A high-robustness port logistics-energy coupling scheduling method and system

Abstract

The application provides a high-robustness port logistics-energy coupling scheduling method and system, relates to the field of port scheduling, and solves the technical problem of low logistics efficiency, poor power grid stability and low economic benefit caused by the uncertainty of deep coupling of port logistics and energy system. The method comprises the following steps: constructing a logistics system model and an energy system model of the port; based on the logistics system model and the energy system model, constructing an energy balance constraint condition, coupling the logistics system model and the energy system model; and based on the logistics-energy coupling model, obtaining state variables and energy variables of the port; based on the cost corresponding to the state variables, establishing a logistics cost objective function, and based on the cost corresponding to the energy variables, constructing an energy cost objective function. The application is used in the process of port logistics-energy coupling scheduling.

Description

Technical Field

[0001] This application relates to the field of port scheduling, and in particular to a highly robust port logistics-energy coupled scheduling method and system. Background Technology

[0002] As global port electrification accelerates, traditional fossil fuel-powered equipment is gradually being replaced by electrified equipment. While this shift improves the utilization rate of clean energy, it also brings new stability challenges to port power grids, such as voltage fluctuations and frequency instability. The introduction of ship-based grid control technology provides flexible support for port power grids; however, the deep coupling between port logistics systems and energy systems brings complex operational problems, such as the variable operating characteristics of equipment, frequent start-up and shutdown disturbances, and the uncertainty of ship operations. This presents port operators, ship aggregators, and other stakeholders with challenges in coordinating and optimizing multiple objectives, including improving logistics efficiency, ensuring grid stability, and optimizing economic benefits, while also navigating a game of interests. Summary of the Invention

[0003] This application provides a highly robust port logistics-energy coupled scheduling method and system, which solves the technical problems of low logistics efficiency, poor power grid stability and low economic benefits caused by the uncertainty of deep coupling between port logistics and energy systems in the prior art.

[0004] To achieve the above objectives, this application adopts the following technical solution:

[0005] Firstly, a highly robust port logistics-energy coupled scheduling method is provided, including:

[0006] A port logistics system model is constructed to transform the port's dynamic logistics demand into quantifiable load power demand and time constraints. The logistics system model includes quay crane operating status variables, quay crane load power demand, gantry crane operating status variables, and gantry crane load power demand.

[0007] Construct a port energy system model to integrate the grid, renewable energy power generation equipment, electric energy storage system, hydrogen energy storage system and the grid connection capabilities of berthing ships, and establish a multi-energy collaborative power supply framework;

[0008] Based on the logistics system model and the energy system model, energy balance constraints are constructed to couple the logistics system model and the energy system model.

[0009] Furthermore, based on the logistics-energy coupling model, the port's state variables and energy variables are obtained; based on the costs corresponding to the state variables, a logistics cost objective function is established, and based on the costs corresponding to the energy variables, an energy cost objective function is constructed; the cost objective functions are used for the optimal scheduling of port economy; the state variables represent the real-time changes in port state, and the energy variables represent the real-time consumption of port energy.

[0010] Based on the above technical solutions, the robust port logistics-energy coupled scheduling method provided in this application, considering the uncertainty of container throughput of arriving vessels and the robust scheduling strategy of the port-ship system supported by vessel network, has several advantages. First, a scheduling model coupling the logistics and energy systems is established, deeply exploring the full-chain coupling characteristics of the port-ship system under complex dynamic logistics scenarios. Second, by constructing a polyhedral uncertainty set of vessel throughput and quantifying the dynamic impact of different confidence level parameters on the conservatism of the scheduling scheme, the system's ability to cope with uncertainty is improved. Finally, with the goal of minimizing system cost, this scheme introduces a column and constraint generation algorithm, effectively solving the two-stage robust optimization problem in the port-ship system, achieving high robustness and economy, and providing new theoretical support and technical guidance for the optimized scheduling of port-ship systems.

[0011] In conjunction with the first aspect above, in one possible implementation, the quay crane operating state variable include:

[0012]

[0013] t 0，j <t crane，j <t leave，j ;

[0014] t crane，j +Δt crane，j <t leave，j ;

[0015] Where t is the scheduling time, j is the ship code, j∈[1,N], N is a positive integer, and t crane，j t represents the time when the quay crane corresponding to the j-th vessel begins loading and unloading operations. 0，j For the arrival time of the j-th ship, t leave，j Let Δt be the departure time of the j-th ship. crane，j Let T be the total loading and unloading operation time of the j-th vessel, and T be a single scheduling cycle.

[0016] In conjunction with the first aspect above, in one possible implementation, the quay crane load power requirement include:

[0017]

[0018] in, Let n be the load power of the quay crane corresponding to the j-th ship at scheduling time t, and n be the total number of quay cranes.

[0019] In conjunction with the first aspect above, in one possible implementation, the gantry crane operating state variable include:

[0020]

[0021] in, The gantry crane load power is the time t during scheduling.

[0022] In conjunction with the first aspect above, in one possible implementation, the gantry crane load power requirement... include:

[0023]

[0024] Among them, P gan，rated The rated power for the operation of the quay crane.

[0025] In conjunction with the first aspect above, in one possible implementation, the energy system model of the port includes:

[0026] State variables of power grid output at scheduling time t

[0027]

[0028] in, The grid power at scheduling time t;

[0029] Charging state variables of energy storage at scheduling time t and discharge state variables

[0030]

[0031] in, Let t be the battery discharge power during scheduling time. Battery charging power for scheduling time t;

[0032] Scheduling time T: Battery state of charge

[0033]

[0034] in, Let E be the state of charge of the battery at time t-1. bat This refers to the rated capacity of the battery.

[0035] P2H state variables of hydrogen energy storage at scheduling time t and H2P state variables

[0036]

[0037] Among them, P2H is electro-hydrogen conversion. The energy fed back to the grid by the fuel cell during the scheduling time t; H2P is the hydrogen-to-electricity conversion. The electrical energy consumed by the electrolyzer to produce hydrogen during the scheduling time t.

[0038] The electro-hydrogen conversion relationship between the electrolyzer and the fuel cell is as follows:

[0039] LHV×H P2H =P P2H η P2H ;

[0040] P H2P =LHV×H H2P η H2P ;

[0041] Where LHV is the calorific value of hydrogen fuel, and H... P2H Hydrogen produced for the hydrogen storage tank electrolyzer, H H2P For the hydrogen consumed by the fuel cell, η P2H For the efficiency of the electrolyzer, η H2P For fuel cell efficiency, P P2H P is the input power of the electrolytic cell. H2P This refers to the output power of the fuel cell;

[0042] Scheduling time t, hydrogen storage tank capacity, state variables

[0043]

[0044] Among them, E hyd This refers to the rated capacity of the hydrogen storage tank.

[0045] Discharge state variables of the j-th ship at scheduling time t charging state variables

[0046]

[0047] in, Let t be the discharge power of the j-th ship at scheduling time t. The charging power of the j-th ship at scheduling time t;

[0048] Energy state of charge of the j-th ship at scheduling time t

[0049]

[0050] in, Let E be the energy state of charge of the j-th ship at time t-1. ship.j Let be the rated capacity of the j-th ship.

[0051] In conjunction with the first aspect above, in one possible implementation, the construction of energy balance constraints includes:

[0052]

[0053]

[0054] in, The actual wind power generation capacity at scheduling time t. Let δ be the actual photovoltaic power generation at scheduling time t. wt For fluctuations in wind power generation, δ pv For fluctuations in photovoltaic power generation, For the wind power predicted generation capacity at scheduling time t, For the photovoltaic power generation predicted at scheduling time t, Let t be the local load power during scheduling. The power fluctuation of the quay crane load during the scheduling time t is the power fluctuation of the quay crane load.

[0055] In conjunction with the first aspect above, in one possible implementation, the logistics cost objective function includes:

[0056]

[0057] Gy≥h-Ey-Mu,u∈μ;

[0058]

[0059] Where y is the decision variable for the first stage, and c, G, h, E, and M are all constant matrices, c = [c rane 0 456 ], c crane The service fee per unit time for a single quay crane. vec(·) represents a vector form, S1 represents the container rating, η represents the second-stage target value, and u = [vec(g j ), δ pv δ wt ], δ wt For fluctuations in wind power generation, δ pv For fluctuations in photovoltaic power generation; Let j be the container fluctuation range of the j-th ship. S represents the lower and upper bounds of the container fluctuation range for each ship, respectively. y Let be the feasible region of variable y. It represents an n-dimensional positive real number.

[0060] In conjunction with the first aspect above, in one possible implementation, the energy cost objective function includes:

[0061]

[0062] Gx≥h-Ex-Mu,u∈μ;

[0063]

[0064] Where x is the decision variable for the second stage. Let λ be the number of containers that need to be transferred during scheduling time t, and λ be the power margin penalty factor. C buy For time-of-use pricing of the power grid, c dis c represents the unit operation and maintenance cost of onshore battery energy storage discharge. cha The unit operation and maintenance cost of charging onshore battery energy storage, c dis,j c represents the unit operation and maintenance cost of the j-th ship discharging electricity. cha,j c represents the unit operation and maintenance cost of charging the j-th ship. car The unit dispatch cost is the vehicle's capacity when fully loaded, where cap is the capacity of a single transfer vehicle, and c is the unit dispatch cost. λ The unit penalty cost for margin deficit; u = [vec(g j ), δ pv δ wt ], δ wt For fluctuations in wind power generation, δ pv For fluctuations in photovoltaic power generation; S x Let x be the feasible region of variable x. It represents an m-dimensional positive real number.

[0065] Secondly, a highly robust port logistics-energy coupled scheduling device is provided, comprising: a communication unit and a processing unit; the communication unit is used to construct a port logistics system model and a port energy system model; based on the logistics system model and the energy system model, energy balance constraints are constructed, and the logistics system model and the energy system model are coupled; the processing unit, based on the logistics-energy coupled model, obtains the port's state variables and energy variables; based on the costs corresponding to the state variables, a logistics cost objective function is established, and based on the costs corresponding to the energy variables, an energy cost objective function is constructed.

[0066] Thirdly, this application provides a highly robust port logistics-energy coupled scheduling device, comprising: a processor and a storage medium; the storage medium includes instructions, and the processor is used to execute the instructions to implement the method described in the first aspect and any possible implementation thereof. This highly robust port logistics-energy coupled scheduling device can be an electronic device or a chip within an electronic device.

[0067] Fourthly, this application provides a highly robust port logistics-energy coupled scheduling system, comprising: a construction module and a scheduling module; wherein, the construction module is used to construct a port logistics system model and a port energy system model; based on the logistics system model and the energy system model, construct energy balance constraints and couple the logistics system model and the energy system model; the scheduling module, based on the logistics-energy coupled model, obtains the port's state variables and energy variables; based on the costs corresponding to the state variables, establishes a logistics cost objective function, and based on the costs corresponding to the energy variables, constructs an energy cost objective function.

[0068] Fifthly, this application provides a computer-readable storage medium storing instructions that, when executed on a robust port logistics-energy coupled scheduling device, cause the robust port logistics-energy coupled scheduling device to perform the methods described in the first aspect and any possible implementation thereof.

[0069] In a sixth aspect, this application provides a computer program product containing instructions that, when the computer program product is run on a highly robust port logistics-energy coupled scheduling device, cause the highly robust port logistics-energy coupled scheduling device to perform the methods described in the first aspect and any possible implementation thereof.

[0070] This application provides a robust port logistics-energy coupled scheduling method and system, comprehensively and deeply considering the uncertainty of ship throughput and the unique ability of ships to participate in network support, successfully filling the gap in existing research on the full-chain coupling characteristics under complex dynamic logistics scenarios. By constructing a refined polyhedral uncertainty set model, this scheme can dynamically and accurately quantify the specific impact of different confidence levels on the conservatism of the scheduling scheme, thereby significantly enhancing the system's ability to cope with various uncertainties. Simultaneously, combined with an efficient column and constraint generation algorithm, the scheme achieves deep collaborative optimization of logistics scheduling and energy scheduling with minimizing total cost as the core objective, ensuring the economical, efficient, and stable operation of the system in complex and ever-changing environments. Simulation analysis results show that this invention not only effectively reduces port operating costs but also significantly improves the overall robustness of the system, providing solid technical support for the intelligent and green transformation of ports.

[0071] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0072] Figure 1 A flowchart illustrating a highly robust port logistics-energy coupled scheduling method provided in this application embodiment;

[0073] Figure 2 A flowchart illustrating the iteration process of the C&CG algorithm provided in this application embodiment;

[0074] Figure 3 A flowchart illustrating the energy scheduling plan provided in this application embodiment;

[0075] Figure 4 A schematic diagram illustrating the relationship between energy storage capacity and electricity price provided in the embodiments of this application;

[0076] Figure 5 A schematic diagram illustrating the relationship between energy storage capacity and new energy output provided in this application embodiment;

[0077] Figure 6 A schematic diagram of a ship energy dispatch plan without considering the power margin of the network structure is provided for an embodiment of this application;

[0078] Figure 7 A schematic diagram of a ship energy dispatch plan considering power margin is provided for an embodiment of this application;

[0079] Figure 8 A comparative diagram of power scheduling plans for ships under two modes, with and without power margin constraints, provided in the embodiments of this application;

[0080] Figure 9 A schematic diagram of the structure of a logistics-energy coupled scheduling device provided in an embodiment of this application;

[0081] Figure 10 This is a schematic diagram of the hardware structure of a logistics-energy coupled scheduling device provided in an embodiment of this application. Detailed Implementation

[0082] In the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. The "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0083] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0084] The robust port logistics-energy coupled scheduling method provided in this application embodiment can be applied to a robust port logistics-energy coupled scheduling system, which includes a construction module and a scheduling module.

[0085] The module is used to build the port's logistics system model and energy system model; based on the logistics system model and energy system model, energy balance constraints are constructed to couple the logistics system model and energy system model.

[0086] The scheduling module, based on the logistics-energy coupling model, obtains the port's state variables and energy variables; based on the costs corresponding to the state variables, it establishes a logistics cost objective function, and based on the costs corresponding to the energy variables, it constructs an energy cost objective function.

[0087] To address the technical problems of low logistics efficiency, poor grid stability, and low economic benefits caused by the uncertainty of deep coupling between port logistics and energy systems in existing technologies, this application provides a highly robust port logistics-energy coupled scheduling method. This method includes: constructing a port logistics system model, transforming dynamic port logistics demand into quantifiable load power demand and time constraints; the logistics system model includes quay crane operating state variables, quay crane load power demand, gantry crane operating state variables, and gantry crane load power demand; and constructing a port energy system model, integrating the power grid, renewable energy generation equipment, energy storage systems, hydrogen energy storage systems, and berthing vessels. This research establishes a multi-energy collaborative power supply framework to enhance network capabilities. Based on logistics and energy system models, energy balance constraints are constructed and coupled. Furthermore, based on the logistics-energy coupling model, port state and energy variables are obtained. A logistics cost objective function is established based on the costs corresponding to the state variables, and an energy cost objective function is constructed based on the costs corresponding to the energy variables. These cost objective functions are used for optimal port economic scheduling. This approach comprehensively considers the uncertainty of ship throughput and the ability of ships to participate in network support, filling a gap in existing research on the full-chain coupling characteristics in complex dynamic logistics scenarios. By constructing a refined polyhedral uncertainty set model, the impact of different confidence levels on the conservatism of the scheduling scheme can be dynamically quantified, significantly enhancing the system's ability to cope with uncertainty. Combined with column and constraint generation algorithms, the scheme aims to minimize total cost, achieving collaborative optimization of logistics and energy scheduling and ensuring the economical and efficient operation of the system in complex environments.

[0088] like Figure 1 As shown in the embodiment of this application, a highly robust port logistics-energy coupled scheduling method includes:

[0089] S201. Construct a logistics system model for the port.

[0090] The logistics system model includes quay crane operating status variables, quay crane load power demand, gantry crane operating status variables, and gantry crane load power demand.

[0091] quay crane operating state variables include:

[0092]

[0093] t 0,j <t crane,j <t leave,j ;

[0094] t crane，j +Δt crane，j <t leave，j ;

[0095] Where t is the scheduling time, j is the ship code, j∈[1,N], N is a positive integer, and t crane，j t represents the time when the quay crane corresponding to the j-th vessel begins loading and unloading operations. 0，j For the arrival time of the j-th ship, t leave，j Let Δt be the departure time of the j-th ship. crane，j Let T be the total loading and unloading operation time of the j-th vessel, and T be a single scheduling cycle.

[0096] It should be noted that the loading and unloading time of a vessel is directly related to the estimated cargo volume reported by the vessel:

[0097]

[0098] Where, N j The rated number of containers for the j-th ship, R j Let be the loading and unloading speed of the j-th ship.

[0099] quay crane load power demand include:

[0100]

[0101] in, Let n be the load power of the quay crane corresponding to the j-th ship at scheduling time t, and n be the total number of quay cranes.

[0102] Based on ship arrival data and quay crane operation status variables, the current dynamic container count in the forward yard can be determined.

[0103]

[0104] in, T represents the number of containers in the yard at the previous moment. j The variable T represents the ship's loading status. When the ship arrives at the port to unload cargo, T... j =1, when the ship arrives at the port to load cargo, T j =-1, when the ship arrives at the port only for charging and not for cargo loading or unloading, T j =0.

[0105] The scheduling of transfer vehicles depends on excess container capacity.

[0106]

[0107] Among them, S max The rated capacity of cargo storage yards in front of ports;

[0108] Number of containers to be transferred in each scheduling period The constraints are:

[0109]

[0110] The current stockpile is overloaded, and the gantry crane is operating to handle the excess. Define the gantry crane's operating state variable. Its operational constraints are:

[0111]

[0112] in, The gantry crane load power is the time t during which the gantry crane is scheduled.

[0113] Gantry crane load power requirements include:

[0114]

[0115] S202, Construct a model of the port's energy system.

[0116] Dispatch time t, power grid output state variables

[0117]

[0118] in, Let t be the grid power during the scheduling time.

[0119] The upper and lower limits of the power output of the power grid are constrained as follows:

[0120]

[0121] in, Limits the power output of the power grid.

[0122] The cost of electricity purchased by the power grid is:

[0123]

[0124] Among them, C grid The total cost of purchasing electricity from the power grid. The time-of-use electricity price for the power grid is t, which is the scheduling time.

[0125] Charging state variables of energy storage at scheduling time t and discharge state variables

[0126]

[0127] in, Let t be the battery discharge power during scheduling time. The battery charging power is the power generated during the scheduling time t.

[0128] The mutual exclusion constraints for energy storage charge and discharge state variables are as follows:

[0129]

[0130] Upper and lower limits of energy storage charging and discharging power constraints:

[0131]

[0132] in, This represents the maximum output power of the battery.

[0133] Scheduling time t Battery state of charge

[0134]

[0135] in, Let E be the state of charge of the battery at time t-1. bat This refers to the rated capacity of the battery.

[0136] The normal operating range constraint for the battery's state of charge is:

[0137]

[0138] in, This represents the minimum state of charge of the battery. This represents the battery's maximum state of charge.

[0139] To ensure the sustainable and cyclical use of energy storage systems, the cycle capacity consistency constraint is as follows:

[0140]

[0141] in, This indicates the initial state of charge of the battery's energy storage. This indicates the state of charge of the battery at the final moment.

[0142] Hydrogen energy storage technology has been developed by leveraging the interconversion between electricity and hydrogen energy. Hydrogen energy storage is based on a "power-to-power" (P2P) conversion process, mainly comprising devices such as electrolyzers, hydrogen storage tanks, and fuel cells. The electrolyzer produces hydrogen by electrolyzing water, converting electrical energy into hydrogen gas which is then stored in the hydrogen storage tank; the fuel cell converts the chemical energy stored in the fuel into electrical energy. Therefore, hydrogen storage in port energy systems has two states: power-to-hydrogen (P2H) and hydrogen-to-electricity (H2P).

[0143] Define the P2H state variable of the energy storage at scheduling time t. and H2P state variables

[0144]

[0145] Among them, P2H is electro-hydrogen conversion. The energy fed back to the grid by the fuel cell during the scheduling time t; H2P is the hydrogen-to-electricity conversion. The electrical energy consumed by the electrolyzer to produce hydrogen during the scheduling time t.

[0146] The mutual exclusion constraints of the state variables for the hydrogen storage tank's electro-hydrogen conversion and hydrogen-to-electricity conversion are as follows:

[0147]

[0148] The upper and lower limits of the interaction power between hydrogen storage and the power grid are constrained as follows:

[0149]

[0150] in, This represents the maximum hydrogen storage capacity.

[0151] The electro-hydrogen conversion relationship between the electrolyzer and the fuel cell is as follows:

[0152] LHV×H P2H =P P2H η P2H ;

[0153] P H2P =LHV×H P2H η P2H ;

[0154] Where LHV is the calorific value of hydrogen fuel, and H... P2H Hydrogen produced for the hydrogen storage tank electrolyzer, H H2P For the hydrogen consumed by the fuel cell, η P2H For the efficiency of the electrolyzer, η H2P For fuel cell efficiency, P P2H P is the input power of the electrolytic cell. H2P This refers to the output power of the fuel cell;

[0155] Scheduling time t, hydrogen storage tank capacity, state variables

[0156]

[0157] Among them: E hyd This refers to the rated capacity of the hydrogen storage tank.

[0158] The normal operating range constraints for the state of charge of the hydrogen storage tank are as follows:

[0159]

[0160] in, The minimum state of charge of the hydrogen storage tank This represents the maximum state of charge of the hydrogen storage tank.

[0161] To ensure the sustainable and cyclical use of energy storage systems, the cycle capacity consistency constraint is as follows:

[0162]

[0163] in, This represents the initial state of charge of the hydrogen energy storage. This indicates the state of charge of the hydrogen energy storage at the final moment.

[0164] During port berthing, vessels need to be fully charged. If necessary, the vessel's primary task can shift from self-charging to ensuring the stable operation of the port-ship system. Therefore, an energy model for vessel participation in grid-type support systems is required.

[0165] Ships have two states: charging and discharging. The discharging state variable of the j-th ship is set at a scheduling time t. charging state variables

[0166]

[0167] in, Let t be the discharge power of the j-th ship at scheduling time t. Let t be the charging power of the j-th ship during scheduling time t.

[0168] Similar to batteries, the mutual exclusion constraints for ship charging and discharging state variables are as follows:

[0169]

[0170] Upper and lower limits of ship charging and discharging power constraints:

[0171]

[0172] in, This represents the maximum power output of the ship.

[0173] Energy state of charge of the j-th ship at scheduling time t

[0174]

[0175] in, Let E be the energy state of charge of the j-th ship at time t-1. ship，j Let be the rated capacity of the j-th ship.

[0176] The normal operating range constraints for the ship's state of charge are as follows:

[0177]

[0178] in, This represents the minimum state of charge of the battery. This represents the battery's maximum state of charge.

[0179] After the vessel completes its charging operations at port, its state of charge meets the following requirements:

[0180]

[0181] in, Let represent the energy charge state of the j-th ship at the final moment.

[0182] S203. Based on the logistics system model and the energy system model, construct energy balance constraints and couple the logistics system model and the energy system model.

[0183] Ideally, the energy balance constraints are as follows:

[0184]

[0185] in, For the wind power predicted generation capacity at scheduling time t, For the photovoltaic power generation predicted at scheduling time t, Let t be the local load power during scheduling. The power fluctuation of the quay crane load during the scheduling time t is the power fluctuation of the quay crane load.

[0186] In real-world scenarios, uncertain events in port and shipping systems pose safety risks, thus necessitating the construction of port and shipping system models that take uncertainty into account.

[0187] The uncertainty of renewable energy output refers to the fact that the power generation capacity of renewable energy sources such as wind and solar energy is not as stable and controllable as that of traditional thermal or nuclear power due to the volatility and unpredictability of the natural conditions they depend on. Therefore, the uncertainty of the range of renewable energy output needs to be considered in dispatch decisions.

[0188]

[0189] in, This refers to the actual wind power generation capacity. δ represents the actual photovoltaic power generation. wt For fluctuations in wind power generation, δ pv This is due to fluctuations in photovoltaic power generation.

[0190] The discrepancy between the number of containers arriving at the port and the pre-stowage plan is caused by actual circumstances such as temporary additions or reductions in port calls during transport. This uncertainty directly impacts port operations, yard management, and cargo handling plans, leading to changes in port resources such as quay cranes, gantry cranes, yard space, transfer vehicle allocation efficiency, and operating costs.

[0191] The actual number of containers arriving at the port may deviate from the predicted value due to factors such as temporary increases or decreases in ports of call. The constraints on the number of containers are as follows:

[0192]

[0193] To handle fluctuations in container numbers and ensure timely completion of loading and unloading plans, quay cranes improve operational efficiency. The impact of fluctuations in the number of individual containers on quay crane power is as follows:

[0194]

[0195] in, For fluctuating container numbers, g min For the minimum number of fluctuations, g max For the largest number of fluctuations, P conta,j This represents the power impact of single-box fluctuations on the quay crane load. This represents the fluctuation in the total quay crane load power.

[0196] The energy balance constraint considering the uncertainties of the port and shipping system is as follows:

[0197]

[0198] S204. Based on the logistics-energy coupling model, obtain the port's state variables and energy variables; based on the costs corresponding to the state variables, establish a logistics cost objective function; based on the costs corresponding to the energy variables, construct an energy cost objective function.

[0199] The cost objective function is used for the optimal scheduling of port economy; the state variable represents the real-time changes in port status; and the energy variable represents the real-time energy consumption of port.

[0200] The optimal scheduling problem of a port vessel system is solved using the Two-Stage Robust Optimization (TSRO) method. This problem considers the energy and logistics scheduling of the port vessel system under the influence of uncertain events. A column and constraint generation algorithm is applied to incorporate the decision variables determined in the first stage into the decision-making process in the second stage, with the goal of minimizing the total operating cost, to achieve the economical and stable operation of the system.

[0201] The first stage is based on "min" to minimize the first-stage cost by optimizing the first-stage decision variable set, denoted as x. The second stage is based on "max-min" to access the uncertain variable "u" in a given polyhedral uncertainty set "μ". This aims to balance the level of uncertainty and minimize the cost in the second stage, thus ensuring robustness. Variable classification: y represents the decisive variable in the first-stage problem, x represents the decisive variable in the second stage, and μ is a polyhedral uncertainty set based on the uncertain variable u.

[0202] Discrete operational states such as ship berthing and departure, and equipment start-up and shutdown require the introduction of binary variables. Given the strong nonlinearity of binary variables, which makes direct dualization difficult in the second stage of TSRO, they must be solved in the first stage. Since ship berthing and departure states are affected by logistics scheduling, logistics scheduling must be completed simultaneously in the first stage. The solved ship berthing and departure states and equipment start-up and shutdown states are used as known parameters and passed to the second stage for subsequent optimization.

[0203] Phase 1: Logistics cost objective function, including:

[0204]

[0205] Gy≥h-Ey-Mu,u∈μ;

[0206]

[0207] Where y is the decision variable for the first stage, and c, G, h, E, and M are all constant matrices, c = [c crane 0 456 ], c crane The service fee per unit time for a single quay crane. vec(·) represents a vector form, S1 represents the container rating, η represents the second-stage target value, and u = [vec(g j ), δ pv δ wt ], δ wt For fluctuations in wind power generation, δ pv For fluctuations in photovoltaic power generation; Let j be the container fluctuation range of the j-th ship. S represents the lower and upper bounds of the container fluctuation range for each ship, respectively. y Let be the feasible region of variable y. It represents an n-dimensional positive real number.

[0208] As can be seen from the above formula, although the first stage of optimization simultaneously decides on binary variables and logistics variables, its objective function only sets cost weights for the quay crane operation status variable, that is, it takes minimizing the quay crane service cost as the optimization objective.

[0209] After solving the first-stage problem, the decision variables obtained from the first-stage problem are used in the second stage. The purpose of the second-stage problem is to handle the uncertainty of the system when receiving the results of the first-stage problem, so as to ensure the robustness of the system.

[0210] Phase Two: Energy Cost Objective Function, including:

[0211]

[0212] Gx≥h-Ex-Mu,u∈μ;

[0213]

[0214] Where x is the decision variable for the second stage. Let λ be the number of containers that need to be transferred during scheduling time t, and λ be the power margin penalty factor. C buy For time-of-use pricing of the power grid, c dis c represents the unit operation and maintenance cost of onshore battery energy storage discharge. cha The unit operation and maintenance cost of charging onshore battery energy storage, c dis,j c represents the unit operation and maintenance cost of the j-th ship discharging electricity. cha,j c represents the unit operation and maintenance cost of charging the j-th ship. car The unit dispatch cost is the vehicle's capacity when fully loaded, where cap is the capacity of a single transfer vehicle, and c is the unit dispatch cost. λ S is the unit penalty cost for margin deficit. x Let x be the feasible region of variable x. It represents an m-dimensional positive real number.

[0215] As can be seen from the above, the objective function of the second stage includes the grid energy purchase cost, the operating cost of each piece of equipment, the operating cost of the transfer vehicle, and the penalty for insufficient margin.

[0216] The constraint matrices G, h, E, and M are determined by the following constraints: energy balance constraint, upper and lower limits of equipment output constraint, dynamic SOC constraint of energy storage, capacity consistency constraint of energy storage cycle, energy storage operating range constraint, dynamic SOC constraint of ship, operating range constraint of ship, relationship between ship power margin and penalty factor constraint, uncertainty constraint of new energy power generation, uncertainty constraint of container quantity, and power fluctuation constraint of quay crane.

[0217] Considering the uncertainty of fluctuating container numbers, the polyhedral uncertainty set is:

[0218]

[0219] Inner layer Since it is a convex optimization problem and strong duality is obviously true, the inner model can be equivalently transformed into a single-layer MIP solution by using Lagrangian duality and the tKT condition. Considering the outer layer, the entire two-layer SP2 is equivalent to the following single-layer model:

[0220]

[0221] Gx≥h-Ey-Mu,u∈μ;

[0222] G T π≤b;

[0223] (Gx-h-Ey+Mu)Q=0,Q>0;

[0224] (bG T Q)x=0;

[0225] Here, Q is the newly introduced Lagrange dual variable.

[0226] Based on the above technical solution, this section explains the mathematical formulas for the two stages of the proposed TSRO method and provides a flowchart of the TSRO method based on the C&CG algorithm. The TSRO-based EMS method minimizes the total system operating cost by iteratively optimizing the first-stage decision variable set y, the second-stage decision variable set x, and the uncertain variable set u.

[0227] In one possible implementation of this application embodiment, S201 further includes:

[0228] The cost of a quay crane consists of two parts: energy consumption cost and service fee; the energy consumption cost is included in the operating cost of the energy output equipment.

[0229] Shore bridge service fees:

[0230]

[0231] Among them, C crane c is the total service fee for the quay crane. crane The service fee per unit time for a single quay crane;

[0232] Transfer vehicle operating cost C trans :

[0233]

[0234] Here, cap represents the capacity of a single transport vehicle.

[0235] In one possible implementation of this application embodiment, S202 further includes:

[0236] The battery cost model includes operation and maintenance costs, and its cumulative expenditure over the scheduling cycle is:

[0237]

[0238] Among them, C bat For total operating and maintenance costs, c dis For the unit operation and maintenance cost of discharge, c cha The unit maintenance cost for charging is Δt, where Δt is the scheduling time interval.

[0239] The hydrogen energy storage cost model includes energy costs and its cumulative expenditure C over the dispatch cycle. hyd :

[0240]

[0241] Among them, c P2H c represents the unit revenue from replenishing hydrogen to the hydrogen storage tank. H2P This indicates the unit cost of selling hydrogen.

[0242] Furthermore, to enhance the resilience of the port and vessel system, vessels participating in network support need to reserve power margins, establishing a margin management mechanism driven by penalty factors. High penalties will force vessel operators to balance short-term arbitrage with long-term safety.

[0243]

[0244] Where k is the proportional coefficient of the penalty factor, a constant determined by the port agreement, used to adjust the intensity of the penalty factor. This is the power margin penalty factor. According to the definition of the penalty factor, the larger the power margin deficit, the heavier the penalty.

[0245] The ship operating cost model includes operating and maintenance costs and margin deficit penalties, with cumulative expenditure C over its dispatch cycle. ship :

[0246]

[0247] For example, data from a typical day at a port is selected for case analysis, with a 24-hour scheduling cycle and a 1-hour unit scheduling period. Port vessel logistics and energy system parameters are shown in Table 1, and vessel arrival schedules are shown in Table 2.

[0248] Table 1 Parameters of Port Ship Logistics and Energy Systems

[0249]

[0250]

[0251] Table 2 Ship Arrival Schedule

[0252]

[0253] Figure 2 The convergence results of the C&CG algorithm are shown in the figure. It can be clearly seen from the figure that after 9 iterations, stable convergence with upper and lower bounds can be achieved, and the robust optimization scheduling scheme of the port and ship system can be obtained by using this algorithm.

[0254] A comprehensive power dispatch plan that takes into account the uncertainties of wind and solar power output and ship container throughput, as well as the participation of ships in grid construction support, is as follows: Figure 3 As shown, this scheme can reliably supply power to the port ship microgrid. Even in the worst-case scenario of wind and solar power output and the start-up of high-power load gantry cranes, the port integrated energy system provides power with the optimal energy dispatch scheme, and the ships actively support the port power grid by connecting to the grid through network control.

[0255] Energy storage capacity changes such as Figure 4 As shown, from 0:00 to 7:00, the energy storage capacity shows a decreasing trend, while it increases from 3:00 to 4:00, indicating that the dispatching results strive to promote energy storage at low electricity prices. From 16:00 to 19:00, the electricity price is relatively low, allowing for charging of the energy storage to meet the capacity consistency constraints of the energy storage cycle and extend battery life. In summary, energy storage achieves peak-valley charging and peak-discharging, effectively reducing the grid's electricity purchase cost.

[0256] The relationship between changes in energy storage capacity and renewable energy output is as follows: Figure 5 As shown in the diagram. From 0:00 to 6:00, wind power accounts for a large proportion of renewable energy output. During this period, the trend of energy storage capacity changes is basically consistent with the fluctuation of wind power output, and energy storage plays a role in increasing the absorption of wind power. From 5:00 to 6:00, the increase in photovoltaic output in the morning leads to hydrogen production by electrolyzers, increasing hydrogen storage capacity. From 08:00 to 12:00, this period is the peak of electricity prices, and renewable energy output is relatively high, providing the required electricity for the load. From 16:00 to 18:00, renewable energy output remains relatively high, charging the energy storage. From 17:00 to 21:00, wind and solar output decrease, but in order to ensure the consistency of energy storage capacity throughout the period and to guarantee the safety of energy storage, the energy storage capacity remains unchanged. The above analysis shows that the energy storage system plays a role in smoothing out renewable energy fluctuations and achieving "peak shaving and valley filling".

[0257] The operating status of gantry cranes is determined by the ship logistics scheduling plan, which also affects the energy flow scheduling plan. Figure 6 and Figure 7 It demonstrates the impact of reserved power margins on logistics and energy systems.

[0258] The results show that, in order to cope with the impact load of gantry crane startup, ships can provide support for the port power grid through reverse charging. Figure 6 and Figure 7The data shows that, both without considering and with reserved grid power margins, vessels in port significantly and in the opposite direction at 11:00 and 15:00 respectively, releasing power to support the port's power grid. Furthermore, vessels proactively charge during off-peak electricity periods to reserve energy for future support needs and their own charging requirements.

[0259] Figure 8 This paper compares the power dispatch plans for ships under two modes: with and without power margin constraints. With the power margin set, the maximum charging peak decreases from 500kW to 380kW, a 32% reduction, reserving a 120kW power margin. From the perspective of the temporal distribution of ship energy storage power, considering the power margin reduces the peak output, mitigating power surges in the ship's power system caused by sudden load changes or renewable energy fluctuations. However, this also means that the charging and discharging capacity of energy storage is limited, potentially preventing it from fully charging and discharging during periods of optimal electricity prices, thus reducing arbitrage opportunities. However, considering the power margin can stabilize the total cost by reducing risk, and even lower the total cost when uncertain events occur frequently.

[0260] Based on the above analysis, the simulation results of the three schemes are summarized in Table 3:

[0261] Table 3. Comparison of Total Costs and Risks of the Three Options

[0262]

[0263] Analysis shows that among the three options, Option 1, however, is constrained by the positive correlation between cost and system power limit. Its significantly insufficient power limit and low robustness lead to a higher potential operational risk. It is worth noting that while the total costs of Options 2 and 3 are very close, Option 2's power limit characteristic may result in a significantly higher actual cost than Option 3 when dealing with high-level risk events. In contrast, Option 3 demonstrates a significant leap in risk resistance compared to Option 2. Therefore, considering both the economic and safety requirements of port vessel system operation, prioritizing the option that trades controllable economic costs for a significantly enhanced risk resilience is more reasonable. This also highlights the comprehensive performance advantage of Option 3 in the robustness-economic trade-off.

[0264] The above primarily describes the solutions of the embodiments of this application from the perspective of device implementation. It is understood that each device, for example, a highly robust port logistics-energy coupling scheduling device, includes at least one of the hardware structures and software modules corresponding to the execution of each function in order to achieve the above-mentioned functions. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software-driven hardware manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0265] This application embodiment can divide a highly robust port logistics-energy coupled scheduling device into functional units based on the above method example. For example, each function can be divided into separate functional units, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents a logical functional division; other division methods may be used in actual implementation.

[0266] When using integrated units, Figure 9 The diagram shows a possible structural schematic of a highly robust port logistics-energy coupling scheduling device (referred to as logistics-energy coupling scheduling device 50) involved in the above embodiments. The logistics-energy coupling scheduling device 50 includes a processing unit 501 and a communication unit 502, and may also include a storage unit 503. Figure 9 The structural diagram shown can be used to illustrate the structure of a highly robust port logistics-energy coupling scheduling device involved in the above embodiments.

[0267] when Figure 9 The schematic diagram shown illustrates the structure of a highly robust port logistics-energy coupling scheduling device involved in the above embodiments. The processing unit 501 is used to control and manage the operation of the highly robust port logistics-energy coupling scheduling device, the communication unit 502 is used for the highly robust port logistics-energy coupling scheduling device to communicate with other devices, and the storage unit 503 is used to store the program code and data of the highly robust port logistics-energy coupling scheduling device.

[0268] For example, communication unit 502 is used to construct a port logistics system model and a port energy system model; based on the logistics system model and the energy system model, energy balance constraints are constructed, and the logistics system model and the energy system model are coupled.

[0269] Processing unit 501 acquires the port's state variables and energy variables based on the logistics-energy coupling model; it establishes a logistics cost objective function based on the costs corresponding to the state variables, and constructs an energy cost objective function based on the costs corresponding to the energy variables.

[0270] The processing unit 501 can be a processor or a controller, and the communication unit 502 can be a communication interface, transceiver, transceiver circuit, transceiver device, etc. The term "communication interface" is a general term and may include one or more interfaces. The storage unit 503 can be a memory. When the logistics-energy coupling scheduling device 50 is a chip, the processing unit 501 can be a processor or a controller, and the communication unit 502 can be an input interface and / or an output interface, pins, or circuits, etc. The storage unit 503 can be a storage unit within the chip (e.g., a register, cache, etc.) or a storage unit located outside the chip (e.g., read-only memory (ROM), random access memory (RAM, etc.)).

[0271] The communication unit can also be called a transceiver unit. The antenna and control circuit with transceiver functions in the logistics-energy coupling scheduling device 50 can be considered as the communication unit 502 of the logistics-energy coupling scheduling device 50, and the processor with processing functions can be considered as the processing unit 501 of the logistics-energy coupling scheduling device 50. Optionally, the device in the communication unit 502 used to implement the receiving function can be considered as a communication unit. The communication unit is used to execute the receiving steps in the embodiments of this application, and the communication unit can be a receiver, a receiver circuit, etc. The device in the communication unit 502 used to implement the transmitting function can be considered as a transmitting unit. The transmitting unit is used to execute the transmitting steps in the embodiments of this application, and the transmitting unit can be a transmitter, a transmitter, a transmitting circuit, etc.

[0272] Figure 9If the integrated units in the process are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of software products. These computer software products are stored in a storage medium and include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. Storage media for storing computer software products include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0273] Figure 9 The units in the process can also be called modules; for example, a processing unit can be called a processing module.

[0274] This application also provides a hardware structure diagram of a highly robust port logistics-energy coupled scheduling device (denoted as logistics-energy coupled scheduling device 60), see [link to diagram]. Figure 10 The logistics-energy coupled scheduling device 60 includes a processor 601, and optionally, a memory 602 connected to the processor 601.

[0275] In the first possible implementation, see Figure 10 The logistics-energy coupled scheduling device 60 also includes a transceiver 603. The processor 601, memory 602, and transceiver 603 are connected via a bus. The transceiver 603 is used to communicate with other devices or communication networks. Optionally, the transceiver 603 may include a transmitter and a receiver. The device in the transceiver 603 that implements the receiving function can be considered as a receiver, which is used to perform the receiving steps in the embodiments of this application. The device in the transceiver 603 that implements the transmitting function can be considered as a transmitter, which is used to perform the transmitting steps in the embodiments of this application.

[0276] Based on the first possible implementation method Figure 10 The structural diagram shown can be used to illustrate the structure of a highly robust port logistics-energy coupling scheduling device involved in the above embodiments.

[0277] in, Figure 10 Alternatively, a system chip in a highly robust port logistics-energy coupled scheduling device can be illustrated. In this case, the actions performed by the aforementioned highly robust port logistics-energy coupled scheduling device can be implemented by this system chip. The specific actions performed are described above and will not be repeated here.

[0278] In implementation, each step of the method provided in this embodiment can be completed by integrated logic circuits in the processor or by instructions in software form. The steps of the method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0279] The processor in this application may include, but is not limited to, at least one of the following: a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a microcontroller unit (MCU), or an artificial intelligence processor, etc., and other computing devices that run software. Each computing device may include one or more cores for executing software instructions to perform calculations or processing. The processor may be a standalone semiconductor chip or integrated with other circuits into a single semiconductor chip. For example, it may form a System-on-a-Chip (SoC) with other circuits (such as encoding / decoding circuits, hardware acceleration circuits, or various bus and interface circuits), or it may be integrated as a built-in processor within an ASIC. The ASIC with the integrated processor may be packaged separately or together with other circuits. In addition to the cores for executing software instructions to perform calculations or processing, the processor may further include necessary hardware accelerators, such as field-programmable gate arrays (FPGAs), programmable logic devices (PLDs), or logic circuits that implement dedicated logic operations.

[0280] The memory in the embodiments of this application may include at least one of the following types: read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions; random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions; or electrically erasable programmable-only memory (EEPROM). In some scenarios, the memory may also be a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0281] This application also provides a computer-readable storage medium including instructions that, when run on a computer, cause the computer to perform any of the methods described above.

[0282] This application also provides a computer program product containing instructions that, when run on a computer, cause the computer to perform any of the methods described above.

[0283] This application also provides a chip including a processor and an interface circuit. The interface circuit is coupled to the processor. The processor is used to run computer programs or instructions to implement the above-described method. The interface circuit is used to communicate with other modules outside the chip.

[0284] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0285] Some of the data in the above formula are calculated by removing dimensions and taking their numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

Claims

1. A high-robustness port logistics-energy coupling scheduling method, characterized in that, The method comprises the following steps: a logistics system model of the port is constructed to convert dynamic logistics demand of the port into quantifiable load power demand and time constraints; the logistics system model comprises a quay crane operating state variable, quay crane load power demand, a gantry crane operating state variable, and gantry crane load power demand; an energy system model of the port is constructed to integrate a power grid, renewable energy power generation equipment, an electric energy storage system, a hydrogen energy storage system, and a network construction capability of a berthed ship, and establish a multi-energy collaborative power supply framework; based on the logistics system model and the energy system model, an energy balance constraint condition is constructed to couple the logistics system model and the energy system model; the construction of the energy balance constraint condition comprises: wherein t is the scheduling time, j is the ship code, j ∈ [1, N], N is a positive integer, is the actual wind power at scheduling time t, is the actual photovoltaic power at scheduling time t, is the grid power at scheduling time t, is the battery discharge power at scheduling time t, is the battery charge power at scheduling time t, is the discharge power of the jth ship at scheduling time t, is the charge power of the jth ship at scheduling time t, is the shore crane load power demand, is the gantry crane load power at scheduling time t, δ wt is the wind power fluctuation, pv is the photovoltaic power fluctuation, is the predicted wind power at scheduling time t, is the predicted photovoltaic power at scheduling time t, is the local load power at scheduling time t, is the shore crane load fluctuation power at scheduling time t; and, based on the logistics-energy coupling model, state variables and energy variables of the port are obtained; a logistics cost objective function is established based on costs corresponding to the state variables, and an energy cost objective function is constructed based on costs corresponding to the energy variables; the cost objective functions are used for optimal scheduling of the port economy; the state variables represent real-time changes in the state of the port, and the energy variables represent real-time consumption of energy of the port.

2. The highly robust port logistics-energy coupling scheduling method according to claim 1, characterized in that, The shore crane operating state variable Comprising: Wherein, t is the scheduling time, j is the ship code, j ∈ [1, N], N is a positive integer, t crane，j is the time when the jth ship starts the loading and unloading operation of the shore crane, t 0，j is the arrival time of the jth ship, t leave，j is the departure time of the jth ship, Δt crane，j is the total loading and unloading operation time of the jth ship, and T is a single scheduling period.

3. The highly robust port logistics-energy coupling scheduling method according to claim 2, characterized in that, The shore power demand comprises: wherein, Pj(t) is the load power of the jth quay crane corresponding to the jth ship at the scheduling time t, and n is the total number of quay cranes.

4. The highly robust port logistics-energy coupling scheduling method according to claim 3, characterized in that, The gantry crane operating state variable Comprises: wherein, Pcrane is the crane load power at the dispatch time t.

5. The highly robust port logistics-energy coupling scheduling method according to claim 4, characterized in that, The gantry crane load power requirement Comprising: where P gan，rated is the rated power of the shore crane.

6. The highly robust port logistics-energy coupling scheduling method according to claim 5, characterized in that, the energy system model of the port comprises: A dispatch time t grid power output state variable : wherein, is the grid power at dispatch time t; scheduling time t the state of charge variable of the electrical energy storage and the state of discharge variable : wherein, Pbatdisch(t) is the battery discharge power at dispatch time t, Pbatcharge(t) is the battery charge power at dispatch time t; scheduling time t battery state of charge : wherein, SoC(t) is the battery state of charge at time t, bat SoC(t) is the battery state of charge at time t, P2H state variable for scheduling time t hydrogen storage energy and H2P state variable where P2H is the electrical to hydrogen conversion, H2P is the hydrogen to electricity conversion, P2H is the electrical to hydrogen conversion, an electro-hydrogen conversion relationship of the electrolyzer and the fuel cell is: where LHV is the lower heating value of hydrogen fuel, H P2H hydrogen produced by the electrolyzer for the hydrogen storage tank, H H2P hydrogen consumed by the fuel cell, η P2H electrolyzer efficiency, η H2P fuel cell efficiency, P P2H input power to the electrolyzer, P H2P output power from the fuel cell; Scheduling time t hydrogen tank capacity state variable : wherein E hyd is the rated capacity of the hydrogen storage tank, is the hydrogen consumed by the fuel cell at the dispatch time t, is the hydrogen produced by the electrolyzer at the dispatch time t; scheduling time t the discharge state variable of the jth vessel charge state variable wherein, Pdischargej(t) is the discharging power of the jth vessel at the dispatch time t, Pchargej(t) is the charging power of the jth vessel at the dispatch time t. scheduling time t the energy state of charge of the jth vessel : wherein, Ej(t) is the energy state of charge of the jth vessel at time t-1, ship,j Cj is the rated capacity of the jth vessel.

7. The highly robust port logistics-energy coupling scheduling method according to claim 1, characterized in that, the logistics cost objective function comprises: Gy≥h-Ey-Mu, u∈μ; where y is the first-stage decision variable, c, G, h, E and M are constant matrices, c = [c crane ,0 456 ] is the service cost of a single quay crane per unit time, crane vec(·) denotes the vector form, S1 denotes the nominal amount of containers, η denotes the second-stage objective value, u = [vec(g j ), δ pv , δ wt ] is the wind power fluctuation, δ wt is the photovoltaic power fluctuation; pv is the container fluctuation range of the jth ship, denote the lower and upper bounds of the container fluctuation range of each ship, respectively, S y is the feasible region of the variable y, denotes the n-dimensional positive real number.​​ 8. The highly robust port logistics-energy coupling scheduling method according to claim 7, characterized in that, the energy cost objective function comprises: Gx≥h-Ex-Mu, u∈μ; where x is the second-stage decision variable, the number of containers to be transferred at scheduling time t, and λ is the power margin penalty factor. C buy the time-of-use price of the grid, c dis the unit operation cost of discharging the onshore battery storage, c cha the unit operation cost of charging the onshore battery storage, c dis,j the unit operation cost of discharging the jth vessel, c cha,j the unit operation cost of charging the jth vessel, c car the unit scheduling cost when the vehicle is fully loaded, cap is the capacity of a single transfer vehicle, and c λ the unit penalty cost for the margin deficit; u = [vec(g j ), δ pv , δ wt ], δ wt the wind power fluctuation, δ pv the photovoltaic power fluctuation; S x the feasible region of variable x, denotes an m-dimensional positive real number.

9. A highly robust port logistics-energy coupling scheduling system, characterized in that, the system comprises a construction module and a scheduling module; the construction module is configured to construct a logistics system model of the port to convert dynamic logistics demand of the port into quantifiable load power demand and time constraints; the logistics system model comprises a quay crane operating state variable, quay crane load power demand, a gantry crane operating state variable, and gantry crane load power demand; an energy system model of the port is constructed to integrate a power grid, renewable energy power generation equipment, an electric energy storage system, a hydrogen energy storage system, and a network construction capability of a berthed ship, and establish a multi-energy collaborative power supply framework; based on the logistics system model and the energy system model, an energy balance constraint condition is constructed to couple the logistics system model and the energy system model; the construction of the energy balance constraint condition comprises: wherein t is the scheduling time, j is the ship code, j ∈ [1, N], N is a positive integer, is the actual wind power at the scheduling time t, is the actual photovoltaic power at the scheduling time t, is the grid power at the scheduling time t, is the battery discharge power at the scheduling time t, is the battery charge power at the scheduling time t, is the discharge power of the jth ship at the scheduling time t, is the charge power of the jth ship at the scheduling time t, is the shore bridge load power demand, is the gantry crane load power at the scheduling time t, δ wt is the wind power fluctuation, pv is the photovoltaic power fluctuation, is the wind power prediction at the scheduling time t, is the photovoltaic power prediction at the scheduling time t, is the local load power at the scheduling time t, is the shore bridge load fluctuation power at the scheduling time t; the scheduling module is configured to obtain state variables and energy variables of the port based on the logistics-energy coupling model; a logistics cost objective function is established based on costs corresponding to the state variables, and an energy cost objective function is constructed based on costs corresponding to the energy variables; the cost objective functions are used for optimal scheduling of the port economy; the state variables represent real-time changes in the state of the port, and the energy variables represent real-time consumption of energy of the port.

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