Electric vehicle charging and discharging power aggregation method, device and computer equipment

Abstract

The present application relates to a method, device and computer equipment for aggregating the charging and discharging power of electric vehicles. The method comprises: receiving a virtual battery model of each electric vehicle sent by a terminal, wherein the virtual battery model of each electric vehicle is a battery model constructed by the terminal according to the corresponding schedulable domain model of the electric vehicle, and the schedulable domain model is a model determined by the terminal according to the charging and discharging power of the electric vehicle at each moment in a preset time period; aggregating the virtual battery models of each electric vehicle corresponding to the edge server to obtain the aggregation result of the charging and discharging power of each electric vehicle. The use of this method can improve the accuracy of the aggregation result of the charging and discharging power of electric vehicles.

Description

Technical Field

[0001] The present application relates to the technical field of virtual power plants, and in particular to a method, device and computer equipment for aggregating charging and discharging power of electric vehicles. Background Art

[0002] As a distributed resource, electric vehicle charging and discharging power has great potential in improving the economy and flexibility of power system operation. Virtual power plant technology can connect geographically dispersed distributed resources to the power system to achieve effective integration and allocation of distributed resources. Therefore, virtual power plant technology can be used to aggregate electric vehicle charging and discharging power, that is, load.

[0003] However, the current method of aggregating the charging and discharging power of a large number of electric vehicles using virtual power plant technology is to directly add boundary values, that is, the power range is determined by adding the maximum power and the minimum power of the electric vehicles respectively, and the energy range is determined by adding the maximum energy and the minimum energy of the electric vehicles respectively. However, the energy range obtained in this way is inaccurate. Therefore, the accuracy of the charging and discharging power aggregation results obtained based on the currently determined energy range and power range is low. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device and computer equipment for aggregating the charging and discharging power of electric vehicles, which can improve the accuracy of the aggregation results of the charging and discharging power of electric vehicles, in response to the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a method for aggregating charging and discharging power of an electric vehicle, which is applied to an edge server. The method comprises:

[0006] A virtual battery model of each electric vehicle sent by a receiving terminal, wherein the virtual battery model of each electric vehicle is a battery model constructed by the terminal according to a schedulable domain model of the corresponding electric vehicle, and the schedulable domain model is a model determined by the terminal according to the charging and discharging power of the electric vehicle at each moment in a preset time period;

[0007] The virtual battery models of the electric vehicles corresponding to the edge servers are aggregated to obtain the aggregation results of the charging and discharging power of the electric vehicles.

[0008] In one embodiment, the method further comprises:

[0009] The charging and discharging power aggregation result is sent to a cloud server, so that the cloud server constructs an objective function and constraint conditions according to the charging and discharging power aggregation result, and constructs an electric power resource optimization scheduling model according to the objective function and the constraint conditions.

[0010] In one embodiment, the virtual battery model of the electric vehicle is represented by the following first objective formula:

[0011]

[0012] Among them, H j is the set of all charging and discharging powers of the jth electric vehicle among the electric vehicles within the preset time period, is the collection of charging and discharging power of the jth electric vehicle at each moment, is the charge and discharge power of the jth electric vehicle at time t, T is the preset time period, A j is the time that the jth electric vehicle stays at the terminal, m is the total number of time periods in the preset time period, ΔT is the time period length, a j is the time when the jth electric car arrives at the terminal, d j is the time when the jth electric car leaves the terminal, is the rated charging and discharging power of the jth electric vehicle, is the initial energy of the jth electric vehicle when it arrives at the terminal, is the energy expected to be achieved when the jth electric vehicle leaves the terminal, is the lower bound of the energy of the j-th electric car at time t, is the upper bound of the energy of the j-th electric car at time t.

[0013] In one embodiment, the virtual battery models of the electric vehicles corresponding to the edge servers are aggregated to obtain the aggregated charging and discharging power of the electric vehicles, including:

[0014] Aggregating the virtual battery models of the electric vehicles to obtain an aggregate;

[0015] Summing the rated charge and discharge powers in the polymer to obtain the power boundary of the polymer;

[0016] Correcting the upper energy bound and the lower energy bound within the aggregate to obtain a corrected upper energy bound and a corrected lower energy bound;

[0017] Obtaining the charging and discharging power aggregation result of each of the electric vehicles according to the power boundary of the aggregate, and the revised energy upper bound and energy lower bound;

[0018] The charge and discharge power aggregation result is expressed by the following second target formula:

[0019]

[0020] Among them, H EVis the collection of all charging and discharging powers of the polymer within the preset time period, is the collection of charge and discharge powers of the polymer at each moment, g t is the charge and discharge power of the polymer at time t, is the power boundary of the aggregate at time t, C t is the accumulated energy of the polymer at time t, L t is the modified lower bound of the energy of the aggregate at time t, U t is the modified upper energy bound of the aggregate at time t.

[0021] In one embodiment, the objective function is Among them, φ t c To clear the energy price for the market, is the power exchanged between the virtual power plant and the electricity market, N DG is the number of distributed generators, is the active output power of the zth distributed generator at time t, is the preset fuel cost function, φ t fu The price of the upstream service at time t is adjusted flexibly, fru t is the total power of all uplink flexible adjustment services at time t, φ t fd The price of the downstream service is adjusted flexibly at time t, frd t is the total power of all downlink flexible adjustment services at time t, m is the total number of time periods in the preset time period, and ΔT is the time period length;

[0022] The constraints include distributed generator output constraints, renewable energy output constraints, electric vehicle power constraints, power flow constraints and network security constraints. The distributed generator output constraints are expressed by the following third objective formula:

[0023] in, is the lower limit of the output of the zth distributed generator at time t, is the actual active power output of the zth distributed generator at time t, is the output upper limit of the zth distributed generator at time t, rd z is the maximum ramp-down rate of the zth distributed generator, ru z is the maximum ramp-up rate of the zth distributed generator, is the reactive power output of the zth distributed generator at time t, is the rated capacity of the zth distributed generator at time t;

[0024] The renewable energy output constraint is expressed by the following fourth objective formula:

[0025] in, is the actual active power output of the dth wind turbine at time t, is the predicted output of the dth wind turbine at time t, is the actual active power output of the s-th PV power station at time t, is the predicted output of the s-th photovoltaic power station at time t, is the reactive power output of the dth wind turbine at time t, is the installed capacity of the dth wind turbine at time t, is the reactive power output of the s-th photovoltaic power station at time t, is the installed capacity of the s-th PV power station at time t;

[0026] The electric vehicle power constraint is expressed by the fifth objective formula: in, is the collection of the charge and discharge powers of the ae-th polymer at each moment, is the set of all charging and discharging powers of the ae-th polymer within the preset time period;

[0027] The power flow constraint is expressed by the following sixth objective formula:

[0028] Among them, P i,t The active power injected into the i-th node in the virtual power plant at time t, V i,t is the voltage amplitude of the ith node at time t, V j,t is the voltage amplitude of the jth node in the virtual power plant at time t, G ij is the conductance of the branch connecting the i-th node and the j-th node, B ij is the susceptance of the branch connecting the i-th node and the j-th node, θ ij,t is the phase difference between the i-th node and the j-th node at time t, Q i,t is the reactive power injected by the i-th node at time t, P ij,t is the active power flow of the branch connected by the i-th node and the j-th node at time t, Q ij,t is the reactive power flow of the branch connected by the ith node and the jth node at time t, j∈i is all the nodes connected to the ith node, I ic =1 means the i-th node is the grid connection point of the virtual power plant, I ic = 0 means the i-th node is not the grid connection point of the virtual power plant. is the actual active power output of the dth wind turbine at the ith node at time t, is the actual active power output of the s-th PV power station at the ith node at time t, is the actual active power output of the zth distributed generator at the ith node at time t, g i,ae,t is the active power of the ae-th aggregate of the i-th node at time t, is the active power of the ith node at time t, is the reactive power output of the dth wind turbine at the ith node at time t, is the reactive power output of the s-th PV power station at the ith node at time t, is the reactive power output of the zth distributed generator at the ith node at time t, is the reactive power of the i-th node at time t;

[0029] The network security constraint is expressed by the following seventh objective formula:

[0030] Among them, P ij is the active power flow of the branch connected by the i-th node and the j-th node, Q ij is the reactive power flow of the branch connected by the i-th node and the j-th node, S ij is the line capacity of the branch connecting the i-th node and the j-th node, V i min is the minimum voltage of the ith node, V i is the voltage amplitude of the ith node, V i max is the maximum voltage of the ith node.

[0031] In a second aspect, the present application also provides a method for aggregating charging and discharging power of an electric vehicle, which is applied to a terminal. The method comprises:

[0032] Obtain the dispatchable domain model of each electric vehicle;

[0033] Constructing a virtual battery model of each electric vehicle according to the dispatchable domain model of each electric vehicle;

[0034] The virtual battery model of each electric vehicle is sent to the edge server, so that the edge server aggregates the virtual battery model of each electric vehicle to obtain the charging and discharging power aggregation result of each electric vehicle.

[0035] In a third aspect, the present application also provides a charging and discharging power aggregation device for an electric vehicle. The device comprises:

[0036] A receiving module, used for receiving a virtual battery model of each electric vehicle sent by a terminal, wherein the virtual battery model of each electric vehicle is a battery model constructed by the terminal according to a schedulable domain model of the corresponding electric vehicle, and the schedulable domain model is a model determined by the terminal according to the charging and discharging power of the electric vehicle at each moment in a preset time period;

[0037] The aggregation module is used to aggregate the virtual battery models of the electric vehicles corresponding to the edge servers to obtain the aggregation results of the charging and discharging power of the electric vehicles.

[0038] In a fourth aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program.

[0039] In a fifth aspect, the present application further provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0040] In a sixth aspect, the present application further provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, any of the above method steps is implemented.

[0041] The above-mentioned electric vehicle charge and discharge power aggregation method, device and computer equipment aggregate the virtual battery models of each electric vehicle sent by the receiving terminal, wherein the virtual battery model of each electric vehicle is a battery model constructed by the terminal according to the corresponding electric vehicle's schedulable domain model, and the schedulable domain model is a model determined by the terminal according to the charge and discharge power of the electric vehicle at each moment in a preset time period, and aggregates the virtual battery models of each electric vehicle corresponding to the edge server to obtain the charge and discharge power aggregation result of each electric vehicle. In the traditional technology, the charge and discharge power aggregation result of the electric vehicle is obtained by directly adding the boundary values ​​of the charge and discharge power and energy of the electric vehicle, while in this embodiment, the boundary value is obtained more accurately by constructing the schedulable domain model of the electric vehicle, thereby solving the problem of inaccurate charge and discharge aggregation results of the electric vehicle in the traditional method. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 An application environment diagram of a virtual power plant scheduling method considering the aggregation of charging and discharging power of electric vehicles provided in an embodiment of the present application;

[0043] Figure 2 A schematic diagram of a process flow of a charging and discharging power aggregation method for an electric vehicle provided in an embodiment of the present application;

[0044] Figure 3 A schematic diagram of a dispatchable domain model of an electric vehicle provided in an embodiment of the present application;

[0045] Figure 4 A schematic diagram of a polymer charging and discharging power polymerization method provided in an embodiment of the present application;

[0046] Figure 5 A schematic diagram of a flow chart of a method for constructing a schedulable domain model provided in an embodiment of the present application;

[0047] Figure 6 A structural block diagram of a charging and discharging power aggregation device for an electric vehicle provided in an embodiment of the present application;

[0048] Figure 7 It is a structural block diagram of another charging and discharging power aggregation device for an electric vehicle provided in an embodiment of the present application;

[0049] Figure 8 Schematic diagram of the internal structure of a computer device in an embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0051] In order to help better understand the overall technical solution of the present application, the application scenarios of the overall technical solution of the present application are now described. Figure 1 This is an application environment diagram of a virtual power plant scheduling method considering electric vehicle charging and discharging power aggregation provided in an embodiment of the present application, referring to Figure 1, the terminal 101 obtains the dispatchable domain model of each electric vehicle, constructs the virtual battery model of each electric vehicle according to the dispatchable domain model of each electric vehicle, and sends the virtual battery model of each electric vehicle to the edge server 102; the edge server 102 receives the virtual battery model of each electric vehicle sent by the terminal 101, aggregates the virtual battery model of each electric vehicle, obtains the charge and discharge power aggregation result of each electric vehicle, and sends the charge and discharge power aggregation result to the cloud server 103. The number of edge servers is determined by the number of nodes in the virtual power plant. One edge server corresponds to one node in the virtual power plant. The number of nodes in the virtual power plant is set in advance by those skilled in the art. The specific value is not limited here. For example, the number of nodes in the virtual power plant can be set to 2, then the edge server 1 can receive the virtual battery model of each electric vehicle sent by the terminal corresponding to node 1, and the edge server 2 can receive the virtual battery model of each electric vehicle sent by the terminal corresponding to node 2. Among them, the terminal 101 communicates with the edge server 102 by wireless communication, and the edge server 102 communicates with the cloud server 103 by wireless communication. The terminal 101 may be, but is not limited to, a charging pile and a device that can be provided to the electric vehicle for charging and discharging, and the wireless communication method includes, but is not limited to, Bluetooth, wireless LAN, cellular network and WIFI.

[0052] The charging and discharging power aggregation method of an electric vehicle provided in the embodiment of the present application can be applied to Figure 1 In the edge server shown. Figure 2 The flowchart of the charging and discharging power aggregation method of an electric vehicle provided in an embodiment of the present application is shown in FIG. 1 . The method is applied to an edge server and includes the following steps:

[0053] S201, receiving a virtual battery model of each electric vehicle sent by a terminal, wherein the virtual battery model of each electric vehicle is a battery model constructed by the terminal according to a schedulable domain model of the corresponding electric vehicle, and the schedulable domain model is a model determined by the terminal according to the charging and discharging power of the electric vehicle at each moment in a preset time period.

[0054] In this embodiment, the virtual battery model of the electric vehicle is obtained by describing the dispatchable domain model of the electric vehicle in the form of a virtual battery. The dispatchable domain of the electric vehicle refers to the set of all feasible charging and discharging powers of the electric vehicle at each moment. Figure 3 As shown, Figure 3 is a schematic diagram of a dispatchable domain model of an electric vehicle provided in an embodiment of the present application, Figure 3 The horizontal axis represents time, and the vertical axis represents energy. a is the time when the electric vehicle arrives at the terminal, and b is the time when the electric vehicle leaves the terminal. E ar is the initial energy of the electric vehicle, E exis the energy expected to be achieved when the electric vehicle leaves, E max is the maximum energy that an electric vehicle can achieve, E min is the minimum energy that an electric vehicle can achieve. The dashed box represents the dispatchable domain of the electric vehicle. The line segment represents the feasible power of the electric vehicle. The feasible power refers to the power that satisfies the four equations in brackets in equation (1).

[0055] S202, aggregate the virtual battery models of the electric vehicles corresponding to the edge servers to obtain the aggregated results of the charging and discharging power of the electric vehicles.

[0056] In the conventional technology, the power and energy boundary values ​​of each electric vehicle are directly added together, that is, the maximum power and the minimum power of each electric vehicle are added together to determine the power range, and the maximum energy and the minimum energy of each electric vehicle are added together to determine the energy range, and the charging and discharging power aggregation results of each electric vehicle are obtained based on the power range and energy range. However, this method has the problem that the energy range obtained is inaccurate, and the charging and discharging power aggregation results obtained after aggregating the charging and discharging power of electric vehicles have low accuracy.

[0057] The above-mentioned electric vehicle charge and discharge power aggregation method, device and computer equipment aggregate the virtual battery models of each electric vehicle sent by the receiving terminal, wherein the virtual battery model of each electric vehicle is a battery model constructed by the terminal according to the corresponding electric vehicle's schedulable domain model, and the schedulable domain model is a model determined by the terminal according to the charge and discharge power of the electric vehicle at each moment in a preset time period, and aggregates the virtual battery models of each electric vehicle corresponding to the edge server to obtain the charge and discharge power aggregation results of each electric vehicle. In the traditional technology, the charge and discharge power aggregation result of the electric vehicle is obtained by directly adding the boundary values ​​of the charge and discharge power and energy of the electric vehicle, while in this embodiment, the boundary value can be obtained more accurately by constructing the schedulable domain model of the electric vehicle, which solves the problem of inaccurate charge and discharge aggregation results of the electric vehicle in the traditional method.

[0058] In some of these embodiments, the virtual battery model of the electric vehicle is represented by the following first objective formula:

[0059]

[0060] Among them, H j is the set of all charging and discharging powers of the jth electric vehicle among all electric vehicles within a preset time period, is the collection of charging and discharging power of the jth electric vehicle at each moment, is the charge and discharge power of the jth electric vehicle at time t, T is the preset time period, A jis the time that the jth electric vehicle stays at the terminal, m is the total number of time periods in the preset time period, ΔT is the time period length, and a j is the time when the jth electric car arrives at the terminal, d j is the time when the jth electric car leaves the terminal, is the rated charging and discharging power of the jth electric vehicle, is the initial energy of the jth electric car when it arrives at the terminal, is the energy expected to be achieved when the jth electric vehicle leaves the terminal, is the lower bound of the energy of the j-th electric car at time t, is the upper bound of the energy of the j-th electric car at time t.

[0061] The energy upper bound of the j-th electric car at time t Specifically expressed as:

[0062]

[0063] in, is the maximum energy that the jth electric vehicle can achieve;

[0064] The energy lower bound of the j-th electric car at time t Specifically expressed as:

[0065]

[0066] in, is the minimum energy that the jth electric car can achieve.

[0067] Figure 4 This is a schematic diagram of a polymer charging and discharging power polymerization method provided in an embodiment of the present application, referring to Figure 4 This embodiment relates to an implementation method of how to determine the charging and discharging power aggregation result of an electric vehicle. Based on the above embodiment, the above S202 includes the following steps:

[0068] S401, aggregating the virtual battery models of the electric vehicles to obtain an aggregate.

[0069] In this embodiment, the aggregate refers to aggregating electric vehicles on the same node to obtain an aggregate, which is convenient for accepting the scheduling of the virtual power plant and reduces the computing burden.

[0070] S402, summing the rated charge and discharge powers in the polymer to obtain the power boundary of the polymer.

[0071] In this embodiment, the power boundary of the aggregate is calculated using the following formula:

[0072]

[0073] Among them, A t is the number of all electric vehicles connected to the terminal for charging and discharging at time t.

[0074] S403, correcting the upper energy bound and the lower energy bound within the aggregate to obtain a corrected upper energy bound and a corrected lower energy bound.

[0075] In this embodiment, from the perspective of energy, in order for all electric vehicles to reach the desired energy at the time of departure, the following constraints should be met:

[0076]

[0077] in, The energy required for electric vehicles, and

[0078] From the perspective of power, the available energy needs to be further limited. The following formula (6) can be used to and Make corrections as follows:

[0079]

[0080] Compared with the traditional aggregation method, the energy boundary can be corrected in the present application, thereby improving the accuracy of the energy boundary and further ensuring the accuracy of the aggregation results of the charging and discharging power of the electric vehicle.

[0081] S404, obtaining the charging and discharging power aggregation result of each electric vehicle according to the power boundary of the aggregate, and the corrected upper and lower energy bounds.

[0082] Among them, the charge and discharge power aggregation result is expressed by the following second objective formula:

[0083]

[0084] Among them, H EV is the collection of all charging and discharging powers of the polymer within a preset time period, is the collection of charging and discharging powers of the polymer at each moment, g t is the charge and discharge power of the polymer at time t, is the power boundary of the aggregate at time t, C t is the accumulated energy of the aggregate at time t, L t is the modified lower bound of the energy of the aggregate at time t, U t is the corrected upper energy bound of the aggregate at time t.

[0085] In this embodiment, through the above-mentioned electric vehicle charge and discharge power aggregation method, all electric vehicles on the same node are aggregated into an aggregate, and the charge and discharge power aggregation results are displayed in the form of virtual batteries, which greatly reduces the calculation burden and has high calculation efficiency. In addition, due to the heterogeneity of the arrival and departure times of different electric vehicles, the traditional method cannot aggregate the charge and discharge power of electric vehicles using the isomorphic polyhedral approximation method. Therefore, the aggregation method in this application has a wider range of application, and the accuracy of the charge and discharge power aggregation results obtained in this application is higher than the accuracy of the charge and discharge power aggregation results obtained by directly adding boundary values ​​in the traditional method.

[0086] In order to ensure the rational and efficient use of electricity resources, virtual power plants are used to integrate and allocate electricity resources, and the aggregated results of the charging and discharging power of electric vehicles are sent to the cloud server of the virtual power plant. Optimized scheduling is carried out within the virtual power plant. The goal of optimized scheduling of the virtual power plant is to minimize the total operating cost. Therefore, the following objective function is established. The following objective function includes the cost of purchasing electricity from the electricity market, the fuel cost of distributed generators, and the benefits of providing flexible regulation services to the electricity market. Flexible regulation services refer to the flexible capacity provided to the electricity market that can increase and decrease power, which is divided into upstream flexible regulation services and downstream flexible regulation services.

[0087] In an embodiment of the present application, the charging and discharging power aggregation results are sent to a cloud server, so that the cloud server constructs an objective function and constraints based on the charging and discharging power aggregation results, and constructs an electric power resource optimization scheduling model based on the objective function and constraints.

[0088] In this embodiment, traditional optimization scheduling mainly focuses on energy exchange and does not consider participating in the auxiliary regulation power market to obtain profits. The power resource optimization scheduling model in this application takes into account the participation of virtual power plants in the energy market and the flexible regulation service market, increases the revenue sources of virtual power plants, and improves the economy and universality of the optimization scheduling of this application.

[0089] In the embodiment of the present application, the objective function is

[0090]

[0091] Among them, φ t c To clear the energy price for the market, is the power exchanged between the virtual power plant and the electricity market, N DG is the number of distributed generators, is the active output power of the zth distributed generator at time t, is the preset fuel cost function, φ t fuThe price of the upstream service at time t is adjusted flexibly, fru t is the total power of all uplink flexible adjustment services at time t, φ t fd The price of the downstream service is adjusted flexibly at time t, frd t is the total power of all downlink flexible adjustment services at time t, m is the total number of time periods in the preset time period, and ΔT is the time period length;

[0092] The constraints include distributed generator output constraints, renewable energy output constraints, electric vehicle power constraints, power flow constraints and network security constraints. The distributed generator output constraints are expressed by the following third objective formula:

[0093] in, is the lower limit of the output of the zth distributed generator at time t, is the actual active power output of the zth distributed generator at time t, is the output upper limit of the zth distributed generator at time t, rd z is the maximum ramp-down rate of the zth distributed generator, ru z is the maximum ramp-up rate of the zth distributed generator, is the reactive power output of the zth distributed generator at time t, is the rated capacity of the zth distributed generator at time t;

[0094] The renewable energy output constraint is expressed by the following fourth objective formula:

[0095] in, is the actual active power output of the dth wind turbine at time t, is the predicted output of the dth wind turbine at time t, is the actual active power output of the s-th PV power station at time t, is the predicted output of the s-th photovoltaic power station at time t, is the reactive power output of the dth wind turbine at time t, is the installed capacity of the dth wind turbine at time t, is the reactive power output of the s-th photovoltaic power station at time t, is the installed capacity of the s-th PV power station at time t;

[0096] The electric vehicle power constraint is expressed by the fifth objective formula: in, is the collection of charging and discharging powers of the ae-th polymer at each moment, is the set of all charging and discharging powers of the ae-th polymer within a preset time period;

[0097] The power flow constraint is expressed by the following sixth objective formula:

[0098] Among them, P i,t The active power injected into the i-th node in the virtual power plant at time t, V i,t is the voltage amplitude of the ith node at time t, V j,t is the voltage amplitude of the jth node in the virtual power plant at time t, G ij is the conductance of the branch connecting the i-th node and the j-th node, B ij is the susceptance of the branch connecting the i-th node and the j-th node, θ ij,t is the phase difference between the i-th node and the j-th node at time t, Q i,t is the reactive power injected by the i-th node at time t, P ij,t is the active power flow of the branch connected by the i-th node and the j-th node at time t, Q ij,t is the reactive power flow of the branch connected by the ith node and the jth node at time t, j∈i is all the nodes connected to the ith node, I ic =1 means the i-th node is the grid connection point of the virtual power plant, I ic = 0 means the i-th node is not the grid connection point of the virtual power plant. is the actual active power output of the dth wind turbine at the ith node at time t, is the actual active power output of the s-th PV power station at the ith node at time t, is the actual active power output of the zth distributed generator at the ith node at time t, g i,ae,t is the active power of the ae-th aggregate of the i-th node at time t, is the active power of the ith node at time t, is the reactive power output of the dth wind turbine at the ith node at time t, is the reactive power output of the s-th PV power station at the ith node at time t, is the reactive power output of the zth distributed generator at the ith node at time t, is the reactive power of the i-th node at time t;

[0099] The network security constraint is expressed by the following seventh objective formula:

[0100] Among them, P ij is the active power flow of the branch connected by the i-th node and the j-th node, Q ij is the reactive power flow of the branch connected by the i-th node and the j-th node, S ijis the line capacity of the branch connecting the i-th node and the j-th node, V i min is the minimum voltage of the ith node, V i is the voltage amplitude of the ith node, V i max is the maximum voltage of the ith node.

[0101] In this embodiment, a commercial solver is used to solve the power resource optimization scheduling model. The decision variable x of the power resource optimization scheduling model is t for

[0102]

[0103] Among them, g ae,t is the charge and discharge power of the aeth polymer at time t.

[0104] By solving the power resource optimization dispatch model, we can get the decision variables, which can be used to and Develop a dispatch plan for distributed generators using decision variables and Develop a dispatch plan for renewable energy wind and solar energy, using the decision variable g ae,t Get the reference power of the electric vehicle cluster and use the decision variables Develop a bidding plan for virtual power plants in the electricity market.

[0105] The optimization scheduling method in this application can obtain scheduling plans for distributed generators and renewable energy, reference power of electric vehicle clusters, and bidding plans for virtual power plants in the power market. The power resource optimization scheduling model also takes into account the participation of virtual power plants in the energy market and the flexible regulation service market, releasing the flexibility potential of electric vehicle charging and discharging power.

[0106] Figure 5 A schematic diagram of a flow chart of a method for constructing a schedulable domain model provided in an embodiment of the present application, the method is applied to a terminal, in one embodiment, such as Figure 5 As shown, the following steps are included:

[0107] S501, obtaining a dispatchable domain model of each electric vehicle.

[0108] In this embodiment, the dispatchable domain model of a single electric vehicle is as follows: Figure 3 shown.

[0109] S502: construct a virtual battery model of each electric vehicle according to the dispatchable domain model of each electric vehicle.

[0110] S503, sending the virtual battery model of each electric vehicle to the edge server, so that the edge server aggregates the virtual battery model of each electric vehicle to obtain the aggregation result of the charging and discharging power of each electric vehicle.

[0111] In this embodiment, obtaining the dispatchable domain model of each electric vehicle can accurately know the power boundary and energy boundary of each electric vehicle. The visualized dispatchable domain model also enables the virtual power plant to grasp the dispatching status of electric vehicles more flexibly. Describing the dispatchable domain model of electric vehicles in the form of virtual batteries can also greatly reduce the computational burden.

[0112] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0113] Based on the same inventive concept, the embodiment of the present application also provides an electric vehicle charging and discharging power aggregation device for implementing the above-mentioned electric vehicle charging and discharging power aggregation method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of one or more electric vehicle charging and discharging power aggregation devices provided below can refer to the limitations of the electric vehicle charging and discharging power aggregation method above, and will not be repeated here.

[0114] Reference Figure 6 , Figure 6 This is a structural block diagram of a charging and discharging power aggregation device for an electric vehicle provided in an embodiment of the present application. The device 600 includes: a receiving module 601 and an aggregation module 602, wherein:

[0115] The receiving module 601 is used to receive the virtual battery model of each electric vehicle sent by the terminal, wherein the virtual battery model of each electric vehicle is a battery model constructed by the terminal according to the schedulable domain model of the corresponding electric vehicle, and the schedulable domain model is a model determined by the terminal according to the charging and discharging power of the electric vehicle at each moment in a preset time period;

[0116] The aggregation module 602 is used to aggregate the virtual battery models of the electric vehicles corresponding to the edge servers to obtain the aggregation results of the charging and discharging power of each electric vehicle.

[0117] The electric vehicle charge and discharge power aggregation device provided in this embodiment aggregates the virtual battery models of each electric vehicle sent by the receiving terminal, wherein the virtual battery model of each electric vehicle is a battery model constructed by the terminal according to the corresponding schedulable domain model of the electric vehicle, and the schedulable domain model is a model determined by the terminal according to the charge and discharge power of the electric vehicle at each moment in a preset time period, and obtains the charge and discharge power aggregation result of each electric vehicle. In the traditional technology, the charge and discharge power aggregation result of the electric vehicle is obtained by directly adding the boundary values ​​of the charge and discharge power and energy of the electric vehicle, while in this embodiment, the boundary value can be obtained more accurately by constructing the schedulable domain model of the electric vehicle, which solves the problem of inaccurate charge and discharge aggregation results of the electric vehicle in the traditional method.

[0118] Optionally, the device 600 further includes:

[0119] The sending module is used to send the charging and discharging power aggregation results to the cloud server, so that the cloud server constructs the objective function and constraint conditions according to the charging and discharging power aggregation results, and constructs the power resource optimization scheduling model according to the objective function and constraint conditions.

[0120] Optionally, the virtual battery model of the electric vehicle is represented by the following first objective formula:

[0121]

[0122] Among them, H j is the set of all charging and discharging powers of the jth electric vehicle among all electric vehicles within a preset time period, is the collection of charging and discharging power of the jth electric vehicle at each moment, is the charge and discharge power of the jth electric vehicle at time t, T is the preset time period, A j is the time that the jth electric vehicle stays at the terminal, m is the total number of time periods in the preset time period, ΔT is the time period length, and a j is the time when the jth electric car arrives at the terminal, d j is the time when the jth electric car leaves the terminal, is the rated charging and discharging power of the jth electric vehicle, is the initial energy of the jth electric car when it arrives at the terminal, is the energy expected to be achieved when the jth electric vehicle leaves the terminal, is the lower bound of the energy of the j-th electric car at time t, is the upper bound of the energy of the j-th electric car at time t.

[0123] Optionally, the aggregation module 602 includes:

[0124] An aggregation unit, used for aggregating the virtual battery models of the electric vehicles to obtain an aggregate;

[0125] A power boundary solving unit, used for summing the rated charge and discharge powers in the polymer to obtain the power boundary of the polymer;

[0126] An energy boundary solving unit is used to correct the upper energy boundary and the lower energy boundary of the aggregate to obtain the corrected upper energy boundary and the lower energy boundary;

[0127] The aggregation result unit is used to obtain the charging and discharging power aggregation result of each electric vehicle according to the power boundary of the aggregate, and the corrected energy upper bound and energy lower bound.

[0128] Optionally, the objective function is Among them, φ t c To clear the energy price for the market, is the power exchanged between the virtual power plant and the electricity market, N DG is the number of distributed generators, is the active output power of the zth distributed generator at time t, is the preset fuel cost function, φ t fu The price of the upstream service at time t is adjusted flexibly, fru t is the total power of all uplink flexible adjustment services at time t, φ t fd The price of the downstream service is adjusted flexibly at time t, frd t is the total power of all downlink flexible adjustment services at time t, m is the total number of time periods in the preset time period, and ΔT is the time period length;

[0129] The constraints include distributed generator output constraints, renewable energy output constraints, electric vehicle power constraints, power flow constraints and network security constraints. The distributed generator output constraints are expressed by the following third objective formula:

[0130] in, is the lower limit of the output of the zth distributed generator at time t, is the actual active power output of the zth distributed generator at time t, is the output upper limit of the zth distributed generator at time t, rd z is the maximum ramp-down rate of the zth distributed generator, ru zis the maximum ramp-up rate of the zth distributed generator, is the reactive power output of the zth distributed generator at time t, is the rated capacity of the zth distributed generator at time t;

[0131] The renewable energy output constraint is expressed by the following fourth objective formula:

[0132] in, is the actual active power output of the dth wind turbine at time t, is the predicted output of the dth wind turbine at time t, is the actual active power output of the s-th PV power station at time t, is the predicted output of the s-th photovoltaic power station at time t, is the reactive power output of the dth wind turbine at time t, is the installed capacity of the dth wind turbine at time t, is the reactive power output of the s-th photovoltaic power station at time t, is the installed capacity of the s-th PV power station at time t;

[0133] The electric vehicle power constraint is expressed by the fifth objective formula: in, is the collection of charging and discharging powers of the ae-th polymer at each moment, is the set of all charging and discharging powers of the ae-th polymer within a preset time period;

[0134] The power flow constraint is expressed by the following sixth objective formula:

[0135] Among them, P i,t The active power injected into the i-th node in the virtual power plant at time t, V i,t is the voltage amplitude of the ith node at time t, V j,t is the voltage amplitude of the jth node in the virtual power plant at time t, G ij is the conductance of the branch connecting the i-th node and the j-th node, B ij is the susceptance of the branch connecting the i-th node and the j-th node, θ ij,t is the phase difference between the i-th node and the j-th node at time t, Q i,t is the reactive power injected by the i-th node at time t, P ij,t is the active power flow of the branch connected by the i-th node and the j-th node at time t, Q ij,t is the reactive power flow of the branch connected by the ith node and the jth node at time t, j∈i is all the nodes connected to the ith node, I ic=1 means the i-th node is the grid connection point of the virtual power plant, I ic = 0 means the i-th node is not the grid connection point of the virtual power plant. is the actual active power output of the dth wind turbine at the ith node at time t, is the actual active power output of the s-th PV power station at the ith node at time t, is the actual active power output of the zth distributed generator at the ith node at time t, g i,ae,t is the active power of the ae-th aggregate of the i-th node at time t, is the active power of the ith node at time t, is the reactive power output of the dth wind turbine at the ith node at time t, is the reactive power output of the s-th PV power station at the ith node at time t, is the reactive power output of the zth distributed generator at the ith node at time t, is the reactive power of the i-th node at time t;

[0136] The network security constraint is expressed by the following seventh objective formula:

[0137] Among them, P ij is the active power flow of the branch connected by the i-th node and the j-th node, Q ij is the reactive power flow of the branch connected by the i-th node and the j-th node, S ij is the line capacity of the branch connecting the i-th node and the j-th node, V i min is the minimum voltage of the ith node, V i is the voltage amplitude of the ith node, V i max is the maximum voltage of the ith node.

[0138] Reference Figure 7 , Figure 7 This is a structural block diagram of another charging and discharging power aggregation device for an electric vehicle provided in an embodiment of the present application. The device 700 includes:

[0139] An acquisition module 701 is used to acquire a dispatchable domain model of each electric vehicle;

[0140] A construction module 702 is used to construct a virtual battery model of each electric vehicle according to the dispatchable domain model of each electric vehicle;

[0141] The sending module 703 is used to send the virtual battery model of each electric vehicle to the edge server, so that the edge server aggregates the virtual battery model of each electric vehicle to obtain the charging and discharging power aggregation result of each electric vehicle.

[0142] This embodiment provides another charging and discharging power aggregation device for electric vehicles. By acquiring the dispatchable domain model of each electric vehicle, the power boundary and energy boundary of each electric vehicle can be accurately known. The visualized dispatchable domain model also enables the virtual power plant to grasp the dispatching status of electric vehicles more flexibly. The dispatchable domain model of the electric vehicle is described in the form of a virtual battery, which can also greatly reduce the computational burden.

[0143] Each module in the above-mentioned charging and discharging power aggregation device of the electric vehicle can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0144] Figure 8 : is an internal structure diagram of a computer device in an embodiment of the present application. In this embodiment, a computer device is provided, and its internal structure diagram can be as follows: Figure 8 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for constructing a scheduling model for a microgrid is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball or a touchpad set on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0145] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0146] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the method for aggregating charging and discharging power of an electric vehicle provided in the above embodiment are implemented. The implementation principle and technical effect are similar to those of the above method embodiment, and will not be repeated here.

[0147] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method for aggregating charging and discharging power of an electric vehicle provided in the above embodiment are implemented. The implementation principle and technical effect are similar to those of the above method embodiment, and will not be repeated here.

[0148] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps of the method for aggregating charging and discharging power of an electric vehicle provided in the above embodiment. Its implementation principle and technical effect are similar to those of the above method embodiment, and will not be repeated here.

[0149] 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, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0150] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present 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), magnetoresistive 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0151] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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.

[0152] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations 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 shall be subject to the attached claims.

Claims

1. A method for aggregating charging and discharging power of an electric vehicle, characterized in that: Applied to an edge server, the method comprises: A virtual battery model of each electric vehicle sent by a receiving terminal, wherein the virtual battery model of each electric vehicle is a battery model constructed by the terminal according to a schedulable domain model of the corresponding electric vehicle, and the schedulable domain model is a model determined by the terminal according to the charging and discharging power of the electric vehicle at each moment in a preset time period; wherein the virtual battery model of the electric vehicle is represented by the following first objective formula: in, is the set of all charging and discharging powers of the jth electric vehicle among the electric vehicles within the preset time period, is the collection of charging and discharging power of the jth electric vehicle at each moment, is the charge and discharge power of the jth electric vehicle at time t, T is the preset time period, is the time that the jth electric vehicle stays at the terminal, m is the total number of time periods in the preset time period, Δ T is the time period length, is the time when the jth electric car arrives at the terminal, is the time when the jth electric car leaves the terminal, is the rated charging and discharging power of the jth electric vehicle, is the initial energy of the jth electric vehicle when it arrives at the terminal, is the energy expected to be achieved when the jth electric vehicle leaves the terminal, is the lower bound of the energy of the j-th electric car at time t, is the upper bound of the energy of the j-th electric car at time t; Aggregating the virtual battery models of the electric vehicles to obtain an aggregate; Summing the rated charge and discharge powers in the polymer to obtain the power boundary of the polymer; Correcting the upper energy bound and the lower energy bound within the aggregate to obtain a corrected upper energy bound and a corrected lower energy bound; Obtaining the charging and discharging power aggregation result of each of the electric vehicles according to the power boundary of the aggregate, and the revised energy upper bound and energy lower bound; The charge and discharge power aggregation result is expressed by the following second target formula: , in, is the collection of all charging and discharging powers of the polymer within the preset time period, is the collection of the charging and discharging powers of the polymer at each moment, is the charge and discharge power of the polymer at time t, is the power boundary of the aggregate at time t, is the accumulated energy of the polymer at time t, is the modified lower bound of the energy of the aggregate at time t, is the modified upper energy bound of the aggregate at time t.

2. The method according to claim 1, characterized in that The method further comprises: The charging and discharging power aggregation result is sent to a cloud server, so that the cloud server constructs an objective function and constraint conditions according to the charging and discharging power aggregation result, and constructs an electric power resource optimization scheduling model according to the objective function and the constraint conditions.

3. The method according to claim 2, characterized in that The objective function is ,in, To clear the energy price for the market, is the power exchanged between the virtual power plant and the electricity market, is the number of distributed generators, is the active output power of the zth distributed generator at time t, is the preset fuel cost function, Flexible adjustment of service price for uplink at time t, is the total power of all uplink flexible adjustment services at time t, Flexible adjustment of service prices for downlink at time t, is the total power of all downlink flexible adjustment services at time t, m is the total number of time periods in the preset time period, Δ T is the time period length; The constraints include distributed generator output constraints, renewable energy output constraints, electric vehicle power constraints, power flow constraints and network security constraints. The distributed generator output constraints are expressed by the following third objective formula: ,in, is the lower limit of the output of the zth distributed generator at time t, is the actual active power output of the zth distributed generator at time t, is the output upper limit of the zth distributed generator at time t, is the maximum ramp-down rate of the zth distributed generator, is the maximum ramp-up rate of the zth distributed generator, is the reactive power output of the zth distributed generator at time t, is the rated capacity of the zth distributed generator at time t; The renewable energy output constraint is expressed by the following fourth objective formula: ,in, is the actual active power output of the dth wind turbine at time t, is the predicted output of the dth wind turbine at time t, is the actual active power output of the s-th PV power station at time t, is the predicted output of the s-th photovoltaic power station at time t, is the reactive power output of the dth wind turbine at time t, is the installed capacity of the dth wind turbine at time t, is the reactive power output of the s-th photovoltaic power station at time t, is the installed capacity of the s-th PV power station at time t; The electric vehicle power constraint is expressed by the fifth objective formula: ,in, is the collection of the charge and discharge powers of the ae-th polymer at each moment, is the set of all charging and discharging powers of the ae-th polymer within the preset time period; The power flow constraint is expressed by the following sixth objective formula: ,in, Inject active power into the i-th node in the virtual power plant at time t, is the voltage amplitude of the ith node at time t, is the voltage amplitude of the jth node in the virtual power plant at time t, is the conductance of the branch connecting the i-th node and the j-th node, is the susceptance of the branch connecting the i-th node and the j-th node, is the phase angle difference between the i-th node and the j-th node at time t, is the reactive power injected by the ith node at time t, is the active power flow of the branch connected by the i-th node and the j-th node at time t, is the reactive power flow of the branch connected by the i-th node and the j-th node at time t, are all nodes connected to the i-th node, The i-th node is the grid connection point of the virtual power plant, The i-th node is not the grid connection point of the virtual power plant, is the actual active power output of the dth wind turbine at the ith node at time t, is the actual active power output of the s-th PV power station at the ith node at time t, is the actual active power output of the zth distributed generator at the ith node at time t, is the active power of the ae-th aggregate of the i-th node at time t, is the active power of the ith node at time t, is the reactive power output of the dth wind turbine at the ith node at time t, is the reactive power output of the s-th PV power station at the ith node at time t, is the reactive power output of the zth distributed generator at the ith node at time t, is the reactive power of the i-th node at time t; The network security constraint is expressed by the following seventh objective formula: ,in, is the active power flow of the branch connected by the i-th node and the j-th node, is the reactive power flow of the branch connected by the i-th node and the j-th node, is the line capacity of the branch connecting the i-th node and the j-th node, is the minimum voltage of the ith node, is the voltage amplitude of the ith node, is the maximum voltage of the ith node.

4. A method for aggregating charging and discharging power of an electric vehicle, characterized in that: Applied to a terminal, the method comprises: Obtain the dispatchable domain model of each electric vehicle; A virtual battery model of each electric vehicle is constructed according to the dispatchable domain model of each electric vehicle; wherein the virtual battery model of the electric vehicle is represented by the following first objective formula: in, is the set of all charging and discharging powers of the j-th electric vehicle among the electric vehicles within a preset time period, is the collection of charging and discharging power of the jth electric vehicle at each moment, is the charge and discharge power of the jth electric vehicle at time t, T is the preset time period, is the time that the jth electric vehicle stays at the terminal, m is the total number of time periods in the preset time period, Δ T is the time period length, is the time when the jth electric car arrives at the terminal, is the time when the jth electric car leaves the terminal, is the rated charging and discharging power of the jth electric vehicle, is the initial energy of the jth electric vehicle when it arrives at the terminal, is the energy expected to be achieved when the jth electric vehicle leaves the terminal, is the lower bound of the energy of the j-th electric car at time t, is the upper bound of the energy of the j-th electric car at time t; Sending the virtual battery model of each electric vehicle to the edge server, so that the edge server aggregates the virtual battery model of each electric vehicle to obtain an aggregate; summing the rated charge and discharge power in the aggregate to obtain the power boundary of the aggregate; correcting the energy upper bound and the energy lower bound in the aggregate to obtain the corrected energy upper bound and the energy lower bound; obtaining the charge and discharge power aggregation result of each electric vehicle according to the power boundary of the aggregate and the corrected energy upper bound and the energy lower bound; The charge and discharge power aggregation result is expressed by the following second target formula: , in, is the collection of all charging and discharging powers of the polymer within the preset time period, is the collection of the charging and discharging powers of the polymer at each moment, is the charge and discharge power of the polymer at time t, is the power boundary of the aggregate at time t, is the accumulated energy of the polymer at time t, is the modified lower bound of the energy of the aggregate at time t, is the modified upper energy bound of the aggregate at time t.

5. A charging and discharging power aggregation device for an electric vehicle, characterized in that: The device comprises: A receiving module is used to receive a virtual battery model of each electric vehicle sent by a terminal, wherein the virtual battery model of each electric vehicle is a battery model constructed by the terminal according to a schedulable domain model of the corresponding electric vehicle, and the schedulable domain model is a model determined by the terminal according to the charging and discharging power of the electric vehicle at each moment in a preset time period; wherein the virtual battery model of the electric vehicle is represented by the following first objective formula: in, is the set of all charging and discharging powers of the jth electric vehicle among the electric vehicles within the preset time period, is the collection of charging and discharging power of the jth electric vehicle at each moment, is the charge and discharge power of the jth electric vehicle at time t, T is the preset time period, is the time that the jth electric vehicle stays at the terminal, m is the total number of time periods in the preset time period, Δ T is the time period length, is the time when the jth electric car arrives at the terminal, is the time when the jth electric car leaves the terminal, is the rated charging and discharging power of the jth electric vehicle, is the initial energy of the jth electric vehicle when it arrives at the terminal, is the energy expected to be achieved when the jth electric vehicle leaves the terminal, is the lower bound of the energy of the j-th electric car at time t, is the upper bound of the energy of the j-th electric car at time t; An aggregation unit of the aggregation module is used to aggregate the virtual battery models of the electric vehicles to obtain an aggregate; A power boundary solving unit of the aggregation module is used to sum the rated charge and discharge power in the aggregate to obtain the power boundary of the aggregate; The energy boundary solving unit of the aggregation module is used to correct the energy upper bound and the energy lower bound in the aggregate to obtain the corrected energy upper bound and the energy lower bound; An aggregation result unit of the aggregation module is used to obtain the charging and discharging power aggregation result of each of the electric vehicles according to the power boundary of the aggregate, and the corrected energy upper bound and energy lower bound; The charge and discharge power aggregation result is expressed by the following second target formula: , in, is the collection of all charging and discharging powers of the polymer within the preset time period, is the collection of the charging and discharging powers of the polymer at each moment, is the charge and discharge power of the polymer at time t, is the power boundary of the aggregate at time t, is the accumulated energy of the polymer at time t, is the modified lower bound of the energy of the aggregate at time t, is the modified upper energy bound of the aggregate at time t.

6. A charging and discharging power aggregation device for an electric vehicle, characterized in that: The device comprises: An acquisition module, used to acquire a dispatchable domain model of each electric vehicle; A construction module is used to construct a virtual battery model of each electric vehicle according to the dispatchable domain model of each electric vehicle; wherein the virtual battery model of the electric vehicle is represented by the following first objective formula: in, is the set of all charging and discharging powers of the j-th electric vehicle among the electric vehicles within a preset time period, is the collection of charging and discharging power of the jth electric vehicle at each moment, is the charge and discharge power of the jth electric vehicle at time t, T is the preset time period, is the time that the jth electric vehicle stays at the terminal, m is the total number of time periods in the preset time period, Δ T is the time period length, is the time when the jth electric car arrives at the terminal, is the time when the jth electric car leaves the terminal, is the rated charging and discharging power of the jth electric vehicle, is the initial energy of the jth electric vehicle when it arrives at the terminal, is the energy expected to be achieved when the jth electric vehicle leaves the terminal, is the lower bound of the energy of the j-th electric car at time t, is the upper bound of the energy of the j-th electric car at time t; A sending module, used for sending the virtual battery model of each electric vehicle to the edge server, so that the edge server aggregates the virtual battery model of each electric vehicle to obtain an aggregate; sums the rated charge and discharge power in the aggregate to obtain the power boundary of the aggregate; corrects the energy upper bound and the energy lower bound in the aggregate to obtain the corrected energy upper bound and the energy lower bound; obtains the charge and discharge power aggregation result of each electric vehicle according to the power boundary of the aggregate and the corrected energy upper bound and the energy lower bound; The charge and discharge power aggregation result is expressed by the following second target formula: , in, is the collection of all charging and discharging powers of the polymer within the preset time period, is the collection of the charging and discharging powers of the polymer at each moment, is the charge and discharge power of the polymer at time t, is the power boundary of the aggregate at time t, is the accumulated energy of the polymer at time t, is the modified lower bound of the energy of the aggregate at time t, is the modified upper energy bound of the aggregate at time t.

7. 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, the steps of the method according to any one of claims 1 to 4 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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