Super charging pile load characteristic evaluation method and system based on multi-dimensional index system

Abstract

The invention discloses a super charging pile load characteristic evaluation method and system based on a multi-dimensional index system, and the method comprises the steps: obtaining the original data of a charging station, constructing an intra-day charging coupling probability density function and the charging frequency distribution of each day in a week, and carrying out the random simulation of the arrival time of a vehicle; constructing an overall power demand curve by using a result obtained by stochastic simulation and the charging power model of each stage; obtaining power data of the super charging station and the low-speed charging station through the overall power demand curve, and performing preprocessing; calculating the preprocessed data by using a double-layer evaluation system to obtain an index value; and obtaining a load difference degree value between the super charging station and the slow charging station through the index value, and giving a comprehensive evaluation result. A systematic super charging pile load characteristic quantitative evaluation system is established, a quantitative comparison basis is provided for the load characteristic difference between a super charging station and a slow charging station, and a scientific basis is provided for charging station site selection and power distribution network optimization.

Description

Technical Field

[0001] This invention relates to the field of electrical engineering and automation technology, and in particular to a method and system for evaluating the load characteristics of supercharging piles based on a multi-dimensional index system. Background Technology

[0002] With the deepening of global energy transition and sustainable development strategies, electric vehicles, as an important representative of green transportation, are experiencing rapid growth. To date, the global electric vehicle fleet has exceeded 20 million vehicles, with China accounting for over 40%, showing explosive growth. This trend has driven the rapid development of charging infrastructure, among which supercharging stations, with their high efficiency and high power, have become key facilities for alleviating "range anxiety" associated with electric vehicles.

[0003] However, the charging load of electric vehicles (EVs) differs significantly from that of traditional loads. The power output of a single supercharger is typically between 60 and 350 kW, far exceeding the 7 to 22 kW of a typical home charging station. When a large number of EVs are concentrated in a specific period for charging, it can easily cause significant impacts on the power distribution network. Studies show that under disordered charging conditions, for every 10% increase in EV penetration, the peak load of the power distribution network may rise by 15% to 25%, further exacerbating the peak-to-valley difference in the power grid and the pressure on system peak regulation. This load concentration phenomenon may not only lead to local grid overload, voltage deviation, and decreased power quality, but also increase the investment and operating costs of power distribution equipment. Current technologies have not yet formed a multi-dimensional indicator evaluation system for superchargers, and lack a quantitative evaluation framework that combines load dispersion and temporal fluctuations. Therefore, it is necessary to propose an innovative method that can systematically characterize the load characteristics of superchargers and support multi-dimensional data analysis and differential assessment to achieve accurate modeling and evaluation of the charging load. Summary of the Invention

[0004] Therefore, the technical problem solved by this invention is: to establish a systematic analysis and evaluation method for the unique load characteristics of supercharging piles; to construct an evaluation index system to accurately characterize the dynamic fluctuation characteristics of supercharging pile loads; and to establish a quantitative comparison method for the differences between supercharging piles and load characteristics.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for evaluating the load characteristics of supercharging piles based on a multi-dimensional index system, which includes: Obtain raw data from supercharging stations and slow charging stations, and construct the intraday charging coupling probability density function and the intraweek daily charging frequency distribution; Using the intraday charging coupling probability density function and the charging frequency distribution of each day of the week, a random simulation of the vehicle's arrival time is performed; Using the results obtained from stochastic simulation and the charging power models for each stage, an overall power demand curve is constructed. The power data of supercharging stations and slow charging stations are obtained through the overall power demand curve and then preprocessed. A two-layer evaluation system based on discreteness and temporal difference characteristics is used to calculate the index value from the preprocessed data. The load difference between supercharging stations and slow charging stations is obtained through the aforementioned index values, and a comprehensive evaluation result is given.

[0006] As a preferred embodiment of the supercharging pile load characteristic evaluation method based on a multi-dimensional index system described in this invention, the intraday charging coupling probability density function divides the day into several 15-minute time intervals, maps the charging station data to these time intervals based on the electric vehicle's access and disconnection times, and calculates the frequency of different time periods as the intraday coupling probability density, expressed as: in, This represents the intraday coupling probability density. express The number of vehicles connected to the charging station at any given time. This indicates the total number of vehicles connected to the charging station on that day.

[0007] The charging frequency distribution for each day of the week is expressed as follows: in, This indicates the charging frequency for each day of the week. Indicates the first day of the week The number of charging vehicles per day This indicates the total number of times vehicles were charged within a week.

[0008] As a preferred embodiment of the supercharging pile load characteristic evaluation method based on a multi-dimensional index system described in this invention, the random simulation of the vehicle arrival time includes randomly selecting one day according to the charging frequency of each day of the week, and then randomly sampling the time interval according to the intra-day coupling probability density to obtain the arrival time of each vehicle.

[0009] As a preferred embodiment of the supercharging pile load characteristic evaluation method based on a multi-dimensional index system described in this invention, the charging power model for each stage is based on the power change characteristics of electric vehicles during actual charging, and introduces a random disturbance term to reflect the differences between vehicles, expressed as follows: when This is the pre-charging phase: when This is the fast charging phase: when This is the recharging phase: when This is the trickle charging stage: in, Indicates time The difference between the time the vehicle begins charging and the time when charging begins. Indicates the maximum charging power; , , , These represent the durations of the pre-charging phase, fast charging phase, replenishing charging phase, and trickle charging phase, respectively. , , , The difference is respectively represented as The appropriate charging power at each stage; , , , The term is a random disturbance that follows a normal distribution with a mean of 0.

[0010] As a preferred embodiment of the supercharging pile load characteristic evaluation method based on a multi-dimensional index system described in this invention, the overall power demand curve is obtained through random simulation. Combining this with the power change process of a single vehicle at each stage, the power curves of all vehicles are superimposed on the time axis in a staggered manner according to their arrival times to obtain the total power curve that changes with time, expressed as: in, express The overall power demand curve at any given time. Indicates that the i-th car is in Charging power at any time Indicates the first The arrival time of the vehicle.

[0011] As a preferred embodiment of the supercharging pile load characteristic evaluation method based on a multidimensional index system described in this invention, the multidimensional index system includes a power dispersion evaluation system and a time-series power difference characteristic evaluation system, which are used to characterize the spatial distribution characteristics and temporal variation characteristics of the charging load, respectively.

[0012] As a preferred embodiment of the supercharging pile load characteristic evaluation method based on a multi-dimensional index system described in this invention, the load difference value is obtained using a quantitative difference evaluation formula; the quantitative difference evaluation formula, in the form of relative difference, calculates the degree of deviation between the supercharging station and the slow charging station on each index, and is expressed as: in, Indicators of supercharging stations The value on, Indicates the indicator of a slow charging station The value on, This indicates the degree of difference.

[0013] Secondly, this invention provides a supercharging pile load characteristic evaluation system based on a multi-dimensional index system, including: a charging behavior modeling module, which acquires raw data from supercharging stations and slow charging stations, constructs a daily charging coupling probability density function and a weekly charging frequency distribution; uses the daily charging coupling probability density function and the weekly charging frequency distribution to perform random simulations of vehicle arrival times; uses the results of the random simulation and charging power models at each stage to construct an overall power demand curve; a multi-dimensional evaluation system construction module, which obtains power data from supercharging stations and slow charging stations through the overall power demand curve and performs preprocessing; uses a two-layer evaluation system based on discreteness and time-series difference characteristics to calculate index values ​​from the preprocessed data; and a difference analysis and comprehensive evaluation module, which obtains the load difference value between supercharging stations and slow charging stations through the index values ​​and provides a comprehensive evaluation result.

[0014] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the supercharging pile load characteristic evaluation method based on a multi-dimensional index system as described in the first aspect of the present invention.

[0015] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the supercharging pile load characteristic evaluation method based on a multi-dimensional index system as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: It establishes a systematic quantitative evaluation system for the load characteristics of supercharging piles, which makes up for the shortcomings of existing qualitative analysis, provides a quantitative comparison basis for the load characteristics differences between supercharging stations and slow charging stations, and provides a scientific basis for charging station site selection and power distribution network optimization. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a method for evaluating the load characteristics of supercharging piles based on a multi-dimensional index system. Detailed Implementation

[0019] Reference Figure 1 This is one embodiment of the present invention, which provides a method for evaluating the load characteristics of supercharging piles based on a multi-dimensional index system, including the following steps: S1: Obtain the raw data of supercharging stations and slow charging stations, and construct the intraday charging coupling probability density function and the daily charging frequency distribution within the week.

[0020] Furthermore, the raw data includes vehicle access time and access time, the total number of vehicles accessing the charging pile on a single day, the maximum charging power of the charging pile, the number of charging times on each day of the week, and the total number of charging trips within a week.

[0021] Furthermore, the intraday charging coupling probability density function: in, This represents the intraday coupling probability density. This represents the number of vehicles connected to the charging station at time t. This indicates the total number of vehicles connected to the charging station on that day.

[0022] Furthermore, the distribution of charging frequency across different days of the week, in, This indicates the charging frequency for each day of the week. This indicates the number of charging vehicles on day d of the week. This indicates the total number of times vehicles were charged within a week.

[0023] S2: Using the intraday charging coupling probability density function and the weekly charging frequency distribution, a random simulation of the vehicle's arrival time is performed.

[0024] Furthermore, the random simulation of vehicle arrival time includes randomly selecting one day based on the charging frequency of each day of the week, and then randomly sampling the time interval according to the intraday coupling probability density to obtain the arrival time of each vehicle.

[0025] It should be noted that by using the above-mentioned two-layer probability sampling mechanism, randomness can be introduced on the basis of statistical regularity to realize the random simulation of vehicle arrival time. This method takes into account the probability distribution of charging behavior during the day and week, so that the generated vehicle access time sequence not only conforms to historical regularity, but also has random disturbance characteristics. Thus, it can truly reproduce the dynamic characteristics of vehicle arrival at charging stations and provide reliable input for the subsequent construction of power demand curves.

[0026] S3: Construct the overall power demand curve using the results obtained from stochastic simulation and the charging power model for each stage.

[0027] Furthermore, the charging power model for each stage is based on the power change characteristics of electric vehicles during actual charging, and introduces a random disturbance term to reflect the differences between vehicles, expressed as: when This is the pre-charging phase: when This is the fast charging phase: when This is the recharging phase: when This is the trickle charging stage: in, Indicates time The difference between the time the vehicle begins charging and the time when charging begins. Indicates the maximum charging power; , , , These represent the durations of the pre-charging phase, fast charging phase, replenishing charging phase, and trickle charging phase, respectively. , , , The difference is respectively represented as The appropriate charging power at each stage; , , , The term is a random disturbance that follows a normal distribution with a mean of 0.

[0028] It should be noted that this model introduces a random perturbation term to make the power curve of each vehicle slightly different, thereby achieving a differentiated simulation of charging behavior between vehicles, which is closer to the actual scenario.

[0029] Furthermore, the overall power demand curve is obtained through random simulation. By combining the power change process of a single vehicle at each stage, the power curves of all vehicles are superimposed on the time axis according to their arrival time to obtain the total power curve that changes with time, as shown below: in, This represents the overall power demand curve at time t. Let represent the charging power of the i-th vehicle at time t. This represents the arrival time of the i-th vehicle.

[0030] It should be noted that the stochastic simulation determines the arrival patterns of vehicles over time, while the power model depicts the power variation characteristics of a single vehicle within a charging cycle. The superposition of these two models is equivalent to summing the charging power curves of different vehicles at different times along the time axis, forming a dynamic change process of the charging station's overall load, thus accurately reflecting the overall power fluctuation pattern over time.

[0031] S4: Obtain the power data of supercharging stations and slow charging stations through the overall power demand curve, and perform preprocessing.

[0032] It should be noted that by combining downsampling and normalization preprocessing, the influence of random noise in the original data is reduced, and the statistical scale of each power sequence is unified, making the data both smooth and comparable. This provides high-quality input for the subsequent calculation of indicators in the two-layer evaluation system, ensuring that the quantitative evaluation results of differences are more stable and accurate.

[0033] S5: Using a two-layer evaluation system based on discreteness and temporal difference characteristics, the preprocessed data is calculated to obtain the index value.

[0034] Furthermore, the two-layer evaluation system is divided into a power dispersion evaluation system and a time-series power difference characteristic evaluation system. The power dispersion evaluation system includes... Coefficient of variation: in, The standard deviation of power data This represents the average value of the power data.

[0035] Kuroshi: in, Indicates the first Power data at any given time. This represents the number of power data points.

[0036] Normalized mean absolute error: in, This represents the maximum value of the data. This represents the mean absolute error of the original power data sequence.

[0037] Furthermore, the time-series power differential characteristic evaluation system includes, Mean of normalized absolute differences: in, It represents the average absolute deviation of the rate of change of power.

[0038] Normalized difference standard deviation: in, It indicates the degree of dispersion of the rate of change of power.

[0039] Normalized root mean square difference: in, It represents the average squared magnitude of the power change.

[0040] Difference coefficient of variation: in, The standard deviation of the difference values. It represents the absolute value of the average of the differences between adjacent data.

[0041] It should be noted that the power dispersion index reflects the statistical stability and fluctuation range of the power sequence, while the time-series difference characteristic index reveals the speed and continuity of power changes. The former reflects the distribution pattern of power at the overall level, while the latter reflects the dynamic stability of power over time. These two indicators complement each other, enabling the system to comprehensively evaluate the load characteristics of charging stations from both static and dynamic perspectives. This allows for a quantitative characterization of load volatility, stability, and trends, thus providing a scientific basis for charging station operation optimization and scheduling decisions.

[0042] S6: Using the aforementioned index values, obtain the load difference value between supercharging stations and slow charging stations, and provide a comprehensive evaluation result.

[0043] Furthermore, the load difference value is obtained using a quantitative difference assessment formula; this formula, employing a relative difference form, calculates the degree of deviation between supercharging stations and slow charging stations on various indicators, and is expressed as follows: in, Indicators of supercharging stations The value on, Indicates the indicator of a slow charging station The value on, This indicates the degree of difference.

[0044] It should be noted that quantitative assessment based on index values ​​enables a scientific comparison and comprehensive analysis of the load characteristics of different types of charging stations. This is because the difference formula measures the degree of deviation between supercharging stations and slow charging stations on various indicators in the form of relative proportions, preserving the physical meaning of the indicators themselves while eliminating the impact of dimensional differences. Through normalized calculation and weighted summation of the differences in multiple indicators, the overall differences between the two types of charging stations in terms of load fluctuation amplitude, temporal stability, and power distribution characteristics can be comprehensively reflected. This method not only enables a quantifiable comparison of load characteristics but also provides an objective basis for operational optimization and resource allocation under different charging modes.

[0045] This embodiment also provides a computer device applicable to the supercharging pile load characteristic evaluation method based on a multi-dimensional index system, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the supercharging pile load characteristic evaluation method based on a multi-dimensional index system as proposed in the above embodiment.

[0046] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0047] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the supercharging pile load characteristic evaluation method based on a multi-dimensional index system as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0048] In summary, this invention achieves the following: First, it acquires raw operational data from supercharging stations and slow charging stations, constructs a daily charging coupling probability density function and a weekly charging frequency distribution reflecting vehicle charging behavior patterns, thus simulating the randomness of vehicle arrival times. Second, it combines a phased charging power model to establish an overall power demand curve that reflects the dynamic characteristics of the group load. Third, it constructs a two-layer evaluation system based on discreteness and temporal difference characteristics to characterize the volatility and stability of charging power in multiple dimensions. Fourth, it improves the smoothness and comparability of power data through downsampling and normalization preprocessing. Fifth, it uses the evaluation system to calculate index values ​​and performs a differential quantitative evaluation formula to calculate and comprehensively evaluate the load characteristics of supercharging stations and slow charging stations. This method realizes a complete technical process from data acquisition and power modeling to index quantification and differential evaluation, and can accurately reflect the load characteristic differences of different types of charging stations in multiple dimensions, providing a scientific basis for charging network planning and power grid scheduling optimization.

[0049] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications can be made to the technical solutions of the present invention or equivalent substitutions can be made to a supercharging pile load characteristic evaluation method and system based on a multi-dimensional index system without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for evaluating the load characteristics of supercharging piles based on a multi-dimensional index system, characterized in that: This includes acquiring raw data from supercharging stations and slow charging stations, constructing the intraday charging coupling probability density function and the intraweek daily charging frequency distribution; Using the intraday charging coupling probability density function and the charging frequency distribution of each day of the week, a random simulation of the vehicle's arrival time is performed; Using the results obtained from stochastic simulation and the charging power models for each stage, an overall power demand curve is constructed. The power data of supercharging stations and slow charging stations are obtained through the overall power demand curve and then preprocessed. A two-layer evaluation system based on discreteness and temporal difference characteristics is used to calculate the index value from the preprocessed data. The load difference between supercharging stations and slow charging stations is obtained through the aforementioned index values, and a comprehensive evaluation result is given.

2. The method for evaluating the load characteristics of supercharging piles based on a multi-dimensional index system as described in claim 1, characterized in that: The intraday charging coupling probability density function divides the day into several 15-minute time intervals. Data from charging stations is mapped to these time intervals based on the electric vehicle's access and disconnection times. The frequency of each time period is then calculated as the intraday coupling probability density, expressed as: in, This represents the intraday coupling probability density. express The number of vehicles connected to the charging station at any given time. This indicates the total number of vehicles connected to the charging station on that day; The charging frequency distribution for each day of the week is expressed as follows: in, This indicates the charging frequency for each day of the week. Indicates the first day of the week The number of charging vehicles per day This indicates the total number of times vehicles were charged within a week.

3. The method for evaluating the load characteristics of supercharging piles based on a multi-dimensional index system as described in claim 2, characterized in that: The random simulation includes randomly selecting one day based on the charging frequency of each day of the week, and then randomly sampling the time interval according to the intraday coupling probability density to obtain the arrival time of each vehicle.

4. The method for evaluating the load characteristics of supercharging piles based on a multi-dimensional index system as described in claim 3, characterized in that: The charging power models for each stage are constructed by simulating the power variation characteristics of electric vehicles during actual charging and introducing random perturbation terms, and are expressed as follows: when This is the pre-charging phase: when This is the fast charging phase: when This is the recharging phase: when This is the trickle charging stage: in, Indicates time The difference between the time the vehicle begins charging and the time when charging begins. Indicates the maximum charging power; , , , These represent the durations of the pre-charging phase, fast charging phase, replenishing charging phase, and trickle charging phase, respectively. , , , The difference is respectively represented as The appropriate charging power at each stage; , , , The term is a random disturbance that follows a normal distribution with a mean of 0.

5. The method for evaluating the load characteristics of supercharging piles based on a multi-dimensional index system as described in claim 4, characterized in that: The overall power demand curve is obtained through random simulation. By combining the power change process of a single vehicle at each stage, the power curves of all vehicles are superimposed on the time axis according to their arrival time to obtain the total power curve that changes with time, as shown below: in, This represents the overall power demand curve at time t. Let represent the charging power of the i-th vehicle at time t. This represents the arrival time of the i-th vehicle.

6. The method for evaluating the load characteristics of supercharging piles based on a multi-dimensional index system as described in claim 5, characterized in that: The dual-layer evaluation system includes a power dispersion evaluation system and a time-series power differential characteristic evaluation system, which are used to characterize the spatial distribution characteristics and temporal variation characteristics of the charging load, respectively.

7. The method for evaluating the load characteristics of supercharging piles based on a multi-dimensional index system as described in claim 6, characterized in that: The load difference value is obtained using a quantitative difference assessment formula; the quantitative difference assessment formula, in the form of relative difference, calculates the degree of deviation between supercharging stations and slow charging stations on various indicators, and is expressed as follows: in, Indicates the supercharging station's indicators The value on, Indicates the indicator of a slow charging station The value on, This indicates the degree of difference.

8. A supercharging pile load characteristic evaluation system based on a multi-dimensional index system, based on the supercharging pile load characteristic evaluation method based on a multi-dimensional index system as described in any one of claims 1 to 7, characterized in that: The system includes: a charging behavior modeling module, which acquires raw data from supercharging stations and slow charging stations, constructs a daily charging coupling probability density function and a weekly charging frequency distribution; uses the daily charging coupling probability density function and the weekly charging frequency distribution for stochastic simulation of vehicle arrival times; and constructs an overall power demand curve using the results of the stochastic simulation and charging power models for each stage; a multi-dimensional evaluation system construction module, which obtains power data from supercharging stations and slow charging stations through the overall power demand curve and performs preprocessing; uses a two-layer evaluation system based on discreteness and time-series difference characteristics to calculate index values ​​from the preprocessed data; and a difference analysis and comprehensive evaluation module, which obtains the load difference value between supercharging stations and slow charging stations through the index values ​​and provides a comprehensive evaluation result.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the supercharging pile load characteristic evaluation method based on a multi-dimensional index system as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the supercharging pile load characteristic evaluation method based on a multi-dimensional index system as described in any one of claims 1 to 7.

Images