Method and device for regulating charging power of electric vehicle cluster and electronic equipment

Abstract

The application provides a charging power adjustment method and device for an electric vehicle cluster and electronic equipment, relates to the technical field of vehicle charging control, and comprises the following steps: determining a plurality of electric vehicles contained in the electric vehicle cluster; for each electric vehicle in the plurality of electric vehicles, generating cumulative energy feasible region information and charging power feasible region information when the electric vehicle accesses a charging pile; aggregating a plurality of cumulative energy feasible region information and a plurality of charging power feasible region information corresponding to the plurality of electric vehicles to obtain a cumulative energy adjustment domain and a charging power adjustment domain of the electric vehicle cluster; and adjusting the charging power of the electric vehicle cluster according to the cumulative energy adjustment domain and the charging power adjustment domain. The application can fully exert the controllability advantage of the electric vehicle cluster, reduce the load randomness problem caused by a large number of electric vehicles without charging, guide the electric vehicles to participate in power grid operation scheduling in a cluster mode, and reduce the complexity of power grid operation control.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of vehicle charging control, in particular to a charging power regulation method and device for an electric vehicle cluster and an electronic device. BACKGROUND

[0002] At present, many scholars at home and abroad have studied the electric vehicle adjustable capacity evaluation problem. The research is mainly aimed at the electric vehicle cluster connected to the continuously adjustable charging power and the V2G charging pile. Since the electric vehicle cluster connected to the start-stop charging pile is aggregated, the aggregated power is discrete, which can easily cause the problem scale to become large and the solution to become slow, so the start-stop charging pile regulation potential is generally ignored. However, the start-stop charging pile is widely distributed and easy to control. If this type of charging pile is not fully considered, the electric vehicle adjustable capacity evaluation will be conservative, which can cause the electric vehicle load to be overloaded in actual operation and cause a series of system problems. On the other hand, the electric vehicle demand has randomness, which makes the regulation potential in the aggregation process also random. Therefore, the randomness of the electric vehicle needs to be considered in the analysis, and an electric vehicle cluster regulation capacity aggregation method that conforms to the actual operation situation needs to be established.

[0003] Fully utilizing the potential of the electric vehicle cluster and orderly guiding a large number of electric vehicles to participate in the power grid operation control is an important means of power grid dispatching and is also conducive to the randomness of the charging load controlled by the power grid. However, the existing aggregation method does not fully consider the randomness of various types of charging piles and charging loads, especially the lack of effective representation of the aggregated power of the electric vehicle cluster that can only be controlled to start and stop. The direct use of integer variables to describe the electric vehicle cluster under a large number of this type of charging piles will cause the problem to be solved slowly. If this type of charging pile is not considered, it is difficult to comprehensively describe the charging characteristics of the electric vehicle cluster. The electric vehicle adjustable capacity aggregation method still has room for improvement. SUMMARY

[0004] The application provides a charging power regulation method and device for an electric vehicle cluster and an electronic device, which can fully utilize the regulation advantage of the electric vehicle cluster, reduce the load randomness problem caused by a large number of electric vehicles without charging, guide the electric vehicle to participate in the power grid operation dispatch in the form of a cluster, and reduce the complexity of the power grid operation control.

[0005] In a first aspect, a charging power regulation method for an electric vehicle cluster is provided, which includes:

[0006] determine a plurality of electric vehicles included in the electric vehicle cluster, wherein the plurality of electric vehicles correspond to the same type of charging pile to be accessed, and the type of charging pile includes any one of a first charging pile, a second charging pile, and a third charging pile, the first charging pile is a charging pile that only allows charging, but the charging power can be adjusted within the maximum charging power; the second charging pile is a charging pile that allows the electric vehicle to discharge externally while charging the electric vehicle, and the charging and discharging power can be adjusted within the maximum charging power and the maximum discharging power; and the third charging pile is a charging pile that can only charge the electric vehicle and cannot continuously adjust the charging power, and the charging power can only be the rated power or 0;

[0007] For each electric vehicle in the plurality of electric vehicles, generate cumulative energy feasible region information and charging power feasible region information when accessing the charging pile;

[0008] Aggregate the plurality of cumulative energy feasible region information and the plurality of charging power feasible region information corresponding to the plurality of electric vehicles to obtain a cumulative energy adjustment domain and a charging power adjustment domain of the electric vehicle cluster;

[0009] Adjust the charging power of the electric vehicle cluster according to the cumulative energy adjustment domain and the charging power adjustment domain.

[0010] Optionally, for each electric vehicle in the plurality of electric vehicles, generating cumulative energy feasible region information and charging power feasible region information when accessing the charging pile, includes:

[0011] Determine the target charging pile type of the charging pile accessed by the electric vehicle cluster;

[0012] Based on the target charging pile type, generate power constraint conditions for each electric vehicle in the plurality of electric vehicles after accessing the charging pile, the power constraint conditions include cumulative energy constraint conditions and charging power constraint conditions of each electric vehicle within a parking period after accessing the charging pile;

[0013] According to the power constraint conditions, generate cumulative energy feasible region information and charging power feasible region information of each electric vehicle within a scheduling period.

[0014] Optionally, based on the target charging pile type, generating power constraint conditions for each electric vehicle in the plurality of electric vehicles after accessing the charging pile, includes:

[0015] Obtain historical charging parameters of each electric vehicle in the electric vehicle cluster after accessing the charging pile at a historical time;

[0016] Fit the historical charging parameters by the Monte Carlo method to obtain target charging parameters of each electric vehicle within a scheduling period;

[0017] determine a power constraint condition of each electric vehicle in the parking time period based on the target charging parameter.

[0018] Optionally, cumulative energy feasible region information and charging power feasible region information of each electric vehicle in a scheduling period are generated according to the power constraint condition, including:

[0019] initial cumulative energy feasible region information and initial charging power feasible region information of each electric vehicle in the parking time period are generated based on the power constraint condition, the initial cumulative energy feasible region information includes a first cumulative energy feasible region upper bound and a first cumulative energy feasible region lower bound, the initial charging power feasible region information includes a first charging power feasible region upper bound and a first charging power feasible region lower bound, or a first charging power feasible value;

[0020] a time range of the initial cumulative energy feasible region information and the initial charging power feasible region information is extended to obtain cumulative energy feasible region information and charging power feasible region information in a scheduling period, the cumulative energy feasible region information includes a second cumulative energy feasible region upper bound and a second cumulative energy feasible region lower bound, the charging power feasible region information includes a second charging power feasible region upper bound and a second charging power feasible region lower bound, or includes a second charging power feasible value.

[0021] Optionally, initial cumulative energy feasible region information and initial charging power feasible region information of each electric vehicle in the parking time period are generated based on the power constraint condition, including:

[0022] a first cumulative energy feasible region upper bound, a first cumulative energy feasible region lower bound, a first charging power feasible region upper bound and a first charging power feasible region lower bound of each electric vehicle in the parking time period are calculated based on the power constraint condition, or a first charging power feasible value is calculated;

[0023] a time range of the initial cumulative energy feasible region information and the initial charging power feasible region information is extended to obtain cumulative energy feasible region information and charging power feasible region information in a scheduling period, including:

[0024] the first cumulative energy feasible region upper bound and the first cumulative energy feasible region lower bound are respectively extended to a second cumulative energy feasible region upper bound and a second cumulative energy feasible region lower bound in a scheduling period;

[0025] the first charging power feasible region upper bound and the first charging power feasible region lower bound are respectively extended to a second charging power feasible region upper bound and a second charging power feasible region lower bound in a scheduling period; or,

[0026] the first charging power feasible value is extended to a second charging power feasible value in a scheduling period.

[0027] Optionally, the plurality of cumulative energy feasible region information and the plurality of charging power feasible region information corresponding to the plurality of electric vehicles are aggregated to obtain a cumulative energy adjustment domain and a charging power adjustment domain of the electric vehicle cluster, including:

[0028] The plurality of second cumulative energy feasible region upper bounds corresponding to the plurality of electric vehicles are aggregated to obtain a third cumulative energy feasible region upper bound, and the plurality of second cumulative energy feasible region lower bounds corresponding to the plurality of electric vehicles are aggregated to obtain a third cumulative energy feasible region lower bound;

[0029] The third cumulative energy feasible region upper bound and the third cumulative energy feasible region lower bound are constructed to obtain a cumulative energy adjustment domain;

[0030] The plurality of second charging power feasible region upper bounds corresponding to the plurality of electric vehicles are aggregated to obtain a third charging power feasible region upper bound, and the plurality of second charging power feasible region lower bounds corresponding to the plurality of electric vehicles are aggregated to obtain a third charging power feasible region lower bound, and the third charging power feasible region upper bound and the third charging power feasible region lower bound are constructed to obtain a charging power adjustment domain; or,

[0031] The plurality of second charging power feasible values corresponding to the plurality of electric vehicles are integrated to obtain a third charging power feasible value, and a charging power adjustment domain containing all third charging power feasible values is constructed.

[0032] Optionally, according to the cumulative energy adjustment domain and the charging power adjustment domain, the charging power of the electric vehicle cluster is adjusted, including:

[0033] The active power value of the electric vehicle cluster at each time is determined according to the cumulative energy adjustment domain and the charging power adjustment domain;

[0034] The charging power of the electric vehicle cluster is adjusted in real time based on the active power value.

[0035] In a second aspect, a charging power adjustment device for an electric vehicle cluster is provided, including:

[0036] A determination module is configured to determine a plurality of electric vehicles included in the electric vehicle cluster, wherein the plurality of electric vehicles correspond to the same type of charging pile to be accessed, and the type of charging pile includes any one of a first charging pile, a second charging pile, and a third charging pile; the first charging pile is a charging pile that only allows charging, but the charging power can be adjusted within the maximum charging power; the second charging pile is a charging pile that charges the electric vehicle and simultaneously allows the electric vehicle to discharge externally through the type of charging pile, and the charging and discharging power can be adjusted within the maximum charging power and the maximum discharging power; and the third charging pile is a charging pile that can only charge the electric vehicle and cannot continuously adjust the charging power, and the charging power can only be the rated power or 0;

[0037] The generating module is configured to generate, for each of the plurality of electric vehicles, cumulative energy feasible region information and charging power feasible region information when accessing the charging pile;

[0038] The aggregating module is configured to aggregate the plurality of cumulative energy feasible region information and the plurality of charging power feasible region information corresponding to the plurality of electric vehicles to obtain cumulative energy regulation region and charging power regulation region of the electric vehicle cluster;

[0039] The adjusting module is configured to adjust the charging power of the electric vehicle cluster according to the cumulative energy regulation region and the charging power regulation region.

[0040] Optionally, the generating module is configured to determine a target charging pile type of the charging pile accessed by the electric vehicle cluster; generate, based on the target charging pile type, a power constraint condition of each of the plurality of electric vehicles after accessing the charging pile, the power constraint condition including a cumulative energy constraint condition and a charging power constraint condition of each of the plurality of electric vehicles within a parking time period after accessing the charging pile; and generate, according to the power constraint condition, the cumulative energy feasible region information and the charging power feasible region information of each of the plurality of electric vehicles within a scheduling period.

[0041] Optionally, in the generating, based on the target charging pile type, of the power constraint condition of each of the plurality of electric vehicles after accessing the charging pile, the generating module is configured to obtain historical charging parameters of each of the plurality of electric vehicles after accessing the charging pile at a historical time; fit the historical charging parameters by a Monte Carlo method to obtain target charging parameters of each of the plurality of electric vehicles within a scheduling period; and determine, based on the target charging parameters, the power constraint condition of each of the plurality of electric vehicles within the parking time period.

[0042] Optionally, in the generating, according to the power constraint condition, of the cumulative energy feasible region information and the charging power feasible region information of each of the plurality of electric vehicles within a scheduling period, the generating module is configured to generate, based on the power constraint condition, initial cumulative energy feasible region information and initial charging power feasible region information of each of the plurality of electric vehicles within the parking time period, the initial cumulative energy feasible region information including a first cumulative energy feasible region upper bound and a first cumulative energy feasible region lower bound, and the initial charging power feasible region information including a first charging power feasible region upper bound and a first charging power feasible region lower bound, or a first charging power feasible value; and extend a time range of the initial cumulative energy feasible region information and the initial charging power feasible region information to obtain the cumulative energy feasible region information and the charging power feasible region information within the scheduling period, the cumulative energy feasible region information including a second cumulative energy feasible region upper bound and a second cumulative energy feasible region lower bound, and the charging power feasible region information including a second charging power feasible region upper bound and a second charging power feasible region lower bound, or a second charging power feasible value.

[0043] Optionally, in the process of generating the initial cumulative energy feasible region information and the initial charging power feasible region information of each electric vehicle in the parking time period based on the power constraint condition, the generating module is configured to calculate a first cumulative energy feasible region upper bound, a first cumulative energy feasible region lower bound, a first charging power feasible region upper bound and a first charging power feasible region lower bound of each electric vehicle in the parking time period based on the power constraint condition, or calculate a first charging power feasible value; correspondingly, in the process of extending the time range of the initial cumulative energy feasible region information and the initial charging power feasible region information to obtain the cumulative energy feasible region information and the charging power feasible region information in a scheduling period, the generating module is configured to extend the first cumulative energy feasible region upper bound and the first cumulative energy feasible region lower bound into a second cumulative energy feasible region upper bound and a second cumulative energy feasible region lower bound in the scheduling period respectively; extend the first charging power feasible region upper bound and the first charging power feasible region lower bound into a second charging power feasible region upper bound and a second charging power feasible region lower bound in the scheduling period respectively; or extend the first charging power feasible value into a second charging power feasible value in the scheduling period.

[0044] Optionally, the aggregating module is configured to accumulate the plurality of second cumulative energy feasible region upper bounds corresponding to the plurality of electric vehicles to obtain a third cumulative energy feasible region upper bound, and accumulate the plurality of second cumulative energy feasible region lower bounds corresponding to the plurality of electric vehicles to obtain a third cumulative energy feasible region lower bound; construct a cumulative energy adjustment region corresponding to the third cumulative energy feasible region upper bound and the third cumulative energy feasible region lower bound; accumulate the plurality of second charging power feasible region upper bounds corresponding to the plurality of electric vehicles to obtain a third charging power feasible region upper bound, and accumulate the plurality of second charging power feasible region lower bounds corresponding to the plurality of electric vehicles to obtain a third charging power feasible region lower bound, and construct a charging power adjustment region corresponding to the third charging power feasible region upper bound and the third charging power feasible region lower bound; or integrate the plurality of second charging power feasible values corresponding to the plurality of electric vehicles to obtain a third charging power feasible value, and construct a charging power adjustment region containing all the third charging power feasible values.

[0045] Optionally, the adjusting module is configured to determine the active power value of the electric vehicle cluster at each time point according to the cumulative energy adjustment region and the charging power adjustment region; and adjust the charging power of the electric vehicle cluster in real time based on the active power value.

[0046] In a third aspect, an electronic device is provided, including a processor and a memory, the memory being configured to store a computer program, and the processor being configured to invoke and run the computer program stored in the memory to execute the method in the first aspect or the implementation manners thereof.

[0047] In a fourth aspect, a computer readable storage medium is provided, configured to store a computer program, and the computer program is configured to make a computer execute the method in the first aspect or the implementation manners thereof.

[0048] By the technical solution provided by the application, after determining a plurality of electric vehicles included in an electric vehicle cluster, cumulative energy feasible region information and charging power feasible region information when accessing a charging pile are generated for each electric vehicle in the plurality of electric vehicles; then, by aggregating a plurality of cumulative energy feasible region information and a plurality of charging power feasible region information corresponding to the plurality of electric vehicles, a cumulative energy regulation domain and a charging power regulation domain of the electric vehicle cluster are obtained; finally, the charging power of the electric vehicle cluster is adjusted according to the cumulative energy regulation domain and the charging power regulation domain. The technical solution in the application considers a plurality of types of charging piles that the electric vehicle can access, establishes an electric vehicle cluster regulation capability aggregation method under each type of charging pile in a power distribution network, can fully exert the controllability advantage of the electric vehicle cluster, reduces the load randomness problem caused by a large number of electric vehicles without charging, guides the electric vehicle to participate in power grid operation scheduling in a cluster manner, and reduces the complexity of power grid operation control.

[0049] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the application. Other features and advantages of the application will be described in detail in the subsequent specific embodiments section. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0051] Figure 1 An application scenario diagram provided by the embodiments of the application;

[0052] Figure 2 A flowchart of a charging power regulation method of an electric vehicle cluster provided by the embodiments of the application;

[0053] Figure 3 An example diagram of initial cumulative energy feasible region information and initial charging power feasible region information of a first charging pile about a single electric vehicle provided by the embodiments of the application;

[0054] Figure 4 An example diagram of initial cumulative energy feasible region information and initial charging power feasible region information of a second charging pile about a single electric vehicle provided by the embodiments of the application;

[0055] Figure 5An example schematic diagram of initial cumulative energy feasible region information and initial charging power feasible region information of a third charging pile about a single electric vehicle is provided for the embodiments of the present application.

[0056] Figure 6 An example schematic diagram of cumulative energy boundary of an aggregator 1 is provided for the embodiments of the present application.

[0057] Figure 7 An example schematic diagram of active power boundary of an aggregator 1 is provided for the embodiments of the present application.

[0058] Figure 8 An example schematic diagram of cumulative energy boundary of an aggregator 2 is provided for the embodiments of the present application.

[0059] Figure 9 An example schematic diagram of active power boundary of an aggregator 2 is provided for the embodiments of the present application.

[0060] Figure 10 An example schematic diagram of cumulative energy boundary of an aggregator 3 is provided for the embodiments of the present application.

[0061] Figure 11 An example schematic diagram of active power boundary of an aggregator 3 is provided for the embodiments of the present application.

[0062] Figure 12 An example schematic diagram of cumulative energy boundary of an aggregator 4 is provided for the embodiments of the present application.

[0063] Figure 13 An example schematic diagram of active power value of an aggregator 4 is provided for the embodiments of the present application.

[0064] Figure 14 A structural schematic diagram of a charging power adjusting device of an electric vehicle cluster is provided for the embodiments of the present application.

[0065] Figure 15 A structural schematic diagram of an electronic device is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0066] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0067] It is to be understood that the terminology "first", "second" and the like used in the specification and the claims of the application as well as the preceding description of the drawings is merely used to distinguish similar objects and does not necessarily imply a specific order or chronology. It is to be understood that the data thus used can be interchanged, where appropriate, so that the embodiments of the application described herein can be carried out in a different order than the one described here. Furthermore, the terms "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusions, for example, processes, methods, systems, products, or servers comprising a list of steps or units are not necessarily limited to those steps or units that are clearly listed, but can include other steps or units that are not clearly listed or inherent to such processes, methods, products, or devices.

[0068] At present, many scholars at home and abroad have studied the adjustable capacity evaluation problem of electric vehicles. The research is mainly aimed at the electric vehicle cluster with continuously adjustable access charging power and V2G charging piles. Since the aggregated power of the electric vehicle cluster with start-stop charging piles controlled by access control is discrete, it is easy to cause the problem scale to become larger and the solution to become slower, so the start-stop charging pile control potential is generally ignored. However, start-stop charging piles are widely distributed and easy to control. If this type of charging pile is not fully considered, the evaluation of the adjustable capacity of electric vehicles will be conservative, causing electric vehicle load overload in actual operation and triggering a series of system problems. On the other hand, the randomness of electric vehicle demand makes the controllable potential in the aggregation process also random, so the randomness of electric vehicles needs to be considered in the analysis, and an electric vehicle cluster regulation capacity aggregation method that conforms to the actual operation situation needs to be established.

[0069] Fully utilizing the potential of electric vehicle clusters and orderly guiding a large number of electric vehicles to participate in grid operation control is an important means of grid dispatching and is also conducive to controlling the randomness of charging load. However, the existing aggregation methods do not fully consider the randomness of various types of charging piles and charging loads, especially lacking effective representation of the aggregated power of electric vehicle clusters that can only be controlled by starting and stopping. The direct use of integer variables to describe electric vehicle clusters under large-scale charging piles of this type will cause the problem to be solved slowly, and if this type of charging pile is not considered, it will be difficult to fully describe the charging characteristics of electric vehicle clusters. The electric vehicle adjustable capacity aggregation method still has room for improvement.

[0070] In order to solve the above technical problems, the application concept of the present application is that, fully considering the operation characteristics of different types of charging piles in actual operation, a single electric vehicle adjustment capacity aggregation method considering multiple types of charging piles is established, after considering the randomness of electric vehicles, the single electric vehicle is aggregated into a cluster, the controllable capacity of the electric vehicle cluster is evaluated, thereby obtaining the constraint of the controllable potential of the electric vehicle cluster in the power grid operation, which is used for power system operation control. The present application can more completely depict the influence of the charging and discharging process of electric vehicles and electric vehicle clusters in the power grid on the active power of the power grid, and distinguishes the aggregation methods of electric vehicle clusters of different scales, improves the calculation speed of the problem, and can be further applied to power grid operation control, which is very important for safe and stable operation of the power grid.

[0071] It should be understood that the technical solutions of the present application can be applied to the following scenarios, but are not limited to:

[0072] In some implementable manners, Figure 1 An application scenario graph provided by the embodiment of the present application is shown in Figure 1 As shown in the figure, the application scenario can include an electronic device 110 and a network device 120. The electronic device 110 can establish a connection with the network device 120 through a wired network or a wireless network.

[0073] For example, the electronic device 110 can be a desktop computer, a notebook computer, a tablet computer, etc., but is not limited thereto. The network device 120 can be a terminal device or a server, but is not limited thereto. In an embodiment of the present application, the electronic device 110 can send a request message to the network device 120, and the request message can be used to request to obtain multiple electric vehicles included in an electric vehicle cluster. Further, the electronic device 110 can receive a response message sent by the network device 120, and the response message includes multiple electric vehicles included in the electric vehicle cluster.

[0074] In addition, Figure 1 For example, one electronic device 110 and one network device 120 are exemplarily given, and in fact, other numbers of electronic devices and network devices can be included, and the present application does not limit this.

[0075] In some other implementable manners, the technical solutions of the present application can also be executed by the above-mentioned electronic device 110, or the technical solutions of the present application can also be executed by the above-mentioned network device 120, and the present application does not limit this.

[0076] After introducing the application scenario of the embodiment of the present application, the technical solutions of the present application will be described in detail as follows:

[0077] Figure 2 A flowchart of a charging power adjustment method of an electric vehicle cluster provided by the embodiment of the present application, and the method can be executed byFigure 1 The electronic device 110 shown performs, but is not limited to, the following. Figure 2 As shown, the method can include the following steps:

[0078] Step 210, determining a plurality of electric vehicles contained in an electric vehicle cluster.

[0079] The electric vehicle cluster refers to a collection of a large number of dispersed electric vehicle resources. In the same electric vehicle cluster, the plurality of electric vehicles correspond to the same type of charging pile to be accessed, and the type of charging pile includes any one of a first charging pile, a second charging pile, and a third charging pile. The first charging pile is a charging pile that only allows charging, but the charging power can be adjusted within the maximum charging power; the second charging pile is a charging pile that charges the electric vehicle and simultaneously allows the electric vehicle to discharge externally through the type of charging pile, and the charging and discharging power can be adjusted within the maximum charging power and the maximum discharging power, such as a V2G (Vehicle-to-Grid) charging pile; and the third charging pile is a charging pile that can only charge the electric vehicle and cannot continuously adjust the charging power, and the charging power can only be the rated power or 0, such as a start-stop charging pile.

[0080] For the execution subject of the present application, the charging station can be connected with a plurality of charging piles of a plurality of charging pile types. After obtaining the plurality of electric vehicles contained in the electric vehicle cluster, the charging power adjustment operation of the subsequent embodiment steps can be performed for the plurality of electric vehicles.

[0081] Step 220, for each electric vehicle in the plurality of electric vehicles, generating cumulative energy feasible region information and charging power feasible region information corresponding to the accessed charging pile.

[0082] For the embodiment of the present disclosure, the step 220 of generating, for each electric vehicle in the plurality of electric vehicles, cumulative energy feasible region information and charging power feasible region information corresponding to the accessed charging pile can include the following steps:

[0083] Step 220-1, determining a target charging pile type of the accessed charging pile of the electric vehicle cluster.

[0084] The target charging pile type is the type of the accessed charging pile of the electric vehicle cluster, which can be any one of the first charging pile, the second charging pile, and the third charging pile, or can also include other types of charging piles, which are not specifically enumerated here. It should be noted that in the following embodiment steps of the present application, the target charging pile type is taken as an example of any one of the first charging pile, the second charging pile, and the third charging pile to explain the technical solutions in the present application, but it does not constitute a specific limitation.

[0085] Step 220-2, based on the target charging pile type, generating a power constraint condition of each electric vehicle in the plurality of electric vehicles after accessing the charging pile.

[0086] For the embodiments of the present disclosure, the embodiment step 220-2 can include: obtaining historical charging parameters of each electric vehicle in the electric vehicle cluster after accessing the charging pile at a historical time; fitting the historical charging parameters by a Monte Carlo method to obtain target charging parameters of each electric vehicle in a scheduling period; and determining a power constraint condition of each electric vehicle in a parking period based on the target charging parameters.

[0087] The power constraint condition includes a cumulative energy constraint condition and a charging power constraint condition of each electric vehicle in the corresponding parking period after accessing the charging pile.

[0088] For the embodiments of the present disclosure, as a possible implementation manner, when the target charging pile type is the first charging pile, the target charging parameters of each electric vehicle in the electric vehicle cluster can be determined based on the above embodiment steps, including: an arrival time of the electric vehicle when accessing the first charging pile , a charging end time , an expected state of charge , and a maximum charging power of the first charging pile . The power constraint condition of each electric vehicle in the parking period can be determined based on the target charging parameters, and can be composed of the following formulas (1) and (2):

[0089] Formula (1)

[0090] Formula (2)

[0091] In formula (1) is the parking period charging power, which indicates that when the charging power is adjusted, the charging power must be ensured to be within the maximum charging power and non-negative; represents the cumulative charging amount from the arrival time to the time , and formula (2) ensures that the cumulative charging amount in the parking period must be within the expected state of charge and the expected state of charge is reached at the leaving time.

[0092] For the embodiments of the present disclosure, as another possible implementation manner, when the target charging pile type is the second charging pile, the target charging parameters of each electric vehicle in the electric vehicle cluster can be determined based on the above embodiment steps, including: an arrival time of the electric vehicle when accessing the second charging pile , a charging end time ​Desired state of charge Maximum negative shift of the state of charge during charging and discharging and maximum positive offset of the state of charge And the maximum charging power and maximum discharging power of the second charging pile. Based on the target charging parameters, the power constraints for each electric vehicle during the parking period can be determined by the following formulas (3) and (4):

[0093] Formula (3)

[0094] Formula (4)

[0095] In formula (3) for The charging power during a given period indicates that the charging power must be maintained between the maximum charging power and the discharge power when adjusting the charging power. Here, the discharge power is expressed as the charging power, but in a negative value. In formula (4) Indicates arrival time Time The cumulative charging amount is used to ensure charging during parking periods. The cumulative charging amount in each time period must be within the maximum allowable positive and negative offset of the state of charge, and the departure time... Charge to the desired state of charge. .

[0096] In another possible implementation of this disclosure, when the target charging pile type is a third charging pile, the target charging parameters for each electric vehicle in the electric vehicle cluster can be determined based on the steps of the above embodiments, including: the arrival time of the electric vehicle when connecting to the third charging pile. Charging end time Desired state of charge and the rated charging power of the third charging pile Based on the target charging parameters, the power constraints for each electric vehicle during the parking period can be determined by the following formulas (5) and (6):

[0097] Formula (5)

[0098] Formula (6)

[0099] In formula (5) for The charging power during a specific period indicates the charging power at which the rated power can only be used. Or 0; in formula (6), Indicates arrival time Time cumulative charging amount of each electric vehicle in the parking period, for ensuring that the cumulative charging amount of each electric vehicle in the parking period is within the expected state of charge cumulative charging amount of each electric vehicle in the parking period must be within the expected state of charge cumulative charging amount of each electric vehicle in the parking period must be within the expected state of charge .

[0100] Step 220-3, generating cumulative energy feasible region information and charging power feasible region information of each electric vehicle in a scheduling period according to the power constraint condition.

[0101] For the embodiments of the present disclosure, the embodiment step 220-3 can include: generating initial cumulative energy feasible region information and initial charging power feasible region information of each electric vehicle in the parking period based on the power constraint condition, the initial cumulative energy feasible region information including a first cumulative energy feasible region upper bound and a first cumulative energy feasible region lower bound, the initial charging power feasible region information including a first charging power feasible region upper bound and a first charging power feasible region lower bound, or including a first charging power feasible value; and extending the time range of the initial cumulative energy feasible region information and the initial charging power feasible region information to obtain cumulative energy feasible region information and charging power feasible region information in a scheduling period, the cumulative energy feasible region information including a second cumulative energy feasible region upper bound and a second cumulative energy feasible region lower bound, the charging power feasible region information including a second charging power feasible region upper bound and a second charging power feasible region lower bound, or including a second charging power feasible value.

[0102] Correspondingly, when generating the initial cumulative energy feasible region information and the initial charging power feasible region information of each electric vehicle in the parking period based on the power constraint condition, the embodiment step 220-3 can further include: calculating the first cumulative energy feasible region upper bound, the first cumulative energy feasible region lower bound, the first charging power feasible region upper bound, and the first charging power feasible region lower bound of each electric vehicle in the parking period based on the power constraint condition, or calculating the first charging power feasible value.

[0103] For the embodiments of the present disclosure, as a possible implementation manner, when the target charging pile type is the first charging pile, the mathematical expressions for calculating the first cumulative energy feasible region upper bound, the first cumulative energy feasible region lower bound, the first charging power feasible region upper bound, and the first charging power feasible region lower bound of each electric vehicle in the parking period based on the power constraint condition can be formulas (7) to (10):

[0104] Formula (7)

[0105] Formula (8)

[0106] Formula (9)

[0107] Equation (10)

[0108] Equation (7) represents the upper bound of the first cumulative energy feasible region , which is the fastest charging energy trajectory of a single electric vehicle. The fastest charging means that the electric vehicle accesses the first charging pile, immediately charges with the maximum charging power to the desired state of charge , and then stops charging and waits until leaves. Equation (5) is the lower bound of the first cumulative energy feasible region , which is the slowest charging energy trajectory of a single electric vehicle. The slowest charging energy trajectory means that the electric vehicle accesses the first charging pile, waits until a certain time, charges with the maximum power to , and leaves. Here, the certain time is defined as the time when the electric vehicle charges with the maximum charging power , and just charges to the desired state of charge at . Based on the definition of the fastest charging energy trajectory and the slowest charging energy trajectory, the upper bound of the first cumulative energy feasible region of the electric vehicle is Equation (7), which corresponds to the upper solid line of the parallelogram in Figure 3 , and the lower bound of the first cumulative energy feasible region is Equation (8), which corresponds to the lower dashed line of the parallelogram in Figure 3 . Equation (9) and Equation (10) are the upper bound of the first charging power feasible region and the lower bound of the first charging power feasible region, respectively, which represent the maximum charging power and the minimum charging power 0 of the electric vehicle, respectively, and correspond to the upper solid line and the lower dashed line of the rectangle in Figure 3 , respectively.

[0109] For the embodiments of the present disclosure, as another possible implementation, when the target charging pile type is the second charging pile, the mathematical expressions for calculating the upper bound of the first cumulative energy feasible region, the lower bound of the first cumulative energy feasible region, the upper bound of the first charging power feasible region, and the lower bound of the first charging power feasible region of each electric vehicle within the parking period based on the power constraint condition are Equations (11) to (14):

[0110] Equation (11)

[0111] Equation (12)

[0112] Equation (13)

[0113] Equation (14)

[0114] Equation (11) represents the upper bound of the first cumulative energy feasible region is the fastest charging energy trajectory of a single electric vehicle. The fastest charging means that the electric vehicle connects to the second charging pile, immediately charges at the maximum charging power to the maximum state of charge , parks and waits, at a certain time, discharges at the maximum discharging power to leaves. Here, the "certain time" is defined as the time from the moment the state of charge charges at the maximum discharging power , and exactly at the time , discharges to the desired value . Equation (12) is the lower bound of the first cumulative energy feasible region is the slowest charging energy trajectory of a single electric vehicle. The slowest charging means that the electric vehicle connects to the second charging pile 2, discharges at the maximum discharging power to the minimum state of charge , parks and waits, at a certain time, charges at the maximum power to leaves at the time. Here, the "certain time" is defined as the time from the moment the state of charge discharges at the maximum charging power , and exactly at the time , charges to the desired value . Based on the definition of the fastest charging energy trajectory and the slowest charging energy trajectory, it can be obtained that the upper bound of the first cumulative energy feasible region of the electric vehicle is equation (11), corresponding to the upper solid line of the parallelogram in Figure 4 , and the lower bound of the first cumulative energy feasible region is equation (12), corresponding to the lower dotted line of the parallelogram in Figure 4 . Equations (13) and (14) are the upper bound of the first charging power feasible region and the lower bound of the first charging power feasible region, respectively, representing the maximum charging power and the maximum discharging power of the electric vehicle, respectively, corresponding to the upper solid line and the lower dotted line of the rectangle in Figure 4 .

[0115] For the embodiments of the present disclosure, as another possible implementation, when the target charging pile type is the third charging pile, the mathematical expressions for calculating the upper bound of the first cumulative energy feasible region and the lower bound of the first cumulative energy feasible region of each electric vehicle within the parking period based on the power constraint condition are equations (15) and (16):

[0116] Equation (15)

[0117] Equation (16)

[0118] Equation (15) represents the upper bound of the first cumulative energy feasible region is the fastest charging energy trajectory of a single electric vehicle. The fastest charging means that the electric vehicle accesses the third charging pile, immediately charges at the rated power of the third charging pile to the desired state of charge , and then stops charging and waits until leaves. Formula (16) is the lower bound of the first cumulative energy feasible region is the slowest charging energy trajectory of a single electric vehicle. The slowest charging energy trajectory means that the electric vehicle accesses the third charging pile, waits until "a certain time", and then charges at the rated power of the third charging pile to the desired state of charge , and leaves. Here, "a certain time" is defined as the time when charging at the rated power exactly charges to the desired state of charge at . Based on the definition of the fastest charging energy trajectory and the slowest charging energy trajectory, the upper bound of the first cumulative energy feasible region of the electric vehicle is formula (15), which corresponds to the upper solid line of the parallelogram in Figure 5 , and the lower bound of the first cumulative energy feasible region is formula (16), which corresponds to the lower dashed line of the parallelogram in Figure 5 . Since the third charging pile can only control the start and stop states, as shown in Figure 5 , the charging power only has two first charging power feasible values: the rated power or 0, at this time, the upper and lower limits of the power are not considered, that is, at this time, the upper bound of the first charging power feasible region and the lower bound of the first charging power feasible region are not considered.

[0119] Correspondingly, when the time range of the extended initial cumulative energy feasible region information and the initial charging power feasible region information is obtained, the cumulative energy feasible region information and the charging power feasible region information in a scheduling period, embodiment step 220-3 can further include: extending the first cumulative energy feasible region upper bound and the first cumulative energy feasible region lower bound to the second cumulative energy feasible region upper bound and the second cumulative energy feasible region lower bound in a scheduling period, respectively; extending the first charging power feasible region upper bound and the first charging power feasible region lower bound to the second charging power feasible region upper bound and the second charging power feasible region lower bound in a scheduling period, respectively; or, extending the first charging power feasible value to the second charging power feasible value in a scheduling period.

[0120] For the embodiments of the present disclosure, as a possible implementation manner, when the target charging pile type is the first charging pile, the time range can be extended to a scheduling period based on the initial cumulative energy feasible region information and the initial charging power feasible region information in the parking period of formula (7) to formula (10):

[0121]

[0122]

[0123]

[0124]

[0125] Correspondingly, when the adjustment domain of the first charging pile is extended to a scheduling period, the second cumulative energy feasible region upper bound of each electric vehicle , the second cumulative energy feasible region lower bound , the second charging power feasible region upper bound , and the second charging power feasible region lower bound can be obtained. Wherein, n is the number of each electric vehicle in the electric vehicle cluster.

[0126] For the embodiment of the disclosure, as another possible implementation, when the target charging pile type is the second charging pile, the initial cumulative energy feasible region information and the initial charging power feasible region information in the parking period of formula (11) to formula (14) can be extended to a scheduling period in terms of time range:

[0127]

[0128]

[0129]

[0130]

[0131] Correspondingly, when the adjustment domain of the second charging pile is extended to a scheduling period, the second cumulative energy feasible region upper bound of each electric vehicle , the second cumulative energy feasible region lower bound , the second charging power feasible region upper bound , and the second charging power feasible region lower bound can be obtained. Wherein, n is the number of each electric vehicle in the electric vehicle cluster.

[0132] For the embodiment of the disclosure, as another possible implementation, when the target charging pile type is the third charging pile, the initial cumulative energy feasible region information and the initial charging power feasible region information in the parking period of formula (15) to formula (16) can be extended to a scheduling period in terms of time range:

[0133]

[0134]

[0135]

[0136] Correspondingly, when the adjustment domain of the third charging pile is extended to a scheduling period, the upper bound of the second cumulative energy feasible domain of each electric vehicle can be obtained , the lower bound of the second cumulative energy feasible domain , and the second charging power feasible value . Wherein, n is the number of each electric vehicle in the electric vehicle cluster.

[0137] Step 230, aggregate the plurality of cumulative energy feasible domain information and the plurality of charging power feasible domain information corresponding to the plurality of electric vehicles to obtain the cumulative energy adjustment domain and the charging power adjustment domain of the electric vehicle cluster.

[0138] For the embodiment of the present disclosure, the aggregation of the plurality of cumulative energy feasible domain information and the plurality of charging power feasible domain information corresponding to the plurality of electric vehicles in step 230 to obtain the cumulative energy adjustment domain and the charging power adjustment domain of the electric vehicle cluster can include the following steps:

[0139] Step 230-1, accumulate the plurality of second cumulative energy feasible domain upper bounds corresponding to the plurality of electric vehicles to obtain the third cumulative energy feasible domain upper bound, and accumulate the plurality of second cumulative energy feasible domain lower bounds corresponding to the plurality of electric vehicles to obtain the third cumulative energy feasible domain lower bound.

[0140] Step 230-2, construct the cumulative energy adjustment domain corresponding to the third cumulative energy feasible domain upper bound and the third cumulative energy feasible domain lower bound.

[0141] For embodiment steps 230-1 and 230-2, as a possible implementation, when the target charging pile type is the first charging pile, the cumulative energy adjustment domain corresponding to the third cumulative energy feasible domain upper bound and the third cumulative energy feasible domain lower bound can be determined as:

[0142]

[0143] In the formula, is the third cumulative energy feasible domain upper bound, is the second cumulative energy feasible domain upper bound of the nth electric vehicle, is the third cumulative energy feasible domain lower bound, is the second cumulative energy feasible domain lower bound of the nth electric vehicle, and N is the number of electric vehicles included in the electric vehicle cluster.

[0144] For embodiment steps 230-1 and 230-2, as another possible implementation, when the target charging pile type is the second charging pile, the cumulative energy adjustment domain corresponding to the third cumulative energy feasible domain upper bound and the third cumulative energy feasible domain lower bound can be determined as:

[0145]

[0146] wherein, is the third cumulative energy feasible region upper bound, is the nth electric vehicle's second cumulative energy feasible region upper bound, is the third cumulative energy feasible region lower bound, is the nth electric vehicle's second cumulative energy feasible region lower bound, and N is the number of electric vehicles included in the electric vehicle cluster.

[0147] For embodiment step 230-1 and 230-2, as another possible implementation, when the target charging pile type is the third charging pile, the cumulative energy adjustment region corresponding to the third cumulative energy feasible region upper bound and the third cumulative energy feasible region lower bound can be determined as:

[0148]

[0149] wherein, is the third cumulative energy feasible region upper bound, is the nth electric vehicle's second cumulative energy feasible region upper bound, is the third cumulative energy feasible region lower bound, is the nth electric vehicle's second cumulative energy feasible region lower bound, and N is the number of electric vehicles included in the electric vehicle cluster.

[0150] Step 230-3a, accumulating the plurality of second charging power feasible region upper bounds corresponding to the plurality of electric vehicles to obtain the third charging power feasible region upper bound, and accumulating the plurality of second charging power feasible region lower bounds corresponding to the plurality of electric vehicles to obtain the third charging power feasible region lower bound, to construct the charging power adjustment region corresponding to the third charging power feasible region upper bound and the third charging power feasible region lower bound.

[0151] For embodiment step 230-3a, as a possible implementation, when the target charging pile type is the first charging pile, the charging power adjustment region corresponding to the third charging power feasible region upper bound and the third charging power feasible region lower bound can be determined as:

[0152]

[0153] wherein, is the third charging power feasible region upper bound, is the nth electric vehicle's second charging power feasible region upper bound, is the third charging power feasible region lower bound, is the nth electric vehicle's second charging power feasible region lower bound, and N is the number of electric vehicles included in the electric vehicle cluster.

[0154] For the embodiment step 230-3a, as another possible implementation, when the target charging pile type is the second charging pile, it can be determined that the charging power adjustment domain corresponding to the third charging power feasible region upper bound and the third charging power feasible region lower bound is:

[0155]

[0156] wherein, is the third charging power feasible region upper bound, is the second charging power feasible region upper bound of the nth electric vehicle, is the third charging power feasible region lower bound, is the second charging power feasible region lower bound of the nth electric vehicle, and N is the number of electric vehicles included in the electric vehicle cluster.

[0157] As the embodiment step 230-3b parallel to the embodiment step 230-3a, the plurality of second charging power feasible values corresponding to the plurality of electric vehicles are integrated to obtain a third charging power feasible value, and a charging power adjustment domain including all third charging power feasible values is constructed.

[0158] For the embodiment step 230-3b, as another possible implementation, when the target charging pile type is the third charging pile, it can be determined that the charging power adjustment domain is:

[0159]

[0160]

[0161] wherein, is the charging power adjustment domain, is the rated power, is an integer variable, and N is the number of electric vehicles included in the electric vehicle cluster.

[0162] When the number of electric vehicles in the electric vehicle cluster accessed by the third charging pile is large, the integer variable is large, resulting in a slow solving speed. If the above formula is still used to represent, the complexity of the model will be increased, which is not conducive to the application of the model in the actual scene.

[0163] At this time, considering the actual characteristics of the third charging pile: the start-stop charging pile is mainly for home use, and the charging active power is generally 7-10 kW. When using continuous variables to represent the aggregated active power, the error is 3.5-5 kW. When the number of electric vehicles in the cluster is large, the error of the aggregated active power is small when using continuous variables to approximate the aggregated active power. In actual use, the active error can be adjusted by the energy storage device of the charging station, or it can be directly ignored. According to the approximate operation, the scale of the aggregated problem and the solving time can be reduced. At this time, the charging power adjustment region corresponding to the upper limit of the third charging power feasible region and the lower limit of the third charging power feasible region can be determined as:

[0164]

[0165] wherein, is the upper limit of the third charging power feasible region, is the rated power, is the lower limit of the third charging power feasible region.

[0166] Step 240, adjusting the charging power of the electric vehicle cluster according to the cumulative energy adjustment region and the charging power adjustment region.

[0167] For the embodiments of the present disclosure, the step 240 of adjusting the charging power of the electric vehicle cluster according to the cumulative energy adjustment region and the charging power adjustment region can include the following steps:

[0168] Step 240-1, determining the active power value of the electric vehicle cluster at each time according to the cumulative energy adjustment region and the charging power adjustment region.

[0169] Step 240-2, adjusting the charging power of the electric vehicle cluster in real time based on the active power value.

[0170] In order to facilitate the understanding of the present scheme, the following examples are given to illustrate the technical scheme in the present application:

[0171] Now consider four aggregators, the charging pile types of aggregators 1 and 2 are the first charging pile and the second charging pile respectively, and the charging pile types of aggregators 3 and 4 are the third charging pile. Take a scheduling period from evening to next morning, study the aggregation of all vehicles from evening to next morning, the time period is 17:00-8:30 the next day, there are 10 electric vehicles in aggregator 4, and there are 1000 electric vehicles in the remaining aggregators. The maximum charging power of the first charging pile is 10 kW, the maximum charging power of the second charging pile is 10 kW, the maximum discharging power is 6 kW, and the rated power of the third charging pile is 10 kW. The arrival and departure times of electric vehicles have randomness. According to historical data statistics, within a scheduling period, the arrival time of an electric vehicle satisfies a normal distribution of , truncated between 17:00-21:00, and the departure time satisfies a normal distribution truncated between 6:30-8:30.

[0172] Scenario One

[0173] Aggregator 1 accesses the first charging post, and Aggregator 1 is specifically an electric vehicle cluster containing 1000 electric vehicles. Referring to the steps of the above embodiment, the cumulative energy boundary of the cumulative energy regulation domain corresponding to Aggregator 1 can be determined as shown in Figure 6 , and the active power boundary of the charging power regulation domain corresponding to Aggregator 1 can be determined as shown in Figure 7 .

[0174] Scenario Two

[0175] Aggregator 2 accesses the second charging post, and Aggregator 2 is specifically an electric vehicle cluster containing 1000 electric vehicles. Referring to the steps of the above embodiment, the cumulative energy boundary of the cumulative energy regulation domain corresponding to Aggregator 2 can be determined as shown in Figure 8 , and the active power boundary of the charging power regulation domain corresponding to Aggregator 2 can be determined as shown in Figure 9 .

[0176] Scenario Three

[0177] Aggregator 3 accesses the third charging post, and Aggregator 3 is specifically an electric vehicle cluster containing 1000 electric vehicles. Referring to the steps of the above embodiment, the cumulative energy boundary of the cumulative energy regulation domain corresponding to Aggregator 3 can be determined as shown in Figure 10 , and the active power boundary of the charging power regulation domain corresponding to Aggregator 3 can be determined as shown in Figure 11 .

[0178] Scenario Four

[0179] Aggregator 4 accesses the third charging post, and Aggregator 4 is specifically an electric vehicle cluster containing 10 electric vehicles. Referring to the steps of the above embodiment, the cumulative energy boundary of the cumulative energy regulation domain corresponding to Aggregator 4 can be determined as shown in Figure 12 , and the active power value of the charging power regulation domain corresponding to Aggregator 4 can be determined as shown in Figure 13 .

[0180] To sum up, the technical scheme in the application can generate cumulative energy feasible region information and charging power feasible region information when each of the plurality of electric vehicles accesses the charging pile after determining the plurality of electric vehicles included in the electric vehicle cluster; then, the cumulative energy feasible region information and the charging power feasible region information corresponding to the plurality of electric vehicles are aggregated to obtain the cumulative energy regulation domain and the charging power regulation domain of the electric vehicle cluster; finally, the charging power of the electric vehicle cluster is adjusted according to the cumulative energy regulation domain and the charging power regulation domain. The technical scheme in the application considers various types of charging piles that can be accessed by electric vehicles, establishes an electric vehicle cluster regulation capability aggregation method under various types of charging piles in a power distribution network, can fully exert the controllability advantage of the electric vehicle cluster, reduces the load randomness problem caused by a large number of electric vehicles without charging, guides the electric vehicles to participate in power grid operation scheduling in a cluster manner, and reduces the complexity of power grid operation control.

[0181] Based on the above Figure 2 The specific description of the electric vehicle cluster charging power regulation method provided is as shown in Figure 14 , and Figure 14 is a structural block diagram of an electric vehicle cluster charging power regulation device according to an example embodiment. As shown in Figure 14 , the device comprises:

[0182] The determination module 1410 is configured to determine a plurality of electric vehicles included in an electric vehicle cluster, wherein the plurality of electric vehicles correspond to the same type of charging pile to be accessed, and the type of charging pile includes any one of a first charging pile, a second charging pile, and a third charging pile. The first charging pile is a charging pile that only allows charging, but the charging power can be adjusted within the maximum charging power. The second charging pile is a charging pile that charges the electric vehicle and simultaneously allows the electric vehicle to discharge externally through the type of charging pile, and the charging and discharging power can be adjusted within the maximum charging power and the maximum discharging power. The third charging pile is a charging pile that can only charge the electric vehicle and cannot continuously adjust the charging power, and the charging power can only be the rated power or 0.

[0183] The generation module 1420 is configured to generate cumulative energy feasible region information and charging power feasible region information when each of the plurality of electric vehicles accesses the charging pile.

[0184] The aggregation module 1430 is configured to aggregate the plurality of cumulative energy feasible region information and the plurality of charging power feasible region information corresponding to the plurality of electric vehicles to obtain the cumulative energy regulation domain and the charging power regulation domain of the electric vehicle cluster.

[0185] The adjustment module 1440 is configured to adjust the charging power of the electric vehicle cluster according to the cumulative energy regulation domain and the charging power regulation domain.

[0186] In some embodiments of the application, the generating module 1420 can be specifically configured to determine a target charging pile type of the charging pile accessed by the cluster of electric vehicles; generate power constraint conditions of each electric vehicle in the cluster of electric vehicles after accessing the charging pile based on the target charging pile type, the power constraint conditions including cumulative energy constraint conditions and charging power constraint conditions of each electric vehicle in the cluster of electric vehicles within a corresponding parking period after accessing the charging pile; and generate cumulative energy feasible region information and charging power feasible region information of each electric vehicle within a scheduling period according to the power constraint conditions.

[0187] In some embodiments of the application, when generating the power constraint conditions of each electric vehicle in the cluster of electric vehicles after accessing the charging pile based on the target charging pile type, the generating module 1420 can be specifically configured to obtain historical charging parameters of each electric vehicle in the cluster of electric vehicles after accessing the charging pile at a historical time; fit the historical charging parameters by a Monte Carlo method to obtain target charging parameters of each electric vehicle within a scheduling period; and determine the power constraint conditions of each electric vehicle within the parking period based on the target charging parameters.

[0188] In some embodiments of the application, when generating the cumulative energy feasible region information and the charging power feasible region information of each electric vehicle within a scheduling period according to the power constraint conditions, the generating module 1420 can be specifically configured to generate initial cumulative energy feasible region information and initial charging power feasible region information of each electric vehicle within the parking period based on the power constraint conditions, the initial cumulative energy feasible region information including a first cumulative energy feasible region upper bound and a first cumulative energy feasible region lower bound, the initial charging power feasible region information including a first charging power feasible region upper bound and a first charging power feasible region lower bound, or a first charging power feasible value; and extend the time range of the initial cumulative energy feasible region information and the initial charging power feasible region information to obtain the cumulative energy feasible region information and the charging power feasible region information within the scheduling period, the cumulative energy feasible region information including a second cumulative energy feasible region upper bound and a second cumulative energy feasible region lower bound, the charging power feasible region information including a second charging power feasible region upper bound and a second charging power feasible region lower bound, or including a second charging power feasible value.

[0189] In some embodiments of the present application, when generating the initial cumulative energy feasible region information and the initial charging power feasible region information of each electric vehicle within the parking time period based on the power constraint condition, the generating module 1420 is specifically configured to calculate the first cumulative energy feasible region upper bound, the first cumulative energy feasible region lower bound, the first charging power feasible region upper bound and the first charging power feasible region lower bound of each electric vehicle within the parking time period based on the power constraint condition, or calculate the first charging power feasible value; correspondingly, when extending the time range of the initial cumulative energy feasible region information and the initial charging power feasible region information to obtain the cumulative energy feasible region information and the charging power feasible region information within a scheduling period, the generating module 1420 is specifically configured to extend the first cumulative energy feasible region upper bound and the first cumulative energy feasible region lower bound to the second cumulative energy feasible region upper bound and the second cumulative energy feasible region lower bound within the scheduling period respectively; extend the first charging power feasible region upper bound and the first charging power feasible region lower bound to the second charging power feasible region upper bound and the second charging power feasible region lower bound within the scheduling period respectively; or extend the first charging power feasible value to the second charging power feasible value within the scheduling period.

[0190] In some embodiments of the present application, the aggregating module 1430 is specifically configured to accumulate the plurality of second cumulative energy feasible region upper bounds corresponding to the plurality of electric vehicles to obtain the third cumulative energy feasible region upper bound, and accumulate the plurality of second cumulative energy feasible region lower bounds corresponding to the plurality of electric vehicles to obtain the third cumulative energy feasible region lower bound; construct a cumulative energy adjustment region corresponding to the third cumulative energy feasible region upper bound and the third cumulative energy feasible region lower bound; accumulate the plurality of second charging power feasible region upper bounds corresponding to the plurality of electric vehicles to obtain the third charging power feasible region upper bound, and accumulate the plurality of second charging power feasible region lower bounds corresponding to the plurality of electric vehicles to obtain the third charging power feasible region lower bound, and construct a charging power adjustment region corresponding to the third charging power feasible region upper bound and the third charging power feasible region lower bound; or integrate the plurality of second charging power feasible values corresponding to the plurality of electric vehicles to obtain the third charging power feasible value, and construct a charging power adjustment region containing all the third charging power feasible values.

[0191] In some embodiments of the present application, the adjusting module 1440 is specifically configured to determine the active power value of the electric vehicle cluster at each time point according to the cumulative energy adjustment region and the charging power adjustment region; and adjust the charging power of the electric vehicle cluster in real time based on the active power value.

[0192] As to the apparatus in the above-mentioned embodiments, the specific manners in which various modules perform operations have been described in detail in the embodiments of the method, and thus will not be described in detail here.

[0193] The embodiment of the application can more completely depict the influence of the charging and discharging process of the electric vehicle and the electric vehicle cluster on the active power of the power grid, and distinguish the aggregation methods of electric vehicle clusters of different scales, improve the calculation speed of the problem, and be further applied to power grid operation control, which is very important for safe and stable operation of the power grid.

[0194] The charging power regulation device of the electric vehicle cluster in the embodiment of the application is described above from the perspective of functional modules in combination with the drawings. It should be understood that the functional modules can be realized in the form of hardware, realized in the form of instructions of software, or realized in the form of a combination of hardware and software modules. Specifically, each step of the charging power regulation method of the electric vehicle cluster in the embodiment of the application can be completed by the integrated logic circuit of hardware in the processor and / or the instructions of software. The steps of the charging power regulation method of the electric vehicle cluster in the embodiment of the application can be directly embodied as hardware decoding processor execution completion, or hardware and software module combination execution completion in the decoding processor. Alternatively, the software module can be located in a mature storage medium in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and combines the hardware to complete the steps in the charging power regulation method of the electric vehicle cluster in the embodiment of the application.

[0195] Figure 15 is a schematic block diagram of an electronic device 1500 according to an embodiment of the application.

[0196] As shown in Figure 15 , the electronic device 1500 can include:

[0197] The memory 1510 is configured to store a computer program and transmit the program code to the processor 1520. In other words, the processor 1520 can call and run the computer program from the memory 1510 to implement the method in the embodiment of the application.

[0198] For example, the processor 1520 can be configured to execute the above method embodiments according to the instructions in the computer program.

[0199] In some embodiments of the application, the processor 1520 can include but is not limited to:

[0200] General processor, Digital Signal Processor (DSP), Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, and the like.

[0201] In some embodiments of the present application, the memory 1510 includes, but is not limited to:

[0202] volatile memory and / or non-volatile memory. The non-volatile memory can be Read-Only Memory (ROM), Programmable ROM (PROM), Erasable PROM (EPROM), Electrically EPROM (EEPROM), or flash memory. The volatile memory can be Random Access Memory (RAM), which is used as the external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synch link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0203] In some embodiments of the present application, the computer program can be divided into one or more modules, which are stored in the memory 1510 and executed by the processor 1520 to complete the method provided by the present application. The one or more modules can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the controller.

[0204] As shown in Figure 15 The electronic device 1500 can also include:

[0205] a transceiver 1530, which can be connected to the processor 1520 or the memory 1510.

[0206] The processor 1520 can control the transceiver 1530 to communicate with other devices, specifically, can send data to other devices or receive data sent by other devices. The transceiver 1530 can include a transmitter and a receiver. The transceiver 1530 can further include an antenna, and the number of antennas can be one or more.

[0207] It should be understood that various components in the electronic device are connected through a bus system, which includes a data bus, a power supply bus, a control bus, and a state signal bus in addition to a data bus.

[0208] The present application also provides a computer storage medium, which stores a computer program, and the computer program enables a computer to execute the method of the above method embodiments when the computer program is executed by the computer. Alternatively, one embodiment of the present application also provides a computer program product containing instructions, and the instructions enable a computer to execute the method of the above method embodiments when the instructions are executed by the computer.

[0209] When implemented by using software, the computer program product can be implemented in the form of a computer program product in whole or in part. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the flow or function according to the embodiments 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 devices. The computer instructions 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 instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center through a wired (for example, coaxial cable, optical fiber, Digital Subscriber Line (DSL)) or wireless (for example, infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. integrated with one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a Digital Video Disc (DVD)), or a semiconductor medium (for example, a Solid State Disk (SSD)), etc.

[0210] Those skilled in the art can understand that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0211] In several embodiments provided by the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are merely illustrative, for example, the division of the modules is only a logical function division, and actual implementation can have another division manner, for example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed modules can be indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0212] The modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical modules, that is, they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiments according to actual needs. For example, the functional modules in each embodiment of the present application can be integrated in one processing module, or each module can be physically present separately, or two or more modules can be integrated in one module.

[0213] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any skilled person in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for regulating charging power of a cluster of electric vehicles, characterized in that, The method comprises the following steps: determining a plurality of electric vehicles included in an electric vehicle cluster, wherein the plurality of electric vehicles correspond to the same type of charging pile to be accessed, and the type of charging pile includes any one of a first charging pile, a second charging pile, and a third charging pile, the first charging pile is a charging pile that only allows charging, but the charging power can be adjusted within the maximum charging power; the second charging pile is a charging pile that charges the electric vehicle and allows the electric vehicle to discharge externally through the type of charging pile, and the charging and discharging power can be adjusted within the maximum charging power and the maximum discharging power; the third charging pile is a charging pile that can only charge the electric vehicle and cannot continuously adjust the charging power, and the charging power can only be the rated power or 0; for each electric vehicle in the plurality of electric vehicles, generating cumulative energy feasible region information and charging power feasible region information when accessing the charging pile; aggregating the cumulative energy feasible region information and the charging power feasible region information corresponding to the plurality of electric vehicles to obtain the cumulative energy regulation domain and the charging power regulation domain of the electric vehicle cluster; adjusting the charging power of the electric vehicle cluster according to the cumulative energy regulation domain and the charging power regulation domain; the method further comprises the following steps: determining a target charging pile type of a charging pile accessed by the electric vehicle cluster; based on the target charging pile type, generating a power constraint condition of each electric vehicle in the plurality of electric vehicles after accessing the charging pile, the power constraint condition including a cumulative energy constraint condition and a charging power constraint condition of the each electric vehicle within a parking time period after accessing the charging pile; generating cumulative energy feasible region information and charging power feasible region information of the each electric vehicle within a scheduling period according to the power constraint condition; the method further comprises the following steps: obtaining historical charging parameters of each electric vehicle in the electric vehicle cluster after accessing the charging pile at a historical time; fitting the historical charging parameters by a Monte Carlo method to obtain target charging parameters of the each electric vehicle within a scheduling period; based on the target charging parameters, determining the power constraint condition of the each electric vehicle within the parking time period; the method further comprises the following steps: based on the power constraint condition, generating initial cumulative energy feasible region information and initial charging power feasible region information of the each electric vehicle within the parking time period, the initial cumulative energy feasible region information including a first cumulative energy feasible region upper bound and a first cumulative energy feasible region lower bound, and the initial charging power feasible region information including a first charging power feasible region upper bound and a first charging power feasible region lower bound, or a first charging power feasible value; The time range of the initial cumulative energy feasible region information and the initial charging power feasible region information is extended to obtain cumulative energy feasible region information and charging power feasible region information in a scheduling period, the cumulative energy feasible region information includes a second cumulative energy feasible region upper bound and a second cumulative energy feasible region lower bound, and the charging power feasible region information includes a second charging power feasible region upper bound and a second charging power feasible region lower bound, or includes a second charging power feasible value.

2. The method of claim 1, wherein, The initial cumulative energy feasible region information and the initial charging power feasible region information of each electric vehicle in the parking period are generated based on the power constraint condition, including: The first cumulative energy feasible region upper bound, the first cumulative energy feasible region lower bound, the first charging power feasible region upper bound and the first charging power feasible region lower bound of each electric vehicle in the parking period are calculated based on the power constraint condition, or a first charging power feasible value is calculated; The time range of the initial cumulative energy feasible region information and the initial charging power feasible region information is extended to obtain cumulative energy feasible region information and charging power feasible region information in a scheduling period, including: The first cumulative energy feasible region upper bound and the first cumulative energy feasible region lower bound are respectively extended to a second cumulative energy feasible region upper bound and a second cumulative energy feasible region lower bound in a scheduling period; The first charging power feasible region upper bound and the first charging power feasible region lower bound are respectively extended to a second charging power feasible region upper bound and a second charging power feasible region lower bound in a scheduling period; or, The first charging power feasible value is extended to a second charging power feasible value in a scheduling period.

3. The method of claim 2, wherein, The multiple cumulative energy feasible region information and the multiple charging power feasible region information corresponding to the multiple electric vehicles are aggregated to obtain a cumulative energy regulation region and a charging power regulation region of the electric vehicle cluster, including: The multiple second cumulative energy feasible region upper bounds corresponding to the multiple electric vehicles are accumulated to obtain a third cumulative energy feasible region upper bound, and the multiple second cumulative energy feasible region lower bounds corresponding to the multiple electric vehicles are accumulated to obtain a third cumulative energy feasible region lower bound; A cumulative energy regulation region corresponding to the third cumulative energy feasible region upper bound and the third cumulative energy feasible region lower bound is constructed; The multiple second charging power feasible region upper bounds corresponding to the multiple electric vehicles are accumulated to obtain a third charging power feasible region upper bound, and the multiple second charging power feasible region lower bounds corresponding to the multiple electric vehicles are accumulated to obtain a third charging power feasible region lower bound, and a charging power regulation region corresponding to the third charging power feasible region upper bound and the third charging power feasible region lower bound is constructed; or, The multiple second charging power feasible values corresponding to the multiple electric vehicles are integrated to obtain a third charging power feasible value, and a charging power regulation region containing all the third charging power feasible values is constructed.

4. The method of claim 3, wherein, The charging power of the electric vehicle cluster is regulated according to the cumulative energy regulation region and the charging power regulation region, including: determining active power values of the electric vehicle cluster at each time point according to the cumulative energy regulation domain and the charging power regulation domain; adjusting charging power of the electric vehicle cluster in real time based on the active power values.

5. A charging power regulating device for a cluster of electric vehicles, characterized by, Comprise: The determination module is used for determining a plurality of electric vehicles included in an electric vehicle cluster, wherein the plurality of electric vehicles correspond to the same type of charging pile to be accessed, the type of charging pile includes any one of a first charging pile, a second charging pile and a third charging pile, the first charging pile is a charging pile that only allows charging, but the charging power can be adjusted within the maximum charging power; the second charging pile is a charging pile that charges electric vehicles and simultaneously allows the electric vehicles to discharge externally through the type of charging pile, and the charging and discharging power of the charging pile can be adjusted within the maximum charging power and the maximum discharging power; and the third charging pile is a charging pile that can only charge electric vehicles and cannot continuously adjust the charging power, and the charging power can only be the rated power or 0; The generation module is used for generating cumulative energy feasible domain information and charging power feasible domain information of each electric vehicle in the plurality of electric vehicles when accessing a charging pile; The aggregation module is used for aggregating a plurality of cumulative energy feasible domain information and a plurality of charging power feasible domain information corresponding to the plurality of electric vehicles to obtain a cumulative energy regulation domain and a charging power regulation domain of the electric vehicle cluster; The adjustment module is used for adjusting charging power of the electric vehicle cluster according to the cumulative energy regulation domain and the charging power regulation domain; The generation module is used for determining a target charging pile type of a charging pile accessed by the electric vehicle cluster, generating power constraint conditions of each electric vehicle in the plurality of electric vehicles after accessing the charging pile based on the target charging pile type, the power constraint conditions including cumulative energy constraint conditions and charging power constraint conditions of each electric vehicle in a parking time period after accessing the charging pile, and generating cumulative energy feasible domain information and charging power feasible domain information of each electric vehicle in a scheduling period according to the power constraint conditions; When generating the power constraint conditions of each electric vehicle in the plurality of electric vehicles after accessing the charging pile based on the target charging pile type, the generation module is used for obtaining historical charging parameters of each electric vehicle in the electric vehicle cluster after accessing the charging pile at historical time points, fitting the historical charging parameters through a Monte Carlo method to obtain target charging parameters of each electric vehicle in a scheduling period, and determining the power constraint conditions of each electric vehicle in the parking time period based on the target charging parameters. In generating the cumulative energy feasible region information and the charging power feasible region information of each electric vehicle in a scheduling period according to the power constraint condition, the generating module is configured to generate initial cumulative energy feasible region information and initial charging power feasible region information of each electric vehicle in the parking period based on the power constraint condition, the initial cumulative energy feasible region information comprising a first cumulative energy feasible region upper bound and a first cumulative energy feasible region lower bound, the initial charging power feasible region information comprising a first charging power feasible region upper bound and a first charging power feasible region lower bound, or a first charging power feasible value; and expand the time range of the initial cumulative energy feasible region information and the initial charging power feasible region information to obtain the cumulative energy feasible region information and the charging power feasible region information in a scheduling period, the cumulative energy feasible region information comprising a second cumulative energy feasible region upper bound and a second cumulative energy feasible region lower bound, the charging power feasible region information comprising a second charging power feasible region upper bound and a second charging power feasible region lower bound, or a second charging power feasible value.

6. An electronic device, comprising: Comprise: a processor and a memory, the memory being configured to store a computer program, and the processor being configured to invoke and run the computer program stored in the memory to execute the method of any one of claims 1-4.

7. A computer readable storage medium characterized in that, a computer program product configured to store a computer program, the computer program causing a computer to execute the method of any one of claims 1-4.

Images