Charging power adjusting method and device of electric vehicle cluster and electronic equipment

Abstract

The invention provides a charging power adjusting method and device for an electric vehicle cluster and electronic equipment, and relates to the technical field of vehicle charging control, and the method comprises the steps: determining a plurality of electric vehicles contained in the electric vehicle cluster; for each electric vehicle in the plurality of electric vehicles, generating accumulated energy feasible region information and charging power feasible region information when the charging pile is accessed; aggregating the multiple pieces of accumulated energy feasible region information and the multiple pieces of charging power feasible region information corresponding to the multiple electric vehicles to obtain an accumulated energy regulation region and a charging power regulation region of the electric vehicle cluster; and adjusting the charging power of the electric vehicle cluster according to the accumulated energy adjustment domain and the charging power adjustment domain. The method can give full play to the advantage of controllability of an electric vehicle cluster, reduces the problem of load randomness caused by the fact that a large number of electric vehicles do not need to be charged, guides the electric vehicles to participate in power grid operation scheduling in a cluster mode, and reduces the complexity of power grid operation control.

Description

Technical Field

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

[0002] At present, many scholars at home and abroad have studied the problem of evaluating the adjustable capacity of electric vehicles. The research is mainly aimed at electric vehicle clusters that are connected to continuously adjustable charging power and allow V2G charging piles. Since the aggregated power of electric vehicle clusters connected to controlled start-stop charging piles is discrete when aggregated, it is easy to cause the problem scale to become larger and the solution to become slower. Therefore, the research on the regulation potential of start-stop charging piles is generally ignored. However, start-stop charging piles are widely distributed and simple 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 demand for electric vehicles is random, which makes the adjustable potential in the aggregation process also random. It is necessary to consider the randomness of electric vehicles in the analysis and establish an electric vehicle cluster regulation capacity aggregation method that conforms to the actual operation situation.

[0003] Giving full play to the potential of electric vehicle clusters and orderly guiding a large number of electric vehicles to participate in the operation and control of the power grid is an important means of power grid dispatching, which is also conducive to the randomness of the power grid controlling the charging load. However, the existing aggregation methods do not fully consider the types of various charging piles and the randomness of charging loads, especially the lack of effective characterization of the aggregated power of electric vehicle clusters that can only control the start and stop. Directly using integer variables to characterize electric vehicle clusters under large-scale charging piles of this type will slow down the problem solving. If this type of charging pile is not considered, it is difficult to fully characterize the charging characteristics of electric vehicle clusters. There is still room for improvement in the aggregation method of electric vehicle adjustable capacity. Summary of the invention

[0004] The present invention provides a charging power regulation method, device and electronic equipment for an electric vehicle cluster, which can give full play to the controllability advantage of the electric vehicle cluster, reduce the load randomness problem caused by a large number of electric vehicles not needing to be charged, guide electric vehicles to participate in power grid operation scheduling in a cluster manner, and reduce the complexity of power grid operation control.

[0005] In a first aspect, a charging power adjustment method for an electric vehicle cluster is provided, comprising:

[0006] 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 piles to be connected, and the charging pile types include any one of a first charging pile, a second charging pile, and a third charging pile, wherein the first charging pile is a charging pile that only allows charging, but the charging power can be adjusted within a 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 this type of charging pile, and the charging and discharging power thereof can be adjusted within a maximum charging power and a maximum discharging power; the third charging pile is a charging pile that can only charge the electric vehicle, and the charging power cannot be continuously adjusted, and the charging power can only be a rated power or 0;

[0007] For each of the plurality of electric vehicles, generating cumulative energy feasible domain information and charging power feasible domain information when connected to a charging pile;

[0008] Aggregate multiple accumulated energy feasible domain information and multiple charging power feasible domain information corresponding to multiple electric vehicles to obtain the accumulated energy regulation domain and charging power regulation domain of the electric vehicle cluster;

[0009] The charging power of the electric vehicle cluster is adjusted according to the accumulated energy adjustment domain and the charging power adjustment domain.

[0010] Optionally, for each of the multiple electric vehicles, cumulative energy feasible domain information and charging power feasible domain information are generated when the charging pile is connected, including:

[0011] Determine the target charging pile type to which the electric vehicle cluster is connected;

[0012] Based on the target charging pile type, a power constraint condition is generated for each of the multiple electric vehicles after the electric vehicle is connected to the charging pile. The power constraint condition includes a cumulative energy constraint condition and a charging power constraint condition in the corresponding parking period after each electric vehicle is connected to the charging pile.

[0013] According to the power constraints, the feasible domain information of the accumulated energy and the feasible domain information of the charging power of each electric vehicle within a scheduling cycle is generated.

[0014] Optionally, based on the target charging pile type, a power constraint condition is generated for each of the multiple electric vehicles after being connected to the charging pile, including:

[0015] Obtain the historical charging parameters of each electric vehicle in the electric vehicle cluster after it is connected to the charging pile at the historical moment;

[0016] The historical charging parameters are fitted by Monte Carlo method to obtain the target charging parameters of each electric vehicle in a scheduling cycle;

[0017] Based on the target charging parameters, the power constraint of each electric vehicle during the parking period is determined.

[0018] Optionally, based on the power constraint condition, the accumulated energy feasible domain information and charging power feasible domain information of each electric vehicle within a scheduling cycle are generated, including:

[0019] Generate initial cumulative energy feasible domain information and initial charging power feasible domain information for each electric vehicle during the parking period based on the power constraint condition, the initial cumulative energy feasible domain information includes a first cumulative energy feasible domain upper bound and a first cumulative energy feasible domain lower bound, and the initial charging power feasible domain information includes a first charging power feasible domain upper bound and a first charging power feasible domain lower bound, or a first charging power feasible value;

[0020] Expand the time range of the initial cumulative energy feasible domain information and the initial charging power feasible domain information to obtain the cumulative energy feasible domain information and the charging power feasible domain information within a scheduling cycle, the cumulative energy feasible domain information includes the second cumulative energy feasible domain upper bound and the second cumulative energy feasible domain lower bound, the charging power feasible domain information includes the second charging power feasible domain upper bound and the second charging power feasible domain lower bound, or includes the second charging power feasible value.

[0021] Optionally, generating the feasible domain information of initial accumulated energy and the feasible domain information of initial charging power for each electric vehicle during the parking period based on the power constraint condition includes:

[0022] Calculate the upper bound of the feasible domain of the first cumulative energy, the lower bound of the feasible domain of the first cumulative energy, the upper bound of the feasible domain of the first charging power and the lower bound of the feasible domain of the first charging power for each electric vehicle during the parking period based on the power constraint condition, or calculate the feasible value of the first charging power;

[0023] The time range of the initial cumulative energy feasible domain information and the initial charging power feasible domain information is expanded to obtain the cumulative energy feasible domain information and the charging power feasible domain information within a scheduling cycle, including:

[0024] The upper bound of the feasible domain of the first accumulated energy and the lower bound of the feasible domain of the first accumulated energy are respectively extended to the upper bound of the feasible domain of the second accumulated energy and the lower bound of the feasible domain of the second accumulated energy within a scheduling cycle;

[0025] The first upper bound of the feasible domain of charging power and the first lower bound of the feasible domain of charging power are respectively extended to the upper bound of the feasible domain of second charging power and the lower bound of the feasible domain of second charging power within one scheduling cycle; or,

[0026] The first charging power feasible value is expanded to a second charging power feasible value within a scheduling period.

[0027] Optionally, aggregating multiple accumulated energy feasible domain information and multiple charging power feasible domain information corresponding to multiple electric vehicles to obtain the accumulated energy regulation domain and charging power regulation domain of the electric vehicle cluster includes:

[0028] Accumulating multiple second cumulative energy feasible domain upper bounds corresponding to multiple electric vehicles to obtain a third cumulative energy feasible domain upper bound, and accumulating multiple second cumulative energy feasible domain lower bounds corresponding to multiple electric vehicles to obtain a third cumulative energy feasible domain lower bound;

[0029] Constructing a cumulative energy adjustment domain corresponding to an upper bound of the feasible domain of the third cumulative energy and a lower bound of the feasible domain of the third cumulative energy;

[0030] Accumulate multiple second charging power feasible domain upper bounds corresponding to multiple electric vehicles to obtain a third charging power feasible domain upper bound, and accumulate multiple second charging power feasible domain lower bounds corresponding to multiple electric vehicles to obtain a third charging power feasible domain lower bound, and construct a charging power regulation domain corresponding to the third charging power feasible domain upper bound and the third charging power feasible domain lower bound; or,

[0031] A plurality of second charging power feasible values ​​corresponding to a plurality of electric vehicles are integrated to obtain a third charging power feasible value, and a charging power regulation domain including all third charging power feasible values ​​is constructed.

[0032] Optionally, adjusting the charging power of the electric vehicle cluster according to the accumulated energy adjustment domain and the charging power adjustment domain includes:

[0033] Determine the active power value of the electric vehicle cluster at each moment according to the accumulated energy regulation domain and the charging power regulation 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 regulating device for an electric vehicle cluster is provided, comprising:

[0036] A determination module is used to determine multiple electric vehicles included in an electric vehicle cluster, wherein the multiple electric vehicles correspond to the same type of charging piles to be connected, and the charging pile types include 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 a maximum charging power; the second charging pile is a charging pile that charges the electric vehicle and allows the electric vehicle to discharge to the outside through this type of charging pile, and its 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 the charging power cannot be continuously adjusted, and the charging power can only be the rated power or 0;

[0037] A generation module, for generating, for each of the plurality of electric vehicles, cumulative energy feasible domain information and charging power feasible domain information when connected to a charging pile;

[0038] An aggregation module, used to aggregate multiple accumulated energy feasible domain information and multiple charging power feasible domain information corresponding to multiple electric vehicles to obtain the accumulated energy regulation domain and charging power regulation domain of the electric vehicle cluster;

[0039] The regulating module is used to regulate the charging power of the electric vehicle cluster according to the accumulated energy regulating domain and the charging power regulating domain.

[0040] Optionally, a generation module is used to determine the target charging pile type to which the electric vehicle cluster is connected; based on the target charging pile type, generate power constraints for each of the multiple electric vehicles after the electric vehicle is connected to the charging pile, the power constraints including the cumulative energy constraints and charging power constraints for each electric vehicle during the corresponding parking period after the electric vehicle is connected to the charging pile; based on the power constraints, generate the cumulative energy feasible domain information and charging power feasible domain information for each electric vehicle within a scheduling cycle.

[0041] Optionally, when generating power constraint conditions for each of multiple electric vehicles after being connected to a charging pile based on the target charging pile type, a module is generated to obtain historical charging parameters of each electric vehicle in the electric vehicle cluster after being connected to the charging pile at historical moments; the historical charging parameters are fitted by the Monte Carlo method to obtain target charging parameters for each electric vehicle within a scheduling cycle; and the power constraint conditions for each electric vehicle during the parking period are determined based on the target charging parameters.

[0042] Optionally, when generating the cumulative energy feasible domain information and charging power feasible domain information of each electric vehicle within a scheduling cycle according to the power constraint condition, a generation module is used to generate the initial cumulative energy feasible domain information and initial charging power feasible domain information of each electric vehicle within the parking period based on the power constraint condition, the initial cumulative energy feasible domain information includes a first cumulative energy feasible domain upper bound and a first cumulative energy feasible domain lower bound, the initial charging power feasible domain information includes a first charging power feasible domain upper bound and a first charging power feasible domain lower bound, or a first charging power feasible value; the time range of the initial cumulative energy feasible domain information and the initial charging power feasible domain information is expanded to obtain the cumulative energy feasible domain information and charging power feasible domain information within a scheduling cycle, the cumulative energy feasible domain information includes a second cumulative energy feasible domain upper bound and a second cumulative energy feasible domain lower bound, the charging power feasible domain information includes a second charging power feasible domain upper bound and a second charging power feasible domain lower bound, or includes a second charging power feasible value.

[0043] Optionally, when generating the initial cumulative energy feasible domain information and the initial charging power feasible domain information of each electric vehicle during the parking period based on the power constraint condition, a generation module is used to calculate the first cumulative energy feasible domain upper bound, the first cumulative energy feasible domain lower bound, the first charging power feasible domain upper bound and the first charging power feasible domain lower bound of each electric vehicle during the parking period based on the power constraint condition, or calculate the first charging power feasible value; accordingly, when expanding the time range of the initial cumulative energy feasible domain information and the initial charging power feasible domain information to obtain the cumulative energy feasible domain information and the charging power feasible domain information within a scheduling cycle, a generation module is used to expand the first cumulative energy feasible domain upper bound and the first cumulative energy feasible domain lower bound to the second cumulative energy feasible domain upper bound and the second cumulative energy feasible domain lower bound within a scheduling cycle, respectively; expand the first charging power feasible domain upper bound and the first charging power feasible domain lower bound to the second charging power feasible domain upper bound and the second charging power feasible domain lower bound within a scheduling cycle, respectively; or expand the first charging power feasible value to the second charging power feasible value within a scheduling cycle.

[0044] Optionally, an aggregation module is used to accumulate multiple second cumulative energy feasible domain upper bounds corresponding to multiple electric vehicles to obtain the third cumulative energy feasible domain upper bound, and accumulate multiple second cumulative energy feasible domain lower bounds corresponding to multiple electric vehicles to obtain the third cumulative energy feasible domain lower bound; construct a cumulative energy adjustment domain corresponding to the third cumulative energy feasible domain upper bound and the third cumulative energy feasible domain lower bound; accumulate multiple second charging power feasible domain upper bounds corresponding to multiple electric vehicles to obtain the third charging power feasible domain upper bound, and accumulate multiple second charging power feasible domain lower bounds corresponding to multiple electric vehicles to obtain the third charging power feasible domain lower bound, and construct a charging power adjustment domain corresponding to the third charging power feasible domain upper bound and the third charging power feasible domain lower bound; or, integrate multiple second charging power feasible values ​​corresponding to multiple electric vehicles to obtain the third charging power feasible value, and construct a charging power adjustment domain including all third charging power feasible values.

[0045] Optionally, a regulation module is used to determine the active power value of the electric vehicle cluster at each moment according to the accumulated energy regulation domain and the charging power regulation domain; and to regulate the charging power of the electric vehicle cluster in real time based on the active power value.

[0046] According to a third aspect, an electronic device is provided, comprising: a processor and a memory, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method in the first aspect or its various implementations.

[0047] According to a fourth aspect, a computer-readable storage medium is provided for storing a computer program, wherein the computer program enables a computer to execute the method according to the first aspect or its various implementations.

[0048] Through the technical solution provided by the present invention, after determining the multiple electric vehicles included in the electric vehicle cluster, the cumulative energy feasible domain information and charging power feasible domain information when connected to the charging pile can be generated for each of the multiple electric vehicles; then, by aggregating the multiple cumulative energy feasible domain information and multiple charging power feasible domain information corresponding to the multiple electric vehicles, the cumulative energy regulation domain and charging power regulation domain of the electric vehicle cluster are obtained; finally, according to the cumulative energy regulation domain and the charging power regulation domain, the charging power of the electric vehicle cluster is adjusted. The technical solution in this application considers the various types of charging piles that electric vehicles can connect to, and establishes a method for aggregating the regulation capabilities of electric vehicle clusters under various types of charging piles in the distribution network, which can give full play to the controllability advantages of electric vehicle clusters, reduce the load randomness problem caused by a large number of electric vehicles not needing to be charged, guide electric vehicles to participate in the operation and dispatch of the power grid in a cluster manner, and reduce the complexity of power grid operation control.

[0049] It should be understood that the above general description and the following detailed description are only exemplary and explanatory and cannot limit the present invention. Other features and advantages of the present invention will be described in detail in the following specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0051] Figure 1 An application scenario diagram provided for an embodiment of the present application;

[0052] Figure 2 A schematic diagram of a flow chart of a charging power adjustment method for an electric vehicle cluster provided in an embodiment of the present application;

[0053] Figure 3 An example schematic diagram of an initial cumulative energy feasible domain information and initial charging power feasible domain information of a first charging pile for a single electric vehicle provided in an embodiment of the present application;

[0054] Figure 4 An example schematic diagram of initial cumulative energy feasible domain information and initial charging power feasible domain information of a second charging pile for a single electric vehicle provided in an embodiment of the present application;

[0055] Figure 5An example schematic diagram of initial cumulative energy feasible domain information and initial charging power feasible domain information of a third charging pile for a single electric vehicle provided in an embodiment of the present application;

[0056] Figure 6 A schematic diagram of an example of the cumulative energy boundary of an aggregator 1 provided in an embodiment of the present application;

[0057] Figure 7 A schematic diagram of an example of active power boundary of an aggregator 1 provided in an embodiment of the present application;

[0058] Figure 8 A schematic diagram of an example of the cumulative energy boundary of an aggregation quotient 2 provided in an embodiment of the present application;

[0059] Fig. 9 A schematic diagram of an example of active power boundary of an aggregator 2 provided in an embodiment of the present application;

[0060] Fig.10 A schematic diagram of an example of the cumulative energy boundary of an aggregation quotient 3 provided in an embodiment of the present application;

[0061] Fig.11 A schematic diagram of an example of active power boundary of an aggregator 3 provided in an embodiment of the present application;

[0062] Fig.12 A schematic diagram of an example of the cumulative energy boundary of an aggregation quotient 4 provided in an embodiment of the present application;

[0063] Fig.13 A schematic diagram of an example of active power value of an aggregator 4 provided in an embodiment of the present application;

[0064] Fig.14 A schematic diagram of the structure of a charging power regulating device for an electric vehicle cluster provided in an embodiment of the present application;

[0065] Fig.15 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0067] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0068] At present, many scholars at home and abroad have studied the problem of evaluating the adjustable capacity of electric vehicles. The research is mainly aimed at electric vehicle clusters that are connected to continuously adjustable charging power and allow V2G charging piles. Since the aggregated power of electric vehicle clusters connected to controlled start-stop charging piles is discrete when aggregated, it is easy to cause the problem scale to become larger and the solution to become slower. Therefore, the research on the regulation potential of start-stop charging piles is generally ignored. However, start-stop charging piles are widely distributed and simple 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 demand for electric vehicles is random, which makes the adjustable potential in the aggregation process also random. It is necessary to consider the randomness of electric vehicles in the analysis and establish an electric vehicle cluster regulation capacity aggregation method that conforms to the actual operation situation.

[0069] Giving full play to the potential of electric vehicle clusters and orderly guiding a large number of electric vehicles to participate in the operation and control of the power grid is an important means of power grid dispatching, which is also conducive to the randomness of the power grid controlling the charging load. However, the existing aggregation methods do not fully consider the types of various charging piles and the randomness of charging loads, especially the lack of effective characterization of the aggregated power of electric vehicle clusters that can only control the start and stop. Directly using integer variables to characterize electric vehicle clusters under large-scale charging piles of this type will slow down the problem solving. If this type of charging pile is not considered, it is difficult to fully characterize the charging characteristics of electric vehicle clusters. There is still room for improvement in the aggregation method of electric vehicle adjustable capacity.

[0070] In order to solve the above technical problems, the inventive concept of the present invention is to fully consider the operating characteristics of different types of charging piles in actual operation, establish a single electric vehicle regulation capacity aggregation method taking into account multiple types of charging piles, and after considering the randomness of electric vehicles, aggregate single electric vehicles into clusters to evaluate the controllable capacity of electric vehicle clusters, thereby obtaining constraints that characterize the controllable potential of electric vehicle clusters in distribution network operation for power system operation control. The present invention can more completely characterize the impact 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 distinguish between aggregation methods of electric vehicle clusters of different sizes, improve the calculation speed of the problem, and can be further applied to power grid operation control, which is very important for the safe and stable operation of the power grid.

[0071] It should be understood that the technical solution of the present invention can be applied to the following scenarios, but is not limited to:

[0072] In some possible implementations, Figure 1 An application scenario diagram provided by an embodiment of the present invention, such as Figure 1 As shown, the application scenario may include an electronic device 110 and a network device 120. The electronic device 110 may establish a connection with the network device 120 via a wired network or a wireless network.

[0073] Exemplarily, the electronic device 110 may be a desktop computer, a laptop computer, a tablet computer, etc., but is not limited thereto. The network device 120 may be a terminal device or a server, but is not limited thereto. In one embodiment of the present invention, the electronic device 110 may send a request message to the network device 120, and the request message may be used to request to obtain multiple electric vehicles included in the electric vehicle cluster. Further, the electronic device 110 may 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] also, Figure 1 An electronic device 110 and a network device 120 are provided as an example, but other numbers of electronic devices and network devices may be included in practice, and the present invention is not limited thereto.

[0075] In some other possible implementations, the technical solution of the present invention may also be executed by the above-mentioned electronic device 110, or the technical solution of the present invention may also be executed by the above-mentioned network device 120, and the present invention does not limit this.

[0076] After introducing the application scenarios of the embodiments of the present invention, the technical solutions of the present invention will be described in detail below:

[0077] Figure 2 A flowchart of a charging power adjustment method for an electric vehicle cluster provided in an embodiment of the present invention. The method can be performed as follows: Figure 1 The electronic device 110 shown in the figure performs, but is not limited to this. Figure 2 As shown, the method may include the following steps:

[0078] Step 210: Determine a plurality of electric vehicles included in the electric vehicle cluster.

[0079] Among them, an electric vehicle cluster refers to a collection of many dispersed electric vehicle resources. In the same electric vehicle cluster, multiple electric vehicles correspond to the same type of charging piles to be connected, and the charging pile types include any one of the first charging pile, the second charging pile, and the 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 allows electric vehicles to discharge to the outside through this type of charging pile, and its charging and discharging power can be adjusted within the maximum charging power and the maximum discharge power, such as a V2G (Vehicle-to-Grid) charging pile; the third charging pile is a charging pile that can only charge electric vehicles and cannot continuously adjust the charging power. The charging power can only be the rated power or 0, such as a start-stop charging pile.

[0080] The executor of the present application may be a charging station, which is respectively connected to multiple charging piles of multiple charging pile types. After obtaining multiple electric vehicles included in the electric vehicle cluster, the charging power adjustment operation of the subsequent embodiment steps can be performed for the multiple electric vehicles.

[0081] Step 220: For each electric vehicle among the multiple electric vehicles, generate the accumulated energy feasible domain information and charging power feasible domain information corresponding to the connected charging pile.

[0082] For the embodiment of the present disclosure, generating the accumulated energy feasible domain information and charging power feasible domain information corresponding to the connected charging pile for each of the multiple electric vehicles in step 220 may include the following steps:

[0083] Step 220 - 1 : Determine the target charging pile type of the charging pile to which the electric vehicle cluster is connected.

[0084] Among them, the target charging pile type is the type of charging pile connected to 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 listed here. It should be noted that in the following embodiment steps of the present application, the target charging pile type is any one of the first charging pile, the second charging pile and the third charging pile as an example to illustrate the technical solution in the present application, but it does not constitute a specific limitation.

[0085] Step 220 - 2 : Based on the target charging pile type, generate a power constraint condition for each of the multiple electric vehicles after being connected to the charging pile.

[0086] For the disclosed embodiment, step 220-2 of the embodiment may include: obtaining the historical charging parameters of each electric vehicle in the electric vehicle cluster after being connected to the charging pile at a historical moment; fitting the historical charging parameters by the Monte Carlo method to obtain the target charging parameters of each electric vehicle within a scheduling cycle; based on the target charging parameters, determining the power constraint conditions of each electric vehicle during the parking period.

[0087] Among them, the power constraint conditions include the cumulative energy constraint conditions and charging power constraint conditions during the corresponding parking period after each electric vehicle is connected to the charging pile.

[0088] For the embodiment of the present disclosure, as a possible implementation method, 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-mentioned embodiment steps, including: the arrival time of the electric vehicle when it is connected to the first charging pile; , Charging end time , Desired state of charge And the maximum charging power of the first charging pile Based on the target charging parameters, the power constraint conditions for each electric vehicle during the parking period can be determined by the following formulas (1) and (2):

[0089] Formula (1)

[0090] Formula (2)

[0091] In formula (1) for The charging power during the time period means that when adjusting the charging power, the charging power must be within the maximum charging power and not negative; Indicates arrival time Time The cumulative charging capacity of The cumulative charge amount in each period must be within the expected state of charge The desired state of charge is reached at the time of leaving .

[0092] For the embodiment of the present disclosure, as another possible implementation, 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-mentioned embodiment steps, including: the arrival time of the electric vehicle when it is connected to the second charging pile; , Charging end time , Desired state of charge , the maximum negative offset of the state of charge allowed during the charge and discharge process and the 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 constraint conditions 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 in the time period indicates that when adjusting the charging power, the charging power must be kept between the maximum charging power and the discharge power. Here, the discharge power is expressed as the charging power, but it takes a negative value. In formula (4), Indicates arrival time Time The accumulated charging capacity is used to ensure the parking period The accumulated charge in each period must be within the maximum positive and negative deviation of the state of charge allowed, and the time of leaving , charge to the desired state of charge .

[0096] For the embodiment of the present disclosure, as another possible implementation, when the target charging pile type is the third charging pile, the target charging parameters of each electric vehicle in the electric vehicle cluster can be determined based on the above-mentioned embodiment steps, including: the arrival time of the electric vehicle when it is connected 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 constraint conditions 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 Charging power during the time period, indicating that the charging power can only be the rated power or 0; in formula (6), Indicates arrival time Time The accumulated charging capacity is used to ensure the parking period The cumulative charge amount in each period must be equal to the expected state of charge Within, and leave the time, charge to the desired state of charge .

[0100] Step 220-3: Generate the accumulated energy feasible domain information and charging power feasible domain information of each electric vehicle within a scheduling cycle according to the power constraint condition.

[0101] For the embodiments of the present disclosure, step 220-3 of the embodiment may include: generating initial cumulative energy feasible domain information and initial charging power feasible domain information for each electric vehicle during the parking period based on power constraints, the initial cumulative energy feasible domain information including a first cumulative energy feasible domain upper bound and a first cumulative energy feasible domain lower bound, the initial charging power feasible domain information including a first charging power feasible domain upper bound and a first charging power feasible domain lower bound, or a first charging power feasible value; expanding the time range of the initial cumulative energy feasible domain information and the initial charging power feasible domain information to obtain the cumulative energy feasible domain information and the charging power feasible domain information within a scheduling cycle, the cumulative energy feasible domain information including a second cumulative energy feasible domain upper bound and a second cumulative energy feasible domain lower bound, the charging power feasible domain information including a second charging power feasible domain upper bound and a second charging power feasible domain lower bound, or including a second charging power feasible value.

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

[0103] For the embodiment of the present disclosure, as a possible implementation, when the target charging pile type is the first charging pile, the mathematical expressions of the feasible upper bound of the first cumulative energy domain, the feasible lower bound of the first cumulative energy domain, the feasible upper bound of the first charging power domain, and the feasible lower bound of the first charging power domain of each electric vehicle during the parking period can be calculated based on the power constraint condition as follows: Formula (7) to Formula (10):

[0104] Formula (7)

[0105] Formula (8)

[0106] Formula (9)

[0107] Formula (10)

[0108] Formula (7) represents the upper bound of the feasible region of the first cumulative energy: , is the energy trajectory of a single electric vehicle when it is charged at the fastest speed. The fastest charging means that the electric vehicle is connected to the first charging pile and immediately charges at the maximum power. Charge to desired state of charge , then stop charging and wait until Leave. Formula (5) is the lower bound of the feasible region of the first cumulative energy , is the slowest charging energy trajectory for a single electric vehicle. The slowest charging energy trajectory means that the electric vehicle is connected to the first charging pile, stops and waits until "some moment" when it is charged at maximum power. Charge to Here, "a moment" is defined as the time from this moment on with the maximum charging power. Charging, just in Always charge to the desired state of charge Based on the definition of the fastest charging energy trajectory and the slowest charging energy trajectory, it can be concluded that the upper bound of the feasible region of the first cumulative energy of the electric vehicle is formula (7), corresponding to Figure 3 The solid line on the upper part of the parallelogram in the middle, the lower bound of the feasible region of the first cumulative energy is formula (8), corresponding to Figure 3 The dashed line at the bottom of the parallelogram in the middle. Formulas (9) and (10) are the upper bound and the lower bound of the feasible domain of the first charging power, respectively, representing the maximum charging power and the minimum charging power of the electric vehicle 0, respectively corresponding to Figure 3 The upper solid line and the lower dotted line of the middle rectangle.

[0109] For the embodiment of the present disclosure, as another possible implementation, when the target charging pile type is the second charging pile, the mathematical expressions of the feasible upper bound of the first cumulative energy domain, the feasible lower bound of the first cumulative energy domain, the feasible upper bound of the first charging power domain, and the feasible lower bound of the first charging power domain of each electric vehicle during the parking period can be calculated based on the power constraint condition as follows: Formula (11) to Formula (14):

[0110] Formula (11)

[0111] Formula (12)

[0112] Formula (13)

[0113] Formula (14)

[0114] Formula (11) represents the upper bound of the feasible region of the first cumulative energy: , is the energy trajectory of a single electric vehicle when it is charged at the fastest speed. The fastest charging means that the electric vehicle is connected to the second charging pile and immediately charges at the maximum power. Charge to maximum state of charge , stop and wait, at a certain moment, discharge at maximum power Discharge to Leave. Here "some moment" is defined as the state of charge from this moment At maximum discharge power Discharge, just in Discharge to the expected value at any time Formula (12) is the lower bound of the feasible region of the first cumulative energy , is the energy trajectory of a single electric vehicle when charging at the slowest speed. The slowest charging means that the electric vehicle is connected to the charging pile 2 and discharges at the maximum power. Discharge to minimum state of charge , stop and wait, and charge at maximum power at a certain time Here, "a moment" is defined as the state of charge from this moment At maximum charging power Charging, just in Always charge to the desired value Based on the definition of the fastest charging energy trajectory and the slowest charging energy trajectory, it can be concluded that the upper bound of the feasible region of the first cumulative energy of the electric vehicle is formula (11), corresponding to Figure 4 The solid line on the upper part of the parallelogram in the middle, the lower bound of the feasible region of the first cumulative energy is formula (12), corresponding to Figure 4 Formulas (13) and (14) are the upper bound and lower bound of the feasible domain of the first charging power, respectively, representing the maximum charging power and maximum discharging power of the electric vehicle, corresponding to Figure 4 The upper solid line and the lower dotted line of the middle rectangle.

[0115] For the embodiment of the present disclosure, as another possible implementation, when the target charging pile type is the third charging pile, the mathematical expressions of the upper bound of the feasible domain of the first cumulative energy and the lower bound of the feasible domain of the first cumulative energy of each electric vehicle during the parking period can be calculated based on the power constraint condition as formula (15) to formula (16):

[0116] Formula (15)

[0117] Formula (16)

[0118] Formula (15) represents the upper bound of the feasible region of the first cumulative energy: , is the energy trajectory of a single electric vehicle when it is charged the fastest. The fastest charging means that the electric vehicle is connected to the third charging pile and immediately charges at the rated power of the third charging pile. Charge to desired state of charge , then stop charging and wait until Leave. Formula (16) is the lower bound of the feasible region of the first cumulative energy , is the slowest charging energy trajectory for a single electric vehicle. The slowest charging energy trajectory means that the electric vehicle is connected to the third charging pile, stops and waits until "some time" at the rated power of the third charging pile. Charge to Here "a moment" is defined as the moment when the rated power Charging, just in Always charge to the desired state of charge Based on the definition of the fastest charging energy trajectory and the slowest charging energy trajectory, it can be concluded that the upper bound of the feasible region of the first cumulative energy of the electric vehicle is formula (15), corresponding to Figure 5 The solid line on the upper part of the parallelogram in the middle, the lower bound of the feasible region of the first cumulative energy is formula (16), corresponding to Figure 5 The third charging pile can only control the start and stop state, such as Figure 5 As shown, the charging power has only two first charging power feasible values: rated power Or 0, at this time, the upper and lower limits of power are not considered, that is, the upper limit of the feasible domain of the first charging power and the lower limit of the feasible domain of the first charging power are not considered at this time.

[0119] Correspondingly, when the time range of the initial cumulative energy feasible domain information and the initial charging power feasible domain information is expanded to obtain the cumulative energy feasible domain information and the charging power feasible domain information within a scheduling cycle, the embodiment step 220-3 may also include: expanding the first cumulative energy feasible domain upper bound and the first cumulative energy feasible domain lower bound to the second cumulative energy feasible domain upper bound and the second cumulative energy feasible domain lower bound within a scheduling cycle respectively; expanding the first charging power feasible domain upper bound and the first charging power feasible domain lower bound to the second charging power feasible domain upper bound and the second charging power feasible domain lower bound within a scheduling cycle respectively; or, expanding the first charging power feasible value to the second charging power feasible value within a scheduling cycle.

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

[0121]

[0122]

[0123]

[0124]

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

[0126] For the embodiment of the present disclosure, as another possible implementation, when the target charging pile type is the second charging pile, the time range can be extended to one scheduling cycle based on the initial cumulative energy feasible domain information and the initial charging power feasible domain information in the parking period in formulas (11) to (14):

[0127]

[0128]

[0129]

[0130]

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

[0132] For the embodiment of the present disclosure, as another possible implementation, when the target charging pile type is the third charging pile, the time range can be extended to one scheduling cycle based on the initial cumulative energy feasible domain information and the initial charging power feasible domain information in the parking period in formulas (15) to (16):

[0133]

[0134]

[0135]

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

[0137] Step 230: Aggregate multiple pieces of accumulated energy feasible domain information and multiple pieces of charging power feasible domain information corresponding to multiple electric vehicles to obtain the accumulated energy regulation domain and the charging power regulation domain of the electric vehicle cluster.

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

[0139] Step 230-1, accumulating multiple second cumulative energy feasible domain upper bounds corresponding to multiple electric vehicles to obtain a third cumulative energy feasible domain upper bound, and accumulating multiple second cumulative energy feasible domain lower bounds corresponding to multiple electric vehicles to obtain a third cumulative energy feasible domain lower bound.

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

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

[0142]

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

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

[0145]

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

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

[0148]

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

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

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

[0152]

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

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

[0155]

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

[0157] As an embodiment step 230-3b parallel to the embodiment step 230-3a, multiple second charging power feasible values ​​corresponding to multiple 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 step 230-3b of the embodiment, as another possible implementation, when the target charging pile type is the third charging pile, the charging power adjustment domain may be determined as:

[0159]

[0160]

[0161] In the formula, is the charging power regulation domain, is the rated power, is an integer variable, and N is the number of electric vehicles contained in the electric vehicle cluster.

[0162] When the number of electric vehicles in the electric vehicle cluster connected to the third charging pile is large, the integer variable The larger feasible domain leads to a slower solution speed. If the above formula is still used, the complexity of the model will increase, which is not conducive to the application of the model in actual scenarios.

[0163] At this time, the actual characteristics of the third charging pile are taken into account: the start-stop charging piles are mainly for household use, and the charging active power is generally 7-10kW. When continuous variables are used to represent the active power after aggregation, the error is 3.5-5kW. When there are a large number of electric vehicles in the cluster, continuous variables are used to approximate the active power after aggregation, and the error is very small. In actual use, the active error can be adjusted using the energy storage device of the charging station, or it can be directly ignored. After approximation based on the actual operating conditions, the scale of the problem after aggregation and the solution time can be reduced. At this time, the charging power adjustment domain corresponding to the upper bound of the feasible domain of the third charging power and the lower bound of the feasible domain of the third charging power can be determined as:

[0164]

[0165] In the formula, is the upper bound of the feasible region of the third charging power, is the rated power, It is the lower bound of the feasible region of the third charging power.

[0166] Step 240: Adjust the charging power of the electric vehicle cluster according to the accumulated energy adjustment domain and the charging power adjustment domain.

[0167] For the embodiment of the present disclosure, adjusting the charging power of the electric vehicle cluster according to the accumulated energy adjustment domain and the charging power adjustment domain in step 240 may include the following steps:

[0168] Step 240 - 1 : Determine the active power value of the electric vehicle cluster at each moment according to the accumulated energy regulation domain and the charging power regulation domain.

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

[0170] To facilitate the understanding of this solution, the following examples are given here to illustrate the technical solution in this application:

[0171] Now consider 4 aggregators. The charging pile types of aggregators 1 and 2 are the first charging pile and the second charging pile respectively. The charging pile types of aggregators 3 and 4 are both the third charging pile. Take a scheduling period from dusk to the next morning, and study the aggregation of all vehicles from dusk to the next morning. The time period is 17:00-8:30 the next day. There are 10 electric vehicles in aggregator 4 and 1,000 electric vehicles in the other aggregators. The maximum charging power of the first charging pile is 10kW, the maximum charging power of the second charging pile is 10kW, the maximum discharge power is 6kW, and the rated power of the third charging pile is 10kW. The arrival and departure times of electric vehicles are random. According to historical data statistics, within a scheduling cycle, the arrival time of electric vehicles meets The normal distribution of is truncated between 17:00-21:00, and the departure time satisfies A normal distribution of , truncated between 6:30-8:30.

[0172] Scene 1

[0173] Aggregator 1 is connected to the first charging pile. Aggregator 1 is specifically an electric vehicle cluster including 1,000 electric vehicles. Referring to the steps of the above embodiment, the accumulated energy boundary of the accumulated energy regulation domain corresponding to aggregator 1 can be determined as follows: Figure 6 As shown, the active power boundary of the charging power regulation domain corresponding to aggregator 1 is determined as follows Figure 7 shown.

[0174] Scene 2

[0175] Aggregator 2 is connected to the second charging pile. Aggregator 2 is specifically an electric vehicle cluster including 1,000 electric vehicles. Referring to the steps of the above embodiment, the accumulated energy boundary of the accumulated energy regulation domain corresponding to aggregator 2 can be determined as follows: Figure 8 As shown, the active power boundary of the charging power regulation domain corresponding to aggregator 2 is determined as follows Fig. 9 shown.

[0176] Scene 3

[0177] Aggregator 3 is connected to the third charging pile. Aggregator 3 is specifically an electric vehicle cluster including 1,000 electric vehicles. Referring to the above-mentioned embodiment steps, the accumulated energy boundary of the accumulated energy regulation domain corresponding to aggregator 3 can be determined as follows: Fig.10 As shown, the active power boundary of the charging power regulation domain corresponding to the aggregator 3 is determined as follows Fig.11 shown.

[0178] Scene 4

[0179] Aggregator 4 is connected to the third charging pile. Aggregator 4 is specifically an electric vehicle cluster including 10 electric vehicles. Referring to the steps of the above embodiment, the accumulated energy boundary of the accumulated energy regulation domain corresponding to aggregator 4 can be determined as follows: Fig.12 As shown, the active power value of the charging power regulation domain corresponding to the aggregator 4 is determined as follows Fig.13 shown.

[0180] In summary, the technical solution in this application can generate the cumulative energy feasible domain information and charging power feasible domain information when connecting to the charging pile for each of the multiple electric vehicles after determining the multiple electric vehicles included in the electric vehicle cluster; then, by aggregating the multiple cumulative energy feasible domain information and multiple charging power feasible domain information corresponding to the multiple electric vehicles, the cumulative energy regulation domain and charging power regulation domain of the electric vehicle cluster are obtained; finally, according to the cumulative energy regulation domain and the charging power regulation domain, the charging power of the electric vehicle cluster is adjusted. The technical solution in this application considers the various types of charging piles that electric vehicles can connect to, and establishes a method for aggregating the regulation capabilities of electric vehicle clusters under various types of charging piles in the distribution network, which can give full play to the controllability advantages of electric vehicle clusters, reduce the load randomness problem caused by a large number of electric vehicles not needing to be charged, guide electric vehicles to participate in the operation and dispatch of the power grid in a cluster manner, and reduce the complexity of power grid operation control.

[0181] Based on the above Figure 2 A detailed description of the charging power regulation method for the electric vehicle cluster is provided, such as Fig.14 As shown, Fig.14 FIG. 1 is a structural block diagram of a charging power regulating device for an electric vehicle cluster according to an exemplary embodiment. Fig.14 As shown, the device comprises:

[0182] The determination module 1410 is used 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 piles to be connected, and the charging pile types include any one of a first charging pile, a second charging pile, and a third charging pile, wherein the first charging pile is a charging pile that only allows charging, but the charging power can be adjusted within a maximum charging power; the second charging pile is a charging pile that charges the electric vehicle and allows the electric vehicle to discharge to the outside through this type of charging pile, and the charging and discharging power thereof can be adjusted within a maximum charging power and a maximum discharging power; the third charging pile is a charging pile that can only charge the electric vehicle, and the charging power cannot be continuously adjusted, and the charging power can only be a rated power or 0;

[0183] The generating module 1420 may be used to generate, for each of the plurality of electric vehicles, feasible domain information of cumulative energy and feasible domain information of charging power when connected to a charging pile;

[0184] Aggregation module 1430, which can be used to aggregate multiple accumulated energy feasible domain information and multiple charging power feasible domain information corresponding to multiple electric vehicles to obtain the accumulated energy regulation domain and charging power regulation domain of the electric vehicle cluster;

[0185] The adjustment module 1440 may be used to adjust the charging power of the electric vehicle cluster according to the accumulated energy adjustment domain and the charging power adjustment domain.

[0186] In some embodiments of the present application, the generation module 1420 can be specifically used to determine the target charging pile type to which the electric vehicle cluster is connected; based on the target charging pile type, generate power constraints for each of the multiple electric vehicles after the charging pile is connected, the power constraints include the cumulative energy constraints and charging power constraints for each electric vehicle during the corresponding parking period after the electric vehicle is connected to the charging pile; based on the power constraints, generate the cumulative energy feasible domain information and charging power feasible domain information for each electric vehicle within a scheduling cycle.

[0187] In some embodiments of the present application, when generating power constraints for each of a plurality of electric vehicles after being connected to a charging pile based on the target charging pile type, a generation module 1420 can be specifically used to obtain historical charging parameters of each electric vehicle in the electric vehicle cluster after being connected to the charging pile at a historical moment; fit the historical charging parameters by the Monte Carlo method to obtain target charging parameters for each electric vehicle within a scheduling cycle; and determine the power constraints for each electric vehicle during the parking period based on the target charging parameters.

[0188] In some embodiments of the present application, when generating the cumulative energy feasible domain information and the charging power feasible domain information of each electric vehicle within a scheduling cycle according to the power constraint condition, the generation module 1420 can be specifically used to generate the initial cumulative energy feasible domain information and the initial charging power feasible domain information of each electric vehicle within the parking period based on the power constraint condition, the initial cumulative energy feasible domain information includes the first cumulative energy feasible domain upper bound and the first cumulative energy feasible domain lower bound, the initial charging power feasible domain information includes the first charging power feasible domain upper bound and the first charging power feasible domain lower bound, or the first charging power feasible value; expand the time range of the initial cumulative energy feasible domain information and the initial charging power feasible domain information to obtain the cumulative energy feasible domain information and the charging power feasible domain information within a scheduling cycle, the cumulative energy feasible domain information includes the second cumulative energy feasible domain upper bound and the second cumulative energy feasible domain lower bound, the charging power feasible domain information includes the second charging power feasible domain upper bound and the second charging power feasible domain lower bound, or includes the second charging power feasible value.

[0189] In some embodiments of the present application, when generating the initial cumulative energy feasible domain information and the initial charging power feasible domain information of each electric vehicle during the parking period based on the power constraint condition, the generation module 1420 can be specifically used to calculate the upper bound of the first cumulative energy feasible domain, the lower bound of the first cumulative energy feasible domain, the upper bound of the first charging power feasible domain and the lower bound of the first charging power feasible domain of each electric vehicle during the parking period based on the power constraint condition, or calculate the feasible value of the first charging power; accordingly, when the time range of the initial cumulative energy feasible domain information and the initial charging power feasible domain information is extended, the feasible value of the first charging power is obtained. When the accumulated energy feasible domain information and charging power feasible domain information within a scheduling cycle are generated, module 1420 can be specifically used to expand the first accumulated energy feasible domain upper bound and the first accumulated energy feasible domain lower bound to the second accumulated energy feasible domain upper bound and the second accumulated energy feasible domain lower bound within a scheduling cycle respectively; expand the first charging power feasible domain upper bound and the first charging power feasible domain lower bound to the second charging power feasible domain upper bound and the second charging power feasible domain lower bound within a scheduling cycle respectively; or expand the first charging power feasible value to the second charging power feasible value within a scheduling cycle.

[0190] In some embodiments of the present application, the aggregation module 1430 can be specifically used to accumulate multiple second cumulative energy feasible domain upper bounds corresponding to multiple electric vehicles to obtain the third cumulative energy feasible domain upper bound, and accumulate multiple second cumulative energy feasible domain lower bounds corresponding to multiple electric vehicles to obtain the third cumulative energy feasible domain lower bound; construct a cumulative energy adjustment domain corresponding to the third cumulative energy feasible domain upper bound and the third cumulative energy feasible domain lower bound; accumulate multiple second charging power feasible domain upper bounds corresponding to multiple electric vehicles to obtain the third charging power feasible domain upper bound, and accumulate multiple second charging power feasible domain lower bounds corresponding to multiple electric vehicles to obtain the third charging power feasible domain lower bound, and construct a charging power adjustment domain corresponding to the third charging power feasible domain upper bound and the third charging power feasible domain lower bound; or, integrate multiple second charging power feasible values ​​corresponding to multiple electric vehicles to obtain the third charging power feasible value, and construct a charging power adjustment domain including all third charging power feasible values.

[0191] In some embodiments of the present application, the adjustment module 1440 can be specifically used to determine the active power value of the electric vehicle cluster at each moment according to the accumulated energy adjustment domain and the charging power adjustment domain; and adjust the charging power of the electric vehicle cluster in real time based on the active power value.

[0192] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0193] The embodiments of the present application can more completely characterize the impact 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 differentiate between the aggregation methods of electric vehicle clusters of different sizes, thereby improving the calculation speed of the problem, which can be further applied to the operation control of the power grid, and is very important for the safe and stable operation of the power grid.

[0194] In the above, the charging power regulating device of the electric vehicle cluster according to the embodiment of the present invention is described from the perspective of the functional module in combination with the accompanying drawings. It should be understood that the functional module can be implemented in the form of hardware, can be implemented by instructions in the form of software, or can be implemented by a combination of hardware and software modules. Specifically, the steps of the charging power regulating method of the electric vehicle cluster in the embodiment of the present invention can be completed by the hardware integrated logic circuit and / or software instructions in the processor, and the steps of the charging power regulating method of the electric vehicle cluster applied in combination with the embodiment of the present invention can be directly embodied as a hardware decoding processor to execute, or a combination of hardware and software modules in the decoding processor to execute. Optionally, the software module can be located in a mature storage medium in the field 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 completes the steps in the above-mentioned charging power regulating method embodiment of the electric vehicle cluster in combination with its hardware.

[0195] Fig.15 is a schematic block diagram of an electronic device 1500 according to an embodiment of the present invention.

[0196] like Fig.15 As shown, the electronic device 1500 may include:

[0197] The memory 1510 and the processor 1520, the memory 1510 is used to store the 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 present invention.

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

[0199] In some embodiments of the present invention, the processor 1520 may include but is not limited to:

[0200] General-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

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

[0202] Volatile memory and / or non-volatile memory. Among them, the non-volatile memory can be read-only memory (ROM), programmable ROM (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM) or flash memory. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct memory bus random access memory (Direct Rambus RAM, DR RAM).

[0203] In some embodiments of the present invention, the computer program may 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 invention. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the controller.

[0204] like Fig.15 As shown, the electronic device 1500 may further include:

[0205] The transceiver 1530 may be connected to the processor 1520 or the memory 1510 .

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

[0207] It should be understood that the various components in the electronic device are connected via a bus system, wherein the bus system includes not only a data bus but also a power bus, a control bus and a status signal bus.

[0208] The present invention also provides a computer storage medium having a computer program stored thereon, which, when executed by a computer, enables the computer to perform the method of the above method embodiment. In other words, an embodiment of the present invention also provides a computer program product containing instructions, which, when executed by a computer, enables the computer to perform the method of the above method embodiment.

[0209] When software is used for implementation, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present invention 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 a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integration. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (Digital Video Disc, DVD)), or a semiconductor medium (e.g., a solid-state drive (Solid State Disk, SSD)), etc.

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

[0211] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the module is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

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

[0213] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in this application, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A charging power adjustment method for an electric vehicle cluster, characterized in that: include: 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 piles to be connected, and the charging pile type includes any one of a first charging pile, a second charging pile, and a third charging pile, wherein the first charging pile is a charging pile that only allows charging, but the charging power can be adjusted within a maximum charging power; the second charging pile is a charging pile that charges the electric vehicle and allows the electric vehicle to discharge to the outside through this type of charging pile, and the charging and discharging power thereof can be adjusted within a maximum charging power and a 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 a rated power or 0; For each of the plurality of electric vehicles, generating cumulative energy feasible domain information and charging power feasible domain information when connected to a charging pile; Aggregating the plurality of accumulated energy feasible domain information and the plurality of charging power feasible domain information corresponding to the plurality of electric vehicles to obtain the accumulated energy regulation domain and the charging power regulation domain of the electric vehicle cluster; The charging power of the electric vehicle cluster is adjusted according to the accumulated energy adjustment domain and the charging power adjustment domain.

2. The method according to claim 1, characterized in that The generating, for each of the plurality of electric vehicles, the cumulative energy feasible domain information and the charging power feasible domain information when connected to the charging pile comprises: Determine the target charging pile type to which the electric vehicle cluster is connected; Based on the target charging pile type, generating a power constraint condition for each of the multiple electric vehicles after connecting to the charging pile, the power constraint condition including a cumulative energy constraint condition and a charging power constraint condition corresponding to a parking period after each electric vehicle is connected to the charging pile; According to the power constraint condition, the feasible domain information of the accumulated energy and the feasible domain information of the charging power of each electric vehicle in a scheduling cycle is generated.

3. The method according to claim 2, characterized in that The generating, based on the target charging pile type, a power constraint condition for each of the plurality of electric vehicles after being connected to the charging pile comprises: Obtaining historical charging parameters of each electric vehicle in the electric vehicle cluster after being connected to the charging pile at a historical moment; Fitting the historical charging parameters by Monte Carlo method to obtain the target charging parameters of each electric vehicle in a scheduling cycle; Based on the target charging parameter, a power constraint condition of each electric vehicle during the parking period is determined.

4. The method according to claim 2, characterized in that: The generating, according to the power constraint condition, the accumulated energy feasible domain information and the charging power feasible domain information of each electric vehicle in a scheduling cycle includes: Based on the power constraint condition, generating the initial cumulative energy feasible domain information and the initial charging power feasible domain information of each electric vehicle in the parking period, wherein the initial cumulative energy feasible domain information includes a first cumulative energy feasible domain upper bound and a first cumulative energy feasible domain lower bound, and the initial charging power feasible domain information includes a first charging power feasible domain upper bound and a first charging power feasible domain lower bound, or a first charging power feasible value; Expand the time range of the initial cumulative energy feasible domain information and the initial charging power feasible domain information to obtain the cumulative energy feasible domain information and the charging power feasible domain information within a scheduling cycle, the cumulative energy feasible domain information includes the second cumulative energy feasible domain upper bound and the second cumulative energy feasible domain lower bound, the charging power feasible domain information includes the second charging power feasible domain upper bound and the second charging power feasible domain lower bound, or includes a second charging power feasible value.

5. The method according to claim 4, characterized in that The generating of the feasible domain information of the initial accumulated energy and the feasible domain information of the initial charging power of each electric vehicle during the parking period based on the power constraint condition includes: Calculate the upper bound of the feasible domain of the first accumulated energy, the lower bound of the feasible domain of the first accumulated energy, the upper bound of the feasible domain of the first charging power and the lower bound of the feasible domain of the first charging power for each electric vehicle during the parking period based on the power constraint condition, or calculate the feasible value of the first charging power; The step of expanding the time range of the initial cumulative energy feasible domain information and the initial charging power feasible domain information to obtain the cumulative energy feasible domain information and the charging power feasible domain information within a scheduling cycle includes: Expanding the first feasible region upper bound of the accumulated energy and the first feasible region lower bound of the accumulated energy to the second feasible region upper bound of the accumulated energy and the second feasible region lower bound of the accumulated energy within a scheduling cycle respectively; The first upper bound of the feasible domain of charging power and the first lower bound of the feasible domain of charging power are respectively extended to the upper bound of the feasible domain of second charging power and the lower bound of the feasible domain of second charging power within one scheduling cycle; or, The first charging power feasible value is expanded to a second charging power feasible value within a scheduling cycle.

6. The method according to claim 5, characterized in that The aggregating the plurality of accumulated energy feasible domain information and the plurality of charging power feasible domain information corresponding to the plurality of electric vehicles to obtain the accumulated energy regulation domain and the charging power regulation domain of the electric vehicle cluster includes: Accumulating a plurality of upper bounds of the feasible domain of the second accumulated energy corresponding to the plurality of electric vehicles to obtain an upper bound of the feasible domain of a third accumulated energy, and accumulating a plurality of lower bounds of the feasible domain of the second accumulated energy corresponding to the plurality of electric vehicles to obtain a lower bound of the feasible domain of a third accumulated energy; Constructing a cumulative energy adjustment domain corresponding to the upper bound of the third cumulative energy feasible domain and the lower bound of the third cumulative energy feasible domain; Accumulate multiple upper bounds of the second charging power feasible domain corresponding to the multiple electric vehicles to obtain an upper bound of the third charging power feasible domain, and accumulate multiple lower bounds of the second charging power feasible domain corresponding to the multiple electric vehicles to obtain a lower bound of the third charging power feasible domain, and construct a charging power regulation domain corresponding to the upper bound of the third charging power feasible domain and the lower bound of the third charging power feasible domain; or, 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 regulation domain including all the third charging power feasible values ​​is constructed.

7. The method according to claim 6, characterized in that The step of adjusting the charging power of the electric vehicle cluster according to the accumulated energy adjustment domain and the charging power adjustment domain includes: Determining the active power value of the electric vehicle cluster at each moment according to the accumulated energy regulation domain and the charging power regulation domain; The charging power of the electric vehicle cluster is adjusted in real time based on the active power value.

8. A charging power adjustment device for an electric vehicle cluster, characterized in that: include: A determination module, 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 piles to be connected, and the type of charging piles includes any one of a first charging pile, a second charging pile and a third charging pile, wherein the first charging pile is a charging pile that only allows charging, but the charging power can be adjusted within a 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 this type of charging pile, and the charging and discharging power thereof can be adjusted within a maximum charging power and a 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; A generating module, configured to generate, for each of the plurality of electric vehicles, cumulative energy feasible domain information and charging power feasible domain information when connected to a charging pile; An aggregation module, used for aggregating the plurality of accumulated energy feasible domain information and the plurality of charging power feasible domain information corresponding to the plurality of electric vehicles, to obtain the accumulated energy regulation domain and the charging power regulation domain of the electric vehicle cluster; The regulating module is used to regulate the charging power of the electric vehicle cluster according to the accumulated energy regulating domain and the charging power regulating domain.

9. An electronic device, characterized in that: include: A processor and a memory, the memory being used to store a computer program, and the processor being used to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store a computer program, wherein the computer program enables a computer to execute the method according to any one of claims 1 to 7.

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