Flexible load aggregation method, device and system for electric vehicle

Abstract

The invention relates to the technical field of data processing, in particular to an electric vehicle flexible load aggregation method, device and system, and the method comprises the steps: determining the travel behavior intensity index of each electric vehicle based on the difference between the historical adjacent charging time of each electric vehicle currently connected to a power distribution network and the mileage data of the electric vehicle after charging; acquiring a charging mode volatility index of each electric vehicle; obtaining a flexible load contribution index of each electric vehicle; based on the similarity of the flexible load contribution indexes between the electric vehicles, dividing all the electric vehicles accessed to the power distribution network in the target area into various categories; and analyzing the charging power of each electric vehicle in each category and the working state of each electric vehicle at each moment to obtain the total charging power of each electric vehicle in each category at each moment. Therefore, the accuracy of flexible load aggregation of the electric vehicle is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device and system for flexible load aggregation of electric vehicles. Background Art

[0002] As a clean, efficient, and sustainable mode of transportation, new energy electric vehicles (EVs) play a vital role in future mobility. As flexible electric vehicles, EVs can actively participate in the operation and control of distribution networks, interacting with the grid through energy exchange. This helps balance loads, reduce peak-to-valley variations, and improve grid efficiency. Currently, the disordered charging and discharging behavior of large numbers of EVs connected to the distribution network has a significant negative impact on the network, resulting in load fluctuations and peak-to-valley variations. Therefore, it is necessary to aggregate the large number of EVs connected to the distribution network to enable centralized management and optimized dispatch of different types of EVs.

[0003] Traditional aggregation methods in existing technologies usually treat electric vehicles as homogeneous load units. However, in actual applications, electric vehicles have significant individual differences in travel behavior, which makes the results of electric vehicle flexible load aggregation in actual applications less accurate and unable to accurately reflect the actual application of electric vehicles, thereby affecting the stable operation of the power grid and the accuracy of scheduling decisions. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a method, device and system for flexible load aggregation of electric vehicles. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for flexible load aggregation of an electric vehicle, the method comprising the following steps:

[0006] Determine the travel behavior intensity index of each electric vehicle based on the differences in the historical charging times of each electric vehicle currently connected to the power distribution network, as well as the mileage data traveled between two consecutive charges; analyze the uncertainty of the historical charging time and charging time distribution of each electric vehicle, and determine the charging pattern volatility index of each electric vehicle in combination with the travel behavior intensity index;

[0007] Determine the flexible load contribution index of each electric vehicle by combining the power consumption of each electric vehicle when connected to the distribution network and the charging mode volatility index;

[0008] Based on the similarity of the flexible load contribution index between electric vehicles, all electric vehicles currently connected to the distribution network in the target area are divided into categories; the charging power of each electric vehicle in each category and its working status at the current moment are analyzed to obtain the total charging power of the electric vehicles in each category at the current moment.

[0009] In one embodiment, the construction of the travel behavior intensity index includes:

[0010] Analyze the difference between the historical charging time and the current charging time of each electric vehicle connected to the distribution network, and determine the weight of each electric vehicle's historical charging time;

[0011] Combined with the weights, the travel behavior intensity index of each electric vehicle is calculated as follows:

[0012] Where, ITB i is the travel behavior intensity index of the i-th electric vehicle currently connected to the distribution network, k is the number of times the i-th electric vehicle currently connected to the distribution network has been charged in the last month, ω i,j is the weight of the jth charging of the i-th electric vehicle currently connected to the distribution network in the last month, S i,j is the mileage of the i-th electric vehicle currently connected to the distribution network between the j-th charging and the j+1-th charging in the last month, β i,j+1 is the j+1th charging time of the i-th electric vehicle currently connected to the distribution network in the last month, β i,j is the jth charging time of the i-th electric vehicle currently connected to the distribution network in the last month, and norm() is the normalization function.

[0013] In one embodiment, the weight of each historical charging of each electric vehicle is the inverse of the gap.

[0014] In one embodiment, the charging mode volatility index is determined as follows:

[0015] The sum of the disorder degree of all charging durations and the disorder degree of all charging times of each electric vehicle currently connected to the distribution network in the past month is calculated, and the charging mode volatility index is the fusion result of the travel behavior intensity index and the sum.

[0016] In one embodiment, the charging mode volatility index is the product of the travel behavior intensity index and the sum value.

[0017] In one embodiment, the flexible load contribution index is determined as follows:

[0018] A normalized value of the charging mode volatility index of each electric vehicle is calculated, and the flexible load contribution index is the ratio of the remaining available power of each electric vehicle when it is connected to the power distribution network to the normalized value.

[0019] In one embodiment, the classification of all electric vehicles connected to the power distribution network in the target area into categories includes:

[0020] The flexible load contribution index of all electric vehicles currently connected to the distribution network in the target area is used as the input of the k-means clustering algorithm, and the output is the categories of electric vehicles.

[0021] In one embodiment, the process of determining the total charging power of the electric vehicles in each category at the current moment is as follows:

[0022] The multiplication result of the charging power and the working state of each electric vehicle in each category is calculated, and the total charging power is the cumulative sum of the multiplication results of all electric vehicles in each category, wherein when the electric vehicle is in the charging state, the working state is 1, and when the electric vehicle is in the power-off state, the working state is 0.

[0023] In a second aspect, the present application also provides an electric vehicle flexible load aggregation device, comprising:

[0024] Charging pattern analysis module: This module determines the travel behavior intensity index of each electric vehicle based on the differences in the historical charging times of each electric vehicle currently connected to the distribution network, as well as the mileage data traveled between two consecutive charges. It also analyzes the uncertainty of the historical charging time and charging time distribution of each electric vehicle, and combines this travel behavior intensity index to determine the charging pattern volatility index of each electric vehicle.

[0025] Flexible load optimization module: combining the power consumption of each electric vehicle when it is connected to the distribution network and the charging mode volatility index to determine the flexible load contribution index of each electric vehicle;

[0026] Flexible load aggregation module: Based on the similarity of the flexible load contribution index between electric vehicles, all electric vehicles currently connected to the distribution network in the target area are divided into categories; the charging power of each electric vehicle in each category and its working status at the current moment are analyzed to obtain the total charging power of electric vehicles in each category at the current moment.

[0027] In a third aspect, an embodiment of the present application further provides an electric vehicle flexible load aggregation system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0028] This application has at least the following beneficial effects:

[0029] This application determines the travel behavior intensity index of each electric vehicle based on the differences in the historical adjacent charging times of each electric vehicle currently connected to the power distribution network, as well as the mileage data traveled between two adjacent charges; the travel behavior intensity index takes into account the changes in the travel behavior patterns of electric vehicle users, reflects the differences in daily driving intensity of different electric vehicles and the urgency of their demand for electricity, and improves the accuracy of individual difference analysis of electric vehicles; analyzes the uncertainty of the historical charging time and charging time distribution of each electric vehicle, and determines the charging pattern volatility index of each electric vehicle in combination with the travel behavior intensity index; the charging pattern volatility index takes into account the historical behavior data of different user groups of electric vehicles, reflects the daily driving intensity of electric vehicles and the volatility of electric vehicle charging patterns, and improves the accuracy of feature extraction of historical charging data of electric vehicles;

[0030] Combined with the power consumption of each electric vehicle when it is connected to the distribution network and the charging mode volatility index, the flexible load contribution index of each electric vehicle is determined; the flexible load contribution index reflects the prominence of the electric vehicle in the charging and discharging demand, and improves the accuracy of flexible load management of electric vehicles; based on the similarity of the flexible load contribution index between electric vehicles, all electric vehicles currently connected to the distribution network in the target area are divided into categories; the charging power of each electric vehicle in each category and its working status at the current moment are analyzed to obtain the total charging power of the electric vehicles in each category at the current moment, taking into account the travel behavior characteristics of different electric vehicles, improving the accuracy of flexible load aggregation of electric vehicles, thereby improving the flexibility and stability of the power grid, and providing convenience for subsequent centralized scheduling of loads of different categories. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0032] Figure 1 A flowchart of a method for aggregating flexible loads of an electric vehicle according to an embodiment of the present application;

[0033] Figure 2 Construct a flow chart for the flexible load aggregation indicator of electric vehicles. DETAILED DESCRIPTION

[0034] To further illustrate the technical means and effectiveness of this application to achieve the intended invention objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of a flexible load aggregation method, device, and system for electric vehicles proposed in this application. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0035] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0036] The following describes in detail a method, device and system for flexible load aggregation of electric vehicles provided by the present application with reference to the accompanying drawings.

[0037] See also Figure 1 , which shows a flowchart of a method for flexible load aggregation of electric vehicles provided by one embodiment of the present application, the method comprising the following steps:

[0038] S1. Determine the travel behavior intensity index of each electric vehicle based on the differences in the historical adjacent charging times of each electric vehicle connected to the distribution network, as well as the mileage data between two adjacent charges; analyze the uncertainty of the historical charging time and charging time distribution of each electric vehicle, and determine the charging mode volatility index of each electric vehicle in combination with the travel behavior intensity index.

[0039] This embodiment collects driving information data of all electric vehicles in any residential community, specifically including: the charging time and charging duration of each historical charging of the electric vehicle, and the mileage of the electric vehicle after each charging; it should be noted that the charging time of each electric vehicle is the corresponding moment when the electric vehicle is connected to the power distribution network, and the mileage of the electric vehicle after each charging is the mileage of the electric vehicle between two adjacent charging times; and the electric vehicles analyzed after this embodiment are all the electric vehicles in the any residential community, and the any residential community is recorded as the target area.

[0040] Flexible loads refer to loads with flexible characteristics that can actively participate in the operation and control of the power grid and interact with the power grid through energy. The scheduling and regulation of flexible loads is one of the important means to alleviate the contradiction between supply and demand. Electric vehicles, as a common flexible load, are of great significance for maintaining the stability of the distribution network and alleviating short-term power shortages during peak load periods. However, the disordered charging and discharging behavior of a large number of electric vehicles after being connected to the distribution network not only fails to alleviate the power supply pressure of the distribution network, but also brings great negative effects, such as load fluctuations in the distribution network and peak-valley contradictions in the distribution network. Therefore, it is necessary to aggregate a large number of electric vehicles connected to the distribution network in order to centrally manage and optimize the scheduling of different categories of electric vehicles. However, in actual applications, there are significant individual differences in the travel behavior characteristics of electric vehicles. This difference makes it difficult for traditional aggregation methods to effectively aggregate electric vehicles.

[0041] In actual EV usage scenarios, the intensity of EV travel varies significantly depending on the type of vehicle used. For example, private cars are primarily used for daily commuting and shopping, and are more likely to be parked for extended periods during peak electricity consumption periods, such as evenings and weekends. Taxis and ride-hailing services, on the other hand, are often used more frequently due to operational needs and require frequent charging. Therefore, the role EVs play in the distribution network varies significantly depending on their travel behavior. Private cars, since they do not require frequent driving, can serve as a power source during peak electricity demand periods, alleviating pressure on the distribution network.

[0042] Therefore, this embodiment evaluates the differences in travel behavior of different electric vehicles based on the intensity of electric vehicle users' travel behavior. Specifically, the greater the charging frequency and the greater the frequency of use of an electric vehicle, the more urgent the electric vehicle's demand for electricity. Furthermore, taking into account changes in user travel behavior patterns, thereby changing the travel intensity of electric vehicles, the earlier historical data at this time has a weaker effect on the evaluation of the travel behavior intensity of electric vehicles at the current moment. Therefore, in order to more accurately represent the travel intensity of electric vehicles in the current state, this embodiment uses the electric vehicle travel data of the last month and performs weighted calculations based on the distance in time. Based on the above analysis, the travel behavior intensity index of each electric vehicle is specifically calculated as follows:

[0043] Where, ω i,j is the weight of the jth charging of the i-th electric vehicle currently connected to the distribution network in the last month, β i,j is the jth charging time of the i-th electric vehicle currently connected to the distribution network in the last month, β i,0 is the charging time of the i-th electric vehicle currently connected to the distribution network;

[0044] Where, ITB i is the travel behavior intensity index of the i-th electric vehicle currently connected to the distribution network, k is the number of times the i-th electric vehicle currently connected to the distribution network has been charged in the past month, S i,j is the mileage of the i-th electric vehicle currently connected to the distribution network between the j-th charging and the j+1-th charging in the last month, β i,j+1 is the j+1th charging time of the i-th electric vehicle currently connected to the distribution network in the last month, β i,j is the jth charging time of the i-th electric vehicle currently connected to the distribution network in the last month, and norm() is the normalization function.

[0045] It should be understood that when the travel intensity of electric vehicles is greater, the greater the number of kilometers the electric vehicle travels after one charge, and the shorter the time interval between two adjacent charges of the electric vehicle, which makes the calculated The greater the value. Furthermore, since the intensity of electric vehicle travel behavior is affected by changes in the user's travel behavior pattern, the travel behavior of electric vehicles within a month is weighted so that the travel behavior closer to the current time has a greater impact on the evaluation of the intensity of electric vehicle travel behavior. Therefore, the closer the electric vehicle's historical charging behavior is to the current time, the greater the weight of the calculated electric vehicle driving data ω i,j The larger the value, the greater the travel behavior intensity index of the electric vehicle calculated. This indicates that the greater the daily driving intensity of the i-th electric vehicle connected to the distribution network, the more urgent the demand for electricity.

[0046] Furthermore, consider the significant uncertainty in electric vehicle users' charging behavior. Specifically, some users, such as office workers, have a fixed daily schedule for electric vehicle use. Their charging patterns are relatively stable, and they often charge at night when electricity prices are relatively low. Meanwhile, other users, such as self-employed individuals, have significant uncertainty in their charging patterns. Their charging times and frequencies vary, making it difficult for these electric vehicles to respond promptly when the distribution network needs power.

[0047] Therefore, this example further evaluates the differences in charging behavior among different electric vehicles based on the stability of their charging behavior. Specifically, the charging time and duration of electric vehicles over the past month are analyzed to quantify the uncertainty of charging patterns. Based on this analysis and combined with the electric vehicle travel behavior intensity index, an electric vehicle charging pattern volatility index is constructed. The specific calculation method is:

[0048] TBF i =(sh(L i )+sh(F i))*ITB i ; where, TBF i is the charging mode volatility index of the i-th electric vehicle currently connected to the distribution network, L i is the set of charging times of all charging times of the i-th electric vehicle currently connected to the distribution network in the last month, F i is the set of charging durations of all charging times of the i-th electric vehicle currently connected to the distribution network in the last month. sh() represents the information entropy calculation function, which reflects the uncertainty and confusion of data distribution. ITB i is the travel behavior intensity index of the i-th electric vehicle currently connected to the distribution network.

[0049] It should be understood that when the travel behavior intensity of electric vehicles is greater, the calculated travel behavior intensity index of electric vehicles is greater; at the same time, when the charging time and charging time distribution of electric vehicles are more irregular, the calculated sh(L i ) and sh(F i ), the larger the volatility index of the charging mode of the i-th electric vehicle connected to the distribution network is, which indicates that the charging mode of the i-th electric vehicle connected to the distribution network has high uncertainty and dynamics.

[0050] S2, determining a flexible load contribution index of each electric vehicle based on the power consumption of each electric vehicle when connected to the power distribution network and the charging mode volatility index.

[0051] Based on the above analysis, the charging mode volatility index of each electric vehicle currently connected to the distribution network can be calculated. The charging mode volatility index fully considers the historical behavior data of electric vehicles, reflecting the daily driving intensity of each electric vehicle and the volatility of the electric vehicle's charging behavior pattern. Therefore, the calculated electric vehicle charging mode volatility index is normalized and used as the weight of the electric vehicle connected to the distribution network. Combined with the remaining available power of the electric vehicle, the flexible load contribution index of the electric vehicle is calculated. The specific calculation method is:

[0052] Where, FLC i is the flexible load contribution index of the i-th electric vehicle currently connected to the distribution network, TBF i is the charging mode volatility index of the i-th electric vehicle currently connected to the distribution network, norm() is the normalization function, SOC i It is the remaining available power when the i-th electric vehicle currently connected to the distribution network starts charging.

[0053] S3. Based on the similarity of the flexible load contribution index between electric vehicles, all electric vehicles currently connected to the distribution network in the target area are divided into categories; the charging power of each electric vehicle in each category and its working status at the current moment are analyzed to obtain the total charging power of the electric vehicles in each category at the current moment.

[0054] This embodiment uses the flexible load contribution index of all electric vehicles currently connected to the distribution network as input, adopts the k-means algorithm for clustering, and sets the number of categories to 5. The implementer can set it according to actual conditions. This embodiment does not impose any restrictions on this. The output is the clustering clusters of all electric vehicles currently connected to the distribution network, that is, all electric vehicles are divided into various categories. Among them, the K-means algorithm is an existing well-known technology and will not be described in detail here. The implementer can select other existing feasible clustering algorithms according to actual conditions, such as the DBSCAN clustering algorithm.

[0055] The clustering results of electric vehicles are analyzed to obtain the influencing factors of the characteristics of different types of electric vehicles. Based on the influencing factors of the characteristics of different types of electric vehicles, an electric vehicle aggregation model is established, specifically:

[0056] Where, is the total charging power of any cluster of electric vehicles at the current time t, P m is the charging power of the mth electric vehicle in any cluster, is the working state of the mth electric vehicle in any cluster at the current time t. It should be noted that when the electric vehicle is in the charging state at the current time t, When the electric vehicle is in a power-off state at the current time t, The flow chart of constructing flexible load aggregation index of electric vehicles is as follows: Figure 2 shown.

[0057] The total charging power within each cluster of electric vehicles reflects the different types of power loads of electric vehicles, which facilitates the subsequent centralized scheduling and management of different types of power loads.

[0058] A flexible load aggregation device for electric vehicles, comprising:

[0059] Charging pattern analysis module: This module determines the travel behavior intensity index of each electric vehicle based on the differences in the historical charging times of each electric vehicle currently connected to the distribution network, as well as the mileage data traveled between two consecutive charges. It also analyzes the uncertainty of the historical charging time and charging time distribution of each electric vehicle, and combines this travel behavior intensity index to determine the charging pattern volatility index of each electric vehicle.

[0060] Flexible load optimization module: combining the power consumption of each electric vehicle when it is connected to the distribution network and the charging mode volatility index to determine the flexible load contribution index of each electric vehicle;

[0061] Flexible load aggregation module: Based on the similarity of the flexible load contribution index between electric vehicles, all electric vehicles currently connected to the distribution network in the target area are divided into categories; the charging power of each electric vehicle in each category and its working status at the current moment are analyzed to obtain the total charging power of electric vehicles in each category at the current moment.

[0062] Based on the same inventive concept as the above method, an embodiment of the present application also provides an electric vehicle flexible load aggregation system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned electric vehicle flexible load aggregation methods are implemented.

[0063] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0064] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0065] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A flexible load aggregation method for electric vehicles, characterized in that: The method comprises the following steps: Determine the travel behavior intensity index of each electric vehicle based on the differences in the historical charging times of each electric vehicle currently connected to the power distribution network, as well as the mileage data traveled between two consecutive charges; analyze the uncertainty of the historical charging time and charging time distribution of each electric vehicle, and determine the charging pattern volatility index of each electric vehicle in combination with the travel behavior intensity index; Determine the flexible load contribution index of each electric vehicle by combining the power consumption of each electric vehicle when connected to the distribution network and the charging mode volatility index; Based on the similarity of the flexible load contribution index between electric vehicles, all electric vehicles currently connected to the distribution network in the target area are divided into categories; the charging power of each electric vehicle in each category and its working status at the current moment are analyzed to obtain the total charging power of the electric vehicles in each category at the current moment.

2. The electric vehicle flexible load aggregation method according to claim 1, characterized in that: The construction of the travel behavior intensity index includes: Analyze the difference between the historical charging time and the current charging time of each electric vehicle connected to the distribution network, and determine the weight of each electric vehicle's historical charging time; Combined with the weights, the travel behavior intensity index of each electric vehicle is calculated as follows: Where, ITB i is the travel behavior intensity index of the i-th electric vehicle currently connected to the distribution network, k is the number of times the i-th electric vehicle currently connected to the distribution network has been charged in the last month, ω i,j is the weight of the jth charging of the i-th electric vehicle currently connected to the distribution network in the last month, S i,j is the mileage of the i-th electric vehicle currently connected to the distribution network between the j-th charging and the j+1-th charging in the last month, β i,j+1 is the j+1th charging time of the i-th electric vehicle currently connected to the distribution network in the last month, β i,j is the jth charging time of the i-th electric vehicle currently connected to the distribution network in the last month, and norm() is the normalization function.

3. The electric vehicle flexible load aggregation method according to claim 2, characterized in that: The weight of each historical charging of each electric vehicle is the inverse of the gap.

4. The electric vehicle flexible load aggregation method according to claim 1, characterized in that: The process of determining the charging mode volatility index is as follows: The sum of the disorder degree of all charging durations and the disorder degree of all charging times of each electric vehicle currently connected to the distribution network in the past month is calculated, and the charging mode volatility index is the fusion result of the travel behavior intensity index and the sum.

5. The electric vehicle flexible load aggregation method according to claim 4, characterized in that: The charging mode volatility index is the product of the travel behavior intensity index and the sum value.

6. The electric vehicle flexible load aggregation method according to claim 1, characterized in that: The process of determining the flexible load contribution index is as follows: A normalized value of the charging mode volatility index of each electric vehicle is calculated, and the flexible load contribution index is the ratio of the remaining available power of each electric vehicle when it is connected to the power distribution network to the normalized value.

7. The electric vehicle flexible load aggregation method according to claim 1, characterized in that: All electric vehicles connected to the distribution network in the target area are divided into categories, including: The flexible load contribution index of all electric vehicles currently connected to the distribution network in the target area is used as the input of the k-means clustering algorithm, and the output is the categories of electric vehicles.

8. The electric vehicle flexible load aggregation method according to claim 1, characterized in that: The process of determining the total charging power of electric vehicles in each category at the current moment is as follows: The multiplication result of the charging power and the working state of each electric vehicle in each category is calculated, and the total charging power is the cumulative sum of the multiplication results of all electric vehicles in each category, wherein when the electric vehicle is in the charging state, the working state is 1, and when the electric vehicle is in the power-off state, the working state is 0.

9. A flexible load aggregation device for electric vehicles, comprising: Charging pattern analysis module: This module determines the travel behavior intensity index of each electric vehicle based on the differences in the historical charging times of each electric vehicle currently connected to the distribution network, as well as the mileage data traveled between two consecutive charges. It also analyzes the uncertainty of the historical charging time and charging time distribution of each electric vehicle, and combines this travel behavior intensity index to determine the charging pattern volatility index of each electric vehicle. Flexible load optimization module: combining the power consumption of each electric vehicle when it is connected to the distribution network and the charging mode volatility index to determine the flexible load contribution index of each electric vehicle; Flexible load aggregation module: Based on the similarity of the flexible load contribution index between electric vehicles, all electric vehicles currently connected to the distribution network in the target area are divided into categories; the charging power of each electric vehicle in each category and its working status at the current moment are analyzed to obtain the total charging power of electric vehicles in each category at the current moment.

10. An electric vehicle flexible load aggregation system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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