A port electric ship shore power and energy storage combined dispatching control method

Abstract

The application discloses a port electric ship shore power and energy storage combined scheduling control method, and relates to the technical field of port energy management and intelligent scheduling. The specific implementation scheme is as follows: real-time operation state data of a berthing area is obtained; the charging demand load of each berthing electric carrier is predicted, and external grid load is superimposed to generate an original total load prediction curve, and an overload risk target period is identified; a combined scheduling optimization model is constructed by taking the weighted sum of the total load peak value and the average charging waiting time of the carrier as the optimization target for the target period; and the model is solved to obtain a time-of-use charging and charging-discharging power instruction sequence and execute the same. Through the application, the charging service efficiency is optimized in coordination under the grid safety and the rigid constraint of the carrier departure.

Description

Technical Field

[0001] This application relates to the field of port energy management and intelligent scheduling technology, specifically to a method for joint scheduling and control of shore power and energy storage for electric ships in ports. Background Technology

[0002] With the increasing green and low-carbon development of ports and the continuous improvement of ship electrification, the application of shore power systems for charging electric ships during berthing is becoming more and more common. At the same time, some ports have also configured energy storage systems to improve power supply flexibility and load regulation capabilities. In this application scenario, the charging load of electric ships, the existing power load of the port area, the power supply capacity of shore power equipment, and the charging and discharging capacity of the energy storage system are all coupled. Especially when multiple electric ships arrive at the port simultaneously, their berthing times overlap, or their departure times are close together, a large superimposed load can easily form at certain times. Therefore, it is necessary to coordinate and control the shore power system and the energy storage system to meet the charging needs of ships and ensure the safety of power supply in the port area.

[0003] Existing shore power dispatching technologies typically only consider load balancing on the grid side or simple orderly charging strategies, often neglecting the varying urgency of vehicle departure times. When multiple vehicles are berthed and charging simultaneously, if power suppression is implemented solely with grid peak shaving as the goal, it can easily lead to some vehicles about to depart being unable to complete their target charging amount, or causing unnecessary long waits, severely impacting port operational efficiency and service quality. Conversely, prioritizing the immediate charging needs of all vehicles may cause the total load to exceed safe limits during certain periods, threatening the stable operation of the grid.

[0004] Therefore, there is an urgent need for a joint dispatch and control scheme that can fully adapt to the rigid constraints of departure time of berthed electric vehicles while ensuring grid security, and achieve dual optimization of grid peak shaving and vehicle charging service efficiency. Summary of the Invention

[0005] In view of this, the purpose of this application is to provide a method for joint scheduling and control of shore power and energy storage for electric vessels in ports, which can realize the coordinated and optimized scheduling of shore power and energy storage systems during overload risk periods, so as to solve the problem of charging service efficiency under the rigid constraints of the departure time of berthed electric vessels in the existing technology, which is difficult to balance the safe operation of the power grid and the rigid constraints of the departure time of berthed electric vessels.

[0006] According to a first aspect of this application, a method for joint dispatch and control of shore power and energy storage for electric ships in ports is provided, comprising: Acquire real-time operational status data of the berthing area, including information on the berthing electric vehicle queue, operating parameters of the energy storage system, external power grid load, and safety limits; Based on the information of the berthed electric vehicles, the charging demand load of each berthed electric vehicle in each discrete time period is predicted. The charging demand load is superimposed with the external power grid load to generate the original total load prediction curve. Based on the original total load prediction curve and the safety limit, the target time period with overload risk is identified. For the target period, the optimization objective is to minimize the weighted sum of the peak total load and the average charging waiting time of the vehicles. The joint optimization variables are the charging power of each berthed electric vehicle in each discrete period and the charging and discharging power of the energy storage system in each discrete period. A joint scheduling optimization model for shore power and energy storage is constructed. The constraints of the joint scheduling optimization model for shore power and energy storage include: each berthed electric vehicle completes the target charging amount before the planned departure; the output power of the shore power equipment does not exceed the rated limit; the operating status and charging and discharging power of the energy storage system do not exceed the corresponding limit; and the total load in each discrete period after optimization does not exceed the safety limit. Solve the joint scheduling optimization model of shore power and energy storage to obtain the time-sharing charging power command sequence of each berthed electric vehicle and the time-sharing charging and discharging power command sequence of the energy storage system within the target time period; Execute the time-sharing charging power command sequence and the time-sharing charging and discharging power command sequence within the target time period.

[0007] In some embodiments, the berthing electric vehicle queue information includes the planned departure time and current battery charge status of each berthing electric vehicle. The prediction of the charging demand load of each berthing electric vehicle in each discrete time period based on the berthing electric vehicle queue information includes: The remaining berthing time is determined based on the planned departure time and current time of each berthing electric vehicle, and the remaining berthing time is divided into multiple charging time slots; The amount to be charged is determined based on the difference between the target charging amount of each docking electric vehicle and the amount of electricity corresponding to the current state of battery charge. Based on the amount of charge to be charged and the charging time slot, a power-time demand distribution matrix for each docking electric vehicle in each discrete time period is generated; The charging load of each docking electric vehicle in each discrete time period is determined based on the power-time demand distribution matrix.

[0008] In some embodiments, the joint scheduling optimization model of shore power and energy storage is solved to obtain the time-of-use charging power command sequence for each berthed electric vehicle within the target time period, including: Based on the planned departure time and the amount of charge to be collected for each berthed electric vehicle, the charging priority of each berthed electric vehicle within the target time period is determined. According to the charging priority from high to low, the charging power of each docking electric vehicle is allocated in each discrete time period. Within the same discrete time period, when the allocable charging power is less than the sum of the charging demand loads of all participating electric mooring vehicles, the charging power of the electric mooring vehicle with the higher charging priority is allocated first.

[0009] In some embodiments, the optimization objective is determined in the following ways: The maximum value of the total load in each discrete time period after optimization within the target time period is determined as the peak value of the total load; For each berthing electric vehicle participating in the scheduling, determine the start time of the discrete period when the berthing electric vehicle is first allocated non-zero charging power, and calculate the time difference between the start time of the discrete period and the arrival time of the berthing electric vehicle. The average charging waiting time of the vehicle is obtained by averaging the time differences corresponding to all the docking electric vehicles participating in the scheduling. The sum of the product of the first weighting coefficient and the total load peak value, and the product of the second weighting coefficient and the average charging waiting time of the vehicle, is used as the optimization objective.

[0010] In some embodiments, when constructing the joint scheduling optimization model of shore power and energy storage, binary auxiliary variables are introduced to characterize the charging and discharging state of the energy storage system in each discrete time period, and mutual exclusion constraints on the charging and discharging states of the energy storage system are established. The constraint method is as follows:

[0011] in, The binary auxiliary variable represents a discrete time period when the value of the binary auxiliary variable is 0. When the energy storage system is in a discharging state, a value of 1 for the binary auxiliary variable indicates a corresponding discrete time period. The energy storage system is in a charging state at that time. and These represent the energy storage system in discrete time periods. The charging power variable and the discharging power variable at that time This indicates the rated power limit of the energy storage system.

[0012] In some embodiments, superimposing the charging demand load with the external power grid load to generate an original total load forecast curve includes: For each discrete time period, obtain the load value of the external power grid load, the sum of the charging demand load of all moored electric vehicles, and the load value of the basic uncontrollable load. The total load forecast for the discrete time period is obtained by adding the load value of the external power grid load, the sum of the charging demand load of all moored electric vehicles in the discrete time period, and the load value of the basic uncontrollable load in the discrete time period. The total load forecast values ​​for each discrete time period are arranged in chronological order to generate the original total load forecast curve.

[0013] In some embodiments, the constraint condition that the total load in each discrete time period does not exceed the safety limit after optimization includes: For each discrete time period within the target time period, the optimized total load for that discrete time period is constrained to be less than or equal to the safety limit. The optimized total load is obtained by summing the optimized charging power of each docking electric vehicle in the discrete time period, the optimized charging and discharging power of the energy storage system, and the basic uncontrollable load. When the energy storage system is in a charging state, its charging power is included as a negative value in the optimized total load; when the energy storage system is in a discharging state, its discharging power is included as a positive value in the optimized total load.

[0014] In some embodiments, solving the joint scheduling optimization model of shore power and energy storage yields a time-sharing charging and discharging power command sequence for the energy storage system within the target time period, including: For each discrete time period within the target time period, calculate the sum of the charging power of each docked electric vehicle in that discrete time period and the sum of the external power grid load; When the sum of the charging power and the sum of the external grid load exceed the safety limit, the energy storage system is controlled to discharge during the discrete time period, and the discharge power of the energy storage system is the difference between the sum of the charging power and the sum of the external grid load exceeding the safety limit, or the maximum allowable discharge power of the energy storage system during the discrete time period. When the sum of the charging power and the sum of the external grid load are less than or equal to the safety limit, the energy storage system is controlled not to discharge during that discrete period, or to charge under the condition that the state of charge constraint of the energy storage system is met.

[0015] According to a second aspect of this application, a port electric vessel shore power and energy storage joint dispatching and control device is provided. The device is used to execute the steps of the port electric vessel shore power and energy storage joint dispatching and control method described in the first aspect and any embodiment of the first aspect of this application, including: The data acquisition module is used to acquire real-time operating status data of the berthing area. The real-time operating status data includes berthing electric vehicle queue information, energy storage system operating parameters, external power grid load and safety limits. The risk identification module is used to predict the charging demand load of each docking electric vehicle in each discrete time period based on the docking electric vehicle queue information, superimpose the charging demand load with the external power grid load to generate an original total load prediction curve, and identify the target time period with overload risk based on the original total load prediction curve and the safety limit. The model building module is used to construct a shore power and energy storage joint scheduling optimization model for the target time period, with the optimization objective being the minimum weighted sum of the total load peak and the average charging waiting time of the vehicles. The model uses the charging power of each berthed electric vehicle in each discrete time period and the charging and discharging power of the energy storage system in each discrete time period as joint optimization variables. The constraints of the shore power and energy storage joint scheduling optimization model include: each berthed electric vehicle completing the target charging amount before planned departure; the output power of the shore power equipment not exceeding the rated limit; the operating status and charging and discharging power of the energy storage system not exceeding the corresponding limit; and the total load in each discrete time period after optimization not exceeding the safety limit. The model solving module is used to solve the joint scheduling optimization model of shore power and energy storage to obtain the time-sharing charging power command sequence of each berthing electric vehicle and the time-sharing charging and discharging power command sequence of the energy storage system during the target time period. The instruction execution module is used to execute the time-sharing charging power instruction sequence and the time-sharing charging and discharging power instruction sequence within the target time period.

[0016] According to a third aspect of this application, a computer device is provided, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which is executed by the at least one processor to enable the at least one processor to implement the steps of the port electric ship shore power and energy storage joint dispatch and control method described in the first aspect and any embodiment of the first aspect of this application.

[0017] One embodiment of the above application has the following advantages or beneficial effects: This application constructs a power-time demand distribution matrix based on the remaining berthing time and the amount of charge waiting for berthed electric vehicles, transforming the rigid physical constraint of vehicle departure time into a calculable spatiotemporal load characteristic, fundamentally different from the scheduling mode of conventional electric vehicles or fixed loads. On this basis, the joint optimization objective is to minimize the weighted sum of the total load peak and the average charging waiting time of the vehicles, and the constraint condition is forcibly embedded with the hard requirement to complete the target charging amount before departure, so that the optimization solution process must find the optimal solution of grid load under the premise of ensuring that the vehicles depart on time.

[0018] Other effects of the above-mentioned alternative methods will be described below in conjunction with specific embodiments. Attached Figure Description

[0019] The accompanying drawings are provided for a better understanding of this solution and do not constitute a limitation of this application. Wherein: Figure 1 This is a schematic flowchart of the port electric ship shore power and energy storage joint dispatch and control method provided in the embodiments of this application; Figure 2 A structural block diagram of a port electric ship shore power and energy storage joint dispatch and control device provided in the embodiments of this application; Figure 3 This is a structural block diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0020] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0021] See Figure 1 This is a flowchart illustrating the port electric vessel shore power and energy storage joint dispatch and control method provided in this application embodiment. This method addresses the grid overload risk and service efficiency conflict faced by the berthing area when electric vehicles are charging intensively. It coordinates and regulates the power output of shore power equipment and energy storage systems. Figure 1 The execution subject of the method shown can be a combination of software and / or hardware, specifically, it can be one or more of various types of terminals, hardware systems, cloud computing, etc.

[0022] Figure 1 The port electric vessel shore power and energy storage joint dispatch and control method shown includes steps S100 to S500, as detailed below: S100: Obtain real-time operational status data of the berthing area. The real-time operational status data includes information on the berthing electric vehicle queue, operating parameters of the energy storage system, external power grid load, and safety limits.

[0023] Specifically, the berthing electric vehicle queue information reflects the load demand characteristics of the shore power system in the current and future periods, and can be collected in real time through the terminal management system, vehicle battery management system, or smart pile terminal interaction terminal. In this embodiment, berthing electric vehicles are a broader concept, including not only various types of electric vessels (such as pure electric container ships, electric ferries, electric port tugboats, etc.), but also other mobile or fixed electric equipment performing charging operations in the berthing area, such as electric engineering vehicles, mobile energy storage vehicles, and floating operation platforms. To facilitate understanding of the applicability of the technical solution in specific engineering scenarios and the basis for parameter configuration, the following provides reference parameter ranges using three typical berthing electric vehicles as examples: For pure electric container ships, the total capacity of their power batteries is typically between 2000kWh and 10000kWh, and the rated charging power for shore power access is between 500kW and 2000kW. Constrained by port liner schedules, their typical departure time window is concentrated within 2 to 4 hours after arrival, and is often strictly tied to the port's loading and unloading operation window; for electric ferries, the total capacity of their power batteries is typically between 800kWh and 3000kWh. For ferries, the total capacity of their power batteries is typically between 300kWh and 1500kWh, and the rated charging power for shore power access is between 100kW and 500kW. Their operating mode is intermittent standby, and the typical departure interval is relatively regular (e.g., every 30 minutes to 2 hours). For electric port tugboats, the total capacity of their power batteries is typically between 300kWh and 1500kWh, and the rated charging power for shore power access is between 100kW and 500kW. Their operating mode is intermittent standby, and the typical departure time window depends on the dispatch instructions, usually departing within 0.5 to 2 hours after receiving the task notification.

[0024] The aforementioned parameter range reflects the typical technical specifications of mainstream vehicles in current port electrification applications. Those skilled in the art can select or adjust specific parameter values ​​within this range based on the actual port equipment configuration and operating conditions, using them as input data for subsequent charging demand forecasting and joint scheduling optimization model construction. This broad definition ensures that the technical solution of this application is not limited to specific vehicle types, but is applicable to all berthing power consumption scenarios with departure time constraints or operational window constraints. The operating parameters of the energy storage system include, but are not limited to, the current state of charge, maximum charge / discharge power limits, battery health status, and temperature, used to define the adjustable capacity boundary of the energy storage system within the scheduling cycle. External grid load refers to the background load of the distribution network node where the berthing area is located, usually provided by the grid dispatch center or local energy management system. Safety limits are the red lines for ensuring the safe operation of the grid and equipment; these can be the upper limit of transformer capacity, the thermal stability limit of the line, or the power threshold corresponding to power quality constraints. These limits can be fixed values ​​or time-varying curves dynamically adjusted according to ambient temperature or grid dispatch instructions. By comprehensively acquiring the above multi-dimensional data, a solid data foundation is laid for subsequent accurate forecasting and optimization decisions.

[0025] S200: Based on the queuing information of moored electric vehicles, predict the charging demand load of each moored electric vehicle in each discrete time period, superimpose the charging demand load with the external power grid load to generate the original total load prediction curve, and identify the target time period with overload risk based on the original total load prediction curve and safety limits.

[0026] Specifically, discrete time periods are time units divided into continuous time axes according to preset granularity (such as 5 minutes, 15 minutes, or 1 hour). The choice of granularity depends on the balance between control accuracy requirements and computational resources. The prediction of charging demand load is not a simple instantaneous power reading, but a time-series extrapolation combining the vehicle's remaining berthing time, target charging amount, and battery charging characteristic curves. By aligning and superimposing the predicted time-sharing charging demand of each vehicle with the external power grid load along the time axis, the original total load prediction curve without any control measures is obtained. This curve visually demonstrates the evolution trend of the total load in the berthing area over time under natural charging behavior.

[0027] Based on this, by comparing the original total load forecast curve with the safety limit on a time-by-time basis, the time interval during which the total load is expected to exceed the safety limit can be accurately located, i.e., the target time period. It should be understood that the core purpose of introducing the target time period identification mechanism is to achieve on-demand scheduling: joint optimization is initiated only during periods when overload risk is predicted, while during periods with sufficient load, vehicles are allowed to charge freely or only a basic ordered charging strategy is executed. This trigger-based scheduling mechanism avoids the huge computational overhead of full-time global optimization, while also reducing unnecessary intervention in the normal charging process of vehicles, improving the overall operating efficiency of the system and the user experience.

[0028] S300: For the target period, the optimization objective is to minimize the weighted sum of the peak total load and the average charging waiting time of the vehicles. The joint optimization variables are the charging power of each berthed electric vehicle in each discrete period and the charging and discharging power of the energy storage system in each discrete period. A joint scheduling optimization model for shore power and energy storage is constructed. The constraints of the joint scheduling optimization model for shore power and energy storage include: each berthed electric vehicle completes the target charging amount before the planned departure, the output power of the shore power equipment does not exceed the rated limit, the operating status and charging and discharging power of the energy storage system do not exceed the corresponding limit, and the total load in each discrete period after optimization does not exceed the safety limit.

[0029] Specifically, this step is the core modeling stage of the technical solution. The reason for employing joint optimization instead of single shore power dispatch is that berthing electric vehicles have strict rigid constraints on departure time. If charging power is forcibly suppressed solely for the purpose of grid peak shaving, it is highly likely that some vehicles about to depart will be unable to replenish their power, causing delays. Conversely, if the goal is only to meet the needs of the vehicles, it may lead to localized load overloads that threaten grid safety. Therefore, this embodiment constructs a joint optimization model with dual objectives. The total load peak represents the grid's safety margin and economic indicators, while the average vehicle charging waiting time represents the user's service experience indicators. The weighted sum of these two achieves a mathematical coupling and engineering trade-off between grid interests and user interests.

[0030] The joint optimization variables encompass both the time-sharing charging power of each vehicle on the shore power side and the time-sharing charging and discharging power of the energy storage side. This means the model can coordinate the allocation of two types of heterogeneous regulation resources within the same solution framework: when the load is too high, it can reduce the charging power of the vehicles or instruct the energy storage to discharge; when the load is low, it can increase the charging speed of the vehicles or utilize surplus capacity to charge the energy storage. The constraints constitute the physical boundary of the feasible solution, especially the hard constraint that "each berthed electric vehicle must complete the target charging amount before the planned departure," which fundamentally ensures that the scheduling results will not sacrifice the basic operational requirements of the vehicles. It should be noted that this embodiment only provides a functional definition of the optimization objective, variables, and constraints. The specific mathematical expressions, weight coefficient selection, and mutual exclusion logic will be elaborated in detail in subsequent embodiments to avoid prematurely limiting the algorithm details and thus narrowing the protection scope.

[0031] S400: Solve the joint scheduling optimization model of shore power and energy storage to obtain the time-sharing charging power command sequence of each berthed electric vehicle and the time-sharing charging and discharging power command sequence of the energy storage system within the target time period.

[0032] Specifically, the model solution can be implemented using various algorithms such as mixed-integer linear programming, quadratic programming, particle swarm optimization, or deep reinforcement learning; this application does not limit the specific algorithms used. The solver's input consists of the boundary conditions, predicted data, and model parameters for the target time period. The output consists of two sets of time-series command sequences: one set specifies the charging power settings to be executed for each berthing electric vehicle in each discrete time period, and the other set specifies the charging or discharging power settings to be executed for the energy storage system in each discrete time period. These two sets of command sequences are strictly synchronized in time, together constituting a joint scheduling strategy within the target time period. In practical engineering, if the model fails to converge to the global optimum within a predetermined time, a suboptimal feasible solution that satisfies all constraints is also acceptable to ensure the real-time performance and availability of the scheduling commands.

[0033] S500: Executes time-sharing charging power command sequence and time-sharing charging and discharging power command sequence within the target time period.

[0034] Specifically, the instruction execution module converts the obtained power instruction sequence into control signals recognizable by the underlying devices and distributes them to the shore power pile controllers and energy storage converters via industrial Ethernet, CAN bus, or wireless communication. The shore power piles dynamically adjust their output voltage or current based on the received time-sharing charging power instructions, achieving precise control over the charging rate of the berthing electric vehicles. The energy storage converters switch between charging and discharging modes or smoothly transition between different power levels based on the time-sharing charging and discharging power instructions to mitigate overall load fluctuations. During execution, the system can also be configured with a feedback verification mechanism to monitor the deviation between the actual power and the instruction value in real time. When the deviation exceeds a threshold, an alarm is triggered or a backup control strategy is activated, thus forming a complete closed-loop control link. Through the coordinated scheduling of the entire process described above, this embodiment can, while ensuring the safe operation of the power grid, maximize the adaptation to the departure time constraints of berthing electric vehicles, achieving a dual improvement in energy utilization efficiency and service quality.

[0035] This embodiment further details the charging demand prediction mechanism for berthing electric vehicles and the power allocation strategy based on urgency. As one implementation method, the berthing electric vehicle queue information includes the planned departure time and current battery state of charge of each berthing electric vehicle.

[0036] Specifically, the planned departure time is the core physical constraint that distinguishes berthing vehicles from ordinary electric vehicles or stationary energy storage loads. It is usually obtained from the port scheduling system or the ship's declaration and has a high degree of rigidity and cannot be delayed. The current battery state of charge reflects the current energy reserves of the vehicle. Together, they constitute the basic data dimension for calculating the urgency of the charging task.

[0037] As one implementation method, predicting the charging demand load of each berthing electric vehicle in each discrete time period based on berthing electric vehicle queue information includes: determining the remaining berthing time based on the planned departure time and current time of each berthing electric vehicle, and dividing the remaining berthing time into multiple charging time slots; determining the amount to be charged based on the difference between the target charging amount of each berthing electric vehicle and the amount of charge corresponding to the current battery state of charge; generating a power-time demand distribution matrix for each berthing electric vehicle in each discrete time period based on the amount to be charged and the charging time slots; and determining the charging demand load of each berthing electric vehicle in each discrete time period based on the power-time demand distribution matrix.

[0038] Specifically, the above prediction process is not a simple linear extrapolation, but rather maps discrete power shortages to a finite time window, forming structured spatiotemporal load characteristics.

[0039] First, the time difference between the current moment and the planned departure moment is calculated as the remaining berthing time. This time is then divided into several consecutive charging time slots according to a preset scheduling granularity (e.g., 15 minutes). The net energy required to complete this berthing operation, i.e., the amount of energy to be charged, is obtained by subtracting the actual amount of energy corresponding to the current battery state of charge from the target charging amount. This is a crucial step in constructing the power-time demand distribution matrix. The rows of this matrix represent discrete time periods, and the columns represent different berthing electric vehicles. Each element in the matrix represents the theoretical charging demand intensity of the corresponding vehicle within the corresponding time period.

[0040] For example, for a specific vehicle, if its charging capacity is 600kWh, the remaining berthing time is 4 hours and it is divided into 16 15-minute time slots, then under the ideal uniform charging mode, the demand element value for each time slot is 37.5kW. However, considering the constant voltage trickle charging characteristics at the end of the battery charging period or the mandatory full charge requirement before departure, the element values ​​in the matrix near the departure time may be nonlinearly corrected according to the battery charging curve, or even marked as "rigid demand" to distinguish it from the adjustable "elastic demand".

[0041] This matrix representation allows the optimization model to accurately perceive the load potential and time margin at each moment, avoiding the timing mismatch problem caused by the traditional method's extensive scheduling based solely on instantaneous power or total power. In step S204, the system directly reads or aggregates the element values ​​of each row in the matrix to obtain the predicted total charging demand load value after superimposing all vehicles in each discrete time period, providing accurate input for subsequent identification of overload risk periods.

[0042] As one implementation method, the joint scheduling optimization model of shore power and energy storage is solved to obtain the time-sharing charging power instruction sequence for each berthed electric vehicle within the target time period. This includes: determining the charging priority of each berthed electric vehicle within the target time period based on its planned departure time and the amount of charging to be completed; allocating the charging power of each berthed electric vehicle in each discrete time period in descending order of charging priority; and prioritizing the allocation of charging power to berthed electric vehicles with higher charging priority within the same discrete time period when the allocable charging power is less than the sum of the charging demand loads of the participating berthed electric vehicles.

[0043] Specifically, this mechanism is an important supplement to pure mathematical optimization and a reinforcement of physical constraints. The determination of charging priority is essentially based on the "minimum average charging power requirement," which is the ratio of the amount of energy to be charged to the remaining berthing time. The larger this ratio, the more energy must be injected per unit time, and the higher the risk of the vehicle leaving on time, thus it is given a higher priority.

[0044] For example, vehicle A needs to charge 400 kWh in 2 hours (ratio 200 kW / h), while vehicle B needs to charge 500 kWh in 5 hours (ratio 100 kW / h). Although B's total demand is larger, A's time urgency is much higher than B's, therefore A has a higher priority. During power allocation, when the sum of available shore power and energy storage discharge power in a discrete time period cannot meet the needs of all vehicles, the system will strictly implement a "high-priority preemption" strategy: first, the needs of high-priority vehicles will be fully met, and the remaining power will then be allocated to low-priority vehicles according to their priority order.

[0045] It should be understood that this urgency-based priority mechanism has a dual implementation in this application: firstly, it can serve as an embedded constraint or weighting factor in a mixed-integer programming model, guiding the solver to automatically prioritize high-priority vehicles when searching for feasible solutions; secondly, it can also serve as a result verification and correction rule after the model solution is completed, i.e., when the solved power sequence causes a high-priority vehicle to be unable to complete charging before departure, a local rescheduling or power reallocation is forcibly triggered. This flexible implementation ensures that regardless of the underlying algorithm used, the final scheduling result can strictly adapt to the core physical characteristic of the "rigid constraint of departure time" of berthing vehicles, effectively preventing the risk of vehicle delays due to excessive pursuit of grid peak shaving targets.

[0046] This embodiment further details the refined construction method of the optimization objective and the physical constraints of the energy storage system's operating state. As one implementation method, the optimization objective is determined in the following way: the maximum value of the total load in each discrete time period after optimization within the target time period is determined as the peak value of the total load; for each berthing electric vehicle participating in the scheduling, the starting time of the discrete time period when the berthing electric vehicle is first allocated non-zero charging power is determined, and the time difference between the starting time of the discrete time period and the arrival time of the berthing electric vehicle is calculated; the average time difference corresponding to all berthing electric vehicles participating in the scheduling is averaged to obtain the average charging waiting time of the vehicle; the sum of the product of the first weighting coefficient and the peak value of the total load, and the product of the second weighting coefficient and the average charging waiting time of the vehicle, is taken as the optimization objective.

[0047] Specifically, the design of the aforementioned optimization objectives reflects the deep adaptation of this application to the special operating mode of mooring electric vehicles. Among them, the total load peak reflects the safety margin and transformer capacity utilization rate on the grid side, and is a core indicator of traditional microgrid dispatching.

[0048] However, focusing solely on minimizing peak loads can easily lead to scheduling algorithms excessively delaying vehicle charging start-up times in order to smooth out peak loads. This results in a "hidden waiting" phenomenon where vehicles, despite being physically connected to shore charging stations, cannot receive effective power injection for extended periods. To address this, this embodiment innovatively introduces the average charging waiting time of vehicles as a user-side service metric. It is crucial to emphasize that the starting point for calculating this waiting time is not the moment the vehicle physically arrives at the shore or plugs in, but rather the "starting moment of the discrete period for the first allocation of non-zero charging power." This definition has significant technical implications: it precisely distinguishes between "physical arrival time" and "effective service time," explicitly including the power gap period actively suppressed by the scheduling system due to grid overload in the penalty cost.

[0049] For example, an electric ship arrives at port at 10:00 and connects to shore power. However, because the grid load has reached the safety limit between 10:00 and 10:30, the scheduling model sets its charging power instruction to 0, and only begins allocating 50kW of charging power at 10:30. Therefore, the ship's charging waiting time is 30 minutes, not 0 minutes. This definition forces the optimization solver to find a balance between peak-shaving benefits and delay penalties when searching for the optimal solution, avoiding extreme solutions that sacrifice user experience by simply pursuing grid performance indicators. The values ​​of the first and second weighting coefficients essentially represent the operator's preference for grid security over service efficiency. In practical engineering applications, the first weighting coefficient typically ranges from 0.6 to 0.9, and the second weighting coefficient typically ranges from 0.1 to 0.4. When the port is experiencing peak electricity demand or the transformer load rate is close to the critical value, the first weighting coefficient can be appropriately increased (e.g., to 0.85) to make the scheduling strategy more inclined to peak shaving and valley filling to ensure equipment safety. Conversely, during off-peak hours at night or during periods of critical shipping operations, the second weighting coefficient can be increased (e.g., to 0.35) to prioritize shortening vehicle waiting times and improve turnaround efficiency. It should be understood that the aforementioned weighting coefficients can be either fixed constants or time-varying parameters dynamically adjusted based on real-time load rates or departure urgency; this application does not impose any limitations on this.

[0050] As one implementation method, when constructing the joint dispatch optimization model of shore power and energy storage, binary auxiliary variables are introduced to characterize the charging and discharging state of the energy storage system in each discrete time period, and mutual exclusion constraints on the charging and discharging states of the energy storage system are established. The constraint method is as follows:

[0051] in, This represents a binary auxiliary variable. When the binary auxiliary variable takes a value of 0, it represents the corresponding discrete time period. When the energy storage system is in a discharging state, a value of 1 for the binary auxiliary variable indicates the corresponding discrete time period. The energy storage system is in a charging state at this time. and These represent the energy storage system in discrete time periods. The charging power variable and the discharging power variable at that time This indicates the rated power limit of the energy storage system.

[0052] Specifically, this mutual exclusion constraint is a crucial safeguard to ensure the physical realism of the optimization model's output. Without this constraint, the solver might output non-zero charging power simultaneously within the same discrete time interval during mathematical programming problem-solving. and discharge power At the electrical physics level, this means that the energy storage converter simultaneously performs rectification and inversion operations, which not only violates the operating principle of the equipment but also leads to spurious calculations of energy conversion efficiency and abnormal battery lifespan degradation. This can be addressed by introducing binary auxiliary variables. The above inequality cleverly achieves logical interlocking of the charging and discharging states: when During the charging state, the inequality becomes and This means that the charging power is allowed to take values ​​within the limit, while the discharge term is forced to fail. In actual modeling, it is usually necessary to combine this with... When the Big M method constraint is used to strictly set the value to zero, the formula here is a simplified expression of the core mutual exclusion logic; conversely, when... (In the discharge state) the inequality becomes Furthermore, the charging module is malfunctioning.

[0053] This modeling approach couples continuous power variables with discrete state variables, forming a typical mixed-integer linear programming problem. In the equation... As a rated power limit, its value is derived from the hardware physical boundaries of the energy storage system, specifically the minimum value among the rated AC side power of the energy storage converter, the maximum allowable charge / discharge power of the battery cluster, or the dynamic power limit under the thermal management system. By... Embedding mutual exclusion constraints not only prevents charging and discharging state conflicts but also simultaneously implements power amplitude limiting protection, ensuring that every scheduling command is within the safe operating envelope of the device. It should be understood that although this embodiment demonstrates a mutual exclusion constraint based on a single binary variable, in other embodiments, two independent binary variables can be used to represent the charging and discharging states respectively, with an additional constraint that their sum is less than or equal to 1 to achieve the same effect. These transformations all fall within the scope of this application.

[0054] This embodiment further details the generation of the original total load forecast curve and the symbol conventions in the optimized total load constraints. As one implementation method, the original total load forecast curve is generated by superimposing the charging demand load and the external power grid load. This includes: for each discrete time period, obtaining the load value of the external power grid load, the sum of the charging demand loads of all docked electric vehicles, and the load value of the basic uncontrollable load; adding the load value of the external power grid load, the sum of the charging demand loads of all docked electric vehicles, and the load value of the basic uncontrollable load to obtain the total load forecast value for that discrete time period; and arranging the total load forecast values ​​for each discrete time period in chronological order to generate the original total load forecast curve.

[0055] Specifically, the construction of the original total load forecast curve is essentially a panoramic reconstruction of the future electricity consumption situation in the berthing area. The external grid load reflects the basic power flow transmitted from the upper-level distribution network to the busbar in the berthing area. Its data typically originates from the ultra-short-term load forecast interface of the grid dispatch system or real-time measurements extrapolated from local gateway meters. It exhibits significant periodic fluctuations and is not directly controlled by the shore power system. The sum of the charging demand loads of all berthed electric vehicles in this discrete time period is the result of aggregating the theoretical power demand of all vehicles in port under the same time slice, based on the power-time demand distribution matrix generated in the embodiment. This component represents the total power that the shore power system would naturally absorb without any dispatch intervention.

[0056] The basic uncontrollable loads encompass other rigid electrical equipment within the berthing area besides the charging of electric vehicles, such as dock lighting, monitoring systems, office facilities, water pumps, and ventilation equipment. Although the individual power of these loads is relatively small, their cumulative effect as long-term baseline loads cannot be ignored. By algebraically adding the above three types of heterogeneous loads within the same discrete time period, the system can obtain an unoptimized baseline load curve reflecting natural power consumption behavior. This curve is not only the sole criterion for subsequently identifying target periods of overload risk but also a reference for evaluating the peak-shaving effect of joint dispatch strategies. For example, if the external grid load is 2000kW in a certain discrete time period, the total natural charging demand of the three berthing electric vehicles is 1500kW, and the basic uncontrollable load is 300kW, then the original total load forecast for that time period is 3800kW. If the safety limit at this time is 3500kW, then this time period is marked as the target period requiring joint optimization. This component-based superposition prediction mechanism ensures the comprehensiveness and accuracy of risk assessment, avoiding omissions or misjudgments due to the omission of a certain type of load component.

[0057] As one implementation method, the constraint condition that the total load of each discrete time period after optimization does not exceed the safety limit includes: for each discrete time period within the target time period, constraining the total load after optimization of that discrete time period to be less than or equal to the safety limit; the total load after optimization is obtained by superimposing the optimized charging power of each berthing electric vehicle in that discrete time period, the optimized charging and discharging power of the energy storage system, and the basic uncontrollable load; wherein, when the energy storage system is in the charging state, its charging power is included in the total load after optimization as a negative value, and when the energy storage system is in the discharging state, its discharging power is included in the total load after optimization as a positive value.

[0058] Specifically, this constraint is a mathematical defense line to ensure the safe operation of the power grid, and its core lies in accurately expressing the regulatory effect of the energy storage system on the net load of the power grid. It is important to note that the notation convention of "negative for charging, positive for discharging" for energy storage power in this embodiment is a specific definition for the algebraic operation rules within the optimization model, rather than a negation of electrical and physical conventions. At the physical level, the energy storage system does absorb power from the power grid when charging, leading to an increase in the absolute load on the grid side; when discharging, it injects power into the grid, leading to a decrease in the absolute load on the grid side. However, in constructing the mathematical inequality constraint "the total load after optimization does not exceed the safety limit," this application adopts the aforementioned notation rule to unify the direction of variables and adapt to the standard form requirements of a specific solver. Specifically, when the energy storage system discharges, its discharge power participates in the calculation of the constraint equation in a positive form, representing its positive support or substitution capacity for the grid load; when the energy storage system charges, its charging power participates in the calculation in a negative form, representing its additional occupation of grid output or its reverse effect in the regulation dimension. For example, in a discrete time period, if the sum of the optimized charging power of all docked electric vehicles is 1200kW, the uncontrollable load of the base is 300kW, and the discharge power of the energy storage system is 500kW, then according to the notation conventions of this embodiment, the calculation expression of the optimized total load is reflected in the model as the algebraic sum of each component. The final actual net load of the power grid mapped to the physical layer is 1200 + 300 - 500 = 1000kW, which is constrained within the safety limit. Conversely, if the charging power of the energy storage system is 400kW, it is counted as a negative value in the model, and the actual net load of the power grid mapped to the physical layer is 1200 + 300 + 400 = 1900kW.

[0059] It should be understood that this symbolic processing method is essentially a mathematical modeling technique. Its purpose is to ensure that the optimization solver can correctly handle the different effects of energy storage charging and discharging on the safety boundary within a unified algebraic framework, preventing constraint failure or non-physical solutions caused by symbol confusion. In actual engineering deployment, technicians can make equivalent transformations to the above symbol conventions based on the type of optimization solver or programming habits used, without departing from the core technical concept of "energy storage discharging alleviates overload, charging aggravates overload" in this application. For example, all power values ​​can be unified to positive values ​​and directions can be distinguished by addition and subtraction signs. These transformations are all within the scope of protection of this application. In addition, the safety limit itself can also be a time-varying parameter. For example, the limit can be appropriately reduced during the afternoon in summer when the transformer heat dissipation conditions are poor, and restored to the rated limit at night when heat dissipation is good, so that the constraint conditions are more in line with the actual thermal stability characteristics of the equipment.

[0060] As one implementation method, the joint scheduling optimization model of shore power and energy storage is solved to obtain the time-sharing charging and discharging power command sequence of the energy storage system within the target time period. This includes: for each discrete time period within the target time period, calculating the sum of the charging power of each berthed electric vehicle and the sum of the external grid load during that discrete time period; when the sum of the charging power and the sum of the external grid load are greater than the safety limit, controlling the energy storage system to discharge during that discrete time period, and the discharge power of the energy storage system is the difference between the sum of the charging power and the sum of the external grid load exceeding the safety limit, or the maximum allowable discharge power of the energy storage system during that discrete time period; when the sum of the charging power and the sum of the external grid load are less than or equal to the safety limit, controlling the energy storage system not to discharge during that discrete time period, or charging under the condition of satisfying the state of charge constraints of the energy storage system.

[0061] Specifically, the technical solution provided in this embodiment is logically different from the global optimization solution path based on mathematical programming. It is essentially a threshold-triggered heuristic control strategy, which can be used as an alternative implementation method for model solving or a real-time power correction strategy.

[0062] In practical engineering applications, although the global optimization model can take into account the comprehensive optimality of power grid peak shaving and vehicle service efficiency, its solution process usually involves complex mixed integer operations, which consumes a lot of computing resources and takes a long time, making it difficult to cope with extreme conditions such as millisecond-level sudden load fluctuations or communication link interruptions.

[0063] In contrast, the heuristic strategy employed in this embodiment abandons global prediction and optimization for future periods, instead focusing on instantaneous power balance and safety boundary protection for the current discrete period. Its core logic lies in establishing a rapid closed loop of "detection-calculation-execution": the system monitors in real time the sum of the total charging demand of the docked electric vehicles and the background load of the external power grid. Once this sum exceeds the preset safety limit, the energy storage system's discharge compensation mechanism is immediately triggered. The determination of discharge power follows the principle of "filling on demand and capping capacity," that is, prioritizing the difference exceeding the safety limit as the discharge command to achieve precise load clamping; if the difference exceeds the current physical discharge capacity of the energy storage system (such as being limited by the inverter's rated power, the battery's maximum discharge rate, or the available power at the current SOC), then saturation output is performed with the maximum allowable discharge power as the upper limit to alleviate overload pressure to the greatest extent. Conversely, when the sum of loads is within the safe range, the energy storage system remains silent or performs opportunistic charging when spare capacity allows, to store energy to cope with potential subsequent overload risks.

[0064] It should be understood that while this threshold-based control method may not be as refined as the global optimization model in terms of overall economy and service experience—for example, it cannot predict and smooth future load peaks in advance, nor can it coordinate the charging waiting order of vehicles from a global perspective—it possesses extremely high response speed and robustness. In scenarios with limited computing power at the edge computing gateway, model solver failure, loss of upper-level scheduling data, or extremely high real-time requirements for grid security, this embodiment can serve as a reliable safety fallback, ensuring that the power load in the docking area is always limited within the physical safety envelope. Furthermore, this embodiment can also run in parallel with the aforementioned optimization model, forming a two-layer control architecture of "optimization layer + safety layer": the optimization layer is responsible for generating long-term baseline scheduling plans, pursuing maximum comprehensive benefits; the safety layer acts as a high-frequency, real-time watchdog, performing secondary verification and dynamic correction on the execution results of optimization commands. When the actual load unexpectedly exceeds the limit due to prediction deviations or equipment response lags, the safety layer takes over control for rapid intervention, and then returns control to the optimization layer after the system returns to steady state. This hierarchical collaborative mechanism retains the performance advantages of global optimization while compensating for the vulnerability of pure model control in uncertain environments, significantly improving the engineering practical value and operational reliability of the shore power and energy storage joint dispatch system.

[0065] Please see Figure 2 This is a structural block diagram of the port electric ship shore power and energy storage joint dispatch control device provided in the embodiments of this application. As an implementation of the port electric ship shore power and energy storage joint dispatch control method described above, this application provides an embodiment of the port electric ship shore power and energy storage joint dispatch control device, as shown in the figure. This embodiment of the port electric ship shore power and energy storage joint dispatch control device is similar to... Figure 1 Corresponding to the method embodiment shown, the joint dispatch and control system for shore power and energy storage of electric ships in ports can be specifically applied to various electronic devices.

[0066] This embodiment of a port electric vessel shore power and energy storage joint dispatch and control device includes: The data acquisition module is configured to acquire real-time operating status data of the berthing area. The real-time operating status data includes berthing electric vehicle queue information, energy storage system operating parameters, external power grid load and safety limits. The risk identification module is configured to predict the charging demand load of each berthing electric vehicle in each discrete time period based on the berthing electric vehicle queue information, superimpose the charging demand load with the external power grid load to generate an original total load prediction curve, and identify target time periods with overload risk based on the original total load prediction curve and the safety limit. The model building module is configured to, for the target time period, minimize the weighted sum of the peak total load and the average charging waiting time of the vehicles as the optimization objective, and use the charging power of each berthed electric vehicle in each discrete time period and the charging and discharging power of the energy storage system in each discrete time period as joint optimization variables to construct a joint scheduling optimization model for shore power and energy storage. The constraints of the joint scheduling optimization model for shore power and energy storage include: each berthed electric vehicle completes the target charging amount before the planned departure, the output power of the shore power equipment does not exceed the rated limit, the operating status and charging and discharging power of the energy storage system do not exceed the corresponding limit, and the total load in each discrete time period after optimization does not exceed the safety limit. The model solving module is configured to solve the joint scheduling optimization model of shore power and energy storage, and obtain the time-sharing charging power command sequence of each berthing electric vehicle and the time-sharing charging and discharging power command sequence of the energy storage system within the target time period; The instruction execution module is configured to execute the time-sharing charging power instruction sequence and the time-sharing charging and discharging power instruction sequence during the target time period.

[0067] Please see Figure 3 , Figure 3 This is a structural block diagram of a computer device provided in an embodiment of this application.

[0068] A computer device according to an embodiment of this application includes: a memory 301 and a processor 302. The memory 301 stores a computer program. When the computer program is executed by the processor 302, the processor 302 performs the steps of the port electric ship shore power and energy storage joint scheduling and control method as described in the above embodiment.

[0069] The memory 301 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. The memory 301 has a storage space 303 for program code 313 for performing any of the method steps described above. For example, the storage space 303 for program code may include various program codes 304 for implementing the various steps in the above method. These program codes may be read from or written to one or more computer program products, including program code carriers such as hard disks, compact discs (CDs), memory cards, or floppy disks. The program code may be compressed, for example, in an appropriate form.

[0070] When these codes are run by a computing processing device, they cause the computing processing device to perform the various steps in the methods described above. These program codes can be read from or written to one or more computer program products, including program code carriers such as hard disks, compact discs (CDs), memory cards, or floppy disks. The program codes can be compressed, for example, in an appropriate form. When these codes are run by a computing processing device, they cause the computing processing device to perform the steps of the port electric vessel shore power and energy storage joint scheduling and control method described in the above embodiments.

[0071] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0072] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0073] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for joint dispatch and control of shore power and energy storage for electric ships in ports, characterized in that, include: Acquire real-time operational status data of the berthing area, including information on the berthing electric vehicle queue, operating parameters of the energy storage system, external power grid load, and safety limits; Based on the information of the berthed electric vehicles, the charging demand load of each berthed electric vehicle in each discrete time period is predicted. The charging demand load is superimposed with the external power grid load to generate the original total load prediction curve. Based on the original total load prediction curve and the safety limit, the target time period with overload risk is identified. For the target period, the optimization objective is to minimize the weighted sum of the peak total load and the average charging waiting time of the vehicles. The joint optimization variables are the charging power of each berthed electric vehicle in each discrete period and the charging and discharging power of the energy storage system in each discrete period. A joint scheduling optimization model for shore power and energy storage is constructed. The constraints of the joint scheduling optimization model for shore power and energy storage include: each berthed electric vehicle completes the target charging amount before the planned departure; the output power of the shore power equipment does not exceed the rated limit; the operating status and charging and discharging power of the energy storage system do not exceed the corresponding limit; and the total load in each discrete period after optimization does not exceed the safety limit. Solve the joint scheduling optimization model of shore power and energy storage to obtain the time-sharing charging power command sequence of each berthed electric vehicle and the time-sharing charging and discharging power command sequence of the energy storage system within the target time period; Execute the time-sharing charging power command sequence and the time-sharing charging and discharging power command sequence within the target time period.

2. The method according to claim 1, characterized in that, The berthing electric vehicle queue information includes the planned departure time and current battery charge status of each berthing electric vehicle. The prediction of the charging demand load of each berthing electric vehicle in each discrete time period based on the berthing electric vehicle queue information includes: The remaining berthing time is determined based on the planned departure time and current time of each berthing electric vehicle, and the remaining berthing time is divided into multiple charging time slots; The amount to be charged is determined based on the difference between the target charging amount of each docking electric vehicle and the amount of electricity corresponding to the current state of battery charge. Based on the amount of charge to be charged and the charging time slot, a power-time demand distribution matrix for each docking electric vehicle in each discrete time period is generated; The charging load of each docking electric vehicle in each discrete time period is determined based on the power-time demand distribution matrix.

3. The method according to claim 2, characterized in that, Solving the joint scheduling optimization model of shore power and energy storage yields the time-of-use charging power command sequence for each berthing electric vehicle during the target time period, including: Based on the planned departure time and the amount of charge to be collected for each berthed electric vehicle, the charging priority of each berthed electric vehicle within the target time period is determined. According to the charging priority from high to low, the charging power of each docking electric vehicle is allocated in each discrete time period. Within the same discrete time period, when the allocable charging power is less than the sum of the charging demand loads of all participating electric mooring vehicles, the charging power of the electric mooring vehicle with the higher charging priority is allocated first.

4. The method according to claim 1, characterized in that, The optimization objective was determined in the following way: The maximum value of the total load in each discrete time period after optimization within the target time period is determined as the peak value of the total load; For each berthing electric vehicle participating in the scheduling, determine the start time of the discrete period when the berthing electric vehicle is first allocated non-zero charging power, and calculate the time difference between the start time of the discrete period and the arrival time of the berthing electric vehicle. The average charging waiting time of the vehicle is obtained by averaging the time differences corresponding to all the docking electric vehicles participating in the scheduling. The sum of the product of the first weighting coefficient and the total load peak value, and the product of the second weighting coefficient and the average charging waiting time of the vehicle, is taken as the optimization objective.

5. The method according to claim 4, characterized in that, When constructing the joint dispatch optimization model for shore power and energy storage, binary auxiliary variables are introduced to characterize the charging and discharging state of the energy storage system in each discrete time period. Mutually exclusive constraints on the charging and discharging states of the energy storage system are established, and the constraint method is as follows: in, The binary auxiliary variable represents a discrete time period when the binary auxiliary variable takes a value of 0. When the energy storage system is in a discharging state, a value of 1 for the binary auxiliary variable indicates a corresponding discrete time period. The energy storage system is in a charging state at that time. and These represent the energy storage system in discrete time periods. The charging power variable and the discharging power variable at that time This indicates the rated power limit of the energy storage system.

6. The method according to claim 1, characterized in that, The original total load forecast curve is generated by superimposing the charging demand load with the external power grid load, including: For each discrete time period, obtain the load value of the external power grid load, the sum of the charging demand load of all moored electric vehicles, and the load value of the basic uncontrollable load. The total load forecast for the discrete time period is obtained by adding the load value of the external power grid load, the sum of the charging demand load of all moored electric vehicles in the discrete time period, and the load value of the basic uncontrollable load in the discrete time period. The total load forecast values ​​for each discrete time period are arranged in chronological order to generate the original total load forecast curve.

7. The method according to claim 6, characterized in that, The optimized constraints that prevent the total load in each discrete time period from exceeding the safety limit include: For each discrete time period within the target time period, the optimized total load for that discrete time period is constrained to be less than or equal to the safety limit. The optimized total load is obtained by summing the optimized charging power of each docking electric vehicle in the discrete time period, the optimized charging and discharging power of the energy storage system, and the basic uncontrollable load. When the energy storage system is in a charging state, its charging power is included as a negative value in the optimized total load; when the energy storage system is in a discharging state, its discharging power is included as a positive value in the optimized total load.

8. The method according to claim 1, characterized in that, Solving the joint scheduling optimization model of shore power and energy storage yields the time-sharing charging and discharging power command sequence of the energy storage system within the target time period, including: For each discrete time period within the target time period, calculate the sum of the charging power of each docked electric vehicle in that discrete time period and the sum of the external power grid load; When the sum of the charging power and the sum of the external grid load exceed the safety limit, the energy storage system is controlled to discharge during the discrete time period, and the discharge power of the energy storage system is the difference between the sum of the charging power and the sum of the external grid load exceeding the safety limit, or the maximum allowable discharge power of the energy storage system during the discrete time period. When the sum of the charging power and the sum of the external grid load are less than or equal to the safety limit, the energy storage system is controlled not to discharge during that discrete period, or to charge under the condition that the state of charge constraint of the energy storage system is met.

9. A port electric vessel shore power and energy storage joint dispatch and control device, the device being used to execute the steps of the port electric vessel shore power and energy storage joint dispatch and control method according to any one of claims 1 to 8, characterized in that, include: The data acquisition module is used to acquire real-time operating status data of the berthing area. The real-time operating status data includes berthing electric vehicle queue information, energy storage system operating parameters, external power grid load and safety limits. The risk identification module is used to predict the charging demand load of each docking electric vehicle in each discrete time period based on the docking electric vehicle queue information, superimpose the charging demand load with the external power grid load to generate an original total load prediction curve, and identify the target time period with overload risk based on the original total load prediction curve and the safety limit. The model building module is used to construct a joint scheduling optimization model for shore power and energy storage for the target period, with the optimization objective being the minimum weighted sum of the peak total load and the average charging waiting time of the vehicles, and with the charging power of each berthed electric vehicle in each discrete period and the charging and discharging power of the energy storage system in each discrete period as joint optimization variables. The constraints of the joint scheduling optimization model of shore power and energy storage include: each berthed electric vehicle completes the target charging amount before the planned departure, the output power of the shore power equipment does not exceed the rated limit, the operating status and charging and discharging power of the energy storage system do not exceed the corresponding limit, and the total load of each discrete time period after optimization does not exceed the safety limit. The model solving module is used to solve the joint scheduling optimization model of shore power and energy storage to obtain the time-sharing charging power command sequence of each berthing electric vehicle and the time-sharing charging and discharging power command sequence of the energy storage system during the target time period. The instruction execution module is used to execute the time-sharing charging power instruction sequence and the time-sharing charging and discharging power instruction sequence within the target time period.

10. A computer device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to implement the steps of the port electric vessel shore power and energy storage joint dispatch control method according to any one of claims 1 to 8.

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