A port electric truck charging method for wind power on-site consumption

Abstract

The application discloses a kind of port electric truck ordered charging methods for wind power local consumption.First, with the minimum port electricity purchase cost as the goal, considering the power balance constraint, electric truck SOC constraint, minimum residual power and maximum charging power constraint and logistics scheduling constraint, the port electric truck logistics and charging scheduling model is constructed, and particle swarm optimization algorithm is used to solve optimization;Second, in the logistics operation and scheduling module, read the port logistics task of the day, judge the charging demand according to the minimum residual power of electric truck, assign the electric truck transportation that meets the power and distance requirements;Finally, in the charging pile selection and charging module, assign electric truck charging according to the principle of proximity, and judge the charging state according to the maximum charging power, calculate the charging duration and power of electric truck, and calculate the port electricity purchase cost in the electricity cost calculation module.The application adopts the ordered charging strategy of port electric truck, realizes the goal of wind power self-generation and self-use, maximization local consumption.

Description

Technical Field

[0001] This invention relates to the field of application technology of port electric container trucks and local wind power consumption, specifically to an orderly charging method for port electric container trucks for local wind power consumption. Background Technology

[0002] Improving the energy efficiency management of carbon emissions in ports is of great significance for achieving energy conservation, emission reduction, and energy transition in the transportation industry. Adopting measures such as wind turbine power supply and full electrification of loads is the mainstream trend for green development of ports now and for some time to come.

[0003] However, existing source-load optimization scheduling methods primarily aim to reduce fluctuations and are not suitable for port scenarios with rigid transportation constraints. Furthermore, electric truck charging strategies rely solely on remaining power, failing to reflect the temporal coupling between port transportation scheduling and efficient local wind power utilization. Additionally, although all wind power output can be fed into the grid, a difference exists between the grid-connected electricity price and the purchased electricity price. To maximize port economic efficiency, the level of wind power self-consumption and local utilization should be improved. To address this issue, a method for orderly charging of port electric trucks, geared towards local wind power utilization, is proposed.

[0004] A review of existing literature revealed that a collaborative scheduling optimization strategy for the entire port energy flow-logistics process considering multi-source incentives (Pu Yue, Liu Haoming, Wang Jian, Yang Zhihao. Collaborative scheduling optimization for the entire port energy flow-logistics process considering multi-source incentives [J / OL]. Proceedings of the CSEE: 1-20. DOI: 10.13334 / j.0258-8013.pcsee.221326.) was proposed. An improved non-dominated sorting genetic algorithm was used to solve the scheduling optimization model for the entire logistics process. The scheduling strategy, which considers grid demand incentives and pollution reduction and carbon reduction incentives, can effectively respond to the grid peak shaving and valley filling requirements and reduce the port's electricity demand during peak periods. While this study combines energy flow and logistics, it does not consider methods for orderly charging of electric trucks. The authorized publication number CN103904749 B discloses an orderly charging control method for electric vehicles that considers wind power output fluctuations, aiming to optimize the effect of smoothing wind power output fluctuations while also taking into account user interests. The invention publication number CN 107867187 A discloses an orderly charging control method for electric vehicles oriented towards wind power absorption, determining the charging order of vehicles based on wind power output. The authorized publication number CN111216586 B discloses an orderly charging control method for electric vehicles in residential communities that considers wind power absorption, using the Monte Carlo method to simulate the charging behavior of electric vehicles and employing a differential evolution algorithm to obtain the pre-allocation curve of charging power for electric vehicles in the community. These three invention patents have different control objects than this invention, and none of them consider the rigid constraints of electric vehicle logistics, nor are they applicable to the scenarios of electric truck logistics and charging scheduling in ports. Summary of the Invention

[0005] The purpose of this invention is to provide an orderly charging method for port electric trucks for local wind power consumption, so as to solve the time-series coupling problem between port transportation scheduling and local wind power consumption.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for orderly charging of port electric container trucks for local wind power consumption, characterized by employing a particle swarm optimization algorithm, the method comprising the following steps:

[0008] Step 1: With the goal of minimizing electricity purchase costs, considering constraints such as charging capacity, number of electric trucks, number of charging piles, and logistics scheduling, the minimum remaining power and maximum charging capacity are used as control variables to carry out rolling port electric truck logistics and charging scheduling.

[0009] Step 2: Enter the logistics operation module, read the port's logistics operation tasks for the day, including: the coordinates of the starting quay crane, start time, end time, number of 20-foot and 40-foot containers, coordinates of each electric truck, remaining battery power, driving speed, response time, quay crane loading and unloading time, and yard loading and unloading time. Divide the electric trucks into dispatchable and non-dispatchable states according to the minimum remaining battery power, and calculate the driving distance of the dispatchable electric trucks from the starting point of the quay crane based on the known coordinates of the electric trucks and the port's electric truck driving requirements.

[0010] Step 3: Enter the logistics dispatch module, assign the corresponding dispatch vehicles according to the principle of proximity, number of vehicles and remaining power, and at the same time, calculate the operating distance, remaining power and driving time of the responding trucks, and update the coordinates of each electric truck.

[0011] Step 4: Enter the charging pile selection module, extract the coordinates of the unschedulable vehicles in Step 2, traverse and calculate the distance of each vehicle to the available charging pile, and assign each electric truck to the nearest charging pile according to the principle of proximity and avoiding vehicle conflicts, and update the selected vehicles and charging piles to the charging status.

[0012] Step 5: Enter the charging module, calculate the vehicle's start charging time, the charging capacity during that period, and the remaining capacity, and compare the remaining capacity with the maximum charging capacity. When the maximum charging capacity is reached, update the vehicle status to be schedulable and update the charging pile status to be idle.

[0013] Step 6: Enter the electricity cost calculation module and calculate the electricity purchase cost for this period based on the day-ahead forecast wind power output, day-ahead forecast benchmark load, time-of-use electricity price, and wind power grid connection price.

[0014] Preferably, the scheduling time step in step 1 is 15 minutes.

[0015] Furthermore, step 1 includes an objective function and constraints:

[0016] The objective function established to minimize electricity purchase costs is: Where: T is the entire scheduling period, p toe (t) represents the time-of-use electricity price for the t-th time period, p wind For wind power grid connection tariff, E buy (t) represents the amount of electricity purchased in time period t, E sell (t) represents the electricity sold during time period t.

[0017] The established constraints include 1) power balance constraints, 2) electric truck SOC constraints, 3) upper and lower limit constraints of control variables, and 4) logistics scheduling constraints.

[0018] Power balance constraint: P wind(t)+η t E buy (t)=P ART (t)+P load (t)+(1-η t E sell (t).

[0019] Among them, P wind (t) represents the day-ahead forecast of wind power generation during time period t, η t The state variable representing the purchase or sale of electricity by the power grid during time period t is: P ART (t) represents the charging power of the electric truck during time period t, P load (t) represents the day-ahead forecast baseline load power for time period t.

[0020] Electric truck SOC constraints:

[0021] Where N represents the number of electric trucks. and This represents the sum of the electricity levels at the start and end times of the dispatching quay crane for electric container trucks. Based on this constraint, the logistics requirements of the dispatchable electric container trucks in the next dispatching cycle can be met.

[0022] Minimum remaining battery capacity and maximum charging capacity upper and lower limits constraints:

[0023] Among them: soc last Represents the minimum remaining battery capacity, SOC charge This represents the maximum charging capacity. These are the upper and lower limits of the minimum remaining battery power, respectively. These are the upper and lower limits of the maximum charging capacity, respectively.

[0024] Logistics scheduling constraints:

[0025] in: This represents the sum of the number of containers actually transported by electric trucks within the T-schedule period. It represents the total number of containers that should be transported by electric trucks within the T-schedule period.

[0026] Furthermore, in step 2, the logistics operation task and calculation process are as follows:

[0027] Since the electric container trucks in the port are unmanned, their locations are determined by historical scheduling tasks and are usually randomly distributed within the dock area. To ensure path isolation from external transport vehicles, the driving direction of the electric container trucks is fixed as unidirectional. Using the dock plane as the object, a coordinate system is established with the origin at the lower left corner of the plane. The distances between each electric container truck and the starting point of the quay crane for logistics operations are calculated, and the relationships are as follows:

[0028]

[0029] Among them, D ij x represents the distance from each electric truck to the starting point of the quay crane. i ,y i Represents the x and y coordinates of each electric truck, x j ,y j The x and y coordinates represent the starting point of the quay crane. Since electric container trucks are required to travel horizontally from left to right, and no branching or U-turns are permitted in between, x... right Represents the x-coordinate of the right boundary, when x i >x j When, it means that the electric truck is closer to the right boundary, calculated according to formula (1), and vice versa.

[0030] Furthermore, in step 3, the port logistics scheduling module process is as follows:

[0031] Based on the distance calculated in step 2, when the distances are the same, the vehicle with the most remaining battery power is selected as the second priority to avoid multiple selections.

[0032] The distance traveled by the truck in response to the operation is the sum of the distance from the electric truck's original position to the starting point of the quay crane and the distance from the starting point of the quay crane to the ending point of the yard. The relationship is as follows:

[0033]

[0034] Among them, D ik The distance represented by represents the distance the selected electric truck travels from its original location, through the quay crane's starting point, to the yard's ending point; n represents the fixed number of vehicles per dispatch round; x represents the distance the truck travels from its original location, through the quay crane's starting point, to the yard's ending point; x represents the fixed number of vehicles per dispatch round. k ,y k The x and y coordinates represent the endpoint of the storage yard. Since the electric container truck travels horizontally from right to left from the starting point of the quay crane to the right boundary of the storage yard, and no branching or U-turns are allowed in between, x... left Represents the x-coordinate of the left boundary, when x j >x k When the time is right, it means that the quay bridge is closer to the left boundary, calculated according to formula (1), and vice versa.

[0035] Furthermore, in step 4, the process for selecting a charging station for the electric truck is as follows:

[0036] When a truck needs charging, to avoid multiple trucks selecting the same charging station, when an available charging station is selected, its status is changed to charging to avoid conflict.

[0037] The distance D from the selected electric truck to the charging station ip With respect to the provisions of right 4, D ij Consistent,

[0038] Where, x i ,y i The horizontal and vertical coordinates represent the available charging stations, and p represents the number of electric trucks assigned to charging in the current time period.

[0039] Furthermore, in step 5, the process of calculating the charging parameters is as follows:

[0040] Calculate the charging duration and charge amount for each scheduling period. The charging duration is:

[0041]

[0042] Among them, T t charge The duration of charging in time period t. t1 indicates whether there are any new charging trucks in time period t. If there are none, it is 0; if there are, it is 1. t1 represents the response time of the electric truck. 0.25 means 0.25h.

[0043] The charging capacity is: P ART (t)=p charge T t charge .

[0044] As a preferred option, the present invention selects the particle swarm optimization algorithm for solving the problem.

[0045] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0046] This invention utilizes information such as predicted wind power output, baseline load, and port logistics operations to establish an orderly charging model for electric trucks with the goal of minimizing electricity purchase costs. It uses the minimum remaining power and maximum charging power as control objects to perform time-coupled scheduling optimization, thereby realizing the port's ability to absorb wind power locally. At the same time, it meets the rigid constraints of logistics and is applicable to the scheduling problems of electric truck logistics and charging in port scenarios. Attached Figure Description

[0047] The technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. However, it should be understood that these drawings are designed for illustrative purposes only and are not intended to limit the scope of the present invention. Furthermore, these drawings are only intended to conceptually illustrate the processes and illustrations described herein and are not necessarily drawn to scale.

[0048] Figure 1 This invention provides a flowchart of the orderly charging process for electric trucks designed for on-site wind power consumption.

[0049] Figure 2 This is a schematic diagram of a fixed driving direction for an electric container truck in a port, provided by the present invention.

[0050] Figure 3 This invention provides a flowchart of port electric container truck logistics operations and charging scheduling.

[0051] Figure 4 This is a schematic diagram of the day-ahead predicted wind power output curve and the baseline load curve provided in an embodiment of the present invention;

[0052] Figure 5 This is a comparison chart of electricity purchase costs provided in an embodiment of the present invention;

[0053] Figure 6 This is a comparison chart of wind power local consumption rates provided in an embodiment of the present invention. Detailed Implementation

[0054] The following is in conjunction with the appendix Figure 1 To be continued Figure 3 The specific embodiments described below will further illustrate the present invention in detail. It should be understood that the specific embodiments described below are only for illustrating the present invention and are not intended to limit the scope of the present invention.

[0055] Example

[0056] Taking a port terminal's logistics operations on a particular day as an example, the terminal has 76 electric container trucks, 13 charging piles, and 2 wind turbines, each with a capacity of 4.5MW. The time-of-use electricity price adopts Tianjin's general industrial and commercial electricity price (35-110kV voltage level), and the wind power grid connection price is 0.3655 yuan / kWh, according to the attached... Figure 1 and attached Figure 3 The process shown executes logistics and charging scheduling methods.

[0057] Step 1: Determine the scheduling time granularity as 15 minutes. The day-ahead forecast wind power output and baseline load for the terminal are shown in the attached figure. Figure 4 As shown.

[0058] The particle swarm optimization algorithm is used, with a given number of 30 particles, 100 iterations, and inertia weights. w The variable weight w is 1. aThe fitness function is set to minimize the port's electricity purchase cost, with an individual learning factor C1 of 0.99 and a group learning factor C2 of 1.5.

[0059] Where: T is the entire scheduling period, p toe (t) represents the time-of-use electricity price for the t-th time period, p wind For wind power grid connection tariff, E buy (t) represents the amount of electricity purchased in time period t, E sell (t) represents the electricity sold during time period t.

[0060] The constraints include 1) power balance constraints, 2) electric truck SOC constraints, 3) minimum remaining power and maximum charging power upper and lower limits constraints, and 4) logistics scheduling constraints.

[0061] Power balance constraint: P wind (t)+η t E buy (t)=P ART (t)+P load (t)+(1-η t E sell (t).

[0062] Among them, P wind (t) represents the day-ahead forecast of wind power generation during time period t, η t The state variable representing the purchase or sale of electricity by the power grid during time period t is: P ART (t) represents the charging power of the electric truck during time period t, P load (t) represents the day-ahead forecast baseline load power for time period t.

[0063] Electric truck SOC constraints:

[0064] Where N represents the number of electric trucks. and These represent the sum of the electric truck's power at the start and end times, respectively. Based on this constraint, the logistics requirements of the available electric trucks in the next scheduling cycle can be met.

[0065] Minimum remaining battery capacity and maximum charging capacity upper and lower limits constraints:

[0066] Among them: soc last Represents the minimum remaining battery capacity, SOC charge This represents the maximum charging capacity. These are the upper and lower limits of the minimum remaining battery power, respectively. These are the upper and lower limits of the maximum charging capacity, respectively.

[0067] Logistics scheduling constraints:

[0068] in: This represents the sum of the number of containers actually transported by electric trucks within the T-schedule period. It represents the total number of containers that should be transported by electric trucks within the T-schedule period.

[0069] Minimum remaining power SOC last and maximum charging capacity SoC charge As control variables, the logistics and charging scheduling of electric container trucks in ports are carried out, and the optimal minimum remaining power and maximum charging power are obtained by solving the problem.

[0070] Step 2: Enter the logistics operation module and read the port's logistics operation tasks for the day, including: starting quay crane coordinates (x j ,y j ), start time t start End time t end Quantity B of 20-foot and 40-foot containers 20 B 40 The coordinates (x, y) of each electric truck i ,y i i = 1,...,76, Remaining battery power soc(i), i = 1,...,76, Driving speed v Response time t1, quay crane loading and unloading time t2, yard loading and unloading time t3, power consumption per kilometer of electric truck travel, self-discharge rate.

[0071] Electric trucks are categorized into dispatchable and standby charging states based on their minimum remaining battery power. The standby charging state means they are not dispatchable. The travel distance of a dispatchable electric truck from the starting point of the quay crane is calculated using a formula. The travel direction of the electric trucks is shown in the appendix. Figure 2 As shown;

[0072]

[0073] Among them, D ij x represents the distance from each electric truck to the starting point of the quay crane. i ,y i Represents the x and y coordinates of each electric truck, x j ,y j The x and y coordinates represent the starting point of the quay crane. Since the horizontal direction of electric container trucks within the yard area is specified as left to right, and no branching or U-turns are allowed in between, x... right Represents the x-coordinate of the right boundary, when x i >x j When, it means that the electric truck is closer to the right boundary, calculated according to formula (1), and vice versa.

[0074] Step 3: Enter the logistics scheduling module, select 7 vehicles according to the ratio of quay crane to electric truck of 1:7 and the principle of proximity. When a conflict occurs due to the same distance, select the electric truck with more remaining power for assignment.

[0075] The assigned operating distance of the electric container truck is calculated according to the formula. The operating distance of the electric container truck is the sum of the distance from the original position to the starting point of the quay crane and the distance from the starting point of the quay crane to the ending point of the stockyard. The relationship is as follows:

[0076]

[0077] Among them, D ik The distance represented by represents the distance the selected electric truck travels from its original location, through the quay crane's starting point, to the yard's ending point; n represents the fixed number of vehicles per dispatch round; x represents the distance the truck travels from its original location, through the quay crane's starting point, to the yard's ending point; x represents the fixed number of vehicles per dispatch round. k ,y k The x and y coordinates represent the endpoint of the storage yard. Since the electric container truck travels horizontally from right to left from the starting point of the quay crane to the right boundary of the storage yard, and no branching or U-turns are allowed in between, x... left Represents the x-coordinate of the left boundary, when x j >x k When the time is right, it means that the quay bridge is closer to the left boundary, calculated according to formula (1), and vice versa.

[0078] Calculate the travel time of the electric truck as D. ij / v, update the coordinates of each calling electric truck for that time period to x. k ,y k ;

[0079] Step 4: Extract the coordinates of the trucks to be charged from Step 2, read the available charging stations, and iterate through and calculate the distance from each vehicle to an available charging station:

[0080]

[0081] Where, x i ,y i The horizontal and vertical coordinates represent the available charging stations, and p represents the number of electric trucks assigned to charging in the current time period.

[0082] Update the selected vehicle and charging station to charging status, and arrange the vehicle to select the charging station in order to avoid conflicts when multiple vehicles select the same charging station.

[0083] When the number of available charging stations is greater than the number of trucks waiting to be charged during a given time period, the matching information for each vehicle to the charging station is output; when the number of available charging stations is less than the number of trucks waiting to be charged during a given time period, it means that there are not enough charging stations, and the nearest electric truck is selected for charging first, and the trucks that have not been charged are carried over to the next time period for scheduling.

[0084] Step 5: Enter the charging module to calculate the vehicle's charging start time and the charging capacity during that period;

[0085] Charging time is:

[0086]

[0087] Among them, T t charge The duration of charging in time period t. t1 indicates whether there are any new charging trucks in time period t. If there are none, it is 0; if there are, it is 1. t1 represents the response time of the electric truck. 0.25 means 0.25h.

[0088] The charging capacity is: P ART (t)=p charge *T t charge .

[0089] Update the remaining battery level of the charging card:

[0090] and the remaining power With maximum charging capacity SoC charge When comparing, At that time, update the vehicle status to be dispatchable and update the charging pile status to be idle.

[0091] Step 6: Enter the electricity cost calculation module and calculate the electricity purchase cost for this period based on the day-ahead forecast wind power output, day-ahead forecast benchmark load, time-of-use electricity price, and wind power grid connection price. Refer to the objective function formula in Step 1.

[0092] Preferably, the scheduling time step in step 1 is 15 minutes.

[0093] In this example, the electricity purchase costs generated by orderly charging and disorderly charging are compared, such as... Figure 5 As shown.

[0094] In this example, the on-site absorption rate of port wind power corresponding to orderly charging and disordered charging is compared. Figure 6 As shown.

[0095] It can be seen that after the implementation of the method proposed in this invention, the time-series coupling characteristics between wind power and charging load are improved, and the local consumption rate is increased. Therefore, the implementation results fully verify the feasibility and effectiveness of the method proposed in this invention.

[0096] The technical means disclosed in this invention are not limited to those disclosed in the above embodiments and should not be considered as limiting the scope of this invention. They also include technical solutions composed of any combination of the above technical features. All equivalent variations and improvements made within the scope of this invention's claims should fall within the patent coverage of this invention.

Claims

1. A method for orderly charging of port electric container trucks for local wind power consumption, characterized in that: The method includes the following steps: Step 1: With the goal of minimizing the port's electricity purchase cost, considering the constraints of power balance, electric truck SOC, minimum remaining power and maximum charging power upper and lower limits, and logistics scheduling, a port electric truck logistics and charging scheduling model is constructed using the minimum remaining power and maximum charging power as optimization variables, and the particle swarm optimization algorithm is used to optimize the scheduling model. Step 2: Enter the logistics operation module, read the port's logistics operation tasks for the day, divide the electric trucks into dispatchable and non-dispatchable states according to the minimum remaining power, and calculate the driving distance of the dispatchable electric trucks from the starting point of the quay crane based on the known coordinates of the electric trucks and the port's electric truck driving requirements. Step 3: Enter the logistics scheduling module, assign the corresponding electric trucks according to the principle of proximity, the number of electric trucks and the remaining power, and at the same time, calculate the operating distance, remaining power and driving time of the corresponding electric trucks, and update the coordinates of each electric truck. Step 4: Enter the charging pile selection module, extract the coordinates of the unschedulable electric trucks in Step 2, traverse and calculate the distance from each electric truck to the available charging pile, and assign each electric truck to the nearest charging pile according to the principle of proximity and avoiding electric truck conflicts, and update the selected electric truck and charging pile to the charging status. Step 5: Enter the charging module, calculate the start time of electric truck charging, the charging amount during this period and the remaining amount of electricity, and compare the remaining amount of electricity with the maximum charging amount. When the maximum charging amount is reached, update the electric truck status to dispatchable status and update the charging pile status to idle status. Step 6: Enter the electricity cost calculation module and calculate the electricity purchase cost for this period based on the predicted wind power output, predicted base load, time-of-use electricity price, and wind power grid connection price for this period.

2. The method for orderly charging of port electric container trucks for local wind power consumption as described in claim 1, characterized in that: Step 1, the objective function for minimizing electricity purchase costs is: ,in: T For the entire scheduling cycle, For the first t Time-of-use electricity pricing for different time periods For wind power grid connection price, Representing the t Electricity purchased during the period Representing the t Electricity sales volume during a given time period.

3. The method for orderly charging of port electric container trucks for local wind power consumption as described in claim 2, characterized in that: Step 1 establishes constraints including: 1) power balance constraints, 2) electric truck SOC constraints, 3) upper and lower limit constraints of control variables, and 4) logistics scheduling constraints. 1) Power balance constraint: ,in: represent t Day-ahead forecast of wind power generation during the period represent t State variables for electricity purchase or sale by the power grid during a given time period: , represent t The charging capacity of electric trucks during certain time periods. represent t The day-ahead forecast baseline load for the time period; 2) SOC constraints for electric trucks: Where N represents the number of electric trucks, and These represent the sum of the electric truck's power at the start and end of the dispatching process, respectively. Based on this constraint, the logistics requirements of the available electric trucks in the next dispatching cycle can be met. 3) Minimum remaining battery capacity and maximum charging capacity upper and lower limits constraints: , in: Represents the minimum remaining battery power. This represents the maximum charging capacity. These are the upper and lower limits of the minimum remaining battery power, respectively. These are the upper and lower limits of the maximum charging capacity, respectively; 4) Logistics scheduling constraints: , Indicates in T The sum of the number of containers actually transported by electric trucks within the scheduling cycle. Indicated as in T The total number of containers that should be transported by electric trucks within the scheduling cycle.

4. The method for orderly charging of port electric container trucks for local wind power consumption as described in claim 1, characterized in that: Step 2: Since the port's electric container trucks are unmanned, their locations are determined by historical scheduling tasks and are typically randomly distributed within the dock area. To ensure path isolation from external transport electric container trucks, the driving direction of the electric container trucks is fixed as unidirectional. A coordinate system is established with the dock plane as the object, with the origin set at the lower left corner of the plane. The distances between each electric container truck and the starting point of the logistics operation quay crane are calculated, and the relationships are as follows: ,in This represents the distance from each electric truck to the starting point of the quay crane. Representing the horizontal and vertical axes of each electric truck, The coordinates represent the horizontal and vertical axes of the starting point of the quay crane. Since electric container trucks are required to travel horizontally from left to right, and no branching or U-turns are permitted in between, therefore... Represents the x-coordinate of the right boundary, when When, it means that the electric truck is closer to the right boundary, calculated according to formula (1).

5. The method for orderly charging of port electric container trucks for local wind power consumption as described in claim 4, characterized in that: Step 3: Calculate the distances iteratively. When distances are the same, select the truck with the highest remaining battery power as the second priority to avoid multiple selections of the electric truck. The operating distance of the electric truck is the sum of the distance from its original position to the starting point of the quay crane and the distance from the starting point of the quay crane to the end point of the yard. The relationship is as follows: ,in This represents the distance the selected electric container truck travels from its original location, through the starting point of the quay crane, to the ending point of the storage yard. n This represents the fixed number of electric trucks in each round of dispatching. The horizontal and vertical coordinates represent the endpoint of the storage yard. Since the electric container trucks travel horizontally from right to left from the starting point of the quay crane to the right boundary of the storage yard, and no branching or U-turns are allowed in between, therefore... Represents the x-coordinate of the left boundary, when When, it means that the quay bridge is closer to the left boundary, calculated according to formula (1).

6. The method for orderly charging of port electric container trucks for local wind power consumption as described in claim 4, characterized in that: Step 4: When an electric truck needs charging, to avoid multiple vehicles selecting the same charging station, when an available charging station is selected, its status is changed to charging to avoid conflicts. The distance from the selected electric truck to the charging station is... and Consistent, , Representing the horizontal and vertical coordinates of each available charging station. p This represents the number of electric trucks assigned to charging during the current time period.

7. The method for orderly charging of port electric container trucks for local wind power consumption as described in claim 1, characterized in that: Step 5: Calculate the charging duration and charge amount for each scheduling period. The charging duration is: in, For the first t The duration of charging during a given period. For the first t The time period should indicate whether there are any newly added electric trucks requiring charging; if not, the value is 0, otherwise 1. This represents the response time of the electric truck; 0.25 indicates 0.25 hours. The charging capacity is: , This refers to the distance from the electric truck to the charging station.

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