Decentralization new energy metering automatic distribution method

Abstract

The invention discloses a decentralized new energy metering automatic distribution method, and relates to the technical field of energy distribution, and the method comprises the steps: collecting the new energy power production data of a target region in real time, and enabling the production data to comprise the total power amount which can be distributed to a charging station network by a new energy power generation system; the reference electric quantity is distributed to the charging station network by the current power grid system; acquiring operation data of each charging station in a preset geographical range, wherein the operation data at least comprises monthly planned electricity consumption, actual electricity consumption in a period and charging pile operation state data of each charging station; the new energy electric power is reasonably distributed to the charging stations with different requirements, it can be ensured that the electric power generated by new energy power generation is fully utilized, electric power allocation is conducted according to the real-time requirements of the charging stations through a dynamic distribution strategy, the new energy electric power can be timely transmitted to the required places, the consumption proportion of new energy is increased, and the energy utilization rate is increased. And healthy development of the new energy industry is promoted.

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 decentralization of energy production, storage and consumption through distributed energy resources (DER) and related technologies, thereby reducing dependence on traditional centralized energy systems; In the prior art, renewable energy power generation as an emerging energy source has been widely used in all walks of life. With the popularization of new energy vehicles, more and more charging stations are being built. However, in the actual application process, a certain amount of renewable energy power is generally allocated to each charging station in advance. However, in the actual application process, because the flow rate of each charging station is different, the required allocated power resources are inconsistent, and in the process of charging new energy vehicles with charging piles, overheating 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 certain losses to the allocated power resources, and then cause uneven distribution of new energy. Summary of the invention

[0003] In order to solve the above problems, the present invention provides a decentralized new energy metering automatic allocation method.

[0004] The present invention provides a decentralized new energy metering automatic allocation method, which is applied to a new energy charging station network and includes the following steps: Step S1: Real-time collection of new energy power production data of the target area, wherein 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 benchmark amount of power that the current power grid system allocates to the charging station network; Step S2: Acquire the operating data of each charging station within a preset geographical range, wherein the operating data at least includes the monthly planned power consumption, the actual power consumption within the cycle and the charging pile operating status data of each charging station; Step S3: Based on the deviation analysis between the historical power demand and the actual power consumption of each charging station, the charging stations are dynamically divided into two categories: power redundancy type and power shortage type; Step S4: Establish a differentiated allocation strategy based on the classification results and dynamically adjust the allocation ratio of new energy electricity among charging stations.

[0005] Preferably, the step of S3 includes: S31: Setting a statistical period, collecting the power replenishment records of each charging station within the period, wherein the power replenishment records refer to the number of times the charging station needs to request power replenishment from the power grid after obtaining the allocated power; S32: Record the frequency of power replenishment for each charging station, and mark it as an abnormal node when the frequency exceeds a threshold; S33: Classify the charging stations with abnormal nodes exceeding a first preset ratio as power shortage type, and the rest as power redundancy type.

[0006] Preferably, the step S33 also includes an abnormal data correction process: Step S331: Perform periodic analysis on the supplementary power time series of abnormal nodes to identify power demand with regular fluctuation characteristics; Step S332: when periodic power demand is detected, data correction is performed on the supplementary power frequency of the corresponding abnormal node; Step S333: Re-classify based on the corrected supplementary power frequency, and if the corrected frequency still exceeds the threshold, maintain the power shortage classification.

[0007] Preferably, the specific method for obtaining the actual power consumption of the charging station is: 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, the overheat protection triggering record of each charging pile is counted, and the overheat protection includes true overheat protection and false overheat protection; Step S43: Counting the power consumption corresponding to the true overheat protection as normal energy consumption; Step S44: marking the power consumption of the charging pile based on false overheat protection as abnormal power consumption; Step S45: obtaining the extra power consumption in the abnormal power consumption; 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.

[0008] Preferably, the method for obtaining the additional power consumption in step S45 is: Get the duration of false overheat protection; Get the power consumption per minute during false overheat protection; The additional power consumption caused by false overheat protection is calculated based on the duration and power consumption per minute.

[0009] Preferably, the method for determining true overheat protection and false overheat protection is: When an overheat protection signal is detected, a "fault injection" operation is performed on the cooling system or charging parameters; During the fault injection, the response characteristics of multiple key parameters of each charging pile are continuously acquired; Compare and analyze the monitored response characteristics with the pre-established true overheating and false overheating response characteristic library; Based on the comparison results, determine whether this overheating protection is true overheating protection or false overheating protection.

[0010] Preferably, the specific method of the pre-established true overheating and false overheating response feature library is: Overheating tests for different types of charging equipment, batteries, and various conditions that cause overheating; Synchronously record the changes of multiple key parameters as response characteristics; The parameter changes recorded in each test are collated to form a data set containing the characteristics of true and false overheating responses under various operating conditions; The features in the data set are classified and sorted, and a true overheating response feature library and a false overheating response feature library are constructed respectively.

[0011] Preferably, the specific method for judging whether the overheat protection is true overheat protection or false overheat protection based on the comparison result is: Select a similarity calculation method; The real-time response feature vector is respectively calculated to have similarity with all feature vectors in the true overheating response feature library and the false overheating response feature library; Find the true overheating and false overheating feature vectors that have the highest similarity to the real-time response features.

[0012] Preferably, judging that the overheat protection is a false overheat protection based on the comparison result includes: Since it has been determined to be a false overheating protection, the cooling system power is reduced to normal levels.

[0013] Preferably, after judging that the overheat protection is a false overheat protection based on the comparison result, the method further includes: Use high-precision, redundantly configured sensors or detection equipment using different principles to re-test the temperature and other related parameters, and record the re-test data; Record all relevant data when the overheat protection is triggered for the first time, including but not limited to the temperature, current, voltage, device operating status, and charging stage information at that time.

[0014] By rationally allocating renewable energy power to charging stations with different needs, it is helpful to ensure that the power generated by renewable energy power generation is fully utilized. In the traditional mode, renewable energy power generation is affected by natural conditions. For example, wind power generation during periods of strong wind and photovoltaic power generation during periods of sufficient sunlight will generate a large amount of power. If it cannot be consumed in time, it will cause energy waste. The dynamic allocation strategy allocates power according to the real-time needs of charging stations, so that renewable energy power can be delivered to where it is needed in a timely manner, increasing the proportion of renewable energy consumption and promoting the healthy development of the new energy industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flow chart of the allocation method of the present invention. DETAILED DESCRIPTION

[0016] like Figure 1 As shown: A decentralized new energy metering automatic allocation method, applied to a new energy charging station network, includes the following steps: Step S1: Real-time collection of new energy power production data in the target area, the production data including the total amount of power that the new energy power generation system can allocate to the charging station network, and the benchmark amount of power that the current power grid system allocates to the charging station network; It should be noted that, in the context of vigorously developing new energy today, the reasonable allocation of new energy electricity is crucial to improving energy utilization efficiency and ensuring the stable operation of charging stations. Real-time grasp of the production of new energy electricity is the basis for reasonable allocation. In this city's new energy power system, the new energy power generation system is mainly composed of photovoltaic and wind power generation. Through the sensors and monitoring systems installed on various power generation equipment, we can collect in real time the total amount of electricity that the new energy power generation system can allocate to the charging station network and realize new energy metering; for example, on a certain day, after system statistics, the actual power generation of photovoltaic and wind power reached 7800kWh. At the same time, we also need to understand the benchmark power allocated by the current power grid system to the charging station network. This part of the power is allocated to the charging station network on a daily basis by the traditional power grid based on historical power consumption data and planning. These data are like our "resource list" for power distribution, which allows us to clearly know how much power is available for allocation.

[0017] Step S2: Obtaining the operating data of each charging station within a preset geographical range, the operating data at least including the monthly planned power consumption of each charging station, the actual power consumption within the cycle, and the charging pile operating status data; Furthermore, in order to allocate electricity more accurately, we also need to understand the operating data of each charging station within the 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 electricity consumption, actual electricity consumption during the cycle, and charging pile operation status data; The monthly planned electricity consumption is set in advance based on factors such as the number of vehicles in the area where the charging station is located and the charging demand forecast. For example, the monthly planned electricity consumption of charging station A is 15,000 kWh, which means that according to expectations, the charging station will need approximately this much electricity in a month to meet the vehicle charging needs; The actual power consumption during the cycle reflects the power consumption of the charging station during its actual operation. By counting the data for this week, we found that the actual power consumption of charging station A this week reached 18,000 kWh, which exceeded the planned power consumption by 20%. This may be due to the recent increase in the number of vehicles in the area or the increase in the frequency of vehicle charging. The operating status data of the charging pile is also very important information. By monitoring the operating status of the charging pile, we can understand the usage of the charging pile. For example, the charging pile of charging station A is at full load during peak hours, which further shows that the power demand of the charging station is large, while the average daily utilization rate of charging station B is 60%, indicating that its charging pile has a certain idle time; the idle rate of charging station C at night exceeds 40%, indicating that its power demand at night is lower.

[0018] Step S3: Based on the deviation analysis between the historical power demand and the actual power consumption of each charging station, the charging stations are dynamically divided into two categories: power redundancy type and power shortage type; Specifically, based on the deviation analysis between the historical power demand and the actual power consumption of each charging station, we can dynamically divide the charging stations into two categories: power redundancy type and power shortage type; the specific deviation rate calculation formula is: , through calculation, we get the deviation rate of each charging station: The deviation rate of charging station A is +20%, which means that its actual power consumption exceeds the planned power consumption, and it has exceeded the planned power consumption for three consecutive weeks. Therefore, we classify it as a power shortage charging station. The deviation rate of charging station B is -16.7%, which means that its actual power consumption is lower than the planned power consumption, and the utilization rate of charging piles is continuously low, so it is classified as a power redundant charging station; The deviation rate of charging station C is -6.25%. The actual power consumption is also lower than the planned power consumption, and there is an obvious power consumption valley at night. It is also a power redundant charging station. This classification method helps us formulate different power distribution strategies for different types of charging stations and improve the rationality of power distribution.

[0019] Step S4: Establish a differentiated allocation strategy based on the classification results and dynamically adjust the allocation ratio of new energy electricity among charging stations.

[0020] Specifically, based on the classification results of charging stations, we have established a differentiated allocation strategy to dynamically adjust the allocation ratio of new energy electricity among charging stations. The specific allocation principles are as follows: For charging stations with power shortage, such as charging station A, we will increase the proportion of new energy allocation to 60% to meet their larger power demand; For charging stations with power redundancy, such as charging stations B and C, since their power demand is relatively low, we will reduce the proportion of new energy allocation to 30%, and the grid benchmark power will prioritize the basic needs of each charging station; In summary, through this differentiated allocation strategy, we not only reduce our reliance on a single power grid and achieve decentralization, but also improve the utilization efficiency of renewable energy power, reduce the pressure on the power grid during peak load periods, and reduce the abandonment rate of renewable energy power; By rationally allocating renewable energy electricity to charging stations with different needs, it is helpful to ensure that the electricity generated by renewable energy power generation is fully utilized. Under the traditional model, renewable energy power generation is affected by natural conditions. For example, wind power generation will generate a lot of electricity during periods of strong wind and photovoltaic power generation during periods of sufficient sunlight. If it cannot be consumed in time, it will cause energy waste. The dynamic allocation strategy automatically allocates electricity according to the real-time needs of the charging station, so that renewable energy electricity can be delivered to where it is needed in time, thereby increasing the consumption ratio of new energy and promoting the healthy development of the new energy industry.

[0021] As a further embodiment, the step of S3 includes: S31: Setting a statistical period, collecting the power replenishment records of each charging station within the period, where the power replenishment records refer to the number of times the charging station needs to request power replenishment from the power grid after obtaining the allocated power; It should be understood that in order to accurately determine the power supply and demand status of each charging station, a reasonable statistical period needs to be set first. The setting of the statistical period should take into account many factors, such as the periodic characteristics of renewable energy power generation (such as photovoltaic power generation is affected by day and 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; During this statistical period, we collected power replenishment records of each charging station. The power replenishment record refers to the number of times the charging station needs to request power replenishment from the power grid after obtaining the allocated power. This is because, even if the charging station supplies power according to the pre-allocated new energy power and the power grid benchmark power, due to the uncertainty of actual charging demand, there may be insufficient power. At this time, the charging station needs to request additional power from the power grid. For example, charging station A obtained the allocated new energy power and power grid benchmark power within a week, but on Monday, Wednesday and Friday, due to the significant increase in the number of charging vehicles, the allocated power could not meet the demand, so it requested power replenishment from the power grid once each. Then the power replenishment record of charging station A during this statistical period is 3 times.

[0022] S32: Record the frequency of power replenishment for each charging station, and mark it as an abnormal node when the frequency exceeds a threshold; It should be understood that the power replenishment records of each charging station are collated and the frequency of their power replenishment within the statistical period is counted. This frequency can intuitively reflect whether the power demand of the charging station exceeds the supply capacity of the allocated power; In order to distinguish between normal and abnormal power replenishment situations, we need to set a threshold. The threshold setting needs to be determined based on historical data, the average power 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 statistical periods in the past, it is found that the average power replenishment frequency of most charging stations in a week is 2 times, so we can set the threshold to 3 times; When the power replenishment frequency of a charging station exceeds this threshold, it will be marked as an abnormal node, which means that the power demand of the charging station has experienced abnormal fluctuations during the current statistical period, and the allocated power may not be able to meet its actual needs. Taking the previous example, the power replenishment frequency of charging station A is 3 times, which exceeds the set threshold by 2 times, then charging station A will be marked as an abnormal node.

[0023] S33: Classify the charging stations with abnormal nodes exceeding a first preset ratio as power shortage type, and the rest as power redundancy type.

[0024] It should be understood that after marking all abnormal nodes, we need to calculate the proportion of abnormal nodes in each charging station. The calculation method of the abnormal node proportion is: the number of abnormal nodes divided by the total number of charging stations; Next, we set the first preset ratio. The setting of this ratio also needs to be combined with the actual situation, taking into account the overall balance of new energy power distribution, the distribution of various charging stations, and the requirements for distinguishing between power-scarce and power-redundant charging stations. Suppose we set the first preset ratio to 30%; If the proportion of abnormal nodes in a charging station exceeds the first preset proportion, the charging station is classified as power shortage type, which indicates that the power demand of the charging station exceeds the allocated power in many cases during the statistical period, and more power supply is needed to meet its operation needs. On the contrary, if the proportion of abnormal nodes does not exceed the first preset proportion, the charging station is classified as power redundancy type, which indicates that the allocated power can basically meet the demand, and there may even be a certain amount of power surplus. For example, in an area containing 10 charging stations, after statistics and marking, it is found that charging station A has 4 abnormal nodes, and the abnormal nodes account for 40%, which exceeds the first preset ratio of 30%. In this case, charging station A is classified as power shortage type; while charging station B has only 2 abnormal nodes, and the abnormal nodes account for 20%, which does not exceed the first preset ratio, so charging station B is classified as power redundancy type; Through the above steps, we can scientifically and reasonably divide charging stations into power shortage type and power redundancy type according to the power replenishment situation, providing a basis for subsequent differentiated power distribution strategies.

[0025] As a further embodiment, step S33 also includes an abnormal data correction process: Step S331: Perform periodic analysis on the supplementary power time series of abnormal nodes to identify power demand with regular fluctuation characteristics; It should be understood that after the charging stations are initially divided into power shortage type and power redundancy type, 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 the abnormal nodes. The supplementary power time series records the specific time when the abnormal nodes request supplementary power from the power grid each time during the statistical period; Periodicity analysis is a method of mining time series data to find potential periodic patterns. In the scenario of new energy charging stations, many factors will cause electricity demand to fluctuate regularly. For example, some charging stations are located in commercial centers, and their electricity demand may fluctuate periodically with the change of weekdays and weekends. On weekends, due to the large flow of people, the charging demand will also increase accordingly. Some charging stations are close to industrial parks, and their electricity demand may be related to the company's commuting time, showing the periodic characteristics of daily morning and evening peaks. By using time series analysis techniques, such as Fourier transform and autocorrelation analysis, to analyze the time series of supplementary power at abnormal nodes, we can identify these electricity demands with regular fluctuation characteristics; for example, by converting time series data to the frequency domain through Fourier transform, we can find out 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 determined that the electricity demand of the abnormal node has periodic fluctuations related to weekdays and weekends.

[0026] Step S332: when periodic power demand is detected, data correction is performed on the supplementary power frequency of the corresponding abnormal node; It should be understood that once it is detected that an abnormal node has periodic power demand, it is necessary to perform data correction on its power replenishment frequency, because these periodic power demands may be caused by some predictable factors rather than a real power supply shortage; The method of data correction can be determined based on 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 electricity replenishment, then the normal increase in electricity demand on weekends can be calculated based on historical data, and then the frequency of electricity replenishment on weekends can be adjusted; assuming that the abnormal node replenishes electricity twice on average on weekends during the statistical period, and according to historical data, it should replenish electricity once on weekends due to normal demand increase, then the frequency of electricity replenishment on weekends can be corrected to once; Through data correction, false anomalies caused by periodic factors can be eliminated, so that the frequency of power replenishment can more accurately reflect the actual power demand of the charging station.

[0027] Step S333: Re-classify based on the corrected supplementary power frequency, and if the corrected frequency still exceeds the threshold, maintain the power shortage classification.

[0028] It should be understood that after completing the data correction of the frequency of power replenishment of abnormal nodes, it is necessary to re-classify the charging station based on the corrected power replenishment frequency; that is, calculate the abnormal node ratio 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; If the corrected supplementary power frequency still exceeds the previously set threshold, it means that even if the influence of periodic factors is excluded, the power demand of the charging station is still large and the allocated power cannot meet its actual demand. In this case, its power shortage classification is maintained. For example, after data correction, the supplementary power frequency of charging station A is corrected from 5 times to 3 times, but it still exceeds the threshold by 2 times. In this case, charging station A is still classified as power shortage type. Through the abnormal data correction process, the accuracy of charging station classification can be improved, and misclassification due to periodic electricity demand can be avoided, so that the subsequent new energy power distribution strategy can be more scientific and reasonable.

[0029] As a further embodiment, a specific method for obtaining the actual power consumption of a charging station is: Step S41: Collect the energy consumption of each charging pile in the charging station and record the energy consumption of each charging pile; It should be understood that in order to accurately obtain the power consumption of the entire charging station, the energy consumption of each charging pile in the station must first be collected. This process relies on a high-precision power metering device installed on the charging pile, which can monitor and record the power consumption of the charging pile during the charging process in real time; In actual operation, these metering devices will collect energy consumption data at certain time intervals (such as every minute or every hour) and store it in a local data recording module. After that, the 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 in different time periods, providing basic data for subsequent statistics and analysis.

[0030] Step S42: After obtaining the energy consumption of each charging pile in the charging station, the overheat protection triggering record of each charging pile is counted, and the overheat protection includes true overheat protection and false overheat protection; It should be understood that after obtaining the energy consumption data of each charging pile, it is necessary to collect statistics on the overheat protection triggering records of the charging pile. Overheat protection is a safety mechanism set by the charging pile to prevent damage to the equipment due to excessive temperature. When the temperature of the charging pile exceeds a certain threshold, the overheat protection device will automatically start, and the cooling system will start to dissipate heat, increasing power consumption; Overheat protection can be divided into true overheat protection and false overheat protection. True overheat protection is triggered by the real increase in temperature due to long-term high-load operation of the charging pile and poor heat dissipation; while false overheat protection may be caused by sensor failure, electromagnetic interference and other factors. In order to accurately distinguish between 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 and other information. By comprehensively analyzing these data, it can be determined whether the overheat protection is true or false.

[0031] Step S43: The power consumption corresponding to the true overheat protection is counted as normal energy consumption; specifically, when it is determined that a charging pile triggers the true overheat protection, the power consumed during the overheat protection is counted as normal energy consumption; this is because the true overheat protection is triggered during the normal operation of the charging pile due to the actual temperature increase, and the power consumed is necessary to complete the charging task.

[0032] Step S44: marking the power consumption of the charging pile based on false overheat protection as abnormal power consumption; Specifically, unlike true overheating protection, false overheating protection is triggered due to reasons other than actual temperature increase, and the power consumed is not for normal charging tasks. Therefore, the power consumption of the charging pile based on false overheating protection is marked as abnormal power consumption.

[0033] Step S45: obtaining the extra power consumption in the abnormal power consumption; 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; 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.

[0034] 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.

[0035] 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; 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.

[0036] As a further embodiment, the method for obtaining the additional power consumption in step S45 is: 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.

[0037] 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; 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; The additional power consumption caused by the false overheat protection is calculated based on the duration and the power consumption per minute. It should be understood that after obtaining the duration and power consumption per minute of the false overheat protection, the additional power consumption can be calculated. The specific calculation method is to add up the power consumption per minute to obtain the total power consumption during the entire false overheat protection period.

[0038] It should be noted that in actual calculations, in order to improve calculation efficiency and accuracy, a computer program can be used to complete the cumulative calculation: this can quickly and accurately determine the additional power consumption caused by the false overheating protection, thereby providing a reliable basis for the subsequent calibration of the actual power consumption of the charging station: Specifically, under the condition of limited supply of renewable energy power, accurately grasping the extra power consumption of charging stations due to false overheat protection can make power distribution more scientific and reasonable: the power grid can accurately distribute power according to the actual power demand of each charging station, including normal energy consumption and accurate extra power consumption, which is conducive to avoiding waste and unreasonable allocation of power resources; By eliminating the extra power consumption interference caused by false overheat protection, the power load of the charging station can be predicted more accurately: this helps the power grid to better dispatch and balance power, reduce the impact of excessive power load fluctuations on the stability of the power system, and ensure the safe operation of the entire power system; Accurate power distribution can make more efficient use of new energy power: reducing unnecessary extra power consumption means that more new energy power can be allocated to charging stations that really need it, increasing the proportion of new energy consumption and promoting the development of the new energy industry. The method for obtaining extra power consumption in step S45 is: Obtain the duration of the pseudo overheat protection; It should be understood that to obtain the duration of the pseudo overheat protection, it is necessary to rely on the monitoring system inside the charging pile, which will accurately record the time point from the triggering to the release of the overheat protection device. When the pseudo overheat protection is triggered, the monitoring system will immediately start the timing function and stop the timing when the overheat protection is released.

[0039] 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; 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; The additional power consumption caused by the false overheat protection is calculated based on the duration and the power consumption per minute. It should be understood that after obtaining the duration and power consumption per minute of the false overheat protection, the additional power consumption can be calculated. The specific calculation method is to add up the power consumption per minute to obtain the total power consumption during the entire false overheat protection period.

[0040] It should be noted that in actual calculations, in order to improve calculation efficiency and accuracy, a computer program can be used to complete the cumulative calculation: this can quickly and accurately determine the additional power consumption caused by the false overheating protection, thereby providing a reliable basis for the subsequent calibration of the actual power consumption of the charging station: Specifically, under the condition of limited supply of renewable energy power, accurately grasping the extra power consumption of charging stations due to false overheat protection can make power distribution more scientific and reasonable: the power grid can accurately distribute power according to the actual power demand of each charging station, including normal energy consumption and accurate extra power consumption, which is conducive to avoiding waste and unreasonable allocation of power resources; By eliminating the extra power consumption interference caused by false overheat protection, the power load of the charging station can be predicted more accurately: this helps the power grid to better dispatch and balance power, reduce the impact of excessive power load fluctuations on the stability of the power system, and ensure the safe operation of the entire power system; Accurate power distribution can make more efficient use of renewable energy electricity: reducing unnecessary additional power consumption means that more renewable energy electricity can be allocated to charging stations that really need it, thereby increasing the proportion of new energy consumption and promoting the development of the new energy industry.

[0041] As a further embodiment, the method for determining true overheat protection and false overheat protection is as follows: When an overheat protection signal is detected, a "fault injection" operation is performed on the cooling system or charging parameters in a targeted manner. It should be noted that a safe fault injection mechanism is designed in the control system of the charging device, for example, temporarily reducing the speed of the cooling fan or fine-tuning the charging current. During the fault injection period, the response characteristics of multiple key parameters of each charging pile are continuously obtained; it should be noted that the response characteristics are the temperature change rate, current and voltage fluctuations, and the adjustment feedback of the power of the cooling system. When facing these small disturbances, the response characteristics of key parameters will be different for true overheating and false overheating; for example, in the case of true overheating, reducing the speed of the cooling fan may cause the temperature to rise rapidly; while in the case of false overheating, the temperature may not change significantly.

[0042] Compare and analyze the monitored response characteristics with the pre-established true overheating and false overheating response characteristic library; Based on the comparison results, determine whether this overheating protection is true overheating protection or false overheating protection.

[0043] It should be noted that the similarity calculation and other methods are used to determine whether the overheat protection is true or false. If the similarity with the false overheat response characteristics exceeds 90%, it is determined to be false overheat protection, otherwise it is true overheat protection.

[0044] As a further embodiment, the specific method of pre-establishing the true overheating and false overheating response feature library is: Overheating tests are conducted on different types of charging equipment, batteries, and various working conditions that may cause overheating. Specifically, in actual application environments, real overheating conditions are simulated, such as increasing the charging current, blocking the heat dissipation channel, etc., to make the device overheat. False overheating conditions are simulated, such as artificially creating sensor interference, changing the ambient temperature for a short period of time, etc., which may lead to misjudgment of overheating protection.

[0045] The changes of multiple key parameters are recorded synchronously as response characteristics. It should be noted that the key parameters include but are not limited to the temperature change rate, current fluctuation amplitude, voltage fluctuation amplitude, heat dissipation component power change and 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 rises from 5A to 6A instantly, and the current fluctuation amplitude is 1A, etc.

[0046] The parameter changes recorded in each test are collated to form a data set containing the true and false overheat response characteristics under various working conditions; it should be noted that the obtained data are integrated to generate a data set, which contains various parameters.

[0047] The features in the data set are classified and sorted to construct a true overheating response feature library and a false overheating response feature library. It should be noted that the features in each library are stored in the form of feature vectors. For example, a true overheating response feature vector may be , corresponding to the temperature change rate, current fluctuation amplitude, voltage fluctuation amplitude, heat dissipation component power change and pressure change respectively.

[0048] As a further embodiment, the specific method for judging whether the overheat protection is true overheat protection or false overheat protection based on the comparison result is: Select a similarity calculation method; specifically, a variety of similarity calculation methods can be used, such as Euclidean distance, cosine similarity, etc., which will not be elaborated here.

[0049] The real-time response feature vector is respectively calculated to have similarity with all feature vectors in the true overheating response feature library and the false overheating response feature library; Find the true overheating and false overheating feature vectors that have the highest similarity to the real-time response features.

[0050] For example, taking the Euclidean distance as an example, suppose there is a feature vector in the true overheating response feature library , 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 the feature vector in the library; Then, the n feature vectors in the true overheat response feature library are calculated to obtain the Euclidean distance , and the Euclidean distance is calculated from the m feature vectors in the pseudo-overheating response feature library ; Assume that the minimum Euclidean distance to a feature vector in the pseudo-overheating feature library is , and the minimum Euclidean distance to a feature vector in the true overheating feature library is , calculate the similarity and (Here, the similarity is obtained by normalizing the distance. The smaller the distance, the higher the similarity). If S2>0.9 and S2>S1, the overheat protection is determined to be a false overheat protection; otherwise, it is determined to be a true overheat protection.

[0051] As a further embodiment, after judging that the overheat protection is a false overheat protection based on the comparison result, the method includes: Since it has been determined to be a false overheating protection, the cooling system power is reduced to normal levels.

[0052] It should be noted that during the overheat protection triggering period, the cooling system usually runs at a higher power to cope with possible high temperature conditions. 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 lot of electricity, and thus cause inaccurate energy statistics results; and after it is determined to be a false overheat protection, continuing to maintain high-power operation will cause unnecessary energy waste. Reducing the power of the cooling system to a normal level can minimize energy consumption and improve energy utilization efficiency while meeting the normal cooling needs of the charging pile, and make the power consumption statistics closer to the actual situation, which is conducive to providing a basis for subsequent power allocation; And after reducing the power of the cooling system, relevant personnel can be notified to investigate and repair the cause of the false overheating protection as soon as possible, which may involve calibrating or replacing the sensor, checking whether the electromagnetic shielding measures are in place, etc.

[0053] As a further embodiment, after judging that the overheat protection is a false overheat protection based on the comparison result, the method further includes: Use high-precision, redundantly configured sensors or detection equipment using different principles to re-test the temperature and other related parameters, and record the re-test data; Record all relevant data when the overheat protection is triggered for the first time, including but not limited to the temperature, current, voltage, device operating status, and charging stage information at that time.

[0054] It should be noted that if thermistor is used to detect the temperature initially, it can be retested with an infrared thermometer to further confirm whether overheating really exists, providing a comprehensive basis for subsequent analysis; Furthermore, it is also important to record the relevant data when the overheat protection is triggered for the first time. The temperature data at that time can reflect the temperature of the charging pile at the moment when the overheat protection is triggered. Even if it is judged as a false overheat protection, there may be a trend of abnormal temperature increase, but it is not to the extent that the overheat protection needs to be triggered. By comparing the temperature at the first trigger and the temperature detected again, the trend and amplitude of the temperature change can be analyzed. Current and voltage data are also key information. Abnormal current or voltage fluctuations may cause the sensor to misjudge, thereby triggering false overheat protection; for example, when the current is too large during charging, the electromagnetic environment around the temperature sensor may change, affecting its measurement accuracy and causing false overheat protection. Recording the current and voltage values ​​when it is first triggered can help technicians analyze whether there is an electrical fault or interference factor. The equipment operation status information includes whether the various components of the charging pile are working properly, such as whether the cooling fan is running and whether the charging interface is well connected. If the cooling fan fails when the overheat protection is triggered for the first time, it may cause the local temperature to rise and cause the sensor to misjudge. Recording the charging stage information, such as whether fast charging or slow charging is in progress and whether charging is about to be completed, is also important 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. The relevant data when the overheat protection is triggered for the first time will be stored in the data storage module together with the data from the re-detection, providing comprehensive data support for subsequent fault diagnosis and equipment optimization. Technicians can find out the root cause of the false overheat protection through in-depth analysis of these data, such as sensor failure, electromagnetic interference, equipment aging, etc., and take corresponding measures to repair and improve it, thereby improving the stability and reliability of the charging pile.

[0055] The above are only preferred implementations of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technical staff in this technical field, some improvements and modifications 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, applied to a new energy charging station network, characterized in that: The following steps are involved: Step S1: Real-time collection of new energy power production data of the target area, wherein 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 benchmark amount of power that the current power grid system allocates to the charging station network; Step S2: Acquire the operating data of each charging station within a preset geographical range, wherein the operating data at least includes the monthly planned power consumption, the actual power consumption within the cycle and the charging pile operating status data of each charging station; Step S3: Based on the deviation analysis between the historical power demand and the actual power consumption of each charging station, the charging stations are dynamically divided into two categories: power redundancy type and power shortage type; Step S4: Establish a differentiated allocation strategy based on the classification results and dynamically adjust the allocation ratio of new energy electricity among charging stations.

2. A decentralized new energy metering automatic allocation method according to claim 1, characterized in that: The steps of S3 include: S31: Setting a statistical period, collecting the power replenishment records of each charging station within the period, wherein the power replenishment records refer to the number of times the charging station needs to request power replenishment from the power grid after obtaining the allocated power; S32: Record the frequency of power replenishment for each charging station, and mark it as an abnormal node when the frequency exceeds a threshold; S33: Classify the charging stations with abnormal nodes exceeding a first preset ratio as power shortage type, and classify the rest as power redundancy type.

3. A decentralized new energy metering automatic allocation method according to claim 2, characterized in that: The step S33 also includes an abnormal data correction process: Step S331: Perform periodic analysis on the supplementary power time series of abnormal nodes to identify power demand with regular fluctuation characteristics; Step S332: when periodic power demand is detected, data correction is performed on the supplementary power frequency of the corresponding abnormal node; Step S333: Re-classify based on the corrected supplementary power frequency, and if the corrected frequency still exceeds the threshold, maintain the power shortage classification.

4. A decentralized new energy metering automatic allocation method according to claim 3, characterized in that: The specific method for obtaining the actual power consumption of the charging station is: 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, the overheat protection triggering record of each charging pile is counted, and the overheat protection includes true overheat protection and false overheat protection; Step S43: Counting the power consumption corresponding to the true overheat protection as normal energy consumption; Step S44: marking the power consumption of the charging pile based on false overheat protection as abnormal power consumption; Step S45: obtaining the extra power consumption in the abnormal power consumption; 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.

5. A decentralized new energy metering automatic allocation method according to claim 4, characterized in that: The method for obtaining the additional power consumption in step S45 is: Get the duration of false overheat protection; Get the power consumption per minute during false overheat protection; The additional power consumption caused by false overheat protection is calculated based on the duration and power consumption per minute.

6. A decentralized new energy metering automatic allocation method according to claim 5, characterized in that: The judgment method of true overheat protection and false overheat protection is as follows: When an overheat protection signal is detected, a "fault injection" operation is performed on the cooling system or charging parameters in a targeted manner; During the fault injection, the response characteristics of multiple key parameters of each charging pile are continuously acquired; Compare and analyze the monitored response characteristics with the pre-established true overheating and false overheating response characteristic library; Based on the comparison results, determine whether this overheating protection is true overheating protection or false overheating protection.

7. A decentralized new energy metering automatic allocation method according to claim 6, characterized in that: The specific method of the pre-established true overheating and false overheating response feature library is: Overheating tests for different types of charging equipment, batteries, and various conditions that cause overheating; Synchronously record the changes of multiple key parameters as response characteristics; The parameter changes recorded in each test are collated to form a data set containing the characteristics of true and false overheating responses under various operating conditions; The features in the data set are classified and sorted, and a true overheating response feature library and a false overheating response feature library are constructed respectively.

8. A decentralized new energy metering automatic allocation method according to claim 4, characterized in that: The specific method for judging whether the overheat protection is true overheat protection or false overheat protection based on the comparison results is as follows: Select a similarity calculation method; The real-time response feature vector is respectively subjected to similarity calculation with all feature vectors in the true overheating response feature library and the false overheating response feature library; Find the true overheating and false overheating feature vectors that have the highest similarity to the real-time response features.

9. A decentralized new energy metering automatic allocation method according to claim 3, characterized in that: Based on the comparison results, it is judged that this overheat protection is a false overheat protection, including: Since it has been determined to be a false overheating protection, the cooling system power is reduced to normal levels.

10. A decentralized new energy metering automatic allocation method according to claim 7, characterized in that: After judging that the overheat protection is a false overheat protection based on the comparison result, the method further includes: Use high-precision, redundantly configured sensors or detection equipment using different principles to re-test the temperature and other related parameters, and record the re-test data; Record all relevant data when the overheat protection is triggered for the first time, including but not limited to the temperature, current, voltage, device operating status, and charging stage information at that time.

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