Electric vehicle dispatching method and system for emergency power supply of distribution network

Abstract

The present invention provides an electric vehicle scheduling method and system for emergency power supply of distribution networks, and relates to the field of cloud computing technology. The present invention uses an edge information screening model to collect and process the data of electric vehicles. It only needs to upload the coordinate positions of electric vehicles that have passed the screening to the cloud platform, remove a large amount of redundant data, and alleviate the computing pressure and data transmission pressure of the cloud platform. Since the edge node is closely linked to the user end, the data transmission is faster, and the data can be delivered to the electric vehicle users in a timely and accurate manner, which is more conducive to the rapid restoration of power grid elasticity. At the same time, the cloud is used for path planning, and the extremely strong computing power of the cloud platform is combined with the information uploaded by the edge to perform calculations to meet the calculations for real-time scheduling of electric vehicles.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud computing, and in particular to an electric vehicle dispatching method and system for emergency power supply of a distribution network. Background Art

[0002] In recent years, extreme natural disasters such as typhoons have caused frequent power outages in urban distribution networks, resulting in huge economic losses and posing great challenges to the continuous power supply in urban areas. With the development of the times and the advancement of policies, the number of electric vehicle (EV) users in my country has grown rapidly. In response to the problem of distribution network accidents caused by natural disasters such as typhoons, a strategy to improve the resilience of urban distribution networks using vehicle-to-grid (V2G) has been proposed to enhance the disaster resistance of urban power grids.

[0003] In order to better carry out V2G activities, real-time dispatch analysis of electric vehicles is required. However, due to the strong randomness of the movement of electric vehicles, V2G's participation in grid auxiliary services urgently needs to solve its real-time, accuracy and security issues. However, real-time dispatching requires high data transmission capabilities, and collecting basic information about electric vehicles will generate a huge amount of data that needs to be collected and analyzed to help plan the driving routes of electric vehicles. This will cause high latency in the cloud computing center and occupy a large network bandwidth, leading to problems such as cloud platform network congestion and data loss. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides an electric vehicle scheduling method and system for emergency power supply to distribution networks, which solves the technical problems of existing V2G participation in grid auxiliary services, such as cloud platform network congestion and data loss due to the spatiotemporal uncertainty of electric vehicles and a large amount of vehicle data transmission.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] In a first aspect, the present invention provides an electric vehicle dispatching method for emergency power supply of a distribution network, comprising:

[0009] The edge node obtains the power outage area information and determines the target discharge station;

[0010] The edge node publishes the help information to the electric vehicle user end and receives the feedback of the user's voluntary choice of vehicle grid connection activities and the current data of the electric vehicle;

[0011] The edge node pre-processes the current data of electric vehicles, analyzes the information of electric vehicles, selects vehicles that meet the discharge conditions, and uploads the coordinates of the selected electric vehicles to the cloud platform;

[0012] The cloud platform calculates the optimal route to the target discharge station based on vehicle information;

[0013] The cloud platform synchronizes the calculation results to electric vehicle users in real time and guides users to the target discharge station.

[0014] Preferably, the current data includes: the state of charge of the electric vehicle, the real-time location of the electric vehicle, and the departure time of the electric vehicle.

[0015] Preferably, the vehicles meeting the discharge conditions include:

[0016] The SOC value of the vehicle is within a preset normal range of the SOC value, the estimated arrival time of the vehicle is within a preset time range, the distance between the vehicle and the target discharge station is within a preset maximum distance range, and the capacity of the vehicle is within a battery capacity configuration range.

[0017] Preferably, the edge node pre-processes the current data of the electric vehicle, including: detecting and correcting the current data using a standard deviation method.

[0018] Preferably, the cloud platform calculates the optimal path to the target discharge station according to the vehicle information, including:

[0019] (1) The location of the electric vehicle when the user receives the request is recorded as the starting point A, and the demand point closest to the electric vehicle is recorded as P;

[0020] (2) Assume that there are n nodes X1, X2, X3, ..., Xn between point A and point P. The node set is denoted as X. The distance between every two adjacent nodes is denoted as d, and the distance matrix is ​​denoted as D.

[0021] (3) According to the starting point A, the end point P, the node set X, and the distance matrix D, determine the numbers of the two adjacent nodes where the electric vehicle is located, and calculate the distances between the electric vehicle and the two nodes, which are recorded as Slast and Snext respectively;

[0022] (4) Calculate the shortest path when the electric vehicle travels from the direction of the previous node, denoted by Dlast. The set of planned path nodes is [A, Xi, P], where Xi represents the set of nodes that the electric vehicle passes through on the way from the direction of the previous node to the end point;

[0023] (5) Calculate the shortest path for the electric vehicle to travel from the next node, denoted by Dnext. The set of planned path nodes is [A, Xj, P], where Xj represents the set of nodes that the electric vehicle passes through on the way from the next node to the end point;

[0024] (6) Determine the size of Slast+Dlast and Snext+Dnext. The path corresponding to the smaller value is the shortest driving path for the electric vehicle from the current location to the charging station.

[0025] Preferably, the Dlast and Dnext are calculated by Floyd algorithm.

[0026] In a second aspect, the present invention provides an electric vehicle dispatching system for emergency power supply to a distribution network, the electric vehicle dispatching system comprising:

[0027] The edge node data processing module is used to obtain information about the power outage area and determine the target discharge station. It is also used to publish help information to the electric vehicle user end and receive feedback from users who voluntarily choose to connect their vehicles to the grid and the current data of the electric vehicle. It is also used to pre-process the current data of the electric vehicle, analyze the information of the electric vehicle, select the vehicles that meet the discharge conditions, and upload the coordinates of the selected electric vehicles to the cloud platform.

[0028] The cloud platform is used to calculate the optimal route to the target discharge station based on vehicle information; it is also used to synchronize the calculation results to the electric vehicle users in real time to guide the users to the target discharge station.

[0029] Preferably, the calculating the optimal path to the target discharge station according to the vehicle information includes:

[0030] (1) The location of the electric vehicle when the user receives the request is recorded as the starting point A, and the demand point closest to the electric vehicle is recorded as P;

[0031] (2) Assume that there are n nodes X1, X2, X3, ..., Xn between point A and point P. The node set is denoted as X. The distance between every two adjacent nodes is denoted as d, and the distance matrix is ​​denoted as D.

[0032] (3) According to the starting point A, the end point P, the node set X, and the distance matrix D, determine the numbers of the two adjacent nodes where the electric vehicle is located, and calculate the distances between the electric vehicle and the two nodes, which are recorded as Slast and Snext respectively;

[0033] (4) Calculate the shortest path when the electric vehicle travels from the direction of the previous node, denoted by Dlast. The set of planned path nodes is [A, Xi, P], where Xi represents the set of nodes that the electric vehicle passes through on the way from the direction of the previous node to the end point;

[0034] (5) Calculate the shortest path for the electric vehicle to travel from the next node, denoted by Dnext. The set of planned path nodes is [A, Xj, P], where Xj represents the set of nodes that the electric vehicle passes through on the way from the next node to the end point;

[0035] (6) Determine the size of Slast+Dlast and Snext+Dnext. The path corresponding to the smaller value is the shortest driving path for the electric vehicle from the current location to the charging station.

[0036] In a third aspect, the present invention provides a computer-readable storage medium storing a computer program for an electric vehicle dispatching method for emergency power supply to a distribution network, wherein the computer program enables a computer to execute the above-mentioned electric vehicle dispatching method for emergency power supply to a distribution network.

[0037] In a third aspect, the present invention provides an electronic device, comprising:

[0038] One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include a method for executing the electric vehicle dispatching method for emergency power supply to the distribution network as described above.

[0039] (III) Beneficial effects

[0040] The present invention provides an electric vehicle dispatching method and system for emergency power supply of distribution network. Compared with the prior art, it has the following beneficial effects:

[0041] The present invention provides an electric vehicle dispatching method for emergency power supply of a distribution network, and the method performs the following steps at the edge end: the edge node obtains the information of the power outage area and determines the target discharge station; the edge node publishes the help information to the electric vehicle user end, and receives the feedback of the user's voluntary choice to conduct V2G activities and the current data of the electric vehicle; the edge node pre-processes the current data of the electric vehicle, analyzes the information of the electric vehicle to select the vehicle that meets the discharge conditions, and uploads the coordinate position of the screened electric vehicle to the cloud platform; the cloud platform performs the following steps: the cloud platform calculates the optimal path to the target discharge station according to the vehicle information; the cloud platform synchronizes the calculation results to the electric vehicle user in real time, and guides the user to the target discharge station. The present invention uses the information screening model of the edge end to collect and process the data of the electric vehicle, and only needs to upload the coordinate position of the screened electric vehicle to the cloud platform, removes a large amount of redundant data, and alleviates the calculation pressure and data transmission pressure of the cloud platform. Since the edge node is closely linked to the user end, the data transmission is faster, and the data can be delivered to the electric vehicle user in a timely and accurate manner, which is more conducive to the rapid recovery of the elasticity of the power grid. At the same time, the cloud is used for path planning, and the extremely strong computing power of the cloud platform is combined with the information uploaded by the edge to perform calculations to meet the real-time scheduling needs of electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 The present invention is a block diagram of an electric vehicle scheduling method for emergency power supply to a distribution network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] The embodiment of the present application provides an electric vehicle dispatching method and system for emergency power supply of distribution network, which solves the technical problems such as cloud platform network congestion and data loss caused by the spatiotemporal uncertainty of electric vehicles and the large amount of vehicle data transmission in the existing V2G grid auxiliary service. The edge node can collect and process the real-time data of electric vehicles, which alleviates the data transmission pressure of cloud computing and ensures the security of data.

[0046] The technical solution in the embodiment of the present application is to solve the above technical problems, and the overall idea is as follows:

[0047] The existing technology has the following deficiencies: (1) Real-time dispatch of electric vehicles has extremely high requirements for the timeliness, accuracy and security of information. Due to the strong temporal and spatial uncertainty of electric vehicle driving, a large amount of historical data is required for training, which occupies a large amount of cloud storage space. Data transmission between electric vehicles and the cloud may cause network congestion and data loss, reducing system efficiency. (2) Communication delay and aggregation delay will affect the stability of the control system. As the number of electric vehicles participating in V2G increases, the complexity and delay of the control algorithm will also increase.

[0048] To solve the above problems, an embodiment of the present invention proposes an electric vehicle scheduling method and system for emergency power supply of distribution network based on cloud-edge collaboration, establishes an implementation path optimization model, and obtains a scheduling plan based on the current area with abnormal power supply after the disaster and the real-time location of the electric vehicle.

[0049] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0050] The embodiment of the present invention provides an electric vehicle scheduling method for emergency power supply of a distribution network, the method running on a cloud computing system, the cloud computing system comprising a cloud platform and at least one edge resource cluster, each edge resource cluster having a plurality of edge nodes; Figure 1 As shown, the method includes:

[0051] S1. The edge node obtains the power outage area information and determines the target discharge station;

[0052] S2, the edge node publishes the help information to the electric vehicle user end, and receives the feedback of the user's voluntary choice to conduct V2G activities and the current data of the electric vehicle;

[0053] S3, the edge node pre-processes the current data of the electric vehicle, analyzes the information of the electric vehicle, selects the vehicles that meet the discharge conditions, and uploads the coordinates of the selected electric vehicles to the cloud platform;

[0054] S4, the cloud platform calculates the optimal path to the target discharge station based on the vehicle information;

[0055] S5. The cloud platform synchronizes the calculation results to the electric vehicle users in real time and guides the users to the target discharge station.

[0056] The embodiment of the present invention uses the information screening model at the edge to collect and process the data of electric vehicles. It only needs to upload the coordinate positions of the electric vehicles that have passed the screening to the cloud platform, remove a large amount of redundant data, and alleviate the computing pressure and data transmission pressure of the cloud platform. Since the edge node is closely linked to the user end, the data transmission is faster, and the data can be delivered to the electric vehicle users in a timely and accurate manner, which is more conducive to the rapid restoration of grid elasticity. At the same time, the cloud is used for path planning, and the calculation is performed through the extremely strong computing power of the cloud platform combined with the information uploaded by the edge to meet the calculation of real-time scheduling of electric vehicles.

[0057] The following is a detailed description of each step:

[0058] In step S1, the edge node obtains the power outage area information and determines the target discharge station. The specific implementation process is as follows:

[0059] The edge nodes obtain in real time the specific locations of battery swap stations in areas where some lines are damaged and power supply is insufficient due to typhoon disasters, which serve as the final destination of electric vehicles - the target discharge station.

[0060] In step S2, the edge node publishes the help information to the electric vehicle user end, and receives the feedback of the user's voluntary choice to conduct V2G activities and the current data of the electric vehicle. The specific real-time process is as follows:

[0061] The edge node publishes the help information to the electric vehicle user end, and the user chooses whether to go to the V2G station for discharge activities. The edge node receives the information that the user voluntarily chooses to carry out V2G activities.

[0062] The edge node collects the current data of the electric vehicles of users who are willing to discharge. The current data includes: the state of charge (SOC) of the electric vehicle, the real-time location of the electric vehicle, and the departure time of the electric vehicle (the time when the electric vehicle accepts the request).

[0063] In step S3, the edge node pre-processes the current data of the electric vehicle, analyzes the information of the electric vehicle, selects the vehicles that meet the discharge conditions, and uploads the coordinates of the selected electric vehicles to the cloud platform. The specific implementation process is as follows:

[0064] S301, pre-processing the current data of the electric vehicle, including: detecting and correcting abnormal data on the collected data. Specifically:

[0065] The standard deviation method is used to detect and correct data. Its basic idea is to use the mean and standard deviation of the data to determine whether a data point deviates from the normal range. The advantage of the standard deviation method is that it is simple and easy to use, and can quickly identify outliers that deviate from the normal range. In the specific implementation process, methods such as Isolation Forest and Local Outlier Factor in machine learning can also be used to detect and correct abnormal data.

[0066] S302, analyzing the information of electric vehicles to select vehicles that meet the discharge conditions, and uploading the coordinates of the selected electric vehicles to the cloud platform. Including:

[0067] (1) Screening out electric vehicles with abnormal SOC values ​​that cannot be discharged sufficiently and quickly. The present invention sets the normal range of SOC values ​​to be 30% to 100%, that is, vehicles with SOC values ​​below 30% are vehicles that cannot be discharged sufficiently and quickly.

[0068] (2) The time when the electric vehicle receives the request is collected as the departure time and the arrival time is estimated. If the arrival time exceeds the preset time, it is considered as a vehicle that does not meet the time requirements and is screened out. The preset time is determined based on the estimated recovery time of the power grid accident.

[0069] (3) Vehicles that are too far from the target discharge station are screened out. The range is set to a circle with a radius of 20 kilometers and the target discharge station as the center, ensuring that electric vehicles can arrive as quickly as possible.

[0070] (4) Screen out vehicles that do not meet the battery capacity requirements.

[0071] Emin≤Ei≤Emax(1)

[0072]

[0073] Si≤Smax(3)

[0074] Where: Emin and Emax are the minimum and maximum battery capacity configurations of electric vehicles respectively; Ei,t is the remaining battery capacity of electric vehicle i at time t after receiving the request; Ei is the rated battery capacity of electric vehicle i; and are the lowest and highest state of charge (SOC) of the electric vehicle respectively; Si is the distance between electric vehicle i and the target discharge station, and Smax is the set maximum distance from the target discharge station.

[0075] (5) The coordinate positions of the screened electric vehicles are uploaded to the cloud platform by the edge nodes.

[0076] In step S4, the cloud platform calculates the optimal path to the target discharge station based on the vehicle information. The specific implementation process is as follows:

[0077] S401, the location of the electric vehicle when the user receives the request is recorded as the starting point A, and the demand point closest to the electric vehicle is recorded as P;

[0078] S402, assuming that there are n nodes X1, X2, X3, ..., Xn between point A and point P, the node set is denoted as X; the distance between every two adjacent nodes is denoted as d, and the distance matrix is ​​denoted as D;

[0079] S403, according to the starting point A, the end point P, the node set X, and the distance matrix D, determine the numbers of the two adjacent nodes where the electric vehicle is located, and calculate the distances between the electric vehicle and the two nodes, which are recorded as Slast and Snext respectively;

[0080] S404, using the Floyd algorithm to calculate the shortest path for the electric vehicle to travel from the previous node, denoted by Dlast, and planning the path node set as [A, Xi, P], where Xi represents the node set that the electric vehicle passes through on the way from the previous node to the end point;

[0081] S405, using the Floyd algorithm to calculate the shortest path for the electric vehicle to travel from the next node, denoted by Dnext, and planning the path node set as [A, Xj, P], where Xj represents the node set that the electric vehicle passes through on the way from the next node to the end point; it should be noted that in the specific implementation process, Dlast and Dnext can also be calculated by other algorithms, such as the Dijkstra algorithm;

[0082] S406. Determine the size of Slast+Dlast and Snext+Dnext. The path corresponding to the smaller value is the shortest driving path from the current position of the electric vehicle to the charging station.

[0083] In step S5, the cloud platform synchronizes the calculation results to the electric vehicle user in real time and guides the user to the target discharge station. The specific implementation process is as follows:

[0084] The cloud platform synchronizes the calculation results to the electric vehicle users in real time, and the electric vehicles go to the target discharge station for discharge according to the shortest path obtained.

[0085] The embodiment of the present invention further provides an electric vehicle dispatching system for emergency power supply to a distribution network, the electric vehicle dispatching system comprising:

[0086] The edge node data processing module is used to obtain information about the power outage area and determine the target discharge station. It is also used to publish help information to the electric vehicle user end and receive feedback from users who voluntarily choose to connect their vehicles to the grid and the current data of the electric vehicle. It is also used to pre-process the current data of the electric vehicle, analyze the information of the electric vehicle, select the vehicles that meet the discharge conditions, and upload the coordinates of the selected electric vehicles to the cloud platform.

[0087] The cloud platform is used to calculate the optimal route to the target discharge station based on vehicle information; it is also used to synchronize the calculation results to the electric vehicle users in real time to guide the users to the target discharge station.

[0088] It can be understood that the electric vehicle dispatching system for emergency power supply to distribution network provided by the embodiment of the present invention corresponds to the above-mentioned electric vehicle dispatching method for emergency power supply to distribution network. The explanation, examples, beneficial effects and other parts of the relevant contents can refer to the corresponding contents in the electric vehicle dispatching method for emergency power supply to distribution network, and will not be repeated here.

[0089] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program for scheduling electric vehicles for emergency power supply to a distribution network, wherein the computer program enables a computer to execute the method for scheduling electric vehicles for emergency power supply to a distribution network as described above.

[0090] An embodiment of the present invention further provides an electronic device, including:

[0091] one or more processors;

[0092] Memory; and

[0093] One or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include a method for executing the electric vehicle dispatching method for emergency power supply to the distribution network as described above.

[0094] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0095] The embodiment of the present invention uses the information screening model at the edge to collect and process the data of electric vehicles. It only needs to upload the coordinate positions of the electric vehicles that have passed the screening to the cloud platform, remove a large amount of redundant data, and alleviate the computing pressure and data transmission pressure of the cloud platform. Since the edge node is closely linked to the user end, the data transmission is faster, and the data can be delivered to the electric vehicle users in a timely and accurate manner, which is more conducive to the rapid restoration of grid elasticity. At the same time, the cloud is used for path planning, and the calculation is performed through the extremely strong computing power of the cloud platform combined with the information uploaded by the edge to meet the calculation of real-time scheduling of electric vehicles.

[0096] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0097] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. 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 invention.

Claims

1. A method for dispatching electric vehicles for emergency power supply of distribution network, characterized in that: include: The edge nodes obtain the specific locations of battery swap stations in areas where some lines are damaged and power supply is insufficient due to typhoon disasters in real time, and use them as target discharge stations for electric vehicles; The edge node publishes the help information to the electric vehicle user end and receives the feedback of the user's voluntary choice of vehicle grid connection activities and the current data of the electric vehicle; The edge node pre-processes the current data of electric vehicles, analyzes the information of electric vehicles, selects vehicles that meet the discharge conditions, and uploads the coordinates of the selected electric vehicles to the cloud platform; The cloud platform calculates the optimal route to the target discharge station based on vehicle information; The cloud platform synchronizes the calculation results to the electric vehicle users in real time, guiding them to the target discharge station; in, The current data includes: the state of charge of the electric vehicle, the real-time location of the electric vehicle, and the departure time of the electric vehicle; The edge node pre-processes the current data of the electric vehicle, including: detecting and correcting the current data using a standard deviation method; The vehicles meeting the discharge conditions include: the SOC value of the vehicle is within a preset normal SOC value range, the estimated arrival time of the vehicle is within a preset time range, the distance between the vehicle and the target discharge station is within a preset maximum distance range, and the capacity of the vehicle is within the battery capacity configuration range; The cloud platform calculates the optimal path to the target discharge station according to the vehicle information, including: (1) The location of the electric vehicle when the user receives the request is recorded as the starting point A, and the demand point closest to the electric vehicle is recorded as P; (2) Assume that there are n nodes X1, X2, X3, ..., X4 between point A and point P. n , the node set is recorded as X; the distance between every two adjacent nodes is recorded as d, and the distance matrix is ​​recorded as D; (3) According to the starting point A, the end point P, the node set X, and the distance matrix D, the numbers of the two adjacent nodes where the electric vehicle is located are determined, and the distances between the electric vehicle and the two nodes are calculated, which are recorded as S last and S next ; (4) Calculate the shortest path for the electric vehicle to travel from the previous node. last , the set of planning path nodes is [A,X i ,P],X i Represents the set of nodes that the electric vehicle passes through on the way from the previous node to the end point; (5) Calculate the shortest path for the electric vehicle to travel from the next node. next , the set of planning path nodes is [A,X j ,P],X j Represents the set of nodes that the electric vehicle passes through on the way from the next node to the end point; (6) Determine S last +D last and S next +D next The path corresponding to the smaller value is the shortest driving path for the electric vehicle from the current location to the charging station.

2. The electric vehicle dispatching method for emergency power supply to distribution network according to claim 1, characterized in that: The D last and D next Calculated using the Floyd algorithm.

3. An electric vehicle dispatching system for emergency power supply of distribution network, characterized in that: The electric vehicle dispatching system comprises: The edge node data processing module is used for edge nodes to obtain in real time the specific locations of battery swap stations in areas where some lines are damaged and power supply is insufficient due to typhoon disasters, as target discharge stations for electric vehicles; it is also used to publish help information to the electric vehicle user end, and receive feedback from users who voluntarily choose to connect their vehicles to the grid and the current data of the electric vehicle; it is also used to pre-process the current data of the electric vehicle, analyze the information of the electric vehicle, select vehicles that meet the discharge conditions, and upload the coordinates of the selected electric vehicles to the cloud platform; The cloud platform is used to calculate the optimal route to the target discharge station based on vehicle information; it is also used to synchronize the calculation results to the electric vehicle users in real time to guide the users to the target discharge station; The current data includes: the state of charge of the electric vehicle, the real-time location of the electric vehicle, and the departure time of the electric vehicle; The edge node pre-processes the current data of the electric vehicle, including: detecting and correcting the current data using a standard deviation method; The vehicles meeting the discharge conditions include: the SOC value of the vehicle is within a preset normal SOC value range, the estimated arrival time of the vehicle is within a preset time range, the distance between the vehicle and the target discharge station is within a preset maximum distance range, and the capacity of the vehicle is within the battery capacity configuration range; The cloud platform calculates the optimal path to the target discharge station according to the vehicle information, including: (1) The location of the electric vehicle when the user receives the request is recorded as the starting point A, and the demand point closest to the electric vehicle is recorded as P; (2) Assume that there are n nodes X1, X2, X3, ..., X4 between point A and point P. n , the node set is recorded as X; the distance between every two adjacent nodes is recorded as d, and the distance matrix is ​​recorded as D; (3) According to the starting point A, the end point P, the node set X, and the distance matrix D, the numbers of the two adjacent nodes where the electric vehicle is located are determined, and the distances between the electric vehicle and the two nodes are calculated, which are recorded as S last and S next ; (4) Calculate the shortest path for the electric vehicle to travel from the previous node. last , the set of planning path nodes is [A,X i ,P],X i Represents the set of nodes that the electric vehicle passes through on the way from the previous node to the end point; (5) Calculate the shortest path for the electric vehicle to travel from the next node. next , the set of planning path nodes is [A,X j ,P],X j Represents the set of nodes that the electric vehicle passes through on the way from the next node to the end point; (6) Determine S last +D last and S next +D next The path corresponding to the smaller value is the shortest driving path for the electric vehicle from the current location to the charging station.

4. A computer-readable storage medium, characterized in that: It stores a computer program for an electric vehicle dispatching method for emergency power supply to a distribution network, wherein the computer program enables a computer to execute the electric vehicle dispatching method for emergency power supply to a distribution network as claimed in any one of claims 1 to 2.

5. An electronic device, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include a method for executing the electric vehicle dispatching method for emergency power supply to a distribution network as described in any one of claims 1 to 2.

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