Distribution network dispatching method, device, terminal and medium based on electric vehicle collaboration

Abstract

The present application discloses a distribution network dispatching method, device, terminal and medium based on electric vehicle collaboration, which relates to the field of distribution network technology. The solution provided by the present application first obtains extreme scenario quantitative data and disaster resilience information of the distribution network, and obtains the disaster damage simulation results of the distribution network through a preset disaster simulation model. According to the historical extreme scenario data of the distribution network, combined with the preset resilience entropy calculation formula, the resilience entropy of the distribution network in the extreme scenario is calculated as a parameter for comprehensive quantitative indicators such as disaster probability, recovery time, and user demand satisfaction. Then, the resilience entropy and the disaster damage simulation results are input into the preset resource scheduling optimization model, so as to optimize the distribution network resource scheduling scheme through the resource scheduling optimization model and the preset optimization algorithm, and execute the resource scheduling of the distribution network in extreme scenarios through the optimal solution obtained by the optimization, thereby improving the disaster resilience of the distribution network in the face of extreme scenarios.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network, and in particular to a distribution network scheduling method, device, terminal and medium based on electric vehicle collaboration. Background Art

[0002] With global climate change and the increasing frequency of natural disasters, the resilience of distribution networks in extreme scenarios (such as typhoons, floods, and earthquakes) has become a key area of ​​research for power systems. Traditional distribution network resource scheduling focuses on economic efficiency and reliability, but when faced with extreme disasters, it is prone to insufficient recovery capabilities and prolonged power outages, resulting in technical issues such as weak resilience. Summary of the Invention

[0003] The present application provides a distribution network scheduling method, device, terminal and medium based on electric vehicle collaboration, which are used to solve the technical problem of weak disaster resilience of existing distribution networks when facing extreme disasters.

[0004] To solve the above technical problems, the first aspect of the present application provides a distribution network scheduling method based on electric vehicle collaboration, comprising:

[0005] Acquire extreme scenario quantitative data and information on the disaster resilience of the distribution network, and obtain disaster damage simulation results of the distribution network through a preset disaster simulation model, wherein the disaster simulation model includes a line damage probability function, which is used to reflect the influence of the extreme scenario intensity and line disaster resilience on the line damage probability;

[0006] Calculate the resilience entropy of the distribution network under extreme scenarios based on historical extreme scenario data of the distribution network and a preset resilience entropy calculation formula;

[0007] The resilience entropy and the disaster damage simulation results are input into a preset resource scheduling optimization model, so as to optimize the distribution network resource scheduling scheme through the resource scheduling optimization model in combination with a preset optimization algorithm, and execute the resource scheduling of the distribution network under extreme scenarios through the optimal solution obtained by the optimization, wherein the constraints of the resource scheduling optimization model include: electric vehicle scheduling constraints and power grid operation constraints.

[0008] Preferably, the toughness entropy calculation formula is specifically:

[0009]

[0010] Where, is the toughness entropy, is the probability of occurrence of extreme scenario s, is the average recovery time under extreme scenario s, is the user demand satisfaction in extreme scenario s, is the weight of the extreme scenario s, and S is the set of extreme scenarios.

[0011] Preferably, the objective function of the resource scheduling optimization model is specifically:

[0012]

[0013] Where, are the weight coefficients of each optimization objective, is the resilience entropy, C is the configuration cost, S is the user demand satisfaction, F is the resource coordination efficiency, and G is the electric vehicle transfer cost.

[0014] Preferably, the electric vehicle scheduling constraints include: electric vehicle charging and discharging constraints, electric vehicle battery energy constraints, and electric vehicle transfer constraints.

[0015] Preferably, the power grid operation constraints specifically include:

[0016]

[0017]

[0018]

[0019]

[0020] Where, is the power injected into node i by the generator at time t, is the power injected into node i by distributed energy storage at time t, is the power demand of node i at time t, is the damage simulation result of line li under extreme scenario s, is the transmission power of line ij at time t, is the maximum transmission capacity of line ij, is the voltage of node i at time t, and are the lower and upper limits of the voltage at node i, and are the resistance and reactance of line ij respectively, M is the line voltage drop constraint coefficient, and are the reference voltages of nodes i and j respectively, and are the active power and reactive power of line ij respectively.

[0021] Preferably, the electric vehicle transfer constraints specifically include:

[0022]

[0023]

[0024] Where, is the number of electric vehicles at node i at time t+1, is the number of electric vehicles at node i at time t, is the number of electric vehicles transferred from node i to node j within time t, is the number of electric vehicles transferred from node j to node i within time t, is the set of time periods during which electric vehicles are allowed to transfer from node i to j.

[0025] Preferably, the electric vehicle battery energy constraint conditions specifically include:

[0026]

[0027]

[0028] Where, is the battery energy state of the electric vehicle at node i at time t+1, is the battery energy state of the electric vehicle at node i at time t, For charging efficiency, is the discharge efficiency, is the charging power of the electric vehicle at node i at time t, is the electric vehicle discharge power at node i at time t, is the minimum energy capacity of electric vehicle batteries, The maximum energy capacity of an electric vehicle battery.

[0029] A second aspect of the present application provides a distribution network dispatching device based on electric vehicle collaboration, comprising:

[0030] A disaster damage simulation unit is used to obtain quantitative data of extreme scenarios and information on the disaster resilience of the distribution network, and obtain disaster damage simulation results of the distribution network through a preset disaster simulation model, wherein the disaster simulation model includes a line damage probability function, which is used to reflect the influence of the intensity of extreme scenarios and the disaster resilience of lines on the line damage probability;

[0031] A resilience entropy calculation unit, configured to calculate the resilience entropy of the distribution network under extreme scenarios based on historical extreme scenario data of the distribution network and a preset resilience entropy calculation formula;

[0032] A scheduling scheme optimization unit is used to input the resilience entropy and the disaster damage simulation results into a preset resource scheduling optimization model, so as to optimize the distribution network resource scheduling scheme through the resource scheduling optimization model in combination with a preset optimization algorithm, and execute the resource scheduling of the distribution network under extreme scenarios through the optimal solution obtained by the optimization.

[0033] A third aspect of the present application provides a distribution network dispatching terminal based on electric vehicle collaboration, comprising: a memory and a processor;

[0034] The memory is used to store program code, and the program code is used to implement the distribution network scheduling based on electric vehicle collaboration as provided in the first aspect of the present application;

[0035] The processor is configured to read and execute the program code.

[0036] The fourth aspect of the present application provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores program code, and the program code is used to be read and executed by a processor to implement the distribution network scheduling based on electric vehicle collaboration provided in the first aspect of the present application.

[0037] It can be seen from the above technical solutions that this application has the following advantages:

[0038] The solution provided in this application first obtains quantitative data of extreme scenarios and disaster resilience information of the distribution network, and obtains disaster damage simulation results of the distribution network through a preset disaster simulation model. According to the historical extreme scenario data of the distribution network and combined with the preset resilience entropy calculation formula, the resilience entropy of the distribution network in extreme scenarios is calculated as a parameter for comprehensive quantitative indicators such as disaster occurrence probability, recovery time, and user demand satisfaction. The resilience entropy and disaster damage simulation results are then input into a preset resource scheduling optimization model to optimize the distribution network resource scheduling scheme through the resource scheduling optimization model in combination with a preset optimization algorithm. The optimal solution obtained through the optimization is used to execute resource scheduling of the distribution network in extreme scenarios, thereby achieving low-cost and high-resilience balanced resource allocation for the distribution network, and improving the disaster resilience of the distribution network in the face of extreme scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0040] Figure 1A flow chart of an embodiment of a distribution network scheduling method based on electric vehicle collaboration provided in this application.

[0041] Figure 2 A schematic diagram of the calculation flow of the optimization process of the distribution network resource scheduling scheme in the distribution network scheduling method based on electric vehicle collaboration provided in this application.

[0042] Figure 3 This is a structural diagram of an embodiment of a distribution network dispatching device based on electric vehicle collaboration provided in this application.

[0043] Figure 4 This is a structural diagram of an embodiment of a distribution network dispatching terminal based on electric vehicle collaboration provided in this application. DETAILED DESCRIPTION

[0044] The embodiments of the present application provide a distribution network scheduling method, device, terminal and medium based on electric vehicle collaboration, which are used to solve the technical problem of weak disaster resilience of existing distribution networks when facing extreme disasters.

[0045] In order to make the purpose, features, and advantages of the invention of this application more obvious and easy to understand, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described below are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0046] First, a detailed description of an embodiment of a distribution network scheduling method based on electric vehicle collaboration provided by this application is as follows:

[0047] See also Figure 1 , an embodiment of the present application provides a distribution network scheduling method based on electric vehicle collaboration, including:

[0048] Step 101: Acquire extreme scenario quantitative data and disaster resilience information of the distribution network, and obtain disaster damage simulation results of the distribution network through a preset disaster simulation model;

[0049] It should be noted that, first, quantitative data of extreme scenarios such as wind speed, rainfall, etc., as well as disaster resilience information of the distribution network, such as quantitative data of the disaster resistance capabilities of each line and node in the distribution network, are obtained. Then, the obtained quantitative data of extreme scenarios and disaster resilience information of the distribution network are input into the preset disaster simulation model to obtain the simulation results of disaster damage to the distribution network.

[0050] More specifically, the disaster simulation model includes a line damage probability function, which is used to reflect the impact of extreme scenario intensity and line disaster resilience on line damage probability. The expression of the line damage probability function is as follows:

[0051]

[0052] Where, is the damage probability of line ij under scenario s, is the quantitative value of the disaster intensity of scenario s, For the line Quantitative data on disaster resilience, is the damage probability function, which is usually a nonlinear function used to describe the impact of extreme scenario intensity and line disaster resistance on the damage probability, and k is a constant.

[0053] More specifically, the disaster damage simulation results mentioned in this embodiment are: line damage state quantity , It is a binary variable that indicates whether line ij is damaged in extreme scenario s. It can be based on the damage probability Random generation, for example, generating a random number ,if ,but (Line damaged); otherwise (The line is normal); in this method, automatic isolating switches are installed at both ends of the line. After a fault occurs, the switches are automatically disconnected, and the fault will not spread along the line.

[0054] Step 102: Calculate the resilience entropy of the distribution network under extreme scenarios based on historical extreme scenario data of the distribution network and a preset resilience entropy calculation formula.

[0055] It should be noted that the resilience entropy provided in this application is specifically used to comprehensively quantify indicators such as disaster probability, recovery time, and user demand satisfaction, thereby comprehensively quantifying the resilience loss of the distribution network in extreme scenarios. Its calculation method is as follows:

[0056]

[0057] Where, is the toughness entropy, is the probability of occurrence of extreme scenario s, is the average recovery time under extreme scenario s, is the user demand satisfaction in extreme scenario s, is the weight of the extreme scenario s, and S is the set of extreme scenarios.

[0058] Step 103: Input the resilience entropy and the disaster damage simulation results into a preset resource scheduling optimization model, so as to optimize the distribution network resource scheduling scheme through the resource scheduling optimization model in combination with a preset optimization algorithm, and execute the resource scheduling of the distribution network in extreme scenarios through the optimal solution obtained by the optimization.

[0059] It should be noted that, based on the resilience entropy sum, this embodiment establishes a multi-objective optimization model for distribution network resilience improvement based on the collaborative optimization of resilience entropy and electric vehicles. The overall objective function is:

[0060]

[0061] Where, are the weight coefficients of each optimization objective, is the resilience entropy, C is the configuration cost, which aims to reduce the economic cost of resource configuration, S is the user demand satisfaction, which is used to ensure the power supply reliability of critical loads, F is the resource coordination efficiency, which aims to improve the coordinated utilization efficiency of multiple types of resources, and G is the electric vehicle transfer cost, which aims to reduce the cost of transferring electric vehicles between different nodes.

[0062] More specifically, the configuration cost C can be calculated as follows:

[0063]

[0064] Where i is the node number, N is the set of all nodes, t is the time number, T is the set of all time periods, is the unit cost of deploying electric vehicles at node i at time t, The unit cost of deploying distributed energy storage for node i, is the number of electric vehicles at node i at time t, is the distributed energy storage capacity of node i.

[0065] The calculation method of user demand satisfaction S can refer to the following expression:

[0066]

[0067] Where, is the user demand priority of node i (the higher the value, the higher the priority), is the user demand satisfaction of node i in scenario s, which is determined by the difference between the load value of the node during normal operation and the actual power supply of the node during disaster.

[0068] The calculation method of resource coordination efficiency F can refer to the following expression:

[0069]

[0070] Where, is the synergistic efficiency coefficient of the electric vehicle (depending on the charge and discharge rate), is the synergistic efficiency coefficient of distributed energy storage (depends on the energy conversion efficiency).

[0071] The calculation method of electric vehicle transfer cost G can refer to the following expression:

[0072]

[0073] Where j is the target node number, is the unit cost of transferring an electric vehicle from node i to node j within time t, is the number of electric vehicles transferred from node i to node j within time t.

[0074] Furthermore, among the constraints of the established model, there are constraints on distribution network operation and related constraints on electric vehicle collaborative optimization. The constraints of the resource scheduling optimization model mentioned in this embodiment include: electric vehicle scheduling constraints and grid operation constraints. Among them, the electric vehicle scheduling constraints include: electric vehicle charging and discharging constraints, electric vehicle battery energy constraints, and electric vehicle transfer constraints, as shown in the following example:

[0075] More specifically, the charging and discharging constraints of electric vehicles are as follows:

[0076]

[0077] Where, is the charging power of the electric vehicle at node i at time t, which is determined by the power and number of cars charging at node i at time t; is the maximum charging power allowed for node i, is the electric vehicle discharge power at node i at time t, which is determined by the power and number of cars discharged at node i at time t. is the maximum discharge power allowed for node i, where the constraint formula is This means that electric vehicles cannot be charged and discharged at the same time and node.

[0078] The specific energy constraints of electric vehicle batteries are as follows:

[0079]

[0080]

[0081] Where, is the battery energy state of the electric vehicle at node i at time t+1, is the battery energy state of the electric vehicle at node i at time t, is the charging efficiency (the value range is 0~1), is the discharge efficiency (range is 0~1), is the charging power of the electric vehicle at node i at time t, is the electric vehicle discharge power at node i at time t, is the minimum energy capacity of electric vehicle batteries, The maximum energy capacity of an electric vehicle battery.

[0082] The specific constraints on electric vehicle transfer include:

[0083]

[0084]

[0085] Where, is the number of electric vehicles at node i at time t+1, is the number of electric vehicles at node i at time t, is the number of electric vehicles transferred from node i to node j within time t, is the number of electric vehicles transferred from node j to node i within time t, which means updating the number of electric vehicles at the node. is the set of time periods during which electric vehicles are allowed to transfer from node i to j, which means that the transfer time is constrained.

[0086] The grid operation constraints include:

[0087]

[0088]

[0089]

[0090]

[0091] Where, is the power injected into node i by the generator at time t, is the power injected into node i by distributed energy storage at time t, is the power demand of node i at time t, is the damage simulation result of line li under extreme scenario s, is the transmission power of line ij at time t, is the maximum transmission capacity of line ij, is the voltage of node i at time t, and are the lower and upper limits of the voltage at node i, and are the resistance and reactance of line ij, and are the reference voltages of nodes i and j respectively, and are the active power and reactive power of line ij respectively, M is the line voltage drop constraint coefficient, which is a sufficiently large positive number. If the line is in a fault state, this constraint will be relaxed.

[0092] It can be understood that, based on this embodiment, Take line fault as an example to illustrate, therefore, the grid operation constraint condition is The expression: If the line li is damaged, its transmission power is 0. Indicates line fault, then Also needs to be adjusted accordingly .

[0093] Based on the above resource scheduling optimization model, the preset optimization algorithm is combined to optimize the distribution network resource scheduling scheme. The optimal solution set provides a series of trade-offs rather than a single solution, allowing decision makers to select the most appropriate solution based on actual needs. Regarding the selection of an optimization algorithm, particle swarm optimization, ant colony optimization, or non-dominated sorting genetic algorithm can generally be used. The following is an example of an embodiment of this application using the improved non-dominated sorting genetic algorithm II (N-NSGA-II) based on neighborhood metrics to obtain the optimal solution for resource scheduling in extreme scenarios through optimization. The pseudo code of the NSGA-II algorithm based on neighborhood metrics provided in this embodiment is shown in Table 1:

[0094]

[0095] Among them, m represents the population size, k represents the maximum evolutionary generation, and p c and p v They represent the crossover probability and mutation probability respectively, K represents the neighborhood related parameter (K ​​nearest neighbor method); P t is the parent population of generation t, Q t is the offspring population of generation t, and the two are combined to get R t After performing non-dominated sorting and congestion calculation, a temporary candidate set P' is formed t If the scale exceeds m, further screening is performed based on the neighborhood metric NM(x) to obtain P (t+1) ; Through selection, crossover, and mutation (t+1) Generate offspring Q (t+1) , the iteration count t increases until it reaches k; finally, Pk Perform non-dominated sorting and output the solution set with rank=1, which is the approximate Pareto frontier.

[0096] The optimization process of this embodiment introduces a neighborhood measure and a secondary screening mechanism in the re-ranking stage after the population is merged. The calculation flow chart is as follows: Figure 2 shown.

[0097] Given a multi-objective optimization problem with N objective functions:

[0098]

[0099] in, is the decision variable, is a feasible solution space, if there are two solutions and satisfy:

[0100]

[0101] Then it is called Dominate , when a solution When a solution is not dominated by any other solution, it is called a non-dominated solution (Pareto solution). N-NSGA-II's fast non-dominated sorting will assign a rank to each individual, providing a basis for subsequent screening.

[0102] Regarding the neighborhood measure (NeighbourhoodMeasure), this embodiment is to characterize the local density and define the distance between individuals As follows (taking the target space as an example):

[0103]

[0104] in, and There are two solutions, and Is the solution and The value on the nth objective function, N represents the number of objective functions, Characterizes the solution in the target space and solution This formula is used to measure the proximity between solutions and provides a basis for improving the subsequent neighborhood determination and local density analysis of the algorithm.

[0105] Based on a given threshold ϵ or K-nearest neighbor idea, define:

[0106]

[0107] Thus the neighborhood metric can be set as:

[0108]

[0109] Here we use K nearest neighbors, is the average distance of the K individuals closest to x1, etc.

[0110] Next, a secondary elimination process based on the neighborhood is performed. If redundancy still exists after the initial truncation based on "level + crowding distance" (for example, if the population size must be strictly maintained at n), additional individuals with severe local crowding are removed. NM(x) can be sorted from largest to smallest, removing individuals until the size meets the requirement.

[0111] Finally, after the above modeling and solving steps, an approximate Pareto front representing the optimal solution is obtained, allowing decision makers to choose the most appropriate energy storage deployment and electric vehicle deployment solutions.

[0112] The above is a detailed description of an embodiment of a distribution network dispatching method based on electric vehicle collaboration provided by this application. The following is a detailed description of an embodiment of a distribution network dispatching device based on electric vehicle collaboration provided by this application.

[0113] See also Figure 3 , the present application provides a distribution network dispatching device based on electric vehicle collaboration, comprising:

[0114] The disaster damage simulation unit 201 is used to obtain quantitative data of extreme scenarios and information on the disaster resilience of the distribution network. Using a preset disaster simulation model, it obtains simulation results of the damage to the distribution network. The disaster simulation model includes a line damage probability function, which reflects the influence of the intensity of the extreme scenario and the line disaster resilience on the line damage probability.

[0115] The resilience entropy calculation unit 202 is used to calculate the resilience entropy of the distribution network under extreme scenarios based on historical extreme scenario data of the distribution network and a preset resilience entropy calculation formula;

[0116] The scheduling scheme optimization unit 203 is used to input the resilience entropy and disaster damage simulation results into a preset resource scheduling optimization model, so as to optimize the distribution network resource scheduling scheme through the resource scheduling optimization model in combination with a preset optimization algorithm, and execute the resource scheduling of the distribution network under extreme scenarios through the optimal solution obtained through the optimization.

[0117] like Figure 4 As shown, the present application also provides a distribution network dispatching terminal based on electric vehicle collaboration, including: a memory 33 and a processor 31, the memory 33 and the processor 31 can be connected via a communication bus 34;

[0118] The memory 33 is used to store program codes, and the program codes are used to implement the distribution network scheduling based on electric vehicle collaboration as in the above embodiment;

[0119] The processor 31 is used to read and execute program codes.

[0120] In addition, the present application also provides a computer-readable storage medium, characterized in that program code is stored in the computer-readable storage medium, and the program code is used to be read and executed by a processor to implement the distribution network scheduling based on electric vehicle collaboration provided in the above embodiment.

[0121] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the terminals, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0122] In the several embodiments provided in this application, it should be understood that the disclosed terminals, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0123] The terms "first," "second," "third," "fourth," and the like (if any) in the specification of the present application and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present application described herein, for example, can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or apparatus.

[0124] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0125] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0126] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0127] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0128] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A distribution network dispatching method based on electric vehicle collaboration, characterized in that: include: Acquire extreme scenario quantitative data and information on the disaster resilience of the distribution network, and obtain disaster damage simulation results of the distribution network through a preset disaster simulation model, wherein the disaster simulation model includes a line damage probability function, which is used to reflect the influence of the extreme scenario intensity and line disaster resilience on the line damage probability; Calculate the resilience entropy of the distribution network under extreme scenarios based on historical extreme scenario data of the distribution network and a preset resilience entropy calculation formula; Inputting the resilience entropy and the disaster damage simulation results into a preset resource scheduling optimization model, so as to optimize the distribution network resource scheduling scheme through the resource scheduling optimization model in combination with a preset optimization algorithm, and executing the resource scheduling of the distribution network under extreme scenarios through the optimal solution obtained by the optimization, wherein the constraints of the resource scheduling optimization model include: electric vehicle scheduling constraints and power grid operation constraints; The toughness entropy calculation formula is specifically: ； Where, is the toughness entropy, is the probability of occurrence of extreme scenario s, is the average recovery time under extreme scenario s, is the user demand satisfaction in extreme scenario s, is the weight of the extreme scenario s, and S is the set of extreme scenarios.

2. A distribution network dispatching method based on electric vehicle collaboration according to claim 1, characterized in that: The objective function of the resource scheduling optimization model is specifically: ； Where, are the weight coefficients of each optimization objective, is the resilience entropy, C is the configuration cost, S is the user demand satisfaction, F is the resource coordination efficiency, and G is the electric vehicle transfer cost.

3. The distribution network dispatching method based on electric vehicle collaboration according to claim 1, characterized in that: The electric vehicle scheduling constraints include: electric vehicle charging and discharging constraints, electric vehicle battery energy constraints, and electric vehicle transfer constraints.

4. The distribution network dispatching method based on electric vehicle collaboration according to claim 1, characterized in that: The power grid operation constraints specifically include: ； ； ； ； Where, is the power injected into node i by the generator at time t, is the power injected into node i by distributed energy storage at time t, is the power demand of node i at time t, is the damage simulation result of line li under extreme scenario s, is the transmission power of line ij at time t, is the maximum transmission capacity of line ij, is the voltage of node i at time t, and are the lower and upper limits of the voltage at node i, and are the resistance and reactance of line ij respectively, M is the line voltage drop constraint coefficient, and are the reference voltages of nodes i and j respectively, and are the active power and reactive power of line ij respectively.

5. The distribution network dispatching method based on electric vehicle collaboration according to claim 3 is characterized in that: The electric vehicle transfer constraints specifically include: ； ； Where, is the number of electric vehicles at node i at time t+1, is the number of electric vehicles at node i at time t, is the number of electric vehicles transferred from node i to node j within time t, is the number of electric vehicles transferred from node j to node i within time t, is the set of time periods during which electric vehicles are allowed to transfer from node i to j.

6. A distribution network dispatching method based on electric vehicle collaboration according to claim 3, characterized in that: The electric vehicle battery energy constraint conditions specifically include: ； ； Where, is the battery energy state of the electric vehicle at node i at time t+1, is the battery energy state of the electric vehicle at node i at time t, For charging efficiency, is the discharge efficiency, is the charging power of the electric vehicle at node i at time t, is the electric vehicle discharge power at node i at time t, is the minimum energy capacity of electric vehicle batteries, The maximum energy capacity of an electric vehicle battery.

7. A distribution network dispatching device based on electric vehicle collaboration, characterized in that: include: A disaster damage simulation unit is used to obtain quantitative data of extreme scenarios and information on the disaster resilience of the distribution network, and obtain disaster damage simulation results of the distribution network through a preset disaster simulation model, wherein the disaster simulation model includes a line damage probability function, which is used to reflect the influence of the intensity of extreme scenarios and the disaster resilience of lines on the line damage probability; A resilience entropy calculation unit, configured to calculate the resilience entropy of the distribution network under extreme scenarios based on historical extreme scenario data of the distribution network and a preset resilience entropy calculation formula; a scheduling scheme optimization unit, configured to input the resilience entropy and the disaster damage simulation result into a preset resource scheduling optimization model, so as to optimize the distribution network resource scheduling scheme through the resource scheduling optimization model in combination with a preset optimization algorithm, and execute resource scheduling of the distribution network under extreme scenarios through the optimal solution obtained through the optimization; The toughness entropy calculation formula is specifically: ； Where, is the toughness entropy, is the probability of occurrence of extreme scenario s, is the average recovery time under extreme scenario s, is the user demand satisfaction in extreme scenario s, is the weight of the extreme scenario s, and S is the set of extreme scenarios.

8. A distribution network dispatching terminal based on electric vehicle collaboration, characterized in that: include: memory and processor; The memory is used to store program code, and the program code is used to implement the distribution network scheduling method based on electric vehicle collaboration according to any one of claims 1 to 6; The processor is configured to read and execute the program code.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores program code, which is used to be read and executed by a processor to implement the distribution network scheduling method based on electric vehicle collaboration as described in any one of claims 1 to 6.

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