A method, system, equipment and medium for adjusting the power of an electric vehicle charging pile

Abstract

This invention belongs to the field of power technology, specifically relating to a method, system, equipment, and medium for regulating the power of electric vehicle charging piles. The method includes the following steps: acquiring historical power grid operation data for each distribution area and establishing an objective function; obtaining a Pareto optimal solution set using a multi-objective optimization method based on particle swarm optimization algorithm according to the objective function; calculating the per-unit voltage values ​​of all distribution network nodes in each time period based on the Pareto optimal solution set; selecting solutions where no voltage crossover occurs at the per-unit voltage values ​​of the distribution network nodes, calculating the corresponding total voltage deviation; selecting the solution with the smallest total voltage deviation as the optimal solution for day-ahead optimization; and controlling the charging and discharging of the charging piles according to the optimal solution for day-ahead optimization. Accuracy is improved by filtering the Pareto solution set obtained from the multi-objective optimization results.

Description

Technical Field

[0001] This invention belongs to the field of power technology, specifically relating to a method, system, equipment, and medium for adjusting the power of electric vehicle charging piles. Background Technology

[0002] With the integration of elements such as high proportion of renewable distributed generation (RDG), energy storage, and electric vehicles, urban power distribution networks are being promoted to develop in a low-carbon and intelligent direction. The coordinated optimization control of "source, grid, load, storage, and charging" is an important part of the operation of urban power distribution networks.

[0003] In the "source-grid-load-storage-charging" model, the source side includes micro-gas-fired power generation, photovoltaic, and wind power connected to medium voltage, as well as residential photovoltaic power connected to low voltage. The grid side mainly consists of distribution network lines and related equipment; the load side mainly consists of electric vehicles and other conventional electricity loads; the storage side is the energy storage system; and the charging side is the electric vehicle charging pile. The coordination among source, grid, load, storage, and charging is essentially the interaction and coordination of active and reactive power.

[0004] Charging terminals are further divided into commercial charging stations and conventional parking space charging piles (including home charging piles and office area parking space charging piles). Home charging piles and office area parking space charging piles are characterized by users having longer parking times, strong regularity, concentrated charging times, and similar user behavior characteristics.

[0005] The continuous growth in the number of electric vehicles and the disorderly charging behavior of large-scale electric vehicles may cause a series of problems such as excessive grid load due to peak-on-peak charging and charging queues, which will increase the peak-valley difference of the grid, affect the safety and economy of grid operation, and exacerbate the operating pressure of the distribution network and the pressure to expand the capacity of distribution network transformers. Conversely, during the off-peak period, the electricity consumption decreases and the electric vehicle load is withdrawn, which further increases the peak-valley difference of the distribution network and reduces the overall utilization rate of equipment.

[0006] Current research on electric vehicles' participation in power grid optimization management focuses on setting charging prices / service fees for electric vehicles through economic analysis. The aim is to guide the orderly charging of electric vehicles at different times through pricing, thereby achieving peak shaving and valley filling. While this approach can play a role in peak shaving and valley filling of the distribution network to some extent, it still carries a significant risk of charging congestion when users are not highly price-sensitive, thus failing to achieve the desired peak shaving and valley filling effect.

[0007] Other studies employ single-objective optimization without comprehensively considering distribution network operation safety, economy, and peak-valley differences. Some studies only consider charging piles charging at maximum power in their optimization algorithms, without adjusting the charging power according to grid load demand. Summary of the Invention

[0008] The purpose of this invention is to provide a method, system, device and medium for adjusting the power of electric vehicle charging piles, so as to solve the technical problem that the existing methods for adjusting the power of electric vehicle charging piles are not practical and have difficulty in achieving peak shaving and valley filling effects.

[0009] To achieve the above objectives, the present invention employs the following technical solution:

[0010] Firstly, a method for adjusting the power of an electric vehicle charging station includes the following steps:

[0011] Obtain historical power grid operation data for each distribution area and establish an objective function;

[0012] Based on the objective function, a multi-objective optimization method based on particle swarm optimization algorithm is used to obtain the Pareto optimal solution set;

[0013] Calculate the per-unit voltage values ​​of all distribution network nodes for each time period based on the Pareto optimal solution set;

[0014] Calculate the solution corresponding to the voltage per unit value of the distribution network node where there is no voltage crossing, and calculate the total voltage deviation corresponding to the solution;

[0015] The solution with the smallest total voltage deviation is selected as the optimal solution for the current day optimization.

[0016] The charging and discharging of the charging piles is controlled based on the optimal solution optimized recently.

[0017] A further improvement of this invention is that, when obtaining the Pareto optimal solution set using a multi-objective optimization method based on particle swarm optimization, the specific steps include:

[0018] S1. Randomly generate the initial particle swarm;

[0019] S2. Calculate the objective function value and constraint function value for each point;

[0020] S3. Classify the particle swarm according to the objective function value;

[0021] S4. Calculate the constraint penalty, inferior solution penalty, and total penalty for each point based on the constraint function value and the classification result.

[0022] S5. Calculate fitness based on the total penalty for each point;

[0023] S6. Based on the fitness of each point, update the velocity and position of the particles, generate new group positions, and update the local and global optima at the same time.

[0024] S7. Add the points with a total penalty of 0 to the candidate list of non-dominated solution set, and check the candidate list of non-dominated solution set. Keep the first level non-dominated points and delete the other points to obtain the first candidate list of non-dominated solution set.

[0025] S8. Check if the first non-dominated solution candidate list has converged. If it has not converged, return to S2. If it has converged, delete the points in the first non-dominated solution candidate list whose distance from other points is less than a preset value, and obtain the second non-dominated solution candidate list.

[0026] S9. Output the Pareto optimal solution set and the objective function value corresponding to the Pareto optimal solution set in the candidate list of the second non-dominated solution set.

[0027] A further improvement of the present invention is that the objective function includes a total objective function, minimizing the operating cost of the transformer area and minimizing the peak-to-valley difference of the daily load curve of the transformer area.

[0028] A further improvement of the present invention is that the objective function is subject to the following constraints: power balance constraint, transformer power limit, upper and lower limit constraints of distributed energy output, distributed energy output power ramping constraint, reserve capacity constraint, energy storage unit constraint, energy storage charging and discharging power limit constraint, energy storage unit remaining capacity constraint, and energy storage remaining capacity balance constraint.

[0029] A further improvement of the present invention is that: when calculating the per-unit voltage values ​​of all distribution network nodes in each time period based on the Pareto optimal solution set, each solution of the Pareto optimal solution set is extracted, and power flow calculation is performed on each time period in each solution to obtain the per-unit voltage values ​​of all distribution network nodes in each time period.

[0030] A further improvement of the present invention is that, when controlling the charging and discharging of charging piles according to the optimal solution optimized in the previous day, the following steps are specifically included: During real-time operation during the day, in each time period, when the total access power of all charging piles in the area is less than the output power of the charging piles in the previous day's optimization result, the charging piles are charged at full power; when the access power is less than the output power optimized in the previous day, some electric vehicles can be charged with limited power according to the user level or user setting information.

[0031] Secondly, an electric vehicle charging pile power regulation system includes a charging service platform, a user terminal, and an intelligent charging terminal.

[0032] The intelligent charging terminal is used to obtain real-time charging information of charging piles / stations and interact with the charging service platform;

[0033] The user terminal interacts with the charging service platform to obtain charging information;

[0034] The charging service platform acquires historical power grid operation data and real-time charging information from smart charging terminals for each distribution area, and performs charging and discharging control.

[0035] A further improvement of the present invention is that the charging service platform includes:

[0036] Objective function establishment module: used to obtain historical power grid operation data for each distribution area and establish the objective function;

[0037] Pareto optimal solution set generation module: used to obtain the Pareto optimal solution set based on the objective function using a multi-objective optimization method based on particle swarm optimization algorithm;

[0038] Distribution network node voltage per-unit value calculation module: used to calculate the voltage per-unit value of all distribution network nodes in each time period based on the Pareto optimal solution set;

[0039] Total voltage deviation calculation module: used to select the solution corresponding to the voltage per unit value of the distribution network node where there is no voltage crossing, calculate the solution, and calculate the total voltage deviation corresponding to the solution;

[0040] The day-ahead optimization optimal solution selection module is used to select the set of solutions with the smallest total voltage deviation as the day-ahead optimization optimal solution.

[0041] Charge / discharge control module: Used to control the charging and discharging of the charging pile based on the currently optimized optimal solution.

[0042] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for adjusting the power of an electric vehicle charging pile.

[0043] Fourthly, a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for adjusting the power of an electric vehicle charging station.

[0044] Compared with the prior art, the present invention has at least the following beneficial effects:

[0045] This invention addresses the problem of large peak-valley power differences in distribution network areas caused by the large influx of new energy power sources and the disorderly charging of electric vehicles, which in turn affects the operational stability of the distribution network, leading to increased operating losses and the pressure to expand the capacity of distribution network transformers.

[0046] This invention controls the charging and discharging of charging piles through the recently optimized optimal solution, thereby smoothing load fluctuations in the distribution network, reducing operating losses, and ultimately achieving the goal of optimizing equipment configuration and slowing down the expansion of distribution network transformers, while ensuring the safe and stable operation of the distribution network.

[0047] This invention constructs a multi-objective optimization algorithm model and solves the multi-objective optimization problem using the particle swarm optimization algorithm. Then, power flow calculations and evaluation of the calculation results are used to filter the Pareto solution set of the multi-objective optimization results, thereby improving accuracy. Attached Figure Description

[0048] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0049] In the attached diagram:

[0050] Figure 1 This is an overall flowchart of a power adjustment method for electric vehicle charging piles according to the present invention;

[0051] Figure 2 This is a flowchart illustrating the process of obtaining the Pareto optimal solution set using a multi-objective optimization method based on particle swarm optimization algorithm in an electric vehicle charging pile power adjustment method of the present invention.

[0052] Figure 3 This is a schematic diagram of the power regulation system for an electric vehicle charging pile according to the present invention;

[0053] Figure 4 This is a structural block diagram of a charging service platform in an electric vehicle charging pile power regulation system according to the present invention. Detailed Implementation

[0054] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0055] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0056] Example 1

[0057] A method for adjusting the power of an electric vehicle charging station, such as Figure 1 As shown, the specific steps include:

[0058] Obtain historical power grid operation data for each distribution area and establish an objective function;

[0059] The Pareto optimal solution set is obtained by using a multi-objective optimization method based on particle swarm optimization algorithm according to the objective function.

[0060] Each solution in the Pareto optimal solution set is extracted in turn, and power flow calculation is performed for each time period in each solution to obtain the per-unit voltage value of all distribution network nodes in each time period;

[0061] Determine whether there is a voltage overshoot at the per-unit voltage value of each distribution network node. If a voltage overshoot exists, discard the solution with the voltage overshoot; if no voltage overshoot exists, calculate the total voltage deviation corresponding to the solution.

[0062] The set with the smallest total voltage deviation is selected as the optimal solution for the current day optimization.

[0063] During real-time operation, in each time period, when the total access power of all charging piles in the area is less than the output power of the charging piles in the previous day's optimization results, the charging piles will charge at full power; when the access power is less than the previous day's optimized output power, some electric vehicles can be charged with limited power according to user level or user settings.

[0064] When obtaining the Pareto optimal solution set using a multi-objective optimization method based on particle swarm optimization, the specific steps include:

[0065] S1. Randomly generate the initial particle swarm;

[0066] S2. Calculate the objective function value and constraint function value for each point;

[0067] S3. Classify the particle swarm according to the objective function value;

[0068] S4. Calculate the constraint penalty, inferior solution penalty, and total penalty for each point based on the constraint function value and the classification result.

[0069] S5. Calculate fitness based on the total penalty for each point;

[0070] S6. Based on the fitness of each point, update the velocity and position of the particles, generate new group positions, and update the local and global optima at the same time.

[0071] S7. Add the points with a total penalty of 0 to the candidate list of non-dominated solution set, and check the candidate list of non-dominated solution set. Keep the first level non-dominated points and delete the other points to obtain the first candidate list of non-dominated solution set.

[0072] S8. Check if the first non-dominated solution candidate list has converged. If it has not converged, return to S2. If it has converged, delete the points in the first non-dominated solution candidate list whose distance from other points is less than a preset value, and obtain the second non-dominated solution candidate list.

[0073] S9. Output the Pareto optimal solution set and the objective function value corresponding to the Pareto optimal solution set in the candidate list of the second non-dominated solution set.

[0074] The advantage of obtaining a Pareto optimal solution set by using a multi-objective optimization method based on particle swarm optimization algorithm compared to traditional optimization algorithms is that it can obtain an optimal solution set, rather than just a single optimal solution, thereby obtaining more choices and improving accuracy.

[0075] To prevent the "peak-on-peak" phenomenon caused by the disorderly charging of large-scale electric vehicles, reduce the peak-valley difference in transformer area load, and promote the economical operation of the distribution network, this invention selects minimizing the operating cost of transformer areas and minimizing the peak-valley difference in the daily load curve of transformer areas as objectives, and performs multi-objective optimization. The overall objective function is:

[0076] min[f1,f2];

[0077] In the formula, f1 represents the operating cost of the transformer area, and f2 represents the peak-to-valley difference of the daily load curve of the transformer area.

[0078] Minimizing the operating cost of a distribution transformer area (DCE) involves taking into account the day-ahead forecast power of renewable energy sources and loads, the peak-to-valley difference in electricity prices, the peak-to-valley difference in loads, and the lifespan of energy storage. This is achieved by optimizing the charging and discharging of energy storage and the output of controllable equipment, and determining the exchange power between the DCE and the upstream grid, thereby minimizing the total system operating cost. This can be expressed as:

[0079]

[0080] In the formula: T represents the total number of time periods for day-ahead optimization. In this embodiment, day-ahead optimization is used, so 96 time periods are used for calculation; N DG C represents the total number of distributed energy sources. f (·) represents the fuel cost of operating distributed energy resources; C OM (·) indicates the maintenance cost of operating distributed energy resources; The output of distributed energy source i during time period t; This represents the charging and discharging power of energy storage during time period t. A positive value indicates energy storage discharging, while a negative value indicates energy storage charging. This represents the interaction power between the transformer in the distribution area and the upstream power grid. A positive value indicates that electrical energy flows from the upstream power grid into the distribution network in the distribution area. The calculation method is as follows: C pp (·) represents the total electricity cost of the distribution system; C bat (·) represents the cost of energy storage equipment lifespan loss, which can be estimated using the "rainflow counting method".

[0081] N DG C f (·), C OM (·) and C pp (·) can all be obtained through the charging service platform established by the power grid.

[0082] Minimizing the peak-to-valley difference of the daily load curve in the distribution area, that is, optimizing the control of electric vehicle charging power without changing the traditional load, so as to minimize the peak-to-valley difference of the 24-hour daily load curve in the distribution area, can be expressed as:

[0083]

[0084] In the formula: This refers to the base load power excluding electric vehicle charging and discharging. This represents the charging and discharging power of the electric vehicle during time period t.

[0085] The current optimization requirements are constraint function values, including:

[0086] Power balance constraints:

[0087]

[0088] In the formula: Let t be the system load demand power during time period t.

[0089] Transformer power limit in the distribution area:

[0090]

[0091] In the formula: and These are the minimum and maximum allowable power for the transformer in the distribution area, respectively.

[0092] Upper and lower limits of distributed energy output constraints:

[0093]

[0094] In the formula: and Let be the minimum and maximum allowable output power of the i-th distributed power source, respectively.

[0095] Reserve capacity constraints:

[0096]

[0097] In the formula: R t This refers to the system's reserve rate.

[0098] Distributed energy output ramping constraints:

[0099]

[0100] In the formula: and These are the upper and lower limits of the ramp rate for distributed power sources.

[0101] Energy storage unit constraints:

[0102] The remaining capacity of the energy storage unit during time period t is related to the remaining capacity during time period t-1, the charge and discharge capacity during the [t-1, t] period, and the self-discharge capacity.

[0103] During energy storage charging, the remaining capacity in time period t is:

[0104]

[0105] In the formula: σ represents the remaining energy storage capacity during time period t; σ is the energy storage self-discharge rate. η is the charging power of energy storage during period t; c For energy storage charging efficiency; E bat Δt represents the total capacity of the energy storage battery; Δt represents the scheduling time period.

[0106] When the stored energy is discharged, the remaining capacity during time period t is:

[0107]

[0108] In the formula: η is the discharge power during energy storage period t; d For energy storage discharge efficiency;

[0109] Energy storage operation constraints mainly include charging and discharging power limits, capacity constraints, and energy storage remaining capacity balance constraints.

[0110] (1) Limits on energy storage charging and discharging power:

[0111]

[0112] In the formula: and These represent the maximum values ​​of the energy storage charging and discharging power, respectively.

[0113] (2) Remaining capacity constraints of energy storage units:

[0114]

[0115] In the formula: and These are the upper and lower limits of the State of Charge (SOC) of the energy storage unit.

[0116] (3) Since the dispatching of microgrids exhibits a certain periodicity, in order to ensure that the energy storage system meets the operation requirements of the next day, the remaining capacity of the energy storage unit at the end of the dispatching period should be equal to the remaining capacity at the beginning of the dispatching period:

[0117]

[0118] The calculation of the total voltage deviation specifically includes the following steps:

[0119] Each solution S in the Pareto optimal solution set is sequentially... j Extract the values ​​and perform power flow calculations for each time period in the solution to obtain the per-unit voltage values ​​of all distribution network nodes in each time period. And judge If a voltage limit is exceeded, the solution is discarded. If no voltage limit is exceeded, the solution S is calculated using the following formula. j The corresponding total voltage deviation V Total,j :

[0120]

[0121] In the formula, N G This represents the number of distribution network nodes.

[0122] Example 2

[0123] like Figure 2 and 3 As shown, an electric vehicle charging pile power regulation system includes a charging service platform responsible for acquiring historical power grid operation data for each distribution area, and calculating new energy power generation forecast data and electric vehicle charging load forecast data based on the historical operation data; at the same time, the charging service platform is responsible for interacting with user terminals and charging piles / stations.

[0124] The user terminal interacts with the charging service app and the charging service platform to obtain information such as charging prices, charging station availability, charging reservations, and real-time charging information. Users can also set charging parameters online.

[0125] The intelligent charging terminal is connected to distributed charging piles or centralized charging stations to obtain real-time charging information.

[0126] The smart charging terminal also interacts with the charging service platform to upload real-time charging information and obtain charging control commands;

[0127] The charging service platform specifically includes:

[0128] Objective function establishment module: used to obtain historical power grid operation data for each distribution area and establish the objective function;

[0129] Pareto optimal solution set generation module: used to obtain the Pareto optimal solution set based on the objective function using a multi-objective optimization method based on particle swarm optimization algorithm;

[0130] Distribution network node voltage per-unit value calculation module: used to calculate the voltage per-unit value of all distribution network nodes in each time period based on the Pareto optimal solution set;

[0131] Total voltage deviation calculation module: used to select the solution corresponding to the voltage per unit value of the distribution network node where there is no voltage crossing, calculate the solution, and calculate the total voltage deviation corresponding to the solution;

[0132] The day-ahead optimization optimal solution selection module is used to select the set of solutions with the smallest total voltage deviation as the day-ahead optimization optimal solution.

[0133] Charge / discharge control module: Used to control the charging and discharging of the charging pile based on the currently optimized optimal solution.

[0134] Example 3

[0135] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a power adjustment method for an electric vehicle charging pile according to Embodiment 1.

[0136] Example 4:

[0137] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements a power adjustment method for an electric vehicle charging pile according to Embodiment 1.

[0138] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative in all respects and are not the only ones. All modifications within the scope of this invention or its equivalents are included in this invention.

[0139] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0140] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0141] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0142] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for adjusting the power of an electric vehicle charging pile, characterized in that, Includes the following steps: Obtain historical power grid operation data for each distribution area and establish an objective function; Based on the objective function, a multi-objective optimization method based on particle swarm optimization algorithm is used to obtain the Pareto optimal solution set; Calculate the per-unit voltage values ​​of all distribution network nodes for each time period based on the Pareto optimal solution set; Calculate the solution corresponding to the voltage per unit value of the distribution network node where there is no voltage crossing, and calculate the total voltage deviation corresponding to the solution; The solution with the smallest total voltage deviation is selected as the optimal solution for the current day optimization. The charging and discharging of the charging piles are controlled based on the optimal solution optimized recently. The objective function includes a total objective function; the total objective function is: ； In the formula, This indicates the operating cost of the transformer substation. This indicates the peak-to-valley difference in the daily load curve of the transformer substation. The objective function is subject to the following constraints: power balance constraint, transformer power limit, upper and lower limit constraints of distributed energy output, distributed energy output power ramping constraint, reserve capacity constraint, energy storage unit constraint, energy storage charging and discharging power limit constraint, energy storage unit remaining capacity constraint, and energy storage remaining capacity balance constraint.

2. The method for adjusting the power of an electric vehicle charging pile according to claim 1, characterized in that, When obtaining the Pareto optimal solution set using a multi-objective optimization method based on particle swarm optimization, the specific steps include: S1. Randomly generate the initial particle swarm; S2. Calculate the objective function value and constraint function value for each point; S3. Classify the particle swarm according to the objective function value; S4. Calculate the constraint penalty, inferior solution penalty, and total penalty for each point based on the constraint function value and the classification result. S5. Calculate fitness based on the total penalty for each point; S6. Based on the fitness of each point, update the velocity and position of the particles, generate new group positions, and update the local and global optima at the same time. S7. Add the points with a total penalty of 0 to the candidate list of non-dominated solution set, and check the candidate list of non-dominated solution set. Keep the first level non-dominated points and delete the other points to obtain the first candidate list of non-dominated solution set. S8. Check if the first non-dominated solution candidate list has converged. If it has not converged, return to S2. If it has converged, delete the points in the first non-dominated solution candidate list whose distance from other points is less than a preset value, and obtain the second non-dominated solution candidate list. S9. Output the Pareto optimal solution set and the objective function value corresponding to the Pareto optimal solution set in the candidate list of the second non-dominated solution set.

3. The method for adjusting the power of an electric vehicle charging pile according to claim 1, characterized in that, When calculating the per-unit voltage values ​​of all distribution network nodes in each time period based on the Pareto optimal solution set, each solution of the Pareto optimal solution set is extracted, and power flow calculation is performed for each time period in each solution to obtain the per-unit voltage values ​​of all distribution network nodes in each time period.

4. The method for adjusting the power of an electric vehicle charging pile according to claim 1, characterized in that, When controlling the charging and discharging of charging piles according to the optimal solution optimized recently, the specific steps include: During real-time operation during the day, in each time period, when the total access power of all charging piles in the area is less than the output power of the charging piles in the previous optimization result, the charging piles are charged at full power; when the access power is greater than the output power optimized recently, some electric vehicles can be charged with limited power according to the user level or user settings information.

5. A power regulation system for an electric vehicle charging pile, characterized in that, This includes charging service platforms, user terminals, and smart charging terminals. The intelligent charging terminal is used to obtain real-time charging information of charging piles / stations and interact with the charging service platform; The user terminal interacts with the charging service platform to obtain charging information; The charging service platform acquires historical power grid operation data and real-time charging information of smart charging terminals for each distribution area, and performs charging and discharging control. The charging service platform includes: Objective function establishment module: used to obtain historical power grid operation data for each distribution area and establish the objective function; Pareto optimal solution set generation module: used to obtain the Pareto optimal solution set based on the objective function using a multi-objective optimization method based on particle swarm optimization algorithm; Distribution network node voltage per-unit value calculation module: used to calculate the voltage per-unit value of all distribution network nodes in each time period based on the Pareto optimal solution set; Total voltage deviation calculation module: used to select the solution corresponding to the voltage per unit value of the distribution network node where there is no voltage crossing, calculate the solution, and calculate the total voltage deviation corresponding to the solution; The day-ahead optimization optimal solution selection module is used to select the set of solutions with the smallest total voltage deviation as the day-ahead optimization optimal solution. Charge and discharge control module: used to control the charging and discharging of the charging pile according to the currently optimized optimal solution; The objective function includes a total objective function; the total objective function is: ； In the formula, This indicates the operating cost of the transformer substation. This indicates the peak-to-valley difference in the daily load curve of the transformer substation. The objective function is subject to the following constraints: power balance constraint, transformer power limit, upper and lower limit constraints of distributed energy output, distributed energy output power ramping constraint, reserve capacity constraint, energy storage unit constraint, energy storage charging and discharging power limit constraint, energy storage unit remaining capacity constraint, and energy storage remaining capacity balance constraint.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the electric vehicle charging pile power adjustment method according to any one of claims 1-4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the electric vehicle charging pile power adjustment method according to any one of claims 1-4.

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