Port collaborative scheduling method based on information gap decision theory

Abstract

A port collaborative scheduling method based on an information gap decision theory comprises the following steps: S1, establishing a port integrated energy system framework model for a port, and finally constructing a berth logistics system; s2, collecting equipment parameters in the port integrated energy system framework model; s3, establishing a logistics side minimum energy consumption cost target under berth-logistics-energy collaborative scheduling; s4, establishing a minimum energy consumption cost target of a berth side under berth-logistics-energy collaborative scheduling; s5, establishing a common coupling electric energy balance relation; s6, establishing and solving an overall target under berth-logistics cooperative scheduling; s7, performing modeling and solving under uncertainty based on an information gap decision theory; according to the method, the ship energy demand, the container throughput dynamic and the logistics demand are integrated, and meanwhile, the operation limitation of the port comprehensive energy system is considered. And an electricity-hydrogen coupling transportation method is adopted to dynamically optimize hydrogenation, battery replacement time windows, logistics operation and berth allocation under time-of-use electricity price.

Description

Technical Field

[0001] This invention relates to the field of port scheduling technology, specifically a port collaborative scheduling method based on information gap decision theory. This method reduces port energy costs through berth-logistics-energy collaborative scheduling and characterizes the comprehensive uncertainty that a port can bear under different strategies based on information gap decision theory. Background Technology

[0002] Modern ports are rapidly evolving from traditional cargo handling nodes into comprehensive hubs integrating logistics, energy, and information technology. While this evolution has enhanced the intelligence and integration of port operations, it has also brought more complex management and scheduling challenges. This stems primarily from the spatiotemporal coupling of shore power system loads, the coordinated scheduling of the electricity-hydrogen coupled logistics system, and the stochastic fluctuations in renewable energy and ship loads. These closely related factors lead to a highly complex optimization problem with significant uncertainty. Summary of the Invention

[0003] To address the aforementioned shortcomings of existing technologies, this invention proposes a port collaborative scheduling method based on information gap decision theory. This method comprehensively integrates the energy demand characteristics (cooling, heating, and electricity) of ships during berthing, the dynamic evolution of container throughput, and the operational rhythm of logistics transportation, while also considering the operational constraints of the port's integrated energy system. In terms of optimization, an electricity-hydrogen coupled transportation system is introduced, combined with a time-of-use electricity pricing mechanism, to dynamically and collaboratively optimize the time windows for hydrogen refueling and battery swapping operations, logistics task allocation, and berth scheduling, thereby achieving effective control of port operating costs and a comprehensive improvement in system robustness.

[0004] The specific technical solution is as follows: A port collaborative scheduling method based on information gap decision theory includes the following steps: S1, establish a framework model for the port's integrated energy system, and ultimately construct a berth logistics system; S2, collect equipment parameters in the framework model of the port's integrated energy system; S3, establish the target for minimum energy cost on the logistics side under the coordinated scheduling of berths, logistics and energy; S4, establish the minimum energy cost target on the berth side under the coordinated scheduling of berth-logistics-energy; S5, establish a common coupled power balance relationship; S6, Establish and solve the overall objective under berth-logistics collaborative scheduling; S7, Modeling and Solving Uncertainty Based on Information Gap Decision Theory.

[0005] Furthermore, in step S1, the port integrated energy system framework model includes a berth-side system, an electric energy storage system, a gas turbine, a heat exchanger, a thermal energy storage system, a heat pump, an electric chiller, a logistics-side system, an energy hub, and a berth logistics system, as detailed below: The berth-side system includes green power generation devices and shore power systems; the green power generation devices include photovoltaic power generation devices and / or wind power generation devices; Electric energy storage systems are used to store electrical energy to achieve power balance and peak shaving and valley filling. Gas turbines are used to provide a stable supply of electricity during peak load periods. Heat exchangers are used to recover exhaust heat from gas turbines for heat supply or to drive absorption chillers for refrigeration. Thermal energy storage systems are used to store the heat energy generated by heat exchangers and regulate heat load demand. Heat pumps and electric chillers are used to provide heat and cold energy using electrical energy. The logistics-side system includes battery swapping stations, hydrogen stations, water electrolysis devices, hydrogen refueling stations, and hydrogen energy storage systems. Among them, battery swapping stations provide energy sources for electric trucks, while hydrogen stations, hydrogen refueling stations, and hydrogen energy storage systems provide energy sources for hydrogen trucks. The energy hub is used for the coupling and conversion of electrical energy into cold and hot energy between the berth-side system, electric energy storage system, gas turbine, heat exchanger, drive absorption chiller, thermal energy storage system, heat pump, electric chiller and logistics-side system of the entire port integrated energy system, and finally to build the berth logistics system. Berth logistics system, used to coordinate the berthing and logistics transportation of ships; The green power generation device, gas turbine, and energy storage system regulate electrical energy. The gas turbine achieves combined heat, cooling and power generation through a heat exchanger and an absorption chiller.

[0006] Furthermore, the green power generation device includes: a photovoltaic power generation device and a wind power generation device.

[0007] Furthermore, in step S2, the equipment parameters in the port integrated energy system framework model include: Berth-side system parameters: maximum output power, heating efficiency, and power generation efficiency of the gas turbine; maximum power of photovoltaic power generation; maximum power of wind power generation; energy storage capacity limit, charge / discharge efficiency, and charge / discharge power of the electric energy storage system; energy storage capacity limit, charge / discharge efficiency, and charge / discharge power of the thermal energy storage system; maximum output power and heating efficiency of the heat pump; maximum output power and heating efficiency of the heat exchanger; maximum output power and cooling efficiency of the electric chiller; maximum output power and cooling efficiency of the absorption chiller. Logistics-side system parameters: number of electric trucks; number of hydrogen trucks; planned ship arrival times; number of port berths; number of containers carried by ships; hourly energy consumption of ships; container truck transportation efficiency; energy consumption of a single container transported by a container truck; maximum number of batteries and charging power at battery swapping stations; maximum hydrogen production power and efficiency at hydrogen stations; capacity limitations and hydrogen charging / discharging efficiency of hydrogen storage systems.

[0008] Furthermore, in step S3, a target for the minimum energy cost on the logistics side is established under the coordinated scheduling of berths, logistics, and energy, as follows:

[0009] in: yes Energy consumption on the logistics side at all times yes Momentary energy comprehensive price, yes Energy consumption of a battery swapping station at all times. yes Energy consumption of an electrolytic cell at any given time, measured in megawatts (MW). S3.1, Establish the scheduling object: This includes electric trucks, hydrogen trucks, battery swapping station charging power, and hydrogen production capacity at hydrogen stations; S3.2, Set the scheduling space: S3.2.1, Set the scheduling boundaries for container truck transportation tasks: To meet the operational requirements with the minimum number of trucks, the following constraints apply:

[0010] in, It is a ship Arrival time; It is a ship The time until the transportation and unloading task is completed; It is a ship The number of containers carried; This refers to the number of containers transported by a container truck per hour. yes Time allocated to ships The number of electric trucks; yes Time allocated to ships The number of hydrogen-powered trucks; This is the maximum number of electric trucks; This is the maximum number of hydrogen-powered trucks; It is a ship Latest departure time; It is a binary variable representing a ship. Is it in Constant berthing; the energy consumption equations for electric trucks and hydrogen-powered trucks are as follows:

[0011]

[0012] in, yes Real-time energy consumption of electric trucks; yes Energy consumption of hydrogen-powered trucks at all times; It is the energy consumption of an electric truck transporting a container; This refers to the energy consumption of a hydrogen-powered truck transporting a container; the unit of energy consumption is megawatts, and the unit of time is hours. S3.2.2, Set operational constraints for battery swapping stations and hydrogen stations in the logistics-side system, including: Set capacity limits for battery swapping stations:

[0013]

[0014]

[0015] ,

[0016] in, ; yes The total energy stored in the battery swapping station at any given time; yes The charging power of the battery swapping station at all times; yes The remaining energy of the battery when the user unloads it; yes Battery energy available for battery swapping at any time; It is the charging efficiency coefficient; It is a constant charging power; yes The number of batteries that need to be charged constantly; It is the SoC coefficient for unloading the battery during battery swapping; yes The number of batteries that need to be replaced frequently; This refers to the rated capacity of a single battery. The evolution equation for the hydrogen production process at a hydrogen station is as follows:

[0017]

[0018]

[0019]

[0020] in, ; yes Hydrogen output flow rate at any time; yes Electrolytic cell power at all times; It is the efficiency coefficient of the electrolytic cell; It is the electro-hydrogen conversion efficiency coefficient; It is the lower heating value of hydrogen; It is the density of hydrogen gas; yes Hydrogen production rate of the electrolyzer at any given time; It is the scheduling cycle Total domestic hydrogen production; The equation for the state of charge evolution of a hydrogen energy storage system is:

[0021] Among them, the initial and final state constraints within the scheduling period , , , , ; for The energy storage capacity, minimum energy storage capacity, and maximum energy storage capacity of the instantaneous energy storage system; for The hydrogen charging power, hydrogen discharging power, and maximum power of the hydrogen energy storage system at any given time, in kg. It is a binary variable. The charging and discharging status of the energy storage system at any given time; The charging and discharging efficiency of the hydrogen energy storage system; all power units mentioned above, except for the charging and discharging power, are megawatts.

[0022] Furthermore, in step S4, a minimum energy cost target for the berth side is established under the coordinated scheduling of berth-logistics-energy, as detailed below:

[0023] in, yes Energy consumption at the berth side at any given time. yes Momentary energy comprehensive price, yes Energy consumption of the refrigeration unit at all times. yes The energy consumption of heat pump units at all times yes Total energy consumption of ships berthed at the berth side at any given time; S4.1, Establish the scheduling object: The berth allocation for arriving vessels, power consumption for electricity and gas, photovoltaic power, wind power, gas turbine power, heat pump power, electric chiller power, and electric energy storage system power; the arriving vessels include refrigerated container ships and cruise ships; S4.2, Set the scheduling space: S4.2.1, setting the berth scheduling boundaries for arriving and departing vessels, including: Time constraints of the scheduling cycle: ; Uniqueness and continuity constraints in berth allocation: ; Dock time matching constraints: ; Assuming a fixed number of berths, the number of ships that can berth simultaneously at any given time must not exceed the total number of berths, thus creating a berth capacity constraint.

[0024] in, It is a ship The estimated arrival time, It is a ship The actual arrival time It is the total scheduling time. It is the total number of ships. It is a binary variable representing a ship. Is it in Always berth, It is a ship Estimated berthing time, This refers to the total number of berths; all time units mentioned above are in hours. S4.2.2, Set ship energy consumption state constraints, including: The total energy consumption of the berth side per unit time is:

[0025] The heat energy demand per unit time is:

[0026] The berth-side vessel cooling load per unit time is:

[0027] in, yes Cruise ship electrical load at all times yes Refrigerated container ship electrical load at all times yes Refrigerated container ships are constantly operating under cold load. yes The cruise ship's heat load at all times is in megawatts (MW). S4.2.3, Set operating constraints for energy equipment, including: Power limitations for photovoltaic and wind power generation:

[0028] in, for Real-time photovoltaic and wind power generation and maximum photovoltaic and wind power generation; The equation for converting electrical energy into thermal energy in a gas turbine unit is as follows:

[0029] in, for The electrical and thermal energy generated by the gas turbine unit at all times; , These are the power generation efficiency and heating efficiency of the gas turbine unit, respectively. The heat exchanger unit exchanges the following heat energy:

[0030] in, for The heat energy generated by the heat exchanger unit at all times; For the heat exchange efficiency of the heat exchanger unit; The equation for the conversion of thermal energy to cold energy in an absorption refrigerant unit is:

[0031] in, for The cold energy generated by the absorption chiller unit at all times; The refrigeration efficiency of the absorption chiller unit; The equation for a heat pump unit to convert electrical energy into heat energy is:

[0032] in, , for The heat energy generated by the heat pump unit at all times; The heating efficiency of the heat pump unit; The equation for an electric chiller unit to convert electrical energy into cooling energy is:

[0033] in, ; for The cooling energy generated by the instantaneous electric chiller unit; The refrigeration efficiency of the electric chiller unit; The state-of-charge evolution equation for an electric energy storage system is:

[0034] Among them, the initial and final state constraints within the scheduling period , , , , ; for The energy storage capacity, minimum energy storage capacity, and maximum energy storage capacity of the instantaneous energy storage system; for The charging power, discharging power, and maximum power of the instantaneous energy storage system; It is a binary variable. The charging and discharging status of the energy storage system at any given time; These are the charging and discharging efficiencies of the energy storage system; The equation for the state of charge evolution of a thermal energy storage system is:

[0035] Among them, the initial and final state constraints within the scheduling period , , , , ; for The amount of heat storage, minimum heat storage, and maximum heat storage of the thermal energy storage system at all times; for The charging power, discharging power, and maximum power of the thermal energy storage system at all times; It is a binary variable. The charging and discharging status of the thermal energy storage system at all times; These represent the charge and discharge efficiencies of the thermal energy storage system; all power units mentioned above are megawatts (MW). S4.2.4, setting power balance constraints under multi-energy coupling, including: Restrictions on electricity and gas purchases from the upper-level power grid:

[0036] in, They are respectively Purchase electricity and gas at any time; These refer to the maximum electricity purchase volume and the maximum gas purchase volume, respectively; all units are megawatts. The cold balance equation is:

[0037] The heat balance equation is: .

[0038] Further, in step S5, the establishment of the common coupling power balance relationship is as follows: .

[0039] Furthermore, in step S6, establishing and solving the overall objective under the berth-logistics collaborative scheduling specifically includes: Establish the comprehensive energy price calculation equation:

[0040] Among them, the objective function under cooperative scheduling ; yes Fuel purchase price at any time; yes The price of electricity purchased from the grid at all times; , They are respectively The weighting of fuel purchase price and electricity purchase price at any given time.

[0041] Furthermore, in step S7, the modeling and solution under uncertainty based on information gap decision theory specifically includes: S7.1, Constructing an uncertainty model: Uncertainties include photovoltaic power generation and wind power generation All arriving ships' power load and cruise ship heat load and the cold load of refrigerated container ships ; express and Uncertainty, and They represent and The uncertainty is thus expressed as:

[0042]

[0043] S7.2, Construct risk avoidance strategies, including: Under this strategy, it is necessary to maximize uncertainty, and the uncertainty equation is expressed as:

[0044] The operating costs under this strategy are:

[0045] in, ; It is the optimal cost when the uncertain input data matches the predicted value; This is the maximum cost that port operators can afford; Under this strategy, the uncertainties of ship load and wind / solar power generation are expressed as follows:

[0046]

[0047] S7.3, Construct a risk-seeking strategy, including: Under this strategy, we need to minimize uncertainty, and the uncertainty equation is expressed as:

[0048] The operating costs under this strategy are:

[0049] in, ; Under this strategy, the uncertainties of ship load and wind / solar power generation are expressed as follows:

[0050]

[0051] in, yes Weighting coefficients; yes Weighting coefficients; yes Weighting coefficients; yes Weighting coefficients; S7.4 is based on the uncertainty model constructed in S7.1, the risk aversion strategy constructed in S7.2 and the risk seeking strategy constructed in S7.3. It is modeled in MATLAB and solved using the GROUBI solver.

[0052] The beneficial effects of this invention are as follows: This invention fully considers various influencing factors such as port logistics activities, electricity price information, renewable energy uncertainty, and berth resource allocation, achieving joint optimization scheduling of key variables such as ship berthing time, container truck dispatch types and quantities, and energy equipment output status. This method utilizes time-of-use electricity price signals to guide the coordinated adjustment of energy and logistics operations, fully leveraging the flexible scheduling capabilities of electric-hydrogen coupled transportation vehicles, and significantly reducing the overall operating costs of ports. Simultaneously, this invention can quantify the range of uncertainty that the port system can withstand under different risk preferences (risk aversion and risk seeking) strategies, thereby improving the adaptability and robustness of scheduling strategies under various complex situations. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of the port integrated energy system framework model of the present invention.

[0054] Figure 2 This is a flowchart illustrating the collaborative scheduling method according to an embodiment of the present invention.

[0055] Figure 3 This is a schematic diagram of the power dispatching results after coordinated dispatching according to an embodiment of the present invention.

[0056] Figure 4 This is a schematic diagram illustrating the robustness range analysis of comprehensive load fluctuations under different critical operating cost deviations in an embodiment of the present invention.

[0057] Figure 5 This is a schematic diagram illustrating the opportunity range analysis of comprehensive load fluctuations under different critical operating cost deviations in an embodiment of the present invention.

[0058] Figure 6 This is a schematic diagram of the berth scheduling results after collaborative scheduling in an embodiment of the present invention. Detailed Implementation

[0059] This embodiment relates to a port berth-logistics-energy coordinated scheduling method based on information gap decision theory, including: Step S1: Construct as follows Figure 1 The Port Integrated Energy System (PIES) framework model shown ultimately constructs the Berth Logistics System (PBLS). The framework model includes the Berth Side System (PBS), Electric Energy Storage System (EES), Gas Turbine (GT), Heat Exchanger (HX), Thermal Energy Storage System (TES), Heat Pump (HP), Electric Refrigeration Unit (ER), Logistics Side System (PLS), Energy Hub (EH), and Berth Logistics System (PBLS), as detailed below: Berth-side system (PBS) includes green power generation units and shore power systems; the green power generation units include photovoltaic (PV) power generation units and wind (WT) power generation units for providing green electricity; Electric energy storage systems (EES) are used to store electrical energy to achieve power balance and peak shaving and valley filling. Gas turbines (GTs) are used to provide stable electrical energy during peak load periods. Heat exchangers (HX) are used to recover exhaust heat from gas turbines (GT) for heat supply or to drive absorption chillers (AC) for refrigeration. Thermal energy storage systems (TES) are used to store the heat energy generated by heat exchangers (HX) and release it when heat demand changes, thereby regulating heat load demand. Heat pumps (HP) and electric chillers (ER) are used to provide heat and cold energy using electrical energy, respectively. The logistics-side system (PLS) includes a battery swapping station (BSS), a hydrogen refueling station (HS), a water electrolysis unit (EL), a hydrogen refueling station (HRS), and a hydrogen energy storage system (HES). The battery swapping station (BSS) provides energy for electric ECT trucks, while the hydrogen refueling station (HS), hydrogen refueling station (HRS), and hydrogen energy storage system (HES) provide energy for hydrogen CT trucks. In this embodiment, the battery swapping station (BSS) and the hydrogen refueling station (HS) provide energy to the electric ECT trucks and hydrogen CT trucks through the port area power grid or green electricity, respectively. The Energy Hub (EH) is used to couple and convert electrical energy into cold and hot energy between the berth-side system (PBS), electric energy storage system (EES), gas turbine (GT), heat exchanger (HX), driven absorption chiller (AC), thermal energy storage system (TES), heat pump (HP), electric chiller (ER), and logistics-side system (PLS) of the entire port integrated energy system (PIES). This enables multi-source complementarity and spatiotemporal coordination to meet the diverse energy needs of ship operations and logistics transportation, and to build an efficient, clean, and flexible berth logistics system (PBLS). Berth Logistics System (PBLS) is used to coordinate the berthing and logistics transportation of ships. The green power generation unit, gas turbine (GT), and energy storage system (EES) regulate electrical energy. The gas turbine (GT) achieves combined heat, cooling and power supply through a heat exchanger (HX) and an absorption chiller (AC).

[0060] Step S2: The port operator collects equipment information from the port's integrated energy system framework model, gathering the following parameters: Berth-side system parameters: maximum output power, heating efficiency, and power generation efficiency of the gas turbine; maximum power of photovoltaic power generation; maximum power of wind power generation; energy storage capacity limit, charge / discharge efficiency, and charge / discharge power of the electric energy storage system; energy storage capacity limit, charge / discharge efficiency, and charge / discharge power of the thermal energy storage system; maximum output power and heating efficiency of the heat pump; maximum output power and heating efficiency of the heat exchanger; maximum output power and cooling efficiency of the electric chiller; maximum output power and cooling efficiency of the absorption chiller. Logistics-side system parameters: number of electric trucks; number of hydrogen trucks; planned ship arrival times; number of port berths; number of containers carried by ships; hourly energy consumption of ships; container truck transportation efficiency; energy consumption of a single container transported by a container truck; maximum number of batteries and charging power at battery swapping stations; maximum hydrogen production power and efficiency at hydrogen stations; capacity limitations and hydrogen charging / discharging efficiency of hydrogen storage systems.

[0061] Step S3: Establish the minimum energy cost target for the logistics side under the coordinated scheduling of berths, logistics, and energy, as follows:

[0062] in: yes Energy consumption on the logistics side at all times yes Momentary energy comprehensive price, yes Energy consumption of a battery swapping station at all times. yes Energy consumption of an electrolyzer at any given time, measured in megawatts (MW).

[0063] S3.1, Establish the scheduling object: This includes electric trucks (ECT), hydrogen trucks (HCT), battery swapping station charging power, and hydrogen production capacity at hydrogen stations; S3.2, Set the scheduling space: S3.2.1, Set the scheduling boundaries for container truck transportation tasks: Truck allocation must ensure that container transport tasks are completed within the specified operating time, with the minimum number of trucks required to meet the operational needs, as follows:

[0064] To ensure that vessels complete unloading within the designated operating window, upper and lower limits are set to ensure that the start and end times of operations comply with the planned berthing and latest departure requirements. Meanwhile, to reflect the limited nature of port resources, the number of trucks allocated to any ship at any given time must be less than or equal to the port's available vehicle limit, thus reflecting the feasibility constraints of resource allocation. ;in, It is a ship Arrival time; It is a ship The time until the transportation and unloading task is completed; It is a ship The number of containers carried; This refers to the number of containers transported by a container truck per hour. yes Time allocated to ships The number of electric trucks; yes Time allocated to ships The number of hydrogen-powered trucks; This is the maximum number of electric trucks; This is the maximum number of hydrogen-powered trucks; It is a ship Latest departure time; It is a binary variable representing a ship. Is it in Always ready to dock; To achieve synergistic optimization of energy and logistics, the impact of electric trucks (ECT) and hydrogen trucks (HCT) on the port's energy system during transportation needs to be considered and quantified using energy consumption calculation formulas, as follows:

[0065]

[0066] in, yes Real-time energy consumption of electric trucks; yes Energy consumption of hydrogen-powered trucks at all times; It is the energy consumption of an electric truck transporting a container; This refers to the energy consumption of a hydrogen-powered truck transporting a container; all energy consumption units are megawatts (MW), and all time units are hours (h).

[0067] S3.2.2, Set operational constraints for logistics power supply equipment, including: Set capacity limits for battery swapping stations:

[0068] Charging power equation using constant power charging method Even after being replaced during actual operation, the batteries still retain some energy. User battery swapping energy demand equation Battery quantity limit at battery swapping stations ; It is the charging efficiency coefficient; It is a constant charging power; It is the SoC coefficient for unloading the battery during battery swapping; yes The number of batteries that need to be replaced frequently; This refers to the rated capacity of a single battery. The hydrogen station produces hydrogen through water electrolysis using an electrolytic cell. The specific process evolution equation is as follows:

[0069]

[0070]

[0071]

[0072] Hydrogen production power limitations , yes Electrolytic cell power at all times; yes Hydrogen output flow rate at any time; It is the efficiency coefficient of the electrolytic cell; It is the electro-hydrogen conversion efficiency coefficient; It is the lower heating value of hydrogen; It is the density of hydrogen gas; yes Hydrogen production rate of the electrolyzer at any given time; It is the scheduling cycle Total domestic hydrogen production; The equation for the state of charge evolution of a hydrogen energy storage system is:

[0073] Among them, the initial and final state constraints within the scheduling period , , , , ; for The energy storage capacity, minimum energy storage capacity, and maximum energy storage capacity of the instantaneous energy storage system; for The hydrogen charging power, hydrogen discharging power, and maximum power of the hydrogen energy storage system at any given time, in kg. It is a binary variable. The charging and discharging status of the energy storage system at any given time; The charging and discharging efficiency of the hydrogen energy storage system; all power units mentioned above, except for the charging and discharging power, are megawatts (MW).

[0074] Step S4: Establish the minimum energy cost target on the berth side under the coordinated scheduling of berth-logistics-energy, as follows:

[0075] in, yes Energy consumption at the berth side at any given time. yes Momentary energy comprehensive price, yes Energy consumption of the refrigeration unit at all times. yes The energy consumption of heat pump units at all times yes Total energy consumption of ships berthed at the berth side at any given time; S4.1, Establish the scheduling object: Arriving vessels include berth allocation, power consumption for electricity and gas, photovoltaic power, wind power, gas turbine power, heat pump power, electric chiller power, and electric energy storage system power; arriving vessels include refrigerated container ships and cruise ships. S4.2, Set the scheduling space: S4.2.1, setting the berth scheduling boundaries for arriving and departing vessels, including: The actual berthing time of each vessel must be later than its expected arrival time, and the time constraints of the scheduling cycle must be met: ; Each vessel must occupy a unique berth continuously during its stay in port, and each berth can only be allocated to one vessel at any given time. This method ensures the uniqueness and continuity of berth allocation. ; To ensure that the time a vessel spends in port matches its reported berthing time, its scheduled berthing duration is introduced as a reference, requiring that the total berth occupancy time within the scheduling cycle matches this, thus forming a berthing time matching constraint: ; Given the limited availability of port berth resources, assuming a fixed number of berths, the number of ships that can berth simultaneously at any given time must not exceed the total number of berths, thus creating a berth capacity constraint: ; in, It is a ship The estimated arrival time, It is a ship The actual arrival time It is the total scheduling time. It is the total number of ships. It is a binary variable representing a ship. Is it in Always berth, It is a ship Estimated berthing time, This refers to the total number of berths. All time units mentioned above are hours (h). S4.2.2, Set ship energy consumption state constraints, including: Each arriving vessel must report its estimated electricity and heat consumption per unit time for each period to the port operator before arrival. Therefore, the total electricity consumption per unit time at the berth is: The total energy consumption of the berth side per unit time is:

[0076] The heat energy demand per unit time is:

[0077] The cooling load of refrigerated container ships is significantly affected by their logistics operations. As refrigerated containers are transported and unloaded, the number of containers requiring continuous cooling on board gradually decreases, thus significantly impacting the cooling load demand. Therefore, the berth-side cooling load of the ship per unit time is:

[0078] in, yes Cruise ship electrical load at all times yes Refrigerated container ship electrical load at all times yes Refrigerated container ships are constantly operating under cold load. yes The cruise ship's heat load at any given time is in megawatts (MW). S4.2.3, Set operating constraints for energy equipment, including: Power limitations for photovoltaic and wind power generation:

[0079] in, for Real-time photovoltaic and wind power generation and maximum photovoltaic and wind power generation; The equation for converting electrical energy into thermal energy in a gas turbine unit is as follows:

[0080] in, for The electrical and thermal energy generated by the gas turbine unit at all times; These are the power generation efficiency and heating efficiency of the gas turbine unit, respectively. The heat exchanger unit exchanges the following heat energy:

[0081] in, for The heat energy generated by the heat exchanger unit at all times; For the heat exchange efficiency of the heat exchanger unit; The equation for the conversion of thermal energy to cold energy in an absorption refrigerant unit is:

[0082] in, for The cold energy generated by the absorption chiller unit at all times; The refrigeration efficiency of the absorption chiller unit; The equation for a heat pump unit to convert electrical energy into heat energy is:

[0083] in, , for The heat energy generated by the heat pump unit at all times; The heating efficiency of the heat pump unit; The equation for an electric chiller unit to convert electrical energy into cooling energy is:

[0084] in, ; for The cooling energy generated by the instantaneous electric chiller unit; The refrigeration efficiency of the electric chiller unit; The state-of-charge evolution equation for an electric energy storage system is:

[0085] Among them, the initial and final state constraints within the scheduling period , , , , ; for The energy storage capacity, minimum energy storage capacity, and maximum energy storage capacity of the instantaneous energy storage system; for The charging power, discharging power, and maximum power of the instantaneous energy storage system; It is a binary variable. The charging and discharging status of the energy storage system at any given time; These are the charging and discharging efficiencies of the energy storage system; The equation for the state of charge evolution of a thermal energy storage system is:

[0086] Among them, the initial and final state constraints within the scheduling period , , , , ; for The amount of heat storage, minimum heat storage, and maximum heat storage of the thermal energy storage system at all times; for The charging power, discharging power, and maximum power of the thermal energy storage system at all times; It is a binary variable. The charging and discharging status of the thermal energy storage system at all times; These are the charge and discharge efficiencies of the thermal energy storage system; all power units mentioned above are megawatts (MW). S4.2.4, setting power balance constraints under multi-energy coupling, including: Restrictions on electricity and gas purchases from the upper-level power grid:

[0087] in, They are respectively Purchase electricity and gas at any time; These refer to the maximum electricity purchase and the maximum gas purchase, respectively; all units are megawatts (MW). The cold balance equation is:

[0088] The heat balance equation is: .

[0089] Step S5: Establish the common coupling power balance relationship as follows: .

[0090] Step S6: Establish and solve the overall objective under berth-logistics collaborative scheduling, specifically including: Establish the comprehensive energy price calculation equation:

[0091] Among them, the objective function under cooperative scheduling ; yes Fuel purchase price at any time; yes The price of electricity purchased from the grid at all times; They are respectively The weighting of fuel purchase price and electricity purchase price at any given time.

[0092] Step S7 involves modeling and solving under uncertainty based on information gap decision theory, specifically as follows: S7.1, Constructing an uncertainty model: In this embodiment, the uncertainty includes photovoltaic power generation. and wind power generation All arriving ships' power load and cruise ship heat load and the cold load of refrigerated container ships ; express and Uncertainty, and They represent and The uncertainty is thus expressed as:

[0093] S7.2, Construct a risk aversion strategy (RA), including: Under this strategy, it is necessary to maximize uncertainty, and the uncertainty equation is expressed as:

[0094] The operating costs under this strategy are:

[0095] in, ; It is the optimal cost when the uncertain input data matches the predicted value; This is the maximum cost that port operators can afford; Under this strategy, the uncertainties of ship load and wind / solar power generation are expressed as follows:

[0096] S7.3, Construct a risk-seeking strategy (RS), including: Under this strategy, we need to minimize uncertainty, and the uncertainty equation is expressed as:

[0097] The operating costs under this strategy are:

[0098] in, ; Under this strategy, the uncertainties of ship load and wind / solar power generation are expressed as follows:

[0099] in, yes Weighting coefficients; yes Weighting coefficients; yes Weighting coefficients; yes Weighting coefficients; S7.4, such as Figure 2 As shown, for the uncertainty model constructed in S7.1, the risk aversion strategy (RA) constructed in S7.2 and the risk seeking strategy (RS) constructed in S7.3 are modeled in MATLAB and solved using the GROUBI solver, specifically: Combining the uncertainty model in step S7.1, a model based on information gap decision theory is constructed, and... The value is assigned to 0, and the objective function is obtained under normal circumstances using the GUROBI solver. optimal value Then, the obtained optimal value is substituted into S7.2 and S7.3 to calculate the comprehensive fluctuation that can be tolerated within the cost threshold.

[0100] In the specific experiment, the simulation environment was built based on some data from a port in Tianjin: the time-of-use electricity price is shown in Table 1, the ship arrival information is shown in Table 2, the parameters of the port's energy supply equipment are shown in Table 3, and the parameters of the energy storage equipment are shown in Table 4; the total scheduling cycle was set to 72 hours, and the scheduling interval was set to 1 hour; the simulation was built in MATLAB R2019a and solved using the GUROBI 10.0.1 solver.

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[0105] The power optimization results after adopting this collaborative scheduling method are as follows: Figure 3 As shown in the figure, during peak and normal electricity price periods, port operators prioritize gas turbine power generation to reduce operating costs. However, due to the limited heat storage capacity of the port, gas turbines alone cannot fully meet the total electricity demand. Therefore, during off-peak periods, the system tends to purchase electricity from the main grid to achieve optimal cost scheduling. The energy storage system plays an important role in peak shaving during high electricity price periods. The overall energy consumption of the port shows a significant downward trend during peak electricity price periods, fully verifying the superiority of the proposed collaborative scheduling method in reducing electricity load during high electricity price periods.

[0106] Figure 4 and Figure 5 The study demonstrates the range of overall volatility that ports can tolerate under risk-averse and risk-seeking strategies: under the risk-averse strategy, ports can tolerate 45.59% of the overall volatility; under the risk-seeking strategy, ports can tolerate 27% of the overall volatility.

[0107] Figure 6 The results of vessel berth scheduling under the collaborative scheduling method in this embodiment are shown. Compared with the original plan, the berthing order of vessels 4-9 and 11-14 has been rearranged. This change effectively reshapes the energy curve, reduces the operating costs of port berths, and most vessels requiring logistics transportation appear during the parity and low-price periods, thus reducing logistics transportation costs. Overall, this method reduces port operating costs.

Claims

1. A port collaborative scheduling method based on information gap decision theory, characterized in that, Includes the following steps: S1, establish a framework model for the port's integrated energy system, and ultimately construct a berth logistics system; S2, collect equipment parameters in the framework model of the port's integrated energy system; S3, establish the target for minimum energy cost on the logistics side under the coordinated scheduling of berths, logistics and energy; S4, establish the minimum energy cost target on the berth side under the coordinated scheduling of berth-logistics-energy; S5, establish a common coupled power balance relationship; S6, Establish and solve the overall objective under berth-logistics collaborative scheduling; S7, Modeling and Solving Uncertainty Based on Information Gap Decision Theory.

2. The port collaborative scheduling method based on information gap decision theory according to claim 1, characterized in that, In step S1, the port integrated energy system framework model includes: The berth-side system includes green power generation devices and shore power systems; the green power generation devices include photovoltaic power generation devices and / or wind power generation devices; Electric energy storage systems are used to store electrical energy to achieve power balance and peak shaving and valley filling. Gas turbines are used to provide a stable supply of electricity during peak load periods. Heat exchangers are used to recover exhaust heat from gas turbines for heat supply or to drive absorption chillers for refrigeration. Thermal energy storage systems are used to store the heat energy generated by heat exchangers and regulate heat load demand. Heat pumps and electric chillers are used to provide heat and cold energy using electrical energy. The logistics-side system includes battery swapping stations, hydrogen stations, water electrolysis devices, hydrogen refueling stations, and hydrogen energy storage systems. Among them, battery swapping stations provide energy sources for electric trucks, while hydrogen stations, hydrogen refueling stations, and hydrogen energy storage systems provide energy sources for hydrogen trucks. The energy hub is used for the coupling and conversion of electrical energy into cold and hot energy between the berth-side system, electric energy storage system, gas turbine, heat exchanger, drive absorption chiller, thermal energy storage system, heat pump, electric chiller and logistics-side system of the entire port integrated energy system, thereby constructing the berth logistics system; Berth logistics system, used to coordinate the berthing and logistics transportation of ships; The green power generation device, gas turbine, and energy storage system regulate electrical energy. The gas turbine achieves combined heat, cooling and power generation through a heat exchanger and an absorption chiller.

3. The port collaborative scheduling method based on information gap decision theory according to claim 2, characterized in that, The green power generation devices include: photovoltaic power generation devices and wind power generation devices.

4. The port collaborative scheduling method based on information gap decision theory according to claim 3, characterized in that, In step S2, the equipment parameters in the port integrated energy system framework model include: Berth-side system parameters: maximum output power, heating efficiency, and power generation efficiency of the gas turbine; maximum power of photovoltaic power generation; maximum power of wind power generation; energy storage capacity limit, charge / discharge efficiency, and charge / discharge power of the electric energy storage system; energy storage capacity limit, charge / discharge efficiency, and charge / discharge power of the thermal energy storage system; maximum output power and heating efficiency of the heat pump; maximum output power and heating efficiency of the heat exchanger; maximum output power and cooling efficiency of the electric chiller; maximum output power and cooling efficiency of the absorption chiller. Logistics-side system parameters: number of electric trucks; number of hydrogen trucks; planned ship arrival times; number of port berths; number of containers carried by ships; hourly energy consumption of ships; container truck transportation efficiency; energy consumption of a single container transported by a container truck; maximum number of batteries and charging power at battery swapping stations; maximum hydrogen production power and efficiency at hydrogen stations; capacity limitations and hydrogen charging / discharging efficiency of hydrogen storage systems.

5. A port collaborative scheduling method based on information gap decision theory according to claim 4, characterized in that, In step S3, the minimum energy cost target for the logistics side under the coordinated scheduling of berths, logistics, and energy is established, as follows: ， in: yes Energy consumption on the logistics side at all times yes Momentary energy comprehensive price, yes Energy consumption of a battery swapping station at all times. yes Energy consumption of an electrolytic cell at any given time, measured in megawatts (MW). S3.1, Establish the scheduling object: This includes electric trucks, hydrogen trucks, battery swapping station charging power, and hydrogen production capacity at hydrogen stations; S3.2, Set the scheduling space: S3.2.1, Set the scheduling boundaries for container truck transportation tasks: To meet the operational requirements with the minimum number of trucks, the following constraints apply: ， in, , , ; It is a ship Arrival time; It is a ship The time until the transportation and unloading task is completed; It is a ship The number of containers carried; This refers to the number of containers transported by a container truck per hour. yes Time allocated to ships The number of electric trucks; yes Time allocated to ships The number of hydrogen-powered trucks; This is the maximum number of electric trucks; This is the maximum number of hydrogen-powered trucks; It is a ship Latest departure time; It is a binary variable representing a ship. Is it in Constant berthing; the energy consumption equations for electric trucks and hydrogen-powered trucks are as follows: ， in, yes Real-time energy consumption of electric trucks; yes Energy consumption of hydrogen-powered trucks at all times; It is the energy consumption of an electric truck transporting a container; This refers to the energy consumption of a hydrogen-powered truck transporting a container; the unit of energy consumption is megawatts, and the unit of time is hours. S3.2.2, Set operational constraints for battery swapping stations and hydrogen stations in the logistics-side system, including: Set capacity limits for battery swapping stations: ， in, ; yes The total energy stored in the battery swapping station at any given time; yes The charging power of the battery swapping station at all times; yes The remaining energy of the battery when the user unloads it; yes Battery energy available for battery swapping at any time; It is the charging efficiency coefficient; It is a constant charging power; yes The number of batteries that need to be charged constantly; It is the SoC coefficient for unloading the battery during battery swapping; yes The number of batteries that need to be replaced frequently; This refers to the rated capacity of a single battery. The evolution equation for the hydrogen production process at a hydrogen station is as follows: ， in, ; yes Hydrogen output flow rate at any time; yes Electrolytic cell power at all times; It is the efficiency coefficient of the electrolytic cell; It is the electro-hydrogen conversion efficiency coefficient; It is the lower heating value of hydrogen; It is the density of hydrogen gas; yes Hydrogen production rate of the electrolyzer at any given time; It is the scheduling cycle Total domestic hydrogen production; The equation for the state of charge evolution of a hydrogen energy storage system is: ， Among them, the initial and final state constraints within the scheduling period , , , , ; for The energy storage capacity, minimum energy storage capacity, and maximum energy storage capacity of the instantaneous energy storage system; for The hydrogen charging power, hydrogen discharging power, and maximum power of the hydrogen energy storage system at any given time, in kg. It is a binary variable. The charging and discharging status of the energy storage system at any given time; The charging and discharging efficiency of the hydrogen energy storage system; all power units mentioned above, except for the charging and discharging power, are megawatts.

6. A port collaborative scheduling method based on information gap decision theory according to claim 5, characterized in that, In step S4, the minimum energy cost target on the berth side under the coordinated scheduling of berth-logistics-energy is established, as follows: ， in, yes Energy consumption at the berth side at any given time. yes Momentary energy comprehensive price, yes Energy consumption of the refrigeration unit at all times. yes The energy consumption of heat pump units at all times yes Total energy consumption of ships berthed at the berth side at any given time; S4.1, Establish the scheduling object: The berth allocation for arriving vessels, power consumption for electricity and gas, photovoltaic power, wind power, gas turbine power, heat pump power, electric chiller power, and electric energy storage system power; the arriving vessels include refrigerated container ships and cruise ships; S4.2, Set the scheduling space: S4.2.1, setting the berth scheduling boundaries for arriving and departing vessels, including: Time constraints of the scheduling cycle: ; Uniqueness and continuity constraints in berth allocation: ; Dock time matching constraints: ; Assuming a fixed number of berths, the number of ships that can berth simultaneously at any given time must not exceed the total number of berths, thus creating a berth capacity constraint. ， in, It is a ship The estimated arrival time, It is a ship The actual arrival time It is the total scheduling time. It is the total number of ships. It is a binary variable representing a ship. Is it in Always berth, It is a ship Estimated berthing time, This refers to the total number of berths; all time units mentioned above are in hours. S4.2.2, Set ship energy consumption state constraints, including: The total energy consumption of the berth side per unit time is: ， The heat energy demand per unit time is: ， The berth-side vessel cooling load per unit time is: ， in, yes Cruise ship electrical load at all times yes Refrigerated container ship electrical load at all times yes Refrigerated container ships are constantly operating under cold load. yes The cruise ship's heat load at all times is in megawatts (MW). S4.2.3, Set operating constraints for energy equipment, including: Power limitations for photovoltaic and wind power generation: ， in, for Real-time photovoltaic and wind power generation and maximum photovoltaic and wind power generation; The equation for converting electrical energy into thermal energy in a gas turbine unit is as follows: ， in, for The electrical and thermal energy generated by the gas turbine unit at all times; These are the power generation efficiency and heating efficiency of the gas turbine unit, respectively. The heat exchanger unit exchanges the following heat energy: ， in, for The heat energy generated by the heat exchanger unit at all times; For the heat exchange efficiency of the heat exchanger unit; The equation for the conversion of thermal energy to cold energy in an absorption refrigerant unit is: ， in, for The cold energy generated by the absorption chiller unit at all times; The refrigeration efficiency of the absorption chiller unit; The equation for a heat pump unit to convert electrical energy into heat energy is: ， in, , for The heat energy generated by the heat pump unit at all times; The heating efficiency of the heat pump unit; The equation for an electric chiller unit to convert electrical energy into cooling energy is: ， in, ; for The cooling energy generated by the instantaneous electric chiller unit; The refrigeration efficiency of the electric chiller unit; The state-of-charge evolution equation for an electric energy storage system is: ， Among them, the initial and final state constraints within the scheduling period , , , , ; for The energy storage capacity, minimum storage capacity, and maximum storage capacity of the instantaneous energy storage system; for The charging power, discharging power, and maximum power of the instantaneous energy storage system; It is a binary variable. The charging and discharging status of the energy storage system at any given time; These are the charging and discharging efficiencies of the energy storage system; The equation for the state of charge evolution of a thermal energy storage system is: ， Among them, the initial and final state constraints within the scheduling period , , , , ; for The amount of heat storage, minimum heat storage, and maximum heat storage of the thermal energy storage system at all times; for The charging power, discharging power, and maximum power of the thermal energy storage system at all times; It is a binary variable. The charging and discharging status of the thermal energy storage system at all times; These represent the charge and discharge efficiencies of the thermal energy storage system; all power units mentioned above are megawatts (MW). S4.2.4, setting power balance constraints under multi-energy coupling, including: Restrictions on electricity and gas purchases from the upper-level power grid: ， in, They are respectively Purchase electricity and gas at any time; These refer to the maximum electricity purchase volume and the maximum gas purchase volume, respectively; all units are megawatts. The cold balance equation is: ， The heat balance equation is: 。 7. A port collaborative scheduling method based on information gap decision theory according to claim 6, characterized in that, In step S5, establishing the common coupling power balance relationship is as follows: 。 8. A port collaborative scheduling method based on information gap decision theory according to claim 7, characterized in that, In step S6, establishing and solving the overall objective under the berth-logistics collaborative scheduling specifically includes: Establish the comprehensive energy price calculation equation: ， Among them, the objective function under cooperative scheduling ; yes Fuel purchase price at any time; yes The price of electricity purchased from the grid at all times; , They are respectively The weighting of fuel purchase price and electricity purchase price at any given time.

9. A port collaborative scheduling method based on information gap decision theory according to claim 8, characterized in that, In step S7, the modeling and solution under uncertainty based on information gap decision theory specifically includes: S7.1, Constructing an uncertainty model: Uncertainties include photovoltaic power generation and wind power generation All arriving ships' power load and cruise ship heat load and the cold load of refrigerated container ships ; express and Uncertainty, and They represent and The uncertainty is thus expressed as: ， S7.2, Construct risk avoidance strategies, including: Under this strategy, it is necessary to maximize uncertainty, and the uncertainty equation is expressed as: ， The operating costs under this strategy are: ， in, ; It is the optimal cost when the uncertain input data matches the predicted value; This is the maximum cost that port operators can afford; Under this strategy, the uncertainties of ship load and wind / solar power generation are expressed as follows: ， ， S7.3, Construct a risk-seeking strategy, including: Under this strategy, we need to minimize uncertainty, and the uncertainty equation is expressed as: ， The operating costs under this strategy are: ， in, ; Under this strategy, the uncertainties of ship load and wind / solar power generation are expressed as follows: ， in, yes Weighting coefficients; yes Weighting coefficients; yes Weighting coefficients; yes Weighting coefficients; S7.4 is based on the uncertainty model constructed in S7.1, the risk aversion strategy constructed in S7.2 and the risk seeking strategy constructed in S7.

3. It is modeled in MATLAB and solved using the GROUBI solver.

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