Charging station demand response processing method and apparatus, computer device and storage medium

Abstract

The present application relates to a charging station demand response processing method and apparatus, a computer device, a storage medium and a computer program product. The method comprises: on the basis of traffic information of a target area, operating information of a charging station located in the target area, and vehicle information of an electric vehicle located in the target area, obtaining a path set for the electric vehicle to participate in a demand response; on the basis of the path set, obtaining target cost information of the electric vehicle participating in the demand response at the charging station; on the basis of the cost information, obtaining the degree of willingness of the electric vehicle participating in the demand response at the charging station; and on the basis of the degree of willingness, obtaining an evaluation result for demand response capability of the charging station. On the basis of the target cost information of the electric vehicle participating in the demand response at the charging station, the present method can determine the degree of willingness of the electric vehicle participating in the demand response at the charging station; and then, by means of analyzing the degree of willingness, achieve prediction and evaluation of the demand response capability of the charging station, thereby improving the accuracy of evaluating the demand response capability of the charging station.

Description

Demand response processing methods, devices, computer equipment, and storage media for charging stations Technical Field

[0001] This application relates to the field of power control technology, and in particular to a charging station demand response processing method, apparatus, computer equipment, storage medium and computer program product. Background Technology

[0002] With the popularization and promotion of new energy vehicles, electric vehicles are becoming increasingly common, and the management of their charging is facing more and more adjustments. Charging stations, as the link between the power grid and electric vehicle owners, can draw energy from the grid to control the charging and discharging of electric vehicles. However, the scale and configuration of each charging station are different, so the responsiveness of each charging station to the demand of electric vehicles also varies.

[0003] Traditional technologies typically assess a charging station's demand response capability based on its historical demand response capacity on similar days. However, this approach cannot predict demand response capability in real-time based on vehicle distribution and traffic conditions, resulting in low accuracy in assessing a charging station's demand response capability.

[0004] Summary of the Invention

[0005] Therefore, it is necessary to provide a charging station demand response processing method, apparatus, computer equipment, computer-readable storage medium, and computer program product to address the above-mentioned technical problems.

[0006] Firstly, this application provides a charging station demand response processing method. The method includes:

[0007] Based on the traffic information of the target area, the operation information of the charging stations located in the target area, and the vehicle information of the electric vehicles located in the target area, a set of paths for the electric vehicles to participate in demand response is obtained.

[0008] Based on the set of paths, the target cost information for the electric vehicle to participate in demand response at the charging station is obtained;

[0009] Based on the target cost information, the willingness of the electric vehicle to participate in demand response at the charging station is obtained;

[0010] Based on the stated willingness level, an assessment result is obtained regarding the demand response capability of the charging station.

[0011] In one embodiment, based on the set of paths, the target cost information for the electric vehicle's participation in demand response at the charging station is obtained, including:

[0012] Obtain the initial cost information of the electric vehicle not participating in demand response;

[0013] Based on the set of paths, the response cost information of the electric vehicle participating in demand response at the charging station is obtained;

[0014] Based on the constraint information of the electric vehicle and the initial cost information, the response cost information is optimized to obtain the target cost information of the electric vehicle participating in demand response at the charging station.

[0015] In one embodiment, the response cost information of the electric vehicle participating in demand response at the charging station is obtained based on the path set, including:

[0016] Based on the set of paths, the mileage cost information of the electric vehicle participating in demand response at the charging station is obtained, as well as the total travel time of the electric vehicle from its current location to its destination location.

[0017] Based on the operation information of the charging station, the charging and discharging cost information of the electric vehicle participating in demand response at the charging station is obtained;

[0018] Based on the total driving time and the operation information of the charging station, the time cost information of the electric vehicle participating in demand response at the charging station is obtained;

[0019] Based on the mileage cost information, the charging and discharging cost information, and the time cost information, the response cost information of the electric vehicle participating in demand response at the charging station is obtained.

[0020] In one embodiment, before performing cost optimization processing on the response cost information based on the constraint information of the electric vehicle and the initial cost information to obtain the target cost information of the electric vehicle participating in demand response at the charging station, the method further includes:

[0021] Based on the battery capacity of the electric vehicle, the battery capacity constraint information of the electric vehicle is obtained;

[0022] Based on the maximum time taken for the electric vehicle to travel from the current location to the destination location, the time constraint information of the electric vehicle is obtained;

[0023] The constraint information of the electric vehicle is obtained based on the battery capacity constraint information and the time consumption constraint information.

[0024] In one embodiment, obtaining initial cost information regarding the electric vehicle's non-participation in demand response includes:

[0025] Based on the current location and destination location of the electric vehicle, the initial travel route information of the electric vehicle that does not participate in demand response is obtained;

[0026] Obtain the initial cost information of the electric vehicle under the initial travel path information.

[0027] In one embodiment, based on traffic information of the target area, operational information of charging stations located within the target area, and vehicle information of electric vehicles located within the target area, a set of paths for the electric vehicles to participate in demand response is obtained, including:

[0028] Based on the traffic information of the target area, a weighted bidirectional connectivity map of the target area is obtained; the weighted bidirectional connectivity map is used to represent the road connectivity information and road weights of the target area.

[0029] Based on the weighted bidirectional connectivity graph, the travel path information of the electric vehicle from its current location to the charging station and from the charging station to its destination is obtained.

[0030] Based on the travel route information, a set of routes in which the electric vehicle participates in demand response at all charging stations within the target area is obtained.

[0031] In one embodiment, based on the target cost information, the willingness of the electric vehicle to participate in demand response at the charging station is obtained, including:

[0032] Obtain the vehicle type of the electric vehicle;

[0033] If the target cost information meets the preset cost conditions, the target cost information is input into the willingness prediction model corresponding to the vehicle type to obtain the willingness of the electric vehicle to participate in demand response at the charging station.

[0034] In one embodiment, based on the willingness level, an assessment result is obtained regarding the demand response capability of the charging station, including:

[0035] Based on the willingness level and the charging and discharging rate between the charging station and the electric vehicle, a first evaluation result of the electric vehicle's participation in demand response at the charging station is obtained;

[0036] Based on the first assessment results of each electric vehicle participating in demand response at the charging station within the target area, as well as the total driving time, waiting time, and charging / discharging time of each electric vehicle at the charging station, an assessment result for the demand response capability of the charging station is obtained.

[0037] Secondly, this application also provides a charging station demand response processing device. The device includes:

[0038] The route determination module is used to obtain a set of routes for the electric vehicle to participate in demand response based on traffic information of the target area, operation information of charging stations located in the target area, and vehicle information of electric vehicles located in the target area.

[0039] The cost optimization module is used to obtain the target cost information of the electric vehicle participating in demand response at the charging station based on the path set.

[0040] The willingness determination module is used to determine the willingness of the electric vehicle to participate in demand response at the charging station based on the target cost information.

[0041] The capability assessment module is used to obtain an assessment result of the demand response capability of the charging station based on the willingness level.

[0042] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0043] Based on the traffic information of the target area, the operation information of the charging stations located in the target area, and the vehicle information of the electric vehicles located in the target area, a set of paths for the electric vehicles to participate in demand response is obtained.

[0044] Based on the set of paths, the target cost information for the electric vehicle to participate in demand response at the charging station is obtained;

[0045] Based on the target cost information, the willingness of the electric vehicle to participate in demand response at the charging station is obtained;

[0046] Based on the stated willingness level, an assessment result is obtained regarding the demand response capability of the charging station.

[0047] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0048] Based on the traffic information of the target area, the operation information of the charging stations located in the target area, and the vehicle information of the electric vehicles located in the target area, a set of paths for the electric vehicles to participate in demand response is obtained.

[0049] Based on the set of paths, the target cost information for the electric vehicle to participate in demand response at the charging station is obtained;

[0050] Based on the target cost information, the willingness of the electric vehicle to participate in demand response at the charging station is obtained;

[0051] Based on the stated willingness level, an assessment result is obtained regarding the demand response capability of the charging station.

[0052] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0053] Based on the traffic information of the target area, the operation information of the charging stations located in the target area, and the vehicle information of the electric vehicles located in the target area, a set of paths for the electric vehicles to participate in demand response is obtained.

[0054] Based on the set of paths, the target cost information for the electric vehicle to participate in demand response at the charging station is obtained;

[0055] Based on the target cost information, the willingness of the electric vehicle to participate in demand response at the charging station is obtained;

[0056] Based on the stated willingness level, an assessment result is obtained regarding the demand response capability of the charging station.

[0057] The aforementioned charging station demand response processing method, apparatus, computer equipment, storage medium, and computer program product, based on traffic information of the target area, operational information of charging stations within the target area, and vehicle information of electric vehicles within the target area, obtain a set of paths for electric vehicles to participate in demand response; based on the path set, obtain target cost information for electric vehicles participating in demand response at charging stations; based on the cost information, obtain the willingness of electric vehicles to participate in demand response at charging stations; and based on the willingness, obtain an assessment result of the demand response capability of the charging station. Using this method, the willingness of electric vehicles to participate in demand response at charging stations can be determined through the target cost information, and then the demand response capability of charging stations can be predicted and assessed by analyzing the willingness of electric vehicles, effectively improving the accuracy of the assessment of the demand response capability of charging stations. Attached Figure Description

[0058] Figure 1 is an application environment diagram of a charging station demand response processing method in one embodiment;

[0059] Figure 2 is a flowchart illustrating a charging station demand response processing method in one embodiment;

[0060] Figure 3 is a flowchart illustrating the steps for obtaining target cost information of electric vehicles participating in demand response at charging stations in one embodiment.

[0061] Figure 4 is a flowchart illustrating the process of obtaining the set of paths for electric vehicles to participate in demand response in one embodiment;

[0062] Figure 5 is a flowchart illustrating the charging station demand response processing method in another embodiment;

[0063] Figure 6 is a schematic diagram of the framework of a charging station demand response processing system in one embodiment;

[0064] Figure 7 is a structural block diagram of a charging station demand response processing device in one embodiment;

[0065] Figure 8 is an internal structure diagram of a computer device in one embodiment. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0067] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, electric vehicle data, charging station data, etc.) involved in this application are all information and data authorized by the user (e.g., authorized by the owner of the electric vehicle) or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0068] The charging station demand response processing method provided in this application embodiment can be applied to the application environment shown in Figure 1. In this environment, electric vehicle 101 and charging station 102 communicate with server 103 via a network. A data storage system can store the data that server 103 needs to process. The data storage system can be integrated onto server 103 or hosted on the cloud or other network servers. Charging station 102 can provide charging and discharging services to electric vehicle 101. Server 103 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0069] In one embodiment, as shown in Figure 2, a charging station demand response processing method is provided. Taking the application of this method to the server in Figure 1 as an example, it can be understood that this method can also be applied to a terminal, or to a system including both a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0070] Step S201: Based on the traffic information of the target area, the operation information of the charging stations located in the target area, and the vehicle information of the electric vehicles located in the target area, obtain the set of paths for electric vehicles to participate in demand response.

[0071] The target area refers to a specific geographical region where demand response analysis is required; it can be a region in a real-world scenario. Traffic information describes the traffic conditions in the target area; for example, target traffic information includes road connectivity information, road location information, and charging station location information. Operational information describes the charging station's power operation; for example, operational information includes charging station pricing information (such as charging electricity costs and service fees), charging and discharging rates, and current service queue times. Vehicle information describes the electric vehicle's driving status; for example, vehicle information includes the electric vehicle's current location, destination location, and battery capacity information.

[0072] Specifically, the server can connect to the external target area's traffic operation system, the charging station's power trading system and charging station operation system, and the electric vehicle's onboard operation system. The server then obtains traffic information for the target area through the traffic operation system, and counts all charging stations located within the target area. It then obtains the operation information of all charging stations located within the target area through the power trading system and charging station operation system. Furthermore, it obtains the current location of each electric vehicle through the onboard operation system, thereby identifying the electric vehicles located within the target area based on their current locations, and also obtains the vehicle information of the electric vehicles located within the target area through the onboard operation system.

[0073] Furthermore, for each electric vehicle, the server can sequentially designate each charging station within the target area as a waypoint for the electric vehicle, essentially treating it as the electric vehicle traveling to each charging station to participate in demand response. The server uses an optimal path search algorithm to obtain the travel path information of the electric vehicle from its current location to each charging station and from each charging station to its destination.

[0074] Step S202: Based on the path set, obtain the target cost information for electric vehicles participating in demand response at charging stations.

[0075] Demand response refers to adjusting electricity demand within a specific time frame based on grid load conditions and changes in energy demand, in order to achieve balanced and efficient operation of the power system.

[0076] It should be noted that electric vehicles participating in demand response at charging stations refers to the charging stations flexibly adjusting parameters such as charging time and charging / discharging power of electric vehicles based on the power system conditions and market demand, in order to achieve flexible response to power demand and thereby improve the operating efficiency of charging stations and electric vehicles.

[0077] Specifically, the server determines the cost for each electric vehicle to participate in demand response at each charging station based on the route information in the route set. Then, based on the constraints of the electric vehicle, the server optimizes this cost to reduce the overall cost of demand response. Finally, the server obtains the target cost information for each electric vehicle participating in demand response at each charging station. The target cost information represents the cost required for the electric vehicle to participate in demand response after cost optimization.

[0078] Step S203: Based on the target cost information, obtain the willingness of electric vehicles to participate in demand response at charging stations.

[0079] Among them, willingness level is used to measure the probability that electric vehicles are willing to participate in demand response at the charging station.

[0080] Specifically, the server can use a willingness prediction model to calculate the willingness of each electric vehicle to participate in demand response at each charging station based on the target cost information of each electric vehicle.

[0081] Step S204: Based on the willingness level, obtain the assessment results of the demand response capability of the charging station.

[0082] Specifically, for each charging station, the server combines the willingness of all electric vehicles in the target area to use the charging station to calculate the charging station's demand response capability for all electric vehicles in the target area; then, based on the arrival time of all electric vehicles at the charging station, the charging and discharging duration, the preset future time, and the charging station's demand response capability, it calculates a time series that can represent the charging station's demand response capability in the future, and sets this time series as the evaluation result of the charging station's demand response capability in the future.

[0083] The above-mentioned charging station demand response processing method can determine the willingness of electric vehicles to participate in demand response at charging stations by using the target cost information of electric vehicles participating in demand response at charging stations. Then, by analyzing the willingness of electric vehicles, the demand response capability of charging stations can be predicted and evaluated, which effectively improves the accuracy of the assessment of the demand response capability of charging stations.

[0084] In one embodiment, as shown in Figure 3, step S202 above, based on the path set, obtains the target cost information for electric vehicles participating in demand response at charging stations, specifically including the following:

[0085] Step S301: Obtain initial cost information for electric vehicles not participating in demand response.

[0086] The initial cost information is used to characterize the total cost incurred when electric vehicles do not participate in demand response.

[0087] Step S302: Based on the path set, obtain the response cost information of electric vehicles participating in demand response at charging stations.

[0088] Among them, response cost information is used to represent the total cost required for an electric vehicle to participate in demand response according to a certain travel route information.

[0089] Step S303: Based on the constraint information and initial cost information of the electric vehicle, the response cost information is optimized to obtain the target cost information of the electric vehicle participating in demand response at the charging station.

[0090] The target cost information is used to characterize the maximum cost optimization space for electric vehicles participating in demand response. The maximum cost optimization space represents the costs that can be optimized, adjusted, and reduced in terms of operation, charging, and energy consumption of electric vehicles. Therefore, the server can optimize and reduce the cost of electric vehicles participating in demand response activities based on the target cost information, thereby making electric vehicles more efficient and economical in participating in demand response activities.

[0091] Specifically, the server can first calculate the driving distance of the electric vehicle without participating in demand response, thus obtaining initial travel path information. Based on this initial travel path information, the server then calculates the initial cost information for the electric vehicle without participating in demand response. For each electric vehicle, the server can calculate the response cost information for participating in demand response at each charging station within the target area, based on the electric vehicle's path set. The server determines the constraints on the electric vehicle (including constraints related to time consumption and battery capacity). Under these constraints, the server performs cost optimization processing on the response cost information of the electric vehicle under each travel path information, based on the initial cost information, thus obtaining the target cost information for the electric vehicle under each travel path information. Since the travel path information represents the driving path of the electric vehicle when participating in demand response at a charging station, the target cost information of the electric vehicle under each travel path information can also represent the target cost information of the electric vehicle participating in demand response at each charging station.

[0092] In practical applications, maximizing the target cost information for electric vehicles participating in demand response at charging stations can be used as the objective to minimize the costs associated with such participation. The objective function for the target cost information can be expressed by the following formula: Max ΔC i =Max C 0,I -(β·(T) drive,i,j +T wait,i,j +T charge,i,j )+C loss,i,j +(C charge,i,j +Cbalance,i,j ))

[0093] In the formula, ΔC i Let C represent the cost optimization space for electric vehicle i, where i ∈ I, and I is the set of all electric vehicles within the target region; 0,i This indicates the initial travel path information of electric vehicle i. 0i The initial cost information is given below; β represents the coefficient of the initial time cost information; T drive,i,j Let T represent the total travel time of electric vehicle i to charging station j and then to its destination, where j ∈ J, and J is the set of all charging stations within the target area; wait,i,j T represents the waiting time at charging station j; charge,i,j C represents the charging and discharging time of charging station j; loss,i,j This represents the mileage cost information for electric vehicle i; C charge,i,j This represents the charging and discharging costs that electric vehicle i must pay when participating in demand response; C balance,i,j This represents the cost of charging electric vehicle i to maintain the minimum battery level constraint after it reaches its destination.

[0094] In this embodiment, based on the constraint information of electric vehicles and the initial cost information of electric vehicles not participating in demand response, the response cost information of electric vehicles participating in demand response at charging stations is optimized to obtain the target cost information of electric vehicles participating in demand response at charging stations. On the one hand, this allows subsequent steps to determine the willingness of electric vehicles to participate in demand response at a certain charging station based on the target cost information of electric vehicles participating in demand response at that charging station. On the other hand, it can also reduce the cost of electric vehicles participating in demand response activities based on the target cost information, thereby making it more efficient and economical for electric vehicles to participate in demand response activities.

[0095] In one embodiment, step S302 above, obtaining the response cost information of the electric vehicle participating in demand response at the charging station based on the path set, specifically includes the following: obtaining the mileage cost information of the electric vehicle participating in demand response at the charging station based on the path set, and the total travel time of the electric vehicle from its current location to its destination location; obtaining the charging and discharging cost information of the electric vehicle participating in demand response at the charging station based on the charging station's operation information; obtaining the time cost information of the electric vehicle participating in demand response at the charging station based on the total travel time and the charging station's operation information; and obtaining the response cost information of the electric vehicle participating in demand response at the charging station based on the mileage cost information, charging and discharging cost information, and time cost information.

[0096] The mileage cost information indicates the distance an electric vehicle needs to travel to a charging station to participate in demand response. The charging and discharging cost information indicates the expenses incurred by an electric vehicle in traveling to a charging station to participate in demand response. The time cost information indicates the total time required for an electric vehicle to travel to a charging station to participate in demand response.

[0097] Specifically, the server obtains the mileage cost information C of electric vehicle i participating in demand response at charging station j based on the path set L(i). loss,i,j And the total travel time T of electric vehicle i from its current location to its destination location. drive,i,j The server obtains the charging and discharging cost information of electric vehicle i participating in demand response at charging station j based on the operation information of charging station j. For example, it could be the charging and discharging cost C of electric vehicle i included in the operation information. charge,i,j And charging and replenishment costs C balance,i,j Summing is performed to obtain the charging and discharging cost information required for electric vehicle i to participate in demand response at charging station j, i.e., the charging and discharging cost information is (C charge,i,j +C balance,i,j The server calculates the total travel time T. drive,i,j And the queuing time T included in the operation information of charging station j. wait,i,j and charge / discharge duration T charge,i,j We perform summation to obtain the time cost information of electric vehicle i participating in demand response at charging station j, i.e., the time cost information is T. drive,i,j +T wait,i,j +T charge,i,j Then, the server can use the preset initial time cost information coefficient β to weight the time cost information, so that the server obtains the weighted time cost information of electric vehicle i when participating in demand response at charging station j, that is, the weighted time cost information is β·(T drive,i,j +T wait,i,j +T charge,i,j The server can access mileage cost information C. loss,i,j Charging and discharging cost information (C) charge,i,j +C balance,i,j ) and weighted time cost information β·(T) drive,i,j +T wait,i,j +T charge,i,j The summation is performed to obtain the response cost information of electric vehicle i participating in demand response at charging station j, i.e., (β·(T)). drive,i,j +T wait,i,j +T charge,i,j )+C loss,i,j +(C charge,i,j +C balance,i,j This refers to response cost information.

[0098] In this embodiment, by using the mileage cost information, charging and discharging cost information, and time cost information of electric vehicles, the response cost information required for electric vehicles to go to charging stations to participate in demand response is reasonably obtained. This provides reliable data support for optimizing the cost consumption of electric vehicles when participating in demand response at charging stations, and helps to more accurately determine the willingness of electric vehicles to go to charging stations.

[0099] In one embodiment, before performing cost optimization processing on the response cost information based on the constraint information and initial cost information of the electric vehicle to obtain the target cost information of the electric vehicle participating in demand response at the charging station in step S303, the method further includes: obtaining battery capacity constraint information of the electric vehicle based on the battery capacity of the electric vehicle; obtaining time constraint information of the electric vehicle based on the maximum time taken by the electric vehicle from the current location to the destination location; and obtaining constraint information of the electric vehicle based on the battery capacity constraint information and the time constraint information.

[0100] Among them, battery capacity constraint information is used to constrain the charging and discharging time of electric vehicles at charging stations. Time consumption constraint information is used to constrain the time cost information of electric vehicles at charging stations. Maximum consumption time represents the maximum time that an electric vehicle is allowed to spend from its current location to its destination location.

[0101] Specifically, the server can determine the charging and discharging duration of the electric vehicle at the charging station based on the upper and lower limits of the electric vehicle's battery capacity and the charging and discharging rate of the electric vehicle at the charging station; then, based on the charging and discharging duration of the electric vehicle at the charging station, it obtains the battery capacity constraint information of the electric vehicle. The server can also determine the time cost information of the electric vehicle at the charging station based on the preset maximum time taken for the electric vehicle to travel from its current location to its destination; then, based on the time cost information, it determines the time consumption constraint information of the electric vehicle. Finally, the server sets the battery capacity constraint information and the time consumption constraint information as the constraint information of the electric vehicle.

[0102] In practical applications, when the server calculates target cost information, one of the parameter variables is the formation path selection when electric vehicles participate in demand response; that is, one of the parameter variables is l. i，j ∈L(i), the other parameter variable is the charging and discharging time T of the electric vehicle. charge,i,j The battery capacity constraint information for the target cost information can be set as follows: the product of the charge / discharge rate and the charge / discharge duration must not be less than the lower limit of the battery capacity, and this product must not be greater than the upper limit of the battery capacity. The battery capacity constraint information can be represented in the following form: SOC min,i ≤T charge,i,j ·R charge,j ≤SOC max,i

[0103] In the formula, SOC min,i R represents the lower limit of the battery capacity of electric vehicle i; charge,j This represents the charge / discharge rate, which is determined by the maximum charge / discharge rate supported by electric vehicle i and charging station j; SOC max,i This indicates the upper limit of the battery capacity of electric vehicle i.

[0104] The time constraint information for the target cost information can be set as follows: the time cost information of the electric vehicle does not exceed the maximum allowable time for the electric vehicle to reach the destination. The time constraint information can then be represented in the following form: T drive,i,j +T wait,i,j +T charge,i,j ≤T last,i,j

[0105] In the formula, T last,i,j This indicates the maximum time allowed for an electric vehicle to reach its destination.

[0106] In this embodiment, the upper and lower limits of the electric vehicle's battery capacity are used to obtain battery capacity constraint information, which is then used to constrain the charging and discharging time in the target cost information. The maximum time consumed by the electric vehicle is used to obtain time consumption constraint information, which is then used to constrain the time cost information in the target cost information. This allows subsequent steps to optimize the response cost information by using the battery capacity constraint information and the time consumption constraint information to constrain the time cost information and charging and discharging time in the target cost information, thereby improving the accuracy and reliability of the obtained target cost information.

[0107] In one embodiment, step S301 above, obtaining the initial cost information of the electric vehicle not participating in demand response, specifically includes the following: obtaining the initial travel path information of the electric vehicle not participating in demand response based on the current location and destination location of the electric vehicle; and obtaining the initial cost information of the electric vehicle under the initial travel path information.

[0108] Specifically, the server calculates the initial path information for electric vehicles not participating in demand response. This can be done using a minimum path algorithm (such as Dijkstra's algorithm) to calculate the shortest path from the current location to the destination location based on traffic information (or road connectivity information) in the target area. This shortest path is then set as the initial travel path information for electric vehicles not participating in demand response. 0i Furthermore, the server can calculate the initial cost information of electric vehicles not participating in demand response based on the initial travel path information.

[0109] In this embodiment, the initial travel path information of the electric vehicle without participating in demand response is obtained by using the current location and destination location of the electric vehicle. Then, the initial cost information of the electric vehicle under the initial travel path information is calculated, realizing the reasonable acquisition of the initial cost information of the electric vehicle and providing a reliable processing basis for subsequent cost optimization.

[0110] In one embodiment, step S201 above, which obtains a set of paths for electric vehicles to participate in demand response based on traffic information of the target area, operation information of charging stations located in the target area, and vehicle information of electric vehicles located in the target area, specifically includes the following: obtaining a weighted bidirectional connectivity graph of the target area based on traffic information of the target area; the weighted bidirectional connectivity graph is used to represent road connectivity information and road weights of the target area; obtaining travel path information of electric vehicles from their current location to charging stations and from charging stations to their destination location based on the weighted bidirectional connectivity graph; and obtaining a set of paths for electric vehicles to participate in demand response at all charging stations in the target area based on the travel path information.

[0111] The weighted bidirectional connectivity graph is used to model the traffic network of the target area. In the weighted bidirectional connectivity graph, vertices represent nodes (such as intersections and charging stations), edges represent roads, and weights represent the distance between two nodes and the road congestion status.

[0112] Figure 4 illustrates the process of obtaining the set of paths for electric vehicles to participate in demand response. Specifically, the server can obtain real-time traffic information of the target area based on the traffic operation system of the target area; according to graph theory, it extracts the bidirectional connectivity graph corresponding to the road network of the target area from the real-time traffic information, and sets the weight of each road based on the congestion information and road length of each road in the target area, finally obtaining a weighted bidirectional connectivity graph. The server obtains electric vehicle i in the target area, where i∈I, I is the set of all electric vehicles in the target area, and also obtains charging station j in the target area, j∈J, J is the set of all charging stations in the target area; based on the current location and destination location of electric vehicle i, the server adds charging station j as a waypoint of electric vehicle i's journey path, and calculates the journey path information l of electric vehicle i from its current location to charging station j and from charging station j to its destination location based on the weighted bidirectional connectivity graph. i，j Among them, the trip route information l i，j This can also indicate that electric vehicle i participated in demand response at charging station j. Similarly, the server continues to calculate the travel path information l of electric vehicle i from its current location to charging station j+1 and from charging station j+1 to its destination location. i，j+1Repeat this step until the travel path information of electric vehicle i participating in demand response at each charging station J in the target area has been calculated, and set as the path set L(i) of electric vehicle i participating in demand response at all charging stations J in the target area.

[0113] Furthermore, the server continues to calculate the set of paths L(i+1) for the next electric vehicle i+1 in the target area to participate in demand response at all charging stations J in the target area. This step is repeated until the server calculates the total set of paths L for all electric vehicles I in the target area to participate in demand response at all charging stations J.

[0114] In this embodiment, a weighted bidirectional connectivity map of the target area is obtained based on the traffic information of the target area. Then, based on the bidirectional connectivity map, the travel path information of the electric vehicle from its current location to the charging station and from the charging station to its destination is obtained. Based on the travel path information of the electric vehicle participating in demand response at all charging stations in the target area, a path set of the electric vehicle participating in demand response at all charging stations in the target area is formed, thereby realizing the reasonable acquisition of the path set of the electric vehicle participating in demand response.

[0115] In one embodiment, step S203 above, which obtains the willingness of electric vehicles to participate in demand response at charging stations based on target cost information, specifically includes the following: obtaining the vehicle type of the electric vehicle; if the target cost information meets the preset cost conditions, then inputting the target cost information into the willingness prediction model corresponding to the vehicle type to obtain the willingness of electric vehicles to participate in demand response at charging stations.

[0116] The willingness prediction function represents the probability that an electric vehicle will participate in demand response and charge / discharge at a charging station. The preset cost condition refers to the judgment conditions set for target cost information; for example, the preset cost condition could be set as the optimal choice when the target cost information represents the electric vehicle's participation in demand response.

[0117] Specifically, the server can classify electric vehicles into three types: sensitive (K1), general (K2), and insensitive (K3). Different intention prediction models can be set for different types of electric vehicles. These intention prediction models can be constructed based on probability density functions. In practical applications, the intention prediction model can be represented by a probability density function p with target cost information in the range [0,∞). If the electric vehicle participates in demand response at a certain charging station, the travel path information l... i，j The corresponding target cost information ΔC i If an electric vehicle is not the optimal choice for participating in demand response at that charging station, the server can set the probability of that electric vehicle participating in demand response at that charging station to 0, i.e., p. i，j=0. If an electric vehicle participates in demand response at a certain charging station, the travel path information l i，j The corresponding target cost information ΔC i If the electric vehicle is the optimal choice for participating in demand response at that charging station, the server can input the target cost information corresponding to the electric vehicle's participation in demand response at that charging station into the willingness prediction model corresponding to the electric vehicle type. The server then obtains the willingness level of the electric vehicle to participate in demand response at that charging station, which can be represented as p. i，j =p(ΔC) i i).

[0118] In this embodiment, if the target cost information meets the preset cost conditions, the target cost information is input into the willingness prediction model corresponding to the type of electric vehicle. The willingness prediction model outputs the willingness of electric vehicles to participate in demand response at charging stations. The willingness level can then be used to analyze the willingness of electric vehicle owners to participate in demand response, laying the foundation for predicting the demand response capability of charging stations.

[0119] In one embodiment, step S204 above, which obtains an assessment result of the demand response capability of the charging station based on the willingness level, specifically includes the following: obtaining a first assessment result of the electric vehicle's participation in demand response at the charging station based on the willingness level and the charging and discharging rate between the charging station and the electric vehicle; obtaining an assessment result of the charging station's demand response capability based on the first assessment result of each electric vehicle's participation in demand response at the charging station within the target area, as well as the total driving time, waiting time at the charging station, and charging and discharging time of each electric vehicle.

[0120] The first assessment result characterizes the expected demand response capability of electric vehicles at the charging station. The assessment result of the charging station's demand response capability characterizes the charging station's demand response capability over a future time period. Waiting time refers to the length of time an electric vehicle needs to queue when it goes to the charging station to participate in demand response charging and discharging services.

[0121] Specifically, the server calculates a first assessment result of the electric vehicles' willingness to participate in demand response at the charging station based on the charging and discharging rates between the charging station and the electric vehicles. Based on the first assessment result of each electric vehicle's participation in demand response at the charging station within the target area, as well as the total driving time, waiting time at the charging station, and charging / discharging time of each electric vehicle, the server calculates an assessment result of the charging station's demand response capability in future times. If the assessment results for the charging station at multiple future times are calculated, the assessment result can also be expressed in the form of a time series.

[0122] In practical applications, the first evaluation result E of electric vehicle i participating in demand response at charging station j is...i，j It can be represented as: E i，j =R charge,j ·p i，j

[0123] If we combine the first assessment results of all electric vehicles in the target area participating in demand response at this charging station, we can also calculate the second assessment result of the charging station. The second assessment result can be expressed as E. j E j =∑E i，j =∑(R) charge,j ·p i，j )

[0124] Then, based on the initial assessment results of each electric vehicle's participation in demand response at charging stations within the target area, as well as the total driving time, waiting time at charging stations, and charging / discharging time of each electric vehicle, the assessment result E of the charging station's demand response capability can be calculated. j (t), the evaluation result E j (t) can be represented as: E j (t)=∑(E i，j ·ε(tT charge,i,j )-ε(tT drive,i,j -T charge,i,j -T wait,i,j ))

[0125] In the formula, t represents future time.

[0126] In this embodiment, a first evaluation result of electric vehicles' participation in demand response at a charging station is obtained by considering the willingness of all electric vehicles in the target area to participate in demand response at a certain charging station and the charging and discharging rate between the charging station and the electric vehicles. Then, based on the first evaluation result of each electric vehicle's participation in demand response at the charging station in the target area, as well as the total driving time, waiting time at the charging station, and charging and discharging time of each electric vehicle, an evaluation result of the charging station's demand response capability is obtained. This achieves an accurate evaluation of the charging station's demand response capability in the future, and the evaluation result can guide the charging station operator to participate in demand response optimization decisions.

[0127] In one embodiment, as shown in Figure 5, another charging station demand response processing method is provided. Taking the application of this method to the server in Figure 1 as an example, the method includes the following steps:

[0128] Step S501: Based on the traffic information of the target area, the operation information of the charging stations located in the target area, and the vehicle information of the electric vehicles located in the target area, obtain the set of paths for electric vehicles to participate in demand response.

[0129] Step S502: Based on the path set, obtain the response cost information of electric vehicles participating in demand response at charging stations.

[0130] Step S503: Based on the current location and destination location of the electric vehicle, obtain the initial travel route information of the electric vehicle when it does not participate in demand response; obtain the initial cost information of the electric vehicle under the initial travel route information.

[0131] Step S504: Obtain battery capacity constraint information for the electric vehicle based on its battery capacity.

[0132] Step S505: Obtain the time constraint information of the electric vehicle based on the maximum time taken for the electric vehicle to travel from the current location to the destination location; obtain the constraint information of the electric vehicle based on the battery capacity constraint information and the time constraint information.

[0133] Step S506: Based on the constraint information and initial cost information of the electric vehicle, the response cost information is optimized to obtain the target cost information of the electric vehicle participating in demand response at the charging station.

[0134] Step S507: Obtain the vehicle type of the electric vehicle; if the target cost information meets the preset cost conditions, input the target cost information into the willingness prediction model corresponding to the vehicle type to obtain the willingness of the electric vehicle to participate in demand response at the charging station.

[0135] Step S508: Based on the willingness level and the charging and discharging rate between the charging station and the electric vehicle, obtain the first assessment result of the electric vehicle's participation in demand response at the charging station.

[0136] Step S509: Based on the first assessment results of each electric vehicle's participation in demand response at the charging station within the target area, as well as the total driving time, waiting time at the charging station, and charging / discharging time of each electric vehicle, an assessment result for the demand response capability of the charging station is obtained.

[0137] The above-mentioned charging station demand response processing method can achieve the following beneficial effects: it can determine the willingness of electric vehicles to participate in demand response at charging stations by using the target cost information of electric vehicles participating in demand response at charging stations, and then predict and evaluate the demand response capability of charging stations by analyzing the willingness of electric vehicles, thus effectively improving the accuracy of the assessment of the demand response capability of charging stations.

[0138] To more clearly illustrate the charging station demand response processing method provided in this disclosure, a specific embodiment is used to describe the above-mentioned charging station demand response processing method in detail below. As shown in Figure 6, another charging station demand response processing method is provided, which can be applied to the server in Figure 1. The server can also be equipped with a charging station demand response processing system, which includes a data access module, an orderly charging decision analysis module, and a demand response resource capacity prediction module.

[0139] The data access module also includes a map and traffic information acquisition submodule, a power grid demand response information acquisition submodule, a charging station information acquisition submodule, and an electric vehicle information acquisition submodule.

[0140] 1) The map and traffic information acquisition submodule connects to the traffic operation system to obtain the road location and road connections in the target area, as well as the location distribution of charging stations and traffic congestion in the target area;

[0141] 2) The grid-side demand response information acquisition submodule connects to the power trading system to obtain day-ahead, intraday, and real-time power market disclosure information on charging stations. The operation information includes demand areas, demand volume, and demand periods.

[0142] 3) The charging station information acquisition submodule obtains the charging station price information in the target area by connecting to the charging station or the charging station operation system. Currently, the charging station price information includes the charging electricity fee and service fee. In addition, the charging station information acquisition submodule also needs to obtain other aspects of the charging station's operation information, including the charging and discharging rate of the charging station and the current queuing waiting time of the charging station.

[0143] 4) The electric vehicle information acquisition submodule connects to the on-board operation system of electric vehicles to obtain the real-time location of each vehicle, and at the same time reads the current destination and itinerary of each electric vehicle, as well as the current battery capacity information of each electric vehicle.

[0144] The orderly charging decision analysis module analyzes the travel path of electric vehicles based on the optimal path search module, analyzes the cost optimization space of electric vehicles participating in demand response based on the charging cost calculation module, and inputs the target cost information of electric vehicles choosing different travel paths into the willingness conversion model based on the demand response participation willingness analysis module, and outputs the willingness of any electric vehicle to participate in demand response charging and discharging at any charging station.

[0145] The demand response resource capacity prediction module predicts the demand response capacity of charging stations based on willingness, and obtains the evaluation results of the demand response capacity of charging stations.

[0146] In this embodiment, the willingness of electric vehicles to participate in demand response at charging stations can be determined by the target cost information of electric vehicles participating in demand response at charging stations. Then, by analyzing the willingness of electric vehicles, the demand response capability of charging stations can be predicted and evaluated, which effectively improves the accuracy of the evaluation of the demand response capability of charging stations.

[0147] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0148] Based on the same inventive concept, this application also provides a charging station demand response processing device for implementing the charging station demand response processing method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the charging station demand response processing device provided below can be found in the limitations of the charging station demand response processing method described above, and will not be repeated here.

[0149] In one embodiment, as shown in FIG7, a charging station demand response processing device 700 is provided, comprising:

[0150] The route determination module 701 is used to obtain a set of routes for electric vehicles to participate in demand response based on traffic information of the target area, operation information of charging stations located in the target area, and vehicle information of electric vehicles located in the target area.

[0151] The cost optimization module 702 is used to obtain the target cost information of electric vehicles participating in demand response at charging stations based on the path set.

[0152] The willingness determination module 703 is used to obtain the willingness of electric vehicles to participate in demand response at charging stations based on the target cost information.

[0153] The capability assessment module 704 is used to obtain the assessment results of the demand response capability of the charging station based on the willingness level.

[0154] In one embodiment, the cost optimization module 702 is further configured to obtain initial cost information of electric vehicles not participating in demand response; obtain response cost information of electric vehicles participating in demand response at charging stations based on the path set; and perform cost optimization processing on the response cost information based on the constraint information and initial cost information of electric vehicles to obtain target cost information of electric vehicles participating in demand response at charging stations.

[0155] In one embodiment, the charging station demand response processing device 700 further includes a response cost determination module, configured to obtain, based on a path set, mileage cost information of the electric vehicle participating in demand response at the charging station, and the total travel time of the electric vehicle from its current location to its destination; obtain charging and discharging cost information of the electric vehicle participating in demand response at the charging station based on the charging station's operation information; obtain time cost information of the electric vehicle participating in demand response at the charging station based on the total travel time and the charging station's operation information; and obtain response cost information of the electric vehicle participating in demand response at the charging station based on the mileage cost information, charging and discharging cost information, and time cost information.

[0156] In one embodiment, the charging station demand response processing device 700 further includes a constraint determination module, which is used to obtain battery capacity constraint information of the electric vehicle based on the battery capacity of the electric vehicle; obtain time constraint information of the electric vehicle based on the maximum time taken for the electric vehicle to travel from the current location to the destination location; and obtain constraint information of the electric vehicle based on the battery capacity constraint information and the time constraint information.

[0157] In one embodiment, the charging station demand response processing device 700 further includes an initial cost determination module, used to obtain initial travel path information of the electric vehicle when it does not participate in demand response based on the current location and destination location of the electric vehicle; and to obtain initial cost information of the electric vehicle under the initial travel path information.

[0158] In one embodiment, the path determination module 701 is further configured to obtain a weighted bidirectional connectivity map of the target area based on the traffic information of the target area; the weighted bidirectional connectivity map is used to characterize the road connectivity information and road weights of the target area; based on the weighted bidirectional connectivity map, the travel path information of the electric vehicle from its current location to the charging station and from the charging station to its destination location is obtained; based on the travel path information, the set of paths in which the electric vehicle participates in demand response at all charging stations within the target area is obtained.

[0159] In one embodiment, the willingness determination module 703 is further configured to obtain the vehicle type of the electric vehicle; if the target cost information meets the preset cost conditions, the target cost information is input into the willingness prediction model corresponding to the vehicle type to obtain the willingness of the electric vehicle to participate in demand response at the charging station.

[0160] In one embodiment, the capability assessment module 704 is further configured to obtain a first assessment result of electric vehicles participating in demand response at the charging station based on willingness and the charging / discharging rate between the charging station and the electric vehicle; and to obtain an assessment result of the demand response capability of the charging station based on the first assessment result of each electric vehicle participating in demand response at the charging station within the target area, as well as the total driving time, waiting time at the charging station, and charging / discharging time of each electric vehicle.

[0161] Each module in the aforementioned charging station demand response processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0162] In one embodiment, a computer device, which may be a server, is provided, and its internal structure is shown in Figure 8. The computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores data such as traffic information, operational information, vehicle information, and route sets. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a charging station demand response processing method.

[0163] Those skilled in the art will understand that the structure shown in Figure 8 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or may combine certain components, or may have different component arrangements.

[0164] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0165] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0166] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0167] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0168] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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, they should be considered to be within the scope of this specification.

[0169] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A demand response processing method for charging stations, characterized in that, The method includes: Based on the traffic information of the target area, the operation information of the charging stations located in the target area, and the vehicle information of the electric vehicles located in the target area, a set of paths for the electric vehicles to participate in demand response is obtained. Based on the set of paths, the target cost information for the electric vehicle to participate in demand response at the charging station is obtained; Based on the target cost information, the willingness of the electric vehicle to participate in demand response at the charging station is obtained; Based on the stated willingness level, an assessment result is obtained regarding the demand response capability of the charging station.

2. The method according to claim 1, characterized in that, The step of obtaining the target cost information for the electric vehicle's participation in demand response at the charging station based on the path set includes: Obtain the initial cost information of the electric vehicle not participating in demand response; Based on the set of paths, the response cost information of the electric vehicle participating in demand response at the charging station is obtained; Based on the constraint information of the electric vehicle and the initial cost information, the response cost information is optimized to obtain the target cost information of the electric vehicle participating in demand response at the charging station.

3. The method according to claim 2, characterized in that, The step of obtaining the response cost information of the electric vehicle participating in demand response at the charging station based on the path set includes: Based on the set of paths, the mileage cost information of the electric vehicle participating in demand response at the charging station is obtained, as well as the total travel time of the electric vehicle from its current location to its destination location. Based on the operation information of the charging station, the charging and discharging cost information of the electric vehicle participating in demand response at the charging station is obtained; Based on the total driving time and the operation information of the charging station, the time cost information of the electric vehicle participating in demand response at the charging station is obtained; Based on the mileage cost information, the charging and discharging cost information, and the time cost information, the response cost information of the electric vehicle participating in demand response at the charging station is obtained.

4. The method according to claim 3, characterized in that, Before performing cost optimization processing on the response cost information based on the constraint information and initial cost information of the electric vehicle to obtain the target cost information of the electric vehicle participating in demand response at the charging station, the process further includes: Based on the battery capacity of the electric vehicle, the battery capacity constraint information of the electric vehicle is obtained; Based on the maximum time taken for the electric vehicle to travel from the current location to the destination location, the time constraint information of the electric vehicle is obtained; The constraint information of the electric vehicle is obtained based on the battery capacity constraint information and the time consumption constraint information.

5. The method according to claim 2, characterized in that, The process of obtaining the initial cost information for the electric vehicle not participating in demand response includes: Based on the current location and destination location of the electric vehicle, the initial travel route information of the electric vehicle that does not participate in demand response is obtained; Obtain the initial cost information of the electric vehicle under the initial travel path information.

6. The method according to claim 1, characterized in that, The step of obtaining a set of paths for electric vehicles to participate in demand response based on traffic information of the target area, operational information of charging stations within the target area, and vehicle information of electric vehicles within the target area includes: Based on the traffic information of the target area, a weighted bidirectional connectivity map of the target area is obtained; the weighted bidirectional connectivity map is used to represent the road connectivity information and road weights of the target area. Based on the weighted bidirectional connectivity graph, the travel path information of the electric vehicle from its current location to the charging station and from the charging station to its destination is obtained. Based on the travel route information, a set of routes in which the electric vehicle participates in demand response at all charging stations within the target area is obtained.

7. The method according to claim 1, characterized in that, The step of obtaining the willingness of the electric vehicle to participate in demand response at the charging station based on the target cost information includes: Obtain the vehicle type of the electric vehicle; If the target cost information meets the preset cost conditions, the target cost information is input into the willingness prediction model corresponding to the vehicle type to obtain the willingness of the electric vehicle to participate in demand response at the charging station.

8. The method according to claim 1, characterized in that, The process of obtaining an assessment result of the demand response capability of the charging station based on the willingness level includes: Based on the willingness level and the charging and discharging rate between the charging station and the electric vehicle, a first evaluation result of the electric vehicle's participation in demand response at the charging station is obtained; Based on the first assessment results of each electric vehicle participating in demand response at the charging station within the target area, as well as the total driving time, waiting time, and charging / discharging time of each electric vehicle at the charging station, an assessment result for the demand response capability of the charging station is obtained.

9. A charging station demand response processing device, characterized in that, The device includes: The route determination module is used to obtain a set of routes for the electric vehicle to participate in demand response based on traffic information of the target area, operation information of charging stations located in the target area, and vehicle information of electric vehicles located in the target area. The cost optimization module is used to obtain the target cost information of the electric vehicle participating in demand response at the charging station based on the path set. The willingness determination module is used to determine the willingness of the electric vehicle to participate in demand response at the charging station based on the target cost information. The capability assessment module is used to obtain an assessment result of the demand response capability of the charging station based on the willingness level.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

12. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

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