheduling costs of the quay crane, the yard crane, the truck, and the cold box, respectively, as follows: The shore crane scheduling cost includes the shore crane startup cost and the loading and unloading operation cost for serving each ship, which is represented as: in formula (3): c QC,on and c QC,w are the start-up cost and average per hour handling cost of a single quay crane, respectively; r QC is the total number of quay cranes; r C,i denotes the number of containers to be handled for the ith ship; T L is the scheduling period of the logistics system; denotes the 0-1 state variable of the kth quay crane serving the ith ship at time t, the work is 1, otherwise 0; is the handling efficiency of the kth quay crane; r ship is the number of arriving ships in a period; The yard crane scheduling cost includes the yard crane startup cost and the stacking operation cost for serving each ship, which is denoted as: In formula (4): c GC,on and c GC,w respectively represent the start-up cost and average per hour stacking cost of a single yard crane; r GC is the total number of yard cranes; is a 0-1 state variable representing the work of the nth yard crane serving the ith ship in the t period, which is 1 if working, otherwise 0; is the stacking efficiency of the nth yard crane; The truck scheduling cost includes the truck startup cost and the transportation cost for serving each ship, which is denoted as: In formula (5): c AGV,on and c AGV,w respectively represent the start-up cost of a single truck and the average per-hour container handling cost; r AGV represents the total number of trucks; represents the 0-1 state variable of the mth truck serving the ith ship container at the t period, 1 for stop and charge, and 0 for container; is the container handling efficiency of the mth truck. The cold box scheduling cost is the sum of the startup refrigeration cost of the cold box, which can be denoted as: in formula (6): c RC,on represents the start-up cost of a single cold box; r RC is the total number of cold boxes; is a refrigeration status 0-1 variable for the fth cold box, 1 if refrigerating, otherwise 0; The ship berthing / departure time and berthing position constraints: each ship can only be berthed after its arrival, and the actual departure time of each ship cannot be later than the planned latest departure time, and the berthing times of any two ships cannot conflict, as shown in equation (7); the berthing positions of all ships cannot exceed the range of the port coastline, and the berthing positions and times of any two ships cannot conflict, as shown in equation (8); t arr,i ≤t ber,i ,t ber,i +t SPS,i ≤t dep,i ,t ber,i +t SPS,i ≤t ber,q +M(1-h i,q ) (7) l 0,i + i ≤L,l 0,i + i ≤l q +M(1-z i,q ),z i,q +z q,i +h i,q +h q,i ≥1 (8) M is an infinite constant; t dep,i denotes the planned departure time of ship i; h i,q denotes the berthing time 0-1 state variable of ship i and ship q, and takes the value 1 if ship i berths before ship q, otherwise 0; h q,i denotes the berthing time 0-1 state variable of ship q and ship i, and takes the value 1 if ship q berths before ship i, otherwise 0; h 0,i denotes the initial position of the berth of ship i; h i and h q denote the length of ship i and ship q respectively, which includes the berthing safety distance between ships; L is the length of the shore line; z i,q denotes the berthing position 0-1 state variable of ship i and ship q, and takes the value 1 if ship i berths on the left side of ship q, otherwise 0; z q,i denotes the berthing position 0-1 state variable of ship q and ship i, and takes the value 1 if ship q berths on the left side of ship i, otherwise 0; h i,q denotes the berthing time 0-1 state variable of ship i and ship q, and takes the value 1 if ship i berths before ship q, otherwise 0; h q,i denotes the berthing time 0-1 state variable of ship q and ship i, and takes the value 1 if ship q berths before ship i, otherwise 0; The logistics equipment scheduling constraints: each shore power, yard crane, yard bridge and truck can only serve one ship in each time period, which can be represented by equation (9); the number of each type of equipment operating in each time period cannot exceed its total number, as shown in equation (10); the number of yard cranes serving each ship is limited by the total number of yard cranes that can be allocated to the ship, which can be represented by equation (11); assuming that the loading and unloading tasks of each ship can be evenly distributed to the yard cranes, the length of time each ship occupies the berth shore power is the sum of the loading and unloading preparation time and the time for transporting all containers, as shown in equation (12); In formulas (9)-(12): represents the 0-1 variable of the jth shore power supply state for the ith ship at time t, and the supply is 1, otherwise 0; r SPS is the total number of shore power charging piles; r QC,i,max and r QC,i,min respectively represent the upper and lower limits of the number of shore cranes that can serve the loading and unloading of the ship i; t rea,i is the loading and unloading preparation time of the ship i; The logistics equipment operation constraints: the loading and unloading efficiency of each yard crane, the stacking efficiency of the yard bridge, and the container transportation efficiency of the truck in each time period are subject to upper and lower limits, which can be represented by equation (13); the power consumption of the ship during berthing is subject to upper and lower limits of the shore power supply, as shown in equation (14); the charging power and state of charge of each truck are subject to upper and lower limits, and the state of charge of each truck at the end of the scheduling period must be the same as at the beginning of the scheduling period, as shown in equation (15); the refrigeration power of each cold box and the change in temperature inside the box are also subject to upper and lower limits, and the temperature inside the cold box at the end of the scheduling period should be the same as at the beginning of the scheduling period, as shown in equation (16); In formula (13)-(16), E k t represents the loading and unloading efficiency of the kth quay crane at time t, E n t represents the stacking efficiency of the nth yard crane at time t, and E m t represents the loading and unloading efficiency of the mth truck at time t; n respectively represent the maximum loading and unloading efficiency of a single quay crane, truck and yard crane; QC,k,max AGV,m,max GC,n,max respectively represent the maximum loading and unloading efficiency of a single quay crane, truck and yard crane; P j represents the power supply of the jth quay power supply; respectively represent the upper and lower limits of the power supply of the jth quay power supply; P m t represents the charging power of the mth truck at time t; respectively represent the maximum and minimum charging power of the mth truck; SoC m t represents the state of charge of the mth truck at time t; SoC AGV,m,max AGV,m,min respectively represent the maximum and minimum state of charge of the mth truck, and the state of charge at the initial time of scheduling and at the end of the scheduling period; P f represents the refrigeration power of the fth cold box; respectively represent the maximum and minimum refrigeration power of the fth cold box; ΔT f represents the change in the temperature in the fth cold box after the fth cold box starts refrigeration for a time interval; ΔT RC,f,max RC,f,min respectively represent the maximum and minimum change in the temperature in the fth cold box, and the temperature in the fth cold box at the initial time of scheduling and at the end of the scheduling period.​​​​​​​​​​​​ The total power demand of the port container logistics system in time period t is the sum of the power of all types of logistics equipment in that time period, denoted as: In formula (17), represents the total power demand of the port container logistics system at time t; and respectively represent the total power of the shore power, the quay crane, the yard crane, the truck and the cold box at time t, as follows, respectively: The total power of the shore power in time period t is the sum of the shore power supply power for serving ships, which is also the sum of the power of the berthing ships, denoted as: In formula (18): is the 0-1 variable for the i-th vessel berthing status, 1 if the vessel is connected to shore power, otherwise 0; denotes the power demand of the i-th vessel at time period t. The total power of the yard bridge in time period t is the sum of the operating power of the yard bridge serving the loading and unloading operations of each ship in that time period, as shown in equation (19); the operating power of a single yard bridge is represented by the working power of the frame structure, lifting mechanism and trolley transportation mechanism, as well as the container loading and unloading rate, as shown in equation (20): In formula (19), (20): represents the operating power of the kth quay crane at time t; H QC,k , R QC,k and D QC,k are the lifting height, outreach distance and horizontal span of the kth quay crane, respectively; and are the average power and operating speed of the lifting motor of the kth quay crane, respectively; and are the average power and operating speed of the horizontal operating trolley, respectively; The total power of the yard bridge in time period t is the sum of the operating power of the yard bridge serving the loading and unloading operations of each ship in that time period, as shown in equation (19); the operating power of a single yard bridge is represented by the working power of the frame structure, lifting mechanism and trolley transportation mechanism, as well as the container loading and unloading rate, as shown in equation (20): In formula (21) and (22): Pn(t) represents the power of the nth portal crane at time t; Hn(t) represents the lifting height of the crane of the nth portal crane at time t; Ln(t) represents the horizontal span of the nth portal crane at time t; GC,n and D GC,n respectively represent the lifting height and the horizontal span of the nth portal crane; and respectively represent the average power and the running speed of the lifting motor and the horizontal running trolley of the nth portal crane. The total power of the truck in time period t is the sum of the charging power of all trucks in the non-operating charging state in that time period, which can be represented by equation (23); the charging / discharging process of each truck can be represented by the state of charge, as shown in equation (24), where the first term represents the state of charge of the truck in time period t-1, and the second term represents the change in state of charge after the truck is in the non-operating charging or startup transportation state for a period of time; in formulas (23), (24): denotes the state of charge of the t-1 period set of trucks; and respectively, the charging and discharging efficiency of the mth truck; denotes the power of the mth truck carrying a single container; denotes the battery rated capacity of the mth truck; Δt L is the unit time interval of logistics scheduling of the logistics system; The total power of the cold box in the time period is the sum of the refrigeration power of all the cold boxes started in the time period, as shown in equation (25); the change in the internal temperature of each cold box is determined by the refrigeration power and can be represented by equation (26), wherein the first term is the change in the internal temperature of the box due to the ambient temperature, and the second term represents the change in the internal temperature of the box after the refrigeration is powered for a period of time; in equations (25) and (26): Tf(t) is the internal temperature of the fth cold box at time t; T0(t) is the external ambient temperature at time t; e denotes the natural exponential function; A RC,f and m RC,f respectively represent the external surface area of the fth cold box and the weight of the loaded cargo; z RC and c RC are respectively the thermal conductivity of the cold box and the specific heat capacity in the box.

6. The method of claim 1, wherein: The multi-energy flow scheduling objective function of the port integrated energy system aims to minimize the operation cost, and the objective function can be represented as: In formula (27): C PIES represents the total cost of the port comprehensive energy system operation; C net , C om , C loss , and are the energy purchasing cost, energy equipment operation and maintenance cost, energy storage wear and tear cost, CO2 transportation and storage cost, and emission treatment cost of a scheduling period, respectively. S er and S pc respectively represent the reduction of carbon emissions and the income from participating in carbon market trading of the reduction amount, and the income from participating in the peak shaving and valley filling demand response of the power system auxiliary service market, respectively as follows: In formulas (28)-(34), and are the electricity price and the natural gas price at time t, respectively; and represent the power purchased from the external grid and the natural gas grid by the energy system at time t, respectively; ω,om represents the unit operating and maintenance cost of each energy device, where ω∈{photovoltaic, wind power, gas turbine, waste heat boiler, gas boiler, absorption chiller, electric refrigeration device, electric-to-gas device, carbon capture device, battery, thermal storage tank}; represents the τ power of each energy device at time t, τ∈{e, h, c, g}, where e, h, c, g represent electricity, heat, cold, and natural gas, respectively; is the difference in the power purchased from the grid by the port before and after responding to the peak shaving and valley filling demand at time t; BS,loss and HS,loss represent the unit wear and tear cost of the battery and the thermal storage tank, respectively; represents the charging power of the battery, represents the discharging power of the battery, represents the charging power of the thermal storage tank, represents the discharging power of the thermal storage tank; is the charging efficiency of the thermal storage tank, is the discharging efficiency of the thermal storage tank, is the charging efficiency of the battery, is the discharging efficiency of the battery; and represent the unit transmission and storage cost of CO2 and the unit emission treatment cost, respectively; and are the unit carbon emissions of electricity and natural gas combustion, respectively; represents the amount of CO2 captured by the electric-to-gas device at time t; is the gas production power of the electric-to-gas device; Δt E is the unit time interval of the integrated energy system of the port; T p and T v represent the time periods of the peak shaving and valley filling demand set by the grid, respectively; is the peak shaving-valley filling subsidy unit price; The energy network supply constraint, i.e., the port integrated energy system scheduling is constrained by the upper and lower limits of the external power grid and natural gas network supply, as shown in equations (39) and (40) respectively: in formulas (39)-(40): and G net,max respectively represent the maximum electric power supplied to the port by the external electric grid, the maximum natural gas rate supplied to the port by the natural gas grid; The energy equipment operation constraint, i.e., the operation of each energy equipment needs to meet the output constraint condition, which can be represented by equation (41); the gas turbine unit also needs to meet the climbing constraint, as shown in equation (42); the energy of the energy storage device needs to meet the capacity constraint and the energy at the end of the scheduling should be the same as that at the beginning of the scheduling, as shown in equation (43); in formulas (41)-(43): and G net,max Pmaxgridand Pmaxgrid, nat represent the maximum power supplied to the port by the external grid and the natural gas grid, respectively; Pmaxgas, t and Pmin gas, t are the maximum and minimum power output of the gas turbine, respectively, and Pmaxgas, t and Pmin gas, t are the maximum and minimum power output of the gas turbine, respectively, and Pgas, t-1 and Rgas, t-1 represent the power output of the gas turbine and the thermal efficiency of the gas boiler at time t-1, respectively; GT,max and R GT,min Pmaxgas, t and Pmin gas, t are the maximum and minimum power output of the gas turbine, respectively, GB,max and R GB,min Pmaxgas, t and Pmin gas, t are the maximum and minimum power output of the gas turbine, respectively; Eendand Eendrepresent the energy remaining in the battery and the thermal storage tank at the end of time t, respectively; BS / HS,max , E BS / HS,min Eupand E1owrepresent the upper and lower limits of the energy that can be stored in the battery and the thermal storage tank, respectively, and Estartand Eendrepresent the energy stored in the battery and the thermal storage tank at the start and end of dispatch, respectively.

7. The method of claim 1, wherein: The step D is based on the principle of flexible flow line workshop scheduling, and the port container logistics scheduling flow model is based on the continuity of the ship berthing, container loading and unloading, transportation, stacking and refrigeration operation process. The whole process of logistics scheduling problem is abstracted as a multi-stage operation scheduling problem of flow line, and a port container logistics scheduling flow model based on the principle of flexible flow line workshop scheduling is established. The port container logistics scheduling flow includes five stages of ship berthing, shore power connection, shore crane unloading, truck container transportation, yard crane stacking and cold box refrigeration. The amount of containers to be unloaded by the ship is the amount of work to be processed in each stage, and each stage is carried out synchronously under the constraints of sequence and equipment operation; The specific steps are as follows: the ship logistics work of each ship is divided into time windows according to the operation time of each stage, and is expressed as: C,1 C,2 C,i , …}​​ W i,s = [t str,i,s ,t end,i,s ]s∈{1,2,...,5} (46) In formula (46): W i,s denotes a ship logistics job r C,i a job time window corresponding to the s-th stage; t str,i,s and t end,i,s are respectively the earliest start time and the latest end time of the ship logistics job r C,i corresponding to the s-th stage; The time window boundaries of each stage are as follows: The logistics equipment serving each stage can only work within the corresponding time window, which is represented by: t str,i,1 t str,i,2 t str,i,3 t str,i,4 and t str,i,5 respectively represent the earliest start operation time of the ship logistics work r C,i corresponding to the first to fifth stages; t end,i,1 , t end,i,2 , t end,i,3 , t end,i,4 and t end,i,5 are the ship logistics jobs r C,i the latest end time of the jobs corresponding to the 1st to 5th stages; and are 0-1 state variables of the jth shore power, the kth quay crane, the mth truck, and the nth yard crane serving the ith ship logistics job at time t, job = 1, otherwise 0; is a 0-1 refrigeration state variable of the fth cold box at time t, refrigeration = 1, otherwise 0; The available time interval of stage one is determined by the planned time of the ship in the port, and the end time of the ship berthing is determined by the end time of stage two unloading, i.e., the time of stage one; therefore, stage one and stage two need to be carried out within the planned time window of the ship in the port, which is represented by: In formula (49), W i,1 and W i,2 respectively represent the time window for ship i to carry out stage one and stage two operations; W i is the time window for ship i logistics work.

8. The method of claim 1, wherein: In the solving method in the step F, the collaborative scheduling problem of the port comprehensive energy system and the port container logistics system is decomposed into a sub-problem based on the balance constraint of the supply and demand of electricity, heat and cold energy, and a balance constraint of the supply and demand relationship of energy is introduced into the coupling variable of electricity, heat and cold and to represent the coupling relationship between the energy output of the port comprehensive energy system and the total energy demand of the port, and the common constraint of the supply and demand relationship of electricity, heat and cold energy is simplified and represented as the energy supply and demand common constraint of formula (58): Based on the constraint of equation (58), the convergence consistency of the port integrated energy system multi-energy flow scheduling objective function, the berth scheduling objective function with shore power, and the joint scheduling objective function of shore crane, yard crane, truck and cold box is increased by using Lagrange relaxation method. The augmented Lagrange function of the coordinated scheduling optimization problem of the port integrated energy system and the port container logistics system is represented as the overall energy flow-logistics scheduling objective function of equation (59): In equation (59), a, b and c are the weights of the total cost of the port integrated energy system scheduling, the average time of the ship in the port and the total cost of the port container logistics system scheduling respectively; the last three terms represent the synergistic effect between the power, heat and cold output of the port integrated energy system and the energy demand of the port container logistics system and the port infrastructure; T is the coordination scheduling period; and are the Lagrange multipliers of the electricity, heat, and cold energy supply-demand consensus constraints, respectively; p e , p h , and p c are the constant step sizes of the consensus constraints, respectively; The coupling variable is calculated by equation (60), and the Lagrange multiplier is updated by equation (61) according to the latest coupling variable obtained by iteration; (60), (61) respectively, represent the coupling variables obtained after the (v+1)th iteration; and respectively, represent the coupling variables obtained after the (v+1)th iteration; and respectively, are the Lagrange multipliers after the vth iteration; respectively, represent the Lagrange multipliers obtained after the (v+1)th iteration; The energy flow-logistics overall scheduling objective function of formula (59) is solved by using the coupling variable and the Lagrange multiplier in each iteration, and the iteration stops when the residual error of the coupling variable and the consensus variable is less than the residual error constraint value E, that is, the judgment criterion of formula (62) is met; In formula (62), E is the residual error constraint value; Subsequently, the port comprehensive energy system shares the coupling variable information with the port container logistics system, and determines the final port energy flow and logistics scheduling plan.

9. The method of claim 8, wherein: In the solving method in step F, the solving steps of the port comprehensive energy system and the port container logistics system collaborative scheduling based on ADMM and MNSGA-II are as follows: 1) input the port comprehensive energy system and the port container logistics system parameters, the data of the scheduled ships on a certain day, and the predicted data of the wind power and photovoltaic output and the basic electricity, heat and cold load; 2) establish the energy supply and demand consensus constraint formula (58) of the port comprehensive energy system and the port container logistics system, and construct the energy flow-logistics overall scheduling objective function formula (59); 3) initialize the optimization variables, the initial population number and the maximum iteration number of MNSGA-II, the energy coupling variable of ADMM, the objective function weight, the consensus constraint Lagrange multiplier and the step size, the residual error constraint value, and the maximum collaborative scheduling solving number; 4) randomly generate N initial populations, calculate the port container logistics system whole-process scheduling objective function in the port container logistics system joint optimization scheduling model and the operation energy consumption model, perform non-dominated sorting and congestion degree comparison sorting on the calculation results, and select the most suitable population as the initial parent population by using the bidding competition method, and enter the iteration; 5) perform crossover and mutation on the parent population to form a child population, and calculate the port container logistics system whole-process scheduling objective function in the port container logistics system joint optimization scheduling model and the operation energy consumption model; 6) combine the child population and the parent population, perform non-dominated sorting and congestion degree sorting on the combined population, and use the elite strategy to select the most suitable individuals to form a new population; When the maximum iteration number is reached, the iteration stops, and step 7) is entered; Otherwise, the new population is the parent population, and step 5) is returned to; 7) obtain a feasible logistics scheduling scheme from the Pareto solution set, obtain the power consumption scheme from the container logistics whole-process power demand model in the port container logistics system joint optimization scheduling model and the operation energy consumption model, and set the initial energy coupling variable of ADMM to the power demand of the optimal solution, and enter the iteration; 8) calculate the energy flow-logistics overall scheduling objective function formula (59), update the energy coupling variable according to formula (60), and update the Lagrange multiplier according to formula (61); when formula (62) is met, the iteration stops, and step 9) is entered; Otherwise, step 8) is cycled; 9) if the maximum collaborative scheduling solving number is not reached, return to step 7); Otherwise, terminate the iteration, and output the final port energy flow and logistics scheduling plan.

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