Method and device for determining scheduling potential of electric vehicle cluster, equipment and medium

Abstract

The invention relates to a scheduling potential determination method and device for an electric vehicle cluster, equipment and a medium. The method comprises the following steps: acquiring target scheduling demand information of an electric vehicle cluster in a current time period in an area to which a power system belongs; the target scheduling demand information comprises charging demand information and battery state information of each electric vehicle, and charging and discharging parameters of each charging pile in an area to which the power system belongs; obtaining a regulation potential prediction model of the electric vehicle cluster; the regulation and control potential prediction model is determined according to a regulation and control potential function and a scheduling constraint function of the electric vehicle cluster, the regulation and control potential function is used for representing the change condition of adjustable power of the electric vehicle cluster in a future time period, and the scheduling constraint function is used for constraining charging and discharging of the electric vehicle cluster; and based on the regulation and control potential prediction model, according to the target scheduling demand information, determining a schedulable range of the electric vehicle cluster in a future time period. By adopting the method, the scheduling potential of the electric vehicle cluster can be quickly and accurately determined.

Description

Technical Field

[0001] The present application relates to the technical field of power grid dispatching, and in particular to a method, device, equipment and medium for determining the dispatching potential of an electric vehicle cluster. Background Art

[0002] The use of electric vehicles is growing rapidly around the world and is seen as a promising alternative to traditional gasoline vehicles. The demand-side distributed resources represented by electric vehicle loads are numerous and small in capacity, and their penetration rate on the demand side is increasing rapidly, significantly increasing the flexibility of the distribution network. In order to further enhance the forward-looking understanding of the potential of demand-side distributed resources represented by electric vehicles to participate in power system regulation, it is necessary to conduct an aggregate assessment of the regulation capabilities of electric vehicle resources.

[0003] However, existing studies have evaluated the dispatching potential of electric vehicle clusters by constructing objective functions and constraints that reflect the charging and discharging process of electric vehicles, and solving the objective functions. Existing mathematical optimization-based methods are difficult to quickly fit the mapping relationship from the initial state of the system to the complex dynamic response. Once parameters such as vehicle demand change, they need to be recalculated, and the problem solving time is long. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, equipment and medium for determining the scheduling potential of an electric vehicle cluster in response to the above-mentioned technical problems, which can quickly and accurately determine the scheduling potential of an electric vehicle cluster.

[0005] In a first aspect, the present application provides a method for determining the dispatching potential of an electric vehicle cluster, comprising:

[0006] Obtaining target dispatch demand information of electric vehicle clusters in the area to which the power system belongs in the current period; wherein the target dispatch demand information includes charging demand information and battery status information of each electric vehicle, and charging and discharging parameters of each charging pile in the area to which the power system belongs;

[0007] Acquire a regulation potential prediction model of the electric vehicle cluster; wherein the regulation potential prediction model is determined according to a regulation potential function and a scheduling constraint function of the electric vehicle cluster, the regulation potential function is used to characterize changes in the adjustable power of the electric vehicle cluster in a future period, and the scheduling constraint function is used to constrain the charging and discharging of the electric vehicle cluster;

[0008] Based on the regulation potential prediction model and according to the target scheduling demand information, the dispatchable range of the electric vehicle cluster in the future period is determined.

[0009] In one embodiment, the regulation potential function is constructed based on a power upper limit variable representing an adjustable power upper limit value of the electric vehicle cluster in a future period, and a power lower limit variable representing an adjustable power lower limit value of the electric vehicle cluster in a future period.

[0010] In one embodiment, the regulatory potential function is constructed in the following manner:

[0011] Using the difference between the power upper limit variable and the power lower limit variable as a first intermediate function;

[0012] The product of the square of the first intermediate function and a preset weight coefficient is used as the second intermediate function;

[0013] The regulation potential function is constructed according to the difference between the first intermediate function and the second intermediate function.

[0014] In one embodiment, the scheduling constraint function includes the charging demand constraint of the electric vehicle cluster, the physical constraint of electric vehicle charging and discharging, and the physical constraint of charging and discharging of charging piles; the scheduling constraint function is constructed in the following manner:

[0015] Constructing the charging demand constraint according to the state of charge parameters and charging time parameters of each electric vehicle;

[0016] According to the charging and discharging condition parameters and charge state parameters of each electric vehicle, and the charging and discharging parameters of each charging pile, constructing the charging and discharging physical constraints of the electric vehicle;

[0017] The charging and discharging physical constraints of the charging pile are constructed according to the power upper limit variable, the power lower limit variable and the charging time parameters of each electric vehicle.

[0018] In one embodiment, the electric vehicle cluster regulation potential prediction model is constructed in the following manner:

[0019] Constructing an initial regulation potential prediction model according to the regulation potential function and the scheduling constraint function;

[0020] Acquire sample data; wherein the sample data includes historical dispatch demand information of the electric vehicle cluster in a reference period and a dispatchable range in a comparison period; wherein the comparison period is a period after the reference period;

[0021] The sample data is used to train the initial regulation potential prediction model to obtain the regulation potential prediction model of the electric vehicle cluster.

[0022] In one embodiment, obtaining sample data includes:

[0023] Obtaining historical demand information of the electric vehicle cluster within a reference period;

[0024] According to the historical demand information, the regulation potential function and the scheduling constraint function are updated respectively to obtain an updated regulation potential function and an updated scheduling constraint function;

[0025] Taking the maximum function value of the updated regulation potential function as the goal and the updated dispatch constraint function as the constraint condition, the updated regulation potential function is solved to obtain the dispatchable range of the electric vehicle in the comparison period.

[0026] In one of the embodiments, the charging demand information of each electric vehicle includes the remaining state of charge of the electric vehicle before charging, the target state of charge after charging, and the estimated charging period; the battery status information of each electric vehicle includes the maximum chargeable state of charge and the minimum chargeable state of charge of the power battery of the electric vehicle; and the charging and discharging parameters of each charging pile include the maximum charging power and the maximum discharging power of the charging pile.

[0027] In a second aspect, the present application also provides a device for determining the dispatching potential of an electric vehicle cluster, comprising:

[0028] An information acquisition module is used to obtain target dispatch demand information of electric vehicle clusters in the area to which the power system belongs in the current period; wherein the target dispatch demand information includes charging demand information and battery status information of each electric vehicle, and charging and discharging parameters of each charging pile in the area to which the power system belongs;

[0029] A model acquisition module, used to acquire a regulation potential prediction model of the electric vehicle cluster; wherein the regulation potential prediction model is determined according to a regulation potential function and a scheduling constraint function of the electric vehicle cluster, the regulation potential function is used to characterize changes in the adjustable power of the electric vehicle cluster in a future period, and the scheduling constraint function is used to constrain the charging and discharging of the electric vehicle cluster;

[0030] A potential determination module is used to determine the dispatchable range of the electric vehicle cluster in the future period based on the regulation potential prediction model and the target scheduling demand information.

[0031] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0032] Obtaining target dispatch demand information of electric vehicle clusters in the area to which the power system belongs in the current period; wherein the target dispatch demand information includes charging demand information and battery status information of each electric vehicle, and charging and discharging parameters of each charging pile in the area to which the power system belongs;

[0033] Acquire a regulation potential prediction model of the electric vehicle cluster; wherein the regulation potential prediction model is determined according to a regulation potential function and a scheduling constraint function of the electric vehicle cluster, the regulation potential function is used to characterize changes in the adjustable power of the electric vehicle cluster in a future period, and the scheduling constraint function is used to constrain the charging and discharging of the electric vehicle cluster;

[0034] Based on the regulation potential prediction model and according to the target scheduling demand information, the dispatchable range of the electric vehicle cluster in the future period is determined.

[0035] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0036] Obtaining target dispatch demand information of electric vehicle clusters in the area to which the power system belongs in the current period; wherein the target dispatch demand information includes charging demand information and battery status information of each electric vehicle, and charging and discharging parameters of each charging pile in the area to which the power system belongs;

[0037] Acquire a regulation potential prediction model of the electric vehicle cluster; wherein the regulation potential prediction model is determined according to a regulation potential function and a scheduling constraint function of the electric vehicle cluster, the regulation potential function is used to characterize changes in the adjustable power of the electric vehicle cluster in a future period, and the scheduling constraint function is used to constrain the charging and discharging of the electric vehicle cluster;

[0038] Based on the regulation potential prediction model and according to the target scheduling demand information, the dispatchable range of the electric vehicle cluster in the future period is determined.

[0039] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0040] Obtaining target dispatch demand information of electric vehicle clusters in the area to which the power system belongs in the current period; wherein the target dispatch demand information includes charging demand information and battery status information of each electric vehicle, and charging and discharging parameters of each charging pile in the area to which the power system belongs;

[0041] Acquire a regulation potential prediction model of the electric vehicle cluster; wherein the regulation potential prediction model is determined according to a regulation potential function and a scheduling constraint function of the electric vehicle cluster, the regulation potential function is used to characterize changes in the adjustable power of the electric vehicle cluster in a future period, and the scheduling constraint function is used to constrain the charging and discharging of the electric vehicle cluster;

[0042] Based on the regulation potential prediction model and according to the target scheduling demand information, the dispatchable range of the electric vehicle cluster in the future period is determined.

[0043] The above-mentioned method, device, equipment and medium for determining the dispatching potential of electric vehicle clusters introduces a regulation potential prediction model of electric vehicle clusters. Since the regulation potential prediction model is determined based on the regulation potential function and dispatching constraint function of the electric vehicle cluster, it is equivalent to comprehensively considering the changes in the adjustable power of each electric vehicle in the electric vehicle cluster, ensuring that the constructed regulation potential prediction model can accurately simulate the changes in the adjustable power of the electric vehicle cluster during the charging process; further, combining the regulation potential prediction model and the charging demand information and battery status information of each electric vehicle, as well as the target dispatching demand information of the charging and discharging parameters of each charging pile in the area to which the power system belongs, the accuracy of the dispatchable range of the determined electric vehicle cluster in the future period is guaranteed; at the same time, by introducing the regulation potential prediction model, the efficiency of determining the dispatchable range of the electric vehicle cluster in the future period is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 A schematic flow chart of a method for determining the dispatch potential of an electric vehicle cluster in one embodiment;

[0046] Figure 2 A schematic diagram of a process for constructing a regulation potential function in one embodiment;

[0047] Figure 3 A schematic diagram of a process for constructing a scheduling constraint function in one embodiment;

[0048] Figure 4 A schematic diagram of a process for constructing a regulation potential prediction model in one embodiment;

[0049] Figure 5A schematic diagram of a process for obtaining sample data in one embodiment;

[0050] Figure 6 It is a schematic diagram of a rolling evaluation calculation result when the maximum adjustable potential is taken as a target in one embodiment;

[0051] Figure 7 A schematic flow chart of a method for determining the dispatch potential of an electric vehicle cluster in another embodiment;

[0052] Figure 8 A structural block diagram of a device for determining the dispatch potential of an electric vehicle cluster in one embodiment; Fig. 9 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0053] 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.

[0054] The method for determining the dispatching potential of an electric vehicle cluster provided in the embodiment of the present application can be applied to the dispatching process of a power grid system, and specifically can be applied to an application environment in which the dispatching potential of an electric vehicle cluster is evaluated before the power grid system is dispatched. The method for determining the dispatching potential of an electric vehicle cluster provided in the embodiment of the present application can be executed by a computer device, which can be a server or a terminal with powerful computing capabilities.

[0055] In an exemplary embodiment, Figure 1 As shown, a method for determining the dispatching potential of an electric vehicle cluster is provided, and the method is applied to a server as an example for explanation, and specifically includes the following steps:

[0056] S101, obtaining target dispatch demand information of electric vehicle clusters in the area to which the power system belongs during the current period.

[0057] Among them, the electric vehicle cluster within the area to which the power system belongs is a cluster composed of electric vehicles within the area to which the power system belongs; specifically, in an embodiment of the present application, the electric vehicle cluster within the area to which the power system belongs may be a cluster composed of electric vehicles connected to charging piles within the area to which the power system belongs.

[0058] The target scheduling demand information includes the charging demand information and battery status information of each electric vehicle, as well as the charging and discharging parameters of each charging pile in the area to which the power system belongs. In the embodiment of the present application, the charging demand information of each electric vehicle includes the remaining state of charge of the electric vehicle before charging, the target state of charge after charging, and the expected charging period; the battery status information of each electric vehicle includes the maximum chargeable state of charge and the minimum chargeable state of charge of the power battery of the electric vehicle; the charging and discharging parameters of each charging pile include the maximum charging power and the maximum discharging power of the charging pile.

[0059] Optionally, when the electric vehicle is connected to the charging pile, the charging pile collects various demand information related to the adjustable potential assessment of the electric vehicle cluster, including the charging demand of each user, the battery status information of each electric vehicle, the charging and discharging fixed parameter information of the charging pile, etc. The charging demand of each user includes the remaining battery state of charge (State of Charge, SOC) of the vehicle in the current period, the estimated departure time of the vehicle, and the target battery SOC of the vehicle; the battery status information of the electric vehicle includes the maximum energy of the power battery, the maximum and minimum SOC of the power battery that can be charged; the charging and discharging fixed parameter information of the charging pile includes the maximum charging power and the maximum discharge power of the charging pile, etc.

[0060] S102, obtaining a regulation potential prediction model for an electric vehicle cluster.

[0061] Among them, the regulation potential prediction model is a model for predicting the scheduling potential of electric vehicle clusters in future time periods. In the embodiment of the present application, the regulation potential prediction model can be determined based on the regulation potential function and scheduling constraint function of the electric vehicle cluster. The regulation potential function is used to characterize the change of the adjustable power of the electric vehicle cluster in the future time period, and the scheduling constraint function is used to constrain the charging and discharging of the electric vehicle cluster. In the embodiment of the present application, the regulation potential function is constructed based on the power upper limit variable that characterizes the upper limit value of the adjustable power of the electric vehicle cluster in the future time period, and the power lower limit variable that characterizes the lower limit value of the adjustable power of the electric vehicle cluster in the future time period.

[0062] The scheduling constraint functions include the charging demand constraints of the electric vehicle cluster, the physical constraints of electric vehicle charging and discharging, and the physical constraints of charging and discharging of charging piles.

[0063] Optionally, the regulation potential prediction model can be constructed in advance based on preset rules. In an embodiment of the present application, the regulation potential prediction model can be based on a neural network model and constructed and trained according to the regulation potential function and scheduling constraint function of the electric vehicle cluster. Further, the pre-constructed regulation potential prediction model can be directly obtained from the memory.

[0064] S103, based on the regulation potential prediction model and according to the target scheduling demand information, determine the dispatchable range of the electric vehicle cluster in the future period.

[0065] The dispatchable range of the electric vehicle cluster in the future period can be represented by the adjustable power upper limit value and the adjustable power lower limit value of the electric vehicle cluster at each moment in the future period.

[0066] Optionally, the target scheduling demand information can be input into the regulation potential prediction model so that the model parameters of the regulation potential prediction model can perform computational analysis on the target scheduling demand information, and the output result of the regulation potential prediction model can be used as the dispatchable range of the electric vehicle cluster in the future period.

[0067] In the above-mentioned method for determining the dispatching potential of electric vehicle clusters, by introducing the regulation potential prediction model of electric vehicle clusters, since the regulation potential prediction model is determined based on the regulation potential function and dispatching constraint function of the electric vehicle cluster, it is equivalent to comprehensively considering the changes in the adjustable power of each electric vehicle in the electric vehicle cluster, ensuring that the constructed regulation potential prediction model can accurately simulate the changes in the adjustable power of the electric vehicle cluster during the charging process; further, combining the regulation potential prediction model and the charging demand information and battery status information of each electric vehicle, as well as the target dispatching demand information of the charging and discharging parameters of each charging pile in the area to which the power system belongs, the accuracy of the dispatchable range of the determined electric vehicle cluster in the future period is guaranteed; at the same time, by introducing the regulation potential prediction model, the efficiency of determining the dispatchable range of the electric vehicle cluster in the future period is improved.

[0068] Optionally, in one embodiment, Figure 2 As shown, a method for constructing a regulation potential function is provided, which specifically includes the following steps:

[0069] S201: Use the difference between the upper power limit variable and the lower power limit variable as a first intermediate function.

[0070] Optionally, the power upper limit variable and the power lower limit variable may be subtracted, and the difference obtained by the subtraction is used as the first intermediate function. Specifically, the first intermediate function may be expressed by the following formula (1):

[0071] (1)

[0072] in, represents the first intermediate function; for The power upper limit variable of the electric vehicle cluster during the period; for The power lower limit variable of the electric vehicle cluster during the period.

[0073] S202: multiplying the square of the first intermediate function by a preset weight coefficient as a second intermediate function.

[0074] Optionally, the product of the square of the first intermediate function and the preset weight coefficient can be used as the second intermediate function. Specifically, the second intermediate function can be expressed by the following formula (2):

[0075] (2)

[0076] in, represents the second intermediate function; is the preset weight coefficient, the quadratic term The adjustable potential of the electric vehicle cluster is distributed more evenly in time, and the weight coefficient can adjust the intensity of the effect of the quadratic term on the objective function.

[0077] S203: construct a regulation potential function according to the difference between the first intermediate function and the second intermediate function.

[0078] Optionally, the process of constructing the regulation potential function according to the difference between the first intermediate function and the second intermediate function can be expressed by the following formula (3):

[0079] (3)

[0080] (4)

[0081] (5)

[0082] in, represents the regulatory potential function; A power upper limit variable and a power lower limit variable set of the electric vehicle cluster within an adjustable time range of the electric vehicle cluster; The departure time of the electric vehicle that is expected to leave the latest during the current period. is the initial time; is the number of electric vehicles in the electric vehicle cluster; For the The estimated departure time of the electric car last night.

[0083] In an embodiment of the present application, by introducing a first intermediate function, a second intermediate function and a preset weight coefficient, a method for accurately and quickly constructing a control potential function is provided; at the same time, based on the power upper limit variable and the power lower limit variable, the constructed control potential function can accurately characterize the power changes of the electric vehicle cluster during the charging and discharging process.

[0084] Optionally, the scheduling constraint function includes the charging demand constraint of the electric vehicle cluster, the physical constraint of electric vehicle charging and discharging, and the physical constraint of charging and discharging of the charging pile; based on the above embodiment, in one embodiment, Figure 3 As shown, a method for constructing a scheduling constraint function is provided, which specifically includes the following steps:

[0085] S301: construct charging demand constraints according to the state of charge parameters and charging time parameters of each electric vehicle.

[0086] Among them, the charging demand constraint is the constraint on the amount of electricity required by the electric vehicle cluster during the charging process. The state of charge parameter is a parameter that characterizes the change in the state of charge of the power battery of the electric vehicle during the charging process; and the charging time parameter is a parameter that characterizes the charging time of the electric vehicle.

[0087] Optionally, the process of constructing charging demand constraints according to the state of charge parameters and charging time parameters of each electric vehicle can be expressed by the following formula (6) to formula (10):

[0088] (6)

[0089] (7)

[0090] (8)

[0091] (9)

[0092] (10)

[0093] in, is the state of charge parameter, indicating the The remaining battery state of charge of an electric vehicle before charging; is the state of charge parameter, indicating the The target battery state of charge of an electric vehicle after charging; is the state of charge parameter, indicating the The maximum charge state of the power battery of an electric vehicle; is the state of charge parameter, indicating the The minimum charge state of the power battery of an electric vehicle; is the charging time parameter.

[0094] S302: constructing physical constraints for charging and discharging of electric vehicles according to charging and discharging operating parameters and state of charge parameters of each electric vehicle and charging and discharging parameters of each charging pile.

[0095] Optionally, according to the charging and discharging condition parameters and charge state parameters of each electric vehicle, and the charging and discharging parameters of each charging pile, the process of constructing the charging and discharging physical constraints of the electric vehicle can be expressed by the following formula (11)-formula (17):

[0096] (11)

[0097] (12)

[0098] (13)

[0099] (14)

[0100] (15)

[0101] (16)

[0102] (17)

[0103] in, and are the charge and discharge parameters, respectively representing the The charging and discharging decision parameters of an electric vehicle, both of which are not 1 at the same time; and are the charging and discharging condition parameters, representing the charging and discharging efficiency of electric vehicles respectively; The duration of each period; is the state of charge parameter, indicating the The maximum energy of the power battery of an electric vehicle; and are the charging and discharging parameters of the charging pile, respectively representing the The maximum charging power and maximum discharging power of the charging pile to which the electric vehicle is connected.

[0104] S303: construct charging and discharging physical constraints of the charging pile according to the power upper limit variable, the power lower limit variable and the charging time parameters of each electric vehicle.

[0105] Optionally, according to the power upper limit variable, the power lower limit variable and the charging time parameters of each electric vehicle, the process of constructing the charging and discharging physical constraints of the charging pile can be expressed by the following formula (18):

[0106] (18)

[0107] In this embodiment, through the charging demand constraints of the electric vehicle cluster, the physical constraints of electric vehicle charging and discharging, and the physical constraints of charging and discharging of charging piles, the constructed scheduling constraint function can comprehensively consider the charging demand and charging and discharging conditions of the electric vehicles, as well as the charging and discharging conditions of the charging piles, thereby ensuring the accuracy and comprehensiveness of the constructed scheduling constraint function.

[0108] Optionally, in one embodiment, Figure 4 As shown, a method for constructing a regulation potential prediction model of an electric vehicle cluster is provided, which specifically includes the following steps:

[0109] S401, constructing an initial regulation potential prediction model according to the regulation potential function and the scheduling constraint function.

[0110] Optionally, an initial regulation potential prediction model may be constructed based on a neural network framework according to parameters in the regulation potential function and the scheduling constraint function.

[0111] Exemplarily, the input of the constructed initial regulation potential prediction model can be the historical scheduling demand information of the electric vehicle cluster during the reference period, such as the charging demand of each user, the battery status information of each electric vehicle, the charging and discharging of the charging piles and other fixed parameter information; the output can be the dispatchable range of the electric vehicle cluster during the comparison period; wherein the comparison period is the period after the reference period.

[0112] S402, obtaining sample data.

[0113] The sample data includes the historical dispatch demand information of the electric vehicle cluster in the reference period and the dispatchable range in the comparison period; the reference period can be any pre-set historical period; the comparison period is the period after the reference period.

[0114] Optionally, historical dispatch demand information of the electric vehicle cluster during the reference period can be obtained from all charging piles in the area to which the power system belongs, and the dispatchable range of the electric vehicle cluster during the comparison period can be calculated based on the historical dispatch demand information; further, the historical dispatch demand information of the electric vehicle cluster during the reference period and the dispatchable range during the comparison period are used as sample data.

[0115] S403, using sample data to train the initial regulation potential prediction model to obtain the regulation potential prediction model of the electric vehicle cluster.

[0116] Optionally, in order to ensure the integrity and availability of the sample data, the sample data may be preprocessed first. Further, the preprocessed sample data is divided into a training set and a test set, and the training set is used to train the initial regulation potential prediction model, and the test set is used to train and test the initially trained regulation potential prediction model, and when the calculated loss value meets the requirements, the training process is terminated, and the regulation potential prediction model of the electric vehicle cluster can be obtained.

[0117] In an embodiment of the present application, an initial regulation potential prediction model is constructed based on the regulation potential function and the scheduling constraint function, and the initial regulation potential prediction model is further trained to ensure the accuracy of the constructed regulation potential prediction model of the electric vehicle cluster; in addition, the introduced regulation potential prediction model realizes the rapid fitting and solution of the parameter system between the regulation potential function and the scheduling constraint function.

[0118] Optionally, in one embodiment, Figure 5 As shown, a method for obtaining sample data is provided to refine the above S402, and specifically includes the following steps:

[0119] S501, obtaining historical demand information of an electric vehicle cluster within a reference period.

[0120] Optionally, historical dispatch demand information of the electric vehicle cluster during the reference period can be obtained from all charging piles in the area to which the power system belongs.

[0121] S502: According to the historical demand information, the regulation potential function and the scheduling constraint function are updated respectively to obtain an updated regulation potential function and an updated scheduling constraint function.

[0122] Optionally, the parameters involved in the historical demand information are substituted into the regulation potential function and the scheduling constraint function respectively to update the regulation potential function and the scheduling constraint function, thereby obtaining an updated regulation potential function and an updated scheduling constraint function.

[0123] S503, taking the maximum function value of the updated regulation potential function as the goal and the updated dispatch constraint function as the constraint condition, solving the updated regulation potential function to obtain the dispatchable range of the electric vehicle in the comparison period.

[0124] Optionally, a preset solution algorithm is used, with the goal of maximizing the function value of the updated control potential function and the updated scheduling constraint function as a constraint condition, to solve the updated control potential function, and the values ​​of the upper power limit variable and the lower power limit variable when the function value of the control potential function is maximum are taken as the dispatchable range of the electric vehicle during the comparison period.

[0125] In the embodiment of the present application, by obtaining historical demand information and solving the regulation potential function based on the historical demand information, the accuracy of the dispatchable range of electric vehicles in the comparison period is ensured, thereby ensuring the accuracy of the regulation potential prediction model trained using sample data.

[0126] Optionally, in order to clearly and intuitively describe the implementation process and effect of the method for determining the dispatching potential of an electric vehicle cluster provided in an embodiment of the present application, in one embodiment, a specific example is provided. Specifically, the method is implemented using the Pycharm development platform and the Python 3.8 programming language, and the test and verification of this embodiment are completed using a PC equipped with an Intel Xeon-X5650 2.6GHz CPU and 24G memory.

[0127] In one embodiment, a technical verification is carried out around a residential electric vehicle charging station. An operation scenario is constructed based on the charging demand information data of electric vehicles in a residential area for verification. It is equipped with 30 bidirectional charging and discharging charging piles. The evaluation and regulation interval is 15 minutes, and it can be regulated at all times of the day. The rated charging and discharging power of the charging pile is 30kW. It is assumed that the capacity of the power battery of all accessible electric vehicles is 120kWh, the maximum SOC is 0.98, and the minimum SOC is 0.02. Because the verification scenario is a residential area, the common charging mode of electric vehicles is "late return and early departure". The vehicle arrival time follows a uniform distribution from 7pm to 9pm, and the vehicle's expected departure time follows a uniform distribution from 7am to 10am. Here, the maximum charging power and maximum discharge power of the charging piles connected to all electric vehicles, and the maximum and minimum SOC of the power battery are set as fixed values ​​and are not used as inputs. Four types of variables, including the current remaining battery charge state of all electric vehicles, the expected departure time of the vehicle, the target battery of the vehicle, and the maximum energy of the power battery of all electric vehicles, are used as inputs to the regulation potential prediction model. The hyperparameter settings of the regulation potential prediction model are shown in Table 1.

[0128] Table 1 Model structure of the regulatory potential prediction model

[0129]

[0130] 2000 samples were generated by the method for obtaining sample data provided in the above embodiment, 80% of the samples were randomly selected as training sets and 20% as test sets, and the back propagation neural network was selected as the regulation potential prediction model. After debugging, when the number of electric vehicles was 30, the back propagation neural network with 3 hidden layers and 50 neurons in each layer had the best effect.

[0131] like Figure 6The figure shows the rolling evaluation calculation results when the maximum adjustable potential is the target. At this time, it is assumed that the power execution trajectory given by the control agency dispatches the electric vehicle cluster around the midline of the adjustable power upper and lower bounds. During the rolling optimization process, the adjustable potential evaluation model did not have an unsolvable situation. The feasibility of the evaluation model means that as long as the upper and lower bounds of the cluster power in the future period are within the future period, the electric vehicle user demand can be met.

[0132] The comparison results of the inference speed of the regulation potential prediction model (proxy model) and the direct solution of the regulation potential function (mechanism model) are shown in Table 2, which compares the average single inference time (excluding other program execution time) of the regulation potential prediction model and the mechanism model when evaluating the flexibility of cluster regulation with different numbers of electric vehicles. It can be clearly seen from Table 2 that the average inference time of the regulation potential prediction model is around 1ms to 3ms.

[0133] Table 2 Comparison of inference speed between proxy model and optimized model (unit: seconds)

[0134]

[0135] Figure 7 FIG. 1 is a flow chart of a method for determining the dispatching potential of an electric vehicle cluster in another embodiment. Based on the above embodiment, this embodiment provides an optional example of a method for determining the dispatching potential of an electric vehicle cluster. Figure 7 The specific implementation process is as follows:

[0136] S701: Use the difference between the upper power limit variable and the lower power limit variable as a first intermediate function.

[0137] S702: Multiply the square of the first intermediate function by a preset weight coefficient as a second intermediate function.

[0138] S703: construct a regulation potential function according to the difference between the first intermediate function and the second intermediate function.

[0139] S704: construct a charging demand constraint in a scheduling constraint function according to the state of charge parameters and charging time parameters of each electric vehicle.

[0140] S705: constructing electric vehicle charging and discharging physical constraints in the scheduling constraint function according to the charging and discharging operating condition parameters and charge state parameters of each electric vehicle and the charging and discharging parameters of each charging pile.

[0141] S706, constructing charging and discharging physical constraints of the charging pile in the scheduling constraint function according to the power upper limit variable, the power lower limit variable and the charging time parameters of each electric vehicle.

[0142] S707: construct an initial regulation potential prediction model based on the regulation potential function and the scheduling constraint function.

[0143] S708, obtaining sample data.

[0144] Among them, the sample data includes the historical dispatch demand information of the electric vehicle cluster in the reference period and the dispatchable range in the comparison period; the comparison period is the period after the reference period.

[0145] Optionally, obtain historical demand information of the electric vehicle cluster during a reference period; based on the historical demand information, update the regulation potential function and the scheduling constraint function respectively to obtain an updated regulation potential function and an updated scheduling constraint function; with the maximum function value of the updated regulation potential function as the goal and the updated scheduling constraint function as the constraint condition, solve the updated regulation potential function to obtain the dispatchable range of the electric vehicle during the comparison period.

[0146] S709, using sample data to train the initial regulation potential prediction model to obtain the regulation potential prediction model of the electric vehicle cluster.

[0147] S710, obtaining target dispatch demand information of electric vehicle clusters in the area to which the power system belongs during the current period.

[0148] Among them, the target scheduling demand information includes the charging demand information and battery status information of each electric vehicle, as well as the charging and discharging parameters of each charging pile in the area to which the power system belongs.

[0149] S711, based on the regulation potential prediction model and according to the target scheduling demand information, determine the dispatchable range of the electric vehicle cluster in the future period.

[0150] The specific process of S701-S711 above can refer to the description of the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0151] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned 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 does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can 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 to be 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.

[0152] Based on the same inventive concept, the embodiment of the present application also provides a device for determining the dispatching potential of an electric vehicle cluster for implementing the method for determining the dispatching potential of an electric vehicle cluster involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the device for determining the dispatching potential of one or more electric vehicle clusters provided below can refer to the limitations of the method for determining the dispatching potential of an electric vehicle cluster above, and will not be repeated here.

[0153] In an exemplary embodiment, Figure 8 As shown, a device 800 for determining the dispatching potential of an electric vehicle cluster is provided, comprising: an information acquisition module 810, a model acquisition module 820 and a potential determination module 830, wherein:

[0154] The information acquisition module 810 is used to obtain the target scheduling demand information of the electric vehicle cluster in the area to which the power system belongs in the current period; wherein the target scheduling demand information includes the charging demand information and battery status information of each electric vehicle, and the charging and discharging parameters of each charging pile in the area to which the power system belongs.

[0155] The model acquisition module 820 is used to obtain the regulation potential prediction model of the electric vehicle cluster; wherein the regulation potential prediction model is determined based on the regulation potential function and the scheduling constraint function of the electric vehicle cluster, the regulation potential function is used to characterize the change of the adjustable power of the electric vehicle cluster in the future period, and the scheduling constraint function is used to constrain the charging and discharging of the electric vehicle cluster.

[0156] The potential determination module 830 is used to determine the dispatchable range of the electric vehicle cluster in the future period based on the regulation potential prediction model and the target dispatch demand information.

[0157] The above-mentioned dispatching potential determination device of the electric vehicle cluster introduces the regulation potential prediction model of the electric vehicle cluster. Since the regulation potential prediction model is determined according to the regulation potential function and the dispatching constraint function of the electric vehicle cluster, it is equivalent to comprehensively considering the change of the adjustable power of each electric vehicle in the electric vehicle cluster, ensuring that the constructed regulation potential prediction model can accurately simulate the change of the adjustable power of the electric vehicle cluster during the charging process; further, combining the regulation potential prediction model and the charging demand information and battery status information of each electric vehicle, as well as the target dispatching demand information of the charging and discharging parameters of each charging pile in the area to which the power system belongs, the accuracy of the dispatchable range of the determined electric vehicle cluster in the future period is guaranteed; at the same time, by introducing the regulation potential prediction model, the efficiency of determining the dispatchable range of the electric vehicle cluster in the future period is improved.

[0158] In one embodiment, the regulation potential function is constructed based on a power upper limit variable representing an adjustable power upper limit value of the electric vehicle cluster in a future period, and a power lower limit variable representing an adjustable power lower limit value of the electric vehicle cluster in a future period.

[0159] In one embodiment, the electric vehicle cluster dispatch potential determination device 800 further includes:

[0160] The first function unit is used to use the difference between the upper power limit variable and the lower power limit variable as a first intermediate function.

[0161] The second function unit is used to take the product of the square of the first intermediate function and the preset weight coefficient as the second intermediate function.

[0162] The first construction unit is used to construct a regulation potential function according to the difference between the first intermediate function and the second intermediate function.

[0163] In one embodiment, the scheduling potential determination device 800 further includes:

[0164] The second constructing unit is used to construct a charging demand constraint according to the state of charge parameters and charging time parameters of each electric vehicle.

[0165] The third construction unit is used to construct the electric vehicle charging and discharging physical constraints according to the charging and discharging operating condition parameters and charge state parameters of each electric vehicle and the charging and discharging parameters of each charging pile.

[0166] The fourth construction unit is used to construct charging and discharging physical constraints of the charging pile according to the power upper limit variable, the power lower limit variable and the charging time parameters of each electric vehicle.

[0167] In one embodiment, the scheduling potential determination device 800 further includes:

[0168] The first construction module is used to construct an initial regulation potential prediction model according to the regulation potential function and the scheduling constraint function.

[0169] The data acquisition module is used to acquire sample data; wherein the sample data includes the historical dispatch demand information of the electric vehicle cluster in the reference period and the dispatchable range in the comparison period; the comparison period is the period after the reference period.

[0170] The model training module is used to train the initial regulation potential prediction model using sample data to obtain the regulation potential prediction model of the electric vehicle cluster.

[0171] In one embodiment, the data acquisition module is specifically used for:

[0172] Obtain the historical demand information of the electric vehicle cluster during the reference period; update the regulation potential function and the scheduling constraint function according to the historical demand information to obtain an updated regulation potential function and an updated scheduling constraint function; take the maximum function value of the updated regulation potential function as the goal, take the updated scheduling constraint function as the constraint condition, solve the updated regulation potential function, and obtain the dispatchable range of electric vehicles during the comparison period.

[0173] In one embodiment, the charging demand information of each electric vehicle includes the remaining state of charge of the electric vehicle before charging, the target state of charge after charging, and the estimated charging period; the battery status information of each electric vehicle includes the maximum chargeable state of charge and the minimum chargeable state of charge of the power battery of the electric vehicle; and the charging and discharging parameters of each charging pile include the maximum charging power and the maximum discharging power of the charging pile.

[0174] Each module in the above-mentioned electric vehicle cluster dispatch potential determination device 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 a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0175] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig. 9 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for determining the scheduling potential of an electric vehicle cluster is implemented.

[0176] Those skilled in the art will understand that Fig. 9 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.

[0177] In an exemplary 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 following steps are implemented:

[0178] Obtaining target dispatch demand information of electric vehicle clusters in the area to which the power system belongs in the current period; wherein the target dispatch demand information includes charging demand information and battery status information of each electric vehicle, and charging and discharging parameters of each charging pile in the area to which the power system belongs;

[0179] Obtaining a regulation potential prediction model for an electric vehicle cluster; wherein the regulation potential prediction model is determined according to a regulation potential function and a dispatch constraint function of the electric vehicle cluster, the regulation potential function is used to characterize changes in adjustable power of the electric vehicle cluster in a future period, and the dispatch constraint function is used to constrain charging and discharging of the electric vehicle cluster;

[0180] Based on the regulation potential prediction model and the target scheduling demand information, the dispatchable range of the electric vehicle cluster in the future period is determined.

[0181] In one embodiment, the regulation potential function is constructed based on a power upper limit variable representing an adjustable power upper limit value of the electric vehicle cluster in a future period, and a power lower limit variable representing an adjustable power lower limit value of the electric vehicle cluster in a future period.

[0182] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0183] The difference between the power upper limit variable and the power lower limit variable is used as the first intermediate function; the product of the square of the first intermediate function and the preset weight coefficient is used as the second intermediate function; and a control potential function is constructed according to the difference between the first intermediate function and the second intermediate function.

[0184] In one embodiment, the scheduling constraint function includes the charging demand constraint of the electric vehicle cluster, the physical constraint of electric vehicle charging and discharging, and the physical constraint of charging and discharging of the charging pile; when the processor executes the computer program, the following steps are also implemented:

[0185] According to the state of charge parameters and charging time parameters of each electric vehicle, the charging demand constraints are constructed; according to the charging and discharging operating parameters and state of charge parameters of each electric vehicle, as well as the charging and discharging parameters of each charging pile, the electric vehicle charging and discharging physical constraints are constructed; according to the power upper limit variable, the power lower limit variable and the charging time parameters of each electric vehicle, the charging and discharging physical constraints of the charging pile are constructed.

[0186] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0187] According to the regulation potential function and the dispatch constraint function, an initial regulation potential prediction model is constructed; sample data is obtained; wherein the sample data includes the historical dispatch demand information of the electric vehicle cluster in the reference period and the dispatchable range in the comparison period; the comparison period is the period after the reference period; the sample data is used to train the initial regulation potential prediction model to obtain the regulation potential prediction model of the electric vehicle cluster.

[0188] In one embodiment, when the processor executes the computer program to obtain sample data, the processor further implements the following steps:

[0189] Obtain the historical demand information of the electric vehicle cluster during the reference period; update the regulation potential function and the scheduling constraint function according to the historical demand information to obtain an updated regulation potential function and an updated scheduling constraint function; take the maximum function value of the updated regulation potential function as the goal, take the updated scheduling constraint function as the constraint condition, solve the updated regulation potential function, and obtain the dispatchable range of electric vehicles during the comparison period.

[0190] In one embodiment, the charging demand information of each electric vehicle includes the remaining state of charge of the electric vehicle before charging, the target state of charge after charging, and the estimated charging period; the battery status information of each electric vehicle includes the maximum chargeable state of charge and the minimum chargeable state of charge of the power battery of the electric vehicle; and the charging and discharging parameters of each charging pile include the maximum charging power and the maximum discharging power of the charging pile.

[0191] 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 following steps are implemented:

[0192] Obtaining target dispatch demand information of electric vehicle clusters in the area to which the power system belongs in the current period; wherein the target dispatch demand information includes charging demand information and battery status information of each electric vehicle, and charging and discharging parameters of each charging pile in the area to which the power system belongs;

[0193] Obtaining a regulation potential prediction model for an electric vehicle cluster; wherein the regulation potential prediction model is determined according to a regulation potential function and a dispatch constraint function of the electric vehicle cluster, the regulation potential function is used to characterize changes in adjustable power of the electric vehicle cluster in a future period, and the dispatch constraint function is used to constrain charging and discharging of the electric vehicle cluster;

[0194] Based on the regulation potential prediction model and the target scheduling demand information, the dispatchable range of the electric vehicle cluster in the future period is determined.

[0195] In one embodiment, the regulation potential function is constructed based on a power upper limit variable representing an adjustable power upper limit value of the electric vehicle cluster in a future period, and a power lower limit variable representing an adjustable power lower limit value of the electric vehicle cluster in a future period.

[0196] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0197] The difference between the power upper limit variable and the power lower limit variable is used as the first intermediate function; the product of the square of the first intermediate function and the preset weight coefficient is used as the second intermediate function; and a control potential function is constructed according to the difference between the first intermediate function and the second intermediate function.

[0198] In one embodiment, the scheduling constraint function includes the charging demand constraint of the electric vehicle cluster, the physical constraint of electric vehicle charging and discharging, and the physical constraint of charging and discharging of the charging pile; when the processor executes the computer program, the following steps are also implemented:

[0199] According to the state of charge parameters and charging time parameters of each electric vehicle, the charging demand constraints are constructed; according to the charging and discharging operating parameters and state of charge parameters of each electric vehicle, as well as the charging and discharging parameters of each charging pile, the electric vehicle charging and discharging physical constraints are constructed; according to the power upper limit variable, the power lower limit variable and the charging time parameters of each electric vehicle, the charging and discharging physical constraints of the charging pile are constructed.

[0200] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0201] According to the regulation potential function and the dispatch constraint function, an initial regulation potential prediction model is constructed; sample data is obtained; wherein the sample data includes the historical dispatch demand information of the electric vehicle cluster in the reference period and the dispatchable range in the comparison period; the comparison period is the period after the reference period; the sample data is used to train the initial regulation potential prediction model to obtain the regulation potential prediction model of the electric vehicle cluster.

[0202] In one embodiment, when the processor executes the computer program to obtain sample data, the processor further implements the following steps:

[0203] Obtain the historical demand information of the electric vehicle cluster during the reference period; update the regulation potential function and the scheduling constraint function according to the historical demand information to obtain an updated regulation potential function and an updated scheduling constraint function; take the maximum function value of the updated regulation potential function as the goal, take the updated scheduling constraint function as the constraint condition, solve the updated regulation potential function, and obtain the dispatchable range of electric vehicles during the comparison period.

[0204] In one embodiment, the charging demand information of each electric vehicle includes the remaining state of charge of the electric vehicle before charging, the target state of charge after charging, and the estimated charging period; the battery status information of each electric vehicle includes the maximum chargeable state of charge and the minimum chargeable state of charge of the power battery of the electric vehicle; and the charging and discharging parameters of each charging pile include the maximum charging power and the maximum discharging power of the charging pile.

[0205] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0206] Obtaining target dispatch demand information of electric vehicle clusters in the area to which the power system belongs in the current period; wherein the target dispatch demand information includes charging demand information and battery status information of each electric vehicle, and charging and discharging parameters of each charging pile in the area to which the power system belongs;

[0207] Obtaining a regulation potential prediction model for an electric vehicle cluster; wherein the regulation potential prediction model is determined according to a regulation potential function and a dispatch constraint function of the electric vehicle cluster, the regulation potential function is used to characterize changes in adjustable power of the electric vehicle cluster in a future period, and the dispatch constraint function is used to constrain charging and discharging of the electric vehicle cluster;

[0208] Based on the regulation potential prediction model and the target scheduling demand information, the dispatchable range of the electric vehicle cluster in the future period is determined.

[0209] In one embodiment, the regulation potential function is constructed based on a power upper limit variable representing an adjustable power upper limit value of the electric vehicle cluster in a future period, and a power lower limit variable representing an adjustable power lower limit value of the electric vehicle cluster in a future period.

[0210] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0211] The difference between the power upper limit variable and the power lower limit variable is used as the first intermediate function; the product of the square of the first intermediate function and the preset weight coefficient is used as the second intermediate function; and a control potential function is constructed according to the difference between the first intermediate function and the second intermediate function.

[0212] In one embodiment, the scheduling constraint function includes the charging demand constraint of the electric vehicle cluster, the physical constraint of electric vehicle charging and discharging, and the physical constraint of charging and discharging of the charging pile; when the processor executes the computer program, the following steps are also implemented:

[0213] According to the state of charge parameters and charging time parameters of each electric vehicle, the charging demand constraints are constructed; according to the charging and discharging operating parameters and state of charge parameters of each electric vehicle, as well as the charging and discharging parameters of each charging pile, the electric vehicle charging and discharging physical constraints are constructed; according to the power upper limit variable, the power lower limit variable and the charging time parameters of each electric vehicle, the charging and discharging physical constraints of the charging pile are constructed.

[0214] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0215] According to the regulation potential function and the dispatch constraint function, an initial regulation potential prediction model is constructed; sample data is obtained; wherein the sample data includes the historical dispatch demand information of the electric vehicle cluster in the reference period and the dispatchable range in the comparison period; the comparison period is the period after the reference period; the sample data is used to train the initial regulation potential prediction model to obtain the regulation potential prediction model of the electric vehicle cluster.

[0216] In one embodiment, when the processor executes the computer program to obtain sample data, the processor further implements the following steps:

[0217] Obtain the historical demand information of the electric vehicle cluster during the reference period; update the regulation potential function and the scheduling constraint function according to the historical demand information to obtain an updated regulation potential function and an updated scheduling constraint function; take the maximum function value of the updated regulation potential function as the goal, take the updated scheduling constraint function as the constraint condition, solve the updated regulation potential function, and obtain the dispatchable range of electric vehicles during the comparison period.

[0218] In one embodiment, the charging demand information of each electric vehicle includes the remaining state of charge of the electric vehicle before charging, the target state of charge after charging, and the estimated charging period; the battery status information of each electric vehicle includes the maximum chargeable state of charge and the minimum chargeable state of charge of the power battery of the electric vehicle; and the charging and discharging parameters of each charging pile include the maximum charging power and the maximum discharging power of the charging pile.

[0219] It should be noted that the user information (including but not limited to target scheduling demand 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, and the collection, use and processing of relevant data must comply with relevant regulations.

[0220] 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 this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), 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.

[0221] 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.

[0222] 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 determining the dispatching potential of an electric vehicle cluster, characterized in that: The method comprises: Obtaining target dispatch demand information of electric vehicle clusters in the area to which the power system belongs in the current period; wherein the target dispatch demand information includes charging demand information and battery status information of each electric vehicle, and charging and discharging parameters of each charging pile in the area to which the power system belongs; Acquire a regulation potential prediction model of the electric vehicle cluster; wherein the regulation potential prediction model is determined according to a regulation potential function and a scheduling constraint function of the electric vehicle cluster, the regulation potential function is used to characterize changes in the adjustable power of the electric vehicle cluster in a future period, and the scheduling constraint function is used to constrain the charging and discharging of the electric vehicle cluster; Based on the regulation potential prediction model and according to the target scheduling demand information, the dispatchable range of the electric vehicle cluster in the future period is determined.

2. The method according to claim 1, characterized in that The regulation potential function is constructed according to a power upper limit variable representing an adjustable power upper limit value of the electric vehicle cluster in a future period, and a power lower limit variable representing an adjustable power lower limit value of the electric vehicle cluster in a future period.

3. The method according to claim 2, characterized in that The regulatory potential function is constructed in the following way: Using the difference between the power upper limit variable and the power lower limit variable as a first intermediate function; The product of the square of the first intermediate function and a preset weight coefficient is used as the second intermediate function; The regulation potential function is constructed according to the difference between the first intermediate function and the second intermediate function.

4. The method according to claim 2, characterized in that: The scheduling constraint function includes the charging demand constraint of the electric vehicle cluster, the physical constraint of electric vehicle charging and discharging, and the physical constraint of charging and discharging of charging piles; the scheduling constraint function is constructed in the following way: Constructing the charging demand constraint according to the state of charge parameters and charging time parameters of each electric vehicle; According to the charging and discharging condition parameters and charge state parameters of each electric vehicle, and the charging and discharging parameters of each charging pile, constructing the charging and discharging physical constraints of the electric vehicle; The charging and discharging physical constraints of the charging pile are constructed according to the power upper limit variable, the power lower limit variable and the charging time parameters of each electric vehicle.

5. The method according to claim 1, characterized in that The regulation potential prediction model of the electric vehicle cluster is constructed in the following way: Constructing an initial regulation potential prediction model according to the regulation potential function and the scheduling constraint function; Acquire sample data; wherein the sample data includes historical dispatch demand information of the electric vehicle cluster in a reference period and a dispatchable range in a comparison period; the comparison period is a period after the reference period; The sample data is used to train the initial regulation potential prediction model to obtain the regulation potential prediction model of the electric vehicle cluster.

6. The method according to claim 5, characterized in that The obtaining of sample data comprises: Obtaining historical demand information of the electric vehicle cluster within a reference period; According to the historical demand information, the regulation potential function and the scheduling constraint function are updated respectively to obtain an updated regulation potential function and an updated scheduling constraint function; Taking the maximum function value of the updated regulation potential function as the goal and the updated dispatch constraint function as the constraint condition, the updated regulation potential function is solved to obtain the dispatchable range of the electric vehicle in the comparison period.

7. The method according to any one of claims 1 to 6, characterized in that: The charging demand information of each electric vehicle includes the remaining state of charge of the electric vehicle before charging, the target state of charge after charging and the expected charging period; the battery status information of each electric vehicle includes the maximum chargeable state of charge and the minimum chargeable state of charge of the power battery of the electric vehicle; the charging and discharging parameters of each charging pile include the maximum charging power and the maximum discharging power of the charging pile.

8. A device for determining the dispatching potential of an electric vehicle cluster, characterized in that: The device comprises: An information acquisition module is used to obtain target dispatch demand information of electric vehicle clusters in the area to which the power system belongs in the current period; wherein the target dispatch demand information includes charging demand information and battery status information of each electric vehicle, and charging and discharging parameters of each charging pile in the area to which the power system belongs; A model acquisition module, used to acquire a regulation potential prediction model of the electric vehicle cluster; wherein the regulation potential prediction model is determined according to a regulation potential function and a scheduling constraint function of the electric vehicle cluster, the regulation potential function is used to characterize changes in the adjustable power of the electric vehicle cluster in a future period, and the scheduling constraint function is used to constrain the charging and discharging of the electric vehicle cluster; A potential determination module is used to determine the dispatchable range of the electric vehicle cluster in the future period based on the regulation potential prediction model and the target scheduling demand information.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. 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 7 are implemented.

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