Charging control method based on edge computing for integrated charging and storage

Abstract

The present invention discloses an integrated charging and storage control method based on edge computing, which belongs to the field of power control technology and solves the problem of how to use a small amount of data to accurately predict and obtain the total charging amount of an electric vehicle and control the energy storage module to store appropriate electric energy, thereby avoiding the problem that electric energy still needs to be obtained from the power grid during peak hours, and the load and fluctuation of the power grid during peak hours cannot be reduced, and the energy storage module stores excessive energy, resulting in waste of electric power resources. The method comprises: statistically obtaining a data set of electric vehicle historical charging amounts in a target charging area in the same time period as the next peak period, analyzing and obtaining a first predicted charging amount of all electric vehicles in the target charging area during the next peak period; analyzing and obtaining the health status of each energy storage battery; analyzing and obtaining a second predicted charging amount that the energy storage module needs to obtain from the power grid during the current off-peak period or flat-valley period, and controlling the power grid to output electric energy to the energy storage module for storage.

Description

Technical Field

[0001] The present invention belongs to the field of power control technology, and specifically is a charging and storage integrated charging control method based on edge computing. Background Art

[0002] The core concept of the integrated charging system is to use the energy storage system to store electricity during periods of low grid load, and provide additional power support for charging electric vehicles during peak hours, thereby achieving the goal of shifting peak loads. The edge computing node is a core component of the edge computing architecture, located between the data source (such as sensors, devices) and the cloud computing center, and is responsible for data processing, analysis and storage at the edge of the network.

[0003] However, in the prior art, it is often necessary to obtain electricity from the power grid during peak hours, or the energy storage module stores excess electricity, resulting in a waste of resources, and the data storage capacity and data processing capabilities of edge computing nodes are limited. How to use a small amount of data to accurately predict the total charge of an electric vehicle and control the energy storage module to store appropriate electricity needs to be solved urgently. To this end, the present invention proposes an integrated charging control method based on edge computing. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes an integrated charging control method based on edge computing, which solves the problem of how to use a small amount of data to accurately predict the total charging amount of an electric vehicle and control the energy storage module to store appropriate electric energy, thereby avoiding the need to obtain electric energy from the power grid during peak hours, failing to reduce the load and fluctuation of the power grid during peak hours, and avoiding the problem of excessive energy storage in the energy storage module, resulting in waste of electric power resources.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The charging control method of integrated charging and storage based on edge computing includes:

[0007] Step S1: Statistically obtain a historical charging amount data set of electric vehicles in the same time period as the target charging area and the next peak period, and analyze and obtain a first predicted charging amount of all electric vehicles in the target charging area during the next peak period based on the historical charging amount data set;

[0008] Step S2: obtaining recent charge and discharge data of the energy storage module in the target charging area, and obtaining the health status of each energy storage battery included in the energy storage module in the target charging area based on the acquired recent charge and discharge data of the energy storage module in the target charging area;

[0009] Step S3: Obtain the identifiers of the energy storage batteries in the target charging area whose current health status is marked as normal by statistics; analyze and obtain the second predicted charging amount that the energy storage module needs to obtain from the power grid during the current off-peak period or flat-valley period based on the power data of the energy storage batteries in the energy storage module whose health status is normal and the first predicted charging amount of all electric vehicles during the next peak period; and control the power grid to output electric energy to the energy storage module for storage based on the second predicted charging amount.

[0010] Furthermore, the electric vehicle historical charging amount data set for the target charging area in the same time interval as the next peak period includes the charging amounts of all electric vehicles in the target charging area in the corresponding time interval on each day within n consecutive historical dates.

[0011] Furthermore, the method of obtaining a first predicted charging amount of all electric vehicles in the target charging area during the next peak period based on the historical charging amount data set analysis includes:

[0012] Arrange the charging capacity values ​​in the historical charging capacity data set in chronological order;

[0013] Performing a cumulative calculation on each historical charging capacity value contained in the historical charging capacity data set in the order of arrangement to obtain a cumulative historical charging capacity data set;

[0014] Performing adjacent mean calculation on each of the once accumulated historical charging capacity values ​​contained in the once accumulated historical charging capacity data set to obtain an adjacent equalized data set of the once accumulated historical charging capacity data set;

[0015] Based on each accumulated historical charging amount value contained in the historical charging amount data set after the first accumulation, a first-order differential equation of the charging amount of all electric vehicles in the target charging area within the time interval corresponding to the next peak period is calculated;

[0016] The approximate time response of the first-order differential equation of the charging amount of all electric vehicles in the target charging area corresponding to the time interval of the next peak period is obtained by calculation;

[0017] After one cumulative reduction, the charging amount of all electric vehicles in the target charging area corresponding to the time interval of the next peak period is obtained;

[0018] Then, a first predicted charging amount of all electric vehicles in the target charging area in the corresponding time interval of the next peak period is obtained.

[0019] Furthermore, the recent charging and discharging data of the energy storage module in the target charging area include: the charging power WC and charging time TC of each energy storage battery in the energy storage module in the target charging area during the previous valley period or flat valley period, and the starting energy storage power WS, discharging power WD and discharging time TD of each energy storage battery in the energy storage module in the target charging area during the previous peak period.

[0020] Furthermore, in the recent charging and discharging data of the energy storage module in the target charging area, the time of the last low-valley period or the flat-valley period comes first, and the time of the last peak period comes later; and the current time is set as the low-valley period or the flat-valley period, and after the current low-valley period or the flat-valley period passes, it will be the next peak period.

[0021] Furthermore, the health status of each energy storage battery included in the energy storage module of the target charging area is obtained based on the recent charge and discharge data analysis of the energy storage module of the target charging area, including:

[0022] Based on the charging capacity WC and charging time TC of each energy storage battery in the energy storage module of the target charging area during the previous low valley period or flat valley period, the charging rate vC of each energy storage battery in the energy storage module during the previous low valley period or flat valley period is calculated. The formula is:

[0023] Based on the discharge power WD and discharge time TD of each energy storage battery in the energy storage module of the target charging area during the previous peak period, the discharge rate vD of each energy storage battery in the energy storage module during the previous peak period is calculated. The formula is:

[0024] Based on the charging capacity WC of each energy storage battery in the energy storage module of the target charging area during the previous low valley period or flat valley period and the starting energy storage capacity WS and the discharging capacity WD during the previous peak period, the charging efficiency ηC of each energy storage battery in the energy storage module during the previous low valley period or flat valley period and the discharging efficiency ηD during the previous peak period are calculated; the formula is:

[0025] Based on the charging capacity WC and charging time TC of each energy storage battery in the energy storage module of the target charging area during the previous low-valley period or flat-valley period, and the discharging capacity WD and discharging time TD during the previous peak period, the energy storage loss rate LS of each energy storage battery in the energy storage module during the previous low-valley period or flat-valley period and the previous peak period is calculated. The formula is:

[0026] After normalizing the charging rate vC and charging efficiency ηC of each energy storage battery in the energy storage module in the previous low-valley period or flat-valley period, the discharge rate vD and discharge efficiency ηD in the previous peak period, and the energy storage loss rate LS in the previous low-valley period or flat-valley period and the previous peak period, the deviation of each parameter value from the corresponding standard value is calculated, and a weighted sum calculation is performed to obtain the current health status index of each energy storage battery in the energy storage module.

[0027] Furthermore, the calculated health status index of each energy storage battery of the energy storage module is compared with a preset health status index threshold to determine whether the health status of the corresponding energy storage battery is normal;

[0028] If the health status index of the energy storage battery corresponding to the energy storage module is less than the preset health status index threshold, the health status of the corresponding energy storage battery is marked as normal and no warning is performed;

[0029] If the health status index of the energy storage battery corresponding to the energy storage module is greater than or equal to the preset health status index threshold, the health status of the corresponding energy storage battery is marked as abnormal, and an early warning signal of the abnormal health status of the energy storage battery with the corresponding identifier is generated and sent to the administrator's user end for processing.

[0030] Further, the identifier of the energy storage battery in the energy storage module of the target charging area that is marked as normal in the current health state is obtained by statistics, and according to the power data of the energy storage battery in the energy storage module that is in a normal health state and the first predicted charging amount of all electric vehicles in the next peak period, the second predicted charging amount that the energy storage module needs to obtain from the power grid during the current valley period or flat valley period is analyzed and obtained, and according to the second predicted charging amount, the power grid is controlled to output electric energy to the energy storage module for storage; including:

[0031] Obtain the identifier of the energy storage battery in the target charging area that is marked as normal in the current health state, detect the current remaining power WLr of the energy storage battery corresponding to the energy storage battery identifier marked as normal, and obtain the minimum power WXr of each energy storage battery; wherein r represents the number of the energy storage battery in the target charging area that is marked as normal in the current health state, r=1,2...R; R represents the total number of energy storage batteries in the target charging area that are marked as normal in the current health state;

[0032] According to the remaining power WLr and the minimum power WXr of each energy storage battery in the energy storage module of the target charging area with normal health status, and the first predicted charging amount W1 of all electric vehicles in the target charging area during the next peak period, the second predicted charging amount W2 that the energy storage module needs to obtain from the power grid during the current off-peak period or flat-valley period is calculated. The formula is:

[0033] In the formula, ɑ represents the power conversion coefficient between electric vehicles and energy storage modules in the target charging area, and β represents the power conversion coefficient between the power grid and energy storage modules in the target charging area;

[0034] Based on the calculated second predicted charging amount W2 that the energy storage module needs to obtain from the power grid during the current off-peak period or flat-peak period, the power distribution control module in the target charging area controls the power grid to output the second predicted charging amount of electric energy to the energy storage module.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. In the present invention, the predicted charging amount of electric vehicles in the target charging area during the next peak period is obtained by analyzing the historical charging data set of electric vehicles in the same time interval as the next peak period in the target charging area, including one accumulation, adjacent mean calculation and first-order differential equation calculation. This method is suitable for small sample data, missing or incomplete data, and is suitable for processing the instability, nonlinearity and irregularity of the charging time and charging amount of electric vehicles; the calculation cost is low, and a large amount of computing resources are not required. It has a good effect for scenarios that require accurate prediction in the near future, can quickly respond to data changes, and update the prediction results in time. It has a certain robustness and can effectively reduce the impact of abnormal data on the prediction results; it is suitable for the low storage and low data processing characteristics of edge computing nodes. By analyzing a small amount of historical charging value, the charging amount of all electric vehicles in the next peak period can be accurately predicted, thereby improving the data processing efficiency of edge computing nodes; and it lays a data foundation for the subsequent energy storage module to accurately and appropriately store the required electric energy during the current valley period or flat valley period, ensuring that there will be no shortage of electricity to affect the power supply during the peak period, and no excessive storage will cause waste of resources.

[0037] 2. In the present invention, the health status of the energy storage battery is analyzed and calculated by analyzing the charging and discharging data of the energy storage battery in the target charging area during the last low valley period or flat valley period and the last peak period in time, which can reflect the current health status and performance changes of the energy storage battery in real time and provide real-time health assessment results. The data of the most recent period is highly correlated with the current battery status, which can more accurately reflect the actual use of the energy storage battery and the performance change trend, and can more accurately predict the health status of the battery and future performance changes, which helps to discover potential problems in time, and then quickly respond and adjust to prevent batteries with abnormal health status from affecting system performance and safety; at the same time, it is also convenient for subsequent data reference for power distribution control work.

[0038] 3. In the present invention, the power data of the energy storage batteries in a normal healthy state in the target charging area and the first predicted charging amount of all electric vehicles in the next peak period are analyzed to obtain the second predicted charging amount that the energy storage module needs to obtain from the power grid during the current off-peak period or flat period, and the power grid is controlled to output electric energy to the energy storage module for storage according to the second predicted charging amount; the storage amount of the energy storage module in the target charging area during the current off-peak period or flat period can be accurately controlled to avoid the need to obtain electric energy from the power grid during peak periods, which cannot reduce the load and fluctuation of the power grid during peak electricity consumption periods, and avoid the problem of excessive energy storage in the energy storage module, resulting in waste of electric power resources; at the same time, the present invention can achieve accurate control of the energy storage of the energy storage module by simply sampling a small amount of data, thereby improving the data processing efficiency of the edge computing node. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic flow chart of the charging control method of the integrated charging and storage based on edge computing of the present invention. DETAILED DESCRIPTION

[0040] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] like Figure 1 As shown, the charging control method of the integrated charging and storage based on edge computing includes the following steps:

[0042] Step S1: Statistically obtain a historical charging amount data set of electric vehicles in the same time period as the target charging area and the next peak period, and analyze and obtain a first predicted charging amount of all electric vehicles in the target charging area during the next peak period based on the historical charging amount data set;

[0043] Step S2: obtaining recent charge and discharge data of the energy storage module in the target charging area, and obtaining the health status of each energy storage battery included in the energy storage module in the target charging area based on the acquired recent charge and discharge data of the energy storage module in the target charging area;

[0044] Step S3: Obtaining the identifiers of the energy storage batteries in the target charging area that are marked as normal in the current health state by statistics, analyzing and obtaining the second predicted charging amount that the energy storage module needs to obtain from the power grid during the current off-peak period or flat-valley period based on the power data of the energy storage batteries in the energy storage module that are in normal health state and the first predicted charging amount of all electric vehicles during the next peak period, and controlling the power grid to output electric energy to the energy storage module for storage based on the second predicted charging amount;

[0045] It is understandable that 24 hours a day can be divided into peak hours, off-peak hours and valley hours. For example, the peak hours are from 7:00 to 10:00 in the morning and from 17:00 to 22:00 in the afternoon, the off-peak hours are from 11:00 to 17:00 in the afternoon, and the valley hours are from 22:00 to 7:00 the next morning. These time periods are divided to optimize the use of power resources, balance the load of the power grid, and encourage users to use electricity during periods with lower electricity prices. The specific time period division and electricity price policy will vary according to the power supply situation in different regions. For details, please refer to the specific regulations of the local power company.

[0046] In the prior art, a charging and storage integrated system is provided for the charging area, and the system includes a power grid, a power distribution control module, an energy storage module and a power consumption module, wherein the power distribution control module is used to control the power output of the power grid, the power input and output of the energy storage module and the power input of the power consumption module, wherein the power consumption module may be a charging pile used by an electric vehicle, and the power is input into the power battery of the electric vehicle through the charging pile; during off-peak or flat periods, the power distribution control module controls the power grid to transmit the power to the energy storage module for energy storage and the power consumption module for power consumption; during peak periods, the power distribution control module first controls the energy storage module to transmit the stored power to the power consumption module, and when the power stored in the energy storage module is insufficient, the power grid is controlled to transmit the power to the power consumption module, thereby realizing peak shifting and valley filling, reducing the load and fluctuation of the power grid during peak periods, and reducing the power consumption cost of electric vehicles;

[0047] However, with the widespread popularity of electric vehicles, more and more electric vehicles need to be charged, and the charging time of electric vehicles is random, and the number of electric vehicles going to the target charging area for charging is also unstable; for example, when the target charging area is in peak hours, if the energy storage module does not store enough electricity at this time, the load and fluctuation of the power grid during the peak period of electricity consumption cannot be reduced, which increases the electricity cost of electric vehicles. If the energy storage module stores enough electricity at this time, it will also waste electricity, because the energy storage module is composed of at least one energy storage battery, and the energy storage battery will gradually age over time, causing additional leakage of stored electricity and wasting electricity;

[0048] In order to further optimize energy utilization and improve energy storage efficiency, this application introduces an electric vehicle charging capacity prediction model, which can accurately predict the specific charging capacity requirements of electric vehicles in the target charging area during the next peak period. Through this model, the energy storage module can accurately store the required electrical energy during off-peak or flat periods, ensuring that there will be no shortage of electricity during peak periods that affects power supply, and no excessive storage that causes waste of resources;

[0049] As a preferred embodiment of the present invention, in step S1, the electric vehicle historical charging amount data set of the target charging area in the same time interval as the next peak period includes the charging amount W of all electric vehicles in the target charging area in the corresponding time interval on each day within n consecutive historical dates. (0) (i); where i represents the historical date number, i = 1, 2...n;

[0050] Among them, the charging amount of all electric vehicles in the corresponding interval is obtained by statistics from the historical electric vehicle charging records;

[0051] In step S1, the first predicted charging amount of all electric vehicles in the target charging area during the next peak period is obtained based on the analysis of the historical charging amount data set; including:

[0052] The charging capacity values ​​in the historical charging capacity data set are arranged and numbered in chronological order, and the historical charging capacity data set is marked as:

[0053] W (0) = {W (0) (1),W (0) (2),…,W (0) (i),…,W (0) (n)}; where (0) is the superscript of the historical charging amount of the electric vehicle, indicating the mark of the original data before accumulation;

[0054] Each historical charging capacity value contained in the historical charging capacity data set is accumulated and calculated in order to obtain an accumulated historical charging capacity data set W. (1) = {W (1) (1),W (1) (2),…,W (1) (j),…,W (1) (n)}; where (1) is the superscript of the historical charging amount of the electric vehicle after one accumulation, indicating the mark of the processed data after one accumulation; the one-time accumulation formula is: j=1,2……n;

[0055] The adjacent average value of each accumulated historical charging capacity data set after the accumulation is calculated to obtain the adjacent average data set S of the historical charging capacity data set after the accumulation. (1) ={S (1) (2),S (1) (3),…,S (1) (j),…,S (1) (n)}; the calculation formula for the adjacent mean is:

[0056]

[0057] Based on the accumulated historical charging capacity values ​​contained in the historical charging capacity data set after one accumulation, the first-order differential equation of the charging capacity of all electric vehicles in the target charging area in the corresponding time interval of the next peak period is calculated: In the formula, a represents the development coefficient, and b represents the grey action; the expressions of a and b are:

[0058]

[0059] make Then Y = Bλ;

[0060] Using the least squares method to solve it, we get:

[0061] The approximate time response of the first-order differential equation for the charging amount of all electric vehicles in the target charging area corresponding to the time interval of the next peak period is obtained by calculation:

[0062] Where x = 0, 1, 2, ... n;

[0063] After one cumulative reduction, the charging capacity of all electric vehicles in the target charging area corresponding to the time interval of the next peak period is obtained as follows:

[0064] Then, the first predicted charging amount W1 of all electric vehicles in the target charging area corresponding to the time interval of the next peak period is obtained, and the formula is:

[0065] In the formula, n+1 represents the number of the next peak period;

[0066] In the present invention, by analyzing the historical charging data set of electric vehicles in the target charging area in the same time interval as the next peak period, including one accumulation, adjacent mean calculation and first-order differential equation calculation, the predicted charging amount of electric vehicles in the target charging area in the next peak period is obtained. This method is suitable for small sample data, missing or incomplete data, and is suitable for processing the instability, nonlinearity and irregularity of the charging time and charging amount of electric vehicles; the calculation cost is low, and a large amount of computing resources are not required. It has a good effect for scenarios that require accurate prediction in the near future, can quickly respond to data changes, and update the prediction results in time. It has a certain robustness and can effectively reduce the impact of abnormal data on the prediction results; it is suitable for the low storage and low data processing characteristics of edge computing nodes. By analyzing a small amount of historical charging value values, the charging amount of all electric vehicles in the next peak period can be accurately predicted, thereby improving the data processing efficiency of edge computing nodes; and it lays a data foundation for the subsequent energy storage module to accurately and appropriately store the required electric energy in the current valley period or flat valley period, ensuring that there will be no shortage of electricity to affect the power supply during the peak period, and no excessive storage will cause waste of resources;

[0067] It should be noted that this method is not limited to predicting the charging amount of electric vehicles during peak hours, but can also predict the charging amount of electric vehicles during other periods;

[0068] As a preferred embodiment of the present invention, in step S2, the recent charging and discharging data of the energy storage module in the target charging area includes: the charging power WC and charging time TC of each energy storage battery in the energy storage module in the target charging area during the previous low valley period or flat valley period, and the starting energy storage power WS, discharging power WD and discharging time TD of each energy storage battery in the energy storage module in the target charging area during the previous peak period;

[0069] It can be understood that the time of the last valley period or flat valley period in the recent charge and discharge data of the energy storage module in the target charging area comes first, and the time of the last peak period comes later; and the current time is set as the valley period or flat valley period, and the next peak period will come after the current valley period or flat valley period has passed; it should be noted that the valley period or flat valley period is adjacent to the peak period;

[0070] As time goes by and as external factors affect it, the performance of the energy storage battery will decline, including a reduction in energy storage capacity and output power, affecting the overall performance and efficiency of the energy storage module. There are also some safety hazards, which may lead to battery overheating, short circuit, battery expansion and other problems, and then cause fire, explosion and other serious safety accidents. It is necessary to monitor and analyze the health status of each energy storage battery in the energy storage module, which is also convenient for the subsequent determination of the target charging area. The prediction of the charging amount of the energy storage module in the current low or flat period provides reliable conditions;

[0071] In step S2, the health status of each energy storage battery included in the energy storage module of the target charging area is obtained based on the recent charge and discharge data of the energy storage module of the target charging area, including:

[0072] Based on the charging capacity WC and charging time TC of each energy storage battery in the energy storage module of the target charging area during the previous low valley period or flat valley period, the charging rate vC of each energy storage battery in the energy storage module during the previous low valley period or flat valley period is calculated. The formula is:

[0073] Based on the discharge power WD and discharge time TD of each energy storage battery in the energy storage module of the target charging area during the previous peak period, the discharge rate vD of each energy storage battery in the energy storage module during the previous peak period is calculated. The formula is:

[0074] Based on the charging capacity WC of each energy storage battery in the energy storage module of the target charging area during the previous low valley period or flat valley period and the starting energy storage capacity WS and the discharging capacity WD during the previous peak period, the charging efficiency ηC of each energy storage battery in the energy storage module during the previous low valley period or flat valley period and the discharging efficiency ηD during the previous peak period are calculated; the formula is:

[0075]

[0076] Based on the charging capacity WC and charging time TC of each energy storage battery in the energy storage module of the target charging area during the previous low-valley period or flat-valley period, and the discharging capacity WD and discharging time TD during the previous peak period, the energy storage loss rate LS of each energy storage battery in the energy storage module during the previous low-valley period or flat-valley period and the previous peak period is calculated. The formula is:

[0077] After normalizing the charging rate vC and charging efficiency ηC of each energy storage battery in the previous low-valley period or flat-valley period, the discharge rate vD and discharge efficiency ηD of each energy storage battery in the previous peak period, and the energy storage loss rate LS of the previous low-valley period or flat-valley period and the previous peak period, the deviation of each parameter value from the corresponding standard value is calculated, and a weighted sum calculation is performed to obtain the current health status index of each energy storage battery in the energy storage module;

[0078] For example, the calculation formula for the deviation between the parameter value of the charging rate of each energy storage battery in the previous low valley period or flat valley period and the corresponding standard value is: Wherein, vC0 is the standard value of the charging rate; other parameter values ​​are similar; the weighted summation calculation refers to assigning a corresponding weight coefficient to the deviation value of each parameter value from the corresponding standard value, and then summing them; the weight coefficients of the charging rate deviation value, the discharging rate deviation value, the charging efficiency deviation value, the discharging efficiency deviation value and the energy storage loss rate deviation value are 0.3, 0.3, 0.2, 0.15 and 0.05 respectively;

[0079] It can be understood that when the charging rate deviation value is smaller, the discharging rate deviation value is smaller, the charging efficiency deviation value is smaller, the discharging efficiency deviation value is smaller, and the energy storage loss rate deviation value is smaller, the health status index is smaller, and the health status of the energy storage battery is more normal;

[0080] Compare the calculated health status index of each energy storage battery in the energy storage module with the preset health status index threshold to determine whether the health status of the corresponding energy storage battery is normal;

[0081] If the health status index of the energy storage battery corresponding to the energy storage module is less than the preset health status index threshold, the health status of the corresponding energy storage battery is marked as normal and no warning is performed;

[0082] If the health status index of the energy storage battery corresponding to the energy storage module is greater than or equal to the preset health status index threshold, the health status of the corresponding energy storage battery is marked as abnormal, and an abnormal health status warning signal of the energy storage battery with the corresponding identifier is generated and sent to the administrator's user end to request processing;

[0083] In the present invention, the health status of the energy storage battery is analyzed and calculated by analyzing the charging and discharging data of the energy storage battery in the target charging area during the last valley period or flat valley period and the last peak period in time, so that the current health status and performance changes of the energy storage battery can be reflected in real time, and real-time health assessment results can be provided. The data of the most recent period is highly correlated with the current battery status, and can more accurately reflect the actual use of the energy storage battery and the performance change trend, and can also more accurately predict the health status of the battery and future performance changes, which is helpful to timely discover potential problems, and then quickly respond and adjust to prevent batteries with abnormal health status from affecting system performance and safety; at the same time, it is also convenient for subsequent data reference for power distribution control work;

[0084] As a preferred embodiment of the present invention, in step S3, the identifier of the energy storage battery marked as normal in the current health state in the energy storage module of the target charging area is statistically obtained, and according to the power data of the energy storage battery in the energy storage module with a normal health state and the first predicted charging amount of all electric vehicles in the next peak period, the second predicted charging amount that the energy storage module needs to obtain from the power grid during the current valley period or flat valley period is analyzed and obtained, and the power grid is controlled to output electric energy to the energy storage module for storage according to the second predicted charging amount; including:

[0085] Obtain the identifier of the energy storage battery in the target charging area energy storage module that is marked as normal in the current health state, detect the current remaining power WLr of the energy storage battery corresponding to the energy storage battery identifier marked as normal in the health state, and obtain the minimum power WXr of each energy storage battery; wherein r represents the number of the energy storage battery in the target charging area energy storage module that is marked as normal in the current health state, r=1,2...R; R represents the total number of energy storage batteries in the target charging area energy storage module that are marked as normal in the current health state; it should be noted that the energy storage battery needs to store a minimum amount of power to help extend its service life, and over-discharging or overcharging may damage the battery and reduce its performance and life;

[0086] According to the remaining power WLr and the minimum power WXr of each energy storage battery in the energy storage module of the target charging area with normal health status, and the first predicted charging amount W1 of all electric vehicles in the target charging area during the next peak period, the second predicted charging amount W2 that the energy storage module needs to obtain from the power grid during the current off-peak period or flat-valley period is calculated. The formula is:

[0087]

[0088] In the formula, ɑ represents the power conversion coefficient between electric vehicles and energy storage modules in the target charging area, and β represents the power conversion coefficient between the power grid and energy storage modules in the target charging area. The values ​​of ɑ and β are obtained by conversion according to the corresponding conversion devices, and ɑ and β are generally around 90%.

[0089] Based on the calculated second predicted charging amount W2 that the energy storage module needs to obtain from the power grid during the current off-peak period or flat-peak period, the power distribution control module of the target charging area controls the power grid to output the second predicted charging amount of electric energy to the energy storage module;

[0090] In the present invention, the power data of the energy storage batteries in a normal healthy state in the target charging area and the first predicted charging amount of all electric vehicles in the next peak period are analyzed to obtain the second predicted charging amount that the energy storage module needs to obtain from the power grid during the current off-peak period or the flat period, and the power grid is controlled to output electric energy to the energy storage module for storage according to the second predicted charging amount; the storage amount of the energy storage module in the target charging area in the current off-peak period or the flat period can be accurately controlled to avoid the need to obtain electric energy from the power grid during peak periods, which cannot reduce the load and fluctuation of the power grid during peak electricity consumption periods, and avoid the problem of excessive energy storage in the energy storage module, resulting in waste of electric power resources; at the same time, the present invention can achieve accurate control of the energy storage of the energy storage module by simply sampling a small amount of data, thereby improving the data processing efficiency of the edge computing node.

[0091] The above formulas are all calculated by removing dimensions and taking numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data.

[0092] In the embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation; the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the method of this embodiment.

[0093] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An integrated charging and storage charging control method based on edge computing, characterized in that: include: Step S1: Statistically obtain a historical charging amount data set of electric vehicles in the same time period as the target charging area and the next peak period, and analyze and obtain a first predicted charging amount of all electric vehicles in the target charging area during the next peak period based on the historical charging amount data set; Step S2: obtaining recent charge and discharge data of the energy storage module in the target charging area, and obtaining the health status of each energy storage battery included in the energy storage module in the target charging area based on the acquired recent charge and discharge data of the energy storage module in the target charging area; Step S3: Obtaining the identifiers of the energy storage batteries in the target charging area that are marked as normal in the current health state by statistics, analyzing and obtaining the second predicted charging amount that the energy storage module needs to obtain from the power grid during the current off-peak period or flat-valley period based on the power data of the energy storage batteries in the energy storage module that are in normal health state and the first predicted charging amount of all electric vehicles during the next peak period, and controlling the power grid to output electric energy to the energy storage module for storage based on the second predicted charging amount; The electric vehicle historical charging amount data set for the target charging area in the same time interval as the next peak period includes the charging amount of all electric vehicles in the target charging area in the corresponding time interval on each day within n consecutive historical dates; The method of obtaining a first predicted charging amount of all electric vehicles in the target charging area during the next peak period based on the historical charging amount data set analysis includes: Arrange the charging capacity values ​​in the historical charging capacity data set in chronological order; Performing a cumulative calculation on each historical charging capacity value contained in the historical charging capacity data set in the order of arrangement to obtain a cumulative historical charging capacity data set; Performing adjacent mean calculation on each of the once accumulated historical charging capacity values ​​contained in the once accumulated historical charging capacity data set to obtain an adjacent equalized data set of the once accumulated historical charging capacity data set; Based on each accumulated historical charging amount value contained in the historical charging amount data set after the first accumulation, a first-order differential equation of the charging amount of all electric vehicles in the target charging area within the time interval corresponding to the next peak period is calculated; The approximate time response of the first-order differential equation of the charging amount of all electric vehicles in the target charging area corresponding to the time interval of the next peak period is obtained by calculation; After one cumulative reduction, the charging amount of all electric vehicles in the target charging area corresponding to the time interval of the next peak period is obtained; Then, a first predicted charging amount of all electric vehicles in the target charging area in the corresponding time interval of the next peak period is obtained.

2. The charging control method based on edge computing for integrated charging and storage according to claim 1 is characterized in that: The recent charging and discharging data of the energy storage module in the target charging area include: the charging power WC and charging time TC of each energy storage battery in the energy storage module in the target charging area during the previous low-valley period or flat-valley period, and the starting energy storage power WS, discharging power WD and discharging time TD of each energy storage battery in the energy storage module in the target charging area during the previous peak period.

3. The charging control method based on edge computing for charging and storage integration according to claim 2 is characterized in that: In the recent charging and discharging data of the energy storage module in the target charging area, the time of the last low-valley period or flat-valley period comes first, and the time of the last peak period comes later; and the current time is set as the low-valley period or flat-valley period, and after the current low-valley period or flat-valley period passes, it will be the next peak period.

4. The charging control method based on edge computing according to claim 2 is characterized in that: The method of obtaining the health status of each energy storage battery included in the energy storage module of the target charging area based on the recent charge and discharge data of the energy storage module of the target charging area includes: Based on the charging capacity WC and charging time TC of each energy storage battery in the energy storage module of the target charging area during the previous low valley period or flat valley period, the charging rate vC of each energy storage battery in the energy storage module during the previous low valley period or flat valley period is calculated. The formula is: ; Based on the discharge power WD and discharge time TD of each energy storage battery in the energy storage module of the target charging area during the previous peak period, the discharge rate vD of each energy storage battery in the energy storage module during the previous peak period is calculated. The formula is: ; Based on the charging capacity WC of each energy storage battery in the energy storage module of the target charging area during the previous low valley period or flat valley period and the starting energy storage capacity WS and discharge capacity WD during the previous peak period, calculate the charging efficiency of each energy storage battery in the energy storage module during the previous low valley period or flat valley period and the discharge efficiency during the previous peak period ; The formula is: , ; Based on the charging capacity WC and charging time TC of each energy storage battery in the energy storage module of the target charging area during the previous low-valley period or flat-valley period, and the discharging capacity WD and discharging time TD during the previous peak period, the energy storage loss rate LS of each energy storage battery in the energy storage module during the previous low-valley period or flat-valley period and the previous peak period is calculated. The formula is: ; The charging rate vC and charging efficiency of each energy storage battery in the energy storage module during the previous low-valley period or flat-valley period will be calculated. , the discharge rate vD and discharge efficiency during the previous peak period After normalizing the energy storage loss rate LS during the previous low or flat period and the previous peak period, the deviation of each parameter value from the corresponding standard value is calculated, and a weighted sum calculation is performed to obtain the current health status index of each energy storage battery in the energy storage module.

5. The charging control method based on edge computing for charging and storage integration according to claim 4 is characterized in that: Compare the calculated health status index of each energy storage battery in the energy storage module with the preset health status index threshold to determine whether the health status of the corresponding energy storage battery is normal; If the health status index of the energy storage battery corresponding to the energy storage module is less than the preset health status index threshold, the health status of the corresponding energy storage battery is marked as normal and no warning is performed; If the health status index of the energy storage battery corresponding to the energy storage module is greater than or equal to the preset health status index threshold, the health status of the corresponding energy storage battery is marked as abnormal, and an early warning signal of the abnormal health status of the energy storage battery with the corresponding identifier is generated and sent to the administrator's user end for processing.

6. The charging control method based on edge computing for charging and storage integration according to claim 1 is characterized in that: Statistically obtain the identifier of the energy storage battery in the target charging area that is marked as normal in the current health state, analyze and obtain the second predicted charging amount that the energy storage module needs to obtain from the power grid during the current off-peak period or flat-valley period based on the power data of the energy storage battery in the energy storage module that is in a normal health state and the first predicted charging amount of all electric vehicles during the next peak period, and control the power grid to output electric energy to the energy storage module for storage according to the second predicted charging amount; including: Obtain the identifier of the energy storage battery in the target charging area that is marked as normal in the current health state, detect the current remaining power WLr of the energy storage battery corresponding to the energy storage battery identifier marked as normal, and obtain the minimum power WXr of each energy storage battery; wherein r represents the number of the energy storage battery in the target charging area that is marked as normal in the current health state, r=1,2...R; R represents the total number of energy storage batteries in the target charging area that are marked as normal in the current health state; According to the remaining power WLr and the minimum power WXr of each energy storage battery in the energy storage module of the target charging area with normal health status, and the first predicted charging amount W1 of all electric vehicles in the target charging area during the next peak period, the second predicted charging amount W2 that the energy storage module needs to obtain from the power grid during the current off-peak period or flat-valley period is calculated. The formula is: ; In the formula, ɑ represents the power conversion coefficient between electric vehicles and energy storage modules in the target charging area, and β represents the power conversion coefficient between the power grid and energy storage modules in the target charging area; Based on the calculated second predicted charging amount W2 that the energy storage module needs to obtain from the power grid during the current off-peak period or off-peak period, the power distribution control module in the target charging area controls the power grid to output the second predicted charging amount of electric energy to the energy storage module.

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