Charging processing method, device, electronic device and computer readable storage medium

Abstract

The present invention discloses a charging processing method, device, electronic device and computer-readable storage medium. The method includes: constructing an objective function for determining the power curve of a target charging pile, wherein the objective function is obtained based on the charging target strategy of the target charging pile; determining the charging demand constraint and load constraint of the objective function; solving the objective function based on the charging demand constraint and the load constraint to obtain the power curve of the target charging pile. The present invention solves the technical problem that it is difficult to comprehensively consider the constraints of the charging power when determining the charging power of the charging pile in the related art, so that the obtained charging power does not meet the actual expectations.

Description

Technical Field

[0001] The present invention relates to the field of computers, and in particular to a charging processing method, device, electronic device and computer-readable storage medium. Background Art

[0002] Transportation electrification is one of the main means to significantly reduce carbon emissions. In recent years, it has received widespread attention from countries around the world, resulting in a continuous and substantial increase in the number of electric vehicles in the world. With the growth of electric vehicle ownership, infrastructure such as charging piles and charging stations are also being built. From the perspective of the power grid, the charging load of electric vehicles is a high-power load. If it is not properly regulated, it will affect the safe operation of the power grid. The most common situation is that the transformer capacity of the charging station is overloaded. However, electric vehicles can usually be used as flexible loads. For charging facilities in typical areas such as residential or commercial areas, the parking time of electric vehicles is usually much longer than the actual time required for charging, which makes it possible for charging stations to regulate the charging load of electric vehicles. Through orderly charging control at the charging station level, power overload of the charging station can be avoided, the charging load can be smoothed, and the operating cost of the charging station can be saved in areas with time-of-use electricity prices. Therefore, a reasonable orderly charging operation strategy within the station is needed.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present invention provide a charging processing method, device, electronic device and computer-readable storage medium to at least solve the technical problem in the related art that it is difficult to comprehensively consider the constraints of the charging power when determining the charging power of a charging pile, so that the obtained charging power does not meet the actual expectations.

[0005] According to one aspect of an embodiment of the present invention, a charging processing method is provided, comprising: constructing an objective function for determining a power curve of a target charging pile, wherein the objective function is obtained based on a charging target strategy of the target charging pile; determining a charging demand constraint and a load constraint of the objective function; and solving the objective function based on the charging demand constraint and the load constraint to obtain the power curve of the target charging pile.

[0006] Optionally, determining the charging demand constraint of the objective function includes: determining historical charging energy data and historical charging power data of the target charging pile within a predetermined time period; constructing the charging power constraint and charging energy constraint of the target charging pile based on the historical charging energy data and the historical charging power data; and determining the charging demand constraint based on the charging power constraint and the charging energy constraint.

[0007] Optionally, determining the historical charging energy data and the historical charging power data of the target charging pile within the predetermined time period includes: obtaining the target charging pile data of the target charging pile within the predetermined time period and the target electric vehicle charging data of the target electric vehicle connected to the target charging pile; determining the historical charging energy data and the historical charging power data of the target charging pile within the predetermined time period based on the target charging pile data and the target electric vehicle charging data.

[0008] Optionally, determining the charging demand constraint based on the charging power constraint and the charging energy constraint includes: determining a unified constraint expression of the charging power constraint and the charging energy constraint based on the charging power constraint and the charging energy constraint; converting the unified constraint expression into a conditional value-at-risk CVaR constraint corresponding to the target charging pile; and determining the charging demand constraint based on the CVaR constraint.

[0009] Optionally, determining the charging demand constraint based on the CVaR constraint includes: determining the charging demand constraint under predetermined conservative parameters based on the CVaR constraint, wherein the predetermined conservative parameters are used to control the degree of conservatism of the obtained power curve of the target charging pile.

[0010] Optionally, determining the load constraint of the objective function includes: obtaining the capacity data of the target distribution transformer corresponding to the target charging pile within the predetermined time period, and the load curve of other equipment connected to the target distribution transformer; determining the load constraint of the objective function based on the capacity data of the target distribution transformer and the load curve of the other equipment.

[0011] According to one aspect of an embodiment of the present invention, a charging processing device is provided, including: a construction module, used to construct an objective function for determining a power curve of a target charging pile, wherein the objective function is obtained based on the charging target strategy of the target charging pile; a determination module, used to determine the charging demand constraint and the load constraint of the objective function; and a solution module, used to solve the objective function based on the charging demand constraint and the load constraint to obtain the power curve of the target charging pile.

[0012] According to one aspect of an embodiment of the present invention, an electronic device is provided, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement any of the charging processing methods described above.

[0013] According to one aspect of an embodiment of the present invention, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute any of the charging processing methods described above.

[0014] According to one aspect of an embodiment of the present invention, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the charging processing method described in any one of the above is implemented.

[0015] In an embodiment of the present invention, an objective function for determining a power curve of a target charging pile is constructed, wherein the objective function is obtained based on the charging target strategy of the target charging pile, and the charging demand constraint and the load constraint of the objective function are determined, and then the objective function can be solved based on the charging demand constraint and the load constraint to obtain the power curve of the target charging pile. Since the objective function is obtained based on the charging target strategy, the objective function is in line with actual expectations, and since the charging demand constraint and the compliance constraint are determined, a comprehensive consideration is made when determining the power curve, so that the obtained power curve meets actual expectations, thereby solving the technical problem in the related art that it is difficult to comprehensively consider the constraints of the charging power when determining the charging power of the charging pile, so that the obtained charging power does not meet actual expectations. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0017] Figure 1 is a flow chart of a charging processing method according to an embodiment of the present invention;

[0018] Figure 2 is a structural block diagram of a charging processing device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0020] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention 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 interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0021] First, some nouns or terms that appear in the description of the embodiments of the present application are subject to the following explanations:

[0022] CvaR: Conditional Value at Risk is an investment risk measurement method developed on the basis of VaR (Value at Risk). Compared with VaR, CVaR satisfies subadditivity, positive homogeneity, monotonicity and transitive invariance, so CVaR is a consistent risk measurement method.

[0023] Example 1

[0024] According to an embodiment of the present invention, an embodiment of a charging processing method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0025] Figure 1 is a flow chart of a charging processing method according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0026] Step S102, constructing an objective function for determining a power curve of a target charging pile, wherein the objective function is obtained according to a charging target strategy of the target charging pile;

[0027] Step S104, determining the charging demand constraint and the load constraint of the objective function;

[0028] Step S106, based on the charging demand constraint and the load constraint, solve the objective function to obtain the power curve of the target charging pile.

[0029] Through the above steps, an objective function for determining the power curve of the target charging pile is constructed, wherein the objective function is obtained according to the charging target strategy of the target charging pile, and the charging demand constraint and load constraint of the objective function are determined, and then the objective function can be solved based on the charging demand constraint and the load constraint to obtain the power curve of the target charging pile. Since the objective function is obtained according to the charging target strategy, the objective function is in line with actual expectations. Since the charging demand constraint and the compliance constraint are determined, a comprehensive consideration is made when determining the power curve, so that the obtained power curve meets the actual expectations, thereby solving the technical problem that it is difficult to comprehensively consider the constraints of the charging power when determining the charging power of the charging pile in the related technology, so that the obtained charging power does not meet the actual expectations.

[0030] As an optional embodiment, an objective function for determining the power curve of the target charging pile is constructed, wherein the objective function is obtained based on the charging target strategy of the target charging pile. The charging target strategy can be determined in different identities. For example, when acting as a charging station operator, the charging target strategy can be an objective function for minimizing operating costs or an objective function for maximizing revenue. Customized settings can be made according to actual applications and scenarios. An objective function that meets the charging target strategy is obtained, and the objective function is solved to obtain the optimal power curve under the charging target strategy. The power curve is made to meet the charging target strategy and meet user expectations.

[0031] As an optional embodiment, the charging demand constraint and the load constraint of the objective function are determined, wherein the charging demand constraint is a constraint set based on the charging demand, and the load constraint is a constraint set based on the load carrying capacity. By determining the charging demand constraint and the load constraint of the objective function, a comprehensive consideration is made, which not only considers the charging demand but also the load capacity, so that the obtained power curve is effective, reasonable, and comprehensive.

[0032] As an optional embodiment, determining the charging demand constraint of the objective function may include multiple ways, for example, the historical charging energy data and the historical charging power data of the target charging pile within a predetermined time period may be determined, and the charging power constraint and the charging energy constraint of the target charging pile may be constructed based on the historical charging energy data and the historical charging power data, and the charging demand constraint may be determined based on the charging power constraint and the charging energy constraint. The historical charging energy record and the historical charging power record are obtained because the historical records often enable the charging law that can reflect the charging demand, for example, electric vehicles in residential areas are generally charged in the evening and parked until the next morning, and electric vehicles in commercial areas or office areas are generally parked in the morning until the evening, etc., which makes it possible to use data to predict future charging demand. Therefore, the historical charging energy data and the historical charging power data of the target charging pile within a predetermined time period are used for processing, so that the obtained charging power constraint and the charging energy constraint are more reasonable. Optionally, when constructing the charging power constraint and the charging energy constraint of the target charging pile based on the historical charging energy data and the historical charging power data, the charging power constraint and the charging energy constraint of the target charging pile may be constructed with reference to the power-energy boundary model.

[0033] As an optional embodiment, when determining the historical charging energy data and historical charging power data of the target charging pile within the predetermined time period, it is known that the historical charging energy data and historical charging power data of the target charging pile are generally not directly obtainable. Therefore, the target charging pile data of the target charging pile within the predetermined time period and the target electric vehicle charging data of the target electric vehicle connected to the target charging pile can be obtained, and the historical charging energy data and historical charging power data of the target charging pile within the predetermined time period can be determined based on the target charging pile data and the target electric vehicle charging data. For example, the arrival time period number t of the target electric vehicle connected to each target charging pile a , leaving time period number t d , initial charge E a , expected power E d and other information, as well as the maximum battery capacity E max and the charging power P of the charging pile chg , the charging demand of the target electric vehicle can be calculated, and then the charging demand of the target charging pile can be determined.

[0034] As an optional embodiment, when determining the charging demand constraint based on the charging power constraint and the charging energy constraint, a unified constraint expression of the charging power constraint and the charging energy constraint can be determined based on the charging power constraint and the charging energy constraint; the unified constraint expression can be converted into the conditional value at risk CVaR constraint corresponding to the target charging pile; and the charging demand constraint can be determined based on the CVaR constraint, so that the obtained charging demand constraint can be more reasonable.

[0035] Optionally, in order to make the conservative degree of the charging demand constraint adjustable, the charging demand constraint under predetermined conservative parameters may also be determined based on the CVaR constraint, wherein the predetermined conservative parameters are used to control the conservative degree of the power curve of the target charging pile.

[0036] As an optional embodiment, the load constraint of the objective function is determined, the capacity data of the target distribution transformer corresponding to the target charging pile within a predetermined time period and the load curve of other equipment connected to the target distribution transformer are obtained, and the load constraint of the objective function is determined based on the capacity data of the target distribution transformer and the load curve of other equipment. That is, the load is guaranteed to ensure the reasonable operation of the target charging pile.

[0037] Based on the above embodiments and optional embodiments, an optional implementation is provided, which is described in detail below.

[0038] In an optional implementation of the present invention, a control scheme for orderly charging operation of electric vehicles in a charging station based on distributed robust opportunity constraints is provided. When the historical operation data of the charging station is known, including the arrival time, departure time, starting power, expected power and other data of the historical charging demand, distributed robust opportunity constraints are used to construct the charging demand for each charging pile in the station, which can take into account the regularity and uncertainty of the charging demand, formulate a reasonable power curve for the charging station, and realize the safe and economical operation of the charging station. The following is a detailed introduction to the optional implementation of the present invention:

[0039] S1, determine the objective function of the target charging pile;

[0040] The method provided in the optional implementation mode of the present invention is to design an orderly charging operation strategy for electric vehicles from the perspective of the charging station operator, so the goal should be to maximize the revenue of the charging station operator or minimize the operating cost. The specific mathematical expression is given below, taking minimizing the operating cost as an example. The operation optimization objective function of each target charging pile in the charging station with the goal of minimizing the operating cost is:

[0041]

[0042] Where c(t) is the electricity price of time-sharing electricity in time period t, P j (t) is the power of charging pile j in time period t, ΔT refers to the time interval, and J is the set of all charging piles in the station.

[0043] S2, constructing the constraint conditions of the objective function of the target charging pile, wherein the constraint conditions include charging demand constraint and load constraint;

[0044] (I) Regarding charging demand constraints:

[0045] 1) First, discretize the time and divide the time window (same as the above-mentioned predetermined time period) into a total of T time periods according to the time interval of ΔT. Then obtain historical data, in which the arrival time period number t of a single electric vehicle is known. a , leaving time period number t d , initial charge E a , expected power E d and other information, as well as the maximum battery capacity E max and the charging power P of the charging pile chg , we can calculate the power-energy boundary p (t), e (t), That is, the power lower bound, power upper bound, energy lower bound, and energy upper bound are used to characterize the charging demand of a single electric vehicle. The power boundary represents the power range allowed for electric vehicles to charge in each time period. Without considering the discharge of the vehicle to the power grid, if there is a car connected to the charging pile, the power range in this period is 0 to the rated power. If there is no car connected, the power can only be 0. The energy boundary represents the possible charging plan of the electric vehicle. The basic principle is that the charging process must reach the expected power before leaving, but it cannot exceed the battery capacity. By superimposing the power-energy boundaries of all electric vehicles connected to a charging pile one after another, the demand function of a single charging pile can be obtained, that is:

[0046]

[0047]

[0048] Among them, I j is the set of electric vehicles connected to charging station j, P j (t), E j (t), is the power and energy boundary of charging pile j in time period t.

[0049] Then the charging power constraint and charging energy constraint of all charging piles in the charging station can be obtained as follows:

[0050]

[0051]

[0052] 2) Since the charging power constraint and charging energy constraint constructed in step 1) are constructed with the charging demand of electric vehicles known, certain rules can be obtained based on historical data. Generally speaking, the charging demand of electric vehicles in residential areas, commercial areas and other destinations has certain rules. For example, electric vehicles in residential areas are generally charged in the evening and parked until the next morning, and electric vehicles in commercial areas or office areas are generally parked from morning to evening, etc. This makes it possible to use data to predict future charging demand. In the real-time control stage, in step 1), the power and energy boundary parameters P j (t), E j (t), It can be regarded as a random variable. For a charging station, if it saves the charging demand data of the historically connected electric vehicles, it can construct charging power constraints and charging energy constraints for each historical operation day. Correspondingly, the charging pile power-energy boundary generated in this way is a sample of the corresponding random variable. Considering that both the power and energy boundaries are random variables, the above charging pile power and energy boundary constraints should be described by chance constraints, that is:

[0053]

[0054]

[0055]

[0056]

[0057]

[0058] Where 1-η represents the confidence level of the above constraints.

[0059] It should be noted that for convenience, the confidence levels of all the above constraints can be the same. Let ξ represent the vector of all random variables, and x represents the decision variable P. j (t), then all the expressions in the above chance constraints are linear inequalities about the random variable ξ. Without loss of generality, they can be rewritten as the following unified constraint expression:

[0060]

[0061] Where K is the number of all opportunity constraints. Take K = 1 as an example. k (x) represents the kth dimension P j (t), the other column vectors are 0, which become row vectors after transposition, b k (x) = -Pj (t), ξ is k-dimensional.

[0062] 3) To use the above opportunity constraint for calculation, it is also necessary to determine the distribution of the random variable ξ. For this purpose, a distributed robust opportunity constraint can be used. The principle is to find the distribution with the highest probability of constraint violation in the possible distribution set. After the historical sample of ξ is known, a distributed robust opportunity constraint based on Wasserstein distance is used. To this end, the opportunity constraint is first rewritten as a conditional value at risk (CVaR) constraint, namely:

[0063]

[0064] Rewrite the expected expression as the maximum combination of two linear functions, that is:

[0065]

[0066] where a k,1 (x) = a k (x),a k,2 (x)=0,b k,1 (x,β k )=b(x)-(1-η)β k ,b k,2 (x,β k )=ηβ k .

[0067] The distributed robust chance constraint based on Wasserstein distance requires first roughly setting the set where the random variables are located. In this patent, the multifaceted set Then the expression of CVAR constraint transformed into distributed robust chance constraint is as follows:

[0068]

[0069]

[0070]

[0071] ||C T γ k,dh -a k,h (x)|| ∞ ≤λ k

[0072] γ k,dh ≥0

[0073] It should be noted that D is the number of random variable samples, represents the dth sample of ξ, and ε represents the radius of the Wasserstein sphere. k ,s k ,γ k,dh They are all dual variables generated in the conversion process and added to the optimization model as decision variables. It should also be noted that the polyhedron is a set of random variables ξ, which is usually roughly set, such as S = {ξ│ξ≥0}. C and d are just coefficients in the polyhedron. C is the coefficient matrix, and d is the right-hand vector, which has no specific physical meaning. For example, for the above example, C = -I (negative unit matrix) and d = 0 (all zero vector).

[0074] (ii) load constraints, i.e., constraints to ensure that the overall load is not overloaded at the charging station level;

[0075]

[0076] Among them, Cap is the capacity of the distribution transformer where the charging station is located, and L(t) is the load curve of the basic load (residential load, industrial load, commercial load, etc.) simultaneously connected to the transformer.

[0077] S3, solve the objective function according to the above constraints to obtain the power curve P that should be allocated to each charging pile j (t).

[0078]

[0079] st

[0080]

[0081]

[0082]

[0083] Through the above optional implementation, at least the following beneficial effects can be achieved:

[0084] (1) The charging operator can collect historical charging demand data, and the calculated charging pile power curve can adapt to the uncertainty of charging demand;

[0085] (2) Using distributed robust chance constraints to describe the uncertainty of charging demand has the advantage of adjustable conservativeness, that is, charging station operators can control the conservativeness of decisions by setting different conservative parameters, thereby obtaining different control effects, which is flexible.

[0086] (3) It can be applied to the orderly charging of a charging station. The power curve result obtained by the method provided in the optional embodiment of the present invention can maximize the profit of the charging station operator while ensuring the safe operation of the charging station and the power grid.

[0087] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0088] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of various embodiments of the present invention.

[0089] Example 2

[0090] According to an embodiment of the present invention, a device for implementing the above charging processing method is also provided. Figure 2 is a structural block diagram of a charging processing device according to an embodiment of the present invention. Figure 2 As shown, the device includes: a construction module 202, a determination module 204 and a solution module 206. The device is described in detail below.

[0091] A construction module 202 is used to construct an objective function for determining the power curve of a target charging pile, wherein the objective function is obtained based on the charging target strategy of the target charging pile; a determination module 204 is connected to the above construction module 202, and is used to determine the charging demand constraints and load constraints of the objective function; a solution module 206 is connected to the above determination module 204, and is used to solve the objective function based on the charging demand constraints and load constraints to obtain the power curve of the target charging pile.

[0092] It should be noted here that the above-mentioned construction module 202, determination module 204 and solution module 206 correspond to steps S102 to S106 in implementing the charging processing method, and the instances and application scenarios implemented by the multiple modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned embodiment 1.

[0093] Example 3

[0094] According to another aspect of an embodiment of the present invention, there is further provided an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor, wherein the processor is configured to execute the instructions to implement any one of the above-mentioned charging processing methods.

[0095] Example 4

[0096] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device can execute any of the above-mentioned charging processing methods.

[0097] Example 5

[0098] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the charging processing method described in any one of the above is implemented.

[0099] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0100] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0101] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units 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 units or modules, which can be electrical or other forms.

[0102] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0103] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0104] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0105] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A charging processing method, characterized in that: include: Constructing an objective function for determining a power curve of a target charging pile, wherein the objective function is obtained according to a charging target strategy of the target charging pile; Determining a charging demand constraint and a load constraint of the objective function; Based on the charging demand constraint and the load constraint, solving the objective function to obtain a power curve of the target charging pile; Wherein, determining the charging demand constraint of the objective function includes: Determine the historical charging energy data and the historical charging power data of the target charging pile within a predetermined time period; Based on the historical charging energy data and the historical charging power data, constructing a charging power constraint and a charging energy constraint of the target charging pile; Determining the charging demand constraint according to the charging power constraint and the charging energy constraint; Wherein, determining the charging demand constraint according to the charging power constraint and the charging energy constraint includes: Determining a unified constraint expression of the charging power constraint and the charging energy constraint according to the charging power constraint and the charging energy constraint; Convert the unified constraint expression into a conditional value at risk CVaR constraint corresponding to the target charging pile; Based on the CVaR constraint, the charging demand constraint is determined.

2. The method according to claim 1, characterized in that The determining of the historical charging energy data and the historical charging power data of the target charging pile within a predetermined time period includes: Acquire target charging pile data of the target charging pile and target electric vehicle charging data of the target electric vehicle connected to the target charging pile within the predetermined time period; According to the target charging pile data and the target electric vehicle charging data, historical charging energy data and historical charging power data of the target charging pile within a predetermined time period are determined.

3. The method according to claim 1, characterized in that The determining the charging demand constraint based on the CVaR constraint includes: Based on the CVaR constraint, the charging demand constraint under a predetermined conservative parameter is determined, wherein the predetermined conservative parameter is used to control the conservative degree of the obtained power curve of the target charging pile.

4. The method according to any one of claims 1 to 3, characterized in that The determining of the load constraint of the objective function comprises: Acquire the capacity data of the target distribution transformer corresponding to the target charging pile within a predetermined time period, and the load curves of other devices connected to the target distribution transformer; The load constraint of the objective function is determined according to the capacity data of the target distribution transformer and the load curves of the other equipment.

5. A charging processing device, characterized in that: include: A construction module, used to construct an objective function for determining a power curve of a target charging pile, wherein the objective function is obtained according to a charging target strategy of the target charging pile; A determination module, used to determine the charging demand constraint and the load constraint of the objective function; A solution module, used to solve the objective function based on the charging demand constraint and the load constraint to obtain a power curve of the target charging pile; The determination module is further used to determine the historical charging energy data and the historical charging power data of the target charging pile within a predetermined time period; construct the charging power constraint and the charging energy constraint of the target charging pile based on the historical charging energy data and the historical charging power data; determine the charging demand constraint according to the charging power constraint and the charging energy constraint; Among them, the determination module is also used to determine the unified constraint expression of the charging power constraint and the charging energy constraint based on the charging power constraint and the charging energy constraint; convert the unified constraint expression into the conditional value at risk CVaR constraint corresponding to the target charging pile; and determine the charging demand constraint based on the CVaR constraint.

6. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the charging processing method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the charging processing method according to any one of claims 1 to 4.

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

Images