A power distribution method, system, and medium for charging piles within a charging station.

Abstract

This application relates to a power allocation method, system, and medium for charging piles in a charging station. The method includes collecting real-time demand information and real-time status information of charging piles, and performing data preprocessing on the real-time power demand information and real-time charging pile status information; inputting the historical data of the charging piles and the real-time demand data into a pre-trained power demand prediction model, and outputting the power demand of each charging pile for a certain period of time in the future through the power demand prediction model, generating a power curve for each charging pile based on the power demand prediction results at different times; inputting the power curve of the charging pile into a pre-established power allocation model, and solving the power allocation model based on an optimization algorithm to obtain the optimal power allocation scheme for each charging pile. This application optimizes the resource utilization rate of the charging station and improves charging efficiency by dynamically adjusting the power allocation of charging piles.

Description

Technical Field

[0001] This application relates to the field of charging station charging system technology, and in particular to a power distribution method, system and medium for charging piles in a charging station. Background Technology

[0002] Electric vehicles, as a clean and efficient mode of transportation, have gained high attention and widespread recognition from governments and consumers worldwide. In recent years, the electric vehicle market has experienced explosive growth.

[0003] The rapid popularization of electric vehicles has brought about huge charging demand, driving the vigorous development of charging infrastructure. As an important place for electric vehicles to replenish their energy, the scale of construction and operational efficiency of charging stations directly affect the user experience and the promotion of electric vehicles. However, compared with the rapidly growing number of electric vehicles, the construction speed of charging infrastructure is still lagging behind, and problems such as difficulty in charging and slow charging continue to plague many electric vehicle users.

[0004] Charging stations are typically equipped with multiple charging piles to meet the charging needs of different electric vehicles. The power requirements of each charging pile vary, mainly depending on factors such as the electric vehicle's battery capacity, remaining charge, and charging mode. For example, the charging power for household electric vehicles is generally around 7kW, while the charging power for commercial electric vehicles can reach 350kW or even higher. Furthermore, the overall power capacity of a charging station is limited, usually determined by the transformer capacity, and cannot simultaneously meet the maximum power requirements of all charging piles. Therefore, how to rationally allocate the power of each charging pile within a limited power capacity to meet the charging needs of different electric vehicles and maximize the utilization rate and operational efficiency of the charging station has become a critical issue that urgently needs to be addressed.

[0005] Existing power allocation methods for charging stations mainly employ a simple equal-distribution strategy, that is, allocating power capacity equally based on the number of charging piles. While this strategy is simple to implement, it has several drawbacks:

[0006] Unable to dynamically adjust power allocation according to actual needs: The equal distribution strategy ignores the differences in charging needs of different electric vehicles, resulting in insufficient power for some charging piles to meet the fast charging needs, while other charging piles may have excess power, resulting in resource waste.

[0007] This leads to resource waste and low charging efficiency: Due to unreasonable power allocation, some electric vehicles need to wait longer to complete charging, which reduces the operating efficiency and service quality of charging stations and increases users' waiting time and usage costs. Summary of the Invention

[0008] The purpose of this application is to provide a power allocation method, system, and medium for charging piles in a charging station, which can optimize the resource utilization of the charging station and improve the overall charging efficiency of the charging station by dynamically adjusting the power allocation of each charging pile.

[0009] To achieve the above objectives, this application provides the following technical solution:

[0010] In a first aspect, embodiments of this application provide a power allocation method for charging piles within a charging station, comprising the following steps:

[0011] Collect real-time demand information and real-time status information of charging piles, and perform data preprocessing on the real-time power demand information and real-time charging pile status information.

[0012] The historical data and real-time demand data of the charging piles are input into a pre-trained power demand prediction model. The power demand prediction model outputs the power demand of each charging pile in the future within a certain period of time. Based on the power demand prediction results at different times, the power curve of each charging pile is generated.

[0013] The power curve of the charging pile is input into a pre-established power allocation model, and the optimal power allocation scheme for each charging pile is obtained by solving the power allocation model based on the optimization algorithm.

[0014] The method further includes the following steps:

[0015] Real-time demand information and real-time charging pile status information are collected and monitored, and the optimal power allocation scheme for each charging pile is iteratively adjusted based on the real-time demand information and real-time charging pile status information.

[0016] The historical data and real-time demand data of the charging piles are input into a pre-trained power demand prediction model. The power demand prediction model outputs the power demand of each charging pile over a certain period of time in the future. Based on the power demand prediction results at different times, a power curve for each charging pile is generated, specifically including:

[0017] The historical data and real-time demand data of the charging piles are input into a pre-trained LSTM neural network model. The LSTM neural network model outputs the power demand of each charging pile in the future within a certain period of time. Based on the power demand prediction results at different times, the power curve of each charging pile is generated.

[0018] The power curve of the charging pile is input into a pre-established power allocation model. An optimization algorithm is used to solve the power allocation model to obtain the optimal power allocation scheme for each charging pile. Specifically, this includes:

[0019] The objective function of the power allocation model is to minimize the total power fluctuation of the charging station, and the specific formula is as follows:

[0020]

[0021] in, Let be the total power of the charging station at time t. The average power of the charging station.

[0022] The constraint functions of the power allocation model include:

[0023]

[0024] Among them, P i (t) represents the power of the i-th device, P tatal,max The maximum operating power allowed by the system.

[0025] The power allocation model is pre-established and trained specifically as follows:

[0026] Initialization parameters:

[0027] 1) Set the maximum power limit Pmax for the charging station;

[0028] 2) Set the time step Δt;

[0029] 3) Obtain the current charging demand Ei for all charging stations;

[0030] 4) Set the vehicle charging power range:

[0031] 0≤Pi(t)≤Pi,max where Pi,max is the maximum power of the i-th charging pile;

[0032] Calculate the objective function:

[0033] The optimization objective is to minimize power fluctuations at the charging station:

[0034]

[0035] in: It is the total power at the current time t; It is the average power: ;

[0036] Calculate the current power requirement:

[0037] For each charging station

[0038] 1) Calculate the remaining charging time:

[0039] If Ti is too short, meaning the vehicle is about to be fully charged, reduce its power allocation priority;

[0040] 2) Calculate the total power demand for the current time window: ;

[0041] 3) Calculate the degree of power fluctuation:

[0042] Power allocation is adjusted based on σ(t);

[0043] Perform dynamic optimization:

[0044] Optimization is performed using gradient descent:

[0045] 1) Calculate the adjustment direction

[0046] If the total power P(t) exceeds the limit, reduce the power of some charging piles with higher power.

[0047] If power fluctuations are large, smooth the power curve; if some vehicles have insufficient battery power, prioritize power allocation.

[0048] 2) Construction optimization problem

[0049] Constraints: , ;

[0050] 3) Solve using an optimization algorithm: Use gradient descent to solve;

[0051] Adjust charging power in real time:

[0052] At each time step t:

[0053] 1) Update Pi(t) based on the new power allocation scheme calculated by optimization;

[0054] 2) Calculate the new total power P(t), ensuring it does not exceed the maximum power;

[0055] 3) Record power fluctuations. If the fluctuations are still large, continue to optimize.

[0056] The method also includes pre-building and training an LSTM neural network model.

[0057] The input data for the LSTM neural network model is the historical charging pile power data P(t) over the past 4 hours.

[0058] The output of the LSTM neural network model is the power prediction value for the next 10 minutes, i.e., the power prediction curves P(t+1), P(t+2), ... P(t+n).

[0059] Secondly, embodiments of this application provide a power distribution system for charging piles within a charging station, comprising:

[0060] The data acquisition and preprocessing module is used to acquire real-time demand information and real-time status information of charging piles, and to perform data preprocessing on the real-time power demand information and real-time charging pile status information.

[0061] The power demand prediction module is used to input the historical data and real-time demand data of the charging pile into a pre-trained power demand prediction model, and output the power demand of each charging pile in the future within a certain period of time through the power demand prediction model. Based on the power demand prediction results at different times, the module generates the power curve of each charging pile.

[0062] The power allocation module is used to input the power curve of the charging pile into a pre-established power allocation model, and solve the power allocation model based on the optimization algorithm to obtain the optimal power allocation scheme for each charging pile.

[0063] Thirdly, embodiments of this application provide a computer-readable storage medium storing program code, which, when executed by a processor, implements the steps of the power allocation method for charging piles in a charging station as described above.

[0064] Fourthly, embodiments of this application provide an electronic device, including:

[0065] Memory, used to store computer programs;

[0066] When the processor executes the computer program stored in the memory, it performs the steps of the power allocation method for charging piles in the charging station as described above.

[0067] Compared with the prior art, the beneficial effects of the present invention are:

[0068] a. Reduce power fluctuations: By dynamically adjusting power allocation through optimization algorithms, the total power fluctuations of charging stations are reduced, thereby improving the stability of the power grid;

[0069] b. Improve charging efficiency: Allocate power according to the actual needs of each charging station to avoid resource waste and improve charging efficiency;

[0070] c. Enhance adaptability: Through real-time monitoring and dynamic adjustment, enhance the charging station's ability to adapt to emergencies and ensure the smooth progress of the charging process. Attached Figure Description

[0071] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0072] Figure 1 This is a flowchart of the method in this application;

[0073] Figure 2 This is a diagram of the LSTM neural network model of this application;

[0074] Figure 3 This is a flowchart of the power allocation process in this application;

[0075] Figure 4 This is a system block diagram of this application;

[0076] Figure 5 This is a diagram of the electronic device described in this application. Detailed Implementation

[0077] The technical solutions of the embodiments of this application will now be described with reference to the accompanying drawings. It should be noted that similar reference numerals and letters in the following drawings indicate similar items; therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0078] The terms “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0079] The terms “first,” “second,” etc., are used only to distinguish one entity or operation from another, and should not be construed as indicating or implying relative importance, nor as requiring or implying any such actual relationship or order between these entities or operations.

[0080] like Figure 1 As shown, this application provides a power allocation method for charging piles within a charging station, comprising the following steps:

[0081] S1: Collect real-time demand information and real-time status information of charging piles, and perform data preprocessing on the real-time demand information and real-time status information of charging piles. The real-time demand information and status information of charging piles include power demand information, battery status information, charging time information, etc. The above information can be collected by equipping each charging pile with a sensor or by other means of obtaining the above information.

[0082] S2: Input the historical data and real-time demand data of the charging piles into a pre-trained power demand prediction model. The power demand prediction model outputs the power demand of each charging pile over a certain period of time. Based on the power demand prediction results at different times, a power curve for each charging pile is generated. It is worth noting that before using the power allocation method for power allocation, the power demand prediction model needs to be trained. The power demand prediction model can be an LSTM neural network model. The power demand prediction flowchart is as follows: Figure 2 As shown.

[0083] S3: Input the power curve of the charging pile into the pre-established power allocation model, and solve the power allocation model based on the optimization algorithm to obtain the optimal power allocation scheme for each charging pile. It is worth noting that before using the power allocation method for power allocation, it is also necessary to establish and train the power allocation model. For example, the flowchart of the power allocation model solution is as follows: Figure 3 As shown.

[0084] In one embodiment of the present invention, the charging station further includes a charging station control center. The collected power demand information, battery status information, charging time information and other information are transmitted to the charging station control center through a wireless communication module (exemplary: HPLC / Lora). The charging station control center performs data cleaning and format conversion on the received data to ensure the accuracy and consistency of the data.

[0085] In one embodiment of the present invention, the method further includes the following steps: real-time acquisition and monitoring of real-time demand information and real-time charging pile status information, and iterative adjustment of the optimal power allocation scheme for each charging pile based on the real-time demand information and real-time charging pile status information.

[0086] It is worth noting that the power allocation method for charging piles within a charging station provided above is based on the real-time status of the charging piles and user demand. By predicting power demand at different times and inputting this demand into a pre-trained power allocation model, the power allocation is dynamically adjusted. This process is dynamic; after outputting the optimal power allocation scheme for the current moment, iteratively inputting real-time collected and monitored demand information and real-time charging pile status information, and iteratively adjusting the optimal power allocation scheme for each charging pile based on this information. This method reduces the overall power fluctuation of the charging station, improves grid stability, allocates power according to the actual demand of each charging pile, avoids resource waste, improves charging efficiency, and enhances the charging station's adaptability to emergencies through real-time monitoring and dynamic adjustment, ensuring the smooth operation of the charging process.

[0087] In one embodiment of the present invention, the historical data of the charging pile and the real-time demand data are input into a pre-trained LSTM neural network model. The LSTM neural network model outputs the power demand of each charging pile within a certain period of time in the future. Based on the power demand prediction results at different times, a power curve of each charging pile is generated. For example, the output power demand of each charging pile within a certain period of time in the future can be the power at multiple time points such as 1 minute, 2 minutes...15 minutes in the future. Then, the power curve of each charging pile is output with time and power as the horizontal and vertical axes, and the power demand curve of each charging pile is used as the input of the power allocation model.

[0088] LSTM, as an improved recurrent neural network, adds a cell state structure to the original network structure to control the transmission of global information. It uses three gating units—forget gate, input gate, and output gate—to control the updating of cell state information values. LSTM greatly alleviates the long-term dependency problem of traditional RNN models, reduces the loss of long-distance historical information, and outputs more accurate prediction results. The specific LSTM model is as follows... Figure 2 ,

[0089] Historical data was used to train the model, and an LSTM model suitable for ultra-short-term power prediction of charging piles was constructed.

[0090] The following is the Python build code.

[0091] # Building an LSTM Network

[0092] model = Sequential([

[0093] LSTM(units=50, return_sequences=True, input_shape=(lookback, 1)),

[0094] LSTM (units=50),

[0095] Dense(units=25, activation="relu"),

[0096] Dense(units=1) # Output predicted power])

[0097] # Compilation Model

[0098] model.compile(optimizer="adam", loss="mse")

[0099] # Training the model

[0100] model.fit(X_train, y_train, epochs=50, batch_size=32, validation_data=(X_test, y_test)).

[0101] Predicted power data curve

[0102] Input data: Historical charging pile power data P(t) for 4 hours.

[0103] Model output: Power prediction values ​​for the next 10 minutes, i.e., the power prediction curve.

[0104] P(t+1), P(t+2), ... P(t+n).

[0105] In one embodiment of the present invention, the step of inputting the power curve of the charging pile into a pre-established power allocation model, and solving the power allocation model based on an optimization algorithm to obtain the optimal power allocation scheme for each charging pile specifically includes:

[0106] The objective function of the power allocation model does not minimize the total power fluctuation of the charging station. The specific formula is as follows:

[0107]

[0108] in, Let be the total power of the charging station at time t. The average power of the charging station.

[0109] For example, the ant colony algorithm is used to solve the power allocation model to obtain the optimal power allocation scheme for each charging pile. The solution results are then transmitted to the control unit of the charging pile to adjust the power output of each charging pile in real time.

[0110] In one embodiment of the present invention, the constraint function of the power allocation model includes:

[0111]

[0112] Among them, P i (t) represents the power of the i-th device, P tatal,max The maximum operating power allowed by the system.

[0113] First, the power allocation model is established and trained. Specifically, this method takes into account the following:

[0114] 1) Charging requirements (ensure each vehicle is fully charged)

[0115] 2) Smooth total power (reduce fluctuations)

[0116] 3) Battery charging characteristics (avoid sudden changes)

[0117] 4) Peak shaving and valley filling (rationally allocate power and avoid overload)

[0118] Actual execution steps

[0119] 1. Initialize parameters

[0120] 1) Set the maximum power limit Pmax for the charging station.

[0121] 2) Set the time step Δt (e.g., 1 min or 5 min).

[0122] 3) Obtain the current charging demand Ei (unit: kWh) for all charging piles.

[0123] 4) Set the vehicle charging power range:

[0124] 0≤Pi(t)≤Pi,max where Pi,max is the maximum power of the i-th charging pile.

[0125] 2. Calculate the objective function.

[0126] The optimization objective is to minimize power fluctuations at the charging station:

[0127]

[0128] in: It is the total power at the current time t; It is the average power:

[0129] 3. Calculate the current power demand.

[0130] For each charging station

[0131] 1) Calculate the remaining charging time:

[0132] If Ti is too short (i.e., the vehicle is about to be fully charged), reduce its power allocation priority.

[0133] 2) Calculate the total power demand for the current time window:

[0134] 3) Calculate the degree of power fluctuation:

[0135] If σ(t) is too large, adjust the power distribution.

[0136] Perform dynamic optimization

[0137] Optimization is performed using gradient descent:

[0138] 1) Calculate the adjustment direction

[0139] If the total power P(t) exceeds the limit, reduce the power of some charging piles with higher power.

[0140] If power fluctuations are large, smooth the power curve; if some vehicles have insufficient battery power, prioritize power allocation.

[0141] 2) Construction optimization problem

[0142] Constraints: ( )

[0143] 3) Solve using optimization algorithms: Use gradient descent to solve.

[0144] 5. Adjust charging power in real time

[0145] At each time step t:

[0146] 1) Update Pi(t) based on the new power allocation scheme calculated by optimization;

[0147] 2) Calculate the new total power P(t), ensuring it does not exceed the maximum power;

[0148] 3) Record power fluctuations. If the fluctuations are still large, continue to optimize.

[0149] In one embodiment of the present invention, such as Figure 4 As shown, a power distribution system for charging piles within a charging station is provided, comprising the following modules:

[0150] The data acquisition and preprocessing module is used to acquire real-time demand information and real-time status information of charging piles, and to perform data preprocessing on the real-time power demand information and real-time status information of charging piles.

[0151] The power demand prediction module is used to input the historical data and real-time demand data of the charging pile into a pre-trained power demand prediction model, and output the power demand of each charging pile in the future within a certain period of time through the power demand prediction model. Based on the power demand prediction results at different times, the module generates the power curve of each charging pile.

[0152] The power allocation module is used to input the power curve of the charging pile into a pre-established power allocation model, and solve the power allocation model based on the optimization algorithm to obtain the optimal power allocation scheme for each charging pile.

[0153] In one embodiment of this application, an electronic device is provided, such as Figure 5 As shown, the electronic device includes:

[0154] At least one processor 601 and a memory 602 connected to at least one processor 601. In this embodiment, the specific connection medium between the processor 601 and the memory 602 is not limited. Figure 5 The example shown is the connection between processor 601 and memory 602 via bus 600. Bus 600 is... Figure 5 The connections between other components are indicated by thick lines and are for illustrative purposes only, not as limiting information. The 600 bus can be divided into address bus, data bus, control bus, etc., for ease of representation. Figure 5 The term 601 is represented by a single thick line, but this does not imply that there is only one bus or one type of bus. Alternatively, the processor 601 may also be referred to as a controller; the name is not limited. In one embodiment of this application, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the aforementioned power distribution method for charging piles in a charging station.

[0155] In this embodiment, memory 602 stores instructions executable by at least one processor 601. By executing the instructions stored in memory 602, at least one processor 601 can execute the power allocation method for charging piles within the charging station described above. Processor 601 can implement... Figure 5 The functions of each module in the device shown.

[0156] The processor 601 serves as the control center of the device, connecting to various parts of the control equipment via various interfaces and lines. It monitors the device as a whole by running or executing instructions stored in the memory 602 and accessing data stored in the memory 602, thus controlling the device's various functions and processing data. In one possible design, the processor 601 may include one or more processing units. The processor 601 may integrate an application processor and a modem processor. The application processor primarily handles the operating system, user interface, and applications, while the modem processor primarily handles wireless communication. It is understood that the modem processor may not be integrated into the processor 601. In some embodiments, the processor 601 and the memory 602 may be implemented on the same chip; in other embodiments, they may be implemented on separate chips.

[0157] The processor 601 can be a general-purpose processor, such as a central processing unit (CPU), digital signal processor, application-specific integrated circuit, field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the power allocation method for charging piles in a charging station disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0158] Memory 602, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory 602 may include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic storage, magnetic disk, optical disk, etc. Memory 602 can be any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto. In the embodiments of this application, memory 602 may also be a circuit or any other device capable of implementing storage functions for storing program instructions and / or data.

[0159] By designing and programming the processor 601, the code corresponding to the power allocation method of the charging piles in the charging station in the aforementioned embodiment can be embedded into the chip, thereby enabling the chip to execute the code during operation. Figure 1 The steps of the power distribution method for charging piles in the charging station shown in the embodiment are described below. How to design and program the processor 601 is a technique well-known to those skilled in the art and will not be elaborated upon here.

[0160] In one embodiment of the present invention, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when the computer program is executed by a processor, the steps of a power distribution method for charging piles in a charging station as described in any of the above claims are performed.

[0161] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A power distribution method for charging piles within a charging station, characterized in that, Includes the following steps: Collect real-time power demand information and real-time charging pile status information of charging piles, and perform data preprocessing on the real-time power demand information and real-time charging pile status information. Historical data and real-time power demand data of charging piles are input into a pre-trained power demand prediction model. The power demand prediction model outputs the power demand of each charging pile in the future within a certain period of time. Based on the power demand prediction results at different times, a power curve of each charging pile is generated. The power curve of the charging pile is input into a pre-established power allocation model, and the optimal power allocation scheme for each charging pile is obtained by solving the power allocation model based on the optimization algorithm. The method further includes the following steps: Real-time power demand information and real-time charging pile status information are collected and monitored, and the optimal power allocation scheme for each charging pile is iteratively adjusted based on the real-time power demand information and real-time charging pile status information. Historical data of charging piles and real-time power demand data are input into a pre-trained power demand prediction model. The model outputs the power demand of each charging pile over a certain period of time. Based on the power demand prediction results at different times, a power curve for each charging pile is generated, specifically including: The historical data and real-time demand data of the charging piles are input into a pre-trained LSTM neural network model. The LSTM neural network model outputs the power demand of each charging pile in the future within a certain period of time. Based on the power demand prediction results at different times, the power curve of each charging pile is generated. The power curve of the charging pile is input into a pre-established power allocation model. An optimization algorithm is used to solve the power allocation model to obtain the optimal power allocation scheme for each charging pile. Specifically, this includes: The objective function of the power allocation model is to minimize the total power fluctuation of the charging station, and the specific formula is as follows: ， in, Let be the total power of the charging station at time t. The average power of the charging station. T represents the total number of time periods; The constraint functions of the power allocation model include: ， Among them, P i (t) represents the power of the i-th device, P tatal,max The maximum operating power allowed by the system.

2. The power distribution method for charging piles in a charging station according to claim 1, characterized in that, The power allocation model is pre-established and trained specifically as follows: Initialization parameters: 1) Set the maximum power limit Pmax for the charging station; 2) Set the time step Δt; 3) Obtain the current charging demand Ei for all charging stations; 4) Set the vehicle charging power range: 0≤Pi(t)≤Pi,max where Pi,max is the maximum power of the i-th charging pile; Calculate the objective function: The optimization objective is to minimize power fluctuations at the charging station: ， in: It is the total power at the current time t; It is the average power: ; Calculate the current power requirement: For each charging station 1) Calculate the remaining charging time: , If Ti is too short, meaning the vehicle is about to be fully charged, reduce its power allocation priority; 2) Calculate the total power demand for the current time window: ; 3) Calculate the degree of power fluctuation: Power allocation is adjusted based on σ(t); Perform dynamic optimization: Optimization is performed using gradient descent: 1) Calculate the adjustment direction If the total power P(t) exceeds the limit, reduce the power of some charging piles with higher power. If power fluctuations are large, smooth the power curve; if some vehicles have insufficient battery power, prioritize power allocation. 2) Construction optimization problem Constraints: , ; 3) Solve using an optimization algorithm: Use gradient descent to solve; Adjust charging power in real time: At each time step t: 1) Update Pi(t) based on the new power allocation scheme calculated by optimization; 2) Calculate the new total power P(t), ensuring it does not exceed the maximum power; 3) Record power fluctuations. If the fluctuations are still large, continue to optimize.

3. The power distribution method for charging piles in a charging station according to claim 2, characterized in that, The method also includes pre-building and training an LSTM neural network model. The input data for the LSTM neural network model is the historical charging pile power data P(t) over the past 4 hours. The output of the LSTM neural network model is the power prediction value for the next 10 minutes, i.e., the power prediction curves P(t+1), P(t+2), ... P(t+n).

4. A power distribution system for charging piles within a charging station, used to implement the method described in any one of claims 1-3, characterized in that, include, The data acquisition and preprocessing module is used to acquire real-time power demand information and real-time charging pile status information of the charging pile, and to perform data preprocessing on the real-time power demand information and real-time charging pile status information. The power demand prediction module is used to input the historical data and real-time power demand data of the charging piles into a pre-trained power demand prediction model, and output the power demand of each charging pile in the future within a certain period of time through the power demand prediction model. Based on the power demand prediction results at different times, the module generates the power curve of each charging pile. The power allocation module is used to input the power curve of the charging pile into a pre-established power allocation model, and solve the power allocation model based on the optimization algorithm to obtain the optimal power allocation scheme for each charging pile.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program code, which, when executed by a processor, implements the steps of the power allocation method for charging piles in a charging station as described in any one of claims 1 to 3.

6. An electronic device, characterized in that, include: Memory, used to store computer programs; When the processor executes the computer program stored in the memory, it performs the steps of the power allocation method for charging piles in a charging station as described in any one of claims 1 to 3.

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