Dispatch method, device, computer equipment and storage medium of a vehicle

Abstract

The present application relates to a scheduling method, device, computer device, and storage medium for a transportation vehicle. After obtaining the initial values of each transportation vehicle to be scheduled, based on preset constraint conditions and with the goal of maximizing the microgrid revenue, the initial values and related parameters of each transportation vehicle to be scheduled are input into a preset objective function for calculation to obtain the output results of each transportation vehicle to be scheduled. Then, a scheduling strategy is determined according to the output results, and each transportation vehicle to be scheduled is scheduled according to the scheduling strategy. Among them, the initial values at least include the charging power and discharging power of the transportation vehicle to be scheduled at different time periods; the output results at least include the target charging power and target discharging power of the transportation vehicle to be scheduled at different time periods. The above scheduling method for transportation vehicles improves the scheduling efficiency of transportation vehicles by aiming at maximizing the microgrid revenue.

Description

Technical Field

[0001] The present application relates to the technical field of electric transportation vehicles, and particularly to a scheduling method, device, computer device, and storage medium for a transportation vehicle. Background Art

[0002] With the popular application of electric transportation vehicles, especially with the increasing number of electric vehicles, in some communities with a relatively high usage rate of electric vehicles, there will be problems such as difficult charging of electric vehicles, serious line loss, voltage drop, and in severe cases, peak-on-peak phenomena. Therefore, how to efficiently and safely schedule electric vehicles between different zones has become an urgent technical problem to be solved.

[0003] Currently, the architectures used in existing electric vehicle scheduling methods are divided into two categories. The first category is a non-cloud-based architecture, where electric vehicles on charging piles are centrally managed in a microgrid, and the charging and discharging scheduling of electric vehicles is performed by an intelligent power grid provider according to the surveyed user needs. The second category is a cloud-based architecture, that is, a cloud platform is used to determine user needs by collecting big data, and electric vehicles are scheduled and managed according to the collected user needs.

[0004] However, the above-mentioned electric vehicle scheduling methods have the problem of low scheduling efficiency. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide a scheduling method, device, computer device, and storage medium for a transportation vehicle that can improve the scheduling efficiency of the transportation vehicle.

[0006] In a first aspect, a scheduling method for a transportation vehicle, the method includes:

[0007] Obtain the initial values of each transportation vehicle to be scheduled; the initial values at least include the charging power and discharging power of the transportation vehicle to be scheduled at different time periods;

[0008] Based on preset constraint conditions and with the goal of maximizing the microgrid revenue, input the initial values and relevant parameters of each transportation vehicle to be scheduled into a preset objective function for calculation, and obtain the output results of each transportation vehicle to be scheduled; the output results at least include the target charging power and target discharging power of the transportation vehicle to be scheduled at different time periods;

[0009] Determine a scheduling strategy according to the output results, and schedule each transportation vehicle to be scheduled according to the scheduling strategy.

[0010] In one of the embodiments, the method further includes:

[0011] Perform out-of-bounds processing on the initial values of each transportation vehicle to be scheduled to obtain the first processed initial values of each transportation vehicle to be scheduled;

[0012] Based on preset constraint conditions and aiming at maximizing the benefits of the microgrid, the initial values and relevant parameters of each vehicle to be scheduled are input into a preset objective function for calculation to obtain output results, including:

[0013] Based on preset constraint conditions and aiming at maximizing the benefits of the microgrid, the first processed initial values and relevant parameters of each vehicle to be scheduled are input into a preset objective function for calculation to obtain output results.

[0014] In one embodiment, the method further includes:

[0015] Performing chained processing on the first processed initial values of each vehicle to be scheduled to obtain the second processed initial values of each vehicle to be scheduled; the chained processing is used to perform charge and discharge compensation for the vehicles to be scheduled in different time periods;

[0016] Based on preset constraint conditions and aiming at maximizing the benefits of the microgrid, the initial values and relevant parameters of each vehicle to be scheduled are input into a preset objective function for calculation to obtain output results, including:

[0017] Based on preset constraint conditions and aiming at maximizing the benefits of the microgrid, the second processed initial values and relevant parameters of each vehicle to be scheduled are input into a preset objective function for calculation to obtain output results.

[0018] In one embodiment, the method further includes:

[0019] Screening the output results of each vehicle to be scheduled to obtain the candidate output results of each vehicle to be scheduled; the candidate output results at least include the candidate charging power and candidate discharging power of the vehicle to be scheduled in different time periods;

[0020] Determine whether the scheduling parameters of each vehicle to be scheduled obtained by screening meet the preset conditions. If not, use the candidate output results of each vehicle to be scheduled as the initial values of each vehicle to be scheduled, and return to execute the step of inputting the initial values and relevant parameters of each vehicle to be scheduled into a preset objective function for calculation based on preset constraint conditions and aiming at maximizing the benefits of the microgrid to obtain output results until the scheduling parameters of each vehicle to be scheduled obtained by screening meet the preset conditions, and obtain the target output results;

[0021] Determine the scheduling strategy according to the output results, and schedule each vehicle to be scheduled according to the scheduling strategy, including:

[0022] Determine the scheduling strategy according to the target output results, and schedule each vehicle to be scheduled according to the scheduling strategy.

[0023] In one embodiment, the output results of each vehicle to be scheduled are screened to obtain candidate output results of each vehicle to be scheduled, including:

[0024] Evaluating the fitness of the output results of each vehicle to be scheduled to obtain evaluation results of each vehicle to be scheduled;

[0025] Screening the output results of each vehicle to be scheduled according to the evaluation results of each vehicle to be scheduled to obtain intermediate output results of each vehicle to be scheduled;

[0026] Determining the intermediate output results of each vehicle to be scheduled as the candidate output results of each vehicle to be scheduled.

[0027] In one embodiment, the method further includes:

[0028] Performing density estimation on the intermediate output results of each vehicle to be scheduled to obtain estimation results of each vehicle to be scheduled;

[0029] Screening the intermediate output results of each vehicle to be scheduled according to the estimation results of each vehicle to be scheduled to obtain candidate output results of each vehicle to be scheduled.

[0030] In one embodiment, obtaining the initial values of each vehicle to be scheduled includes:

[0031] Obtaining the transition probabilities of each vehicle to be scheduled in different regions;

[0032] Determining the initial values of each vehicle to be scheduled according to the transition probabilities of each vehicle to be scheduled in different regions, the state of charge of the battery of each vehicle to be scheduled, user behavior habits, and environmental parameters of each vehicle to be scheduled in different time periods.

[0033] In a second aspect, a scheduling method device for a vehicle, the device includes:

[0034] An acquisition module, configured to acquire the initial values of each vehicle to be scheduled; the initial values at least include the charging power and discharging power of the vehicle to be scheduled in different time periods;

[0035] A calculation module, configured to calculate, based on preset constraint conditions and with the goal of maximizing the microgrid revenue, input the initial values and related parameters of each vehicle to be scheduled into a preset objective function to obtain output results of each vehicle to be scheduled; the output results at least include the target charging power and target discharging power of the vehicle to be scheduled in different time periods;

[0036] A scheduling module, configured to determine a scheduling strategy according to the output result, and schedule each vehicle to be scheduled according to the scheduling strategy.

[0037] In a third aspect, a computer device includes a memory and a processor. The memory stores a computer program. The processor, when executing the computer program, implements the steps of the method according to any one of claims 1 to 7.

[0038] In a fourth aspect, a computer-readable storage medium stores a computer program. The computer program, when executed by a processor, implements the steps of the method according to any one of claims 1 to 7.

[0039] For the above vehicle scheduling method, device, computer device and storage medium, after obtaining the initial values of each vehicle to be scheduled, based on preset constraint conditions and with the goal of maximizing the microgrid revenue, the initial values and related parameters of each vehicle to be scheduled are input into a preset objective function for calculation to obtain the output results of each vehicle to be scheduled. Then, a scheduling strategy is determined according to the output results, and each vehicle to be scheduled is scheduled according to the scheduling strategy. Among them, the initial values at least include the charging power and discharging power of the vehicle to be scheduled in different time periods; the output results at least include the target charging power and target discharging power of the vehicle to be scheduled in different time periods. The above vehicle scheduling method optimizes the target charging power and target discharging power of the vehicle in different time periods with the goal of maximizing the microgrid revenue, so that the scheduling strategy determined by the target charging power and target discharging power can maximize the revenue of the entire microgrid, thereby improving the scheduling efficiency of the vehicle. In addition, this solution also fully considers the optimization problem of the discharging power in the vehicle scheduling process, further improving the microgrid revenue and making the vehicle scheduling strategy more reasonable and perfect. Description of the Drawings

[0040] Figure 1 It is an application environment diagram of the vehicle scheduling method in an embodiment;

[0041] Figure 2 It is a schematic flowchart of the vehicle scheduling method in an embodiment;

[0042] Figure 3 It is a schematic flowchart of the vehicle scheduling method in an embodiment;

[0043] Figure 4 It is a schematic flowchart of the vehicle scheduling method in an embodiment;

[0044] Figure 5 It is a schematic flowchart of the vehicle scheduling method in an embodiment;

[0045] Figure 6 For Figure 5 Schematic flowchart of an implementation manner of S108 in an embodiment;

[0046] Figure 7 For Figure 5 Schematic flowchart of an implementation manner of S108 in an embodiment;

[0047] Figure 8 For Figure 2 Schematic flowchart of an implementation manner of S101 in an embodiment;

[0048] Figure 9 Schematic flowchart of the scheduling method of a vehicle in an embodiment;

[0049] Figure 10 Schematic flowchart of the method for a terminal to generate a scheduling method of a vehicle;

[0050] Figure 11 Block diagram of the structure of a vehicle scheduling device in an embodiment;

[0051] Figure 12 Internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0052] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0053] With the popularization and application of electric vehicles, especially with the increasing number of electric vehicles, in some communities with a relatively high utilization rate of electric vehicles, there will be problems such as difficult charging of electric vehicles, serious line loss, voltage drop, and in severe cases, peak-on-peak phenomena. Then, how to efficiently and safely schedule electric vehicles between different partitions has become an urgent technical problem to be solved at present. At present, the architectures used in existing electric vehicle scheduling methods are divided into two categories. The first category is the non-cloud-based architecture, where electric vehicles on charging piles are centrally managed in a microgrid, and the charging and discharging scheduling of electric vehicles is executed by an intelligent power grid provider according to the surveyed user needs. The second category is the cloud-based architecture, that is, a cloud platform is used to determine user needs by collecting big data and schedule and manage electric vehicles according to the collected user needs. However, the above-mentioned electric vehicle scheduling methods have the problem of low scheduling efficiency. Based on this, the present application provides a data collection method to solve the above problems, and the following embodiments will describe the above method in detail.

[0054] The method for a vehicle provided by the present application can be applied to, for example Figure 1In the application environment shown, it includes a terminal 102, multiple charging piles 104, and a transportation vehicle 106. Among them, the terminal 102 is respectively connected to multiple charging piles 104 in different regions, and the transportation vehicle 106 can be charged and discharged by connecting to any charging pile 104. The multiple charging piles 104 can be in different regions, such as residential areas, office areas, leisure areas, entertainment areas, etc., and the type of the region is not limited. The terminal 102 is used to calculate the charging power and discharging power of each transportation vehicle 108 in different time periods, determine the charging and discharging strategy according to the charging power and discharging power of each transportation vehicle 106, and instruct the charging piles 104 in different regions to charge and discharge the corresponding transportation vehicle 106 in different time periods according to the full charging strategy. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, and portable wearable devices.

[0055] Those skilled in the art can understand that Figure 1 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0056] In one embodiment, as Figure 2 shown, a scheduling method for a transportation vehicle is provided. Taking the terminal in as an example, the method includes the following steps: Figure 1 shown, a scheduling method for a transportation vehicle is provided. Taking the terminal in as an example, the method includes the following steps:

[0057] S101, obtain the initial values of each transportation vehicle to be scheduled.

[0058] Among them, the initial values at least include the charging power and discharging power of the transportation vehicle to be scheduled in different time periods. It may also include the charging and discharging methods, charging and discharging durations, charge limits, etc. of each transportation vehicle to be scheduled in different time periods, which are not limited here.

[0059] In this embodiment, the terminal can determine the initial values of each transportation vehicle to be scheduled by obtaining the regional transfer probability matrix, charge state, user's travel habits, environmental temperature and humidity in 24 time periods of the day, etc. of each transportation vehicle to be scheduled. Among them, the charge state, user's travel habits, environmental temperature and humidity in 24 time periods of the day can be obtained from the local database or from the big data platform.

[0060] Optionally, the regional transfer probability matrix of each transportation vehicle to be scheduled in different time periods within the current 24 time periods can be represented by the following relational expressions (4)-(5), where: Relational expression (4) is obtained from relational expressions (1)-(3).

[0061] P[Xn+1 (t n+1 ) = j|X n (t n ) = i,...,X 1 (t 1 ) = x 1 ,X 0 (t 0 ) = x 0 = P[X n+1 (t n+1 ) = j|X n (t n ) = i] = p ij (1)

[0062] The above relation (1) represents the probability that each vehicle to be scheduled transfers from the current area to another area. Taking the office area and the residential area as an example, it can be the probability of transferring from the residential area to the office area or the probability of transferring from the office area to the residential area. Among them, X n represents that at the current time t n each vehicle to be scheduled is in area i, that is, X n (t n ) = i, where i is the area where each vehicle to be scheduled is located at the current time t n ; X n+1 represents that at the next time t n+1 each vehicle to be scheduled is in area j, that is, X n+1 (t n+1 ) = j, where j is the area where each vehicle to be scheduled is located at the next time t n+1 ; the area X 0 at time t 0 is x 0 , and the area X 1 at time t 1 is x 1 ; at the same time, t 0 ≤ t 1 ≤...≤ t n ≤ t n+1 ; P represents the area transfer probability function; P ij represents the probability of transferring to the next area j under the condition of the current area i, and a certain future area x n+1 is only related to the current area x n and has nothing to do with the previous areas x n-1 ,...,x 1 ,x 0 .

[0063] H ij (t) = P(T n ≤ t|X n (tn ) = i, X n+1 (t n+1 ) = j) (2)

[0064] In the above relation (2), H ij (t) represents the conditional distribution function of the residence duration of each vehicle to be scheduled from one area to another area within a period of time. Wherein: T n represents the actual residence duration; t represents the state transition duration from area i to j; X n represents the current t n moment when each vehicle to be scheduled is in area i, that is, X n (t n ) = i, i is the area where each vehicle to be scheduled is located at the current t n moment; X n+1 represents the next moment t n+1 when each vehicle to be scheduled is in area j, that is, X n+1 (t n+1 ) = j, j is the area where each vehicle to be scheduled is located at the next moment t n+1 when each vehicle to be scheduled is located.

[0065] G ij (t) = P(X n+1 = j, T n ≤ t|X n = i, T n-1 ) = p ij H ij (t) (3)

[0066] In the above relation (3), G ij (t) represents the probability that each vehicle to be scheduled transfers to area j within a period of time t when it is in area i. Wherein: X n+1 represents the next moment t n+1 when each vehicle to be scheduled is in area j, that is, X n+1 (t n+1 ) = j, j is the area where each vehicle to be scheduled is located at the next moment t n+1 when each vehicle to be scheduled is located; T n represents the actual residence duration; X n represents the current t n moment when each vehicle to be scheduled is in area i, that is, X n (t n ) = i, i is the area where each vehicle to be scheduled is located at the current t n moment; T n-1 represents the actual residence duration of each vehicle to be scheduled from the previous area to the current area i. P ijrepresents the probability of transferring to the next area j under the condition of the current area i; H ij (t) represents the conditional distribution function of the residence time of each vehicle to be scheduled from one area to another area within a period of time.

[0067]

[0068] A in the above relation (4) ij (t) represents the area transfer probability matrix of each vehicle to be scheduled for 24 time periods. Taking the residential area and the office area as an example, where 1 represents the residential area and 2 represents the office area. Then G 1，1 (t) represents the probability that each vehicle to be scheduled does not transfer within a period of time t when it is in the residential area; G 1，2 (t) represents the probability that each vehicle to be scheduled transfers to the office area within a period of time t when it is in the residential area; G 1，2 (t) represents the probability that each vehicle to be scheduled transfers to the residential area within a period of time t when it is in the office area; G 1，2 (t) represents the probability that each vehicle to be scheduled does not transfer within a period of time t when it is in the office area; t represents time.

[0069]

[0070] The above relation (5) represents the constraint condition for G m,n (t). m and n represent the codes of two areas. Taking the residential area and the office area as an example, 1 represents the residential area and 2 represents the office area, m = 1 or m = 2, n = 1 or n = 2.

[0071] S102. Based on the preset constraint conditions and with the goal of maximizing the microgrid revenue, input the initial values and relevant parameters of each vehicle to be scheduled into the preset objective function for calculation to obtain the output results of each vehicle to be scheduled.

[0072] Among them, the preset constraint conditions may include the charge and discharge power constraints of each vehicle to be scheduled, the state of charge constraints, the charge and discharge time constraints of each vehicle to be scheduled, etc. The initial values of each vehicle to be scheduled may include the target charge power and target discharge power of each vehicle to be scheduled, etc. The relevant parameters may include the charging price of each vehicle to be scheduled at time t, the discharge price of each vehicle to be scheduled, the interaction time with the large power grid, the interaction cost between each area microgrid and the large power grid, the wind power generation cost, the photovoltaic power generation cost, the power generation cost of the battery, etc. The objective function is the charge and discharge model of each vehicle to be scheduled when the microgrid revenue is maximized. The output results at least include the target charge power and target discharge power of the vehicle to be scheduled in different time periods, and also include the revenue when the microgrid revenue is maximized, etc.

[0073] In this embodiment, the charge-discharge power constraint conditions for each vehicle to be scheduled can be represented by relational expressions (6)-(8):

[0074]

[0075]

[0076]

[0077] Among them, relational expressions (6)-(7) indicate that the charge-discharge power of each vehicle to be scheduled should be between the maximum charge-discharge power and the minimum charge-discharge power, and relational expression (8) represents the charge-discharge state constraint for each vehicle to be scheduled, that is, each vehicle to be scheduled cannot be in a charging state and a discharging state at the same time. represents the charging power of each vehicle to be scheduled, represents the discharging power of each vehicle to be scheduled, represents the maximum charging power of each vehicle to be scheduled, represents the maximum discharging power of each vehicle to be scheduled, If then each vehicle to be scheduled is only in a charging state. If then each vehicle to be scheduled is only in a discharging state. If or then it means that each vehicle to be scheduled is neither in a charging state nor in a discharging state.

[0078] The constraint conditions for the charge state of each vehicle to be scheduled can be represented by relational expressions (9)-(10):

[0079]

[0080]

[0081] Among them, S t represents the SOC of each vehicle to be scheduled at time t, Q 0 represents the battery capacity of each vehicle to be scheduled, S max represents the maximum value of the battery SOC, S min represents the minimum value of the battery SOC, and the dimension of all is 1.

[0082] The charge-discharge time constraint conditions for each vehicle to be scheduled can be represented by relational expressions (11)-(15):

[0083]

[0084]

[0085]

[0086]

[0087]

[0088] where t k represents the k-th moment in the daily itinerary of each vehicle to be scheduled, and t k+1 represents the (k + 1)-th moment in the daily itinerary of each vehicle to be scheduled. represents the charging duration of each vehicle to be scheduled in the k-th time period. represents the discharging duration of each vehicle to be scheduled in the k-th time period. represents the moment when each vehicle to be scheduled charges during driving. represents the moment when each vehicle to be scheduled discharges during driving.

[0089] Optionally, the objective function aiming at maximizing the microgrid revenue is represented by relation (16):

[0090]

[0091] where F 1 represents the revenue when the microgrid revenue is maximized. is the initial value of each vehicle to be scheduled, representing the charging power of each vehicle to be scheduled in the t time period; is the initial value of each vehicle to be scheduled, representing the discharging power of each vehicle to be scheduled at the t moment; Δt represents the interaction time between the microgrid and the main grid; represents the charging price of each vehicle to be scheduled in the t time period, obtained from relations (17)-(19); represents the discharging price of each vehicle to be scheduled in the t time period, obtained from relations (17)-(20); R s (t) represents the interaction cost between each regional microgrid and the main grid, obtained from relation (21); g M represents the power generation cost of wind, light, and battery, obtained from relation (22).

[0092] M h (t) = βe -βt (17)

[0093] where M h(t) represents the probability distribution function that the parking area of each vehicle to be scheduled is a residential area. β is a constant, t represents the time period, and a day is divided into 24 time periods, where 0 ≤ t ≤ 24.

[0094]

[0095] Among them, M w (t) represents the probability distribution function that the parking area of each vehicle to be scheduled is a residential area, a n is a constant, b n is a constant, c n is a constant, t represents the time period, and a day is divided into 24 time periods, where 0 ≤ t ≤ 24.

[0096]

[0097] Among them, represents the charging price of each vehicle to be scheduled in the t-th time period, represents the charging price of each vehicle to be scheduled in the residential area in the t-th time period, M h (t) represents the probability that the parking area of each vehicle to be scheduled is a residential area, represents the charging price of each vehicle to be scheduled in the office area in the t-th time period, M w (t) represents the probability that the parking area of each vehicle to be scheduled is an office area.

[0098]

[0099] Among them, represents the discharging price of each vehicle to be scheduled in the t-th time period, represents the discharging price of each vehicle to be scheduled in the residential area in the t-th time period, M h (t) represents the probability that the parking area of each vehicle to be scheduled is a residential area, represents the discharging price of each vehicle to be scheduled in the office area in the t-th time period, M w (t) represents the probability that the parking area of each vehicle to be scheduled is an office area.

[0100]

[0101] Among them, R s (t) represents the interaction cost between the microgrid of each area and the large grid, C buy represents the electricity purchase price of the microgrid of each area, C sell represents the electricity selling price of the microgrid of each area, P l (t) represents the interaction power between the microgrid of each area and the large grid, and Δt represents the interaction time between the microgrid and the large grid.

[0102]

[0103] Among them, g M represents the power generation costs of wind, light, and the storage battery; represents the cost coefficient of the energy storage system; represents the cost coefficient of the wind power generation system; represents the cost coefficient of the photovoltaic power generation system; is the power of the storage battery; is the power generation power of the wind power generation system; is the power generation power of the photovoltaic power generation system.

[0104] By using the relational expressions (16)-(22) to calculate the objective function, the revenue when the microgrid revenue is maximized and the target charging power and target discharging power of each vehicle to be scheduled at different time periods when the microgrid revenue is maximized can be obtained.

[0105] S103. Determine a scheduling strategy according to the output result, and schedule each vehicle to be scheduled according to the scheduling strategy.

[0106] In this embodiment, the terminal obtains the target charging power and target discharging power of each vehicle to be scheduled at different time periods when the microgrid revenue is maximized according to the output result, and further determines a scheduling strategy according to the target charging power and target discharging power at different time periods. For example, which vehicles are scheduled in which time periods, the specific charging duration and discharging duration of the vehicles, etc. Then, according to the scheduling strategy, the charging pile is instructed to perform charging and discharging operations on the scheduled vehicles at different time periods, which can maximize the revenue of the entire microgrid.

[0107] The above-mentioned scheduling method, device, computer equipment and storage medium for transportation tools, after obtaining the initial values of each transportation tool to be scheduled, based on preset constraint conditions and with the goal of maximizing the microgrid revenue, input the initial values and related parameters of each transportation tool to be scheduled into a preset objective function for calculation to obtain the output results of each transportation tool to be scheduled, and then determine the scheduling strategy according to the output results, and schedule each transportation tool to be scheduled according to the scheduling strategy. Among them, the initial values at least include the charging power and discharging power of the transportation tool to be scheduled at different time periods; the output results at least include the target charging power and target discharging power of the transportation tool to be scheduled at different time periods. The above-mentioned scheduling method for transportation tools optimizes the target charging power and target discharging power of the transportation tool at different time periods with the goal of maximizing the microgrid revenue, so that the scheduling strategy determined by the target charging power and target discharging power can maximize the revenue of the entire microgrid, thereby improving the scheduling efficiency of the transportation tool. In addition, this solution also fully considers the optimization problem of the discharging power during the transportation tool scheduling process, further improving the microgrid revenue and making the scheduling strategy of the transportation tool more reasonable.

[0108] In one embodiment, after the terminal finishes the steps of S101 above, as Figure 3 shown, the terminal can further perform the following steps:

[0109] S104, perform out-of-bounds processing on the initial values of each transportation tool to be scheduled to obtain the first processed initial values of each transportation tool to be scheduled.

[0110] Among them, the out-of-bounds processing is to screen the obtained initial values of each transportation tool to be scheduled and remove unreasonable initial values; the first processed initial values are the screened initial values.

[0111] In this embodiment, after obtaining the initial values of each transportation tool to be scheduled, the charging power and discharging power of each transportation tool to be scheduled at different time periods, the charging and discharging methods of each transportation tool to be scheduled at different time periods, the charging and discharging duration, the charge limit, etc. can be calculated using relational expressions (6)-(15), and then the initial values in which the charging power and discharging power of each transportation tool to be scheduled at different time periods are less than or equal to the preset transportation tool charging and discharging power threshold, the initial values in which the charging and discharging duration is less than or equal to the preset charging and discharging duration threshold, and the initial values in which the charge state is less than or equal to the preset charge state threshold are screened out to obtain the first processed initial values.

[0112] S105, based on preset constraint conditions and with the goal of maximizing the microgrid revenue, input the first processed initial values and related parameters of each transportation tool to be scheduled into a preset objective function for calculation to obtain the output results.

[0113] In this embodiment, the first processing initial values and related parameters of each vehicle to be scheduled are calculated using the relational expression (16) to obtain the target charging power, target discharging power, and maximum microgrid benefit of each vehicle to be scheduled at different time periods.

[0114] In one embodiment, after the terminal finishes the above-mentioned step S104, as Figure 4 shown, the terminal may further execute the following steps:

[0115] S106, perform a chain processing on the first processing initial values of each vehicle to be scheduled to obtain the second processing initial values of each vehicle to be scheduled.

[0116] Among them, the chain processing is used to perform charge and discharge compensation for the vehicles to be scheduled in different time periods.

[0117] In this embodiment, due to the random behavior of individual vehicles to be scheduled, for example, without charging according to the output charging duration and then switching to the running state or the parked state, which may cause problems in the SOC in one time period, and there may be out-of-bounds behavior in the SOC from this time period until the last time period. Therefore, the method of charge and discharge compensation is adopted. Taking the driving duration of the vehicle to be scheduled as the radius, search forward for the parking time period, and use the electricity price comparison to determine the charge and discharge compensation time and compensation power, and complete the chain adjustment to obtain the second processing initial values of each vehicle to be scheduled.

[0118] S107, based on the preset constraint conditions and with the goal of maximizing the microgrid benefit, input the second processing initial values and related parameters of each vehicle to be scheduled into the preset objective function for calculation to obtain the output result.

[0119] In this embodiment, the second processing initial values and related parameters of each vehicle to be scheduled are calculated using the relational expression (16) to obtain the target charging power, target discharging power, and maximum microgrid benefit of each vehicle to be scheduled at different time periods.

[0120] The method for obtaining the output result in this step of the embodiment is basically the same as the method for obtaining the output result in the step S102 in the foregoing Figure 2 embodiment. For the detailed description, please refer to the foregoing description and will not be elaborated here.

[0121] In one embodiment, after the terminal finishes the above-mentioned step S107, as Figure 5 shown, the terminal may further execute the following steps:

[0122] S108, screen the output results of each vehicle to be scheduled to obtain the candidate output results of each vehicle to be scheduled.

[0123] Among them, the candidate output results at least include the candidate charging power and candidate discharging power of the transportation vehicles to be scheduled in different time periods. The candidate output results may also include the charging and discharging methods, charging and discharging durations, charge limits, etc. of each transportation vehicle to be scheduled in different time periods, which are not limited here.

[0124] In this embodiment, by solving the objective function, multiple groups of the target charging power, target discharging power of each transportation vehicle to be scheduled in different time periods and the corresponding microgrid benefits can be obtained. The target charging power and target discharging power of a group of each transportation vehicle to be scheduled in different time periods can be represented by the relational expression (23):

[0125]

[0126] where z r represents the matrix of the target charging power and target discharging power of each transportation vehicle to be scheduled in different time periods, and Z N,T represents the target charging and discharging power of the Nth vehicle in the Tth time period. By screening multiple groups of the target charging power, target discharging power of each transportation vehicle to be scheduled in different time periods and the corresponding microgrid benefits, optionally, a microgrid benefit threshold can be preset, the obtained microgrid benefits can be sorted, and the microgrid benefits greater than the preset threshold can be screened out, so as to obtain the candidate output results of each transportation vehicle to be scheduled.

[0127] S109. Determine whether the scheduling parameters of each transportation vehicle to be scheduled screened out meet the preset conditions. If not, use the candidate output results of each transportation vehicle to be scheduled as the initial values of each transportation vehicle to be scheduled, and return to execute the step of inputting the initial values of each transportation vehicle to be scheduled and related parameters into the preset objective function for calculation based on the preset constraint conditions and with the maximization of the microgrid benefits as the goal to obtain the output results until the scheduling parameters of each transportation vehicle to be scheduled screened out meet the preset conditions, so as to obtain the target output results.

[0128] Among them, the scheduling parameters of each transportation vehicle to be scheduled may include the target charging power and target discharging power of each transportation vehicle to be scheduled, the charge state, the charging and discharging time of each transportation vehicle to be scheduled, the current iteration number, etc.

[0129] The preset conditions may include the target charging power thresholds of each vehicle to be scheduled in advance, the target discharge thresholds of each vehicle to be scheduled, the charging duration thresholds of each vehicle to be scheduled, the discharge duration thresholds of each vehicle to be scheduled, the state of charge thresholds, the preset number of iterations, etc. When the scheduling parameters of each vehicle to be scheduled include the target charging power of each vehicle to be scheduled, if the target charging power of the vehicle is greater than the target charging power threshold of each preset vehicle to be scheduled, it is determined that the scheduling parameters of the vehicle meet the preset conditions; when the scheduling parameters of each vehicle to be scheduled include the target discharge power of each vehicle to be scheduled, if the target discharge power of the vehicle is greater than the target discharge power threshold of each preset vehicle to be scheduled, it is determined that the scheduling parameters of the vehicle meet the preset conditions; when the scheduling parameters of each vehicle to be scheduled include the charging duration of each vehicle to be scheduled, if the charging duration of the vehicle is greater than the charging duration threshold of each preset vehicle to be scheduled, it is determined that the scheduling parameters of the vehicle meet the preset conditions; when the scheduling parameters of each vehicle to be scheduled include the discharge duration of each vehicle to be scheduled, if the discharge duration of the vehicle is greater than the discharge duration threshold of each preset vehicle to be scheduled, it is determined that the scheduling parameters of the vehicle meet the preset conditions; when the scheduling parameters of each vehicle to be scheduled include the state of charge of each vehicle to be scheduled, if the state of charge of the vehicle is greater than the state of charge threshold of each preset vehicle to be scheduled, it is determined that the scheduling parameters of the vehicle meet the preset conditions; when the scheduling parameters of each vehicle to be scheduled include the number of iterations of each vehicle to be scheduled, if the number of iterations of the vehicle is greater than the preset number of iterations of each preset vehicle to be scheduled, it is determined that the scheduling parameters of the vehicle meet the preset conditions.

[0130] In this embodiment, if the scheduling parameters of each vehicle to be scheduled screened out do not meet the preset conditions, it means that at least one of the target charging power, target discharge power, charging duration, discharge duration, state of charge, and number of iterations of each vehicle to be scheduled does not meet the preset conditions. In this scenario, the candidate charging power and candidate discharge power of each vehicle to be scheduled at different time periods are used as the initial values. After out-of-bounds processing and chained processing, the obtained initial values are based on the preset constraint conditions and aim to maximize the microgrid revenue. The initial values and related parameters of each vehicle to be scheduled are input into the preset objective function for calculation to obtain the output results of each vehicle to be scheduled. Until the output results meet the preset conditions, the target results are output. It should be noted that if the scheduling parameters of each vehicle to be scheduled screened out meet the preset conditions, it means that the target charging power, target discharge power, charging duration, discharge duration, state of charge, and number of iterations of each vehicle to be scheduled all meet the preset conditions. In this scenario, the target results are directly output.

[0131] S110. Determine a scheduling strategy based on the target output result, and schedule each vehicle to be scheduled according to the scheduling strategy.

[0132] The method of scheduling in this step of the embodiment is basically the same as the method of scheduling in step S103 of the foregoing Figure 2 embodiment. For a detailed description, please refer to the foregoing description and will not be elaborated here.

[0133] In one embodiment, the present application also provides a specific implementation manner of "screening the output results of each vehicle to be scheduled to obtain the candidate output results of each vehicle to be scheduled", such as Figure 6 shown, including:

[0134] S201. Perform a fitness evaluation on the output results of each vehicle to be scheduled to obtain the evaluation results of each vehicle to be scheduled.

[0135] In this embodiment, the SPEA2-SDE algorithm is used to perform a fitness evaluation on the microgrid benefits corresponding to the target charging power and target discharging power of multiple groups of each vehicle to be scheduled at different time periods, and obtain the evaluation results corresponding to multiple groups of microgrid benefits.

[0136] S202. Screen the output results of each vehicle to be scheduled according to the evaluation results of each vehicle to be scheduled to obtain the intermediate output results of each vehicle to be scheduled.

[0137] In this embodiment, after using the SPEA2-SDE algorithm to perform a fitness evaluation on multiple groups of microgrid benefits, the greater the microgrid benefit, the higher the fitness evaluation. Multiple groups of microgrid benefits with higher fitness evaluations are selected, corresponding to the target charging power and target discharging power of multiple groups of each vehicle to be scheduled at different time periods, as the intermediate output results of each vehicle to be scheduled.

[0138] S203. Determine the intermediate output results of each vehicle to be scheduled as the candidate output results of each vehicle to be scheduled.

[0139] In this embodiment, the SPEA2-SDE algorithm is used to determine the intermediate output results of each vehicle to be scheduled with higher fitness evaluations as the candidate output results of each vehicle to be scheduled.

[0140] In one embodiment, after performing the steps of S203 above, as Figure 7 shown, the following steps may also be performed:

[0141] S204. Perform density estimation on the intermediate output results of each vehicle to be scheduled to obtain the estimation results of each vehicle to be scheduled.

[0142] In this embodiment, the SPEA2-SDE algorithm is used to perform density estimation on the intermediate output results of multiple groups of each vehicle to be scheduled. The greater the microgrid revenue, the higher the density estimation. Multiple groups of microgrid revenues with higher density are selected, corresponding to the target charging power and target discharging power of each vehicle to be scheduled at different time periods, which are the intermediate output results of each vehicle to be scheduled.

[0143] S205. Screen the intermediate output results of each vehicle to be scheduled according to the estimation results of each vehicle to be scheduled, and obtain the candidate output results of each vehicle to be scheduled.

[0144] In this embodiment, the SPEA2-SDE algorithm is used to determine the intermediate output results of each vehicle to be scheduled with higher density as the candidate output results of each vehicle to be scheduled.

[0145] In one embodiment, the present application also provides a specific implementation manner of the above-mentioned "obtaining the initial values of each vehicle to be scheduled", as Figure 8 shown, including:

[0146] S301. Obtain the transfer probabilities of each vehicle to be scheduled in different regions.

[0147] The method for obtaining the transfer probabilities of each vehicle to be scheduled in different regions in this step of the embodiment is basically the same as the method for obtaining the transfer probabilities of each vehicle to be scheduled in different regions in step S102 of the foregoing Figure 2 embodiment. For detailed description, please refer to the foregoing description and will not be elaborated here.

[0148] S302. Determine the initial values of each vehicle to be scheduled according to the transfer probabilities of each vehicle to be scheduled in different regions, the state of the battery charge of each vehicle to be scheduled, the user behavior habits, and the environmental parameters of each vehicle to be scheduled at different time periods.

[0149] The method for determining the initial values of each vehicle to be scheduled in this step of the embodiment is basically the same as the method for determining the initial values of each vehicle to be scheduled in step S102 of the foregoing Figure 2 embodiment. For detailed description, please refer to the foregoing description and will not be elaborated here.

[0150] This embodiment also fully considers the influence of the environment on the objective function during the vehicle scheduling process, further improves the microgrid revenue, and makes the vehicle scheduling strategy more perfect.

[0151] Combining all the above embodiments, the present application also provides a vehicle scheduling method, as Figure 9 shown. This method includes:

[0152] S401. Obtain the transfer probabilities of each vehicle to be scheduled in different regions.

[0153] S402. Determine the initial values of each vehicle to be scheduled based on the transfer probabilities of each vehicle to be scheduled in different regions, the state of the battery charge of each vehicle to be scheduled, user behavior habits, and the environmental parameters of each vehicle to be scheduled in different time periods.

[0154] S403. Perform out-of-bounds processing on the initial values of each vehicle to be scheduled to obtain the first processed initial values of each vehicle to be scheduled.

[0155] S404. Perform chained processing on the first processed initial values of each vehicle to be scheduled to obtain the second processed initial values of each vehicle to be scheduled.

[0156] S405. Based on preset constraint conditions and with the goal of maximizing the microgrid revenue, input the second processed initial values and relevant parameters of each vehicle to be scheduled into a preset objective function for calculation to obtain an output result.

[0157] S406. Perform fitness evaluation on the output results of each vehicle to be scheduled to obtain the evaluation results of each vehicle to be scheduled.

[0158] S407. Screen the output results of each vehicle to be scheduled according to the evaluation results of each vehicle to be scheduled to obtain the intermediate output results of each vehicle to be scheduled.

[0159] S408. Perform density estimation on the intermediate output results of each vehicle to be scheduled to obtain the estimation results of each vehicle to be scheduled.

[0160] S409. Screen the intermediate output results of each vehicle to be scheduled according to the estimation results of each vehicle to be scheduled to obtain the candidate output results of each vehicle to be scheduled.

[0161] S410. Determine whether the scheduling parameters of each vehicle to be scheduled selected meet the preset conditions. If not, execute step S411; if so, execute step S412.

[0162] S411. Use the candidate output results of each vehicle to be scheduled as the initial values of each vehicle to be scheduled and return to execute step S403.

[0163] S412. Obtain the target output result.

[0164] S413. Determine the scheduling strategy according to the target output result and schedule each vehicle to be scheduled according to the scheduling strategy.

[0165] The above steps are described in the foregoing description. For the detailed content, please refer to the foregoing description and will not be elaborated here.

[0166] Correspondingly, based on the above method, a scheduling method for a vehicle is further provided. As Figure 10 shown, this method includes:

[0167] First, set n as the nth vehicle to be scheduled, N as the total number of vehicles participating in the vehicle scheduling method, which is N, t as the current iteration number, with an initial value of t = 1, and T as the total number of iterations. Set the initial value of n = 1. That is, the terminal can determine the initial value of the first vehicle to be scheduled based on the transfer probability of the first vehicle to be scheduled in different regions, the state of the battery charge of the first vehicle to be scheduled, the user's behavior habits, and the environmental parameters of the first vehicle to be scheduled in different time periods. Then, perform out-of-bounds processing to obtain the first initial value, and then perform chain processing to obtain the second processed initial value. After that, according to the objective function, output the microgrid benefits corresponding to the target charging power and target discharging power of multiple groups of the first vehicle. Determine whether n is equal to N. If n = N, then output the target charging power, target discharging power, and corresponding microgrid benefits of each vehicle to be scheduled. If n ≠ N, then set n + 1, that is, n = n + 1, and return to execute the step of determining the initial value of the vehicle to be scheduled. When the target charging power, target discharging power, and corresponding microgrid benefits of each vehicle to be scheduled are output, determine whether t is equal to 1. If t = 1, then use the target charging power, target discharging power, and corresponding microgrid benefits of each vehicle to be scheduled as the candidate charging power, candidate discharging power, and corresponding microgrid benefits of each vehicle to be scheduled. Then, use the SPDEA2-SDE algorithm to evaluate the fitness of the candidate charging power, candidate discharging power, and corresponding microgrid benefits of each vehicle to be scheduled as the intermediate output result. Then, use the SPDEA2-SDE algorithm to perform density estimation on the intermediate output result as the estimation result. Determine whether t is equal to T. If t = T, then output the estimation result to obtain the charging power and discharging power of each vehicle to be scheduled when the microgrid benefit is maximized. If t ≠ T, then set t + 1, that is, t = t + 1, and return to execute the step of n = 1. If t ≠ 1, then directly execute the step of using the SPDEA2-SDE algorithm to evaluate the fitness of the candidate charging power, candidate discharging power, and corresponding microgrid benefits of each vehicle to be scheduled as the intermediate output result.

[0168] It should be understood that although Figure 2-10 the steps in the flowchart Figure 2-10At least a part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily executed and completed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in rotation with at least a part of other steps or steps or stages in other steps.

[0169] In one embodiment, as Figure 11 shown, a scheduling device for a transportation vehicle is provided, including:

[0170] An acquisition module 11, configured to acquire initial values of each transportation vehicle to be scheduled; the initial values at least include the charging power and discharging power of the transportation vehicle to be scheduled at different time periods.

[0171] A first calculation module 12, configured to, based on preset constraint conditions and with the goal of maximizing the microgrid revenue, input the initial values and related parameters of each transportation vehicle to be scheduled into a preset objective function for calculation, and obtain output results of each transportation vehicle to be scheduled; the output results at least include the target charging power and target discharging power of the transportation vehicle to be scheduled at different time periods.

[0172] A first scheduling module 13, configured to determine a scheduling strategy according to the output results, and schedule each transportation vehicle to be scheduled according to the scheduling strategy.

[0173] For the specific limitations on the scheduling device of the transportation vehicle, reference can be made to the limitations on the scheduling method of the transportation vehicle in the foregoing text, which will not be elaborated here. Each module in the above-mentioned scheduling device of the transportation vehicle can be implemented in whole or in part through software, hardware, and their combination. The above-mentioned modules can be embedded in the processor in the computer device in the form of hardware or be independent of it, or can be stored in the memory in the computer device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to the above-mentioned modules.

[0174] In one embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 12As shown. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, operator networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for quickly locating faults in a converter valve control system. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0175] Those skilled in the art can understand that Figure 12 the structure shown in

[0176] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented:

[0177] Obtain the initial values of each vehicle to be scheduled; the initial values at least include the charging power and discharging power of the vehicle to be scheduled at different time periods;

[0178] Based on preset constraint conditions and with the goal of maximizing the microgrid revenue, input the initial values and related parameters of each vehicle to be scheduled into a preset objective function for calculation to obtain the output results of each vehicle to be scheduled; the output results at least include the target charging power and target discharging power of the vehicle to be scheduled at different time periods;

[0179] Determine a scheduling strategy according to the output results, and schedule each vehicle to be scheduled according to the scheduling strategy.

[0180] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, the following steps are implemented:

[0181] Obtain the initial values of each vehicle to be scheduled; the initial values at least include the charging power and discharging power of the vehicle to be scheduled at different time periods;

[0182] Based on the preset constraint conditions and with the goal of maximizing the microgrid revenue, input the initial values and relevant parameters of each vehicle to be scheduled into the preset objective function for calculation to obtain the output results of each vehicle to be scheduled; the output results at least include the target charging power and target discharging power of the vehicle to be scheduled at different time periods;

[0183] Determine the scheduling strategy according to the output results, and schedule each vehicle to be scheduled according to the scheduling strategy.

[0184] For the computer-readable storage medium provided in the above embodiment, its implementation principle and technical effects are similar to those of the above method embodiment, and will not be elaborated here.

[0185] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0186] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0187] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A scheduling method for a means of transportation, characterized in that, the method includes: Obtaining the initial values of each means of transportation to be scheduled; the initial values at least include the charging power and discharging power of the means of transportation to be scheduled at different time periods; Based on preset constraint conditions and aiming at maximizing the microgrid revenue, the initial values and related parameters of each of the to-be-scheduled transportation vehicles are input into a preset objective function for calculation to obtain the output results of each of the to-be-scheduled transportation vehicles; the output results at least include the target charging power and target discharging power of the to-be-scheduled transportation vehicles at different time periods; the objective function aiming at maximizing the microgrid revenue is as follows: Among them, F 1 represents the revenue when the microgrid revenue is maximized, is the initial value of each of the to-be-scheduled transportation vehicles, representing the charging power of each of the to-be-scheduled transportation vehicles at time t; is the initial value of each of the to-be-scheduled transportation vehicles, representing the discharging power of each of the to-be-scheduled transportation vehicles at time t; Δt represents the interaction time between the microgrid and the main grid; represents the charging price of each of the to-be-scheduled transportation vehicles in time period t; represents the discharging price of each of the to-be-scheduled transportation vehicles in time period t; R s (t) represents the interaction cost between the microgrid and the main grid in each region; g M represents the power generation cost of wind, light, and battery; Determining a scheduling strategy according to the output result, and scheduling each means of transportation to be scheduled according to the scheduling strategy.

2. The method according to claim 1, characterized in that, the method further includes: Performing out-of-bounds processing on the initial values of each means of transportation to be scheduled to obtain the first processed initial values of each means of transportation to be scheduled; Based on preset constraint conditions and with the goal of maximizing the microgrid revenue, inputting the initial values and related parameters of each means of transportation to be scheduled into a preset objective function for calculation to obtain an output result, including: Based on preset constraint conditions and with the goal of maximizing the microgrid revenue, inputting the first processed initial values and related parameters of each means of transportation to be scheduled into a preset objective function for calculation to obtain an output result.

3. The method according to claim 2, characterized in that, the method further includes: Performing chain processing on the first processed initial values of each means of transportation to be scheduled to obtain the second processed initial values of each means of transportation to be scheduled; the chain processing is used for charge and discharge compensation of the means of transportation to be scheduled at different time periods; Based on preset constraint conditions and with the goal of maximizing the microgrid revenue, inputting the initial values and related parameters of each means of transportation to be scheduled into a preset objective function for calculation to obtain an output result, including: Based on preset constraint conditions and with the goal of maximizing the microgrid revenue, inputting the second processed initial values and related parameters of each means of transportation to be scheduled into a preset objective function for calculation to obtain an output result.

4. The method according to any one of claims 1-3, characterized in that, the method further includes: Screening the output results of each means of transportation to be scheduled to obtain the candidate output results of each means of transportation to be scheduled; the candidate output results at least include the candidate charging power and candidate discharging power of the means of transportation to be scheduled at different time periods; Determining whether the scheduling parameters of each means of transportation to be scheduled screened out meet the preset conditions. If not, taking the candidate output results of each means of transportation to be scheduled as the initial values of each means of transportation to be scheduled, and returning to execute the step of inputting the initial values and related parameters of each means of transportation to be scheduled into a preset objective function for calculation based on preset constraint conditions and with the goal of maximizing the microgrid revenue to obtain an output result until the scheduling parameters of each means of transportation to be scheduled screened out meet the preset conditions, and obtaining the target output result; Determining a scheduling strategy according to the output result, and scheduling each means of transportation to be scheduled according to the scheduling strategy, including: Determining a scheduling strategy according to the target output result, and scheduling each means of transportation to be scheduled according to the scheduling strategy.

5. The method according to claim 4, wherein, the screening of the output results of each of the to-be-scheduled transportation means to obtain the candidate output results of each of the to-be-scheduled transportation means includes: evaluating the fitness of the output results of each of the to-be-scheduled transportation means to obtain the evaluation results of each of the to-be-scheduled transportation means; screening the output results of each of the to-be-scheduled transportation means according to the evaluation results of each of the to-be-scheduled transportation means to obtain the intermediate output results of each of the to-be-scheduled transportation means; determining the intermediate output results of each of the to-be-scheduled transportation means as the candidate output results of each of the to-be-scheduled transportation means.

6. The method according to claim 5, wherein, the method further includes: estimating the density of the intermediate output results of each of the to-be-scheduled transportation means to obtain the estimation results of each of the to-be-scheduled transportation means; screening the intermediate output results of each of the to-be-scheduled transportation means according to the estimation results of each of the to-be-scheduled transportation means to obtain the candidate output results of each of the to-be-scheduled transportation means.

7. The method according to claim 1, wherein, the obtaining of the initial values of each of the to-be-scheduled transportation means includes: obtaining the transition probabilities of each of the to-be-scheduled transportation means in different regions; determining the initial values of each of the to-be-scheduled transportation means according to the transition probabilities of each of the to-be-scheduled transportation means in different regions, the state of charge of the battery of each of the to-be-scheduled transportation means, the user behavior habits, and the environmental parameters of each of the to-be-scheduled transportation means in different time periods.

8. A scheduling device for a transportation means, wherein, the device includes: an obtaining module, configured to obtain the initial values of each of the to-be-scheduled transportation means; the initial values at least include the charging power and discharging power of the to-be-scheduled transportation means in different time periods; a calculating module, configured to calculate, based on a preset constraint condition and with the maximization of the microgrid revenue as the objective, by inputting the initial values and related parameters of each of the to-be-scheduled transportation means into a preset objective function, to obtain the output results of each of the to-be-scheduled transportation means; the output results at least include the target charging power and target discharging power of the to-be-scheduled transportation means in different time periods; the objective function with the maximization of the microgrid revenue as the objective is: Among them, F 1 represents the revenue when the revenue of the microgrid is maximized, is the initial value of each of the vehicles to be scheduled, representing the charging power of each of the vehicles to be scheduled in the t period; is the initial value of each of the vehicles to be scheduled, representing the discharging power of each of the vehicles to be scheduled at the t moment; Δt represents the interaction time between the microgrid and the main grid; represents the charging price of each of the vehicles to be scheduled in the t period; represents the discharging price of each of the vehicles to be scheduled in the t period; R s (t) represents the interaction cost between the microgrid in each region and the main grid; g M represents the power generation cost of wind, light, and battery; a scheduling module, configured to determine a scheduling strategy according to the output results and schedule each of the to-be-scheduled transportation means according to the scheduling strategy.

9. A computer device, including a memory and a processor, the memory stores a computer program, wherein, when the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, on which a computer program is stored, wherein, when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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