Collaborative optimization method for mobile shore power dispatching and berth allocation

Abstract

The invention discloses a collaborative optimization method for mobile shore power dispatching and berth allocation. The collaborative optimization method comprises the following steps: a model construction module for constructing a collaborative optimization model for mobile shore power dispatching and berth allocation; the data acquisition module is used for collecting ship information, berth information and shore power information data; the intelligent decision-making module is used for solving the collaborative optimization model by applying an optimization algorithm; and the distribution execution module is used for automatically or semi-automatically executing shore power dispatching and berth distribution operation according to a calculation result. According to the collaborative optimization method for mobile shore power dispatching and berth allocation, dispatching and allocation of shore power and berth resources under the multi-target condition are optimized, the method is suitable for various ports where mobile shore power resources and berth allocation need to be optimized, high flexibility and adaptability are achieved, the utilization rate of berths and shore power is increased, and the cost is reduced. Fuel oil use and pollution emission during the berthing period of the ship at the port are reduced, remarkable environmental protection benefits are achieved, and the port is assisted to achieve green operation and dual-carbon targets.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power engineering, and in particular to a collaborative optimization method for mobile shore power dispatching and berth allocation. Background Art

[0002] With the improvement of global environmental awareness and the strict requirements of ship emission standards, ship electrification has become a development trend in the shipping industry. As an effective means to reduce pollution emissions from ships during port berthing, shore power technology allows ships to replace or reduce the use of their own auxiliary diesel generators by connecting to shore power during berthing, thereby significantly reducing pollutant emissions. However, traditional shore power berth allocation methods often rely on manual operation and experience judgment, resulting in unbalanced resource allocation and inefficiency.

[0003] In busy ports, the shortage of berths and shore power resources is particularly prominent. Due to the uncertainty of ship berthing time, traditional methods are difficult to achieve optimal allocation of berths and shore power resources, resulting in waste of resources and increased waiting time for ships. Fixed shore power facilities require the installation of fixed interfaces at each berth, which is costly and difficult to adjust according to the real-time needs of ships.

[0004] In contrast, mobile shore power systems have higher flexibility and adaptability. Mobile shore power equipment can be moved between different berths according to actual needs, making it more efficient to serve multiple ships and optimize resource utilization. Therefore, it is particularly necessary to develop a collaborative optimization method for mobile shore power scheduling and berth allocation. Summary of the invention

[0005] The purpose of the present invention is to provide a collaborative optimization method for mobile shore power scheduling and berth allocation, including a data acquisition module, an intelligent decision-making module and an allocation execution module. The method realizes dynamic optimization allocation and scheduling of berth and shore power resources by comprehensively considering factors such as the ship's berthing time, power demand, availability of shore power facilities and real-time status of berths.

[0006] To achieve the above object, the present invention provides a collaborative optimization method for mobile shore power scheduling and berth allocation, comprising the following steps:

[0007] S1, model building module: building a collaborative optimization model for mobile shore power dispatching and berth allocation;

[0008] S2, data acquisition module: collects ship information, berth information and shore power information data;

[0009] S3, intelligent decision-making module: using optimization algorithms to solve collaborative optimization models;

[0010] S4, allocation execution module: automatically or semi-automatically execute shore power dispatch and berth allocation operations according to the calculation results.

[0011] Preferably, step S1 specifically includes the following steps:

[0012] S11, determining the objective function;

[0013] The ship time cost consists of three parts: the cost of not arriving at the port as planned, the cost of waiting for berthing, and the cost of delayed departure. The objective function is to minimize the ship time cost, as shown in formula (1):

[0014]

[0015] Where F1 is the time cost of the ship, i∈S is the number of the ship arriving at the port within the planning period, S is the set of ships arriving at the port within the planning period, and x i is the actual arrival time of ship i, X i The planned arrival time of ship i, w i is the waiting time for ship i to berth, g i is the berthing time of ship i, z i is the actual departure time of ship i, Z i is the planned departure time of ship i, C1 is the unit time cost of the actual arrival at the port not as planned, C2 is the unit time cost of the ship waiting for berthing, C3 is the unit time cost of the ship berthing, and C4 is the unit time cost of the ship's delayed departure;

[0016] The objective function 2 is to minimize the shore power charging cost under the time-of-use electricity price, as shown in formula (2):

[0017]

[0018] Where F2 is the shore power charging cost under the time-of-use electricity price, E iτ is the power consumption of ship i in period τ, C τ is the unit electricity price cost in the τ period, K is the set of different electricity price periods, and τ∈K is the number of different electricity price periods;

[0019] The objective function three is to minimize the shore power operation time cost, as shown in formula (3):

[0020] minF3=T connect / disconnect +T move (3)

[0021] Where F3 is the shore power operation time cost, T connect / disconnect is the sum of the time required for shore power connection and disconnection of the mobile shore power, T move The sum of the time required for the mobile shore power to move;

[0022] S12, setting constraints;

[0023] The power consumption of ship i in each period is shown in formula (4):

[0024]

[0025] In the formula, e iτ is the charging time of ship i in period τ, p i is the average shore power required by ship i during berthing;

[0026] The sum of the time required for mobile shore power connection and disconnection is shown in formula (5):

[0027]

[0028] In the formula, c iτ Indicates whether ship i uses shore power during the τ period, b jkτ Indicates whether shore power k is at berth j during period τ, is the unit time required for shore power connection, is the unit time required for shore power disconnection, a i is the berth number of ship i;

[0029] The time required for mobile shore power to move is shown in formula (6):

[0030]

[0031] Where P is the mobile shore power set, N is the discretized berth segment set, T is the planning period time set, j, j′∈N is the discretized continuous berth segment number, k∈P is the mobile shore power number, t∈T is the time, m jj′kt Indicates whether the shore power k moves from berth segment j to berth segment j′ at time t, The unit time required for shore power movement;

[0032] The waiting time constraint for berthing is as shown in formula (7):

[0033]

[0034] In the formula, y i is the actual berthing time of ship i;

[0035] The berthing time of a ship depends on the sum of its charging time, charging waiting time and shore power operation time in each period, as well as the service time provided to the ship. Considering that shore power use and cargo loading and unloading are carried out in parallel, the berthing time should be the larger value of the charging time and the service time, as shown in formula (8):

[0036]

[0037] In the formula, g i is the docking time of ship i, e iτ is the charging time of ship i in period τ, The waiting time for ship i to charge, f i Length of service for ship i, d i Indicates whether ship i needs service;

[0038] The ship berthing and unberthing time constraints are as shown in formula (9):

[0039]

[0040] The ship's berthing time should not be earlier than the arrival time, as shown in formula (10):

[0041]

[0042] The ship's berthing time should not be later than the departure time, as shown in formula (11):

[0043]

[0044] Only one ship can berth at the same berth at the same time, as shown in formulas (12)-(15):

[0045]

[0046] Where M is a sufficiently large positive number, i,i′∈S is the number of the ship arriving at the port within the planning period, and o ii′ Indicates whether ship i berths before ship i′, u ii′ Indicates whether the berthing number of ship i is less than the berthing number of ship i′;

[0047] Each berth can only be allocated one shore power supply at the same time, as shown in formula (16):

[0048]

[0049] Where b jkt Indicates whether shore power k is at berth j at time t;

[0050] Each shore power can only be in one berth at the same time, as shown in formula (17):

[0051]

[0052] The relative order of the single rail shore power cannot be changed at any time, and the shore power cannot cross each other, as shown in formula (18):

[0053]

[0054] Where n kt represents the berth number where shore power k is located at time t;

[0055] To ensure that the shore power allocated to the berth at each time has a surplus of idle shore power, as shown in formula (19):

[0056]

[0057] Where P jt is the number of shore power options available at berth j at time t;

[0058] Each mobile shore power device can only be reallocated once at a time, as shown in formula (20):

[0059]

[0060] The decision variables are non-negatively constrained, as shown in formula (21):

[0061]

[0062] Define 0-1 variables as shown in formula (22):

[0063]

[0064] Preferably, in step S3, the model constructed in step S1 is solved using the R-NSGA-III algorithm based on the relevant data obtained in step S2, and the specific steps are as follows:

[0065] S31, reference point set construction: according to historical scheduling experience and actual needs, the expected values ​​of multiple objective functions are input, each expected point represents a preferred position in the target space, and the expected point set is mapped to the reference point set;

[0066] S32, initializing the population: first, setting the initial parameters of the R-NSGA-III algorithm, including mutation probability, population size, and number of iterations; then generating an initial population, which contains multiple candidate solutions;

[0067] S33, selection and inheritance: Perform selection, crossover and mutation operations on the initial population to generate a new offspring population, and gradually improve the quality of the population by making feasible solutions with better objective function values ​​have a greater probability of being inherited to the next generation;

[0068] S34, Population merging: Merge the offspring population obtained through selection, crossover and mutation with the original population to form an extended population;

[0069] S35, fast non-dominated sort: perform fast non-dominated sort on the merged population to distinguish solutions with different non-dominated levels;

[0070] S36, minimum habitat selection, applying the minimum habitat selection mechanism based on reference points to select representative solutions from the merged population and construct a new initial population;

[0071] S37, iterative and termination condition judgment: check whether the algorithm meets the preset number of iterations or convergence criteria; if not, return to step S33 to continue iterating; if satisfied, continue to step S38;

[0072] S38. Output of Pareto optimal solution set: The Pareto optimal solution set is finally output, which includes the optimal scheduling and berth allocation plan of mobile shore power equipment for each ship berth demand within the time period.

[0073] Therefore, the present invention adopts the above-mentioned collaborative optimization method of mobile shore power scheduling and berth allocation, and the beneficial effects are as follows:

[0074] (1) The present invention can comprehensively optimize costs: through the R-NSGA-III multi-objective optimization algorithm, the ship time cost, electricity usage cost and shore power operation time cost are comprehensively optimized to minimize the overall operating cost.

[0075] (2) The present invention can perform dynamic resource allocation: realize dynamic optimization allocation of berth and shore power resources, maximize the use of existing resources, and improve the operational efficiency and flexibility of the port.

[0076] (3) The present invention can reduce waiting time: by optimizing scheduling, the waiting time of ships in ports can be reduced, thereby improving the turnover efficiency of ships.

[0077] (4) The present invention can reduce environmental pollution: by increasing the utilization rate of shore power, reducing fuel consumption and pollution emissions during ship docking at the port, and realizing green port operation.

[0078] (5) The present invention can improve the utilization rate of berths and shore power: by rationally allocating and scheduling berths and shore power, the utilization rate of berths and shore power can be improved and resource waste can be reduced.

[0079] (6) The present invention has flexible adaptability: it is suitable for the needs of ports of different sizes and types and has good adaptability and scalability.

[0080] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 It is a flow chart of an embodiment of a collaborative optimization method for mobile shore power dispatching and berth allocation of the present invention;

[0082] Figure 2It is a problem diagram of an embodiment of a collaborative optimization method for mobile shore power scheduling and berth allocation of the present invention;

[0083] Figure 3 It is a flow chart of the R-NSGA-III algorithm of an embodiment of a collaborative optimization method for mobile shore power scheduling and berth allocation of the present invention. DETAILED DESCRIPTION

[0084] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0085] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0086] Figure 1 This is a flow chart of an embodiment of a collaborative optimization method for mobile shore power scheduling and berth allocation of the present invention, showing the main steps and operation flow of the method.

[0087] In order to better understand the collaborative optimization method of mobile shore power scheduling and berth allocation of the present invention, as shown in Figure 2 , which is a schematic diagram of an embodiment of the present invention. The figure shows the dispatching and berth allocation of the mobile shore power equipment in the port, as well as the interaction process with the ship. The figure depicts that the power supply system provides stable power to the shore power equipment, and the mobile shore power equipment can be moved between different berth sections to meet the charging needs of the ship, and also shows the berth sections where the ship can dock.

[0088] like Figure 1 As shown, the following is a detailed description of the specific steps of the mobile shore power scheduling and berth allocation collaborative optimization method of the present invention:

[0089] S1. Model building module:

[0090] In order to construct the collaborative optimization method of mobile shore power scheduling and berth allocation of the present invention, the following assumptions are set to simplify the model analysis and focus on the core optimization problem:

[0091] It is assumed that the arrival time, departure time, expected berthing time and power demand of each ship are known. This means that the declared dam crossing information of all ships is known, without considering external interference, and without considering the interference of weather, accidents, etc. on the entry, departure and berthing of ships.

[0092] It is assumed that the ship's service time (i.e. loading and unloading time) can be carried out simultaneously with shore power charging without affecting the charging efficiency.

[0093] Assuming that all arriving ships are equipped with shore power connections, the berth is able to provide sufficient shore power for all ships. This means that there is no need to consider whether the ship can use shore power.

[0094] Assume that a monorail mobile shore power supply is used. The mobile equipment can be moved between different berths but cannot cross, keeping the relative position between shore power stations unchanged to meet the power needs of different ships.

[0095] Assume that the port is divided into berth sections, each of which can accommodate one ship. The division of berth sections is fixed and does not change over time.

[0096] It is assumed that the shore power supply can meet the needs of all ships and the power quality supplied is stable without power interruption or shortage.

[0097] It is assumed that there is a certain time cost when the monorail mobile shore power equipment moves between berths. These moving time costs are known and fixed.

[0098] Constructing a collaborative optimization model for mobile shore power dispatching and berth allocation includes the following steps:

[0099] S11, determining the objective function;

[0100] The ship time cost consists of three parts: the cost of not arriving at the port as planned, the cost of waiting for berthing, and the cost of delayed departure. The objective function is to minimize the ship time cost, as shown in formula (1):

[0101]

[0102] Where F1 is the time cost of the ship, i∈S is the number of the ship arriving at the port within the planning period, S is the set of ships arriving at the port within the planning period, and x i is the actual arrival time of ship i, X i Planned arrival time for ship i, w i is the waiting time for ship i to berth, g i is the berthing time of ship i, z i is the actual departure time of ship i, Z i is the planned departure time of ship i, C1 is the unit time cost of the ship not arriving at the port as planned, C2 is the unit time cost of the ship waiting for berthing, C3 is the unit time cost of the ship berthing, and C4 is the unit time cost of the ship's delayed departure.

[0103] The objective function 2 is to minimize the shore power charging cost under the time-of-use electricity price, as shown in formula (2):

[0104]

[0105] Where F2 is the shore power charging cost under the time-of-use electricity price, E iτ is the power consumption of ship i in period τ, C τ is the unit electricity price cost in the τ period, K is a set of different electricity price periods, and τ∈K is the number of the different electricity price periods.

[0106] The objective function three is to minimize the shore power operation time cost, as shown in formula (3):

[0107] minF3=T connect / disconnect +T move (3)

[0108] Where F3 is the shore power operation time cost, T connect / disconnect is the sum of the time required for shore power connection and disconnection of the mobile shore power, T move It is the total time required for the mobile shore power to move.

[0109] S12, setting constraints;

[0110] 1) The power consumption of ship i in each period is shown in formula (4):

[0111]

[0112] In the formula, e iτ is the charging time of ship i in period τ, p i is the average shore power required by ship i during berthing;

[0113] 2) The sum of the time required for mobile shore power connection and disconnection, as shown in formula (5):

[0114]

[0115] In the formula, c iτ Indicates whether ship i uses shore power during the τ period, b jkτ Indicates whether shore power k is at berth j during period τ, is the unit time required for shore power connection, is the unit time required for shore power disconnection, a i is the berth number of ship i;

[0116] 3) The time required for the mobile shore power to move is shown in formula (6):

[0117]

[0118] Where P is the mobile shore power set, N is the discretized berth segment set, T is the planning period time set, j, j′∈N is the discretized continuous berth segment number, k∈P is the mobile shore power number, t∈T is the time, m jj′ktIndicates whether the shore power k moves from berth segment j to berth segment j′ at time t, The unit time required for shore power movement;

[0119] 4) Ship waiting time constraint, as shown in formula (7):

[0120]

[0121] In the formula, y i is the berthing time of ship i;

[0122] 5) The berthing time of a ship depends on the sum of its charging time, charging waiting time and shore power operation time in each period, as well as the service time provided to the ship. Considering that shore power use and cargo loading and unloading are carried out in parallel, the berthing time should be the larger value of the shore power use time and the service time, as shown in formula (8):

[0123]

[0124] In the formula, g i is the berthing time of ship i, e iτ is the charging time of ship i in period τ, The waiting time for ship i to charge, f i Length of service for ship i, d i Indicates whether ship i needs service (loading, unloading);

[0125] 6) Ship departure time constraint, as shown in formula (9):

[0126]

[0127] 7) The ship’s berthing time should not be earlier than the port arrival time, as shown in formula (10):

[0128]

[0129] 8) The ship’s berthing time should not be later than the departure time, as shown in formula (11):

[0130]

[0131] 9) Only one ship can berth at the same berth at the same time, as shown in formulas (12)-(15):

[0132]

[0133] Where M is a sufficiently large positive number, i,i′∈S is the number of the ship arriving at the port within the planning period, and o ii′ Indicates whether ship i berths before ship i′, u ii′Indicates whether the berthing number of ship i is less than the berthing number of ship i′;

[0134] 10) Each berth can only be allocated one shore power at the same time, as shown in formula (16):

[0135]

[0136] Where b jkt Indicates whether shore power k is at berth j at time t;

[0137] 11) Each shore power can only be in one berth at the same time, as shown in formula (17):

[0138]

[0139] 12) The relative order of the single rail shore power cannot be changed at any time, and the shore power cannot cross each other, as shown in formula (18):

[0140]

[0141] Where n kt is the berth number where shore power k is located at time t;

[0142] 13) Ensure that there is idle shore power remaining for the shore power allocated to the berth at each time, as shown in formula (19):

[0143]

[0144] Where P jt is the number of shore power options available at berth j at time t;

[0145] 14) Each mobile shore power device can only be reallocated once at a time, as shown in formula (20):

[0146]

[0147] 15) The decision variable is non-negative, as shown in formula (21):

[0148]

[0149] 16) Define 0-1 variables, as shown in formula (22):

[0150]

[0151] S2, data acquisition module: collects ship information, berth information and shore power information data;

[0152] Ship information: including the ship's estimated arrival time, estimated departure time, charging requirements, service requirements, etc.

[0153] Berth information: including berth number, location, availability, etc.

[0154] Shore power information data; including the number, location, power, etc. of mobile shore power equipment.

[0155] Determine the berth data, shore power data, ship berthing data, and other required relevant data in the step S1 model of the terminal to be studied.

[0156] S3, intelligent decision-making module: using optimization algorithms to solve collaborative optimization models;

[0157] like Figure 3 As shown, the R-NSGA-III algorithm is used to solve the model constructed in step S1 based on the relevant data obtained in step S2. The specific steps are as follows:

[0158] S31. Reference point set construction: According to historical scheduling experience and actual needs, the expected values ​​of multiple objective functions are input. Each expected point represents a preferred position in the target space, and the expected point set is mapped to the reference point set.

[0159] Specifically, the expected points are normalized, reference directions are generated, contractions and shifts are performed, extreme points are added, and finally a reference point set is obtained. The reference point set is used to guide the search direction of the algorithm, ensuring diversity and global search capabilities.

[0160] S32, initializing the population: first set the initial parameters of the R-NSGA-III algorithm, including mutation probability, population size, number of iterations, etc.;

[0161] The mutation probability is used to control the frequency of mutation operations, and the strategy of eliminating duplicate individuals is adopted to ensure the diversity of the population. The maximum number of iterations executed by the algorithm is set to ensure that the algorithm has enough time for optimization.

[0162] Then, random sampling is performed to generate an initial population containing multiple candidate solutions. The population size is determined by the number of reference points and the number of reference directions generated.

[0163] S33, Selection and inheritance: Perform selection, crossover and mutation operations on the initial population to generate a new offspring population, and gradually improve the quality of the population by making feasible solutions with better objective function values ​​have a greater probability of being inherited to the next generation.

[0164] Selection operation: Divide the population into non-dominated levels and select all solutions in the first few non-dominated levels to ensure that high-quality solutions are retained; for solutions of the same level, select a part of them according to their crowding to maintain diversity, and the selected solutions will enter the next generation population.

[0165] Crossover operation: Perform a crossover operation to generate new offspring individuals; common crossover methods that can be used include single-point crossover, multi-point crossover, and uniform crossover.

[0166] Mutation operation: mutation operation is performed on offspring individuals to introduce randomness to increase the diversity of solutions; mutation operation generates new solutions by randomly changing part of the genes of individuals.

[0167] S34. Population merging: The offspring population obtained through selection, crossover and mutation is merged with the original population to form an extended population. The extended population contains more candidate solutions, providing a wider solution space for further optimization, which helps to improve the global search capability of the algorithm.

[0168] S35. Fast non-dominated sort: Perform fast non-dominated sort on the merged population to distinguish solutions of different non-dominated levels.

[0169] The individuals in the population are divided into different levels according to the dominance relationship. The non-dominated solution has a higher level. The population is divided according to the non-dominated level, and the individuals with high non-dominated level are retained first.

[0170] S36, Minimum habitat selection: Associate individuals in the population with reference points and calculate the distance from each individual to the reference point. Apply the minimum habitat selection mechanism based on the reference point to select representative solutions from the merged population and construct a new initial population to prepare for the next round of iteration.

[0171] S37, iterative and termination condition judgment: check whether the algorithm meets the preset number of iterations or convergence criteria; if not, return to step S33 to continue iterating; if satisfied, continue to step S38.

[0172] S38. Output of Pareto optimal solution set: Finally, the Pareto optimal solution set is output, which includes the optimal scheduling and berth allocation scheme of mobile shore power equipment for each ship berth demand within the time period. The output Pareto optimal solution set provides decision makers with a variety of optimization schemes to meet different practical needs. The Pareto frontier is displayed through visualization tools to help decision makers choose the optimal solution.

[0173] S4, allocation execution module: automatically or semi-automatically execute shore power dispatch and berth allocation operations according to the calculation results.

[0174] The dispatcher selects the most appropriate shore power dispatch and berth allocation scheme from the Pareto frontier, including the ship's berthing time, berth number, shore power facilities used, shore power movement and other decision-making information. The selected scheme is analyzed in depth to evaluate the optimization efficiency of the model and its application value in actual dispatch. The selected scheme is implemented to ensure that the ship berths and uses shore power facilities as planned.

[0175] Therefore, the present invention adopts the above-mentioned collaborative optimization method of mobile shore power scheduling and berth allocation, and uses the R-NSGA-III multi-objective optimization algorithm to optimize the ship time cost, electricity usage cost and shore power operation time cost; introduces monorail mobile shore power equipment to optimize its movement path and time between different berths to ensure the flexibility and efficiency of power supply; collects and processes relevant data of ships, berths and shore power in real time to support dynamic scheduling decisions and improve response speed and accuracy.

[0176] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A collaborative optimization method for mobile shore power dispatching and berth allocation, characterized in that: The following steps are involved: S1, model building module: building a collaborative optimization model for mobile shore power dispatching and berth allocation; S2, data acquisition module: collects ship information, berth information and shore power information data; S3, intelligent decision-making module: using optimization algorithms to solve collaborative optimization models; S4, allocation execution module: automatically or semi-automatically execute shore power dispatch and berth allocation operations according to the calculation results.

2. A collaborative optimization method for mobile shore power scheduling and berth allocation according to claim 1, characterized in that: Step S1 specifically includes the following steps: S11, determining the objective function; The ship time cost consists of three parts: the cost of not arriving at the port as planned, the cost of waiting for berthing, and the cost of delayed departure. The objective function is to minimize the ship time cost, as shown in formula (1): Where F1 is the time cost of the ship, i∈S is the number of the ship arriving at the port within the planning period, S is the set of ships arriving at the port within the planning period, and x i is the actual arrival time of ship i, X i The planned arrival time of ship i, w i is the waiting time for ship i to berth, g i is the berthing time of ship i, z i is the actual departure time of ship i, Z i is the planned departure time of ship i, C1 is the unit time cost of the actual arrival at the port not as planned, C2 is the unit time cost of the ship waiting for berthing, C3 is the unit time cost of the ship berthing, and C4 is the unit time cost of the ship's delayed departure; The objective function 2 is to minimize the shore power charging cost under the time-of-use electricity price, as shown in formula (2): Where F2 is the shore power charging cost under the time-of-use electricity price, E iτ is the power consumption of ship i in period τ, C τ is the unit electricity price cost in the τ period, K is the set of different electricity price periods, and τ∈K is the number of different electricity price periods; The objective function three is to minimize the shore power operation time cost, as shown in formula (3): minF3=T connect / disconnect +T move (3) Where F3 is the shore power operation time cost, T connect / disconnect is the sum of the time required for shore power connection and disconnection of the mobile shore power, T move The sum of the time required for the mobile shore power to move; S12, setting constraints; The power consumption of ship i in each period is shown in formula (4): In the formula, e iτ is the charging time of ship i in period τ, p i is the average shore power required by ship i during berthing; The sum of the time required for mobile shore power connection and disconnection is shown in formula (5): In the formula, c iτ Indicates whether ship i uses shore power during the τ period, b jkτ Indicates whether shore power k is at berth j during period τ, is the unit time required for shore power connection, is the unit time required for shore power disconnection, a i is the berth number of ship i; The time required for mobile shore power to move is shown in formula (6): Where P is the mobile shore power set, N is the discretized berth segment set, T is the planning period time set, j, j′∈N is the discretized continuous berth segment number, k∈P is the mobile shore power number, t∈T is the time, m jj′kt Indicates whether the shore power k moves from berth segment j to berth segment j′ at time t, The unit time required for shore power movement; The waiting time constraint for berthing is as shown in formula (7): In the formula, y i is the actual berthing time of ship i; The berthing time of a ship depends on the sum of its charging time, charging waiting time and shore power operation time in each period, as well as the service time provided to the ship. Considering that shore power use and cargo loading and unloading are carried out in parallel, the berthing time should be the larger value of the charging time and the service time, as shown in formula (8): In the formula, g i is the docking time of ship i, e iτ is the charging time of ship i in period τ, The waiting time for ship i to charge, f i Length of service for ship i, d i Indicates whether ship i needs service; The ship berthing and unberthing time constraints are as shown in formula (9): The ship's berthing time should not be earlier than the arrival time, as shown in formula (10): The ship's berthing time should not be later than the departure time, as shown in formula (11): Only one ship can berth at the same berth at the same time, as shown in formulas (12)-(15): Where M is a sufficiently large positive number, i,i′∈S is the number of the ship arriving at the port within the planning period, and o ii′ Indicates whether ship i berths before ship i′, u ii′ Indicates whether the berthing number of ship i is less than the berthing number of ship i′; Each berth can only be allocated one shore power supply at the same time, as shown in formula (16): Where b jkt Indicates whether shore power k is at berth j at time t; Each shore power can only be in one berth at the same time, as shown in formula (17): The relative order of the single rail shore power cannot be changed at any time, and the shore power cannot cross each other, as shown in formula (18): Where n kt represents the berth number where shore power k is located at time t; To ensure that the shore power allocated to the berth at each time has a surplus of idle shore power, as shown in formula (19): Where P jt is the number of shore power options available at berth j at time t; Each mobile shore power device can only be reallocated once at a time, as shown in formula (20): The decision variables are non-negatively constrained, as shown in formula (21): Define 0-1 variables as shown in formula (22):

3. A collaborative optimization method for mobile shore power dispatching and berth allocation according to claim 2, characterized in that: In step S3, the model constructed in step S1 is solved using the R-NSGA-III algorithm based on the relevant data obtained in step S2. The specific steps are as follows: S31, reference point set construction: according to historical scheduling experience and actual needs, the expected values ​​of multiple objective functions are input, each expected point represents a preferred position in the target space, and the expected point set is mapped to the reference point set; S32, initializing the population: first, setting the initial parameters of the R-NSGA-III algorithm, including mutation probability, population size, and number of iterations; then generating an initial population, which contains multiple candidate solutions; S33, selection and inheritance: Perform selection, crossover and mutation operations on the initial population to generate a new offspring population, and gradually improve the quality of the population by making feasible solutions with better objective function values ​​have a greater probability of being inherited to the next generation; S34, Population merging: Merge the offspring population obtained through selection, crossover and mutation with the original population to form an extended population; S35, fast non-dominated sort: perform fast non-dominated sort on the merged population to distinguish solutions with different non-dominated levels; S36, minimum habitat selection, applying the minimum habitat selection mechanism based on reference points to select representative solutions from the merged population and construct a new initial population; S37, iterative and termination condition judgment: check whether the algorithm meets the preset number of iterations or convergence criteria; if not, return to step S33 to continue iterating; if satisfied, continue to step S38; S38. Output of Pareto optimal solution set: The Pareto optimal solution set is finally output, which includes the optimal scheduling and berth allocation plan of mobile shore power equipment for each ship berth demand within the time period.

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