A Decentralized Automatic Allocation Method for New Energy Metering

Abstract

The present invention discloses a decentralized new energy metering and automatic distribution method, which relates to the technical field of energy distribution. It includes real-time collection of new energy power production data in a target area, where the production data includes the total amount of power that the new energy power generation system can allocate to the charging station network, and the reference power consumption allocated by the current power grid system to the charging station network; obtaining the operation data of each charging station within a preset geographical range, where the operation data at least includes the monthly planned power consumption of each charging station, the actual power consumption within a cycle, and the operation status data of the charging piles; by reasonably distributing new energy power to charging stations with different demands, it is beneficial to ensure the full utilization of the power generated by new energy power generation. Then, through a dynamic distribution strategy, power is allocated according to the real-time demands of the charging stations, enabling new energy power to be timely transported to where it is needed, improving the consumption ratio of new energy, and promoting the healthy development of the new energy industry.

Description

Technical Field

[0001] The present invention relates to the technical field of energy distribution, and more specifically, to a decentralized new energy metering automatic distribution method. Background Art

[0002] Decentralized new energy refers to the realization of the decentralization of energy production, storage, and consumption through distributed energy resources (DER) and related technologies, thereby reducing the dependence on traditional centralized energy systems;

[0003] In the prior art, new energy power generation, as a new type of energy, has been widely applied in various industries. With the popularization of new energy vehicles, more and more charging stations have been built. However, in the actual application process, generally, a fixed amount of new energy power is allocated to each charging station in advance. However, in the actual application process, due to the different traffic flows of each charging station, the required allocated power resources are inconsistent. And during the charging process of electric vehicles by charging piles, overheat protection may occur due to external factors. At this time, the built-in heat dissipation system will increase the power for heat dissipation, which will cause a certain loss of the allocated power resources, and then lead to the occurrence of uneven new energy distribution. Summary of the Invention

[0004] To solve the above problems, the present invention provides a decentralized new energy metering automatic distribution method.

[0005] The present invention provides a decentralized new energy metering automatic distribution method, which is applied to a new energy charging station network and includes the following steps:

[0006] Step S1: Real-time collect the new energy power production data of the target area, where the production data includes the total power that the new energy power generation system can allocate to the charging station network, and the reference power allocated by the current power grid system to the charging station network;

[0007] Step S2: Obtain the operation data of each charging station within a preset geographical range, where the operation data at least includes the monthly planned power consumption, the actual power consumption within a period, and the charging pile operation status data of each charging station;

[0008] Step S3: Based on the deviation analysis of the historical power consumption demand and the actual power consumption of each charging station, dynamically divide the charging stations into two categories: power redundant type and power shortage type;

[0009] Step S4: Establish a differential distribution strategy according to the classification results, and dynamically adjust the distribution ratio of new energy power among each charging station.

[0010] Preferably, the steps of S3 include:

[0011] S31: Set the statistical period and collect the power replenishment records of each charging station within the period. The power replenishment record refers to the number of times a charging station still needs to request power replenishment from the power grid after obtaining the allocated power.

[0012] S32: Record the power replenishment frequency for each charging station. When the frequency exceeds the threshold, mark it as an abnormal node.

[0013] S33: Classify the charging stations with the proportion of abnormal nodes exceeding the first preset ratio as power shortage types, and the rest as power redundancy types.

[0014] Preferably, the step S33 further includes an abnormal data correction process:

[0015] Step S331: Conduct a periodic analysis of the power replenishment time series of abnormal nodes to identify the power consumption demands with regular fluctuation characteristics.

[0016] Step S332: When detecting periodic power consumption demands, correct the data of the power replenishment frequency of the corresponding abnormal nodes.

[0017] Step S333: Re - execute the classification based on the corrected power replenishment frequency. If the corrected frequency still exceeds the threshold, maintain the power shortage type classification.

[0018] Preferably, the specific method for obtaining the actual power consumption of the charging station is as follows:

[0019] Step S41: Collect the energy consumption of each charging pile in the charging station and record the energy consumption of each charging pile.

[0020] Step S42: After obtaining the energy consumption of each charging pile in the charging station, count the overheat protection trigger records of each charging pile. The overheat protection includes true overheat protection and false overheat protection.

[0021] Step S43: Count the power consumption corresponding to the true overheat protection as normal energy consumption.

[0022] Step S44: Mark the power consumption of the charging pile based on false overheat protection as abnormal power consumption.

[0023] Step S45: Obtain the additional power consumption in the abnormal power consumption.

[0024] Step S46: Add the normal energy consumption and the abnormal power consumption and then deduct the additional power consumption, and output the calibrated actual power consumption.

[0025] Preferably, the method for obtaining the additional power consumption in step S45 is as follows:

[0026] Obtain the duration of false overheat protection.

[0027] Obtain the power consumption per minute during the pseudo-overheat protection process;

[0028] Calculate the additional power consumption generated by the pseudo-overheat protection based on the duration and the power consumption per minute.

[0029] Preferably, the method for judging the true overheat protection and the pseudo-overheat protection is as follows:

[0030] When an overheat protection signal is detected, perform a "fault injection" operation on the heat dissipation system or the charging parameters specifically;

[0031] During the fault injection period, continuously obtain the response characteristics of multiple key parameters of each charging pile;

[0032] Compare and analyze the monitored response characteristics with the pre-established true overheat and pseudo-overheat response characteristic libraries;

[0033] Based on the comparison result, judge whether this overheat protection belongs to true overheat protection or pseudo-overheat protection.

[0034] Preferably, the specific method for the pre-established true overheat and pseudo-overheat response characteristic libraries is as follows:

[0035] Conduct overheat tests for different types of charging devices, batteries, and various working conditions that cause overheating;

[0036] Simultaneously record the changes in multiple key parameters as response characteristics;

[0037] Sort out the parameter changes recorded in each test to form a data set containing true and pseudo-overheat response characteristics under various working conditions;

[0038] Classify and sort out the characteristics in the data set, and respectively construct a true overheat response characteristic library and a pseudo-overheat response characteristic library.

[0039] Preferably, the specific method for judging whether this overheat protection belongs to true overheat protection or pseudo-overheat protection based on the comparison result is as follows:

[0040] Select a similarity calculation method;

[0041] Calculate the similarity between the real-time response feature vectors and all the feature vectors in the true overheat response characteristic library and the pseudo-overheat response characteristic library respectively;

[0042] Find the true overheat and pseudo-overheat feature vectors with the highest similarity to the real-time response features.

[0043] Preferably, after judging that this overheat protection belongs to pseudo-overheat protection based on the comparison result, it includes:

[0044] After it is determined that the overheat protection is false, the power of the cooling system is reduced to the normal level.

[0045] Preferably, after it is determined that the overheat protection this time belongs to false overheat protection based on the comparison result, the following steps are further included:

[0046] Use high-precision, redundant-configured sensors or detection devices using different principles to re-detect the temperature and other related parameters, and record the data of the re-detection.

[0047] Record all relevant data when the overheat protection is first triggered, including but not limited to the temperature, current, voltage at that time, as well as the device operating state and charging stage information.

[0048] By reasonably distributing new energy power to charging stations with different demands, it is beneficial to ensure the full utilization of the power generated by new energy power generation. In the traditional mode, new energy power generation is affected by natural conditions. For example, wind power generation will generate a large amount of power during periods of strong wind, and photovoltaic power generation will generate a large amount of power during periods of sufficient sunlight. If it cannot be consumed in time, it will cause energy waste. The dynamic distribution strategy adjusts the power according to the real-time demands of the charging stations, enabling new energy power to be delivered to where it is needed in a timely manner, increasing the consumption ratio of new energy, and promoting the healthy development of the new energy industry. Description of the Drawings

[0049] Figure 1 It is a flowchart of the distribution method of the present invention. Detailed Embodiments

[0050] As Figure 1 shown: A decentralized new energy metering automatic distribution method is applied to a new energy charging station network, including the following steps:

[0051] Step S1: Real-time collect the new energy power production data of the target area. The production data includes the total amount of power that the new energy power generation system can allocate to the charging station network, and the reference power allocated by the current power grid system to the charging station network.

[0052] It should be noted that in the context of vigorously developing new energy today, the reasonable allocation of new energy power is crucial for improving energy utilization efficiency and ensuring the stable operation of charging stations. Real-time understanding of the production situation of new energy power is the basis for reasonable allocation. In the new energy power system of this city, the new energy power generation system mainly consists of photovoltaic and wind power generation. Through sensors and monitoring systems installed on each power generation device, we can collect in real time the total amount of power that the new energy power generation system can allocate to the charging station network, realizing new energy metering. For example, on a certain day, after system statistics, the actual power generation of photovoltaic and wind power reached 7,800 kWh. At the same time, we also need to know the benchmark power allocated by the current power grid system to the charging station network. This part of the power is fixed by the traditional power grid and allocated to the charging station network daily according to historical power consumption data and plans. These data are like the "resource list" for our power allocation, enabling us to clearly know how much power is available for deployment.

[0053] Step S2: Obtain the operation data of each charging station within a preset geographical range. The operation data at least includes the monthly planned power consumption, actual power consumption within a period, and the operation status data of charging piles of each charging station.

[0054] Furthermore, in order to allocate power more accurately, we also need to understand the operation data of each charging station within a preset geographical range. Here, we selected three representative charging stations A, B, and C for analysis. For each charging station, we collected their monthly planned power consumption, actual power consumption within a period, and the operation status data of charging piles.

[0055] The monthly planned power consumption is formulated in advance based on factors such as the number of vehicles in the area where the charging station is located and the prediction of charging demand. For example, the monthly planned power consumption of charging station A is 15,000 kWh, which means that, as expected, the charging station needs approximately this much power within a month to meet the vehicle charging demand.

[0056] The actual power consumption within a period reflects the power consumption situation of the charging station during actual operation. By counting this week's data, we found that the actual power consumption of charging station A this week reached 18,000 kWh, exceeding the planned power consumption by 20%. This may be due to an increase in the number of vehicles in the area recently, or an increase in the vehicle charging frequency, etc.

[0057] The operation status data of charging piles is also very important information. By monitoring the operation status of charging piles, we can understand the usage situation of charging piles. For example, during peak hours, the charging piles at charging station A are operating at full load, which further indicates that the charging station has a large power demand. The daily utilization rate of charging station B is 60%, indicating that there is a certain amount of idle time for its charging piles. The night idle rate of charging station C exceeds 40%, indicating that its power demand at night is relatively low.

[0058] Step S3: Based on the deviation analysis of the historical electricity demand and actual power consumption of each charging station, the charging stations are dynamically divided into two categories: power redundant type and power shortage type;

[0059] Specifically, according to the deviation analysis of the historical electricity demand and actual power consumption of each charging station, we can dynamically divide the charging stations into two categories: power redundant type and power shortage type; The specific deviation rate calculation formula is: Through calculation, we obtained the deviation rates of each charging station:

[0060] The deviation rate of charging station A is +20%, which indicates that its actual power consumption exceeds the planned power consumption, and the situation of over-planned power consumption has occurred for 3 consecutive weeks. Therefore, we classify it as a power shortage type charging station;

[0061] The deviation rate of charging station B is -16.7%, indicating that its actual power consumption is lower than the planned power consumption, and the utilization rate of charging piles continues to be low. Therefore, it is classified as a power redundant type charging station;

[0062] The deviation rate of charging station C is -6.25%, and similarly, its actual power consumption is lower than the planned power consumption, and there is an obvious electricity consumption trough at night. It also belongs to the power redundant type charging station;

[0063] This classification method helps us formulate different power distribution strategies for different types of charging stations, improving the rationality of power distribution.

[0064] Step S4: Establish a differential distribution strategy based on the classification results, and dynamically adjust the distribution ratio of new energy power among each charging station.

[0065] Specifically, according to the classification results of the charging stations, we established a differential distribution strategy to dynamically adjust the distribution ratio of new energy power among each charging station. The specific distribution principles are as follows:

[0066] For power shortage type charging stations, such as charging station A, in order to meet its large power demand, we increase the new energy distribution ratio to 60%;

[0067] For power redundant type charging stations, such as charging stations B and C, since their power demands are relatively low, we reduce the new energy distribution ratio to 30%, and the grid reference power is preferentially guaranteed to meet the basic needs of each charging station;

[0068] In summary, through this differential distribution strategy, we not only reduce the dependence on a single power grid, achieve decentralization, but also improve the utilization efficiency of new energy power, reduce the pressure on the power grid during peak load periods, and at the same time reduce the curtailment rate of new energy power;

[0069] By reasonably distributing new - energy power to charging stations with different demands, it is conducive to ensuring the full utilization of the power generated by new - energy power generation. In the traditional mode, new - energy power generation is affected by natural conditions. For example, wind power generation generates a large amount of electricity during periods with strong winds, and photovoltaic power generation generates a large amount of electricity during periods with sufficient sunlight. If it cannot be consumed in time, it will cause energy waste. The dynamic distribution strategy automatically adjusts the power according to the real - time demands of charging stations, enabling new - energy power to be delivered to where it is needed in time, increasing the consumption ratio of new - energy and promoting the healthy development of the new - energy industry.

[0070] As a further embodiment, the steps of S3 include:

[0071] S31: Set a statistical period and collect the power - replenishment records of each charging station within the period. The power - replenishment record refers to the number of times a charging station needs to request additional power from the power grid after receiving the allocated power.

[0072] It should be understood that in order to accurately judge the power supply - demand situation of each charging station, a reasonable statistical period needs to be set first. The setting of the statistical period needs to comprehensively consider various factors, such as the periodic characteristics of new - energy power generation (for example, photovoltaic power generation is affected by day - night and seasons, and wind power generation is affected by weather changes), the daily operation rules of charging stations, and the fluctuation of power demand. Usually, a week, a month, or a quarter can be selected as the statistical period. Here, we take a week as an example.

[0073] Within this statistical period, we collect the power - replenishment records of each charging station. The power - replenishment record refers to the number of times a charging station needs to request additional power from the power grid after receiving the allocated power. This is because, even if the charging station supplies power according to the pre - allocated new - energy power and the grid benchmark power, due to the uncertainty of actual charging demand, there may be a situation of insufficient power. At this time, the charging station needs to request additional power from the power grid. For example, charging station A has received the allocated new - energy power and the grid benchmark power within a week, but on Monday, Wednesday, and Friday of this week, due to a large increase in the number of charging vehicles, the allocated power cannot meet the demand, so it requests additional power from the power grid once each day. Then the power - replenishment record of charging station A within this statistical period is 3 times.

[0074] S32: Record the frequency of power replenishment for each charging station, and mark it as an abnormal node when the frequency exceeds the threshold.

[0075] It should be understood that the power - replenishment records of each charging station are sorted out, and the frequency of power replenishment within the statistical period is calculated. This frequency can intuitively reflect whether the power demand of the charging station exceeds the supply capacity of the allocated power.

[0076] To distinguish between normal and abnormal power replenishment situations, we need to set a threshold. The setting of the threshold needs to be determined based on historical data, the average power consumption demand in the area where the charging station is located, and the overall situation of new energy power distribution; for example, by analyzing the data of multiple past statistical periods, it is found that the average power replenishment frequency of most charging stations within a week is 2 times, then we can set the threshold to 3 times;

[0077] When the power replenishment frequency of a certain charging station exceeds this threshold, it is marked as an abnormal node, which means that there is an abnormal fluctuation in the power demand of this charging station during the current statistical period, and the allocated power may not meet its actual demand. Taking the previous example, the power replenishment frequency of charging station A is 3 times, exceeding the set threshold of 2 times, then charging station A will be marked as an abnormal node.

[0078] S33: Classify the charging stations with the proportion of abnormal nodes exceeding the first preset proportion as power shortage types, and the rest as power redundancy types.

[0079] It should be understood that after marking all the abnormal nodes, we need to calculate the proportion of abnormal nodes in each charging station. The calculation method of the proportion of abnormal nodes is: the number of abnormal nodes divided by the total number of charging stations;

[0080] Next, we set the first preset proportion. The setting of this proportion also needs to be combined with the actual situation, considering factors such as the overall balance of new energy power distribution, the distribution of each charging station, and the requirements for distinguishing power shortage type and power redundancy type charging stations. Suppose we set the first preset proportion to 30%;

[0081] If the proportion of abnormal nodes in a certain charging station exceeds the first preset proportion, then this charging station is classified as a power shortage type, which indicates that there are more situations where the power demand of this charging station exceeds the allocated power during the statistical period, and more power supply is needed to meet its operation requirements. On the contrary, if the proportion of abnormal nodes does not exceed the first preset proportion, then this charging station is classified as a power redundancy type, indicating that the allocated power can basically meet the demand, and there may even be a certain amount of power surplus;

[0082] For example, in an area with 10 charging stations, after statistics and marking, it is found that charging station A has 4 abnormal nodes, and the proportion of abnormal nodes is 40%, exceeding the first preset proportion of 30%, then charging station A is classified as a power shortage type; while charging station B only has 2 abnormal nodes, and the proportion of abnormal nodes is 20%, not exceeding the first preset proportion, so charging station B is classified as a power redundancy type;

[0083] Through the above steps, we can scientifically and reasonably classify charging stations into power shortage types and power redundancy types based on the power replenishment situation of the charging stations, providing a basis for subsequent differential power distribution strategies.

[0084] As a further embodiment, step S33 further includes an abnormal data correction process:

[0085] Step S331: Perform a periodic analysis on the supplementary power time series of abnormal nodes to identify the electricity consumption demands with regular fluctuation characteristics;

[0086] It should be understood that after initially classifying charging stations into power shortage types and power redundancy types, in order to ensure the accuracy of the classification, it is necessary to conduct an in-depth analysis of the supplementary power time series of abnormal nodes. The supplementary power time series records the specific time when each abnormal node requests supplementary power from the power grid within the statistical period;

[0087] Periodic analysis is a method of mining time series data to find potential periodic patterns. In the scenario of new energy charging stations, many factors can cause the electricity consumption demand to show regular fluctuations; for example, some charging stations are located in commercial centers, and their electricity consumption demand may show periodic fluctuations with the change of weekdays and weekends. Since the number of people is large on weekends, the charging demand will also increase accordingly; there are also some charging stations near industrial parks, and their electricity consumption demand may be related to the working hours of enterprises, showing periodic characteristics of morning and evening peaks every day;

[0088] By using time series analysis techniques, such as Fourier transform, autocorrelation analysis and other methods, to analyze the supplementary power time series of abnormal nodes, we can identify these electricity consumption demands with regular fluctuation characteristics; for example, by converting the time series data to the frequency domain through Fourier transform and finding the main frequency components, if it is found that the period corresponding to a certain frequency component matches the period of weekdays and weekends, then it can be judged that the electricity consumption demand of the abnormal node has periodic fluctuations related to weekdays and weekends.

[0089] Step S332: When periodic electricity consumption demands are detected, perform data correction on the supplementary power frequencies of the corresponding abnormal nodes;

[0090] It should be understood that once periodic electricity consumption demands are detected in abnormal nodes, it is necessary to perform data correction on their supplementary power frequencies, because these periodic electricity consumption demands may be caused by some predictable factors rather than real power supply shortages;

[0091] The method of data correction can be determined according to the characteristics of periodic electricity demand. For example, if it is found that the electricity demand of a certain abnormal node peaks every weekend, resulting in an increase in the frequency of supplementary power, then the normal increase in electricity demand on weekends can be calculated based on historical data, and then the frequency of supplementary power on weekends can be adjusted; assuming that the abnormal node supplements power 2 times on average on weekends within the statistical period, and according to historical data, it should supplement power 1 time due to normal demand increase on weekends, then the frequency of supplementary power on weekends can be corrected to 1 time;

[0092] Through data correction, those false anomalies caused by periodic factors can be excluded, enabling the frequency of supplementary power to more accurately reflect the actual power demand of the charging station.

[0093] Step S333: Re - perform classification based on the corrected frequency of supplementary power. If the corrected frequency still exceeds the threshold, maintain the classification of power shortage type.

[0094] It should be understood that after completing the data correction of the frequency of supplementary power for abnormal nodes, it is necessary to re - perform the classification of the charging station based on the corrected frequency of supplementary power; that is, calculate the proportion of abnormal nodes again and compare it with the first preset ratio to determine whether the charging station belongs to the power shortage type or the power redundancy type;

[0095] If the corrected frequency of supplementary power still exceeds the previously set threshold, it indicates that even after excluding the influence of periodic factors, the power demand of this charging station is still large, and the allocated power cannot meet its actual demand. Then maintain its classification of power shortage type. For example, after data correction, the frequency of supplementary power of charging station A is corrected from the original 5 times to 3 times, but still exceeds the threshold of 2 times. Then charging station A is still classified as the power shortage type;

[0096] Through the abnormal data correction process, the accuracy of charging station classification can be improved, avoiding misclassification caused by periodic electricity demand, so that the subsequent new - energy power distribution strategy is more scientific and reasonable.

[0097] As a further embodiment, the specific method for obtaining the actual power consumption of the charging station is as follows:

[0098] Step S41: Collect the energy consumption of each charging pile in the charging station and record the energy consumption of each charging pile.

[0099] It should be understood that in order to accurately obtain the power consumption of the entire charging station, it is necessary to first collect the energy consumption of each charging pile in the station. This process relies on a high - precision power metering device installed on the charging pile, which can real - time monitor and record the power consumption of the charging pile during the charging process;

[0100] In actual operation, these metering devices collect energy consumption data at regular time intervals (such as every minute or every hour) and store it in the local data recording module. After that, this data will be transmitted to the central management system of the charging station for further analysis and processing. In this way, we can clearly understand the energy consumption of each charging pile at different time periods, providing basic data for subsequent statistics and analysis.

[0101] Step S42: After obtaining the energy consumption of each charging pile in the charging station, count the overheat protection trigger records of each charging pile. The overheat protection includes true overheat protection and false overheat protection.

[0102] It should be understood that after obtaining the energy consumption data of each charging pile, it is necessary to count the overheat protection trigger records of the charging pile. Overheat protection is a safety mechanism set by the charging pile to prevent equipment damage due to excessive temperature. When the temperature of the charging pile exceeds a certain threshold, the overheat protection device will be automatically activated, and the cooling system will start to dissipate heat, increasing power consumption.

[0103] Overheat protection can be divided into true overheat protection and false overheat protection. True overheat protection is triggered due to the actual increase in temperature caused by the charging pile running at high load for a long time, poor heat dissipation, etc.; while false overheat protection may be caused by factors such as sensor failure and electromagnetic interference.

[0104] To accurately distinguish these two situations, it is necessary to analyze the relevant data when the overheat protection is triggered. For example, check the working status of the charging pile, ambient temperature, sensor readings, etc. By comprehensively analyzing this data, it can be determined whether the overheat protection is true or false.

[0105] Step S43: Count the power consumption corresponding to true overheat protection as normal energy consumption. Specifically, when it is determined that a certain charging pile triggers true overheat protection, the power consumed during this overheat protection period is counted as normal energy consumption. This is because true overheat protection is triggered during the normal operation of the charging pile due to the actual increase in temperature, and the power consumed is necessary to complete the charging task.

[0106] Step S44: Mark the power consumption of the charging pile based on false overheat protection as abnormal power consumption.

[0107] Specifically, different from true overheat protection, false overheat protection is triggered due to reasons other than the actual increase in temperature, and the power consumed is not for the normal charging task. Therefore, the power consumption of the charging pile based on false overheat protection is marked as abnormal power consumption.

[0108] Step S45: Obtain the additional power consumption in the abnormal power consumption.

[0109] It should be understood that after marking the abnormal power consumption, it is necessary to further analyze the additional power consumption. The additional power consumption refers to the unnecessary power consumption caused by abnormal conditions such as false overheat protection;

[0110] To obtain the additional power consumption, a detailed analysis of the abnormal power consumption is required. For example, the power consumption during the normal charging process and the false overheating protection period should be compared to find the difference between the two. This difference is the additional power consumption.

[0111] Step S46: Add the normal power consumption and the abnormal power consumption, deduct the extra power consumption, and output the actual power consumption after calibration.

[0112] It should be understood that the normal power consumption and the abnormal power consumption are added together to obtain the total power consumption. Then, the extra power consumption is deducted from the total power consumption to obtain the actual power consumption after calibration;

[0113] The actual power consumption after calibration more accurately reflects the actual power consumed by the charging station to complete the charging task, and eliminates unnecessary power consumption caused by abnormal conditions such as false overheating protection. This data is of great significance for the rational allocation and management of new energy electricity.

[0114] As a further embodiment, the method for obtaining the additional power consumption in step S45 is:

[0115] Obtain the duration of the false overheating protection; it should be understood that to obtain the duration of the false overheating protection, it is necessary to rely on the monitoring system inside the charging pile. The system will accurately record the time point from the triggering to the release of the overheating protection device. When the false overheating protection is triggered, the monitoring system will immediately start the timing function and stop the timing when the overheating protection is released.

[0116] Obtain the power consumption per minute during the pseudo overheat protection process; It should be understood that in order to obtain the power consumption per minute during the pseudo overheat protection process, it is necessary to use a high-precision power metering device installed on the charging pile. This device can not only monitor the total power consumption in real time, but also count the power consumption in units of minutes;

[0117] During the pseudo overheat protection period, the power metering device will continuously collect power data and feed back the power consumption information every minute to the central management system of the charging station. For example, by analyzing the data of the power metering device, it is found that a charging pile consumes 0.2 kWh of electricity in the first minute of the pseudo overheat protection, and 0.21 kWh of electricity in the second minute, and so on, thereby obtaining the power consumption data every minute during the entire pseudo overheat protection process;

[0118] The additional power consumption caused by false overheat protection is calculated based on the duration and power consumption per minute. It should be understood that after obtaining the duration of false overheat protection and the power consumption per minute, the additional power consumption can be calculated. The specific calculation method is to accumulate the power consumption per minute to obtain the total power consumption during the entire false overheat protection period.

[0119] It should be noted that in actual calculations, to improve calculation efficiency and accuracy, a computer program can be used to complete the accumulation calculation: in this way, the additional power consumption caused by false overheat protection can be quickly and accurately obtained, providing a reliable basis for subsequent calibration of the actual power consumption of the charging station:

[0120] Specifically, in the case of limited new energy power supply, accurately grasping the additional power consumption of the charging station due to false overheat protection can make the power distribution more scientific and reasonable: the power grid can perform precise power distribution according to the actual power consumption needs of each charging station, including normal energy consumption and accurate additional power consumption, which is conducive to avoiding waste and unreasonable allocation of power resources;

[0121] By eliminating the interference of the additional power consumption caused by false overheat protection, the power consumption load of the charging station can be predicted more accurately: this helps the power grid to better perform power dispatching and balancing, reducing the impact on the stability of the power system caused by excessive fluctuations in the power consumption load and ensuring the safe operation of the entire power system;

[0122] Precise power distribution can make more effective use of new energy power: reducing unnecessary additional power consumption means that more new energy power can be allocated to charging stations with real needs, increasing the consumption ratio of new energy and promoting the development of the new energy industry. The method for obtaining the additional power consumption in step S45 is as follows:

[0123] Obtain the duration of false overheat protection; it should be understood that to obtain the duration of false overheat protection, the monitoring system inside the charging pile needs to be relied on. This system will accurately record the time points from when the overheat protection device is triggered to when it is released. When the false overheat protection is triggered, the monitoring system will immediately start the timing function and stop timing when the overheat protection is released.

[0124] Obtain the power consumption per minute during the false overheat protection process; it should be understood that in order to obtain the power consumption per minute during the false overheat protection process, a high-precision power metering device installed on the charging pile needs to be used. This device can not only monitor the total power consumption in real time but also count the power consumption in minutes;

[0125] During the false overheat protection period, the power metering device continuously collects power data and feeds back the power consumption information per minute to the central management system of the charging station. For example, by analyzing the data of the power metering device, it is found that a certain charging pile consumed 0.2 kWh of electricity in the first minute of false overheat protection, 0.21 kWh in the second minute, and so on, thereby obtaining the power consumption data per minute during the entire false overheat protection process;

[0126] Based on the duration and the power consumption per minute, the additional power consumption caused by the false overheat protection is calculated. It should be understood that after obtaining the duration of the false overheat protection and the power consumption per minute, the additional power consumption can be calculated. The specific calculation method is to accumulate the power consumption per minute to obtain the total power consumption during the entire false overheat protection period.

[0127] It should be noted that in actual calculations, in order to improve the calculation efficiency and accuracy, a computer program can be used to complete the accumulation calculation: in this way, the additional power consumption caused by the false overheat protection can be quickly and accurately obtained, and then a reliable basis can be provided for subsequent calibration of the actual power consumption of the charging station:

[0128] Specifically, in the case of limited new energy power supply, accurately grasping the additional power consumption caused by false overheat protection in the charging station can make the power distribution more scientific and reasonable: the power grid can perform precise power distribution according to the actual power consumption needs of each charging station, including normal energy consumption and accurate additional power consumption, which is conducive to avoiding waste and unreasonable allocation of power resources;

[0129] By eliminating the interference of the additional power consumption caused by false overheat protection, the power consumption load of the charging station can be predicted more accurately: this helps the power grid to better perform power dispatching and balancing, reduce the impact on the stability of the power system caused by excessive fluctuations in the power consumption load, and ensure the safe operation of the entire power system;

[0130] Precise power distribution can make more effective use of new energy power: reducing unnecessary additional power consumption means that more new energy power can be allocated to charging stations with real needs, increasing the consumption ratio of new energy, and promoting the development of the new energy industry.

[0131] As a further embodiment, the method for judging true overheat protection and false overheat protection is as follows:

[0132] When an overheat protection signal is detected, a "fault injection" operation is carried out on the heat dissipation system or charging parameters in a targeted manner; it should be noted that in the control system of the charging device, a safe fault injection mechanism is designed, for example, briefly reducing the rotation speed of the cooling fan or slightly adjusting the charging current;

[0133] During fault injection, continuously obtain the response characteristics of multiple key parameters of each charging pile; it should be noted that the response characteristics include the temperature change rate, the fluctuation of current and voltage, and the adjustment feedback of the scattered system power, etc. When facing these small disturbances, the response characteristics of the key parameters of true overheating and false overheating are different; for example, during true overheating, reducing the speed of the cooling fan may cause the temperature to rise rapidly; while during false overheating, the temperature may not change significantly.

[0134] Compare and analyze the monitored response characteristics with the pre-established true overheating and false overheating response characteristic libraries.

[0135] Based on the comparison results, determine whether this overheat protection belongs to true overheat protection or false overheat protection.

[0136] It should be noted that by methods such as similarity calculation, determine whether this overheat protection is true or false. If the similarity with the false overheating response characteristics exceeds 90%, it is determined as false overheat protection; otherwise, it is true overheat protection.

[0137] As a further embodiment, the specific method for pre-establishing the true overheating and false overheating response characteristic libraries is as follows:

[0138] Conduct overheat tests for different types of charging devices, batteries, and various working conditions that cause overheating; specifically, in the actual application environment, simulate true overheating conditions, such as by increasing the charging current, blocking the heat dissipation channel, etc., to make the device generate real overheating phenomena; simulate false overheating conditions, such as artificially creating sensor interference, short-term changing the ambient temperature, etc., to trigger misjudged overheat protection.

[0139] Synchronously record the changes of multiple key parameters as response characteristics; it should be noted that the key parameters include but are not limited to the temperature change rate, the current fluctuation amplitude, the voltage fluctuation amplitude, the power change of the heat dissipation component, and the pressure change (if there is a pressure sensor), etc. For example, in a true overheating test, it is recorded that the temperature rises by 10°C within 1 minute, that is, the temperature change rate is 10°C per minute; the current instantaneously rises from 5A to 6A, and the current fluctuation amplitude is 1A, etc.

[0140] Organize the parameter changes recorded in each test to form a data set containing true and false overheating response characteristics under various working conditions; it should be noted that the obtained data is integrated to generate a data set, and this data set contains various parameters.

[0141] Classify and organize the characteristics in the data set, and respectively construct a true overheating response characteristic library and a false overheating response characteristic library. It should be noted that the characteristics in each library are stored in the form of feature vectors. For example, a true overheating response feature vector may be , respectively corresponding to the temperature change rate, current fluctuation amplitude, voltage fluctuation amplitude, power change of the heat dissipation component, and pressure change.

[0142] As a further embodiment, the specific method for judging whether this overheat protection is true overheat protection or false overheat protection based on the comparison result is as follows:

[0143] Select a similarity calculation method; specifically, various similarity calculation methods can be used, such as Euclidean distance, cosine similarity, etc., which will not be elaborated here too much.

[0144] Calculate the similarity between the real-time response feature vector and all feature vectors in the true overheat response feature library and the false overheat response feature library respectively;

[0145] Find the true overheat and false overheat feature vectors with the highest similarity to the real-time response feature.

[0146] For example, taking Euclidean distance as an example, assume there is a feature vector in the true overheat response feature library , and the response feature vector obtained by real-time monitoring is , then the Euclidean distance . The smaller the distance, the higher the similarity between the real-time response feature and this feature vector in the library;

[0147] Then calculate the Euclidean distances respectively for the n feature vectors in the true overheat response feature library, and calculate the Euclidean distances respectively for the m feature vectors in the false overheat response feature library;

[0148] Assume the minimum Euclidean distance from a certain feature vector in the false overheat feature library is , and the minimum Euclidean distance from a certain feature vector in the true overheat feature library is , calculate the similarities and (here the similarity is obtained by normalizing the distance, and the smaller the distance, the higher the similarity). If S2 > 0.9 and S2 > S1, then it is determined that this overheat protection is false overheat protection; otherwise, it is determined to be true overheat protection.

[0149] As a further embodiment, after it is determined that this overheat protection belongs to false overheat protection based on the comparison result, it includes:

[0150] In view of the fact that it has been determined to be false overheat protection, reduce the power of the heat dissipation system to the normal level.

[0151] It should be noted that during the triggering of overheat protection, the cooling system usually operates at a high power to cope with possible high-temperature situations. For example, a high-power cooling fan will run at high speed, and the coolant circulation pump will also increase the flow rate, which will undoubtedly consume a large amount of electrical energy, and thus will cause inaccurate results in energy statistics; after it is determined that the overheat protection is false, continuing to maintain high-power operation will cause unnecessary energy waste. Reducing the power of the cooling system to the normal level can minimize energy consumption to the greatest extent while meeting the normal heat dissipation requirements of the charging pile, improve energy utilization efficiency, and make the power consumption statistics closer to the actual situation, which is conducive to providing a basis for subsequent power distribution;

[0152] Moreover, after reducing the power of the cooling system, it is possible to notify relevant personnel to quickly investigate and repair the reasons for the false overheat protection, which may involve calibrating or replacing sensors, checking whether the electromagnetic shielding measures are in place, etc.

[0153] As a further embodiment, after determining that the overheat protection is a false overheat protection based on the comparison result, it further includes:

[0154] Using high-precision, redundant-configured sensors or detection devices with different principles to re-detect the temperature and other relevant parameters, and record the data obtained from the re-detection;

[0155] Record all relevant data at the time of the first trigger of overheat protection, including but not limited to the temperature, current, voltage, equipment operating status, and charging stage information at that time.

[0156] It should be noted that if a thermistor is initially used to detect the temperature, it can be re-measured with an infrared thermometer to further confirm whether there is really an overheat situation, providing a comprehensive basis for subsequent analysis;

[0157] Furthermore, it is equally crucial to record the relevant data at the time of the first trigger of overheat protection. The temperature data at that time can reflect the temperature situation of the charging pile at the moment of triggering overheat protection. Even if it is judged as a false overheat protection, there may still be a trend of abnormal temperature increase, but it does not reach the level that really requires triggering overheat protection. By comparing the temperature at the first trigger with the re-detected temperature, the trend and amplitude of temperature change can be analyzed.

[0158] The current and voltage data are also key information. Abnormal current or voltage fluctuations may cause the sensor to misjudge, thus triggering false overheat protection; for example, when there is a sudden large current during the charging process, it may change the electromagnetic environment around the temperature sensor, affecting its measurement accuracy and resulting in a false overheat protection alarm. Recording the current and voltage values at the first trigger can help technicians analyze whether there are electrical faults or interference factors;

[0159] The device operation status information includes whether each component of the charging pile is working properly, such as whether the cooling fan is running, whether the charging interface is well connected, etc. If the cooling fan fails when the overheat protection is triggered for the first time, it may cause a local temperature rise, leading to misjudgment by the sensor. Recording the charging stage information, such as whether fast charging or slow charging is in progress, whether the charging is about to be completed, etc., is also of great significance for analyzing the cause of overheat protection. In different charging stages, the power and current of the charging pile change differently, which may have different effects on the temperature;

[0160] These relevant data when the overheat protection is triggered for the first time will be stored in the data storage module together with the data detected again, providing comprehensive data support for subsequent fault diagnosis and equipment optimization. Technicians can find out the root causes of false overheat protection, such as sensor failure, electromagnetic interference, equipment aging, etc., through in-depth analysis of these data, and take corresponding measures for repair and improvement to improve the stability and reliability of the charging pile.

[0161] The above is only the preferred implementation mode of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions within the idea of the present invention belong to the protection scope of the present invention. It should be pointed out that for those of ordinary skill in the art of this technology, several improvements and refinements made without departing from the principle of the present invention should also be regarded as the protection scope of this template.

Claims

1. A decentralized new energy metering automatic allocation method, which is applied to a new energy charging station network, is characterized in that It includes the following steps: Step S1: Collect the new energy power production data of the target area in real time. The production data includes the total power that the new energy power generation system can allocate to the charging station network, and the benchmark power allocated by the current power grid system to the charging station network. Step S2: Obtain the operation data of each charging station within a preset geographical range. The operation data at least includes the monthly planned power consumption, the actual power consumption during the period, and the charging pile operation status data of each charging station. Step S3: Based on the deviation analysis of the historical power consumption demand and the actual power consumption of each charging station, the charging stations are dynamically divided into two categories: power redundant type and power shortage type. Step S4: Establish a differential allocation strategy according to the classification results, and dynamically adjust the allocation ratio of new energy power among the charging stations. For charging stations with power shortage, increase the new energy allocation ratio to 60%. For power redundant charging stations, reduce the new energy allocation ratio to 30%. The specific method for obtaining the actual power consumption of the charging station is as follows: Step S41: Collect the energy consumption of each charging pile in the charging station and record the energy consumption of each charging pile. Step S42: After obtaining the energy consumption of each charging pile in the charging station, count the overheat protection trigger records of each charging pile. The overheat protection includes true overheat protection and false overheat protection. Step S43: Count the power consumption corresponding to the true overheat protection as the normal energy consumption. Step S44: Mark the power consumption of the charging pile based on false overheat protection as abnormal power consumption. Step S45: Obtain the additional power consumption in the abnormal power consumption. Step S46: Add the normal energy consumption and the abnormal power consumption and then deduct the additional power consumption, and output the calibrated actual power consumption. The method for obtaining the additional power consumption in Step S45 is as follows: Obtain the duration of the false overheat protection. Obtain the power consumption per minute during the false overheat protection process. Calculate the additional power consumption generated by the false overheat protection based on the duration and the power consumption per minute. The method for judging the true overheat protection and the false overheat protection is as follows: When the overheat protection signal is detected, perform a "fault injection" operation on the heat dissipation system or the charging parameters. During the fault injection period, continuously obtain the response characteristics of multiple key parameters of each charging pile. Compare and analyze the monitored response characteristics with the pre-established true overheat and false overheat response characteristic libraries. Based on the comparison results, judge whether this overheat protection belongs to true overheat protection or false overheat protection. The specific method for the pre-established true overheat and false overheat response characteristic libraries is as follows: Conduct overheat tests for different types of charging equipment, batteries, and various working conditions that cause overheating. Synchronously record the changes in multiple key parameters as the response characteristics. Organize the parameter changes recorded in each test to form a data set containing true and false overheat response characteristics under various working conditions. Classify and organize the characteristics in the data set, and respectively construct a true overheat response characteristic library and a false overheat response characteristic library.

2. The automatic distribution method for decentralized new energy metering according to claim 1, characterized in that The steps of S3 include: S31: Set a statistical period and collect the power replenishment records of each charging station within the period. The power replenishment record refers to the number of times a charging station still needs to request power replenishment from the power grid after obtaining the allocated power. S32: Record the power replenishment frequency for each charging station. When the frequency exceeds the threshold, mark it as an abnormal node. S33: Classify the charging stations with the proportion of abnormal nodes exceeding the first preset ratio as power shortage types, and the rest as power redundancy types.

3. The automatic decentralized new energy metering and allocation method according to claim 2, wherein The step S33 also includes an abnormal data correction process: Step S331: Conduct a periodic analysis of the power replenishment time series of abnormal nodes to identify the electricity consumption demand with regular fluctuation characteristics. Step S332: When detecting the periodic electricity consumption demand, perform data correction on the power replenishment frequency of the corresponding abnormal node. Step S333: Re - execute the classification based on the corrected power replenishment frequency. If the corrected frequency still exceeds the threshold, maintain the power shortage type classification.

4. The automatic distribution method of decentralized new energy metering according to claim 1, characterized in that The specific method for judging whether this overheat protection is true overheat protection or false overheat protection based on the comparison result is: Select a similarity calculation method; Calculate the similarity between the real - time response feature vector and all feature vectors in the true overheat response feature library and the false overheat response feature library respectively. Find the true overheat and false overheat feature vectors with the highest similarity to the real - time response feature.

5. A decentralized new energy metering and automatic distribution method according to claim 3, characterized in that, After judging that this overheat protection belongs to false overheat protection based on the comparison result, it includes: In view of the fact that it has been judged as false overheat protection, reduce the power of the heat dissipation system to the normal level.

6. The automatic decentralized new energy metering and allocation method according to claim 1, characterized in that After judging that this overheat protection belongs to false overheat protection based on the comparison result, it also includes: Use high - precision and redundant - configured sensors to re - detect the temperature and its related parameters, and record the re - detected data. Record all relevant data when the overheat protection is first triggered, including the temperature, current, voltage at that time, as well as the equipment operation status and charging stage information.

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