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

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

[0001] The present application relates to the technical field of power control, and in particular to a charging station demand response processing method and device, a computer device, a storage medium, and a computer program product. BACKGROUND

[0002] With the popularization and promotion of new energy vehicles, electric vehicles driven by new energy are becoming more and more popular, and the management of their charging also faces more adjustments. As a link between the power grid and electric vehicle owners, the charging station can absorb energy from the power grid to control the charging and discharging of electric vehicles. However, the scale and configuration of each charging station are not the same, so the demand response capability of each charging station for electric vehicles is also different.

[0003] In the traditional technology, the demand response capability of the charging station is usually evaluated based on the demand response capability of the charging station on the historical similar day. However, this method cannot estimate the demand response capability in real time according to the real-time vehicle distribution and traffic conditions, and has the defect of low accuracy in evaluating the demand response capability of the charging station.

[0004] SUMMARY

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

[0006] In a first aspect, the present application provides a charging station demand response processing method. The method comprises:

[0007] obtaining a path set of the electric vehicle participating in demand response according to traffic information of a target area, operation information of a charging station located in the target area, and vehicle information of an electric vehicle located in the target area;

[0008] obtaining target cost information of the electric vehicle participating in demand response at the charging station according to the path set;

[0009] obtaining the willingness degree of the electric vehicle participating in demand response at the charging station according to the target cost information;

[0010] obtaining an evaluation result of the demand response capability of the charging station according to the willingness degree.

[0011] In one embodiment, obtaining target cost information of the electric vehicle participating in demand response at the charging station according to the path set comprises:

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

[0013] obtain response cost information of the electric vehicle participating in the demand response at the charging station according to the path set;

[0014] perform cost optimization processing on the response cost information based on the constraint information of the electric vehicle and the initial cost information, to obtain target cost information of the electric vehicle participating in the demand response at the charging station.

[0015] In one of the embodiments, the obtaining of the response cost information of the electric vehicle participating in the demand response at the charging station according to the path set comprises:

[0016] obtaining mileage cost information of the electric vehicle participating in the demand response at the charging station according to the path set, and total driving time length of the electric vehicle from a current position of the electric vehicle to a destination position of the electric vehicle;

[0017] obtaining charging and discharging cost information of the electric vehicle participating in the demand response at the charging station according to the operation information of the charging station;

[0018] obtaining time cost information of the electric vehicle participating in the demand response at the charging station according to the total driving time length and the operation information of the charging station;

[0019] obtaining the response cost information of the electric vehicle participating in the demand response at the charging station according to the mileage cost information, the charging and discharging cost information and the time cost information.

[0020] In one of the embodiments, before the performing of the 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 the demand response at the charging station, the method further comprises:

[0021] obtaining battery capacity constraint information of the electric vehicle according to the battery capacity of the electric vehicle;

[0022] obtaining time consumption constraint information of the electric vehicle according to the maximum time consumption length of the electric vehicle from the current position to the destination position;

[0023] obtaining the constraint information of the electric vehicle according to the battery capacity constraint information and the time consumption constraint information.

[0024] In one of the embodiments, the obtaining of the initial cost information of the electric vehicle not participating in the demand response comprises:

[0025] According to the current position of the electric vehicle and the destination position of the electric vehicle, initial travel path information of the electric vehicle not participating in demand response is obtained;

[0026] Initial cost information of the electric vehicle under the initial travel path information is obtained.

[0027] In one of the embodiments, according to the traffic information of the target area, the operation information of the charging station located in the target area, and the vehicle information of the electric vehicle located in the target area, a path set of the electric vehicle participating in demand response is obtained, including:

[0028] According to the traffic information of the target area, a weighted bidirectional connection graph of the target area is obtained; the weighted bidirectional connection graph is used to represent road connectivity information and road weight of the target area;

[0029] According to the weighted bidirectional connection graph, travel path information of the electric vehicle from the current position of the electric vehicle to the charging station and from the charging station to the destination position of the electric vehicle is obtained;

[0030] According to the travel path information, a path set of the electric vehicle participating in demand response at all charging stations in the target area is obtained.

[0031] In one of the embodiments, according to the target cost information, a willingness degree of the electric vehicle participating in demand response at the charging station is obtained, including:

[0032] The type of the electric vehicle is obtained.

[0033] If the target cost information meets a preset cost condition, the target cost information is input into a willingness prediction model corresponding to the type of the electric vehicle, and a willingness degree of the electric vehicle participating in demand response at the charging station is obtained.

[0034] In one of the embodiments, according to the willingness degree, an evaluation result of demand response capability of the charging station is obtained, including:

[0035] According to the willingness degree and the charging and discharging rate between the charging station and the electric vehicle, a first evaluation result of the electric vehicle participating in demand response at the charging station is obtained.

[0036] According to the first evaluation result of each electric vehicle participating in demand response at the charging station in the target area, and the total travel time length, the waiting time length at the charging station and the charging and discharging time length of each electric vehicle, an evaluation result of demand response capability of the charging station is obtained.

[0037] In a second aspect, the present application provides a charging station demand response processing device. The device comprises:

[0038] a path determination module configured to obtain a path set of the electric vehicle participating in the demand response according to traffic information of a target area, operation information of a charging station located in the target area, and vehicle information of the electric vehicle located in the target area;

[0039] a cost optimization module configured to obtain target cost information of the electric vehicle participating in the demand response at the charging station according to the path set;

[0040] a willingness determination module configured to obtain a willingness degree of the electric vehicle participating in the demand response at the charging station according to the target cost information;

[0041] an ability evaluation module configured to obtain an evaluation result of a demand response ability of the charging station according to the willingness degree.

[0042] In a third aspect, the present application provides a computer device. The computer device comprises a memory and a processor. The memory stores a computer program. The processor implements the following steps when executing the computer program:

[0043] obtaining a path set of the electric vehicle participating in the demand response according to traffic information of a target area, operation information of a charging station located in the target area, and vehicle information of the electric vehicle located in the target area;

[0044] obtaining target cost information of the electric vehicle participating in the demand response at the charging station according to the path set;

[0045] obtaining a willingness degree of the electric vehicle participating in the demand response at the charging station according to the target cost information;

[0046] obtaining an evaluation result of a demand response ability of the charging station according to the willingness degree.

[0047] In a fourth aspect, the present application provides a computer readable storage medium. The computer readable storage medium stores a computer program. The computer program is executed by a processor to implement the following steps:

[0048] obtaining a path set of the electric vehicle participating in the demand response according to traffic information of a target area, operation information of a charging station located in the target area, and vehicle information of the electric vehicle located in the target area;

[0049] obtaining target cost information of the electric vehicle participating in the demand response at the charging station according to the path set;

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

[0051] According to the willingness degree, an evaluation result of demand response capability of the charging station is obtained.

[0052] In a fifth aspect, a computer program product is provided. The computer program product includes a computer program, which, when executed by a processor, implements the following steps:

[0053] According to traffic information of a target region, operation information of charging stations located in the target region, and vehicle information of electric vehicles located in the target region, a path set of the electric vehicles participating in demand response is obtained;

[0054] According to the path set, target cost information of the electric vehicles participating in demand response at the charging station is obtained;

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

[0056] According to the willingness degree, an evaluation result of demand response capability of the charging station is obtained.

[0057] The charging station demand response processing method, device, computer device, storage medium, and computer program product described above obtain a path set of electric vehicles participating in demand response according to traffic information of a target region, operation information of charging stations located in the target region, and vehicle information of electric vehicles located in the target region; obtain target cost information of the electric vehicles participating in demand response at the charging station according to the path set; obtain a willingness degree of the electric vehicles to participate in demand response at the charging station according to the cost information; and obtain an evaluation result of demand response capability of the charging station according to the willingness degree. By using the method, the willingness degree of the electric vehicles to participate in demand response at the charging station can be determined through the target cost information of the electric vehicles participating in demand response at the charging station, and the prediction and evaluation of demand response capability of the charging station can be realized by analyzing the willingness degree of the electric vehicles, thereby effectively improving the evaluation accuracy of demand response capability of the charging station. BRIEF DESCRIPTION OF DRAWINGS

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

[0059] FIG. 2 is a flowchart of a charging station demand response processing method in an embodiment;

[0060] FIG. 3 is a flowchart of a step of obtaining target cost information of electric vehicles participating in demand response at a charging station in an embodiment;

[0061] FIG. 4 is a flow diagram illustrating a method for obtaining a set of paths for an electric vehicle to participate in demand response, according to an embodiment;

[0062] FIG. 5 is a flow diagram illustrating a method for demand response processing of a charging station, according to an embodiment;

[0063] FIG. 6 is a schematic diagram illustrating a framework of a demand response processing system of a charging station, according to an embodiment;

[0064] FIG. 7 is a block diagram illustrating a structure of a demand response processing apparatus of a charging station, according to an embodiment;

[0065] FIG. 8 is a diagram illustrating an internal structure of a computer device, according to an embodiment. DETAILED DESCRIPTION

[0066] To make the objects, technical solutions, and advantages of the present application clearer, the present application will be described in further detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are merely intended to explain the present application and should not be used to limit the present application.

[0067] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, data of electric vehicles, data of charging stations, etc.) involved in the present application are all information and data authorized by users (for example, authorized by the owner of an electric vehicle) or fully authorized by all parties, and the collection, use, and processing of related data need to comply with relevant regulations.

[0068] The demand response processing method of a charging station provided by the embodiments of the present application can be applied in the application environment as shown in FIG. 1. In the application environment, an electric vehicle 101 and a charging station 102 communicate with a server 103 through a network. A data storage system can store data required to be processed by the server 103. The data storage system can be integrated on the server 103, or placed on a cloud or other network server. The charging station 102 can provide charging and discharging services for the electric vehicle 101. The server 103 can be implemented by an independent server or a server cluster composed of multiple servers.

[0069] In an embodiment, as shown in FIG. 2, a demand response processing method of a charging station is provided. The method is applied to the server in FIG. 1 as an example. It should be understood that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and can be implemented through the interaction of the terminal and the server. In this embodiment, the method includes the following steps:

[0070] In step S201, a path set of the electric vehicle participating in demand response is obtained according to traffic information of a target region, operation information of a charging station located in the target region, and vehicle information of the electric vehicle located in the target region.

[0071] The target region refers to a certain geographical region that needs to be analyzed for demand response. The target region can be a certain region in a real scene. The traffic information is used to describe the traffic situation of the target region. For example, the target traffic information includes road connectivity information, road location information, charging station location information, and the like. The operation information is used to describe the information of the charging station in terms of power operation. For example, the operation information includes charging station price information (such as charging electricity fee, service fee), charging and discharging rate, current service queuing time, and the like. The vehicle information is used to describe the driving situation of the electric vehicle. For example, the vehicle information includes the current position, the destination position, the battery capacity information, and the like of the electric vehicle.

[0072] Specifically, the server can be connected with an external traffic operation system of the target region, a power transaction system and a charging station operation system of the charging station, and a vehicle-mounted operation system of the electric vehicle. Then, the server obtains the traffic information of the target region through the traffic operation system, and counts all the charging stations located in the target region. Then, the server obtains the operation information of all the charging stations located in the target region through the power transaction system and the charging station operation system. Then, the server obtains the current positions of the electric vehicles through the vehicle-mounted operation system, so as to obtain the electric vehicles located in the target region according to the current positions. Then, the server obtains the vehicle information of the electric vehicles located in the target region through the vehicle-mounted operation system.

[0073] Further, for each electric vehicle, the server can sequentially set each charging station located in the target region as a passing point of the electric vehicle, that is, as the electric vehicle going to each charging station to participate in demand response. The server obtains the travel path information of the electric vehicle from the current position to each charging station and from each charging station to the destination position through a best path search algorithm.

[0074] In step S202, target cost information of the electric vehicle participating in demand response at the charging station is obtained according to the path set.

[0075] The demand response refers to adjusting the power demand in a specific time according to the load condition and energy demand change of the power grid, so as to realize the balance and efficient operation of the power system.

[0076] It should be noted that the electric vehicle participating in demand response at the charging station refers to that the charging station flexibly adjusts the charging time, charging and discharging power and the like of the electric vehicle according to the power system condition and market demand, so as to realize the flexible response of the power demand, thereby improving the operation efficiency of the charging station and the electric vehicle.

[0077] Specifically, the server determines a cost of each electric vehicle participating in demand response at each charging station according to each travel path information in the path set, and performs cost optimization on the cost based on the constraint information of the electric vehicle to reduce the cost required for the electric vehicle to participate in demand response, and finally the server obtains target cost information of 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 the cost optimization.

[0078] In step S203, willingness degree of the electric vehicle participating in demand response at the charging station is obtained according to the target cost information.

[0079] The willingness degree is used to measure the willingness probability of the electric vehicle participating in demand response at the charging station.

[0080] Specifically, the server can calculate the willingness degree of each electric vehicle participating in demand response at each charging station according to the target cost information of each electric vehicle through a willingness prediction model.

[0081] In step S204, an evaluation result of demand response capability of the charging station is obtained according to the willingness degree.

[0082] Specifically, for each charging station, the server combines the willingness degrees of all electric vehicles located in the target area at the charging station to calculate the demand response capability of the charging station to all electric vehicles in the target area, and then calculates a time sequence representing the demand response capability of the charging station at the future time according to the time of all electric vehicles arriving at the charging station, the charging and discharging time length, the preset future time, and the demand response capability of the charging station, and sets the time sequence as the evaluation result of the demand response capability of the charging station at the future time.

[0083] In the above charging station demand response processing method, the willingness degree of the electric vehicle participating in demand response at the charging station can be determined through the target cost information of the electric vehicle participating in demand response at the charging station, and the prediction and evaluation of the demand response capability of the charging station can be realized by analyzing the willingness degree of the electric vehicle, thereby effectively improving the evaluation accuracy of the demand response capability of the charging station.

[0084] In one embodiment, as shown in FIG. 3, the above step S202 obtains the target cost information of the electric vehicle participating in demand response at the charging station according to the path set, and specifically includes the following contents:

[0085] In step S301, initial cost information of the electric vehicle not participating in demand response is obtained.

[0086] The initial cost information is used to represent the total cost consumed by the electric vehicle when not participating in demand response.

[0087] In step S302, response cost information of the electric vehicle participating in demand response at the charging station is obtained according to the path set.

[0088] The response cost information is used to represent the total cost consumed by the electric vehicle participating in demand response according to certain trip path information.

[0089] In step S303, cost optimization processing is performed on the response cost information based on the constraint information and the initial cost information of the electric vehicle, to obtain target cost information of the electric vehicle participating in demand response at the charging station.

[0090] The target cost information is used to represent the maximum cost optimization space of the electric vehicle participating in demand response. The maximum cost optimization space represents the cost that can be optimized, adjusted and reduced by the electric vehicle in terms of operation, charging, energy consumption, etc., and thus the server can optimize and reduce the cost of the electric vehicle participating in demand response activities based on the target cost information, so that the electric vehicle can be more efficient and economical when participating in demand response activities.

[0091] Specifically, the server can first calculate the driving trip of the electric vehicle without participating in demand response, and then obtain initial trip path information. Then, the server calculates the initial cost information of the electric vehicle without participating in demand response according to the initial trip path information. For each electric vehicle, the server can calculate the response cost information of the electric vehicle participating in demand response at each charging station in the target area according to the path set of the electric vehicle. The server determines the constraint information of the electric vehicle (including the constraints in terms of time consumption and battery capacity, etc.), and under the constraints of the constraint information, the server performs cost optimization processing on the response cost information of the electric vehicle under each trip path information based on the initial cost information, and then obtains the target cost information of the electric vehicle under each trip path information. Since the trip path information represents the driving path of the electric vehicle participating in demand response at the charging station, the target cost information of the electric vehicle under each trip path information also represents the target cost information of the electric vehicle participating in demand response at each charging station.

[0092] In actual application, the target cost information of the electric vehicle participating in demand response at the charging station can be maximized as a target to minimize the cost required by the electric vehicle participating in demand response activities to the greatest extent. The objective function of the target cost information can be represented 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] ΔC i represents the cost optimization space of the electric vehicle i, i∈I, I is a set of all electric vehicles in the target area; C 0,i represents the initial cost information of the electric vehicle i under the initial travel path information l 0i ; β represents the coefficient of the initial time cost information; T drive,i,j represents the total travel time length consumed by the electric vehicle i to go to the charging station j for charging and then go to the destination location, j∈J, J is a set of all charging stations in the target area; T wait,i,j represents the waiting time of the charging station j; T charge,i,j represents the charging and discharging time of the charging station j; C loss,i,j represents the mileage cost information of the electric vehicle i; C charge,i,j represents the charging and discharging fee paid by the electric vehicle i when participating in the demand response; C balance,i,j represents the charging supplement fee for maintaining the minimum power constraint of the electric vehicle i after arriving at the destination location.

[0094] In this embodiment, the response cost information of the electric vehicle participating in the demand response at the charging station is processed based on the constraint information of the electric vehicle and the initial cost information of the electric vehicle not participating in the demand response, and the target cost information of the electric vehicle participating in the demand response at the charging station is obtained. On the one hand, the willingness of the electric vehicle to participate in the demand response at a certain charging station can be determined based on the target cost information of the electric vehicle participating in the demand response at the charging station in the subsequent step, and on the other hand, the cost of the electric vehicle participating in the demand response activity can be reduced based on the target cost information, so that the electric vehicle can be more efficient and economical when participating in the demand response activity.

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

[0096] The mileage cost information represents the mileage of the electric vehicle required to travel to the charging station to participate in the demand response. The charging and discharging cost information represents the cost consumed by the electric vehicle required to travel to the charging station to participate in the demand response. The time cost information represents the total time length consumed by the electric vehicle required to travel to the charging station to participate in the demand response.

[0097] Specifically, the server obtains the mileage cost information C loss,i,j of the electric vehicle i participating in the demand response at the charging station j according to the path set L(i). drive,i,j The server obtains the charging and discharging cost information of the electric vehicle i participating in the demand response at the charging station j according to the operation information of the charging station j, which can be the sum of the charging and discharging cost C charge,i,j of the electric vehicle i contained in the operation information and the charging supplement cost C balance,i,j , i.e., the charging and discharging cost information is (C charge,i,j +C balance,i,j ). The server obtains the time cost information of the electric vehicle i participating in the demand response at the charging station j by summing the total travel time length T drive,i,j and the queuing waiting time length T wait,i,j and the charging and discharging time length T charge,i,j contained in the operation information of the 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 further perform weighting processing on the time cost information by using a preset initial time cost information coefficient β, and obtains the weighted time cost information of the electric vehicle i participating in the demand response at the charging station j, i.e., the weighted time cost information is β·(T drive,i,j +T wait,i,j +T charge,i,j ). The server can obtain the response cost information of the electric vehicle i participating in the demand response at the charging station j by summing the mileage cost information C loss,i,j , the charging and discharging cost information (C charge,i,j +C balance,i,j ), and the weighted time cost information β·(T drive,i,j +T wait,i,j +T charge,i,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 )) is the response cost information.

[0098] In the embodiment, the response cost information required when the electric vehicle goes to the charging station to participate in the demand response is reasonably obtained through the mileage cost information, the charging and discharging cost information and the time cost information of the electric vehicle, which provides reliable data support for optimizing the cost consumption of the electric vehicle when participating in the demand response at the charging station, and is beneficial to more accurately determining the willingness of the electric vehicle to go to the charging station.

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

[0100] The battery capacity constraint information is used to constrain the charging and discharging duration of the electric vehicle at the charging station. The time consumption constraint information is used to constrain the time cost information of the electric vehicle at the charging station. The maximum time consumption duration represents the maximum duration allowed for the electric vehicle to go from the current location to the destination location.

[0101] Specifically, the server can determine the charging and discharging duration of the electric vehicle at the charging station according to the upper limit of the battery capacity and the lower limit of the battery capacity of the electric vehicle and the charging and discharging rate of the electric vehicle at the charging station, and then obtain the battery capacity constraint information of the electric vehicle according to the charging and discharging duration of the electric vehicle at the charging station. The server can determine the time cost information of the electric vehicle at the charging station according to the preset maximum time consumption duration of the electric vehicle from the current location to the destination location, and then determine the time consumption constraint information of the electric vehicle according to the time cost information. Then the server sets the battery capacity constraint information and the time consumption constraint information as the constraint information of the electric vehicle.

[0102] In actual application, when the server calculates the target cost information, one of the parameter variables is the path selection of the electric vehicle participating in the demand response, that is, one of the parameter variables is l i，j ∈L(i), and the other parameter variable is the charging and discharging duration T charge,i,j of the electric vehicle. And the battery capacity constraint information of the target cost information can be set as the product of the charging and discharging rate and the charging and discharging duration being not less than the lower limit of the battery capacity, and the product being not greater than the upper limit of the battery capacity. The battery capacity constraint information can be represented in the following form: min,i ≤T charge,i,j ·R charge,j ≤SOC max,i

[0103] SOCi min,i represents the lower limit of the battery capacity of the electric vehicle i; R charge,j represents the charging / discharging rate, which is determined according to the maximum charging / discharging rate supported by the electric vehicle i and the charging station j; SOC max,i represents the upper limit of the battery capacity of the electric vehicle i.

[0104] The time-consuming constraint information of the target cost information can be set as that the time cost information of the electric vehicle does not exceed the maximum allowed time-consuming of the electric vehicle arriving at the destination location, and the time-consuming constraint information can 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 represents the maximum time-consuming allowed for the electric vehicle to arrive at the destination location.

[0106] In this embodiment, the battery capacity constraint information of the electric vehicle is obtained through the upper and lower limits of the battery capacity of the electric vehicle, so as to constrain the charging / discharging time length in the target cost information through the battery capacity constraint information; the time-consuming constraint information of the electric vehicle is obtained through the maximum time-consuming of the electric vehicle, so as to constrain the time cost information in the target cost information through the time-consuming constraint information; so that when the subsequent step performs the cost optimization processing on the response cost information, the time cost information and the charging / discharging time length in the target cost information can be constrained based on the battery capacity constraint information and the time-consuming constraint information, thereby improving the accuracy and reliability of the target cost information obtained by processing.

[0107] In one embodiment, the step S301 of obtaining the initial cost information of the electric vehicle not participating in the demand response specifically includes the following contents: obtaining the initial travel path information of the electric vehicle not participating in the demand response according to the current location of the electric vehicle and the 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 of the electric vehicle not participating in the demand response, which can be calculated by a minimum path algorithm (for example, Dijkstra) based on the traffic information (or the road connectivity information in the traffic information) of the target area, so as to obtain the shortest path of the electric vehicle from the current location to the destination location, and set the shortest path as the initial travel path information l 0i of the electric vehicle not participating in the demand response; and then the server can calculate the initial cost information of the electric vehicle not participating in the demand response according to the initial travel path information.

[0109] In the embodiment, the initial travel path information of the electric vehicle not participating in the demand response is obtained through the current position of the electric vehicle and the destination position of the electric vehicle, and then the initial cost information of the electric vehicle under the initial travel path information is calculated, so that the reasonable acquisition of the initial cost information of the electric vehicle is realized, and a reliable processing basis is provided for subsequent cost optimization processing.

[0110] In one embodiment, the step S201 obtains the path set of the electric vehicle participating in the demand response according to the traffic information of the target region, the operation information of the charging station located in the target region, and the vehicle information of the electric vehicle located in the target region, and specifically includes the following contents: obtaining a weighted bidirectional connection graph of the target region according to the traffic information of the target region; the weighted bidirectional connection graph is used to represent the road connection information and road weight of the target region; obtaining travel path information of the electric vehicle from the current position of the electric vehicle to the charging station and from the charging station to the destination position of the electric vehicle according to the weighted bidirectional connection graph; obtaining the path set of the electric vehicle participating in the demand response at all charging stations in the target region according to the travel path information.

[0111] The weighted bidirectional connection graph is used to model the traffic network of the target region. The vertex of the weighted bidirectional connection graph represents a node (such as an intersection or a charging station), the edge represents a road, and the weight represents the distance and road congestion between two nodes.

[0112] FIG. 4 is a flowchart of obtaining the path set of the electric vehicle participating in the demand response. Specifically, the server can obtain real-time traffic information of the target region based on the traffic operation system of the target region; a bidirectional connection graph corresponding to the road traffic network of the target region is extracted from the real-time traffic information according to graph theory, and the weight of each road is set in combination with the congestion information and the length of each road in the target region, and finally a weighted bidirectional connection graph is obtained. The server obtains an electric vehicle i in the target region, where i∈I, I is a set of all electric vehicles in the target region, and also obtains a charging station j in the target region, j∈J, J is a set of all charging stations in the target region; the server obtains the current position and the destination position of the electric vehicle i, and adds the charging station j as a way point of the travel path of the electric vehicle i, and based on the weighted bidirectional connection graph, the travel path information l i，j of the electric vehicle i from the current position to the charging station j and from the charging station j to the destination position of the electric vehicle i is calculated. i，j The electric vehicle i participating in the demand response at the charging station j can also be represented. Similarly, the server continues to calculate the travel path information l i，j+1, and the step is repeated until the travel path information of the electric vehicle i participating in demand response at each charging station J in the target area is calculated and set as the path set L(i) of the electric vehicle i participating in demand response at all charging stations J in the target area.

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

[0114] In the embodiment, the weighted bidirectional connection graph of the target area is obtained according to the traffic information of the target area; then the travel path information of the electric vehicle from the current position of the electric vehicle to the charging station and from the charging station to the destination position of the electric vehicle is obtained according to the bidirectional connection graph; and the path set of the electric vehicle participating in demand response at all charging stations in the target area is formed based on the travel path information of the electric vehicle participating in demand response at all charging stations in the target area, so that the path set of the electric vehicle participating in demand response is reasonably obtained.

[0115] In one embodiment, the above step S203 obtains the willingness of the electric vehicle participating in demand response at the charging station according to the target cost information, and specifically includes the following contents: obtaining the vehicle type of the electric vehicle; if the target cost information meets the preset cost condition, inputting the target cost information into the willingness prediction model corresponding to the vehicle type to obtain the willingness of the electric vehicle participating in demand response at the charging station.

[0116] The willingness prediction function is used to represent the probability of the willingness of the electric vehicle participating in demand response for charging and discharging at the charging station. The preset cost condition refers to a judgment condition set for the target cost information, for example, the preset cost condition can be set as that the target cost information belongs to the optimal choice when the electric vehicle participates in demand response.

[0117] Specifically, the server can divide the electric vehicles into three vehicle types: sensitive type (K1), general type (K2), and non-sensitive type (K3), and different willingness prediction models can be set for electric vehicles of different vehicle types; wherein the willingness prediction model can be constructed based on a probability density function; in actual application, the willingness prediction model can be represented by a probability density function p of [0, ∞) through the target cost information. If the travel path information l i，j of the electric vehicle participating in demand response at a certain charging station does not belong to the optimal choice when the electric vehicle participates in demand response, the server can set the probability of the electric vehicle participating in demand response at the charging station as 0, that is, p i . i，j= 0. If the electric vehicle participates in demand response at a charging station in a travel path information l i，j corresponding to the target cost information AC i is the optimal selection when the electric vehicle participates in demand response, the server can input the target cost information corresponding to the electric vehicle participating in demand response at the charging station into the willingness prediction model corresponding to the vehicle type of the electric vehicle, and then the server obtains the willingness degree of the electric vehicle participating in demand response at the charging station, that is, the willingness degree can be represented as p i，j = p(AC i , i).

[0118] In this embodiment, if the target cost information satisfies the preset cost condition, the target cost information is input into the willingness prediction model corresponding to the vehicle type of the electric vehicle, so as to output the willingness degree of the electric vehicle participating in demand response at the charging station through the willingness prediction model, and then the willingness degree can be used to analyze the willingness of the owner of the electric vehicle to participate in demand response, which lays a foundation for predicting the demand response capability of the charging station.

[0119] In one embodiment, the step S204 obtains an evaluation result of the demand response capability of the charging station according to the willingness degree, and specifically includes the following contents: obtaining a first evaluation result of the electric vehicle participating in demand response at the charging station according to the willingness degree and the charging and discharging rate between the charging station and the electric vehicle; and obtaining the evaluation result of the demand response capability of the charging station according to the first evaluation result of each electric vehicle participating in demand response at the charging station in the target area, and the total driving time, the waiting time at the charging station and the charging and discharging time of each electric vehicle.

[0120] The first evaluation result is used to represent the expected capability value of the electric vehicle participating in demand response at the charging station. The evaluation result of the demand response capability of the charging station is used to represent the demand response capability value possessed by the charging station in the future time period. The waiting time refers to the queuing time of the electric vehicle going to the charging station to participate in demand response for charging and discharging service.

[0121] Specifically, the server calculates the first evaluation result of the electric vehicle participating in demand response at the charging station according to the willingness degree of the electric vehicle at the charging station and the charging and discharging rate between the charging station and the electric vehicle. The evaluation result of the demand response capability of the charging station in the future time is calculated according to the first evaluation result of each electric vehicle participating in demand response at the charging station in the target area, and the total driving time, the waiting time at the charging station and the charging and discharging time of each electric vehicle. If the evaluation result of the charging station in multiple future times is calculated, the evaluation result can also be represented in the form of time sequence.

[0122] In actual application, the first evaluation result Ei，j may be expressed as: E i，j = R charge,j · p i，j

[0123] If the first evaluation result of all electric vehicles in the target area participating in demand response at the charging station is combined, the second evaluation result of the charging station can also be calculated, and the second evaluation result can be expressed as E j : E j =∑E i，j =∑(R charge,j ·p i，j )

[0124] Further, according to the first evaluation result of each electric vehicle in the target area participating in demand response at the charging station, and the total driving time, waiting time at the charging station and charging and discharging time of each electric vehicle, the evaluation result E j (t) of the demand response capability of the charging station can be calculated, and the evaluation result E j (t) can be expressed as: E j (t) =∑(E i，j ·ε(t-T charge,i,j )-ε(t-T drive,i,j -T charge,i,j -T wait,i,j )

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

[0126] In the embodiment, the first evaluation result of the electric vehicle participating in demand response at the charging station is obtained by the willingness degree of all electric vehicles in the target area participating in demand response at the charging station and the charging and discharging rate between the charging station and the electric vehicle; and further, the evaluation result of the demand response capability of the charging station is obtained according to the first evaluation result of each electric vehicle in the target area participating in demand response at the charging station, and the total driving time, waiting time at the charging station and charging and discharging time of each electric vehicle, so as to realize accurate evaluation of the demand response capability of the charging station at future time, and further to guide the charging station operation subject to participate in demand response optimization decision based on the evaluation result.

[0127] In one embodiment, as shown in FIG. 5, another charging station demand response processing method is provided, which is applied to the server in FIG. 1 as an example for illustration, including the following steps:

[0128] Step S501, according to the traffic information of the target area, the operation information of the charging station located in the target area and the vehicle information of the electric vehicle located in the target area, a path set of the electric vehicle participating in demand response is obtained.

[0129] In step S502, the response cost information of the electric vehicle participating in the demand response at the charging station is obtained according to the path set.

[0130] In step S503, the initial travel path information of the electric vehicle not participating in the demand response is obtained according to the current position of the electric vehicle and the destination position of the electric vehicle; and the initial cost information of the electric vehicle under the initial travel path information is obtained.

[0131] In step S504, the battery capacity constraint information of the electric vehicle is obtained according to the battery capacity of the electric vehicle.

[0132] In step S505, the time consumption constraint information of the electric vehicle is obtained according to the maximum time consumption duration of the electric vehicle from the current position to the destination position; and the constraint information of the electric vehicle is obtained according to the battery capacity constraint information and the time consumption constraint information.

[0133] In step S506, the target cost information of the electric vehicle participating in the demand response at the charging station is obtained by performing cost optimization processing on the response cost information based on the constraint information of the electric vehicle and the initial cost information.

[0134] In step S507, the vehicle type of the electric vehicle is obtained; and if the target cost information meets the preset cost condition, the target cost information is input into a willingness prediction model corresponding to the vehicle type to obtain the willingness degree of the electric vehicle participating in the demand response at the charging station.

[0135] In step S508, the first evaluation result of the electric vehicle participating in the demand response at the charging station is obtained according to the willingness degree and the charging and discharging rate between the charging station and the electric vehicle.

[0136] In step S509, the evaluation result of the demand response capability of the charging station is obtained according to the first evaluation result of each electric vehicle participating in the demand response at the charging station in the target area, and the total travel duration, the waiting duration at the charging station and the charging and discharging duration of each electric vehicle.

[0137] The above charging station demand response processing method can achieve the following beneficial effects: the willingness degree of the electric vehicle participating in the demand response at the charging station can be determined through the target cost information of the electric vehicle participating in the demand response at the charging station, and the prediction and evaluation of the demand response capability of the charging station can be realized by analyzing the willingness degree of the electric vehicle, thereby effectively improving the evaluation accuracy of the demand response capability of the charging station.

[0138] In order to more clearly illustrate the charging station demand response processing method provided by the embodiments of the present disclosure, the charging station demand response processing method is specifically described below with one specific embodiment. As shown in FIG. 6, another charging station demand response processing method is provided, which can be applied to the server in FIG. 1, and the server can also be built 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 capability prediction module.

[0139] The data access module further includes a map and traffic information acquisition submodule, a power grid side 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 obtains the road position and road connection of the target area by interfacing with the traffic operation system, obtains the position distribution of the charging stations in the target area and the traffic congestion of the road, and obtains the position distribution of the charging stations in the target area and the traffic congestion of the road.

[0141] 2) The power grid side demand response information acquisition submodule obtains the operation information of the charging station disclosed by the power market in the day-ahead, day-in and real-time through the interface with the power transaction system, and the operation information includes the demand area, demand amount and demand period, etc.

[0142] 3) The charging station information acquisition submodule obtains the price information of the charging station in the target area by interfacing with the charging station or the charging station operation system, and the current price information of the charging station includes the charging electricity fee and the service fee. In addition, the charging station information acquisition submodule also needs to obtain other aspects of the operation information of the charging station, and the operation also includes 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 obtains the real-time position of each vehicle by interfacing with the vehicle-mounted operation system of the electric vehicle, reads the current destination and travel arrangement of each electric vehicle, and obtains the current battery capacity information of each electric vehicle.

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

[0145] The demand response resource capability prediction module predicts the demand response capability of the charging station based on the willingness degree, and obtains the evaluation result of the demand response capability of the charging station.

[0146] In the embodiment, the willingness of the electric vehicle to participate in the demand response at the charging station can be determined according to the target cost information of the electric vehicle participating in the demand response at the charging station, and the prediction and evaluation of the demand response capability of the charging station can be realized by analyzing the willingness of the electric vehicle, thereby effectively improving the evaluation accuracy of the demand response capability of the charging station.

[0147] It should be understood that, although each step in the flowchart involved in each embodiment as described above is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least part of other steps or steps or stages in other steps.

[0148] Based on the same inventive concept, the embodiments of the present application also provide a charging station demand response processing device for implementing the above-mentioned charging station demand response processing method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more charging station demand response processing device embodiments provided below can refer to the limitations of the charging station demand response processing method in the above text, which will not be repeated here.

[0149] In one embodiment, as shown in FIG. 7, a charging station demand response processing device 700 is provided, which includes:

[0150] The path determination module 701 is configured to obtain a path set of the electric vehicle participating in the demand response according to the traffic information of the target area, the operation information of the charging station located in the target area, and the vehicle information of the electric vehicle located in the target area.

[0151] The cost optimization module 702 is configured to obtain target cost information of the electric vehicle participating in the demand response at the charging station according to the path set.

[0152] The willingness determination module 703 is configured to obtain the willingness of the electric vehicle to participate in the demand response at the charging station according to the target cost information.

[0153] The capability evaluation module 704 is configured to obtain an evaluation result of the demand response capability of the charging station according to the willingness.

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

[0155] In one embodiment, the charging station demand response processing apparatus 700 further comprises a response cost determination module configured to obtain mileage cost information of the electric vehicle participating in the demand response at the charging station according to the path set, and total driving time of the electric vehicle from a current position of the electric vehicle to a destination position of the electric vehicle, obtain charging and discharging cost information of the electric vehicle participating in the demand response at the charging station according to operation information of the charging station, obtain time cost information of the electric vehicle participating in the demand response at the charging station according to the total driving time and the operation information of the charging station, and obtain response cost information of the electric vehicle participating in the demand response at the charging station according to the mileage cost information, the charging and discharging cost information and the time cost information.

[0156] In one embodiment, the charging station demand response processing apparatus 700 further comprises a constraint determination module configured to obtain battery capacity constraint information of the electric vehicle according to a battery capacity of the electric vehicle, obtain time consumption constraint information of the electric vehicle according to a maximum time consumption duration of the electric vehicle from the current position to the destination position, and obtain constraint information of the electric vehicle according to the battery capacity constraint information and the time consumption constraint information.

[0157] In one embodiment, the charging station demand response processing apparatus 700 further comprises an initial cost determination module configured to obtain initial travel path information of the electric vehicle not participating in the demand response according to a current position of the electric vehicle and a destination position of the electric vehicle, and acquire 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 connection graph of the target region according to traffic information of the target region, wherein the weighted bidirectional connection graph is used to represent road connectivity information and road weight of the target region, obtain travel path information of the electric vehicle from the current position of the electric vehicle to the charging station and from the charging station to the destination position of the electric vehicle according to the weighted bidirectional connection graph, and obtain a path set of the electric vehicle participating in the demand response at all charging stations in the target region according to the travel path information.

[0159] In one embodiment, the willingness determination module 703 is further configured to acquire a vehicle type of the electric vehicle, and input the target cost information into a willingness prediction model corresponding to the vehicle type to obtain a willingness degree of the electric vehicle participating in the demand response at the charging station if the target cost information satisfies a preset cost condition.

[0160] In an embodiment, the capability evaluation module 704 is further configured to obtain a first evaluation result of the electric vehicle participating in the demand response at the charging station according to the willingness degree and the charging / discharging rate between the charging station and the electric vehicle, and obtain an evaluation result of the demand response capability of the charging station according to the first evaluation result of each electric vehicle participating in the demand response at the charging station in the target region, and the total driving time, the waiting time at the charging station and the charging / discharging time of each electric vehicle.

[0161] The modules in the charging station demand response processing apparatus can be implemented by software, hardware or a combination thereof. The modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a memory in a computer device in software form, so as to be called and executed by a processor to perform the operations corresponding to the modules.

[0162] In an embodiment, a computer device is provided, which can be a server. An internal structure diagram of the computer device can be shown in FIG. 8. The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for running of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store traffic information, running information, vehicle information, path sets and the like. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with terminals outside through a network connection. The computer program is executed by the processor to implement a charging station demand response processing method.

[0163] Those skilled in the art can understand that the structure shown in FIG. 8 is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. Specifically, the computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0164] In an embodiment, a computer device is also provided, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0165] In an embodiment, a computer readable storage medium is provided, having stored thereon a computer program which, when executed by a processor, implements the steps of any of the above method embodiments.

[0166] In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of any of the above method embodiments.

[0167] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, the computer program can include the processes of the above-mentioned embodiments. Any reference to a memory, database or other medium used in the embodiments provided in the present application can include at least one of a non-volatile memory and a volatile memory. The non-volatile memory can include a read-only memory (ROM), a magnetic tape, a floppy disk, a flash memory, an optical storage, a high-density embedded non-volatile memory, a resistive memory (ReRAM), a magnetoresistive random access memory (MRAM), a ferroelectric random access memory (FRAM), a phase change memory (PCM), a graphene memory, etc. The volatile memory can include a random access memory (RAM) or an external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as a static random access memory (SRAM) or a dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., and is not limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., and is not limited thereto.

[0168] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present disclosure.

[0169] The above-described embodiments are merely illustrative of several embodiments of the present application, which are described in more detail and in a specific manner, but should not be construed as limiting the scope of the patent of the present application. It should be noted that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for demand response processing of charging stations, characterized by, The method comprises: According to the traffic information of the target area, the operation information of the charging station located in the target area and the vehicle information of the electric vehicle located in the target area, a path set of the electric vehicle participating in demand response is obtained; According to the path set, target cost information of the electric vehicle participating in demand response at the charging station is obtained; According to the target cost information, the willingness degree of the electric vehicle participating in demand response at the charging station is obtained; According to the willingness degree, an evaluation result of the demand response capability of the charging station is obtained.

2. The method of claim 1, wherein, According to the path set, the target cost information of the electric vehicle participating in demand response at the charging station is obtained, comprising: Obtaining initial cost information of the electric vehicle not participating in demand response; According to the path set, 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 subjected to cost optimization processing to obtain the target cost information of the electric vehicle participating in demand response at the charging station.

3. The method of claim 2, wherein, According to the path set, the target cost information of the electric vehicle participating in demand response at the charging station is obtained, comprising: According to the path set, mileage cost information of the electric vehicle participating in demand response at the charging station and total driving time length of the electric vehicle from the current position of the electric vehicle to the destination position of the electric vehicle are obtained; According to the operation information of the charging station, charging and discharging cost information of the electric vehicle participating in demand response at the charging station is obtained; According to the total driving time length and the operation information of the charging station, time cost information of the electric vehicle participating in demand response at the charging station is obtained; According to 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 of claim 3, wherein, Before the cost optimization processing of the response cost information based on the constraint information of the electric vehicle and the initial cost information, the target cost information of the electric vehicle participating in demand response at the charging station is obtained, further comprising: According to the battery capacity of the electric vehicle, battery capacity constraint information of the electric vehicle is obtained; According to the maximum time-consuming time length of the electric vehicle from the current position to the destination position, time-consuming constraint information of the electric vehicle is obtained; According to the battery capacity constraint information and the time-consuming constraint information, constraint information of the electric vehicle is obtained.

5. The method of claim 2, wherein, The initial cost information of the electric vehicle not participating in demand response is obtained, comprising: According to the current position of the electric vehicle and the destination position of the electric vehicle, initial travel path information of the electric vehicle not participating in demand response is obtained; Obtaining initial cost information of the electric vehicle under the initial travel path information.

6. The method of claim 1, wherein, According to the traffic information of the target area, the operation information of the charging station located in the target area and the vehicle information of the electric vehicle located in the target area, a path set of the electric vehicle participating in demand response is obtained, comprising: According to the traffic information of the target region, a weighted bidirectional connection graph of the target region is obtained; the weighted bidirectional connection graph is used to represent road connectivity information and road weight of the target region; According to the weighted bidirectional connection graph, travel path information of the electric vehicle from a current position of the electric vehicle to the charging station and from the charging station to a destination position of the electric vehicle is obtained; According to the travel path information, a path set of the electric vehicle participating in demand response at all charging stations in the target region is obtained.

7. The method of claim 1, wherein, The obtaining of the willingness degree of the electric vehicle participating in demand response at the charging station according to the target cost information comprises: obtaining a vehicle type of the electric vehicle; if the target cost information satisfies a preset cost condition, inputting the target cost information into a willingness prediction model corresponding to the vehicle type to obtain the willingness degree of the electric vehicle participating in demand response at the charging station.

8. The method of claim 1, wherein, The obtaining of the evaluation result of the demand response capability of the charging station according to the willingness degree comprises: obtaining a first evaluation result of the electric vehicle participating in demand response at the charging station according to the willingness degree and a charging / discharging rate between the charging station and the electric vehicle; obtaining the evaluation result of the demand response capability of the charging station according to the first evaluation result of the electric vehicle participating in demand response at the charging station, and a total driving time length, a waiting time length at the charging station and a charging / discharging time length of each electric vehicle in the target region.

9. A charging station demand response processing apparatus characterized by comprising: The device comprises: a path determination module configured to obtain a path set of the electric vehicle participating in demand response according to traffic information of a target region, operation information of a charging station located in the target region and vehicle information of an electric vehicle located in the target region; a cost optimization module configured to obtain target cost information of the electric vehicle participating in demand response at the charging station according to the path set; a willingness determination module configured to obtain a willingness degree of the electric vehicle participating in demand response at the charging station according to the target cost information; a capability evaluation module configured to obtain an evaluation result of a demand response capability of the charging station according to the willingness degree. 10.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-9. The processor executes the computer program to realize the steps of the method in any one of claims 1 to 8.

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

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

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