Evaluation method, device and equipment for adjustable capability of electric vehicle cluster and medium

Abstract

The invention discloses an evaluation method, device and equipment for the adjustable capability of an electric vehicle cluster and a medium, relates to the technical field of vehicle network interaction and smart power grids, and can improve the evaluation efficiency of the adjustable capability of the electric vehicle cluster. According to the scheme, the method comprises the steps that charge state changes of a power battery of each electric vehicle in a cluster of the electric vehicles at the current moment and the last moment are obtained; the electric vehicles in the cluster are divided into a first subgroup and a second subgroup according to the charge state change, the charge state change of the first subgroup is larger than a target threshold value, and the charge state change of the second subgroup is smaller than the target threshold value; for the first electric vehicle in the first subgroup, calculating a first adjustable power range of the first subgroup by adopting a mixed integer-based linear programming model; for a second electric vehicle in the second subgroup, calculating a second adjustable power range of the second subgroup by adopting a finite difference algorithm; and determining the adjustable capability of the cluster according to the first adjustable power range and the second adjustable power range.

Description

Technical Field

[0001] The present application relates to the field of vehicle-grid interaction and smart grid technology, and in particular to a method, device, equipment and medium for evaluating the adjustable capability of an electric vehicle cluster. Background Art

[0002] With the continuous promotion of renewable energy and low carbon, electric vehicles, which represent new energy, have developed rapidly in recent years. However, the rapid development of electric vehicles has brought a series of new challenges to the power grid, such as overload of distribution transformers and increased load peak-to-valley differences. A large number of existing studies have shown that V2G technology can not only solve these challenges, but also use electric vehicles as distributed flexibility resources and aggregate them to participate in power grid services.

[0003] At present, a large number of studies have been conducted on the participation of electric vehicle clusters in grid regulation. A key point of its technical feasibility is to evaluate the adjustable capacity of electric vehicle clusters, that is, the power range within which electric vehicle clusters can charge and discharge with the grid at the current moment. However, the existing models that aggregate a large number of electric vehicles have a heavy computational burden and a long solution time, making it difficult to cope with rapidly changing grid dispatch instructions. Summary of the invention

[0004] The present application provides a method, device, equipment and medium for evaluating the adjustable capability of an electric vehicle cluster, which can improve the evaluation efficiency of the adjustable capability of an electric vehicle cluster.

[0005] In order to achieve the above purpose, this application adopts the following technical solutions: In a first aspect of an embodiment of the present application, a method for evaluating the adjustable capability of an electric vehicle cluster is provided, the method comprising: Obtain the charge state change of the power battery of each electric vehicle in the electric vehicle cluster at the current moment and the previous moment; Dividing the electric vehicles in the cluster into a first subgroup and a second subgroup according to the charge state change, the charge state change of the first subgroup being greater than a target threshold, and the charge state change of the second subgroup being less than the target threshold; For a first electric vehicle in the first subgroup, a first adjustable power range of the first subgroup is calculated based on a mixed integer linear programming model; For the second electric vehicles in the second subgroup, a second adjustable power range of the second subgroup is calculated using a finite difference algorithm; The adjustable capability of the cluster is determined according to the first adjustable power range and the second adjustable power range.

[0006] As a possible implementation, the mixed integer linear programming model includes: an objective function and a plurality of constraints, and for the first electric vehicle in the first subgroup, the first adjustable power range of the first subgroup is calculated based on the mixed integer linear programming model, including: Acquire input information, the input information including: time information of the first electric vehicle being connected to the power grid, battery parameters of a first power battery corresponding to the first electric vehicle, and battery charging information; After determining that the input information satisfies the corresponding constraint condition, the first adjustable power range is calculated using the input information and the objective function.

[0007] As a possible implementation method, the objective function is:

[0008] in, F 1 is the minimum adjustable power of the first subgroup, F 2 is the maximum adjustable power of the first subgroup, t1 is the time when the i-th first electric vehicle is connected to the grid, t2 is the time when the i-th first electric vehicle is disconnected from the grid, M is the set of the first subgroup, represents the state of charge of the power battery of the first electric vehicle at time t, cap i Represents the total capacity of the power battery of the i-th electric car.

[0009] As a possible implementation manner, the constraint conditions include: power battery safety constraint, user charging demand constraint, power battery technical parameter constraint and power battery charging and discharging energy change constraint.

[0010] As a possible implementation, The power battery safety constraints are:

[0011] in, Indicates the preset minimum SOC value, Represents the preset maximum SOC value; The user charging demand constraint is:

[0012] in, is the SOC when the first electric vehicle is disconnected from the grid, Represents the minimum SOC requirement when the electric vehicle is disconnected from the grid; The power battery technical parameter constraints are:

[0013] in, represents the battery charging power at time t, Represents the battery discharge power at time t; Represents the charging state of the battery, which is a zero-one variable, 1 represents charging, and 0 represents not charging; Represents the discharge state of the battery; Represents the maximum charging power of the battery. Represents the maximum discharge power of the battery; The power battery charge and discharge energy variation constraint is:

[0014] in, is the energy of the i-th electric car at time t, is the time granularity, The efficiency of battery charging and discharging.

[0015] As a possible implementation manner, the calculating and obtaining the first adjustable power range by using the input information and the objective function includes: Input information satisfying the constraint condition is input into the objective function to obtain the first adjustable power range.

[0016] As a possible implementation, for the second electric vehicle in the second subgroup, calculating the second adjustable power range of the second subgroup based on a finite difference algorithm includes: Determine the current adjustable power of each second electric vehicle according to the adjustable power, sensitivity, charge state of the second electric vehicle at the previous moment and the charge state of the second electric vehicle at the current moment; wherein the sensitivity is determined based on the change in the adjustable power of the second electric vehicle under a preset disturbance value; The second adjustable power range is determined according to the current adjustable power of each second electric vehicle.

[0017] The embodiment of the present application provides a device for evaluating the adjustable capability of an electric vehicle cluster, the device comprising: An acquisition module, used to acquire the charge state change of the power battery of each electric vehicle in the electric vehicle cluster at the current moment and the previous moment; a processing module, configured to divide the electric vehicles in the cluster into a first subgroup and a second subgroup according to the charge state change, wherein the charge state change of the first subgroup is greater than a target threshold, and the charge state change of the second subgroup is less than the target threshold; A first calculation module, configured to calculate, for a first electric vehicle in the first subgroup, a first adjustable power range of the first subgroup by using a mixed integer linear programming model; A second calculation module, configured to calculate a second adjustable power range of the second subgroup by using a finite difference algorithm for the second electric vehicle in the second subgroup; A determination module is used to determine the adjustable capability of the cluster according to the first adjustable power range and the second adjustable power range.

[0018] According to a third aspect of an embodiment of the present application, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for evaluating the adjustable capability of an electric vehicle cluster in the first aspect of an embodiment of the present application is implemented.

[0019] According to a fourth aspect of the embodiments of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for evaluating the adjustable capacity of an electric vehicle cluster described in the first aspect of the embodiments of the present application is implemented.

[0020] The beneficial effects brought by the technical solution provided by the embodiment of the present application include at least: An embodiment of the present application provides an evaluation method for the adjustable capacity of an electric vehicle cluster, the method comprising: obtaining a change in the state of charge of a power battery of each electric vehicle in the cluster of electric vehicles at a current moment and a previous moment; dividing the electric vehicles in the cluster into a first subgroup and a second subgroup according to the change in the state of charge, the change in the state of charge of the first subgroup being greater than a target threshold, and the change in the state of charge of the second subgroup being less than the target threshold; for a first electric vehicle in the first subgroup, calculating a first adjustable power range of the first subgroup based on a mixed integer linear programming model; for a second electric vehicle in the second subgroup, calculating a second adjustable power range of the second subgroup based on a finite difference algorithm; determining the adjustable capacity of the cluster according to the first adjustable power range and the second adjustable power range, thereby enabling online rapid calculation of the adjustable capacity in a scenario where a large number of electric vehicles are gathered, and supporting the electric vehicle cluster to respond quickly to power grid regulation in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flow chart of a method for evaluating adjustable capacity of an electric vehicle cluster provided in an embodiment of the present application; Figure 2 An electric vehicle travel data graph provided in an embodiment of the present application; Figure 3 A schematic diagram of interaction between an electric vehicle cluster and a power grid provided in an embodiment of the present application; Figure 4 A structural diagram of an electric vehicle cluster adjustable capability evaluation device provided in an embodiment of the present application; Figure 5A schematic diagram of the internal structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0023] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present disclosure, unless otherwise specified, "plurality" means two or more.

[0024] Additionally, the use of “based on” or “according to” is meant to be open and inclusive, as a process, step, calculation, or other action “based on” or “according to” one or more conditions or values ​​may, in practice, be based on additional conditions or beyond values.

[0025] The present application embodiment provides a method for evaluating the adjustable capability of an electric vehicle cluster, such as Figure 1 As shown, the method comprises the following steps: Step 101: Obtain the charge state change of the power battery of each electric vehicle in the electric vehicle cluster at the current moment and the previous moment.

[0026] The state of charge change is the difference between the SOC of the power battery of each electric vehicle at the current moment and the SOC at the previous moment, and the state of charge change is obtained.

[0027] Step 102: Divide the electric vehicles in the cluster into a first subgroup and a second subgroup according to the charge state change, the charge state change of the first subgroup being greater than a target threshold, and the charge state change of the second subgroup being less than the target threshold.

[0028] The calculation process of the target threshold D is as follows:

[0029] in, is the SOC change of the power battery of the i-th electric vehicle between the current moment and the previous moment, M is the number of electric vehicles in the cluster, is the SOC of the i-th electric vehicle at time t.

[0030] Step 103: for a first electric vehicle in the first subgroup, a first adjustable power range of the first subgroup is calculated based on a mixed integer linear programming model.

[0031] Optionally, before executing step 101, the method for evaluating the adjustable capability of an electric vehicle cluster further includes: constructing an objective function and multiple constraints based on a mixed integer linear programming model.

[0032] Specifically, the objective function and multiple constraints constructed are as follows: The objective function is:

[0033] in, F 1 is the minimum adjustable power of the first subgroup, F 2 is the maximum adjustable power of the first subgroup, t1 is the time when the i-th first electric vehicle is connected to the grid, t2 is the time when the i-th first electric vehicle is disconnected from the grid, M is the set of the first subgroup, represents the state of charge of the power battery of the first electric vehicle at time t, cap i Represents the total capacity of the power battery of the i-th electric car.

[0034] The constraints include: power battery safety constraints, user charging demand constraints, power battery technical parameter constraints and power battery charging and discharging energy change constraints.

[0035] Specifically, The power battery safety constraints are:

[0036] in, Indicates the preset minimum SOC value, Represents the preset maximum SOC value; The user charging demand constraint is:

[0037] in, is the SOC when the first electric vehicle is disconnected from the grid, Represents the minimum SOC requirement when the electric vehicle is disconnected from the grid; The power battery technical parameter constraints are:

[0038] in, represents the battery charging power at time t, Represents the battery discharge power at time t; Represents the charging state of the battery, which is a zero-one variable, 1 represents charging, and 0 represents not charging; Represents the discharge state of the battery; Represents the maximum charging power of the battery. Represents the maximum discharge power of the battery; The power battery charge and discharge energy variation constraint is:

[0039] in, is the energy of the i-th electric car at time t, is the time granularity, The efficiency of battery charging and discharging.

[0040] After constructing the objective function and multiple constraints based on the mixed integer linear programming model, the process of calculating the first adjustable power range of the first subgroup based on the mixed integer linear programming model in step 103 may be: Acquire input information, the input information including: time information of the first electric vehicle being connected to the power grid, battery parameters of a first power battery corresponding to the first electric vehicle, and battery charging information; After determining that the input information satisfies the corresponding constraint condition, the input information satisfying the constraint condition is input into the objective function to obtain the first adjustable power range.

[0041] The time information of the first electric vehicle connecting to the grid includes: the time when the first electric vehicle connected to the grid and the time when the first electric vehicle was disconnected from the grid. The battery parameters include: the total capacity, maximum charging power and maximum discharging power of the power battery of the first electric vehicle. The battery charging information includes: battery SOC change, battery charging and discharging power, battery charging and discharging status, etc.

[0042] Step 104: For the second electric vehicles in the second subgroup, a second adjustable power range of the second subgroup is calculated using a finite difference algorithm.

[0043] Optionally, the current adjustable power of each second electric vehicle is determined according to the adjustable power, sensitivity, charge state of the second electric vehicle at the previous moment and the charge state of the second electric vehicle at the current moment; wherein the sensitivity is determined based on the change in the adjustable power of the second electric vehicle under a preset disturbance value; The second adjustable power range is determined according to the current adjustable power of each second electric vehicle.

[0044] Specifically, the obtained adjustable power, sensitivity, charge state of each second electric vehicle at the previous moment and the charge state of the second electric vehicle at the current moment can be input into the following formula to calculate the current adjustable power of each second electric vehicle.

[0045]

[0046]

[0047] in, represents the adjustable power of the i-th second electric car at time t, Represents sensitivity, represents the state of charge of the i-th second electric vehicle at time t, is the state of charge of the i-th second electric vehicle at time t-1, is the adjustable power of the i-th second electric vehicle when the state of charge is S at time t, is the preset disturbance value.

[0048] After the adjustable power of each second electric vehicle in the second subgroup is obtained, the adjustable power of each second electric vehicle is summed up to obtain the second adjustable power range of the second subgroup.

[0049] Step 105: Determine the adjustable capacity of the cluster according to the first adjustable power range and the second adjustable power range.

[0050] Among them, the adjustable capacity is the power range within which the electric vehicle cluster can charge and discharge with the power grid at the current moment.

[0051] After the first adjustable power range and the second adjustable power range are obtained, the first adjustable power range and the second adjustable power range are summed to obtain the adjustable power range of the cluster, that is, the adjustable capacity.

[0052] An embodiment of the present application provides an evaluation method for the adjustable capacity of an electric vehicle cluster, the method comprising: obtaining a change in the state of charge of a power battery of each electric vehicle in the cluster of electric vehicles at a current moment and a previous moment; dividing the electric vehicles in the cluster into a first subgroup and a second subgroup according to the change in the state of charge, the change in the state of charge of the first subgroup being greater than a target threshold, and the change in the state of charge of the second subgroup being less than the target threshold; for a first electric vehicle in the first subgroup, calculating a first adjustable power range of the first subgroup based on a mixed integer linear programming model; for a second electric vehicle in the second subgroup, calculating a second adjustable power range of the second subgroup based on a finite difference algorithm; determining the adjustable capacity of the cluster according to the first adjustable power range and the second adjustable power range, thereby enabling online rapid calculation of the adjustable capacity in a scenario where a large number of electric vehicles are gathered, and supporting the electric vehicle cluster to respond quickly to power grid regulation in real time.

[0053] Please refer to Figure 2 The electric vehicle travel data of the embodiment of the present application is derived from the actual data of a certain working day in a certain industrial park, and the arrival and departure times of the electric vehicles, that is, the time when the electric vehicles are connected to and disconnected from the power grid. Figure 2 It can be seen that the behavior of electric vehicles in the industrial park has obvious regularity, most of them arrive between 7:00 and 9:00 in the morning and leave between 18:00 and 20:00 at night. It is reasonable to assume that the capacity of electric vehicles is 60kWh and the maximum charging and discharging power is 30kW.

[0054] See also Figure 3 , the interaction mode between electric vehicle clusters and power grids is described in detail. Electric vehicle clusters need to calculate their own V2G adjustable capacity and report it to the local power grid as the basis for issuing regulation instructions. Electric vehicle clusters are a good frequency regulation resource, but the frequency regulation instructions change very quickly, which requires the adjustable capacity of electric vehicle clusters to be calculated quickly.

[0055] Table 1. The time for calculating the adjustable capacity of electric vehicle clusters by different methods

[0056] According to the data in Table 1, the FDM method is used to quickly calculate the adjustable capacity of electric vehicle clusters, which will have a small error, but the calculation speed is greatly improved. The maximum calculation time at a single moment is reduced from 30.68 seconds to 5.08 seconds, and the total calculation time is reduced from 1248.84 seconds to 116.38 seconds.

[0057] The evaluation method of the adjustable capacity of electric vehicle clusters provided by this application is used to evaluate the industrial park scene on weekdays. It can be seen that the upper and lower limits of the adjustable capacity of electric vehicle clusters begin to increase around 8:00 in the morning, reach a peak around 12:00, and gradually decrease around 18:00. Correspondingly, it can be seen that this is because most employees in the industrial park arrive at the park at 8:00 in the morning, the vehicles are full at around 12:00, and the employees leave in groups at 18:00 in the evening. The results show that the adjustable capacity of electric vehicle clusters is closely related to the car owner's car usage behavior, which verifies the correctness of the evaluation method of the adjustable capacity of electric vehicle clusters.

[0058] The method for evaluating the adjustable capacity of an electric vehicle cluster provided in the embodiment of the present application is based on the travel behavior of each electric vehicle, the technical parameters of the power battery, the charging requirements of the user, and other information. The mixed integer linear programming model is used to evaluate the upper and lower limits of the adjustable capacity of the electric vehicle cluster, which is used as the basis for the grid to issue dispatching instructions and support the realization of intelligent and flexible interaction between "cars and grids". In addition, the FDM method can greatly improve the efficiency of model solving and reduce the calculation time while ensuring accuracy, and is suitable for online rapid calculation of adjustable capacity in scenarios where a large number of electric vehicles are gathered.

[0059] like Figure 4 As shown, an embodiment of the present application provides an evaluation device for adjustable capability of an electric vehicle cluster, the device comprising: An acquisition module 11 is used to acquire the charge state change of the power battery of each electric vehicle in the electric vehicle cluster at the current moment and the previous moment; A processing module 12, configured to divide the electric vehicles in the cluster into a first subgroup and a second subgroup according to the charge state change, wherein the charge state change of the first subgroup is greater than a target threshold, and the charge state change of the second subgroup is less than the target threshold; A first calculation module 13 is used to calculate a first adjustable power range of the first subgroup by using a mixed integer linear programming model for a first electric vehicle in the first subgroup; A second calculation module 14 is used to calculate a second adjustable power range of the second subgroup by using a finite difference algorithm for the second electric vehicle in the second subgroup; The determination module 15 is configured to determine the adjustable capability of the cluster according to the first adjustable power range and the second adjustable power range.

[0060] In one embodiment, the mixed integer linear programming model includes: an objective function and multiple constraints, and the first calculation module 13 is specifically used for: Acquire input information, the input information including: time information of the first electric vehicle being connected to the power grid, battery parameters of a first power battery corresponding to the first electric vehicle, and battery charging information; After determining that the input information satisfies the corresponding constraint condition, the first adjustable power range is calculated using the input information and the objective function.

[0061] In one embodiment, the objective function is:

[0062] in, F 1 is the minimum adjustable power of the first subgroup, F 2 is the maximum adjustable power of the first subgroup, t1 is the time when the i-th first electric vehicle is connected to the grid, t2 is the time when the i-th first electric vehicle is disconnected from the grid, M is the set of the first subgroup, represents the state of charge of the power battery of the first electric vehicle at time t, cap i Represents the total capacity of the power battery of the i-th electric car.

[0063] In one embodiment, the constraint conditions include: power battery safety constraint, user charging demand constraint, power battery technical parameter constraint and power battery charging and discharging energy change constraint.

[0064] In one embodiment, The power battery safety constraints are:

[0065] in, Indicates the preset minimum SOC value, Represents the preset maximum SOC value; The user charging demand constraint is:

[0066] in, is the SOC when the first electric vehicle is disconnected from the grid, Represents the minimum SOC requirement when the electric vehicle is disconnected from the grid; The power battery technical parameter constraints are:

[0067] in, represents the battery charging power at time t, Represents the battery discharge power at time t; Represents the charging state of the battery, which is a zero-one variable, 1 represents charging, and 0 represents not charging; Represents the discharge state of the battery; Represents the maximum charging power of the battery. Represents the maximum discharge power of the battery; The power battery charge and discharge energy variation constraint is:

[0068] in, is the energy of the i-th electric car at time t, is the time granularity, The efficiency of battery charging and discharging.

[0069] In one embodiment, the first calculation module 13 is specifically used for: Input information satisfying the constraint condition is input into the objective function to obtain the first adjustable power range.

[0070] In one embodiment, the second calculation module 14 is specifically used for: Determine the current adjustable power of each second electric vehicle according to the adjustable power, sensitivity, charge state of the second electric vehicle at the previous moment and the charge state of the second electric vehicle at the current moment; wherein the sensitivity is determined based on the change in the adjustable power of the second electric vehicle under a preset disturbance value; The second adjustable power range is determined according to the current adjustable power of each second electric vehicle.

[0071] The electric vehicle cluster adjustable capacity evaluation device provided in this embodiment can execute the above-mentioned electric vehicle cluster adjustable capacity evaluation method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here. Each module in the above-mentioned electric vehicle cluster adjustable capacity evaluation device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor of the electronic device in the form of hardware, or can be stored in the memory of the electronic device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0072] The executor of the method for evaluating the adjustable capability of an electric vehicle cluster provided in the embodiment of the present application may be an electronic device, which may be a computer device, a terminal device, a server or a server cluster, and the embodiment of the present application does not specifically limit this.

[0073] Figure 5 This is a schematic diagram of the internal structure of an electronic device provided in an embodiment of the present application. Figure 5As shown, the electronic device includes a processor and a memory connected via a system bus. The processor is used to provide computing and control capabilities. The memory may include a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The computer program can be executed by the processor to implement the steps of the method for evaluating the adjustable capability of an electric vehicle cluster provided in each of the above embodiments. The internal memory provides a cache operating environment for the operating system and the computer program in the non-volatile storage medium.

[0074] Those skilled in the art will understand that Figure 5 The internal structure diagram of the electronic device shown in the figure is only a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of the present application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0075] In another embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for evaluating the adjustable capacity of an electric vehicle cluster as in the embodiment of the present application are implemented.

[0076] In another embodiment of the present application, a computer program product is also provided. The computer program product includes computer instructions. When the computer instructions are executed on the electric vehicle cluster adjustable capability evaluation device, the electric vehicle cluster adjustable capability evaluation device executes each step of the electric vehicle cluster adjustable capability evaluation method in the method flow shown in the above method embodiment.

[0077] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using a software program, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer execution instruction is loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instruction can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instruction can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that can be accessed by the computer or a data storage device such as a server, data center, etc. that contains one or more servers that can be integrated with the medium. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium (eg, a solid state disk (SSD)).

[0078] 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.

[0079] The above 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 invention patent. 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 patent of the present application shall be subject to the attached claims.

Claims

1. A method for evaluating the adjustable capacity of an electric vehicle cluster, characterized in that: The method comprises: Obtain the charge state change of the power battery of each electric vehicle in the electric vehicle cluster at the current moment and the previous moment; Dividing the electric vehicles in the cluster into a first subgroup and a second subgroup according to the charge state change, the charge state change of the first subgroup being greater than a target threshold, and the charge state change of the second subgroup being less than the target threshold; For a first electric vehicle in the first subgroup, a first adjustable power range of the first subgroup is calculated based on a mixed integer linear programming model; For the second electric vehicles in the second subgroup, a second adjustable power range of the second subgroup is calculated using a finite difference algorithm; The adjustable capability of the cluster is determined according to the first adjustable power range and the second adjustable power range.

2. The method according to claim 1, characterized in that The mixed integer linear programming model includes: an objective function and a plurality of constraints. For the first electric vehicle in the first subgroup, the first adjustable power range of the first subgroup is calculated based on the mixed integer linear programming model, including: Acquire input information, the input information including: time information of the first electric vehicle being connected to the power grid, battery parameters of a first power battery corresponding to the first electric vehicle, and battery charging information; After determining that the input information satisfies the corresponding constraint condition, the first adjustable power range is calculated using the input information and the objective function.

3. The method according to claim 2, characterized in that The objective function is: in, F 1 is the minimum adjustable power of the first subgroup, F 2 is the maximum adjustable power of the first subgroup, t1 is the time when the i-th first electric vehicle is connected to the grid, t2 is the time when the i-th first electric vehicle is disconnected from the grid, M is the set of the first subgroup, represents the state of charge of the power battery of the first electric vehicle at time t, cap i Represents the total capacity of the power battery of the i-th electric car.

4. The method according to claim 2, characterized in that: The constraints include: power battery safety constraints, user charging demand constraints, power battery technical parameter constraints and power battery charge and discharge energy change constraints.

5. The method according to claim 4, characterized in that The power battery safety constraints are: in, Indicates the preset minimum SOC value, Represents the preset maximum SOC value; The user charging demand constraint is: in, is the SOC when the first electric vehicle is disconnected from the grid, Represents the minimum SOC requirement when the electric vehicle is disconnected from the grid; The power battery technical parameter constraints are: in, represents the battery charging power at time t, Represents the battery discharge power at time t; Represents the charging state of the battery, which is a zero-one variable, 1 represents charging, and 0 represents not charging; Represents the discharge state of the battery; Represents the maximum charging power of the battery. Represents the maximum discharge power of the battery; The power battery charge and discharge energy variation constraint is: in, is the energy of the i-th electric car at time t, is the time granularity, The efficiency of battery charging and discharging.

6. The method according to claim 2, characterized in that The calculating and obtaining the first adjustable power range by using the input information and the objective function includes: Input information satisfying the constraint condition is input into the objective function to obtain the first adjustable power range.

7. The method according to claim 1, characterized in that The method of calculating the second adjustable power range of the second electric vehicle in the second subgroup by using a finite difference algorithm comprises: Determine the current adjustable power of each second electric vehicle according to the adjustable power, sensitivity, charge state of the second electric vehicle at the previous moment and the charge state of the second electric vehicle at the current moment; wherein the sensitivity is determined based on the change in the adjustable power of the second electric vehicle under a preset disturbance value; The second adjustable power range is determined according to the current adjustable power of each second electric vehicle.

8. An evaluation device for the adjustable capacity of an electric vehicle cluster, characterized in that: The device comprises: An acquisition module, used to acquire the charge state change of the power battery of each electric vehicle in the electric vehicle cluster at the current moment and the previous moment; a processing module, configured to divide the electric vehicles in the cluster into a first subgroup and a second subgroup according to the charge state change, wherein the charge state change of the first subgroup is greater than a target threshold, and the charge state change of the second subgroup is less than the target threshold; A first calculation module, configured to calculate, for a first electric vehicle in the first subgroup, a first adjustable power range of the first subgroup by using a mixed integer linear programming model; A second calculation module, configured to calculate a second adjustable power range of the second subgroup by using a finite difference algorithm for the second electric vehicle in the second subgroup; A determination module is configured to determine the adjustable capability of the cluster according to the first adjustable power range and the second adjustable power range.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for evaluating the adjustable capability of an electric vehicle cluster as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the method for evaluating the adjustable capability of an electric vehicle cluster as claimed in any one of claims 1 to 7 is implemented.

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