Method for Judging Privately Built Charging Piles Based on User Electricity Load Data

Abstract

This invention discloses a method for identifying privately built charging piles based on user electricity load data. The method includes: acquiring electricity load data from all registered users and extracting their electricity consumption characteristics; acquiring electricity load data from residential users and extracting their electricity consumption characteristics; comparing the residential user's electricity consumption characteristics with those of the registered users, and determining whether the residential user is using a privately built charging pile based on the comparison result. By analyzing the electricity load data of registered users and comparing it with the electricity characteristics of residential users, if the residential user's electricity consumption characteristics match those of the registered users, it indicates that the residential user has a privately built charging pile. This facilitates State Grid staff in monitoring users with privately built charging piles and standardizing the installation of charging piles, thus contributing to the safe operation of the State Grid.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle charging pile technology, specifically a method for judging privately built charging piles based on user electricity load data. Background Technology

[0002] Energy storage for new energy electric vehicles is typically achieved through designated charging stations. These charging stations require reporting and approval before installation and use. Once approved, they are recorded as independent meters in the State Grid system. However, the large-scale construction of charging stations can impact the stable operation of the power grid. Electric vehicles require long charging times and high charging power. Generally, for users who have reported charging stations, power resources are allocated rationally based on the distribution of these stations during grid construction to avoid a surge in electricity load due to excessive charging stations, which could affect the safe operation of the grid. However, due to certain limitations, some charging stations are installed without State Grid approval. These stations lack oversight and are not included in the grid's records. Therefore, when allocating power resources to users, the high load from charging stations is not considered. In situations like summer, a surge in electricity consumption can lead to excessive load, affecting the safe operation of the grid and hindering the planning and development of charging station construction. Therefore, a method is needed to determine whether residential users (those who have not reported for charging) have illegally installed charging stations. Summary of the Invention

[0003] The purpose of this invention is to provide a method for judging unauthorized charging piles based on user electricity load data. By analyzing the electricity load data of users who have applied for charging pile installation and comparing it with the electricity load data of residential users, if the electricity load data of residential users matches that of users who have applied for installation, it indicates that the residential user has unauthorized charging pile installation. This facilitates the State Grid staff to supervise users with unauthorized charging pile installation and to standardize the installation of charging piles, which is beneficial to the safe operation of the State Grid.

[0004] This application provides a method for identifying privately built charging piles based on user electricity load data, including the following steps:

[0005] S1. Obtain the electricity load data of all registered users and store it in the first array A, denoted as A(A1, A2, A3, ... A2). m Based on A, extract the electricity consumption characteristics of users who have already applied for connection;

[0006] S2. Obtain the electricity load data of residential users and extract the electricity consumption characteristics of residential users from the electricity load data;

[0007] S3. Determine whether the electricity consumption characteristics of residential users and those of users who have already applied for charging services meet the similarity criteria. If they do, then determine that the residential user is using a privately built charging pile.

[0008] Furthermore, the charging characteristics of registered users include: charging time period, charging power, charging duration, and electricity consumption per charge.

[0009] Furthermore, the specific process of analyzing the electricity consumption characteristics of registered users based on A is as follows:

[0010] S11. Tag the daily electricity load data of each registered user with a charging tag and extract the charging characteristics. Analyze the extracted charging characteristics for all days to obtain the charging pile load characteristics.

[0011] S12. Perform S11 on the electricity load data of each registered user in A to obtain the charging pile load characteristics of all registered users and store them in the second data group B, denoted as B. A (B1, B2, B3, ...);

[0012] S13. Perform feature analysis on the second data group B to obtain the electricity consumption characteristics of the registered users.

[0013] Furthermore, the process of tagging the daily electricity load data of each registered user in step S11 is as follows:

[0014] Obtain the daily electricity load data for each registered user, denoted as A. m (a1, a2, ..., ax);

[0015] For A m For each data point: electricity load data is collected at fixed time intervals each day, resulting in n electricity load points for each day. These n electricity load points are then numbered and dated in chronological order and stored in the first electricity load database for each day, denoted as Pax(P1, P2, ..., P...). n );

[0016] Add half of the electricity load points from the first electricity load database of the next two consecutive days in Am to the first electricity load database of the previous day; thus obtaining the second electricity load database P ax'(P1, P2, ..., P) for each day. n P n+1 , ..., P 1.5n P' includes 1.5n electrical load points;

[0017] Determine the charging status of each electrical load point in Pax', including positive charging and no charging; and mark the charging status; thus obtaining the charging status mark of each electrical load point; the determination method is: determine the charging status of each electrical load point based on the load size corresponding to each electrical load point;

[0018] By combining the charging status labels of 1.5n power load points, a complete charging label for each day can be obtained.

[0019] Furthermore, the specific process for extracting charging features is as follows:

[0020] Extract the start and end times of charging each day: Iterate through the complete charging tags for each day, find the tag segments corresponding to the sudden changes in charging status, and use regular expressions to index and extract the power load points corresponding to the tag segments in the complete charging tags for each day as the start and end times of charging respectively; thus obtaining several start and end times of charging.

[0021] Calculate the daily charging time: Extract several charging time periods based on several charging start and end times, calculate the length of each charging time period, and select the longest charging time period to calculate the daily charging time.

[0022] Calculate the daily electricity consumption for charging once: Calculate the daily electricity consumption for charging once based on the load size of the electrical load point corresponding to the longest charging time period.

[0023] Furthermore, the charging pile load characteristics include the number of charging interval days. The process for obtaining the number of charging interval days is as follows:

[0024] Get the charging duration and start and end times of each registered user for all days. Based on the daily charging duration, determine whether there is charging activity on that day. If so, tag the electricity load data for that day with a "charging activity" label.

[0025] Based on the charging dates corresponding to all electricity load data with charging tags for that day, calculate the number of days between two adjacent charging dates for each user, and then filter out the maximum, minimum, and average charging interval days for each user.

[0026] Furthermore, the process of obtaining the load characteristics of charging piles also includes:

[0027] Calculate the peak charging time for a single user: Based on the start and end times of charging for all days, count the frequency at which each registered user starts charging at various times each day, and select the time with the highest frequency as the peak charging time for that registered user.

[0028] Calculate the average charging time for a single user: Calculate the average charging time for a single registered user based on the charging time for all days.

[0029] Calculate the average electricity consumption per charge for a single user: Calculate the average electricity consumption per charge for a single registered user based on the electricity consumption per charge over all days.

[0030] Calculate the average starting load value for charging for a single user: Based on the load size corresponding to the charging start time point for all days, calculate the average starting load value for charging for a single registered user.

[0031] Furthermore, the process of obtaining the electricity consumption characteristics of registered users includes:

[0032] Charging time period: Count the peak charging times of all charging pile users and group them according to time to get several time periods. Calculate the total frequency of charging at each time in each time period. Select the time period with the highest total frequency as the charging time period for all charging pile users and record the start and end times of charging corresponding to the charging time period.

[0033] Charging power of charging piles: The charging power of the charging piles corresponding to each registered user is determined based on the average starting load value of charging for each registered user. All registered users are grouped according to the corresponding charging power of the charging piles.

[0034] Charging time of charging piles: This includes calculating the average single charging time of charging piles with different power based on the average charging time of all registered users in different charging power groups;

[0035] Electricity consumption per charge: The average electricity consumption per charge for charging piles of different power levels is calculated based on the average electricity consumption per charge for all registered users in different charging power groups.

[0036] Furthermore, the process of extracting the electricity consumption characteristics of residential users is as follows:

[0037] Analyze the electricity load data of residential users to extract the maximum daily load value of residential users;

[0038] Calculate the electricity consumption of residential users during the charging period of the charging pile based on the electricity load data of residential users.

[0039] Find the time periods in the residential user's electricity load data that remain at the charging power of the charging pile within a continuous time period; record them as suspected charging time periods; calculate the suspected charging duration based on the suspected charging time periods;

[0040] Based on the suspected charging time period, the load values ​​corresponding to the start and end times of the suspected charging time period are extracted from the electricity load data of residential users, so as to obtain the load increase at the start of suspected charging and the load decrease at the end of suspected charging.

[0041] Furthermore, when a residential user's maximum daily load is greater than or equal to the minimum charging power of the charging pile, and any of the following conditions are met, the residential user is deemed to be using a privately built charging pile:

[0042] Condition 1: The electricity consumption of residential users exceeds the electricity consumption range of charging piles used by users who have already applied for installation;

[0043] Condition 2: The suspected charging time of residential users is greater than or equal to the charging time of charging piles of users who have already applied for installation;

[0044] Condition 3: The suspected increase in charging load for residential users is greater than or equal to the load corresponding to the start time of charging during the charging period of the charging pile.

[0045] Condition 4: The suspected reduction in load after the end of charging for residential users is greater than or equal to the load corresponding to the end of the charging period at the charging pile.

[0046] The beneficial effects of this invention are as follows:

[0047] This application extracts features from the electricity load data of registered users, starting from the most granular data dimension of 96 daily load points for registered users. It fully considers the data differences arising from different charging pile models and charging habits, deeply mining the electricity consumption characteristics of registered users. Comparing these characteristics with those of residential users, users whose electricity consumption characteristics are highly similar to those of registered users are most likely to have privately installed charging piles. Identifying and monitoring users who may have privately installed charging piles, and incorporating the electricity load of these charging piles into the State Grid's operation, helps to rationally allocate power resources, ensure the safe operation of the State Grid, and also contributes to the planning of charging pile construction and development. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the method for judging privately built charging piles based on user electricity load data according to the present invention.

[0049] Figure 2 This is a logic diagram for determining private stakes according to a specific embodiment of the present invention.

[0050] Figure 3 This is a specific embodiment of the present invention showing the electricity load points of registered users and their corresponding charging status markings;

[0051] Figure 4 A schematic diagram of a complete daily charging tag provided for a specific embodiment of the present invention;

[0052] Figure 5 This is a specific embodiment of the present invention showing the capture of charging start and end time points;

[0053] Figure 6 This is a specific embodiment of the present invention providing the charging feature extraction information for registered users;

[0054] Figure 7A daily electricity load trend for residential users is provided as a specific embodiment of the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0057] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0058] Furthermore, for clarity and brevity, descriptions of well-known structures, functions, and configurations may have been omitted. Those skilled in the art will recognize that various changes and modifications can be made to the examples described herein without departing from the spirit and scope of this disclosure.

[0059] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0060] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0061] Example 1

[0062] like Figure 1 As shown, this embodiment provides a method for judging privately built charging piles based on user electricity load data, including the following steps:

[0063] S1. Obtain the electricity load data of all registered users and store it in the first array A, denoted as A(A1, A2, A3, ... A2). m Based on A, extract the electricity consumption characteristics of users who have already applied for connection;

[0064] S2. Obtain the electricity load data of residential users and extract the electricity consumption characteristics of residential users from the electricity load data;

[0065] S3. Determine whether the electricity consumption characteristics of residential users and those of users who have already applied for charging services meet the similarity criteria. If they do, then determine that the residential user is using a privately built charging pile.

[0066] Specifically, the process of analyzing the electricity consumption characteristics of registered users based on A is as follows:

[0067] Step 1: Preprocessing of electricity load data

[0068] S11. Clean the data. To remove some invalid data in A, calculate the historical maximum load of each registered user. If the historical maximum load of a registered user is 0 or close to 0, it indicates that the registered user has not charged. All data of the user is invalid. Delete the data of the registered user from A.

[0069] S12. Extract the charging pile load characteristics and obtain the daily electricity load data for each valid registered user in A, denoted as A. m (a1, a2, ..., ax); Tag the daily electricity load data of each registered user with a charging tag and extract the charging characteristics. Analyze the charging characteristics of all days to obtain the charging pile load characteristics.

[0070] 1. Affix the charging tag. The specific process is as follows:

[0071] 1.1. Collect power load points: For A m For each data point in the database, electricity load data is collected at fixed time intervals each day, resulting in n electricity load points for each day. These n electricity load points are then numbered and dated in chronological order and stored in the first electricity load database for each day, denoted as Pax(P1, P2, ..., P...). n In one specific implementation, the daily electricity load data is collected at 15-minute intervals, and a total of 96 time points are collected for each registered user each day, corresponding to 96 time points from P1 to P96 (for example, P1 represents 0:15, P2 represents 0:30, P3 represents 0:45, and so on). The interval between two adjacent load points is 15 minutes, or 0.25 hours.

[0072] 1.2 Determine the charging status of each electrical load point in Pax, including positive charging and no charging; and mark the charging status; obtain the charging status mark of each electrical load point; the determination method is: determine the charging status of each electrical load point according to the load size corresponding to each electrical load point;

[0073] Specifically, when a charging station is not charging, its load may be much smaller than when it is charging, but still greater than 0 (e.g., 0.005kW). At the very end of the charging process, the load may decrease significantly, but still be significantly greater than the load when not charging. Generally, the minimum power of a common electric vehicle charging station is 5kW. To determine the charging status of each user's electrical load point at any given time, points with a load less than 1kW can be marked as "0" (not charging), and others as "1" (charging).

[0074] 1.3 Obtain the daily charging tags for registered users. Combine the charging status labels of n power load points to obtain the daily charging tag; specifically, obtain a string of 96 characters composed of 0s and 1s, called the daily charging tag, which clearly records the charging status of the user's 96 power load points in one day, such as... Figure 3 As shown, the power load points P1 to P8 (partial) of a certain package user on a certain date and the corresponding charging status are marked.

[0075] 1.4 Correct the daily charging labels. There are cases where a certain power load point is in the state of not charging, but several power load points before and after it are in the state of positive charging, such as 101. Considering that users are unlikely to deliberately stop charging for 15 minutes in the middle, it is considered that load data was missed at this point. Therefore, the charging status of the load point is changed to positive charging, and "101" in the charging label is replaced with "111".

[0076] 1.5. Improve the daily charging tags by adding half of the charging status labels from the charging tags of the next two consecutive days in Am to the charging tags of the previous day, and update the daily charging tags to obtain the complete daily charging tags.

[0077] Analysis of user charging start times revealed that most users prefer to start charging between 11 PM and midnight, with charging sessions typically lasting several hours. Therefore, many charging processes span multiple dates. Studying charging pile load characteristics involves charging duration, start and end times, and load changes throughout the charging process. Therefore, the analysis should be based on a complete charging process, not individual dates. To record the entire charging process, the load data for the first half of the following day can be added to the load data for each user on each date. For example, the load data for user A on July 1st should include the load data for the first 12 hours of July 2nd, resulting in new load data for user A on July 1st. This would give user A 144 load points on July 1st. Figure 4This provides the electricity load data for a registered user on July 1st, along with the complete composition of the charging tag for that day. Similarly, user A's load data for July 2nd requires the addition of the load data for the first half of July 3rd, and so on for other dates.

[0078] 2. Extract charging features, the specific process is as follows:

[0079] 2.1 Extracting the start and end times of charging each day: Traverse the complete charging tags for each day, find the tag segments corresponding to the sudden changes in charging status, and use regular expressions to index and extract the corresponding power load points from the corresponding tag segments as the start and end times of charging; thus obtaining several start and end times of charging.

[0080] For example, in a charging tag with a character length of 144, one or more segments contain interfering (or erroneous) data, such as "1001001". Therefore, the tag segments "0011" and "1100" representing a sudden change in charging status are used as the start and end markers for charging, respectively, instead of "01" and "10". Figure 5 Regular expressions are used to extract the start and end indices of the charging segment within the complete charging tag set (the indices corresponding to the start and end times of each charging action), such as... Figure 5 The charging start index is 58 and the charging end index is 65, which correspond to two times of the day, P58 and P65, respectively. Points P58 and P65 are used as the start and end times of charging.

[0081] 2.2 Calculate the daily charging time: Extract several charging time periods based on several charging start and end times, calculate the length of each charging time period, and select the longest charging time period to calculate the daily charging time.

[0082] The extracted charging time periods may be one or more segments. These charging time periods need to be categorized by date. Since later tags in the complete charging tag list belong to the next day, if the earliest charging start marker is "0011," i.e., 0:45 AM (P3), then charging activities with a starting index less than 99 (i.e., before 0:45 AM the next day) are counted as the current day; otherwise, they are counted as the next day. This solves the problem of load data being distributed across two days and thus unanalyzable when charging across multiple dates.

[0083] 2.3 Calculate the daily charging power consumption: Calculate the daily charging power consumption based on the load size of the electrical load point corresponding to the longest charging time period.

[0084] Given that it's relatively rare for a single user to charge multiple times a day, to analyze the load characteristics of a single charging session, the longest charging segment of the day is taken as the start and end time of that day's charging activity. For example... Figure 3 The longest charging period for user number 123 on June 15th was from P95 to P109, while user number 234 charged once, from P58 to P68. Since the two adjacent load points are 0.25 hours apart, taking user 234 as an example, the charging duration for that user on that day is calculated as 0.25 * (end time index - start time index + 1). Therefore, the electricity consumption for one charge for that user is 0.25 * (the sum of load values ​​between the start and end times of charging). Figure 6 .

[0085] 3. Analysis of charging pile load characteristics, the specific process is as follows:

[0086] 3.1 Calculating Peak Charging Times for Individual Users: Based on the start and end times of charging for all days, count the frequency of each registered user starting charging at various times each day, and select the time with the highest frequency as the peak charging time for that registered user; count the frequency of each user starting charging at various times (e.g., user A starts charging at 10 PM 19 times, starts charging at 8 PM 8 times, ...), and select the time with the highest frequency for each user. The data obtained at this time represents the most frequent charging times for multiple charging pile users. For example, user A started charging at 10 PM for 19 days, ranking first in frequency, so this user prefers to start charging their car at 10 PM.

[0087] 3.2 Calculate the average charging time for a single user: Calculate the average charging time for a single registered user based on the charging time for all days; statistical analysis of the data shows that the average charging time for registered users is generally concentrated between 3.5 and 8 hours.

[0088] 3.3 Calculate the average electricity consumption per charge for a single user: Calculate the average electricity consumption per charge for a single registered user based on the electricity consumption per charge for all days.

[0089] 3.4 Calculate the average starting load value for charging for a single user: Calculate the average starting load value for charging for a single registered user based on the load size corresponding to the charging start time point for all days.

[0090] 3.5 Calculate the charging interval days. The calculation process is as follows:

[0091] 3.5.1 Obtain the charging duration and start and end times for each registered user for all days. Based on the daily charging duration, determine whether there is charging activity on that day. If so, tag the electricity load data for that day with a "charging activity" label.

[0092] 3.5.2 Based on the charging dates corresponding to all the electricity load data with charging tags for the current day, calculate the number of days between two adjacent charging dates for each user, and filter out the maximum, minimum and average charging interval days for each user.

[0093] Step 2: Analysis of Charging Pile Load Characteristics

[0094] S13. Perform S12 on the electricity load data of each registered user in A to obtain the charging pile load characteristics of all registered users and store them in the second data group B, denoted as B. A (B1, B2, B3, ...);

[0095] S14. Perform feature analysis on the second data group B to obtain the electricity consumption characteristics of the registered users.

[0096] 4. Extract the electricity usage characteristics of registered users. The specific process includes:

[0097] 4.1 Charging Time Periods: Peak charging times for all registered users were statistically analyzed and grouped into time periods. The total frequency of charging starts at each time point within each time period was calculated. The time period with the highest total frequency was selected as the charging time period for all registered users, and the start and end times of each charging time period were recorded. Research revealed that most people start charging between 10 PM and midnight, especially between 11 PM and midnight. Therefore, if a user has installed a private charging pile, its load is likely to suddenly increase between 11 PM and midnight.

[0098] 4.2 Charging power of charging piles: Based on the average starting load value of charging for a single registered user, the charging power of the charging pile corresponding to a single registered user is determined. All registered users are grouped according to the corresponding charging power of the charging pile. The load value at the start of charging for charging piles with different power is statistically analyzed. It is found that the starting load of a 7kW charging pile is generally between 6 and 7.5kW. Such a load will generally last for at least 4 hours. The average starting load of a 5kW charging pile is 4.22kW.

[0099] 4.3 Charging Time: This includes calculating the average single charging time for charging piles of different power levels based on the average charging time of all registered users in different charging power groups. Based on the average single charging time for charging piles of different power levels, the overall average single charging time for charging piles is approximately 4.57 hours. Users with charging piles of 7kW and 8kW power are the most numerous and representative, with an average charging time of 4.66 hours for this group.

[0100] 4.4 Electricity Consumption per Charge: Based on the average electricity consumption per charge for all registered users in different charging power groups, the average electricity consumption per charge for charging piles of different power levels is calculated. The average electricity consumption per charge for most registered users is concentrated between 15 and 50 kWh, while the average electricity consumption per charge for users with charging piles of 7 kW and 8 kW is 32.51 kWh.

[0101] In step S2, the process of extracting the electricity consumption characteristics of residential users is as follows:

[0102] S21. Preprocess the electricity load data of residential users according to the processing method for the electricity load data of users who have already applied for connection, including:

[0103] The electricity load data of residential users is collected at 15-minute intervals, resulting in a total of 96 electricity load values ​​for each residential user per day, corresponding to 96 time points from P1 to P96 (for example, P1 represents 0:15, P2 represents 0:30, P3 represents 0:45, and so on). The interval between two adjacent load points is 15 minutes, or 0.25 hours.

[0104] S22. Extract the maximum daily load value of residential users from the daily electricity load data of residential users;

[0105] S23. Calculate the electricity consumption of residential users during the charging period of the charging pile based on the electricity load data of residential users.

[0106] S24. Find the time periods in the residential user's electricity load data that remain at the charging power of the charging pile within a continuous time period; record them as suspected charging time periods; calculate the suspected charging duration based on the suspected charging time periods;

[0107] S25. Based on the suspected charging time period, extract the load values ​​corresponding to the start and end times of the suspected charging time period from the electricity load data of residential users to obtain the load increase at the start of suspected charging and the load decrease at the end of suspected charging.

[0108] Step 3: Judging Privately Built Charging Stations by Residential Users

[0109] (1) Constructing judgment indicators

[0110] An analysis of 96 daily electricity load points for residential users revealed that the load was generally quite irregular (frequent fluctuations, inconsistent timing of peak electricity consumption, and varying load magnitude), and each user's electricity consumption habits also differed slightly. Figure 7The trend of 96 electricity load points for a residential user on a certain date is presented. There are some differences between the daily load characteristics of residential users and those of users with installed charging piles: Residential users' daily peak electricity consumption is generally around 9 PM, with a significant decrease in load after midnight. The load generally does not reach the charging pile's load level (e.g., 7-20kW) or remain at that level for several hours. In contrast, charging pile usage is generally concentrated between 10 PM and 9 AM the next day, and the load level of a single charging pile is maintained for several hours (e.g., a 7kW charging pile's load starts at 11 PM, remains around 7.2kW, and maintains this load level for about 4 hours, after which the load decreases to near zero).

[0111] Based on the aforementioned differences in characteristics, and combining the charging time period, charging start time, and charging duration of charging piles among the electricity consumption characteristics of registered users, the following four indicators are constructed to identify potential unauthorized charging piles: suspected early morning charging hours (based on the load performance of residential users between 0:15 and 10:00, the number of hours during which a charging pile was in use during the suspected period; for example, if a user's load is greater than 7kW between 1:00 and 5:00, then this indicator is 4 hours), suspected load reduction at the end of charging (possibly a sudden decrease in load at the end of charging), suspected nighttime charging hours (defined the same as suspected early morning charging hours, but the time range is 20:00 to 0:00), and suspected load increase at the start of charging (possibly a sudden increase in load at the start of charging). If a user's suspected early morning charging duration is greater than or equal to 4 hours, or the suspected load reduction at the end of charging is greater than or equal to 7kW, or the suspected nighttime charging duration is greater than or equal to 3.5 hours, or the suspected load increase at the start of charging is greater than or equal to 7kW, then the user is determined to have an unauthorized charging pile.

[0112] (2) Determine the characteristics of the charging pile to be installed

[0113] To obtain the performance of each indicator when the charging pile utilization rate of each user is the highest (the charging load changes the most and the charging time is the longest; if the maximum value is not taken, the obtained indicator value may be close to that of residential users, affecting subsequent judgment), the maximum value of the four indicators for each user is taken. The performance of the four indicators of the 22 users who applied for charging piles is as follows: the maximum suspected charging hours in the early morning generally reach 4 hours or more, but some users tend to charge in the hours before midnight; the maximum suspected load reduction at the end of charging generally reaches 7kW or more; the maximum suspected nighttime charging hours generally reach 3.5 hours or more; the maximum suspected load increase at the start of charging generally reaches 7kW or more.

[0114] Users of different charging stations may experience variations in these four indicators due to differences in charging station models (different power outputs, different power curves during charging) and charging habits (when to charge, how much battery power remains before starting charging, and how long to charge, etc.). For example... Figure 6For example, user number 8 has a maximum suspected charging hour of 0 hours at midnight, but a maximum suspected charging hour of 3.5 hours at night, indicating that this user generally only completes charging within a few hours before midnight. For user number 17, the maximum suspected charging hour at night is 0 hours, but the maximum suspected charging hour at midnight is 4.25 hours, indicating that this user mainly charges the vehicle after midnight. For user number 6, the maximum suspected load reduction at the end of charging is 6.29kW, which is different from other users, but the maximum suspected load increase at the beginning of charging is 7.43kW, indicating that the load of this charging pile decreases slowly near the end of charging, rather than suddenly becoming 0, which is different from other models of charging piles.

[0115] (3) Logic for judging private piles

[0116] like Figure 2 As shown, based on the above analysis of the differences between the electricity consumption characteristics of residential users and the electricity consumption characteristics of charging piles, the following logic for judging private charging piles is formulated:

[0117] If a residential user's maximum daily load is greater than or equal to the minimum charging power of the charging pile (5kW), and any of the following conditions are met, then the residential user is considered to be using a privately built charging pile:

[0118] Condition 1: The electricity consumption of residential users exceeds the electricity consumption range of charging piles used by users who have already applied for installation;

[0119] Condition 2: The suspected charging time of residential users is greater than or equal to the charging time of charging piles of users who have already applied for installation (3.5h or 4h);

[0120] Condition 3: The suspected increase in charging load of residential users is greater than or equal to the load corresponding to the start time of charging at the charging station (7kW).

[0121] Condition 4: The suspected reduction in load after the end of charging for residential users is greater than or equal to the load corresponding to the end of the charging period of the charging pile (7kW).

[0122] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Based on the technical essence of the present invention, any simple modifications, equivalent substitutions, and improvements made to the above embodiments within the spirit and principles of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for identifying privately built charging piles based on user electricity load data, characterized in that, Includes the following steps: S1. Obtain the electricity load data of all registered users and store it in the first array A, denoted as A(A1, A2, A3, ... A2). m Based on A, extract the electricity consumption characteristics of users who have already applied for connection; The specific process for extracting the electricity consumption characteristics of registered users based on A is as follows: S11. Tag the daily electricity load data of each registered user with a charging tag and extract the charging characteristics. Analyze the extracted charging characteristics for all days to obtain the charging pile load characteristics. The process of tagging the daily electricity load data of each registered user in step S11 is as follows: Obtain the daily electricity load data for each registered user, denoted as A. m (a1, a2, ..., ax); For A m For each data point: electricity load data is collected at fixed time intervals each day, resulting in n electricity load points for each day. These n electricity load points are then numbered and dated in chronological order and stored in the first electricity load database for each day, denoted as Pax(P1, P2, ..., P...). n ); Add half of the electricity load points from the first electricity load database of the next two consecutive days in Am to the first electricity load database of the previous day; thus obtaining the second electricity load database P ax' (P1, P2, ..., P) for each day. n P n+1 , ..., P 1.5n P' includes 1.5n electrical load points; Determine the charging status of each electrical load point in Pax', including positive charging and no charging; and mark the charging status. The charging status label of each power load point is obtained; the determination method is: determine the charging status of each power load point according to the load size corresponding to each power load point; The charging status labels of 1.5n power load points are combined to obtain a complete charging label for each day; S12. Perform S11 on the electricity load data of each registered user in A to obtain the charging pile load characteristics of all registered users and store them in the second data group B, denoted as B. A (B1, B2, B3, ...); S13. Perform feature analysis on the second data group B to obtain the electricity consumption characteristics of the registered users; S2. Obtain the electricity load data of residential users and extract the electricity consumption characteristics of residential users from the electricity load data; S3. Determine whether the electricity consumption characteristics of residential users and those of users who have already applied for charging services meet the similarity criteria. If they do, then determine that the residential user is using a privately built charging pile.

2. The method for judging privately built charging piles based on user electricity load data according to claim 1, characterized in that, The electricity consumption characteristics of registered users include: charging time period, charging power, charging duration, and electricity consumption per charge.

3. The method for judging privately built charging piles based on user electricity load data according to claim 1, characterized in that, The specific process for extracting charging features is as follows: Extract the start and end times of charging each day: Iterate through the complete charging tags for each day, find the tag segments corresponding to the sudden changes in charging status, and use regular expressions to index and extract the power load points corresponding to the tag segments in the complete charging tags for each day as the start and end times of charging respectively; thus obtaining several start and end times of charging. Calculate the daily charging time: Extract several charging time periods based on several charging start and end times, calculate the length of each charging time period, and select the longest charging time period to calculate the daily charging time. Calculate the daily electricity consumption for charging once: Calculate the daily electricity consumption for charging once based on the load size of the electrical load point corresponding to the longest charging time period.

4. The method for judging privately built charging piles based on user electricity load data according to claim 3, characterized in that, The charging pile load characteristics include the number of days between charging intervals. The process of obtaining the number of days between charging intervals is as follows: Get the charging duration and start and end times of each registered user for all days. Based on the daily charging duration, determine whether there is charging activity on that day. If so, tag the electricity load data for that day with a "charging activity" label. Based on the charging dates corresponding to all electricity load data with charging tags for that day, calculate the number of days between two adjacent charging dates for each user, and then filter out the maximum, minimum, and average charging interval days for each user.

5. The method for judging privately built charging piles based on user electricity load data according to claim 3, characterized in that, The process of obtaining the load characteristics of charging piles also includes: Calculate the peak charging time for a single user: Based on the start and end times of charging for all days, count the frequency at which each registered user starts charging at various times each day, and select the time with the highest frequency as the peak charging time for that registered user. Calculate the average charging time for a single user: Calculate the average charging time for a single registered user based on the charging time for all days. Calculate the average electricity consumption per charge for a single user: Calculate the average electricity consumption per charge for a single registered user based on the electricity consumption per charge over all days. Calculate the average starting load value for charging for a single user: Based on the load size corresponding to the charging start time point for all days, calculate the average starting load value for charging for a single registered user.

6. The method for judging privately built charging piles based on user electricity load data according to claim 5, characterized in that, The process of obtaining the electricity usage characteristics of registered users includes: Charging time period: Count the peak charging times of all charging pile users and group them according to time to get several time periods. Calculate the total frequency of charging at each time in each time period. Select the time period with the highest total frequency as the charging time period for all charging pile users and record the start and end times of charging corresponding to the charging time period. Charging power of charging piles: The charging power of the charging piles corresponding to each registered user is determined based on the average starting load value of charging for each registered user. All registered users are grouped according to the corresponding charging power of the charging piles. Charging time of charging piles: This includes calculating the average single charging time of charging piles with different power based on the average charging time of all registered users in different charging power groups; Electricity consumption per charge: The average electricity consumption per charge for charging piles of different power levels is calculated based on the average electricity consumption per charge for all registered users in different charging power groups.

7. The method for judging privately built charging piles based on user electricity load data according to claim 2, characterized in that, The process of extracting the electricity consumption characteristics of residential users is as follows: Analyze the electricity load data of residential users to extract the maximum daily load value of residential users; Calculate the electricity consumption of residential users during the charging period of the charging pile based on the electricity load data of residential users. Find the time periods in the electricity load data of residential users that remain at the charging power of the charging pile; record them as suspected charging time periods; calculate the suspected charging duration based on the suspected charging time periods. Based on the suspected charging time period, the load values ​​corresponding to the start and end times of the suspected charging time period are extracted from the electricity load data of residential users, so as to obtain the load increase at the start of suspected charging and the load decrease at the end of suspected charging.

8. The method for judging privately built charging piles based on user electricity load data according to claim 7, characterized in that, If a residential user's maximum daily load is greater than or equal to the minimum charging power of the charging pile, and any of the following conditions are met, the residential user is considered to be using a privately built charging pile: Condition 1: The electricity consumption of residential users exceeds the electricity consumption range of charging piles used for one charge by users who have already applied for installation; Condition 2: The suspected charging time of residential users is greater than or equal to the charging time of charging piles of users who have already applied for installation; Condition 3: The suspected increase in charging load for residential users is greater than or equal to the load corresponding to the start time of charging during the charging period of the charging pile. Condition 4: The suspected reduction in load after the end of charging for residential users is greater than or equal to the load corresponding to the end of the charging period at the charging pile.

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