Electric vehicle charging station charging load control method and device

Abstract

The application discloses an electric vehicle charging station charging load control method and device. The method comprises the following steps: obtaining target data associated with a target charging station in a target time period, the target data comprising: an upper limit of the capacity of a distribution transformer in a target area to which the target charging station belongs, a basic load capacity under the distribution transformer, the number of alternating current charging piles and direct current charging piles in the target charging station, a charging price, and charging demand information of a charging vehicle; constructing a target function and a constraint condition according to the target data and a target working state of the target charging station, the target working state being a to-be-solved quantity, the target function at least comprising one of the following: a charging cost optimization function, a charging time penalty function, a charging fluctuation penalty function, and a power grid peak load shifting optimization function; and solving the target function under the constraint condition to obtain a target charging control strategy of the target charging station. The application solves the technical problem that the charging strategy of a charging station lacks adaptability to power supply in a target area in the related art.

Description

Technical Field

[0001] This application relates to the field of vehicle charging technology, and more specifically, to a method and apparatus for controlling the charging load of an electric vehicle charging station. Background Technology

[0002] Electric vehicles, as a new mode of transportation and a distributed power load with energy storage capabilities, not only meet the demands of energy conservation and emission reduction but also reduce dependence on traditional fossil fuels, making them an important component of the energy internet. Research on technologies related to orderly charging at public charging stations can effectively improve the grid's capacity to support electric vehicle loads, reduce the impact on other users, and enhance the economic efficiency of the energy system.

[0003] However, the existing orderly charging strategy of public charging stations does not fully consider the impact on the entire distribution area. In actual application, it does not fully utilize the charging resources of electric vehicles. In particular, the charging strategy adapted to the heavily overloaded distribution area is not considered. If the load of a certain distribution area exceeds its design capacity, it will lead to insufficient power supply or power overload, which may cause grid failure in severe cases.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This application provides a method and apparatus for controlling the charging load of an electric vehicle charging station, which at least solves the technical problem in the related art that the charging strategy of the charging station is not compatible with the power supply of the power distribution area.

[0006] According to one aspect of the embodiments of this application, a method for controlling the charging load of an electric vehicle charging station is provided, comprising: acquiring target data associated with a target charging station within a target time period, wherein the target data includes at least: the upper limit of the capacity of the distribution transformer in the transformer substation to which the target charging station belongs, the basic load capacity under the distribution transformer, the number of AC charging piles and DC charging piles in the target charging station, the charging price, and the charging demand information of charging vehicles; constructing an objective function and constraints based on the target data and the target operating state of the target charging station, wherein the target operating state is an unknown quantity to be solved, and the objective function includes at least one of the following: a charging cost optimization function, a charging time penalty function, a charging fluctuation penalty function, and a power grid peak shaving and valley filling optimization function; solving the objective function under the constraints to obtain a target charging control strategy for the target charging station, wherein the target charging control strategy includes the target operating state.

[0007] Optionally, the initial time of the target time period is the charging start time of the charging vehicle, and the target time period includes multiple sampling time periods; the charging price includes the charging price in each sampling time period; the charging demand information of the charging vehicle includes the battery capacity, initial state of charge, target state of charge, and charging end time of the vehicle; the target operating status includes the on / off status and output power of each AC charging pile and DC charging pile in the target charging station in each sampling time period.

[0008] Optionally, an objective function is constructed based on the target data and the target operating state of the target charging station, including: obtaining a preset charging cost weight, and determining a charging cost optimization function based on the charging cost weight, the charging price in each sampling time period, and the output power of each AC charging pile and DC charging pile in each sampling time period; and / or, obtaining a preset charging time weight, and determining a charging time penalty function based on the charging time weight and the output power of each AC charging pile and DC charging pile in each sampling time period; and / or, obtaining a preset charging fluctuation weight, and determining the charging time penalty function based on the charging time weight and the output power of each AC charging pile and DC charging pile in each sampling time period. Within a segment, the power fluctuation value is used to determine the charging fluctuation penalty function based on the charging fluctuation weight and the power fluctuation value, where the power fluctuation value is the absolute value of the difference in output power of the same charging pile in two adjacent sampling time periods; and / or, the preset peak shaving and valley filling weight and the revenue generated by the unit peak shaving and valley filling amount are obtained to determine the total load fluctuation value of the distribution transformer in each sampling time period. Based on the peak shaving and valley filling weight, revenue and total load fluctuation value, the power grid peak shaving and valley filling optimization function is determined, where the total load fluctuation value is the square of the difference between the total load of the distribution transformer in the sampling time period and the average power of the distribution transformer in the target time period.

[0009] Optionally, the sum of the charging cost weight and the peak shaving and valley filling weight is 1.

[0010] Optionally, constraints are constructed based on target data and the target operating state of the target charging station, including: constructing charging capacity constraints based on the charging demand information of the charging vehicle, wherein the charging capacity constraints include: the remaining capacity at the end of vehicle charging is not less than the target remaining capacity; and / or, constructing upper and lower limit constraints on charging pile power, wherein the upper and lower limit constraints on charging pile power include: for each sampling time period before the end of vehicle charging, the output power of the DC charging pile during the sampling time period is not greater than a preset maximum DC output power threshold, and the output power of the AC charging pile during the sampling time period is not less than a preset minimum AC output power threshold and not greater than a preset maximum AC output power threshold; and / or, constructing continuous charging constraints on AC charging piles, wherein the continuous charging constraints on AC charging piles include: the AC charging pile continuously charges from the access time to the end of vehicle charging; and / or, constructing power fluctuation constraints, wherein the power fluctuation constraints include: for each sampling time period, the output power of each AC charging pile and DC charging pile during the sampling time period is not less than a preset minimum AC output power threshold and not greater than a preset maximum AC output power threshold; and / or, constructing power fluctuation constraints, wherein the power fluctuation constraints include: for each sampling time period, the output power of each AC charging pile and DC charging pile during the sampling time period is not greater than a preset maximum AC output power threshold. The power fluctuation value within a segment is not less than the difference between the output power within the sampling time period and the output power within the next sampling time period; and / or, based on the upper limit of the distribution transformer's capacity and the base load capacity under the distribution transformer, a transformer capacity constraint condition is constructed, wherein the transformer capacity constraint condition includes: for each sampling time period, the sum of the output power of each AC charging pile and DC charging pile within the sampling time period and the sum of the base load capacity are not greater than the upper limit of the distribution transformer's capacity; and / or, a total transformer load constraint condition is constructed, wherein the total transformer load constraint condition includes: for each sampling time period, the total load of the distribution transformer within the sampling time period is not greater than the upper limit of the distribution transformer's capacity; and / or, a total transformer load fluctuation constraint condition is constructed, wherein the total transformer load fluctuation constraint condition includes: for each sampling time period, the total load fluctuation value of the distribution transformer within the sampling time period is not less than a preset load fluctuation threshold, the load fluctuation threshold being determined by the total load of the distribution transformer within the sampling time period and the average power of the distribution transformer within the target time period.

[0011] Optionally, an objective function and constraints are constructed based on the target data and the target operating state of the target charging station. This includes: responding to the charging control requirements input by the target object, selecting a function corresponding to the charging control requirements from the charging cost optimization function, charging time penalty function, charging fluctuation penalty function, and grid peak shaving and valley filling optimization function to form the objective function. The charging control requirements include at least one of the following: minimum charging cost, shortest charging time, minimum charging power fluctuation, and maximum peak shaving and valley filling benefits; and constructing constraints corresponding to the objective function.

[0012] Optionally, if the time spent solving the objective function exceeds a preset time threshold and no result is obtained, the solution to the objective function is stopped, and the preset charging control strategy is used as the target charging control strategy.

[0013] According to another aspect of the embodiments of this application, a charging load control device for electric vehicle charging stations is also provided, comprising: an acquisition module, configured to acquire target data associated with a target charging station within a target time period, wherein the target data includes at least: the upper limit of the capacity of the distribution transformer in the area to which the target charging station belongs, the basic load capacity under the distribution transformer, the number of AC charging piles and DC charging piles in the target charging station, the charging price, and the charging demand information of charging vehicles; a construction module, configured to construct an objective function and constraints based on the target data and the target operating state of the target charging station, wherein the target operating state is an unknown quantity to be solved, and the objective function includes at least one of the following: a charging cost optimization function, a charging time penalty function, a charging fluctuation penalty function, and a power grid peak shaving and valley filling optimization function; and a solution module, configured to solve the objective function under constraints to obtain a target charging control strategy for the target charging station, wherein the target charging control strategy includes the target operating state.

[0014] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-described electric vehicle charging station charging load control method by running the computer program.

[0015] According to another aspect of the embodiments of this application, an electronic device is also provided, the electronic device including: a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the above-described electric vehicle charging station charging load control method through the computer program.

[0016] In this embodiment, the impact of factors such as charging costs, charging time, charging power, and peak shaving and valley filling benefits are fully considered. Different objective functions are set for different charging needs. By flexibly adjusting the objective functions to meet actual needs, the optimal operation mode of public charging stations can be achieved from an economic perspective. Considering the upper limit of the distribution transformer capacity and the basic load capacity in the area where the target charging station is located, the power demand of public charging stations can be prevented from exceeding the grid load capacity, thus minimizing the impact of public charging stations on public areas. This solves the technical problem of the lack of adaptability between the charging strategy of charging stations and the power supply of the area in related technologies. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0018] Figure 1 This is a schematic diagram of the structure of an optional computer terminal according to an embodiment of this application;

[0019] Figure 2 This is a schematic flowchart of an optional electric vehicle charging station charging load control method according to an embodiment of this application;

[0020] Figure 3 This is a schematic diagram of an optional electric vehicle charging station charging load control device according to an embodiment of this application. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0022] It should be noted that the terms "first," "second," etc., used in the specification, claims, and drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] To better understand the embodiments of this application, the following is a translation and explanation of some nouns or terms that appear in the description of the embodiments of this application:

[0024] Distribution area: In a power grid, a distribution area refers to the smallest unit of power supply in the power system and is also the interface between the power system and users. Each distribution area consists of a transformer and corresponding low-voltage distribution equipment, which is responsible for converting the electrical energy of the high-voltage power grid into low-voltage electrical energy for users.

[0025] Example 1

[0026] According to an embodiment of this application, a charging load control method for an electric vehicle charging station is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0027] The methods and embodiments provided in this application can be executed on mobile terminals, computer terminals, or similar computing devices. Figure 1 A hardware block diagram of a computer terminal (or mobile device) for implementing a charging load control method for electric vehicle charging stations is shown. Figure 1 As shown, the computer terminal 10 (or mobile device 10) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0028] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0029] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the electric vehicle charging station charging load control method in this embodiment of the application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby implementing the above-mentioned application vulnerability detection method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0030] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0031] The display can be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0032] Under the above operating environment, embodiments of this application provide a method for controlling the charging load of an electric vehicle charging station, such as... Figure 2 As shown, the method includes the following steps:

[0033] Step S202: Obtain target data associated with the target charging station within the target time period. The target data includes at least: the upper limit of the capacity of the distribution transformer in the area to which the target charging station belongs, the basic load capacity under the distribution transformer, the number of AC charging piles and DC charging piles in the target charging station, the charging price, and the charging demand information of the charging vehicles.

[0034] Optionally, the target time period is denoted as T, and its initial time is the charging start time of the charging vehicle. The target time period T usually includes multiple sampling time periods.

[0035] To establish a more effective orderly charging control strategy model for public charging stations and reduce their impact on public distribution areas, it is necessary to first obtain data such as the upper limit of the distribution transformer's capacity and the basic load capacity under the distribution transformer within the distribution area where the target charging station is located. This data serves as the foundational information for the orderly charging strategy of the public charging station. The upper limit of the distribution transformer's capacity can be denoted as... The base load capacity of the distribution transformer in the t-th sampling time period can be denoted as P. b,t .

[0036] Since charging prices may change continuously, we can obtain the charging prices for each sampling time period, and denote the charging price for the t-th sampling time period as ρ. t .

[0037] Charging demand information for charging vehicles typically includes: the vehicle's battery capacity E. k Initial State of Charge (SOC) at the start of charging k,0 The expected target state of charge (SOC) upon completion of charging. k,e Vehicle charging end time t k,e It should be noted that if the charging station is idle, meaning no vehicle is charging at that station, the relevant information returned by the charging station will be 0, i.e., t k,e =0, SOC k,0 =0, SOC k,e =0,E k =0.

[0038] The number of AC charging piles in the target charging station can be denoted as N. AC The number of DC charging stations can be denoted as N. DC The target operating status of the charging pile includes: the on / off status and output power of the charging pile during each sampling time period, where 0-1 variables are introduced. This reflects the on / off state of the k-th AC charging pile and the DC charging pile during the t-th sampling time period, where 1 indicates on and 0 indicates off; the output power of the k-th AC charging pile and the DC charging pile during the t-th sampling time period are denoted as follows:

[0039] Step S204: Construct an objective function and constraints based on the target data and the target operating state of the target charging station. The target operating state is an unknown quantity to be solved. The objective function includes at least one of the following: charging cost optimization function, charging time penalty function, charging fluctuation penalty function, and power grid peak shaving and valley filling optimization function.

[0040] As an optional implementation, the charging cost optimization function can be constructed as follows: Obtain a preset charging cost weight, and determine the charging cost optimization function based on the charging cost weight, the charging price in each sampling time period, and the output power of each AC charging pile and DC charging pile in each sampling time period. The specific calculation formula is as follows:

[0041]

[0042] In the formula, ω is the charging cost weight, which is usually associated with the peak shaving and valley filling weight in the power grid peak shaving and valley filling optimization function. The sum of the two is 1. Specifically, the value range of ω is [0,1]. Without considering the peak shaving and valley filling factor, ω is directly taken as 1.

[0043] Optionally, the charging time penalty function can be constructed as follows: obtain a preset charging time weight, and determine the charging time penalty function based on the charging time weight and the output power of each AC charging pile and DC charging pile in each sampling time period. The specific calculation formula is as follows:

[0044]

[0045] In the formula, μ t Weighting of charging time μ t The weight can be a sequence that increases over time, for example, it can be μ. t =0.00001t, because an increasing sequence can gradually increase the impact of charging time on the overall goal without drastically increasing the weight of charging time. In specific applications, it can be set according to the actual situation.

[0046] Optionally, the charging time penalty function can be constructed as follows: Obtain a preset charging fluctuation weight, determine the power fluctuation value of each AC charging pile and DC charging pile in each sampling time period, and determine the charging fluctuation penalty function based on the charging fluctuation weight and the power fluctuation value. The power fluctuation value is the absolute value of the difference in output power between two adjacent sampling time periods for the same charging pile. The specific calculation formula is as follows:

[0047]

[0048] In the formula, β is the charging fluctuation weight β. In this application example, in order to balance the importance of charging fluctuation and other objectives, β is preset to 0.00001. In specific applications, the choice of β value can also be determined according to specific circumstances. Let represent the power fluctuation values ​​of the k-th AC charging pile and the DC charging pile during the t-th sampling time period, respectively. These represent the output power of the k-th AC charging pile and the DC charging pile during the (t+1)-th sampling time period, respectively.

[0049] Optionally, the power grid peak shaving and valley filling optimization function can be constructed as follows: Obtain the preset peak shaving and valley filling weights and the revenue generated per unit of peak shaving and valley filling; determine the total load fluctuation value of the distribution transformer in each sampling time period; and determine the power grid peak shaving and valley filling optimization function based on the peak shaving and valley filling weights, revenue, and total load fluctuation value. The total load fluctuation value is the square of the difference between the total load of the distribution transformer in the sampling time period and the average power of the distribution transformer in the target time period. The specific calculation formula is as follows:

[0050]

[0051] In the formula, 1-ω is the peak shaving and valley filling weight, c represents the revenue generated per unit of peak shaving and valley filling, and ΔP t This represents the total load fluctuation value of the distribution transformer during the t-th sampling time period. P a,t This represents the total load of the distribution transformer during the t-th sampling time period. This represents the average power of the distribution transformer within the target time period T.

[0052] As an optional implementation method, corresponding constraints can be constructed according to different objective functions. The types of constraints typically include: charging power constraints, charging pile power upper and lower limit constraints, AC charging pile continuous charging constraints, power fluctuation constraints, transformer capacity constraints, transformer total load constraints, and transformer total load fluctuation constraints.

[0053] Optionally, charging capacity constraints can be constructed based on the charging demand information of the charging vehicle. These constraints include ensuring that the remaining battery power at the end of charging is not less than the target remaining battery power. Simply put, this means ensuring that when the vehicle leaves after charging, the initial battery power plus the power supplied by the charging station reaches the vehicle's expected target battery power. Specifically, the formulas for the charging capacity constraints for AC charging stations and DC charging stations are as follows:

[0054]

[0055]

[0056] Optionally, the constructed upper and lower limit constraints for charging pile power include: for each sampling time period before the end of vehicle charging, the output power of the DC charging pile during the sampling time period is not greater than a preset maximum DC output power threshold, and the output power of the AC charging pile during the sampling time period is not less than a preset minimum AC output power threshold and not greater than a preset maximum AC output power threshold. Specifically, the formulas for the upper and lower limit constraints for AC charging piles and DC charging piles are as follows:

[0057]

[0058]

[0059] In the formula, Indicates the maximum DC output power threshold. This represents the minimum output power threshold for AC power. This indicates the maximum AC output power threshold.

[0060] Optionally, the continuous charging constraint for the AC charging pile includes: the AC charging pile continuously charges from the moment of connection to the moment the vehicle finishes charging. The specific formula is:

[0061]

[0062] In the formula, This indicates the on / off state of the k-th AC charging pile during the (t-1)-th sampling time period.

[0063] Optionally, the constructed power fluctuation constraint conditions include: for each sampling time period, the power fluctuation value of each AC charging pile and DC charging pile within the sampling time period is not less than the difference between the output power within the sampling time period and the output power within the next sampling time period. The formulas for the power fluctuation constraint conditions of AC charging piles and DC charging piles are as follows:

[0064]

[0065]

[0066] Optionally, transformer capacity constraints can be constructed based on the upper limit of the distribution transformer's capacity and the base load capacity under the distribution transformer. These constraints include: for each sampling time period, the sum of the output power of all AC and DC charging piles during that time period, plus the sum of the base load capacity, does not exceed the upper limit of the distribution transformer's capacity. The specific formula is as follows:

[0067]

[0068] Optionally, the constructed total transformer load constraint includes: for each sampling time period, the total load of the distribution transformer during the sampling time period does not exceed the upper limit of the distribution transformer's capacity. The specific formula is expressed as follows:

[0069]

[0070] Optionally, the constructed total load fluctuation constraint conditions for the transformer include: for each sampling time period, the total load of the distribution transformer is the sum of the output power of each AC charging pile and DC charging pile during the sampling time period and the sum of the basic load capacity; the total load fluctuation value of the distribution transformer during the sampling time period is not less than the preset load fluctuation threshold; the load fluctuation threshold is determined by the total load of the distribution transformer during the sampling time period and the average power of the distribution transformer during the target time period.

[0071] Among them, due to the total load fluctuation ΔP t Since it is a quadratic function, the quadratic form increases the computational complexity, so the total load fluctuation ΔP is... t The linearization of the quadratic function yields its expression as follows: The final formula for the total transformer load fluctuation constraint is:

[0072]

[0073] As an optional implementation, when constructing the objective function and constraints based on the target data and the target operating state of the target charging station, one or more functions corresponding to the charging control requirements can be selected from the charging cost optimization function, charging time penalty function, charging fluctuation penalty function, and grid peak shaving and valley filling optimization function to form the objective function in response to the charging control requirements input by the target object. The charging control requirements include at least one of the following: minimum charging cost, shortest charging time, minimum charging power fluctuation, and maximum peak shaving and valley filling benefit. Constraints corresponding to the objective function are then constructed. This process allows for flexible adjustment of the objective function to meet actual needs.

[0074] For example, when the charging control requirements are to minimize charging costs, shorten charging time, and minimize charging power fluctuations, the objective function can be determined as the sum of the charging cost optimization function, the charging time penalty function, and the charging fluctuation penalty function. The corresponding objective function and constraints are as follows:

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084] Optionally, when the charging control requirements are to minimize charging costs, shorten charging time, minimize charging power fluctuations, and maximize peak shaving and valley filling benefits, the objective function can be determined as the sum of the charging cost optimization function, the charging time penalty function, the charging fluctuation penalty function, and the grid peak shaving and valley filling optimization function. The corresponding objective function and constraints are as follows:

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095] Step S206: Solve the objective function under constraints to obtain the target charging control strategy for the target charging station, wherein the target charging control strategy includes the target operating state.

[0096] Optionally, if the time spent solving the objective function exceeds a preset time threshold and no result is obtained, the solution to the objective function is stopped, and the preset charging control strategy is used as the target charging control strategy.

[0097] In this embodiment, the impact of factors such as charging costs, charging time, charging power, and peak shaving and valley filling benefits are fully considered. Different objective functions are set for different charging needs. By flexibly adjusting the objective functions to meet actual needs, the optimal operation mode of public charging stations can be achieved from an economic perspective. Considering the upper limit of the distribution transformer capacity and the basic load capacity in the area where the target charging station is located, the power demand of public charging stations can be prevented from exceeding the grid load capacity, thus minimizing the impact of public charging stations on public areas. This solves the technical problem of the lack of adaptability between the charging strategy of charging stations and the power supply of the area in related technologies.

[0098] Example 2

[0099] According to an embodiment of this application, an electric vehicle charging station charging load control device is also provided for implementing the electric vehicle charging station charging load control method in Embodiment 1, such as... Figure 3 As shown, the electric vehicle charging station charging load control device includes at least an acquisition module 31, a construction module 32, and a solution module 33, wherein:

[0100] The acquisition module 31 is used to acquire target data associated with the target charging station within the target time period. The target data includes at least: the upper limit of the capacity of the distribution transformer in the area to which the target charging station belongs, the basic load capacity under the distribution transformer, the number of AC charging piles and DC charging piles in the target charging station, the charging price, and the charging demand information of the charging vehicles.

[0101] Optionally, the initial time of the target time period is the charging start time of the charging vehicle, and the target time period includes multiple sampling time periods; the charging price includes the charging price in each sampling time period; the charging demand information of the charging vehicle includes the battery capacity, initial state of charge, target state of charge, and charging end time of the vehicle; the target operating status includes the on / off status and output power of each AC charging pile and DC charging pile in the target charging station in each sampling time period.

[0102] Module 32 is used to construct the objective function and constraints based on the target data and the target operating state of the target charging station. The target operating state is the unknown quantity to be solved. The objective function includes at least one of the following: charging cost optimization function, charging time penalty function, charging fluctuation penalty function, and power grid peak shaving and valley filling optimization function.

[0103] As an optional implementation, the construction module can construct the charging cost optimization function in the following way: obtain the preset charging cost weight, and determine the charging cost optimization function based on the charging cost weight, the charging price in each sampling time period, and the output power of each AC charging pile and DC charging pile in each sampling time period.

[0104] Optionally, the construction module can construct the charging time penalty function in the following way: obtain the preset charging time weight, and determine the charging time penalty function based on the charging time weight and the output power of each AC charging pile and DC charging pile in each sampling time period.

[0105] Optionally, the construction module can construct the charging time penalty function in the following way: obtain the preset charging fluctuation weight, determine the power fluctuation value of each AC charging pile and DC charging pile in each sampling time period, and determine the charging fluctuation penalty function based on the charging fluctuation weight and the power fluctuation value, wherein the power fluctuation value is the absolute value of the difference in output power of the same charging pile in two adjacent sampling time periods.

[0106] Optionally, the construction module can construct the power grid peak shaving and valley filling optimization function in the following way: obtain the preset peak shaving and valley filling weights and the revenue generated by a unit of peak shaving and valley filling, determine the total load fluctuation value of the distribution transformer in each sampling time period, and determine the power grid peak shaving and valley filling optimization function based on the peak shaving and valley filling weights, revenue, and total load fluctuation value, wherein the total load fluctuation value is the square of the difference between the total load of the distribution transformer in the sampling time period and the average power of the distribution transformer in the target time period; wherein the sum of the charging cost weight and the peak shaving and valley filling weight is 1.

[0107] As an optional implementation method, corresponding constraints can be constructed according to different objective functions. The types of constraints typically include: charging power constraints, charging pile power upper and lower limit constraints, AC charging pile continuous charging constraints, power fluctuation constraints, transformer capacity constraints, transformer total load constraints, and transformer total load fluctuation constraints.

[0108] Optionally, the construction module can construct charging power constraints based on the charging demand information of the charging vehicle. The charging power constraints include: the remaining power at the end of the vehicle charging is not less than the target remaining power.

[0109] Optionally, the upper and lower limit constraints of the charging pile power constructed by the construction module include: for each sampling time period before the end of vehicle charging, the output power of the DC charging pile during the sampling time period is not greater than the preset maximum DC output power threshold, and the output power of the AC charging pile during the sampling time period is not less than the preset minimum AC output power threshold and not greater than the preset maximum AC output power threshold.

[0110] Optionally, the continuous charging constraints for the AC charging pile constructed by the construction module include: the AC charging pile continuously charges from the time of connection to the time when the vehicle ends charging.

[0111] Optionally, the power fluctuation constraint constructed by the module includes: for each sampling time period, the power fluctuation value of each AC charging pile and DC charging pile in the sampling time period is not less than the difference between the output power in the sampling time period and the output power in the next sampling time period.

[0112] Optionally, the construction module can construct transformer capacity constraints based on the upper limit of the distribution transformer's capacity and the basic load capacity under the distribution transformer. The transformer capacity constraints include: for each sampling time period, the sum of the output power of each AC charging pile and DC charging pile during the sampling time period and the sum of the basic load capacity are not greater than the upper limit of the distribution transformer's capacity.

[0113] Optionally, the total load constraint for the transformer constructed by the construction module includes: for each sampling time period, the total load of the distribution transformer during the sampling time period is not greater than the upper limit of the distribution transformer's capacity.

[0114] Optionally, the total load fluctuation constraint conditions for the transformer constructed by the construction module include: for each sampling time period, the total load fluctuation value of the distribution transformer during the sampling time period is not less than the preset load fluctuation threshold, and the load fluctuation threshold is determined by the total load of the distribution transformer during the sampling time period and the average power of the distribution transformer during the target time period.

[0115] As an optional implementation, when constructing the objective function and constraints based on the target data and the target operating state of the target charging station, the construction module can respond to the charging control requirements input by the target object. It can select one or more functions corresponding to the charging control requirements from the charging cost optimization function, charging time penalty function, charging fluctuation penalty function, and grid peak shaving and valley filling optimization function to form the objective function. The charging control requirements include at least one of the following: minimum charging cost, shortest charging time, minimum charging power fluctuation, and maximum peak shaving and valley filling benefit. Constraints corresponding to the objective function are then constructed. This process allows for flexible adjustment of the objective function to meet actual needs.

[0116] The solver module 33 is used to solve the objective function under constraints to obtain the target charging control strategy for the target charging station, wherein the target charging control strategy includes the target operating state.

[0117] Optionally, if the time spent by the solving module in solving the objective function exceeds a preset time threshold and no result is obtained, the solving of the objective function is stopped, and the preset charging control strategy is used as the target charging control strategy.

[0118] It should be noted that each module in the electric vehicle charging station charging load control device in this application embodiment corresponds one-to-one with each implementation step of the electric vehicle charging station charging load control method in embodiment 1. Since embodiment 1 has been described in detail, some details not shown in this embodiment can be referred to embodiment 1, and will not be elaborated further here.

[0119] Example 3

[0120] According to an embodiment of this application, a non-volatile storage medium is also provided, which includes a stored computer program, wherein the device containing the non-volatile storage medium executes the electric vehicle charging station charging load control method in Embodiment 1 by running the computer program.

[0121] Specifically, the device containing the non-volatile storage medium executes the following steps by running the computer program: acquiring target data associated with the target charging station within the target time period, wherein the target data includes at least: the upper limit of the capacity of the distribution transformer in the area to which the target charging station belongs, the basic load capacity under the distribution transformer, the number of AC charging piles and DC charging piles in the target charging station, the charging price, and the charging demand information of charging vehicles; constructing an objective function and constraints based on the target data and the target operating state of the target charging station, wherein the target operating state is an unknown quantity to be solved, and the objective function includes at least one of the following: a charging cost optimization function, a charging time penalty function, a charging fluctuation penalty function, and a power grid peak shaving and valley filling optimization function; solving the objective function under the constraints to obtain a target charging control strategy for the target charging station, wherein the target charging control strategy includes the target operating state.

[0122] According to an embodiment of this application, a processor is also provided for running a computer program, wherein the computer program executes the electric vehicle charging station charging load control method in Embodiment 1.

[0123] Specifically, the computer program executes the following steps during runtime: acquiring target data associated with the target charging station within the target time period, wherein the target data includes at least: the upper limit of the capacity of the distribution transformer in the area to which the target charging station belongs, the basic load capacity under the distribution transformer, the number of AC and DC charging piles in the target charging station, the charging price, and the charging demand information of charging vehicles; constructing an objective function and constraints based on the target data and the target operating state of the target charging station, wherein the target operating state is an unknown quantity to be solved, and the objective function includes at least one of the following: a charging cost optimization function, a charging time penalty function, a charging fluctuation penalty function, or a power grid peak shaving and valley filling optimization function; solving the objective function under the constraints to obtain the target charging control strategy for the target charging station, wherein the target charging control strategy includes the target operating state.

[0124] According to an embodiment of this application, an electronic device is also provided, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the electric vehicle charging station charging load control method of Embodiment 1 through the computer program.

[0125] Specifically, the processor is configured to execute the following steps via a computer program: acquire target data associated with the target charging station within a target time period, wherein the target data includes at least: the upper limit of the capacity of the distribution transformer in the area to which the target charging station belongs, the basic load capacity under the distribution transformer, the number of AC charging piles and DC charging piles in the target charging station, the charging price, and the charging demand information of charging vehicles; construct an objective function and constraints based on the target data and the target operating state of the target charging station, wherein the target operating state is an unknown quantity to be solved, and the objective function includes at least one of the following: a charging cost optimization function, a charging time penalty function, a charging fluctuation penalty function, and a power grid peak shaving and valley filling optimization function; solve the objective function under the constraints to obtain a target charging control strategy for the target charging station, wherein the target charging control strategy includes the target operating state.

[0126] The sequence numbers of the above embodiments are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0127] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0128] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0129] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0130] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0131] If the integrated unit is implemented as 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 this application, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0132] The above are merely preferred embodiments of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for controlling the charging load of an electric vehicle charging station, characterized in that, include: Obtain target data associated with the target charging station within the target time period, wherein the target data includes at least: the upper limit of the capacity of the distribution transformer in the area to which the target charging station belongs, the basic load capacity under the distribution transformer, the number of AC charging piles and DC charging piles in the target charging station, the charging price, and the charging demand information of the charging vehicles. Based on the target data and the target operating state of the target charging station, an objective function and constraints are constructed. Specifically, a preset charging fluctuation weight is obtained, and the power fluctuation values ​​of each AC and DC charging pile in each sampling time period are determined. A charging fluctuation penalty function is determined based on the charging fluctuation weight and the power fluctuation values, where the power fluctuation value is the absolute value of the difference in output power between two adjacent sampling time periods for the same charging pile. Power fluctuation constraints are constructed, including: for each sampling time period, the power fluctuation value of each AC and DC charging pile in that sampling time period is not less than the difference between the output power in that sampling time period and the output power in the next sampling time period. The target operating state is the unknown quantity to be solved. The objective function includes at least: a charging cost optimization function, a charging time penalty function, the charging fluctuation penalty function, and a power grid peak shaving and valley filling optimization function. Solving the objective function under the constraints yields a target charging control strategy for the target charging station, wherein the target charging control strategy includes the target operating state; The construction of the objective function and constraints based on the target data and the target operating state of the target charging station includes: In response to the charging control requirements input by the target object, the objective function is formed by selecting a function corresponding to the charging control requirements from the charging cost optimization function, the charging time penalty function, the charging fluctuation penalty function, and the power grid peak shaving and valley filling optimization function. The charging control requirements include: minimum charging cost, shortest charging time, minimum charging power fluctuation, and maximum peak shaving and valley filling benefit. When the charging control requirements are minimum charging cost, shortest charging time, and minimum charging power fluctuation, the objective function is determined to be the sum of the charging cost optimization function, the charging time penalty function, and the charging fluctuation penalty function. Construct the constraints corresponding to the objective function.

2. The method according to claim 1, characterized in that, The initial time of the target time period is the charging start time of the charging vehicle, and the target time period includes multiple sampling time periods; The charging price includes: the charging price within each sampling time period; The charging demand information of the charging vehicle includes: the battery capacity of the charging vehicle, the initial state of charge, the target state of charge, and the time when the vehicle charging ends. The target operating status includes: the on / off status and output power of each AC charging pile and DC charging pile in the target charging station during each sampling time period.

3. The method according to claim 2, characterized in that, Based on the target data and the target operating state of the target charging station, a target function is constructed, including: Obtain a preset charging cost weight, and determine the charging cost optimization function based on the charging cost weight, the charging price in each sampling time period, and the output power of each AC charging pile and DC charging pile in each sampling time period; and / or, Obtain a preset charging time weight, and determine the charging time penalty function based on the charging time weight and the output power of each AC charging pile and DC charging pile in each sampling time period; and / or, Obtain the preset peak shaving and valley filling weights and the revenue generated by a unit peak shaving and valley filling amount, determine the total load fluctuation value of the distribution transformer in each sampling time period, and determine the power grid peak shaving and valley filling optimization function based on the peak shaving and valley filling weights, the revenue, and the total load fluctuation value, wherein the total load fluctuation value is the square of the difference between the total load of the distribution transformer in the sampling time period and the average power of the distribution transformer in the target time period.

4. The method according to claim 3, characterized in that, The sum of the charging cost weight and the peak shaving and valley filling weight is 1.

5. The method according to claim 3, characterized in that, Based on the target data and the target operating state of the target charging station, constraints are constructed, including: Based on the charging demand information of the charging vehicle, charging capacity constraints are constructed, wherein the charging capacity constraints include: the remaining capacity of the vehicle at the end of charging is not less than the target remaining capacity; and / or, A power upper and lower limit constraint condition for charging piles is constructed, wherein the power upper and lower limit constraint condition for charging piles includes: for each sampling time period before the end of vehicle charging, the output power of the DC charging pile during the sampling time period is not greater than a preset maximum DC output power threshold, and the output power of the AC charging pile during the sampling time period is not less than a preset minimum AC output power threshold and not greater than a preset maximum AC output power threshold; and / or, A continuous charging constraint condition for the AC charging pile is constructed, wherein the continuous charging constraint condition for the AC charging pile includes: the AC charging pile continuously charges from the time of connection to the time of vehicle charging completion; and / or, Based on the upper limit of the distribution transformer's capacity and the base load capacity under the distribution transformer, a transformer capacity constraint condition is constructed. This constraint condition includes: for each sampling time period, the sum of the output power of all AC charging piles and DC charging piles during that sampling time period, plus the sum of the base load capacity, does not exceed the upper limit of the distribution transformer's capacity; and / or, A total load constraint condition for the transformer is constructed, wherein the total load constraint condition includes: for each sampling time period, the total load of the distribution transformer during the sampling time period does not exceed the upper limit of the distribution transformer's capacity; and / or, A total load fluctuation constraint condition for the transformer is constructed, wherein the total load fluctuation constraint condition includes: for each sampling time period, the total load fluctuation value of the distribution transformer in the sampling time period is not greater than a preset load fluctuation threshold, and the load fluctuation threshold is determined by the total load of the distribution transformer in the sampling time period and the average power of the distribution transformer in the target time period.

6. The method according to claim 1, characterized in that, The method further includes: If the time spent solving the objective function exceeds a preset time threshold and no result is obtained, the solution to the objective function is stopped, and the preset charging control strategy is used as the target charging control strategy.

7. A charging load control device for an electric vehicle charging station, characterized in that, include: The acquisition module is used to acquire target data associated with the target charging station within a target time period. The target data includes at least: the upper limit of the capacity of the distribution transformer in the area to which the target charging station belongs, the basic load capacity under the distribution transformer, the number of AC charging piles and DC charging piles in the target charging station, the charging price, and the charging demand information of the charging vehicles. The construction module is used to construct an objective function and constraints based on the target data and the target operating state of the target charging station. This includes obtaining a preset charging fluctuation weight, determining the power fluctuation values ​​of each AC and DC charging pile in each sampling time period, and determining a charging fluctuation penalty function based on the charging fluctuation weight and the power fluctuation values. The power fluctuation value is the absolute value of the difference in output power between two adjacent sampling time periods for the same charging pile. Power fluctuation constraints are constructed, including: for each sampling time period, the power fluctuation value of each AC and DC charging pile in that sampling time period is not less than the difference between the output power in that sampling time period and the output power in the next sampling time period. The target operating state is the unknown quantity to be solved. The objective function includes at least: a charging cost optimization function, a charging time penalty function, a charging fluctuation penalty function, and a power grid peak shaving and valley filling optimization function. In response to the charging control requirements input by the target object, the objective function is formed by selecting functions corresponding to the charging control requirements from the charging cost optimization function, the charging time penalty function, the charging fluctuation penalty function, and the power grid peak shaving and valley filling optimization function. The charging control requirements include: minimum charging cost, shortest charging time, minimum charging power fluctuation, and highest peak shaving and valley filling benefit. When the charging control requirements are minimum charging cost, shortest charging time, and minimum charging power fluctuation, the objective function is determined to be the sum of the charging cost optimization function, the charging time penalty function, and the charging fluctuation penalty function. The constraints corresponding to the objective function are then constructed. The solution module is used to solve the objective function under the constraints to obtain the target charging control strategy for the target charging station, wherein the target charging control strategy includes the target operating state.

8. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored computer program, wherein the device containing the non-volatile storage medium executes the electric vehicle charging station charging load control method according to any one of claims 1 to 6 by running the computer program.

9. An electronic device, characterized in that, include: A memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the electric vehicle charging station charging load control method according to any one of claims 1 to 6 through the computer program.

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