Artificial intelligence-based photovoltaic energy storage safety collaborative management method and system

Abstract

The application discloses a photovoltaic energy storage safety collaborative management method and system based on artificial intelligence, and relates to the technical field of photovoltaic energy storage collaborative management.The photovoltaic energy storage safety collaborative management method based on artificial intelligence comprises the following steps: photovoltaic anti-interference management, energy storage safety collaborative management, and energy storage operation management optimization.The application obtains a photovoltaic anti-interference management result based on acquired safety collaborative management data, then judges whether to execute anti-interference response stable management optimization, if yes, sends an energy storage system charging and discharging collaborative management instruction after executing the anti-interference response stable management optimization, otherwise directly sends the energy storage system charging and discharging collaborative management instruction, and finally judges whether to perform energy storage collaborative operation safety management optimization based on an acquired energy storage operation safety management result, so that the photovoltaic system and the energy storage system are collaboratively managed more timely, and the problem that the photovoltaic system and the energy storage system are not collaboratively managed timely in the prior art is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of photovoltaic energy storage collaborative management, and particularly relates to a photovoltaic energy storage safety collaborative management method and system based on artificial intelligence. BACKGROUND

[0002] Photovoltaic energy storage is an integrated technology of "photovoltaic power generation + energy storage system". The photovoltaic energy storage system usually consists of photovoltaic components, energy storage batteries and inverters as the main components. When connected to the grid, it is necessary to ensure that the photovoltaic system and the energy storage system can work in coordination to ensure the stability and safety of the grid.

[0003] In the grid-connected mode, the prior art first needs to collect various real-time data of the photovoltaic energy storage system. Common data types include: photovoltaic component data (such as output power, voltage, current of photovoltaic components), energy storage system data (such as battery voltage, temperature, charge and discharge current, remaining power), inverter data (such as input voltage, current, output voltage, current of the inverter) and grid data (such as frequency, voltage, load demand of the grid) and the like; then the real-time data is transmitted to the central control system and the real-time updating of the data is ensured; based on the collected data, artificial intelligence, machine learning and big data analysis technology are used to generate control decisions such as energy storage battery charge and discharge adjustment and inverter power adjustment, for example, based on photovoltaic power generation, the current state of the energy storage battery, grid demand and other information, the charge and discharge strategy is optimized using artificial intelligence algorithms (such as reinforcement learning, optimization algorithm, etc.); according to the generated control decisions, measures are taken, such as adjusting the charge and discharge strategy of the battery, the output voltage and power of the inverter, to ensure the safe and stable operation of the photovoltaic energy storage system.

[0004] The harmonic interference formed by the high-frequency voltage or current fluctuations generated by various industrial equipment in the grid (such as harmonic pollution caused by current impact, instantaneous load changes and other factors when industrial equipment starts, resulting in frequent harmonics in the grid), usually in the form of non-sinusoidal waves, is superimposed on the grid voltage or current, causing distortion of the voltage and current waveform of the grid. The inverter may not be able to clearly identify this distortion, resulting in misjudgment or missed judgment. For example, the inverter may mistake the fluctuation caused by harmonics as a normal load change of the grid, without triggering the corresponding protection mechanism, resulting in delayed protection action and affecting the timeliness of safety management.

[0005] In addition, it is also necessary to consider that if the inverter fails to accurately detect abnormal fluctuations in the power grid (such as harmonic interference), the charging and discharging strategy of the energy storage system may not be adjusted in time. The charging and discharging of the energy storage battery requires precise control of current and voltage, and harmonic interference may cause distortion of the output power of the inverter, thereby affecting the charging process of the energy storage battery. If the inverter fails to effectively identify these fluctuations, the battery may be subjected to overcharging or overdischarging, resulting in untimely safety control of the energy storage system. SUMMARY

[0006] The present application provides a photovoltaic energy storage safety collaborative control method and system based on artificial intelligence, which solves the problem of untimely collaborative control of photovoltaic systems and energy storage systems in the prior art, and achieves the effect of more timely collaborative control of photovoltaic systems and energy storage systems.

[0007] The present application provides a photovoltaic energy storage safety collaborative control method based on artificial intelligence, which comprises: in grid-connected mode, obtaining safety collaborative control data reflecting the safety performance of the photovoltaic system within a control period, obtaining a photovoltaic anti-interference control result based on the obtained safety collaborative control data to quantify the anti-interference response stability of the inverter process of the photovoltaic system in grid-connected mode; determining whether to perform anti-interference response stability control optimization according to the photovoltaic anti-interference control result, if yes, sending a charging and discharging collaborative control instruction of the energy storage system after performing the anti-interference response stability control optimization, if no, directly sending the charging and discharging collaborative control instruction of the energy storage system, the charging and discharging collaborative control instruction of the energy storage system being used to obtain an energy storage operation safety control result by using energy storage collaborative control data reflecting the safety performance of the energy storage system to quantify the safety performance of the energy storage battery operation in the energy storage system, the anti-interference response stability control optimization indicating that the inverter pulse width modulation is combined with the photovoltaic anti-interference control result; determining whether to perform energy storage collaborative operation safety control optimization based on the obtained energy storage operation safety control result, if yes, sending a control data remote synchronization instruction after performing the energy storage collaborative operation safety control optimization, otherwise, directly sending the control data remote synchronization instruction, the energy storage collaborative operation safety control optimization indicating that the charging and discharging process of the energy storage battery in the energy storage system is adjusted in combination with the energy storage operation safety control result.

[0008] The application provides an artificial intelligence-based photovoltaic energy storage safety collaborative control system, which comprises a photovoltaic anti-interference control module, an energy storage safety collaborative control module and an operation safety control optimization module; the photovoltaic anti-interference control module is used for obtaining safety collaborative control data for reflecting the safety performance of a photovoltaic system in a control period in a grid-connected mode, obtaining a photovoltaic anti-interference control result based on the obtained safety collaborative control data to quantify the anti-interference response stability of the inverter process of the photovoltaic system in the grid-connected mode; the energy storage safety collaborative control module is used for judging whether to perform anti-interference response stability control optimization according to the photovoltaic anti-interference control result, if yes, sending an energy storage system charging and discharging collaborative control instruction after performing the anti-interference response stability control optimization, and if no, directly sending the energy storage system charging and discharging collaborative control instruction; and the operation safety control optimization module is used for judging whether to perform energy storage collaborative operation safety control optimization based on the obtained energy storage operation safety control result, if yes, sending a control data remote synchronization instruction after performing the energy storage collaborative operation safety control optimization, and if no, directly sending the control data remote synchronization instruction.

[0009] The one or more technical solutions provided in the embodiments of the application have at least the following technical effects or advantages:

[0010] 1. In the grid-connected mode, harmonic interference and load fluctuation in the power grid may cause the inverter to misjudge power grid abnormalities, thereby affecting the output stability of the photovoltaic system, and further causing the safety control of the photovoltaic system to be not timely, and further causing the charging and discharging strategy of the energy storage system to be unable to be adjusted in time, and the charging and discharging process of the energy storage system may also be disturbed, so that the protection mechanism of the energy storage system cannot respond quickly, causing the safety control of the energy storage system to be not timely, the application obtains the photovoltaic anti-interference control result based on the obtained safety collaborative control data to quantify the anti-interference response stability of the inverter process of the photovoltaic system in the grid-connected mode, which helps to monitor the inverter process of the photovoltaic system and the influence of power grid fluctuation in real time, ensures that the photovoltaic system can still maintain stable output and good anti-interference ability under the condition of power grid fluctuation or load fluctuation, then judges whether to perform anti-interference response stability control optimization according to the photovoltaic anti-interference control result, dynamically adjusts the inverter parameters to improve the anti-interference response stability of the inverter process of the photovoltaic system, which can reduce the risk caused by frequent inverter restart or adjustment, more timely safety control of the photovoltaic system, and finally judges whether to perform energy storage collaborative operation safety control optimization based on the obtained energy storage operation safety control result, dynamically adjusts the charging and discharging parameters, which can effectively cope with different needs of the energy storage battery under different working conditions, thereby enhancing the reliability and stability of the energy storage system, realizing more timely collaborative control of the photovoltaic system and the energy storage system, and effectively solving the problem of not timely collaborative control of the photovoltaic system and the energy storage system in the prior art.

[0011] 2、By corresponding to anti-interference control qualified when the photovoltaic anti-interference control result, do not execute anti-interference response stable control optimization, send energy storage system charge-discharge cooperative control instruction and obtain energy storage operation safety control result, further realize the cooperative control of photovoltaic system and energy storage system, when the photovoltaic anti-interference control result corresponds to anti-interference control unqualified, execute anti-interference response stable control optimization, higher carrier frequency can make the output ac waveform of inverter closer to sine wave. When the carrier frequency is lower, the output voltage waveform may have greater fluctuation, increasing the carrier frequency can effectively reduce the distortion of waveform, reduce the harmonic component, the dead time refers to the "interval time" between two groups of switching elements in inverter, in order to avoid short circuit, by shortening the dead time, the switching control of inverter is more accurate, can switch the switch state faster, improve the dynamic response ability of system, the adjustment of inverter carrier frequency and inverter dead time helps to improve the reliability of photovoltaic system safety control.

[0012] 3、By obtaining the deviation between the energy storage safety control quantity and the energy storage safety control reference value to obtain the energy storage operation safety control result, the energy storage charging safety coefficient and the energy storage discharging safety coefficient can quantify the safety of the battery in different working states, which helps to more accurately judge the safety performance of the energy storage battery charging and discharging process, then according to the energy storage operation safety control result, judge whether to carry out energy storage cooperative operation safety control optimization, too high charging power may cause the temperature of energy storage battery too high, increase the rate of internal chemical reaction, and then speed up the aging process of energy storage battery, increase the risk of thermal runaway, at the same time, deep discharge will cause greater pressure on energy storage battery, excessive discharge will cause irreversible decline of energy storage battery capacity, shorten the service life of battery, energy storage cooperative operation safety control optimization helps to more efficient safety control of energy storage system

[0013] 4, the arithmetic mean result of the obtained energy storage charging safety factor and the energy storage safety control quantity deviation degree is input into the energy storage charging power control linear regression model after weighting processing of assigning respective weights, and the charging power control value for reducing the initial charging power in the energy storage battery charging process is output correspondingly, overcharging of the energy storage battery will damage the performance of the battery, shorten the service life of the battery, and even cause safety problems such as thermal runaway, according to the energy storage charging safety factor and the safety control quantity, the initial charging power can be avoided to be too large, thereby reducing the overcharging risk, realizing the improvement of safety control reliability in the energy storage battery charging process, the arithmetic mean result of the obtained energy storage discharging safety factor and the energy storage safety control quantity deviation degree is input into the energy storage discharging depth control linear regression model after weighting processing of assigning respective weights, and the discharging depth control value for reducing the initial discharging depth in the energy storage battery discharging process is output correspondingly, over-discharge is a main reason for damage of the energy storage battery, according to the energy storage discharging safety factor and the energy storage safety control quantity, the discharging depth can be dynamically adjusted to effectively avoid over-discharge of the battery in the discharging process, avoid the battery power below its safe working range, thereby prolonging the service life of the battery, avoiding irreversible attenuation of the battery capacity, and improving the reliability of safety control in the energy storage battery discharging process. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0015] Figure 1 The flow chart of the photovoltaic energy storage safety collaborative control method based on artificial intelligence provided by the embodiment of the present application;

[0016] Figure 2 The photovoltaic anti-interference control result acquisition flow chart of the photovoltaic energy storage safety collaborative control method based on artificial intelligence provided by the embodiment of the present application;

[0017] Figure 3 The energy storage collaborative operation safety control optimization flow chart of the photovoltaic energy storage safety collaborative control method based on artificial intelligence provided by the embodiment of the present application;

[0018] Figure 4 The structural schematic diagram of the photovoltaic energy storage safety collaborative control system based on artificial intelligence provided by the embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be described clearly and completely below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the described embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without any inventive effort fall within the scope of protection of the present application.

[0020] Unless otherwise defined, technical terms or scientific terms used in the present application shall have the ordinary meaning understood by a person of ordinary skill in the art to which the present application pertains. The terms "first", "second" and similar terms used in the present application do not denote any order, quantity or importance, but are used to distinguish different components. Similarly, the terms "one", "a" or "the" and similar terms do not denote a quantity limitation, but mean that at least one exists. The terms "include" or "contain" and similar terms mean that the elements or objects before the terms encompass the elements or objects listed after the terms and their equivalents, without excluding other elements or objects. The terms "connect" or "connected" and similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0021] It should be noted that "up", "down", "left", "right", "front", "back" and the like used in the present application are only used to represent relative positional relationships, and when the absolute positions of the described objects change, the relative positional relationships can also change accordingly.

[0022] The present application aims at the problem of not timely coordination and control of photovoltaic systems and energy storage systems in the prior art, and provides an artificial intelligence-based photovoltaic energy storage safety coordination and control method and system, which achieves the effect of more timely coordination and control of photovoltaic systems and energy storage systems.

[0023] As shown in Figure 1 The flowchart of the artificial intelligence-based photovoltaic energy storage safety coordination and control method provided by the embodiments of the present application is shown in Figure 1 The method comprises the following steps: photovoltaic anti-interference control, energy storage safety coordination and control, and energy storage operation control optimization.

[0024] The first step of the artificial intelligence-based photovoltaic energy storage safety coordination and control method is photovoltaic anti-interference control, which specifically comprises: in a grid-connected mode, acquiring safety coordination and control data for reflecting the safety performance of the photovoltaic system in a control period, wherein the safety coordination and control data comprises grid load fluctuation data, grid quality control data and inverter safety control data, and the specific acquisition steps of the safety coordination and control data are as follows:

[0025] S1, obtaining power grid load fluctuation data in a control period through a smart meter, the power grid load fluctuation data including a load frequency fluctuation amplitude and a load power fluctuation amplitude.

[0026] S2, if the load frequency fluctuation amplitude is not greater than a load frequency fluctuation limit value and the load power fluctuation amplitude is not greater than a load power fluctuation limit value, obtaining inverter safety control data after obtaining power grid quality control data in the control period, otherwise, directly obtaining inverter safety control data in the control period and judging whether to obtain power grid quality control data. In the case that the load fluctuation amplitude meets the requirements, the photovoltaic system can skip some links of processing, for example, not directly obtaining inverter safety control data, but first obtaining power grid quality control data; if the power grid load fluctuation amplitude and the load power fluctuation amplitude are both within the limit value, only power grid quality control data needs to be obtained, and then inverter safety control data is obtained, which avoids over-reliance on inverter safety control data. This optimized data acquisition process not only reduces the processing burden of the photovoltaic system, but also helps to speed up the control decision, realizes real-time monitoring and rapid response of the photovoltaic system.

[0027] Among them, the power grid quality control data includes a power grid voltage change rate and a power grid frequency change rate, and the inverter safety control data includes inverter input data and inverter output data; specifically, the inverter input data includes inverter input voltage and inverter input current, and the inverter output data includes inverter output voltage, inverter output current and output total harmonic distortion; the load frequency fluctuation limit value and the load power fluctuation limit value are obtained from a preset database, and the power grid quality control data and the inverter safety control data are obtained through a smart meter.

[0028] It should be noted that whether to obtain power grid quality control data is judged, and the specific process is as follows:

[0029] S21, obtaining inverter voltage control indicators based on inverter safety control data to reflect the deviation stability degree of inverter input voltage and inverter output voltage, inverter current control indicators to reflect the deviation stability degree of inverter input current and inverter output current, and inverter harmonic control indicators to reflect the output harmonic response stability degree.

[0030] The inverter voltage control indicator represents the result of inverse proportional processing of the deviation of inverter input voltage and inverter output voltage, the inverter current control indicator represents the result of inverse proportional processing of the deviation of inverter input current and inverter output current, and the inverter harmonic control indicator represents the result of inverse proportional processing of the output total harmonic distortion; wherein, the deviation inverse proportional processing means that the difference operation takes absolute value and then performs inverse operation.

[0031] S22, the inverter voltage control index, inverter current control index and inverter harmonic control index are arithmetically averaged to obtain a photovoltaic inverter control score to quantitatively reflect the stability of the photovoltaic system inverter process in the grid-connected mode.

[0032] It should be understood that as the inverter voltage control index, inverter current control index and inverter harmonic control index increase, the photovoltaic inverter control score also increases; at the same time, the parameters in the photovoltaic inverter control score are related to each other. Harmonics are nonlinear distortions of current and voltage waveforms, and their generation is usually related to voltage fluctuations, current fluctuations and nonlinear working characteristics of inverters. If the inverter voltage control index is not effectively controlled, it may cause distortion of the output voltage, thereby increasing the harmonic content. That is, as the inverter voltage control index decreases, the inverter harmonic control index decreases, and the photovoltaic inverter control score also decreases.

[0033] S23, the photovoltaic inverter control score is compared with the inverter control reference limit value, if the photovoltaic inverter control score is not greater than the inverter control reference limit value, the grid quality control data is obtained; otherwise, the photovoltaic inverter control score is verified based on the relative deviation, specifically: the relative deviation of the photovoltaic inverter control score is obtained, if the relative deviation of the photovoltaic inverter control score is in the inverter control stability limit interval, the grid quality control data is not obtained, and the relative deviation of the photovoltaic inverter control score is continuously monitored whether it is in the inverter control stability limit interval, otherwise, the grid quality control data is obtained, the relative deviation of the photovoltaic inverter control score represents the result of the relative deviation processing of the photovoltaic inverter control score and the inverter control upper limit value, and the relative deviation processing represents the absolute value of the difference between the photovoltaic inverter control score and the inverter control upper limit value and the inverter control upper limit value.

[0034] As a further scheme, the photovoltaic anti-interference control result is obtained based on the obtained safety coordination control data to quantitatively reflect the anti-interference response stability of the photovoltaic system inverter process in the grid-connected mode; for example, Figure 2As shown, the photovoltaic anti-interference control result acquisition flowchart of the photovoltaic energy storage safety collaborative management method based on artificial intelligence provided by the embodiment of the present application, the corresponding logic is: if the load frequency fluctuation amplitude is not greater than the load frequency fluctuation limit value, and the load power fluctuation amplitude is not greater than the load power fluctuation limit value, the power grid quality anti-interference control judgment is performed, otherwise, the load fluctuation anti-interference control judgment is performed, if the power grid quality anti-interference control judgment is performed, the power grid-anti-interference control quantity is obtained to obtain the photovoltaic anti-interference control result, if the load fluctuation anti-interference control judgment is performed, the load-anti-interference control quantity is obtained to obtain the photovoltaic anti-interference control result, the photovoltaic anti-interference control result includes anti-interference control qualified and anti-interference control unqualified, anti-interference control qualified represents the photovoltaic anti-interference control result corresponding to the condition that the power grid-anti-interference control quantity is greater than the power grid-anti-interference control reference value and the load-anti-interference control quantity is greater than the load-anti-interference control reference value, and anti-interference control unqualified represents the photovoltaic anti-interference control result corresponding to the condition that the power grid-anti-interference control quantity is not greater than the power grid-anti-interference control reference value or the load-anti-interference control quantity is not greater than the load-anti-interference control reference value. The specific acquisition steps of the photovoltaic anti-interference control result are as follows:

[0035] The photovoltaic inverter control score is obtained, if the load frequency fluctuation amplitude is not greater than the load frequency fluctuation limit value, and the load power fluctuation amplitude is not greater than the load power fluctuation limit value, the power grid quality anti-interference control judgment is performed, otherwise, the load fluctuation anti-interference control judgment is performed.

[0036] Among them, the power grid quality anti-interference control judgment is specifically: the power grid-anti-interference factor and the photovoltaic inverter control score are interactively processed to obtain the power grid-anti-interference control quantity, the power grid-anti-interference control quantity is compared with the power grid-anti-interference control reference value, and the photovoltaic anti-interference control result is obtained.

[0037] Specifically, the power grid-anti-interference factor is used to describe the influence degree of the power grid quality control data on the stability evaluation of the photovoltaic system inverter process anti-interference response, which is the result of weighting processing by assigning respective weights to the arithmetic mean of the relative deviation processing results of the power grid quality control data, specifically, the product operation of the power grid voltage change rate relative deviation processing result and the power grid frequency change rate relative deviation processing result is respectively carried out with the power grid voltage change rate weight and the power grid frequency change rate weight obtained from the preset database, the power grid quality control data relative deviation processing result is used to reflect the relative deviation degree of the power grid quality control limit value and the power grid quality control data, including the power grid voltage change rate relative deviation processing result and the power grid frequency change rate relative deviation processing result, the relative deviation degree is the result of relative deviation processing.

[0038] The load fluctuation anti-interference control judgment is performed, and specifically, the load-anti-interference factor and the photovoltaic inverter control score are interactively processed to obtain a load-anti-interference control amount, the load-anti-interference control amount is compared with a load-anti-interference control reference value, and a photovoltaic anti-interference control result is obtained.

[0039] Specifically, the load-anti-interference factor is used to describe the influence degree of the grid load fluctuation data on the anti-interference response stability evaluation of the photovoltaic system inverter process. The load-anti-interference factor is the result of weighting and arithmetic averaging of the respective weights assigned to the relative deviation processing results of the grid load fluctuation data. Specifically, the load frequency fluctuation amplitude relative deviation processing result and the load power fluctuation amplitude relative deviation processing result are multiplied by the load frequency fluctuation amplitude weight and the load power fluctuation amplitude weight obtained from the preset database, respectively. The relative deviation processing result of the grid load fluctuation data is used to reflect the relative deviation degree of the grid load fluctuation limit value and the grid load fluctuation data, including the load frequency fluctuation amplitude relative deviation processing result and the load power fluctuation amplitude relative deviation processing result.

[0040] The photovoltaic anti-interference control result includes anti-interference control qualified and anti-interference control unqualified. Anti-interference control qualified means that the grid-anti-interference control amount is greater than the grid-anti-interference control reference value and the load-anti-interference control amount is greater than the load-anti-interference control reference value. Anti-interference control unqualified means that the grid-anti-interference control amount is not greater than the grid-anti-interference control reference value or the load-anti-interference control amount is not greater than the load-anti-interference control reference value. The grid-anti-interference control reference value and the load-anti-interference control reference value are obtained from the preset database.

[0041] It should be noted that the interactive processing is a multiplication operation. As the grid-anti-interference factor increases, the photovoltaic inverter control score increases, and the grid-anti-interference control amount also increases. Similarly, as the load-anti-interference factor increases, the photovoltaic inverter control score increases, and the load-anti-interference control amount also increases.

[0042] During grid connection, the photovoltaic system may be affected by frequency fluctuations, voltage fluctuations, load changes and other interference factors from the grid, and even by external environmental factors such as climate change and load fluctuations. Disturbances in the grid, such as voltage sag and grid frequency fluctuations, can affect the photovoltaic system. By quantitatively evaluating the anti-interference response stability of the photovoltaic system inverter process in grid connection mode, it is helpful to more efficiently control the safety of the photovoltaic system.

[0043] The second step of the photovoltaic energy storage safety collaborative management method based on artificial intelligence is energy storage safety collaborative management, which specifically comprises: determining whether to perform anti-interference response stability management optimization according to the photovoltaic anti-interference management result, and specifically comprising:

[0044] determining whether the photovoltaic anti-interference management result corresponds to an anti-interference management pass, if yes, not performing the anti-interference response stability management optimization, sending an energy storage system charging and discharging collaborative management instruction and obtaining an energy storage operation safety management result, otherwise, performing the anti-interference response stability management optimization, and sending the energy storage system charging and discharging collaborative management instruction after performing the anti-interference response stability management optimization.

[0045] The energy storage system charging and discharging collaborative management instruction is used to obtain the energy storage operation safety management result by reflecting the energy storage collaborative management data of the safety performance of the energy storage system, so as to quantify the safety performance of the energy storage battery operation in the energy storage system; the specific steps for obtaining the energy storage operation safety management result are as follows:

[0046] B11, obtaining the energy storage collaborative management data in the management period through the energy management system (EMS), the energy storage collaborative management data including energy storage battery charging data and energy storage battery discharging data, the energy storage battery charging data including energy storage charging voltage, energy storage charging current and energy storage charging power, and the energy storage battery discharging data including energy storage discharging voltage, energy storage discharging current and energy storage discharging power.

[0047] B12, performing deviation inverse proportional processing on the energy storage battery charging data and the energy storage battery charging reference limit value to obtain an energy storage charging safety coefficient to represent the safety performance of the energy storage battery charging process, the energy storage battery charging reference limit value being the arithmetic mean of the upper limit value and the lower limit value of the energy storage battery charging.

[0048] B13, performing deviation inverse proportional processing on the energy storage battery discharging data and the energy storage battery discharging reference limit value to obtain an energy storage discharging safety coefficient to represent the safety performance of the energy storage battery discharging process, the energy storage battery discharging reference limit value being the arithmetic mean of the upper limit value and the lower limit value of the energy storage battery discharging.

[0049] B14, determining whether the obtained energy storage charging safety coefficient and the energy storage discharging safety coefficient are both not 0, if yes, performing harmonic average processing on the obtained energy storage charging safety coefficient and the energy storage discharging safety coefficient to obtain an energy storage safety management quantity, otherwise, marking the energy storage safety management quantity as an abnormal value and sending an energy storage collaborative management abnormality prompt.

[0050] The energy storage charging safety factor and the energy storage discharging safety factor are related to each other in the energy storage safety control quantity, and jointly act on the safety performance evaluation of the energy storage battery in the energy storage system. Specifically, with the increase of the energy storage charging safety factor and the energy storage discharging safety factor, the energy storage safety control quantity increases accordingly. The charging state of the battery directly affects the discharging state. If the voltage of the battery exceeds the upper limit of charging or is overcharged during the charging process, it may cause damage to the internal battery, thereby affecting the performance and safety during subsequent discharging. Conversely, if the battery is over-discharged during discharging, it may cause damage to the battery, resulting in the risk of overcharging during subsequent charging. Therefore, the energy storage charging safety factor and the energy storage discharging safety factor can reflect the health status of the battery in the entire cycle, that is, with the decrease of the energy storage charging safety factor, the energy storage discharging safety factor decreases accordingly, and the energy storage safety control quantity also decreases accordingly.

[0051] B15, based on the deviation between the obtained energy storage safety control quantity and the energy storage safety control reference value, to obtain an energy storage operation safety control result. The energy storage operation safety control result includes safety control qualified and safety control unqualified. Safety control qualified means that the energy storage safety control quantity is greater than the energy storage operation safety control result corresponding to the energy storage safety control reference value. Safety control unqualified means that the energy storage safety control quantity is not greater than the energy storage operation safety control result corresponding to the energy storage safety control reference value.

[0052] Among them, the anti-interference response stable control optimization means that the inverter pulse width modulation is combined with the photovoltaic anti-interference control result, including inverter carrier frequency control and inverter dead time control.

[0053] B21, inverter carrier frequency control, specifically: judging whether to perform grid quality anti-interference control judgment, if yes, based on the grid-anti-interference control quantity to query the carrier frequency control mapping value from the constructed carrier frequency control optimization mapping table, otherwise, based on the load-anti-interference control quantity to query the carrier frequency control mapping value from the constructed carrier frequency control optimization mapping table, and gradually increase the initial carrier frequency of the inverter according to the step length corresponding to the obtained carrier frequency control mapping value. Increasing the carrier frequency helps to reduce the harmonic content of the inverter output. The voltage waveform of the inverter output is smoother at high frequency, and the harmonic distortion is lower, which can be closer to a pure sine wave. At the same time, a higher carrier frequency can improve the speed of the inverter responding to changes in grid load. High-frequency modulation can quickly adjust the output of the inverter, thereby adapting to fluctuations in load and ensuring the stability of the photovoltaic system.

[0054] B22, inverter dead time management, specifically: determine whether to perform grid quality anti-interference management control judgment, if yes, query the dead time management mapping value from the constructed dead time management optimization mapping table based on the grid-anti-interference management control quantity, otherwise, query the dead time management mapping value from the constructed dead time management optimization mapping table based on the load-anti-interference management control quantity, and gradually reduce the initial dead time of the inverter according to the step corresponding to the obtained dead time management mapping value; the dead time is the time interval introduced by the inverter in the switching device conversion process in order to avoid the simultaneous conduction of two switching devices. Reducing the dead time can reduce switching loss and improve the efficiency of the inverter, thereby improving the safety management reliability of the photovoltaic system.

[0055] It should be noted that the anti-interference response stability management optimization also includes:

[0056] C1, if the grid quality anti-interference management control judgment is performed, query the management optimization cycle for reducing the management cycle corresponding step from the constructed management cycle optimization mapping table based on the grid-anti-interference management control quantity, otherwise, query the management optimization cycle for reducing the management cycle corresponding step from the constructed management cycle optimization mapping table based on the load-anti-interference management control quantity.

[0057] C2, determine whether to trigger the load fluctuation priority transmission condition, if triggered, sequentially transmit to the cloud platform based on the obtained transmission priority, specifically: if the grid quality management data is obtained, the grid load fluctuation data transmission is performed first, then the inverter safety management control data transmission is performed, and finally the grid quality management data transmission is performed, achieving more accurate photovoltaic system safety management effect, if the grid quality management data is not obtained, the inverter safety management control data transmission is performed after the grid load fluctuation data transmission; if not triggered, sequentially transmit to the cloud platform based on the obtained limit priority, specifically: sequentially perform grid load fluctuation data transmission, grid quality management data transmission and inverter safety management control data transmission, achieving more timely safety management of the photovoltaic system; the load fluctuation priority transmission condition includes that the load frequency fluctuation amplitude is not greater than the load frequency fluctuation limit value or the load power fluctuation amplitude is not greater than the load power fluctuation limit value.

[0058] The third step of the photovoltaic energy storage safety collaborative management method based on artificial intelligence is: energy storage operation management optimization, specifically: determine whether to perform energy storage collaborative operation safety management optimization based on the obtained energy storage operation safety management result, the specific process is as follows:

[0059] D1, if the energy storage operation safety control result corresponds to safety control qualification, no energy storage collaborative operation safety control optimization is performed, and a control data remote synchronization instruction is sent to synchronize the light storage collaborative control parameters to the cloud platform, the light storage collaborative control parameters including safety collaborative control data, energy storage collaborative control data, photovoltaic anti-interference control results, and energy storage operation safety control results.

[0060] D2, if the energy storage operation safety control result corresponds to safety control disqualification, energy storage collaborative operation safety control optimization is performed, and after the energy storage collaborative operation safety control optimization, a control data remote synchronization instruction is sent, as shown in Figure 3 The corresponding logic is that the energy storage collaborative operation safety control optimization includes energy storage system charging control and energy storage system discharging control. The energy storage system charging control obtains a charging power control value for reducing the initial charging power in the energy storage battery charging process by obtaining an energy storage charging safety coefficient and an energy storage safety control quantity. The energy storage system discharging control obtains a discharging depth control value for reducing the initial discharging depth in the energy storage battery discharging process by obtaining an energy storage discharging safety coefficient and an energy storage safety control quantity.

[0061] Specifically, the energy storage collaborative operation safety control optimization means adjusting the charging and discharging process of the energy storage battery in the energy storage system in combination with the energy storage operation safety control result. The energy storage collaborative operation safety control optimization includes energy storage system charging control and energy storage system discharging control.

[0062] D21, energy storage system charging control, the specific process is: the arithmetically averaged result of the energy storage charging safety coefficient and the energy storage safety control quantity deviation degree after being assigned respective weights for weighting processing is input to the energy storage charging power control linear regression model, and the corresponding output is a charging power control value for reducing the initial charging power in the energy storage battery charging process. The respective weights for weighting processing are specifically the product operation of the energy storage charging safety coefficient and the energy storage safety control quantity deviation degree with the energy storage charging safety weight and the energy storage safety control weight obtained from the preset database, respectively.

[0063] D22, energy storage system discharging control, the specific process is: the arithmetically averaged result of the energy storage discharging safety coefficient and the energy storage safety control quantity deviation degree after being assigned respective weights for weighting processing is input to the energy storage discharging depth control linear regression model, and the corresponding output is a discharging depth control value for reducing the initial discharging depth in the energy storage battery discharging process. The respective weights for weighting processing are specifically the product operation of the energy storage discharging safety coefficient and the energy storage safety control quantity deviation degree with the energy storage discharging safety weight and the energy storage safety control weight obtained from the preset database, respectively.

[0064] It should be noted that the energy storage charging power control linear regression model is a linear regression model pre-trained to fit the mapping relationship between the arithmetic mean of the energy storage charging safety coefficient and the energy storage safety control quantity deviation degree after being assigned respective weights for weighting processing and the charging power control value. The input of the linear regression model is the arithmetic mean of the energy storage charging safety coefficient and the energy storage safety control quantity deviation degree after being assigned respective weights for weighting processing, and the charging power control value set by the preset staff according to the arithmetic mean of the energy storage charging safety coefficient and the energy storage safety control quantity deviation degree after being assigned respective weights for weighting processing. The energy storage discharge depth control linear regression model is a linear regression model pre-trained to fit the mapping relationship between the arithmetic mean of the energy storage discharge safety coefficient and the energy storage safety control quantity deviation degree after being assigned respective weights for weighting processing and the discharge depth control value. The input of the linear regression model is the arithmetic mean of the energy storage discharge safety coefficient and the energy storage safety control quantity deviation degree after being assigned respective weights for weighting processing, and the discharge depth control value set by the preset staff according to the arithmetic mean of the energy storage discharge safety coefficient and the energy storage safety control quantity deviation degree after being assigned respective weights for weighting processing. The energy storage charging power control linear regression model and the energy storage discharge depth control linear regression model are trained based on the least square method through the scikit-learn framework.

[0065] It should be understood that high power during battery charging may cause battery overheating or rapid aging, especially in the initial charging stage. By reducing the initial charging power, the temperature rise of the battery can be reduced, and the risk of heat runaway caused by excessive current can be avoided, thereby improving the effectiveness of energy storage system safety control. In addition, excessive discharge depth may cause battery over-discharge, thereby causing changes in the internal chemical substances of the battery, and even causing battery swelling, liquid leakage, or heat runaway. By reducing the initial discharge depth, these risks can be effectively reduced to ensure the safety of the battery during discharge and improve the reliability of the energy storage system safety control.

[0066] As shown in Figure 4 The structure of the photovoltaic energy storage safety collaborative control system based on artificial intelligence provided by the embodiment of the present application is shown in the structure diagram of the photovoltaic energy storage safety collaborative control system based on artificial intelligence provided by the embodiment of the present application. The photovoltaic energy storage safety collaborative control system based on artificial intelligence provided by the embodiment of the present application comprises a photovoltaic anti-interference control module, an energy storage safety collaborative control module, and a running safety control optimization module.

[0067] The photovoltaic anti-interference control module is used to obtain safety collaborative control data reflecting the safety performance of the photovoltaic system in a control period in a grid-connected mode, and obtain a photovoltaic anti-interference control result based on the obtained safety collaborative control data to quantify the anti-interference response stability of the photovoltaic system inverter process in the grid-connected mode.

[0068] The energy storage safety collaborative control module is used for judging whether to execute anti-interference response stable control optimization according to the photovoltaic anti-interference control result, if yes, sending the energy storage system charging and discharging collaborative control instruction after executing the anti-interference response stable control optimization, if no, directly sending the energy storage system charging and discharging collaborative control instruction.

[0069] The operation safety control optimization module is used for judging whether to perform energy storage collaborative operation safety control optimization based on the obtained energy storage operation safety control result, if yes, sending the control data remote synchronization instruction after performing the energy storage collaborative operation safety control optimization, otherwise, directly sending the control data remote synchronization instruction.

[0070] The photovoltaic system and the energy storage system are interrelated, and the electric energy generated by photovoltaic power generation is usually transmitted to the energy storage system for storage. If the safety control of the photovoltaic system is not timely, it may cause abnormal conditions such as overvoltage and overcurrent of the energy storage system when accepting power, and further cause damage or overheating of the battery of the energy storage system. If the safety control of the photovoltaic system is delayed, the energy storage system may fail to take timely measures due to power fluctuations or sudden conditions. The present application synchronously controls the safety of the photovoltaic system and the energy storage system, which can improve the rapid response capability of the photovoltaic system and the energy storage system to abnormal conditions, and realize more timely collaborative control between the photovoltaic system and the energy storage system.

[0071] The following points need to be explained:

[0072] (1) The drawings of the embodiments of the present application only involve the structures involved in the embodiments of the present application, and other structures can be referred to the usual design.

[0073] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present application, the thickness of the layer or region is magnified or reduced, that is, these drawings are not drawn according to the actual proportion. It can be understood that when an element such as a layer, film, region or substrate is referred to as being located "on" or "under" another element, the element can be "directly" located "on" or "under" another element or there can be an intermediate element.

[0074] (3) In the case of no conflict, the embodiments of the present application and the features in the embodiments can be combined with each other to obtain new embodiments.

[0075] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A photovoltaic energy storage safety collaborative management and control method based on artificial intelligence, characterized in that, Includes the following steps: In grid-connected mode, safety collaborative management data reflecting the safety performance of the photovoltaic system within the management and control period is acquired. Based on the acquired safety collaborative management data, the photovoltaic anti-interference management result is obtained to quantify the anti-interference response stability of the photovoltaic system's inverter process in grid-connected mode. The specific steps for acquiring the safety collaborative management data are as follows: Acquire grid load fluctuation data within the control period, wherein the grid load fluctuation data includes load frequency fluctuation amplitude and load power fluctuation amplitude; If the load frequency fluctuation amplitude is not greater than the load frequency fluctuation limit and the load power fluctuation amplitude is not greater than the load power fluctuation limit, then the inverter safety management data is obtained after obtaining the grid quality management data within the management period; otherwise, the inverter safety management data within the management period is obtained directly and it is determined whether to obtain the grid quality management data. The grid quality management data includes the grid voltage change rate and the grid frequency change rate, and the inverter safety management data includes the inverter input data and the inverter output data. The inverter input data includes inverter input voltage and inverter input current, and the inverter output data includes inverter output voltage, inverter output current, and total harmonic distortion (THD). The security collaborative management and control data includes power grid load fluctuation data, power grid quality management and control data, and inverter security management and control data. Based on the photovoltaic anti-interference control results, it is determined whether to perform anti-interference response stability control optimization. If yes, after performing anti-interference response stability control optimization, a charging and discharging coordinated control command for the energy storage system is sent. If no, a charging and discharging coordinated control command for the energy storage system is sent directly. The charging and discharging coordinated control command for the energy storage system is used to obtain the energy storage operation safety control results through energy storage coordinated control data that reflects the safety performance of the energy storage system, so as to quantify the safety performance of the energy storage battery operation in the energy storage system. The anti-interference response stability control optimization means combining the photovoltaic anti-interference control results to perform inverter pulse width modulation. Based on the obtained energy storage operation safety management and control results, it is determined whether to perform energy storage collaborative operation safety management and control optimization. If so, a remote synchronization command for management and control data is sent after the energy storage collaborative operation safety management and control optimization is performed. Otherwise, a remote synchronization command for management and control data is sent directly. The energy storage collaborative operation safety management and control optimization means adjusting the charging and discharging process of the energy storage battery in the energy storage system in combination with the energy storage operation safety management and control results.

2. The photovoltaic energy storage safety collaborative management and control method based on artificial intelligence according to claim 1, characterized in that, The specific process for determining whether to acquire power grid quality control data is as follows: Based on inverter safety management data, inverter voltage management indicators are obtained to reflect the stability of the deviation between inverter input voltage and inverter output voltage, inverter current management indicators to reflect the stability of the deviation between inverter input current and inverter output current, and inverter harmonic management indicators to reflect the stability of output harmonic response. The inverter voltage control index, inverter current control index, and inverter harmonic control index are arithmetically averaged to obtain the photovoltaic inverter control score, which quantitatively reflects the stability of the photovoltaic system inverter process under grid-connected mode. The photovoltaic inverter control score is compared with the inverter control reference limit. If the photovoltaic inverter control score is not greater than the inverter control reference limit, then the grid quality control data is obtained. Otherwise, the verification and judgment are based on the relative deviation of the photovoltaic inverter control score. Specifically, the relative deviation of the photovoltaic inverter control score is obtained. If the relative deviation of the photovoltaic inverter control score is within the inverter control stability limit range, the grid quality control data is not obtained. Otherwise, the grid quality control data is obtained. The relative deviation of the photovoltaic inverter control score represents the result of relative deviation processing of the photovoltaic inverter control score and the inverter control upper limit value.

3. The photovoltaic energy storage safety collaborative management and control method based on artificial intelligence according to claim 2, characterized in that, The specific steps for obtaining the photovoltaic anti-interference control results are as follows: If the load frequency fluctuation amplitude is not greater than the load frequency fluctuation limit and the load power fluctuation amplitude is not greater than the load power fluctuation limit, then the grid quality anti-interference control judgment is made; otherwise, the load fluctuation anti-interference control judgment is made. The judgment of power grid quality anti-interference control is specifically as follows: the power grid-anti-interference factor and the photovoltaic inverter control score are interactively processed to obtain the power grid-anti-interference control quantity, and the power grid-anti-interference control quantity is compared with the power grid-anti-interference control reference value to obtain the photovoltaic anti-interference control result. The power grid-anti-interference factor is used to describe the degree of influence of power grid quality control data on the stability assessment of anti-interference response of photovoltaic system inverter process. The load fluctuation anti-interference control judgment is specifically as follows: the load-anti-interference factor and the photovoltaic inverter control score are interactively processed to obtain the load-anti-interference control quantity, and the load-anti-interference control quantity is compared with the load-anti-interference control reference value to obtain the photovoltaic anti-interference control result. The load-anti-interference factor is used to describe the degree of influence of grid load fluctuation data on the stability assessment of the anti-interference response of the photovoltaic system inverter process. The photovoltaic anti-interference control results include anti-interference control qualified and anti-interference control unqualified; The "interference control qualified" means that the power grid-interference control quantity is greater than the power grid-interference control reference value and the load-interference control quantity is greater than the load-interference control reference value. The failure of anti-interference control indicates that the power grid-anti-interference control quantity is not greater than the power grid-anti-interference control reference value or the load-anti-interference control quantity is not greater than the load-anti-interference control reference value.

4. The photovoltaic energy storage safety collaborative management and control method based on artificial intelligence according to claim 3, characterized in that, The specific steps for determining whether to perform anti-interference response stability management optimization based on the photovoltaic anti-interference management results are as follows: Determine whether the photovoltaic anti-interference control result is qualified. If so, do not execute the anti-interference response stability control optimization, send the energy storage system charge and discharge collaborative control command and obtain the energy storage operation safety control result. Otherwise, execute the anti-interference response stability control optimization and send the energy storage system charge and discharge collaborative control command after executing the anti-interference response stability control optimization. The anti-interference response stability management optimization includes inverter carrier frequency management and inverter dead time management; The inverter carrier frequency control is specifically as follows: determine whether to perform grid quality anti-interference control; if so, query the constructed carrier frequency control optimization mapping table based on the grid-anti-interference control quantity to obtain the carrier frequency control mapping value; otherwise, query the constructed carrier frequency control optimization mapping table based on the load-anti-interference control quantity to obtain the carrier frequency control mapping value, and gradually increase the initial carrier frequency of the inverter according to the step size corresponding to the obtained carrier frequency control mapping value. The inverter dead time management specifically involves: determining whether to perform grid quality anti-interference management; if so, querying the existing dead time management optimization mapping table based on the grid-anti-interference management quantity to obtain the dead time management mapping value; otherwise, querying the existing dead time management optimization mapping table based on the load-anti-interference management quantity to obtain the dead time management mapping value, and gradually reducing the initial dead time of the inverter according to the step size corresponding to the obtained dead time management mapping value.

5. The photovoltaic energy storage safety collaborative management and control method based on artificial intelligence according to claim 4, characterized in that, The optimization of anti-interference response stability control further includes: If a power grid quality anti-interference control judgment is to be made, the control optimization period for reducing the corresponding step size of the control period is obtained by querying the existing control cycle optimization mapping table based on the power grid-anti-interference control quantity; otherwise, the control optimization period is obtained by querying the existing control cycle optimization mapping table based on the load-anti-interference control quantity. Determine whether the load fluctuation priority transmission condition is triggered. If triggered, transmit the data to the cloud platform sequentially based on the acquired transmission priority. Specifically, if power grid quality control data is acquired, power grid load fluctuation data transmission is prioritized, followed by inverter safety control data transmission, and finally power grid quality control data transmission. If power grid quality control data is not acquired, inverter safety control data transmission is performed after power grid load fluctuation data transmission. If not triggered, the data will be transmitted to the cloud platform in sequence based on the priority of the acquired restrictions, specifically: power grid load fluctuation data transmission, power grid quality control data transmission, and inverter safety control data transmission in sequence. The load fluctuation priority transmission condition includes that the load frequency fluctuation amplitude is not greater than the load frequency fluctuation limit or the load power fluctuation amplitude is not greater than the load power fluctuation limit.

6. The photovoltaic energy storage safety collaborative management and control method based on artificial intelligence according to claim 3, characterized in that, The specific steps for obtaining the safety management and control results of energy storage operation are as follows: Acquire energy storage collaborative management and control data within the management and control period. The energy storage collaborative management and control data includes energy storage battery charging data and energy storage battery discharging data. The energy storage battery charging data includes energy storage charging voltage, energy storage charging current, and energy storage charging power. The energy storage battery discharging data includes energy storage discharging voltage, energy storage discharging current, and energy storage discharging power. The energy storage battery charging data and the energy storage battery charging reference limit are processed inversely proportionally to obtain the energy storage charging safety factor to characterize the safety performance of the energy storage battery charging process. The energy storage battery discharge data and the energy storage battery discharge reference limit are processed inversely proportionally to obtain the energy storage discharge safety factor to characterize the safety performance of the energy storage battery discharge process. Determine whether the obtained energy storage charging safety factor and energy storage discharging safety factor are both non-zero. If so, perform harmonic averaging on the obtained energy storage charging safety factor and energy storage discharging safety factor to obtain the energy storage safety control quantity. Otherwise, mark the energy storage safety control quantity as an abnormal value and send an energy storage collaborative control abnormality prompt. The energy storage operation safety management result is obtained based on the degree of deviation between the obtained energy storage safety management quantity and the energy storage safety management reference value. The energy storage operation safety management result includes qualified safety management and unqualified safety management. Qualified safety management means that the energy storage safety management quantity is greater than the energy storage safety management reference value, and unqualified safety management means that the energy storage safety management quantity is not greater than the energy storage safety management reference value.

7. The photovoltaic energy storage safety collaborative management and control method based on artificial intelligence according to claim 6, characterized in that, The specific process for determining whether to optimize the safety management of energy storage collaborative operation based on the acquired energy storage operation safety management results is as follows: If the safety management result of energy storage operation is qualified, no optimization of energy storage collaborative operation safety management will be performed, and a remote synchronization command for management data will be sent to synchronize the photovoltaic-storage collaborative management parameters to the cloud platform. The photovoltaic-storage collaborative management parameters include safety collaborative management data, energy storage collaborative management data, photovoltaic anti-interference management results, and energy storage operation safety management results. If the safety management result of energy storage operation is unqualified, then the safety management optimization of energy storage collaborative operation will be carried out, and a remote synchronization command for management data will be sent after the optimization. The optimization of energy storage collaborative operation safety management includes energy storage system charging management and energy storage system discharging management.

8. The photovoltaic energy storage safety collaborative management and control method based on artificial intelligence according to claim 7, characterized in that, The specific process for charging control of the energy storage system is as follows: The arithmetic mean of the obtained energy storage charging safety factor and the deviation degree of energy storage safety control quantity are weighted and then input into the linear regression model of energy storage charging power control. The corresponding output is the charging power control value used to reduce the initial charging power during the charging process of energy storage battery. The specific process for the discharge control of the energy storage system is as follows: the obtained energy storage discharge safety factor and the degree of deviation of energy storage safety control quantity are assigned their respective weights and the arithmetic average result is then input into the linear regression model for energy storage discharge depth control, and the corresponding output is the discharge depth control value used to reduce the initial discharge depth during the discharge process of the energy storage battery.

9. A photovoltaic energy storage safety collaborative management and control system based on artificial intelligence, employing the photovoltaic energy storage safety collaborative management and control method based on artificial intelligence as described in any one of claims 1-8, characterized in that, include: Photovoltaic anti-interference control module, energy storage safety collaborative control module, and operation safety control optimization module; The photovoltaic anti-interference control module is used to acquire safety collaborative control data reflecting the safety performance of the photovoltaic system within the control period in grid-connected mode, and to obtain photovoltaic anti-interference control results based on the acquired safety collaborative control data to quantify the anti-interference response stability of the photovoltaic system inverter process in grid-connected mode. The energy storage safety collaborative management and control module is used to determine whether to perform anti-interference response stability management and control optimization based on the photovoltaic anti-interference management and control results. If yes, it sends the energy storage system charge and discharge collaborative management and control command after performing anti-interference response stability management and control optimization; otherwise, it directly sends the energy storage system charge and discharge collaborative management and control command. The operation safety management and optimization module is used to determine whether to perform energy storage collaborative operation safety management and optimization based on the acquired energy storage operation safety management and optimization results. If so, it sends a remote synchronization command for management and control data after performing energy storage collaborative operation safety management and optimization; otherwise, it directly sends a remote synchronization command for management and control data.

Images