Photovoltaic energy storage system based on microgrid monitoring

Abstract

The invention relates to the technical field of photovoltaic energy storage, and discloses a photovoltaic energy storage system based on micro-grid monitoring, which comprises an energy storage battery and an energy storage inverter, and also comprises a battery management system used for monitoring the operation data of the energy storage battery, the operation data comprising the actual capacity of the energy storage battery; the micro-grid monitoring unit is used for monitoring temperature data of the energy storage battery; the database is used for storing operation data and temperature data of the energy storage battery pack; the energy storage battery abnormity analysis unit is used for performing deviation comparison according to the actual capacity change curve and the ideal capacity attenuation curve of the energy storage battery and judging the attenuation level of the capacity of the energy storage battery, and the attenuation level comprises normality, risk and abnormity; and when the attenuation level is at risk, identifying to obtain a risk time period, predicting the loss capacity according to the operation data and temperature data of the risk time period, comparing the predicted loss capacity with the actual residual capacity, and judging 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] The photovoltaic energy storage system is an integrated energy solution that combines photovoltaic power generation with energy storage technology. Through energy storage technology, the system can store excess electricity generated by photovoltaic power generation during the day for use at night, rainy days or during peak electricity consumption, thereby improving energy self-sufficiency and reducing dependence on the power grid. In the photovoltaic storage system, the most core unit is the energy storage battery, which can store the remaining power. In the process of energy storage battery management, its safety is crucial to the photovoltaic energy storage system. Therefore, a battery management system is set up in the existing technology to monitor the status of the battery, and timely repair or replacement is performed when an abnormality is found in the battery to avoid disasters caused by battery failure.

[0003] In existing photovoltaic energy storage systems, in addition to monitoring battery parameters, the degree of battery aging, that is, the battery life loss status, is used to judge whether the battery is operating normally. When the degree of battery aging exceeds the normal standard, it means that there are risks or abnormalities in its operation, so it needs to be repaired or replaced to reduce the adverse effects of battery failure on the photovoltaic energy storage system.

[0004] In the existing abnormality judgment process of energy storage batteries, due to the large differences in the usage conditions of different energy storage batteries, when compared with the life loss under ideal conditions, if the difference standard is set too high, only more obvious energy storage battery failures can be judged. If the difference standard is set too low, misjudgment problems are prone to occur. Therefore, how to more accurately judge the 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 determine the safety of energy storage batteries, the present application provides a photovoltaic energy storage system based on microgrid monitoring.

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

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

[0008] 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;

[0009] Microgrid monitoring unit, used to monitor the temperature data of energy storage batteries;

[0010] A database for storing operating data and temperature data of energy storage battery packs;

[0011] 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 attenuation curve to determine the attenuation level of the energy storage battery capacity, wherein the attenuation levels include 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 remaining capacity, and the state of the energy storage battery is determined based on the comparison result.

[0012] By adopting the above technical solution, by comparing the predicted loss capacity with the actual remaining capacity, the cause of the energy storage battery capacity loss can be determined. If the cause is fault damage, the energy storage battery needs to be repaired and replaced in time to ensure its safety in use. If the cause is caused by use loss and other reasons, although the capacity loss of the energy storage battery is slightly larger than the ideal capacity attenuation curve, it means that the safety risk of its operation is relatively low. Therefore, the above process can more accurately determine the safety of the energy storage battery.

[0013] Optionally, the deviation comparison process includes:

[0014] By formula:

[0015] (1)

[0016] (2)

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

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

[0019] If g(t)<g1, the attenuation level is judged to be normal;

[0020] If g(t)∈[g1,g2], the attenuation level is judged to be risky;

[0021] If g(t)>g2, the attenuation level is judged to be abnormal;

[0022] 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), for The maximum value in the period 0~t, for The maximum value in the period 0~t, , is the proportionality coefficient.

[0023] By adopting the above technical solution, the capacity difference at the current time point and the capacity difference change status in the historical period are combined. Compared with the method of judging only by the value of the capacity difference curve at time point t, the deviation amount can better reflect the overall deviation status of the past period, thereby improving the accuracy and comprehensiveness of the judgment.

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

[0025] By formula Calculate and obtain the capacity difference change curve h(t);

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

[0027] If h(t) exists in a time interval greater than k1, the time interval greater than k1 is regarded as the risk interval;

[0028] Otherwise, the period t-△t~t is selected as the risk interval, and △t is the preset fixed period.

[0029] By adopting the above technical solution, it is possible to obtain time intervals with greater risks for analysis, which reduces the amount of calculation and better judges the problem of greater capacity loss of energy storage batteries, thereby judging the abnormality of the energy storage batteries.

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

[0031] Obtain battery cycle life, charge and discharge curves, and charging voltage curves from energy storage battery status data;

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

[0033] Determine the basic loss capacity based on the battery cycle life in the risk range;

[0034] 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;

[0035] 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.

[0036] By adopting the above technical solution, the loss amount can be predicted through the charge and discharge curve, charging voltage curve and temperature curve in the risk interval, and then the abnormal state of the energy storage battery can be judged through comparison;

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

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

[0039] The first influence coefficient E1 is calculated by formula (3)-(6);

[0040] (3)

[0041] (4)

[0042] (5)

[0043] (6)

[0044] 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.

[0045] By adopting the above technical solution, the impact degree of the historical charge and discharge curve on the capacity of the energy storage battery can be judged.

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

[0047] By formula Calculate and obtain the second influence coefficient E2;

[0048] 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.

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

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

[0051] By formula The third influence coefficient E3 is calculated;

[0052] 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.

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

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

[0055] By formula Calculate the predicted energy storage battery loss capacity during the risk period ;

[0056] 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.

[0057] By adopting the above technical solution, it is possible to obtain the predicted energy storage battery loss capacity, and 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 determining the state of the energy storage battery according to the comparison result includes:

[0059] By formula Calculate the predicted capacity difference , the predicted capacity difference Compare with the preset fixed threshold Cth:

[0060] like , then the energy storage battery attenuation level is judged to be abnormal;

[0061] Otherwise, the energy storage battery attenuation level is judged to be normal;

[0062] in, 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 to accurately determine the abnormality of the energy storage battery.

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

[0065] The present invention can judge the cause of the capacity loss of the energy storage battery by comparing the predicted loss capacity with the actual remaining capacity. If the cause is fault damage, the energy storage battery needs to be repaired and replaced in time to ensure its safety in use. If the cause is caused by use loss and other reasons, although the capacity loss of the energy storage battery is slightly larger than the ideal capacity decay curve, it means that the safety risk of its operation is relatively low, so the safety of the energy storage battery can be judged more accurately. At the same time, the present invention obtains the risk period by identification. Since the actual capacity change curve of the risk period is quite different from the ideal capacity decay curve, the result calculated according to the corresponding data of the risk period is more representative and the amount of calculation is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0067] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings.

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

[0069] The present application embodiment discloses a photovoltaic energy storage system based on microgrid monitoring, referring to Figure 1, including energy storage batteries, energy storage inverters, battery management systems, microgrid monitoring units, databases and energy storage battery abnormality analysis units, wherein the energy storage batteries are used to store the electricity generated by photovoltaics, the energy storage inverters are used to bidirectionally convert electricity to realize charge and discharge control, the battery management system is used to monitor the operation data of the energy storage batteries, the operation data includes the actual capacity of the energy storage batteries, charge and discharge data, charging voltage data, etc., the microgrid monitoring unit is used to monitor the temperature data of the energy storage batteries, which usually sets sensors on the energy storage batteries to realize its functions, the database is used to receive and store the operation data and temperature data of the energy storage battery group, and finally the energy storage battery abnormality analysis unit compares the deviation between the actual capacity change curve of the energy storage battery and the ideal capacity attenuation curve to judge the attenuation level of the energy storage battery capacity, the attenuation level includes normal, risk and abnormal; in this process, the actual capacity change curve and the ideal capacity attenuation curve can be used to preliminarily judge the more obvious abnormal attenuation problem of the energy storage battery life, when the actual capacity change curve is close to the ideal capacity attenuation curve, it means that the energy storage battery state is relatively normal, on the contrary ... When the attenuation curves differ greatly, it indicates that the state of the energy storage battery is abnormal. In order to more accurately judge the capacity attenuation of the energy storage battery, this embodiment further divides the attenuation level of risk. When the attenuation level is at risk, it is between normal and abnormal. This embodiment first identifies and obtains the risk period. Since the actual capacity change curve of the risk period is greatly different from the ideal capacity attenuation curve, the result calculated according to the corresponding data of the risk period is more representative and reduces the amount of calculation. The loss capacity is predicted according to the operation data and temperature data of the risk period, and the predicted loss capacity is compared with the actual remaining capacity. The state of the energy storage battery is judged according to the comparison result. In this process, by comparing the predicted loss capacity with the actual remaining capacity, the cause of the capacity loss of the energy storage battery can be judged. If the cause is fault damage, the energy storage battery needs to be repaired and replaced in time to ensure its safety in use. If the cause is caused by use loss and other reasons, although the capacity loss of the energy storage battery is slightly larger than the ideal capacity attenuation curve, it indicates that the risk of safety in its operation is low. Therefore, the above process can more accurately judge the safety of the use of the energy storage battery.

[0070] In one embodiment, a deviation comparison process is provided, including: using the formula:

[0071] (1)

[0072] (2)

[0073] Calculate and obtain the deviation g(t) at the current time point t; where Cd(t) is the ideal capacity decay curve, which is obtained based on the relationship between the capacity and the number of cycle life of the same type of energy storage battery measured under the standard use environment, and the number of cycle life of the energy storage battery in 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 life 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 based on empirical control data. is the derivative of C(t), for The maximum value in the period of 0~t is calculated by the calculation process of formula (1), which combines the capacity difference at the current time point with the capacity difference change state in the historical period, and at the same time uses the proportional coefficient , Adjust the weight and proportional coefficient , The value of is set according to the test data. Compared with the method of judging by the value of the capacity difference curve at the time point t alone, the deviation g(t) can better reflect the overall deviation state of the past period, improve the accuracy and comprehensiveness of the judgment, and then compare the deviation g(t) with the preset deviation interval [g1, g2]. The boundary of the preset deviation interval [g1, g2] is a preset fixed value, which is obtained by fitting the measured data of the energy storage battery under different states. If g(t) < g1, it means that the battery capacity loss difference is small and the loss rate is in the normal range, because the attenuation level is judged to be normal; if g(t) > g2, it means that the battery capacity loss difference is large or the loss rate is in the abnormal range, so it needs to be replaced or repaired in time, so the attenuation level is judged to be abnormal; if g(t)∈[g1, g2], the attenuation level is judged to be risky, and it is necessary to further judge based on 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 a risk period is provided, including: The capacity difference change curve h(t) is calculated and obtained. The capacity difference change curve h(t) reflects the capacity change rate of the energy storage battery. h(t) is compared with the preset threshold k1. The preset threshold k1 is obtained by fitting the capacity loss rate curves of multiple groups of normally operating energy storage batteries. Therefore, it is a critical value. Therefore, if h(t) has a time interval greater than k1, it means that the corresponding interval has a large abnormal risk, and the time interval greater than k1 is used as the risk interval; otherwise, the t-△t~t period is selected as the risk interval, and △t is a preset fixed period, which is set according to the user's selection. The longer the preset fixed period is selected, the more accurate the calculation result is, and the corresponding calculation amount is larger. Through the above process, the time interval with a larger risk can be obtained for analysis, which reduces the calculation amount and can better judge the problem of large capacity loss of the energy storage battery, and then judge the abnormality of the energy storage battery.

[0075] In one embodiment, the process of predicting the loss capacity of the energy storage battery includes: obtaining the number of battery cycle life, charge and discharge curve, and charging voltage curve in the energy storage battery status data; obtaining the temperature curve in the energy storage battery status data; determining the basic loss capacity according to the number of battery cycle life in the risk interval; calculating the first influence coefficient according to the charge and 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 loss capacity by the first influence coefficient, the second influence coefficient, and the third influence coefficient to obtain the predicted loss capacity of the energy storage battery; since the energy storage battery is mainly affected by its charge and discharge process data, input power data, and environmental factors in addition to the number of battery cycle life during use, the present application predicts its loss amount through the charge and discharge curve, the charging voltage curve, and the temperature curve in the risk interval, and then can realize the judgment of the abnormal state of the energy storage battery through comparison; 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 summarize the factors affecting a large proportion of the energy storage battery failure risk, so although the calculated predicted loss capacity has a certain error, it has a high reference value in the comparison and judgment process.

[0076] In one embodiment, a calculation process of a first influence coefficient is provided, including: identifying a historical charge and discharge curve, obtaining a charging interval, a floating charge interval, and a discharging interval. The above identification process is based on the prior art and will not be described in detail herein. The first influence coefficient E1 is calculated by formulas (3)-(6);

[0077] (3)

[0078] (4)

[0079] (5)

[0080] (6)

[0081] Wherein, Ep is the discharge influence coefficient, Ec is the charge influence coefficient, and Ef is the float charge influence coefficient. The influence of the discharge influence coefficient, the charge influence coefficient, and the float charge influence coefficient are comprehensively calculated by formula (3). n is the number of discharge intervals, i is a positive integer and i∈[1, n]; is the discharge percentage of the ith discharge interval. When the discharge percentage is greater than 50%, it has a higher impact on the capacity loss of the energy storage battery, and the more it exceeds, the greater the impact. Therefore, by calculating the part exceeding 50%, and then using the discharge control function Determine the degree of influence. In addition, the discharge rate also affects the capacity loss of the energy storage battery. The discharge percentage of the discharge interval is divided by the duration of the i-th discharge interval. Calculate the discharge rate, subtract the discharge base rate vd, and use the discharge rate control function Calculate the degree of influence; m is the number of charging intervals, j is a positive integer and j∈[1,m]; during the charging process, overcharge will also affect the capacity loss of the energy storage battery, and the maximum percentage of power passing through the jth charging interval Subtract 80%, and then use the charge capacity comparison function , and then judge the impact of overcharge in each charging interval. At the same time, the charging rate will also have a high impact on the capacity loss of the energy storage battery. The length of the jth charging interval , the maximum percentage of power in the jth charging interval and vc is the portion of the charging rate exceeding the charging basic rate during the charging basic rate calculation process, which is calculated by the charging rate comparison function. The influence degree is calculated. In addition, 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 a floating charge time comparison function, which calculates the time of each floating charge interval to obtain the impact on the loss of energy storage battery capacity. It should be noted that the above discharge rate and charging rate are obtained according to the standard data corresponding to the energy storage battery model in experience, and the discharge amount comparison function, discharge rate comparison function, charging amount comparison function, charging rate comparison function and floating charge time comparison function in the above embodiment are all obtained by fitting the test data. In the test process, since the impact of a single charge and discharge process on the energy storage battery capacity is difficult to obtain, the energy storage battery capacity change data for a longer period of time is obtained by controlling the variables, and the total impact ratio is evenly divided according to the number of cycle life of the energy storage battery to obtain the single impact amount, and then the corresponding impact value is obtained through the different intervals where the data is located, and then the total impact coefficient is obtained by superimposing all the times.

[0082] In one embodiment, a calculation process of the second influence coefficient is provided, including: using the formula The second influence coefficient E2 is calculated; wherein, 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, which is obtained according to the standard corresponding to the energy storage battery. is the overvoltage step comparison function, It is an undervoltage step control function. In the above technical solution, the magnitude of the charging voltage exceeding U2 or being lower than U1 will also affect the influence coefficient. However, in specific applications, the charging voltage is within a controllable range, so it is difficult to exceed the standard range. Therefore, this embodiment only considers the influence of overvoltage and undervoltage time on the life of the energy storage battery. The overvoltage step control function and the undervoltage step control function are obtained by fitting the test data. The capacity change data of the energy storage battery for a long period of time are obtained by controlling the variables. The total influence ratio is evenly divided according to the number of cycle life of the energy storage battery, so as to obtain the single influence amount, and then obtain the corresponding influence value according to the different intervals where the data is located, and then obtain the total influence coefficient by superposition of all times.

[0083] In one embodiment, a calculation process of the third influence coefficient is provided, including: using the formula The third influence coefficient E3 is calculated; wherein, 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, which is obtained based on empirical data. Same as the second influence coefficient E2, since in actual processes, the photovoltaic energy storage system will be equipped with a corresponding heat dissipation device, the probability of exceeding a large range of standard temperatures is low. Therefore, the influence of the temperature exceeding the standard time on the life of the energy storage battery is considered, where, is the high temperature step control function, is the low temperature step control function. Both the high temperature step control function and the low temperature step control function are obtained by fitting the test data. The data determination process is the same as the above-mentioned overvoltage step control function and undervoltage step control function, which will not be described in detail here.

[0084] In one embodiment, a calculation process for predicting the loss capacity of an energy storage battery is provided, including: using the 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, is the reference loss capacity corresponding to the number of battery cycle life x. Through the above process, 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.

[0085] The process of judging the status of the energy storage battery based on the comparison results includes:

[0086] By formula Calculate the predicted capacity difference , the predicted capacity difference Compare with the preset fixed threshold Cth, Risk period The maximum difference is that the capacity of the energy storage battery is irreversible. is the difference between the capacity at the end of the risk period and the capacity at the start of the risk period. The fixed threshold Cth is set based on empirical data fitting. , it means that the predicted loss capacity is quite different from the actual remaining capacity. Therefore, it can be judged that the reason for the large loss of battery capacity is not the influence of controllable factors, but the existence of abnormal faults. Therefore, the attenuation level of the energy storage battery is judged to be abnormal. Otherwise, the attenuation level of the energy storage battery is judged to be normal. Through the above process, it is possible to judge the problem of large capacity loss of the energy storage battery and realize accurate judgment of the abnormality of the energy storage battery.

[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 cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify 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 the operation data and temperature data of the energy storage battery pack; 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 operating data and temperature data of the risk period. The predicted loss capacity is compared with the actual remaining capacity, and the status of the energy storage battery is judged based on the comparison results.

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 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.

5. A photovoltaic energy storage system based on microgrid monitoring according to claim 4, 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.

6. A photovoltaic energy storage system based on microgrid monitoring according to claim 5, 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.

7. A photovoltaic energy storage system based on microgrid monitoring according to claim 6, 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.

8. A photovoltaic energy storage system based on microgrid monitoring according to claim 7, 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.

9. A photovoltaic energy storage system based on microgrid monitoring according to claim 8, 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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