A photovoltaic energy storage system based on microgrid monitoring

Abstract

This application relates to the technical field of photovoltaic energy storage, and discloses a photovoltaic energy storage system based on microgrid monitoring, including an energy storage battery and an energy storage inverter, and further including: a battery management system for monitoring the operation data of the energy storage battery, where the operation data includes the actual capacity of the energy storage battery; a microgrid monitoring unit for monitoring the temperature data of the energy storage battery; a database for storing the operation data and temperature data of the energy storage battery pack; an energy storage battery anomaly analysis unit for comparing the deviation between the actual capacity change curve of the energy storage battery and the ideal capacity decay curve to determine the decay level of the energy storage battery capacity, where the decay level includes normal, risk, and anomaly; when the decay level is at risk, identify the risk period, predict the lost capacity based on the operation data and temperature data during the risk period, and compare the predicted lost capacity with the actual remaining capacity, and determine the state of the energy storage battery according to the comparison result.

Description

Technical Field

[0001] The present application relates to the technical field of photovoltaic energy storage, and in particular to a photovoltaic energy storage system based on microgrid monitoring. Background Art

[0002] A photovoltaic energy storage system is an integrated energy solution that combines photovoltaic power generation and energy storage technology. Through energy storage technology, the system can store the excess electric energy generated by photovoltaic power generation during the day for use at night, on rainy days, or during peak electricity consumption periods, improving the energy self-sufficiency rate and reducing dependence on the power grid. In a photovoltaic storage system, the most core unit is the energy storage battery, which can complete the function of storing the remaining electric energy. During the management process of the energy storage battery, its safety is crucial for the photovoltaic energy storage system. Therefore, in the prior art, a battery management system is set up to monitor the state of the battery, and when an abnormality is found in the battery, it is repaired or replaced in time to avoid disasters caused by battery failures.

[0003] In the existing photovoltaic energy storage systems, in addition to monitoring the parameters of the battery, the normal operation of the battery is also judged by the degree of battery aging, that is, the state of battery life loss. When the degree of battery aging exceeds the conventional standard, it indicates that there are risks or abnormalities during its operation. Therefore, it is necessary to repair or replace it to reduce the adverse effects caused by battery failures on the photovoltaic energy storage system.

[0004] In the existing process of judging abnormal energy storage batteries, due to the large differences in the usage conditions of different energy storage batteries, when comparing with the life loss in the ideal state, if the difference standard is set too high, only obvious energy storage battery failures can be judged. If the difference standard is set too low, misjudgment problems are likely to occur. Therefore, how to more accurately judge the usage safety of energy storage batteries is the fundamental problem to be solved by the present invention. Summary of the Invention

[0005] In order to more accurately judge the usage safety of energy storage batteries, the present application provides a photovoltaic energy storage system based on microgrid monitoring.

[0006] In a first aspect, the present application provides a photovoltaic energy storage system based on microgrid monitoring, adopting the following technical solution:

[0007] A photovoltaic energy storage system based on microgrid monitoring includes an energy storage battery and an energy storage inverter, and further includes:

[0008] A battery management system for monitoring the operation data of the energy storage battery, where the operation data includes the actual capacity of the energy storage battery;

[0009] A microgrid monitoring unit for monitoring the temperature data of the energy storage battery;

[0010] A database for storing the operation data and temperature data of the energy storage battery pack;

[0011] An energy storage battery anomaly analysis unit for comparing the deviation between the actual capacity change curve and the ideal capacity decay curve of the energy storage battery to determine the decay level of the energy storage battery capacity, where the decay levels include normal, risky, and abnormal; when the decay level is risky, identify the risk period, predict the lost capacity based on the operation data and temperature data during the risk period, and compare the predicted lost capacity with the actual remaining capacity, and determine the state of the energy storage battery according to the comparison result.

[0012] By adopting the above technical solution, by comparing the predicted lost capacity with the actual remaining capacity, the reason for the loss of the energy storage battery capacity can be determined. If the reason is a fault damage, the energy storage battery needs to be repaired and replaced in time to ensure its use safety. If the reason is due to usage loss, etc., although the capacity loss of the energy storage battery is slightly larger than the ideal capacity decay curve, it indicates that the risk of its operation is relatively low. Therefore, the use safety of the energy storage battery can be judged more accurately through the above process.

[0013] Optionally, the process of the deviation comparison includes:

[0014] Through the formula:

[0015] (1)

[0016] (2)

[0017] Calculate the deviation amount g(t) at the current time point t;

[0018] Compare the deviation amount g(t) with the preset deviation interval [g1, g2]:

[0019] If g(t) < g1, then determine that the decay level is normal;

[0020] If g(t) ∈ [g1, g2], then determine that the decay level is risky;

[0021] If g(t) > g2, then determine that the decay level is abnormal;

[0022] Where, Cd(t) is the ideal capacity decay curve, Cp(t) is the actual capacity change curve, C(t) is the capacity difference curve, y(t) is the number of charge-discharge cycles of the energy storage battery corresponding to the time point t, is the capacity difference reference value corresponding to y(t), is the derivative function of C(t), is the maximum value in the time period from 0 to t, is The maximum value in the time period from 0 to t, and is the proportionality coefficient.

[0023] By adopting the above technical solution, by comprehensively considering the capacity difference at the current time point and the change state of the capacity difference in the historical time period, compared with the method of simply judging based on the value of the capacity difference curve at time point t, the deviation amount can better reflect the overall deviation state in the past time period, improving the accuracy and comprehensiveness of the judgment.

[0024] Optionally, the process of identifying the risk time period includes:

[0025] Calculate the capacity difference change curve h(t) through the formula ;

[0026] Compare h(t) with the preset threshold k1:

[0027] If there is a time interval where h(t) is greater than k1, then the time interval greater than k1 is used as the risk interval;

[0028] Otherwise, select the time period from t - △t to t as the risk interval, where △t is a preset fixed time period.

[0029] By adopting the above technical solution, it is possible to obtain the time interval with a relatively high risk for analysis, reducing the calculation amount while being more capable of judging the problem of relatively large capacity loss of the energy storage battery, and then judging the abnormality of the energy storage battery.

[0030] Optionally, the process of predicting the capacity loss of the energy storage battery includes:

[0031] Obtain the number of battery cycle lives, charge-discharge curve, and charging voltage curve in the energy storage battery state data;

[0032] Obtain the temperature curve in the energy storage battery state data;

[0033] Determine the basic loss capacity according to the number of battery cycle lives in the risk interval;

[0034] Calculate the first influence coefficient according to the charge-discharge curve in the risk interval, calculate the second influence coefficient according to the charging voltage curve in the risk interval, and calculate the third influence coefficient according to the temperature curve in the risk interval;

[0035] Adjust the basic loss capacity through the first influence coefficient, the second influence coefficient, and the third influence coefficient to obtain the predicted capacity loss of the energy storage battery.

[0036] By adopting the above technical solution, predict its loss amount through the charge-discharge curve, charging voltage curve, and temperature curve in the risk interval, and then it is possible to judge the abnormal state of the energy storage battery through comparison;

[0037] Optionally, the calculation process of the first influence coefficient includes:

[0038] Identify the historical charge and discharge curve to obtain the charging interval, floating charge interval, and discharging interval;

[0039] Calculate the first influence coefficient E1 through formulas (3)-(6);

[0040] (3)

[0041] (4)

[0042] (5)

[0043] (6)

[0044] Among them, Ep is the discharging influence coefficient, Ec is the charging influence coefficient, Ef is the floating charge influence coefficient, n is the number of discharging intervals, i is a positive integer and i ∈ [1, n]; is the discharging percentage of the i-th discharging interval, is the discharging amount control function, is the duration of the i-th discharging interval, vd is the basic discharging rate, is the discharging rate control function, m is the number of charging intervals, j is a positive integer and j ∈ [1, m]; is the maximum percentage of the electric quantity of the j-th charging interval, is the charging amount control function, is the duration of the j-th charging interval, vc is the basic charging rate, is the charging rate control function, q is the number of floating charge intervals, k is a positive integer and k ∈ [1, q]; is the duration of the k-th floating charge interval, is the floating charge duration control function.

[0045] By adopting the above technical solution, the influence degree of the energy storage battery capacity is judged through the historical charge and discharge curve.

[0046] Optionally, the calculation process of the second influence coefficient includes:

[0047] Through the formula Calculate the second influence coefficient E2;

[0048] Among them, is the duration when the charging voltage curve is greater than U2, is the duration when the charging voltage curve is less than U1, [U1, U2] is the charging voltage interval, is the overvoltage step comparison function, is the undervoltage step comparison function.

[0049] By adopting the above technical solution, the influence degree of the historical voltage curve on the energy storage battery capacity is judged.

[0050] Optionally, the calculation process of the third influence coefficient includes:

[0051] Through the formula calculate to obtain the third influence coefficient E3;

[0052] Among them, is the duration when the temperature curve is greater than T2, is the duration when the temperature curve is less than T1, [T1, T2] is the temperature control interval, is the high-temperature step comparison function, is the low-temperature step comparison function.

[0053] By adopting the above technical solution, the influence degree of the historical temperature curve on the energy storage battery capacity is judged.

[0054] Optionally, the calculation process of the predicted energy storage battery loss capacity includes:

[0055] Through the formula calculate to obtain the predicted energy storage battery loss capacity during the risk period ;

[0056] Among them, x is the number of battery cycle lives during the risk period, is the reference loss capacity corresponding to the number of battery cycle lives x.

[0057] By adopting the above technical solution, the predicted energy storage battery loss capacity can be obtained. By comparing the predicted energy storage battery loss capacity with the actual remaining capacity, the state of the energy storage battery can be judged.

[0058] Optionally, the process of judging the state of the energy storage battery according to the comparison result includes:

[0059] Through the formula calculate to obtain the predicted capacity difference , and compare the predicted capacity difference with the preset fixed threshold Cth:

[0060] If , then judge that the attenuation level of the energy storage battery is abnormal;

[0061] Otherwise, judge that the attenuation level of the energy storage battery is normal;

[0062] Among them, is the risk period The maximum difference.

[0063] By adopting the above technical solution, it is possible to determine the problem of large capacity loss of the energy storage battery and achieve an accurate judgment of the abnormality of the energy storage battery.

[0064] In summary, the present application includes at least one of the following beneficial technical effects:

[0065] By comparing the predicted loss capacity with the actual remaining capacity, the present invention can determine the cause of the capacity loss of the energy storage battery. If the cause is a fault damage, the energy storage battery needs to be repaired and replaced in time to ensure its use safety. If the cause is due to usage loss, etc., although the capacity loss of the energy storage battery is slightly larger than the ideal capacity decay curve, it indicates that the risk of its operation is relatively low. Therefore, the use safety of the energy storage battery can be judged more accurately. At the same time, by identifying the risk period, since the actual capacity change curve during the risk period is quite different from the ideal capacity decay curve, the result calculated based on the data corresponding to the risk period is more representative and the calculation amount is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 is a logic block diagram of a photovoltaic energy storage system based on microgrid monitoring according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] The following details the embodiments of the present application, and the examples of the embodiments are shown in the drawings.

[0068] In the description of this specification, the description with reference to the terms "certain embodiments", "one embodiment", "some embodiments", "schematic embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiments or examples are included in at least one embodiment or example of the present application. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiments or examples. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0069] The embodiments of the present application disclose a photovoltaic energy storage system based on microgrid monitoring, referring to Figure 1, including an energy storage battery, an energy storage inverter, a battery management system, a microgrid monitoring unit, a database, and an energy storage battery anomaly analysis unit. Among them, the energy storage battery is used to store the electric energy generated by the photovoltaic system, the energy storage inverter is used for bidirectional conversion of electric energy to achieve charge and discharge control, the battery management system is used to monitor the operation data of the energy storage battery, and the operation data includes the actual capacity of the energy storage battery, charge and discharge data, charging voltage data, etc. The microgrid monitoring unit is used to monitor the temperature data of the energy storage battery, and it usually sets sensors on the energy storage battery to achieve its function. The database is used to receive and store the operation data and temperature data of the energy storage battery pack. Finally, the energy storage battery anomaly analysis unit compares the deviation between the actual capacity change curve and the ideal capacity decay curve of the energy storage battery to judge the decay level of the energy storage battery capacity. The decay levels include normal, risk, and anomaly. During this process, through the actual capacity change curve and the ideal capacity decay curve, it is possible to initially judge the obvious abnormal decay problem of the energy storage battery life. When the actual capacity change curve is relatively close to the ideal capacity decay curve, it indicates that the state of the energy storage battery is relatively normal. On the contrary, when the difference between the actual capacity change curve and the ideal capacity decay curve is large, it indicates that there is an anomaly in the state of the energy storage battery. In order to more accurately judge the capacity decay of the energy storage battery, this embodiment also divides the decay level of risk. When the decay level is at risk, it is between normal and anomaly. This embodiment first identifies the risk period. Since the actual capacity change curve and the ideal capacity decay curve in the risk period are quite different, the result calculated according to the data corresponding to the risk period is more representative, and at the same time, the calculation amount is reduced. The loss capacity is predicted based on the operation data and temperature data in the risk period, and the predicted loss capacity is compared with the actual remaining capacity to judge the state of the energy storage battery. During this process, through the comparison of the predicted loss capacity and the actual remaining capacity, it is possible to judge the cause of the capacity loss of the energy storage battery. If the cause is a fault damage, the energy storage battery needs to be repaired and replaced in time to ensure its use safety. If the cause is due to reasons such as use wear, although the capacity loss of the energy storage battery is slightly larger than the ideal capacity decay curve, it indicates that the risk of its operation is relatively low. Therefore, through the above process, the use safety of the energy storage battery can be judged more accurately.

[0070] In one embodiment, a process of deviation comparison is given, including: By the formula:

[0071] (1)

[0072] (2)

[0073] Calculate the deviation amount g(t) at the current time point t; where Cd(t) is the ideal capacity decay curve, which is obtained based on the correspondence between the capacity and the number of cycle lives of energy storage batteries of the same type measured under standard usage environments, corresponding to the number of cycle lives of the energy storage battery during the risk period, Cp(t) is the actual capacity change curve, C(t) is the capacity difference curve, and y(t) is the number of cycle lives of the energy storage battery corresponding to the time point t. is the capacity difference reference value corresponding to y(t), and its corresponding process is obtained according to empirical comparison data. is the derivative function of C(t). is the maximum value in the time period from 0 to t. Through the calculation process of formula (1), the capacity difference at the current time point and the change state of the capacity difference in the historical period are comprehensively considered. At the same time, through the proportionality coefficients and weight adjustment is performed. The values of the proportionality coefficients and are set according to the test data. Compared with the method of judging only based on the value of the capacity difference curve at the time point t, the deviation amount g(t) can better reflect the overall deviation state in the past period, improving the accuracy and comprehensiveness of the judgment. Then, the deviation amount g(t) is compared with the preset deviation interval [g1, g2]. The boundaries of the preset deviation interval [g1, g2] are preset fixed values, which are obtained by fitting the measurement data of energy storage batteries in different states. If g(t) < g1, it means that the battery capacity loss difference is small and the loss rate is within the normal range, so the decay level is judged to be normal; if g(t) > g2, it means that there is a large battery capacity loss difference or the loss rate is in an abnormal range or a combination of them, and it needs to be replaced or repaired in time, so the decay level is judged to be abnormal; if g(t) ∈ [g1, g2], the decay level is judged to be at risk, and further judgment needs to be made according to the comparison between the predicted loss capacity and the actual remaining capacity. Through the above process, the abnormal state of the energy storage battery can be judged more accurately and comprehensively.

[0074] In one embodiment, a process for identifying the risk period is given, including: through the formula Calculate to obtain the capacity difference change curve h(t). The capacity difference change curve h(t) reflects the capacity change rate of the energy storage battery. Compare h(t) with the preset threshold k1. The preset threshold k1 is obtained by fitting the capacity loss rate curves of multiple normally operating energy storage batteries, so it is a critical value. Therefore, if there is a time interval where h(t) is greater than k1, it indicates that the abnormal risk in the corresponding interval is relatively large. Take the time interval where h(t) is greater than k1 as the risk interval; otherwise, select the time period from t - △t to t as the risk interval. △t is a preset fixed time period, which is set according to the user's selection. The longer the selected duration of the preset fixed time period, the more accurate the calculation result, but the corresponding calculation amount is larger. Through the above process, it is possible to obtain the time interval with a relatively large risk for analysis, reduce the calculation amount, and more accurately judge the problem of large capacity loss of the energy storage battery, thereby judging the abnormality of the energy storage battery.

[0075] In one embodiment, the process of predicting the lost capacity of the energy storage battery includes: obtaining the number of battery cycle lives, charge-discharge curve, and charging voltage curve in the energy storage battery state data; obtaining the temperature curve in the energy storage battery state data; determining the basic lost capacity according to the number of battery cycle lives in the risk interval; calculating the first influence coefficient according to the charge-discharge curve in the risk interval, calculating the second influence coefficient according to the charging voltage curve in the risk interval, and calculating the third influence coefficient according to the temperature curve in the risk interval; adjusting the basic lost capacity through the first influence coefficient, the second influence coefficient, and the third influence coefficient to obtain the predicted lost capacity of the energy storage battery. Since during the use of the energy storage battery, in addition to the number of battery cycle lives, it is mainly affected by its charge-discharge process data, input power data, and environmental factors. Therefore, in this application, through the charge-discharge curve, charging voltage curve, and temperature curve in the risk interval, its loss amount is predicted, and then the abnormal state of the energy storage battery can be judged through comparison; at the same time, it should be noted that although the factors affecting the life of the energy storage battery are not limited to the factors proposed in this embodiment, the factors proposed in this embodiment cover a relatively large proportion of the influencing factors except for the fault risk of the energy storage battery. Therefore, although there is a certain error in the calculated predicted lost capacity, it has a relatively high reference value in the comparison and judgment process.

[0076] In one embodiment, a calculation process of the first influence coefficient is given, including: identifying the historical charge-discharge curve to obtain the charging interval, floating charge interval, and discharging interval. The above identification process is based on the prior art and will not be elaborated here. Calculate the first influence coefficient E1 through formulas (3)-(6);

[0077] (3)

[0078] (4)

[0079] (5)

[0080] (6)

[0081] Among them, Ep is the discharge influence coefficient, Ec is the charge influence coefficient, and Ef is the floating charge influence coefficient. The influence of the comprehensive discharge influence coefficient, charge influence coefficient, and floating charge influence coefficient is obtained through formula (3). n is the number of discharge intervals, i is a positive integer and i ∈ [1, n]; is the discharge percentage of the i-th discharge interval. When the discharge percentage each time is greater than 50%, it has a relatively high impact on the capacity loss of the energy storage battery, and the more it exceeds, the greater the impact degree. Therefore, by calculating the part that exceeds 50%, and then through the discharge amount comparison function to judge its influence degree. In addition, the discharge rate also affects the capacity loss degree of the energy storage battery. The discharge rate is calculated by dividing the discharge percentage of the discharge interval by the duration of the i-th discharge interval and then subtracting the discharge base rate vd. The influence degree is calculated through the discharge rate comparison function ; m is the number of charge intervals, j is a positive integer and j ∈ [1, m]; During the charging process, overcharging will also affect the capacity loss of the energy storage battery. By subtracting 80% from the maximum percentage of the charge in the j-th charge interval and then through the charge amount comparison function , the influence caused by overcharging in each charge interval is judged. At the same time, the charging rate also has a relatively high impact on the capacity loss of the energy storage battery. By using the duration of the j-th charge interval , the maximum percentage of the charge in the j-th charge interval and vc is the part where the charging rate exceeds the charging base rate in the charging rate calculation process. The influence degree is calculated through the charging rate comparison function ; In addition, q is the number of floating charge intervals, k is a positive integer and k ∈ [1, q]; is the duration of the k-th floating charge interval, It is a floating charge duration comparison function. By calculating the duration of each floating charge interval, the impact on the capacity loss of the energy storage battery can be obtained. It should be noted that the above discharge rate and charge rate are both obtained according to the standard data corresponding to the energy storage battery model in the experience, and the discharge amount comparison function, discharge rate comparison function, charge amount comparison function, charge rate comparison function and floating charge duration comparison function in the above embodiments are all obtained by fitting test data. During the test process, since it is difficult to obtain the impact of a single charge-discharge process on the capacity of the energy storage battery, data on the change in the capacity of the energy storage battery over a long period of time is obtained by controlling variables. The total impact ratio is evenly divided according to the number of cycles of the energy storage battery life, and then the impact amount for a single time is obtained. Then, the corresponding impact value is obtained according to the different intervals where the data is located, and finally, the total impact coefficient is obtained through the superposition of all times.

[0082] In one embodiment, a calculation process of a second impact coefficient is given, including: through the formula The second impact coefficient E2 is calculated; where is the duration when the charging voltage curve is greater than U2, is the duration when the charging voltage curve is less than U1, and [U1, U2] is the charging voltage interval, which is obtained according to the standard corresponding to the energy storage battery. is the overvoltage step comparison function, is the undervoltage step comparison function. In the above technical solution, the magnitude of the charging voltage exceeding U2 or being lower than U1 will also affect the impact coefficient. However, in specific applications, the charging voltage is within a controllable range, so it is difficult to exceed the standard by a large margin. Therefore, this embodiment only considers the impact of overvoltage and undervoltage time on the life of the energy storage battery. Among them, the overvoltage step comparison function and the undervoltage step comparison function are both obtained by fitting test data. Data on the change in the capacity of the energy storage battery over a long period of time is obtained by controlling variables. The total impact ratio is evenly divided according to the number of cycles of the energy storage battery life, and then the impact amount for a single time is obtained. Then, the corresponding impact value is obtained according to the different intervals where the data is located, and finally, the total impact coefficient is obtained through the superposition of all times.

[0083] In one embodiment, a calculation process of a third impact coefficient is given, including: through the formula The third impact coefficient E3 is calculated; where is the duration when the temperature curve is greater than T2, is the duration when the temperature curve is less than T1, and [T1, T2] is the temperature control interval, which is obtained according to the empirical data. Similar to the second impact coefficient E2, since in the actual process, the photovoltaic energy storage system will be provided with a corresponding heat dissipation device, the probability of exceeding the standard temperature by a large margin is relatively low. Therefore, the impact of the temperature exceeding the standard time on the life of the energy storage battery is considered. Among them, is a high-temperature step reference function, is a low-temperature step reference function. Both the high-temperature step reference function and the low-temperature step reference function are obtained by fitting test data. The data measurement process is the same as that of the overvoltage step reference function and the undervoltage step reference function described above, and will not be elaborated here.

[0084] In one embodiment, the calculation process for predicting the loss capacity of the energy storage battery is given, including: through the formula calculate the predicted loss capacity of the energy storage battery during the risk period ; where x is the number of battery cycle lives during the risk period, is the reference loss capacity corresponding to the number of battery cycle lives x. Through the above process, the predicted loss capacity of the energy storage battery can be obtained. By comparing the predicted loss capacity of the energy storage battery with the actual remaining capacity, the state of the energy storage battery can be judged.

[0085] The process of judging the state of the energy storage battery according to the comparison result includes:

[0086] Through the formula calculate the predicted capacity difference , and compare the predicted capacity difference with the preset fixed threshold Cth. is the maximum difference during the risk period . Since the capacity of the energy storage battery is irreversible, therefore is the difference between the capacity corresponding to the end time point and the capacity corresponding to the start time point during the risk period. The fixed threshold Cth is set by fitting empirical data. Therefore, if , it means that the difference between the predicted loss capacity and the actual remaining capacity is relatively large. Therefore, it can be judged that the reason for the large loss of battery capacity is not the influence of controllable factors, but there is an abnormal fault. Therefore, it is judged that the attenuation level of the energy storage battery is abnormal. Otherwise, it is judged that the attenuation level of the energy storage battery is normal. Through the above process, the problem of large loss of energy storage battery capacity can be judged, and the abnormality of the energy storage battery can be accurately judged.

[0087] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A photovoltaic energy storage system based on microgrid monitoring, comprising an energy storage battery and an energy storage inverter, characterized in that: Also includes: A battery management system, used to monitor the operating data of the energy storage battery, wherein the operating data includes the actual capacity of the energy storage battery; Microgrid monitoring unit, used to monitor the temperature data of energy storage batteries; A database for storing operating data and temperature data of energy storage battery packs; The energy storage battery abnormality analysis unit is used to compare the deviation between the actual capacity change curve of the energy storage battery and the ideal capacity decay curve to determine the decay level of the energy storage battery capacity, wherein the decay level includes normal, risk and abnormal; When the attenuation level is at risk, the risk period is identified, and the loss capacity is predicted based on the operation data and temperature data of the risk period. The predicted loss capacity is compared with the actual loss capacity, and the state of the energy storage battery is determined based on the comparison results. The process of predicting the loss capacity of energy storage batteries includes: Obtain battery cycle life, charge and discharge curves, and charging voltage curves from energy storage battery status data; Obtain the temperature curve in the energy storage battery status data; Determine the basic loss capacity based on the battery cycle life in the risk range; The first influence coefficient is calculated according to the charge and discharge curve of the risk interval, the second influence coefficient is calculated according to the charging voltage curve of the risk interval, and the third influence coefficient is calculated according to the temperature curve of the risk interval; The basic loss capacity is adjusted by the first influence coefficient, the second influence coefficient and the third influence coefficient to obtain the predicted energy storage battery loss capacity.

2. A photovoltaic energy storage system based on microgrid monitoring according to claim 1, characterized in that: The deviation comparison process includes: By formula: （1） （2） Calculate and obtain the deviation g(t) at the current time point t; Compare the deviation g(t) with the preset deviation interval [g1, g2]: If g(t)<g1, the attenuation level is judged to be normal; If g(t)∈[g1,g2], the attenuation level is judged to be risky; If g(t)>g2, the attenuation level is judged to be abnormal; Among them, Cd(t) is the ideal capacity attenuation curve, Cp(t) is the actual capacity change curve, C(t) is the capacity difference curve, and y(t) is the cycle life of the energy storage battery corresponding to time point t. is the capacity difference reference value corresponding to y(t), is the derivative of C(t), , is the proportionality coefficient.

3. A photovoltaic energy storage system based on microgrid monitoring according to claim 2, characterized in that: The process of identifying risk periods includes: By formula Calculate and obtain the capacity difference change curve h(t); Compare h(t) with the preset threshold k1: If h(t) exists in a time interval greater than k1, the time interval greater than k1 is regarded as the risk interval; Otherwise, the period t-△t~t is selected as the risk interval, and △t is the preset fixed period.

4. A photovoltaic energy storage system based on microgrid monitoring according to claim 3, characterized in that: The calculation process of the first influence coefficient includes: Identify historical charge and discharge curves to obtain the charging interval, floating charge interval, and discharge interval; The first influence coefficient E1 is calculated by formula (3)-(6); （3） （4） （5） （6） Wherein, Ep is the discharge influence coefficient, Ec is the charge influence coefficient, Ef is the float charge influence coefficient, n is the number of discharge intervals, i is a positive integer and i∈[1,n]; is the discharge percentage of the i-th discharge interval, is the discharge capacity comparison function, is the duration of the ith discharge interval, vd is the basic discharge rate, is the discharge rate control function, m is the number of charging intervals, j is a positive integer and j∈[1,m]; is the maximum percentage of power in the jth charging interval, is the charge capacity comparison function, is the duration of the jth charging interval, vc is the basic charging rate, is the charging rate control function, q is the number of floating charge intervals, k is a positive integer and k∈[1,q]; is the duration of the kth floating charge interval, It is the floating charge duration comparison function.

5. A photovoltaic energy storage system based on microgrid monitoring according to claim 4, characterized in that: The calculation process of the second influence coefficient includes: By formula The second influence coefficient E2 is obtained by calculation; in, is the time duration that the charging voltage curve is greater than U2, is the duration of the charging voltage curve being less than U1, [U1, U2] is the charging voltage interval, is the overvoltage step comparison function, It is the undervoltage step comparison function.

6. A photovoltaic energy storage system based on microgrid monitoring according to claim 5, characterized in that: The calculation process of the third influence coefficient includes: By formula The third influence coefficient E3 is calculated; in, is the time duration that the temperature curve is greater than T2, is the duration of the temperature curve being less than T1, [T1, T2] is the temperature control interval, is the high temperature step control function, is the low temperature step control function.

7. A photovoltaic energy storage system based on microgrid monitoring according to claim 6, characterized in that: The calculation process of predicting the energy storage battery loss capacity includes: By formula Calculate the predicted energy storage battery loss capacity during the risk period ; Where x is the number of battery cycle life in the risk period, It is the reference loss capacity corresponding to the battery cycle life number x.

8. A photovoltaic energy storage system based on microgrid monitoring according to claim 7, characterized in that: The process of judging the status of the energy storage battery based on the comparison results includes: By formula Calculate the predicted capacity difference , the predicted capacity difference Compare with the preset fixed threshold Cth: like , then the energy storage battery attenuation level is judged to be abnormal; Otherwise, the energy storage battery attenuation level is judged to be normal; in, Risk period The maximum difference.

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